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6,900	doc-pypi-links.sh	ilius_pyglossary/scripts/doc-pypi-links.sh	#!/bin/bash grep -roh 'https://pypi.org/project/[^)]*' doc/p/ \| sort \| uniq --count	85	Python	.py	2	41	71	0.609756	ilius/pyglossary	2,176	238	22	GPL-3.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,901	dump.py	ilius_pyglossary/scripts/dump.py	#!/usr/bin/env python3 import sys from pprint import pformat from pyglossary.glossary import Glossary glos = Glossary() glos.read(sys.argv[1]) for entry in glos: print("Words: " + pformat(entry.l_word)) print("Definitions: " + pformat(entry.defis)) print("-------------------------")	290	Python	.py	10	27.4	46	0.689531	ilius/pyglossary	2,176	238	22	GPL-3.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,902	sort-jsonl.py	ilius_pyglossary/scripts/wiktextract/sort-jsonl.py	#!/usr/bin/env python # read json lines from stdin, # sort them by "word" key and print import operator import sys from json import loads data: "list[tuple[str, str]]" = [] for line in sys.stdin: line = line.strip() # noqa: PLW2901 if not line: continue row = loads(line) data.append((row.get("word"), line)) data.sort(key=operator.itemgetter(0)) for _, line in data: print(line)	394	Python	.py	16	22.8125	37	0.723118	ilius/pyglossary	2,176	238	22	GPL-3.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,903	extract-schema.py	ilius_pyglossary/scripts/wiktextract/extract-schema.py	import json import sys from collections import Counter from collections import OrderedDict as odict from dataclasses import dataclass from typing import Any @dataclass(slots=True) class Node: Type: str = "" Dict: "dict[str, Node] \| None" = None KeyScore: "Counter \| None" = None ListOf: "Node \| None" = None def keyScoreList(self): return [f"{count:.1f}: {key}" for key, count in self.KeyScore.most_common()] @property def __dict__(self): if self.Dict: assert self.ListOf is None keys = [key for key, _ in self.KeyScore.most_common()] try: keys.remove("word") except ValueError: pass else: keys.insert(0, "word") return { "__dict__": odict((key, self.Dict[key].__dict__) for key in keys), # "__key_score__": self.keyScoreList(), } if self.ListOf: return {"__list_of__": self.ListOf.__dict__} return self.Type schema = Node(Type="dict") valueSet: "dict[str, set]" = {} def addToValueSet(value: "str \| int \| float \| bool", path: list[str]): if isinstance(value, str) and "://" in value: return pathStr = ".".join(path) if pathStr in valueSet: valueSet[pathStr].add(value) return valueSet[pathStr] = {value} def getSchemaNode(path: list[str]): node = schema for name in path: if name == "[]": node.Type = "list" if not node.ListOf: node.ListOf = Node() node = node.ListOf continue node.Type = "dict" if not node.Dict: node.Dict = {} node.KeyScore = Counter() if name in node.Dict: node = node.Dict[name] else: newNode = Node() node.Dict[name] = newNode node = newNode return node def updateSchema(_type: str, path: list[str]): node = getSchemaNode(path) prevType = node.Type if prevType and prevType != _type: print( f"mismatch types for path={'.'.join(path)}, {prevType} and {_type}", ) node.Type = _type def parseList(data: list[Any], path: list[str], node: Node): node.Type = "list" if not node.ListOf: node.ListOf = Node() if not data: return itemsPath = path + ["[]"] itemTypes = set() for item in data: itemTypes.add(type(item).__name__) if isinstance(item, dict): parseDict(item, itemsPath, node.ListOf) continue if isinstance(item, list): parseList(item, itemsPath, node.ListOf) continue if isinstance(item, str \| int \| float \| bool): addToValueSet(item, path) itemTypesStr = " \| ".join(sorted(itemTypes)) updateSchema(itemTypesStr, path + ["[]"]) def parseDict(data: "dict[str, Any]", path: list[str], node: Node): if not node.Dict: node.Dict = {} node.KeyScore = Counter() for index, (key, value) in enumerate(data.items()): node.KeyScore[key] += min(1, 50 - index) / 50 if key in node.Dict: childNode = node.Dict[key] else: childNode = node.Dict[key] = Node() if isinstance(value, dict): parseDict(value, path + [key], childNode) continue if isinstance(value, list): parseList(value, path + [key], childNode) continue if isinstance(value, str \| int \| float \| bool): updateSchema(type(value).__name__, path + [key]) addToValueSet(value, path + [key]) jsonl_path = sys.argv[1] with open(jsonl_path, encoding="utf-8") as _file: for line in _file: line = line.strip() # noqa: PLW2901 if not line: continue try: data = json.loads(line) except Exception: print(f"bad line: {line}") continue parseDict(data, [], schema) with open(f"{jsonl_path}.schema.json", mode="w", encoding="utf-8") as _file: json.dump( schema.__dict__, _file, indent="\t", ) commonValuesList = [ (key, sorted(values)) for key, values in valueSet.items() if len(values) < 20 and len(str(values)) < 100 ] def commonValuesSortKey(item): _key, values = item return abs(len(values) - 5) commonValuesList.sort(key=commonValuesSortKey) with open(f"{jsonl_path}-common-values.json", mode="w", encoding="utf-8") as _file: json.dump(dict(commonValuesList), _file, indent="\t")	3,917	Python	.py	138	25.384058	83	0.679754	ilius/pyglossary	2,176	238	22	GPL-3.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,904	fix-css.py	ilius_pyglossary/scripts/appledict/fix-css.py	import sys from os.path import dirname, realpath, splitext sys.path.insert(0, dirname(dirname(dirname(realpath(__file__))))) from pyglossary.apple_utils import substituteAppleCSS for fpath in sys.argv[1:]: if fpath.endswith("-fixed.css"): continue fpathNoExt, _ = splitext(fpath) fpathNew = fpathNoExt + "-fixed.css" with open(fpath, "rb") as _file: text = _file.read() text = substituteAppleCSS(text) with open(fpathNew, "wb") as _file: _file.write(text) print("Created", fpathNew) print()	508	Python	.py	16	29.6875	65	0.740286	ilius/pyglossary	2,176	238	22	GPL-3.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,905	doc-pypi-links.sh	ilius_pyglossary/scripts/doc-pypi-links.sh	#!/bin/bash grep -roh 'https://pypi.org/project/[^)]*' doc/p/ \| sort \| uniq --count	85	Python	.pyp	2	41	71	0.609756	ilius/pyglossary	2,176	238	22	GPL-3.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,906	pyglossary.pyw	ilius_pyglossary/pyglossary.pyw	#!/usr/bin/env python3 import sys from os.path import dirname sys.path.insert(0, dirname(__file__)) from pyglossary.ui.main import main main()	147	Python	.pyw	6	22.833333	37	0.781022	ilius/pyglossary	2,176	238	22	GPL-3.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,907	__main__.py	codespell-project_codespell/codespell_lib/__main__.py	import sys from ._codespell import _script_main if __name__ == "__main__": sys.exit(_script_main())	106	Python	.py	4	24	36	0.65	codespell-project/codespell	1,850	470	225	GPL-2.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,908	_text_util.py	codespell-project_codespell/codespell_lib/_text_util.py	# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; version 2 of the License. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see # https://www.gnu.org/licenses/old-licenses/gpl-2.0.html. """ Copyright (C) 2010-2011 Lucas De Marchi <lucas.de.marchi@gmail.com> Copyright (C) 2011 ProFUSION embedded systems """ def fix_case(word: str, fixword: str) -> str: if word == word.capitalize(): return ", ".join(w.strip().capitalize() for w in fixword.split(",")) if word == word.upper(): return fixword.upper() # they are both lower case # or we don't have any idea return fixword	1,038	Python	.py	25	39	76	0.732938	codespell-project/codespell	1,850	470	225	GPL-2.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,909	_codespell.py	codespell-project_codespell/codespell_lib/_codespell.py	# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; version 2 of the License. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see # https://www.gnu.org/licenses/old-licenses/gpl-2.0.html. """ Copyright (C) 2010-2011 Lucas De Marchi <lucas.de.marchi@gmail.com> Copyright (C) 2011 ProFUSION embedded systems """ import argparse import configparser import ctypes import fnmatch import itertools import os import re import sys import textwrap from typing import ( Any, Dict, Iterable, List, Match, Optional, Pattern, Sequence, Set, TextIO, Tuple, ) if sys.platform == "win32": from ctypes import wintypes ENABLE_VIRTUAL_TERMINAL_PROCESSING = 0x0004 STD_OUTPUT_HANDLE = wintypes.HANDLE(-11) from ._spellchecker import Misspelling, build_dict from ._text_util import fix_case # autogenerated by setuptools_scm from ._version import ( # type: ignore[import-not-found] __version__ as VERSION, # noqa: N812 ) word_regex_def = r"[\w\-'â€™]+" # noqa: RUF001 # While we want to treat characters like ( or " as okay for a starting break, # these may occur unescaped in URIs, and so we are more restrictive on the # endpoint. Emails are more restrictive, so the endpoint remains flexible. uri_regex_def = ( "(\\b(?:https?\|[ts]?ftp\|file\|git\|smb)://[^\\s]+(?=$\|\\s)\|" "\\b[\\w.%+-]+@[\\w.-]+\\b)" ) inline_ignore_regex = re.compile(r"[^\w\s]\s?codespell:ignore\b(\s+(?P<words>[\w,]))?") USAGE = """ \t%prog [OPTIONS] [file1 file2 ... fileN] """ supported_languages_en = ("en", "en_GB", "en_US", "en_CA", "en_AU") supported_languages = supported_languages_en # Users might want to link this file into /usr/local/bin, so we resolve the # symbolic link path to the real path if necessary. _data_root = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data") _builtin_dictionaries = ( # name, desc, name, err in aspell, correction in aspell, \ # err dictionary array, rep dictionary array # The arrays must contain the names of aspell dictionaries # The aspell tests here aren't the ideal state, but the None's are # realistic for obscure words ("clear", "for unambiguous errors", "", False, None, supported_languages_en, None), ( "rare", "for rare (but valid) words that are likely to be errors", "_rare", None, None, None, None, ), ( "informal", "for making informal words more formal", "_informal", True, True, supported_languages_en, supported_languages_en, ), ( "usage", "for replacing phrasing with recommended terms", "_usage", None, None, None, None, ), ( "code", "for words from code and/or mathematics that are likely to be typos in other contexts (such as uint)", # noqa: E501 "_code", None, None, None, None, ), ( "names", "for valid proper names that might be typos", "_names", None, None, None, None, ), ( "en-GB_to_en-US", "for corrections from en-GB to en-US", "_en-GB_to_en-US", True, True, ("en_GB",), ("en_US",), ), ) _builtin_default = "clear,rare" # docs say os.EX_USAGE et al. are only available on Unix systems, so to be safe # we protect and just use the values they are on macOS and Linux EX_OK = 0 EX_USAGE = 64 EX_DATAERR = 65 EX_CONFIG = 78 # OPTIONS: # # ARGUMENTS: # dict_filename The file containing the dictionary of misspellings. # If set to '-', it will be read from stdin # file1 .. fileN Files to check spelling class QuietLevels: NONE = 0 ENCODING = 1 BINARY_FILE = 2 DISABLED_FIXES = 4 NON_AUTOMATIC_FIXES = 8 FIXES = 16 CONFIG_FILES = 32 class GlobMatch: def __init__(self, pattern: List[str]) -> None: self.pattern_list: List[str] = pattern def match(self, filename: str) -> bool: return any(fnmatch.fnmatch(filename, p) for p in self.pattern_list) class TermColors: def __init__(self) -> None: self.FILE = "\033[33m" self.WWORD = "\033[31m" self.FWORD = "\033[32m" self.DISABLE = "\033[0m" def disable(self) -> None: self.FILE = "" self.WWORD = "" self.FWORD = "" self.DISABLE = "" class Summary: def __init__(self) -> None: self.summary: Dict[str, int] = {} def update(self, wrongword: str) -> None: if wrongword in self.summary: self.summary[wrongword] += 1 else: self.summary[wrongword] = 1 def __str__(self) -> str: keys = list(self.summary.keys()) keys.sort() return "\n".join( [f"{key}{self.summary.get(key):{15 - len(key)}}" for key in keys] ) class FileOpener: def __init__( self, use_chardet: bool, quiet_level: int, ignore_multiline_regex: Optional[Pattern[str]], ) -> None: self.use_chardet = use_chardet if use_chardet: self.init_chardet() self.quiet_level = quiet_level self.ignore_multiline_regex = ignore_multiline_regex def init_chardet(self) -> None: try: from chardet.universaldetector import UniversalDetector except ImportError as e: msg = ( "There's no chardet installed to import from. " "Please, install it and check your PYTHONPATH " "environment variable" ) raise ImportError(msg) from e self.encdetector = UniversalDetector() def open(self, filename: str) -> Tuple[List[str], str]: if self.use_chardet: return self.open_with_chardet(filename) return self.open_with_internal(filename) def open_with_chardet(self, filename: str) -> Tuple[List[str], str]: self.encdetector.reset() with open(filename, "rb") as fb: for line in fb: self.encdetector.feed(line) if self.encdetector.done: break self.encdetector.close() encoding = self.encdetector.result["encoding"] try: f = open(filename, encoding=encoding, newline="") except UnicodeDecodeError: print(f"ERROR: Could not detect encoding: {filename}", file=sys.stderr) raise except LookupError: print( f"ERROR: Don't know how to handle encoding {encoding}: {filename}", file=sys.stderr, ) raise else: lines = self.get_lines(f) f.close() return lines, f.encoding def open_with_internal(self, filename: str) -> Tuple[List[str], str]: encoding = None first_try = True for encoding in ("utf-8", "iso-8859-1"): if first_try: first_try = False elif not self.quiet_level & QuietLevels.ENCODING: print(f'WARNING: Trying next encoding "{encoding}"', file=sys.stderr) with open(filename, encoding=encoding, newline="") as f: try: lines = self.get_lines(f) except UnicodeDecodeError: if not self.quiet_level & QuietLevels.ENCODING: print( f'WARNING: Cannot decode file using encoding "{encoding}": ' f"{filename}", file=sys.stderr, ) else: break else: # reading with encoding "iso-8859-1" cannot fail with UnicodeDecodeError msg = "Unknown encoding" raise RuntimeError(msg) # pragma: no cover return lines, encoding def get_lines(self, f: TextIO) -> List[str]: if self.ignore_multiline_regex: text = f.read() pos = 0 text2 = "" for m in re.finditer(self.ignore_multiline_regex, text): text2 += text[pos : m.start()] # Replace with blank lines so line numbers are unchanged. text2 += "\n" m.group().count("\n") pos = m.end() text2 += text[pos:] lines = text2.split("\n") else: lines = f.readlines() return lines # -.-:-.-:-.-:-.:-.-:-.-:-.-:-.-:-.:-.-:-.-:-.-:-.-:-.:-.-:- # If someday this breaks, we can just switch to using RawTextHelpFormatter, # but it has the disadvantage of not wrapping our long lines. class NewlineHelpFormatter(argparse.HelpFormatter): """Help formatter that preserves newlines and deals with lists.""" def _split_lines(self, text: str, width: int) -> List[str]: parts = text.split("\n") out = [] for part in parts: # Eventually we could allow others... indent_start = "- " offset = len(indent_start) if part.startswith(indent_start) else 0 part = part[offset:] part = self._whitespace_matcher.sub(" ", part).strip() parts = textwrap.wrap(part, width - offset) parts = [" " * offset + p for p in parts] if offset: parts[0] = indent_start + parts[0][offset:] out.extend(parts) return out def _toml_to_parseconfig(toml_dict: Dict[str, Any]) -> Dict[str, Any]: """Convert a dict read from a TOML file to the parseconfig.read_dict() format.""" return { k: "" if v is True else ",".join(v) if isinstance(v, list) else v for k, v in toml_dict.items() if v is not False } def _supports_ansi_colors() -> bool: if sys.platform == "win32": # Windows Terminal enables ANSI escape codes by default. In other cases # it is disabled. # See https://ss64.com/nt/syntax-ansi.html for more information. kernel32 = ctypes.WinDLL("kernel32") # fmt: off kernel32.GetConsoleMode.argtypes = ( wintypes.HANDLE, # _In_ hConsoleHandle wintypes.LPDWORD, # _Out_ lpMode ) # fmt: on kernel32.GetConsoleMode.restype = wintypes.BOOL mode = wintypes.DWORD() handle = kernel32.GetStdHandle(STD_OUTPUT_HANDLE) if not kernel32.GetConsoleMode(handle, ctypes.byref(mode)): # TODO: print a warning with the error message on stderr? return False return (mode.value & ENABLE_VIRTUAL_TERMINAL_PROCESSING) != 0 elif sys.platform == "wasi": # WASI disables ANSI escape codes for security reasons. # See https://github.com/WebAssembly/WASI/issues/162. return False elif sys.stdout.isatty(): return True return False def parse_options( args: Sequence[str], ) -> Tuple[argparse.Namespace, argparse.ArgumentParser, List[str]]: parser = argparse.ArgumentParser(formatter_class=NewlineHelpFormatter) parser.set_defaults(colors=_supports_ansi_colors()) parser.add_argument("--version", action="version", version=VERSION) parser.add_argument( "-d", "--disable-colors", action="store_false", dest="colors", help="disable colors, even when printing to terminal", ) parser.add_argument( "-c", "--enable-colors", action="store_true", dest="colors", help="enable colors, even when not printing to terminal", ) parser.add_argument( "-w", "--write-changes", action="store_true", default=False, help="write changes in place if possible", ) parser.add_argument( "-D", "--dictionary", action="append", help="comma-separated list of custom dictionary files that " "contain spelling corrections. If this flag is not specified " 'or equals "-" then the default dictionary is used.', ) builtin_opts = "\n- ".join( [""] + [f"{d[0]!r} {d[1]}" for d in _builtin_dictionaries] ) parser.add_argument( "--builtin", dest="builtin", default=_builtin_default, metavar="BUILTIN-LIST", help="comma-separated list of builtin dictionaries " 'to include (when "-D -" or no "-D" is passed). ' "Current options are:" + builtin_opts + "\n" "The default is %(default)r.", ) parser.add_argument( "--ignore-regex", action="store", type=str, help="regular expression that is used to find " "patterns to ignore by treating as whitespace. " "When writing regular expressions, consider " "ensuring there are boundary non-word chars, " 'e.g., "\\bmatch\\b". Defaults to ' "empty/disabled.", ) parser.add_argument( "--ignore-multiline-regex", action="store", type=str, help="regular expression that is used to ignore " "text that may span multi-line regions. " "The regex is run with re.DOTALL. For example to " "allow skipping of regions of Python code using " "begin/end comments one could use: " "--ignore-multiline-regex " "'# codespell:ignore-begin \\n.# codespell:ignore-end \\n'. " "Defaults to empty/disabled.", ) parser.add_argument( "-I", "--ignore-words", action="append", metavar="FILES", help="comma-separated list of files that contain " "words to be ignored by codespell. Files must contain " "1 word per line. Words are case sensitive based on " "how they are written in the dictionary file.", ) parser.add_argument( "-L", "--ignore-words-list", action="append", metavar="WORDS", help="comma-separated list of words to be ignored " "by codespell. Words are case sensitive based on " "how they are written in the dictionary file.", ) parser.add_argument( "--uri-ignore-words-list", action="append", metavar="WORDS", help="comma-separated list of words to be ignored " "by codespell in URIs and emails only. Words are " "case sensitive based on how they are written in " 'the dictionary file. If set to "", all ' "misspelling in URIs and emails will be ignored.", ) parser.add_argument( "-r", "--regex", action="store", type=str, help="regular expression that is used to find words. " "By default any alphanumeric character, the " "underscore, the hyphen, and the apostrophe are " "used to build words. This option cannot be " "specified together with --write-changes.", ) parser.add_argument( "--uri-regex", action="store", type=str, help="regular expression that is used to find URIs " "and emails. A default expression is provided.", ) parser.add_argument( "-s", "--summary", action="store_true", default=False, help="print summary of fixes", ) parser.add_argument( "--count", action="store_true", default=False, help="print the number of errors as the last line of stderr", ) parser.add_argument( "-S", "--skip", action="append", help="comma-separated list of files to skip. It " "accepts globs as well. E.g.: if you want " "codespell to skip .eps and .txt files, " 'you\'d give ".eps,.txt" to this option.', ) parser.add_argument( "-x", "--exclude-file", action="append", type=str, metavar="FILES", help="ignore whole lines that match those in " "the comma-separated list of files EXCLUDE. " "The lines in these files should match the " "to-be-excluded lines exactly", ) parser.add_argument( "-i", "--interactive", action="store", type=int, default=0, choices=range(0, 4), help="set interactive mode when writing changes:\n" "- 0: no interactivity.\n" "- 1: ask for confirmation.\n" "- 2: ask user to choose one fix when more than one is available.\n" "- 3: both 1 and 2", metavar="MODE", ) parser.add_argument( "-q", "--quiet-level", action="store", type=int, default=34, choices=range(0, 64), help="bitmask that allows suppressing messages:\n" "- 0: print all messages.\n" "- 1: disable warnings about wrong encoding.\n" "- 2: disable warnings about binary files.\n" "- 4: omit warnings about automatic fixes that were disabled in the dictionary.\n" # noqa: E501 "- 8: don't print anything for non-automatic fixes.\n" "- 16: don't print the list of fixed files.\n" "- 32: don't print configuration files.\n" "As usual with bitmasks, these levels can be " "combined; e.g. use 3 for levels 1+2, 7 for " "1+2+4, 23 for 1+2+4+16, etc. " "The default mask is %(default)s.", metavar="LEVEL", ) parser.add_argument( "-e", "--hard-encoding-detection", action="store_true", default=False, help="use chardet to detect the encoding of each " "file. This can slow down codespell, but is more " "reliable in detecting encodings other than " "utf-8, iso8859-1, and ascii.", ) parser.add_argument( "-f", "--check-filenames", action="store_true", default=False, help="check file names as well", ) parser.add_argument( "-H", "--check-hidden", action="store_true", default=False, help='check hidden files and directories (those starting with ".") as well.', ) parser.add_argument( "-A", "--after-context", type=int, metavar="LINES", help="print LINES of trailing context", ) parser.add_argument( "-B", "--before-context", type=int, metavar="LINES", help="print LINES of leading context", ) parser.add_argument( "-C", "--context", type=int, metavar="LINES", help="print LINES of surrounding context", ) parser.add_argument( "--stdin-single-line", action="store_true", help="output just a single line for each misspelling in stdin mode", ) parser.add_argument("--config", type=str, help="path to config file.") parser.add_argument("--toml", type=str, help="path to a pyproject.toml file.") parser.add_argument("files", nargs="", help="files or directories to check") # Parse command line options. options = parser.parse_args(list(args)) # Load config files and look for ``codespell`` options. cfg_files = ["setup.cfg", ".codespellrc"] if options.config: cfg_files.append(options.config) config = configparser.ConfigParser(interpolation=None) # Read toml before other config files. toml_files = [] tomllib_raise_error = False if os.path.isfile("pyproject.toml"): toml_files.append("pyproject.toml") if options.toml: toml_files.append(options.toml) tomllib_raise_error = True if toml_files: if sys.version_info >= (3, 11): import tomllib else: try: import tomli as tomllib # type: ignore[no-redef] except ImportError as e: if tomllib_raise_error: msg = ( f"tomllib or tomli are required to read pyproject.toml " f"but could not be imported, got: {e}" ) raise ImportError(msg) from None tomllib = None # type: ignore[assignment] if tomllib is not None: for toml_file in toml_files: with open(toml_file, "rb") as f: data = tomllib.load(f).get("tool", {}) if "codespell" in data: data["codespell"] = _toml_to_parseconfig(data["codespell"]) config.read_dict(data) # Collect which config files are going to be used used_cfg_files = [] for cfg_file in cfg_files: _cfg = configparser.ConfigParser() _cfg.read(cfg_file) if _cfg.has_section("codespell"): used_cfg_files.append(cfg_file) # Use config files config.read(used_cfg_files) if config.has_section("codespell"): # Build a "fake" argv list using option name and value. cfg_args = [] for key in config["codespell"]: # Add option as arg. cfg_args.append(f"--{key}") # If value is blank, skip. val = config["codespell"][key] if val: cfg_args.append(val) # Parse config file options. options = parser.parse_args(cfg_args) # Re-parse command line options to override config. options = parser.parse_args(list(args), namespace=options) if not options.files: options.files.append(".") return options, parser, used_cfg_files def process_ignore_words( words: Iterable[str], ignore_words: Set[str], ignore_words_cased: Set[str] ) -> None: for word in words: word = word.strip() if word == word.lower(): ignore_words.add(word) else: ignore_words_cased.add(word) def parse_ignore_words_option( ignore_words_option: List[str], ) -> Tuple[Set[str], Set[str]]: ignore_words: Set[str] = set() ignore_words_cased: Set[str] = set() if ignore_words_option: for comma_separated_words in ignore_words_option: process_ignore_words( (word.strip() for word in comma_separated_words.split(",")), ignore_words, ignore_words_cased, ) return (ignore_words, ignore_words_cased) def build_exclude_hashes(filename: str, exclude_lines: Set[str]) -> None: with open(filename, encoding="utf-8") as f: exclude_lines.update(line.rstrip() for line in f) def build_ignore_words( filename: str, ignore_words: Set[str], ignore_words_cased: Set[str] ) -> None: with open(filename, encoding="utf-8") as f: process_ignore_words( (line.strip() for line in f), ignore_words, ignore_words_cased ) def is_hidden(filename: str, check_hidden: bool) -> bool: bfilename = os.path.basename(filename) return bfilename not in ("", ".", "..") and ( not check_hidden and bfilename[0] == "." ) def is_text_file(filename: str) -> bool: with open(filename, mode="rb") as f: s = f.read(1024) return b"\x00" not in s def ask_for_word_fix( line: str, match: Match[str], misspelling: Misspelling, interactivity: int, colors: TermColors, ) -> Tuple[bool, str]: wrongword = match.group() if interactivity <= 0: return misspelling.fix, fix_case(wrongword, misspelling.data) line_ui = ( f"{line[:match.start()]}" f"{colors.WWORD}{wrongword}{colors.DISABLE}" f"{line[match.end():]}" ) if misspelling.fix and interactivity & 1: r = "" fixword = fix_case(wrongword, misspelling.data) while not r: print(f"{line_ui}\t{wrongword} ==> {fixword} (Y/n) ", end="", flush=True) r = sys.stdin.readline().strip().upper() if not r: r = "Y" if r not in ("Y", "N"): print("Say 'y' or 'n'") r = "" if r == "N": misspelling.fix = False elif (interactivity & 2) and not misspelling.reason: # if it is not disabled, i.e. it just has more than one possible fix, # we ask the user which word to use r = "" opt = [w.strip() for w in misspelling.data.split(",")] while not r: print(f"{line_ui} Choose an option (blank for none): ", end="") for i, o in enumerate(opt): fixword = fix_case(wrongword, o) print(f" {i}) {fixword}", end="") print(": ", end="", flush=True) n = sys.stdin.readline().strip() if not n: break try: i = int(n) r = opt[i] except (ValueError, IndexError): print("Not a valid option\n") if r: misspelling.fix = True misspelling.data = r return misspelling.fix, fix_case(wrongword, misspelling.data) def print_context( lines: List[str], index: int, context: Tuple[int, int], ) -> None: # context = (context_before, context_after) for i in range(index - context[0], index + context[1] + 1): if 0 <= i < len(lines): print(f"{'>' if i == index else ':'} {lines[i].rstrip()}") def _ignore_word_sub( text: str, ignore_word_regex: Optional[Pattern[str]], ) -> str: if ignore_word_regex: text = ignore_word_regex.sub(" ", text) return text def extract_words( text: str, word_regex: Pattern[str], ignore_word_regex: Optional[Pattern[str]], ) -> List[str]: return word_regex.findall(_ignore_word_sub(text, ignore_word_regex)) def extract_words_iter( text: str, word_regex: Pattern[str], ignore_word_regex: Optional[Pattern[str]], ) -> List[Match[str]]: return list(word_regex.finditer(_ignore_word_sub(text, ignore_word_regex))) def apply_uri_ignore_words( check_matches: List[Match[str]], line: str, word_regex: Pattern[str], ignore_word_regex: Optional[Pattern[str]], uri_regex: Pattern[str], uri_ignore_words: Set[str], ) -> List[Match[str]]: if not uri_ignore_words: return check_matches for uri in uri_regex.findall(line): for uri_word in extract_words(uri, word_regex, ignore_word_regex): if uri_word in uri_ignore_words: # determine/remove only the first among matches for i, match in enumerate(check_matches): if match.group() == uri_word: check_matches = check_matches[:i] + check_matches[i + 1 :] break return check_matches def parse_file( filename: str, colors: TermColors, summary: Optional[Summary], misspellings: Dict[str, Misspelling], ignore_words_cased: Set[str], exclude_lines: Set[str], file_opener: FileOpener, word_regex: Pattern[str], ignore_word_regex: Optional[Pattern[str]], uri_regex: Pattern[str], uri_ignore_words: Set[str], context: Optional[Tuple[int, int]], options: argparse.Namespace, ) -> int: bad_count = 0 lines = None changed = False if filename == "-": f = sys.stdin encoding = "utf-8" lines = f.readlines() else: if options.check_filenames: for word in extract_words(filename, word_regex, ignore_word_regex): if word in ignore_words_cased: continue lword = word.lower() if lword not in misspellings: continue fix = misspellings[lword].fix fixword = fix_case(word, misspellings[lword].data) if summary and fix: summary.update(lword) cfilename = f"{colors.FILE}{filename}{colors.DISABLE}" cwrongword = f"{colors.WWORD}{word}{colors.DISABLE}" crightword = f"{colors.FWORD}{fixword}{colors.DISABLE}" reason = misspellings[lword].reason if reason: if options.quiet_level & QuietLevels.DISABLED_FIXES: continue creason = f" \| {colors.FILE}{reason}{colors.DISABLE}" else: if options.quiet_level & QuietLevels.NON_AUTOMATIC_FIXES: continue creason = "" bad_count += 1 print(f"{cfilename}: {cwrongword} ==> {crightword}{creason}") # ignore irregular files if not os.path.isfile(filename): return bad_count try: text = is_text_file(filename) except PermissionError as e: print(f"WARNING: {e.strerror}: {filename}", file=sys.stderr) return bad_count except OSError: return bad_count if not text: if not options.quiet_level & QuietLevels.BINARY_FILE: print(f"WARNING: Binary file: {filename}", file=sys.stderr) return bad_count try: lines, encoding = file_opener.open(filename) except OSError: return bad_count for i, line in enumerate(lines): if line.rstrip() in exclude_lines: continue extra_words_to_ignore = set() match = inline_ignore_regex.search(line) if match: extra_words_to_ignore = set( filter(None, (match.group("words") or "").split(",")) ) if not extra_words_to_ignore: continue fixed_words = set() asked_for = set() # If all URI spelling errors will be ignored, erase any URI before # extracting words. Otherwise, apply ignores after extracting words. # This ensures that if a URI ignore word occurs both inside a URI and # outside, it will still be a spelling error. if "" in uri_ignore_words: line = uri_regex.sub(" ", line) check_matches = extract_words_iter(line, word_regex, ignore_word_regex) if "" not in uri_ignore_words: check_matches = apply_uri_ignore_words( check_matches, line, word_regex, ignore_word_regex, uri_regex, uri_ignore_words, ) for match in check_matches: word = match.group() if word in ignore_words_cased: continue lword = word.lower() if lword in misspellings and lword not in extra_words_to_ignore: # Sometimes we find a 'misspelling' which is actually a valid word # preceded by a string escape sequence. Ignore such cases as # they're usually false alarms; see issue #17 among others. char_before_idx = match.start() - 1 if ( char_before_idx >= 0 and line[char_before_idx] == "\\" # bell, backspace, formfeed, newline, carriage-return, tab, vtab. and word.startswith(("a", "b", "f", "n", "r", "t", "v")) and lword[1:] not in misspellings ): continue context_shown = False fix = misspellings[lword].fix fixword = fix_case(word, misspellings[lword].data) if options.interactive and lword not in asked_for: if context is not None: context_shown = True print_context(lines, i, context) fix, fixword = ask_for_word_fix( lines[i], match, misspellings[lword], options.interactive, colors=colors, ) asked_for.add(lword) if summary and fix: summary.update(lword) if word in fixed_words: # can skip because of re.sub below continue if options.write_changes and fix: changed = True lines[i] = re.sub(rf"\b{word}\b", fixword, lines[i]) fixed_words.add(word) continue # otherwise warning was explicitly set by interactive mode if ( options.interactive & 2 and not fix and not misspellings[lword].reason ): continue cfilename = f"{colors.FILE}{filename}{colors.DISABLE}" cline = f"{colors.FILE}{i + 1}{colors.DISABLE}" cwrongword = f"{colors.WWORD}{word}{colors.DISABLE}" crightword = f"{colors.FWORD}{fixword}{colors.DISABLE}" reason = misspellings[lword].reason if reason: if options.quiet_level & QuietLevels.DISABLED_FIXES: continue creason = f" \| {colors.FILE}{reason}{colors.DISABLE}" else: if options.quiet_level & QuietLevels.NON_AUTOMATIC_FIXES: continue creason = "" # If we get to this point (uncorrected error) we should change # our bad_count and thus return value bad_count += 1 if (not context_shown) and (context is not None): print_context(lines, i, context) if filename != "-": print( f"{cfilename}:{cline}: {cwrongword} " f"==> {crightword}{creason}" ) elif options.stdin_single_line: print(f"{cline}: {cwrongword} ==> {crightword}{creason}") else: print( f"{cline}: {line.strip()}\n\t{cwrongword} " f"==> {crightword}{creason}" ) if changed: if filename == "-": print("---") for line in lines: print(line, end="") else: if not options.quiet_level & QuietLevels.FIXES: print( f"{colors.FWORD}FIXED:{colors.DISABLE} {filename}", file=sys.stderr, ) with open(filename, "w", encoding=encoding, newline="") as f: f.writelines(lines) return bad_count def flatten_clean_comma_separated_arguments( arguments: Iterable[str], ) -> List[str]: """ >>> flatten_clean_comma_separated_arguments(["a, b ,\n c, d,", "e"]) ['a', 'b', 'c', 'd', 'e'] >>> flatten_clean_comma_separated_arguments([]) [] """ return [ item.strip() for argument in arguments for item in argument.split(",") if item ] def _script_main() -> int: """Wrap to main() for setuptools.""" try: return main(sys.argv[1:]) except KeyboardInterrupt: # User has typed CTRL+C sys.stdout.write("\n") return 130 def _usage_error(parser: argparse.ArgumentParser, message: str) -> int: parser.print_usage() print(message, file=sys.stderr) return EX_USAGE def main(args: str) -> int: """Contains flow control""" try: options, parser, used_cfg_files = parse_options(args) except configparser.Error as e: print( f"ERROR: ill-formed config file: {e.message}", file=sys.stderr, ) return EX_CONFIG # Report used config files if not options.quiet_level & QuietLevels.CONFIG_FILES: if len(used_cfg_files) > 0: print("Used config files:") for ifile, cfg_file in enumerate(used_cfg_files, start=1): print(f" {ifile}: {cfg_file}") if options.interactive > 0: options.write_changes = True if options.regex and options.write_changes: return _usage_error( parser, "ERROR: --write-changes cannot be used together with --regex", ) word_regex = options.regex or word_regex_def try: word_regex = re.compile(word_regex) except re.error as e: return _usage_error( parser, f'ERROR: invalid --regex "{word_regex}" ({e})', ) if options.ignore_regex: try: ignore_word_regex = re.compile(options.ignore_regex) except re.error as e: return _usage_error( parser, f'ERROR: invalid --ignore-regex "{options.ignore_regex}" ({e})', ) else: ignore_word_regex = None if options.ignore_multiline_regex: try: ignore_multiline_regex = re.compile( options.ignore_multiline_regex, re.DOTALL ) except re.error as e: return _usage_error( parser, f"ERROR: invalid --ignore-multiline-regex " f'"{options.ignore_multiline_regex}" ({e})', ) else: ignore_multiline_regex = None ignore_words, ignore_words_cased = parse_ignore_words_option( options.ignore_words_list ) if options.ignore_words: ignore_words_files = flatten_clean_comma_separated_arguments( options.ignore_words ) for ignore_words_file in ignore_words_files: if not os.path.isfile(ignore_words_file): return _usage_error( parser, f"ERROR: cannot find ignore-words file: {ignore_words_file}", ) build_ignore_words(ignore_words_file, ignore_words, ignore_words_cased) uri_regex = options.uri_regex or uri_regex_def try: uri_regex = re.compile(uri_regex) except re.error as e: return _usage_error( parser, f'ERROR: invalid --uri-regex "{uri_regex}" ({e})', ) uri_ignore_words = set( itertools.chain(parse_ignore_words_option(options.uri_ignore_words_list)) ) dictionaries = flatten_clean_comma_separated_arguments(options.dictionary or ["-"]) use_dictionaries = [] for dictionary in dictionaries: if dictionary == "-": # figure out which builtin dictionaries to use use = sorted(set(options.builtin.split(","))) for u in use: for builtin in _builtin_dictionaries: if builtin[0] == u: use_dictionaries.append( os.path.join(_data_root, f"dictionary{builtin[2]}.txt") ) break else: return _usage_error( parser, f"ERROR: Unknown builtin dictionary: {u}", ) else: if not os.path.isfile(dictionary): return _usage_error( parser, f"ERROR: cannot find dictionary file: {dictionary}", ) use_dictionaries.append(dictionary) misspellings: Dict[str, Misspelling] = {} for dictionary in use_dictionaries: build_dict(dictionary, misspellings, ignore_words) colors = TermColors() if not options.colors: colors.disable() summary = Summary() if options.summary else None context = None if options.context is not None: if (options.before_context is not None) or (options.after_context is not None): return _usage_error( parser, "ERROR: --context/-C cannot be used together with " "--context-before/-B or --context-after/-A", ) context_both = max(0, options.context) context = (context_both, context_both) elif (options.before_context is not None) or (options.after_context is not None): context_before = 0 context_after = 0 if options.before_context is not None: context_before = max(0, options.before_context) if options.after_context is not None: context_after = max(0, options.after_context) context = (context_before, context_after) exclude_lines: Set[str] = set() if options.exclude_file: exclude_files = flatten_clean_comma_separated_arguments(options.exclude_file) for exclude_file in exclude_files: build_exclude_hashes(exclude_file, exclude_lines) file_opener = FileOpener( options.hard_encoding_detection, options.quiet_level, ignore_multiline_regex, ) glob_match = GlobMatch( flatten_clean_comma_separated_arguments(options.skip) if options.skip else [] ) try: glob_match.match("/random/path") # does not need a real path except re.error: return _usage_error( parser, "ERROR: --skip/-S has been fed an invalid glob, " "try escaping special characters", ) bad_count = 0 for filename in sorted(options.files): # ignore hidden files if is_hidden(filename, options.check_hidden): continue if os.path.isdir(filename): for root, dirs, files in os.walk(filename): if glob_match.match(root): # skip (absolute) directories dirs.clear() continue if is_hidden(root, options.check_hidden): # dir itself hidden continue for file_ in sorted(files): # ignore hidden files in directories if is_hidden(file_, options.check_hidden): continue if glob_match.match(file_): # skip files continue fname = os.path.join(root, file_) if glob_match.match(fname): # skip paths continue bad_count += parse_file( fname, colors, summary, misspellings, ignore_words_cased, exclude_lines, file_opener, word_regex, ignore_word_regex, uri_regex, uri_ignore_words, context, options, ) # skip (relative) directories dirs[:] = [ dir_ for dir_ in dirs if not glob_match.match(dir_) and not is_hidden(dir_, options.check_hidden) ] elif not glob_match.match(filename): # skip files bad_count += parse_file( filename, colors, summary, misspellings, ignore_words_cased, exclude_lines, file_opener, word_regex, ignore_word_regex, uri_regex, uri_ignore_words, context, options, ) if summary: print("\n-------8<-------\nSUMMARY:") print(summary) if options.count: print(bad_count, file=sys.stderr) return EX_DATAERR if bad_count else EX_OK	44,133	Python	.py	1,191	26.893367	124	0.558973	codespell-project/codespell	1,850	470	225	GPL-2.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,910	__init__.py	codespell-project_codespell/codespell_lib/__init__.py	from ._codespell import _script_main, main from ._version import __version__ # type: ignore[import-not-found] __all__ = ["__version__", "_script_main", "main"]	162	Python	.py	3	52.666667	67	0.664557	codespell-project/codespell	1,850	470	225	GPL-2.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,911	_spellchecker.py	codespell-project_codespell/codespell_lib/_spellchecker.py	# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; version 2 of the License. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see # https://www.gnu.org/licenses/old-licenses/gpl-2.0.html. """ Copyright (C) 2010-2011 Lucas De Marchi <lucas.de.marchi@gmail.com> Copyright (C) 2011 ProFUSION embedded systems """ from typing import ( Dict, Set, ) # Pass all misspellings through this translation table to generate # alternative misspellings and fixes. alt_chars = (("'", "â€™"),) # noqa: RUF001 class Misspelling: def __init__(self, data: str, fix: bool, reason: str) -> None: self.data = data self.fix = fix self.reason = reason def add_misspelling( key: str, data: str, misspellings: Dict[str, Misspelling], ) -> None: data = data.strip() if "," in data: fix = False data, reason = data.rsplit(",", 1) reason = reason.lstrip() else: fix = True reason = "" misspellings[key] = Misspelling(data, fix, reason) def build_dict( filename: str, misspellings: Dict[str, Misspelling], ignore_words: Set[str], ) -> None: with open(filename, encoding="utf-8") as f: translate_tables = [(x, str.maketrans(x, y)) for x, y in alt_chars] for line in f: [key, data] = line.split("->") # TODO: For now, convert both to lower. # Someday we can maybe add support for fixing caps. key = key.lower() data = data.lower() if key not in ignore_words: add_misspelling(key, data, misspellings) # generate alternative misspellings/fixes for x, table in translate_tables: if x in key: alt_key = key.translate(table) alt_data = data.translate(table) if alt_key not in ignore_words: add_misspelling(alt_key, alt_data, misspellings)	2,393	Python	.py	65	30.307692	75	0.635893	codespell-project/codespell	1,850	470	225	GPL-2.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,912	test_basic.py	codespell-project_codespell/codespell_lib/tests/test_basic.py	import contextlib import inspect import os import os.path as op import re import subprocess import sys from io import StringIO from pathlib import Path from shutil import copyfile from typing import Any, Generator, Optional, Tuple, Union from unittest import mock import pytest import codespell_lib as cs_ from codespell_lib._codespell import ( EX_CONFIG, EX_DATAERR, EX_OK, EX_USAGE, uri_regex_def, ) def test_constants() -> None: """Test our EX constants.""" assert EX_OK == 0 assert EX_USAGE == 64 assert EX_DATAERR == 65 assert EX_CONFIG == 78 class MainWrapper: """Compatibility wrapper for when we used to return the count.""" @staticmethod def main( args: Any, count: bool = True, std: bool = False, ) -> Union[int, Tuple[int, str, str]]: args = tuple(str(arg) for arg in args) if count: args = ("--count", args) code = cs_.main(args) frame = inspect.currentframe() assert frame is not None frame = frame.f_back assert frame is not None capsys = frame.f_locals["capsys"] stdout, stderr = capsys.readouterr() assert code in (EX_OK, EX_USAGE, EX_DATAERR, EX_CONFIG) if code == EX_DATAERR: # have some misspellings code = int(stderr.split("\n")[-2]) elif code == EX_OK and count: code = int(stderr.split("\n")[-2]) assert code == 0 if std: return (code, stdout, stderr) return code cs = MainWrapper() def run_codespell( args: Tuple[Any, ...] = (), cwd: Optional[Path] = None, ) -> int: """Run codespell.""" args = tuple(str(arg) for arg in args) proc = subprocess.run( # noqa: S603 ["codespell", "--count", args], # noqa: S607 cwd=cwd, capture_output=True, encoding="utf-8", check=False, ) return int(proc.stderr.split("\n")[-2]) def test_command(tmp_path: Path) -> None: """Test running the codespell executable.""" # With no arguments does "." assert run_codespell(cwd=tmp_path) == 0 (tmp_path / "bad.txt").write_text("abandonned\nAbandonned\nABANDONNED\nAbAnDoNnEd") assert run_codespell(cwd=tmp_path) == 4 def test_basic( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test some basic functionality.""" assert cs.main("_does_not_exist_") == 0 fname = tmp_path / "tmp" fname.touch() result = cs.main("-D", "foo", fname, std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == EX_USAGE, "missing dictionary" assert "cannot find dictionary" in stderr assert cs.main(fname) == 0, "empty file" with fname.open("a") as f: f.write("this is a test file\n") assert cs.main(fname) == 0, "good" with fname.open("a") as f: f.write("abandonned\n") assert cs.main(fname) == 1, "bad" with fname.open("a") as f: f.write("abandonned\n") assert cs.main(fname) == 2, "worse" with fname.open("a") as f: f.write("tim\ngonna\n") assert cs.main(fname) == 2, "with a name" assert cs.main("--builtin", "clear,rare,names,informal", fname) == 4 with fname.open("w") as f: # overwrite the file f.write("var = 'nwe must check codespell likes escapes nin strings'\n") assert cs.main(fname) == 1, "checking our string escape test word is bad" # the first one is missed because the apostrophe means its not currently # treated as a word on its own with fname.open("w") as f: # overwrite the file f.write("var = '\\nwe must check codespell likes escapes \\nin strings'\n") assert cs.main(fname) == 0, "with string escape" result = cs.main(fname, "--builtin", "foo", std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == EX_USAGE # bad type assert "Unknown builtin dictionary" in stderr result = cs.main(fname, "-D", tmp_path / "foo", std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == EX_USAGE # bad dict assert "cannot find dictionary" in stderr fname.unlink() with (tmp_path / "bad.txt").open("w", newline="") as f: f.write( "abandonned\nAbandonned\nABANDONNED\nAbAnDoNnEd\nabandonned\rAbandonned\r\nABANDONNED \n AbAnDoNnEd" # noqa: E501 ) assert cs.main(tmp_path) == 8 result = cs.main("-w", tmp_path, std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == 0 assert "FIXED:" in stderr with (tmp_path / "bad.txt").open(newline="") as f: new_content = f.read() assert cs.main(tmp_path) == 0 assert ( new_content == "abandoned\nAbandoned\nABANDONED\nabandoned\nabandoned\rAbandoned\r\nABANDONED \n abandoned" # noqa: E501 ) (tmp_path / "bad.txt").write_text("abandonned abandonned\n") assert cs.main(tmp_path) == 2 result = cs.main("-q", "16", "-w", tmp_path, count=False, std=True) assert isinstance(result, tuple) code, stdout, stderr = result assert code == 0 assert not stdout assert not stderr assert cs.main(tmp_path) == 0 # empty directory (tmp_path / "empty").mkdir() assert cs.main(tmp_path) == 0 def test_default_word_parsing( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: fname = tmp_path / "backtick" with fname.open("a") as f: f.write("`abandonned`\n") assert cs.main(fname) == 1, "bad" fname = tmp_path / "apostrophe" fname.write_text("woudn't\n", encoding="utf-8") # U+0027 assert cs.main(fname) == 1, "misspelling containing typewriter apostrophe U+0027" fname.write_text("woudn’t\n", encoding="utf-8") # U+2019 # noqa: RUF001 assert cs.main(fname) == 1, "misspelling containing typographic apostrophe U+2019" def test_bad_glob( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: # disregard invalid globs, properly handle escaped globs g = tmp_path / "glob" g.mkdir() fname = g / "[b-a].txt" fname.write_text("abandonned\n") assert cs.main(g) == 1 # bad glob is invalid result = cs.main("--skip", "[b-a].txt", g, std=True) assert isinstance(result, tuple) code, _, stderr = result if sys.hexversion < 0x030A05F0: # Python < 3.10.5 raises re.error assert code == EX_USAGE, "invalid glob" assert "invalid glob" in stderr else: # Python >= 3.10.5 does not match assert code == 1 # properly escaped glob is valid, and matches glob-like file name assert cs.main("--skip", "[[]b-a[]].txt", g) == 0 @pytest.mark.skipif(sys.platform != "linux", reason="Only supported on Linux") def test_permission_error( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test permission error handling.""" fname = tmp_path / "unreadable.txt" fname.write_text("abandonned\n") result = cs.main(fname, std=True) assert isinstance(result, tuple) _, _, stderr = result assert "WARNING:" not in stderr fname.chmod(0o000) result = cs.main(fname, std=True) assert isinstance(result, tuple) _, _, stderr = result assert "WARNING:" in stderr def test_interactivity( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test interaction""" # Windows can't read a currently-opened file, so here we use # NamedTemporaryFile just to get a good name fname = tmp_path / "tmp" fname.touch() try: assert cs.main(fname) == 0, "empty file" fname.write_text("abandonned\n") with mock.patch.object(sys, "argv", ("-i", "-1", fname)): with pytest.raises(SystemExit) as e: cs.main("-i", "-1", fname) assert e.type is SystemExit assert e.value.code != 0 with FakeStdin("n\n"): result = cs.main("-w", "-i", "3", fname, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 0 assert "==>" in stdout with FakeStdin("x\ny\n"): assert cs.main("-w", "-i", "3", fname) == 0 assert cs.main(fname) == 0 finally: fname.unlink() # New example fname = tmp_path / "tmp2" fname.write_text("abandonned\n") try: assert cs.main(fname) == 1 with FakeStdin(" "): # blank input -> Y assert cs.main("-w", "-i", "3", fname) == 0 assert cs.main(fname) == 0 finally: fname.unlink() # multiple options fname = tmp_path / "tmp3" fname.write_text("ackward\n") try: assert cs.main(fname) == 1 with FakeStdin(" \n"): # blank input -> nothing assert cs.main("-w", "-i", "3", fname) == 0 assert cs.main(fname) == 1 with FakeStdin("0\n"): # blank input -> nothing assert cs.main("-w", "-i", "3", fname) == 0 assert cs.main(fname) == 0 assert fname.read_text() == "awkward\n" fname.write_text("ackward\n") assert cs.main(fname) == 1 with FakeStdin("x\n1\n"): # blank input -> nothing result = cs.main("-w", "-i", "3", fname, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 0 assert "a valid option" in stdout assert cs.main(fname) == 0 assert fname.read_text() == "backward\n" finally: fname.unlink() def test_summary( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test summary functionality.""" fname = tmp_path / "tmp" fname.touch() result = cs.main(fname, std=True, count=False) assert isinstance(result, tuple) code, stdout, stderr = result assert code == 0 assert not stdout assert not stderr, "no output" result = cs.main(fname, "--summary", std=True) assert isinstance(result, tuple) code, stdout, stderr = result assert code == 0 assert stderr == "0\n" assert "SUMMARY" in stdout assert len(stdout.split("\n")) == 5 fname.write_text("abandonned\nabandonned") assert code == 0 result = cs.main(fname, "--summary", std=True) assert isinstance(result, tuple) code, stdout, stderr = result assert stderr == "2\n" assert "SUMMARY" in stdout assert len(stdout.split("\n")) == 7 assert "abandonned" in stdout.split()[-2] def test_ignore_dictionary( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test ignore dictionary functionality.""" bad_name = tmp_path / "bad.txt" bad_name.write_text( "1 abandonned 1\n" "2 abandonned 2\n" "3 abandonned 3\r\n" "4 abilty 4\n" "5 abilty 5\n" "6 abilty 6\r\n" "7 ackward 7\n" "8 ackward 8\n" "9 ackward 9\r\n" "abondon\n" ) assert cs.main(bad_name) == 10 fname = tmp_path / "ignore.txt" fname.write_text("abandonned\nabilty\r\nackward") assert cs.main("-I", fname, bad_name) == 1 # missing file in ignore list fname_missing = tmp_path / "missing.txt" result = cs.main("-I", fname_missing, bad_name, std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == EX_USAGE assert "ERROR:" in stderr # comma-separated list of files fname_dummy1 = tmp_path / "dummy1.txt" fname_dummy1.touch() fname_dummy2 = tmp_path / "dummy2.txt" fname_dummy2.touch() assert cs.main("-I", fname_dummy1, "-I", fname, "-I", fname_dummy2, bad_name) == 1 assert cs.main("-I", f"{fname_dummy1},{fname},{fname_dummy2}", bad_name) == 1 def test_ignore_words_with_cases( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test case-sensitivity implemented for -I and -L options in #3272.""" bad_name = tmp_path / "MIS.txt" bad_name.write_text( "1 MIS (Management Information System) 1\n" "2 Les Mis (1980 musical) 2\n" "3 mis 3\n" ) assert cs.main(bad_name) == 3 assert cs.main(bad_name, "-f") == 4 fname = tmp_path / "ignore.txt" fname.write_text("miS") assert cs.main("-I", fname, bad_name) == 3 assert cs.main("-LmiS", bad_name) == 3 assert cs.main("-I", fname, "-f", bad_name) == 4 assert cs.main("-LmiS", "-f", bad_name) == 4 fname.write_text("MIS") assert cs.main("-I", fname, bad_name) == 2 assert cs.main("-LMIS", bad_name) == 2 assert cs.main("-I", fname, "-f", bad_name) == 2 assert cs.main("-LMIS", "-f", bad_name) == 2 fname.write_text("MIS\nMis") assert cs.main("-I", fname, bad_name) == 1 assert cs.main("-LMIS,Mis", bad_name) == 1 assert cs.main("-I", fname, "-f", bad_name) == 1 assert cs.main("-LMIS,Mis", "-f", bad_name) == 1 fname.write_text("mis") assert cs.main("-I", fname, bad_name) == 0 assert cs.main("-Lmis", bad_name) == 0 assert cs.main("-I", fname, "-f", bad_name) == 0 assert cs.main("-Lmis", "-f", bad_name) == 0 def test_ignore_word_list( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test ignore word list functionality.""" (tmp_path / "bad.txt").write_text("abandonned\nabondon\nabilty\n") assert cs.main(tmp_path) == 3 assert cs.main("-Labandonned,someword", "-Labilty", tmp_path) == 1 @pytest.mark.parametrize( ("content", "expected_error_count"), [ # recommended form ("abandonned abondon abilty # codespell:ignore abondon", 2), ("abandonned abondon abilty // codespell:ignore abondon,abilty", 1), ( "abandonned abondon abilty /* codespell:ignore abandonned,abondon,abilty", 0, ), # ignore unused ignore ("abandonned abondon abilty # codespell:ignore nomenklatur", 3), # wildcard form ("abandonned abondon abilty # codespell:ignore ", 0), ("abandonned abondon abilty # codespell:ignore", 0), ("abandonned abondon abilty # codespell:ignore\n", 0), ("abandonned abondon abilty # codespell:ignore\r\n", 0), ("abandonned abondon abilty # codespell:ignore # noqa: E501\n", 0), ("abandonned abondon abilty # codespell:ignore # noqa: E501\n", 0), ("abandonned abondon abilty # codespell:ignore# noqa: E501\n", 0), ("abandonned abondon abilty # codespell:ignore, noqa: E501\n", 0), ("abandonned abondon abilty #codespell:ignore\n", 0), # ignore these for safety ("abandonned abondon abilty # codespell:ignorenoqa: E501\n", 3), ("abandonned abondon abilty codespell:ignore\n", 3), ("abandonned abondon abilty codespell:ignore\n", 3), # ignore these as they aren't valid ("abandonned abondon abilty # codespell:igore\n", 4), # showcase different comment markers ("abandonned abondon abilty ' codespell:ignore\n", 0), ('abandonned abondon abilty " codespell:ignore\n', 0), ("abandonned abondon abilty ;; codespell:ignore\n", 0), ("abandonned abondon abilty /* codespell:ignore /\n", 0), # prose examples ( "You could also use line based igore ( codespell:ignore ) to igore ", 0, ), ("You could also use line based igore (codespell:ignore) to igore ", 0), ( "You could also use line based igore (codespell:ignore igore) to igore ", 0, ), ( "You could also use line based igore (codespell:ignore igare) to igore ", 2, ), ], ) def test_inline_ignores( tmpdir: pytest.TempPathFactory, capsys: pytest.CaptureFixture[str], content: str, expected_error_count: int, ) -> None: d = str(tmpdir) with open(op.join(d, "bad.txt"), "w", encoding="utf-8") as f: f.write(content) assert cs.main(d) == expected_error_count def test_custom_regex( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test custom word regex.""" (tmp_path / "bad.txt").write_text("abandonned_abondon\n") assert cs.main(tmp_path) == 0 assert cs.main("-r", "[a-z]+", tmp_path) == 2 result = cs.main("-r", "[a-z]+", "--write-changes", tmp_path, std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == EX_USAGE assert "ERROR:" in stderr def test_exclude_file( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test exclude file functionality.""" bad_name = tmp_path / "bad.txt" # check all possible combinations of lines to ignore and ignores combinations = "".join( f"{n} abandonned {n}\n" f"{n} abandonned {n}\r\n" f"{n} abandonned {n} \n" f"{n} abandonned {n} \r\n" for n in range(1, 5) ) bad_name.write_bytes( (combinations + "5 abandonned 5\n6 abandonned 6").encode("utf-8") ) assert cs.main(bad_name) == 18 fname = tmp_path / "tmp.txt" fname.write_bytes( b"1 abandonned 1\n" b"2 abandonned 2\r\n" b"3 abandonned 3 \n" b"4 abandonned 4 \r\n" b"6 abandonned 6\n" ) assert cs.main(bad_name) == 18 assert cs.main("-x", fname, bad_name) == 1 # comma-separated list of files fname_dummy1 = tmp_path / "dummy1.txt" fname_dummy1.touch() fname_dummy2 = tmp_path / "dummy2.txt" fname_dummy2.touch() assert cs.main("-x", fname_dummy1, "-x", fname, "-x", fname_dummy2, bad_name) == 1 assert cs.main("-x", f"{fname_dummy1},{fname},{fname_dummy2}", bad_name) == 1 def test_encoding( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test encoding handling.""" # Some simple Unicode things fname = tmp_path / "tmp" fname.touch() # with CaptureStdout() as sio: assert cs.main(fname) == 0 fname.write_bytes("naïve\n".encode()) assert cs.main(fname) == 0 assert cs.main("-e", fname) == 0 with fname.open("ab") as f: f.write(b"naieve\n") assert cs.main(fname) == 1 # Encoding detection (only try ISO 8859-1 because UTF-8 is the default) fname.write_bytes(b"Speling error, non-ASCII: h\xe9t\xe9rog\xe9n\xe9it\xe9\n") # check warnings about wrong encoding are enabled with "-q 0" result = cs.main("-q", "0", fname, std=True, count=True) assert isinstance(result, tuple) code, stdout, stderr = result assert code == 1 assert "Speling" in stdout assert "iso-8859-1" in stderr # check warnings about wrong encoding are disabled with "-q 1" result = cs.main("-q", "1", fname, std=True, count=True) assert isinstance(result, tuple) code, stdout, stderr = result assert code == 1 assert "Speling" in stdout assert "iso-8859-1" not in stderr # Binary file warning fname.write_bytes(b"\x00\x00naiive\x00\x00") result = cs.main(fname, std=True, count=False) assert isinstance(result, tuple) code, stdout, stderr = result assert code == 0 assert not stdout assert not stderr result = cs.main("-q", "0", fname, std=True, count=False) assert isinstance(result, tuple) code, stdout, stderr = result assert code == 0 assert not stdout assert "WARNING: Binary file" in stderr def test_unknown_encoding_chardet( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test opening a file with unknown encoding using chardet""" fname = tmp_path / "tmp" fname.touch() assert cs.main("--hard-encoding-detection", fname) == 0 def test_ignore( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test ignoring of files and directories.""" goodtxt = tmp_path / "good.txt" goodtxt.write_text("this file is okay") assert cs.main(tmp_path) == 0 badtxt = tmp_path / "bad.txt" badtxt.write_text("abandonned") assert cs.main(tmp_path) == 1 assert cs.main("--skip=bad", tmp_path) == 0 assert cs.main("--skip=bad.txt", tmp_path) == 0 subdir = tmp_path / "ignoredir" subdir.mkdir() (subdir / "bad.txt").write_text("abandonned") assert cs.main(tmp_path) == 2 assert cs.main("--skip=bad", tmp_path) == 0 assert cs.main("--skip=whatever.txt,bad,whatelse.txt", tmp_path) == 0 assert cs.main("--skip=whatever.txt,\n bad* ,", tmp_path) == 0 assert cs.main("--skip=ignoredir", tmp_path) == 1 assert cs.main("--skip=ignoredir", tmp_path) == 1 assert cs.main("--skip=ignoredir/bad", tmp_path) == 1 assert cs.main(f"--skip={tmp_path}", tmp_path) == 0 badjs = tmp_path / "bad.js" copyfile(badtxt, badjs) assert cs.main("--skip=.js", goodtxt, badtxt, badjs) == 1 def test_check_filename( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test filename check.""" fname = tmp_path / "abandonned.txt" # Empty file fname.touch() assert cs.main("-f", tmp_path) == 1 # Normal file with contents fname.write_text(".") assert cs.main("-f", tmp_path) == 1 # Normal file with binary contents fname.write_bytes(b"\x00\x00naiive\x00\x00") assert cs.main("-f", tmp_path) == 1 @pytest.mark.skipif( (not hasattr(os, "mkfifo") or not callable(os.mkfifo)), reason="requires os.mkfifo" ) def test_check_filename_irregular_file( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test irregular file filename check.""" # Irregular file (!isfile()) os.mkfifo(tmp_path / "abandonned") assert cs.main("-f", tmp_path) == 1 def test_check_hidden( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test ignoring of hidden files.""" # visible file # # tmp_path # └── test.txt # fname = tmp_path / "test.txt" fname.write_text("erorr\n") assert cs.main(fname) == 1 assert cs.main(tmp_path) == 1 # hidden file # # tmp_path # └── .test.txt # hidden_file = tmp_path / ".test.txt" fname.rename(hidden_file) assert cs.main(hidden_file) == 0 assert cs.main(tmp_path) == 0 assert cs.main("--check-hidden", hidden_file) == 1 assert cs.main("--check-hidden", tmp_path) == 1 # hidden file with typo in name # # tmp_path # └── .abandonned.txt # typo_file = tmp_path / ".abandonned.txt" hidden_file.rename(typo_file) assert cs.main(typo_file) == 0 assert cs.main(tmp_path) == 0 assert cs.main("--check-hidden", typo_file) == 1 assert cs.main("--check-hidden", tmp_path) == 1 assert cs.main("--check-hidden", "--check-filenames", typo_file) == 2 assert cs.main("--check-hidden", "--check-filenames", tmp_path) == 2 # hidden directory # # tmp_path # ├── .abandonned # │ ├── .abandonned.txt # │ └── subdir # │ └── .abandonned.txt # └── .abandonned.txt # assert cs.main(tmp_path) == 0 assert cs.main("--check-hidden", tmp_path) == 1 assert cs.main("--check-hidden", "--check-filenames", tmp_path) == 2 hidden = tmp_path / ".abandonned" hidden.mkdir() copyfile(typo_file, hidden / typo_file.name) subdir = hidden / "subdir" subdir.mkdir() copyfile(typo_file, subdir / typo_file.name) assert cs.main(tmp_path) == 0 assert cs.main("--check-hidden", tmp_path) == 3 assert cs.main("--check-hidden", "--check-filenames", tmp_path) == 8 # check again with a relative path try: rel = op.relpath(tmp_path) except ValueError: # Windows: path is on mount 'C:', start on mount 'D:' pass else: assert cs.main(rel) == 0 assert cs.main("--check-hidden", rel) == 3 assert cs.main("--check-hidden", "--check-filenames", rel) == 8 # hidden subdirectory # # tmp_path # ├── .abandonned # │ ├── .abandonned.txt # │ └── subdir # │ └── .abandonned.txt # ├── .abandonned.txt # └── subdir # └── .abandonned # └── .abandonned.txt subdir = tmp_path / "subdir" subdir.mkdir() hidden = subdir / ".abandonned" hidden.mkdir() copyfile(typo_file, hidden / typo_file.name) assert cs.main(tmp_path) == 0 assert cs.main("--check-hidden", tmp_path) == 4 assert cs.main("--check-hidden", "--check-filenames", tmp_path) == 11 def test_case_handling( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test that capitalized entries get detected properly.""" # Some simple Unicode things fname = tmp_path / "tmp" fname.touch() # with CaptureStdout() as sio: assert cs.main(fname) == 0 fname.write_bytes(b"this has an ACII error") result = cs.main(fname, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 1 assert "ASCII" in stdout result = cs.main("-w", fname, std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == 0 assert "FIXED" in stderr assert fname.read_text(encoding="utf-8") == "this has an ASCII error" def _helper_test_case_handling_in_fixes( tmp_path: Path, capsys: pytest.CaptureFixture[str], reason: bool, ) -> None: dictionary_name = tmp_path / "dictionary.txt" if reason: dictionary_name.write_text("adoptor->adopter, adaptor, reason\n") else: dictionary_name.write_text("adoptor->adopter, adaptor,\n") # the misspelled word is entirely lowercase fname = tmp_path / "bad.txt" fname.write_text("early adoptor\n") result = cs.main("-D", dictionary_name, fname, std=True) assert isinstance(result, tuple) _, stdout, _ = result # all suggested fixes must be lowercase too assert "adopter, adaptor" in stdout # the reason, if any, must not be modified if reason: assert "reason" in stdout # the misspelled word is capitalized fname.write_text("Early Adoptor\n") result = cs.main("-D", dictionary_name, fname, std=True) assert isinstance(result, tuple) _, stdout, _ = result # all suggested fixes must be capitalized too assert "Adopter, Adaptor" in stdout # the reason, if any, must not be modified if reason: assert "reason" in stdout # the misspelled word is entirely uppercase fname.write_text("EARLY ADOPTOR\n") result = cs.main("-D", dictionary_name, fname, std=True) assert isinstance(result, tuple) _, stdout, _ = result # all suggested fixes must be uppercase too assert "ADOPTER, ADAPTOR" in stdout # the reason, if any, must not be modified if reason: assert "reason" in stdout # the misspelled word mixes lowercase and uppercase fname.write_text("EaRlY AdOpToR\n") result = cs.main("-D", dictionary_name, fname, std=True) assert isinstance(result, tuple) _, stdout, _ = result # all suggested fixes should be lowercase assert "adopter, adaptor" in stdout # the reason, if any, must not be modified if reason: assert "reason" in stdout def test_case_handling_in_fixes( tmp_path: Path, capsys: pytest.CaptureFixture[str] ) -> None: """Test that the case of fixes is similar to the misspelled word.""" _helper_test_case_handling_in_fixes(tmp_path, capsys, reason=False) _helper_test_case_handling_in_fixes(tmp_path, capsys, reason=True) def test_context( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test context options.""" (tmp_path / "context.txt").write_text( "line 1\nline 2\nline 3 abandonned\nline 4\nline 5" ) # symmetric context, fully within file result = cs.main("-C", "1", tmp_path, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 1 lines = stdout.split("\n") assert len(lines) == 5 assert lines[0] == ": line 2" assert lines[1] == "> line 3 abandonned" assert lines[2] == ": line 4" # requested context is bigger than the file result = cs.main("-C", "10", tmp_path, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 1 lines = stdout.split("\n") assert len(lines) == 7 assert lines[0] == ": line 1" assert lines[1] == ": line 2" assert lines[2] == "> line 3 abandonned" assert lines[3] == ": line 4" assert lines[4] == ": line 5" # only before context result = cs.main("-B", "2", tmp_path, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 1 lines = stdout.split("\n") assert len(lines) == 5 assert lines[0] == ": line 1" assert lines[1] == ": line 2" assert lines[2] == "> line 3 abandonned" # only after context result = cs.main("-A", "1", tmp_path, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 1 lines = stdout.split("\n") assert len(lines) == 4 assert lines[0] == "> line 3 abandonned" assert lines[1] == ": line 4" # asymmetric context result = cs.main("-B", "2", "-A", "1", tmp_path, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 1 lines = stdout.split("\n") assert len(lines) == 6 assert lines[0] == ": line 1" assert lines[1] == ": line 2" assert lines[2] == "> line 3 abandonned" assert lines[3] == ": line 4" # both '-C' and '-A' on the command line result = cs.main("-C", "2", "-A", "1", tmp_path, std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == EX_USAGE lines = stderr.split("\n") assert "ERROR" in lines[0] # both '-C' and '-B' on the command line result = cs.main("-C", "2", "-B", "1", tmp_path, std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == EX_USAGE lines = stderr.split("\n") assert "ERROR" in lines[0] def test_ignore_regex_option( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test ignore regex option functionality.""" # Invalid regex. result = cs.main("--ignore-regex=(", std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == EX_USAGE assert "usage:" in stdout fname = tmp_path / "flag.txt" fname.write_text("# Please see http://example.com/abandonned for info\n") # Test file has 1 invalid entry, and it's not ignored by default. assert cs.main(fname) == 1 # An empty regex is the default value, and nothing is ignored. assert cs.main(fname, "--ignore-regex=") == 1 assert cs.main(fname, '--ignore-regex=""') == 1 # Non-matching regex results in nothing being ignored. assert cs.main(fname, "--ignore-regex=^$") == 1 # A word can be ignored. assert cs.main(fname, "--ignore-regex=abandonned") == 0 # Ignoring part of the word can result in odd behavior. assert cs.main(fname, "--ignore-regex=nn") == 0 fname.write_text("abandonned donn\n") # Test file has 2 invalid entries. assert cs.main(fname) == 2 # Ignoring donn breaks them both. assert cs.main(fname, "--ignore-regex=donn") == 0 # Adding word breaks causes only one to be ignored. assert cs.main(fname, r"--ignore-regex=\bdonn\b") == 1 def test_ignore_multiline_regex_option( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test ignore regex option functionality.""" # Invalid regex. result = cs.main("--ignore-multiline-regex=(", std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == EX_USAGE assert "usage:" in stdout fname = tmp_path / "flag.txt" fname.write_text( """ Please see http://example.com/abandonned for info # codespell:ignore-begin ''' abandonned abandonned ''' # codespell:ignore-end abandonned """ ) assert cs.main(fname) == 4 assert ( cs.main( fname, "--ignore-multiline-regex", "codespell:ignore-begin.codespell:ignore-end", ) == 2 ) def test_uri_regex_option( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test --uri-regex option functionality.""" # Invalid regex. result = cs.main("--uri-regex=(", std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == EX_USAGE assert "usage:" in stdout fname = tmp_path / "flag.txt" fname.write_text("# Please see http://abandonned.com for info\n") # By default, the standard regex is used. assert cs.main(fname) == 1 assert cs.main(fname, "--uri-ignore-words-list=abandonned") == 0 # If empty, nothing matches. assert cs.main(fname, "--uri-regex=", "--uri-ignore-words-list=abandonned") == 0 # Can manually match urls. assert ( cs.main(fname, "--uri-regex=\\bhttp.\\b", "--uri-ignore-words-list=abandonned") == 0 ) # Can also match arbitrary content. fname.write_text("abandonned") assert cs.main(fname) == 1 assert cs.main(fname, "--uri-ignore-words-list=abandonned") == 1 assert cs.main(fname, "--uri-regex=.") == 1 assert cs.main(fname, "--uri-regex=.", "--uri-ignore-words-list=abandonned") == 0 def test_uri_ignore_words_list_option_uri( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test ignore regex option functionality.""" fname = tmp_path / "flag.txt" fname.write_text("# Please see http://example.com/abandonned for info\n") # Test file has 1 invalid entry, and it's not ignored by default. assert cs.main(fname) == 1 # An empty list is the default value, and nothing is ignored. assert cs.main(fname, "--uri-ignore-words-list=") == 1 # Non-matching regex results in nothing being ignored. assert cs.main(fname, "--uri-ignore-words-list=foo,example") == 1 # A word can be ignored. assert cs.main(fname, "--uri-ignore-words-list=abandonned") == 0 assert cs.main(fname, "--uri-ignore-words-list=foo,abandonned,bar") == 0 assert cs.main(fname, "--uri-ignore-words-list=") == 0 # The match must be for the complete word. assert cs.main(fname, "--uri-ignore-words-list=abandonn") == 1 fname.write_text("abandonned http://example.com/abandonned\n") # Test file has 2 invalid entries. assert cs.main(fname) == 2 # Ignoring the value in the URI won't ignore the word completely. assert cs.main(fname, "--uri-ignore-words-list=abandonned") == 1 assert cs.main(fname, "--uri-ignore-words-list=") == 1 # The regular --ignore-words-list will ignore both. assert cs.main(fname, "--ignore-words-list=abandonned") == 0 variation_option = "--uri-ignore-words-list=abandonned" # Variations where an error is ignored. for variation in ( "# Please see http://abandonned for info\n", '# Please see "http://abandonned" for info\n', # This variation could be un-ignored, but it'd require a # more complex regex as " is valid in parts of URIs. '# Please see "http://foo"abandonned for info\n', "# Please see https://abandonned for info\n", "# Please see ftp://abandonned for info\n", "# Please see http://example/abandonned for info\n", "# Please see http://example.com/abandonned for info\n", "# Please see http://exam.com/ple#abandonned for info\n", "# Please see http://exam.com/ple?abandonned for info\n", "# Please see http://127.0.0.1/abandonned for info\n", "# Please see http://[2001:0db8:85a3:0000:0000:8a2e:0370" ":7334]/abandonned for info\n", ): fname.write_text(variation) assert cs.main(fname) == 1, variation assert cs.main(fname, variation_option) == 0, variation # Variations where no error is ignored. for variation in ( "# Please see abandonned/ for info\n", "# Please see http:abandonned for info\n", "# Please see foo/abandonned for info\n", "# Please see http://foo abandonned for info\n", ): fname.write_text(variation) assert cs.main(fname) == 1, variation assert cs.main(fname, variation_option) == 1, variation def test_uri_ignore_words_list_option_email( tmp_path: Path, capsys: pytest.CaptureFixture[str], ) -> None: """Test ignore regex option functionality.""" fname = tmp_path / "flag.txt" fname.write_text("# Please see example@abandonned.com for info\n") # Test file has 1 invalid entry, and it's not ignored by default. assert cs.main(fname) == 1 # An empty list is the default value, and nothing is ignored. assert cs.main(fname, "--uri-ignore-words-list=") == 1 # Non-matching regex results in nothing being ignored. assert cs.main(fname, "--uri-ignore-words-list=foo,example") == 1 # A word can be ignored. assert cs.main(fname, "--uri-ignore-words-list=abandonned") == 0 assert cs.main(fname, "--uri-ignore-words-list=foo,abandonned,bar") == 0 assert cs.main(fname, "--uri-ignore-words-list=") == 0 # The match must be for the complete word. assert cs.main(fname, "--uri-ignore-words-list=abandonn") == 1 fname.write_text("abandonned example@abandonned.com\n") # Test file has 2 invalid entries. assert cs.main(fname) == 2 # Ignoring the value in the URI won't ignore the word completely. assert cs.main(fname, "--uri-ignore-words-list=abandonned") == 1 assert cs.main(fname, "--uri-ignore-words-list=") == 1 # The regular --ignore-words-list will ignore both. assert cs.main(fname, "--ignore-words-list=abandonned") == 0 variation_option = "--uri-ignore-words-list=abandonned" # Variations where an error is ignored. for variation in ( "# Please see example@abandonned for info\n", "# Please see abandonned@example for info\n", "# Please see abandonned@example.com for info\n", "# Please see mailto:abandonned@example.com?subject=Test for info\n", ): fname.write_text(variation) assert cs.main(fname) == 1, variation assert cs.main(fname, variation_option) == 0, variation # Variations where no error is ignored. for variation in ( "# Please see example @ abandonned for info\n", "# Please see abandonned@ example for info\n", "# Please see mailto:foo@example.com?subject=Test abandonned for info\n", ): fname.write_text(variation) assert cs.main(fname) == 1, variation assert cs.main(fname, variation_option) == 1, variation def test_uri_regex_def() -> None: uri_regex = re.compile(uri_regex_def) # Tests based on https://mathiasbynens.be/demo/url-regex true_positives = ( "http://foo.com/blah_blah", "http://foo.com/blah_blah/", "http://foo.com/blah_blah_(wikipedia)", "http://foo.com/blah_blah_(wikipedia)_(again)", "http://www.example.com/wpstyle/?p=364", "https://www.example.com/foo/?bar=baz&inga=42&quux", "http://✪df.ws/123", "http://userid:password@example.com:8080", "http://userid:password@example.com:8080/", "http://userid@example.com", "http://userid@example.com/", "http://userid@example.com:8080", "http://userid@example.com:8080/", "http://userid:password@example.com", "http://userid:password@example.com/", "http://142.42.1.1/", "http://142.42.1.1:8080/", "http://➡.ws/䨹", "http://⌘.ws", "http://⌘.ws/", "http://foo.com/blah_(wikipedia)#cite-1", "http://foo.com/blah_(wikipedia)_blah#cite-1", "http://foo.com/unicode_(✪)_in_parens", "http://foo.com/(something)?after=parens", "http://☺.damowmow.com/", "http://code.google.com/events/#&product=browser", "http://j.mp", "ftp://foo.bar/baz", "http://foo.bar/?q=Test%20URL-encoded%20stuff", "http://مثال.إختبار", "http://例子.测试", "http://उदाहरण.परीक्षा", "http://-.~_!$&'()+,;=:%40:80%2f::::::@example.com", "http://1337.net", "http://a.b-c.de", "http://223.255.255.254", ) true_negatives = ( "http://", "//", "//a", "///a", "///", "foo.com", "rdar://1234", "h://test", "://should.fail", "ftps://foo.bar/", ) false_positives = ( "http://.", "http://..", "http://../", "http://?", "http://??", "http://??/", "http://#", "http://##", "http://##/", "http:///a", "http://-error-.invalid/", "http://a.b--c.de/", "http://-a.b.co", "http://a.b-.co", "http://0.0.0.0", "http://10.1.1.0", "http://10.1.1.255", "http://224.1.1.1", "http://1.1.1.1.1", "http://123.123.123", "http://3628126748", "http://.www.foo.bar/", "http://www.foo.bar./", "http://.www.foo.bar./", "http://10.1.1.1", ) boilerplate = "Surrounding text %s more text" for uri in true_positives + false_positives: assert uri_regex.findall(uri) == [uri], uri assert uri_regex.findall(boilerplate % uri) == [uri], uri for uri in true_negatives: assert not uri_regex.findall(uri), uri assert not uri_regex.findall(boilerplate % uri), uri def test_quiet_level_32( tmp_path: Path, tmpdir: pytest.TempPathFactory, capsys: pytest.CaptureFixture[str], ) -> None: d = tmp_path / "files" d.mkdir() conf = str(tmp_path / "setup.cfg") with open(conf, "w", encoding="utf-8") as f: # It must contain a "codespell" section. f.write("[codespell]\n") args = ("--config", conf) # Config files should NOT be in output. result = cs.main(str(d), args, "--quiet-level=32", std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 0 assert "Used config files:" not in stdout # Config files SHOULD be in output. result = cs.main(str(d), args, "--quiet-level=2", std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 0 assert "Used config files:" in stdout assert "setup.cfg" in stdout def test_ill_formed_ini_config_file( tmp_path: Path, tmpdir: pytest.TempPathFactory, capsys: pytest.CaptureFixture[str], ) -> None: d = tmp_path / "files" d.mkdir() conf = str(tmp_path / "setup.cfg") with open(conf, "w", encoding="utf-8") as f: # It should contain but lacks a section. f.write("foobar =\n") args = ("--config", conf) # Should not raise a configparser.Error exception. result = cs.main(str(d), args, std=True) assert isinstance(result, tuple) code, _, stderr = result assert code == 78 assert "ill-formed config file" in stderr @pytest.mark.parametrize("kind", ["cfg", "cfg_multiline", "toml", "toml_list"]) def test_config_toml( tmp_path: Path, capsys: pytest.CaptureFixture[str], kind: str, ) -> None: """Test loading options from a config file or toml.""" d = tmp_path / "files" d.mkdir() (d / "bad.txt").write_text("abandonned donn\n") (d / "good.txt").write_text("good") (d / "abandonned.txt").write_text("") # Should fail when checking all files. result = cs.main(d, "--check-filenames", count=True, std=True) assert isinstance(result, tuple) code, stdout, _ = result # Code in this case is not exit code, but count of misspellings. assert code == 3 assert "bad.txt" in stdout assert "abandonned.txt" in stdout if kind.startswith("cfg"): conffile = tmp_path / "setup.cfg" args = ("--config", conffile) if kind == "cfg": text = """\ [codespell] skip = bad.txt, whatever.txt count = """ else: assert kind == "cfg_multiline" text = """\ [codespell] skip = whatever.txt, bad.txt , , count = """ conffile.write_text(text) else: if sys.version_info < (3, 11): pytest.importorskip("tomli") tomlfile = tmp_path / "pyproject.toml" args = ("--toml", tomlfile) if kind == "toml": text = """\ [tool.codespell] skip = 'bad.txt,whatever.txt' check-filenames = false count = true """ else: assert kind == "toml_list" text = """\ [tool.codespell] skip = ['bad.txt', 'whatever.txt'] check-filenames = false count = true """ tomlfile.write_text(text) # Should pass when skipping bad.txt or abandonned.txt result = cs.main(d, args, std=True) assert isinstance(result, tuple) code, stdout, _ = result assert code == 0 assert "bad.txt" not in stdout assert "abandonned.txt" not in stdout # And both should automatically work if they're in cwd cwd = Path.cwd() try: os.chdir(tmp_path) result = cs.main(d, count=True, std=True) assert isinstance(result, tuple) code, stdout, _ = result finally: os.chdir(cwd) assert code == 0 assert "bad.txt" not in stdout assert "abandonned.txt" not in stdout @contextlib.contextmanager def FakeStdin(text: str) -> Generator[None, None, None]: oldin = sys.stdin try: in_ = StringIO(text) sys.stdin = in_ yield finally: sys.stdin = oldin def run_codespell_stdin( text: str, args: Tuple[Any, ...], cwd: Optional[Path] = None, ) -> int: """Run codespell in stdin mode and return number of lines in output.""" proc = subprocess.run( # noqa: S603 ["codespell", args, "-"], # noqa: S607 cwd=cwd, input=text, capture_output=True, encoding="utf-8", check=False, ) output = proc.stdout # get number of lines return output.count("\n") def test_stdin(tmp_path: Path) -> None: """Test running the codespell executable.""" input_file_lines = 4 text = "" for _ in range(input_file_lines): text += "abandonned\n" for single_line_per_error in (True, False): args: Tuple[str, ...] = () if single_line_per_error: args = ("--stdin-single-line",) # we expect 'input_file_lines' number of lines with # --stdin-single-line and input_file_lines 2 lines without it assert run_codespell_stdin( text, args=args, cwd=tmp_path ) == input_file_lines * (2 - int(single_line_per_error))	47,142	Python	.py	1,273	30.933229	126	0.612962	codespell-project/codespell	1,850	470	225	GPL-2.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,913	test_dictionary.py	codespell-project_codespell/codespell_lib/tests/test_dictionary.py	import glob import os import os.path as op import pathlib import re from typing import Any, Dict, Iterable, Optional, Set, Tuple import pytest from codespell_lib._codespell import ( _builtin_dictionaries, supported_languages, word_regex_def, ) spellers = {} root = pathlib.Path(__file__).parent.parent try: import aspell # type: ignore[import] _test_data_dir = op.join(op.dirname(__file__), "..", "tests", "data") for lang in supported_languages: _wordlist = op.join(_test_data_dir, f"{lang}-additional.wordlist") if op.isfile(_wordlist): spellers[lang] = aspell.Speller( ("lang", lang), ("size", "80"), ("wordlists", _wordlist) ) else: spellers[lang] = aspell.Speller(("lang", lang), ("size", "80")) except ImportError as e: if os.getenv("REQUIRE_ASPELL", "false").lower() == "true": msg = ( "Cannot run complete tests without aspell when " f"REQUIRE_ASPELL=true. Got error during import:\n{e}" ) raise RuntimeError(msg) from e global_err_dicts: Dict[str, Dict[str, Any]] = {} global_pairs: Set[Tuple[str, str]] = set() # Filename, should be seen as errors in aspell or not _data_dir = op.join(op.dirname(__file__), "..", "data") _fnames_in_aspell = [ (op.join(_data_dir, f"dictionary{d[2]}.txt"), d[3:5], d[5:7]) for d in _builtin_dictionaries ] fname_params = pytest.mark.parametrize( "fname, in_aspell, in_dictionary", _fnames_in_aspell ) def test_dictionaries_exist() -> None: """Test consistency of dictionaries.""" doc_fnames = {op.basename(f[0]) for f in _fnames_in_aspell} got_fnames = {op.basename(f) for f in glob.glob(op.join(_data_dir, ".txt"))} assert doc_fnames == got_fnames @fname_params def test_dictionary_formatting( fname: str, in_aspell: Tuple[bool, bool], in_dictionary: Tuple[Iterable[str], Iterable[str]], ) -> None: """Test that all dictionary entries are valid.""" errors = [] with open(fname, encoding="utf-8") as fid: for line in fid: err, rep = line.split("->") err = err.lower() rep = rep.rstrip("\n") try: _check_err_rep(err, rep, in_aspell, fname, in_dictionary) except AssertionError as exp: errors.append(str(exp).split("\n", maxsplit=1)[0]) if errors: msg = "\n" + "\n".join(errors) raise AssertionError(msg) @pytest.mark.parametrize( "filename", [ (root / "data").rglob("dictionary.txt"), (root / "tests/data").rglob(".wordlist"), ], ) def test_dictionary_sorting(filename: pathlib.Path) -> None: relative_path = filename.relative_to(root) previous_line = None with filename.open(encoding="utf-8") as file: for current_line in file: current_line = current_line.strip().lower() if previous_line is not None: assert previous_line < current_line, f"{relative_path} is not sorted" previous_line = current_line def _check_aspell( phrase: str, msg: str, in_aspell: Optional[bool], fname: str, languages: Iterable[str], ) -> None: if not spellers: # if no spellcheckers exist return # cannot check if in_aspell is None: return # don't check if " " in phrase: for word in phrase.split(): _check_aspell(word, msg, in_aspell, fname, languages) return # stop normal checking as we've done each word above this_in_aspell = any( spellers[lang].check(phrase.encode(spellers[lang].ConfigKeys()["encoding"][1])) for lang in languages ) end = f"be in aspell dictionaries ({', '.join(languages)}) for dictionary {fname}" if in_aspell: # should be an error in aspell assert this_in_aspell, f"{msg} should {end}" else: # shouldn't be assert not this_in_aspell, f"{msg} should not {end}" whitespace = re.compile(r"\s") start_whitespace = re.compile(r"^\s") start_comma = re.compile(r"^,") whitespace_comma = re.compile(r"\s,") comma_whitespaces = re.compile(r",\s\s") comma_without_space = re.compile(r",[^ ]") whitespace_end = re.compile(r"\s+$") single_comma = re.compile(r"^[^,],\s$") def _check_err_rep( err: str, rep: str, in_aspell: Tuple[Optional[bool], Optional[bool]], fname: str, languages: Tuple[Iterable[str], Iterable[str]], ) -> None: assert whitespace.search(err) is None, f"error {err!r} has whitespace" assert "," not in err, f"error {err!r} has a comma" assert len(rep) > 0, f"error {err}: correction {rep!r} must be non-empty" assert not start_whitespace.match( rep ), f"error {err}: correction {rep!r} cannot start with whitespace" _check_aspell(err, f"error {err!r}", in_aspell[0], fname, languages[0]) prefix = f"error {err}: correction {rep!r}" for regex, msg in ( (start_comma, "%s starts with a comma"), ( whitespace_comma, "%s contains a whitespace character followed by a comma", ), ( comma_whitespaces, "%s contains a comma followed by multiple whitespace characters", ), (comma_without_space, "%s contains a comma not* followed by a space"), (whitespace_end, "%s has a trailing space"), (single_comma, "%s has a single entry but contains a trailing comma"), ): assert not regex.search(rep), msg % (prefix,) del msg if rep.count(","): assert rep.endswith( "," ), f'error {err}: multiple corrections must end with trailing ","' reps = [r.strip() for r in rep.split(",")] reps = [r for r in reps if len(r)] for r in reps: assert err != r.lower(), f"error {err!r} corrects to itself amongst others" _check_aspell( r, f"error {err}: correction {r!r}", in_aspell[1], fname, languages[1], ) # aspell dictionary is case sensitive, so pass the original case into there # we could ignore the case, but that would miss things like days of the # week which we want to be correct reps = [r.lower() for r in reps] assert len(set(reps)) == len( reps ), f'error {err}: corrections "{rep}" are not (lower-case) unique' @pytest.mark.parametrize( ("err", "rep", "match"), [ ("a a", "bar", "has whitespace"), ("a,a", "bar", "has a comma"), ("a", "", "non-empty"), ("a", " bar", "start with whitespace"), ("a", ",bar", "starts with a comma"), ("a", "bar,bat", ".not.followed by a space"), ("a", "bar ", "trailing space"), ("a", "b ,ar", "contains a whitespace.followed by a comma"), ("a", "bar,", "single entry.comma"), ("a", "bar, bat", 'must end with trailing ","'), ("a", "a, bar,", "corrects to itself amongst others"), ("a", "a", "corrects to itself"), ("a", "bar, Bar,", "unique"), ], ) def test_error_checking(err: str, rep: str, match: str) -> None: """Test that our error checking works.""" with pytest.raises(AssertionError, match=match): _check_err_rep( err, rep, (None, None), "dummy", (supported_languages, supported_languages), ) @pytest.mark.skipif(not spellers, reason="requires aspell-en") @pytest.mark.parametrize( ("err", "rep", "err_aspell", "rep_aspell", "match"), [ # This doesn't raise any exceptions, so skip for now: # pytest.param('a', 'uvw, bar,', None, None, 'should be in aspell'), ("abcdef", "uvwxyz, bar,", True, None, "should be in aspell"), ("a", "uvwxyz, bar,", False, None, "should not be in aspell"), ("a", "abcdef, uvwxyz,", None, True, "should be in aspell"), ("abcdef", "uvwxyz, bar,", True, True, "should be in aspell"), ("abcdef", "uvwxyz, bar,", False, True, "should be in aspell"), ("a", "bar, back,", None, False, "should not be in aspell"), ("a", "bar, back, Wednesday,", None, False, "should not be in aspell"), ("abcdef", "ghijkl, uvwxyz,", True, False, "should be in aspell"), ("abcdef", "uvwxyz, bar,", False, False, "should not be in aspell"), # Multi-word corrections # One multi-word, both parts ("a", "abcdef uvwxyz", None, True, "should be in aspell"), ("a", "bar back", None, False, "should not be in aspell"), ("a", "bar back Wednesday", None, False, "should not be in aspell"), # Second multi-word, both parts ( "a", "bar back, abcdef uvwxyz, bar,", None, True, "should be in aspell", ), ( "a", "abcdef uvwxyz, bar back, ghijkl,", None, False, "should not be in aspell", ), # One multi-word, second part ("a", "bar abcdef", None, True, "should be in aspell"), ("a", "abcdef back", None, False, "should not be in aspell"), ], ) def test_error_checking_in_aspell( err: str, rep: str, err_aspell: Optional[bool], rep_aspell: Optional[bool], match: str, ) -> None: """Test that our error checking works with aspell.""" with pytest.raises(AssertionError, match=match): _check_err_rep( err, rep, (err_aspell, rep_aspell), "dummy", (supported_languages, supported_languages), ) # allow some duplicates, like "m-i-n-i-m-i-s-e", or "c-a-l-c-u-l-a-t-a-b-l-e" # correction in left can appear as typo in right allowed_dups = { ("dictionary.txt", "dictionary_code.txt"), ("dictionary.txt", "dictionary_en-GB_to_en-US.txt"), ("dictionary.txt", "dictionary_names.txt"), ("dictionary.txt", "dictionary_rare.txt"), ("dictionary.txt", "dictionary_usage.txt"), ("dictionary_code.txt", "dictionary_rare.txt"), ("dictionary_rare.txt", "dictionary_en-GB_to_en-US.txt"), ("dictionary_rare.txt", "dictionary_usage.txt"), } @fname_params @pytest.mark.dependency(name="dictionary loop") def test_dictionary_looping( fname: str, in_aspell: Tuple[bool, bool], in_dictionary: Tuple[bool, bool], ) -> None: """Test that all dictionary entries are valid.""" this_err_dict = {} short_fname = op.basename(fname) word_regex = re.compile(word_regex_def) with open(fname, encoding="utf-8") as fid: for line in fid: err, rep = line.split("->") err = err.lower() assert ( err not in this_err_dict ), f"error {err!r} already exists in {short_fname}" rep = rep.rstrip("\n") reps = [r.strip() for r in rep.lower().split(",")] reps = [r for r in reps if len(r)] this_err_dict[err] = reps # 1. check the dict against itself (diagonal) for err, reps in this_err_dict.items(): assert word_regex.fullmatch( err ), f"error {err!r} does not match default word regex '{word_regex_def}'" for r in reps: assert r not in this_err_dict, ( f"error {err}: correction {r} is an error itself " f"in the same dictionary file {short_fname}" ) pair = (short_fname, short_fname) assert pair not in global_pairs global_pairs.add(pair) for other_fname, other_err_dict in global_err_dicts.items(): # error duplication (eventually maybe we should just merge?) for err in this_err_dict: assert err not in other_err_dict, ( f"error {err!r} in dictionary {short_fname} " f"already exists in dictionary {other_fname}" ) # 2. check corrections in this dict against other dicts (upper) pair = (short_fname, other_fname) if pair not in allowed_dups: for err, reps in this_err_dict.items(): assert err not in other_err_dict, ( f"error {err!r} in dictionary {short_fname} " f"already exists in dictionary {other_fname}" ) for r in reps: assert r not in other_err_dict, ( f"error {err}: correction {r} from dictionary {short_fname} " f"is an error itself in dictionary {other_fname}" ) assert pair not in global_pairs global_pairs.add(pair) # 3. check corrections in other dicts against this dict (lower) pair = (other_fname, short_fname) if pair not in allowed_dups: for err in other_err_dict: for r in other_err_dict[err]: assert r not in this_err_dict, ( f"error {err}: correction {r} from dictionary {other_fname} " f"is an error itself in dictionary {short_fname}" ) assert pair not in global_pairs global_pairs.add(pair) global_err_dicts[short_fname] = this_err_dict @pytest.mark.dependency(depends=["dictionary loop"]) def test_ran_all() -> None: """Test that all pairwise tests ran.""" for f1, _, _ in _fnames_in_aspell: f1 = op.basename(f1) for f2, _, _ in _fnames_in_aspell: f2 = op.basename(f2) assert (f1, f2) in global_pairs assert len(global_pairs) == len(_fnames_in_aspell) ** 2	13,590	Python	.py	343	31.708455	87	0.582324	codespell-project/codespell	1,850	470	225	GPL-2.0	9/5/2024, 5:10:09 PM (Europe/Amsterdam)
6,914	setup.py	piskvorky_gensim/setup.py	#!/usr/bin/env python # -- coding: utf-8 -- # # Copyright (C) 2014 Radim Rehurek <radimrehurek@seznam.cz> # Licensed under the GNU LGPL v2.1 - https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html """ Run with:: python ./setup.py install """ import itertools import os import platform import shutil import sys from collections import OrderedDict from setuptools import Extension, find_packages, setup, distutils from setuptools.command.build_ext import build_ext c_extensions = OrderedDict([ ('gensim.models.word2vec_inner', 'gensim/models/word2vec_inner.c'), ('gensim.corpora._mmreader', 'gensim/corpora/_mmreader.c'), ('gensim.models.fasttext_inner', 'gensim/models/fasttext_inner.c'), ('gensim._matutils', 'gensim/_matutils.c'), ('gensim.models.nmf_pgd', 'gensim/models/nmf_pgd.c'), ('gensim.similarities.fastss', 'gensim/similarities/fastss.c'), ]) cpp_extensions = OrderedDict([ ('gensim.models.doc2vec_inner', 'gensim/models/doc2vec_inner.cpp'), ('gensim.models.word2vec_corpusfile', 'gensim/models/word2vec_corpusfile.cpp'), ('gensim.models.fasttext_corpusfile', 'gensim/models/fasttext_corpusfile.cpp'), ('gensim.models.doc2vec_corpusfile', 'gensim/models/doc2vec_corpusfile.cpp'), ]) def need_cython(): """Return True if we need Cython to translate any of the extensions. If the extensions have already been translated to C/C++, then we don't need to install Cython and perform the translation. """ expected = list(c_extensions.values()) + list(cpp_extensions.values()) return any([not os.path.isfile(f) for f in expected]) def make_c_ext(use_cython=False): for module, source in c_extensions.items(): if use_cython: source = source.replace('.c', '.pyx') extra_args = [] # extra_args.extend(['-g', '-O0']) # uncomment if optimization limiting crash info yield Extension( module, sources=[source], language='c', extra_compile_args=extra_args, ) def make_cpp_ext(use_cython=False): extra_args = [] system = platform.system() if system == 'Linux': extra_args.append('-std=c++11') elif system == 'Darwin': extra_args.extend(['-stdlib=libc++', '-std=c++11']) # extra_args.extend(['-g', '-O0']) # uncomment if optimization limiting crash info for module, source in cpp_extensions.items(): if use_cython: source = source.replace('.cpp', '.pyx') yield Extension( module, sources=[source], language='c++', extra_compile_args=extra_args, extra_link_args=extra_args, ) # # We use use_cython=False here for two reasons: # # 1. Cython may not be available at this stage # 2. The actual translation from Cython to C/C++ happens inside CustomBuildExt # ext_modules = list(itertools.chain(make_c_ext(use_cython=False), make_cpp_ext(use_cython=False))) class CustomBuildExt(build_ext): """Custom build_ext action with bootstrapping. We need this in order to use numpy and Cython in this script without importing them at module level, because they may not be available at that time. """ def finalize_options(self): build_ext.finalize_options(self) import builtins import numpy # # Prevent numpy from thinking it is still in its setup process # http://stackoverflow.com/questions/19919905/how-to-bootstrap-numpy-installation-in-setup-py # # Newer numpy versions don't support this hack, nor do they need it. # https://github.com/pyvista/pyacvd/pull/23#issue-1298467701 # try: builtins.__NUMPY_SETUP__ = False except Exception as ex: print(f'could not use __NUMPY_SETUP__ hack (numpy version: {numpy.__version__}): {ex}') self.include_dirs.append(numpy.get_include()) if need_cython(): import Cython.Build Cython.Build.cythonize(list(make_c_ext(use_cython=True)), language_level=3) Cython.Build.cythonize(list(make_cpp_ext(use_cython=True)), language_level=3) class CleanExt(distutils.cmd.Command): description = 'Remove C sources, C++ sources and binaries for gensim extensions' user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): for root, dirs, files in os.walk('gensim'): files = [ os.path.join(root, f) for f in files if os.path.splitext(f)[1] in ('.c', '.cpp', '.so') ] for f in files: self.announce('removing %s' % f, level=distutils.log.INFO) os.unlink(f) if os.path.isdir('build'): self.announce('recursively removing build', level=distutils.log.INFO) shutil.rmtree('build') cmdclass = {'build_ext': CustomBuildExt, 'clean_ext': CleanExt} WHEELHOUSE_UPLOADER_COMMANDS = {'fetch_artifacts', 'upload_all'} if WHEELHOUSE_UPLOADER_COMMANDS.intersection(sys.argv): import wheelhouse_uploader.cmd cmdclass.update(vars(wheelhouse_uploader.cmd)) LONG_DESCRIPTION = u""" ============================================== gensim -- Topic Modelling in Python ============================================== \|GA\|_ \|Wheel\|_ .. \|GA\| image:: https://github.com/RaRe-Technologies/gensim/actions/workflows/tests.yml/badge.svg?branch=develop .. \|Wheel\| image:: https://img.shields.io/pypi/wheel/gensim.svg .. _GA: https://github.com/RaRe-Technologies/gensim/actions .. _Downloads: https://pypi.org/project/gensim/ .. _License: https://radimrehurek.com/gensim/intro.html#licensing .. _Wheel: https://pypi.org/project/gensim/ Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. Features --------- * All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core) * Intuitive interfaces * easy to plug in your own input corpus/datastream (simple streaming API) * easy to extend with other Vector Space algorithms (simple transformation API) * Efficient multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning. * Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers. * Extensive `documentation and Jupyter Notebook tutorials <https://github.com/RaRe-Technologies/gensim/#documentation>`_. If this feature list left you scratching your head, you can first read more about the `Vector Space Model <https://en.wikipedia.org/wiki/Vector_space_model>`_ and `unsupervised document analysis <https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ on Wikipedia. Installation ------------ This software depends on `NumPy and Scipy <https://scipy.org/install/>`_, two Python packages for scientific computing. You must have them installed prior to installing `gensim`. It is also recommended you install a fast BLAS library before installing NumPy. This is optional, but using an optimized BLAS such as MKL, `ATLAS <https://math-atlas.sourceforge.net/>`_ or `OpenBLAS <https://xianyi.github.io/OpenBLAS/>`_ is known to improve performance by as much as an order of magnitude. On OSX, NumPy picks up its vecLib BLAS automatically, so you don't need to do anything special. Install the latest version of gensim:: pip install --upgrade gensim Or, if you have instead downloaded and unzipped the `source tar.gz <https://pypi.org/project/gensim/>`_ package:: python setup.py install For alternative modes of installation, see the `documentation <https://radimrehurek.com/gensim/#install>`_. Gensim is being `continuously tested <https://radimrehurek.com/gensim/#testing>`_ under all `supported Python versions <https://github.com/RaRe-Technologies/gensim/wiki/Gensim-And-Compatibility>`_. Support for Python 2.7 was dropped in gensim 4.0.0 â€“ install gensim 3.8.3 if you must use Python 2.7. How come gensim is so fast and memory efficient? Isn't it pure Python, and isn't Python slow and greedy? -------------------------------------------------------------------------------------------------------- Many scientific algorithms can be expressed in terms of large matrix operations (see the BLAS note above). Gensim taps into these low-level BLAS libraries, by means of its dependency on NumPy. So while gensim-the-top-level-code is pure Python, it actually executes highly optimized Fortran/C under the hood, including multithreading (if your BLAS is so configured). Memory-wise, gensim makes heavy use of Python's built-in generators and iterators for streamed data processing. Memory efficiency was one of gensim's `design goals <https://radimrehurek.com/gensim/intro.html#design-principles>`_, and is a central feature of gensim, rather than something bolted on as an afterthought. Documentation ------------- * `QuickStart`_ * `Tutorials`_ * `Tutorial Videos`_ * `Official Documentation and Walkthrough`_ Citing gensim ------------- When `citing gensim in academic papers and theses <https://scholar.google.cz/citations?view_op=view_citation&hl=en&user=9vG_kV0AAAAJ&citation_for_view=9vG_kV0AAAAJ:u-x6o8ySG0sC>`_, please use this BibTeX entry:: @inproceedings{rehurek_lrec, title = {{Software Framework for Topic Modelling with Large Corpora}}, author = {Radim {\\v R}eh{\\r u}{\\v r}ek and Petr Sojka}, booktitle = {{Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks}}, pages = {45--50}, year = 2010, month = May, day = 22, publisher = {ELRA}, address = {Valletta, Malta}, language={English} } ---------------- Gensim is open source software released under the `GNU LGPLv2.1 license <https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html>`_. Copyright (c) 2009-now Radim Rehurek .. _Official Documentation and Walkthrough: https://radimrehurek.com/gensim/ .. _Tutorials: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials .. _Tutorial Videos: https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#videos .. _QuickStart: https://radimrehurek.com/gensim/gensim_numfocus/auto_examples/core/run_core_concepts.html """ distributed_env = ['Pyro4 >= 4.27'] visdom_req = ['visdom >= 0.1.8, != 0.1.8.7'] # packages included for build-testing everywhere core_testenv = [ 'pytest', 'pytest-cov', 'testfixtures', ] if not sys.platform.lower().startswith("win") and sys.version_info[:2] < (3, 11): core_testenv.append('POT') if not sys.platform.lower().startswith("win") and sys.version_info[:2] < (3, 10): # # nmslib wheels not available for Python 3.10 and 3.11 as of Dec 2022 # core_testenv.append('nmslib') # Add additional requirements for testing on Linux that are skipped on Windows. linux_testenv = core_testenv[:] + visdom_req # Skip problematic/uninstallable packages (& thus related conditional tests) in Windows builds. # We still test them in Linux via Travis, see linux_testenv above. # See https://github.com/RaRe-Technologies/gensim/pull/2814 win_testenv = core_testenv[:] # # This list partially duplicates requirements_docs.txt. # The main difference is that we don't include version pins here unless # absolutely necessary, whereas requirements_docs.txt includes pins for # everything, by design. # # For more info about the difference between the two: # # https://packaging.python.org/discussions/install-requires-vs-requirements/ # # # We pin the Sphinx-related packages to specific versions here because we want # our documentation builds to be reproducible. Different versions of Sphinx # can generate slightly different output, and because we keep some of the output # under version control, we want to keep these differences to a minimum. # docs_testenv = core_testenv + distributed_env + visdom_req + [ 'sphinx==5.1.1', 'sphinx-gallery==0.11.1', 'sphinxcontrib.programoutput==0.17', 'sphinxcontrib-napoleon==0.7', 'matplotlib', # expected by sphinx-gallery 'memory_profiler', 'annoy', 'Pyro4', 'scikit-learn', 'nltk', 'testfixtures', 'statsmodels', 'pandas', ] # # see https://github.com/piskvorky/gensim/pull/3535 # NUMPY_STR = 'numpy >= 1.18.5, < 2.0' install_requires = [ NUMPY_STR, # # scipy 1.14.0 and onwards removes deprecated sparsetools submodule # 'scipy >= 1.7.0, <1.14.0', 'smart_open >= 1.8.1', ] setup( name='gensim', version='4.3.3', description='Python framework for fast Vector Space Modelling', long_description=LONG_DESCRIPTION, ext_modules=ext_modules, cmdclass=cmdclass, packages=find_packages(), author=u'Radim Rehurek', author_email='me@radimrehurek.com', url='https://radimrehurek.com/gensim/', project_urls={ 'Source': 'https://github.com/RaRe-Technologies/gensim', }, download_url='https://pypi.org/project/gensim/', license='LGPL-2.1-only', keywords='Singular Value Decomposition, SVD, Latent Semantic Indexing, ' 'LSA, LSI, Latent Dirichlet Allocation, LDA, ' 'Hierarchical Dirichlet Process, HDP, Random Projections, ' 'TFIDF, word2vec', platforms='any', zip_safe=False, classifiers=[ # from https://pypi.org/classifiers/ 'Development Status :: 5 - Production/Stable', 'Environment :: Console', 'Intended Audience :: Science/Research', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', 'Programming Language :: Python :: 3.11', 'Programming Language :: Python :: 3.12', 'Programming Language :: Python :: 3 :: Only', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Scientific/Engineering :: Information Analysis', 'Topic :: Text Processing :: Linguistic', ], test_suite="gensim.test", python_requires='>=3.8', install_requires=install_requires, tests_require=linux_testenv, extras_require={ 'distributed': distributed_env, 'test-win': win_testenv, 'test': linux_testenv, 'docs': docs_testenv, }, include_package_data=True, )	14,778	Python	.py	311	42.44373	402	0.69066	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,915	gensim Quick Start.ipynb	piskvorky_gensim/gensim Quick Start.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,916	install_wheel.py	piskvorky_gensim/continuous_integration/install_wheel.py	"""Install the built wheel for testing under AppVeyor. Assumes that gensim/dist contains a single wheel to install. """ import os import subprocess curr_dir = os.path.dirname(__file__) dist_path = os.path.join(curr_dir, '..', 'dist') wheels = [ os.path.join(dist_path, f) for f in os.listdir(dist_path) if f.endswith('.whl') ] assert len(wheels) == 1, "wheels = %r" % wheels command = 'pip install --pre --force-reinstall'.split() + [wheels[0]] subprocess.check_call(command)	487	Python	.py	14	33	69	0.708511	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,917	check_wheels.py	piskvorky_gensim/continuous_integration/check_wheels.py	#!/usr/bin/env python # -- coding: utf-8 -- # # Copyright (C) 2019 RaRe Technologies s.r.o. # Licensed under the GNU LGPL v2.1 - https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html """Print available wheels for a particular Python package.""" import re import sys import requests def to_int(value): value = ''.join((x for x in value if x.isdigit())) try: return int(value) except Exception: return 0 def to_tuple(version): return tuple(to_int(x) for x in version.split('.')) def main(): project = sys.argv[1] json = requests.get('https://pypi.org/pypi/%s/json' % project).json() for version in sorted(json['releases'], key=to_tuple): print(version) wheel_packages = [ p for p in json['releases'][version] if p['packagetype'] == 'bdist_wheel' ] for p in wheel_packages: print(' %(python_version)s %(filename)s' % p) if __name__ == '__main__': main()	985	Python	.py	30	27.7	95	0.620908	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,918	upgrade_pip_py310.py	piskvorky_gensim/continuous_integration/upgrade_pip_py310.py	# This script needs to be able run under both Python 2 and 3 without crashing # It only achieves the desired effect under Py3.10 on Linux and MacOS. import subprocess import sys import tempfile if sys.platform in ('linux', 'darwin') and sys.version_info[:2] == (3, 10): import urllib.request with tempfile.NamedTemporaryFile(suffix='.py') as fout: urllib.request.urlretrieve("https://bootstrap.pypa.io/get-pip.py", fout.name) subprocess.call([sys.executable, fout.name])	495	Python	.py	10	46.1	85	0.742268	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,919	Poincare Evaluation.ipynb	piskvorky_gensim/docs/notebooks/Poincare Evaluation.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluation of Poincare Embeddings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how well Poincare embeddings perform on the tasks detailed in the [original paper](https://arxiv.org/pdf/1705.08039.pdf) about the embeddings.\n", "\n", "The following two external, open-source implementations are used - \n", "1. [C++](https://github.com/TatsuyaShirakawa/poincare-embedding)\n", "2. [Numpy](https://github.com/nishnik/poincare_embeddings)\n", "\n", "This is the list of tasks - \n", "1. WordNet reconstruction\n", "2. WordNet link prediction\n", "3. Link prediction in collaboration networks (evaluation incomplete)\n", "4. Lexical entailment on HyperLex\n", "\n", "A more detailed explanation of the tasks and the evaluation methodology is present in the individual evaluation subsections." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Setup\n", "\n", "The following section performs the following - \n", "1. Imports required python libraries and downloads the wordnet data\n", "2. Clones the repositories containing the C++ and Numpy implementations of the Poincare embeddings\n", "3. Applies patches containing minor changes to the implementations.\n", "4. Compiles the C++ sources to create a binary" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/misha/git/gensim\n" ] } ], "source": [ "%cd ../.." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mYou are using pip version 19.0.1, however version 19.1 is available.\r\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\r\n" ] } ], "source": [ "# Some libraries need to be installed that are not part of Gensim\n", "! pip install click>=6.7 nltk>=3.2.5 prettytable>=0.7.2 pygtrie>=2.2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package wordnet to /home/misha/nltk_data...\n", "[nltk_data] Package wordnet is already up-to-date!\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import csv\n", "from collections import OrderedDict\n", "from IPython.display import display, HTML\n", "import logging\n", "import os\n", "import pickle\n", "import random\n", "import re\n", "\n", "import click\n", "from gensim.models.poincare import PoincareModel, PoincareRelations, \\\n", " ReconstructionEvaluation, LinkPredictionEvaluation, \\\n", " LexicalEntailmentEvaluation, PoincareKeyedVectors\n", "from gensim.utils import check_output\n", "import nltk\n", "from prettytable import PrettyTable\n", "from smart_open import smart_open\n", "\n", "logging.basicConfig(level=logging.INFO)\n", "nltk.download('wordnet')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please set the variable `parent_directory` below to change the directory to which the repositories are cloned." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/misha/git/gensim/docs/notebooks\n" ] } ], "source": [ "%cd docs/notebooks/" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "current_directory = os.getcwd()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Change this variable to `False` to not remove and re-download repos for external implementations\n", "force_setup = False\n", "\n", "# The poincare datasets, models and source code for external models are downloaded to this directory\n", "parent_directory = os.path.join(current_directory, 'poincare')\n", "! mkdir -p {parent_directory}" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/misha/git/gensim/docs/notebooks/poincare\n" ] } ], "source": [ "%cd {parent_directory}\n", "\n", "# Clone repos\n", "np_repo_name = 'poincare-np-embedding'\n", "if force_setup and os.path.exists(np_repo_name):\n", " ! rm -rf {np_repo_name}\n", "clone_np_repo = not os.path.exists(np_repo_name)\n", "if clone_np_repo:\n", " ! git clone https://github.com/nishnik/poincare_embeddings.git {np_repo_name}\n", "\n", "cpp_repo_name = 'poincare-cpp-embedding'\n", "if force_setup and os.path.exists(cpp_repo_name):\n", " ! rm -rf {cpp_repo_name}\n", "clone_cpp_repo = not os.path.exists(cpp_repo_name)\n", "if clone_cpp_repo:\n", " ! git clone https://github.com/TatsuyaShirakawa/poincare-embedding.git {cpp_repo_name}\n", "\n", "patches_applied = False" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Apply patches\n", "if clone_cpp_repo and not patches_applied:\n", " %cd {cpp_repo_name}\n", " ! git apply ../poincare_burn_in_eps.patch\n", "\n", "if clone_np_repo and not patches_applied:\n", " %cd ../{np_repo_name}\n", " ! git apply ../poincare_numpy.patch\n", " \n", "patches_applied = True" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/misha/git/gensim/docs/notebooks/poincare/poincare-cpp-embedding\n", "/home/misha/git/gensim/docs/notebooks/poincare/poincare-cpp-embedding/work\n", "-- The C compiler identification is GNU 7.4.0\n", "-- The CXX compiler identification is GNU 7.4.0\n", "-- Check for working C compiler: /usr/bin/cc\n", "-- Check for working C compiler: /usr/bin/cc -- works\n", "-- Detecting C compiler ABI info\n", "-- Detecting C compiler ABI info - done\n", "-- Detecting C compile features\n", "-- Detecting C compile features - done\n", "-- Check for working CXX compiler: /usr/bin/c++\n", "-- Check for working CXX compiler: /usr/bin/c++ -- works\n", "-- Detecting CXX compiler ABI info\n", "-- Detecting CXX compiler ABI info - done\n", "-- Detecting CXX compile features\n", "-- Detecting CXX compile features - done\n", "-- Looking for pthread.h\n", "-- Looking for pthread.h - found\n", "-- Looking for pthread_create\n", "-- Looking for pthread_create - not found\n", "-- Check if compiler accepts -pthread\n", "-- Check if compiler accepts -pthread - yes\n", "-- Found Threads: TRUE \n", "-- Configuring done\n", "-- Generating done\n", "-- Build files have been written to: /home/misha/git/gensim/docs/notebooks/poincare/poincare-cpp-embedding/work\n", "\u001b[35m\u001b[1mScanning dependencies of target poincare_embedding\u001b[0m\n", "[ 50%] \u001b[32mBuilding CXX object CMakeFiles/poincare_embedding.dir/src/poincare_embedding.cpp.o\u001b[0m\n", "[100%] \u001b[32m\u001b[1mLinking CXX executable poincare_embedding\u001b[0m\n", "[100%] Built target poincare_embedding\n", "/home/misha/git/gensim/docs/notebooks\n" ] } ], "source": [ "# Compile the code for the external c++ implementation into a binary\n", "%cd {parent_directory}/{cpp_repo_name}\n", "!mkdir -p work\n", "%cd work\n", "!cmake ..\n", "!make\n", "%cd {current_directory}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You might need to install an updated version of `cmake` to be able to compile the source code. Please make sure that the binary `poincare_embedding` has been created before proceeding by verifying the above cell does not raise an error." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cpp_binary_path = os.path.join(parent_directory, cpp_repo_name, 'work', 'poincare_embedding')\n", "assert(os.path.exists(cpp_binary_path)), 'Binary file doesnt exist at %s' % cpp_binary_path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Training\n", "\n", "### 2.1 Create the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# These directories are auto created in the current directory for storing poincare datasets and models\n", "data_directory = os.path.join(parent_directory, 'data')\n", "models_directory = os.path.join(parent_directory, 'models')\n", "\n", "# Create directories\n", "! mkdir -p {data_directory}\n", "! mkdir -p {models_directory}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "82115 nouns\n", "743241 hypernyms\n" ] } ], "source": [ "# Prepare the WordNet data\n", "# Can also be downloaded directly from -\n", "# https://github.com/jayantj/gensim/raw/wordnet_data/docs/notebooks/poincare/data/wordnet_noun_hypernyms.tsv\n", "\n", "wordnet_file = os.path.join(data_directory, 'wordnet_noun_hypernyms.tsv')\n", "if not os.path.exists(wordnet_file):\n", " ! python {parent_directory}/{cpp_repo_name}/scripts/create_wordnet_noun_hierarchy.py {wordnet_file}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2019-05-10 12:18:20-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", "Resolving people.ds.cam.ac.uk (people.ds.cam.ac.uk)... 131.111.3.47\n", "Connecting to people.ds.cam.ac.uk (people.ds.cam.ac.uk)\|131.111.3.47\|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 183900 (180K) [application/zip]\n", "Saving to: ‘/home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’\n", "\n", "/home/misha/git/gen 100%[===================>] 179.59K 158KB/s in 1.1s \n", "\n", "2019-05-10 12:18:22 (158 KB/s) - ‘/home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", "\n", "Archive: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex-data.zip\n", " creating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/\n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/hyperlex-verbs.txt \n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/hyperlex-nouns.txt \n", " creating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/\n", " creating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/random/\n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/random/hyperlex_training_all_random.txt \n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/random/hyperlex_test_all_random.txt \n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/random/hyperlex_dev_all_random.txt \n", " creating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/lexical/\n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/lexical/hyperlex_dev_all_lexical.txt \n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/lexical/hyperlex_test_all_lexical.txt \n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/splits/lexical/hyperlex_training_all_lexical.txt \n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/hyperlex-all.txt \n", " inflating: /home/misha/git/gensim/docs/notebooks/poincare/data/hyperlex/README.txt \n" ] } ], "source": [ "# Prepare the HyperLex data\n", "hyperlex_url = \"http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\"\n", "! wget {hyperlex_url} -O {data_directory}/hyperlex-data.zip\n", "if os.path.exists(os.path.join(data_directory, 'hyperlex')):\n", " ! rm -r {data_directory}/hyperlex\n", "! unzip {data_directory}/hyperlex-data.zip -d {data_directory}/hyperlex/\n", "hyperlex_file = os.path.join(data_directory, 'hyperlex', 'nouns-verbs', 'hyperlex-nouns.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Training [C++ embeddings](https://github.com/TatsuyaShirakawa/poincare-embedding)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def train_cpp_model(\n", " binary_path, data_file, output_file, dim, epochs, neg,\n", " num_threads, epsilon, burn_in, seed=0):\n", " \"\"\"Train a poincare embedding using the c++ implementation\n", " \n", " Args:\n", " binary_path (str): Path to the compiled c++ implementation binary\n", " data_file (str): Path to tsv file containing relation pairs\n", " output_file (str): Path to output file containing model\n", " dim (int): Number of dimensions of the trained model\n", " epochs (int): Number of epochs to use\n", " neg (int): Number of negative samples to use\n", " num_threads (int): Number of threads to use for training the model\n", " epsilon (float): Constant used for clipping below a norm of one\n", " burn_in (int): Number of epochs to use for burn-in init (0 means no burn-in)\n", " \n", " Notes: \n", " If `output_file` already exists, skips training\n", " \"\"\"\n", " if os.path.exists(output_file):\n", " print('File %s exists, skipping' % output_file)\n", " return\n", " args = {\n", " 'dim': dim,\n", " 'max_epoch': epochs,\n", " 'neg_size': neg,\n", " 'num_thread': num_threads,\n", " 'epsilon': epsilon,\n", " 'burn_in': burn_in,\n", " 'learning_rate_init': 0.1,\n", " 'learning_rate_final': 0.0001,\n", " }\n", " cmd = [binary_path, data_file, output_file]\n", " for option, value in args.items():\n", " cmd.append(\"--%s\" % option)\n", " cmd.append(str(value))\n", " \n", " return check_output(args=cmd)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_sizes = [5, 10, 20, 50, 100, 200]\n", "default_params = {\n", " 'neg': 20,\n", " 'epochs': 50,\n", " 'threads': 8,\n", " 'eps': 1e-6,\n", " 'burn_in': 0,\n", " 'batch_size': 10,\n", " 'reg': 0.0\n", "}\n", "\n", "non_default_params = {\n", " 'neg': [10],\n", " 'epochs': [200],\n", " 'burn_in': [10]\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def cpp_model_name_from_params(params, prefix):\n", " param_keys = ['burn_in', 'epochs', 'neg', 'eps', 'threads']\n", " name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n", " return '%s_%s' % (prefix, '_'.join(name))\n", "\n", "def train_model_with_params(params, train_file, model_sizes, prefix, implementation):\n", " \"\"\"Trains models with given params for multiple model sizes using the given implementation\n", " \n", " Args:\n", " params (dict): parameters to train the model with\n", " train_file (str): Path to tsv file containing relation pairs\n", " model_sizes (list): list of dimension sizes (integer) to train the model with\n", " prefix (str): prefix to use for the saved model filenames\n", " implementation (str): whether to use the numpy or c++ implementation,\n", " allowed values: 'numpy', 'c++'\n", " \n", " Returns:\n", " tuple (model_name, model_files)\n", " model_files is a dict of (size, filename) pairs\n", " Example: ('cpp_model_epochs_50', {5: 'models/cpp_model_epochs_50_dim_5'})\n", " \"\"\"\n", " files = {}\n", " if implementation == 'c++':\n", " model_name = cpp_model_name_from_params(params, prefix)\n", " elif implementation == 'numpy':\n", " model_name = np_model_name_from_params(params, prefix)\n", " elif implementation == 'gensim':\n", " model_name = gensim_model_name_from_params(params, prefix)\n", " else:\n", " raise ValueError('Given implementation %s not found' % implementation)\n", " for model_size in model_sizes:\n", " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", " output_file = os.path.join(models_directory, output_file_name)\n", " print('Training model %s of size %d' % (model_name, model_size))\n", " if implementation == 'c++':\n", " out = train_cpp_model(\n", " cpp_binary_path, train_file, output_file, model_size,\n", " params['epochs'], params['neg'], params['threads'],\n", " params['eps'], params['burn_in'], seed=0)\n", " elif implementation == 'numpy':\n", " train_external_numpy_model(\n", " python_script_path, train_file, output_file, model_size,\n", " params['epochs'], params['neg'], seed=0)\n", " elif implementation == 'gensim':\n", " train_gensim_model(\n", " train_file, output_file, model_size, params['epochs'],\n", " params['neg'], params['burn_in'], params['batch_size'], params['reg'], seed=0)\n", " else:\n", " raise ValueError('Given implementation %s not found' % implementation)\n", " files[model_size] = output_file\n", " return (model_name, files)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_files = {}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 5\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 10\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 20\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 50\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 100\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 200\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 5\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 10\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 20\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 50\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 100\n", "Training model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 200\n", "Training model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 5\n", "Training model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 10\n", "Training model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 20\n", "Training model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 50\n", "Training model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 100\n", "Training model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 200\n", "Training model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 5\n", "Training model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 10\n", "Training model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 20\n", "Training model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 50\n", "Training model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 100\n", "Training model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 200\n" ] } ], "source": [ "model_files['c++'] = {}\n", "# Train c++ models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'cpp_model', 'c++')\n", "model_files['c++'][model_name] = {}\n", "for dim, filepath in files.items():\n", " model_files['c++'][model_name][dim] = filepath\n", "# Train c++ models with non-default params\n", "for param, values in non_default_params.items():\n", " params = default_params.copy()\n", " for value in values:\n", " params[param] = value\n", " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'cpp_model', 'c++')\n", " model_files['c++'][model_name] = {}\n", " for dim, filepath in files.items():\n", " model_files['c++'][model_name][dim] = filepath" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Training [numpy embeddings](https://github.com/nishnik/poincare_embeddings) (non-gensim)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "python_script_path = os.path.join(parent_directory, np_repo_name, 'poincare.py')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def np_model_name_from_params(params, prefix):\n", " param_keys = ['neg', 'epochs']\n", " name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n", " return '%s_%s' % (prefix, '_'.join(name))\n", "\n", "def train_external_numpy_model(\n", " script_path, data_file, output_file, dim, epochs, neg, seed=0):\n", " \"\"\"Train a poincare embedding using an external numpy implementation\n", " \n", " Args:\n", " script_path (str): Path to the Python training script\n", " data_file (str): Path to tsv file containing relation pairs\n", " output_file (str): Path to output file containing model\n", " dim (int): Number of dimensions of the trained model\n", " epochs (int): Number of epochs to use\n", " neg (int): Number of negative samples to use\n", " \n", " Notes: \n", " If `output_file` already exists, skips training\n", " \"\"\"\n", " if os.path.exists(output_file):\n", " print('File %s exists, skipping' % output_file)\n", " return\n", " args = {\n", " 'input-file': data_file,\n", " 'output-file': output_file,\n", " 'dimensions': dim,\n", " 'epochs': epochs,\n", " 'learning-rate': 0.01,\n", " 'num-negative': neg,\n", " }\n", " cmd = ['python', script_path]\n", " for option, value in args.items():\n", " cmd.append(\"--%s\" % option)\n", " cmd.append(str(value))\n", " \n", " return check_output(args=cmd)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training model np_model_epochs_50_neg_20 of size 5\n", "Training model np_model_epochs_50_neg_20 of size 10\n", "Training model np_model_epochs_50_neg_20 of size 20\n", "Training model np_model_epochs_50_neg_20 of size 50\n", "Training model np_model_epochs_50_neg_20 of size 100\n", "Training model np_model_epochs_50_neg_20 of size 200\n" ] } ], "source": [ "model_files['numpy'] = {}\n", "# Train models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'np_model', 'numpy')\n", "model_files['numpy'][model_name] = {}\n", "for dim, filepath in files.items():\n", " model_files['numpy'][model_name][dim] = filepath" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 Training gensim embeddings" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def gensim_model_name_from_params(params, prefix):\n", " param_keys = ['neg', 'epochs', 'burn_in', 'batch_size', 'reg']\n", " name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n", " return '%s_%s' % (prefix, '_'.join(name))\n", "\n", "def train_gensim_model(\n", " data_file, output_file, dim, epochs, neg, burn_in, batch_size, reg, seed=0):\n", " \"\"\"Train a poincare embedding using gensim implementation\n", " \n", " Args:\n", " data_file (str): Path to tsv file containing relation pairs\n", " output_file (str): Path to output file containing model\n", " dim (int): Number of dimensions of the trained model\n", " epochs (int): Number of epochs to use\n", " neg (int): Number of negative samples to use\n", " burn_in (int): Number of epochs to use for burn-in initialization\n", " batch_size (int): Size of batch to use for training\n", " reg (float): Coefficient used for l2-regularization while training\n", " \n", " Notes: \n", " If `output_file` already exists, skips training\n", " \"\"\"\n", " if os.path.exists(output_file):\n", " print('File %s exists, skipping' % output_file)\n", " return\n", " train_data = PoincareRelations(data_file)\n", " model = PoincareModel(train_data, size=dim, negative=neg, burn_in=burn_in, regularization_coeff=reg)\n", " model.train(epochs=epochs, batch_size=batch_size)\n", " model.save(output_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "non_default_params_gensim = [\n", " {'neg': 10,},\n", " {'burn_in': 10,},\n", " {'batch_size': 50,},\n", " {'neg': 10, 'reg': 1, 'burn_in': 10, 'epochs': 200},\n", "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.models.poincare:loading relations from train data..\n", "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training model gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20_reg_0.0 of size 5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.models.poincare:loaded 743241 relations from train data, 82114 nodes\n", "INFO:gensim.models.poincare:training model of size 5 with 1 workers on 743241 relations for 50 epochs and 0 burn-in epochs, using lr=0.10000 burn-in lr=0.01000 negative=20\n", "INFO:gensim.models.poincare:starting training (50 epochs)----------------------------------------\n", "INFO:gensim.models.poincare:training on epoch 1, examples #9990-#10000, loss: 30.71\n", "INFO:gensim.models.poincare:time taken for 10000 examples: 2.11 s, 4749.76 examples / s\n", "INFO:gensim.models.poincare:training on epoch 1, examples #19990-#20000, loss: 30.62\n", "INFO:gensim.models.poincare:time taken for 10000 examples: 2.04 s, 4909.96 examples / s\n", "INFO:gensim.models.poincare:training on epoch 1, examples #29990-#30000, loss: 30.55\n", "INFO:gensim.models.poincare:time taken for 10000 examples: 2.06 s, 4855.20 examples / s\n", "INFO:gensim.models.poincare:training on epoch 1, examples #39990-#40000, loss: 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"INFO:gensim.models.poincare:time taken for 10000 examples: 1.92 s, 5211.54 examples / s\n" ] } ], "source": [ "model_files['gensim'] = {}\n", "# Train models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'gensim_model', 'gensim')\n", "model_files['gensim'][model_name] = {}\n", "for dim, filepath in files.items():\n", " model_files['gensim'][model_name][dim] = filepath\n", "# Train models with non-default params\n", "for new_params in non_default_params_gensim:\n", " params = default_params.copy()\n", " params.update(new_params)\n", " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_model', 'gensim')\n", " model_files['gensim'][model_name] = {}\n", " for dim, filepath in files.items():\n", " model_files['gensim'][model_name][dim] = filepath" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Loading the embeddings" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def transform_cpp_embedding_to_kv(input_file, output_file, encoding='utf8'):\n", " \"\"\"Given a C++ embedding tsv filepath, converts it to a KeyedVector-supported file\"\"\"\n", " with smart_open(input_file, 'rb') as f:\n", " lines = [line.decode(encoding) for line in f]\n", " if not len(lines):\n", " raise ValueError(\"file is empty\")\n", " first_line = lines[0]\n", " parts = first_line.rstrip().split(\"\\t\")\n", " model_size = len(parts) - 1\n", " vocab_size = len(lines)\n", " with smart_open(output_file, 'w') as f:\n", " f.write('%d %d\\n' % (vocab_size, model_size))\n", " for line in lines:\n", " f.write(line.replace('\\t', ' '))\n", "\n", "def transform_numpy_embedding_to_kv(input_file, output_file, encoding='utf8'):\n", " \"\"\"Given a numpy poincare embedding pkl filepath, converts it to a KeyedVector-supported file\"\"\"\n", " np_embeddings = pickle.load(open(input_file, 'rb'))\n", " random_embedding = np_embeddings[list(np_embeddings.keys())[0]]\n", " \n", " model_size = random_embedding.shape[0]\n", " vocab_size = len(np_embeddings)\n", " with smart_open(output_file, 'w') as f:\n", " f.write('%d %d\\n' % (vocab_size, model_size))\n", " for key, vector in np_embeddings.items():\n", " vector_string = ' '.join('%.6f' % value for value in vector)\n", " f.write('%s %s\\n' % (key, vector_string))\n", "\n", "def load_poincare_cpp(input_filename):\n", " \"\"\"Load embedding trained via C++ Poincare model.\n", "\n", " Parameters\n", " ----------\n", " filepath : str\n", " Path to tsv file containing embedding.\n", "\n", " Returns\n", " -------\n", " PoincareKeyedVectors instance.\n", "\n", " \"\"\"\n", " keyed_vectors_filename = input_filename + '.kv'\n", " transform_cpp_embedding_to_kv(input_filename, keyed_vectors_filename)\n", " embedding = PoincareKeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", " os.unlink(keyed_vectors_filename)\n", " return embedding\n", "\n", "def load_poincare_numpy(input_filename):\n", " \"\"\"Load embedding trained via Python numpy Poincare model.\n", "\n", " Parameters\n", " ----------\n", " filepath : str\n", " Path to pkl file containing embedding.\n", "\n", " Returns:\n", " PoincareKeyedVectors instance.\n", "\n", " \"\"\"\n", " keyed_vectors_filename = input_filename + '.kv'\n", " transform_numpy_embedding_to_kv(input_filename, keyed_vectors_filename)\n", " embedding = PoincareKeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", " os.unlink(keyed_vectors_filename)\n", " return embedding\n", "\n", "def load_poincare_gensim(input_filename):\n", " \"\"\"Load embedding trained via Gensim PoincareModel.\n", "\n", " Parameters\n", " ----------\n", " filepath : str\n", " Path to model file.\n", "\n", " Returns:\n", " PoincareKeyedVectors instance.\n", "\n", " \"\"\"\n", " model = PoincareModel.load(input_filename)\n", " return model.kv\n", "\n", "def load_model(implementation, model_file):\n", " \"\"\"Convenience function over functions to load models from different implementations.\n", " \n", " Parameters\n", " ----------\n", " implementation : str\n", " Implementation used to create model file ('c++'/'numpy'/'gensim').\n", " model_file : str\n", " Path to model file.\n", " \n", " Returns\n", " -------\n", " PoincareKeyedVectors instance\n", " \n", " Notes\n", " -----\n", " Raises ValueError in case of invalid value for `implementation`\n", "\n", " \"\"\"\n", " if implementation == 'c++':\n", " return load_poincare_cpp(model_file)\n", " elif implementation == 'numpy':\n", " return load_poincare_numpy(model_file)\n", " elif implementation == 'gensim':\n", " return load_poincare_gensim(model_file)\n", " else:\n", " raise ValueError('Invalid implementation %s' % implementation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Evaluation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def display_results(task_name, results):\n", " \"\"\"Display evaluation results of multiple embeddings on a single task in a tabular format\n", " \n", " Args:\n", " task_name (str): name the task being evaluated\n", " results (dict): mapping between embeddings and corresponding results\n", " \n", " \"\"\"\n", " result_table = PrettyTable()\n", " result_table.field_names = [\"Model Description\", \"Metric\"] + [str(dim) for dim in sorted(model_sizes)]\n", " for model_name, model_results in results.items():\n", " metrics = [metric for metric in model_results.keys()]\n", " dims = sorted([dim for dim in model_results[metrics[0]].keys()])\n", " description = model_description_from_name(model_name)\n", " row = [description, '\\n'.join(metrics) + '\\n']\n", " for dim in dims:\n", " scores = ['%.2f' % model_results[metric][dim] for metric in metrics]\n", " row.append('\\n'.join(scores))\n", " result_table.add_row(row)\n", " result_table.align = 'r'\n", " result_html = result_table.get_html_string()\n", " search = \"<table>\"\n", " insert_at = result_html.index(search) + len(search)\n", " new_row = \"\"\"\n", " <tr>\n", " <th colspan=\"1\" style=\"text-align:left\">%s</th>\n", " <th colspan=\"1\"></th>\n", " <th colspan=\"%d\" style=\"text-align:center\"> Dimensions</th>\n", " </tr>\"\"\" % (task_name, len(model_sizes))\n", " result_html = result_html[:insert_at] + new_row + result_html[insert_at:]\n", " display(HTML(result_html))\n", " \n", "def model_description_from_name(model_name):\n", " if model_name.startswith('gensim'):\n", " implementation = 'Gensim'\n", " elif model_name.startswith('cpp'):\n", " implementation = 'C++'\n", " elif model_name.startswith('np'):\n", " implementation = 'Numpy'\n", " else:\n", " raise ValueError('Unsupported implementation for model: %s' % model_name)\n", " description = []\n", " for param_key in sorted(default_params.keys()):\n", " pattern = '%s_([^_])_?' % param_key\n", " match = re.search(pattern, model_name)\n", " if match:\n", " description.append(\"%s=%s\" % (param_key, match.groups()[0]))\n", " return \"%s: %s\" % (implementation, \", \".join(description))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 WordNet reconstruction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this task, embeddings are learnt using the entire transitive closure of the WordNet noun hypernym hierarchy. Subsequently, for every hypernym pair `(u, v)`, the rank of `v` amongst all nodes that do not have a positive edge with `v` is computed. The final metric `mean_rank` is the average of all these ranks. The `MAP` metric is the mean of the Average Precision of the rankings for all positive nodes for a given node `u`.\n", "\n", "Note that this task tests representation capacity of the learnt embeddings, and not the generalization ability." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reconstruction_results = OrderedDict()\n", "metrics = ['mean_rank', 'MAP']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "for implementation, models in sorted(model_files.items()):\n", " for model_name, files in models.items():\n", " if model_name in reconstruction_results:\n", " continue\n", " reconstruction_results[model_name] = OrderedDict()\n", " for metric in metrics:\n", " reconstruction_results[model_name][metric] = {}\n", " for model_size, model_file in files.items():\n", " print('Evaluating model %s of size %d' % (model_name, model_size))\n", " embedding = load_model(implementation, model_file)\n", " eval_instance = ReconstructionEvaluation(wordnet_file, embedding)\n", " eval_result = eval_instance.evaluate(max_n=1000)\n", " for metric in metrics:\n", " reconstruction_results[model_name][metric][model_size] = eval_result[metric]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "display_results('WordNet Reconstruction', reconstruction_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Results from the paper -\n", "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_paper.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figures above illustrate a few things - \n", "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n", "2. The results from the original paper have not been achieved by our implementation. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n", "3. Using burn-in and regularization leads to much better results with low model sizes, however the results do not improve significantly with increasing model size. This might have to do with tuning the regularization coefficient, which the paper does not mention." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 WordNet link prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This task is similar to the reconstruction task described above, except that the list of relations is split into a training and testing set, and the mean rank reported is for the edges in the test set.\n", "\n", "Therefore, this tests the ability of the model to predict unseen edges between nodes, i.e. generalization ability, as opposed to the representation capacity tested in the Reconstruction task" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.2.1 Preparing data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def train_test_split(data_file, test_ratio=0.1):\n", " \"\"\"Creates train and test files from given data file, returns train/test file names\n", " \n", " Args:\n", " data_file (str): path to data file for which train/test split is to be created\n", " test_ratio (float): fraction of lines to be used for test data\n", " \n", " Returns\n", " (train_file, test_file): tuple of strings with train file and test file paths\n", " \"\"\"\n", " train_filename = data_file + '.train'\n", " test_filename = data_file + '.test'\n", " if os.path.exists(train_filename) and os.path.exists(test_filename):\n", " print('Train and test files already exist, skipping')\n", " return (train_filename, test_filename)\n", " root_nodes, leaf_nodes = get_root_and_leaf_nodes(data_file)\n", " test_line_candidates = []\n", " line_count = 0\n", " all_nodes = set()\n", " with smart_open(data_file, 'rb') as f:\n", " for i, line in enumerate(f):\n", " node_1, node_2 = line.split()\n", " all_nodes.update([node_1, node_2])\n", " if (\n", " node_1 not in leaf_nodes\n", " and node_2 not in leaf_nodes\n", " and node_1 not in root_nodes\n", " and node_2 not in root_nodes\n", " and node_1 != node_2\n", " ):\n", " test_line_candidates.append(i)\n", " line_count += 1\n", "\n", " num_test_lines = int(test_ratio line_count)\n", " if num_test_lines > len(test_line_candidates):\n", " raise ValueError('Not enough candidate relations for test set')\n", " print('Choosing %d test lines from %d candidates' % (num_test_lines, len(test_line_candidates)))\n", " test_line_indices = set(random.sample(test_line_candidates, num_test_lines))\n", " train_line_indices = set(l for l in range(line_count) if l not in test_line_indices)\n", " \n", " train_set_nodes = set()\n", " with smart_open(data_file, 'rb') as f:\n", " train_file = smart_open(train_filename, 'wb')\n", " test_file = smart_open(test_filename, 'wb')\n", " for i, line in enumerate(f):\n", " if i in train_line_indices:\n", " train_set_nodes.update(line.split())\n", " train_file.write(line)\n", " elif i in test_line_indices:\n", " test_file.write(line)\n", " else:\n", " raise AssertionError('Line %d not present in either train or test line indices' % i)\n", " train_file.close()\n", " test_file.close()\n", " assert len(train_set_nodes) == len(all_nodes), 'Not all nodes from dataset present in train set relations'\n", " return (train_filename, test_filename)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_root_and_leaf_nodes(data_file):\n", " \"\"\"Return keys of root and leaf nodes from a file with transitive closure relations\n", " \n", " Args:\n", " data_file(str): file path containing transitive closure relations\n", " \n", " Returns:\n", " (root_nodes, leaf_nodes) - tuple containing keys of root and leaf nodes\n", " \"\"\"\n", " root_candidates = set()\n", " leaf_candidates = set()\n", " with smart_open(data_file, 'rb') as f:\n", " for line in f:\n", " nodes = line.split()\n", " root_candidates.update(nodes)\n", " leaf_candidates.update(nodes)\n", " \n", " with smart_open(data_file, 'rb') as f:\n", " for line in f:\n", " node_1, node_2 = line.split()\n", " if node_1 == node_2:\n", " continue\n", " leaf_candidates.discard(node_1)\n", " root_candidates.discard(node_2)\n", " \n", " return (leaf_candidates, root_candidates)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wordnet_train_file, wordnet_test_file = train_test_split(wordnet_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.2.2 Training models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Training models for link prediction\n", "lp_model_files = {}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lp_model_files['c++'] = {}\n", "# Train c++ models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'cpp_lp_model', 'c++')\n", "lp_model_files['c++'][model_name] = {}\n", "for dim, filepath in files.items():\n", " lp_model_files['c++'][model_name][dim] = filepath\n", "# Train c++ models with non-default params\n", "for param, values in non_default_params.items():\n", " params = default_params.copy()\n", " for value in values:\n", " params[param] = value\n", " model_name, files = train_model_with_params(params, wordnet_train_file, model_sizes, 'cpp_lp_model', 'c++')\n", " lp_model_files['c++'][model_name] = {}\n", " for dim, filepath in files.items():\n", " lp_model_files['c++'][model_name][dim] = filepath" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lp_model_files['numpy'] = {}\n", "# Train numpy models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'np_lp_model', 'numpy')\n", "lp_model_files['numpy'][model_name] = {}\n", "for dim, filepath in files.items():\n", " lp_model_files['numpy'][model_name][dim] = filepath" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lp_model_files['gensim'] = {}\n", "# Train models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'gensim_lp_model', 'gensim')\n", "lp_model_files['gensim'][model_name] = {}\n", "for dim, filepath in files.items():\n", " lp_model_files['gensim'][model_name][dim] = filepath\n", "# Train models with non-default params\n", "for new_params in non_default_params_gensim:\n", " params = default_params.copy()\n", " params.update(new_params)\n", " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_lp_model', 'gensim')\n", " lp_model_files['gensim'][model_name] = {}\n", " for dim, filepath in files.items():\n", " lp_model_files['gensim'][model_name][dim] = filepath" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4.2.3 Evaluating models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lp_results = OrderedDict()\n", "metrics = ['mean_rank', 'MAP']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for implementation, models in sorted(lp_model_files.items()):\n", " for model_name, files in models.items():\n", " lp_results[model_name] = OrderedDict()\n", " for metric in metrics:\n", " lp_results[model_name][metric] = {}\n", " for model_size, model_file in files.items():\n", " print('Evaluating model %s of size %d' % (model_name, model_size))\n", " embedding = load_model(implementation, model_file)\n", " eval_instance = LinkPredictionEvaluation(wordnet_train_file, wordnet_test_file, embedding)\n", " eval_result = eval_instance.evaluate(max_n=1000)\n", " for metric in metrics:\n", " lp_results[model_name][metric][model_size] = eval_result[metric]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "display_results('WordNet Link Prediction', lp_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Results from the paper -\n", "![Link Prediction Paper](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_paper.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results follow similar trends as the reconstruction results. Repeating here for ease of reading - \n", "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n", "2. The results from the original paper have not been achieved by our implementation. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the link prediction task.\n", "4. Using burn-in and regularization leads to better results with low model sizes, however the results do not improve significantly with increasing model size.\n", "\n", "The main difference from the reconstruction results is that mean ranks for link prediction are slightly worse most of the time than the corresponding reconstruction results. This is to be expected, as link prediction is performed on a held-out test set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 HyperLex Lexical Entailment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Lexical Entailment task is performed using the HyperLex dataset, a collection of 2163 noun pairs and scores that denote \"To what degree is noun A a type of noun Y\". For example - \n", " \n", "`girl person 9.85`\n", "\n", "These scores are out of 10.\n", "\n", "The [spearman's correlation score](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) is computed for the predicted and actual similarity scores, with the models trained on the entire WordNet noun hierarchy.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "entailment_results = OrderedDict()\n", "eval_instance = LexicalEntailmentEvaluation(hyperlex_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "for implementation, models in sorted(model_files.items()):\n", " for model_name, files in models.items():\n", " if model_name in entailment_results:\n", " continue\n", " entailment_results[model_name] = OrderedDict()\n", " entailment_results[model_name]['spearman'] = {}\n", " for model_size, model_file in files.items():\n", " print('Evaluating model %s of size %d' % (model_name, model_size))\n", " embedding = load_model(implementation, model_file)\n", " entailment_results[model_name]['spearman'][model_size] = eval_instance.evaluate_spearman(embedding)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "display_results('Lexical Entailment (HyperLex)', entailment_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Results from paper (for Poincaré Embeddings, as well as other embeddings from previous papers) - \n", "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_paper.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some observations - \n", "1. We achieve a max spearman score of 0.48, fairly close to the spearman score of 0.512 mentioned in the paper.\n", "2. The best results are obtained with 20 negative examples, a batch size of 10, and no burn-in, however the differences are too low to make a meaningful conclusion.\n", "\n", "However, there are a few ambiguities and caveats - \n", "1. The paper does not mention which hyperparameters and model size have been used for the above mentioned result. Hence it is possible that the results are achieved with a significantly lower model size than the one we use, which would imply that our implementation still has some way to go.\n", "2. The same word can have multiple nodes in the WordNet dataset for different senses of the word, and it is unclear in the paper how to decide which node to pick. For the above results, we have gone with the sane default of picking the particular sense that has the maximum similarity score with the target word.\n", "3. Certain words in the HyperLex dataset seem to be absent from the WordNet data - the paper does not mention any such thing. Pairs containing missing words have been omitted from the evaluation (182/2163).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.4 Link Prediction on Collaboration Networks\n", "\n", "The paper also describes a variant of the Poincaré model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n", "\n", "This variant has not been implemented yet, and is therefore not a part of our experiments." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Next Steps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. The model can be investigated further to understand why it doesn't produce results as good as the paper. It is possible that this might be due to training details not present in the paper, or due to us incorrectly interpreting some ambiguous parts of the paper. We have not been able to clarify all such ambiguities in communication with the authors.\n", "2. Optimizing the training process further - with a model size of 50 dimensions and a dataset with ~700k relations and ~80k nodes, the Gensim implementation takes around 45 seconds to complete an epoch (~15k relations per second), whereas the open source C++ implementation takes around 1/6th the time (~95k relations per second).\n", "3. Implementing the variant of the model mentioned in the paper for symmetric graphs and evaluating on the scientific collaboration datasets described earlier in the report." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	169,348	Python	.py	2,654	58.102864	437	0.666603	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,920	WordRank_wrapper_quickstart.ipynb	piskvorky_gensim/docs/notebooks/WordRank_wrapper_quickstart.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# WordRank wrapper tutorial on Lee Corpus\n", "\n", "WordRank is a new word embedding algorithm which captures the semantic similarities in a text data well. See this [notebook](Wordrank_comparisons.ipynb) for it's comparisons to other popular embedding models. This tutorial will serve as a guide to use the WordRank wrapper in gensim. You need to install [WordRank](https://bitbucket.org/shihaoji/wordrank) before proceeding with this tutorial.\n", "\n", "\n", "# Train model\n", "\n", "We'll use [Lee corpus](https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/test/test_data/lee.cor) for training which is already available in gensim. Now for Wordrank, two parameters `dump_period` and `iter` needs to be in sync as it dumps the embedding file with the start of next iteration. For example, if you want results after 10 iterations, you need to use `iter=11` and `dump_period` can be anything that gives mod 0 with resulting iteration, in this case 2 or 5.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: 'wordrank/glove/vocab_count': 'wordrank/glove/vocab_count'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-a9079683a958>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'../../gensim/test/test_data/lee.cor'\u001b[0m \u001b[0;31m# sample corpus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m 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\u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1522\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1523\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'wordrank/glove/vocab_count': 'wordrank/glove/vocab_count'" ] } ], "source": [ "from gensim.models.wrappers import Wordrank\n", "\n", "wr_path = 'wordrank' # path to Wordrank directory\n", "out_dir = 'model' # name of output directory to save data to\n", "data = '../../gensim/test/test_data/lee.cor' # sample corpus\n", "\n", "model = Wordrank.train(wr_path, data, out_dir, iter=11, dump_period=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, you can use any of the Keyed Vector function in gensim, on this model for further tasks. For example," ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.most_similar('President')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.similarity('President', 'military')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "As Wordrank provides two sets of embeddings, the word and context embedding, you can obtain their addition by setting ensemble parameter to 1 in the train method.\n", "\n", "# Save and Load models\n", "In case, you have trained the model yourself using demo scripts in Wordrank, you can then simply load the embedding files in gensim. \n", "\n", "Also, Wordrank doesn't return the embeddings sorted according to the word frequency in corpus, so you can use the sorted_vocab parameter in the load method. But for that, you need to provide the vocabulary file generated in the 'matrix.toy' directory(if you used default names in demo) where all the metadata is stored." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "wr_word_embedding = 'wordrank.words'\n", "vocab_file = 'vocab.txt'\n", "\n", "model = Wordrank.load_wordrank_model(wr_word_embedding, vocab_file, sorted_vocab=1)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "If you want to load the ensemble embedding, you similarly need to provide the context embedding file and set ensemble to 1 in `load_wordrank_model` method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "wr_context_file = 'wordrank.contexts'\n", "model = Wordrank.load_wordrank_model(wr_word_embedding, vocab_file, wr_context_file, sorted_vocab=1, ensemble=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can save these sorted embeddings using the standard gensim methods." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tempfile import mkstemp\n", "\n", "fs, temp_path = mkstemp(\"gensim_temp\") # creates a temp file\n", "model.save(temp_path) # save the model" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Evaluating models\n", "Now that the embeddings are loaded in Word2Vec format and sorted according to the word frequencies in corpus, you can use the evaluations provided by gensim on this model.\n", "\n", "For example, it can be evaluated on following Word Analogies and Word Similarity benchmarks. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "word_analogies_file = 'datasets/questions-words.txt'\n", "model.accuracy(word_analogies_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "word_similarity_file = 'datasets/ws-353.txt'\n", "model.evaluate_word_pairs(word_similarity_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These methods take an [optional parameter](https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec.accuracy) restrict_vocab which limits which test examples are to be considered.\n", "\n", "The results here don't look good because the training corpus is very small. To get meaningful results one needs to train on 500k+ words.\n", "\n", "# Conclusion\n", "We learned to use Wordrank wrapper on a sample corpus and also how to directly load the Wordrank embedding files in gensim. Once loaded, you can use the standard gensim methods on this embedding." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	13,859	Python	.py	209	62.110048	1,767	0.678828	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,921	downloader_api_tutorial.ipynb	piskvorky_gensim/docs/notebooks/downloader_api_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial for using Gensim's API for downloading corpuses/models\n", "Let's start by importing the api module." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import logging\n", "import gensim.downloader as api\n", "\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, lets download the text8 corpus and load it to memory (automatically)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[==================================================] 100.0% 31.6/31.6MB downloaded\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-11-10 14:49:45,787 : INFO : text8 downloaded\n" ] } ], "source": [ "corpus = api.load('text8')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the corpus has been downloaded and loaded, let's create a word2vec model of our corpus." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-11-10 14:50:02,458 : INFO : collecting all words and their counts\n", "2017-11-10 14:50:02,461 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n", "2017-11-10 14:50:08,402 : INFO : collected 253854 word types from a corpus of 17005207 raw words and 1701 sentences\n", "2017-11-10 14:50:08,403 : INFO : Loading a fresh vocabulary\n", "2017-11-10 14:50:08,693 : INFO : min_count=5 retains 71290 unique words (28% of original 253854, drops 182564)\n", "2017-11-10 14:50:08,694 : INFO : min_count=5 leaves 16718844 word corpus (98% of original 17005207, drops 286363)\n", "2017-11-10 14:50:08,870 : INFO : deleting the raw counts dictionary of 253854 items\n", "2017-11-10 14:50:08,898 : INFO : sample=0.001 downsamples 38 most-common words\n", "2017-11-10 14:50:08,899 : INFO : downsampling leaves estimated 12506280 word corpus (74.8% of prior 16718844)\n", "2017-11-10 14:50:08,900 : INFO : estimated required memory for 71290 words and 100 dimensions: 92677000 bytes\n", "2017-11-10 14:50:09,115 : INFO : resetting layer weights\n", "2017-11-10 14:50:09,703 : INFO : training model with 3 workers on 71290 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n", "2017-11-10 14:50:10,718 : INFO : PROGRESS: at 1.66% examples, 1020519 words/s, in_qsize 5, out_qsize 0\n", "2017-11-10 14:50:11,715 : INFO : PROGRESS: at 3.29% examples, 1017921 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:12,715 : INFO : PROGRESS: at 4.71% examples, 976739 words/s, in_qsize 4, out_qsize 0\n", "2017-11-10 14:50:13,729 : INFO : PROGRESS: at 6.35% examples, 989118 words/s, in_qsize 4, out_qsize 1\n", 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1031459 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:51,850 : INFO : PROGRESS: at 69.47% examples, 1030985 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:52,857 : INFO : PROGRESS: at 71.18% examples, 1031954 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:53,862 : INFO : PROGRESS: at 72.83% examples, 1031823 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:54,864 : INFO : PROGRESS: at 74.46% examples, 1031628 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:55,866 : INFO : PROGRESS: at 76.17% examples, 1031962 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:56,870 : INFO : PROGRESS: at 77.77% examples, 1031167 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:57,875 : INFO : PROGRESS: at 79.37% examples, 1030337 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:58,880 : INFO : PROGRESS: at 80.99% examples, 1029831 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:50:59,881 : INFO : PROGRESS: at 82.67% examples, 1030029 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:00,881 : INFO : PROGRESS: at 84.39% examples, 1030874 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:01,886 : INFO : PROGRESS: at 86.03% examples, 1030988 words/s, in_qsize 2, out_qsize 0\n", "2017-11-10 14:51:02,892 : INFO : PROGRESS: at 87.72% examples, 1031570 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:03,895 : INFO : PROGRESS: at 89.41% examples, 1031964 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:04,902 : INFO : PROGRESS: at 91.09% examples, 1032271 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:05,910 : INFO : PROGRESS: at 92.53% examples, 1029888 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:06,912 : INFO : PROGRESS: at 94.03% examples, 1028192 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:07,916 : INFO : PROGRESS: at 95.74% examples, 1028660 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:08,919 : INFO : PROGRESS: at 97.47% examples, 1029434 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:09,923 : INFO : PROGRESS: at 99.18% examples, 1029952 words/s, in_qsize 0, out_qsize 0\n", "2017-11-10 14:51:10,409 : INFO : worker thread finished; awaiting finish of 2 more threads\n", "2017-11-10 14:51:10,409 : INFO : worker thread finished; awaiting finish of 1 more threads\n", "2017-11-10 14:51:10,415 : INFO : worker thread finished; awaiting finish of 0 more threads\n", "2017-11-10 14:51:10,416 : INFO : training on 85026035 raw words (62530433 effective words) took 60.7s, 1029968 effective words/s\n" ] } ], "source": [ "from gensim.models.word2vec import Word2Vec\n", "\n", "model = Word2Vec(corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our word2vec model, let's find words that are similar to 'tree'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-11-10 14:51:10,422 : INFO : precomputing L2-norms of word weight vectors\n" ] }, { "data": { "text/plain": [ "[(u'trees', 0.7245415449142456),\n", " (u'leaf', 0.6882676482200623),\n", " (u'bark', 0.645646333694458),\n", " (u'avl', 0.6076173782348633),\n", " (u'cactus', 0.6019535064697266),\n", " (u'flower', 0.6010029315948486),\n", " (u'fruit', 0.5908031463623047),\n", " (u'bird', 0.5886812806129456),\n", " (u'leaves', 0.5771278142929077),\n", " (u'pond', 0.5627825856208801)]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.most_similar('tree')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use the API to download many corpora and models. You can get the list of all the models and corpora that are provided, by using the code below:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"models\": {\n", " \"glove-twitter-25\": {\n", " \"description\": \"Pre-trained vectors, 2B tweets, 27B tokens, 1.2M vocab, uncased. https://nlp.stanford.edu/projects/glove/\", \n", " \"parameters\": \"dimensions = 25\", \n", " \"file_name\": \"glove-twitter-25.gz\", \n", " \"papers\": \"https://nlp.stanford.edu/pubs/glove.pdf\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-twitter-25.txt`\", \n", " \"checksum\": \"50db0211d7e7a2dcd362c6b774762793\"\n", " }, \n", " \"glove-twitter-100\": {\n", " \"description\": \"Pre-trained vectors, 2B tweets, 27B tokens, 1.2M vocab, uncased. https://nlp.stanford.edu/projects/glove/\", \n", " \"parameters\": \"dimensions = 100\", \n", " \"file_name\": \"glove-twitter-100.gz\", \n", " \"papers\": \"https://nlp.stanford.edu/pubs/glove.pdf\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-twitter-100.txt`\", \n", " \"checksum\": \"b04f7bed38756d64cf55b58ce7e97b15\"\n", " }, \n", " \"glove-wiki-gigaword-100\": {\n", " \"description\": \"Pre-trained vectors ,Wikipedia 2014 + Gigaword 5,6B tokens, 400K vocab, uncased. https://nlp.stanford.edu/projects/glove/\", \n", " \"parameters\": \"dimensions = 100\", \n", " \"file_name\": \"glove-wiki-gigaword-100.gz\", \n", " \"papers\": \"https://nlp.stanford.edu/pubs/glove.pdf\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-100.txt`\", \n", " \"checksum\": \"40ec481866001177b8cd4cb0df92924f\"\n", " }, \n", " \"glove-twitter-200\": {\n", " \"description\": \"Pre-trained vectors, 2B tweets, 27B tokens, 1.2M vocab, uncased. https://nlp.stanford.edu/projects/glove/\", \n", " \"parameters\": \"dimensions = 200\", \n", " \"file_name\": \"glove-twitter-200.gz\", \n", " \"papers\": \"https://nlp.stanford.edu/pubs/glove.pdf\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-twitter-200.txt`\", \n", " \"checksum\": \"e52e8392d1860b95d5308a525817d8f9\"\n", " }, \n", " \"glove-wiki-gigaword-50\": {\n", " \"description\": \"Pre-trained vectors ,Wikipedia 2014 + Gigaword 5,6B tokens, 400K vocab, uncased. https://nlp.stanford.edu/projects/glove/\", \n", " \"parameters\": \"dimension = 50\", \n", " \"file_name\": \"glove-wiki-gigaword-50.gz\", \n", " \"papers\": \"https://nlp.stanford.edu/pubs/glove.pdf\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-50.txt`\", \n", " \"checksum\": \"c289bc5d7f2f02c6dc9f2f9b67641813\"\n", " }, \n", " \"glove-twitter-50\": {\n", " \"description\": \"Pre-trained vectors, 2B tweets, 27B tokens, 1.2M vocab, uncased. https://nlp.stanford.edu/projects/glove/\", \n", " \"parameters\": \"dimensions = 50\", \n", " \"file_name\": \"glove-twitter-50.gz\", \n", " \"papers\": \"https://nlp.stanford.edu/pubs/glove.pdf\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-twitter-50.txt`\", \n", " \"checksum\": \"c168f18641f8c8a00fe30984c4799b2b\"\n", " }, \n", " \"__testing_word2vec-matrix-synopsis\": {\n", " \"description\": \"Word vecrors of the movie matrix\", \n", " \"parameters\": \"dimentions = 50\", \n", " \"file_name\": \"__testing_word2vec-matrix-synopsis.gz\", \n", " \"papers\": \"\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v using a preprocessed corpus. Converted to w2v format with `python3.5 -m gensim.models.word2vec -train <input_filename> -iter 50 -output <output_filename>`\", \n", " \"checksum\": \"534dcb8b56a360977a269b7bfc62d124\"\n", " }, \n", " \"glove-wiki-gigaword-200\": {\n", " \"description\": \"Pre-trained vectors ,Wikipedia 2014 + Gigaword 5,6B tokens, 400K vocab, uncased. https://nlp.stanford.edu/projects/glove/\", \n", " \"parameters\": \"dimentions = 200\", \n", " \"file_name\": \"glove-wiki-gigaword-200.gz\", \n", " \"papers\": \"https://nlp.stanford.edu/pubs/glove.pdf\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-200.txt`\", \n", " \"checksum\": \"59652db361b7a87ee73834a6c391dfc1\"\n", " }, \n", " \"word2vec-google-news-300\": {\n", " \"description\": \"Pre-trained vectors trained on part of Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. The phrases were obtained using a simple data-driven approach described in 'Distributed Representations of Words and Phrases and their Compositionality', https://code.google.com/archive/p/word2vec/\", \n", " \"parameters\": \"dimension = 300\", \n", " \"file_name\": \"word2vec-google-news-300.gz\", \n", " \"papers\": \"https://arxiv.org/abs/1301.3781, https://arxiv.org/abs/1310.4546, https://www.microsoft.com/en-us/research/publication/linguistic-regularities-in-continuous-space-word-representations/?from=http%3A%2F%2Fresearch.microsoft.com%2Fpubs%2F189726%2Frvecs.pdf\", \n", " \"parts\": 1, \n", " \"checksum\": \"a5e5354d40acb95f9ec66d5977d140ef\"\n", " }, \n", " \"glove-wiki-gigaword-300\": {\n", " \"description\": \"Pre-trained vectors, Wikipedia 2014 + Gigaword 5, 6B tokens, 400K vocab, uncased. https://nlp.stanford.edu/projects/glove/\", \n", " \"parameters\": \"dimensions = 300\", \n", " \"file_name\": \"glove-wiki-gigaword-300.gz\", \n", " \"papers\": \"https://nlp.stanford.edu/pubs/glove.pdf\", \n", " \"parts\": 1, \n", " \"preprocessing\": \"Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-300.txt`\", \n", " \"checksum\": \"29e9329ac2241937d55b852e8284e89b\"\n", " }\n", " }, \n", " \"corpora\": {\n", " \"__testing_matrix-synopsis\": {\n", " \"source\": \"http://www.imdb.com/title/tt0133093/plotsummary?ref_=ttpl_pl_syn#synopsis\", \n", " \"checksum\": \"1767ac93a089b43899d54944b07d9dc5\", \n", " \"parts\": 1, \n", " \"description\": \"Synopsis of the movie matrix\", \n", " \"file_name\": \"__testing_matrix-synopsis.gz\"\n", " }, \n", " \"fake-news\": {\n", " \"source\": \"Kaggle\", \n", " \"checksum\": \"5e64e942df13219465927f92dcefd5fe\", \n", " \"parts\": 1, \n", " \"description\": \"It contains text and metadata scraped from 244 websites tagged as 'bullshit' here by the BS Detector Chrome Extension by Daniel Sieradski.\", \n", " \"file_name\": \"fake-news.gz\"\n", " }, \n", " \"__testing_multipart-matrix-synopsis\": {\n", " \"description\": \"Synopsis of the movie matrix\", \n", " \"source\": \"http://www.imdb.com/title/tt0133093/plotsummary?ref_=ttpl_pl_syn#synopsis\", \n", " \"file_name\": \"__testing_multipart-matrix-synopsis.gz\", \n", " \"checksum-0\": \"c8b0c7d8cf562b1b632c262a173ac338\", \n", " \"checksum-1\": \"5ff7fc6818e9a5d9bc1cf12c35ed8b96\", \n", " \"checksum-2\": \"966db9d274d125beaac7987202076cba\", \n", " \"parts\": 3\n", " }, \n", " \"text8\": {\n", " \"source\": \"https://mattmahoney.net/dc/text8.zip\", \n", " \"checksum\": \"68799af40b6bda07dfa47a32612e5364\", \n", " \"parts\": 1, \n", " \"description\": \"Cleaned small sample from wikipedia\", \n", " \"file_name\": \"text8.gz\"\n", " }, \n", " \"wiki-en\": {\n", " \"description\": \"Extracted Wikipedia dump from October 2017. Produced by `python -m gensim.scripts.segment_wiki -f enwiki-20171001-pages-articles.xml.bz2 -o wiki-en.gz`\", \n", " \"source\": \"https://dumps.wikimedia.org/enwiki/20171001/\", \n", " \"file_name\": \"wiki-en.gz\", \n", " \"parts\": 4, \n", " \"checksum-0\": \"a7d7d7fd41ea7e2d7fa32ec1bb640d71\", \n", " \"checksum-1\": \"b2683e3356ffbca3b6c2dca6e9801f9f\", \n", " \"checksum-2\": \"c5cde2a9ae77b3c4ebce804f6df542c2\", \n", " \"checksum-3\": \"00b71144ed5e3aeeb885de84f7452b81\"\n", " }, \n", " \"20-newsgroups\": {\n", " \"source\": \"http://qwone.com/~jason/20Newsgroups/\", \n", " \"checksum\": \"c92fd4f6640a86d5ba89eaad818a9891\", \n", " \"parts\": 1, \n", " \"description\": \"The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups\", \n", " \"file_name\": \"20-newsgroups.gz\"\n", " }\n", " }\n", "}\n" ] } ], "source": [ "import json\n", "data_list = api.info()\n", "print(json.dumps(data_list, indent=4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to get detailed information about the model/corpus, use:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"source\": \"Kaggle\", \n", " \"checksum\": \"5e64e942df13219465927f92dcefd5fe\", \n", " \"parts\": 1, \n", " \"description\": \"It contains text and metadata scraped from 244 websites tagged as 'bullshit' here by the BS Detector Chrome Extension by Daniel Sieradski.\", \n", " \"file_name\": \"fake-news.gz\"\n", "}\n" ] } ], "source": [ "fake_news_info = api.info('fake-news')\n", "print(json.dumps(fake_news_info, indent=4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, you do not want to load the model to memory. You would just want to get the path to the model. For that, use :" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/ivan/gensim-data/glove-wiki-gigaword-50/glove-wiki-gigaword-50.gz\n" ] } ], "source": [ "print(api.load('glove-wiki-gigaword-50', return_path=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to load the model to memory, then:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-11-10 14:51:59,199 : INFO : loading projection weights from /home/ivan/gensim-data/glove-wiki-gigaword-50/glove-wiki-gigaword-50.gz\n", "2017-11-10 14:52:18,380 : INFO : loaded (400000, 50) matrix from /home/ivan/gensim-data/glove-wiki-gigaword-50/glove-wiki-gigaword-50.gz\n", "2017-11-10 14:52:18,405 : INFO : precomputing L2-norms of word weight vectors\n" ] }, { "data": { "text/plain": [ "[(u'plastic', 0.7942505478858948),\n", " (u'metal', 0.770871639251709),\n", " (u'walls', 0.7700636386871338),\n", " (u'marble', 0.7638524174690247),\n", " (u'wood', 0.7624281048774719),\n", " (u'ceramic', 0.7602593302726746),\n", " (u'pieces', 0.7589111924171448),\n", " (u'stained', 0.7528817057609558),\n", " (u'tile', 0.748193621635437),\n", " (u'furniture', 0.746385931968689)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = api.load(\"glove-wiki-gigaword-50\")\n", "model.most_similar(\"glass\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In corpora, the corpus is never loaded to memory, all corpuses wrapped to special class `Dataset` and provide `__iter__` method" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }	25,975	Python	.py	495	46.727273	405	0.568053	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,922	soft_cosine_benchmark.ipynb	piskvorky_gensim/docs/notebooks/soft_cosine_benchmark.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Benchmark: Implement Levenshtein term similarity matrix and fast SCM between corpora ([RaRe-Technologies/gensim PR #2016][#2016])\n", "\n", " [#2016]: https://github.com/RaRe-Technologies/gensim/pull/2016 (Implement Levenshtein term similarity matrix and fast SCM between corpora - Pull Request #2016)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d429fedf094e00c4bb5c27589d5befb53b2e4b13\r\n" ] } ], "source": [ "!git rev-parse HEAD" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from copy import deepcopy\n", "from datetime import timedelta\n", "from itertools import product\n", "import logging\n", "from math import floor, ceil, log10\n", "import pickle\n", "from random import sample, seed, shuffle\n", "from time import time\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from tqdm import tqdm_notebook\n", "\n", "def tqdm(iterable, total=None, desc=None):\n", " if total is None:\n", " total = len(iterable)\n", " for num_done, element in enumerate(tqdm_notebook(iterable, total=total)):\n", " logger.info(\"%s: %d / %d\", desc, num_done, total)\n", " yield element\n", "\n", "from gensim.corpora import Dictionary\n", "import gensim.downloader as api\n", "from gensim.similarities.index import AnnoyIndexer\n", "from gensim.similarities import SparseTermSimilarityMatrix\n", "from gensim.similarities import UniformTermSimilarityIndex\n", "from gensim.similarities import LevenshteinSimilarityIndex\n", "from gensim.similarities import WordEmbeddingSimilarityIndex\n", "from gensim.utils import simple_preprocess\n", "\n", "RANDOM_SEED = 12345\n", "\n", "logger = logging.getLogger()\n", "fhandler = logging.FileHandler(filename='matrix_speed.log', mode='a')\n", "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n", "fhandler.setFormatter(formatter)\n", "logger.addHandler(fhandler)\n", "logger.setLevel(logging.INFO)\n", "\n", "pd.set_option('display.max_rows', None, 'display.max_seq_items', None)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "\"\"\"Repeatedly run a benchmark callable given various configurations and\n", "get a list of results.\n", "\n", "Return a list of results of repeatedly running a benchmark callable.\n", "\n", "Parameters\n", "----------\n", "benchmark : callable tuple -> dict\n", " A benchmark callable that accepts a configuration and returns results.\n", "configurations : iterable of tuple\n", " An iterable of configurations that are used for calling the benchmark function.\n", "results_filename : str\n", " A filename of a file that will be used to persistently store the results using\n", " pickle. If the file exists, then the function will load the stored results\n", " instead of calling the benchmark callable.\n", "\n", "Returns\n", "-------\n", "iterable of tuple\n", " The return values of the individual invocations of the benchmark callable.\n", "\n", "\"\"\"\n", "def benchmark_results(benchmark, configurations, results_filename):\n", " try:\n", " with open(results_filename, \"rb\") as file:\n", " results = pickle.load(file)\n", " except IOError:\n", " configurations = list(configurations)\n", " shuffle(configurations)\n", " results = list(tqdm(\n", " (benchmark(configuration) for configuration in configurations),\n", " total=len(configurations), desc=\"benchmark\"))\n", " with open(results_filename, \"wb\") as file:\n", " pickle.dump(results, file)\n", " return results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement Levenshtein term similarity matrix\n", "\n", "In Gensim PR [#1827][], we added a base implementation of the soft cosine measure (SCM). The base implementation would create term similarity matrices using a single complex procedure. In the Gensim PR [#2016][], we split the procedure into:\n", "\n", "- TermSimilarityIndex builder classes that produce the $k$ most similar terms for a given term $t$ that are distinct from $t$ along with the term similarities, and\n", "- the SparseTermSimilarityMatrix director class that constructs term similarity matrices and consumes term similarities produced by TermSimilarityIndex instances.\n", "\n", "One of the benefits of this separation is that we can easily measure the speed at which a TermSimilarityIndex builder class produces term similarities and compare this speed with the speed at which the SparseTermSimilarityMatrix director class consumes term similarities. This allows us to see which of the classes are a bottleneck that slows down the construction of term similarity matrices.\n", "\n", "In this notebook, we measure all the currently available builder and director classes. For the measurements, we use the [Google News word embeddings][word2vec-google-news-300] distributed with the C implementation of Word2Vec. From the word embeddings, we will derive a dictionary of 2.01M terms.\n", "\n", " [word2vec-google-news-300]: https://github.com/mmihaltz/word2vec-GoogleNews-vectors (word2vec-GoogleNews-vectors)\n", " [#1827]: https://github.com/RaRe-Technologies/gensim/pull/1827 (Implement Soft Cosine Measure - Pull Request #1827)\n", " [#2016]: https://github.com/RaRe-Technologies/gensim/pull/2016 (Implement Levenshtein term similarity matrix and fast SCM between corpora - Pull Request #2016)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "full_model = api.load(\"word2vec-google-news-300\")\n", "\n", "try:\n", " full_dictionary = Dictionary.load(\"matrix_speed.dictionary\")\n", "except IOError:\n", " full_dictionary = Dictionary([[term] for term in full_model.vocab.keys()])\n", " full_dictionary.save(\"matrix_speed.dictionary\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Director class benchmark\n", "#### SparseTermSimilarityMatrix\n", "First, we measure the speed at which the SparseTermSimilarityMatrix director class consumes term similarities." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def benchmark(configuration):\n", " dictionary, nonzero_limit, symmetric, positive_definite, repetition = configuration\n", " index = UniformTermSimilarityIndex(dictionary)\n", " \n", " start_time = time()\n", " matrix = SparseTermSimilarityMatrix(\n", " index, dictionary, nonzero_limit=nonzero_limit, symmetric=symmetric,\n", " positive_definite=positive_definite, dtype=np.float16).matrix\n", " end_time = time()\n", " \n", " duration = end_time - start_time\n", " return {\n", " \"dictionary_size\": len(dictionary),\n", " \"nonzero_limit\": nonzero_limit,\n", " \"matrix_nonzero\": matrix.nnz,\n", " \"repetition\": repetition,\n", " \"symmetric\": symmetric,\n", " \"positive_definite\": positive_definite,\n", " \"duration\": duration, }" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4aef903a70e24247ad3c889237ed4c48", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "dictionary_sizes = [10k for k in range(3, int(ceil(log10(len(full_dictionary)))))]\n", "seed(RANDOM_SEED)\n", "dictionaries = []\n", "for size in tqdm(dictionary_sizes, desc=\"dictionaries\"):\n", " dictionary = Dictionary([sample(list(full_dictionary.values()), size)])\n", " dictionaries.append(dictionary)\n", "dictionaries.append(full_dictionary)\n", "nonzero_limits = [1, 10, 100]\n", "symmetry = (True, False)\n", "positive_definiteness = (True, False)\n", "repetitions = range(10)\n", "\n", "configurations = product(dictionaries, nonzero_limits, symmetry, positive_definiteness, repetitions)\n", "results = benchmark_results(benchmark, configurations, \"matrix_speed.director_results\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following tables show how long it takes to construct a term similarity matrix (the duration column), how many nonzero elements there are in the matrix (the matrix_nonzero column) and the mean term similarity consumption speed (the consumption_speed column) as we vary the dictionary size (the dictionary_size column) the maximum number of nonzero elements outside the diagonal in every column of the matrix (the nonzero_limit column), the matrix symmetry constraint (the symmetric column), and the matrix positive definiteness constraing (the positive_definite** column). Ten independendent measurements were taken. The top table shows the mean values and the bottom table shows the standard deviations.\n", "\n", "We can see that the symmetry and positive definiteness constraints severely limit the number of nonzero elements in the resulting matrix. This in turn increases the consumption speed, since we end up throwing away most of the elements that we consume. The effects of the dictionary size on the mean term similarity consumption speed are minor to none." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(results)\n", "df[\"consumption_speed\"] = df.dictionary_size * df.nonzero_limit / df.duration\n", "df = df.groupby([\"dictionary_size\", \"nonzero_limit\", \"symmetric\", \"positive_definite\"])\n", "\n", "def display(df):\n", " df[\"duration\"] = [timedelta(0, duration) for duration in df[\"duration\"]]\n", " df[\"matrix_nonzero\"] = [int(nonzero) for nonzero in df[\"matrix_nonzero\"]]\n", " df[\"consumption_speed\"] = [\"%.02f Kword pairs / s\" % (speed / 1000) for speed in df[\"consumption_speed\"]]\n", " return df" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>duration</th>\n", " <th>matrix_nonzero</th>\n", " <th>consumption_speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>nonzero_limit</th>\n", " <th>symmetric</th>\n", " <th>positive_definite</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"12\" valign=\"top\">10000</th>\n", " <th rowspan=\"4\" valign=\"top\">1</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:00:00.435533</td>\n", " <td>20000</td>\n", " <td>22.96 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.492606</td>\n", " <td>20000</td>\n", " <td>20.30 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:00.185563</td>\n", " <td>10002</td>\n", " <td>53.90 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.240471</td>\n", " <td>10002</td>\n", " <td>41.59 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">10</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:00:02.687836</td>\n", " <td>110000</td>\n", " <td>37.21 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.615492</td>\n", " <td>20000</td>\n", " <td>162.49 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:00.501188</td>\n", " <td>10118</td>\n", " <td>199.53 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:01.380586</td>\n", " <td>10010</td>\n", " <td>72.44 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:00:25.262807</td>\n", " <td>1010000</td>\n", " <td>39.58 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:01.132524</td>\n", " <td>20000</td>\n", " <td>883.02 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:03.595666</td>\n", " <td>20198</td>\n", " <td>278.13 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:11.818912</td>\n", " <td>10100</td>\n", " <td>84.61 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"12\" valign=\"top\">2010000</th>\n", " <th rowspan=\"4\" valign=\"top\">1</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:01:31.786585</td>\n", " <td>4020000</td>\n", " <td>21.90 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:01:40.954580</td>\n", " <td>4020000</td>\n", " <td>19.91 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:39.050064</td>\n", " <td>2010002</td>\n", " <td>51.48 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:49.238437</td>\n", " <td>2010002</td>\n", " <td>40.82 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">10</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:09:35.470373</td>\n", " <td>22110000</td>\n", " <td>34.93 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:02:02.920334</td>\n", " <td>4020000</td>\n", " <td>163.52 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:01:39.576693</td>\n", " <td>2010118</td>\n", " <td>201.88 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:04:35.646501</td>\n", " <td>2010010</td>\n", " <td>72.92 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>01:42:01.747568</td>\n", " <td>203010000</td>\n", " <td>32.88 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:03:36.420778</td>\n", " <td>4020000</td>\n", " <td>928.75 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:10:58.434060</td>\n", " <td>2020198</td>\n", " <td>305.30 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:39:40.319479</td>\n", " <td>2010100</td>\n", " <td>84.44 Kword pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration \\\n", "dictionary_size nonzero_limit symmetric positive_definite \n", "10000 1 False False 00:00:00.435533 \n", " True 00:00:00.492606 \n", " True False 00:00:00.185563 \n", " True 00:00:00.240471 \n", " 10 False False 00:00:02.687836 \n", " True 00:00:00.615492 \n", " True False 00:00:00.501188 \n", " True 00:00:01.380586 \n", " 100 False False 00:00:25.262807 \n", " True 00:00:01.132524 \n", " True False 00:00:03.595666 \n", " True 00:00:11.818912 \n", "2010000 1 False False 00:01:31.786585 \n", " True 00:01:40.954580 \n", " True False 00:00:39.050064 \n", " True 00:00:49.238437 \n", " 10 False False 00:09:35.470373 \n", " True 00:02:02.920334 \n", " True False 00:01:39.576693 \n", " True 00:04:35.646501 \n", " 100 False False 01:42:01.747568 \n", " True 00:03:36.420778 \n", " True False 00:10:58.434060 \n", " True 00:39:40.319479 \n", "\n", " matrix_nonzero \\\n", "dictionary_size nonzero_limit symmetric positive_definite \n", "10000 1 False False 20000 \n", " True 20000 \n", " True False 10002 \n", " True 10002 \n", " 10 False False 110000 \n", " True 20000 \n", " True False 10118 \n", " True 10010 \n", " 100 False False 1010000 \n", " True 20000 \n", " True False 20198 \n", " True 10100 \n", "2010000 1 False False 4020000 \n", " True 4020000 \n", " True False 2010002 \n", " True 2010002 \n", " 10 False False 22110000 \n", " True 4020000 \n", " True False 2010118 \n", " True 2010010 \n", " 100 False False 203010000 \n", " True 4020000 \n", " True False 2020198 \n", " True 2010100 \n", "\n", " consumption_speed \n", "dictionary_size nonzero_limit symmetric positive_definite \n", "10000 1 False False 22.96 Kword pairs / s \n", " True 20.30 Kword pairs / s \n", " True False 53.90 Kword pairs / s \n", " True 41.59 Kword pairs / s \n", " 10 False False 37.21 Kword pairs / s \n", " True 162.49 Kword pairs / s \n", " True False 199.53 Kword pairs / s \n", " True 72.44 Kword pairs / s \n", " 100 False False 39.58 Kword pairs / s \n", " True 883.02 Kword pairs / s \n", " True False 278.13 Kword pairs / s \n", " True 84.61 Kword pairs / s \n", "2010000 1 False False 21.90 Kword pairs / s \n", " True 19.91 Kword pairs / s \n", " True False 51.48 Kword pairs / s \n", " True 40.82 Kword pairs / s \n", " 10 False False 34.93 Kword pairs / s \n", " True 163.52 Kword pairs / s \n", " True False 201.88 Kword pairs / s \n", " True 72.92 Kword pairs / s \n", " 100 False False 32.88 Kword pairs / s \n", " True 928.75 Kword pairs / s \n", " True False 305.30 Kword pairs / s \n", " True 84.44 Kword pairs / s " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.mean()).loc[\n", " [10000, len(full_dictionary)], :, :].loc[\n", " :, [\"duration\", \"matrix_nonzero\", \"consumption_speed\"]]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>duration</th>\n", " <th>matrix_nonzero</th>\n", " <th>consumption_speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>nonzero_limit</th>\n", " <th>symmetric</th>\n", " <th>positive_definite</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"12\" valign=\"top\">10000</th>\n", " <th rowspan=\"4\" valign=\"top\">1</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:00:00.005334</td>\n", " <td>0</td>\n", " <td>0.28 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.004072</td>\n", " <td>0</td>\n", " <td>0.17 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:00.003124</td>\n", " <td>0</td>\n", " <td>0.90 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.001797</td>\n", " <td>0</td>\n", " <td>0.31 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">10</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:00:00.011986</td>\n", " <td>0</td>\n", " <td>0.17 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.005972</td>\n", " <td>0</td>\n", " <td>1.59 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:00.002869</td>\n", " <td>0</td>\n", " <td>1.15 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.011411</td>\n", " <td>0</td>\n", " <td>0.60 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:00:00.111118</td>\n", " <td>0</td>\n", " <td>0.17 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.007611</td>\n", " <td>0</td>\n", " <td>5.94 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:00.030875</td>\n", " <td>0</td>\n", " <td>2.38 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.050198</td>\n", " <td>0</td>\n", " <td>0.36 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"12\" valign=\"top\">2010000</th>\n", " <th rowspan=\"4\" valign=\"top\">1</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:00:00.767305</td>\n", " <td>0</td>\n", " <td>0.18 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.172432</td>\n", " <td>0</td>\n", " <td>0.03 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:00.346239</td>\n", " <td>0</td>\n", " <td>0.46 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.177075</td>\n", " <td>0</td>\n", " <td>0.15 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">10</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:00:05.156655</td>\n", " <td>0</td>\n", " <td>0.31 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.631676</td>\n", " <td>0</td>\n", " <td>0.83 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:01.216067</td>\n", " <td>0</td>\n", " <td>2.41 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.547773</td>\n", " <td>0</td>\n", " <td>0.14 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">False</th>\n", " <th>False</th>\n", " <td>00:04:10.371035</td>\n", " <td>0</td>\n", " <td>1.24 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.634416</td>\n", " <td>0</td>\n", " <td>2.73 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">True</th>\n", " <th>False</th>\n", " <td>00:00:06.586767</td>\n", " <td>0</td>\n", " <td>3.05 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:09.030932</td>\n", " <td>0</td>\n", " <td>0.32 Kword pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration \\\n", "dictionary_size nonzero_limit symmetric positive_definite \n", "10000 1 False False 00:00:00.005334 \n", " True 00:00:00.004072 \n", " True False 00:00:00.003124 \n", " True 00:00:00.001797 \n", " 10 False False 00:00:00.011986 \n", " True 00:00:00.005972 \n", " True False 00:00:00.002869 \n", " True 00:00:00.011411 \n", " 100 False False 00:00:00.111118 \n", " True 00:00:00.007611 \n", " True False 00:00:00.030875 \n", " True 00:00:00.050198 \n", "2010000 1 False False 00:00:00.767305 \n", " True 00:00:00.172432 \n", " True False 00:00:00.346239 \n", " True 00:00:00.177075 \n", " 10 False False 00:00:05.156655 \n", " True 00:00:00.631676 \n", " True False 00:00:01.216067 \n", " True 00:00:00.547773 \n", " 100 False False 00:04:10.371035 \n", " True 00:00:00.634416 \n", " True False 00:00:06.586767 \n", " True 00:00:09.030932 \n", "\n", " matrix_nonzero \\\n", "dictionary_size nonzero_limit symmetric positive_definite \n", "10000 1 False False 0 \n", " True 0 \n", " True False 0 \n", " True 0 \n", " 10 False False 0 \n", " True 0 \n", " True False 0 \n", " True 0 \n", " 100 False False 0 \n", " True 0 \n", " True False 0 \n", " True 0 \n", "2010000 1 False False 0 \n", " True 0 \n", " True False 0 \n", " True 0 \n", " 10 False False 0 \n", " True 0 \n", " True False 0 \n", " True 0 \n", " 100 False False 0 \n", " True 0 \n", " True False 0 \n", " True 0 \n", "\n", " consumption_speed \n", "dictionary_size nonzero_limit symmetric positive_definite \n", "10000 1 False False 0.28 Kword pairs / s \n", " True 0.17 Kword pairs / s \n", " True False 0.90 Kword pairs / s \n", " True 0.31 Kword pairs / s \n", " 10 False False 0.17 Kword pairs / s \n", " True 1.59 Kword pairs / s \n", " True False 1.15 Kword pairs / s \n", " True 0.60 Kword pairs / s \n", " 100 False False 0.17 Kword pairs / s \n", " True 5.94 Kword pairs / s \n", " True False 2.38 Kword pairs / s \n", " True 0.36 Kword pairs / s \n", "2010000 1 False False 0.18 Kword pairs / s \n", " True 0.03 Kword pairs / s \n", " True False 0.46 Kword pairs / s \n", " True 0.15 Kword pairs / s \n", " 10 False False 0.31 Kword pairs / s \n", " True 0.83 Kword pairs / s \n", " True False 2.41 Kword pairs / s \n", " True 0.14 Kword pairs / s \n", " 100 False False 1.24 Kword pairs / s \n", " True 2.73 Kword pairs / s \n", " True False 3.05 Kword pairs / s \n", " True 0.32 Kword pairs / s " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.apply(lambda x: (x - x.mean()).std())).loc[\n", " [10000, len(full_dictionary)], :, :].loc[\n", " :, [\"duration\", \"matrix_nonzero\", \"consumption_speed\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Builder class benchmark\n", "#### UniformTermSimilarityIndex\n", "First, we measure the speed at which the UniformTermSimilarityIndex builder class produces term similarities. UniformTermSimilarityIndex is a dummy class that just generates a sequence of constants. It produces much more term similarities per second than the SparseTermSimilarityMatrix is capable of consuming and its results will serve as an upper limit." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def benchmark(configuration):\n", " dictionary, nonzero_limit, repetition = configuration\n", " \n", " start_time = time()\n", " index = UniformTermSimilarityIndex(dictionary)\n", " end_time = time()\n", " constructor_duration = end_time - start_time\n", " \n", " start_time = time()\n", " for term in dictionary.values():\n", " for _j, _k in zip(index.most_similar(term, topn=nonzero_limit), range(nonzero_limit)):\n", " pass\n", " end_time = time()\n", " production_duration = end_time - start_time\n", " \n", " return {\n", " \"dictionary_size\": len(dictionary),\n", " \"nonzero_limit\": nonzero_limit,\n", " \"repetition\": repetition,\n", " \"constructor_duration\": constructor_duration,\n", " \"production_duration\": production_duration, }" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "nonzero_limits = [1, 10, 100, 1000]\n", "\n", "configurations = product(dictionaries, nonzero_limits, repetitions)\n", "results = benchmark_results(benchmark, configurations, \"matrix_speed.builder_results.uniform\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following tables show how long it takes to retrieve the most similar terms for all terms in a dictionary (the production_duration column) and the mean term similarity production speed (the production_speed column) as we vary the dictionary size (the dictionary_size column), and the maximum number of most similar terms that will be retrieved (the nonzero_limit column). Ten independendent measurements were taken. The top table shows the mean values and the bottom table shows the standard deviations.\n", "\n", "The production_speed is proportional to nonzero_limit." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(results)\n", "df[\"processing_speed\"] = df.dictionary_size ** 2 / df.production_duration\n", "df[\"production_speed\"] = df.dictionary_size * df.nonzero_limit / df.production_duration\n", "df = df.groupby([\"dictionary_size\", \"nonzero_limit\"])\n", "\n", "def display(df):\n", " df[\"constructor_duration\"] = [timedelta(0, duration) for duration in df[\"constructor_duration\"]]\n", " df[\"production_duration\"] = [timedelta(0, duration) for duration in df[\"production_duration\"]]\n", " df[\"processing_speed\"] = [\"%.02f Kword pairs / s\" % (speed / 1000) for speed in df[\"processing_speed\"]]\n", " df[\"production_speed\"] = [\"%.02f Kword pairs / s\" % (speed / 1000) for speed in df[\"production_speed\"]]\n", " return df" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>production_duration</th>\n", " <th>production_speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>nonzero_limit</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th>1</th>\n", " <td>00:00:00.002973</td>\n", " <td>336.41 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:00:00.005372</td>\n", " <td>1861.64 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:00.026752</td>\n", " <td>3738.79 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1000</th>\n", " <td>00:00:00.290265</td>\n", " <td>3449.16 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">2010000</th>\n", " <th>1</th>\n", " <td>00:00:06.318446</td>\n", " <td>318.12 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:00:10.783611</td>\n", " <td>1863.96 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:53.108644</td>\n", " <td>3785.04 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1000</th>\n", " <td>00:09:45.103741</td>\n", " <td>3437.36 Kword pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " production_duration production_speed\n", "dictionary_size nonzero_limit \n", "1000 1 00:00:00.002973 336.41 Kword pairs / s\n", " 10 00:00:00.005372 1861.64 Kword pairs / s\n", " 100 00:00:00.026752 3738.79 Kword pairs / s\n", " 1000 00:00:00.290265 3449.16 Kword pairs / s\n", "2010000 1 00:00:06.318446 318.12 Kword pairs / s\n", " 10 00:00:10.783611 1863.96 Kword pairs / s\n", " 100 00:00:53.108644 3785.04 Kword pairs / s\n", " 1000 00:09:45.103741 3437.36 Kword pairs / s" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.mean()).loc[\n", " [1000, len(full_dictionary)], :, :].loc[\n", " :, [\"production_duration\", \"production_speed\"]]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>production_duration</th>\n", " <th>production_speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>nonzero_limit</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th>1</th>\n", " <td>00:00:00.000017</td>\n", " <td>1.93 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:00:00.000062</td>\n", " <td>21.50 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:00.000408</td>\n", " <td>56.66 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1000</th>\n", " <td>00:00:00.010500</td>\n", " <td>123.82 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">2010000</th>\n", " <th>1</th>\n", " <td>00:00:00.023495</td>\n", " <td>1.18 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:00:00.035587</td>\n", " <td>6.16 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:00.535765</td>\n", " <td>37.76 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1000</th>\n", " <td>00:00:15.037816</td>\n", " <td>89.56 Kword pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " production_duration production_speed\n", "dictionary_size nonzero_limit \n", "1000 1 00:00:00.000017 1.93 Kword pairs / s\n", " 10 00:00:00.000062 21.50 Kword pairs / s\n", " 100 00:00:00.000408 56.66 Kword pairs / s\n", " 1000 00:00:00.010500 123.82 Kword pairs / s\n", "2010000 1 00:00:00.023495 1.18 Kword pairs / s\n", " 10 00:00:00.035587 6.16 Kword pairs / s\n", " 100 00:00:00.535765 37.76 Kword pairs / s\n", " 1000 00:00:15.037816 89.56 Kword pairs / s" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.apply(lambda x: (x - x.mean()).std())).loc[\n", " [1000, len(full_dictionary)], :, :].loc[\n", " :, [\"production_duration\", \"production_speed\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### LevenshteinSimilarityIndex\n", "Next, we measure the speed at which the LevenshteinSimilarityIndex builder class produces term similarities. LevenshteinSimilarityIndex is currently just a na√Øve implementation that produces much fewer term similarities per second than the SparseTermSimilarityMatrix class is capable of consuming." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def benchmark(configuration):\n", " dictionary, nonzero_limit, query_terms, repetition = configuration\n", " \n", " start_time = time()\n", " index = LevenshteinSimilarityIndex(dictionary)\n", " end_time = time()\n", " constructor_duration = end_time - start_time\n", " \n", " start_time = time()\n", " for term in query_terms:\n", " for _j, _k in zip(index.most_similar(term, topn=nonzero_limit), range(nonzero_limit)):\n", " pass\n", " end_time = time()\n", " production_duration = end_time - start_time\n", " \n", " return {\n", " \"dictionary_size\": len(dictionary),\n", " \"mean_query_term_length\": np.mean([len(term) for term in query_terms]),\n", " \"nonzero_limit\": nonzero_limit,\n", " \"repetition\": repetition,\n", " \"constructor_duration\": constructor_duration,\n", " \"production_duration\": production_duration, }" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "nonzero_limits = [1, 10, 100]\n", "seed(RANDOM_SEED)\n", "min_dictionary = sorted((len(dictionary), dictionary) for dictionary in dictionaries)[0][1]\n", "query_terms = sample(list(min_dictionary.values()), 10)\n", "\n", "configurations = product(dictionaries, nonzero_limits, [query_terms], repetitions)\n", "results = benchmark_results(benchmark, configurations, \"matrix_speed.builder_results.levenshtein\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following tables show how long it takes to retrieve the most similar terms for ten randomly sampled terms from a dictionary (the production_duration column), the mean term similarity production speed (the production_speed column) and the mean term similarity processing speed (the processing_speed column) as we vary the dictionary size (the dictionary_size column), and the maximum number of most similar terms that will be retrieved (the nonzero_limit column). Ten independendent measurements were taken. The top table shows the mean values and the bottom table shows the standard deviations.\n", "\n", "The production_speed is proportional to nonzero_limit / dictionary_size. The processing_speed is constant." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(results)\n", "df[\"processing_speed\"] = df.dictionary_size * len(query_terms) / df.production_duration\n", "df[\"production_speed\"] = df.nonzero_limit * len(query_terms) / df.production_duration\n", "df = df.groupby([\"dictionary_size\", \"nonzero_limit\"])\n", "\n", "def display(df):\n", " df[\"constructor_duration\"] = [timedelta(0, duration) for duration in df[\"constructor_duration\"]]\n", " df[\"production_duration\"] = [timedelta(0, duration) for duration in df[\"production_duration\"]]\n", " df[\"processing_speed\"] = [\"%.02f Kword pairs / s\" % (speed / 1000) for speed in df[\"processing_speed\"]]\n", " df[\"production_speed\"] = [\"%.02f word pairs / s\" % speed for speed in df[\"production_speed\"]]\n", " return df" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>production_duration</th>\n", " <th>production_speed</th>\n", " <th>processing_speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>nonzero_limit</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">1000</th>\n", " <th>1</th>\n", " <td>00:00:00.055994</td>\n", " <td>178.61 word pairs / s</td>\n", " <td>178.61 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:00:00.056097</td>\n", " <td>1782.70 word pairs / s</td>\n", " <td>178.27 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:00.056212</td>\n", " <td>17791.65 word pairs / s</td>\n", " <td>177.92 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">1000000</th>\n", " <th>1</th>\n", " <td>00:01:20.618070</td>\n", " <td>0.12 word pairs / s</td>\n", " <td>124.05 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:01:20.048238</td>\n", " <td>1.25 word pairs / s</td>\n", " <td>124.92 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:01:20.064999</td>\n", " <td>12.49 word pairs / s</td>\n", " <td>124.90 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">2010000</th>\n", " <th>1</th>\n", " <td>00:02:44.069399</td>\n", " <td>0.06 word pairs / s</td>\n", " <td>122.51 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:02:43.914601</td>\n", " <td>0.61 word pairs / s</td>\n", " <td>122.63 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:02:43.892408</td>\n", " <td>6.10 word pairs / s</td>\n", " <td>122.64 Kword pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " production_duration production_speed \\\n", "dictionary_size nonzero_limit \n", "1000 1 00:00:00.055994 178.61 word pairs / s \n", " 10 00:00:00.056097 1782.70 word pairs / s \n", " 100 00:00:00.056212 17791.65 word pairs / s \n", "1000000 1 00:01:20.618070 0.12 word pairs / s \n", " 10 00:01:20.048238 1.25 word pairs / s \n", " 100 00:01:20.064999 12.49 word pairs / s \n", "2010000 1 00:02:44.069399 0.06 word pairs / s \n", " 10 00:02:43.914601 0.61 word pairs / s \n", " 100 00:02:43.892408 6.10 word pairs / s \n", "\n", " processing_speed \n", "dictionary_size nonzero_limit \n", "1000 1 178.61 Kword pairs / s \n", " 10 178.27 Kword pairs / s \n", " 100 177.92 Kword pairs / s \n", "1000000 1 124.05 Kword pairs / s \n", " 10 124.92 Kword pairs / s \n", " 100 124.90 Kword pairs / s \n", "2010000 1 122.51 Kword pairs / s \n", " 10 122.63 Kword pairs / s \n", " 100 122.64 Kword pairs / s " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.mean()).loc[\n", " [1000, 1000000, len(full_dictionary)], :].loc[\n", " :, [\"production_duration\", \"production_speed\", \"processing_speed\"]]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>production_duration</th>\n", " <th>production_speed</th>\n", " <th>processing_speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>nonzero_limit</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">1000</th>\n", " <th>1</th>\n", " <td>00:00:00.000673</td>\n", " <td>2.16 word pairs / s</td>\n", " <td>2.16 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:00:00.000409</td>\n", " <td>13.06 word pairs / s</td>\n", " <td>1.31 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:00.000621</td>\n", " <td>196.80 word pairs / s</td>\n", " <td>1.97 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">1000000</th>\n", " <th>1</th>\n", " <td>00:00:00.810661</td>\n", " <td>0.00 word pairs / s</td>\n", " <td>1.23 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:00:00.110013</td>\n", " <td>0.00 word pairs / s</td>\n", " <td>0.17 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:00.164959</td>\n", " <td>0.03 word pairs / s</td>\n", " <td>0.26 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">2010000</th>\n", " <th>1</th>\n", " <td>00:00:01.159273</td>\n", " <td>0.00 word pairs / s</td>\n", " <td>0.85 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>00:00:00.429011</td>\n", " <td>0.00 word pairs / s</td>\n", " <td>0.32 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:00.433687</td>\n", " <td>0.02 word pairs / s</td>\n", " <td>0.32 Kword pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " production_duration production_speed \\\n", "dictionary_size nonzero_limit \n", "1000 1 00:00:00.000673 2.16 word pairs / s \n", " 10 00:00:00.000409 13.06 word pairs / s \n", " 100 00:00:00.000621 196.80 word pairs / s \n", "1000000 1 00:00:00.810661 0.00 word pairs / s \n", " 10 00:00:00.110013 0.00 word pairs / s \n", " 100 00:00:00.164959 0.03 word pairs / s \n", "2010000 1 00:00:01.159273 0.00 word pairs / s \n", " 10 00:00:00.429011 0.00 word pairs / s \n", " 100 00:00:00.433687 0.02 word pairs / s \n", "\n", " processing_speed \n", "dictionary_size nonzero_limit \n", "1000 1 2.16 Kword pairs / s \n", " 10 1.31 Kword pairs / s \n", " 100 1.97 Kword pairs / s \n", "1000000 1 1.23 Kword pairs / s \n", " 10 0.17 Kword pairs / s \n", " 100 0.26 Kword pairs / s \n", "2010000 1 0.85 Kword pairs / s \n", " 10 0.32 Kword pairs / s \n", " 100 0.32 Kword pairs / s " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.apply(lambda x: (x - x.mean()).std())).loc[\n", " [1000, 1000000, len(full_dictionary)], :].loc[\n", " :, [\"production_duration\", \"production_speed\", \"processing_speed\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### WordEmbeddingSimilarityIndex\n", "Lastly, we measure the speed at which the WordEmbeddingSimilarityIndex builder class constructs an instance and produces term similarities. Gensim currently supports slow and precise nearest neighbor search, and also approximate nearest neighbor search using [ANNOY][]. We evaluate both options.\n", "\n", " [ANNOY]: https://github.com/spotify/annoy (Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def benchmark(configuration):\n", " (model, dictionary), nonzero_limit, annoy_n_trees, query_terms, repetition = configuration\n", " use_annoy = annoy_n_trees > 0\n", " model.init_sims()\n", " \n", " start_time = time()\n", " if use_annoy:\n", " annoy = AnnoyIndexer(model, annoy_n_trees)\n", " kwargs = {\"indexer\": annoy}\n", " else:\n", " kwargs = {}\n", " index = WordEmbeddingSimilarityIndex(model, kwargs=kwargs)\n", " end_time = time()\n", " constructor_duration = end_time - start_time\n", " \n", " start_time = time()\n", " for term in query_terms:\n", " for _j, _k in zip(index.most_similar(term, topn=nonzero_limit), range(nonzero_limit)):\n", " pass\n", " end_time = time()\n", " production_duration = end_time - start_time\n", " \n", " return {\n", " \"dictionary_size\": len(dictionary),\n", " \"mean_query_term_length\": np.mean([len(term) for term in query_terms]),\n", " \"nonzero_limit\": nonzero_limit,\n", " \"use_annoy\": use_annoy,\n", " \"annoy_n_trees\": annoy_n_trees,\n", " \"repetition\": repetition,\n", " \"constructor_duration\": constructor_duration,\n", " \"production_duration\": production_duration, }" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "842bb1a60f814110a8f20eb44a973397", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "models = []\n", "for dictionary in tqdm(dictionaries, desc=\"models\"):\n", " if dictionary == full_dictionary:\n", " models.append(full_model)\n", " continue\n", " model = full_model.__class__(full_model.vector_size)\n", " model.vocab = {word: deepcopy(full_model.vocab[word]) for word in dictionary.values()}\n", " model.index2entity = []\n", " vector_indices = []\n", " for index, word in enumerate(full_model.index2entity):\n", " if word in model.vocab.keys():\n", " model.index2entity.append(word)\n", " model.vocab[word].index = len(vector_indices)\n", " vector_indices.append(index)\n", " model.vectors = full_model.vectors[vector_indices]\n", " models.append(model)\n", "annoy_n_trees = [0] + [10k for k in range(3)]\n", "seed(RANDOM_SEED)\n", "query_terms = sample(list(min_dictionary.values()), 1000)\n", "\n", "configurations = product(zip(models, dictionaries), nonzero_limits, annoy_n_trees, [query_terms], repetitions)\n", "results = benchmark_results(benchmark, configurations, \"matrix_speed.builder_results.wordembeddings\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following tables show how long it takes to construct an ANNOY index and the builder class instance (the constructor_duration column), how long it takes to retrieve the most similar terms for 1,000 randomly sampled terms from a dictionary (the production_duration column), the mean term similarity production speed (the production_speed column) and the mean term similarity processing speed (the processing_speed column) as we vary the dictionary size (the dictionary_size column), the maximum number of most similar terms that will be retrieved (the nonzero_limit column), and the number of constructed ANNOY trees (the annoy_n_trees column). Ten independendent measurements were taken. The top table shows the mean values and the bottom table shows the standard deviations.\n", "\n", "If we do not use ANNOY (annoy_n_trees${}=0$), then production_speed is proportional to nonzero_limit / dictionary_size. \n", "If we do use ANNOY (annoy_n_trees${}>0$), then production_speed is proportional to nonzero_limit / (annoy_n_trees)*${}^{1/2}$." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(results)\n", "df[\"processing_speed\"] = df.dictionary_size len(query_terms) / df.production_duration\n", "df[\"production_speed\"] = df.nonzero_limit * len(query_terms) / df.production_duration\n", "df = df.groupby([\"dictionary_size\", \"nonzero_limit\", \"annoy_n_trees\"])\n", "\n", "def display(df):\n", " df[\"constructor_duration\"] = [timedelta(0, duration) for duration in df[\"constructor_duration\"]]\n", " df[\"production_duration\"] = [timedelta(0, duration) for duration in df[\"production_duration\"]]\n", " df[\"processing_speed\"] = [\"%.02f Kword pairs / s\" % (speed / 1000) for speed in df[\"processing_speed\"]]\n", " df[\"production_speed\"] = [\"%.02f Kword pairs / s\" % (speed / 1000) for speed in df[\"production_speed\"]]\n", " return df" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>constructor_duration</th>\n", " <th>production_duration</th>\n", " <th>production_speed</th>\n", " <th>processing_speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>nonzero_limit</th>\n", " <th>annoy_n_trees</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">1000000</th>\n", " <th rowspan=\"3\" valign=\"top\">1</th>\n", " <th>0</th>\n", " <td>00:00:00.000007</td>\n", " <td>00:00:19.962977</td>\n", " <td>0.05 Kword pairs / s</td>\n", " <td>50094.22 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00:00:30.268797</td>\n", " <td>00:00:00.097011</td>\n", " <td>10.32 Kword pairs / s</td>\n", " <td>10320061.76 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:06:23.415982</td>\n", " <td>00:00:00.160870</td>\n", " <td>6.24 Kword pairs / s</td>\n", " <td>6236688.27 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">100</th>\n", " <th>0</th>\n", " <td>00:00:00.000008</td>\n", " <td>00:00:22.868372</td>\n", " <td>4.37 Kword pairs / s</td>\n", " <td>43729.34 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00:00:31.154876</td>\n", " <td>00:00:00.156238</td>\n", " <td>641.91 Kword pairs / s</td>\n", " <td>6419086.99 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:06:23.290572</td>\n", " <td>00:00:01.297445</td>\n", " <td>77.13 Kword pairs / s</td>\n", " <td>771277.71 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">2010000</th>\n", " <th rowspan=\"3\" valign=\"top\">1</th>\n", " <th>0</th>\n", " <td>00:00:00.000007</td>\n", " <td>00:01:55.303216</td>\n", " <td>0.01 Kword pairs / s</td>\n", " <td>17432.79 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00:01:34.004196</td>\n", " <td>00:00:00.190463</td>\n", " <td>5.25 Kword pairs / s</td>\n", " <td>10561607.14 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:23:29.796006</td>\n", " <td>00:00:00.339500</td>\n", " <td>2.96 Kword pairs / s</td>\n", " <td>5954865.50 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">100</th>\n", " <th>0</th>\n", " <td>00:00:00.000007</td>\n", " <td>00:02:11.926861</td>\n", " <td>0.76 Kword pairs / s</td>\n", " <td>15236.46 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00:01:35.813414</td>\n", " <td>00:00:00.301120</td>\n", " <td>332.38 Kword pairs / s</td>\n", " <td>6680879.02 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:23:05.155399</td>\n", " <td>00:00:03.031527</td>\n", " <td>33.42 Kword pairs / s</td>\n", " <td>671683.05 Kword pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " constructor_duration \\\n", "dictionary_size nonzero_limit annoy_n_trees \n", "1000000 1 0 00:00:00.000007 \n", " 1 00:00:30.268797 \n", " 100 00:06:23.415982 \n", " 100 0 00:00:00.000008 \n", " 1 00:00:31.154876 \n", " 100 00:06:23.290572 \n", "2010000 1 0 00:00:00.000007 \n", " 1 00:01:34.004196 \n", " 100 00:23:29.796006 \n", " 100 0 00:00:00.000007 \n", " 1 00:01:35.813414 \n", " 100 00:23:05.155399 \n", "\n", " production_duration \\\n", "dictionary_size nonzero_limit annoy_n_trees \n", "1000000 1 0 00:00:19.962977 \n", " 1 00:00:00.097011 \n", " 100 00:00:00.160870 \n", " 100 0 00:00:22.868372 \n", " 1 00:00:00.156238 \n", " 100 00:00:01.297445 \n", "2010000 1 0 00:01:55.303216 \n", " 1 00:00:00.190463 \n", " 100 00:00:00.339500 \n", " 100 0 00:02:11.926861 \n", " 1 00:00:00.301120 \n", " 100 00:00:03.031527 \n", "\n", " production_speed \\\n", "dictionary_size nonzero_limit annoy_n_trees \n", "1000000 1 0 0.05 Kword pairs / s \n", " 1 10.32 Kword pairs / s \n", " 100 6.24 Kword pairs / s \n", " 100 0 4.37 Kword pairs / s \n", " 1 641.91 Kword pairs / s \n", " 100 77.13 Kword pairs / s \n", "2010000 1 0 0.01 Kword pairs / s \n", " 1 5.25 Kword pairs / s \n", " 100 2.96 Kword pairs / s \n", " 100 0 0.76 Kword pairs / s \n", " 1 332.38 Kword pairs / s \n", " 100 33.42 Kword pairs / s \n", "\n", " processing_speed \n", "dictionary_size nonzero_limit annoy_n_trees \n", "1000000 1 0 50094.22 Kword pairs / s \n", " 1 10320061.76 Kword pairs / s \n", " 100 6236688.27 Kword pairs / s \n", " 100 0 43729.34 Kword pairs / s \n", " 1 6419086.99 Kword pairs / s \n", " 100 771277.71 Kword pairs / s \n", "2010000 1 0 17432.79 Kword pairs / s \n", " 1 10561607.14 Kword pairs / s \n", " 100 5954865.50 Kword pairs / s \n", " 100 0 15236.46 Kword pairs / s \n", " 1 6680879.02 Kword pairs / s \n", " 100 671683.05 Kword pairs / s " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.mean()).loc[\n", " [1000000, len(full_dictionary)], [1, 100], [0, 1, 100]].loc[\n", " :, [\"constructor_duration\", \"production_duration\", \"production_speed\", \"processing_speed\"]]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>constructor_duration</th>\n", " <th>production_duration</th>\n", " <th>production_speed</th>\n", " <th>processing_speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>nonzero_limit</th>\n", " <th>annoy_n_trees</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">1000000</th>\n", " <th rowspan=\"3\" valign=\"top\">1</th>\n", " <th>0</th>\n", " <td>00:00:00.000002</td>\n", " <td>00:00:00.115644</td>\n", " <td>0.00 Kword pairs / s</td>\n", " <td>286.27 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00:00:01.854097</td>\n", " <td>00:00:00.003517</td>\n", " <td>0.37 Kword pairs / s</td>\n", " <td>367959.55 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:04.702035</td>\n", " <td>00:00:00.010444</td>\n", " <td>0.35 Kword pairs / s</td>\n", " <td>350506.05 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">100</th>\n", " <th>0</th>\n", " <td>00:00:00.000002</td>\n", " <td>00:00:00.104872</td>\n", " <td>0.02 Kword pairs / s</td>\n", " <td>198.86 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00:00:01.163678</td>\n", " <td>00:00:00.008939</td>\n", " <td>36.14 Kword pairs / s</td>\n", " <td>361441.71 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:06.818568</td>\n", " <td>00:00:00.036979</td>\n", " <td>2.07 Kword pairs / s</td>\n", " <td>20741.69 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">2010000</th>\n", " <th rowspan=\"3\" valign=\"top\">1</th>\n", " <th>0</th>\n", " <td>00:00:00.000001</td>\n", " <td>00:00:00.653177</td>\n", " <td>0.00 Kword pairs / s</td>\n", " <td>97.50 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00:00:04.677209</td>\n", " <td>00:00:00.005679</td>\n", " <td>0.16 Kword pairs / s</td>\n", " <td>311832.91 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:01:38.562684</td>\n", " <td>00:00:00.029887</td>\n", " <td>0.22 Kword pairs / s</td>\n", " <td>434681.25 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">100</th>\n", " <th>0</th>\n", " <td>00:00:00.000001</td>\n", " <td>00:00:00.979613</td>\n", " <td>0.01 Kword pairs / s</td>\n", " <td>111.85 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00:00:03.207474</td>\n", " <td>00:00:00.009479</td>\n", " <td>10.18 Kword pairs / s</td>\n", " <td>204614.80 Kword pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>00:00:55.119595</td>\n", " <td>00:00:00.419531</td>\n", " <td>3.46 Kword pairs / s</td>\n", " <td>69543.35 Kword pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " constructor_duration \\\n", "dictionary_size nonzero_limit annoy_n_trees \n", "1000000 1 0 00:00:00.000002 \n", " 1 00:00:01.854097 \n", " 100 00:00:04.702035 \n", " 100 0 00:00:00.000002 \n", " 1 00:00:01.163678 \n", " 100 00:00:06.818568 \n", "2010000 1 0 00:00:00.000001 \n", " 1 00:00:04.677209 \n", " 100 00:01:38.562684 \n", " 100 0 00:00:00.000001 \n", " 1 00:00:03.207474 \n", " 100 00:00:55.119595 \n", "\n", " production_duration \\\n", "dictionary_size nonzero_limit annoy_n_trees \n", "1000000 1 0 00:00:00.115644 \n", " 1 00:00:00.003517 \n", " 100 00:00:00.010444 \n", " 100 0 00:00:00.104872 \n", " 1 00:00:00.008939 \n", " 100 00:00:00.036979 \n", "2010000 1 0 00:00:00.653177 \n", " 1 00:00:00.005679 \n", " 100 00:00:00.029887 \n", " 100 0 00:00:00.979613 \n", " 1 00:00:00.009479 \n", " 100 00:00:00.419531 \n", "\n", " production_speed \\\n", "dictionary_size nonzero_limit annoy_n_trees \n", "1000000 1 0 0.00 Kword pairs / s \n", " 1 0.37 Kword pairs / s \n", " 100 0.35 Kword pairs / s \n", " 100 0 0.02 Kword pairs / s \n", " 1 36.14 Kword pairs / s \n", " 100 2.07 Kword pairs / s \n", "2010000 1 0 0.00 Kword pairs / s \n", " 1 0.16 Kword pairs / s \n", " 100 0.22 Kword pairs / s \n", " 100 0 0.01 Kword pairs / s \n", " 1 10.18 Kword pairs / s \n", " 100 3.46 Kword pairs / s \n", "\n", " processing_speed \n", "dictionary_size nonzero_limit annoy_n_trees \n", "1000000 1 0 286.27 Kword pairs / s \n", " 1 367959.55 Kword pairs / s \n", " 100 350506.05 Kword pairs / s \n", " 100 0 198.86 Kword pairs / s \n", " 1 361441.71 Kword pairs / s \n", " 100 20741.69 Kword pairs / s \n", "2010000 1 0 97.50 Kword pairs / s \n", " 1 311832.91 Kword pairs / s \n", " 100 434681.25 Kword pairs / s \n", " 100 0 111.85 Kword pairs / s \n", " 1 204614.80 Kword pairs / s \n", " 100 69543.35 Kword pairs / s " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.apply(lambda x: (x - x.mean()).std())).loc[\n", " [1000000, len(full_dictionary)], [1, 100], [0, 1, 100]].loc[\n", " :, [\"constructor_duration\", \"production_duration\", \"production_speed\", \"processing_speed\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement fast SCM between corpora\n", "\n", "In Gensim PR [#1827][], we added a base implementation of the soft cosine measure (SCM). The base implementation would compute SCM between single documents using the softcossim function. In the Gensim PR [#2016][], we intruduced the SparseTermSimilarityMatrix.inner_product method, which computes SCM not only between single documents, but also between a document and a corpus, and between two corpora.\n", "\n", "For the measurements, we use the [Google News word embeddings][word2vec-google-news-300] distributed with the C implementation of Word2Vec. From the word embeddings, we will derive a dictionary of 2.01m terms. As a corpus, we will use a random sample of 100K articles from the 4.92m English [Wikipedia articles][enwiki].\n", "\n", " [word2vec-google-news-300]: https://github.com/mmihaltz/word2vec-GoogleNews-vectors (word2vec-GoogleNews-vectors)\n", " [enwiki]: https://github.com/RaRe-Technologies/gensim-data/releases/tag/wiki-english-20171001 (wiki-english-20171001)\n", " [#1827]: https://github.com/RaRe-Technologies/gensim/pull/1827 (Implement Soft Cosine Measure - Pull Request #1827)\n", " [#2016]: https://github.com/RaRe-Technologies/gensim/pull/2016 (Implement Levenshtein term similarity matrix and fast SCM between corpora - Pull Request #2016)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "full_model = api.load(\"word2vec-google-news-300\")\n", "\n", "try:\n", " with open(\"matrix_speed.corpus\", \"rb\") as file:\n", " full_corpus = pickle.load(file) \n", "except IOError:\n", " original_corpus = list(tqdm(api.load(\"wiki-english-20171001\"), desc=\"original_corpus\", total=4924894))\n", " seed(RANDOM_SEED)\n", " full_corpus = [\n", " simple_preprocess(u'\\n'.join(article[\"section_texts\"]))\n", " for article in tqdm(sample(original_corpus, 105), desc=\"full_corpus\", total=105)]\n", " del original_corpus\n", " with open(\"matrix_speed.corpus\", \"wb\") as file:\n", " pickle.dump(full_corpus, file)\n", "\n", "try:\n", " full_dictionary = Dictionary.load(\"matrix_speed.dictionary\")\n", "except IOError:\n", " full_dictionary = Dictionary([[term] for term in full_model.vocab.keys()])\n", " full_dictionary.save(\"matrix_speed.dictionary\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SCM between two documents\n", "First, we measure the speed at which the inner_product method produces term similarities between single documents." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "def benchmark(configuration):\n", " (matrix, dictionary, nonzero_limit), corpus, normalized, repetition = configuration\n", " corpus_size = len(corpus)\n", " corpus = [dictionary.doc2bow(doc) for doc in corpus]\n", " corpus = [vec for vec in corpus if len(vec) > 0]\n", " \n", " start_time = time()\n", " for vec1 in corpus:\n", " for vec2 in corpus:\n", " matrix.inner_product(vec1, vec2, normalized=normalized)\n", " end_time = time()\n", " duration = end_time - start_time\n", " \n", " return {\n", " \"dictionary_size\": matrix.matrix.shape[0],\n", " \"matrix_nonzero\": matrix.matrix.nnz,\n", " \"nonzero_limit\": nonzero_limit,\n", " \"normalized\": normalized,\n", " \"corpus_size\": corpus_size,\n", " \"corpus_actual_size\": len(corpus),\n", " \"corpus_nonzero\": sum(len(vec) for vec in corpus),\n", " \"mean_document_length\": np.mean([len(doc) for doc in corpus]),\n", " \"repetition\": repetition,\n", " \"duration\": duration, }" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "110675d5552847819754f0dc5b1c19e1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "744e400d597440f79b5923dafb1974fc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0f84efc0c79a4628a9543736fc5f0c9a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8a185a8e530e4481b90056222f5f0a1c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/mnt/storage/home/novotny/.virtualenvs/gensim/lib/python3.4/site-packages/gensim/matutils.py:738: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "seed(RANDOM_SEED)\n", "dictionary_sizes = [1000, 100000]\n", "dictionaries = []\n", "for size in tqdm(dictionary_sizes, desc=\"dictionaries\"):\n", " dictionary = Dictionary([sample(list(full_dictionary.values()), size)])\n", " dictionaries.append(dictionary)\n", "min_dictionary = sorted((len(dictionary), dictionary) for dictionary in dictionaries)[0][1]\n", "\n", "corpus_sizes = [100, 1000]\n", "corpora = []\n", "for size in tqdm(corpus_sizes, desc=\"corpora\"):\n", " corpus = sample(full_corpus, size)\n", " corpora.append(corpus)\n", "\n", "models = []\n", "for dictionary in tqdm(dictionaries, desc=\"models\"):\n", " if dictionary == full_dictionary:\n", " models.append(full_model)\n", " continue\n", " model = full_model.__class__(full_model.vector_size)\n", " model.vocab = {word: deepcopy(full_model.vocab[word]) for word in dictionary.values()}\n", " model.index2entity = []\n", " vector_indices = []\n", " for index, word in enumerate(full_model.index2entity):\n", " if word in model.vocab.keys():\n", " model.index2entity.append(word)\n", " model.vocab[word].index = len(vector_indices)\n", " vector_indices.append(index)\n", " model.vectors = full_model.vectors[vector_indices]\n", " models.append(model)\n", "\n", "nonzero_limits = [1, 10, 100]\n", "matrices = []\n", "for (model, dictionary), nonzero_limit in tqdm(\n", " list(product(zip(models, dictionaries), nonzero_limits)), desc=\"matrices\"):\n", " annoy = AnnoyIndexer(model, 1)\n", " index = WordEmbeddingSimilarityIndex(model, kwargs={\"indexer\": annoy})\n", " matrix = SparseTermSimilarityMatrix(index, dictionary, nonzero_limit=nonzero_limit)\n", " matrices.append((matrix, dictionary, nonzero_limit))\n", " del annoy\n", "\n", "normalization = (True, False)\n", "repetitions = range(10)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "configurations = product(matrices, corpora, normalization, repetitions)\n", "results = benchmark_results(benchmark, configurations, \"matrix_speed.inner-product_results.doc_doc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following tables show how long it takes to compute the inner_product method between all document vectors in a corpus (the duration column), how many nonzero elements there are in a corpus matrix (the corpus_nonzero column), how many nonzero elements there are in a term similarity matrix (the matrix_nonzero column) and the mean document similarity production speed (the speed column) as we vary the dictionary size (the dictionary_size column), the size of the corpus (the corpus_size column), the maximum number of nonzero elements in a single column of the matrix (the nonzero_limit column), and the matrix symmetry constraint (the symmetric column). Ten independendent measurements were taken. The top table shows the mean values and the bottom table shows the standard deviations.\n", "\n", "The speed is proportional to the square of the number of unique terms shared by the two document vectors. In our scenario as well as the standard IR scenario, this means speed is constant. Computing a normalized inner product (normalized${}={}$True) results in a constant speed decrease." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(results)\n", "df[\"speed\"] = df.corpus_actual_size2 / df.duration\n", "del df[\"corpus_actual_size\"]\n", "df = df.groupby([\"dictionary_size\", \"corpus_size\", \"nonzero_limit\", \"normalized\"])\n", "\n", "def display(df):\n", " df[\"duration\"] = [timedelta(0, duration) for duration in df[\"duration\"]]\n", " df[\"speed\"] = [\"%.02f Kdoc pairs / s\" % (speed / 1000) for speed in df[\"speed\"]]\n", " return df" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>duration</th>\n", " <th>corpus_nonzero</th>\n", " <th>matrix_nonzero</th>\n", " <th>speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>corpus_size</th>\n", " <th>nonzero_limit</th>\n", " <th>normalized</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">1000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.007383</td>\n", " <td>3.0</td>\n", " <td>1000.0</td>\n", " <td>1.23 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.009028</td>\n", " <td>3.0</td>\n", " <td>1000.0</td>\n", " <td>1.01 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.007657</td>\n", " <td>3.0</td>\n", " <td>84944.0</td>\n", " <td>1.19 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.008238</td>\n", " <td>3.0</td>\n", " <td>84944.0</td>\n", " <td>1.10 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.414364</td>\n", " <td>26.0</td>\n", " <td>1000.0</td>\n", " <td>1.39 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.473789</td>\n", " <td>26.0</td>\n", " <td>1000.0</td>\n", " <td>1.22 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.430833</td>\n", " <td>26.0</td>\n", " <td>84944.0</td>\n", " <td>1.35 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.453477</td>\n", " <td>26.0</td>\n", " <td>84944.0</td>\n", " <td>1.27 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">100000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:05.236376</td>\n", " <td>423.0</td>\n", " <td>101868.0</td>\n", " <td>1.29 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:05.623463</td>\n", " <td>423.0</td>\n", " <td>101868.0</td>\n", " <td>1.20 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:05.083829</td>\n", " <td>423.0</td>\n", " <td>8202884.0</td>\n", " <td>1.33 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:05.576003</td>\n", " <td>423.0</td>\n", " <td>8202884.0</td>\n", " <td>1.21 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:08:59.285347</td>\n", " <td>5162.0</td>\n", " <td>101868.0</td>\n", " <td>1.26 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:09:57.693219</td>\n", " <td>5162.0</td>\n", " <td>101868.0</td>\n", " <td>1.14 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:09:23.213450</td>\n", " <td>5162.0</td>\n", " <td>8202884.0</td>\n", " <td>1.21 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:10:10.612458</td>\n", " <td>5162.0</td>\n", " <td>8202884.0</td>\n", " <td>1.12 Kdoc pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 00:00:00.007383 \n", " True 00:00:00.009028 \n", " 100 False 00:00:00.007657 \n", " True 00:00:00.008238 \n", " 1000 1 False 00:00:00.414364 \n", " True 00:00:00.473789 \n", " 100 False 00:00:00.430833 \n", " True 00:00:00.453477 \n", "100000 100 1 False 00:00:05.236376 \n", " True 00:00:05.623463 \n", " 100 False 00:00:05.083829 \n", " True 00:00:05.576003 \n", " 1000 1 False 00:08:59.285347 \n", " True 00:09:57.693219 \n", " 100 False 00:09:23.213450 \n", " True 00:10:10.612458 \n", "\n", " corpus_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 3.0 \n", " True 3.0 \n", " 100 False 3.0 \n", " True 3.0 \n", " 1000 1 False 26.0 \n", " True 26.0 \n", " 100 False 26.0 \n", " True 26.0 \n", "100000 100 1 False 423.0 \n", " True 423.0 \n", " 100 False 423.0 \n", " True 423.0 \n", " 1000 1 False 5162.0 \n", " True 5162.0 \n", " 100 False 5162.0 \n", " True 5162.0 \n", "\n", " matrix_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 1000.0 \n", " True 1000.0 \n", " 100 False 84944.0 \n", " True 84944.0 \n", " 1000 1 False 1000.0 \n", " True 1000.0 \n", " 100 False 84944.0 \n", " True 84944.0 \n", "100000 100 1 False 101868.0 \n", " True 101868.0 \n", " 100 False 8202884.0 \n", " True 8202884.0 \n", " 1000 1 False 101868.0 \n", " True 101868.0 \n", " 100 False 8202884.0 \n", " True 8202884.0 \n", "\n", " speed \n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 1.23 Kdoc pairs / s \n", " True 1.01 Kdoc pairs / s \n", " 100 False 1.19 Kdoc pairs / s \n", " True 1.10 Kdoc pairs / s \n", " 1000 1 False 1.39 Kdoc pairs / s \n", " True 1.22 Kdoc pairs / s \n", " 100 False 1.35 Kdoc pairs / s \n", " True 1.27 Kdoc pairs / s \n", "100000 100 1 False 1.29 Kdoc pairs / s \n", " True 1.20 Kdoc pairs / s \n", " 100 False 1.33 Kdoc pairs / s \n", " True 1.21 Kdoc pairs / s \n", " 1000 1 False 1.26 Kdoc pairs / s \n", " True 1.14 Kdoc pairs / s \n", " 100 False 1.21 Kdoc pairs / s \n", " True 1.12 Kdoc pairs / s " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.mean()).loc[\n", " [1000, 100000], :, [1, 100], :].loc[\n", " :, [\"duration\", \"corpus_nonzero\", \"matrix_nonzero\", \"speed\"]]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>duration</th>\n", " <th>corpus_nonzero</th>\n", " <th>matrix_nonzero</th>\n", " <th>speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>corpus_size</th>\n", " <th>nonzero_limit</th>\n", " <th>normalized</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">1000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.000871</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.13 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.001315</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.14 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.000893</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.12 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000631</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.08 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.014460</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.05 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.025250</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.07 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.039088</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.11 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.023602</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.06 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">100000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.276359</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.07 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.278806</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.06 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.286781</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.07 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.313397</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.06 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:14.321101</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.03 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:23.526104</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.05 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:05.899527</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.01 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:24.454422</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.05 Kdoc pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 00:00:00.000871 \n", " True 00:00:00.001315 \n", " 100 False 00:00:00.000893 \n", " True 00:00:00.000631 \n", " 1000 1 False 00:00:00.014460 \n", " True 00:00:00.025250 \n", " 100 False 00:00:00.039088 \n", " True 00:00:00.023602 \n", "100000 100 1 False 00:00:00.276359 \n", " True 00:00:00.278806 \n", " 100 False 00:00:00.286781 \n", " True 00:00:00.313397 \n", " 1000 1 False 00:00:14.321101 \n", " True 00:00:23.526104 \n", " 100 False 00:00:05.899527 \n", " True 00:00:24.454422 \n", "\n", " corpus_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "100000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "\n", " matrix_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "100000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "\n", " speed \n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 0.13 Kdoc pairs / s \n", " True 0.14 Kdoc pairs / s \n", " 100 False 0.12 Kdoc pairs / s \n", " True 0.08 Kdoc pairs / s \n", " 1000 1 False 0.05 Kdoc pairs / s \n", " True 0.07 Kdoc pairs / s \n", " 100 False 0.11 Kdoc pairs / s \n", " True 0.06 Kdoc pairs / s \n", "100000 100 1 False 0.07 Kdoc pairs / s \n", " True 0.06 Kdoc pairs / s \n", " 100 False 0.07 Kdoc pairs / s \n", " True 0.06 Kdoc pairs / s \n", " 1000 1 False 0.03 Kdoc pairs / s \n", " True 0.05 Kdoc pairs / s \n", " 100 False 0.01 Kdoc pairs / s \n", " True 0.05 Kdoc pairs / s " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.apply(lambda x: (x - x.mean()).std())).loc[\n", " [1000, 100000], :, [1, 100], :].loc[\n", " :, [\"duration\", \"corpus_nonzero\", \"matrix_nonzero\", \"speed\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SCM between a document and a corpus\n", "Next, we measure the speed at which the inner_product method produces term similarities between documents and a corpus." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "def benchmark(configuration):\n", " (matrix, dictionary, nonzero_limit), corpus, normalized, repetition = configuration\n", " corpus_size = len(corpus)\n", " corpus = [dictionary.doc2bow(doc) for doc in corpus if doc]\n", " \n", " start_time = time()\n", " for vec in corpus:\n", " matrix.inner_product(vec, corpus, normalized=normalized)\n", " end_time = time()\n", " duration = end_time - start_time\n", " \n", " return {\n", " \"dictionary_size\": matrix.matrix.shape[0],\n", " \"matrix_nonzero\": matrix.matrix.nnz,\n", " \"nonzero_limit\": nonzero_limit,\n", " \"normalized\": normalized,\n", " \"corpus_size\": corpus_size,\n", " \"corpus_actual_size\": len(corpus),\n", " \"corpus_nonzero\": sum(len(vec) for vec in corpus),\n", " \"mean_document_length\": np.mean([len(doc) for doc in corpus]),\n", " \"repetition\": repetition,\n", " \"duration\": duration, }" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "configurations = product(matrices, corpora, normalization, repetitions)\n", "results = benchmark_results(benchmark, configurations, \"matrix_speed.inner-product_results.doc_corpus\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The speed is inversely proportional to matrix_nonzero. Computing a normalized inner product (normalized${}={}$True) results in a constant speed decrease." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(results)\n", "df[\"speed\"] = df.corpus_actual_size2 / df.duration\n", "del df[\"corpus_actual_size\"]\n", "df = df.groupby([\"dictionary_size\", \"corpus_size\", \"nonzero_limit\", \"normalized\"])\n", "\n", "def display(df):\n", " df[\"duration\"] = [timedelta(0, duration) for duration in df[\"duration\"]]\n", " df[\"speed\"] = [\"%.02f Kdoc pairs / s\" % (speed / 1000) for speed in df[\"speed\"]]\n", " return df" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>duration</th>\n", " <th>corpus_nonzero</th>\n", " <th>matrix_nonzero</th>\n", " <th>speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>corpus_size</th>\n", " <th>nonzero_limit</th>\n", " <th>normalized</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">1000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.009363</td>\n", " <td>3.0</td>\n", " <td>1000.0</td>\n", " <td>1117.12 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.010948</td>\n", " <td>3.0</td>\n", " <td>1000.0</td>\n", " <td>954.13 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.014128</td>\n", " <td>3.0</td>\n", " <td>84944.0</td>\n", " <td>728.91 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.018164</td>\n", " <td>3.0</td>\n", " <td>84944.0</td>\n", " <td>551.78 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.072091</td>\n", " <td>26.0</td>\n", " <td>1000.0</td>\n", " <td>13872.12 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.079284</td>\n", " <td>26.0</td>\n", " <td>1000.0</td>\n", " <td>12615.36 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.162483</td>\n", " <td>26.0</td>\n", " <td>84944.0</td>\n", " <td>6188.43 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.203081</td>\n", " <td>26.0</td>\n", " <td>84944.0</td>\n", " <td>4924.48 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">100000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.278253</td>\n", " <td>423.0</td>\n", " <td>101868.0</td>\n", " <td>36.05 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.298519</td>\n", " <td>423.0</td>\n", " <td>101868.0</td>\n", " <td>33.56 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:36.326167</td>\n", " <td>423.0</td>\n", " <td>8202884.0</td>\n", " <td>0.28 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:36.928802</td>\n", " <td>423.0</td>\n", " <td>8202884.0</td>\n", " <td>0.27 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:07.403301</td>\n", " <td>5162.0</td>\n", " <td>101868.0</td>\n", " <td>135.08 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:07.794943</td>\n", " <td>5162.0</td>\n", " <td>101868.0</td>\n", " <td>128.29 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:05:55.674712</td>\n", " <td>5162.0</td>\n", " <td>8202884.0</td>\n", " <td>2.81 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:06:05.561398</td>\n", " <td>5162.0</td>\n", " <td>8202884.0</td>\n", " <td>2.74 Kdoc pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 00:00:00.009363 \n", " True 00:00:00.010948 \n", " 100 False 00:00:00.014128 \n", " True 00:00:00.018164 \n", " 1000 1 False 00:00:00.072091 \n", " True 00:00:00.079284 \n", " 100 False 00:00:00.162483 \n", " True 00:00:00.203081 \n", "100000 100 1 False 00:00:00.278253 \n", " True 00:00:00.298519 \n", " 100 False 00:00:36.326167 \n", " True 00:00:36.928802 \n", " 1000 1 False 00:00:07.403301 \n", " True 00:00:07.794943 \n", " 100 False 00:05:55.674712 \n", " True 00:06:05.561398 \n", "\n", " corpus_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 3.0 \n", " True 3.0 \n", " 100 False 3.0 \n", " True 3.0 \n", " 1000 1 False 26.0 \n", " True 26.0 \n", " 100 False 26.0 \n", " True 26.0 \n", "100000 100 1 False 423.0 \n", " True 423.0 \n", " 100 False 423.0 \n", " True 423.0 \n", " 1000 1 False 5162.0 \n", " True 5162.0 \n", " 100 False 5162.0 \n", " True 5162.0 \n", "\n", " matrix_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 1000.0 \n", " True 1000.0 \n", " 100 False 84944.0 \n", " True 84944.0 \n", " 1000 1 False 1000.0 \n", " True 1000.0 \n", " 100 False 84944.0 \n", " True 84944.0 \n", "100000 100 1 False 101868.0 \n", " True 101868.0 \n", " 100 False 8202884.0 \n", " True 8202884.0 \n", " 1000 1 False 101868.0 \n", " True 101868.0 \n", " 100 False 8202884.0 \n", " True 8202884.0 \n", "\n", " speed \n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 1117.12 Kdoc pairs / s \n", " True 954.13 Kdoc pairs / s \n", " 100 False 728.91 Kdoc pairs / s \n", " True 551.78 Kdoc pairs / s \n", " 1000 1 False 13872.12 Kdoc pairs / s \n", " True 12615.36 Kdoc pairs / s \n", " 100 False 6188.43 Kdoc pairs / s \n", " True 4924.48 Kdoc pairs / s \n", "100000 100 1 False 36.05 Kdoc pairs / s \n", " True 33.56 Kdoc pairs / s \n", " 100 False 0.28 Kdoc pairs / s \n", " True 0.27 Kdoc pairs / s \n", " 1000 1 False 135.08 Kdoc pairs / s \n", " True 128.29 Kdoc pairs / s \n", " 100 False 2.81 Kdoc pairs / s \n", " True 2.74 Kdoc pairs / s " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.mean()).loc[\n", " [1000, 100000], :, [1, 100], :].loc[\n", " :, [\"duration\", \"corpus_nonzero\", \"matrix_nonzero\", \"speed\"]]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>duration</th>\n", " <th>corpus_nonzero</th>\n", " <th>matrix_nonzero</th>\n", " <th>speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>corpus_size</th>\n", " <th>nonzero_limit</th>\n", " <th>normalized</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">1000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.002120</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>242.09 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.002387</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>207.64 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.002531</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>130.94 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000911</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>27.68 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.000587</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>112.92 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.001191</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>187.31 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.011944</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>513.79 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.001793</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>43.54 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">100000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.016156</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>2.06 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.013451</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.47 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:01.339787</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.01 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:01.617340</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.01 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.038961</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.71 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.024154</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.40 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:07.604805</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.06 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:14.799519</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.10 Kdoc pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 00:00:00.002120 \n", " True 00:00:00.002387 \n", " 100 False 00:00:00.002531 \n", " True 00:00:00.000911 \n", " 1000 1 False 00:00:00.000587 \n", " True 00:00:00.001191 \n", " 100 False 00:00:00.011944 \n", " True 00:00:00.001793 \n", "100000 100 1 False 00:00:00.016156 \n", " True 00:00:00.013451 \n", " 100 False 00:00:01.339787 \n", " True 00:00:01.617340 \n", " 1000 1 False 00:00:00.038961 \n", " True 00:00:00.024154 \n", " 100 False 00:00:07.604805 \n", " True 00:00:14.799519 \n", "\n", " corpus_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "100000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "\n", " matrix_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "100000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "\n", " speed \n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 242.09 Kdoc pairs / s \n", " True 207.64 Kdoc pairs / s \n", " 100 False 130.94 Kdoc pairs / s \n", " True 27.68 Kdoc pairs / s \n", " 1000 1 False 112.92 Kdoc pairs / s \n", " True 187.31 Kdoc pairs / s \n", " 100 False 513.79 Kdoc pairs / s \n", " True 43.54 Kdoc pairs / s \n", "100000 100 1 False 2.06 Kdoc pairs / s \n", " True 1.47 Kdoc pairs / s \n", " 100 False 0.01 Kdoc pairs / s \n", " True 0.01 Kdoc pairs / s \n", " 1000 1 False 0.71 Kdoc pairs / s \n", " True 0.40 Kdoc pairs / s \n", " 100 False 0.06 Kdoc pairs / s \n", " True 0.10 Kdoc pairs / s " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.apply(lambda x: (x - x.mean()).std())).loc[\n", " [1000, 100000], :, [1, 100], :].loc[\n", " :, [\"duration\", \"corpus_nonzero\", \"matrix_nonzero\", \"speed\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SCM between two corpora\n", "Lastly, we measure the speed at which the inner_product method produces term similarities between entire corpora." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "def benchmark(configuration):\n", " (matrix, dictionary, nonzero_limit), corpus, normalized, repetition = configuration\n", " corpus_size = len(corpus)\n", " corpus = [dictionary.doc2bow(doc) for doc in corpus]\n", " corpus = [vec for vec in corpus if len(vec) > 0]\n", " \n", " start_time = time()\n", " matrix.inner_product(corpus, corpus, normalized=normalized)\n", " end_time = time()\n", " duration = end_time - start_time\n", " \n", " return {\n", " \"dictionary_size\": matrix.matrix.shape[0],\n", " \"matrix_nonzero\": matrix.matrix.nnz,\n", " \"nonzero_limit\": nonzero_limit,\n", " \"normalized\": normalized,\n", " \"corpus_size\": corpus_size,\n", " \"corpus_actual_size\": len(corpus),\n", " \"corpus_nonzero\": sum(len(vec) for vec in corpus),\n", " \"mean_document_length\": np.mean([len(doc) for doc in corpus]),\n", " \"repetition\": repetition,\n", " \"duration\": duration, }" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "84e1344be5d944fa98368e6b3994944a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=2), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/mnt/storage/home/novotny/.virtualenvs/gensim/lib/python3.4/site-packages/gensim/matutils.py:738: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "nonzero_limits = [1000]\n", "dense_matrices = []\n", "for (model, dictionary), nonzero_limit in tqdm(\n", " list(product(zip(models, dictionaries), nonzero_limits)), desc=\"matrices\"):\n", " annoy = AnnoyIndexer(model, 1)\n", " index = WordEmbeddingSimilarityIndex(model, kwargs={\"indexer\": annoy})\n", " matrix = SparseTermSimilarityMatrix(index, dictionary, nonzero_limit=nonzero_limit)\n", " matrices.append((matrix, dictionary, nonzero_limit))\n", " del annoy" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "configurations = product(matrices + dense_matrices, corpora + [full_corpus], normalization, repetitions)\n", "results = benchmark_results(benchmark, configurations, \"matrix_speed.inner-product_results.corpus_corpus\")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(results)\n", "df[\"speed\"] = df.corpus_actual_size**2 / df.duration\n", "del df[\"corpus_actual_size\"]\n", "df = df.groupby([\"dictionary_size\", \"corpus_size\", \"nonzero_limit\", \"normalized\"])\n", "\n", "def display(df):\n", " df[\"duration\"] = [timedelta(0, duration) for duration in df[\"duration\"]]\n", " df[\"speed\"] = [\"%.02f Kdoc pairs / s\" % (speed / 1000) for speed in df[\"speed\"]]\n", " return df" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>duration</th>\n", " <th>corpus_nonzero</th>\n", " <th>matrix_nonzero</th>\n", " <th>speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>corpus_size</th>\n", " <th>nonzero_limit</th>\n", " <th>normalized</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"24\" valign=\"top\">1000</th>\n", " <th rowspan=\"8\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.001403</td>\n", " <td>3.0</td>\n", " <td>1000.0</td>\n", " <td>6.69 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.005313</td>\n", " <td>3.0</td>\n", " <td>1000.0</td>\n", " <td>1.70 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">10</th>\n", " <th>False</th>\n", " <td>00:00:00.001565</td>\n", " <td>3.0</td>\n", " <td>8634.0</td>\n", " <td>5.80 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.005307</td>\n", " <td>3.0</td>\n", " <td>8634.0</td>\n", " <td>1.70 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.003172</td>\n", " <td>3.0</td>\n", " <td>84944.0</td>\n", " <td>3.05 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.008461</td>\n", " <td>3.0</td>\n", " <td>84944.0</td>\n", " <td>1.07 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1000</th>\n", " <th>False</th>\n", " <td>00:00:00.021377</td>\n", " <td>3.0</td>\n", " <td>838588.0</td>\n", " <td>0.42 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.055234</td>\n", " <td>3.0</td>\n", " <td>838588.0</td>\n", " <td>0.16 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.001376</td>\n", " <td>26.0</td>\n", " <td>1000.0</td>\n", " <td>418.61 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.005019</td>\n", " <td>26.0</td>\n", " <td>1000.0</td>\n", " <td>114.78 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">10</th>\n", " <th>False</th>\n", " <td>00:00:00.001511</td>\n", " <td>26.0</td>\n", " <td>8634.0</td>\n", " <td>381.50 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.005208</td>\n", " <td>26.0</td>\n", " <td>8634.0</td>\n", " <td>110.60 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.003539</td>\n", " <td>26.0</td>\n", " <td>84944.0</td>\n", " <td>164.03 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.008502</td>\n", " <td>26.0</td>\n", " <td>84944.0</td>\n", " <td>67.81 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1000</th>\n", " <th>False</th>\n", " <td>00:00:00.021548</td>\n", " <td>26.0</td>\n", " <td>838588.0</td>\n", " <td>26.73 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.054425</td>\n", " <td>26.0</td>\n", " <td>838588.0</td>\n", " <td>10.59 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">100000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.019915</td>\n", " <td>2914.0</td>\n", " <td>1000.0</td>\n", " <td>391443.20 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.026118</td>\n", " <td>2914.0</td>\n", " <td>1000.0</td>\n", " <td>298377.75 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">10</th>\n", " <th>False</th>\n", " <td>00:00:00.020152</td>\n", " <td>2914.0</td>\n", " <td>8634.0</td>\n", " <td>386722.55 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.026998</td>\n", " <td>2914.0</td>\n", " <td>8634.0</td>\n", " <td>288567.14 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.028345</td>\n", " <td>2914.0</td>\n", " <td>84944.0</td>\n", " <td>274905.36 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.041069</td>\n", " <td>2914.0</td>\n", " <td>84944.0</td>\n", " <td>189709.57 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1000</th>\n", " <th>False</th>\n", " <td>00:00:00.089978</td>\n", " <td>2914.0</td>\n", " <td>838588.0</td>\n", " <td>86598.15 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.185611</td>\n", " <td>2914.0</td>\n", " <td>838588.0</td>\n", " <td>41971.58 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"24\" valign=\"top\">100000</th>\n", " <th rowspan=\"8\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.003345</td>\n", " <td>423.0</td>\n", " <td>101868.0</td>\n", " <td>2013.92 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.008857</td>\n", " <td>423.0</td>\n", " <td>101868.0</td>\n", " <td>760.13 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">10</th>\n", " <th>False</th>\n", " <td>00:00:00.032639</td>\n", " <td>423.0</td>\n", " <td>814154.0</td>\n", " <td>206.66 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.080591</td>\n", " <td>423.0</td>\n", " <td>814154.0</td>\n", " <td>83.46 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.488467</td>\n", " <td>423.0</td>\n", " <td>8202884.0</td>\n", " <td>13.77 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:01.454507</td>\n", " <td>423.0</td>\n", " <td>8202884.0</td>\n", " <td>4.62 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1000</th>\n", " <th>False</th>\n", " <td>00:00:04.973667</td>\n", " <td>423.0</td>\n", " <td>89912542.0</td>\n", " <td>1.35 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:15.035711</td>\n", " <td>423.0</td>\n", " <td>89912542.0</td>\n", " <td>0.45 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.010141</td>\n", " <td>5162.0</td>\n", " <td>101868.0</td>\n", " <td>67139.73 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.016685</td>\n", " <td>5162.0</td>\n", " <td>101868.0</td>\n", " <td>40798.02 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">10</th>\n", " <th>False</th>\n", " <td>00:00:00.041392</td>\n", " <td>5162.0</td>\n", " <td>814154.0</td>\n", " <td>16444.18 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.091686</td>\n", " <td>5162.0</td>\n", " <td>814154.0</td>\n", " <td>7425.08 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.508916</td>\n", " <td>5162.0</td>\n", " <td>8202884.0</td>\n", " <td>1338.94 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:01.497556</td>\n", " <td>5162.0</td>\n", " <td>8202884.0</td>\n", " <td>454.49 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1000</th>\n", " <th>False</th>\n", " <td>00:00:05.101489</td>\n", " <td>5162.0</td>\n", " <td>89912542.0</td>\n", " <td>133.44 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:15.325415</td>\n", " <td>5162.0</td>\n", " <td>89912542.0</td>\n", " <td>44.42 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"8\" valign=\"top\">100000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:37.145526</td>\n", " <td>525310.0</td>\n", " <td>101868.0</td>\n", " <td>192578.80 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:45.729004</td>\n", " <td>525310.0</td>\n", " <td>101868.0</td>\n", " <td>156431.36 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">10</th>\n", " <th>False</th>\n", " <td>00:00:44.981806</td>\n", " <td>525310.0</td>\n", " <td>814154.0</td>\n", " <td>159029.88 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:54.245450</td>\n", " <td>525310.0</td>\n", " <td>814154.0</td>\n", " <td>131871.88 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:01:15.925860</td>\n", " <td>525310.0</td>\n", " <td>8202884.0</td>\n", " <td>94216.21 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:01:29.232076</td>\n", " <td>525310.0</td>\n", " <td>8202884.0</td>\n", " <td>80177.08 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1000</th>\n", " <th>False</th>\n", " <td>00:03:17.140191</td>\n", " <td>525310.0</td>\n", " <td>89912542.0</td>\n", " <td>36286.25 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:04:05.865666</td>\n", " <td>525310.0</td>\n", " <td>89912542.0</td>\n", " <td>29097.14 Kdoc pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 00:00:00.001403 \n", " True 00:00:00.005313 \n", " 10 False 00:00:00.001565 \n", " True 00:00:00.005307 \n", " 100 False 00:00:00.003172 \n", " True 00:00:00.008461 \n", " 1000 False 00:00:00.021377 \n", " True 00:00:00.055234 \n", " 1000 1 False 00:00:00.001376 \n", " True 00:00:00.005019 \n", " 10 False 00:00:00.001511 \n", " True 00:00:00.005208 \n", " 100 False 00:00:00.003539 \n", " True 00:00:00.008502 \n", " 1000 False 00:00:00.021548 \n", " True 00:00:00.054425 \n", " 100000 1 False 00:00:00.019915 \n", " True 00:00:00.026118 \n", " 10 False 00:00:00.020152 \n", " True 00:00:00.026998 \n", " 100 False 00:00:00.028345 \n", " True 00:00:00.041069 \n", " 1000 False 00:00:00.089978 \n", " True 00:00:00.185611 \n", "100000 100 1 False 00:00:00.003345 \n", " True 00:00:00.008857 \n", " 10 False 00:00:00.032639 \n", " True 00:00:00.080591 \n", " 100 False 00:00:00.488467 \n", " True 00:00:01.454507 \n", " 1000 False 00:00:04.973667 \n", " True 00:00:15.035711 \n", " 1000 1 False 00:00:00.010141 \n", " True 00:00:00.016685 \n", " 10 False 00:00:00.041392 \n", " True 00:00:00.091686 \n", " 100 False 00:00:00.508916 \n", " True 00:00:01.497556 \n", " 1000 False 00:00:05.101489 \n", " True 00:00:15.325415 \n", " 100000 1 False 00:00:37.145526 \n", " True 00:00:45.729004 \n", " 10 False 00:00:44.981806 \n", " True 00:00:54.245450 \n", " 100 False 00:01:15.925860 \n", " True 00:01:29.232076 \n", " 1000 False 00:03:17.140191 \n", " True 00:04:05.865666 \n", "\n", " corpus_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 3.0 \n", " True 3.0 \n", " 10 False 3.0 \n", " True 3.0 \n", " 100 False 3.0 \n", " True 3.0 \n", " 1000 False 3.0 \n", " True 3.0 \n", " 1000 1 False 26.0 \n", " True 26.0 \n", " 10 False 26.0 \n", " True 26.0 \n", " 100 False 26.0 \n", " True 26.0 \n", " 1000 False 26.0 \n", " True 26.0 \n", " 100000 1 False 2914.0 \n", " True 2914.0 \n", " 10 False 2914.0 \n", " True 2914.0 \n", " 100 False 2914.0 \n", " True 2914.0 \n", " 1000 False 2914.0 \n", " True 2914.0 \n", "100000 100 1 False 423.0 \n", " True 423.0 \n", " 10 False 423.0 \n", " True 423.0 \n", " 100 False 423.0 \n", " True 423.0 \n", " 1000 False 423.0 \n", " True 423.0 \n", " 1000 1 False 5162.0 \n", " True 5162.0 \n", " 10 False 5162.0 \n", " True 5162.0 \n", " 100 False 5162.0 \n", " True 5162.0 \n", " 1000 False 5162.0 \n", " True 5162.0 \n", " 100000 1 False 525310.0 \n", " True 525310.0 \n", " 10 False 525310.0 \n", " True 525310.0 \n", " 100 False 525310.0 \n", " True 525310.0 \n", " 1000 False 525310.0 \n", " True 525310.0 \n", "\n", " matrix_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 1000.0 \n", " True 1000.0 \n", " 10 False 8634.0 \n", " True 8634.0 \n", " 100 False 84944.0 \n", " True 84944.0 \n", " 1000 False 838588.0 \n", " True 838588.0 \n", " 1000 1 False 1000.0 \n", " True 1000.0 \n", " 10 False 8634.0 \n", " True 8634.0 \n", " 100 False 84944.0 \n", " True 84944.0 \n", " 1000 False 838588.0 \n", " True 838588.0 \n", " 100000 1 False 1000.0 \n", " True 1000.0 \n", " 10 False 8634.0 \n", " True 8634.0 \n", " 100 False 84944.0 \n", " True 84944.0 \n", " 1000 False 838588.0 \n", " True 838588.0 \n", "100000 100 1 False 101868.0 \n", " True 101868.0 \n", " 10 False 814154.0 \n", " True 814154.0 \n", " 100 False 8202884.0 \n", " True 8202884.0 \n", " 1000 False 89912542.0 \n", " True 89912542.0 \n", " 1000 1 False 101868.0 \n", " True 101868.0 \n", " 10 False 814154.0 \n", " True 814154.0 \n", " 100 False 8202884.0 \n", " True 8202884.0 \n", " 1000 False 89912542.0 \n", " True 89912542.0 \n", " 100000 1 False 101868.0 \n", " True 101868.0 \n", " 10 False 814154.0 \n", " True 814154.0 \n", " 100 False 8202884.0 \n", " True 8202884.0 \n", " 1000 False 89912542.0 \n", " True 89912542.0 \n", "\n", " speed \n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 6.69 Kdoc pairs / s \n", " True 1.70 Kdoc pairs / s \n", " 10 False 5.80 Kdoc pairs / s \n", " True 1.70 Kdoc pairs / s \n", " 100 False 3.05 Kdoc pairs / s \n", " True 1.07 Kdoc pairs / s \n", " 1000 False 0.42 Kdoc pairs / s \n", " True 0.16 Kdoc pairs / s \n", " 1000 1 False 418.61 Kdoc pairs / s \n", " True 114.78 Kdoc pairs / s \n", " 10 False 381.50 Kdoc pairs / s \n", " True 110.60 Kdoc pairs / s \n", " 100 False 164.03 Kdoc pairs / s \n", " True 67.81 Kdoc pairs / s \n", " 1000 False 26.73 Kdoc pairs / s \n", " True 10.59 Kdoc pairs / s \n", " 100000 1 False 391443.20 Kdoc pairs / s \n", " True 298377.75 Kdoc pairs / s \n", " 10 False 386722.55 Kdoc pairs / s \n", " True 288567.14 Kdoc pairs / s \n", " 100 False 274905.36 Kdoc pairs / s \n", " True 189709.57 Kdoc pairs / s \n", " 1000 False 86598.15 Kdoc pairs / s \n", " True 41971.58 Kdoc pairs / s \n", "100000 100 1 False 2013.92 Kdoc pairs / s \n", " True 760.13 Kdoc pairs / s \n", " 10 False 206.66 Kdoc pairs / s \n", " True 83.46 Kdoc pairs / s \n", " 100 False 13.77 Kdoc pairs / s \n", " True 4.62 Kdoc pairs / s \n", " 1000 False 1.35 Kdoc pairs / s \n", " True 0.45 Kdoc pairs / s \n", " 1000 1 False 67139.73 Kdoc pairs / s \n", " True 40798.02 Kdoc pairs / s \n", " 10 False 16444.18 Kdoc pairs / s \n", " True 7425.08 Kdoc pairs / s \n", " 100 False 1338.94 Kdoc pairs / s \n", " True 454.49 Kdoc pairs / s \n", " 1000 False 133.44 Kdoc pairs / s \n", " True 44.42 Kdoc pairs / s \n", " 100000 1 False 192578.80 Kdoc pairs / s \n", " True 156431.36 Kdoc pairs / s \n", " 10 False 159029.88 Kdoc pairs / s \n", " True 131871.88 Kdoc pairs / s \n", " 100 False 94216.21 Kdoc pairs / s \n", " True 80177.08 Kdoc pairs / s \n", " 1000 False 36286.25 Kdoc pairs / s \n", " True 29097.14 Kdoc pairs / s " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.mean()).loc[\n", " [1000, 100000], :, [1, 10, 100, 1000], :].loc[\n", " :, [\"duration\", \"corpus_nonzero\", \"matrix_nonzero\", \"speed\"]]" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>duration</th>\n", " <th>corpus_nonzero</th>\n", " <th>matrix_nonzero</th>\n", " <th>speed</th>\n", " </tr>\n", " <tr>\n", " <th>dictionary_size</th>\n", " <th>corpus_size</th>\n", " <th>nonzero_limit</th>\n", " <th>normalized</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"12\" valign=\"top\">1000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.000292</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.48 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000225</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.08 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.000747</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.02 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000488</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.07 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.000027</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>8.10 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000069</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.56 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.000309</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>16.26 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000268</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>2.24 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">100000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.000576</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>11256.03 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000574</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>6512.19 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.000562</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>5233.50 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000609</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>2743.63 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"12\" valign=\"top\">100000</th>\n", " <th rowspan=\"4\" valign=\"top\">100</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.000152</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>98.97 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000322</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>28.10 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.004997</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.14 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.022206</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.07 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">1000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.000210</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1420.00 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.000192</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>467.23 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.019022</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>45.91 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.004431</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.35 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">100000</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>False</th>\n", " <td>00:00:00.024466</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>126.77 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:00.062447</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>213.64 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">100</th>\n", " <th>False</th>\n", " <td>00:00:00.087692</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>108.55 Kdoc pairs / s</td>\n", " </tr>\n", " <tr>\n", " <th>True</th>\n", " <td>00:00:01.065889</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>968.80 Kdoc pairs / s</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 00:00:00.000292 \n", " True 00:00:00.000225 \n", " 100 False 00:00:00.000747 \n", " True 00:00:00.000488 \n", " 1000 1 False 00:00:00.000027 \n", " True 00:00:00.000069 \n", " 100 False 00:00:00.000309 \n", " True 00:00:00.000268 \n", " 100000 1 False 00:00:00.000576 \n", " True 00:00:00.000574 \n", " 100 False 00:00:00.000562 \n", " True 00:00:00.000609 \n", "100000 100 1 False 00:00:00.000152 \n", " True 00:00:00.000322 \n", " 100 False 00:00:00.004997 \n", " True 00:00:00.022206 \n", " 1000 1 False 00:00:00.000210 \n", " True 00:00:00.000192 \n", " 100 False 00:00:00.019022 \n", " True 00:00:00.004431 \n", " 100000 1 False 00:00:00.024466 \n", " True 00:00:00.062447 \n", " 100 False 00:00:00.087692 \n", " True 00:00:01.065889 \n", "\n", " corpus_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 100000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "100000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 100000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "\n", " matrix_nonzero \\\n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 100000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "100000 100 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 1000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", " 100000 1 False 0.0 \n", " True 0.0 \n", " 100 False 0.0 \n", " True 0.0 \n", "\n", " speed \n", "dictionary_size corpus_size nonzero_limit normalized \n", "1000 100 1 False 1.48 Kdoc pairs / s \n", " True 0.08 Kdoc pairs / s \n", " 100 False 1.02 Kdoc pairs / s \n", " True 0.07 Kdoc pairs / s \n", " 1000 1 False 8.10 Kdoc pairs / s \n", " True 1.56 Kdoc pairs / s \n", " 100 False 16.26 Kdoc pairs / s \n", " True 2.24 Kdoc pairs / s \n", " 100000 1 False 11256.03 Kdoc pairs / s \n", " True 6512.19 Kdoc pairs / s \n", " 100 False 5233.50 Kdoc pairs / s \n", " True 2743.63 Kdoc pairs / s \n", "100000 100 1 False 98.97 Kdoc pairs / s \n", " True 28.10 Kdoc pairs / s \n", " 100 False 0.14 Kdoc pairs / s \n", " True 0.07 Kdoc pairs / s \n", " 1000 1 False 1420.00 Kdoc pairs / s \n", " True 467.23 Kdoc pairs / s \n", " 100 False 45.91 Kdoc pairs / s \n", " True 1.35 Kdoc pairs / s \n", " 100000 1 False 126.77 Kdoc pairs / s \n", " True 213.64 Kdoc pairs / s \n", " 100 False 108.55 Kdoc pairs / s \n", " True 968.80 Kdoc pairs / s " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "display(df.apply(lambda x: (x - x.mean()).std())).loc[\n", " [1000, 100000], :, [1, 100], :].loc[\n", " :, [\"duration\", \"corpus_nonzero\", \"matrix_nonzero\", \"speed\"]]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 2 }	209,285	Python	.py	4,605	38.368512	834	0.351583	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,923	lda_model_difference.ipynb	piskvorky_gensim/docs/notebooks/lda_model_difference.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of two LDA models & visualize difference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In this notebook, I want to show how you can compare models with itself and with other model and why you need it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First, clean up 20 newsgroups dataset. We will use it for fitting LDA." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from string import punctuation\n", "from nltk import RegexpTokenizer\n", "from nltk.stem.porter import PorterStemmer\n", "from nltk.corpus import stopwords\n", "from sklearn.datasets import fetch_20newsgroups\n", "\n", "\n", "newsgroups = fetch_20newsgroups()\n", "eng_stopwords = set(stopwords.words('english'))\n", "\n", "tokenizer = RegexpTokenizer('\\s+', gaps=True)\n", "stemmer = PorterStemmer()\n", "translate_tab = {ord(p): u\" \" for p in punctuation}\n", "\n", "def text2tokens(raw_text):\n", " \"\"\"\n", " Convert raw test to list of stemmed tokens\n", " \"\"\"\n", " clean_text = raw_text.lower().translate(translate_tab)\n", " tokens = [token.strip() for token in tokenizer.tokenize(clean_text)]\n", " tokens = [token for token in tokens if token not in eng_stopwords]\n", " stemmed_tokens = [stemmer.stem(token) for token in tokens]\n", " \n", " return [token for token in stemmed_tokens if len(token) > 2] # skip short tokens\n", "\n", "dataset = [text2tokens(txt) for txt in newsgroups['data']] # convert a documents to list of tokens" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from gensim.corpora import Dictionary\n", "dictionary = Dictionary(documents=dataset, prune_at=None)\n", "dictionary.filter_extremes(no_below=5, no_above=0.3, keep_n=None) # use Dictionary to remove un-relevant tokens\n", "dictionary.compactify()\n", "\n", "d2b_dataset = [dictionary.doc2bow(doc) for doc in dataset] # convert list of tokens to bag of word representation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Second, fit two LDA models." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 31s, sys: 9.94 s, total: 2min 40s\n", "Wall time: 4min 54s\n" ] } ], "source": [ "%%time\n", "\n", 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f=1;for(c=l(t,u,t.length,e),u=0;u<o;u++)f=e;this.imuln(f),this.words[0]+c<67108864?this.words[0]+=c:this._iaddn(c)}},a.prototype.copy=function(t){t.words=new Array(this.length);for(var e=0;e<this.length;e++)t.words[e]=this.words[e];t.length=this.length,t.negative=this.negative,t.red=this.red},a.prototype.clone=function(){var t=new a(null);return this.copy(t),t},a.prototype._expand=function(t){for(;this.length<t;)this.words[this.length++]=0;return this},a.prototype.strip=function(){for(;this.length>1&&0===this.words[this.length-1];)this.length--;return this._normSign()},a.prototype._normSign=function(){return 1===this.length&&0===this.words[0]&&(this.negative=0),this},a.prototype.inspect=function(){return(this.red?\"<BN-R: \":\"<BN: \")+this.toString(16)+\">\"};var c=[\"\",\"0\",\"00\",\"000\",\"0000\",\"00000\",\"000000\",\"0000000\",\"00000000\",\"000000000\",\"0000000000\",\"00000000000\",\"000000000000\",\"0000000000000\",\"00000000000000\",\"000000000000000\",\"0000000000000000\",\"00000000000000000\",\"000000000000000000\",\"0000000000000000000\",\"00000000000000000000\",\"000000000000000000000\",\"0000000000000000000000\",\"00000000000000000000000\",\"000000000000000000000000\",\"0000000000000000000000000\"],u=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],f=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];function h(t,e,r){r.negative=e.negative^t.negative;var n=t.length+e.length\|0;r.length=n,n=n-1\|0;var i=0\|t.words[0],a=0\|e.words[0],o=ia,s=67108863&o,l=o/67108864\|0;r.words[0]=s;for(var c=1;c<n;c++){for(var u=l>>>26,f=67108863&l,h=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=h;p++){var d=c-p\|0;u+=(o=(i=0\|t.words[d])(a=0\|e.words[p])+f)/67108864\|0,f=67108863&o}r.words[c]=0\|f,l=0\|u}return 0!==l?r.words[c]=0\|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0\|e\|\|1,16===(t=t\|\|10)\|\|\"hex\"===t){r=\"\";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i\|a)).toString(16);r=0!==(a=s>>>24-i&16777215)\|\|o!==this.length-1?c[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0\|t)&&t>=2&&t<=36){var h=u[t],p=f[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var g=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?g+r:c[h-g.length]+g+r}for(this.isZero()&&(r=\"0\"+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}n(!1,\"Base should be between 2 and 36\")},a.prototype.toNumber=function(){var t=this.words[0];return 2===this.length?t+=67108864this.words[1]:3===this.length&&1===this.words[2]?t+=4503599627370496+67108864this.words[1]:this.length>2&&n(!1,\"Number can only safely store up to 53 bits\"),0!==this.negative?-t:t},a.prototype.toJSON=function(){return this.toString(16)},a.prototype.toBuffer=function(t,e){return n(\"undefined\"!=typeof o),this.toArrayLike(o,t,e)},a.prototype.toArray=function(t,e){return this.toArrayLike(Array,t,e)},a.prototype.toArrayLike=function(t,e,r){var i=this.byteLength(),a=r\|\|Math.max(1,i);n(i<=a,\"byte array longer than desired length\"),n(a>0,\"Requested array length <= 0\"),this.strip();var o,s,l=\"le\"===e,c=new t(a),u=this.clone();if(l){for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[s]=o;for(;s<a;s++)c[s]=0}else{for(s=0;s<a-i;s++)c[s]=0;for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[a-s-1]=o}return c},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()\|\|(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]\|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative\|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative\|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative\|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0\|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26\|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]\|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0\|r.words[a])+(0\|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0\|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0\|r.words[o])-(0\|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0\|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var 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mt=(c+(n=n+Math.imul(f,H)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,G)\|0)+Math.imul(h,H)\|0))<<13)\|0;c=((a=a+Math.imul(h,G)\|0)+(i>>>13)\|0)+(mt>>>26)\|0,mt&=67108863,n=Math.imul(m,V),i=(i=Math.imul(m,U))+Math.imul(y,V)\|0,a=Math.imul(y,U),n=n+Math.imul(d,H)\|0,i=(i=i+Math.imul(d,G)\|0)+Math.imul(g,H)\|0,a=a+Math.imul(g,G)\|0;var yt=(c+(n=n+Math.imul(f,Y)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,X)\|0)+Math.imul(h,Y)\|0))<<13)\|0;c=((a=a+Math.imul(h,X)\|0)+(i>>>13)\|0)+(yt>>>26)\|0,yt&=67108863,n=Math.imul(b,V),i=(i=Math.imul(b,U))+Math.imul(_,V)\|0,a=Math.imul(_,U),n=n+Math.imul(m,H)\|0,i=(i=i+Math.imul(m,G)\|0)+Math.imul(y,H)\|0,a=a+Math.imul(y,G)\|0,n=n+Math.imul(d,Y)\|0,i=(i=i+Math.imul(d,X)\|0)+Math.imul(g,Y)\|0,a=a+Math.imul(g,X)\|0;var xt=(c+(n=n+Math.imul(f,$)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,J)\|0)+Math.imul(h,$)\|0))<<13)\|0;c=((a=a+Math.imul(h,J)\|0)+(i>>>13)\|0)+(xt>>>26)\|0,xt&=67108863,n=Math.imul(k,V),i=(i=Math.imul(k,U))+Math.imul(M,V)\|0,a=Math.imul(M,U),n=n+Math.imul(b,H)\|0,i=(i=i+Math.imul(b,G)\|0)+Math.imul(_,H)\|0,a=a+Math.imul(_,G)\|0,n=n+Math.imul(m,Y)\|0,i=(i=i+Math.imul(m,X)\|0)+Math.imul(y,Y)\|0,a=a+Math.imul(y,X)\|0,n=n+Math.imul(d,$)\|0,i=(i=i+Math.imul(d,J)\|0)+Math.imul(g,$)\|0,a=a+Math.imul(g,J)\|0;var bt=(c+(n=n+Math.imul(f,Q)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,tt)\|0)+Math.imul(h,Q)\|0))<<13)\|0;c=((a=a+Math.imul(h,tt)\|0)+(i>>>13)\|0)+(bt>>>26)\|0,bt&=67108863,n=Math.imul(T,V),i=(i=Math.imul(T,U))+Math.imul(S,V)\|0,a=Math.imul(S,U),n=n+Math.imul(k,H)\|0,i=(i=i+Math.imul(k,G)\|0)+Math.imul(M,H)\|0,a=a+Math.imul(M,G)\|0,n=n+Math.imul(b,Y)\|0,i=(i=i+Math.imul(b,X)\|0)+Math.imul(_,Y)\|0,a=a+Math.imul(_,X)\|0,n=n+Math.imul(m,$)\|0,i=(i=i+Math.imul(m,J)\|0)+Math.imul(y,$)\|0,a=a+Math.imul(y,J)\|0,n=n+Math.imul(d,Q)\|0,i=(i=i+Math.imul(d,tt)\|0)+Math.imul(g,Q)\|0,a=a+Math.imul(g,tt)\|0;var _t=(c+(n=n+Math.imul(f,rt)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,nt)\|0)+Math.imul(h,rt)\|0))<<13)\|0;c=((a=a+Math.imul(h,nt)\|0)+(i>>>13)\|0)+(_t>>>26)\|0,_t&=67108863,n=Math.imul(C,V),i=(i=Math.imul(C,U))+Math.imul(L,V)\|0,a=Math.imul(L,U),n=n+Math.imul(T,H)\|0,i=(i=i+Math.imul(T,G)\|0)+Math.imul(S,H)\|0,a=a+Math.imul(S,G)\|0,n=n+Math.imul(k,Y)\|0,i=(i=i+Math.imul(k,X)\|0)+Math.imul(M,Y)\|0,a=a+Math.imul(M,X)\|0,n=n+Math.imul(b,$)\|0,i=(i=i+Math.imul(b,J)\|0)+Math.imul(_,$)\|0,a=a+Math.imul(_,J)\|0,n=n+Math.imul(m,Q)\|0,i=(i=i+Math.imul(m,tt)\|0)+Math.imul(y,Q)\|0,a=a+Math.imul(y,tt)\|0,n=n+Math.imul(d,rt)\|0,i=(i=i+Math.imul(d,nt)\|0)+Math.imul(g,rt)\|0,a=a+Math.imul(g,nt)\|0;var wt=(c+(n=n+Math.imul(f,at)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,ot)\|0)+Math.imul(h,at)\|0))<<13)\|0;c=((a=a+Math.imul(h,ot)\|0)+(i>>>13)\|0)+(wt>>>26)\|0,wt&=67108863,n=Math.imul(O,V),i=(i=Math.imul(O,U))+Math.imul(I,V)\|0,a=Math.imul(I,U),n=n+Math.imul(C,H)\|0,i=(i=i+Math.imul(C,G)\|0)+Math.imul(L,H)\|0,a=a+Math.imul(L,G)\|0,n=n+Math.imul(T,Y)\|0,i=(i=i+Math.imul(T,X)\|0)+Math.imul(S,Y)\|0,a=a+Math.imul(S,X)\|0,n=n+Math.imul(k,$)\|0,i=(i=i+Math.imul(k,J)\|0)+Math.imul(M,$)\|0,a=a+Math.imul(M,J)\|0,n=n+Math.imul(b,Q)\|0,i=(i=i+Math.imul(b,tt)\|0)+Math.imul(_,Q)\|0,a=a+Math.imul(_,tt)\|0,n=n+Math.imul(m,rt)\|0,i=(i=i+Math.imul(m,nt)\|0)+Math.imul(y,rt)\|0,a=a+Math.imul(y,nt)\|0,n=n+Math.imul(d,at)\|0,i=(i=i+Math.imul(d,ot)\|0)+Math.imul(g,at)\|0,a=a+Math.imul(g,ot)\|0;var kt=(c+(n=n+Math.imul(f,lt)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,ct)\|0)+Math.imul(h,lt)\|0))<<13)\|0;c=((a=a+Math.imul(h,ct)\|0)+(i>>>13)\|0)+(kt>>>26)\|0,kt&=67108863,n=Math.imul(D,V),i=(i=Math.imul(D,U))+Math.imul(R,V)\|0,a=Math.imul(R,U),n=n+Math.imul(O,H)\|0,i=(i=i+Math.imul(O,G)\|0)+Math.imul(I,H)\|0,a=a+Math.imul(I,G)\|0,n=n+Math.imul(C,Y)\|0,i=(i=i+Math.imul(C,X)\|0)+Math.imul(L,Y)\|0,a=a+Math.imul(L,X)\|0,n=n+Math.imul(T,$)\|0,i=(i=i+Math.imul(T,J)\|0)+Math.imul(S,$)\|0,a=a+Math.imul(S,J)\|0,n=n+Math.imul(k,Q)\|0,i=(i=i+Math.imul(k,tt)\|0)+Math.imul(M,Q)\|0,a=a+Math.imul(M,tt)\|0,n=n+Math.imul(b,rt)\|0,i=(i=i+Math.imul(b,nt)\|0)+Math.imul(_,rt)\|0,a=a+Math.imul(_,nt)\|0,n=n+Math.imul(m,at)\|0,i=(i=i+Math.imul(m,ot)\|0)+Math.imul(y,at)\|0,a=a+Math.imul(y,ot)\|0,n=n+Math.imul(d,lt)\|0,i=(i=i+Math.imul(d,ct)\|0)+Math.imul(g,lt)\|0,a=a+Math.imul(g,ct)\|0;var Mt=(c+(n=n+Math.imul(f,ft)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,ht)\|0)+Math.imul(h,ft)\|0))<<13)\|0;c=((a=a+Math.imul(h,ht)\|0)+(i>>>13)\|0)+(Mt>>>26)\|0,Mt&=67108863,n=Math.imul(F,V),i=(i=Math.imul(F,U))+Math.imul(N,V)\|0,a=Math.imul(N,U),n=n+Math.imul(D,H)\|0,i=(i=i+Math.imul(D,G)\|0)+Math.imul(R,H)\|0,a=a+Math.imul(R,G)\|0,n=n+Math.imul(O,Y)\|0,i=(i=i+Math.imul(O,X)\|0)+Math.imul(I,Y)\|0,a=a+Math.imul(I,X)\|0,n=n+Math.imul(C,$)\|0,i=(i=i+Math.imul(C,J)\|0)+Math.imul(L,$)\|0,a=a+Math.imul(L,J)\|0,n=n+Math.imul(T,Q)\|0,i=(i=i+Math.imul(T,tt)\|0)+Math.imul(S,Q)\|0,a=a+Math.imul(S,tt)\|0,n=n+Math.imul(k,rt)\|0,i=(i=i+Math.imul(k,nt)\|0)+Math.imul(M,rt)\|0,a=a+Math.imul(M,nt)\|0,n=n+Math.imul(b,at)\|0,i=(i=i+Math.imul(b,ot)\|0)+Math.imul(_,at)\|0,a=a+Math.imul(_,ot)\|0,n=n+Math.imul(m,lt)\|0,i=(i=i+Math.imul(m,ct)\|0)+Math.imul(y,lt)\|0,a=a+Math.imul(y,ct)\|0,n=n+Math.imul(d,ft)\|0,i=(i=i+Math.imul(d,ht)\|0)+Math.imul(g,ft)\|0,a=a+Math.imul(g,ht)\|0;var At=(c+(n=n+Math.imul(f,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,gt)\|0)+Math.imul(h,dt)\|0))<<13)\|0;c=((a=a+Math.imul(h,gt)\|0)+(i>>>13)\|0)+(At>>>26)\|0,At&=67108863,n=Math.imul(F,H),i=(i=Math.imul(F,G))+Math.imul(N,H)\|0,a=Math.imul(N,G),n=n+Math.imul(D,Y)\|0,i=(i=i+Math.imul(D,X)\|0)+Math.imul(R,Y)\|0,a=a+Math.imul(R,X)\|0,n=n+Math.imul(O,$)\|0,i=(i=i+Math.imul(O,J)\|0)+Math.imul(I,$)\|0,a=a+Math.imul(I,J)\|0,n=n+Math.imul(C,Q)\|0,i=(i=i+Math.imul(C,tt)\|0)+Math.imul(L,Q)\|0,a=a+Math.imul(L,tt)\|0,n=n+Math.imul(T,rt)\|0,i=(i=i+Math.imul(T,nt)\|0)+Math.imul(S,rt)\|0,a=a+Math.imul(S,nt)\|0,n=n+Math.imul(k,at)\|0,i=(i=i+Math.imul(k,ot)\|0)+Math.imul(M,at)\|0,a=a+Math.imul(M,ot)\|0,n=n+Math.imul(b,lt)\|0,i=(i=i+Math.imul(b,ct)\|0)+Math.imul(_,lt)\|0,a=a+Math.imul(_,ct)\|0,n=n+Math.imul(m,ft)\|0,i=(i=i+Math.imul(m,ht)\|0)+Math.imul(y,ft)\|0,a=a+Math.imul(y,ht)\|0;var Tt=(c+(n=n+Math.imul(d,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(d,gt)\|0)+Math.imul(g,dt)\|0))<<13)\|0;c=((a=a+Math.imul(g,gt)\|0)+(i>>>13)\|0)+(Tt>>>26)\|0,Tt&=67108863,n=Math.imul(F,Y),i=(i=Math.imul(F,X))+Math.imul(N,Y)\|0,a=Math.imul(N,X),n=n+Math.imul(D,$)\|0,i=(i=i+Math.imul(D,J)\|0)+Math.imul(R,$)\|0,a=a+Math.imul(R,J)\|0,n=n+Math.imul(O,Q)\|0,i=(i=i+Math.imul(O,tt)\|0)+Math.imul(I,Q)\|0,a=a+Math.imul(I,tt)\|0,n=n+Math.imul(C,rt)\|0,i=(i=i+Math.imul(C,nt)\|0)+Math.imul(L,rt)\|0,a=a+Math.imul(L,nt)\|0,n=n+Math.imul(T,at)\|0,i=(i=i+Math.imul(T,ot)\|0)+Math.imul(S,at)\|0,a=a+Math.imul(S,ot)\|0,n=n+Math.imul(k,lt)\|0,i=(i=i+Math.imul(k,ct)\|0)+Math.imul(M,lt)\|0,a=a+Math.imul(M,ct)\|0,n=n+Math.imul(b,ft)\|0,i=(i=i+Math.imul(b,ht)\|0)+Math.imul(_,ft)\|0,a=a+Math.imul(_,ht)\|0;var St=(c+(n=n+Math.imul(m,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(m,gt)\|0)+Math.imul(y,dt)\|0))<<13)\|0;c=((a=a+Math.imul(y,gt)\|0)+(i>>>13)\|0)+(St>>>26)\|0,St&=67108863,n=Math.imul(F,$),i=(i=Math.imul(F,J))+Math.imul(N,$)\|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)\|0,i=(i=i+Math.imul(D,tt)\|0)+Math.imul(R,Q)\|0,a=a+Math.imul(R,tt)\|0,n=n+Math.imul(O,rt)\|0,i=(i=i+Math.imul(O,nt)\|0)+Math.imul(I,rt)\|0,a=a+Math.imul(I,nt)\|0,n=n+Math.imul(C,at)\|0,i=(i=i+Math.imul(C,ot)\|0)+Math.imul(L,at)\|0,a=a+Math.imul(L,ot)\|0,n=n+Math.imul(T,lt)\|0,i=(i=i+Math.imul(T,ct)\|0)+Math.imul(S,lt)\|0,a=a+Math.imul(S,ct)\|0,n=n+Math.imul(k,ft)\|0,i=(i=i+Math.imul(k,ht)\|0)+Math.imul(M,ft)\|0,a=a+Math.imul(M,ht)\|0;var Et=(c+(n=n+Math.imul(b,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(b,gt)\|0)+Math.imul(_,dt)\|0))<<13)\|0;c=((a=a+Math.imul(_,gt)\|0)+(i>>>13)\|0)+(Et>>>26)\|0,Et&=67108863,n=Math.imul(F,Q),i=(i=Math.imul(F,tt))+Math.imul(N,Q)\|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)\|0,i=(i=i+Math.imul(D,nt)\|0)+Math.imul(R,rt)\|0,a=a+Math.imul(R,nt)\|0,n=n+Math.imul(O,at)\|0,i=(i=i+Math.imul(O,ot)\|0)+Math.imul(I,at)\|0,a=a+Math.imul(I,ot)\|0,n=n+Math.imul(C,lt)\|0,i=(i=i+Math.imul(C,ct)\|0)+Math.imul(L,lt)\|0,a=a+Math.imul(L,ct)\|0,n=n+Math.imul(T,ft)\|0,i=(i=i+Math.imul(T,ht)\|0)+Math.imul(S,ft)\|0,a=a+Math.imul(S,ht)\|0;var Ct=(c+(n=n+Math.imul(k,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(k,gt)\|0)+Math.imul(M,dt)\|0))<<13)\|0;c=((a=a+Math.imul(M,gt)\|0)+(i>>>13)\|0)+(Ct>>>26)\|0,Ct&=67108863,n=Math.imul(F,rt),i=(i=Math.imul(F,nt))+Math.imul(N,rt)\|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)\|0,i=(i=i+Math.imul(D,ot)\|0)+Math.imul(R,at)\|0,a=a+Math.imul(R,ot)\|0,n=n+Math.imul(O,lt)\|0,i=(i=i+Math.imul(O,ct)\|0)+Math.imul(I,lt)\|0,a=a+Math.imul(I,ct)\|0,n=n+Math.imul(C,ft)\|0,i=(i=i+Math.imul(C,ht)\|0)+Math.imul(L,ft)\|0,a=a+Math.imul(L,ht)\|0;var Lt=(c+(n=n+Math.imul(T,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(T,gt)\|0)+Math.imul(S,dt)\|0))<<13)\|0;c=((a=a+Math.imul(S,gt)\|0)+(i>>>13)\|0)+(Lt>>>26)\|0,Lt&=67108863,n=Math.imul(F,at),i=(i=Math.imul(F,ot))+Math.imul(N,at)\|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)\|0,i=(i=i+Math.imul(D,ct)\|0)+Math.imul(R,lt)\|0,a=a+Math.imul(R,ct)\|0,n=n+Math.imul(O,ft)\|0,i=(i=i+Math.imul(O,ht)\|0)+Math.imul(I,ft)\|0,a=a+Math.imul(I,ht)\|0;var zt=(c+(n=n+Math.imul(C,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(C,gt)\|0)+Math.imul(L,dt)\|0))<<13)\|0;c=((a=a+Math.imul(L,gt)\|0)+(i>>>13)\|0)+(zt>>>26)\|0,zt&=67108863,n=Math.imul(F,lt),i=(i=Math.imul(F,ct))+Math.imul(N,lt)\|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)\|0,i=(i=i+Math.imul(D,ht)\|0)+Math.imul(R,ft)\|0,a=a+Math.imul(R,ht)\|0;var Ot=(c+(n=n+Math.imul(O,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(O,gt)\|0)+Math.imul(I,dt)\|0))<<13)\|0;c=((a=a+Math.imul(I,gt)\|0)+(i>>>13)\|0)+(Ot>>>26)\|0,Ot&=67108863,n=Math.imul(F,ft),i=(i=Math.imul(F,ht))+Math.imul(N,ft)\|0,a=Math.imul(N,ht);var It=(c+(n=n+Math.imul(D,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(D,gt)\|0)+Math.imul(R,dt)\|0))<<13)\|0;c=((a=a+Math.imul(R,gt)\|0)+(i>>>13)\|0)+(It>>>26)\|0,It&=67108863;var Pt=(c+(n=Math.imul(F,dt))\|0)+((8191&(i=(i=Math.imul(F,gt))+Math.imul(N,dt)\|0))<<13)\|0;return c=((a=Math.imul(N,gt))+(i>>>13)\|0)+(Pt>>>26)\|0,Pt&=67108863,l[0]=vt,l[1]=mt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=kt,l[8]=Mt,l[9]=At,l[10]=Tt,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=zt,l[16]=Ot,l[17]=It,l[18]=Pt,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul\|\|(p=h),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?h(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0\|t.words[u])(0\|e.words[c]),h=67108863&f;s=67108863&(h=h+s\|0),i+=(o=(o=o+(f/67108864\|0)\|0)+(h>>>26)\|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t\|\|t===r-1)return t;for(var n=0,i=0;i<e;i++)n\|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2Math.PI/s),c=Math.sin(2Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],v=r[u+p+o],m=n[u+p+o],y=fv-hm;m=fm+hv,v=y,r[u+p]=d+v,n[u+p]=g+m,r[u+p+o]=d-v,n[u+p+o]=g-m,p!==s&&(y=lf-ch,h=lh+cf,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1\|Math.max(e,t),n=1&r,i=0;for(r=r/2\|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192Math.round(t[2n+1]/e)+Math.round(t[2n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864\|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0\|t[o],r[2o]=8191&a,a>>>=13,r[2o+1]=8191&a,a>>>=13;for(o=2e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]u[p]-l[p]f[p];l[p]=s[p]f[p]+l[p]u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0\|this.words[r])t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864\|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26\|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0\|this.words[e])-s<<r;this.words[e]=l\|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u\|\|c>=i);c--){var f=0\|this.words[c];this.words[c]=u<<26-a\|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0\|this.words[0])<t?(this.words[0]=t-(0\|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0\|this.words[i+r])+s;var l=(0\|t.words[i])e;s=((a-=67108863&l)>>26)-(l/67108864\|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0\|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0\|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0\|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0\|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864(0\|n.words[i.length+f])+(0\|n.words[i.length+f-1]);for(h=Math.min(h/o\|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()\|\|(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s\|\|null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length\|\|this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0\|\|1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(er+(0\|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0\|this.words[r])+67108864e;this.words[r]=i/t\|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()\|\|o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()\|\|l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i\|\|0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]\|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0\|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0\|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0\|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0\|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0\|this.words[r],i=0\|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function x(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function _(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function w(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(y,m),y.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0\|t.words[n];t.words[n-10]=(4194303&a)<<4\|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},y.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0\|t.words[r];e+=977n,t.words[r]=67108863&e,e=64n+(e/67108864\|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(x,m),i(b,m),i(_,m),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19(0\|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new y;else if(\"p224\"===t)e=new x;else if(\"p192\"===t)e=new b;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new _}return v[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},w.prototype._verify2=function(t,e){n(0==(t.negative\|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2uu).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var g=p,v=0;0!==g.cmp(s);v++)g=g.redSqr();n(v<d);var m=this.pow(f,new a(1).iushln(d-v-1));h=h.redMul(m),f=m.redSqr(),p=p.redMul(f),d=v}return h},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var 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0!==(z=H?l.sweepBipartite(t,e,F,N,X,Z,R,K,W,Y):l.sweepBipartite(t,e,R,K,W,Y,F,N,X,Z)))return z}else M(O++,D+1,R,K,F,N,H,-1/0,1/0),M(O++,D+1,F,N,R,K,1^H,-1/0,1/0);if(K<B){var Q=c(t,D,F,N,X,Z),tt=X[IQ+D],et=g(t,D,Q,N,X,Z,tt);if(et<N&&M(O++,D,K,B,et,N,(4\|H)+(G?16:0),tt,q),F<Q&&M(O++,D,K,B,F,Q,(2\|H)+(G?16:0),U,tt),Q+1===et){if(void 0!==(z=G?T(t,D,e,K,B,W,Y,Q,X,Z[Q]):A(t,D,e,H,K,B,W,Y,Q,X,Z[Q])))return z}else if(Q<et){var rt;if(G){if(rt=y(t,D,K,B,W,Y,tt),K<rt){var nt=g(t,D,K,rt,W,Y,tt);if(D===t-2){if(K<nt&&void 0!==(z=l.sweepComplete(t,e,K,nt,W,Y,Q,et,X,Z)))return z;if(nt<rt&&void 0!==(z=l.sweepBipartite(t,e,nt,rt,W,Y,Q,et,X,Z)))return z}else K<nt&&M(O++,D+1,K,nt,Q,et,16,-1/0,1/0),nt<rt&&(M(O++,D+1,nt,rt,Q,et,0,-1/0,1/0),M(O++,D+1,Q,et,nt,rt,1,-1/0,1/0))}}else rt=H?x(t,D,K,B,W,Y,tt):y(t,D,K,B,W,Y,tt),K<rt&&(D===t-2?z=H?l.sweepBipartite(t,e,Q,et,X,Z,K,rt,W,Y):l.sweepBipartite(t,e,K,rt,W,Y,Q,et,X,Z):(M(O++,D+1,K,rt,Q,et,H,-1/0,1/0),M(O++,D+1,Q,et,K,rt,1^H,-1/0,1/0)))}}}}};var n=t(\"typedarray-pool\"),i=t(\"bit-twiddle\"),a=t(\"./brute\"),o=a.partial,s=a.full,l=t(\"./sweep\"),c=t(\"./median\"),u=t(\"./partition\"),f=128,h=1<<22,p=1<<22,d=u(\"!(lo>=p0)&&!(p1>=hi)\",[\"p0\",\"p1\"]),g=u(\"lo===p0\",[\"p0\"]),v=u(\"lo<p0\",[\"p0\"]),m=u(\"hi<=p0\",[\"p0\"]),y=u(\"lo<=p0&&p0<=hi\",[\"p0\"]),x=u(\"lo<p0&&p0<=hi\",[\"p0\"]),b=6,_=2,w=n.mallocInt32(1024),k=n.mallocDouble(1024);function M(t,e,r,n,i,a,o,s,l){var c=bt;w[c]=e,w[c+1]=r,w[c+2]=n,w[c+3]=i,w[c+4]=a,w[c+5]=o;var u=_t;k[u]=s,k[u+1]=l}function A(t,e,r,n,i,a,o,s,l,c,u){var f=2t,h=lf,p=c[h+e];t:for(var d=i,g=if;d<a;++d,g+=f){var v=o[g+e],m=o[g+e+t];if(!(p<v\|\|m<p)&&(!n\|\|p!==v)){for(var y,x=s[d],b=e+1;b<t;++b){v=o[g+b],m=o[g+b+t];var _=c[h+b],w=c[h+b+t];if(m<_\|\|w<v)continue t}if(void 0!==(y=n?r(u,x):r(x,u)))return y}}}function T(t,e,r,n,i,a,o,s,l,c){var u=2t,f=su,h=l[f+e];t:for(var p=n,d=nu;p<i;++p,d+=u){var g=o[p];if(g!==c){var v=a[d+e],m=a[d+e+t];if(!(h<v\|\|m<h)){for(var y=e+1;y<t;++y){v=a[d+y],m=a[d+y+t];var x=l[f+y],b=l[f+y+t];if(m<x\|\|b<v)continue t}var _=r(g,c);if(void 0!==_)return _}}}}},{\"./brute\":85,\"./median\":87,\"./partition\":88,\"./sweep\":90,\"bit-twiddle\":80,\"typedarray-pool\":522}],87:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,o,s,l){if(o<=r+1)return r;var c=r,u=o,f=o+r>>>1,h=2t,p=f,d=s[hf+e];for(;c<u;){if(u-c<i){a(t,e,c,u,s,l),d=s[hf+e];break}var g=u-c,v=Math.random()g+c\|0,m=s[hv+e],y=Math.random()g+c\|0,x=s[hy+e],b=Math.random()g+c\|0,_=s[hb+e];m<=x?_>=x?(p=y,d=x):m>=_?(p=v,d=m):(p=b,d=_):x>=_?(p=y,d=x):_>=m?(p=v,d=m):(p=b,d=_);for(var w=h(u-1),k=hp,M=0;M<h;++M,++w,++k){var A=s[w];s[w]=s[k],s[k]=A}var T=l[u-1];l[u-1]=l[p],l[p]=T,p=n(t,e,c,u-1,s,l,d);for(var w=h(u-1),k=hp,M=0;M<h;++M,++w,++k){var A=s[w];s[w]=s[k],s[k]=A}var T=l[u-1];if(l[u-1]=l[p],l[p]=T,f<p){for(u=p-1;c<u&&s[h(u-1)+e]===d;)u-=1;u+=1}else{if(!(p<f))break;for(c=p+1;c<u&&s[hc+e]===d;)c+=1}}return n(t,e,r,f,s,l,s[hf+e])};var n=t(\"./partition\")(\"lo<p0\",[\"p0\"]),i=8;function a(t,e,r,n,i,a){for(var o=2t,s=o(r+1)+e,l=r+1;l<n;++l,s+=o)for(var c=i[s],u=l,f=o(l-1);u>r&&i[f+e]>c;--u,f-=o){for(var h=f,p=f+o,d=0;d<o;++d,++h,++p){var g=i[h];i[h]=i[p],i[p]=g}var v=a[u];a[u]=a[u-1],a[u-1]=v}}},{\"./partition\":88}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=\"abcdef\".split(\"\").concat(e),i=[];t.indexOf(\"lo\")>=0&&i.push(\"lo=e[k+n]\");t.indexOf(\"hi\")>=0&&i.push(\"hi=e[k+o]\");return r.push(n.replace(\"_\",i.join()).replace(\"$\",t)),Function.apply(void 0,r)};var n=\"for(var j=2a,k=jc,l=k,m=c,n=b,o=a+b,p=c;d>p;++p,k+=j){var _;if($)if(m===p)m+=1,l+=j;else{for(var s=0;j>s;++s){var t=e[k+s];e[k+s]=e[l],e[l++]=t}var u=f[p];f[p]=f[m],f[m++]=u}}return m\"},{}],89:[function(t,e,r){\"use strict\";e.exports=function(t,e){e<=4n?i(0,e-1,t):function t(e,r,f){var 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e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new f(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new f(this,[])},u.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new f(this,n)},u.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new f(this,n)},u.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new f(this,n)},u.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new f(this,n)},u.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new 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c=(a=r.right=o(a)).right=o(a.right),r.right=a.left,a.left=r,a.right=c,a._color=r._color,e._color=i,r._color=i,c._color=i,l(r),l(a),u>1&&((f=t[u-2]).left===r?f.left=a:f.right=a),void(t[u-1]=a);if(a.left&&a.left._color===n)return c=(a=r.right=o(a)).left=o(a.left),r.right=c.left,a.left=c.right,c.left=r,c.right=a,c._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(c),u>1&&((f=t[u-2]).left===r?f.left=c:f.right=c),void(t[u-1]=c);if(a._color===i){if(r._color===n)return r._color=i,void(r.right=s(n,a));r.right=s(n,a);continue}a=o(a),r.right=a.left,a.left=r,a._color=r._color,r._color=n,l(r),l(a),u>1&&((f=t[u-2]).left===r?f.left=a:f.right=a),t[u-1]=a,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}else{if((a=r.left).left&&a.left._color===n)return c=(a=r.left=o(a)).left=o(a.left),r.left=a.right,a.right=r,a.left=c,a._color=r._color,e._color=i,r._color=i,c._color=i,l(r),l(a),u>1&&((f=t[u-2]).right===r?f.right=a:f.left=a),void(t[u-1]=a);if(a.right&&a.right._color===n)return c=(a=r.left=o(a)).right=o(a.right),r.left=c.right,a.right=c.left,c.right=r,c.left=a,c._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(c),u>1&&((f=t[u-2]).right===r?f.right=c:f.left=c),void(t[u-1]=c);if(a._color===i){if(r._color===n)return r._color=i,void(r.left=s(n,a));r.left=s(n,a);continue}var f;a=o(a),r.left=a.right,a.right=r,a._color=r._color,r._color=n,l(r),l(a),u>1&&((f=t[u-2]).right===r?f.right=a:f.left=a),t[u-1]=a,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}}}(e),g.left===r?g.left=null:g.right=null,new c(this.tree._compare,e[0])},Object.defineProperty(h,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(h,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(h,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return 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c(this.tree._compare,r[0])},h.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],220:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=607/128,a=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function o(t){if(t<0)return Number(\"0/0\");for(var e=a[0],r=a.length-1;r>0;--r)e+=a[r]/(t+r);var n=t+i+.5;return.5Math.log(2Math.PI)+(t+.5)Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PIe)t(1-e));if(e>100)return Math.exp(o(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var a=e+7+.5;return Math.sqrt(2Math.PI)Math.pow(a,e+.5)Math.exp(-a)r},e.exports.log=o},{}],221:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e\|\|{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas\|\|document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf(\"webgl\")&&a.push(\"experimental-\"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n\|\|null}},{}],222:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this._tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this._labelAngle=[0,0,0],this._labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t\|\|{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]}),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t\|\|\"tickSpacing\"in t\|\|(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort(function(t,e){return t.x-e.x});s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g\|\|u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines\|\|(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],v={model:l,view:l,projection:l};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var m=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t\|\|v;for(var e=this.gl,r=t.model\|\|l,n=t.view\|\|l,i=t.projection\|\|l,a=this.bounds,s=o(r,n,i,a),u=s.cubeEdges,f=s.axis,h=n[12],b=n[13],_=n[14],w=n[15],k=this.pixelRatio(i[3]h+i[7]b+i[11]_+i[15]w)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=u[M],this.lastCubeProps.axis[M]=f[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,u,f);e=this.gl;var T,S=g;for(M=0;M<3;++M)this.backgroundEnable[M]?S[M]=f[M]:S[M]=0;this._background.draw(r,n,i,a,S,this.backgroundColor),this._lines.bind(r,n,i,this);for(M=0;M<3;++M){var E=[0,0,0];f[M]>0?E[M]=a[1][M]:E[M]=a[0][M];for(var C=0;C<2;++C){var L=(M+1+C)%3,z=(M+1+(1^C))%3;this.gridEnable[L]&&this._lines.drawGrid(L,z,this.bounds,E,this.gridColor[L],this.gridWidth[L]this.pixelRatio)}for(C=0;C<2;++C){L=(M+1+C)%3,z=(M+1+(1^C))%3;this.zeroEnable[z]&&Math.min(a[0][z],a[1][z])<=0&&Math.max(a[0][z],a[1][z])>=0&&this._lines.drawZero(L,z,this.bounds,E,this.zeroLineColor[z],this.zeroLineWidth[z]this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]this.pixelRatio);var O=c(m,A[M].primalMinor),I=c(y,A[M].mirrorMinor),P=this.lineTickLength;for(C=0;C<3;++C){var D=k/r[5C];O[C]=P[C]D,I[C]=P[C]D}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,O,this.lineTickColor[M],this.lineTickWidth[M]this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,I,this.lineTickColor[M],this.lineTickWidth[M]this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var R,B;function F(t){(B=[0,0,0])[t]=1}function N(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0?F(n):a>0&&l<0?F(n):a<0&&l>0?F(n):a<0&&l<0?F(n):o>0&&s>0?F(i):o>0&&s<0?F(i):o<0&&s>0?F(i):o<0&&s<0&&F(i)}for(M=0;M<3;++M){var j=A[M].primalMinor,V=A[M].mirrorMinor,U=c(x,A[M].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[M]&&(U[C]+=kj[C]Math.max(this.lineTickLength[C],0)/r[5C]);var q=[0,0,0];if(q[M]=1,this.tickEnable[M]){-3600===this.tickAngle[M]?(this.tickAngle[M]=0,this._tickAlign[M]=\"auto\"):this._tickAlign[M]=-1,R=1,\"auto\"===(T=[this._tickAlign[M],.5,R])[0]?T[0]=0:T[0]=parseInt(\"\"+T[0]),B=[0,0,0],N(M,j,V);for(C=0;C<3;++C)U[C]+=kj[C]this.tickPad[C]/r[5C];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],U,this.tickColor[M],q,B,T)}if(this.labelEnable[M]){R=0,B=[0,0,0],this.labels[M].length>4&&(F(M),R=1),\"auto\"===(T=[this._labelAlign[M],.5,R])[0]?T[0]=0:T[0]=parseInt(\"\"+T[0]);for(C=0;C<3;++C)U[C]+=kj[C]this.labelPad[C]/r[5C];U[M]+=.5(a[0][M]+a[1][M]),this._text.drawLabel(M,this.labelSize[M],this._labelAngle[M],U,this.labelColor[M],[0,0,0],B,T)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":223,\"./lib/cube.js\":224,\"./lib/lines.js\":225,\"./lib/text.js\":227,\"./lib/ticks.js\":228}],223:[function(t,e,r){\"use 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l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":226,\"gl-buffer\":230,\"gl-vao\":310}],224:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a){i(s,e,t),i(s,r,s);for(var p=0,y=0;y<2;++y){u[2]=a[y][2];for(var x=0;x<2;++x){u[1]=a[x][1];for(var b=0;b<2;++b)u[0]=a[b][0],h(l[p],u,s),p+=1}}for(var _=-1,y=0;y<8;++y){for(var w=l[y][3],k=0;k<3;++k)c[y][k]=l[y][k]/w;w<0&&(_<0?_=y:c[y][2]<c[_][2]&&(_=y))}if(_<0){_=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,T=(M+1)%3,S=-1,E=-1,C=0;C<2;++C){var L=C<<M,z=L+(C<<A)+(1-C<<T),O=L+(1-C<<A)+(C<<T);o(c[L],c[z],c[O],f)<0\|\|(C?S=1:E=1)}if(S<0\|\|E<0)E>S&&(_\|=1<<M);else{for(var C=0;C<2;++C){var L=C<<M,z=L+(C<<A)+(1-C<<T),O=L+(1-C<<A)+(C<<T),I=d([l[L],l[z],l[O],l[L+(1<<A)+(1<<T)]]);C?S=I:E=I}E>S&&(_\|=1<<M)}}}for(var P=7^_,D=-1,y=0;y<8;++y)y!==_&&y!==P&&(D<0?D=y:c[D][1]>c[y][1]&&(D=y));for(var R=-1,y=0;y<3;++y){var B=D^1<<y;if(B!==_&&B!==P){R<0&&(R=B);var T=c[B];T[0]<c[R][0]&&(R=B)}}for(var F=-1,y=0;y<3;++y){var B=D^1<<y;if(B!==_&&B!==P&&B!==R){F<0&&(F=B);var T=c[B];T[0]>c[F][0]&&(F=B)}}var N=g;N[0]=N[1]=N[2]=0,N[n.log2(R^D)]=D&R,N[n.log2(D^F)]=D&F;var j=7^F;j===_\|\|j===P?(j=7^R,N[n.log2(F^j)]=j&F):N[n.log2(R^j)]=j&R;for(var V=v,U=_,M=0;M<3;++M)V[M]=U&1<<M?-1:1;return m};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=(t(\"gl-mat4/invert\"),t(\"split-polygon\")),o=t(\"robust-orientation\"),s=new Array(16),l=(new Array(16),new Array(8)),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]r[4i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(ch-uf)}return o}var g=[1,1,1],v=[0,0,0],m={cubeEdges:g,axis:v}},{\"bit-twiddle\":80,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"robust-orientation\":486,\"split-polygon\":503}],225:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3\|0,d=0;d<r[f].length;++d){var g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3\|0;s[f]=h,l[f]=v-h;for(var h=o.length/3\|0,m=0;m<r[f].length;++m){var g=+r[f][m].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3\|0;c[f]=h,u[f]=v-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":226,\"gl-buffer\":230,\"gl-vao\":310}],226:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"#define GLSLIFY 1\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n vec4 pp = projection * view * model * vec4(p, 1.0);\\n return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n vec3 major = position.x * majorAxis;\\n vec3 minor = position.y * minorAxis;\\n\\n vec3 vPosition = major + minor + offset;\\n vec3 pPosition = project(vPosition);\\n vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"#define GLSLIFY 1\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n vec4 pp = projection * view * model * vec4(p, 1.0);\\n return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n vec3 A = project(a);\\n vec3 B = project(b);\\n\\n return atan(\\n (B.y - A.y) * resolution.y,\\n (B.x - A.x) * resolution.x\\n );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio = alignOpt.y;\\nbool enableAlign = (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n return mod_angle((a < 0.0) ?\\n a + TWO_PI :\\n a\\n );\\n}\\n\\nfloat look_upwards(float a) {\\n float b = positive_angle(a);\\n return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n b - PI :\\n b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n // if ratio is set to 0.5 then it is 50%, 50%.\\n // when using a higher ratio e.g. 0.75 the result would\\n // likely be more horizontal than vertical.\\n\\n float b = positive_angle(a);\\n\\n return\\n (b < ( ratio) * HALF_PI) ? 0.0 :\\n (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n 0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n float b = positive_angle(a);\\n float div = TWO_PI / float(n);\\n float c = roundTo(b, div);\\n return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n return\\n (option > 2) ? look_round_n_directions(rawAngle + delta, option) : // option 3-n: round to n directions\\n (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n (option == 1) ? rawAngle + delta : // use free angle, and flip to align with one direction of the axis\\n (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n (option ==-1) ? 0.0 : // useful for backward compatibility, all texts remains horizontal\\n rawAngle; // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n (axis.y == 0.0) &&\\n (axis.z == 0.0);\\n\\nvoid main() {\\n //Compute world offset\\n float axisDistance = position.z;\\n vec3 dataPosition = axisDistance * axis + offset;\\n\\n float beta = angle; // i.e. user defined attributes for each tick\\n\\n float axisAngle;\\n float clipAngle;\\n float flip;\\n\\n if (enableAlign) {\\n axisAngle = (isAxisTitle) ? HALF_PI :\\n computeViewAngle(dataPosition, dataPosition + axis);\\n clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n beta += applyAlignOption(clipAngle, flip * PI);\\n }\\n\\n //Compute plane offset\\n vec2 planeCoord = position.xy * pixelScale;\\n\\n mat2 planeXform = scale * mat2(\\n cos(beta), sin(beta),\\n -sin(beta), cos(beta)\\n );\\n\\n vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n //Compute clip position\\n vec3 clipPosition = project(dataPosition);\\n\\n //Apply text offset in clip coordinates\\n clipPosition += vec3(viewOffset, 0.0);\\n\\n //Done\\n gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"#define GLSLIFY 1\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n vec3 realNormal = signAxis * normal;\\n\\n if(dot(realNormal, enable) > 0.0) {\\n vec3 minRange = min(bounds[0], bounds[1]);\\n vec3 maxRange = max(bounds[0], bounds[1]);\\n vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n } else {\\n gl_Position = vec4(0,0,0,0);\\n }\\n\\n colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n gl_FragColor = colorChannel.x * colors[0] +\\n colorChannel.y * colors[1] +\\n colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":288,glslify:392}],227:[function(t,e,r){(function(r){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window\|\|r.global\|\|{},l=s.__TEXT_CACHE\|\|{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){this.gl;var o=[];function s(t,e,r,n){var i=l[r];i\|\|(i=l[r]={});var s=i[e];s\|\|(s=i[e]=function(t,e){try{return a(t,e)}catch(t){return console.warn(\"error vectorizing 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h=e.model\|\|c,p=e.view\|\|c,m=e.projection\|\|c,y=t.bounds,x=(f=f\|\|a(h,p,m,y)).axis;f.edges;o(u,p,h),o(u,m,u);for(var b=g,_=0;_<3;++_)b[_].lo=1/0,b[_].hi=-1/0,b[_].pixelsPerDataUnit=1/0;var w=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var M=(k+1)%3,A=(k+2)%3,T=v;t:for(var _=0;_<2;++_){var S=[];if(x[k]<0!=!!_){T[k]=y[_][k];for(var E=0;E<2;++E){T[M]=y[E^_][M];for(var C=0;C<2;++C)T[A]=y[C^E^_][A],S.push(T.slice())}for(var E=0;E<w.length;++E){if(0===S.length)continue t;S=i.positive(S,w[E])}for(var E=0;E<S.length;++E)for(var A=S[E],L=d(v,u,A,r,l),C=0;C<3;++C)b[C].lo=Math.min(b[C].lo,A[C]),b[C].hi=Math.max(b[C].hi,A[C]),C!==k&&(b[C].pixelsPerDataUnit=Math.min(b[C].pixelsPerDataUnit,Math.abs(L[C])))}}}return b};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])n,f=(o[1]/o[3]-s[1]/s[3])i;t[a]=.25Math.sqrt(uu+ff)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],v=[0,0,0]},{\"./lib/cube.js\":224,\"extract-frustum-planes\":213,\"gl-mat4/multiply\":256,\"gl-mat4/transpose\":264,\"gl-vec4/transformMat4\":381,\"split-polygon\":503}],230:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.lengthi.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&\"undefined\"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t\|=0)<=0&&(t=1),this.gl.bufferData(this.type,0\|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r\|\|t.ARRAY_BUFFER,n=n\|\|t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:433,\"ndarray-ops\":427,\"typedarray-pool\":522}],231:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=(t(\"gl-vec4\"),function(t,e){for(var r=0;r<t.length;r++)if(t[r]>=e)return r-1;return r}),a=n.create(),o=n.create(),s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t,e,r,l){var c=t[0],u=t[1],f=t[2],h=r[0].length,p=r[1].length,d=r[2].length,g=i(r[0],c),v=i(r[1],u),m=i(r[2],f),y=g+1,x=v+1,b=m+1;if(l&&(g=s(g,0,h-1),y=s(y,0,h-1),v=s(v,0,p-1),x=s(x,0,p-1),m=s(m,0,d-1),b=s(b,0,d-1)),g<0\|\|v<0\|\|m<0\|\|y>=h\|\|x>=p\|\|b>=d)return n.create();var _=(c-r[0][g])/(r[0][y]-r[0][g]),w=(u-r[1][v])/(r[1][x]-r[1][v]),k=(f-r[2][m])/(r[2][b]-r[2][m]);(_<0\|\|_>1\|\|isNaN(_))&&(_=0),(w<0\|\|w>1\|\|isNaN(w))&&(w=0),(k<0\|\|k>1\|\|isNaN(k))&&(k=0);var M=mhp,A=bhp,T=vh,S=xh,E=g,C=y,L=e[T+M+E],z=e[T+M+C],O=e[S+M+E],I=e[S+M+C],P=e[T+A+E],D=e[T+A+C],R=e[S+A+E],B=e[S+A+C],F=n.create();return n.lerp(F,L,z,_),n.lerp(a,O,I,_),n.lerp(F,F,a,w),n.lerp(a,P,D,_),n.lerp(o,R,B,_),n.lerp(a,a,o,w),n.lerp(F,F,a,k),F};e.exports=function(t,e){var r;r=t.positions?t.positions:function(t){for(var e=t[0],r=t[1],n=t[2],i=[],a=0;a<n.length;a++)for(var o=0;o<r.length;o++)for(var s=0;s<e.length;s++)i.push([n[a],r[o],e[s]]);return i}(t.meshgrid);var i=t.meshgrid,a=t.vectors,o={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vertexNormals:[],vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),o;for(var s=0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=1/0,d=-1/0,g=null,v=null,m=[],y=1/0,x=0;x<r.length;x++){var b,_=r[x];c=Math.min(_[0],c),u=Math.max(_[0],u),f=Math.min(_[1],f),h=Math.max(_[1],h),p=Math.min(_[2],p),d=Math.max(_[2],d),b=i?l(_,a,i,!0):a[x],n.length(b)>s&&(s=n.length(b)),x&&(y=Math.min(y,2n.distance(g,_)/(n.length(v)+n.length(b)))),g=_,v=b,m.push(b)}var w=[c,f,p],k=[u,h,d];e&&(e[0]=w,e[1]=k),0===s&&(s=1);var M=1/s;isFinite(y)&&!isNaN(y)\|\|(y=1),o.vectorScale=y;var A=function(t,e,r){var i=n.create();return void 0!==t&&n.set(i,t,e,r),i}(0,1,0),T=t.coneSize\|\|.5;t.absoluteConeSize&&(T=t.absoluteConeSizeM),o.coneScale=T;x=0;for(var S=0;x<r.length;x++)for(var E=(_=r[x])[0],C=_[1],L=_[2],z=m[x],O=n.length(z)M,I=0;I<8;I++){o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vertexIntensity.push(O,O,O),o.vertexIntensity.push(O,O,O),o.vertexNormals.push(A,A,A),o.vertexNormals.push(A,A,A);var P=o.positions.length;o.cells.push([P-6,P-5,P-4],[P-3,P-2,P-1])}return o},e.exports.createConeMesh=t(\"./lib/conemesh\")},{\"./lib/conemesh\":233,\"gl-vec3\":329,\"gl-vec4\":365}],232:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4n+i]e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5i[0](1+o[0]),.5i[1](1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;u<t.length;++u)c[u]=o(t[u],r,a,s,l);for(var f=0,h=1/0,u=0;u<c.length;++u){for(var p=0,d=0;d<2;++d)p+=Math.pow(c[u][d]-e[d],2);p<h&&(h=p,f=u)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),v=0,u=0;u<3;++u){if(g[u]<-.001\|\|g[u]>1.0001)return null;v+=g[u]}if(Math.abs(v-1)>.001)return null;return[f,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=ai[o];return r}(t,g),g]}},{barycentric:61,\"polytope-closest-point/lib/closest_point_2d.js\":464}],233:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./shaders\"),g=(t(\"./closest-point\"),d.meshShader),v=d.pickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,v,y,x,b,_,w,k,M,A,T,S,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=c,this.triangleColors=f,this.triangleNormals=p,this.triangleUVs=h,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=x,this.edgeIds=v,this.edgeVAO=b,this.edgeCount=0,this.pointPositions=_,this.pointColors=k,this.pointUVs=M,this.pointSizes=A,this.pointIds=w,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=S,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1]}var x=y.prototype;function b(t){var e=n(t,v.vertex,v.fragment,null,v.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(t){this.pickId=t},x.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=vy[b]+mx[b]}this.contourCount=s/3\|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},x.update=function(t){t=t\|\|{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4n+a]=i[a];r[4n+3]=255i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],l=[],c=[],h=[],p=[],d=[],g=[],v=[],m=[],y=[],x=[],b=[],_=[],w=[],k=[];this.cells=r,this.positions=n;var M=t.vertexNormals,A=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,S)),A\|\|M\|\|(M=s.vertexNormals(r,n,T));var E=t.vertexColors,C=t.cellColors,L=t.meshColor\|\|[1,1,1,1],z=t.vertexUVs,O=t.vertexIntensity,I=t.cellUVs,P=t.cellIntensity,D=1/0,R=-1/0;if(!z&&!I)if(O)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],R=+t.vertexIntensityBounds[1];else for(var B=0;B<O.length;++B){var F=O[B];D=Math.min(D,F),R=Math.max(R,F)}else if(P)for(B=0;B<P.length;++B){F=P[B];D=Math.min(D,F),R=Math.max(R,F)}else for(B=0;B<n.length;++B){F=n[B][2];D=Math.min(D,F),R=Math.max(R,F)}this.intensity=O\|\|(P?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,P):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var N=t.pointSizes,j=t.pointSize\|\|1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(B=0;B<n.length;++B)for(var V=n[B],U=0;U<3;++U)!isNaN(V[U])&&isFinite(V[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],V[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],V[U]));var q=0,H=0,G=0;t:for(B=0;B<r.length;++B){var W=r[B];switch(W.length){case 1:for(V=n[X=W[0]],U=0;U<3;++U)if(isNaN(V[U])\|\|!isFinite(V[U]))continue t;x.push(V[0],V[1],V[2],V[3]),3===(Z=E?E[X]:C?C[B]:L).length?b.push(Z[0],Z[1],Z[2],1):b.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],_.push($[0],$[1]),N?w.push(N[X]):w.push(j),k.push(B),G+=1;break;case 2:for(U=0;U<2;++U){V=n[X=W[U]];for(var Y=0;Y<3;++Y)if(isNaN(V[Y])\|\|!isFinite(V[Y]))continue t}for(U=0;U<2;++U){V=n[X=W[U]];g.push(V[0],V[1],V[2]),3===(Z=E?E[X]:C?C[B]:L).length?v.push(Z[0],Z[1],Z[2],1):v.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],m.push($[0],$[1]),y.push(B)}H+=1;break;case 3:for(U=0;U<3;++U)for(V=n[X=W[U]],Y=0;Y<3;++Y)if(isNaN(V[Y])\|\|!isFinite(V[Y]))continue t;for(U=0;U<3;++U){var X;V=n[X=W[U]];a.push(V[0],V[1],V[2],V[3]);var Z,$,J,K=i[X];l.push(K[0],K[1],K[2]),3===(Z=E?E[X]:C?C[B]:L).length?c.push(Z[0],Z[1],Z[2],1):c.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],p.push($[0],$[1]),J=M?M[X]:A[B],h.push(J[0],J[1],J[2]),d.push(B)}q+=1}}this.pointCount=G,this.edgeCount=H,this.triangleCount=q,this.pointPositions.update(x),this.pointColors.update(b),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(g),this.edgeColors.update(v),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(c),this.triangleUVs.update(p),this.triangleNormals.update(h),this.triangleIds.update(new Uint32Array(d))}},x.drawTransparent=x.draw=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|m,n=t.view\|\|m,i=t.projection\|\|m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]u[4o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4d+o]this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},x.drawPick=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|m,n=t.view\|\|m,i=t.projection\|\|m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},x.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256t.value[1]+65536t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:Math.floor(r[1]/48),position:n,dataCoordinate:n}},x.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader\|\|function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=b(t),l=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var c=i(t),f=i(t),h=i(t),p=i(t),d=i(t),v=i(t),m=a(t,[{buffer:c,type:t.FLOAT,size:4},{buffer:v,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:h,type:t.FLOAT,size:4},{buffer:p,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:3},{buffer:f,type:t.FLOAT,size:3}]),x=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:x,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),A=i(t),T=i(t),S=i(t),E=i(t),C=i(t),L=a(t,[{buffer:A,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2},{buffer:E,type:t.FLOAT,size:1}]),z=i(t),O=new y(t,l,r,null,null,s,null,null,c,f,v,h,p,d,m,x,k,_,w,M,A,C,T,S,E,L,z,a(t,[{buffer:z,type:t.FLOAT,size:3}]));return O.update(e),O}},{\"./closest-point\":232,\"./shaders\":234,colormap:114,\"gl-buffer\":230,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"gl-shader\":288,\"gl-texture2d\":305,\"gl-vao\":310,ndarray:433,normals:436,\"simplicial-complex-contour\":494,\"typedarray-pool\":522}],234:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat inverse(float m) {\\n return 1.0 / m;\\n}\\n\\nmat2 inverse(mat2 m) {\\n return mat2(m[1][1],-m[0][1],\\n -m[1][0], m[0][0]) / (m[0][0]m[1][1] - m[0][1]m[1][0]);\\n}\\n\\nmat3 inverse(mat3 m) {\\n float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\\n float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\\n float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\\n\\n float b01 = a22 * a11 - a12 * a21;\\n float b11 = -a22 * a10 + a12 * a20;\\n float b21 = a21 * a10 - a11 * a20;\\n\\n float det = a00 * b01 + a01 * b11 + a02 * b21;\\n\\n return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\\n b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\\n b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\\n}\\n\\nmat4 inverse(mat4 m) {\\n float\\n a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\\n a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\\n a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\\n a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\\n\\n b00 = a00 * a11 - a01 * a10,\\n b01 = a00 * a12 - a02 * a10,\\n b02 = a00 * a13 - a03 * a10,\\n b03 = a01 * a12 - a02 * a11,\\n b04 = a01 * a13 - a03 * a11,\\n b05 = a02 * a13 - a03 * a12,\\n b06 = a20 * a31 - a21 * a30,\\n b07 = a20 * a32 - a22 * a30,\\n b08 = a20 * a33 - a23 * a30,\\n b09 = a21 * a32 - a22 * a31,\\n b10 = a21 * a33 - a23 * a31,\\n b11 = a22 * a33 - a23 * a32,\\n\\n det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\\n\\n return mat4(\\n a11 * b11 - a12 * b10 + a13 * b09,\\n a02 * b10 - a01 * b11 - a03 * b09,\\n a31 * b05 - a32 * b04 + a33 * b03,\\n a22 * b04 - a21 * b05 - a23 * b03,\\n a12 * b08 - a10 * b11 - a13 * b07,\\n a00 * b11 - a02 * b08 + a03 * b07,\\n a32 * b02 - a30 * b05 - a33 * b01,\\n a20 * b05 - a22 * b02 + a23 * b01,\\n a10 * b10 - a11 * b08 + a13 * b06,\\n a01 * b08 - a00 * b10 - a03 * b06,\\n a30 * b04 - a31 * b02 + a33 * b00,\\n a21 * b02 - a20 * b04 - a23 * b00,\\n a11 * b07 - a10 * b09 - a12 * b06,\\n a00 * b09 - a01 * b07 + a02 * b06,\\n a31 * b01 - a30 * b03 - a32 * b00,\\n a20 * b03 - a21 * b01 + a22 * b00) / det;\\n}\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have \|\|a\|\| > 0 U \|\|b\|\| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a-b + ba + c0 = -ba + ba + 0 = 0\\n if (v.xv.x > v.zv.z \|\| v.yv.y > v.zv.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2pi (segment/segmentCount)\\n// To go from index to segment index, index - (segment6)\\n//\\nvec3 getConePosition(vec3 d, float index, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n index = mod(index, segmentCount 6.0);\\n\\n float segment = floor(index/6.0);\\n float segmentIndex = index - (segment6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex == 3.0) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n // angle = 2pi ((segment + ((segmentIndex == 1.0 \|\| segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\\n float nextAngle = float(segmentIndex == 1.0 \|\| segmentIndex == 5.0);\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)0.25;\\n vec3 y = v sin(angle) * length(d)0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex <= 2.0) {\\n vec3 tx = u sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\nuniform float vectorScale;\\nuniform float coneScale;\\n\\nuniform float coneOffset;\\n\\nuniform mat4 model\\n , view\\n , projection;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data\\n , f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n normal = normalize(normal * inverse(mat3(model)));\\n\\n // vec4 m_position = model * vec4(conePosition, 1.0);\\n vec4 t_position = view * conePosition;\\n gl_Position = projection * t_position;\\n f_color = color; //vec4(position.w, color.r, 0, 0);\\n f_normal = normal;\\n f_data = conePosition.xyz;\\n f_position = position.xyz;\\n f_eyeDirection = eyePosition - conePosition.xyz;\\n f_lightDirection = lightPosition - conePosition.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data\\n , f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(!gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have \|\|a\|\| > 0 U \|\|b\|\| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a-b + ba + c0 = -ba + ba + 0 = 0\\n if (v.xv.x > v.zv.z \|\| v.yv.y > v.zv.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2pi (segment/segmentCount)\\n// To go from index to segment index, index - (segment6)\\n//\\nvec3 getConePosition(vec3 d, float index, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n index = mod(index, segmentCount 6.0);\\n\\n float segment = floor(index/6.0);\\n float segmentIndex = index - (segment6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex == 3.0) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n // angle = 2pi ((segment + ((segmentIndex == 1.0 \|\| segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\\n float nextAngle = float(segmentIndex == 1.0 \|\| segmentIndex == 5.0);\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)0.25;\\n vec3 y = v sin(angle) * length(d)0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex <= 2.0) {\\n vec3 tx = u sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nuniform float vectorScale;\\nuniform float coneScale;\\nuniform float coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n gl_Position = projection * view * conePosition;\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:392}],235:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34000:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],236:[function(t,e,r){var n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":235}],237:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return this.opacity>=1},l.isTransparent=function(){return this.opacity<1},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view\|\|o,i=r.projection=t.projection\|\|o;r.model=t.model\|\|o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=this.pixelRatio(i[3]a+i[7]s+i[11]l+i[15]c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]),r.capSize=this.capSize[f]u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){\"lineWidth\"in(t=t\|\|{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)\|\|(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)\|\|(this.capSize=[this.capSize,this.capSize,this.capSize])),\"opacity\"in t&&(this.opacity=t.opacity);var e=t.color\|\|[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])\|\|(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])\|\|!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length&&(d=[d[0],d[1],d[2],1]),!isNaN(p[0][s])&&!isNaN(p[1][s])){var g;if(p[0][s]<0)(g=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+f(i,g,d,s);if(p[1][s]>0)(g=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+f(i,g,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":238,\"gl-buffer\":230,\"gl-vao\":310}],238:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = model * vec4(position, 1.0);\\n worldPosition = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n gl_Position = projection * view * worldPosition;\\n fragColor = color;\\n fragPosition = position;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], fragPosition)) discard;\\n\\n gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":288,glslify:392}],239:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i\|\|(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0\|e[1],e=0\|e[0]);if(\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0\|\|e>u\|\|r<0\|\|r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n\|\|{})){if((f=Math.max(0\|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var g=!0;\"depth\"in n&&(g=!!n.depth);var v=!1;\"stencil\"in n&&(v=!!n.stencil);return new d(t,e,r,h,f,g,v,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0\|e,0\|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,v=[0\|e,0\|r];Object.defineProperties(v,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=v,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,v=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var m=0;m<o;++m)t.color[m]=h(r,i,a,v,r.RGBA,r.COLOR_ATTACHMENT0+m);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),m=0;m<t.color.length;++m)t.color[m].dispose(),t.color[m]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var g=d.prototype;function v(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e\|\|t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0\|\|e>i\|\|r<0\|\|r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)\|\|(t=[0\|t,0\|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0\|t[0],r=0\|t[1];return v(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return v(this,t\|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t\|=0,v(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":305}],240:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)\|\|\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));h[g]=d}}for(var v=o(e).split(\"\\n\"),p=0;p<v.length;p++)if(h[p+3]\|\|h[p+2]\|\|h[p+1]){var m=v[p];if(c+=m+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":49,\"gl-constants/lookup\":236,\"glsl-shader-name\":384,\"sprintf-js\":504}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2o/l,u[4]=2s/c,u[6]=2(r[0]-a[0])/l-1,u[7]=2(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2c/f,t[4]=2u/h,t[6]=2(a[0]-l[0])/f-1,t[7]=2(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]this.shape[1];if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]\|0]]}},f.update=function(t){var e=(t=t\|\|{}).shape\|\|[0,0],r=t.x\|\|i(e[0]),o=t.y\|\|i(e[1]),s=t.z\|\|new Float32Array(e[0]e[1]);this.xData=r,this.yData=o;var l=t.colorLevels\|\|[0],c=t.colorValues\|\|[0,0,0,1],u=l.length,f=this.bounds,p=f[0]=r[0],d=f[1]=o[0],g=1/((f[2]=r[r.length-1])-p),v=1/((f[3]=o[o.length-1])-d),m=e[0],y=e[1];this.shape=[m,y];var x=(m-1)(y-1)(h.length>>>1);this.numVertices=x;for(var b=a.mallocUint8(4x),_=a.mallocFloat32(2x),w=a.mallocUint8(2x),k=a.mallocUint32(x),M=0,A=0;A<y-1;++A)for(var T=v(o[A]-d),S=v(o[A+1]-d),E=0;E<m-1;++E)for(var C=g(r[E]-p),L=g(r[E+1]-p),z=0;z<h.length;z+=2){var O,I,P,D,R=h[z],B=h[z+1],F=s[(A+B)m+(E+R)],N=n.le(l,F);if(N<0)O=c[0],I=c[1],P=c[2],D=c[3];else if(N===u-1)O=c[4u-4],I=c[4u-3],P=c[4u-2],D=c[4u-1];else{var j=(F-l[N])/(l[N+1]-l[N]),V=1-j,U=4N,q=4(N+1);O=Vc[U]+jc[q],I=Vc[U+1]+jc[q+1],P=Vc[U+2]+jc[q+2],D=Vc[U+3]+jc[q+3]}b[4M]=255O,b[4M+1]=255I,b[4M+2]=255P,b[4M+3]=255D,_[2M]=.5C+.5L,_[2M+1]=.5T+.5S,w[2M]=R,w[2M+1]=B,k[M]=Am+E,M+=1}this.positionBuffer.update(_),this.weightBuffer.update(w),this.colorBuffer.update(b),this.idBuffer.update(k),a.free(_),a.free(b),a.free(w),a.free(k)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":242,\"binary-search-bounds\":243,\"gl-buffer\":230,\"gl-shader\":288,\"iota-array\":399,\"typedarray-pool\":522}],242:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = vec4(fragColor.rgb fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n fragColor = color;\\n gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n vec2 d = step(.5, vWeight);\\n vec4 id = fragId + pickOffset;\\n id.x += d.x + d.yshape.x;\\n\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n vWeight = weight;\\n\\n fragId = pickId;\\n\\n vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:392}],243:[function(t,e,r){arguments[4][99][0].apply(r,arguments)},{dup:99}],244:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n vec4 startPoint = project(position);\\n vec4 endPoint = project(nextPosition);\\n\\n vec2 A = startPoint.xy / startPoint.w;\\n vec2 B = endPoint.xy / endPoint.w;\\n\\n float clipAngle = atan(\\n (B.y - A.y) * screenShape.y,\\n (B.x - A.x) * screenShape.x\\n );\\n\\n vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n sin(clipAngle),\\n -cos(clipAngle)\\n ) / screenShape;\\n\\n gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n worldPosition = position;\\n pixelArcLength = arcLength;\\n fragColor = color;\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\nlowp vec4 encode_float_1540259130(highp float v) {\\n highp float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n highp vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n highp float e = floor(log2(av));\\n highp float m = av * pow(2.0, -e) - 1.0;\\n \\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n \\n //Unpack exponent\\n highp float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0; \\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n gl_FragColor = vec4(pickId/255.0, encode_float_1540259130(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":288,glslify:392}],245:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl\|\|t.scene&&t.scene.gl,r=u(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),c=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),h=l(new Array(1024),[256,1,4]),p=0;p<1024;++p)h.data[p]=255;var d=a(e,h);d.wrap=e.REPEAT;var g=new v(e,r,o,s,c,d);return g.update(t),g};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=t(\"glsl-read-float\"),s=t(\"binary-search-bounds\"),l=t(\"ndarray\"),c=t(\"./lib/shaders\"),u=c.createShader,f=c.createPickShader,h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function p(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=ii}return Math.sqrt(r)}function d(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}var m=v.prototype;m.isTransparent=function(){return this.opacity<1},m.isOpaque=function(){return this.opacity>=1},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model\|\|h,view:t.view\|\|h,projection:t.projection\|\|h,clipBounds:d(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model\|\|h,view:t.view\|\|h,projection:t.projection\|\|h,pickId:this.pickId,clipBounds:d(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),\"opacity\"in t&&(this.opacity=+t.opacity);var i=[],a=[],o=[],c=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position\|\|t.positions;if(h){var d=t.color\|\|t.colors\|\|[0,0,0,1],g=t.lineWidth\|\|1,v=!1;t:for(e=1;e<h.length;++e){var m,y,x,b=h[e-1],_=h[e];for(a.push(c),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])\|\|isNaN(_[r])\|\|!isFinite(b[r])\|\|!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,v=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(d[0])?(m=d.length>e-1?d[e-1]:d.length>0?d[d.length-1]:[0,0,0,1],y=d.length>e?d[e]:d.length>0?d[d.length-1]:[0,0,0,1]):m=y=d,3===m.length&&(m=[m[0],m[1],m[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var k=c;if(c+=p(b,_),v){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,m[0],m[1],m[2],m[3]);u+=2,v=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,m[0],m[1],m[2],m[3],b[0],b[1],b[2],_[0],_[1],_[2],k,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],c,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],c,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(c),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var M=t.dashes.slice();for(M.unshift(0),e=1;e<M.length;++e)M[e]=M[e-1]+M[e];var A=l(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&s.le(M,M[M.length-1]e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=o(t.value[0],t.value[1],t.value[2],0),r=s.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),l=1-a,c=[0,0,0],u=0;u<3;++u)c[u]=ln[u]+ai[u];var f=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,c,f,this.points[f])}},{\"./lib/shaders\":244,\"binary-search-bounds\":79,\"gl-buffer\":230,\"gl-texture2d\":305,\"gl-vao\":310,\"glsl-read-float\":383,ndarray:433}],246:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=ra-in;return o?(o=1/o,t[0]=ao,t[1]=-no,t[2]=-io,t[3]=ro,t):null}},{}],247:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=uo-sc,h=-ua+sl,p=ca-ol,d=rf+nh+ip;return d?(d=1/d,t[0]=fd,t[1]=(-un+ic)d,t[2]=(sn-io)d,t[3]=hd,t[4]=(ur-il)d,t[5]=(-sr+ia)d,t[6]=pd,t[7]=(-cr+nl)d,t[8]=(or-na)d,t):null}},{}],248:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],249:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],250:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],g=t[14],v=t[15];return(eo-ra)(fv-hg)-(es-na)(uv-hd)+(el-ia)(ug-fd)+(rs-no)(cv-hp)-(rl-io)(cg-fp)+(nl-is)(cd-up)}},{}],251:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=ro,u=no,f=ns,h=io,p=is,d=il,g=ao,v=as,m=al;return t[0]=1-f-d,t[1]=u+m,t[2]=h-v,t[3]=0,t[4]=u-m,t[5]=1-c-d,t[6]=p+g,t[7]=0,t[8]=h+v,t[9]=p-g,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],252:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=ns,f=nl,h=nc,p=il,d=ic,g=ac,v=os,m=ol,y=oc;return t[0]=1-(p+g),t[1]=f+y,t[2]=h-m,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+v,t[7]=0,t[8]=h+m,t[9]=d-v,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],253:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],254:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],v=e[14],m=e[15],y=rs-no,x=rl-io,b=rc-ao,_=nl-is,w=nc-as,k=ic-al,M=ug-fd,A=uv-hd,T=um-pd,S=fv-hg,E=fm-pg,C=hm-pv,L=yC-xE+bS+_T-wA+kM;if(!L)return null;return L=1/L,t[0]=(sC-lE+cS)L,t[1]=(iE-nC-aS)L,t[2]=(gk-vw+m_)L,t[3]=(hw-fk-p_)L,t[4]=(lT-oC-cA)L,t[5]=(rC-iT+aA)L,t[6]=(vb-dk-mx)L,t[7]=(uk-hb+px)L,t[8]=(oE-sT+cM)L,t[9]=(nT-rE-aM)L,t[10]=(dw-gb+my)L,t[11]=(fb-uw-py)L,t[12]=(sA-oS-lM)L,t[13]=(rS-nA+iM)L,t[14]=(gx-d_-vy)L,t[15]=(u_-fx+hy)L,t}},{}],255:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,g=e[0],v=e[1],m=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],k=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(m-k)<1e-6)return n(t);f=g-_,h=v-w,p=m-k,d=1/Math.sqrt(ff+hh+pp),a=x(p=d)-b(h=d),o=b(f=d)-yp,s=yh-xf,(d=Math.sqrt(aa+oo+ss))?(a=d=1/d,o=d,s=d):(a=0,o=0,s=0);l=hs-po,c=pa-fs,u=fo-ha,(d=Math.sqrt(ll+cc+uu))?(l=d=1/d,c=d,u=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(ag+ov+sm),t[13]=-(lg+cv+um),t[14]=-(fg+hv+pm),t[15]=1,t}},{\"./identity\":253}],256:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],v=e[13],m=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=xn+bs+_f+wg,t[1]=xi+bl+_h+wv,t[2]=xa+bc+_p+wm,t[3]=xo+bu+_d+wy,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=xn+bs+_f+wg,t[5]=xi+bl+_h+wv,t[6]=xa+bc+_p+wm,t[7]=xo+bu+_d+wy,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=xn+bs+_f+wg,t[9]=xi+bl+_h+wv,t[10]=xa+bc+_p+wm,t[11]=xo+bu+_d+wy,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=xn+bs+_f+wg,t[13]=xi+bl+_h+wv,t[14]=xa+bc+_p+wm,t[15]=xo+bu+_d+wy,t}},{}],257:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2ino,t[15]=0,t}},{}],258:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,v,m,y,x,b,_,w,k,M,A,T,S,E=n[0],C=n[1],L=n[2],z=Math.sqrt(EE+CC+LL);if(Math.abs(z)<1e-6)return null;E=z=1/z,C=z,L=z,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],v=e[9],m=e[10],y=e[11],x=EEo+a,b=CEo+Li,_=LEo-Ci,w=ECo-Li,k=CCo+a,M=LCo+Ei,A=ELo+Ci,T=CLo-Ei,S=LLo+a,t[0]=sx+fb+g_,t[1]=lx+hb+v_,t[2]=cx+pb+m_,t[3]=ux+db+y_,t[4]=sw+fk+gM,t[5]=lw+hk+vM,t[6]=cw+pk+mM,t[7]=uw+dk+yM,t[8]=sA+fT+gS,t[9]=lA+hT+vS,t[10]=cA+pT+mS,t[11]=uA+dT+yS,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],259:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=ai+cn,t[5]=oi+un,t[6]=si+fn,t[7]=li+hn,t[8]=ci-an,t[9]=ui-on,t[10]=fi-sn,t[11]=hi-ln,t}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=ai-cn,t[1]=oi-un,t[2]=si-fn,t[3]=li-hn,t[8]=an+ci,t[9]=on+ui,t[10]=sn+fi,t[11]=ln+hi,t}},{}],261:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=ai+cn,t[1]=oi+un,t[2]=si+fn,t[3]=li+hn,t[4]=ci-an,t[5]=ui-on,t[6]=fi-sn,t[7]=hi-ln,t}},{}],262:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]n,t[1]=e[1]n,t[2]=e[2]n,t[3]=e[3]n,t[4]=e[4]i,t[5]=e[5]i,t[6]=e[6]i,t[7]=e[7]i,t[8]=e[8]a,t[9]=e[9]a,t[10]=e[10]a,t[11]=e[11]a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],263:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],v=r[1],m=r[2];e===t?(t[12]=e[0]g+e[4]v+e[8]m+e[12],t[13]=e[1]g+e[5]v+e[9]m+e[13],t[14]=e[2]g+e[6]v+e[10]m+e[14],t[15]=e[3]g+e[7]v+e[11]m+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=ng+sv+fm+e[12],t[13]=ig+lv+hm+e[13],t[14]=ag+cv+pm+e[14],t[15]=og+uv+dm+e[15]);return t}},{}],264:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],265:[function(t,e,r){\"use strict\";e.exports=function(t,e){switch(e.length){case 0:break;case 1:t[0]=1/e[0];break;case 4:n(t,e);break;case 9:i(t,e);break;case 16:a(t,e);break;default:throw new Error(\"currently supports matrices up to 4x4\")}return t};var n=t(\"gl-mat2/invert\"),i=t(\"gl-mat3/invert\"),a=t(\"gl-mat4/invert\")},{\"gl-mat2/invert\":246,\"gl-mat3/invert\":247,\"gl-mat4/invert\":254}],266:[function(t,e,r){arguments[4][232][0].apply(r,arguments)},{barycentric:61,dup:232,\"polytope-closest-point/lib/closest_point_2d.js\":464}],267:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec4 m_position = model vec4(position, 1.0);\\n vec4 t_position = view * m_position;\\n gl_Position = projection * t_position;\\n f_color = color;\\n f_normal = normal;\\n f_data = position;\\n f_eyeDirection = eyePosition - position;\\n f_lightDirection = lightPosition - position;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nvec3 normals(vec3 pos) {\\n vec3 fdx = dFdx(pos);\\n vec3 fdy = dFdy(pos);\\n return normalize(cross(fdx, fdy));\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n vec3 normal = normals(f_data);\\n\\n if (dot(N, normal) < 0.0) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\"]),s=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\"]),c=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\"]),f=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\"]),p=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor,1);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:392}],268:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),v=d.meshShader,m=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function k(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,v,m,y,x,b,_,k,M,A,T,S){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=v,this.edgeUVs=m,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=k,this.pointSizes=M,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=T,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var M=k.prototype;function A(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function T(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function S(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function E(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}M.isOpaque=function(){return this.opacity>=1},M.isTransparent=function(){return this.opacity<1},M.pickSlots=1,M.setPickBase=function(t){this.pickId=t},M.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=vy[b]+mx[b]}this.contourCount=s/3\|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},M.update=function(t){t=t\|\|{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4n+a]=i[a];r[4n+3]=255i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],g=[],v=[],m=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,k=t.cellNormals,M=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,A=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=s.faceNormals(r,n,A)),k\|\|w\|\|(w=s.vertexNormals(r,n,M));var T=t.vertexColors,S=t.cellColors,E=t.meshColor\|\|[1,1,1,1],C=t.vertexUVs,L=t.vertexIntensity,z=t.cellUVs,O=t.cellIntensity,I=1/0,P=-1/0;if(!C&&!z)if(L)if(t.vertexIntensityBounds)I=+t.vertexIntensityBounds[0],P=+t.vertexIntensityBounds[1];else for(var D=0;D<L.length;++D){var R=L[D];I=Math.min(I,R),P=Math.max(P,R)}else if(O)for(D=0;D<O.length;++D){R=O[D];I=Math.min(I,R),P=Math.max(P,R)}else for(D=0;D<n.length;++D){R=n[D][2];I=Math.min(I,R),P=Math.max(P,R)}this.intensity=L\|\|(O?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,O):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var B=t.pointSizes,F=t.pointSize\|\|1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(D=0;D<n.length;++D)for(var N=n[D],j=0;j<3;++j)!isNaN(N[j])&&isFinite(N[j])&&(this.bounds[0][j]=Math.min(this.bounds[0][j],N[j]),this.bounds[1][j]=Math.max(this.bounds[1][j],N[j]));var V=0,U=0,q=0;t:for(D=0;D<r.length;++D){var H=r[D];switch(H.length){case 1:for(N=n[W=H[0]],j=0;j<3;++j)if(isNaN(N[j])\|\|!isFinite(N[j]))continue t;m.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?y.push(Y[0],Y[1],Y[2],1):y.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(P-I),0]:z?z[D]:O?[(O[D]-I)/(P-I),0]:[(N[2]-I)/(P-I),0],x.push(X[0],X[1]),B?b.push(B[W]):b.push(F),_.push(D),q+=1;break;case 2:for(j=0;j<2;++j){N=n[W=H[j]];for(var G=0;G<3;++G)if(isNaN(N[G])\|\|!isFinite(N[G]))continue t}for(j=0;j<2;++j){N=n[W=H[j]];p.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?d.push(Y[0],Y[1],Y[2],1):d.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(P-I),0]:z?z[D]:O?[(O[D]-I)/(P-I),0]:[(N[2]-I)/(P-I),0],g.push(X[0],X[1]),v.push(D)}U+=1;break;case 3:for(j=0;j<3;++j)for(N=n[W=H[j]],G=0;G<3;++G)if(isNaN(N[G])\|\|!isFinite(N[G]))continue t;for(j=0;j<3;++j){var W,Y,X,Z;N=n[W=H[j]];i.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?a.push(Y[0],Y[1],Y[2],1):a.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(P-I),0]:z?z[D]:O?[(O[D]-I)/(P-I),0]:[(N[2]-I)/(P-I),0],c.push(X[0],X[1]),Z=w?w[W]:k[D],l.push(Z[0],Z[1],Z[2]),h.push(D)}V+=1}}this.pointCount=q,this.edgeCount=U,this.triangleCount=V,this.pointPositions.update(m),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(v)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},M.drawTransparent=M.draw=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|w,n=t.view\|\|w,i=t.projection\|\|w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]u[4o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4d+o]this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},M.drawPick=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|w,n=t.view\|\|w,i=t.projection\|\|w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},M.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256t.value[1]+65536t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=g(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;var s=o[2],l=0;for(a=0;a<r.length;++a)l+=s[a]this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},M.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension(\"OES_standard_derivatives\")\|\|t.getExtension(\"MOZ_OES_standard_derivatives\")\|\|t.getExtension(\"WEBKIT_OES_standard_derivatives\")))throw new Error(\"derivatives not supported\");var r=function(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=A(t),c=T(t),f=S(t),h=E(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),y=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:y,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),M=i(t),C=i(t),L=i(t),z=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:M,type:t.FLOAT,size:4},{buffer:C,type:t.FLOAT,size:2}]),O=i(t),I=i(t),P=i(t),D=i(t),R=i(t),B=a(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:P,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),F=i(t),N=new k(t,p,r,s,l,c,f,h,d,b,g,y,x,_,w,L,M,C,z,O,R,I,P,D,B,F,a(t,[{buffer:F,type:t.FLOAT,size:3}]));return N.update(e),N}},{\"./lib/closest-point\":266,\"./lib/shaders\":267,colormap:114,\"gl-buffer\":230,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"gl-shader\":288,\"gl-texture2d\":305,\"gl-vao\":310,ndarray:433,normals:436,\"simplicial-complex-contour\":494,\"typedarray-pool\":522}],269:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":272,\"gl-buffer\":230,\"gl-shader\":288}],270:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var v=a[g],m=a[g+2]-v,y=.5(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2m/x,c[g]=2(v-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])d,r.uniforms.color=h[g];var _=6n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,v=s.pixelRatio,m=s.screenBox,y=m[2]-m[0],x=m[3]-m[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var k=p[w],M=p[w+2]-k,A=.5(d[w+2]+d[w]),T=d[w+2]-d[w];e[w]=2M/T,t[w]=2(k-A)/T}e[0]=b/y,t[0]=b/y,e[1]=_/x,t[1]=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,C=s.tickMarkWidth,L=s.tickMarkColor,z=6f[0].length,O=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),I=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),P=0+6O,D=6Math.max(0,I-O),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),B=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),F=z+6R,N=6Math.max(0,B-R);i[0]=2(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]v/y,o[1]=C[1]v/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,F,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2(g[1]-E[0])/x-1,o[0]=C[0]v/y,o[1]=E[0]v/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,P,D)),i[0]=2(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]v/y,o[1]=C[3]v/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,F,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2(g[3]+E[2])/x-1,o[0]=C[2]v/y,o[1]=E[2]v/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,P,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":272,\"binary-search-bounds\":274,\"gl-buffer\":230,\"gl-shader\":288}],271:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=io.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":272,\"gl-buffer\":230,\"gl-shader\":288}],272:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n vec2 delta = normalize(perp(start - end));\\n vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n float dataOffset = textCoordinate.z;\\n vec2 glyphOffset = textCoordinate.xy;\\n mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n glyphMatrix * glyphOffset * textScale + screenOffset;\\n gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n gl_Position = vec4(pos + tickScaledataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:392}],273:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,v=e.tickColor,m=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],k=this.labelCount[t],M=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var T=2/h[2+(1^t)]-h[1^t];u[1^t]=Ts[1^t]-1,d[t]&&(u[1^t]-=Tpg[t],M<A&&i[A]>i[M]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=v[t],r.uniforms.angle=m[t],a.drawArrays(a.TRIANGLES,i[M],i[A]-i[M]))),y[t]&&k&&(u[1^t]-=Tpx[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,k)),u[1^t]=Ts[2+(1^t)]-1,d[t+2]&&(u[1^t]+=Tpg[t+2],M<A&&i[A]>i[M]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=v[t+2],r.uniforms.angle=m[t+2],a.drawArrays(a.TRIANGLES,i[M],i[A]-i[M]))),y[t+2]&&k&&(u[1^t]+=Tpx[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,k))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2(o[u]c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],g=a[o+2]-f,v=i[o],m=i[o+2]-v;p[o]=2l/ug/m,h[o]=2(s-c)/ug/m}d[1]=2t.pixelRatio/(i[3]-i[1]),d[0]=d[1](i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,g=p.text,v=p.font\|\|\"sans-serif\";i=p.fontSize\|\|12;for(var m=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(v,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]i,-n[r+1]i-bi1.2,(d-y)m);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]i,-n[e+1]i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]i,-n[e+1]i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":272,\"binary-search-bounds\":274,\"gl-buffer\":230,\"gl-shader\":288,\"text-cache\":513}],274:[function(t,e,r){arguments[4][99][0].apply(r,arguments)},{dup:99}],275:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]c[3],c[1]c[3],c[2]c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT\|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]u[3],u[1]u[3],u[2]u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]\|\|f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d](e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var v=this.borderLineEnable,m=this.borderLineWidth,y=this.borderLineColor;for(v[1]&&o.drawLine(r[0],r[1]-.5m[1]i,r[0],r[3]+.5m[3]i,m[1],y[1]),v[0]&&o.drawLine(r[0]-.5m[0]i,r[1],r[2]+.5m[2]i,r[1],m[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5m[1]i,r[2],r[3]+.5m[3]i,m[3],y[3]),v[2]&&o.drawLine(r[0]-.5m[0]i,r[3],r[2]+.5m[2]i,r[3],m[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0\|Math.round((t-i[0]/r)n),o=0\|Math.round((e-i[1]/r)n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0\|Math.round(t[0]r),e[1]=0\|Math.round(t[1]r),e[2]=0\|Math.round(t[2]r),e[3]=0\|Math.round(t[3]r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]\|\|e[1]!==t[1]\|\|e[2]!==t[2]\|\|e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0\|Math.round(t[0]e),r[1]=0\|Math.round(t[1]e),r[2]=0\|Math.round(t[2]e),r[3]=0\|Math.round(t[3]e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0\|Math.round((t[2]-t[0])n),0\|Math.round((t[3]-t[1])n)],this.setDirty()},c.update=function(t){t=t\|\|{};var e=this.gl;this.pixelRatio=t.pixelRatio\|\|1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox\|\|[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox\|\|[.125(this.screenBox[2]-this.screenBox[0])/r,.125(this.screenBox[3]-this.screenBox[1])/r,.875(this.screenBox[2]-this.screenBox[0])/r,.875(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox\|\|[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor\|\|[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor\|\|[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable\|\|[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth\|\|[1,1]).slice(),this.gridLineColor=u(t.gridLineColor\|\|[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable\|\|[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth\|\|[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor\|\|[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength\|\|[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth\|\|[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter\|\|[.5(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t&&!t.titleEnable),this.titleAngle=t.titleAngle\|\|0,this.titleColor=(t.titleColor\|\|[0,0,0,1]).slice(),this.labelPad=(t.labelPad\|\|[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle\|\|[0,Math.PI/2,0,3Math.PI/2]).slice(),this.labelEnable=(t.labelEnable\|\|[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad\|\|[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle\|\|[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable\|\|[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable\|\|[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth\|\|[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks\|\|[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels\|\|[\"x\",\"y\"],labelSize:t.labelSize\|\|[12,12],labelFont:t.labelFont\|\|[\"sans-serif\",\"sans-serif\"],title:t.title\|\|\"\",titleSize:t.titleSize\|\|18,titleFont:t.titleFont\|\|\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":269,\"./lib/grid\":270,\"./lib/line\":271,\"./lib/text\":273,\"gl-select-static\":287}],276:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":288,glslify:392}],277:[function(t,e,r){\"use strict\";e.exports=function(t){var e=!1,r=((t=t\|\|{}).pixelRatio\|\|parseFloat(window.devicePixelRatio),t.canvas);if(!r)if(r=document.createElement(\"canvas\"),t.container){var m=t.container;m.appendChild(r)}else document.body.appendChild(r);var y=t.gl;y\|\|(y=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))\|\|(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(r,t.glOptions\|\|{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:p}));if(!y)throw new Error(\"webgl not supported\");var x=t.bounds\|\|[[-10,-10,-10],[10,10,10]],b=new d,_=l(y,[y.drawingBufferWidth,y.drawingBufferHeight],{preferFloat:!p}),w=h(y),k=t.camera\|\|{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:\"turntable\"},M=t.axes\|\|{},A=i(y,M);A.enable=!M.disable;var T=t.spikes\|\|{},S=o(y,T),E=[],C=[],L=[],z=[],O=!0,I=!0,P=new Array(16),D=new Array(16),R={view:null,projection:P,model:D},I=!0,B=[y.drawingBufferWidth,y.drawingBufferHeight],F={gl:y,contextLost:!1,pixelRatio:t.pixelRatio\|\|parseFloat(window.devicePixelRatio),canvas:r,selection:b,camera:n(r,k),axes:A,axesPixels:null,spikes:S,bounds:x,objects:E,shape:B,aspect:t.aspectRatio\|\|[1,1,1],pickRadius:t.pickRadius\|\|10,zNear:t.zNear\|\|.01,zFar:t.zFar\|\|1e3,fovy:t.fovy\|\|Math.PI/4,clearColor:t.clearColor\|\|[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect\|\|null,onrender:t.onrender\|\|null,onclick:t.onclick\|\|null,cameraParams:R,oncontextloss:null,mouseListener:null},N=[y.drawingBufferWidth/F.pixelRatio\|0,y.drawingBufferHeight/F.pixelRatio\|0];function j(){if(!e&&F.autoResize){var t=r.parentNode,n=1,i=1;t&&t!==document.body?(n=t.clientWidth,i=t.clientHeight):(n=window.innerWidth,i=window.innerHeight);var a=0\|Math.ceil(nF.pixelRatio),o=0\|Math.ceil(iF.pixelRatio);if(a!==r.width\|\|o!==r.height){r.width=a,r.height=o;var s=r.style;s.position=s.position\|\|\"absolute\",s.left=\"0px\",s.top=\"0px\",s.width=n+\"px\",s.height=i+\"px\",O=!0}}}F.autoResize&&j();function V(){for(var t=E.length,e=z.length,r=0;r<e;++r)L[r]=0;t:for(var r=0;r<t;++r){var n=E[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(L[a]+i<255){C[r]=a,n.setPickBase(L[a]+1),L[a]+=i;continue t}var o=s(y,B);C[r]=e,z.push(o),L.push(i),n.setPickBase(1),e+=1}else C[r]=-1}for(;e>0&&0===L[e-1];)L.pop(),z.pop().dispose()}window.addEventListener(\"resize\",j),F.update=function(t){e\|\|(t=t\|\|{},O=!0,I=!0)},F.add=function(t){e\|\|(t.axes=A,E.push(t),C.push(-1),O=!0,I=!0,V())},F.remove=function(t){if(!e){var r=E.indexOf(t);r<0\|\|(E.splice(r,1),C.pop(),O=!0,I=!0,V())}},F.dispose=function(){if(!e&&(e=!0,window.removeEventListener(\"resize\",j),r.removeEventListener(\"webglcontextlost\",H),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();_.dispose();for(var t=0;t<z.length;++t)z[t].dispose();w.dispose(),y=null,A=null,S=null,E=[]}};var U=!1,q=0;function H(){if(F.contextLost)return!0;y.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.mouseListener=u(r,function(t,r,n){if(!e){var i=z.length,a=E.length,o=b.object;b.distance=1/0,b.mouse[0]=r,b.mouse[1]=n,b.object=null,b.screen=null,b.dataCoordinate=b.dataPosition=null;var s=!1;if(t&&q)U=!0;else{U&&(I=!0),U=!1;for(var l=0;l<i;++l){var c=z[l].query(r,N[1]-n-1,F.pickRadius);if(c){if(c.distance>b.distance)continue;for(var u=0;u<a;++u){var f=E[u];if(C[u]===l){var h=f.pick(c);h&&(b.buttons=t,b.screen=c.coord,b.distance=c.distance,b.object=f,b.index=h.distance,b.dataPosition=h.position,b.dataCoordinate=h.dataCoordinate,b.data=h,s=!0)}}}}}o&&o!==b.object&&(o.highlight&&o.highlight(null),O=!0),b.object&&(b.object.highlight&&b.object.highlight(b.data),O=!0),(s=s\|\|b.object!==o)&&F.onselect&&F.onselect(b),1&t&&!(1&q)&&F.onclick&&F.onclick(b),q=t}}),r.addEventListener(\"webglcontextlost\",H);var G=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],W=[G[0].slice(),G[1].slice()];function Y(){if(!H()){j();var t=F.camera.tick();R.view=F.camera.matrix,O=O\|\|t,I=I\|\|t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,r=G[0],n=G[1];r[0]=r[1]=r[2]=1/0,n[0]=n[1]=n[2]=-1/0;for(var i=0;i<e;++i){var o=E[i];o.pixelRatio=F.pixelRatio,o.axes=F.axes,O=O\|\|!!o.dirty,I=I\|\|!!o.dirty;var s=o.bounds;if(s)for(var l=s[0],u=s[1],h=0;h<3;++h)r[h]=Math.min(r[h],l[h]),n[h]=Math.max(n[h],u[h])}var p=F.bounds;if(F.autoBounds)for(var h=0;h<3;++h){if(n[h]<r[h])r[h]=-1,n[h]=1;else{r[h]===n[h]&&(r[h]-=1,n[h]+=1);var d=.05(n[h]-r[h]);r[h]=r[h]-d,n[h]=n[h]+d}p[0][h]=r[h],p[1][h]=n[h]}for(var v=!1,h=0;h<3;++h)v=v\|\|W[0][h]!==p[0][h]\|\|W[1][h]!==p[1][h],W[0][h]=p[0][h],W[1][h]=p[1][h];if(I=I\|\|v,O=O\|\|v){if(v){for(var m=[0,0,0],i=0;i<3;++i)m[i]=g((p[1][i]-p[0][i])/10);A.autoTicks?A.update({bounds:p,tickSpacing:m}):A.update({bounds:p})}var x=y.drawingBufferWidth,k=y.drawingBufferHeight;B[0]=x,B[1]=k,N[0]=0\|Math.max(x/F.pixelRatio,1),N[1]=0\|Math.max(k/F.pixelRatio,1),f(P,F.fovy,x/k,F.zNear,F.zFar);for(var i=0;i<16;++i)D[i]=0;D[15]=1;for(var M=0,i=0;i<3;++i)M=Math.max(M,p[1][i]-p[0][i]);for(var i=0;i<3;++i)F.autoScale?D[5i]=F.aspect[i]/(p[1][i]-p[0][i]):D[5i]=1/M,F.autoCenter&&(D[12+i]=.5-D[5i](p[0][i]+p[1][i]));for(var i=0;i<e;++i){var o=E[i];o.axesBounds=p,F.clipToBounds&&(o.clipBounds=p)}b.object&&(F.snapToData?S.position=b.dataCoordinate:S.position=b.dataPosition,S.bounds=p),I&&(I=!1,function(){if(H())return;y.colorMask(!0,!0,!0,!0),y.depthMask(!0),y.disable(y.BLEND),y.enable(y.DEPTH_TEST);for(var t=E.length,e=z.length,r=0;r<e;++r){var n=z[r];n.shape=N,n.begin();for(var i=0;i<t;++i)if(C[i]===r){var a=E[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(R))}n.end()}}()),F.axesPixels=a(F.axes,R,x,k),F.onrender&&F.onrender(),y.bindFramebuffer(y.FRAMEBUFFER,null),y.viewport(0,0,x,k);var T=F.clearColor;y.clearColor(T[0],T[1],T[2],T[3]),y.clear(y.COLOR_BUFFER_BIT\|y.DEPTH_BUFFER_BIT),y.depthMask(!0),y.colorMask(!0,!0,!0,!0),y.enable(y.DEPTH_TEST),y.depthFunc(y.LEQUAL),y.disable(y.BLEND),y.disable(y.CULL_FACE);var L=!1;A.enable&&(L=L\|\|A.isTransparent(),A.draw(R)),S.axes=A,b.object&&S.draw(R),y.disable(y.CULL_FACE);for(var i=0;i<e;++i){var o=E[i];o.axes=A,o.pixelRatio=F.pixelRatio,o.isOpaque&&o.isOpaque()&&o.draw(R),o.isTransparent&&o.isTransparent()&&(L=!0)}if(L){_.shape=B,_.bind(),y.clear(y.DEPTH_BUFFER_BIT),y.colorMask(!1,!1,!1,!1),y.depthMask(!0),y.depthFunc(y.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(var i=0;i<e;++i){var o=E[i];o.isOpaque&&o.isOpaque()&&o.draw(R)}y.enable(y.BLEND),y.blendEquation(y.FUNC_ADD),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.colorMask(!0,!0,!0,!0),y.depthMask(!1),y.clearColor(0,0,0,0),y.clear(y.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(var i=0;i<e;++i){var o=E[i];o.isTransparent&&o.isTransparent()&&o.drawTransparent(R)}y.bindFramebuffer(y.FRAMEBUFFER,null),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.disable(y.DEPTH_TEST),w.bind(),_.color[0].bind(0),w.uniforms.accumBuffer=0,c(y),y.disable(y.BLEND)}O=!1;for(var i=0;i<e;++i)E[i].dirty=!1}}}return function t(){e\|\|F.contextLost\|\|(Y(),requestAnimationFrame(t))}(),F.redraw=function(){e\|\|(O=!0,Y())},F};var n=t(\"3d-view-controls\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"./lib/shader\"),p=t(\"is-mobile\")({tablet:!0});function d(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(tr)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t\|\|t}},{\"./lib/shader\":276,\"3d-view-controls\":44,\"a-big-triangle\":47,\"gl-axes3d\":222,\"gl-axes3d/properties\":229,\"gl-fbo\":239,\"gl-mat4/perspective\":257,\"gl-select-static\":287,\"gl-spikes3d\":297,\"is-mobile\":403,\"mouse-change\":418}],278:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n highp float a = 12.9898;\\n highp float b = 78.233;\\n highp float c = 43758.5453;\\n highp float d = dot(co.xy, vec2(a, b));\\n highp float e = mod(d, 3.14);\\n return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n // if we don't jitter the point size a bit, overall point cloud\\n // saturation 'jumps' on zooming, which is disturbing and confusing\\n gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n if(pointCloud != 0.0) { // pointCloud is truthy\\n // get the same square surface as circle would be\\n gl_PointSize = 0.886;\\n }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n float radius;\\n vec4 baseColor;\\n if(pointCloud != 0.0) { // pointCloud is truthy\\n if(centerFraction == 1.0) {\\n gl_FragColor = color;\\n } else {\\n gl_FragColor = mix(borderColor, color, centerFraction);\\n }\\n } else {\\n radius = length(2.0 gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:392}],279:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t\|\|{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i\|\|l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i\|\|a.free(l),o\|\|a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2r],o=t[2r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatioMath.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2i[0]/a-1,l[7]=-2i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2a],o[2a+1]]}}},{\"./lib/shader\":278,\"gl-buffer\":230,\"gl-shader\":288,\"typedarray-pool\":522}],280:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],g=r[2],v=r[3];(a=cp+ud+fg+hv)<0&&(a=-a,p=-p,d=-d,g=-g,v=-v);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)i)/o,l=Math.sin(ni)/o):(s=1-n,l=n);return t[0]=sc+lp,t[1]=su+ld,t[2]=sf+lg,t[3]=sh+lv,t}},{}],281:[function(t,e,r){\"use strict\";e.exports=function(t){return t\|\|0===t?t.toString():\"\"}},{}],282:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e){var r=i[e];r\|\|(r=i[e]={});if(t in r)return r[t];for(var a=n(t,{textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),o=n(t,{triangles:!0,textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<a.positions.length;++l)for(var c=a.positions[l],u=0;u<2;++u)s[0][u]=Math.min(s[0][u],c[u]),s[1][u]=Math.max(s[1][u],c[u]);return r[t]=[o,a,s]};var i={}},{\"vectorize-text\":527}],283:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),o=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n\\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),s=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale = highlightScale;\\n }\\n\\n vec4 clipCenter = projection view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5lscale(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\"]),l=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},v={vertex:s,fragment:c,attributes:u};function m(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return m(t,f)},r.createOrtho=function(t){return m(t,h)},r.createProject=function(t){return m(t,p)},r.createPickPerspective=function(t){return m(t,d)},r.createPickOrtho=function(t){return m(t,g)},r.createPickProject=function(t){return m(t,v)}},{\"gl-shader\":288,glslify:392}],284:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]r+e[4]n+e[8]i+e[12]a,t[1]=e[1]r+e[5]n+e[9]i+e[13]a,t[2]=e[2]r+e[6]n+e[10]i+e[14]a,t[3]=e[3]r+e[7]n+e[11]i+e[15]a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),m=new g(e,r,n,o,f,h,p,d,v,s,c,u);return m.update(t),m};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},v.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var m=[0,0],y=[0,0,0],x=[0,0,0],b=[0,0,0,1],_=[0,0,0,1],w=f.slice(),k=[0,0,0],M=[[0,0,0],[0,0,0]];function A(t){return t[0]=t[1]=t[2]=0,t}function T(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function S(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function E(t,e,r,n,i){var a,o=e.axesProject,l=e.gl,c=t.uniforms,u=r.model\|\|f,h=r.view\|\|f,d=r.projection\|\|f,g=e.axesBounds,v=function(t){for(var e=M,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);a=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,t.bind(),c.view=h,c.projection=d,c.screenSize=m,c.highlightId=e.highlightId,c.highlightScale=e.highlightScale,c.clipBounds=v,c.pickGroup=e.pickId/255,c.pixelRatio=e.pixelRatio;for(var E=0;E<3;++E)if(o[E]&&e.projectOpacity[E]<1===n){c.scale=e.projectScale[E],c.opacity=e.projectOpacity[E];for(var C=w,L=0;L<16;++L)C[L]=0;for(L=0;L<4;++L)C[5L]=1;C[5E]=0,a[E]<0?C[12+E]=g[0][E]:C[12+E]=g[1][E],s(C,u,C),c.model=C;var z=(E+1)%3,O=(E+2)%3,I=A(y),P=A(x);I[z]=1,P[O]=1;var D=p(0,0,0,T(b,I)),R=p(0,0,0,T(_,P));if(Math.abs(D[1])>Math.abs(R[1])){var B=D;D=R,R=B,B=I,I=P,P=B;var F=z;z=O,O=F}D[0]<0&&(I[z]=-1),R[1]>0&&(P[O]=-1);var N=0,j=0;for(L=0;L<4;++L)N+=Math.pow(u[4z+L],2),j+=Math.pow(u[4O+L],2);I[z]/=Math.sqrt(N),P[O]/=Math.sqrt(j),c.axes[0]=I,c.axes[1]=P,c.fragClipBounds[0]=S(k,v[0],E,-1e8),c.fragClipBounds[1]=S(k,v[1],E,1e8),e.vao.draw(l.TRIANGLES,e.vertexCount),e.lineWidth>0&&(l.lineWidth(e.lineWidth),e.vao.draw(l.LINES,e.lineVertexCount,e.vertexCount))}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function L(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1\|\|a){t.bind();var s=t.uniforms;s.model=n.model\|\|f,s.view=n.view\|\|f,s.projection=n.projection\|\|f,m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,s.screenSize=m,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=C,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}E(e,r,n,i),r.vao.unbind()}function z(t,e,r){var i;i=Array.isArray(t)?e<t.length?t[e]:void 0:t,i=u(i);var a=!0;n(i)&&(i=\"\\u25bc\",a=!1);var o=c(i,r);return{mesh:o[0],lines:o[1],bounds:o[2],visible:a}}v.draw=function(t){L(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},v.drawTransparent=function(t){L(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},v.drawPick=function(t){L(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount\|\|e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t\|\|{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(\"projectOpacity\"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}\"opacity\"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position,i=t.font\|\|\"normal\",a=t.alignment\|\|[0,0],s=[1/0,1/0,1/0],l=[-1/0,-1/0,-1/0],c=t.glyph,u=t.color,f=t.size,h=t.angle,p=t.lineColor,d=-1,g=0,v=0,m=0;if(n.length){m=n.length;t:for(var y=0;y<m;++y){for(var x=n[y],b=0;b<3;++b)if(isNaN(x[b])\|\|!isFinite(x[b]))continue t;var _=(R=z(c,y,i)).mesh,w=R.lines,k=R.bounds;g+=3_.cells.length,v+=2w.edges.length}}var M=g+v,A=o.mallocFloat(3M),T=o.mallocFloat(4M),S=o.mallocFloat(2M),E=o.mallocUint32(M);if(M>0){var C=0,L=g,O=[0,0,0,1],I=[0,0,0,1],P=Array.isArray(u)&&Array.isArray(u[0]),D=Array.isArray(p)&&Array.isArray(p[0]);t:for(y=0;y<m;++y){d+=1;for(x=n[y],b=0;b<3;++b){if(isNaN(x[b])\|\|!isFinite(x[b]))continue t;l[b]=Math.max(l[b],x[b]),s[b]=Math.min(s[b],x[b])}_=(R=z(c,y,i)).mesh,w=R.lines,k=R.bounds;var R,B=R.visible;if(B)if(Array.isArray(u)){if(3===(F=P?y<u.length?u[y]:[0,0,0,0]:u).length){for(b=0;b<3;++b)O[b]=F[b];O[3]=1}else if(4===F.length)for(b=0;b<4;++b)O[b]=F[b]}else O[0]=O[1]=O[2]=0,O[3]=1;else O=[1,1,1,0];if(B)if(Array.isArray(p)){var F;if(3===(F=D?y<p.length?p[y]:[0,0,0,0]:p).length){for(b=0;b<3;++b)I[b]=F[b];I[b]=1}else if(4===F.length)for(b=0;b<4;++b)I[b]=F[b]}else I[0]=I[1]=I[2]=0,I[3]=1;else I=[1,1,1,0];var N=.5;B?Array.isArray(f)?N=y<f.length?+f[y]:12:f?N=+f:this.useOrtho&&(N=12):N=0;var j=0;Array.isArray(h)?j=y<h.length?+h[y]:0:h&&(j=+h);var V=Math.cos(j),U=Math.sin(j);for(x=n[y],b=0;b<3;++b)l[b]=Math.max(l[b],x[b]),s[b]=Math.min(s[b],x[b]);var 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r=this.gl,a=this.shaders[t===r.FRAGMENT_SHADER\|0],l=a[e];if(l&&r.isShader(l.shader))l.count+=1;else{var c=function(t,e,r){var a=t.createShader(e);if(t.shaderSource(a,r),t.compileShader(a),!t.getShaderParameter(a,t.COMPILE_STATUS)){var o=t.getShaderInfoLog(a);try{var s=i(o,r,e)}catch(t){throw console.warn(\"Failed to format compiler error: \"+t),new n(o,\"Error compiling shader:\\n\"+o)}throw new n(o,s.short,s.long)}return a}(r,t,e);l=a[e]=new s(o++,e,t,c,[],1,this)}return l},c.getProgram=function(t,e,r,i){var a=[t.id,e.id,r.join(\":\"),i.join(\":\")].join(\"@\"),o=this.programs[a];return o&&this.gl.isProgram(o)\|\|(this.programs[a]=o=function(t,e,r,i,a){var o=t.createProgram();t.attachShader(o,e),t.attachShader(o,r);for(var s=0;s<i.length;++s)t.bindAttribLocation(o,a[s],i[s]);if(t.linkProgram(o),!t.getProgramParameter(o,t.LINK_STATUS)){var l=t.getProgramInfoLog(o);throw new n(l,\"Error linking program: \"+l)}return 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l=s[0]+(n[0]-o[0])/(o[2]-o[0])(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],296:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vertexPosition = mix(coordinates[0],\\n mix(coordinates[2], coordinates[1], 0.5 (position + 1.0)), abs(position));\\n\\n vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n vec2 delta = weight * clipOffset 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s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model\|\|o,s=t.view\|\|o,l=t.projection\|\|o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":296,\"gl-buffer\":230,\"gl-vao\":310}],298:[function(t,e,r){arguments[4][232][0].apply(r,arguments)},{barycentric:61,dup:232,\"polytope-closest-point/lib/closest_point_2d.js\":464}],299:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat inverse(float m) {\\n return 1.0 / m;\\n}\\n\\nmat2 inverse(mat2 m) {\\n return mat2(m[1][1],-m[0][1],\\n -m[1][0], m[0][0]) / (m[0][0]m[1][1] - m[0][1]m[1][0]);\\n}\\n\\nmat3 inverse(mat3 m) {\\n float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\\n float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\\n float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\\n\\n float b01 = a22 a11 - a12 * a21;\\n float b11 = -a22 * a10 + a12 * a20;\\n float b21 = a21 * a10 - a11 * a20;\\n\\n float det = a00 * b01 + a01 * b11 + a02 * b21;\\n\\n return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\\n b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\\n b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\\n}\\n\\nmat4 inverse(mat4 m) {\\n float\\n a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\\n a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\\n a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\\n a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\\n\\n b00 = a00 * a11 - a01 * a10,\\n b01 = a00 * a12 - a02 * a10,\\n b02 = a00 * a13 - a03 * a10,\\n b03 = a01 * a12 - a02 * a11,\\n b04 = a01 * a13 - a03 * a11,\\n b05 = a02 * a13 - a03 * a12,\\n b06 = a20 * a31 - a21 * a30,\\n b07 = a20 * a32 - a22 * a30,\\n b08 = a20 * a33 - a23 * a30,\\n b09 = a21 * a32 - a22 * a31,\\n b10 = a21 * a33 - a23 * a31,\\n b11 = a22 * a33 - a23 * a32,\\n\\n det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\\n\\n return mat4(\\n a11 * b11 - a12 * b10 + a13 * b09,\\n a02 * b10 - a01 * b11 - a03 * b09,\\n a31 * b05 - a32 * b04 + a33 * b03,\\n a22 * b04 - a21 * b05 - a23 * b03,\\n a12 * b08 - a10 * b11 - a13 * b07,\\n a00 * b11 - a02 * b08 + a03 * b07,\\n a32 * b02 - a30 * b05 - a33 * b01,\\n a20 * b05 - a22 * b02 + a23 * b01,\\n a10 * b10 - a11 * b08 + a13 * b06,\\n a01 * b08 - a00 * b10 - a03 * b06,\\n a30 * b04 - a31 * b02 + a33 * b00,\\n a21 * b02 - a20 * b04 - a23 * b00,\\n a11 * b07 - a10 * b09 - a12 * b06,\\n a00 * b09 - a01 * b07 + a02 * b06,\\n a31 * b01 - a30 * b03 - a32 * b00,\\n a20 * b03 - a21 * b01 + a22 * b00) / det;\\n}\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have \|\|a\|\| > 0 U \|\|b\|\| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a-b + ba + c0 = -ba + ba + 0 = 0\\n if (v.xv.x > v.zv.z \|\| v.yv.y > v.zv.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\nuniform float tubeScale;\\n\\nuniform mat4 model\\n , view\\n , projection;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data\\n , f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n normal = normalize(normal * inverse(mat3(model)));\\n\\n gl_Position = projection * view * tubePosition;\\n f_color = color;\\n f_normal = normal;\\n f_data = tubePosition.xyz;\\n f_position = position.xyz;\\n f_eyeDirection = eyePosition - tubePosition.xyz;\\n f_lightDirection = lightPosition - tubePosition.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data\\n , f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(!gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have \|\|a\|\| > 0 U \|\|b\|\| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a-b + ba + c0 = -ba + ba + 0 = 0\\n if (v.xv.x > v.zv.z \|\| v.yv.y > v.zv.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n gl_Position = projection * view * tubePosition;\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:392}],300:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./shaders\"),g=(t(\"./closest-point\"),d.meshShader),v=d.pickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,v,y,x,b,_,w,k,M,A,T,S,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=c,this.triangleColors=f,this.triangleNormals=p,this.triangleUVs=h,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=x,this.edgeIds=v,this.edgeVAO=b,this.edgeCount=0,this.pointPositions=_,this.pointColors=k,this.pointUVs=M,this.pointSizes=A,this.pointIds=w,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=S,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!1,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.tubeScale=1,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1]}var x=y.prototype;function b(t){var e=n(t,v.vertex,v.fragment,null,v.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(t){this.pickId=t},x.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=vy[b]+mx[b]}this.contourCount=s/3\|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},x.update=function(t){t=t\|\|{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4n+a]=i[a];r[4n+3]=255i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale);var a=[],l=[],c=[],h=[],p=[],d=[],g=[],v=[],m=[],y=[],x=[],b=[],_=[],w=[],k=[];this.cells=r,this.positions=n,this.vectors=i;var M=t.vertexNormals,A=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,S)),A\|\|M\|\|(M=s.vertexNormals(r,n,T));var E=t.vertexColors,C=t.cellColors,L=t.meshColor\|\|[1,1,1,1],z=t.vertexUVs,O=t.vertexIntensity,I=t.cellUVs,P=t.cellIntensity,D=1/0,R=-1/0;if(!z&&!I)if(O)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],R=+t.vertexIntensityBounds[1];else for(var B=0;B<O.length;++B){var F=O[B];D=Math.min(D,F),R=Math.max(R,F)}else if(P)for(B=0;B<P.length;++B){F=P[B];D=Math.min(D,F),R=Math.max(R,F)}else for(B=0;B<n.length;++B){F=n[B][2];D=Math.min(D,F),R=Math.max(R,F)}this.intensity=O\|\|(P?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,P):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var N=t.pointSizes,j=t.pointSize\|\|1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(B=0;B<n.length;++B)for(var V=n[B],U=0;U<3;++U)!isNaN(V[U])&&isFinite(V[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],V[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],V[U]));var q=0,H=0,G=0;t:for(B=0;B<r.length;++B){var W=r[B];switch(W.length){case 1:for(V=n[X=W[0]],U=0;U<3;++U)if(isNaN(V[U])\|\|!isFinite(V[U]))continue t;x.push(V[0],V[1],V[2],V[3]),3===(Z=E?E[X]:C?C[B]:L).length?b.push(Z[0],Z[1],Z[2],1):b.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],_.push($[0],$[1]),N?w.push(N[X]):w.push(j),k.push(B),G+=1;break;case 2:for(U=0;U<2;++U){V=n[X=W[U]];for(var Y=0;Y<3;++Y)if(isNaN(V[Y])\|\|!isFinite(V[Y]))continue t}for(U=0;U<2;++U){V=n[X=W[U]];g.push(V[0],V[1],V[2]),3===(Z=E?E[X]:C?C[B]:L).length?v.push(Z[0],Z[1],Z[2],1):v.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],m.push($[0],$[1]),y.push(B)}H+=1;break;case 3:for(U=0;U<3;++U)for(V=n[X=W[U]],Y=0;Y<3;++Y)if(isNaN(V[Y])\|\|!isFinite(V[Y]))continue t;for(U=0;U<3;++U){var X;V=n[X=W[U]];a.push(V[0],V[1],V[2],V[3]);var Z,$,J,K=i[X];l.push(K[0],K[1],K[2],K[3]),3===(Z=E?E[X]:C?C[B]:L).length?c.push(Z[0],Z[1],Z[2],1):c.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],p.push($[0],$[1]),J=M?M[X]:A[B],h.push(J[0],J[1],J[2]),d.push(B)}q+=1}}this.pointCount=G,this.edgeCount=H,this.triangleCount=q,this.pointPositions.update(x),this.pointColors.update(b),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(g),this.edgeColors.update(v),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(c),this.triangleUVs.update(p),this.triangleNormals.update(h),this.triangleIds.update(new Uint32Array(d))}},x.drawTransparent=x.draw=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|m,n=t.view\|\|m,i=t.projection\|\|m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]u[4o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4d+o]this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},x.drawPick=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|m,n=t.view\|\|m,i=t.projection\|\|m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},x.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256t.value[1]+65536t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:e,position:n,intensity:this.intensity[r[1]],velocity:this.vectors[r[1]].slice(0,3),divergence:this.vectors[r[1]][3],dataCoordinate:n}},x.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader\|\|function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=b(t),l=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var c=i(t),f=i(t),h=i(t),p=i(t),d=i(t),v=i(t),m=a(t,[{buffer:c,type:t.FLOAT,size:4},{buffer:v,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:h,type:t.FLOAT,size:4},{buffer:p,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:3},{buffer:f,type:t.FLOAT,size:4}]),x=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:x,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),A=i(t),T=i(t),S=i(t),E=i(t),C=i(t),L=a(t,[{buffer:A,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2},{buffer:E,type:t.FLOAT,size:1}]),z=i(t),O=new y(t,l,r,null,null,s,null,null,c,f,v,h,p,d,m,x,k,_,w,M,A,C,T,S,E,L,z,a(t,[{buffer:z,type:t.FLOAT,size:3}]));return O.update(e),O}},{\"./closest-point\":298,\"./shaders\":299,colormap:114,\"gl-buffer\":230,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"gl-shader\":288,\"gl-texture2d\":305,\"gl-vao\":310,ndarray:433,normals:436,\"simplicial-complex-contour\":494,\"typedarray-pool\":522}],301:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++){var u=n.length(l[c]);u>o&&(o=u)}var f=t.map(function(t){return function(t,e,r,a){var o,s,l,c=t.points,u=t.velocities,f=t.divergences;n.set(n.create(),0,1,0),n.create(),n.create();n.create();for(var h=[],p=[],d=[],g=[],v=[],m=[],y=0,x=0,b=i.create(),_=i.create(),w=0;w<c.length;w++){o=c[w],s=u[w],l=f[w],0===e&&(l=.05r),x=n.length(s)/a,b=i.create(),n.copy(b,s),b[3]=l;for(var k=0;k<8;k++)v[k]=[o[0],o[1],o[2],k];if(g.length>0)for(k=0;k<8;k++){var M=(k+1)%8;h.push(g[k],v[k],v[M],v[M],g[M],g[k]),d.push(_,b,b,b,_,_),m.push(y,x,x,x,y,y),p.push([h.length-6,h.length-5,h.length-4],[h.length-3,h.length-2,h.length-1])}var A=g;g=v,v=A,A=_,_=b,b=A,A=y,y=x,x=A}return{positions:h,cells:p,vectors:d,vertexIntensity:m}}(t,r,a,o)}),h=[],p=[],d=[],g=[];for(s=0;s<f.length;s++){var v=f[s],m=h.length;h=h.concat(v.positions),d=d.concat(v.vectors),g=g.concat(v.vertexIntensity);for(c=0;c<v.cells.length;c++){var y=v.cells[c],x=[];p.push(x);for(var b=0;b<y.length;b++)x.push(y[b]+m)}}return{positions:h,cells:p,vectors:d,vertexIntensity:g,colormap:e}},o=function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=this.getVelocity(r);n.subtract(a,a,e),n.scale(a,a,1e4),n.add(r,t,[0,i,0]);var o=this.getVelocity(r);n.subtract(o,o,e),n.scale(o,o,1e4),n.add(r,t,[0,0,i]);var s=this.getVelocity(r);return n.subtract(s,s,e),n.scale(s,s,1e4),n.add(r,a,o),n.add(r,r,s),r},s=function(t){return h(t,this.vectors,this.meshgrid,this.clampBorders)},l=function(t,e){for(var r=0;r<t.length;r++){var n=t[r];if(n===e)return r;if(n>e)return r-1}return r},c=n.create(),u=n.create(),f=function(t,e,r){return t<e?e:t>r?r:t},h=function(t,e,r,i){var a=t[0],o=t[1],s=t[2],h=r[0].length,p=r[1].length,d=r[2].length,g=l(r[0],a),v=l(r[1],o),m=l(r[2],s),y=g+1,x=v+1,b=m+1;if(r[0][g]===a&&(y=g),r[1][v]===o&&(x=v),r[2][m]===s&&(b=m),i&&(g=f(g,0,h-1),y=f(y,0,h-1),v=f(v,0,p-1),x=f(x,0,p-1),m=f(m,0,d-1),b=f(b,0,d-1)),g<0\|\|v<0\|\|m<0\|\|y>=h\|\|x>=p\|\|b>=d)return n.create();var _=(a-r[0][g])/(r[0][y]-r[0][g]),w=(o-r[1][v])/(r[1][x]-r[1][v]),k=(s-r[2][m])/(r[2][b]-r[2][m]);(_<0\|\|_>1\|\|isNaN(_))&&(_=0),(w<0\|\|w>1\|\|isNaN(w))&&(w=0),(k<0\|\|k>1\|\|isNaN(k))&&(k=0);var M=mhp,A=bhp,T=vh,S=xh,E=g,C=y,L=e[T+M+E],z=e[T+M+C],O=e[S+M+E],I=e[S+M+C],P=e[T+A+E],D=e[T+A+C],R=e[S+A+E],B=e[S+A+C],F=n.create();return n.lerp(F,L,z,_),n.lerp(c,O,I,_),n.lerp(F,F,c,w),n.lerp(c,P,D,_),n.lerp(u,R,B,_),n.lerp(c,c,u,w),n.lerp(F,F,c,k),F},p=function(t){var e=1/0;t.sort(function(t,e){return t-e});for(var r=1;r<t.length;r++){var n=Math.abs(t[r]-t[r-1]);n<e&&(e=n)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength\|\|1e3,l=t.tubeSize\|\|1,c=t.absoluteTubeSize;t.getDivergence\|\|(t.getDivergence=o),t.getVelocity\|\|(t.getVelocity=s),void 0===t.clampBorders&&(t.clampBorders=!0);var u=[],f=e[0][0],h=e[0][1],d=e[0][2],g=e[1][0],v=e[1][1],m=e[1][2],y=function(t,e){var r=e[0],n=e[1],i=e[2];return r>=f&&r<=g&&n>=h&&n<=v&&i>=d&&i<=m},x=10n.distance(e[0],e[1])/i,b=xx,_=1,w=0;n.create();r.length>=2&&(_=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=0;s<t.length;s++){var l=t[s],c=l[0],u=l[1],f=l[2];i[c]\|\|(e.push(c),i[c]=!0),a[u]\|\|(r.push(u),a[u]=!0),o[f]\|\|(n.push(f),o[f]=!0)}var h=p(e),d=p(r),g=p(n),v=Math.min(h,d,g);return isFinite(v)?v:1}(r));for(var k=0;k<r.length;k++){var M=n.create();n.copy(M,r[k]);var A=[M],T=[],S=t.getVelocity(M),E=M;T.push(S);var C=[],L=t.getDivergence(M,S);(P=n.length(L))>w&&!isNaN(P)&&isFinite(P)&&(w=P),C.push(P),u.push({points:A,velocities:T,divergences:C});for(var z=0;z<100i&&A.length<i&&y(0,M);){z++;var O=n.clone(S),I=n.squaredLength(O);if(0===I)break;if(I>b&&n.scale(O,O,x/Math.sqrt(I)),n.add(O,O,M),S=t.getVelocity(O),n.squaredDistance(E,O)-b>-1e-4b){A.push(O),E=O,T.push(S);L=t.getDivergence(O,S);(P=n.length(L))>w&&!isNaN(P)&&isFinite(P)&&(w=P),C.push(P)}M=O}}for(k=0;k<C.length;k++){var P=C[k];!isNaN(P)&&isFinite(P)\|\|(C[k]=w)}var D=a(u,t.colormap,w,_);return c?D.tubeScale=c:(0===w&&(w=1),D.tubeScale=.5l_/w),D},e.exports.createTubeMesh=t(\"./lib/tubemesh\")},{\"./lib/tubemesh\":300,\"gl-vec3\":329,\"gl-vec4\":365}],302:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n worldCoordinate = vec3(uv.zw, f.x);\\n vec4 worldPosition = model vec4(worldCoordinate, 1.0);\\n vec4 clipPosition = projection * view * worldPosition;\\n gl_Position = clipPosition;\\n kill = f.y;\\n value = f.z;\\n planeCoordinate = uv.xy;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * worldPosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n lightDirection = lightPosition - cameraCoordinate.xyz;\\n eyeDirection = eyePosition - cameraCoordinate.xyz;\\n surfaceNormal = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness) {\\n return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n if ((kill > 0.0) \|\|\\n (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n vec3 N = normalize(surfaceNormal);\\n vec3 V = normalize(eyeDirection);\\n vec3 L = normalize(lightDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n //decide how to interpolate color \\u2014 in vertex or in fragment\\n vec4 surfaceColor = step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) + step(.5, vertexColor) * vColor;\\n\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * view * worldPosition;\\n clipPosition.z = clipPosition.z + zOffset;\\n\\n gl_Position = clipPosition;\\n value = f;\\n kill = -1.0;\\n worldCoordinate = dataCoordinate;\\n planeCoordinate = uv.zw;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if ((kill > 0.0) \|\|\\n (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":288,glslify:392}],303:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:w,offset:0},{buffer:c,size:3,stride:w,offset:16},{buffer:c,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var v=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,f,h,p,d),m={levels:[[],[],[]]};for(var k in t)m[k]=t[k];return m.colormap=m.colormap\|\|\"jet\",v.update(m),v};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),v=t(\"ndarray-gradient\"),m=t(\"./lib/shaders\"),y=m.createShader,x=m.createContourShader,b=m.createPickShader,_=m.createPickContourShader,w=40,k=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],M=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function T(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,c,u,h,p,d,g){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new T([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var C=E.prototype;C.isTransparent=function(){return this.opacity<1},C.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0\|\|this._dynamicCounts[t]>0)return!0;return!1},C.pickSlots=1,C.setPickBase=function(t){this.pickId=t};var L=[0,0,0],z={showSurface:!1,showContour:!1,projections:[k.slice(),k.slice(),k.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis\|\|L,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o\|\|e.surfaceProject[r],n=0;n<3;++n)s=s\|\|e.contourProject[r][n];for(r=0;r<3;++r){var l=z.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5n]=1;l[5r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=z.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return z.showSurface=o,z.showContour=s,z}var I={model:k,view:k,projection:k,inverseModel:k.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},P=k.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t\|\|{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model\|\|k,n.view=t.view\|\|k,n.projection=t.projection\|\|k,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]\|\|this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]\|\|this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=P;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]s[4i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4o+i]this.lightPosition[o];n.lightPosition[i]=c/l}var u=O(n,this);if(u.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]\|\|(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]\|\|(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g])}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}C.draw=function(t){return R.call(this,t,!1)},C.drawTransparent=function(t){return R.call(this,t,!0)};var B={model:k,view:k,projection:k,inverseModel:k,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function F(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function N(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function j(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function V(t){if(Array.isArray(t)){if(Array.isArray(t))return[j(t[0]),j(t[1]),j(t[2])];var e=j(t);return[e.slice(),e.slice(),e.slice()]}}C.drawPick=function(t){t=t\|\|{};var e=this.gl;e.disable(e.CULL_FACE);var r=B;r.model=t.model\|\|k,r.view=t.view\|\|k,r.projection=t.projection\|\|k,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=D;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=O(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},C.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0](t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1](t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,v=f(h?l:1-l),m=0;m<3;++m)c[m]+=this._field[m].get(p,d)v;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],m=0;m<3;++m)r.dataCoordinate[m]=this._field[m].get(r.index[0],r.index[1]);return r},C.update=function(t){t=t\|\|{},this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=N(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=N(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=N(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=V(t.contourColor)),\"contourProject\"in t&&(this.contourProject=N(t.contourProject,function(t){return N(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=V(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=N(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=N(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field\|\|t.coords&&t.coords[2]\|\|null,r=!1;if(e\|\|(e=this._field[2].shape[0]\|\|this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t\|\|\"coords\"in t){var i=(e.shape[0]+2)(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),F(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var p=t.coords;if(!Array.isArray(p)\|\|3!==p.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var d=p[o];for(b=0;b<2;++b)if(d.shape[b]!==a[b])throw new Error(\"gl-surface: coords have incorrect shape\");F(this._field[o],d)}}else if(t.ticks){var g=t.ticks;if(!Array.isArray(g)\|\|2!==g.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var m=g[o];if((Array.isArray(m)\|\|m.length)&&(m=f(m)),m.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var y=f(m.data,a);y.stride[o]=m.stride[0],y.stride[1^o]=0,F(this._field[o],y)}}else{for(o=0;o<2;++o){var x=[0,0];x[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],x,0)}this._field[0].set(0,0,0);for(var b=0;b<a[0];++b)this._field[0].set(b+1,0,b);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),b=0;b<a[1];++b)this._field[1].set(0,b+1,b);this._field[1].set(0,a[1]+1,a[1]-1)}var _=this._field,w=f(s.mallocFloat(3_[2].size2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)v(w.pick(o),_[o],\"mirror\");var k=f(s.mallocFloat(3_[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(b=0;b<a[1]+2;++b){var A=w.get(0,o,b,0),T=w.get(0,o,b,1),E=w.get(1,o,b,0),C=w.get(1,o,b,1),L=w.get(2,o,b,0),z=w.get(2,o,b,1),O=Ez-CL,I=LT-zA,P=AC-TE,D=Math.sqrt(OO+II+PP);D<1e-8?(D=Math.max(Math.abs(O),Math.abs(I),Math.abs(P)))<1e-8?(P=1,I=O=0,D=1):D=1/D:D=1/Math.sqrt(D),k.set(o,b,0,OD),k.set(o,b,1,ID),k.set(o,b,2,PD)}s.free(w.data);var R=[1/0,1/0,1/0],B=[-1/0,-1/0,-1/0],j=1/0,U=-1/0,q=(a[0]-1)(a[1]-1)6,H=s.mallocFloat(n.nextPow2(10q)),G=0,W=0;for(o=0;o<a[0]-1;++o)t:for(b=0;b<a[1]-1;++b){for(var Y=0;Y<2;++Y)for(var X=0;X<2;++X)for(var Z=0;Z<3;++Z){var $=this._field[Z].get(1+o+Y,1+b+X);if(isNaN($)\|\|!isFinite($))continue t}for(Z=0;Z<6;++Z){var J=o+M[Z][0],K=b+M[Z][1],Q=this._field[0].get(J+1,K+1),tt=this._field[1].get(J+1,K+1),et=$=this._field[2].get(J+1,K+1);O=k.get(J+1,K+1,0),I=k.get(J+1,K+1,1),P=k.get(J+1,K+1,2),t.intensity&&(et=t.intensity.get(J,K)),H[G++]=J,H[G++]=K,H[G++]=Q,H[G++]=tt,H[G++]=$,H[G++]=0,H[G++]=et,H[G++]=O,H[G++]=I,H[G++]=P,R[0]=Math.min(R[0],Q),R[1]=Math.min(R[1],tt),R[2]=Math.min(R[2],$),j=Math.min(j,et),B[0]=Math.max(B[0],Q),B[1]=Math.max(B[1],tt),B[2]=Math.max(B[2],$),U=Math.max(U,et),W+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],U=+t.intensityBounds[1]),o=6;o<G;o+=10)H[o]=(H[o]-j)/(U-j);this._vertexCount=W,this._coordinateBuffer.update(H.subarray(0,G)),s.freeFloat(H),s.free(k.data),this.bounds=[R,B],this.intensity=t.intensity\|\|this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===U\|\|(r=!0),this.intensityBounds=[j,U]}if(\"levels\"in t){var rt=t.levels;for(rt=Array.isArray(rt[0])?rt.slice():[[],[],rt],o=0;o<3;++o)rt[o]=rt[o].slice(),rt.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if(rt[o].length!==this.contourLevels[o].length){r=!0;break}for(b=0;b<rt[o].length;++b)if(rt[o][b]!==this.contourLevels[o][b]){r=!0;break t}}this.contourLevels=rt}if(r){_=this._field,a=this.shape;for(var nt=[],it=0;it<3;++it){rt=this.contourLevels[it];var at=[],ot=[],st=[0,0,0];for(o=0;o<rt.length;++o){var lt=h(this._field[it],rt[o]);at.push(nt.length/5\|0),W=0;t:for(b=0;b<lt.cells.length;++b){var ct=lt.cells[b];for(Z=0;Z<2;++Z){var ut=lt.positions[ct[Z]],ft=ut[0],ht=0\|Math.floor(ft),pt=ft-ht,dt=ut[1],gt=0\|Math.floor(dt),vt=dt-gt,mt=!1;e:for(var yt=0;yt<3;++yt){st[yt]=0;var xt=(it+yt+1)%3;for(Y=0;Y<2;++Y){var bt=Y?pt:1-pt;for(J=0\|Math.min(Math.max(ht+Y,0),a[0]),X=0;X<2;++X){var _t=X?vt:1-vt;if(K=0\|Math.min(Math.max(gt+X,0),a[1]),$=yt<2?this._field[xt].get(J,K):(this.intensity.get(J,K)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite($)\|\|isNaN($)){mt=!0;break e}var wt=bt_t;st[yt]+=wt$}}}if(mt){if(Z>0){for(var kt=0;kt<5;++kt)nt.pop();W-=1}continue t}nt.push(st[0],st[1],ut[0],ut[1],st[2]),W+=1}}ot.push(W)}this._contourOffsets[it]=at,this._contourCounts[it]=ot}var Mt=s.mallocFloat(nt.length);for(o=0;o<nt.length;++o)Mt[o]=nt[o];this._contourBuffer.update(Mt),s.freeFloat(Mt)}t.colormap&&this._colorMap.setPixels(function(t){var e=u([l({colormap:t,nshades:S,format:\"rgba\"}).map(function(t){return[t[0],t[1],t[2],255t[3]]})]);return c.divseq(e,255),e}(t.colormap))},C.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},C.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]\|\|this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]\|\|this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12i[0]i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=(this.intensity,h(u,r[o])),g=d.cells,v=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var m=g[e],y=0;y<2;++y){var x=v[m[y]],b=+x[0],_=0\|b,w=0\|Math.min(_+1,i[0]),k=b-_,M=1-k,A=+x[1],T=0\|A,S=0\|Math.min(T+1,i[1]),E=A-T,C=1-E,L=MC,z=ME,O=kC,I=kE,P=Lf.get(_,T)+zf.get(_,S)+Of.get(w,T)+If.get(w,S),D=Lp.get(_,T)+zp.get(_,S)+Op.get(w,T)+Ip.get(w,S);if(isNaN(P)\|\|isNaN(D)){y&&(n-=1);break}a[2n+0]=P,a[2n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2n)),s.freeFloat(a)}}},{\"./lib/shaders\":302,\"binary-search-bounds\":79,\"bit-twiddle\":80,colormap:114,\"gl-buffer\":230,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"gl-texture2d\":305,\"gl-vao\":310,ndarray:433,\"ndarray-gradient\":424,\"ndarray-ops\":427,\"ndarray-pack\":428,\"surface-nets\":508,\"typedarray-pool\":522}],304:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),g=t(\"detect-kerning\"),v=t(\"object-assign\"),m=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var k=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl\|\|a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader\|\|(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};k.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\t\"+(k.normalViewport?\"\":\"vec2 positionOffset = vec2(positionOffset.x,- positionOffset.y);\")+\"\\n\\n\\t\\t\\t\\tvec2 offset = floor(em (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ positionOffset))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\t\"+(k.normalViewport?\"position.y = 1. - position.y;\":\"\")+\"\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth \|\|\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a = maskY;\\n\\t\\t\\t\\tcolor.a = opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},k.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map(function(t){return parseFloat(t)}):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),k.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale\|\|(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate\|\|(this.translate=[0,0]),this.font.length\|\|t.font\|\|(t.font=k.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach(function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(k.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:k.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString\|\|(a=!0,e.font[r]=k.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:m(c,{origin:\"top\",fontSize:k.baseFontSize,fontStyle:u.join(\" \")})},k.fonts[i]=e.font[r]}}),(a\|\|o)&&this.font.forEach(function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2Math.ceil(e.fontSize[i]o.bottom.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)}),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text\|\|a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)e.textOffsets[b]=e.textOffsets[b-1]+t.text[b-1].length,e.count+=t.text[b].length,e.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach(function(t,n){k.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=k.atlasContext.measureText(o).width/k.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);v(t.kerning,g(t.family,{pairs:s}))}}})}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,M=u.mallocFloat(2this.count),A=0,T=0;A<this.counts.length;A++){var S=e.counts[A];if(w)for(var E=0;E<S;E++)M[T++]=t.position[2A],M[T++]=t.position[2A+1];else for(var C=0;C<S;C++)M[T++]=t.position[A][0],M[T++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:M}):this.position=this.regl.buffer({type:\"float\",data:M}),u.freeFloat(M)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text\|\|a){var L=u.mallocUint8(this.count),z=u.mallocFloat(2this.count);this.textWidth=[];for(var O=0,I=0;O<this.counts.length;O++){for(var P=e.counts[O],D=e.font[O]\|\|e.font[0],R=e.fontAtlas[O]\|\|e.fontAtlas[0],B=0;B<P;B++){var F=e.text.charAt(I),N=e.text.charAt(I-1);if(L[I]=R.ids[F],z[2I]=D.width[F],B){var j=z[2I-2],V=z[2I],U=z[2I-1]+.5j+.5V;if(e.kerning){var q=D.kerning[N+F];q&&(U+=.001q)}z[2I+1]=U}else z[2I+1]=.5z[2I];I++}e.textWidth.push(z.length?.5z[2I-2]+z[2I-1]:0)}t.align\|\|(t.align=this.align),this.charBuffer({data:L,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:z,type:\"float\",usage:\"stream\"}),u.freeUint8(L),u.freeFloat(z),r.length&&this.font.forEach(function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(k.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(oi),u=x(si);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:k.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})})}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map(function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5-t}return 0})),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)\|\|(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map(function(t,r){var n=(e.font[r]\|\|e.font[0]).metrics,i=0;return i+=.5n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],k.normalViewport\|\|(i=-1),i})),null!=t.color)if(t.color\|\|(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=u.mallocUint8(G);for(var W=(t.color.subarray\|\|t.color.slice).bind(t.color),Y=0;Y<G;Y+=4)H.set(l(W(Y,Y+4),\"uint8\"),Y)}else{var X=t.color.length;H=u.mallocUint8(4X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]\|\|0,\"uint8\"),4Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position\|\|t.text\|\|t.color\|\|t.baseline\|\|t.align\|\|t.font\|\|t.offset\|\|t.opacity)if(this.color.length>4\|\|this.baselineOffset.length>1\|\|this.align&&this.align.length>1\|\|this.fontAtlas.length>1\|\|this.positionOffset.length>2){var $=Math.max(.5this.position.length\|\|0,.25this.color.length\|\|0,this.baselineOffset.length\|\|0,this.alignOffset.length\|\|0,this.font.length\|\|0,this.opacity.length\|\|0,.5this.positionOffset.length\|\|0);this.batch=Array($);for(var J=0;J<this.batch.length;J++)e.batch[J]={count:e.counts.length>1?e.counts[J]:e.counts[0],offset:e.textOffsets.length>1?e.textOffsets[J]:e.textOffsets[0],color:e.color?e.color.length<=4?e.color:e.color.subarray(4J,4J+4):[0,0,0,255],opacity:Array.isArray(e.opacity)?e.opacity[J]:e.opacity,baseline:null!=e.baselineOffset[J]?e.baselineOffset[J]:e.baselineOffset[0],align:e.align?null!=e.alignOffset[J]?e.alignOffset[J]:e.alignOffset[0]:0,atlas:e.fontAtlas[J]\|\|e.fontAtlas[0],positionOffset:e.positionOffset.length>2?e.positionOffset.subarray(2J,2J+2):e.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color\|\|[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},k.prototype.destroy=function(){},k.prototype.kerning=!0,k.prototype.position={constant:new Float32Array(2)},k.prototype.translate=null,k.prototype.scale=null,k.prototype.font=null,k.prototype.text=\"\",k.prototype.positionOffset=[0,0],k.prototype.opacity=1,k.prototype.color=new Uint8Array([0,0,0,255]),k.prototype.alignOffset=[0,0],k.normalViewport=!1,k.maxAtlasSize=1024,k.atlasCanvas=document.createElement(\"canvas\"),k.atlasContext=k.atlasCanvas.getContext(\"2d\",{alpha:!1}),k.baseFontSize=64,k.fonts={},e.exports=k},{\"bit-twiddle\":80,\"color-normalize\":108,\"css-font\":127,\"detect-kerning\":151,\"es6-weak-map\":209,\"flatten-vertex-data\":216,\"font-atlas\":217,\"font-measure\":218,\"gl-util/context\":306,\"is-plain-obj\":405,\"object-assign\":437,\"parse-rect\":442,\"parse-unit\":444,\"pick-by-alias\":448,regl:478,\"to-px\":516,\"typedarray-pool\":522}],305:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o\|\|function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]\|\|t.RGBA,arguments[4]\|\|t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0\|arguments[1][0],0\|arguments[1][1],arguments[2]\|\|t.RGBA,arguments[3]\|\|t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=c(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0\|e.width,0\|e.height,arguments[2]\|\|t.RGBA,arguments[3]\|\|t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0\|\|o[0]>s\|\|o[1]<0\|\|o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=d(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var f,p,v=0;if(2===o.length)v=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])v=t.ALPHA;else if(2===o[2])v=t.LUMINANCE_ALPHA;else if(3===o[2])v=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");v=t.RGBA}}c!==t.FLOAT\|\|t.getExtension(\"OES_texture_float\")\|\|(c=t.UNSIGNED_BYTE,l=!1);var m=e.size;if(l)f=0===e.offset&&e.data.length===m?e.data:e.data.subarray(e.offset,e.offset+m);else{var y=[o[2],o[2]o[0],1];p=a.malloc(m,r);var x=n(p,o,y,0);\"float32\"!==r&&\"float64\"!==r\|\|c!==t.UNSIGNED_BYTE?i.assign(x,e):u(x,e),f=p.subarray(0,m)}var b=g(t);t.texImage2D(t.TEXTURE_2D,0,v,o[0],o[1],0,v,c,f),l\|\|a.free(p);return new h(t,b,o[0],o[1],v,c)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement\|\|\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement\|\|\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement\|\|\"undefined\"!=typeof ImageData&&t instanceof ImageData}var u=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0\|\|e>i\|\|r<0\|\|r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0\|\|e>a\|\|r<0\|\|r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")\|\|(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return 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t=0;t<this._attribs.length;++t)this._attribs[t].bind(this.gl)},a.prototype.unbind=function(){this._ext.bindVertexArrayOES(null)},a.prototype.dispose=function(){this._ext.deleteVertexArrayOES(this.handle)},a.prototype.update=function(t,e,r){if(this.bind(),n(this.gl,e,t),this.unbind(),this._attribs.length=0,t)for(var a=0;a<t.length;++a){var o=t[a];\"number\"==typeof o?this._attribs.push(new i(a,1,o)):Array.isArray(o)&&this._attribs.push(new i(a,o.length,o[0],o[1],o[2],o[3]))}this._useElements=!!e,this._elementsType=r\|\|this.gl.UNSIGNED_SHORT},a.prototype.draw=function(t,e,r){r=r\|\|0;var n=this.gl;this._useElements?n.drawElements(t,e,this._elementsType,r):n.drawArrays(t,r,e)},e.exports=function(t,e){return new a(t,e,e.createVertexArrayOES())}},{\"./do-bind.js\":307}],310:[function(t,e,r){\"use strict\";var n=t(\"./lib/vao-native.js\"),i=t(\"./lib/vao-emulated.js\");function 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l=i[cr+n.length],c=i[cr+n.length+1];this.keys=i.subarray(l,c),this.bboxes=i.subarray(c),this.insert=this._insertReadonly}else{this.d=e+2r;for(var u=0;u<this.dthis.d;u++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var f=r/et;this.min=-f,this.max=t+f}ur.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},ur.prototype._insertReadonly=function(){throw\"Cannot insert into a GridIndex created from an ArrayBuffer.\"},ur.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},ur.prototype.query=function(t,e,r,n){var i=this.min,a=this.max;if(t<=i&&e<=i&&a<=r&&a<=n)return Array.prototype.slice.call(this.keys);var o=[];return this._forEachCell(t,e,r,n,this._queryCell,o,{}),o},ur.prototype._queryCell=function(t,e,r,n,i,a,o){var s=this.cells[i];if(null!==s)for(var l=this.keys,c=this.bboxes,u=0;u<s.length;u++){var f=s[u];if(void 0===o[f]){var h=4f;t<=c[h+2]&&e<=c[h+3]&&r>=c[h+0]&&n>=c[h+1]?(o[f]=!0,a.push(l[f])):o[f]=!1}}},ur.prototype._forEachCell=function(t,e,r,n,i,a,o){for(var s=this._convertToCellCoord(t),l=this._convertToCellCoord(e),c=this._convertToCellCoord(r),u=this._convertToCellCoord(n),f=s;f<=c;f++)for(var h=l;h<=u;h++){var p=this.dh+f;if(i.call(this,t,e,r,n,p,a,o))return}},ur.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(tthis.scale)+this.padding))},ur.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=cr+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[cr+o]=a,i.set(s,a),a+=s.length}return i[cr+t.length]=a,i.set(this.keys,a),a+=this.keys.length,i[cr+t.length+1]=a,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var fr=self.ImageData,hr={};function pr(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),hr[t]={klass:e,omit:r.omit\|\|[],shallow:r.shallow\|\|[]}}for(var dr in pr(\"Object\",Object),lr.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),r},lr.deserialize=function(t){return new lr(t)},pr(\"Grid\",lr),pr(\"Color\",tt),pr(\"Error\",Error),pr(\"StylePropertyFunction\",Te),pr(\"StyleExpression\",be,{omit:[\"_evaluator\"]}),pr(\"ZoomDependentExpression\",Me),pr(\"ZoomConstantExpression\",ke),pr(\"CompoundExpression\",gt,{omit:[\"_evaluate\"]}),Rt)Rt[dr]._classRegistryKey\|\|pr(\"Expression_\"+dr,Rt[dr]);function gr(t,e){if(null==t\|\|\"boolean\"==typeof t\|\|\"number\"==typeof t\|\|\"string\"==typeof t\|\|t instanceof Boolean\|\|t instanceof Number\|\|t instanceof String\|\|t instanceof Date\|\|t instanceof RegExp)return t;if(t instanceof ArrayBuffer)return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof fr)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1){var o=a[i];n.push(gr(o,e))}return n}if(\"object\"==typeof t){var s=t.constructor,l=s._classRegistryKey;if(!l)throw new Error(\"can't serialize object of unregistered class\");var c={};if(s.serialize)c._serialized=s.serialize(t,e);else{for(var u in t)if(t.hasOwnProperty(u)&&!(hr[l].omit.indexOf(u)>=0)){var f=t[u];c[u]=hr[l].shallow.indexOf(u)>=0?f:gr(f,e)}t instanceof Error&&(c.message=t.message)}return{name:l,properties:c}}throw new Error(\"can't serialize object of type \"+typeof t)}function vr(t){if(null==t\|\|\"boolean\"==typeof t\|\|\"number\"==typeof t\|\|\"string\"==typeof t\|\|t instanceof Boolean\|\|t instanceof Number\|\|t instanceof String\|\|t instanceof Date\|\|t instanceof RegExp\|\|t instanceof ArrayBuffer\|\|ArrayBuffer.isView(t)\|\|t instanceof fr)return t;if(Array.isArray(t))return t.map(function(t){return vr(t)});if(\"object\"==typeof t){var e=t,r=e.name,n=e.properties;if(!r)throw new Error(\"can't deserialize object of anonymous class\");var i=hr[r].klass;if(!i)throw new Error(\"can't deserialize unregistered class \"+r);if(i.deserialize)return i.deserialize(n._serialized);for(var a=Object.create(i.prototype),o=0,s=Object.keys(n);o<s.length;o+=1){var l=s[o];a[l]=hr[r].shallow.indexOf(l)>=0?n[l]:vr(n[l])}return a}throw new Error(\"can't deserialize object of type \"+typeof t)}var mr=function(){this.first=!0};mr.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))};var yr={\"Latin-1 Supplement\":function(t){return t>=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic Supplement\":function(t){return t>=1872&&t<=1919},\"Arabic Extended-A\":function(t){return t>=2208&&t<=2303},\"Hangul Jamo\":function(t){return t>=4352&&t<=4607},\"Unified Canadian Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram Symbols\":function(t){return t>=19904&&t<=19967},\"CJK Unified Ideographs\":function(t){return t>=19968&&t<=40959},\"Yi Syllables\":function(t){return t>=40960&&t<=42127},\"Yi Radicals\":function(t){return t>=42128&&t<=42191},\"Hangul Jamo Extended-A\":function(t){return t>=43360&&t<=43391},\"Hangul Syllables\":function(t){return t>=44032&&t<=55215},\"Hangul Jamo Extended-B\":function(t){return t>=55216&&t<=55295},\"Private Use Area\":function(t){return t>=57344&&t<=63743},\"CJK Compatibility Ideographs\":function(t){return t>=63744&&t<=64255},\"Arabic Presentation Forms-A\":function(t){return t>=64336&&t<=65023},\"Vertical Forms\":function(t){return t>=65040&&t<=65055},\"CJK Compatibility Forms\":function(t){return t>=65072&&t<=65103},\"Small Form Variants\":function(t){return t>=65104&&t<=65135},\"Arabic Presentation Forms-B\":function(t){return t>=65136&&t<=65279},\"Halfwidth and Fullwidth Forms\":function(t){return t>=65280&&t<=65519}};function xr(t){for(var e=0,r=t;e<r.length;e+=1)if(_r(r[e].charCodeAt(0)))return!0;return!1}function br(t){return!(yr.Arabic(t)\|\|yr[\"Arabic Supplement\"](t)\|\|yr[\"Arabic Extended-A\"](t)\|\|yr[\"Arabic Presentation Forms-A\"](t)\|\|yr[\"Arabic Presentation Forms-B\"](t))}function _r(t){return!!(746===t\|\|747===t\|\|!(t<4352)&&(yr[\"Bopomofo Extended\"](t)\|\|yr.Bopomofo(t)\|\|yr[\"CJK Compatibility Forms\"](t)&&!(t>=65097&&t<=65103)\|\|yr[\"CJK Compatibility Ideographs\"](t)\|\|yr[\"CJK Compatibility\"](t)\|\|yr[\"CJK Radicals Supplement\"](t)\|\|yr[\"CJK Strokes\"](t)\|\|!(!yr[\"CJK Symbols 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Ir=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};Ir.prototype.getValue=function(t){return x(this._values[t].value.value)},Ir.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)\|\|(this._values[t]=new Or(this._values[t].property)),this._values[t].value=new zr(this._values[t].property,null===e?void 0:x(e))},Ir.prototype.getTransition=function(t){return x(this._values[t].transition)},Ir.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)\|\|(this._values[t]=new Or(this._values[t].property)),this._values[t].transition=x(e)\|\|void 0},Ir.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},Ir.prototype.transitioned=function(t,e){for(var r=new Dr(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var 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Dr=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Dr.prototype.possiblyEvaluate=function(t){for(var e=new Fr(this._properties),r=0,n=Object.keys(this._values);r<n.length;r+=1){var i=n[r];e._values[i]=this._values[i].possiblyEvaluate(t)}return e},Dr.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var Rr=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};Rr.prototype.getValue=function(t){return x(this._values[t].value)},Rr.prototype.setValue=function(t,e){this._values[t]=new zr(this._values[t].property,null===e?void 0:x(e))},Rr.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},Rr.prototype.possiblyEvaluate=function(t){for(var e=new Fr(this._properties),r=0,n=Object.keys(this._values);r<n.length;r+=1){var 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Br(this,t.expression,e)},jr.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind\|\|\"constant\"!==e.value.kind)return t;if(void 0===t.value.value\|\|void 0===e.value.value)return new Br(this,{kind:\"constant\",value:void 0},t.globals);var n=kt[this.specification.type];return n?new Br(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.globals):t},jr.prototype.evaluate=function(t,e,r){return\"constant\"===t.kind?t.value:t.evaluate(e,r)};var Vr=function(t){this.specification=t};Vr.prototype.possiblyEvaluate=function(t,e){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var r=t.expression.evaluate(e);return this._calculate(r,r,r,e)}return this._calculate(t.expression.evaluate(new Lr(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Lr(Math.floor(e.zoom),e)),t.expression.evaluate(new Lr(Math.floor(e.zoom+1),e)),e)}},Vr.prototype._calculate=function(t,e,r,n){var i=n.zoom,a=i-Math.floor(i),o=n.crossFadingFactor();return 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Zr(t,e){return Math.ceil(t/e)e}Yr.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Yr.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Yr.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.lengththis.bytesPerElement),this._refreshViews())},Yr.prototype.clear=function(){this.length=0},Yr.prototype.resize=function(t){this.reserve(t),this.length=t},Yr.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacitythis.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Yr.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete 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this.int16[a+0]=t,this.int16[a+1]=e,this.int16[a+2]=r,this.int16[a+3]=n,i},e}(Yr);Jr.prototype.bytesPerElement=8,pr(\"StructArrayLayout4i8\",Jr);var Kr=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;this.resize(o+1);var s=6o;return this.int16[s+0]=t,this.int16[s+1]=e,this.int16[s+2]=r,this.int16[s+3]=n,this.int16[s+4]=i,this.int16[s+5]=a,o},e}(Yr);Kr.prototype.bytesPerElement=12,pr(\"StructArrayLayout2i4i12\",Kr);var Qr=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s){var l=this.length;this.resize(l+1);var c=6l,u=12l;return this.int16[c+0]=t,this.int16[c+1]=e,this.int16[c+2]=r,this.int16[c+3]=n,this.uint8[u+8]=i,this.uint8[u+9]=a,this.uint8[u+10]=o,this.uint8[u+11]=s,l},e}(Yr);Qr.prototype.bytesPerElement=12,pr(\"StructArrayLayout4i4ub12\",Qr);var tn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s){var l=this.length;this.resize(l+1);var c=8l;return this.int16[c+0]=t,this.int16[c+1]=e,this.int16[c+2]=r,this.int16[c+3]=n,this.uint16[c+4]=i,this.uint16[c+5]=a,this.uint16[c+6]=o,this.uint16[c+7]=s,l},e}(Yr);tn.prototype.bytesPerElement=16,pr(\"StructArrayLayout4i4ui16\",tn);var en=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3n;return this.float32[i+0]=t,this.float32[i+1]=e,this.float32[i+2]=r,n},e}(Yr);en.prototype.bytesPerElement=12,pr(\"StructArrayLayout3f12\",en);var rn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;this.resize(e+1);var r=1e;return this.uint32[r+0]=t,e},e}(Yr);rn.prototype.bytesPerElement=4,pr(\"StructArrayLayout1ul4\",rn);var nn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.length;this.resize(f+1);var h=12f,p=6f;return this.int16[h+0]=t,this.int16[h+1]=e,this.int16[h+2]=r,this.int16[h+3]=n,this.int16[h+4]=i,this.int16[h+5]=a,this.uint32[p+3]=o,this.uint16[h+8]=s,this.uint16[h+9]=l,this.int16[h+10]=c,this.int16[h+11]=u,f},e}(Yr);nn.prototype.bytesPerElement=24,pr(\"StructArrayLayout6i1ul2ui2i24\",nn);var an=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;this.resize(o+1);var s=6o;return this.int16[s+0]=t,this.int16[s+1]=e,this.int16[s+2]=r,this.int16[s+3]=n,this.int16[s+4]=i,this.int16[s+5]=a,o},e}(Yr);an.prototype.bytesPerElement=12,pr(\"StructArrayLayout2i2i2i12\",an);var on=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=4r;return this.uint8[n+0]=t,this.uint8[n+1]=e,r},e}(Yr);on.prototype.bytesPerElement=4,pr(\"StructArrayLayout2ub4\",on);var sn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p){var d=this.length;this.resize(d+1);var g=20d,v=10d,m=40d;return this.int16[g+0]=t,this.int16[g+1]=e,this.uint16[g+2]=r,this.uint16[g+3]=n,this.uint32[v+2]=i,this.uint32[v+3]=a,this.uint32[v+4]=o,this.uint16[g+10]=s,this.uint16[g+11]=l,this.uint16[g+12]=c,this.float32[v+7]=u,this.float32[v+8]=f,this.uint8[m+36]=h,this.uint8[m+37]=p,d},e}(Yr);sn.prototype.bytesPerElement=40,pr(\"StructArrayLayout2i2ui3ul3ui2f2ub40\",sn);var ln=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;this.resize(e+1);var r=1e;return this.float32[r+0]=t,e},e}(Yr);ln.prototype.bytesPerElement=4,pr(\"StructArrayLayout1f4\",ln);var cn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3n;return this.int16[i+0]=t,this.int16[i+1]=e,this.int16[i+2]=r,n},e}(Yr);cn.prototype.bytesPerElement=6,pr(\"StructArrayLayout3i6\",cn);var un=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=2n,a=4n;return this.uint32[i+0]=t,this.uint16[a+2]=e,this.uint16[a+3]=r,n},e}(Yr);un.prototype.bytesPerElement=8,pr(\"StructArrayLayout1ul2ui8\",un);var fn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3n;return this.uint16[i+0]=t,this.uint16[i+1]=e,this.uint16[i+2]=r,n},e}(Yr);fn.prototype.bytesPerElement=6,pr(\"StructArrayLayout3ui6\",fn);var hn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=2r;return this.uint16[n+0]=t,this.uint16[n+1]=e,r},e}(Yr);hn.prototype.bytesPerElement=4,pr(\"StructArrayLayout2ui4\",hn);var pn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=2r;return this.float32[n+0]=t,this.float32[n+1]=e,r},e}(Yr);pn.prototype.bytesPerElement=8,pr(\"StructArrayLayout2f8\",pn);var dn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;this.resize(i+1);var a=4i;return this.float32[a+0]=t,this.float32[a+1]=e,this.float32[a+2]=r,this.float32[a+3]=n,i},e}(Yr);dn.prototype.bytesPerElement=16,pr(\"StructArrayLayout4f16\",dn);var gn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},radius:{configurable:!0},signedDistanceFromAnchor:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointX.set=function(t){this._structArray.int16[this._pos2+0]=t},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.anchorPointY.set=function(t){this._structArray.int16[this._pos2+1]=t},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.x1.set=function(t){this._structArray.int16[this._pos2+2]=t},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.y1.set=function(t){this._structArray.int16[this._pos2+3]=t},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.x2.set=function(t){this._structArray.int16[this._pos2+4]=t},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.y2.set=function(t){this._structArray.int16[this._pos2+5]=t},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.featureIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.sourceLayerIndex.set=function(t){this._structArray.uint16[this._pos2+8]=t},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.bucketIndex.set=function(t){this._structArray.uint16[this._pos2+9]=t},r.radius.get=function(){return this._structArray.int16[this._pos2+10]},r.radius.set=function(t){this._structArray.int16[this._pos2+10]=t},r.signedDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+11]},r.signedDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+11]=t},r.anchorPoint.get=function(){return new l(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Wr);gn.prototype.size=24;var vn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new gn(this,t)},e}(nn);pr(\"CollisionBoxArray\",vn);var mn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},hidden:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorX.set=function(t){this._structArray.int16[this._pos2+0]=t},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.anchorY.set=function(t){this._structArray.int16[this._pos2+1]=t},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.glyphStartIndex.set=function(t){this._structArray.uint16[this._pos2+2]=t},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.numGlyphs.set=function(t){this._structArray.uint16[this._pos2+3]=t},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.vertexStartIndex.set=function(t){this._structArray.uint32[this._pos4+2]=t},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineStartIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.lineLength.set=function(t){this._structArray.uint32[this._pos4+4]=t},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.segment.set=function(t){this._structArray.uint16[this._pos2+10]=t},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.lowerSize.set=function(t){this._structArray.uint16[this._pos2+11]=t},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.upperSize.set=function(t){this._structArray.uint16[this._pos2+12]=t},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetX.set=function(t){this._structArray.float32[this._pos4+7]=t},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.lineOffsetY.set=function(t){this._structArray.float32[this._pos4+8]=t},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.writingMode.set=function(t){this._structArray.uint8[this._pos1+36]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+37]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+37]=t},Object.defineProperties(e.prototype,r),e}(Wr);mn.prototype.size=40;var yn=function(t){function e(){t.apply(this,arguments)}return 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e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={x:{configurable:!0},y:{configurable:!0},tileUnitDistanceFromAnchor:{configurable:!0}};return r.x.get=function(){return this._structArray.int16[this._pos2+0]},r.x.set=function(t){this._structArray.int16[this._pos2+0]=t},r.y.get=function(){return this._structArray.int16[this._pos2+1]},r.y.set=function(t){this._structArray.int16[this._pos2+1]=t},r.tileUnitDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+2]},r.tileUnitDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+2]=t},Object.defineProperties(e.prototype,r),e}(Wr);_n.prototype.size=6;var wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3t+0]},e.prototype.gety=function(t){return 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Nn=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map(function(t){return t.id}),this.index=t.index,this.layoutVertexArray=new $r,this.indexArray=new fn,this.segments=new Tn,this.programConfigurations=new In(An,t.layers,t.zoom)};function jn(t,e,r){for(var n=0;n<t.length;n++){var i=t[n];if(Zn(i,e))return!0;if(Wn(e,i,r))return!0}return!1}function Vn(t,e){if(1===t.length&&1===t[0].length)return Xn(e,t[0][0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Xn(t,n[i]))return!0;for(var a=0;a<t.length;a++){for(var o=t[a],s=0;s<o.length;s++)if(Xn(e,o[s]))return!0;for(var l=0;l<e.length;l++)if(Hn(o,e[l]))return!0}return!1}function Un(t,e,r){for(var n=0;n<e.length;n++)for(var i=e[n],a=0;a<t.length;a++){var o=t[a];if(o.length>=3)for(var s=0;s<i.length;s++)if(Zn(o,i[s]))return!0;if(qn(o,i,r))return!0}return!1}function qn(t,e,r){if(t.length>1){if(Hn(t,e))return!0;for(var n=0;n<e.length;n++)if(Wn(e[n],t,r))return!0}for(var 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h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var fa=ha;function ha(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(pa,this,e),this.length=this._features.length}function pa(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function da(t,e,r){if(3===t){var n=new fa(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}ha.prototype.feature=function(t){if(t<0\|\|t>=this._features.length)throw new Error(\"feature index out of 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i=n[r],a=i.feature,o=i.index,s=i.sourceLayerIndex;if(this.layers[0]._featureFilter(new Lr(this.zoom),a)){var l=Bn(a);this.addFeature(a,l),e.featureIndex.insert(a,l,o,s,this.index)}}},wa.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},wa.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,oa),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.programConfigurations.upload(t)},wa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},wa.prototype.addFeature=function(t,e){for(var r=this.layers[0].layout,n=r.get(\"line-join\").evaluate(t),i=r.get(\"line-cap\"),a=r.get(\"line-miter-limit\"),o=r.get(\"line-round-limit\"),s=0,l=e;s<l.length;s+=1){var c=l[s];this.addLine(c,t,n,i,a,o)}},wa.prototype.addLine=function(t,e,r,n,i,a){var o=null;e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")&&(o={start:e.properties.mapbox_clip_start,end:e.properties.mapbox_clip_end,tileTotal:void 0});for(var s=\"Polygon\"===va[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){o&&(o.tileTotal=function(t,e,r){for(var n,i,a=0,o=c;o<r-1;o++)n=t[o],i=t[o+1],a+=n.dist(i);return a}(t,0,l)),\"bevel\"===r&&(i=1.05);var u=Dn/(512this.overscaling)15,f=t[c],h=this.segments.prepareSegment(10l,this.layoutVertexArray,this.indexArray);this.distance=0;var p,d,g,v=n,m=s?\"butt\":n,y=!0,x=void 0,b=void 0,_=void 0,w=void 0;this.e1=this.e2=this.e3=-1,s&&(p=t[l-2],w=f.sub(p)._unit()._perp());for(var k=c;k<l;k++)if(!(b=s&&k===l-1?t[c+1]:t[k+1])\|\|!t[k].equals(b)){w&&(_=w),p&&(x=p),p=t[k],w=b?b.sub(p)._unit()._perp():_;var M=(_=_\|\|w).add(w);0===M.x&&0===M.y\|\|M._unit();var A=M.xw.x+M.yw.y,T=0!==A?1/A:1/0,S=A<ya&&x&&b;if(S&&k>c){var E=p.dist(x);if(E>2u){var C=p.sub(p.sub(x)._mult(u/E)._round());this.distance+=C.dist(x),this.addCurrentVertex(C,this.distance,_.mult(1),0,0,!1,h,o),x=C}}var L=x&&b,z=L?r:b?v:m;if(L&&\"round\"===z&&(T<a?z=\"miter\":T<=2&&(z=\"fakeround\")),\"miter\"===z&&T>i&&(z=\"bevel\"),\"bevel\"===z&&(T>2&&(z=\"flipbevel\"),T<i&&(z=\"miter\")),x&&(this.distance+=p.dist(x)),\"miter\"===z)M._mult(T),this.addCurrentVertex(p,this.distance,M,0,0,!1,h,o);else if(\"flipbevel\"===z){if(T>100)M=w.clone().mult(-1);else{var O=_.xw.y-_.yw.x>0?-1:1,I=T_.add(w).mag()/_.sub(w).mag();M._perp()._mult(IO)}this.addCurrentVertex(p,this.distance,M,0,0,!1,h,o),this.addCurrentVertex(p,this.distance,M.mult(-1),0,0,!1,h,o)}else if(\"bevel\"===z\|\|\"fakeround\"===z){var P=_.xw.y-_.yw.x>0,D=-Math.sqrt(TT-1);if(P?(g=0,d=D):(d=0,g=D),y\|\|this.addCurrentVertex(p,this.distance,_,d,g,!1,h,o),\"fakeround\"===z){for(var R=Math.floor(8(.5-(A-.5))),B=void 0,F=0;F<R;F++)B=w.mult((F+1)/(R+1))._add(_)._unit(),this.addPieSliceVertex(p,this.distance,B,P,h,o);this.addPieSliceVertex(p,this.distance,M,P,h,o);for(var N=R-1;N>=0;N--)B=_.mult((N+1)/(R+1))._add(w)._unit(),this.addPieSliceVertex(p,this.distance,B,P,h,o)}b&&this.addCurrentVertex(p,this.distance,w,-d,-g,!1,h,o)}else\"butt\"===z?(y\|\|this.addCurrentVertex(p,this.distance,_,0,0,!1,h,o),b&&this.addCurrentVertex(p,this.distance,w,0,0,!1,h,o)):\"square\"===z?(y\|\|(this.addCurrentVertex(p,this.distance,_,1,1,!1,h,o),this.e1=this.e2=-1),b&&this.addCurrentVertex(p,this.distance,w,-1,-1,!1,h,o)):\"round\"===z&&(y\|\|(this.addCurrentVertex(p,this.distance,_,0,0,!1,h,o),this.addCurrentVertex(p,this.distance,_,1,1,!0,h,o),this.e1=this.e2=-1),b&&(this.addCurrentVertex(p,this.distance,w,-1,-1,!0,h,o),this.addCurrentVertex(p,this.distance,w,0,0,!1,h,o)));if(S&&k<l-1){var j=p.dist(b);if(j>2u){var V=p.add(b.sub(p)._mult(u/j)._round());this.distance+=V.dist(p),this.addCurrentVertex(V,this.distance,w.mult(1),0,0,!1,h,o),p=V}}y=!1}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,e)}},wa.prototype.addCurrentVertex=function(t,e,r,n,i,a,o,s){var l,c=this.layoutVertexArray,u=this.indexArray;s&&(e=ka(e,s)),l=r.clone(),n&&l._sub(r.perp()._mult(n)),_a(c,t,l,a,!1,n,e),this.e3=o.vertexLength++,this.e1>=0&&this.e2>=0&&(u.emplaceBack(this.e1,this.e2,this.e3),o.primitiveLength++),this.e1=this.e2,this.e2=this.e3,l=r.mult(-1),i&&l._sub(r.perp()._mult(i)),_a(c,t,l,a,!0,-i,e),this.e3=o.vertexLength++,this.e1>=0&&this.e2>=0&&(u.emplaceBack(this.e1,this.e2,this.e3),o.primitiveLength++),this.e1=this.e2,this.e2=this.e3,e>ba/2&&!s&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,a,o))},wa.prototype.addPieSliceVertex=function(t,e,r,n,i,a){r=r.mult(n?-1:1);var o=this.layoutVertexArray,s=this.indexArray;a&&(e=ka(e,a)),_a(o,t,r,!1,n,0,e),this.e3=i.vertexLength++,this.e1>=0&&this.e2>=0&&(s.emplaceBack(this.e1,this.e2,this.e3),i.primitiveLength++),n?this.e2=this.e3:this.e1=this.e3},pr(\"LineBucket\",wa,{omit:[\"layers\"]});var Ma=new qr({\"line-cap\":new Nr(I.layout_line[\"line-cap\"]),\"line-join\":new jr(I.layout_line[\"line-join\"]),\"line-miter-limit\":new Nr(I.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Nr(I.layout_line[\"line-round-limit\"])}),Aa={paint:new qr({\"line-opacity\":new jr(I.paint_line[\"line-opacity\"]),\"line-color\":new jr(I.paint_line[\"line-color\"]),\"line-translate\":new Nr(I.paint_line[\"line-translate\"]),\"line-translate-anchor\":new Nr(I.paint_line[\"line-translate-anchor\"]),\"line-width\":new jr(I.paint_line[\"line-width\"]),\"line-gap-width\":new jr(I.paint_line[\"line-gap-width\"]),\"line-offset\":new jr(I.paint_line[\"line-offset\"]),\"line-blur\":new jr(I.paint_line[\"line-blur\"]),\"line-dasharray\":new Vr(I.paint_line[\"line-dasharray\"]),\"line-pattern\":new Vr(I.paint_line[\"line-pattern\"]),\"line-gradient\":new Ur(I.paint_line[\"line-gradient\"])}),layout:Ma},Ta=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new Lr(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n){return r=p({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n)},e}(jr))(Aa.paint.properties[\"line-width\"].specification);Ta.useIntegerZoom=!0;var Sa=function(t){function e(e){t.call(this,e,Aa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setPaintProperty=function(e,r,n){t.prototype.setPaintProperty.call(this,e,r,n),\"line-gradient\"===e&&this._updateGradient()},e.prototype._updateGradient=function(){var t=this._transitionablePaint._values[\"line-gradient\"].value.expression;this.gradient=hi(t,\"lineProgress\"),this.gradientTexture=null},e.prototype.recalculate=function(e){t.prototype.recalculate.call(this,e),this.paint._values[\"line-floorwidth\"]=Ta.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,e)},e.prototype.createBucket=function(t){return new wa(t)},e.prototype.queryRadius=function(t){var e=t,r=Ea($n(\"line-width\",this,e),$n(\"line-gap-width\",this,e)),n=$n(\"line-offset\",this,e);return r/2+Math.abs(n)+Jn(this.paint.get(\"line-translate\"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,a){var o=Kn(t,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),i.angle,a),s=a/2Ea(this.paint.get(\"line-width\").evaluate(e),this.paint.get(\"line-gap-width\").evaluate(e)),c=this.paint.get(\"line-offset\").evaluate(e);return c&&(r=function(t,e){for(var r=[],n=new l(0,0),i=0;i<t.length;i++){for(var a=t[i],o=[],s=0;s<a.length;s++){var c=a[s-1],u=a[s],f=a[s+1],h=0===s?n:u.sub(c)._unit()._perp(),p=s===a.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),g=d.xp.x+d.yp.y;d._mult(1/g),o.push(d._mult(e)._add(u))}r.push(o)}return r}(r,ca)),Un(o,r,s)},e}(Hr);function Ea(t,e){return e>0?e+2t:t}var Ca=Xr([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"}]),La=Xr([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4),za=(Xr([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4),Xr([{name:\"a_placed\",components:2,type:\"Uint8\"}],4)),Oa=(Xr([{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"},{type:\"Int16\",name:\"radius\"},{type:\"Int16\",name:\"signedDistanceFromAnchor\"}]),Xr([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4)),Ia=Xr([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4);function Pa(t,e,r){var n=e.layout.get(\"text-transform\").evaluate(r);return\"uppercase\"===n?t=t.toLocaleUpperCase():\"lowercase\"===n&&(t=t.toLocaleLowerCase()),Cr.applyArabicShaping&&(t=Cr.applyArabicShaping(t)),t}Xr([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Uint16\",name:\"glyphStartIndex\"},{type:\"Uint16\",name:\"numGlyphs\"},{type:\"Uint32\",name:\"vertexStartIndex\"},{type:\"Uint32\",name:\"lineStartIndex\"},{type:\"Uint32\",name:\"lineLength\"},{type:\"Uint16\",name:\"segment\"},{type:\"Uint16\",name:\"lowerSize\"},{type:\"Uint16\",name:\"upperSize\"},{type:\"Float32\",name:\"lineOffsetX\"},{type:\"Float32\",name:\"lineOffsetY\"},{type:\"Uint8\",name:\"writingMode\"},{type:\"Uint8\",name:\"hidden\"}]),Xr([{type:\"Float32\",name:\"offsetX\"}]),Xr([{type:\"Int16\",name:\"x\"},{type:\"Int16\",name:\"y\"},{type:\"Int16\",name:\"tileUnitDistanceFromAnchor\"}]);var Da={\"!\":\"\\ufe15\",\"#\":\"\\uff03\",$:\"\\uff04\",\"%\":\"\\uff05\",\"&\":\"\\uff06\",\"(\":\"\\ufe35\",\")\":\"\\ufe36\",\"\":\"\\uff0a\",\"+\":\"\\uff0b\",\",\":\"\\ufe10\",\"-\":\"\\ufe32\",\".\":\"\\u30fb\",\"/\":\"\\uff0f\",\":\":\"\\ufe13\",\";\":\"\\ufe14\",\"<\":\"\\ufe3f\",\"=\":\"\\uff1d\",\">\":\"\\ufe40\",\"?\":\"\\ufe16\",\"@\":\"\\uff20\",\"[\":\"\\ufe47\",\"\\\\\":\"\\uff3c\",\"]\":\"\\ufe48\",\"^\":\"\\uff3e\",_:\"\\ufe33\",\"`\":\"\\uff40\",\"{\":\"\\ufe37\",\"\|\":\"\\u2015\",\"}\":\"\\ufe38\",\"~\":\"\\uff5e\",\"\\xa2\":\"\\uffe0\",\"\\xa3\":\"\\uffe1\",\"\\xa5\":\"\\uffe5\",\"\\xa6\":\"\\uffe4\",\"\\xac\":\"\\uffe2\",\"\\xaf\":\"\\uffe3\",\"\\u2013\":\"\\ufe32\",\"\\u2014\":\"\\ufe31\",\"\\u2018\":\"\\ufe43\",\"\\u2019\":\"\\ufe44\",\"\\u201c\":\"\\ufe41\",\"\\u201d\":\"\\ufe42\",\"\\u2026\":\"\\ufe19\",\"\\u2027\":\"\\u30fb\",\"\\u20a9\":\"\\uffe6\",\"\\u3001\":\"\\ufe11\",\"\\u3002\":\"\\ufe12\",\"\\u3008\":\"\\ufe3f\",\"\\u3009\":\"\\ufe40\",\"\\u300a\":\"\\ufe3d\",\"\\u300b\":\"\\ufe3e\",\"\\u300c\":\"\\ufe41\",\"\\u300d\":\"\\ufe42\",\"\\u300e\":\"\\ufe43\",\"\\u300f\":\"\\ufe44\",\"\\u3010\":\"\\ufe3b\",\"\\u3011\":\"\\ufe3c\",\"\\u3014\":\"\\ufe39\",\"\\u3015\":\"\\ufe3a\",\"\\u3016\":\"\\ufe17\",\"\\u3017\":\"\\ufe18\",\"\\uff01\":\"\\ufe15\",\"\\uff08\":\"\\ufe35\",\"\\uff09\":\"\\ufe36\",\"\\uff0c\":\"\\ufe10\",\"\\uff0d\":\"\\ufe32\",\"\\uff0e\":\"\\u30fb\",\"\\uff1a\":\"\\ufe13\",\"\\uff1b\":\"\\ufe14\",\"\\uff1c\":\"\\ufe3f\",\"\\uff1e\":\"\\ufe40\",\"\\uff1f\":\"\\ufe16\",\"\\uff3b\":\"\\ufe47\",\"\\uff3d\":\"\\ufe48\",\"\\uff3f\":\"\\ufe33\",\"\\uff5b\":\"\\ufe37\",\"\\uff5c\":\"\\u2015\",\"\\uff5d\":\"\\ufe38\",\"\\uff5f\":\"\\ufe35\",\"\\uff60\":\"\\ufe36\",\"\\uff61\":\"\\ufe12\",\"\\uff62\":\"\\ufe41\",\"\\uff63\":\"\\ufe42\"},Ra=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(l);function Ba(t,e){var r=e.expression;if(\"constant\"===r.kind)return{functionType:\"constant\",layoutSize:r.evaluate(new Lr(t+1))};if(\"source\"===r.kind)return{functionType:\"source\"};for(var n=r.zoomStops,i=0;i<n.length&&n[i]<=t;)i++;for(var a=i=Math.max(0,i-1);a<n.length&&n[a]<t+1;)a++;a=Math.min(n.length-1,a);var o={min:n[i],max:n[a]};return\"composite\"===r.kind?{functionType:\"composite\",zoomRange:o,propertyValue:e.value}:{functionType:\"camera\",layoutSize:r.evaluate(new Lr(t+1)),zoomRange:o,sizeRange:{min:r.evaluate(new Lr(o.min)),max:r.evaluate(new Lr(o.max))},propertyValue:e.value}}pr(\"Anchor\",Ra);var Fa=ga.VectorTileFeature.types,Na=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function ja(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,Math.round(32n),Math.round(32i),a,o,s?s[0]:0,s?s[1]:0)}function Va(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}var Ua=function(t){this.layoutVertexArray=new tn,this.indexArray=new fn,this.programConfigurations=t,this.segments=new Tn,this.dynamicLayoutVertexArray=new en,this.opacityVertexArray=new rn,this.placedSymbolArray=new yn};Ua.prototype.upload=function(t,e){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ca.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.programConfigurations.upload(t),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,La.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,Na,!0),this.opacityVertexBuffer.itemSize=1},Ua.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},pr(\"SymbolBuffers\",Ua);var qa=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Tn,this.collisionVertexArray=new on};qa.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,za.members,!0)},qa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},pr(\"CollisionBuffers\",qa);var Ha=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map(function(t){return t.id}),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex;var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Ba(this.zoom,e[\"text-size\"]),this.iconSizeData=Ba(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout;this.sortFeaturesByY=r.get(\"text-allow-overlap\")\|\|r.get(\"icon-allow-overlap\")\|\|r.get(\"text-ignore-placement\")\|\|r.get(\"icon-ignore-placement\")};Ha.prototype.createArrays=function(){this.text=new Ua(new In(Ca.members,this.layers,this.zoom,function(t){return/^text/.test(t)})),this.icon=new Ua(new In(Ca.members,this.layers,this.zoom,function(t){return/^icon/.test(t)})),this.collisionBox=new qa(an,Oa.members,hn),this.collisionCircle=new qa(an,Ia.members,fn),this.glyphOffsetArray=new bn,this.lineVertexArray=new wn},Ha.prototype.populate=function(t,e){var r=this.layers[0],n=r.layout,i=n.get(\"text-font\"),a=n.get(\"text-field\"),o=n.get(\"icon-image\"),s=(\"constant\"!==a.value.kind\|\|a.value.value.length>0)&&(\"constant\"!==i.value.kind\|\|i.value.value.length>0),l=\"constant\"!==o.value.kind\|\|o.value.value&&o.value.value.length>0;if(this.features=[],s\|\|l){for(var c=e.iconDependencies,u=e.glyphDependencies,f=new Lr(this.zoom),h=0,p=t;h<p.length;h+=1){var d=p[h],g=d.feature,v=d.index,m=d.sourceLayerIndex;if(r._featureFilter(f,g)){var y=void 0;s&&(y=Pa(y=r.getValueAndResolveTokens(\"text-field\",g),r,g));var x=void 0;if(l&&(x=r.getValueAndResolveTokens(\"icon-image\",g)),y\|\|x){var b={text:y,icon:x,index:v,sourceLayerIndex:m,geometry:Bn(g),properties:g.properties,type:Fa[g.type]};if(void 0!==g.id&&(b.id=g.id),this.features.push(b),x&&(c[x]=!0),y)for(var _=i.evaluate(g).join(\",\"),w=u[_]=u[_]\|\|{},k=\"map\"===n.get(\"text-rotation-alignment\")&&\"line\"===n.get(\"symbol-placement\"),M=xr(y),A=0;A<y.length;A++)if(w[y.charCodeAt(A)]=!0,k&&M){var T=Da[y.charAt(A)];T&&(w[T.charCodeAt(0)]=!0)}}}}\"line\"===n.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,f),v=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[v].geometry,!0)]=v,n[g].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter(function(t){return t.geometry})}(this.features))}},Ha.prototype.isEmpty=function(){return 0===this.symbolInstances.length},Ha.prototype.upload=function(t){this.text.upload(t,this.sortFeaturesByY),this.icon.upload(t,this.sortFeaturesByY),this.collisionBox.upload(t),this.collisionCircle.upload(t)},Ha.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.collisionBox.destroy(),this.collisionCircle.destroy()},Ha.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment\|\|0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Ha.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c){for(var u=t.indexArray,f=t.layoutVertexArray,h=t.dynamicLayoutVertexArray,p=t.segments.prepareSegment(4e.length,t.layoutVertexArray,t.indexArray),d=this.glyphOffsetArray.length,g=p.vertexLength,v=0,m=e;v<m.length;v+=1){var y=m[v],x=y.tl,b=y.tr,_=y.bl,w=y.br,k=y.tex,M=p.vertexLength,A=y.glyphOffset[1];ja(f,s.x,s.y,x.x,A+x.y,k.x,k.y,r),ja(f,s.x,s.y,b.x,A+b.y,k.x+k.w,k.y,r),ja(f,s.x,s.y,_.x,A+_.y,k.x,k.y+k.h,r),ja(f,s.x,s.y,w.x,A+w.y,k.x+k.w,k.y+k.h,r),Va(h,s,0),u.emplaceBack(M,M+1,M+2),u.emplaceBack(M+1,M+2,M+3),p.vertexLength+=4,p.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(y.glyphOffset[0])}t.placedSymbolArray.emplaceBack(s.x,s.y,d,this.glyphOffsetArray.length-d,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,!1),t.programConfigurations.populatePaintArrays(t.layoutVertexArray.length,a)},Ha.prototype._addCollisionDebugVertex=function(t,e,r,n,i){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n.x,n.y,Math.round(i.x),Math.round(i.y))},Ha.prototype.addCollisionDebugVertices=function(t,e,r,n,i,a,o,s){var c=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=c.vertexLength,f=i.layoutVertexArray,h=i.collisionVertexArray;if(this._addCollisionDebugVertex(f,h,a,o.anchor,new l(t,e)),this._addCollisionDebugVertex(f,h,a,o.anchor,new l(r,e)),this._addCollisionDebugVertex(f,h,a,o.anchor,new l(r,n)),this._addCollisionDebugVertex(f,h,a,o.anchor,new l(t,n)),c.vertexLength+=4,s){var p=i.indexArray;p.emplaceBack(u,u+1,u+2),p.emplaceBack(u,u+2,u+3),c.primitiveLength+=2}else{var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),c.primitiveLength+=4}},Ha.prototype.generateCollisionDebugBuffers=function(){for(var t=0,e=this.symbolInstances;t<e.length;t+=1){var r=e[t];r.textCollisionFeature={boxStartIndex:r.textBoxStartIndex,boxEndIndex:r.textBoxEndIndex},r.iconCollisionFeature={boxStartIndex:r.iconBoxStartIndex,boxEndIndex:r.iconBoxEndIndex};for(var n=0;n<2;n++){var i=r[0===n?\"textCollisionFeature\":\"iconCollisionFeature\"];if(i)for(var a=i.boxStartIndex;a<i.boxEndIndex;a++){var o=this.collisionBoxArray.get(a),s=o.x1,l=o.y1,c=o.x2,u=o.y2,f=o.radius>0;this.addCollisionDebugVertices(s,l,c,u,f?this.collisionCircle:this.collisionBox,o.anchorPoint,r,f)}}}},Ha.prototype.deserializeCollisionBoxes=function(t,e,r,n,i){for(var a={},o=e;o<r;o++){var s=t.get(o);if(0===s.radius){a.textBox={x1:s.x1,y1:s.y1,x2:s.x2,y2:s.y2,anchorPointX:s.anchorPointX,anchorPointY:s.anchorPointY},a.textFeatureIndex=s.featureIndex;break}a.textCircles\|\|(a.textCircles=[],a.textFeatureIndex=s.featureIndex),a.textCircles.push(s.anchorPointX,s.anchorPointY,s.radius,s.signedDistanceFromAnchor,1)}for(var l=n;l<i;l++){var c=t.get(l);if(0===c.radius){a.iconBox={x1:c.x1,y1:c.y1,x2:c.x2,y2:c.y2,anchorPointX:c.anchorPointX,anchorPointY:c.anchorPointY},a.iconFeatureIndex=c.featureIndex;break}}return a},Ha.prototype.hasTextData=function(){return this.text.segments.get().length>0},Ha.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Ha.prototype.hasCollisionBoxData=function(){return this.collisionBox.segments.get().length>0},Ha.prototype.hasCollisionCircleData=function(){return this.collisionCircle.segments.get().length>0},Ha.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&(this.sortedAngle=t,!(this.text.segments.get().length>1\|\|this.icon.segments.get().length>1))){for(var r=[],n=0;n<this.symbolInstances.length;n++)r.push(n);var i=Math.sin(t),a=Math.cos(t);r.sort(function(t,r){var n=e.symbolInstances[t],o=e.symbolInstances[r];return(in.anchor.x+an.anchor.y\|0)-(io.anchor.x+ao.anchor.y\|0)\|\|o.featureIndex-n.featureIndex}),this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var o=0,s=r;o<s.length;o+=1){var l=s[o],c=e.symbolInstances[l];e.featureSortOrder.push(c.featureIndex);for(var u=0,f=c.placedTextSymbolIndices;u<f.length;u+=1)for(var h=f[u],p=e.text.placedSymbolArray.get(h),d=p.vertexStartIndex+4p.numGlyphs,g=p.vertexStartIndex;g<d;g+=4)e.text.indexArray.emplaceBack(g,g+1,g+2),e.text.indexArray.emplaceBack(g+1,g+2,g+3);var v=e.icon.placedSymbolArray.get(l);if(v.numGlyphs){var m=v.vertexStartIndex;e.icon.indexArray.emplaceBack(m,m+1,m+2),e.icon.indexArray.emplaceBack(m+1,m+2,m+3)}}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},pr(\"SymbolBucket\",Ha,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"],shallow:[\"symbolInstances\"]}),Ha.MAX_GLYPHS=65535,Ha.addDynamicAttributes=Va;var Ga=new qr({\"symbol-placement\":new Nr(I.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Nr(I.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Nr(I.layout_symbol[\"symbol-avoid-edges\"]),\"icon-allow-overlap\":new Nr(I.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new Nr(I.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Nr(I.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Nr(I.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new jr(I.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Nr(I.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Nr(I.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new jr(I.layout_symbol[\"icon-image\"]),\"icon-rotate\":new jr(I.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new Nr(I.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Nr(I.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new jr(I.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new jr(I.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Nr(I.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Nr(I.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Nr(I.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new jr(I.layout_symbol[\"text-field\"]),\"text-font\":new jr(I.layout_symbol[\"text-font\"]),\"text-size\":new jr(I.layout_symbol[\"text-size\"]),\"text-max-width\":new jr(I.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Nr(I.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new jr(I.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new jr(I.layout_symbol[\"text-justify\"]),\"text-anchor\":new jr(I.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Nr(I.layout_symbol[\"text-max-angle\"]),\"text-rotate\":new jr(I.layout_symbol[\"text-rotate\"]),\"text-padding\":new Nr(I.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Nr(I.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new jr(I.layout_symbol[\"text-transform\"]),\"text-offset\":new jr(I.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Nr(I.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new Nr(I.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Nr(I.layout_symbol[\"text-optional\"])}),Wa={paint:new qr({\"icon-opacity\":new jr(I.paint_symbol[\"icon-opacity\"]),\"icon-color\":new jr(I.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new jr(I.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new jr(I.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new jr(I.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Nr(I.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Nr(I.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new jr(I.paint_symbol[\"text-opacity\"]),\"text-color\":new jr(I.paint_symbol[\"text-color\"]),\"text-halo-color\":new jr(I.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new jr(I.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new jr(I.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Nr(I.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Nr(I.paint_symbol[\"text-translate-anchor\"])}),layout:Ga},Ya=function(t){function e(e){t.call(this,e,Wa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e){t.prototype.recalculate.call(this,e),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"line\"===this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"line\"===this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\"))},e.prototype.getValueAndResolveTokens=function(t,e){var r,n=this.layout.get(t).evaluate(e),i=this._unevaluatedLayout._values[t];return i.isDataDriven()\|\|_e(i.value)?n:(r=e.properties,n.replace(/{([^{}]+)}/g,function(t,e){return e in r?String(r[e]):\"\"}))},e.prototype.createBucket=function(t){return new Ha(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e}(Hr),Xa={paint:new qr({\"background-color\":new Nr(I.paint_background[\"background-color\"]),\"background-pattern\":new Vr(I.paint_background[\"background-pattern\"]),\"background-opacity\":new Nr(I.paint_background[\"background-opacity\"])})},Za=function(t){function e(e){t.call(this,e,Xa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Hr),$a={paint:new qr({\"raster-opacity\":new Nr(I.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new Nr(I.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new Nr(I.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new Nr(I.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new Nr(I.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new Nr(I.paint_raster[\"raster-contrast\"]),\"raster-fade-duration\":new Nr(I.paint_raster[\"raster-fade-duration\"])})},Ja={circle:ni,heatmap:pi,hillshade:gi,fill:Ji,\"fill-extrusion\":aa,line:Sa,symbol:Ya,background:Za,raster:function(t){function e(e){t.call(this,e,$a)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Hr)},Ka=i(function(t,e){t.exports=function(){function t(t,e,r){r=r\|\|{},this.w=t\|\|64,this.h=e\|\|64,this.autoResize=!!r.autoResize,this.shelves=[],this.freebins=[],this.stats={},this.bins={},this.maxId=0}function e(t,e,r){this.x=0,this.y=t,this.w=this.free=e,this.h=r}return t.prototype.pack=function(t,e){t=[].concat(t),e=e\|\|{};for(var r,n,i,a,o=[],s=0;s<t.length;s++)if(r=t[s].w\|\|t[s].width,n=t[s].h\|\|t[s].height,i=t[s].id,r&&n){if(!(a=this.packOne(r,n,i)))continue;e.inPlace&&(t[s].x=a.x,t[s].y=a.y,t[s].id=a.id),o.push(a)}return this.shrink(),o},t.prototype.packOne=function(t,r,n){var i,a,o,s,l,c,u,f,h={freebin:-1,shelf:-1,waste:1/0},p=0;if(\"string\"==typeof n\|\|\"number\"==typeof n){if(i=this.getBin(n))return this.ref(i),i;\"number\"==typeof n&&(this.maxId=Math.max(n,this.maxId))}else n=++this.maxId;for(s=0;s<this.freebins.length;s++){if(r===(i=this.freebins[s]).maxh&&t===i.maxw)return this.allocFreebin(s,t,r,n);r>i.maxh\|\|t>i.maxw\|\|r<=i.maxh&&t<=i.maxw&&(o=i.maxwi.maxh-tr)<h.waste&&(h.waste=o,h.freebin=s)}for(s=0;s<this.shelves.length;s++)if(p+=(a=this.shelves[s]).h,!(t>a.free)){if(r===a.h)return this.allocShelf(s,t,r,n);r>a.h\|\|r<a.h&&(o=(a.h-r)t)<h.waste&&(h.freebin=-1,h.waste=o,h.shelf=s)}return-1!==h.freebin?this.allocFreebin(h.freebin,t,r,n):-1!==h.shelf?this.allocShelf(h.shelf,t,r,n):r<=this.h-p&&t<=this.w?(a=new e(p,this.w,r),this.allocShelf(this.shelves.push(a)-1,t,r,n)):this.autoResize?(l=c=this.h,((u=f=this.w)<=l\|\|t>u)&&(f=2Math.max(t,u)),(l<u\|\|r>l)&&(c=2Math.max(r,l)),this.resize(f,c),this.packOne(t,r,n)):null},t.prototype.allocFreebin=function(t,e,r,n){var i=this.freebins.splice(t,1)[0];return i.id=n,i.w=e,i.h=r,i.refcount=0,this.bins[n]=i,this.ref(i),i},t.prototype.allocShelf=function(t,e,r,n){var i=this.shelves[t].alloc(e,r,n);return this.bins[n]=i,this.ref(i),i},t.prototype.shrink=function(){if(this.shelves.length>0){for(var t=0,e=0,r=0;r<this.shelves.length;r++){var n=this.shelves[r];e+=n.h,t=Math.max(n.w-n.free,t)}this.resize(t,e)}},t.prototype.getBin=function(t){return this.bins[t]},t.prototype.ref=function(t){if(1==++t.refcount){var e=t.h;this.stats[e]=1+(0\|this.stats[e])}return t.refcount},t.prototype.unref=function(t){return 0===t.refcount?0:(0==--t.refcount&&(this.stats[t.h]--,delete this.bins[t.id],this.freebins.push(t)),t.refcount)},t.prototype.clear=function(){this.shelves=[],this.freebins=[],this.stats={},this.bins={},this.maxId=0},t.prototype.resize=function(t,e){this.w=t,this.h=e;for(var r=0;r<this.shelves.length;r++)this.shelves[r].resize(t);return!0},e.prototype.alloc=function(t,e,r){if(t>this.free\|\|e>this.h)return null;var n=this.x;return this.x+=t,this.free-=t,new function(t,e,r,n,i,a,o){this.id=t,this.x=e,this.y=r,this.w=n,this.h=i,this.maxw=a\|\|n,this.maxh=o\|\|i,this.refcount=0}(r,n,this.y,t,e,t,this.h)},e.prototype.resize=function(t){return this.free+=t-this.w,this.w=t,!0},t}()}),Qa=function(t,e){var r=e.pixelRatio;this.paddedRect=t,this.pixelRatio=r},to={tl:{configurable:!0},br:{configurable:!0},displaySize:{configurable:!0}};to.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},to.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},to.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(Qa.prototype,to);var eo=function(t){var e=new ui({width:0,height:0}),r={},n=new Ka(0,0,{autoResize:!0});for(var i in t){var a=t[i],o=n.packOne(a.data.width+2,a.data.height+2);e.resize({width:n.w,height:n.h}),ui.copy(a.data,e,{x:0,y:0},{x:o.x+1,y:o.y+1},a.data),r[i]=new Qa(o,a)}n.shrink(),e.resize({width:n.w,height:n.h}),this.image=e,this.positions=r};pr(\"ImagePosition\",Qa),pr(\"ImageAtlas\",eo);var ro=function(t,e,r,n,i){var 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u=a/2,f=Math.floor(i/u),h=1+.4Math.log(c)/Math.LN2,p=Math.floor(fh/2),d=-a/2,g=r,v=n+1,m=d,y=-i/2,x=y-i/4;do{if(--v<0){if(m>y)return;v=0;break}m-=e[v].dist(g),g=e[v]}while(m>x);for(var b=e[v].dist(e[v+1]),_=-p;_<f+p;_++){var w=_u,k=y+w;if(w<0&&(k+=w),w>i&&(k+=w-i),!(k<m)){for(;m+b<k;){if(m+=b,++v+1>=e.length)return;b=e[v].dist(e[v+1])}var M=k-m,A=e[v],T=e[v+1].sub(A)._unit()._mult(M)._add(A)._round(),S=Math.abs(k-d)<u?0:.8(k-d);t.emplaceBack(T.x,T.y,-a/2,-a/2,a/2,a/2,o,s,l,a/2,S)}}};var l=u,c=u;function u(t,e){if(!(this instanceof u))return new u(t,e);if(this.data=t\|\|[],this.length=this.data.length,this.compare=e\|\|f,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)}function f(t,e){return t<e?-1:t>e?1:0}function h(e,r,n){void 0===r&&(r=1),void 0===n&&(n=!1);for(var i=1/0,a=1/0,o=-1/0,s=-1/0,c=e[0],u=0;u<c.length;u++){var f=c[u];(!u\|\|f.x<i)&&(i=f.x),(!u\|\|f.y<a)&&(a=f.y),(!u\|\|f.x>o)&&(o=f.x),(!u\|\|f.y>s)&&(s=f.y)}var h=o-i,g=s-a,v=Math.min(h,g),m=v/2,y=new 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e,r,n}function ut(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return pt(pt({},t.properties),{cluster:!0,cluster_id:t.id,point_count:e,point_count_abbreviated:r})}function ft(t){return t/360+.5}function ht(t){var e=Math.sin(tMath.PI/180),r=.5-.25Math.log((1+e)/(1-e))/Math.PI;return r<0?0:r>1?1:r}function pt(t,e){for(var r in e)t[r]=e[r];return t}function dt(t){return t.x}function gt(t){return t.y}function vt(t,e,r,n,i,a){var o=i-r,s=a-n;if(0!==o\|\|0!==s){var l=((t-r)o+(e-n)s)/(oo+ss);l>1?(r=i,n=a):l>0&&(r+=ol,n+=sl)}return(o=t-r)o+(s=e-n)s}function mt(t,e,r,n){var i={id:t\|\|null,type:e,geometry:r,tags:n,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return function(t){var e=t.geometry,r=t.type;if(\"Point\"===r\|\|\"MultiPoint\"===r\|\|\"LineString\"===r)yt(t,e);else if(\"Polygon\"===r\|\|\"MultiLineString\"===r)for(var n=0;n<e.length;n++)yt(t,e[n]);else if(\"MultiPolygon\"===r)for(n=0;n<e.length;n++)for(var i=0;i<e[n].length;i++)yt(t,e[n][i])}(i),i}function yt(t,e){for(var r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function xt(t,e,r){if(e.geometry){var n=e.geometry.coordinates,i=e.geometry.type,a=Math.pow(r.tolerance/((1<<r.maxZoom)r.extent),2),o=[];if(\"Point\"===i)bt(n,o);else if(\"MultiPoint\"===i)for(var s=0;s<n.length;s++)bt(n[s],o);else if(\"LineString\"===i)_t(n,o,a,!1);else if(\"MultiLineString\"===i)if(r.lineMetrics)for(s=0;s<n.length;s++)return o=[],_t(n[s],o,a,!1),void t.push(mt(e.id,\"LineString\",o,e.properties));else wt(n,o,a,!1);else if(\"Polygon\"===i)wt(n,o,a,!0);else{if(\"MultiPolygon\"!==i){if(\"GeometryCollection\"===i){for(s=0;s<e.geometry.geometries.length;s++)xt(t,{id:e.id,geometry:e.geometry.geometries[s],properties:e.properties},r);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(s=0;s<n.length;s++){var l=[];wt(n[s],l,a,!0),o.push(l)}}t.push(mt(e.id,i,o,e.properties))}}function bt(t,e){e.push(kt(t[0])),e.push(Mt(t[1])),e.push(0)}function _t(t,e,r,n){for(var i,a,o=0,s=0;s<t.length;s++){var l=kt(t[s][0]),c=Mt(t[s][1]);e.push(l),e.push(c),e.push(0),s>0&&(o+=n?(ic-la)/2:Math.sqrt(Math.pow(l-i,2)+Math.pow(c-a,2))),i=l,a=c}var u=e.length-3;e[2]=1,function t(e,r,n,i){for(var a,o=i,s=e[r],l=e[r+1],c=e[n],u=e[n+1],f=r+3;f<n;f+=3){var h=vt(e[f],e[f+1],s,l,c,u);h>o&&(a=f,o=h)}o>i&&(a-r>3&&t(e,r,a,i),e[a+2]=o,n-a>3&&t(e,a,n,i))}(e,0,u,r),e[u+2]=1,e.size=Math.abs(o),e.start=0,e.end=e.size}function wt(t,e,r,n){for(var i=0;i<t.length;i++){var a=[];_t(t[i],a,r,n),e.push(a)}}function kt(t){return t/360+.5}function Mt(t){var e=Math.sin(tMath.PI/180),r=.5-.25Math.log((1+e)/(1-e))/Math.PI;return r<0?0:r>1?1:r}function At(t,e,r,n,i,a,o,s){if(n/=e,a>=(r/=e)&&o<=n)return t;if(a>n\|\|o<r)return null;for(var l=[],c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<=n)l.push(u);else if(!(p>n\|\|d<r)){var g=[];if(\"Point\"===h\|\|\"MultiPoint\"===h)Tt(f,g,r,n,i);else if(\"LineString\"===h)St(f,g,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)Ct(f,g,r,n,i,!1);else if(\"Polygon\"===h)Ct(f,g,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var v=0;v<f.length;v++){var m=[];Ct(f[v],m,r,n,i,!0),m.length&&g.push(m)}if(g.length){if(s.lineMetrics&&\"LineString\"===h){for(v=0;v<g.length;v++)l.push(mt(u.id,h,g[v],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h\|\|(1===g.length?(h=\"LineString\",g=g[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h\|\|(h=3===g.length?\"Point\":\"MultiPoint\"),l.push(mt(u.id,h,g,u.tags))}}}return l.length?l:null}function Tt(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function St(t,e,r,n,i,a,o){for(var s,l,c=Et(t),u=0===i?zt:Ot,f=t.start,h=0;h<t.length-3;h+=3){var p=t[h],d=t[h+1],g=t[h+2],v=t[h+3],m=t[h+4],y=0===i?p:d,x=0===i?v:m,b=!1;o&&(s=Math.sqrt(Math.pow(p-v,2)+Math.pow(d-m,2))),y<r?x>=r&&(l=u(c,p,d,v,m,r),o&&(c.start=f+sl)):y>n?x<=n&&(l=u(c,p,d,v,m,n),o&&(c.start=f+sl)):Lt(c,p,d,g),x<r&&y>=r&&(l=u(c,p,d,v,m,r),b=!0),x>n&&y<=n&&(l=u(c,p,d,v,m,n),b=!0),!a&&b&&(o&&(c.end=f+sl),e.push(c),c=Et(t)),o&&(f+=s)}var _=t.length-3;p=t[_],d=t[_+1],g=t[_+2],(y=0===i?p:d)>=r&&y<=n&&Lt(c,p,d,g),_=c.length-3,a&&_>=3&&(c[_]!==c[0]\|\|c[_+1]!==c[1])&&Lt(c,c[0],c[1],c[2]),c.length&&e.push(c)}function Et(t){var e=[];return e.size=t.size,e.start=t.start,e.end=t.end,e}function Ct(t,e,r,n,i,a){for(var o=0;o<t.length;o++)St(t[o],e,r,n,i,a,!1)}function Lt(t,e,r,n){t.push(e),t.push(r),t.push(n)}function zt(t,e,r,n,i,a){var o=(a-e)/(n-e);return t.push(a),t.push(r+(i-r)o),t.push(1),o}function Ot(t,e,r,n,i,a){var o=(a-r)/(i-r);return t.push(e+(n-e)o),t.push(a),t.push(1),o}function It(t,e){for(var r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o\|\|\"MultiPoint\"===o\|\|\"LineString\"===o)i=Pt(a.geometry,e);else if(\"MultiLineString\"===o\|\|\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(Pt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(Pt(a.geometry[s][c],e));i.push(l)}r.push(mt(a.id,o,i,a.tags))}return r}function Pt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function Dt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(Rt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(Rt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function Rt(t,e,r,n,i,a){return[Math.round(r(tn-i)),Math.round(r(en-a))]}function Bt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,Ft(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function Ft(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a\|\|\"MultiPoint\"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if(\"LineString\"===a)Nt(o,i,t,r,!1,!1);else if(\"MultiLineString\"===a\|\|\"Polygon\"===a)for(s=0;s<i.length;s++)Nt(o,i[s],t,r,\"Polygon\"===a,0===s);else if(\"MultiPolygon\"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)Nt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags\|\|null;if(\"LineString\"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a\|\|\"MultiPolygon\"===a?3:\"LineString\"===a\|\|\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function Nt(t,e,r,n,i,a){var o=nn;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n\|\|e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function jt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0\|\|e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)xt(r,t.features[n],e);else\"Feature\"===t.type?xt(r,t,e):xt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=At(t,1,-1-r,r,0,-1,2,e),a=At(t,1,1-r,2+r,0,-1,2,e);return(i\|\|a)&&(n=At(t,1,-r,1+r,0,-1,2,e)\|\|[],i&&(n=It(i,1).concat(n)),a&&(n=n.concat(It(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function Vt(t,e,r){return 32((1<<t)r+e)+t}function Ut(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new R(n.features),a=V(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength\|\|(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}ot.prototype={options:{minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,reduce:null,initial:function(){return{}},map:function(t){return t}},load:function(t){var e=this.options.log;e&&console.time(\"total time\");var r=\"prepare \"+t.length+\" points\";e&&console.time(r),this.points=t;var n=t.map(lt);e&&console.timeEnd(r);for(var i=this.options.maxZoom;i>=this.options.minZoom;i--){var a=+Date.now();this.trees[i+1]=rt(n,dt,gt,this.options.nodeSize,Float32Array),n=this._cluster(n,i),e&&console.log(\"z%d: %d clusters in %dms\",i,n.length,+Date.now()-a)}return this.trees[this.options.minZoom]=rt(n,dt,gt,this.options.nodeSize,Float32Array),e&&console.timeEnd(\"total time\"),this},getClusters:function(t,e){for(var r=this.trees[this._limitZoom(e)],n=r.range(ft(t[0]),ht(t[3]),ft(t[2]),ht(t[1])),i=[],a=0;a<n.length;a++){var o=r.points[n[a]];i.push(o.numPoints?ct(o):this.points[o.id])}return i},getChildren:function(t,e){for(var r=this.trees[e+1].points[t],n=this.options.radius/(this.options.extentMath.pow(2,e)),i=this.trees[e+1].within(r.x,r.y,n),a=[],o=0;o<i.length;o++){var s=this.trees[e+1].points[i[o]];s.parentId===t&&a.push(s.numPoints?ct(s):this.points[s.id])}return a},getLeaves:function(t,e,r,n){r=r\|\|10,n=n\|\|0;var i=[];return this._appendLeaves(i,t,e,r,n,0),i},getTile:function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options.extent,o=this.options.radius/a,s=(r-o)/i,l=(r+1+o)/i,c={features:[]};return this._addTileFeatures(n.range((e-o)/i,s,(e+1+o)/i,l),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-o/i,s,1,l),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,s,o/i,l),n.points,-1,r,i,c),c.features.length?c:null},getClusterExpansionZoom:function(t,e){for(;e<this.options.maxZoom;){var r=this.getChildren(t,e);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},_appendLeaves:function(t,e,r,n,i,a){for(var o=this.getChildren(e,r),s=0;s<o.length;s++){var l=o[s].properties;if(l.cluster?a+l.point_count<=i?a+=l.point_count:a=this._appendLeaves(t,l.cluster_id,r+1,n,i,a):a<i?a++:t.push(o[s]),t.length===n)break}return a},_addTileFeatures:function(t,e,r,n,i,a){for(var o=0;o<t.length;o++){var s=e[t[o]];a.features.push({type:1,geometry:[[Math.round(this.options.extent(s.xi-r)),Math.round(this.options.extent(s.yi-n))]],tags:s.numPoints?ut(s):this.points[s.id].properties})}},_limitZoom:function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},_cluster:function(t,e){for(var r=[],n=this.options.radius/(this.options.extentMath.pow(2,e)),i=0;i<t.length;i++){var a=t[i];if(!(a.zoom<=e)){a.zoom=e;var o=this.trees[e+1],s=o.within(a.x,a.y,n),l=a.numPoints\|\|1,c=a.xl,u=a.yl,f=null;this.options.reduce&&(f=this.options.initial(),this._accumulate(f,a));for(var h=0;h<s.length;h++){var p=o.points[s[h]];if(e<p.zoom){var d=p.numPoints\|\|1;p.zoom=e,c+=p.xd,u+=p.yd,l+=d,p.parentId=i,this.options.reduce&&this._accumulate(f,p)}}1===l?r.push(a):(a.parentId=i,r.push(st(c/l,u/l,l,i,f)))}}return r},_accumulate:function(t,e){var r=e.numPoints?e.properties:this.options.map(this.points[e.id].properties);this.options.reduce(t,r)}},jt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,debug:0},jt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=Vt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=Bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]\|\|0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom\|\|e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)\|\|n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom\|\|h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var g,v,m,y,x,b,_=.5l.buffer/l.extent,w=.5-_,k=.5+_,M=1+_;g=v=m=y=null,x=At(t,u,r-_,r+k,0,h.minX,h.maxX,l),b=At(t,u,r+w,r+M,0,h.minX,h.maxX,l),t=null,x&&(g=At(x,u,n-_,n+k,1,h.minY,h.maxY,l),v=At(x,u,n+w,n+M,1,h.minY,h.maxY,l),x=null),b&&(m=At(b,u,n-_,n+k,1,h.minY,h.maxY,l),y=At(b,u,n+w,n+M,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g\|\|[],e+1,2r,2n),s.push(v\|\|[],e+1,2r,2n+1),s.push(m\|\|[],e+1,2r+1,2n),s.push(y\|\|[],e+1,2r+1,2n+1)}}},jt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0\|\|t>24)return null;var o=1<<t,s=Vt(t,e=(e%o+o)%o,r);if(this.tiles[s])return Dt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[Vt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?Dt(this.tiles[s],i):null):null};var qt=function(e){function r(t,r,n){e.call(this,t,r,Ut),n&&(this.loadGeoJSON=n)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var t=this;if(this._pendingCallback&&this._pendingLoadDataParams){var e=this._pendingCallback,r=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams,this.loadGeoJSON(r,function(n,i){if(n\|\|!i)return e(n);if(\"object\"!=typeof i)return e(new Error(\"Input data is not a valid GeoJSON object.\"));!function t(e,r){switch(e&&e.type\|\|null){case\"FeatureCollection\":return e.features=e.features.map(z(t,r)),e;case\"Feature\":return e.geometry=t(e.geometry,r),e;case\"Polygon\":case\"MultiPolygon\":return function(t,e){return\"Polygon\"===t.type?t.coordinates=O(t.coordinates,e):\"MultiPolygon\"===t.type&&(t.coordinates=t.coordinates.map(z(O,e))),t}(e,r);default:return e}}(i,!0);try{t._geoJSONIndex=r.cluster?function(t){return new ot(t)}(r.superclusterOptions).load(i.features):new jt(i,r.geojsonVtOptions)}catch(n){return e(n)}t.loaded={};var a={};if(r.request&&r.request.collectResourceTiming){var o=w(r.request.url);o&&(a.resourceTiming={},a.resourceTiming[r.source]=JSON.parse(JSON.stringify(o)))}e(null,a)})}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r}(M),Ht=function(e){var r=this;this.self=e,this.actor=new t.default$7(e,this),this.layerIndexes={},this.workerSourceTypes={vector:M,geojson:qt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isLoaded())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText}};return Ht.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},Ht.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},Ht.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},Ht.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},Ht.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},Ht.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},Ht.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},Ht.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},Ht.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},Ht.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},Ht.prototype.loadRTLTextPlugin=function(e,r,n){try{t.plugin.isLoaded()\|\|(this.self.importScripts(r),n(t.plugin.isLoaded()?null:new Error(\"RTL Text Plugin failed to import scripts from \"+r)))}catch(t){n(t.toString())}},Ht.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e\|\|(e=this.layerIndexes[t]=new n),e},Ht.prototype.getWorkerSource=function(t,e,r){var n=this;if(this.workerSources[t]\|\|(this.workerSources[t]={}),this.workerSources[t][e]\|\|(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){var i={send:function(e,r,i){n.actor.send(e,r,i,t)}};this.workerSources[t][e][r]=new this.workerSourceTypes[e](i,this.getLayerIndex(t))}return this.workerSources[t][e][r]},Ht.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]\|\|(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]\|\|(this.demWorkerSources[t][e]=new A),this.demWorkerSources[t][e]},\"undefined\"!=typeof WorkerGlobalScope&&\"undefined\"!=typeof self&&self instanceof WorkerGlobalScope&&new Ht(self),Ht}),i(0,function(t){var e=t.createCommonjsModule(function(t){function e(t){return!!(\"undefined\"!=typeof window&&\"undefined\"!=typeof document&&Array.prototype&&Array.prototype.every&&Array.prototype.filter&&Array.prototype.forEach&&Array.prototype.indexOf&&Array.prototype.lastIndexOf&&Array.prototype.map&&Array.prototype.some&&Array.prototype.reduce&&Array.prototype.reduceRight&&Array.isArray&&Function.prototype&&Function.prototype.bind&&Object.keys&&Object.create&&Object.getPrototypeOf&&Object.getOwnPropertyNames&&Object.isSealed&&Object.isFrozen&&Object.isExtensible&&Object.getOwnPropertyDescriptor&&Object.defineProperty&&Object.defineProperties&&Object.seal&&Object.freeze&&Object.preventExtensions&&\"JSON\"in window&&\"parse\"in JSON&&\"stringify\"in JSON&&function(){if(!(\"Worker\"in window&&\"Blob\"in window&&\"URL\"in window))return!1;var t,e,r=new Blob([\"\"],{type:\"text/javascript\"}),n=URL.createObjectURL(r);try{e=new Worker(n),t=!0}catch(e){t=!1}return e&&e.terminate(),URL.revokeObjectURL(n),t}()&&\"Uint8ClampedArray\"in window&&function(t){return void 0===r[t]&&(r[t]=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)\|\|r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)\|\|r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)\|\|r.getContext(\"experimental-webgl\",n)}(t)),r[t]}(t&&t.failIfMajorPerformanceCaveat))}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl\|\|{},window.mapboxgl.supported=e);var r={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}}),r=t.default.performance&&t.default.performance.now?t.default.performance.now.bind(t.default.performance):Date.now.bind(Date),n=t.default.requestAnimationFrame\|\|t.default.mozRequestAnimationFrame\|\|t.default.webkitRequestAnimationFrame\|\|t.default.msRequestAnimationFrame,i=t.default.cancelAnimationFrame\|\|t.default.mozCancelAnimationFrame\|\|t.default.webkitCancelAnimationFrame\|\|t.default.msCancelAnimationFrame,a={now:r,frame:function(t){return n(t)},cancelFrame:function(t){return i(t)},getImageData:function(e){var r=t.default.document.createElement(\"canvas\"),n=r.getContext(\"2d\");if(!n)throw new Error(\"failed to create canvas 2d context\");return r.width=e.width,r.height=e.height,n.drawImage(e,0,0,e.width,e.height),n.getImageData(0,0,e.width,e.height)},hardwareConcurrency:t.default.navigator.hardwareConcurrency\|\|4,get devicePixelRatio(){return t.default.devicePixelRatio},supportsWebp:!1};if(t.default.document){var o=t.default.document.createElement(\"img\");o.onload=function(){a.supportsWebp=!0},o.src=\"data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=\"}var s={create:function(e,r,n){var i=t.default.document.createElement(e);return r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.default.document.createElementNS(e,r)}},l=t.default.document?t.default.document.documentElement.style:null;function c(t){if(!l)return null;for(var e=0;e<t.length;e++)if(t[e]in l)return t[e];return t[0]}var u,f=c([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]);s.disableDrag=function(){l&&f&&(u=l[f],l[f]=\"none\")},s.enableDrag=function(){l&&f&&(l[f]=u)};var h=c([\"transform\",\"WebkitTransform\"]);s.setTransform=function(t,e){t.style[h]=e};var p=!1;try{var d=Object.defineProperty({},\"passive\",{get:function(){p=!0}});t.default.addEventListener(\"test\",d,d),t.default.removeEventListener(\"test\",d,d)}catch(t){p=!1}s.addEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&p?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},s.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&p?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var g=function(e){e.preventDefault(),e.stopPropagation(),t.default.removeEventListener(\"click\",g,!0)};s.suppressClick=function(){t.default.addEventListener(\"click\",g,!0),t.default.setTimeout(function(){t.default.removeEventListener(\"click\",g,!0)},0)},s.mousePos=function(e,r){var n=e.getBoundingClientRect();return r=r.touches?r.touches[0]:r,new t.default$1(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},s.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=\"touchend\"===r.type?r.changedTouches:r.touches,o=0;o<a.length;o++)i.push(new t.default$1(a[o].clientX-n.left-e.clientLeft,a[o].clientY-n.top-e.clientTop));return i},s.mouseButton=function(e){return void 0!==t.default.InstallTrigger&&2===e.button&&e.ctrlKey&&t.default.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},s.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var v={API_URL:\"https://api.mapbox.com\",REQUIRE_ACCESS_TOKEN:!0,ACCESS_TOKEN:null},m=\"See https://www.mapbox.com/api-documentation/#access-tokens\";function y(t,e){var r=A(v.API_URL);if(t.protocol=r.protocol,t.authority=r.authority,\"/\"!==r.path&&(t.path=\"\"+r.path+t.path),!v.REQUIRE_ACCESS_TOKEN)return T(t);if(!(e=e\|\|v.ACCESS_TOKEN))throw new Error(\"An API access token is required to use Mapbox GL. \"+m);if(\"s\"===e[0])throw new Error(\"Use a public access token (pk.) with Mapbox GL, not a secret access token (sk.). \"+m);return t.params.push(\"access_token=\"+e),T(t)}function x(t){return 0===t.indexOf(\"mapbox:\")}var b=function(t,e){if(!x(t))return t;var r=A(t);return r.path=\"/v4/\"+r.authority+\".json\",r.params.push(\"secure\"),y(r,e)},_=function(t,e,r,n){var i=A(t);return x(t)?(i.path=\"/styles/v1\"+i.path+\"/sprite\"+e+r,y(i,n)):(i.path+=\"\"+e+r,T(i))},w=/(\\.(png\|jpg)\\d)(?=$)/,k=function(t,e,r){if(!e\|\|!x(e))return t;var n=A(t),i=a.devicePixelRatio>=2\|\|512===r?\"@2x\":\"\",o=a.supportsWebp?\".webp\":\"$1\";return n.path=n.path.replace(w,\"\"+i+o),function(t){for(var e=0;e<t.length;e++)0===t[e].indexOf(\"access_token=tk.\")&&(t[e]=\"access_token=\"+(v.ACCESS_TOKEN\|\|\"\"))}(n.params),T(n)},M=/^(\\w+):\\/\\/([^\\/?])(\\/[^?]+)?\\??(.+)?/;function A(t){var e=t.match(M);if(!e)throw new Error(\"Unable to parse URL object\");return{protocol:e[1],authority:e[2],path:e[3]\|\|\"/\",params:e[4]?e[4].split(\"&\"):[]}}function T(t){var e=t.params.length?\"?\"+t.params.join(\"&\"):\"\";return t.protocol+\"://\"+t.authority+t.path+e}var S=t.default.HTMLImageElement,E=t.default.HTMLCanvasElement,C=t.default.HTMLVideoElement,L=t.default.ImageData,z=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};z.prototype.update=function(t,e){var r=t.width,n=t.height,i=!this.size\|\|this.size[0]!==r\|\|this.size[1]!==n,a=this.context,o=a.gl;this.useMipmap=Boolean(e&&e.useMipmap),o.bindTexture(o.TEXTURE_2D,this.texture),i?(this.size=[r,n],a.pixelStoreUnpack.set(1),this.format!==o.RGBA\|\|e&&!1===e.premultiply\|\|a.pixelStoreUnpackPremultiplyAlpha.set(!0),t instanceof S\|\|t instanceof E\|\|t instanceof C\|\|t instanceof L?o.texImage2D(o.TEXTURE_2D,0,this.format,this.format,o.UNSIGNED_BYTE,t):o.texImage2D(o.TEXTURE_2D,0,this.format,r,n,0,this.format,o.UNSIGNED_BYTE,t.data)):t instanceof S\|\|t instanceof E\|\|t instanceof C\|\|t instanceof L?o.texSubImage2D(o.TEXTURE_2D,0,0,0,o.RGBA,o.UNSIGNED_BYTE,t):o.texSubImage2D(o.TEXTURE_2D,0,0,0,r,n,o.RGBA,o.UNSIGNED_BYTE,t.data),this.useMipmap&&this.isSizePowerOfTwo()&&o.generateMipmap(o.TEXTURE_2D)},z.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST\|\|this.isSizePowerOfTwo()\|\|(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r\|\|t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},z.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},z.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var O=function(){this.images={},this.loaded=!1,this.requestors=[],this.shelfPack=new t.default$2(64,64,{autoResize:!0}),this.patterns={},this.atlasImage=new t.RGBAImage({width:64,height:64}),this.dirty=!0};O.prototype.isLoaded=function(){return this.loaded},O.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},O.prototype.getImage=function(t){return this.images[t]},O.prototype.addImage=function(t,e){this.images[t]=e},O.prototype.removeImage=function(t){delete this.images[t];var e=this.patterns[t];e&&(this.shelfPack.unref(e.bin),delete this.patterns[t])},O.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]\|\|(r=!1)}this.isLoaded()\|\|r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},O.prototype._notify=function(t,e){for(var r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=this.images[a];o&&(r[a]={data:o.data.clone(),pixelRatio:o.pixelRatio,sdf:o.sdf})}e(null,r)},O.prototype.getPixelSize=function(){return{width:this.shelfPack.w,height:this.shelfPack.h}},O.prototype.getPattern=function(e){var r=this.patterns[e];if(r)return r.position;var n=this.getImage(e);if(!n)return null;var i=n.data.width+2,a=n.data.height+2,o=this.shelfPack.packOne(i,a);if(!o)return null;this.atlasImage.resize(this.getPixelSize());var s=n.data,l=this.atlasImage,c=o.x+1,u=o.y+1,f=s.width,h=s.height;t.RGBAImage.copy(s,l,{x:0,y:0},{x:c,y:u},{width:f,height:h}),t.RGBAImage.copy(s,l,{x:0,y:h-1},{x:c,y:u-1},{width:f,height:1}),t.RGBAImage.copy(s,l,{x:0,y:0},{x:c,y:u+h},{width:f,height:1}),t.RGBAImage.copy(s,l,{x:f-1,y:0},{x:c-1,y:u},{width:1,height:h}),t.RGBAImage.copy(s,l,{x:0,y:0},{x:c+f,y:u},{width:1,height:h}),this.dirty=!0;var p=new t.ImagePosition(o,n);return this.patterns[e]={bin:o,position:p},p},O.prototype.bind=function(t){var e=t.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new z(t,this.atlasImage,e.RGBA),this.atlasTexture.bind(e.LINEAR,e.CLAMP_TO_EDGE)};var I=D,P=1e20;function D(t,e,r,n,i,a){this.fontSize=t\|\|24,this.buffer=void 0===e?3:e,this.cutoff=n\|\|.25,this.fontFamily=i\|\|\"sans-serif\",this.fontWeight=a\|\|\"normal\",this.radius=r\|\|8;var o=this.size=this.fontSize+2this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(oo),this.gridInner=new Float64Array(oo),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function R(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[le+s];for(B(n,i,a,o,r),l=0;l<r;l++)t[le+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[le+s];for(B(n,i,a,o,e),s=0;s<e;s++)t[le+s]=Math.sqrt(i[s])}}function B(t,e,r,n,i){r[0]=0,n[0]=-P,n[1]=+P;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+aa-(t[r[o]]+r[o]r[o]))/(2a-2r[o]);s<=n[o];)o--,s=(t[a]+aa-(t[r[o]]+r[o]r[o]))/(2a-2r[o]);r[++o]=a,n[o]=s,n[o+1]=+P}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])(a-r[o])+t[r[o]]}}D.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.sizethis.size),n=0;n<this.sizethis.size;n++){var i=e.data[4n+3]/255;this.gridOuter[n]=1===i?0:0===i?P:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?P:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(R(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),R(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.sizethis.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255(a/this.radius+this.cutoff))))}return r};var F=function(t,e){this.requestTransform=t,this.localIdeographFontFamily=e,this.entries={}};F.prototype.setURL=function(t){this.url=t},F.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a\|\|(a=n.entries[r]={glyphs:{},requests:{}});var o=a.glyphs[i];if(void 0===o)if(o=n._tinySDF(a,r,i))e(null,{stack:r,id:i,glyph:o});else{var s=Math.floor(i/256);if(256s>65535)e(new Error(\"glyphs > 65535 not supported\"));else{var l=a.requests[s];l\|\|(l=a.requests[s]=[],F.loadGlyphRange(r,s,n.url,n.requestTransform,function(t,e){if(e)for(var r in e)a.glyphs[+r]=e[+r];for(var n=0,i=l;n<i.length;n+=1)(0,i[n])(t,e);delete a.requests[s]})),l.push(function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]\|\|null})})}}else e(null,{stack:r,id:i,glyph:o})},function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]\|\|(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}})},F.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&(t.default$4[\"CJK Unified Ideographs\"](n)\|\|t.default$4[\"Hangul Syllables\"](n))){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new F.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},F.loadGlyphRange=function(e,r,n,i,a){var o=256r,s=o+255,l=i(function(t,e){if(!x(t))return t;var r=A(t);return r.path=\"/fonts/v1\"+r.path,y(r,e)}(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.default$3(r.data);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}})},F.TinySDF=I;var N=function(){this.specification=t.default$5.light.position};N.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},N.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var j=new t.Properties({anchor:new t.DataConstantProperty(t.default$5.light.anchor),position:new N,color:new t.DataConstantProperty(t.default$5.light.color),intensity:new t.DataConstantProperty(t.default$5.light.intensity)}),V=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(j),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e){if(!this._validate(t.validateLight,e))for(var r in e){var n=e[r];t.endsWith(r,\"-transition\")?this._transitionable.setTransition(r.slice(0,-\"-transition\".length),n):this._transitionable.setValue(r,n)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r){return t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.default$5})))},r}(t.Evented),U=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.widththis.heightthis.bytes),this.positions={}};U.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.positions[r]\|\|(this.positions[r]=this.addDash(t,e)),this.positions[r]},U.prototype.addDash=function(e,r){var n=r?7:0,i=2n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];for(var s=this.width/a,l=s/2,c=e.length%2==1,u=-n;u<=n;u++)for(var f=this.nextRow+n+u,h=this.widthf,p=c?-e[e.length-1]:0,d=e[0],g=1,v=0;v<this.width;v++){for(;d<v/s;)p=d,d+=e[g],c&&g===e.length-1&&(d+=e[0]),g++;var m=Math.abs(v-ps),y=Math.abs(v-ds),x=Math.min(m,y),b=g%2==1,_=void 0;if(r){var w=n?u/n(l+1):0;if(b){var k=l-Math.abs(w);_=Math.sqrt(xx+kk)}else _=l-Math.sqrt(xx+ww)}else _=(b?1:-1)x;this.data[3+4(h+v)]=Math.max(0,Math.min(255,_+128))}var M={y:(this.nextRow+n+.5)/this.height,height:2n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,M},U.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.RGBA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,this.width,this.height,0,e.RGBA,e.UNSIGNED_BYTE,this.data))};var q=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function H(e,r,n){var i=function(e,r){if(e)return n(e);if(r){var i=t.pick(r,[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\"]);r.vector_layers&&(i.vectorLayers=r.vector_layers,i.vectorLayerIds=i.vectorLayers.map(function(t){return t.id})),n(null,i)}};e.url?t.getJSON(r(b(e.url),t.ResourceType.Source),i):a.frame(function(){return i(null,e)})}q.prototype.broadcast=function(e,r,n){n=n\|\|function(){},t.asyncAll(this.actors,function(t,n){t.send(e,r,n)},n)},q.prototype.send=function(t,e,r,n){return(\"number\"!=typeof n\|\|isNaN(n))&&(n=this.currentActor=(this.currentActor+1)%this.actors.length),this.actors[n].send(t,e,r),n},q.prototype.remove=function(){this.actors.forEach(function(t){t.remove()}),this.actors=[],this.workerPool.release(this.id)},q.Actor=t.default$7;var G=function(t,e){if(isNaN(t)\|\|isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90\|\|this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};G.prototype.wrap=function(){return new G(t.wrap(this.lng,-180,180),this.lat)},G.prototype.toArray=function(){return[this.lng,this.lat]},G.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},G.prototype.toBounds=function(t){var e=360t/40075017,r=e/Math.cos(Math.PI/180this.lat);return new W(new G(this.lng-r,this.lat-e),new G(this.lng+r,this.lat+e))},G.convert=function(t){if(t instanceof G)return t;if(Array.isArray(t)&&(2===t.length\|\|3===t.length))return new G(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new G(Number(t.lng),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var W=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};W.prototype.setNorthEast=function(t){return this._ne=t instanceof G?new G(t.lng,t.lat):G.convert(t),this},W.prototype.setSouthWest=function(t){return this._sw=t instanceof G?new G(t.lng,t.lat):G.convert(t),this},W.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof G)e=t,r=t;else{if(!(t instanceof W))return Array.isArray(t)?t.every(Array.isArray)?this.extend(W.convert(t)):this.extend(G.convert(t)):this;if(e=t._sw,r=t._ne,!e\|\|!r)return this}return n\|\|i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new G(e.lng,e.lat),this._ne=new G(r.lng,r.lat)),this},W.prototype.getCenter=function(){return new G((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},W.prototype.getSouthWest=function(){return this._sw},W.prototype.getNorthEast=function(){return this._ne},W.prototype.getNorthWest=function(){return new G(this.getWest(),this.getNorth())},W.prototype.getSouthEast=function(){return new G(this.getEast(),this.getSouth())},W.prototype.getWest=function(){return this._sw.lng},W.prototype.getSouth=function(){return this._sw.lat},W.prototype.getEast=function(){return this._ne.lng},W.prototype.getNorth=function(){return this._ne.lat},W.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},W.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},W.prototype.isEmpty=function(){return!(this._sw&&this._ne)},W.convert=function(t){return!t\|\|t instanceof W?t:new W(t)};var Y=function(t,e,r){this.bounds=W.convert(this.validateBounds(t)),this.minzoom=e\|\|0,this.maxzoom=r\|\|24};Y.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},Y.prototype.contains=function(t){var e=Math.floor(this.lngX(this.bounds.getWest(),t.z)),r=Math.floor(this.latY(this.bounds.getNorth(),t.z)),n=Math.ceil(this.lngX(this.bounds.getEast(),t.z)),i=Math.ceil(this.latY(this.bounds.getSouth(),t.z));return t.x>=e&&t.x<n&&t.y>=r&&t.y<i},Y.prototype.lngX=function(t,e){return(t+180)(Math.pow(2,e)/360)},Y.prototype.latY=function(e,r){var n=t.clamp(Math.sin(Math.PI/180e),-.9999,.9999),i=Math.pow(2,r)/(2Math.PI);return Math.pow(2,r-1)+.5Math.log((1+n)/(1-n))-i};var X=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),H(this._options,this.map._transformRequest,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new Y(n.bounds,e.minzoom,e.maxzoom)),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},r.prototype.hasTile=function(t){return!this.tileBounds\|\|this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url),i={request:this.map._transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSizee.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:a.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes};function o(t,n){return e.aborted?r(null):t?r(t):(n&&n.resourceTiming&&(e.resourceTiming=n.resourceTiming),this.map._refreshExpiredTiles&&e.setExpiryData(n),e.loadVectorData(n,this.map.painter),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,void 0===e.workerID\|\|\"expired\"===e.state?e.workerID=this.dispatcher.send(\"loadTile\",i,o.bind(this)):\"loading\"===e.state?e.reloadCallback=r:this.dispatcher.send(\"reloadTile\",i,o.bind(this),e.workerID)},r.prototype.abortTile=function(t){this.dispatcher.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0,t.workerID)},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.dispatcher.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0,t.workerID)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),Z=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),H(this._options,this.map._transformRequest,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new Y(n.bounds,e.minzoom,e.maxzoom)),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds\|\|this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url,this.tileSize);e.request=t.getImage(this.map._transformRequest(i,t.ResourceType.Tile),function(t,i){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(t)e.state=\"errored\",r(t);else if(i){n.map._refreshExpiredTiles&&e.setExpiryData(i),delete i.cacheControl,delete i.expires;var a=n.map.painter.context,o=a.gl;e.texture=n.map.painter.getTileTexture(i.width),e.texture?e.texture.update(i,{useMipmap:!0}):(e.texture=new z(a,i,o.RGBA,{useMipmap:!0}),e.texture.bind(o.LINEAR,o.CLAMP_TO_EDGE,o.LINEAR_MIPMAP_NEAREST),a.extTextureFilterAnisotropic&&o.texParameterf(o.TEXTURE_2D,a.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,a.extTextureFilterAnisotropicMax)),e.state=\"loaded\",r(null)}})},r.prototype.abortTile=function(t,e){t.request&&(t.request.abort(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),$=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({},n),this.encoding=n.encoding\|\|\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url,this.tileSize);e.request=t.getImage(this.map._transformRequest(n,t.ResourceType.Tile),function(t,n){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(t)e.state=\"errored\",r(t);else if(n){this.map._refreshExpiredTiles&&e.setExpiryData(n),delete n.cacheControl,delete n.expires;var i=a.getImageData(n),o={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:i,encoding:this.encoding};e.workerID&&\"expired\"!==e.state\|\|(e.workerID=this.dispatcher.send(\"loadDEMTile\",o,function(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",this.dispatcher.send(\"removeDEMTile\",{uid:t.uid,source:this.id},void 0,t.workerID)},r}(Z),J=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this.dispatcher=i,this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type);var o=t.default$8/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster\|\|!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)o,extent:t.default$8,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics\|\|!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.default$8,radius:(n.clusterRadius\|\|50)o,log:!1}},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData(function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}})},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData(function(e){if(e)return r.fire(new t.ErrorEvent(e));var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}),this},r.prototype._updateWorkerData=function(e){var r,n,i=this,a=t.extend({},this.workerOptions),o=this._data;\"string\"==typeof o?(a.request=this.map._transformRequest((r=o,(n=t.default.document.createElement(\"a\")).href=r,n.href),t.ResourceType.Source),a.request.collectResourceTiming=this._collectResourceTiming):a.data=JSON.stringify(o),this.workerID=this.dispatcher.send(this.type+\".\"+a.source+\".loadData\",a,function(t,r){i._removed\|\|r&&r.abandoned\|\|(i._loaded=!0,r&&r.resourceTiming&&r.resourceTiming[i.id]&&(i._resourceTiming=r.resourceTiming[i.id].slice(0)),i.dispatcher.send(i.type+\".\"+a.source+\".coalesce\",null,null,i.workerID),e(t))},this.workerID)},r.prototype.loadTile=function(t,e){var r=this,n=void 0===t.workerID?\"loadTile\":\"reloadTile\",i={type:this.type,uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:a.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID=this.dispatcher.send(n,i,function(i,a){return t.unloadVectorData(),t.aborted?e(null):i?e(i):(t.loadVectorData(a,r.map.painter,\"reloadTile\"===n),e(null))},this.workerID)},r.prototype.abortTile=function(t){t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.dispatcher.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},null,t.workerID)},r.prototype.onRemove=function(){this._removed=!0,this.dispatcher.send(\"removeSource\",{type:this.type,source:this.id},null,this.workerID)},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),K=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),Q=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};Q.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao\|\|this.boundProgram!==e\|\|this.boundLayoutVertexBuffer!==r\|\|l\|\|this.boundIndexBuffer!==i\|\|this.boundVertexOffset!==a\|\|this.boundDynamicVertexBuffer!==o\|\|this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject\|\|u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Q.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes\|\|0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(u,t);a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var v=g[d];v.bind(),v.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},Q.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var tt=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._transformRequest(this.url,t.ResourceType.Image),function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(e.image=a.getImageData(n),e._finishLoading())})},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){this.coordinates=e;var r=this.map,n=e.map(function(t){return r.transform.locationCoordinate(G.convert(t)).zoomTo(0)}),i=this.centerCoord=t.getCoordinatesCenter(n);i.column=Math.floor(i.column),i.row=Math.floor(i.row),this.tileID=new t.CanonicalTileID(i.zoom,i.column,i.row),this.minzoom=this.maxzoom=i.zoom;var a=n.map(function(e){var r=e.zoomTo(i.zoom);return new t.default$1(Math.round((r.column-i.column)t.default$8),Math.round((r.row-i.row)t.default$8))});return this._boundsArray=new t.RasterBoundsArray,this._boundsArray.emplaceBack(a[0].x,a[0].y,0,0),this._boundsArray.emplaceBack(a[1].x,a[1].y,t.default$8,0),this._boundsArray.emplaceBack(a[3].x,a[3].y,0,t.default$8),this._boundsArray.emplaceBack(a[2].x,a[2].y,t.default$8,t.default$8),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var t=this.map.painter.context,e=t.gl;for(var r in this.boundsBuffer\|\|(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO\|\|(this.boundsVAO=new Q),this.texture\|\|(this.texture=new z(t,this.image,e.RGBA),this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE)),this.tiles){var n=this.tiles[r];\"loaded\"!==n.state&&(n.state=\"loaded\",n.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented),et=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this,r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];e.urls.push(e.map._transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",function(){e.map._rerender()}),e.map&&e.video.play(),e._finishLoading())})},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map\|\|(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length\|\|this.video.readyState<2)){var t=this.map.painter.context,e=t.gl;for(var r in this.boundsBuffer\|\|(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO\|\|(this.boundsVAO=new Q),this.texture?this.video.paused\|\|(this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE),e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,this.video)):(this.texture=new z(t,this.video,e.RGBA),this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE)),this.tiles){var n=this.tiles[r];\"loaded\"!==n.state&&(n.state=\"loaded\",n.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(tt),rt=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some(function(t){return!Array.isArray(t)\|\|2!==t.length\|\|t.some(function(t){return\"number\"!=typeof t})})\|\|this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas\|\|n.canvas instanceof t.default.HTMLCanvasElement\|\|this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate\|\|n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this.canvas\|\|(this.canvas=this.options.canvas instanceof t.default.HTMLCanvasElement?this.options.canvas:t.default.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map._rerender()},this.pause=function(){this._playing=!1},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var t=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,t=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,t=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer\|\|(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO\|\|(this.boundsVAO=new Q),this.texture?t?this.texture.update(this.canvas):this._playing&&(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.canvas)):(this.texture=new z(e,this.canvas,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)\|\|r<=0)return!0}return!1},r}(tt),nt={vector:X,raster:Z,\"raster-dem\":$,geojson:J,video:et,image:tt,canvas:rt},it=function(e,r,n,i){var a=new nt[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a};function at(t,e,r,n,i){var a=i.maxPitchScaleFactor(),o=t.tilesIn(r,a);o.sort(ot);for(var s=[],l=0,c=o;l<c.length;l+=1){var u=c[l];s.push({wrappedTileID:u.tileID.wrapped().key,queryResults:u.tile.queryRenderedFeatures(e,u.queryGeometry,u.scale,n,i,a,t.transform.calculatePosMatrix(u.tileID.toUnwrapped()))})}return function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]\|\|{};for(var c in o)for(var u=o[c],f=l[c]=l[c]\|\|{},h=e[c]=e[c]\|\|[],p=0,d=u;p<d.length;p+=1){var g=d[p];f[g.featureIndex]\|\|(f[g.featureIndex]=!0,h.push(g.feature))}}return e}(s)}function ot(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ\|\|r.canonical.y-n.canonical.y\|\|r.wrap-n.wrap\|\|r.canonical.x-n.canonical.x}var st=function(e,r){this.tileID=e,this.uid=t.uniqueId(),this.uses=0,this.tileSize=r,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.expiredRequestCount=0,this.state=\"loading\"};st.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<a.now()\|\|this.fadeEndTime&&e<this.fadeEndTime\|\|(this.fadeEndTime=e)},st.prototype.wasRequested=function(){return\"errored\"===this.state\|\|\"loaded\"===this.state\|\|\"reloading\"===this.state},st.prototype.loadVectorData=function(e,r,n){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",e){if(e.featureIndex&&(this.latestFeatureIndex=e.featureIndex,e.rawTileData?(this.latestRawTileData=e.rawTileData,this.latestFeatureIndex.rawTileData=e.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=e.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.layerIds.map(function(t){return e.getLayer(t)}).filter(Boolean);if(0!==o.length){a.layers=o;for(var s=0,l=o;s<l.length;s+=1)r[l[s].id]=a}}return r}(e.buckets,r.style),n)for(var i in this.buckets){var a=this.buckets[i];a instanceof t.default$14&&(a.justReloaded=!0)}for(var o in this.queryPadding=0,this.buckets){var s=this.buckets[o];this.queryPadding=Math.max(this.queryPadding,r.style.getLayer(s.layerIds[0]).queryRadius(s))}e.iconAtlasImage&&(this.iconAtlasImage=e.iconAtlasImage),e.glyphAtlasImage&&(this.glyphAtlasImage=e.glyphAtlasImage)}else this.collisionBoxArray=new t.CollisionBoxArray},st.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.iconAtlasTexture&&this.iconAtlasTexture.destroy(),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},st.prototype.unloadDEMData=function(){this.dem=null,this.neighboringTiles=null,this.state=\"unloaded\"},st.prototype.getBucket=function(t){return this.buckets[t.id]},st.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploaded\|\|(r.upload(t),r.uploaded=!0)}var n=t.gl;this.iconAtlasImage&&(this.iconAtlasTexture=new z(t,this.iconAtlasImage,n.RGBA),this.iconAtlasImage=null),this.glyphAtlasImage&&(this.glyphAtlasTexture=new z(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},st.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:e,scale:r,tileSize:this.tileSize,posMatrix:o,transform:i,params:n,queryPadding:this.queryPaddinga},t):{}},st.prototype.querySourceFeatures=function(e,r){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData){var n=this.latestFeatureIndex.loadVTLayers(),i=r?r.sourceLayer:\"\",a=n._geojsonTileLayer\|\|n[i];if(a)for(var o=t.default$13(r&&r.filter),s={z:this.tileID.overscaledZ,x:this.tileID.canonical.x,y:this.tileID.canonical.y},l=0;l<a.length;l++){var c=a.feature(l);if(o(new t.default$16(this.tileID.overscaledZ),c)){var u=new t.default$12(c,s.z,s.x,s.y);u.tile=s,e.push(u)}}}},st.prototype.clearMask=function(){this.segments&&(this.segments.destroy(),delete this.segments),this.maskedBoundsBuffer&&(this.maskedBoundsBuffer.destroy(),delete this.maskedBoundsBuffer),this.maskedIndexBuffer&&(this.maskedIndexBuffer.destroy(),delete this.maskedIndexBuffer)},st.prototype.setMask=function(e,r){if(!t.default$10(this.mask,e)&&(this.mask=e,this.clearMask(),!t.default$10(e,{0:!0}))){var n=new t.RasterBoundsArray,i=new t.TriangleIndexArray;this.segments=new t.default$15,this.segments.prepareSegment(0,n,i);for(var a=Object.keys(e),o=0;o<a.length;o++){var s=e[a[o]],l=t.default$8>>s.z,c=new t.default$1(s.xl,s.yl),u=new t.default$1(c.x+l,c.y+l),f=this.segments.prepareSegment(4,n,i);n.emplaceBack(c.x,c.y,c.x,c.y),n.emplaceBack(u.x,c.y,u.x,c.y),n.emplaceBack(c.x,u.y,c.x,u.y),n.emplaceBack(u.x,u.y,u.x,u.y);var h=f.vertexLength;i.emplaceBack(h,h+1,h+2),i.emplaceBack(h+1,h+2,h+3),f.vertexLength+=4,f.primitiveLength+=2}this.maskedBoundsBuffer=r.createVertexBuffer(n,K.members),this.maskedIndexBuffer=r.createIndexBuffer(i)}},st.prototype.hasData=function(){return\"loaded\"===this.state\|\|\"reloading\"===this.state\|\|\"expired\"===this.state},st.prototype.setExpiryData=function(e){var r=this.expirationTime;if(e.cacheControl){var n=t.parseCacheControl(e.cacheControl);n[\"max-age\"]&&(this.expirationTime=Date.now()+1e3n[\"max-age\"])}else e.expires&&(this.expirationTime=new Date(e.expires).getTime());if(this.expirationTime){var i=Date.now(),a=!1;if(this.expirationTime>i)a=!1;else if(r)if(this.expirationTime<r)a=!0;else{var o=this.expirationTime-r;o?this.expirationTime=i+Math.max(o,3e4):a=!0}else a=!0;a?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},st.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)};var lt=function(t,e){this.max=t,this.onRemove=e,this.reset()};lt.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},lt.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout(function(){n.remove(t,a)},r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},lt.prototype.has=function(t){return t.wrapped().key in this.data},lt.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},lt.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},lt.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},lt.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},lt.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this};var ct=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw\|\|delete e.arrayBuffer};ct.prototype.unbindVAO=function(){this.context.extVertexArrayObject&&this.context.bindVertexArrayOES.set(null)},ct.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},ct.prototype.updateData=function(t){var e=this.context.gl;this.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},ct.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var ut={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},ft=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw\|\|delete e.arrayBuffer};ft.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},ft.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},ft.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},ft.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[ut[i.type]],!1,this.itemSize,i.offset+this.itemSize(r\|\|0))}},ft.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var ht=function(e){this.context=e,this.current=t.default$6.transparent};ht.prototype.get=function(){return this.current},ht.prototype.set=function(t){var e=this.current;t.r===e.r&&t.g===e.g&&t.b===e.b&&t.a===e.a\|\|(this.context.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t)};var pt=function(t){this.context=t,this.current=1};pt.prototype.get=function(){return this.current},pt.prototype.set=function(t){this.current!==t&&(this.context.gl.clearDepth(t),this.current=t)};var dt=function(t){this.context=t,this.current=0};dt.prototype.get=function(){return this.current},dt.prototype.set=function(t){this.current!==t&&(this.context.gl.clearStencil(t),this.current=t)};var gt=function(t){this.context=t,this.current=[!0,!0,!0,!0]};gt.prototype.get=function(){return this.current},gt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]\|\|(this.context.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t)};var vt=function(t){this.context=t,this.current=!0};vt.prototype.get=function(){return this.current},vt.prototype.set=function(t){this.current!==t&&(this.context.gl.depthMask(t),this.current=t)};var mt=function(t){this.context=t,this.current=255};mt.prototype.get=function(){return this.current},mt.prototype.set=function(t){this.current!==t&&(this.context.gl.stencilMask(t),this.current=t)};var yt=function(t){this.context=t,this.current={func:t.gl.ALWAYS,ref:0,mask:255}};yt.prototype.get=function(){return this.current},yt.prototype.set=function(t){var e=this.current;t.func===e.func&&t.ref===e.ref&&t.mask===e.mask\|\|(this.context.gl.stencilFunc(t.func,t.ref,t.mask),this.current=t)};var xt=function(t){this.context=t;var e=this.context.gl;this.current=[e.KEEP,e.KEEP,e.KEEP]};xt.prototype.get=function(){return this.current},xt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]\|\|(this.context.gl.stencilOp(t[0],t[1],t[2]),this.current=t)};var bt=function(t){this.context=t,this.current=!1};bt.prototype.get=function(){return this.current},bt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.STENCIL_TEST):e.disable(e.STENCIL_TEST),this.current=t}};var _t=function(t){this.context=t,this.current=[0,1]};_t.prototype.get=function(){return this.current},_t.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]\|\|(this.context.gl.depthRange(t[0],t[1]),this.current=t)};var wt=function(t){this.context=t,this.current=!1};wt.prototype.get=function(){return this.current},wt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.DEPTH_TEST):e.disable(e.DEPTH_TEST),this.current=t}};var kt=function(t){this.context=t,this.current=t.gl.LESS};kt.prototype.get=function(){return this.current},kt.prototype.set=function(t){this.current!==t&&(this.context.gl.depthFunc(t),this.current=t)};var Mt=function(t){this.context=t,this.current=!1};Mt.prototype.get=function(){return this.current},Mt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.BLEND):e.disable(e.BLEND),this.current=t}};var At=function(t){this.context=t;var e=this.context.gl;this.current=[e.ONE,e.ZERO]};At.prototype.get=function(){return this.current},At.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]\|\|(this.context.gl.blendFunc(t[0],t[1]),this.current=t)};var Tt=function(e){this.context=e,this.current=t.default$6.transparent};Tt.prototype.get=function(){return this.current},Tt.prototype.set=function(t){var e=this.current;t.r===e.r&&t.g===e.g&&t.b===e.b&&t.a===e.a\|\|(this.context.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t)};var St=function(t){this.context=t,this.current=null};St.prototype.get=function(){return this.current},St.prototype.set=function(t){this.current!==t&&(this.context.gl.useProgram(t),this.current=t)};var Et=function(t){this.context=t,this.current=1};Et.prototype.get=function(){return this.current},Et.prototype.set=function(e){var r=this.context.lineWidthRange,n=t.clamp(e,r[0],r[1]);this.current!==n&&(this.context.gl.lineWidth(n),this.current=e)};var Ct=function(t){this.context=t,this.current=t.gl.TEXTURE0};Ct.prototype.get=function(){return this.current},Ct.prototype.set=function(t){this.current!==t&&(this.context.gl.activeTexture(t),this.current=t)};var Lt=function(t){this.context=t;var e=this.context.gl;this.current=[0,0,e.drawingBufferWidth,e.drawingBufferHeight]};Lt.prototype.get=function(){return this.current},Lt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]\|\|(this.context.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t)};var zt=function(t){this.context=t,this.current=null};zt.prototype.get=function(){return this.current},zt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t}};var Ot=function(t){this.context=t,this.current=null};Ot.prototype.get=function(){return this.current},Ot.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t}};var It=function(t){this.context=t,this.current=null};It.prototype.get=function(){return this.current},It.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t}};var Pt=function(t){this.context=t,this.current=null};Pt.prototype.get=function(){return this.current},Pt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t}};var Dt=function(t){this.context=t,this.current=null};Dt.prototype.get=function(){return this.current},Dt.prototype.set=function(t){var e=this.context.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t};var Rt=function(t){this.context=t,this.current=null};Rt.prototype.get=function(){return this.current},Rt.prototype.set=function(t){this.current!==t&&this.context.extVertexArrayObject&&(this.context.extVertexArrayObject.bindVertexArrayOES(t),this.current=t)};var Bt=function(t){this.context=t,this.current=4};Bt.prototype.get=function(){return this.current},Bt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t}};var Ft=function(t){this.context=t,this.current=!1};Ft.prototype.get=function(){return this.current},Ft.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t}};var Nt=function(t,e){this.context=t,this.current=null,this.parent=e};Nt.prototype.get=function(){return this.current};var jt=function(t){function e(e,r){t.call(this,e,r),this.dirty=!1}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(this.dirty\|\|this.current!==t){var e=this.context.gl;this.context.bindFramebuffer.set(this.parent),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e.prototype.setDirty=function(){this.dirty=!0},e}(Nt),Vt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;this.context.bindFramebuffer.set(this.parent),e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t}},e}(Nt),Ut=function(t,e,r){this.context=t,this.width=e,this.height=r;var n=t.gl,i=this.framebuffer=n.createFramebuffer();this.colorAttachment=new jt(t,i),this.depthAttachment=new Vt(t,i)};Ut.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();e&&t.deleteTexture(e);var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r),t.deleteFramebuffer(this.framebuffer)};var qt=function(t,e,r){this.func=t,this.mask=e,this.range=r};qt.ReadOnly=!1,qt.ReadWrite=!0,qt.disabled=new qt(519,qt.ReadOnly,[0,1]);var Ht=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Ht.disabled=new Ht({func:519,mask:0},0,0,7680,7680,7680);var Gt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Gt.disabled=new Gt(Gt.Replace=[1,0],t.default$6.transparent,[!1,!1,!1,!1]),Gt.unblended=new Gt(Gt.Replace,t.default$6.transparent,[!0,!0,!0,!0]),Gt.alphaBlended=new Gt([1,771],t.default$6.transparent,[!0,!0,!0,!0]);var Wt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.lineWidthRange=t.getParameter(t.ALIASED_LINE_WIDTH_RANGE),this.clearColor=new ht(this),this.clearDepth=new pt(this),this.clearStencil=new dt(this),this.colorMask=new gt(this),this.depthMask=new vt(this),this.stencilMask=new mt(this),this.stencilFunc=new yt(this),this.stencilOp=new xt(this),this.stencilTest=new bt(this),this.depthRange=new _t(this),this.depthTest=new wt(this),this.depthFunc=new kt(this),this.blend=new Mt(this),this.blendFunc=new At(this),this.blendColor=new Tt(this),this.program=new St(this),this.lineWidth=new Et(this),this.activeTexture=new Ct(this),this.viewport=new Lt(this),this.bindFramebuffer=new zt(this),this.bindRenderbuffer=new Ot(this),this.bindTexture=new It(this),this.bindVertexBuffer=new Pt(this),this.bindElementBuffer=new Dt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new Rt(this),this.pixelStoreUnpack=new Bt(this),this.pixelStoreUnpackPremultiplyAlpha=new Ft(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")\|\|t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")\|\|t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&t.getExtension(\"OES_texture_half_float_linear\")};Wt.prototype.createIndexBuffer=function(t,e){return new ct(this,t,e)},Wt.prototype.createVertexBuffer=function(t,e,r){return new ft(this,t,e,r)},Wt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Wt.prototype.createFramebuffer=function(t,e){return new Ut(this,t,e)},Wt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i\|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i\|=n.DEPTH_BUFFER_BIT,this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Wt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS\|\|t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Wt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS\|\|t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Wt.prototype.setColorMode=function(e){t.default$10(e.blendFunction,Gt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)};var Yt=function(e){function r(t,r,n){var i=this;e.call(this),this.id=t,this.dispatcher=n,this.on(\"data\",function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(i._sourceLoaded=!0),i._sourceLoaded&&!i._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(i.reload(),i.transform&&i.update(i.transform))}),this.on(\"error\",function(){i._sourceErrored=!0}),this._source=it(t,r,n,this),this._tiles={},this._cache=new lt(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._isIdRenderable=this._isIdRenderable.bind(this),this._coveredTiles={}}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,function(){})},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,function(){})},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._tiles)this._tiles[e].upload(t)},r.prototype.getIds=function(){var e=this;return Object.keys(this._tiles).map(Number).sort(function(r,n){var i=e._tiles[r].tileID,a=e._tiles[n].tileID,o=new t.default$1(i.canonical.x,i.canonical.y).rotate(e.transform.angle),s=new t.default$1(a.canonical.x,a.canonical.y).rotate(e.transform.angle);return i.overscaledZ-a.overscaledZ\|\|s.y-o.y\|\|s.x-o.x})},r.prototype.getRenderableIds=function(){return this.getIds().filter(this._isIdRenderable)},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0,{});return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=a.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID})),this.map&&(this.map.painter.tileExtentVAO.vao=null)},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n\|\|Math.abs(n)>1\|\|(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype.getZoom=function(t){return t.zoom+t.scaleZoom(t.tileSize/this._source.tileSize)},r.prototype._findLoadedChildren=function(t,e,r){var n=!1;for(var i in this._tiles){var a=this._tiles[i];if(!(r[i]\|\|!a.hasData()\|\|a.tileID.overscaledZ<=t.overscaledZ\|\|a.tileID.overscaledZ>e)){var o=Math.pow(2,a.tileID.canonical.z-t.canonical.z);if(Math.floor(a.tileID.canonical.x/o)===t.canonical.x&&Math.floor(a.tileID.canonical.y/o)===t.canonical.y)for(r[i]=a.tileID,n=!0;a&&a.tileID.overscaledZ-1>t.overscaledZ;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);if(!s)break;(a=this._tiles[s.key])&&a.hasData()&&(delete r[i],r[s.key]=s)}}}return n},r.prototype.findLoadedParent=function(t,e,r){for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n);if(!i)return;var a=String(i.key),o=this._tiles[a];if(o&&o.hasData())return r[a]=i,o;if(this._cache.has(i))return r[a]=i,this._cache.get(i)}},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map(function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)}):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter(function(t){return n._source.hasTile(t)}))):i=[];var o,s=(this._source.roundZoom?Math.round:Math.floor)(this.getZoom(e)),l=Math.max(s-r.maxOverzooming,this._source.minzoom),c=Math.max(s+r.maxUnderzooming,this._source.minzoom),u=this._updateRetainedTiles(i,s),f={};if(Zt(this._source.type))for(var h=Object.keys(u),p=0;p<h.length;p++){var d=h[p],g=u[d],v=n._tiles[d];if(v&&(void 0===v.fadeEndTime\|\|v.fadeEndTime>=a.now())){n._findLoadedChildren(g,c,u)&&(u[d]=g);var m=n.findLoadedParent(g,l,f);m&&n._addTile(m.tileID)}}for(o in f)u[o]\|\|(n._coveredTiles[o]=!0);for(o in f)u[o]=f[o];for(var y=t.keysDifference(this._tiles,u),x=0;x<y.length;x++)n._removeTile(y[x])}},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s=0;s<t.length;s++){var l=t[s],c=this._addTile(l),u=!1;if(c.hasData())n[l.key]=l;else{u=c.wasRequested(),n[l.key]=l;var f=!0;if(e+1>this._source.maxzoom){var h=l.children(this._source.maxzoom)[0],p=this.getTile(h);p&&p.hasData()?n[h.key]=h:f=!1}else{this._findLoadedChildren(l,o,n);for(var d=l.children(this._source.maxzoom),g=0;g<d.length;g++)if(!n[d[g].key]){f=!1;break}}if(!f)for(var v=l.overscaledZ-1;v>=a;--v){var m=l.scaledTo(v);if(i[m.key])break;if(i[m.key]=!0,!(c=this.getTile(m))&&u&&(c=this._addTile(m)),c&&(n[m.key]=m,u=c.wasRequested(),c.hasData()))break}}}return n},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e);var n=Boolean(r);return n\|\|(r=new st(e,this._source.tileSizee.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n\|\|this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout(function(){r._reloadTile(t,\"expired\"),delete r._timers[t]},n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0\|\|(e.hasData()?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r){for(var n=[],i=this.getIds(),a=1/0,o=1/0,s=-1/0,l=-1/0,c=e[0].zoom,u=0;u<e.length;u++){var f=e[u];a=Math.min(a,f.column),o=Math.min(o,f.row),s=Math.max(s,f.column),l=Math.max(l,f.row)}for(var h=0;h<i.length;h++){var p=this._tiles[i[h]],d=p.tileID,g=Math.pow(2,this.transform.zoom-p.tileID.overscaledZ),v=rp.queryPaddingt.default$8/p.tileSize/g,m=[Xt(d,new t.default$17(a,o,c)),Xt(d,new t.default$17(s,l,c))];if(m[0].x-v<t.default$8&&m[0].y-v<t.default$8&&m[1].x+v>=0&&m[1].y+v>=0){for(var y=[],x=0;x<e.length;x++)y.push(Xt(d,e[x]));n.push({tile:p,tileID:d,queryGeometry:[y],scale:g})}}return n},r.prototype.getVisibleCoordinates=function(){for(var t=this,e=this.getRenderableIds().map(function(e){return t._tiles[e].tileID}),r=0,n=e;r<n.length;r+=1){var i=n[r];i.posMatrix=t.transform.calculatePosMatrix(i.toUnwrapped())}return e},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Zt(this._source.type))for(var t in this._tiles){var e=this._tiles[t];if(void 0!==e.fadeEndTime&&e.fadeEndTime>=a.now())return!0}return!1},r}(t.Evented);function Xt(e,r){var n=r.zoomTo(e.canonical.z);return new t.default$1((n.column-(e.canonical.x+e.wrapMath.pow(2,e.canonical.z)))t.default$8,(n.row-e.canonical.y)t.default$8)}function Zt(t){return\"raster\"===t\|\|\"image\"===t\|\|\"video\"===t}function $t(){return new t.default.Worker(En.workerUrl)}Yt.maxOverzooming=10,Yt.maxUnderzooming=3;var Jt,Kt=function(){this.active={}};function Qt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.default$18.forEach(function(t){t in r&&(n[t]=r[t])}),n}function te(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Qt(t[n],e[t[n].ref]));return t}Kt.prototype.acquire=function(t){if(!this.workers){var e=En.workerCount;for(this.workers=[];this.workers.length<e;)this.workers.push(new $t)}return this.active[t]=!0,this.workers.slice()},Kt.prototype.release=function(t){delete this.active[t],0===Object.keys(this.active).length&&(this.workers.forEach(function(t){t.terminate()}),this.workers=null)};var ee={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function re(t,e,r){r.push({command:ee.addSource,args:[t,e[t]]})}function ne(t,e,r){e.push({command:ee.removeSource,args:[t]}),r[t]=!0}function ie(t,e,r,n){ne(t,r,n),re(t,e,r)}function ae(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.default$10(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.default$10(e[n][i],r[n][i]))return!1;return!0}function oe(e,r,n,i,a,o){var s;for(s in r=r\|\|{},e=e\|\|{})e.hasOwnProperty(s)&&(t.default$10(e[s],r[s])\|\|n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.default$10(e[s],r[s])\|\|n.push({command:o,args:[i,s,r[s],a]}))}function se(t){return t.id}function le(t,e){return t[e.id]=e,t}var ce=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCountthis.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};ce.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},ce.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},ce.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},ce.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},ce.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},ce.prototype._query=function(t,e,r,n,i){if(r<0\|\|t>this.width\|\|n<0\|\|e>this.height)return!i&&[];var a=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var o=0;o<this.boxKeys.length;o++)a.push({key:this.boxKeys[o],x1:this.bboxes[4o],y1:this.bboxes[4o+1],x2:this.bboxes[4o+2],y2:this.bboxes[4o+3]});for(var s=0;s<this.circleKeys.length;s++){var l=this.circles[3s],c=this.circles[3s+1],u=this.circles[3s+2];a.push({key:this.circleKeys[s],x1:l-u,y1:c-u,x2:l+u,y2:c+u})}}else{var f={hitTest:i,seenUids:{box:{},circle:{}}};this._forEachCell(t,e,r,n,this._queryCell,a,f)}return i?a.length>0:a},ce.prototype._queryCircle=function(t,e,r,n){var i=t-r,a=t+r,o=e-r,s=e+r;if(a<0\|\|i>this.width\|\|s<0\|\|o>this.height)return!n&&[];var l=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(i,o,a,s,this._queryCellCircle,l,c),n?l.length>0:l},ce.prototype.query=function(t,e,r,n){return this._query(t,e,r,n,!1)},ce.prototype.hitTest=function(t,e,r,n){return this._query(t,e,r,n,!0)},ce.prototype.hitTestCircle=function(t,e,r){return this._queryCircle(t,e,r,!0)},ce.prototype._queryCell=function(t,e,r,n,i,a,o){var s=o.seenUids,l=this.boxCells[i];if(null!==l)for(var c=this.bboxes,u=0,f=l;u<f.length;u+=1){var h=f[u];if(!s.box[h]){s.box[h]=!0;var p=4h;if(t<=c[p+2]&&e<=c[p+3]&&r>=c[p+0]&&n>=c[p+1]){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[h],x1:c[p],y1:c[p+1],x2:c[p+2],y2:c[p+3]})}}}var d=this.circleCells[i];if(null!==d)for(var g=this.circles,v=0,m=d;v<m.length;v+=1){var y=m[v];if(!s.circle[y]){s.circle[y]=!0;var x=3y;if(this._circleAndRectCollide(g[x],g[x+1],g[x+2],t,e,r,n)){if(o.hitTest)return a.push(!0),!0;var b=g[x],_=g[x+1],w=g[x+2];a.push({key:this.circleKeys[y],x1:b-w,y1:_-w,x2:b+w,y2:_+w})}}}},ce.prototype._queryCellCircle=function(t,e,r,n,i,a,o){var s=o.circle,l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4p;if(this._circleAndRectCollide(s.x,s.y,s.radius,u[d+0],u[d+1],u[d+2],u[d+3]))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,m=0,y=g;m<y.length;m+=1){var x=y[m];if(!l.circle[x]){l.circle[x]=!0;var b=3x;if(this._circlesCollide(v[b],v[b+1],v[b+2],s.x,s.y,s.radius))return a.push(!0),!0}}},ce.prototype._forEachCell=function(t,e,r,n,i,a,o){for(var s=this._convertToXCellCoord(t),l=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),u=this._convertToYCellCoord(n),f=s;f<=c;f++)for(var h=l;h<=u;h++){var p=this.xCellCounth+f;if(i.call(this,t,e,r,n,p,a,o))return}},ce.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(tthis.xScale)))},ce.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(tthis.yScale)))},ce.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return ll>oo+ss},ce.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s\|\|u<=c)return!0;var f=l-s,h=u-c;return ff+hh<=rr};var ue=t.default$19.layout;function fe(e,r,n,i,a){var o=t.mat4.identity(new Float32Array(16));return r?(t.mat4.identity(o),t.mat4.scale(o,o,[1/a,1/a,1]),n\|\|t.mat4.rotateZ(o,o,i.angle)):(t.mat4.scale(o,o,[i.width/2,-i.height/2,1]),t.mat4.translate(o,o,[1,-1,0]),t.mat4.multiply(o,o,e)),o}function he(e,r,n,i,a){var o=t.mat4.identity(new Float32Array(16));return r?(t.mat4.multiply(o,o,e),t.mat4.scale(o,o,[a,a,1]),n\|\|t.mat4.rotateZ(o,o,-i.angle)):(t.mat4.scale(o,o,[1,-1,1]),t.mat4.translate(o,o,[-1,-1,0]),t.mat4.scale(o,o,[2/i.width,2/i.height,1])),o}function pe(e,r){var n=[e.x,e.y,0,1];ke(n,n,r);var i=n[3];return{point:new t.default$1(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function de(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ge(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom,ue.properties[i?\"text-size\":\"icon-size\"]),f=[256/n.width2+1,256/n.height2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,v=!1,m=0;m<d.length;m++){var y=d.get(m);if(y.hidden\|\|y.writingMode===t.WritingMode.vertical&&!v)we(y.numGlyphs,h);else{v=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.vec4.transformMat4(x,x,r),de(x,f)){var b=.5+x[3]/n.transform.cameraToCenterDistance.5,_=t.evaluateSizeForFeature(c,u,y),w=s?_b:_/b,k=new t.default$1(y.anchorX,y.anchorY),M=pe(k,a).point,A={},T=ye(y,w,!1,l,r,a,o,e.glyphOffsetArray,p,h,M,k,A,g);v=T.useVertical,(T.notEnoughRoom\|\|v\|\|T.needsFlipping&&ye(y,w,!0,l,r,a,o,e.glyphOffsetArray,p,h,M,k,A,g).notEnoughRoom)&&we(y.numGlyphs,h)}else we(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ve(t,e,r,n,i,a,o,s,l,c,u,f){var h=s.glyphStartIndex+s.numGlyphs,p=s.lineStartIndex,d=s.lineStartIndex+s.lineLength,g=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(h-1),m=be(tg,r,n,i,a,o,s.segment,p,d,l,c,u,f);if(!m)return null;var y=be(tv,r,n,i,a,o,s.segment,p,d,l,c,u,f);return y?{first:m,last:y}:null}function me(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function ye(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,v=r/24,m=e.lineOffsetXr,y=e.lineOffsetYr;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ve(v,l,m,y,n,f,h,e,c,o,p,!1);if(!w)return{notEnoughRoom:!0};var k=pe(w.first.point,s).point,M=pe(w.last.point,s).point;if(i&&!n){var A=me(e.writingMode,k,M,d);if(A)return A}g=[w.first];for(var T=e.glyphStartIndex+1;T<x-1;T++)g.push(be(vl.getoffsetX(T),m,y,n,f,h,e.segment,b,_,c,o,p,!1));g.push(w.last)}else{if(i&&!n){var S=pe(h,a).point,E=e.lineStartIndex+e.segment+1,C=new t.default$1(c.getx(E),c.gety(E)),L=pe(C,a),z=L.signedDistanceFromCamera>0?L.point:xe(h,C,S,1,a),O=me(e.writingMode,S,z,d);if(O)return O}var I=be(vl.getoffsetX(e.glyphStartIndex),m,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p,!1);if(!I)return{notEnoughRoom:!0};g=[I]}for(var P=0,D=g;P<D.length;P+=1){var R=D[P];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function xe(t,e,r,n,i){var a=pe(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function be(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=i?e-r:e+r,g=d>0?1:-1,v=0;i&&(g=-1,v=Math.PI),g<0&&(v+=Math.PI);for(var m=g>0?l+s:l+s+1,y=m,x=a,b=a,_=0,w=0,k=Math.abs(d);_+w<=k;){if((m+=g)<l\|\|m>=c)return null;if(b=x,void 0===(x=h[m])){var M=new t.default$1(u.getx(m),u.gety(m)),A=pe(M,f);if(A.signedDistanceFromCamera>0)x=h[m]=A.point;else{var T=m-g;x=xe(0===_?o:new t.default$1(u.getx(T),u.gety(T)),M,b,k-_+1,f)}}_+=w,w=b.dist(x)}var S=(k-_)/w,E=x.sub(b),C=E.mult(S)._add(b);return C._add(E._unit()._perp()._mult(ng)),{point:C,angle:v+Math.atan2(x.y-b.y,x.x-b.x),tileDistance:p?{prevTileDistance:m-g===y?0:u.gettileUnitDistanceFromAnchor(m-g),lastSegmentViewportDistance:k-_}:null}}var _e=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function we(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(_e,3n)}}function ke(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]n+r[4]i+r[12],t[1]=r[1]n+r[5]i+r[13],t[3]=r[3]n+r[7]i+r[15],t}t.default$20.mat4;var Me=function(t,e,r){void 0===e&&(e=new ce(t.width+200,t.height+200,25)),void 0===r&&(r=new ce(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100};function Ae(t,e,r){t[e+4]=r?1:0}function Te(e,r,n){return r(t.default$8/(e.tileSizeMath.pow(2,n-e.tileID.overscaledZ)))}Me.prototype.placeCollisionBox=function(t,e,r,n){var i=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),a=ri.perspectiveRatio,o=t.x1a+i.point.x,s=t.y1a+i.point.y,l=t.x2a+i.point.x,c=t.y2a+i.point.y;return!e&&this.grid.hitTest(o,s,l,c)?{box:[],offscreen:!1}:{box:[o,s,l,c],offscreen:this.isOffscreen(o,s,l,c)}},Me.prototype.approximateTileDistance=function(t,e,r,n,i){var a=i?1:n/this.pitchfactor,o=t.lastSegmentViewportDistancer;return t.prevTileDistance+o+(a-1)oMath.abs(Math.sin(e))},Me.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=this.projectAnchor(u,o.anchorX,o.anchorY),v=c/24,m=o.lineOffsetXc,y=o.lineOffsetYc,x=new t.default$1(o.anchorX,o.anchorY),b=ve(v,l,m,y,!1,pe(x,f).point,x,o,s,f,{},!0),_=!1,w=!0,k=g.perspectiveRatioi,M=1/(in),A=0,T=0;b&&(A=this.approximateTileDistance(b.first.tileDistance,b.first.angle,M,g.cameraDistance,p),T=this.approximateTileDistance(b.last.tileDistance,b.last.angle,M,g.cameraDistance,p));for(var S=0;S<e.length;S+=5){var E=e[S],C=e[S+1],L=e[S+2],z=e[S+3];if(!b\|\|z<-A\|\|z>T)Ae(e,S,!1);else{var O=this.projectPoint(u,E,C),I=Lk;if(d.length>0){var P=O.x-d[d.length-4],D=O.y-d[d.length-3];if(II2>PP+DD&&S+8<e.length){var R=e[S+8];if(R>-A&&R<T){Ae(e,S,!1);continue}}}var B=S/5;if(d.push(O.x,O.y,I,B),Ae(e,S,!0),w=w&&this.isOffscreen(O.x-I,O.y-I,O.x+I,O.y+I),!r&&this.grid.hitTestCircle(O.x,O.y,I)){if(!h)return{circles:[],offscreen:!1};_=!0}}}return{circles:_?[]:d,offscreen:w}},Me.prototype.queryRenderedSymbols=function(e){if(0===e.length\|\|0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.default$1(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],v=g.key;if(void 0===f[v.bucketInstanceId]&&(f[v.bucketInstanceId]={}),!f[v.bucketInstanceId][v.featureIndex]){var m=[new t.default$1(g.x1,g.y1),new t.default$1(g.x2,g.y1),new t.default$1(g.x2,g.y2),new t.default$1(g.x1,g.y2)];t.polygonIntersectsPolygon(r,m)&&(f[v.bucketInstanceId][v.featureIndex]=!0,void 0===h[v.bucketInstanceId]&&(h[v.bucketInstanceId]=[]),h[v.bucketInstanceId].push(v.featureIndex))}}return h},Me.prototype.insertCollisionBox=function(t,e,r,n){var i={bucketInstanceId:r,featureIndex:n};(e?this.ignoredGrid:this.grid).insert(i,t[0],t[1],t[2],t[3])},Me.prototype.insertCollisionCircles=function(t,e,r,n){for(var i=e?this.ignoredGrid:this.grid,a={bucketInstanceId:r,featureIndex:n},o=0;o<t.length;o+=4)i.insertCircle(a,t[o],t[o+1],t[o+2])},Me.prototype.projectAnchor=function(t,e,r){var n=[e,r,0,1];return ke(n,n,t),{perspectiveRatio:.5+this.transform.cameraToCenterDistance/n[3].5,cameraDistance:n[3]}},Me.prototype.projectPoint=function(e,r,n){var i=[r,n,0,1];return ke(i,i,e),new t.default$1((i[0]/i[3]+1)/2this.transform.width+100,(-i[1]/i[3]+1)/2this.transform.height+100)},Me.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return ke(i,i,e),{point:new t.default$1((i[0]/i[3]+1)/2this.transform.width+100,(-i[1]/i[3]+1)/2this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3].5}},Me.prototype.isOffscreen=function(t,e,r,n){return r<100\|\|t>=this.screenRightBoundary\|\|n<100\|\|e>this.screenBottomBoundary};var Se=t.default$19.layout,Ee=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};Ee.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var Ce=function(t,e,r,n,i){this.text=new Ee(t?t.text:null,e,r,i),this.icon=new Ee(t?t.icon:null,e,n,i)};Ce.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var Le=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ze=function(t,e){this.transform=t.clone(),this.collisionIndex=new Me(this.transform),this.placements={},this.opacities={},this.stale=!1,this.fadeDuration=e,this.retainedQueryData={}};function Oe(t,e,r){t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0)}ze.prototype.placeLayerTile=function(e,r,n,i){var a=r.getBucket(e),o=r.latestFeatureIndex;if(a&&o&&e.id===a.layerIds[0]){var s=r.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-r.tileID.overscaledZ),u=r.tileSize/t.default$8,f=this.transform.calculatePosMatrix(r.tileID.toUnwrapped()),h=fe(f,\"map\"===l.get(\"text-pitch-alignment\"),\"map\"===l.get(\"text-rotation-alignment\"),this.transform,Te(r,1,this.transform.zoom)),p=fe(f,\"map\"===l.get(\"icon-pitch-alignment\"),\"map\"===l.get(\"icon-rotation-alignment\"),this.transform,Te(r,1,this.transform.zoom));this.retainedQueryData[a.bucketInstanceId]=new function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i}(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,r.tileID),this.placeLayerBucket(a,f,h,p,c,u,n,i,s)}},ze.prototype.placeLayerBucket=function(e,r,n,i,a,o,s,l,c){for(var u=e.layers[0].layout,f=t.evaluateSizeForZoom(e.textSizeData,this.transform.zoom,Se.properties[\"text-size\"]),h=!e.hasTextData()\|\|u.get(\"text-optional\"),p=!e.hasIconData()\|\|u.get(\"icon-optional\"),d=0,g=e.symbolInstances;d<g.length;d+=1){var v=g[d];if(!l[v.crossTileID]){var m=void 0!==v.feature.text,y=void 0!==v.feature.icon,x=!0,b=null,_=null,w=null,k=0,M=0;v.collisionArrays\|\|(v.collisionArrays=e.deserializeCollisionBoxes(c,v.textBoxStartIndex,v.textBoxEndIndex,v.iconBoxStartIndex,v.iconBoxEndIndex)),v.collisionArrays.textFeatureIndex&&(k=v.collisionArrays.textFeatureIndex),v.collisionArrays.textBox&&(m=(b=this.collisionIndex.placeCollisionBox(v.collisionArrays.textBox,u.get(\"text-allow-overlap\"),o,r)).box.length>0,x=x&&b.offscreen);var A=v.collisionArrays.textCircles;if(A){var T=e.text.placedSymbolArray.get(v.placedTextSymbolIndices[0]),S=t.evaluateSizeForFeature(e.textSizeData,f,T);_=this.collisionIndex.placeCollisionCircles(A,u.get(\"text-allow-overlap\"),a,o,v.key,T,e.lineVertexArray,e.glyphOffsetArray,S,r,n,s,\"map\"===u.get(\"text-pitch-alignment\")),m=u.get(\"text-allow-overlap\")\|\|_.circles.length>0,x=x&&_.offscreen}v.collisionArrays.iconFeatureIndex&&(M=v.collisionArrays.iconFeatureIndex),v.collisionArrays.iconBox&&(y=(w=this.collisionIndex.placeCollisionBox(v.collisionArrays.iconBox,u.get(\"icon-allow-overlap\"),o,r)).box.length>0,x=x&&w.offscreen),h\|\|p?p?h\|\|(y=y&&m):m=y&&m:y=m=y&&m,m&&b&&this.collisionIndex.insertCollisionBox(b.box,u.get(\"text-ignore-placement\"),e.bucketInstanceId,k),y&&w&&this.collisionIndex.insertCollisionBox(w.box,u.get(\"icon-ignore-placement\"),e.bucketInstanceId,M),m&&_&&this.collisionIndex.insertCollisionCircles(_.circles,u.get(\"text-ignore-placement\"),e.bucketInstanceId,k),this.placements[v.crossTileID]=new Le(m,y,x\|\|e.justReloaded),l[v.crossTileID]=!0}}e.justReloaded=!1},ze.prototype.commit=function(t,e){this.commitTime=e;var r=!1,n=t&&0!==this.fadeDuration?(this.commitTime-t.commitTime)/this.fadeDuration:1,i=t?t.opacities:{};for(var a in this.placements){var o=this.placements[a],s=i[a];s?(this.opacities[a]=new Ce(s,n,o.text,o.icon),r=r\|\|o.text!==s.text.placed\|\|o.icon!==s.icon.placed):(this.opacities[a]=new Ce(null,n,o.text,o.icon,o.skipFade),r=r\|\|o.text\|\|o.icon)}for(var l in i){var c=i[l];if(!this.opacities[l]){var u=new Ce(c,n,!1,!1);u.isHidden()\|\|(this.opacities[l]=u,r=r\|\|c.text.placed\|\|c.icon.placed)}}r?this.lastPlacementChangeTime=e:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=t?t.lastPlacementChangeTime:e)},ze.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},ze.prototype.updateBucketOpacities=function(t,e,r){t.hasTextData()&&t.text.opacityVertexArray.clear(),t.hasIconData()&&t.icon.opacityVertexArray.clear(),t.hasCollisionBoxData()&&t.collisionBox.collisionVertexArray.clear(),t.hasCollisionCircleData()&&t.collisionCircle.collisionVertexArray.clear();for(var n=t.layers[0].layout,i=new Ce(null,0,!1,!1,!0),a=new Ce(null,0,n.get(\"text-allow-overlap\"),n.get(\"icon-allow-overlap\"),!0),o=0;o<t.symbolInstances.length;o++){var s=t.symbolInstances[o],l=e[s.crossTileID],c=this.opacities[s.crossTileID];l?c=i:c\|\|(c=a,this.opacities[s.crossTileID]=c),e[s.crossTileID]=!0;var u=s.numGlyphVertices>0\|\|s.numVerticalGlyphVertices>0,f=s.numIconVertices>0;if(u){for(var h=je(c.text),p=(s.numGlyphVertices+s.numVerticalGlyphVertices)/4,d=0;d<p;d++)t.text.opacityVertexArray.emplaceBack(h);for(var g=0,v=s.placedTextSymbolIndices;g<v.length;g+=1){var m=v[g];t.text.placedSymbolArray.get(m).hidden=c.text.isHidden()}}if(f){for(var y=je(c.icon),x=0;x<s.numIconVertices/4;x++)t.icon.opacityVertexArray.emplaceBack(y);t.icon.placedSymbolArray.get(o).hidden=c.icon.isHidden()}s.collisionArrays\|\|(s.collisionArrays=t.deserializeCollisionBoxes(r,s.textBoxStartIndex,s.textBoxEndIndex,s.iconBoxStartIndex,s.iconBoxEndIndex));var b=s.collisionArrays;if(b){b.textBox&&t.hasCollisionBoxData()&&Oe(t.collisionBox.collisionVertexArray,c.text.placed,!1),b.iconBox&&t.hasCollisionBoxData()&&Oe(t.collisionBox.collisionVertexArray,c.icon.placed,!1);var _=b.textCircles;if(_&&t.hasCollisionCircleData())for(var w=0;w<_.length;w+=5){var k=l\|\|0===_[w+4];Oe(t.collisionCircle.collisionVertexArray,c.text.placed,k)}}}t.sortFeatures(this.transform.angle),this.retainedQueryData[t.bucketInstanceId]&&(this.retainedQueryData[t.bucketInstanceId].featureSortOrder=t.featureSortOrder),t.hasTextData()&&t.text.opacityVertexBuffer&&t.text.opacityVertexBuffer.updateData(t.text.opacityVertexArray),t.hasIconData()&&t.icon.opacityVertexBuffer&&t.icon.opacityVertexBuffer.updateData(t.icon.opacityVertexArray),t.hasCollisionBoxData()&&t.collisionBox.collisionVertexBuffer&&t.collisionBox.collisionVertexBuffer.updateData(t.collisionBox.collisionVertexArray),t.hasCollisionCircleData()&&t.collisionCircle.collisionVertexBuffer&&t.collisionCircle.collisionVertexBuffer.updateData(t.collisionCircle.collisionVertexArray)},ze.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration},ze.prototype.hasTransitions=function(t){return this.stale\|\|t-this.lastPlacementChangeTime<this.fadeDuration},ze.prototype.stillRecent=function(t){return\"undefined\"!==this.commitTime&&this.commitTime+this.fadeDuration>t},ze.prototype.setStale=function(){this.stale=!0};var Ie=Math.pow(2,25),Pe=Math.pow(2,24),De=Math.pow(2,17),Re=Math.pow(2,16),Be=Math.pow(2,9),Fe=Math.pow(2,8),Ne=Math.pow(2,1);function je(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127t.opacity);return rIe+ePe+rDe+eRe+rBe+eFe+rNe+e}var Ve=function(){this._currentTileIndex=0,this._seenCrossTileIDs={}};Ve.prototype.continuePlacement=function(t,e,r,n,i){for(;this._currentTileIndex<t.length;){var a=t[this._currentTileIndex];if(e.placeLayerTile(n,a,r,this._seenCrossTileIDs),this._currentTileIndex++,i())return!0}};var Ue=function(t,e,r,n,i){this.placement=new ze(t,i),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Ue.prototype.isDone=function(){return this._done},Ue.prototype.continuePlacement=function(t,e,r){for(var n=this,i=a.now(),o=function(){var t=a.now()-i;return!n._forceFullPlacement&&t>2};this._currentPlacementIndex>=0;){var s=e[t[n._currentPlacementIndex]],l=n.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom\|\|s.minzoom<=l)&&(!s.maxzoom\|\|s.maxzoom>l)){if(n._inProgressLayer\|\|(n._inProgressLayer=new Ve),n._inProgressLayer.continuePlacement(r[s.source],n.placement,n._showCollisionBoxes,s,o))return;delete n._inProgressLayer}n._currentPlacementIndex--}this._done=!0},Ue.prototype.commit=function(t,e){return this.placement.commit(t,e),this.placement};var qe=512/t.default$8/2,He=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.key;this.indexedSymbolInstances[o]\|\|(this.indexedSymbolInstances[o]=[]),this.indexedSymbolInstances[o].push({crossTileID:a.crossTileID,coord:this.getScaledCoordinates(a,t)})}};He.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=qe/Math.pow(2,n),a=e.anchor;return{x:Math.floor((r.canonical.xt.default$8+a.x)i),y:Math.floor((r.canonical.yt.default$8+a.y)i)}},He.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0,a=t;i<a.length;i+=1){var o=a[i];if(!o.crossTileID){var s=this.indexedSymbolInstances[o.key];if(s)for(var l=this.getScaledCoordinates(o,e),c=0,u=s;c<u.length;c+=1){var f=u[c];if(Math.abs(f.coord.x-l.x)<=n&&Math.abs(f.coord.y-l.y)<=n&&!r[f.crossTileID]){r[f.crossTileID]=!0,o.crossTileID=f.crossTileID;break}}}}};var Ge=function(){this.maxCrossTileID=0};Ge.prototype.generate=function(){return++this.maxCrossTileID};var We=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};We.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},We.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0,i=e.symbolInstances;n<i.length;n+=1)i[n].crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]\|\|(this.usedCrossTileIDs[t.overscaledZ]={});var a=this.usedCrossTileIDs[t.overscaledZ];for(var o in this.indexes){var s=this.indexes[o];if(Number(o)>t.overscaledZ)for(var l in s){var c=s[l];c.tileID.isChildOf(t)&&c.findMatches(e.symbolInstances,t,a)}else{var u=s[t.scaledTo(Number(o)).key];u&&u.findMatches(e.symbolInstances,t,a)}}for(var f=0,h=e.symbolInstances;f<h.length;f+=1){var p=h[f];p.crossTileID\|\|(p.crossTileID=r.generate(),a[p.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new He(t,e.symbolInstances,e.bucketInstanceId),!0},We.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},We.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]\|\|(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ye=function(){this.layerIndexes={},this.crossTileIDs=new Ge,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ye.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new We);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId\|\|(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ye.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach(function(t){e[t]=!0}),this.layerIndexes)e[r]\|\|delete this.layerIndexes[r]};var Xe=function(e,r){return t.emitValidationErrors(e,r&&r.filter(function(t){return\"source.canvas\"!==t.identifier}))},Ze=t.pick(ee,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),$e=t.pick(ee,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Je=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new q((Jt\|\|(Jt=new Kt),Jt),this),this.imageManager=new O,this.glyphManager=new F(n._transformRequest,i.localIdeographFontFamily),this.lineAtlas=new U(256,512),this.crossTileSymbolIndex=new Ye,this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.default$23,this._loaded=!1,this._resetUpdates();var o=this;this._rtlTextPluginCallback=r.registerForPluginAvailability(function(t){for(var e in o.dispatcher.broadcast(\"loadRTLTextPlugin\",t.pluginURL,t.completionCallback),o.sourceCaches)o.sourceCaches[e].reload()}),this.on(\"data\",function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}})}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!x(e);e=function(t,e){if(!x(t))return t;var r=A(t);return r.path=\"/styles/v1\"+r.path,y(r,e)}(e,r.accessToken);var a=this.map._transformRequest(e,t.ResourceType.Style);t.getJSON(a,function(e,r){e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)})},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),a.frame(function(){n._load(e,!1!==r.validate)})},r.prototype._load=function(e,r){var n=this;if(!r\|\|!Xe(this,t.validateStyle(e))){for(var i in this._loaded=!0,this.stylesheet=e,e.sources)n.addSource(i,e.sources[i],{validate:!1});e.sprite?function(e,r,n){var i,o,s,l=a.devicePixelRatio>1?\"@2x\":\"\";function c(){if(s)n(s);else if(i&&o){var e=a.getImageData(o),r={};for(var l in i){var c=i[l],u=c.width,f=c.height,h=c.x,p=c.y,d=c.sdf,g=c.pixelRatio,v=new t.RGBAImage({width:u,height:f});t.RGBAImage.copy(e,v,{x:h,y:p},{x:0,y:0},{width:u,height:f}),r[l]={data:v,pixelRatio:g,sdf:d}}n(null,r)}}t.getJSON(r(_(e,l,\".json\"),t.ResourceType.SpriteJSON),function(t,e){s\|\|(s=t,i=e,c())}),t.getImage(r(_(e,l,\".png\"),t.ResourceType.SpriteImage),function(t,e){s\|\|(s=t,o=e,c())})}(e.sprite,this.map._transformRequest,function(e,r){if(e)n.fire(new t.ErrorEvent(e));else if(r)for(var i in r)n.imageManager.addImage(i,r[i]);n.imageManager.setLoaded(!0),n.fire(new t.Event(\"data\",{dataType:\"style\"}))}):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var o=te(this.stylesheet.layers);this._order=o.map(function(t){return t.id}),this._layers={};for(var s=0,l=o;s<l.length;s+=1){var c=l[s];(c=t.default$22(c)).setEventedParent(n,{layer:{id:c.id}}),n._layers[c.id]=c}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new V(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type\|\|i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){var e=this;return t.map(function(t){return e._layers[t].serialize()})},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){if(this._changed){var r=Object.keys(this._updatedLayers),n=Object.keys(this._removedLayers);for(var i in(r.length\|\|n.length)&&this._updateWorkerLayers(r,n),this._updatedSources){var a=this._updatedSources[i];\"reload\"===a?this._reloadSource(i):\"clear\"===a&&this._clearSource(i)}for(var o in this._updatedPaintProps)this._layers[o].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates(),this.fire(new t.Event(\"data\",{dataType:\"style\"}))}for(var s in this.sourceCaches)this.sourceCaches[s].used=!1;for(var l=0,c=this._order;l<c.length;l+=1){var u=c[l],f=this._layers[u];f.recalculate(e),!f.isHidden(e.zoom)&&f.source&&(this.sourceCaches[f.source].used=!0)}this.light.recalculate(e),this.z=e.zoom}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),Xe(this,t.validateStyle(e)))return!1;(e=t.clone(e)).layers=te(e.layers);var n=function(e,r){if(!e)return[{command:ee.setStyle,args:[r]}];var n=[];try{if(!t.default$10(e.version,r.version))return[{command:ee.setStyle,args:[r]}];t.default$10(e.center,r.center)\|\|n.push({command:ee.setCenter,args:[r.center]}),t.default$10(e.zoom,r.zoom)\|\|n.push({command:ee.setZoom,args:[r.zoom]}),t.default$10(e.bearing,r.bearing)\|\|n.push({command:ee.setBearing,args:[r.bearing]}),t.default$10(e.pitch,r.pitch)\|\|n.push({command:ee.setPitch,args:[r.pitch]}),t.default$10(e.sprite,r.sprite)\|\|n.push({command:ee.setSprite,args:[r.sprite]}),t.default$10(e.glyphs,r.glyphs)\|\|n.push({command:ee.setGlyphs,args:[r.glyphs]}),t.default$10(e.transition,r.transition)\|\|n.push({command:ee.setTransition,args:[r.transition]}),t.default$10(e.light,r.light)\|\|n.push({command:ee.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r\|\|{},e=e\|\|{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)\|\|ne(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.default$10(e[a],r[a])\|\|(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&ae(e,r,a)?n.push({command:ee.setGeoJSONSourceData,args:[a,r[a].data]}):ie(a,r,n,i)):re(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach(function(t){i[t.source]?n.push({command:ee.removeLayer,args:[t.id]}):o.push(t)}),n=n.concat(a),function(e,r,n){r=r\|\|[];var i,a,o,s,l,c,u,f=(e=e\|\|[]).map(se),h=r.map(se),p=e.reduce(le,{}),d=r.reduce(le,{}),g=f.slice(),v=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:ee.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],g[g.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:ee.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,c=g[g.length-i],n.push({command:ee.addLayer,args:[d[o],c]}),g.splice(g.length-i,0,o),v[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!v[o]&&!t.default$10(s,l))if(t.default$10(s.source,l.source)&&t.default$10(s[\"source-layer\"],l[\"source-layer\"])&&t.default$10(s.type,l.type)){for(u in oe(s.layout,l.layout,n,o,null,ee.setLayoutProperty),oe(s.paint,l.paint,n,o,null,ee.setPaintProperty),t.default$10(s.filter,l.filter)\|\|n.push({command:ee.setFilter,args:[o,l.filter]}),t.default$10(s.minzoom,l.minzoom)&&t.default$10(s.maxzoom,l.maxzoom)\|\|n.push({command:ee.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?oe(s[u],l[u],n,o,u.slice(6),ee.setPaintProperty):t.default$10(s[u],l[u])\|\|n.push({command:ee.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?oe(s[u],l[u],n,o,u.slice(6),ee.setPaintProperty):t.default$10(s[u],l[u])\|\|n.push({command:ee.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:ee.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:ee.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:ee.setStyle,args:[r]}]}return n}(this.serialize(),e).filter(function(t){return!(t.command in $e)});if(0===n.length)return!1;var i=n.filter(function(t){return!(t.command in Ze)});if(i.length>0)throw new Error(\"Unimplemented: \"+i.map(function(t){return t.command}).join(\", \")+\".\");return n.forEach(function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)}),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.addSource=function(e,r,n){var i=this;if(this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Yt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}}),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone(e),e=t.extend(e,{source:i})),!this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n)){var a=t.default$22(e);this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}});var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e]}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n\|\|(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r){this._checkLoaded();var n=this.getLayer(e);if(n){if(!t.default$10(n.filter,r))return null==r?(n.filter=void 0,void this._updateLayer(n)):void(this._validate(t.validateStyle.filter,\"layers.\"+n.id+\".filter\",r)\|\|(n.filter=t.clone(r),this._updateLayer(n)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?t.default$10(i.getLayoutProperty(r),n)\|\|(i.setLayoutProperty(r,n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(t,e){return this.getLayer(t).getLayoutProperty(e)},r.prototype.setPaintProperty=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.default$10(i.getPaintProperty(r),n)){var a=i._transitionablePaint._values[r].value.isDataDriven();i.setPaintProperty(r,n),(i._transitionablePaint._values[r].value.isDataDriven()\|\|a)&&this._updateLayer(i),this._changed=!0,this._updatedPaintProps[e]=!0}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){var e=this;return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,function(t){return t.serialize()}),layers:this._order.map(function(t){return e._layers[t].serialize()})},function(t){return void 0!==t})},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenRenderedFeatures=function(t){for(var e=[],r=this._order.length-1;r>=0;r--)for(var n=this._order[r],i=0,a=t;i<a.length;i+=1){var o=a[i][n];if(o)for(var s=0,l=o;s<l.length;s+=1){var c=l[s];e.push(c)}}return e},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in this.sourceCaches)r.layers&&!i[u]\|\|c.push(at(this.sourceCaches[u],this._layers,e.worldCoordinate,r,n));return this.placement&&c.push(function(t,e,r,n,i){for(var a={},o=n.queryRenderedSymbols(e),s=[],l=0,c=Object.keys(o).map(Number);l<c.length;l+=1){var u=c[l];s.push(i[u])}s.sort(ot);for(var f=function(){var e=p[h],n=e.featureIndex.lookupSymbolFeatures(o[e.bucketInstanceId],e.bucketIndex,e.sourceLayerIndex,r.filter,r.layers,t);for(var i in n){var s=a[i]=a[i]\|\|[],l=n[i];l.sort(function(t,r){var n=e.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(r.featureIndex)-i}return r.featureIndex-t.featureIndex});for(var c=0,u=l;c<u.length;c+=1){var f=u[c];s.push(f.feature)}}},h=0,p=s;h<p.length;h+=1)f();return a}(this._layers,e.viewport,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map(function(e){return t.getTileByID(e)}),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]\|\|(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e){this._checkLoaded();var r=this.light.getLight(),n=!1;for(var i in e)if(!t.default$10(e[i],r[i])){n=!0;break}if(n){var o={now:a.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return(!a\|\|!1!==a.validate)&&Xe(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.default$5},i)))},r.prototype._remove=function(){for(var e in t.evented.off(\"pluginAvailable\",this._rtlTextPluginCallback),this.sourceCaches)this.sourceCaches[e].clearTiles();this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(t,e,r){for(var n=!1,i=!1,o={},s=0,l=this._order;s<l.length;s+=1){var c=l[s],u=this._layers[c];if(\"symbol\"===u.type){if(!o[u.source]){var f=this.sourceCaches[u.source];o[u.source]=f.getRenderableIds().map(function(t){return f.getTileByID(t)}).sort(function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ\|\|(t.tileID.isLessThan(e.tileID)?-1:1)})}var h=this.crossTileSymbolIndex.addLayer(u,o[u.source],t.center.lng);n=n\|\|h}}this.crossTileSymbolIndex.pruneUnusedLayers(this._order);var p=this._layerOrderChanged;if((p\|\|!this.pauseablePlacement\|\|this.pauseablePlacement.isDone()&&!this.placement.stillRecent(a.now()))&&(this.pauseablePlacement=new Ue(t,this._order,p,e,r),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,o),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(this.placement,a.now()),i=!0),n&&this.pauseablePlacement.placement.setStale()),i\|\|n)for(var d=0,g=this._order;d<g.length;d+=1){var v=g[d],m=this._layers[v];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,o[m.source])}return!this.pauseablePlacement.isDone()\|\|this.placement.hasTransitions(a.now())},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r}(t.Evented);Je.getSourceType=function(t){return nt[t]},Je.setSourceType=function(t,e){nt[t]=e},Je.registerForPluginAvailability=t.registerForPluginAvailability;var Ke=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Qe={prelude:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n\\n#if !defined(highp)\\n#define highp\\n#endif\\n\\n#endif\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n\\n#if !defined(highp)\\n#define highp\\n#endif\\n\\n#endif\\n\\n// Unpack a pair of values that have been packed into a single float.\\n// The packed values are assumed to be 8-bit unsigned integers, and are\\n// packed like so:\\n// packedValue = floor(input[0]) 256 + input[1],\\nvec2 unpack_float(const float packedValue) {\\n int packedIntValue = int(packedValue);\\n int v0 = packedIntValue / 256;\\n return vec2(v0, packedIntValue - v0 * 256);\\n}\\n\\nvec2 unpack_opacity(const float packedOpacity) {\\n int intOpacity = int(packedOpacity) / 2;\\n return vec2(float(intOpacity) / 127.0, mod(packedOpacity, 2.0));\\n}\\n\\n// To minimize the number of attributes needed, we encode a 4-component\\n// color into a pair of floats (i.e. a vec2) as follows:\\n// [ floor(color.r * 255) * 256 + color.g * 255,\\n// floor(color.b * 255) * 256 + color.g * 255 ]\\nvec4 decode_color(const vec2 encodedColor) {\\n return vec4(\\n unpack_float(encodedColor[0]) / 255.0,\\n unpack_float(encodedColor[1]) / 255.0\\n );\\n}\\n\\n// Unpack a pair of paint values and interpolate between them.\\nfloat unpack_mix_vec2(const vec2 packedValue, const float t) {\\n return mix(packedValue[0], packedValue[1], t);\\n}\\n\\n// Unpack a pair of paint values and interpolate between them.\\nvec4 unpack_mix_vec4(const vec4 packedColors, const float t) {\\n vec4 minColor = decode_color(vec2(packedColors[0], packedColors[1]));\\n vec4 maxColor = decode_color(vec2(packedColors[2], packedColors[3]));\\n return mix(minColor, maxColor, t);\\n}\\n\\n// The offset depends on how many pixels are between the world origin and the edge of the tile:\\n// vec2 offset = mod(pixel_coord, size)\\n//\\n// At high zoom levels there are a ton of pixels between the world origin and the edge of the tile.\\n// The glsl spec only guarantees 16 bits of precision for highp floats. We need more than that.\\n//\\n// The pixel_coord is passed in as two 16 bit values:\\n// pixel_coord_upper = floor(pixel_coord / 2^16)\\n// pixel_coord_lower = mod(pixel_coord, 2^16)\\n//\\n// The offset is calculated in a series of steps that should preserve this precision:\\nvec2 get_pattern_pos(const vec2 pixel_coord_upper, const vec2 pixel_coord_lower,\\n const vec2 pattern_size, const float tile_units_to_pixels, const vec2 pos) {\\n\\n vec2 offset = mod(mod(mod(pixel_coord_upper, pattern_size) * 256.0, pattern_size) * 256.0 + pixel_coord_lower, pattern_size);\\n return (tile_units_to_pixels * pos + offset) / pattern_size;\\n}\\n\"},background:{fragmentSource:\"uniform vec4 u_color;\\nuniform float u_opacity;\\n\\nvoid main() {\\n gl_FragColor = u_color * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},backgroundPattern:{fragmentSource:\"uniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_mix;\\nuniform float u_opacity;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n gl_FragColor = mix(color1, color2, u_mix) * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n\\n v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\\n v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\\n}\\n\"},circle:{fragmentSource:\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\n\\nvarying vec3 v_data;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize mediump float radius\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize highp vec4 stroke_color\\n #pragma mapbox: initialize mediump float stroke_width\\n #pragma mapbox: initialize lowp float stroke_opacity\\n\\n vec2 extrude = v_data.xy;\\n float extrude_length = length(extrude);\\n\\n lowp float antialiasblur = v_data.z;\\n float antialiased_blur = -max(blur, antialiasblur);\\n\\n float opacity_t = smoothstep(0.0, antialiased_blur, extrude_length - 1.0);\\n\\n float color_t = stroke_width < 0.01 ? 0.0 : smoothstep(\\n antialiased_blur,\\n 0.0,\\n extrude_length - radius / (radius + stroke_width)\\n );\\n\\n gl_FragColor = opacity_t * mix(color * opacity, stroke_color * stroke_opacity, color_t);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform bool u_scale_with_map;\\nuniform bool u_pitch_with_map;\\nuniform vec2 u_extrude_scale;\\nuniform highp float u_camera_to_center_distance;\\n\\nattribute vec2 a_pos;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\n\\nvarying vec3 v_data;\\n\\nvoid main(void) {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize mediump float radius\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize highp vec4 stroke_color\\n #pragma mapbox: initialize mediump float stroke_width\\n #pragma mapbox: initialize lowp float stroke_opacity\\n\\n // unencode the extrusion vector that we snuck into the a_pos vector\\n vec2 extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\\n\\n // multiply a_pos by 0.5, since we had it * 2 in order to sneak\\n // in extrusion data\\n vec2 circle_center = floor(a_pos * 0.5);\\n if (u_pitch_with_map) {\\n vec2 corner_position = circle_center;\\n if (u_scale_with_map) {\\n corner_position += extrude * (radius + stroke_width) * u_extrude_scale;\\n } else {\\n // Pitching the circle with the map effectively scales it with the map\\n // To counteract the effect for pitch-scale: viewport, we rescale the\\n // whole circle based on the pitch scaling effect at its central point\\n vec4 projected_center = u_matrix * vec4(circle_center, 0, 1);\\n corner_position += extrude * (radius + stroke_width) * u_extrude_scale * (projected_center.w / u_camera_to_center_distance);\\n }\\n\\n gl_Position = u_matrix * vec4(corner_position, 0, 1);\\n } else {\\n gl_Position = u_matrix * vec4(circle_center, 0, 1);\\n\\n if (u_scale_with_map) {\\n gl_Position.xy += extrude * (radius + stroke_width) * u_extrude_scale * u_camera_to_center_distance;\\n } else {\\n gl_Position.xy += extrude * (radius + stroke_width) * u_extrude_scale * gl_Position.w;\\n }\\n }\\n\\n // This is a minimum blur distance that serves as a faux-antialiasing for\\n // the circle. since blur is a ratio of the circle's size and the intent is\\n // to keep the blur at roughly 1px, the two are inversely related.\\n lowp float antialiasblur = 1.0 / DEVICE_PIXEL_RATIO / (radius + stroke_width);\\n\\n v_data = vec3(extrude.x, extrude.y, antialiasblur);\\n}\\n\"},clippingMask:{fragmentSource:\"void main() {\\n gl_FragColor = vec4(1.0);\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},heatmap:{fragmentSource:\"#pragma mapbox: define highp float weight\\n\\nuniform highp float u_intensity;\\nvarying vec2 v_extrude;\\n\\n// Gaussian kernel coefficient: 1 / sqrt(2 * PI)\\n#define GAUSS_COEF 0.3989422804014327\\n\\nvoid main() {\\n #pragma mapbox: initialize highp float weight\\n\\n // Kernel density estimation with a Gaussian kernel of size 5x5\\n float d = -0.5 * 3.0 * 3.0 * dot(v_extrude, v_extrude);\\n float val = weight * u_intensity * GAUSS_COEF * exp(d);\\n\\n gl_FragColor = vec4(val, 1.0, 1.0, 1.0);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\n\\nuniform mat4 u_matrix;\\nuniform float u_extrude_scale;\\nuniform float u_opacity;\\nuniform float u_intensity;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_extrude;\\n\\n// Effective \\\"0\\\" in the kernel density texture to adjust the kernel size to;\\n// this empirically chosen number minimizes artifacts on overlapping kernels\\n// for typical heatmap cases (assuming clustered source)\\nconst highp float ZERO = 1.0 / 255.0 / 16.0;\\n\\n// Gaussian kernel coefficient: 1 / sqrt(2 * PI)\\n#define GAUSS_COEF 0.3989422804014327\\n\\nvoid main(void) {\\n #pragma mapbox: initialize highp float weight\\n #pragma mapbox: initialize mediump float radius\\n\\n // unencode the extrusion vector that we snuck into the a_pos vector\\n vec2 unscaled_extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\\n\\n // This 'extrude' comes in ranging from [-1, -1], to [1, 1]. We'll use\\n // it to produce the vertices of a square mesh framing the point feature\\n // we're adding to the kernel density texture. We'll also pass it as\\n // a varying, so that the fragment shader can determine the distance of\\n // each fragment from the point feature.\\n // Before we do so, we need to scale it up sufficiently so that the\\n // kernel falls effectively to zero at the edge of the mesh.\\n // That is, we want to know S such that\\n // weight * u_intensity * GAUSS_COEF * exp(-0.5 * 3.0^2 * S^2) == ZERO\\n // Which solves to:\\n // S = sqrt(-2.0 * log(ZERO / (weight * u_intensity * GAUSS_COEF))) / 3.0\\n float S = sqrt(-2.0 * log(ZERO / weight / u_intensity / GAUSS_COEF)) / 3.0;\\n\\n // Pass the varying in units of radius\\n v_extrude = S * unscaled_extrude;\\n\\n // Scale by radius and the zoom-based scale factor to produce actual\\n // mesh position\\n vec2 extrude = v_extrude * radius * u_extrude_scale;\\n\\n // multiply a_pos by 0.5, since we had it * 2 in order to sneak\\n // in extrusion data\\n vec4 pos = vec4(floor(a_pos * 0.5) + extrude, 0, 1);\\n\\n gl_Position = u_matrix * pos;\\n}\\n\"},heatmapTexture:{fragmentSource:\"uniform sampler2D u_image;\\nuniform sampler2D u_color_ramp;\\nuniform float u_opacity;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n float t = texture2D(u_image, v_pos).r;\\n vec4 color = texture2D(u_color_ramp, vec2(t, 0.5));\\n gl_FragColor = color * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(0.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_world;\\nattribute vec2 a_pos;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos * u_world, 0, 1);\\n\\n v_pos.x = a_pos.x;\\n v_pos.y = 1.0 - a_pos.y;\\n}\\n\"},collisionBox:{fragmentSource:\"\\nvarying float v_placed;\\nvarying float v_notUsed;\\n\\nvoid main() {\\n\\n float alpha = 0.5;\\n\\n // Red = collision, hide label\\n gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\\n\\n // Blue = no collision, label is showing\\n if (v_placed > 0.5) {\\n gl_FragColor = vec4(0.0, 0.0, 1.0, 0.5) * alpha;\\n }\\n\\n if (v_notUsed > 0.5) {\\n // This box not used, fade it out\\n gl_FragColor = .1;\\n }\\n}\",vertexSource:\"attribute vec2 a_pos;\\nattribute vec2 a_anchor_pos;\\nattribute vec2 a_extrude;\\nattribute vec2 a_placed;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_extrude_scale;\\nuniform float u_camera_to_center_distance;\\n\\nvarying float v_placed;\\nvarying float v_notUsed;\\n\\nvoid main() {\\n vec4 projectedPoint = u_matrix vec4(a_anchor_pos, 0, 1);\\n highp float camera_to_anchor_distance = projectedPoint.w;\\n highp float collision_perspective_ratio = clamp(\\n 0.5 + 0.5 * (u_camera_to_center_distance / camera_to_anchor_distance),\\n 0.0, // Prevents oversized near-field boxes in pitched/overzoomed tiles\\n 4.0);\\n\\n gl_Position = u_matrix * vec4(a_pos, 0.0, 1.0);\\n gl_Position.xy += a_extrude * u_extrude_scale * gl_Position.w * collision_perspective_ratio;\\n\\n v_placed = a_placed.x;\\n v_notUsed = a_placed.y;\\n}\\n\"},collisionCircle:{fragmentSource:\"uniform float u_overscale_factor;\\n\\nvarying float v_placed;\\nvarying float v_notUsed;\\nvarying float v_radius;\\nvarying vec2 v_extrude;\\nvarying vec2 v_extrude_scale;\\n\\nvoid main() {\\n float alpha = 0.5;\\n\\n // Red = collision, hide label\\n vec4 color = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\\n\\n // Blue = no collision, label is showing\\n if (v_placed > 0.5) {\\n color = vec4(0.0, 0.0, 1.0, 0.5) * alpha;\\n }\\n\\n if (v_notUsed > 0.5) {\\n // This box not used, fade it out\\n color = .2;\\n }\\n\\n float extrude_scale_length = length(v_extrude_scale);\\n float extrude_length = length(v_extrude) extrude_scale_length;\\n float stroke_width = 15.0 * extrude_scale_length / u_overscale_factor;\\n float radius = v_radius * extrude_scale_length;\\n\\n float distance_to_edge = abs(extrude_length - radius);\\n float opacity_t = smoothstep(-stroke_width, 0.0, -distance_to_edge);\\n\\n gl_FragColor = opacity_t * color;\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\nattribute vec2 a_anchor_pos;\\nattribute vec2 a_extrude;\\nattribute vec2 a_placed;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_extrude_scale;\\nuniform float u_camera_to_center_distance;\\n\\nvarying float v_placed;\\nvarying float v_notUsed;\\nvarying float v_radius;\\n\\nvarying vec2 v_extrude;\\nvarying vec2 v_extrude_scale;\\n\\nvoid main() {\\n vec4 projectedPoint = u_matrix * vec4(a_anchor_pos, 0, 1);\\n highp float camera_to_anchor_distance = projectedPoint.w;\\n highp float collision_perspective_ratio = clamp(\\n 0.5 + 0.5 * (u_camera_to_center_distance / camera_to_anchor_distance),\\n 0.0, // Prevents oversized near-field circles in pitched/overzoomed tiles\\n 4.0);\\n\\n gl_Position = u_matrix * vec4(a_pos, 0.0, 1.0);\\n\\n highp float padding_factor = 1.2; // Pad the vertices slightly to make room for anti-alias blur\\n gl_Position.xy += a_extrude * u_extrude_scale * padding_factor * gl_Position.w * collision_perspective_ratio;\\n\\n v_placed = a_placed.x;\\n v_notUsed = a_placed.y;\\n v_radius = abs(a_extrude.y); // We don't pitch the circles, so both units of the extrusion vector are equal in magnitude to the radius\\n\\n v_extrude = a_extrude * padding_factor;\\n v_extrude_scale = u_extrude_scale * u_camera_to_center_distance * collision_perspective_ratio;\\n}\\n\"},debug:{fragmentSource:\"uniform highp vec4 u_color;\\n\\nvoid main() {\\n gl_FragColor = u_color;\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},fill:{fragmentSource:\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_FragColor = color * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},fillOutline:{fragmentSource:\"#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 outline_color\\n #pragma mapbox: initialize lowp float opacity\\n\\n float dist = length(v_pos - gl_FragCoord.xy);\\n float alpha = 1.0 - smoothstep(0.0, 1.0, dist);\\n gl_FragColor = outline_color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 outline_color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},fillOutlinePattern:{fragmentSource:\"uniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n // find distance to outline for alpha interpolation\\n\\n float dist = length(v_pos - gl_FragCoord.xy);\\n float alpha = 1.0 - smoothstep(0.0, 1.0, dist);\\n\\n\\n gl_FragColor = mix(color1, color2, u_mix) * alpha * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_world;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n\\n v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\\n v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\\n\\n v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},fillPattern:{fragmentSource:\"uniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n gl_FragColor = mix(color1, color2, u_mix) * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n\\n v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\\n v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\\n}\\n\"},fillExtrusion:{fragmentSource:\"varying vec4 v_color;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define highp vec4 color\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float base\\n #pragma mapbox: initialize lowp float height\\n #pragma mapbox: initialize highp vec4 color\\n\\n gl_FragColor = v_color;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec3 u_lightcolor;\\nuniform lowp vec3 u_lightpos;\\nuniform lowp float u_lightintensity;\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_normal_ed;\\n\\nvarying vec4 v_color;\\n\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n\\n#pragma mapbox: define highp vec4 color\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float base\\n #pragma mapbox: initialize lowp float height\\n #pragma mapbox: initialize highp vec4 color\\n\\n vec3 normal = a_normal_ed.xyz;\\n\\n base = max(0.0, base);\\n height = max(0.0, height);\\n\\n float t = mod(normal.x, 2.0);\\n\\n gl_Position = u_matrix * vec4(a_pos, t > 0.0 ? height : base, 1);\\n\\n // Relative luminance (how dark/bright is the surface color?)\\n float colorvalue = color.r * 0.2126 + color.g * 0.7152 + color.b * 0.0722;\\n\\n v_color = vec4(0.0, 0.0, 0.0, 1.0);\\n\\n // Add slight ambient lighting so no extrusions are totally black\\n vec4 ambientlight = vec4(0.03, 0.03, 0.03, 1.0);\\n color += ambientlight;\\n\\n // Calculate cos(theta), where theta is the angle between surface normal and diffuse light ray\\n float directional = clamp(dot(normal / 16384.0, u_lightpos), 0.0, 1.0);\\n\\n // Adjust directional so that\\n // the range of values for highlight/shading is narrower\\n // with lower light intensity\\n // and with lighter/brighter surface colors\\n directional = mix((1.0 - u_lightintensity), max((1.0 - colorvalue + u_lightintensity), 1.0), directional);\\n\\n // Add gradient along z axis of side surfaces\\n if (normal.y != 0.0) {\\n directional = clamp((t + base) pow(height / 150.0, 0.5), mix(0.7, 0.98, 1.0 - u_lightintensity), 1.0);\\n }\\n\\n // Assign final color based on surface + ambient light color, diffuse light directional, and light color\\n // with lower bounds adjusted to hue of light\\n // so that shading is tinted with the complementary (opposite) color to the light color\\n v_color.r += clamp(color.r * directional * u_lightcolor.r, mix(0.0, 0.3, 1.0 - u_lightcolor.r), 1.0);\\n v_color.g += clamp(color.g * directional * u_lightcolor.g, mix(0.0, 0.3, 1.0 - u_lightcolor.g), 1.0);\\n v_color.b += clamp(color.b * directional * u_lightcolor.b, mix(0.0, 0.3, 1.0 - u_lightcolor.b), 1.0);\\n}\\n\"},fillExtrusionPattern:{fragmentSource:\"uniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec4 v_lighting;\\n\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float base\\n #pragma mapbox: initialize lowp float height\\n\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n vec4 mixedColor = mix(color1, color2, u_mix);\\n\\n gl_FragColor = mixedColor * v_lighting;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\nuniform float u_height_factor;\\n\\nuniform vec3 u_lightcolor;\\nuniform lowp vec3 u_lightpos;\\nuniform lowp float u_lightintensity;\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_normal_ed;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec4 v_lighting;\\nvarying float v_directional;\\n\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float base\\n #pragma mapbox: initialize lowp float height\\n\\n vec3 normal = a_normal_ed.xyz;\\n float edgedistance = a_normal_ed.w;\\n\\n base = max(0.0, base);\\n height = max(0.0, height);\\n\\n float t = mod(normal.x, 2.0);\\n float z = t > 0.0 ? height : base;\\n\\n gl_Position = u_matrix * vec4(a_pos, z, 1);\\n\\n vec2 pos = normal.x == 1.0 && normal.y == 0.0 && normal.z == 16384.0\\n ? a_pos // extrusion top\\n : vec2(edgedistance, z * u_height_factor); // extrusion side\\n\\n v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, pos);\\n v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, pos);\\n\\n v_lighting = vec4(0.0, 0.0, 0.0, 1.0);\\n float directional = clamp(dot(normal / 16383.0, u_lightpos), 0.0, 1.0);\\n directional = mix((1.0 - u_lightintensity), max((0.5 + u_lightintensity), 1.0), directional);\\n\\n if (normal.y != 0.0) {\\n directional = clamp((t + base) pow(height / 150.0, 0.5), mix(0.7, 0.98, 1.0 - u_lightintensity), 1.0);\\n }\\n\\n v_lighting.rgb += clamp(directional * u_lightcolor, mix(vec3(0.0), vec3(0.3), 1.0 - u_lightcolor), vec3(1.0));\\n}\\n\"},extrusionTexture:{fragmentSource:\"uniform sampler2D u_image;\\nuniform float u_opacity;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_FragColor = texture2D(u_image, v_pos) * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(0.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_world;\\nattribute vec2 a_pos;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos * u_world, 0, 1);\\n\\n v_pos.x = a_pos.x;\\n v_pos.y = 1.0 - a_pos.y;\\n}\\n\"},hillshadePrepare:{fragmentSource:\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\n\\nuniform sampler2D u_image;\\nvarying vec2 v_pos;\\nuniform vec2 u_dimension;\\nuniform float u_zoom;\\nuniform float u_maxzoom;\\n\\nfloat getElevation(vec2 coord, float bias) {\\n // Convert encoded elevation value to meters\\n vec4 data = texture2D(u_image, coord) * 255.0;\\n return (data.r + data.g * 256.0 + data.b * 256.0 * 256.0) / 4.0;\\n}\\n\\nvoid main() {\\n vec2 epsilon = 1.0 / u_dimension;\\n\\n // queried pixels:\\n // +-----------+\\n // \| \| \| \|\\n // \| a \| b \| c \|\\n // \| \| \| \|\\n // +-----------+\\n // \| \| \| \|\\n // \| d \| e \| f \|\\n // \| \| \| \|\\n // +-----------+\\n // \| \| \| \|\\n // \| g \| h \| i \|\\n // \| \| \| \|\\n // +-----------+\\n\\n float a = getElevation(v_pos + vec2(-epsilon.x, -epsilon.y), 0.0);\\n float b = getElevation(v_pos + vec2(0, -epsilon.y), 0.0);\\n float c = getElevation(v_pos + vec2(epsilon.x, -epsilon.y), 0.0);\\n float d = getElevation(v_pos + vec2(-epsilon.x, 0), 0.0);\\n float e = getElevation(v_pos, 0.0);\\n float f = getElevation(v_pos + vec2(epsilon.x, 0), 0.0);\\n float g = getElevation(v_pos + vec2(-epsilon.x, epsilon.y), 0.0);\\n float h = getElevation(v_pos + vec2(0, epsilon.y), 0.0);\\n float i = getElevation(v_pos + vec2(epsilon.x, epsilon.y), 0.0);\\n\\n // here we divide the x and y slopes by 8 * pixel size\\n // where pixel size (aka meters/pixel) is:\\n // circumference of the world / (pixels per tile * number of tiles)\\n // which is equivalent to: 8 * 40075016.6855785 / (512 * pow(2, u_zoom))\\n // which can be reduced to: pow(2, 19.25619978527 - u_zoom)\\n // we want to vertically exaggerate the hillshading though, because otherwise\\n // it is barely noticeable at low zooms. to do this, we multiply this by some\\n // scale factor pow(2, (u_zoom - u_maxzoom) * a) where a is an arbitrary value\\n // Here we use a=0.3 which works out to the expression below. see \\n // nickidlugash's awesome breakdown for more info\\n // https://github.com/mapbox/mapbox-gl-js/pull/5286#discussion_r148419556\\n float exaggeration = u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;\\n\\n vec2 deriv = vec2(\\n (c + f + f + i) - (a + d + d + g),\\n (g + h + h + i) - (a + b + b + c)\\n ) / pow(2.0, (u_zoom - u_maxzoom) * exaggeration + 19.2562 - u_zoom);\\n\\n gl_FragColor = clamp(vec4(\\n deriv.x / 2.0 + 0.5,\\n deriv.y / 2.0 + 0.5,\\n 1.0,\\n 1.0), 0.0, 1.0);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos = (a_texture_pos / 8192.0) / 2.0 + 0.25;\\n}\\n\"},hillshade:{fragmentSource:\"uniform sampler2D u_image;\\nvarying vec2 v_pos;\\n\\nuniform vec2 u_latrange;\\nuniform vec2 u_light;\\nuniform vec4 u_shadow;\\nuniform vec4 u_highlight;\\nuniform vec4 u_accent;\\n\\n#define PI 3.141592653589793\\n\\nvoid main() {\\n vec4 pixel = texture2D(u_image, v_pos);\\n\\n vec2 deriv = ((pixel.rg * 2.0) - 1.0);\\n\\n // We divide the slope by a scale factor based on the cosin of the pixel's approximate latitude\\n // to account for mercator projection distortion. see #4807 for details\\n float scaleFactor = cos(radians((u_latrange[0] - u_latrange[1]) * (1.0 - v_pos.y) + u_latrange[1]));\\n // We also multiply the slope by an arbitrary z-factor of 1.25\\n float slope = atan(1.25 * length(deriv) / scaleFactor);\\n float aspect = deriv.x != 0.0 ? atan(deriv.y, -deriv.x) : PI / 2.0 * (deriv.y > 0.0 ? 1.0 : -1.0);\\n\\n float intensity = u_light.x;\\n // We add PI to make this property match the global light object, which adds PI/2 to the light's azimuthal\\n // position property to account for 0deg corresponding to north/the top of the viewport in the style spec\\n // and the original shader was written to accept (-illuminationDirection - 90) as the azimuthal.\\n float azimuth = u_light.y + PI;\\n\\n // We scale the slope exponentially based on intensity, using a calculation similar to\\n // the exponential interpolation function in the style spec:\\n // https://github.com/mapbox/mapbox-gl-js/blob/master/src/style-spec/expression/definitions/interpolate.js#L217-L228\\n // so that higher intensity values create more opaque hillshading.\\n float base = 1.875 - intensity * 1.75;\\n float maxValue = 0.5 * PI;\\n float scaledSlope = intensity != 0.5 ? ((pow(base, slope) - 1.0) / (pow(base, maxValue) - 1.0)) * maxValue : slope;\\n\\n // The accent color is calculated with the cosine of the slope while the shade color is calculated with the sine\\n // so that the accent color's rate of change eases in while the shade color's eases out.\\n float accent = cos(scaledSlope);\\n // We multiply both the accent and shade color by a clamped intensity value\\n // so that intensities >= 0.5 do not additionally affect the color values\\n // while intensity values < 0.5 make the overall color more transparent.\\n vec4 accent_color = (1.0 - accent) * u_accent * clamp(intensity * 2.0, 0.0, 1.0);\\n float shade = abs(mod((aspect + azimuth) / PI + 0.5, 2.0) - 1.0);\\n vec4 shade_color = mix(u_shadow, u_highlight, shade) * sin(scaledSlope) * clamp(intensity * 2.0, 0.0, 1.0);\\n gl_FragColor = accent_color * (1.0 - shade_color.a) + shade_color;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos = a_texture_pos / 8192.0;\\n}\\n\"},line:{fragmentSource:\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_width2;\\nvarying vec2 v_normal;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_width2.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_width2.t) or when fading out\\n // (v_width2.s)\\n float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\\n\\n gl_FragColor = color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"\\n\\n// the distance over which the line edge fades out.\\n// Retina devices need a smaller distance to avoid aliasing.\\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\nattribute vec4 a_pos_normal;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform vec2 u_gl_units_to_pixels;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying float v_gamma_scale;\\nvarying highp float v_linesofar;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize mediump float gapwidth\\n #pragma mapbox: initialize lowp float offset\\n #pragma mapbox: initialize mediump float width\\n\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n\\n v_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * 2.0;\\n\\n vec2 pos = a_pos_normal.xy;\\n\\n // x is 1 if it's a round cap, 0 otherwise\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = a_pos_normal.zw;\\n v_normal = normal;\\n\\n // these transformations used to be applied in the JS and native code bases.\\n // moved them into the shader for clarity and simplicity.\\n gapwidth = gapwidth / 2.0;\\n float halfwidth = width / 2.0;\\n offset = -1.0 * offset;\\n\\n float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\\n float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\\n gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\\n\\n // calculate how much the perspective view squishes or stretches the extrude\\n float extrude_length_without_perspective = length(dist);\\n float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\\n v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\\n\\n v_width2 = vec2(outset, inset);\\n}\\n\"},lineGradient:{fragmentSource:\"\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_width2;\\nvarying vec2 v_normal;\\nvarying float v_gamma_scale;\\nvarying highp float v_lineprogress;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_width2.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_width2.t) or when fading out\\n // (v_width2.s)\\n float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\\n\\n // For gradient lines, v_lineprogress is the ratio along the entire line,\\n // scaled to [0, 2^15), and the gradient ramp is stored in a texture.\\n vec4 color = texture2D(u_image, vec2(v_lineprogress, 0.5));\\n\\n gl_FragColor = color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"\\n// the attribute conveying progress along a line is scaled to [0, 2^15)\\n#define MAX_LINE_DISTANCE 32767.0\\n\\n// the distance over which the line edge fades out.\\n// Retina devices need a smaller distance to avoid aliasing.\\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\nattribute vec4 a_pos_normal;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform vec2 u_gl_units_to_pixels;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying float v_gamma_scale;\\nvarying highp float v_lineprogress;\\n\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize mediump float gapwidth\\n #pragma mapbox: initialize lowp float offset\\n #pragma mapbox: initialize mediump float width\\n\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n\\n v_lineprogress = (floor(a_data.z / 4.0) + a_data.w * 64.0) * 2.0 / MAX_LINE_DISTANCE;\\n\\n vec2 pos = a_pos_normal.xy;\\n\\n // x is 1 if it's a round cap, 0 otherwise\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = a_pos_normal.zw;\\n v_normal = normal;\\n\\n // these transformations used to be applied in the JS and native code bases.\\n // moved them into the shader for clarity and simplicity.\\n gapwidth = gapwidth / 2.0;\\n float halfwidth = width / 2.0;\\n offset = -1.0 * offset;\\n\\n float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\\n float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\\n gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\\n\\n // calculate how much the perspective view squishes or stretches the extrude\\n float extrude_length_without_perspective = length(dist);\\n float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\\n v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\\n\\n v_width2 = vec2(outset, inset);\\n}\\n\"},linePattern:{fragmentSource:\"uniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_fade;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_width2.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_width2.t) or when fading out\\n // (v_width2.s)\\n float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\\n\\n float x_a = mod(v_linesofar / u_pattern_size_a.x, 1.0);\\n float x_b = mod(v_linesofar / u_pattern_size_b.x, 1.0);\\n\\n // v_normal.y is 0 at the midpoint of the line, -1 at the lower edge, 1 at the upper edge\\n // we clamp the line width outset to be between 0 and half the pattern height plus padding (2.0)\\n // to ensure we don't sample outside the designated symbol on the sprite sheet.\\n // 0.5 is added to shift the component to be bounded between 0 and 1 for interpolation of\\n // the texture coordinate\\n float y_a = 0.5 + (v_normal.y * clamp(v_width2.s, 0.0, (u_pattern_size_a.y + 2.0) / 2.0) / u_pattern_size_a.y);\\n float y_b = 0.5 + (v_normal.y * clamp(v_width2.s, 0.0, (u_pattern_size_b.y + 2.0) / 2.0) / u_pattern_size_b.y);\\n vec2 pos_a = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, vec2(x_a, y_a));\\n vec2 pos_b = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, vec2(x_b, y_b));\\n\\n vec4 color = mix(texture2D(u_image, pos_a), texture2D(u_image, pos_b), u_fade);\\n\\n gl_FragColor = color * alpha * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\n// the distance over which the line edge fades out.\\n// Retina devices need a smaller distance to avoid aliasing.\\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\\n\\nattribute vec4 a_pos_normal;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform vec2 u_gl_units_to_pixels;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize lowp float offset\\n #pragma mapbox: initialize mediump float gapwidth\\n #pragma mapbox: initialize mediump float width\\n\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n vec2 pos = a_pos_normal.xy;\\n\\n // x is 1 if it's a round cap, 0 otherwise\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = a_pos_normal.zw;\\n v_normal = normal;\\n\\n // these transformations used to be applied in the JS and native code bases.\\n // moved them into the shader for clarity and simplicity.\\n gapwidth = gapwidth / 2.0;\\n float halfwidth = width / 2.0;\\n offset = -1.0 * offset;\\n\\n float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\\n float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\\n gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\\n\\n // calculate how much the perspective view squishes or stretches the extrude\\n float extrude_length_without_perspective = length(dist);\\n float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\\n v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\\n\\n v_linesofar = a_linesofar;\\n v_width2 = vec2(outset, inset);\\n}\\n\"},lineSDF:{fragmentSource:\"\\nuniform sampler2D u_image;\\nuniform float u_sdfgamma;\\nuniform float u_mix;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize mediump float width\\n #pragma mapbox: initialize lowp float floorwidth\\n\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_width2.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_width2.t) or when fading out\\n // (v_width2.s)\\n float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\\n\\n float sdfdist_a = texture2D(u_image, v_tex_a).a;\\n float sdfdist_b = texture2D(u_image, v_tex_b).a;\\n float sdfdist = mix(sdfdist_a, sdfdist_b, u_mix);\\n alpha = smoothstep(0.5 - u_sdfgamma / floorwidth, 0.5 + u_sdfgamma / floorwidth, sdfdist);\\n\\n gl_FragColor = color (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\n// the distance over which the line edge fades out.\\n// Retina devices need a smaller distance to avoid aliasing.\\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\\n\\nattribute vec4 a_pos_normal;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform vec2 u_patternscale_a;\\nuniform float u_tex_y_a;\\nuniform vec2 u_patternscale_b;\\nuniform float u_tex_y_b;\\nuniform vec2 u_gl_units_to_pixels;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize mediump float gapwidth\\n #pragma mapbox: initialize lowp float offset\\n #pragma mapbox: initialize mediump float width\\n #pragma mapbox: initialize lowp float floorwidth\\n\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n vec2 pos = a_pos_normal.xy;\\n\\n // x is 1 if it's a round cap, 0 otherwise\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = a_pos_normal.zw;\\n v_normal = normal;\\n\\n // these transformations used to be applied in the JS and native code bases.\\n // moved them into the shader for clarity and simplicity.\\n gapwidth = gapwidth / 2.0;\\n float halfwidth = width / 2.0;\\n offset = -1.0 * offset;\\n\\n float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\\n float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist =outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\\n gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\\n\\n // calculate how much the perspective view squishes or stretches the extrude\\n float extrude_length_without_perspective = length(dist);\\n float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\\n v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\\n\\n v_tex_a = vec2(a_linesofar * u_patternscale_a.x / floorwidth, normal.y * u_patternscale_a.y + u_tex_y_a);\\n v_tex_b = vec2(a_linesofar * u_patternscale_b.x / floorwidth, normal.y * u_patternscale_b.y + u_tex_y_b);\\n\\n v_width2 = vec2(outset, inset);\\n}\\n\"},raster:{fragmentSource:\"uniform float u_fade_t;\\nuniform float u_opacity;\\nuniform sampler2D u_image0;\\nuniform sampler2D u_image1;\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nuniform float u_brightness_low;\\nuniform float u_brightness_high;\\n\\nuniform float u_saturation_factor;\\nuniform float u_contrast_factor;\\nuniform vec3 u_spin_weights;\\n\\nvoid main() {\\n\\n // read and cross-fade colors from the main and parent tiles\\n vec4 color0 = texture2D(u_image0, v_pos0);\\n vec4 color1 = texture2D(u_image1, v_pos1);\\n if (color0.a > 0.0) {\\n color0.rgb = color0.rgb / color0.a;\\n }\\n if (color1.a > 0.0) {\\n color1.rgb = color1.rgb / color1.a;\\n }\\n vec4 color = mix(color0, color1, u_fade_t);\\n color.a = u_opacity;\\n vec3 rgb = color.rgb;\\n\\n // spin\\n rgb = vec3(\\n dot(rgb, u_spin_weights.xyz),\\n dot(rgb, u_spin_weights.zxy),\\n dot(rgb, u_spin_weights.yzx));\\n\\n // saturation\\n float average = (color.r + color.g + color.b) / 3.0;\\n rgb += (average - rgb) u_saturation_factor;\\n\\n // contrast\\n rgb = (rgb - 0.5) * u_contrast_factor + 0.5;\\n\\n // brightness\\n vec3 u_high_vec = vec3(u_brightness_low, u_brightness_low, u_brightness_low);\\n vec3 u_low_vec = vec3(u_brightness_high, u_brightness_high, u_brightness_high);\\n\\n gl_FragColor = vec4(mix(u_high_vec, u_low_vec, rgb) * color.a, color.a);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_tl_parent;\\nuniform float u_scale_parent;\\nuniform float u_buffer_scale;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n // We are using Int16 for texture position coordinates to give us enough precision for\\n // fractional coordinates. We use 8192 to scale the texture coordinates in the buffer\\n // as an arbitrarily high number to preserve adequate precision when rendering.\\n // This is also the same value as the EXTENT we are using for our tile buffer pos coordinates,\\n // so math for modifying either is consistent.\\n v_pos0 = (((a_texture_pos / 8192.0) - 0.5) / u_buffer_scale ) + 0.5;\\n v_pos1 = (v_pos0 * u_scale_parent) + u_tl_parent;\\n}\\n\"},symbolIcon:{fragmentSource:\"uniform sampler2D u_texture;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_tex;\\nvarying float v_fade_opacity;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n lowp float alpha = opacity * v_fade_opacity;\\n gl_FragColor = texture2D(u_texture, v_tex) * alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"const float PI = 3.141592653589793;\\n\\nattribute vec4 a_pos_offset;\\nattribute vec4 a_data;\\nattribute vec3 a_projected_pos;\\nattribute float a_fade_opacity;\\n\\nuniform bool u_is_size_zoom_constant;\\nuniform bool u_is_size_feature_constant;\\nuniform highp float u_size_t; // used to interpolate between zoom stops when size is a composite function\\nuniform highp float u_size; // used when size is both zoom and feature constant\\nuniform highp float u_camera_to_center_distance;\\nuniform highp float u_pitch;\\nuniform bool u_rotate_symbol;\\nuniform highp float u_aspect_ratio;\\nuniform float u_fade_change;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nuniform mat4 u_matrix;\\nuniform mat4 u_label_plane_matrix;\\nuniform mat4 u_gl_coord_matrix;\\n\\nuniform bool u_is_text;\\nuniform bool u_pitch_with_map;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying float v_fade_opacity;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n vec2 a_pos = a_pos_offset.xy;\\n vec2 a_offset = a_pos_offset.zw;\\n\\n vec2 a_tex = a_data.xy;\\n vec2 a_size = a_data.zw;\\n\\n highp float segment_angle = -a_projected_pos[2];\\n\\n float size;\\n if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {\\n size = mix(a_size[0], a_size[1], u_size_t) / 10.0;\\n } else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {\\n size = a_size[0] / 10.0;\\n } else if (!u_is_size_zoom_constant && u_is_size_feature_constant) {\\n size = u_size;\\n } else {\\n size = u_size;\\n }\\n\\n vec4 projectedPoint = u_matrix * vec4(a_pos, 0, 1);\\n highp float camera_to_anchor_distance = projectedPoint.w;\\n // See comments in symbol_sdf.vertex\\n highp float distance_ratio = u_pitch_with_map ?\\n camera_to_anchor_distance / u_camera_to_center_distance :\\n u_camera_to_center_distance / camera_to_anchor_distance;\\n highp float perspective_ratio = clamp(\\n 0.5 + 0.5 * distance_ratio,\\n 0.0, // Prevents oversized near-field symbols in pitched/overzoomed tiles\\n 4.0);\\n\\n size = perspective_ratio;\\n\\n float fontScale = u_is_text ? size / 24.0 : size;\\n\\n highp float symbol_rotation = 0.0;\\n if (u_rotate_symbol) {\\n // See comments in symbol_sdf.vertex\\n vec4 offsetProjectedPoint = u_matrix vec4(a_pos + vec2(1, 0), 0, 1);\\n\\n vec2 a = projectedPoint.xy / projectedPoint.w;\\n vec2 b = offsetProjectedPoint.xy / offsetProjectedPoint.w;\\n\\n symbol_rotation = atan((b.y - a.y) / u_aspect_ratio, b.x - a.x);\\n }\\n\\n highp float angle_sin = sin(segment_angle + symbol_rotation);\\n highp float angle_cos = cos(segment_angle + symbol_rotation);\\n mat2 rotation_matrix = mat2(angle_cos, -1.0 * angle_sin, angle_sin, angle_cos);\\n\\n vec4 projected_pos = u_label_plane_matrix * vec4(a_projected_pos.xy, 0.0, 1.0);\\n gl_Position = u_gl_coord_matrix * vec4(projected_pos.xy / projected_pos.w + rotation_matrix * (a_offset / 32.0 * fontScale), 0.0, 1.0);\\n\\n v_tex = a_tex / u_texsize;\\n vec2 fade_opacity = unpack_opacity(a_fade_opacity);\\n float fade_change = fade_opacity[1] > 0.5 ? u_fade_change : -u_fade_change;\\n v_fade_opacity = max(0.0, min(1.0, fade_opacity[0] + fade_change));\\n}\\n\"},symbolSDF:{fragmentSource:\"#define SDF_PX 8.0\\n#define EDGE_GAMMA 0.105/DEVICE_PIXEL_RATIO\\n\\nuniform bool u_is_halo;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\n\\nuniform sampler2D u_texture;\\nuniform highp float u_gamma_scale;\\nuniform bool u_is_text;\\n\\nvarying vec2 v_data0;\\nvarying vec3 v_data1;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 fill_color\\n #pragma mapbox: initialize highp vec4 halo_color\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize lowp float halo_width\\n #pragma mapbox: initialize lowp float halo_blur\\n\\n vec2 tex = v_data0.xy;\\n float gamma_scale = v_data1.x;\\n float size = v_data1.y;\\n float fade_opacity = v_data1[2];\\n\\n float fontScale = u_is_text ? size / 24.0 : size;\\n\\n lowp vec4 color = fill_color;\\n highp float gamma = EDGE_GAMMA / (fontScale * u_gamma_scale);\\n lowp float buff = (256.0 - 64.0) / 256.0;\\n if (u_is_halo) {\\n color = halo_color;\\n gamma = (halo_blur * 1.19 / SDF_PX + EDGE_GAMMA) / (fontScale * u_gamma_scale);\\n buff = (6.0 - halo_width / fontScale) / SDF_PX;\\n }\\n\\n lowp float dist = texture2D(u_texture, tex).a;\\n highp float gamma_scaled = gamma * gamma_scale;\\n highp float alpha = smoothstep(buff - gamma_scaled, buff + gamma_scaled, dist);\\n\\n gl_FragColor = color * (alpha * opacity * fade_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"const float PI = 3.141592653589793;\\n\\nattribute vec4 a_pos_offset;\\nattribute vec4 a_data;\\nattribute vec3 a_projected_pos;\\nattribute float a_fade_opacity;\\n\\n// contents of a_size vary based on the type of property value\\n// used for {text,icon}-size.\\n// For constants, a_size is disabled.\\n// For source functions, we bind only one value per vertex: the value of {text,icon}-size evaluated for the current feature.\\n// For composite functions:\\n// [ text-size(lowerZoomStop, feature),\\n// text-size(upperZoomStop, feature) ]\\nuniform bool u_is_size_zoom_constant;\\nuniform bool u_is_size_feature_constant;\\nuniform highp float u_size_t; // used to interpolate between zoom stops when size is a composite function\\nuniform highp float u_size; // used when size is both zoom and feature constant\\n\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\n\\nuniform mat4 u_matrix;\\nuniform mat4 u_label_plane_matrix;\\nuniform mat4 u_gl_coord_matrix;\\n\\nuniform bool u_is_text;\\nuniform bool u_pitch_with_map;\\nuniform highp float u_pitch;\\nuniform bool u_rotate_symbol;\\nuniform highp float u_aspect_ratio;\\nuniform highp float u_camera_to_center_distance;\\nuniform float u_fade_change;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_data0;\\nvarying vec3 v_data1;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 fill_color\\n #pragma mapbox: initialize highp vec4 halo_color\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize lowp float halo_width\\n #pragma mapbox: initialize lowp float halo_blur\\n\\n vec2 a_pos = a_pos_offset.xy;\\n vec2 a_offset = a_pos_offset.zw;\\n\\n vec2 a_tex = a_data.xy;\\n vec2 a_size = a_data.zw;\\n\\n highp float segment_angle = -a_projected_pos[2];\\n float size;\\n\\n if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {\\n size = mix(a_size[0], a_size[1], u_size_t) / 10.0;\\n } else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {\\n size = a_size[0] / 10.0;\\n } else if (!u_is_size_zoom_constant && u_is_size_feature_constant) {\\n size = u_size;\\n } else {\\n size = u_size;\\n }\\n\\n vec4 projectedPoint = u_matrix * vec4(a_pos, 0, 1);\\n highp float camera_to_anchor_distance = projectedPoint.w;\\n // If the label is pitched with the map, layout is done in pitched space,\\n // which makes labels in the distance smaller relative to viewport space.\\n // We counteract part of that effect by multiplying by the perspective ratio.\\n // If the label isn't pitched with the map, we do layout in viewport space,\\n // which makes labels in the distance larger relative to the features around\\n // them. We counteract part of that effect by dividing by the perspective ratio.\\n highp float distance_ratio = u_pitch_with_map ?\\n camera_to_anchor_distance / u_camera_to_center_distance :\\n u_camera_to_center_distance / camera_to_anchor_distance;\\n highp float perspective_ratio = clamp(\\n 0.5 + 0.5 * distance_ratio,\\n 0.0, // Prevents oversized near-field symbols in pitched/overzoomed tiles\\n 4.0);\\n\\n size = perspective_ratio;\\n\\n float fontScale = u_is_text ? size / 24.0 : size;\\n\\n highp float symbol_rotation = 0.0;\\n if (u_rotate_symbol) {\\n // Point labels with 'rotation-alignment: map' are horizontal with respect to tile units\\n // To figure out that angle in projected space, we draw a short horizontal line in tile\\n // space, project it, and measure its angle in projected space.\\n vec4 offsetProjectedPoint = u_matrix vec4(a_pos + vec2(1, 0), 0, 1);\\n\\n vec2 a = projectedPoint.xy / projectedPoint.w;\\n vec2 b = offsetProjectedPoint.xy / offsetProjectedPoint.w;\\n\\n symbol_rotation = atan((b.y - a.y) / u_aspect_ratio, b.x - a.x);\\n }\\n\\n highp float angle_sin = sin(segment_angle + symbol_rotation);\\n highp float angle_cos = cos(segment_angle + symbol_rotation);\\n mat2 rotation_matrix = mat2(angle_cos, -1.0 * angle_sin, angle_sin, angle_cos);\\n\\n vec4 projected_pos = u_label_plane_matrix * vec4(a_projected_pos.xy, 0.0, 1.0);\\n gl_Position = u_gl_coord_matrix * vec4(projected_pos.xy / projected_pos.w + rotation_matrix * (a_offset / 32.0 * fontScale), 0.0, 1.0);\\n float gamma_scale = gl_Position.w;\\n\\n vec2 tex = a_tex / u_texsize;\\n vec2 fade_opacity = unpack_opacity(a_fade_opacity);\\n float fade_change = fade_opacity[1] > 0.5 ? u_fade_change : -u_fade_change;\\n float interpolated_fade_opacity = max(0.0, min(1.0, fade_opacity[0] + fade_change));\\n\\n v_data0 = vec2(tex.x, tex.y);\\n v_data1 = vec3(gamma_scale, size, interpolated_fade_opacity);\\n}\\n\"}},tr=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,er=function(t){var e=Qe[t],r={};e.fragmentSource=e.fragmentSource.replace(tr,function(t,e,n,i,a){return r[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+n+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+n+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n \"+n+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}),e.vertexSource=e.vertexSource.replace(tr,function(t,e,n,i,a){var o=\"float\"===i?\"vec2\":\"vec4\";return r[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float a_\"+a+\"_t;\\nattribute \"+n+\" \"+o+\" a_\"+a+\";\\nvarying \"+n+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+n+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+a+\" = unpack_mix_\"+o+\"(a_\"+a+\", a_\"+a+\"_t);\\n#else\\n \"+n+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float a_\"+a+\"_t;\\nattribute \"+n+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+n+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+n+\" \"+i+\" \"+a+\" = unpack_mix_\"+o+\"(a_\"+a+\", a_\"+a+\"_t);\\n#else\\n \"+n+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})};for(var rr in Qe)er(rr);var nr=Qe,ir=function(t,e,r,n){var i=t.gl;this.program=i.createProgram();var o=r.defines().concat(\"#define DEVICE_PIXEL_RATIO \"+a.devicePixelRatio.toFixed(1));n&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(nr.prelude.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(nr.prelude.vertexSource,e.vertexSource).join(\"\\n\"),c=i.createShader(i.FRAGMENT_SHADER);i.shaderSource(c,s),i.compileShader(c),i.attachShader(this.program,c);var u=i.createShader(i.VERTEX_SHADER);i.shaderSource(u,l),i.compileShader(u),i.attachShader(this.program,u);for(var f=r.layoutAttributes\|\|[],h=0;h<f.length;h++)i.bindAttribLocation(this.program,h,f[h].name);i.linkProgram(this.program),this.numAttributes=i.getProgramParameter(this.program,i.ACTIVE_ATTRIBUTES),this.attributes={},this.uniforms={};for(var p=0;p<this.numAttributes;p++){var d=i.getActiveAttrib(this.program,p);d&&(this.attributes[d.name]=i.getAttribLocation(this.program,d.name))}for(var g=i.getProgramParameter(this.program,i.ACTIVE_UNIFORMS),v=0;v<g;v++){var m=i.getActiveUniform(this.program,v);m&&(this.uniforms[m.name]=i.getUniformLocation(this.program,m.name))}};function ar(e,r,n,i,a){for(var o=0;o<n.length;o++){var s=n[o];if(i.isLessThan(s.tileID))break;if(r.key===s.tileID.key)return;if(s.tileID.isChildOf(r)){for(var l=r.children(1/0),c=0;c<l.length;c++)ar(e,l[c],n.slice(o),i,a);return}}var u=r.overscaledZ-e.overscaledZ,f=new t.CanonicalTileID(u,r.canonical.x-(e.canonical.x<<u),r.canonical.y-(e.canonical.y<<u));a[f.key]=a[f.key]\|\|f}function or(t,e,r,n,i){var a=t.context,o=a.gl,s=i?t.useProgram(\"collisionCircle\"):t.useProgram(\"collisionBox\");a.setDepthMode(qt.disabled),a.setStencilMode(Ht.disabled),a.setColorMode(t.colorModeForRenderPass());for(var l=0;l<n.length;l++){var c=n[l],u=e.getTile(c),f=u.getBucket(r);if(f){var h=i?f.collisionCircle:f.collisionBox;if(h){o.uniformMatrix4fv(s.uniforms.u_matrix,!1,c.posMatrix),i\|\|a.lineWidth.set(1),o.uniform1f(s.uniforms.u_camera_to_center_distance,t.transform.cameraToCenterDistance);var p=Te(u,1,t.transform.zoom),d=Math.pow(2,t.transform.zoom-u.tileID.overscaledZ);o.uniform1f(s.uniforms.u_pixels_to_tile_units,p),o.uniform2f(s.uniforms.u_extrude_scale,t.transform.pixelsToGLUnits[0]/(pd),t.transform.pixelsToGLUnits[1]/(pd)),o.uniform1f(s.uniforms.u_overscale_factor,u.tileID.overscaleFactor()),s.draw(a,i?o.TRIANGLES:o.LINES,r.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,null,h.collisionVertexBuffer,null)}}}}ir.prototype.draw=function(t,e,r,n,i,a,o,s,l){for(var c,u=t.gl,f=(c={},c[u.LINES]=2,c[u.TRIANGLES]=3,c)[e],h=0,p=a.get();h<p.length;h+=1){var d=p[h],g=d.vaos\|\|(d.vaos={});(g[r]\|\|(g[r]=new Q)).bind(t,this,n,o?o.getPaintVertexBuffers():[],i,d.vertexOffset,s,l),u.drawElements(e,d.primitiveLengthf,u.UNSIGNED_SHORT,d.primitiveOffsetf2)}};var sr=t.mat4.identity(new Float32Array(16)),lr=t.default$19.layout;function cr(t,e,r,n,i,a,o,s,l,c){var u,f=t.context,h=f.gl,p=t.transform,d=\"map\"===s,g=\"map\"===l,v=d&&\"line\"===r.layout.get(\"symbol-placement\"),m=d&&!g&&!v,y=g;f.setDepthMode(y?t.depthModeForSublayer(0,qt.ReadOnly):qt.disabled);for(var x=0,b=n;x<b.length;x+=1){var _=b[x],w=e.getTile(_),k=w.getBucket(r);if(k){var M=i?k.text:k.icon;if(M&&M.segments.get().length){var A=M.programConfigurations.get(r.id),T=i\|\|k.sdfIcons,S=i?k.textSizeData:k.iconSizeData;if(u\|\|(u=t.useProgram(T?\"symbolSDF\":\"symbolIcon\",A),A.setUniforms(t.context,u,r.paint,{zoom:t.transform.zoom}),ur(u,t,r,i,m,g,S)),f.activeTexture.set(h.TEXTURE0),h.uniform1i(u.uniforms.u_texture,0),i)w.glyphAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),h.uniform2fv(u.uniforms.u_texsize,w.glyphAtlasTexture.size);else{var E=1!==r.layout.get(\"icon-size\").constantOr(0)\|\|k.iconsNeedLinear,C=g\|\|0!==p.pitch;w.iconAtlasTexture.bind(T\|\|t.options.rotating\|\|t.options.zooming\|\|E\|\|C?h.LINEAR:h.NEAREST,h.CLAMP_TO_EDGE),h.uniform2fv(u.uniforms.u_texsize,w.iconAtlasTexture.size)}h.uniformMatrix4fv(u.uniforms.u_matrix,!1,t.translatePosMatrix(_.posMatrix,w,a,o));var L=Te(w,1,t.transform.zoom),z=fe(_.posMatrix,g,d,t.transform,L),O=he(_.posMatrix,g,d,t.transform,L);h.uniformMatrix4fv(u.uniforms.u_gl_coord_matrix,!1,t.translatePosMatrix(O,w,a,o,!0)),v?(h.uniformMatrix4fv(u.uniforms.u_label_plane_matrix,!1,sr),ge(k,_.posMatrix,t,i,z,O,g,c)):h.uniformMatrix4fv(u.uniforms.u_label_plane_matrix,!1,z),h.uniform1f(u.uniforms.u_fade_change,t.options.fadeDuration?t.symbolFadeChange:1),fr(u,A,t,r,w,M,i,T,g)}}}}function ur(e,r,n,i,a,o,s){var l=r.context.gl,c=r.transform;l.uniform1i(e.uniforms.u_pitch_with_map,o?1:0),l.uniform1f(e.uniforms.u_is_text,i?1:0),l.uniform1f(e.uniforms.u_pitch,c.pitch/3602Math.PI);var u=\"constant\"===s.functionType\|\|\"source\"===s.functionType,f=\"constant\"===s.functionType\|\|\"camera\"===s.functionType;l.uniform1i(e.uniforms.u_is_size_zoom_constant,u?1:0),l.uniform1i(e.uniforms.u_is_size_feature_constant,f?1:0),l.uniform1f(e.uniforms.u_camera_to_center_distance,c.cameraToCenterDistance);var h=t.evaluateSizeForZoom(s,c.zoom,lr.properties[i?\"text-size\":\"icon-size\"]);void 0!==h.uSizeT&&l.uniform1f(e.uniforms.u_size_t,h.uSizeT),void 0!==h.uSize&&l.uniform1f(e.uniforms.u_size,h.uSize),l.uniform1f(e.uniforms.u_aspect_ratio,c.width/c.height),l.uniform1i(e.uniforms.u_rotate_symbol,a?1:0)}function fr(t,e,r,n,i,a,o,s,l){var c=r.context,u=c.gl,f=r.transform;if(s){var h=0!==n.paint.get(o?\"text-halo-width\":\"icon-halo-width\").constantOr(1),p=l?Math.cos(f._pitch)f.cameraToCenterDistance:1;u.uniform1f(t.uniforms.u_gamma_scale,p),h&&(u.uniform1f(t.uniforms.u_is_halo,1),hr(a,n,c,t)),u.uniform1f(t.uniforms.u_is_halo,0)}hr(a,n,c,t)}function hr(t,e,r,n){n.draw(r,r.gl.TRIANGLES,e.id,t.layoutVertexBuffer,t.indexBuffer,t.segments,t.programConfigurations.get(e.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function pr(t,e,r,n,i,o,s,l,c){var u,f,h,p,d=e.context,g=d.gl,v=i.paint.get(\"line-dasharray\"),m=i.paint.get(\"line-pattern\");if(l\|\|c){var y=1/Te(r,1,e.transform.tileZoom);if(v){u=e.lineAtlas.getDash(v.from,\"round\"===i.layout.get(\"line-cap\")),f=e.lineAtlas.getDash(v.to,\"round\"===i.layout.get(\"line-cap\"));var x=u.widthv.fromScale,b=f.widthv.toScale;g.uniform2f(t.uniforms.u_patternscale_a,y/x,-u.height/2),g.uniform2f(t.uniforms.u_patternscale_b,y/b,-f.height/2),g.uniform1f(t.uniforms.u_sdfgamma,e.lineAtlas.width/(256Math.min(x,b)a.devicePixelRatio)/2)}else if(m){if(h=e.imageManager.getPattern(m.from),p=e.imageManager.getPattern(m.to),!h\|\|!p)return;g.uniform2f(t.uniforms.u_pattern_size_a,h.displaySize[0]m.fromScale/y,h.displaySize[1]),g.uniform2f(t.uniforms.u_pattern_size_b,p.displaySize[0]m.toScale/y,p.displaySize[1]);var _=e.imageManager.getPixelSize(),w=_.width,k=_.height;g.uniform2fv(t.uniforms.u_texsize,[w,k])}g.uniform2f(t.uniforms.u_gl_units_to_pixels,1/e.transform.pixelsToGLUnits[0],1/e.transform.pixelsToGLUnits[1])}l&&(v?(g.uniform1i(t.uniforms.u_image,0),d.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(d),g.uniform1f(t.uniforms.u_tex_y_a,u.y),g.uniform1f(t.uniforms.u_tex_y_b,f.y),g.uniform1f(t.uniforms.u_mix,v.t)):m&&(g.uniform1i(t.uniforms.u_image,0),d.activeTexture.set(g.TEXTURE0),e.imageManager.bind(d),g.uniform2fv(t.uniforms.u_pattern_tl_a,h.tl),g.uniform2fv(t.uniforms.u_pattern_br_a,h.br),g.uniform2fv(t.uniforms.u_pattern_tl_b,p.tl),g.uniform2fv(t.uniforms.u_pattern_br_b,p.br),g.uniform1f(t.uniforms.u_fade,m.t))),d.setStencilMode(e.stencilModeForClipping(o));var M=e.translatePosMatrix(o.posMatrix,r,i.paint.get(\"line-translate\"),i.paint.get(\"line-translate-anchor\"));if(g.uniformMatrix4fv(t.uniforms.u_matrix,!1,M),g.uniform1f(t.uniforms.u_ratio,1/Te(r,1,e.transform.zoom)),i.paint.get(\"line-gradient\")){d.activeTexture.set(g.TEXTURE0);var A=i.gradientTexture;if(!i.gradient)return;A\|\|(A=i.gradientTexture=new z(d,i.gradient,g.RGBA)),A.bind(g.LINEAR,g.CLAMP_TO_EDGE),g.uniform1i(t.uniforms.u_image,0)}t.draw(d,g.TRIANGLES,i.id,n.layoutVertexBuffer,n.indexBuffer,n.segments,s)}var dr=function(t,e){if(!t)return!1;var r=e.imageManager.getPattern(t.from),n=e.imageManager.getPattern(t.to);return!r\|\|!n},gr=function(t,e,r){var n=e.context,i=n.gl,a=e.imageManager.getPattern(t.from),o=e.imageManager.getPattern(t.to);i.uniform1i(r.uniforms.u_image,0),i.uniform2fv(r.uniforms.u_pattern_tl_a,a.tl),i.uniform2fv(r.uniforms.u_pattern_br_a,a.br),i.uniform2fv(r.uniforms.u_pattern_tl_b,o.tl),i.uniform2fv(r.uniforms.u_pattern_br_b,o.br);var s=e.imageManager.getPixelSize(),l=s.width,c=s.height;i.uniform2fv(r.uniforms.u_texsize,[l,c]),i.uniform1f(r.uniforms.u_mix,t.t),i.uniform2fv(r.uniforms.u_pattern_size_a,a.displaySize),i.uniform2fv(r.uniforms.u_pattern_size_b,o.displaySize),i.uniform1f(r.uniforms.u_scale_a,t.fromScale),i.uniform1f(r.uniforms.u_scale_b,t.toScale),n.activeTexture.set(i.TEXTURE0),e.imageManager.bind(e.context)},vr=function(t,e,r){var n=e.context.gl;n.uniform1f(r.uniforms.u_tile_units_to_pixels,1/Te(t,1,e.transform.tileZoom));var i=Math.pow(2,t.tileID.overscaledZ),a=t.tileSizeMath.pow(2,e.transform.tileZoom)/i,o=a(t.tileID.canonical.x+t.tileID.wrapi),s=at.tileID.canonical.y;n.uniform2f(r.uniforms.u_pixel_coord_upper,o>>16,s>>16),n.uniform2f(r.uniforms.u_pixel_coord_lower,65535&o,65535&s)};function mr(t,e,r,n,i){if(!dr(r.paint.get(\"fill-pattern\"),t))for(var a=!0,o=0,s=n;o<s.length;o+=1){var l=s[o],c=e.getTile(l),u=c.getBucket(r);u&&(t.context.setStencilMode(t.stencilModeForClipping(l)),i(t,e,r,c,l,u,a),a=!1)}}function yr(t,e,r,n,i,a,o){var s=t.context.gl,l=a.programConfigurations.get(r.id);br(\"fill\",r.paint.get(\"fill-pattern\"),t,l,r,n,i,o).draw(t.context,s.TRIANGLES,r.id,a.layoutVertexBuffer,a.indexBuffer,a.segments,l)}function xr(t,e,r,n,i,a,o){var s=t.context.gl,l=a.programConfigurations.get(r.id),c=br(\"fillOutline\",r.getPaintProperty(\"fill-outline-color\")?null:r.paint.get(\"fill-pattern\"),t,l,r,n,i,o);s.uniform2f(c.uniforms.u_world,s.drawingBufferWidth,s.drawingBufferHeight),c.draw(t.context,s.LINES,r.id,a.layoutVertexBuffer,a.indexBuffer2,a.segments2,l)}function br(t,e,r,n,i,a,o,s){var l,c=r.context.program.get();return e?(l=r.useProgram(t+\"Pattern\",n),(s\|\|l.program!==c)&&(n.setUniforms(r.context,l,i.paint,{zoom:r.transform.zoom}),gr(e,r,l)),vr(a,r,l)):(l=r.useProgram(t,n),(s\|\|l.program!==c)&&n.setUniforms(r.context,l,i.paint,{zoom:r.transform.zoom})),r.context.gl.uniformMatrix4fv(l.uniforms.u_matrix,!1,r.translatePosMatrix(o.posMatrix,a,i.paint.get(\"fill-translate\"),i.paint.get(\"fill-translate-anchor\"))),l}var _r=t.default$20.mat3,wr=t.default$20.mat4,kr=t.default$20.vec3;function Mr(t,e,r,n,i,a,o){var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=t.context.program.get(),f=a.programConfigurations.get(r.id),h=t.useProgram(c?\"fillExtrusionPattern\":\"fillExtrusion\",f);if((o\|\|h.program!==u)&&f.setUniforms(s,h,r.paint,{zoom:t.transform.zoom}),c){if(dr(c,t))return;gr(c,t,h),vr(n,t,h),l.uniform1f(h.uniforms.u_height_factor,-Math.pow(2,i.overscaledZ)/n.tileSize/8)}t.context.gl.uniformMatrix4fv(h.uniforms.u_matrix,!1,t.translatePosMatrix(i.posMatrix,n,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\"))),function(t,e){var r=e.context.gl,n=e.style.light,i=n.properties.get(\"position\"),a=[i.x,i.y,i.z],o=_r.create();\"viewport\"===n.properties.get(\"anchor\")&&_r.fromRotation(o,-e.transform.angle),kr.transformMat3(a,a,o);var s=n.properties.get(\"color\");r.uniform3fv(t.uniforms.u_lightpos,a),r.uniform1f(t.uniforms.u_lightintensity,n.properties.get(\"intensity\")),r.uniform3f(t.uniforms.u_lightcolor,s.r,s.g,s.b)}(h,t),h.draw(s,l.TRIANGLES,r.id,a.layoutVertexBuffer,a.indexBuffer,a.segments,f)}function Ar(e,r,n){var i=e.context,a=i.gl,o=r.fbo;if(o){var s=e.useProgram(\"hillshade\"),l=e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),!0);!function(t,e,r){var n=r.paint.get(\"hillshade-illumination-direction\")(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(n-=e.transform.angle),e.context.gl.uniform2f(t.uniforms.u_light,r.paint.get(\"hillshade-exaggeration\"),n)}(s,e,n);var c=function(e,r){var n=r.toCoordinate(),i=new t.default$17(n.column,n.row+1,n.zoom);return[e.transform.coordinateLocation(n).lat,e.transform.coordinateLocation(i).lat]}(e,r.tileID);i.activeTexture.set(a.TEXTURE0),a.bindTexture(a.TEXTURE_2D,o.colorAttachment.get()),a.uniformMatrix4fv(s.uniforms.u_matrix,!1,l),a.uniform2fv(s.uniforms.u_latrange,c),a.uniform1i(s.uniforms.u_image,0);var u=n.paint.get(\"hillshade-shadow-color\");a.uniform4f(s.uniforms.u_shadow,u.r,u.g,u.b,u.a);var f=n.paint.get(\"hillshade-highlight-color\");a.uniform4f(s.uniforms.u_highlight,f.r,f.g,f.b,f.a);var h=n.paint.get(\"hillshade-accent-color\");if(a.uniform4f(s.uniforms.u_accent,h.r,h.g,h.b,h.a),r.maskedBoundsBuffer&&r.maskedIndexBuffer&&r.segments)s.draw(i,a.TRIANGLES,n.id,r.maskedBoundsBuffer,r.maskedIndexBuffer,r.segments);else{var p=e.rasterBoundsBuffer;e.rasterBoundsVAO.bind(i,s,p,[]),a.drawArrays(a.TRIANGLE_STRIP,0,p.length)}}}function Tr(e,r,n){var i=e.context,a=i.gl;if(r.dem&&r.dem.level){var o=r.dem.level.dim,s=r.dem.getPixels();if(i.activeTexture.set(a.TEXTURE1),i.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture\|\|e.getTileTexture(r.tileSize),r.demTexture){var l=r.demTexture;l.update(s,{premultiply:!1}),l.bind(a.NEAREST,a.CLAMP_TO_EDGE)}else r.demTexture=new z(i,s,a.RGBA,{premultiply:!1}),r.demTexture.bind(a.NEAREST,a.CLAMP_TO_EDGE);i.activeTexture.set(a.TEXTURE0);var c=r.fbo;if(!c){var u=new z(i,{width:o,height:o,data:null},a.RGBA);u.bind(a.LINEAR,a.CLAMP_TO_EDGE),(c=r.fbo=i.createFramebuffer(o,o)).colorAttachment.set(u.texture)}i.bindFramebuffer.set(c.framebuffer),i.viewport.set([0,0,o,o]);var f=t.mat4.create();t.mat4.ortho(f,0,t.default$8,-t.default$8,0,0,1),t.mat4.translate(f,f,[0,-t.default$8,0]);var h=e.useProgram(\"hillshadePrepare\");a.uniformMatrix4fv(h.uniforms.u_matrix,!1,f),a.uniform1f(h.uniforms.u_zoom,r.tileID.overscaledZ),a.uniform2fv(h.uniforms.u_dimension,[2o,2o]),a.uniform1i(h.uniforms.u_image,1),a.uniform1f(h.uniforms.u_maxzoom,n);var p=e.rasterBoundsBuffer;e.rasterBoundsVAO.bind(i,h,p,[]),a.drawArrays(a.TRIANGLE_STRIP,0,p.length),r.needsHillshadePrepare=!1}}function Sr(e,r,n,i,o){var s=i.paint.get(\"raster-fade-duration\");if(s>0){var l=a.now(),c=(l-e.timeAdded)/s,u=r?(l-r.timeAdded)/s:-1,f=n.getSource(),h=o.coveringZoomLevel({tileSize:f.tileSize,roundZoom:f.roundZoom}),p=!r\|\|Math.abs(r.tileID.overscaledZ-h)>Math.abs(e.tileID.overscaledZ-h),d=p&&e.refreshedUponExpiration?1:t.clamp(p?c:1-u,0,1);return e.refreshedUponExpiration&&c>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-d}:{opacity:d,mix:0}}return{opacity:1,mix:0}}function Er(e,r,n){var i=e.context,o=i.gl;i.lineWidth.set(1a.devicePixelRatio);var s=n.posMatrix,l=e.useProgram(\"debug\");i.setDepthMode(qt.disabled),i.setStencilMode(Ht.disabled),i.setColorMode(e.colorModeForRenderPass()),o.uniformMatrix4fv(l.uniforms.u_matrix,!1,s),o.uniform4f(l.uniforms.u_color,1,0,0,1),e.debugVAO.bind(i,l,e.debugBuffer,[]),o.drawArrays(o.LINE_STRIP,0,e.debugBuffer.length);for(var c=function(t,e,r,n){n=n\|\|1;var i,a,o,s,l,c,u,f,h=[];for(i=0,a=t.length;i<a;i++)if(l=Cr[t[i]]){for(f=null,o=0,s=l[1].length;o<s;o+=2)-1===l[1][o]&&-1===l[1][o+1]?f=null:(c=e+l[1][o]n,u=200-l[1][o+1]n,f&&h.push(f.x,f.y,c,u),f={x:c,y:u});e+=l[0]n}return h}(n.toString(),50,0,5),u=new t.PosArray,f=0;f<c.length;f+=2)u.emplaceBack(c[f],c[f+1]);var h=i.createVertexBuffer(u,Ke.members);(new Q).bind(i,l,h,[]),o.uniform4f(l.uniforms.u_color,1,1,1,1);for(var p=r.getTile(n).tileSize,d=t.default$8/(Math.pow(2,e.transform.zoom-n.overscaledZ)p),g=[[-1,-1],[-1,1],[1,-1],[1,1]],v=0;v<g.length;v++){var m=g[v];o.uniformMatrix4fv(l.uniforms.u_matrix,!1,t.mat4.translate([],s,[dm[0],dm[1],0])),o.drawArrays(o.LINES,0,h.length)}o.uniform4f(l.uniforms.u_color,0,0,0,1),o.uniformMatrix4fv(l.uniforms.u_matrix,!1,s),o.drawArrays(o.LINES,0,h.length)}var Cr={\" 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a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4),function t(e,r,n,i){var a=e.gl;a.texImage2D(a.TEXTURE_2D,0,a.RGBA,r.width/4,r.height/4,0,a.RGBA,e.extTextureHalfFloat?e.extTextureHalfFloat.HALF_FLOAT_OES:a.UNSIGNED_BYTE,null),i.colorAttachment.set(n),e.extTextureHalfFloat&&a.checkFramebufferStatus(a.FRAMEBUFFER)!==a.FRAMEBUFFER_COMPLETE&&(e.extTextureHalfFloat=null,i.colorAttachment.setDirty(),t(e,r,n,i))}(t,e,a,i)}}(a,e,n),a.clear({color:t.default$6.transparent}),a.setColorMode(new Gt([o.ONE,o.ONE],t.default$6.transparent,[!0,!0,!0,!0]));for(var s=!0,l=0;l<i.length;l++){var c=i[l];if(!r.hasRenderableParent(c)){var u=r.getTile(c),f=u.getBucket(n);if(f){var h=e.context.program.get(),p=f.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),g=e.transform.zoom;(s\|\|d.program!==h)&&(p.setUniforms(e.context,d,n.paint,{zoom:g}),s=!1),o.uniform1f(d.uniforms.u_extrude_scale,Te(u,1,g)),o.uniform1f(d.uniforms.u_intensity,n.paint.get(\"heatmap-intensity\")),o.uniformMatrix4fv(d.uniforms.u_matrix,!1,c.posMatrix),d.draw(a,o.TRIANGLES,n.id,f.layoutVertexBuffer,f.indexBuffer,f.segments,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o\|\|(o=r.colorRampTexture=new z(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),n.setDepthMode(qt.disabled);var s=e.useProgram(\"heatmapTexture\"),l=r.paint.get(\"heatmap-opacity\");i.uniform1f(s.uniforms.u_opacity,l),i.uniform1i(s.uniforms.u_image,0),i.uniform1i(s.uniforms.u_color_ramp,1);var c=t.mat4.create();t.mat4.ortho(c,0,e.width,e.height,0,0,1),i.uniformMatrix4fv(s.uniforms.u_matrix,!1,c),i.uniform2f(s.uniforms.u_world,i.drawingBufferWidth,i.drawingBufferHeight),e.viewportVAO.bind(e.context,s,e.viewportBuffer,[]),i.drawArrays(i.TRIANGLE_STRIP,0,4)}}(e,n))},line:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"line-opacity\").constantOr(1)){var i=t.context;i.setDepthMode(t.depthModeForSublayer(0,qt.ReadOnly)),i.setColorMode(t.colorModeForRenderPass());for(var a,o=r.paint.get(\"line-dasharray\")?\"lineSDF\":r.paint.get(\"line-pattern\")?\"linePattern\":r.paint.get(\"line-gradient\")?\"lineGradient\":\"line\",s=!0,l=0,c=n;l<c.length;l+=1){var u=c[l],f=e.getTile(u),h=f.getBucket(r);if(h){var p=h.programConfigurations.get(r.id),d=t.context.program.get(),g=t.useProgram(o,p),v=s\|\|g.program!==d,m=a!==f.tileID.overscaledZ;v&&p.setUniforms(t.context,g,r.paint,{zoom:t.transform.zoom}),pr(g,t,f,h,r,u,p,v,m),a=f.tileID.overscaledZ,s=!1}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.context;s.setColorMode(e.colorModeForRenderPass());var l=n.paint.get(\"fill-pattern\")\|\|1!==a.constantOr(t.default$6.transparent).a\|\|1!==o.constantOr(0)?\"translucent\":\"opaque\";e.renderPass===l&&(s.setDepthMode(e.depthModeForSublayer(1,\"opaque\"===e.renderPass?qt.ReadWrite:qt.ReadOnly)),mr(e,r,n,i,yr)),\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")&&(s.lineWidth.set(2),s.setDepthMode(e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,qt.ReadOnly)),mr(e,r,n,i,xr))}},\"fill-extrusion\":function(e,r,n,i){if(0!==n.paint.get(\"fill-extrusion-opacity\"))if(\"offscreen\"===e.renderPass){!function(e,r){var n=e.context,i=n.gl,a=r.viewportFrame;if(e.depthRboNeedsClear&&e.setupOffscreenDepthRenderbuffer(),!a){var o=new z(n,{width:e.width,height:e.height,data:null},i.RGBA);o.bind(i.LINEAR,i.CLAMP_TO_EDGE),(a=r.viewportFrame=n.createFramebuffer(e.width,e.height)).colorAttachment.set(o.texture)}n.bindFramebuffer.set(a.framebuffer),a.depthAttachment.set(e.depthRbo),e.depthRboNeedsClear&&(n.clear({depth:1}),e.depthRboNeedsClear=!1),n.clear({color:t.default$6.transparent}),n.setStencilMode(Ht.disabled),n.setDepthMode(new qt(i.LEQUAL,qt.ReadWrite,[0,1])),n.setColorMode(e.colorModeForRenderPass())}(e,n);for(var a=!0,o=0,s=i;o<s.length;o+=1){var l=s[o],c=r.getTile(l),u=c.getBucket(n);u&&(Mr(e,0,n,c,l,u,a),a=!1)}}else\"translucent\"===e.renderPass&&function(t,e){var r=e.viewportFrame;if(r){var n=t.context,i=n.gl,a=t.useProgram(\"extrusionTexture\");n.setStencilMode(Ht.disabled),n.setDepthMode(qt.disabled),n.setColorMode(t.colorModeForRenderPass()),n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,r.colorAttachment.get()),i.uniform1f(a.uniforms.u_opacity,e.paint.get(\"fill-extrusion-opacity\")),i.uniform1i(a.uniforms.u_image,0);var o=wr.create();wr.ortho(o,0,t.width,t.height,0,0,1),i.uniformMatrix4fv(a.uniforms.u_matrix,!1,o),i.uniform2f(a.uniforms.u_world,i.drawingBufferWidth,i.drawingBufferHeight),t.viewportVAO.bind(n,a,t.viewportBuffer,[]),i.drawArrays(i.TRIANGLE_STRIP,0,4)}}(e,n)},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass\|\|\"translucent\"===t.renderPass){var i=t.context,a=e.getSource().maxzoom;i.setDepthMode(t.depthModeForSublayer(0,qt.ReadOnly)),i.setStencilMode(Ht.disabled),i.setColorMode(t.colorModeForRenderPass());for(var o=0,s=n;o<s.length;o+=1){var l=s[o],c=e.getTile(l);c.needsHillshadePrepare&&\"offscreen\"===t.renderPass?Tr(t,c,a):\"translucent\"===t.renderPass&&Ar(t,c,r)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")){var i,a,o=t.context,s=o.gl,l=e.getSource(),c=t.useProgram(\"raster\");o.setStencilMode(Ht.disabled),o.setColorMode(t.colorModeForRenderPass()),s.uniform1f(c.uniforms.u_brightness_low,r.paint.get(\"raster-brightness-min\")),s.uniform1f(c.uniforms.u_brightness_high,r.paint.get(\"raster-brightness-max\")),s.uniform1f(c.uniforms.u_saturation_factor,(i=r.paint.get(\"raster-saturation\"))>0?1-1/(1.001-i):-i),s.uniform1f(c.uniforms.u_contrast_factor,(a=r.paint.get(\"raster-contrast\"))>0?1/(1-a):1+a),s.uniform3fv(c.uniforms.u_spin_weights,function(t){t=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2r+1)/3,(-Math.sqrt(3)e-r+1)/3,(Math.sqrt(3)e-r+1)/3]}(r.paint.get(\"raster-hue-rotate\"))),s.uniform1f(c.uniforms.u_buffer_scale,1),s.uniform1i(c.uniforms.u_image0,0),s.uniform1i(c.uniforms.u_image1,1);for(var u=n.length&&n[0].overscaledZ,f=0,h=n;f<h.length;f+=1){var p=h[f];o.setDepthMode(t.depthModeForSublayer(p.overscaledZ-u,1===r.paint.get(\"raster-opacity\")?qt.ReadWrite:qt.ReadOnly,s.LESS));var d=e.getTile(p),g=t.transform.calculatePosMatrix(p.toUnwrapped(),!0);d.registerFadeDuration(r.paint.get(\"raster-fade-duration\")),s.uniformMatrix4fv(c.uniforms.u_matrix,!1,g);var v=e.findLoadedParent(p,0,{}),m=Sr(d,v,e,r,t.transform),y=void 0,x=void 0;if(o.activeTexture.set(s.TEXTURE0),d.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.activeTexture.set(s.TEXTURE1),v?(v.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),y=Math.pow(2,v.tileID.overscaledZ-d.tileID.overscaledZ),x=[d.tileID.canonical.xy%1,d.tileID.canonical.yy%1]):d.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),s.uniform2fv(c.uniforms.u_tl_parent,x\|\|[0,0]),s.uniform1f(c.uniforms.u_scale_parent,y\|\|1),s.uniform1f(c.uniforms.u_fade_t,m.mix),s.uniform1f(c.uniforms.u_opacity,m.opacityr.paint.get(\"raster-opacity\")),l instanceof tt){var b=l.boundsBuffer;l.boundsVAO.bind(o,c,b,[]),s.drawArrays(s.TRIANGLE_STRIP,0,b.length)}else if(d.maskedBoundsBuffer&&d.maskedIndexBuffer&&d.segments)c.draw(o,s.TRIANGLES,r.id,d.maskedBoundsBuffer,d.maskedIndexBuffer,d.segments);else{var _=t.rasterBoundsBuffer;t.rasterBoundsVAO.bind(o,c,_,[]),s.drawArrays(s.TRIANGLE_STRIP,0,_.length)}}}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\"),u=c\|\|1!==n.a\|\|1!==i?\"translucent\":\"opaque\";if(t.renderPass===u){var f;if(a.setStencilMode(Ht.disabled),a.setDepthMode(t.depthModeForSublayer(0,\"opaque\"===u?qt.ReadWrite:qt.ReadOnly)),a.setColorMode(t.colorModeForRenderPass()),c){if(dr(c,t))return;f=t.useProgram(\"backgroundPattern\"),gr(c,t,f),t.tileExtentPatternVAO.bind(a,f,t.tileExtentBuffer,[])}else f=t.useProgram(\"background\"),o.uniform4fv(f.uniforms.u_color,[n.r,n.g,n.b,n.a]),t.tileExtentVAO.bind(a,f,t.tileExtentBuffer,[]);o.uniform1f(f.uniforms.u_opacity,i);for(var h=0,p=s.coveringTiles({tileSize:l});h<p.length;h+=1){var d=p[h];c&&vr({tileID:d,tileSize:l},t,f),o.uniformMatrix4fv(f.uniforms.u_matrix,!1,t.transform.calculatePosMatrix(d.toUnwrapped())),o.drawArrays(o.TRIANGLE_STRIP,0,t.tileExtentBuffer.length)}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)Er(t,e,r[n])}},zr=function(e,r){this.context=new Wt(e),this.transform=r,this._tileTextures={},this.setup(),this.numSublayers=Yt.maxUnderzooming+Yt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.depthRboNeedsClear=!0,this.emptyProgramConfiguration=new t.default$24,this.crossTileSymbolIndex=new Ye};function Or(t,e){if(t.row>e.row){var r=t;t=e,e=r}return{x0:t.column,y0:t.row,x1:e.column,y1:e.row,dx:e.column-t.column,dy:e.row-t.row}}function Ir(t,e,r,n,i){var a=Math.max(r,Math.floor(e.y0)),o=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dyt.dx<e.x1:t.x1-e.dy/t.dyt.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,c=e.dx/e.dy,u=t.dx>0,f=e.dx<0,h=a;h<o;h++){var p=lMath.max(0,Math.min(t.dy,h+u-t.y0))+t.x0,d=cMath.max(0,Math.min(e.dy,h+f-e.y0))+e.x0;i(Math.floor(d),Math.ceil(p),h)}}function Pr(t,e,r,n,i,a){var o,s=Or(t,e),l=Or(e,r),c=Or(r,t);s.dy>l.dy&&(o=s,s=l,l=o),s.dy>c.dy&&(o=s,s=c,c=o),l.dy>c.dy&&(o=l,l=c,c=o),s.dy&&Ir(c,s,n,i,a),l.dy&&Ir(c,l,n,i,a)}zr.prototype.resize=function(t,e){var r=this.context.gl;if(this.width=ta.devicePixelRatio,this.height=ea.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var o=i[n];this.style._layers[o].resize()}this.depthRbo&&(r.deleteRenderbuffer(this.depthRbo),this.depthRbo=null)},zr.prototype.setup=function(){var e=this.context,r=new t.PosArray;r.emplaceBack(0,0),r.emplaceBack(t.default$8,0),r.emplaceBack(0,t.default$8),r.emplaceBack(t.default$8,t.default$8),this.tileExtentBuffer=e.createVertexBuffer(r,Ke.members),this.tileExtentVAO=new Q,this.tileExtentPatternVAO=new Q;var n=new t.PosArray;n.emplaceBack(0,0),n.emplaceBack(t.default$8,0),n.emplaceBack(t.default$8,t.default$8),n.emplaceBack(0,t.default$8),n.emplaceBack(0,0),this.debugBuffer=e.createVertexBuffer(n,Ke.members),this.debugVAO=new Q;var i=new t.RasterBoundsArray;i.emplaceBack(0,0,0,0),i.emplaceBack(t.default$8,0,t.default$8,0),i.emplaceBack(0,t.default$8,0,t.default$8),i.emplaceBack(t.default$8,t.default$8,t.default$8,t.default$8),this.rasterBoundsBuffer=e.createVertexBuffer(i,K.members),this.rasterBoundsVAO=new Q;var a=new t.PosArray;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ke.members),this.viewportVAO=new Q},zr.prototype.clearStencil=function(){var e=this.context,r=e.gl;e.setColorMode(Gt.disabled),e.setDepthMode(qt.disabled),e.setStencilMode(new Ht({func:r.ALWAYS,mask:0},0,255,r.ZERO,r.ZERO,r.ZERO));var n=t.mat4.create();t.mat4.ortho(n,0,this.width,this.height,0,0,1),t.mat4.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]);var i=this.useProgram(\"clippingMask\");r.uniformMatrix4fv(i.uniforms.u_matrix,!1,n),this.viewportVAO.bind(e,i,this.viewportBuffer,[]),r.drawArrays(r.TRIANGLE_STRIP,0,4)},zr.prototype._renderTileClippingMasks=function(t){var e=this.context,r=e.gl;e.setColorMode(Gt.disabled),e.setDepthMode(qt.disabled);var n=1;this._tileClippingMaskIDs={};for(var i=0,a=t;i<a.length;i+=1){var o=a[i],s=this._tileClippingMaskIDs[o.key]=n++;e.setStencilMode(new Ht({func:r.ALWAYS,mask:0},s,255,r.KEEP,r.KEEP,r.REPLACE));var l=this.useProgram(\"clippingMask\");r.uniformMatrix4fv(l.uniforms.u_matrix,!1,o.posMatrix),this.tileExtentVAO.bind(this.context,l,this.tileExtentBuffer,[]),r.drawArrays(r.TRIANGLE_STRIP,0,this.tileExtentBuffer.length)}},zr.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Ht({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},zr.prototype.colorModeForRenderPass=function(){var e=this.context.gl;return this._showOverdrawInspector?new Gt([e.CONSTANT_COLOR,e.ONE],new t.default$6(1/8,1/8,1/8,0),[!0,!0,!0,!0]):\"opaque\"===this.renderPass?Gt.unblended:Gt.alphaBlended},zr.prototype.depthModeForSublayer=function(t,e,r){var n=1-((1+this.currentLayer)this.numSublayers+t)this.depthEpsilon,i=n-1+this.depthRange;return new qt(r\|\|this.context.gl.LEQUAL,e,[i,n])},zr.prototype.render=function(e,r){var n=this;for(var i in this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(a.now()),e.sourceCaches){var o=n.style.sourceCaches[i];o.used&&o.prepare(n.context)}var s=this.style._order,l=t.filterObject(this.style.sourceCaches,function(t){return\"raster\"===t.getSource().type\|\|\"raster-dem\"===t.getSource().type}),c=function(e){var r=l[e];!function(e,r){for(var n=e.sort(function(t,e){return t.tileID.isLessThan(e.tileID)?-1:e.tileID.isLessThan(t.tileID)?1:0}),i=0;i<n.length;i++){var a={},o=n[i],s=n.slice(i+1);ar(o.tileID.wrapped(),o.tileID,s,new t.OverscaledTileID(0,o.tileID.wrap+1,0,0,0),a),o.setMask(a,r)}}(r.getVisibleCoordinates().map(function(t){return r.getTile(t)}),n.context)};for(var u in l)c(u);this.renderPass=\"offscreen\";var f,h=[];this.depthRboNeedsClear=!0;for(var p=0;p<s.length;p++){var d=n.style._layers[s[p]];d.hasOffscreenPass()&&!d.isHidden(n.transform.zoom)&&(d.source!==(f&&f.id)&&(h=[],(f=n.style.sourceCaches[d.source])&&(h=f.getVisibleCoordinates()).reverse()),h.length&&n.renderLayer(n,f,d,h))}this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.default$6.black:t.default$6.transparent,depth:1}),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRange=(e._order.length+2)this.numSublayersthis.depthEpsilon,this.renderPass=\"opaque\";var g,v=[];for(this.currentLayer=s.length-1,this.currentLayer;this.currentLayer>=0;this.currentLayer--){var m=n.style._layers[s[n.currentLayer]];m.source!==(g&&g.id)&&(v=[],(g=n.style.sourceCaches[m.source])&&(n.clearStencil(),v=g.getVisibleCoordinates(),g.getSource().isTileClipped&&n._renderTileClippingMasks(v))),n.renderLayer(n,g,m,v)}this.renderPass=\"translucent\";var y,x=[];for(this.currentLayer=0,this.currentLayer;this.currentLayer<s.length;this.currentLayer++){var b=n.style._layers[s[n.currentLayer]];b.source!==(y&&y.id)&&(x=[],(y=n.style.sourceCaches[b.source])&&(n.clearStencil(),x=y.getVisibleCoordinates(),y.getSource().isTileClipped&&n._renderTileClippingMasks(x)),x.reverse()),n.renderLayer(n,y,b,x)}if(this.options.showTileBoundaries){var _=this.style.sourceCaches[Object.keys(this.style.sourceCaches)[0]];_&&Lr.debug(this,_,_.getVisibleCoordinates())}},zr.prototype.setupOffscreenDepthRenderbuffer=function(){var t=this.context;this.depthRbo\|\|(this.depthRbo=t.createRenderbuffer(t.gl.DEPTH_COMPONENT16,this.width,this.height))},zr.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)\|\|(\"background\"===r.type\|\|n.length)&&(this.id=r.id,Lr[r.type](t,e,r,n))},zr.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]l-n[1]s,n[0]s+n[1]l]}var c=[a?n[0]:Te(r,n[0],this.transform.zoom),a?n[1]:Te(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.mat4.translate(u,e,c),u},zr.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},zr.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},zr.prototype._createProgramCached=function(t,e){this.cache=this.cache\|\|{};var r=\"\"+t+(e.cacheKey\|\|\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]\|\|(this.cache[r]=new ir(this.context,nr[t],e,this._showOverdrawInspector)),this.cache[r]},zr.prototype.useProgram=function(t,e){var r=this._createProgramCached(t,e\|\|this.emptyProgramConfiguration);return this.context.program.set(r.program),r};var Dr=t.default$20.vec4,Rr=t.default$20.mat4,Br=t.default$20.mat2,Fr=function(t,e,r){this.tileSize=512,this._renderWorldCopies=void 0===r\|\|r,this._minZoom=t\|\|0,this._maxZoom=e\|\|22,this.latRange=[-85.05113,85.05113],this.width=0,this.height=0,this._center=new G(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._posMatrixCache={},this._alignedPosMatrixCache={}},Nr={minZoom:{configurable:!0},maxZoom:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerPoint:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},unmodified:{configurable:!0},x:{configurable:!0},y:{configurable:!0},point:{configurable:!0}};Fr.prototype.clone=function(){var t=new Fr(this._minZoom,this._maxZoom,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._calcMatrices(),t},Nr.minZoom.get=function(){return this._minZoom},Nr.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Nr.maxZoom.get=function(){return this._maxZoom},Nr.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Nr.renderWorldCopies.get=function(){return this._renderWorldCopies},Nr.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Nr.worldSize.get=function(){return this.tileSizethis.scale},Nr.centerPoint.get=function(){return this.size._div(2)},Nr.size.get=function(){return new t.default$1(this.width,this.height)},Nr.bearing.get=function(){return-this.angle/Math.PI180},Nr.bearing.set=function(e){var r=-t.wrap(e,-180,180)Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=Br.create(),Br.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Nr.pitch.get=function(){return this._pitch/Math.PI180},Nr.pitch.set=function(e){var r=t.clamp(e,0,60)/180Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Nr.fov.get=function(){return this._fov/Math.PI180},Nr.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180Math.PI,this._calcMatrices())},Nr.zoom.get=function(){return this._zoom},Nr.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Nr.center.get=function(){return this._center},Nr.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng\|\|(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Fr.prototype.coveringZoomLevel=function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},Fr.prototype.getVisibleUnwrappedCoordinates=function(e){var r=this.pointCoordinate(new t.default$1(0,0),0),n=this.pointCoordinate(new t.default$1(this.width,0),0),i=Math.floor(r.column),a=Math.floor(n.column),o=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var s=i;s<=a;s++)0!==s&&o.push(new t.UnwrappedTileID(s,e));return o},Fr.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=this.pointCoordinate(this.centerPoint,r),a=new t.default$1(i.column-.5,i.row-.5);return function(e,r,n,i){void 0===i&&(i=!0);var a=1<<e,o={};function s(r,s,l){var c,u,f,h;if(l>=0&&l<=a)for(c=r;c<s;c++)u=Math.floor(c/a),f=(c%a+a)%a,0!==u&&!0!==i\|\|(h=new t.OverscaledTileID(n,u,e,f,l),o[h.key]=h)}return Pr(r[0],r[1],r[2],0,a,s),Pr(r[2],r[3],r[0],0,a,s),Object.keys(o).map(function(t){return o[t]})}(r,[this.pointCoordinate(new t.default$1(0,0),r),this.pointCoordinate(new t.default$1(this.width,0),r),this.pointCoordinate(new t.default$1(this.width,this.height),r),this.pointCoordinate(new t.default$1(0,this.height),r)],e.reparseOverscaled?n:r,this._renderWorldCopies).sort(function(t,e){return a.dist(t.canonical)-a.dist(e.canonical)})},Fr.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Nr.unmodified.get=function(){return this._unmodified},Fr.prototype.zoomScale=function(t){return Math.pow(2,t)},Fr.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Fr.prototype.project=function(e){return new t.default$1(this.lngX(e.lng),this.latY(e.lat))},Fr.prototype.unproject=function(t){return new G(this.xLng(t.x),this.yLat(t.y))},Nr.x.get=function(){return this.lngX(this.center.lng)},Nr.y.get=function(){return this.latY(this.center.lat)},Nr.point.get=function(){return new t.default$1(this.x,this.y)},Fr.prototype.lngX=function(t){return(180+t)this.worldSize/360},Fr.prototype.latY=function(t){return(180-180/Math.PIMath.log(Math.tan(Math.PI/4+tMath.PI/360)))this.worldSize/360},Fr.prototype.xLng=function(t){return 360t/this.worldSize-180},Fr.prototype.yLat=function(t){var e=180-360t/this.worldSize;return 360/Math.PIMath.atan(Math.exp(eMath.PI/180))-90},Fr.prototype.setLocationAtPoint=function(t,e){var r=this.pointCoordinate(e)._sub(this.pointCoordinate(this.centerPoint));this.center=this.coordinateLocation(this.locationCoordinate(t)._sub(r)),this._renderWorldCopies&&(this.center=this.center.wrap())},Fr.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Fr.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Fr.prototype.locationCoordinate=function(e){return new t.default$17(this.lngX(e.lng)/this.tileSize,this.latY(e.lat)/this.tileSize,this.zoom).zoomTo(this.tileZoom)},Fr.prototype.coordinateLocation=function(t){var e=t.zoomTo(this.zoom);return new G(this.xLng(e.columnthis.tileSize),this.yLat(e.rowthis.tileSize))},Fr.prototype.pointCoordinate=function(e,r){void 0===r&&(r=this.tileZoom);var n=[e.x,e.y,0,1],i=[e.x,e.y,1,1];Dr.transformMat4(n,n,this.pixelMatrixInverse),Dr.transformMat4(i,i,this.pixelMatrixInverse);var a=n[3],o=i[3],s=n[0]/a,l=i[0]/o,c=n[1]/a,u=i[1]/o,f=n[2]/a,h=i[2]/o,p=f===h?0:(0-f)/(h-f);return new t.default$17(t.number(s,l,p)/this.tileSize,t.number(c,u,p)/this.tileSize,this.zoom)._zoomTo(r)},Fr.prototype.coordinatePoint=function(e){var r=e.zoomTo(this.zoom),n=[r.columnthis.tileSize,r.rowthis.tileSize,0,1];return Dr.transformMat4(n,n,this.pixelMatrix),new t.default$1(n[0]/n[3],n[1]/n[3])},Fr.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)e.wrap,l=Rr.identity(new Float64Array(16));return Rr.translate(l,l,[so,a.yo,0]),Rr.scale(l,l,[o/t.default$8,o/t.default$8,1]),Rr.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Fr.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=this.latY(f[1]),e=(o=this.latY(f[0]))-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=this.lngX(h[0]),r=(l=this.lngX(h[1]))-s<c.x?c.x/(l-s):0}var p=Math.max(r\|\|0,e\|\|0);if(p)return this.center=this.unproject(new t.default$1(r?(l+s)/2:this.x,e?(o+a)/2:this.y)),this.zoom+=this.scaleZoom(p),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var d=this.y,g=c.y/2;d-g<a&&(i=a+g),d+g>o&&(i=o-g)}if(this.lngRange){var v=this.x,m=c.x/2;v-m<s&&(n=s+m),v+m>l&&(n=l-m)}void 0===n&&void 0===i\|\|(this.center=this.unproject(new t.default$1(void 0!==n?n:this.x,void 0!==i?i:this.y))),this._unmodified=u,this._constraining=!1}},Fr.prototype._calcMatrices=function(){if(this.height){this.cameraToCenterDistance=.5/Math.tan(this._fov/2)this.height;var t=this._fov/2,e=Math.PI/2+this._pitch,r=Math.sin(t)this.cameraToCenterDistance/Math.sin(Math.PI-e-t),n=this.x,i=this.y,a=1.01(Math.cos(Math.PI/2-this._pitch)r+this.cameraToCenterDistance),o=new Float64Array(16);Rr.perspective(o,this._fov,this.width/this.height,1,a),Rr.scale(o,o,[1,-1,1]),Rr.translate(o,o,[0,0,-this.cameraToCenterDistance]),Rr.rotateX(o,o,this._pitch),Rr.rotateZ(o,o,this.angle),Rr.translate(o,o,[-n,-i,0]);var s=this.worldSize/(2Math.PI6378137Math.abs(Math.cos(this.center.lat(Math.PI/180))));Rr.scale(o,o,[1,1,s,1]),this.projMatrix=o;var l=this.width%2/2,c=this.height%2/2,u=Math.cos(this.angle),f=Math.sin(this.angle),h=n-Math.round(n)+ul+fc,p=i-Math.round(i)+uc+fl,d=new Float64Array(o);if(Rr.translate(d,d,[h>.5?h-1:h,p>.5?p-1:p,0]),this.alignedProjMatrix=d,o=Rr.create(),Rr.scale(o,o,[this.width/2,-this.height/2,1]),Rr.translate(o,o,[1,-1,0]),this.pixelMatrix=Rr.multiply(new Float64Array(16),o,this.projMatrix),!(o=Rr.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=o,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Fr.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.default$1(0,0)).zoomTo(this.zoom),r=[e.columnthis.tileSize,e.rowthis.tileSize,0,1];return Dr.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Object.defineProperties(Fr.prototype,Nr);var jr=function(){var e,r,n,i;t.bindAll([\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(e=this._updateHashUnthrottled.bind(this),300,r=!1,n=0,i=function(){n=0,r&&(e(),n=setTimeout(i,300),r=!1)},function(){return r=!0,n\|\|i(),n})};jr.prototype.addTo=function(e){return this._map=e,t.default.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},jr.prototype.remove=function(){return t.default.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},jr.prototype.getHashString=function(t){var e=this._map.getCenter(),r=Math.round(100this._map.getZoom())/100,n=Math.ceil((rMath.LN2+Math.log(512/360/.5))/Math.LN10),i=Math.pow(10,n),a=Math.round(e.lngi)/i,o=Math.round(e.lati)/i,s=this._map.getBearing(),l=this._map.getPitch(),c=\"\";return c+=t?\"#/\"+a+\"/\"+o+\"/\"+r:\"#\"+r+\"/\"+o+\"/\"+a,(s\|\|l)&&(c+=\"/\"+Math.round(10s)/10),l&&(c+=\"/\"+Math.round(l)),c},jr.prototype._onHashChange=function(){var e=t.default.location.hash.replace(\"#\",\"\").split(\"/\");return e.length>=3&&(this._map.jumpTo({center:[+e[2],+e[1]],zoom:+e[0],bearing:+(e[3]\|\|0),pitch:+(e[4]\|\|0)}),!0)},jr.prototype._updateHashUnthrottled=function(){var e=this.getHashString();t.default.history.replaceState(t.default.history.state,\"\",e)};var Vr=function(e){function r(r,n,i,a){void 0===a&&(a={});var o=s.mousePos(n.getCanvasContainer(),i),l=n.unproject(o);e.call(this,r,t.extend({point:o,lngLat:l,originalEvent:i},a)),this._defaultPrevented=!1,this.target=n}e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r;var n={defaultPrevented:{configurable:!0}};return r.prototype.preventDefault=function(){this._defaultPrevented=!0},n.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(r.prototype,n),r}(t.Event),Ur=function(e){function r(r,n,i){var a=s.touchPos(n.getCanvasContainer(),i),o=a.map(function(t){return n.unproject(t)}),l=a.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new t.default$1(0,0)),c=n.unproject(l);e.call(this,r,{points:a,point:l,lngLats:o,lngLat:c,originalEvent:i}),this._defaultPrevented=!1}e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r;var n={defaultPrevented:{configurable:!0}};return r.prototype.preventDefault=function(){this._defaultPrevented=!0},n.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(r.prototype,n),r}(t.Event),qr=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Hr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._delta=0,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};Hr.prototype.isEnabled=function(){return!!this._enabled},Hr.prototype.isActive=function(){return!!this._active},Hr.prototype.enable=function(t){this.isEnabled()\|\|(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},Hr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Hr.prototype.onWheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.default.WheelEvent.DOM_DELTA_LINE?40e.deltaY:e.deltaY,n=a.now(),i=n-(this._lastWheelEventTime\|\|0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type\|\|(this._type=Math.abs(ir)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this.isActive()\|\|this._start(e)),e.preventDefault()}},Hr.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this.isActive()\|\|this._start(t)},Hr.prototype._start=function(e){if(this._delta){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),this._active=!0,this._map.fire(new t.Event(\"movestart\",{originalEvent:e})),this._map.fire(new t.Event(\"zoomstart\",{originalEvent:e})),this._finishTimeout&&clearTimeout(this._finishTimeout);var r=s.mousePos(this._el,e);this._around=G.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(r)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onScrollFrame))}},Hr.prototype._onScrollFrame=function(){var e=this;if(this._frameId=null,this.isActive()){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?1/450:.01,i=2/(1+Math.exp(-Math.abs(this._deltan)));this._delta<0&&0!==i&&(i=1/i);var o=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(oi))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var s=!1;if(\"wheel\"===this._type){var l=Math.min((a.now()-this._lastWheelEventTime)/200,1),c=this._easing(l);r.zoom=t.number(this._startZoom,this._targetZoom,c),l<1?this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onScrollFrame)):s=!0}else r.zoom=this._targetZoom,s=!0;r.setLocationAtPoint(this._around,this._aroundPoint),this._map.fire(new t.Event(\"move\",{originalEvent:this._lastWheelEvent})),this._map.fire(new t.Event(\"zoom\",{originalEvent:this._lastWheelEvent})),s&&(this._active=!1,this._finishTimeout=setTimeout(function(){e._map.fire(new t.Event(\"zoomend\",{originalEvent:e._lastWheelEvent})),e._map.fire(new t.Event(\"moveend\",{originalEvent:e._lastWheelEvent})),delete e._targetZoom},200))}},Hr.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(a.now()-n.start)/n.duration,o=n.easing(i+.01)-n.easing(i),s=.27/Math.sqrt(oo+1e-4).01,l=Math.sqrt(.0729-ss);r=t.bezier(s,l,.25,1)}return this._prevEase={start:a.now(),duration:e,easing:r},r};var Gr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._container=e.getContainer(),t.bindAll([\"_onMouseMove\",\"_onMouseUp\",\"_onKeyDown\"],this)};Gr.prototype.isEnabled=function(){return!!this._enabled},Gr.prototype.isActive=function(){return!!this._active},Gr.prototype.enable=function(){this.isEnabled()\|\|(this._enabled=!0)},Gr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Gr.prototype.onMouseDown=function(e){this.isEnabled()&&e.shiftKey&&0===e.button&&(t.default.document.addEventListener(\"mousemove\",this._onMouseMove,!1),t.default.document.addEventListener(\"keydown\",this._onKeyDown,!1),t.default.document.addEventListener(\"mouseup\",this._onMouseUp,!1),s.disableDrag(),this._startPos=s.mousePos(this._el,e),this._active=!0)},Gr.prototype._onMouseMove=function(t){var e=this._startPos,r=s.mousePos(this._el,t);this._box\|\|(this._box=s.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var n=Math.min(e.x,r.x),i=Math.max(e.x,r.x),a=Math.min(e.y,r.y),o=Math.max(e.y,r.y);s.setTransform(this._box,\"translate(\"+n+\"px,\"+a+\"px)\"),this._box.style.width=i-n+\"px\",this._box.style.height=o-a+\"px\"},Gr.prototype._onMouseUp=function(e){if(0===e.button){var r=this._startPos,n=s.mousePos(this._el,e),i=(new W).extend(this._map.unproject(r)).extend(this._map.unproject(n));this._finish(),s.suppressClick(),r.x===n.x&&r.y===n.y?this._fireEvent(\"boxzoomcancel\",e):this._map.fitBounds(i,{linear:!0}).fire(new t.Event(\"boxzoomend\",{originalEvent:e,boxZoomBounds:i}))}},Gr.prototype._onKeyDown=function(t){27===t.keyCode&&(this._finish(),this._fireEvent(\"boxzoomcancel\",t))},Gr.prototype._finish=function(){this._active=!1,t.default.document.removeEventListener(\"mousemove\",this._onMouseMove,!1),t.default.document.removeEventListener(\"keydown\",this._onKeyDown,!1),t.default.document.removeEventListener(\"mouseup\",this._onMouseUp,!1),this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(s.remove(this._box),this._box=null),s.enableDrag()},Gr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Wr=t.bezier(0,0,.25,1),Yr=function(e,r){this._map=e,this._el=r.element\|\|e.getCanvasContainer(),this._state=\"disabled\",this._button=r.button\|\|\"right\",this._bearingSnap=r.bearingSnap\|\|0,this._pitchWithRotate=!1!==r.pitchWithRotate,t.bindAll([\"_onMouseMove\",\"_onMouseUp\",\"_onBlur\",\"_onDragFrame\"],this)};Yr.prototype.isEnabled=function(){return\"disabled\"!==this._state},Yr.prototype.isActive=function(){return\"active\"===this._state},Yr.prototype.enable=function(){this.isEnabled()\|\|(this._state=\"enabled\")},Yr.prototype.disable=function(){if(this.isEnabled())switch(this._state){case\"active\":this._state=\"disabled\",this._unbind(),this._deactivate(),this._fireEvent(\"rotateend\"),this._pitchWithRotate&&this._fireEvent(\"pitchend\"),this._fireEvent(\"moveend\");break;case\"pending\":this._state=\"disabled\",this._unbind();break;default:this._state=\"disabled\"}},Yr.prototype.onMouseDown=function(e){if(\"enabled\"===this._state){if(\"right\"===this._button){if(this._eventButton=s.mouseButton(e),this._eventButton!==(e.ctrlKey?0:2))return}else{if(e.ctrlKey\|\|0!==s.mouseButton(e))return;this._eventButton=0}s.disableDrag(),t.default.document.addEventListener(\"mousemove\",this._onMouseMove,{capture:!0}),t.default.document.addEventListener(\"mouseup\",this._onMouseUp),t.default.addEventListener(\"blur\",this._onBlur),this._state=\"pending\",this._inertia=[[a.now(),this._map.getBearing()]],this._previousPos=s.mousePos(this._el,e),this._center=this._map.transform.centerPoint,e.preventDefault()}},Yr.prototype._onMouseMove=function(t){this._lastMoveEvent=t,this._pos=s.mousePos(this._el,t),\"pending\"===this._state&&(this._state=\"active\",this._fireEvent(\"rotatestart\",t),this._fireEvent(\"movestart\",t),this._pitchWithRotate&&this._fireEvent(\"pitchstart\",t)),this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onDragFrame))},Yr.prototype._onDragFrame=function(){this._frameId=null;var t=this._lastMoveEvent;if(t){var e=this._map.transform,r=this._previousPos,n=this._pos,i=.8(r.x-n.x),o=-.5(r.y-n.y),s=e.bearing-i,l=e.pitch-o,c=this._inertia,u=c[c.length-1];this._drainInertiaBuffer(),c.push([a.now(),this._map._normalizeBearing(s,u[1])]),e.bearing=s,this._pitchWithRotate&&(this._fireEvent(\"pitch\",t),e.pitch=l),this._fireEvent(\"rotate\",t),this._fireEvent(\"move\",t),delete this._lastMoveEvent,this._previousPos=this._pos}},Yr.prototype._onMouseUp=function(t){if(s.mouseButton(t)===this._eventButton)switch(this._state){case\"active\":this._state=\"enabled\",s.suppressClick(),this._unbind(),this._deactivate(),this._inertialRotate(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Yr.prototype._onBlur=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._fireEvent(\"rotateend\",t),this._pitchWithRotate&&this._fireEvent(\"pitchend\",t),this._fireEvent(\"moveend\",t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Yr.prototype._unbind=function(){t.default.document.removeEventListener(\"mousemove\",this._onMouseMove,{capture:!0}),t.default.document.removeEventListener(\"mouseup\",this._onMouseUp),t.default.removeEventListener(\"blur\",this._onBlur),s.enableDrag()},Yr.prototype._deactivate=function(){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._lastMoveEvent,delete this._previousPos},Yr.prototype._inertialRotate=function(t){var e=this;this._fireEvent(\"rotateend\",t),this._drainInertiaBuffer();var r=this._map,n=r.getBearing(),i=this._inertia,a=function(){Math.abs(n)<e._bearingSnap?r.resetNorth({noMoveStart:!0},{originalEvent:t}):e._fireEvent(\"moveend\",t),e._pitchWithRotate&&e._fireEvent(\"pitchend\",t)};if(i.length<2)a();else{var o=i[0],s=i[i.length-1],l=i[i.length-2],c=r._normalizeBearing(n,l[1]),u=s[1]-o[1],f=u<0?-1:1,h=(s[0]-o[0])/1e3;if(0!==u&&0!==h){var p=Math.abs(u(.25/h));p>180&&(p=180);var d=p/180;c+=fp(d/2),Math.abs(r._normalizeBearing(c,0))<this._bearingSnap&&(c=r._normalizeBearing(0,c)),r.rotateTo(c,{duration:1e3d,easing:Wr,noMoveStart:!0},{originalEvent:t})}else a()}},Yr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Yr.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>0&&e-t[0][0]>160;)t.shift()};var Xr=t.bezier(0,0,.3,1),Zr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._state=\"disabled\",t.bindAll([\"_onMove\",\"_onMouseUp\",\"_onTouchEnd\",\"_onBlur\",\"_onDragFrame\"],this)};Zr.prototype.isEnabled=function(){return\"disabled\"!==this._state},Zr.prototype.isActive=function(){return\"active\"===this._state},Zr.prototype.enable=function(){this.isEnabled()\|\|(this._el.classList.add(\"mapboxgl-touch-drag-pan\"),this._state=\"enabled\")},Zr.prototype.disable=function(){if(this.isEnabled())switch(this._el.classList.remove(\"mapboxgl-touch-drag-pan\"),this._state){case\"active\":this._state=\"disabled\",this._unbind(),this._deactivate(),this._fireEvent(\"dragend\"),this._fireEvent(\"moveend\");break;case\"pending\":this._state=\"disabled\",this._unbind();break;default:this._state=\"disabled\"}},Zr.prototype.onMouseDown=function(e){\"enabled\"===this._state&&(e.ctrlKey\|\|0!==s.mouseButton(e)\|\|(s.addEventListener(t.default.document,\"mousemove\",this._onMove,{capture:!0}),s.addEventListener(t.default.document,\"mouseup\",this._onMouseUp),this._start(e)))},Zr.prototype.onTouchStart=function(e){\"enabled\"===this._state&&(e.touches.length>1\|\|(s.addEventListener(t.default.document,\"touchmove\",this._onMove,{capture:!0,passive:!1}),s.addEventListener(t.default.document,\"touchend\",this._onTouchEnd),this._start(e)))},Zr.prototype._start=function(e){t.default.addEventListener(\"blur\",this._onBlur),this._state=\"pending\",this._previousPos=s.mousePos(this._el,e),this._inertia=[[a.now(),this._previousPos]]},Zr.prototype._onMove=function(t){this._lastMoveEvent=t,t.preventDefault(),this._pos=s.mousePos(this._el,t),this._drainInertiaBuffer(),this._inertia.push([a.now(),this._pos]),\"pending\"===this._state&&(this._state=\"active\",this._fireEvent(\"dragstart\",t),this._fireEvent(\"movestart\",t)),this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onDragFrame))},Zr.prototype._onDragFrame=function(){this._frameId=null;var t=this._lastMoveEvent;if(t){var e=this._map.transform;e.setLocationAtPoint(e.pointLocation(this._previousPos),this._pos),this._fireEvent(\"drag\",t),this._fireEvent(\"move\",t),this._previousPos=this._pos,delete this._lastMoveEvent}},Zr.prototype._onMouseUp=function(t){if(0===s.mouseButton(t))switch(this._state){case\"active\":this._state=\"enabled\",s.suppressClick(),this._unbind(),this._deactivate(),this._inertialPan(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Zr.prototype._onTouchEnd=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._inertialPan(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Zr.prototype._onBlur=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._fireEvent(\"dragend\",t),this._fireEvent(\"moveend\",t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Zr.prototype._unbind=function(){s.removeEventListener(t.default.document,\"touchmove\",this._onMove,{capture:!0,passive:!1}),s.removeEventListener(t.default.document,\"touchend\",this._onTouchEnd),s.removeEventListener(t.default.document,\"mousemove\",this._onMove,{capture:!0}),s.removeEventListener(t.default.document,\"mouseup\",this._onMouseUp),s.removeEventListener(t.default,\"blur\",this._onBlur)},Zr.prototype._deactivate=function(){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._lastMoveEvent,delete this._previousPos,delete this._pos},Zr.prototype._inertialPan=function(t){this._fireEvent(\"dragend\",t),this._drainInertiaBuffer();var e=this._inertia;if(e.length<2)this._fireEvent(\"moveend\",t);else{var r=e[e.length-1],n=e[0],i=r[1].sub(n[1]),a=(r[0]-n[0])/1e3;if(0===a\|\|r[1].equals(n[1]))this._fireEvent(\"moveend\",t);else{var o=i.mult(.3/a),s=o.mag();s>1400&&(s=1400,o._unit()._mult(s));var l=s/750,c=o.mult(-l/2);this._map.panBy(c,{duration:1e3l,easing:Xr,noMoveStart:!0},{originalEvent:t})}}},Zr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Zr.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>0&&e-t[0][0]>160;)t.shift()};var $r=function(e){this._map=e,this._el=e.getCanvasContainer(),t.bindAll([\"_onKeyDown\"],this)};function Jr(t){return t(2-t)}$r.prototype.isEnabled=function(){return!!this._enabled},$r.prototype.enable=function(){this.isEnabled()\|\|(this._el.addEventListener(\"keydown\",this._onKeyDown,!1),this._enabled=!0)},$r.prototype.disable=function(){this.isEnabled()&&(this._el.removeEventListener(\"keydown\",this._onKeyDown),this._enabled=!1)},$r.prototype._onKeyDown=function(t){if(!(t.altKey\|\|t.ctrlKey\|\|t.metaKey)){var e=0,r=0,n=0,i=0,a=0;switch(t.keyCode){case 61:case 107:case 171:case 187:e=1;break;case 189:case 109:case 173:e=-1;break;case 37:t.shiftKey?r=-1:(t.preventDefault(),i=-1);break;case 39:t.shiftKey?r=1:(t.preventDefault(),i=1);break;case 38:t.shiftKey?n=1:(t.preventDefault(),a=-1);break;case 40:t.shiftKey?n=-1:(a=1,t.preventDefault());break;default:return}var o=this._map,s=o.getZoom(),l={duration:300,delayEndEvents:500,easing:Jr,zoom:e?Math.round(s)+e(t.shiftKey?2:1):s,bearing:o.getBearing()+15r,pitch:o.getPitch()+10n,offset:[100-i,100-a],center:o.getCenter()};o.easeTo(l,{originalEvent:t})}};var Kr=function(e){this._map=e,t.bindAll([\"_onDblClick\",\"_onZoomEnd\"],this)};Kr.prototype.isEnabled=function(){return!!this._enabled},Kr.prototype.isActive=function(){return!!this._active},Kr.prototype.enable=function(){this.isEnabled()\|\|(this._enabled=!0)},Kr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Kr.prototype.onTouchStart=function(t){var e=this;this.isEnabled()&&(t.points.length>1\|\|(this._tapped?(clearTimeout(this._tapped),this._tapped=null,this._zoom(t)):this._tapped=setTimeout(function(){e._tapped=null},300)))},Kr.prototype.onDblClick=function(t){this.isEnabled()&&(t.originalEvent.preventDefault(),this._zoom(t))},Kr.prototype._zoom=function(t){this._active=!0,this._map.on(\"zoomend\",this._onZoomEnd),this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)},Kr.prototype._onZoomEnd=function(){this._active=!1,this._map.off(\"zoomend\",this._onZoomEnd)};var Qr=t.bezier(0,0,.15,1),tn=function(e){this._map=e,this._el=e.getCanvasContainer(),t.bindAll([\"_onMove\",\"_onEnd\",\"_onTouchFrame\"],this)};tn.prototype.isEnabled=function(){return!!this._enabled},tn.prototype.enable=function(t){this.isEnabled()\|\|(this._el.classList.add(\"mapboxgl-touch-zoom-rotate\"),this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around)},tn.prototype.disable=function(){this.isEnabled()&&(this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\"),this._enabled=!1)},tn.prototype.disableRotation=function(){this._rotationDisabled=!0},tn.prototype.enableRotation=function(){this._rotationDisabled=!1},tn.prototype.onStart=function(e){if(this.isEnabled()&&2===e.touches.length){var r=s.mousePos(this._el,e.touches[0]),n=s.mousePos(this._el,e.touches[1]);this._startVec=r.sub(n),this._gestureIntent=void 0,this._inertia=[],s.addEventListener(t.default.document,\"touchmove\",this._onMove,{passive:!1}),s.addEventListener(t.default.document,\"touchend\",this._onEnd)}},tn.prototype._getTouchEventData=function(t){var e=s.mousePos(this._el,t.touches[0]),r=s.mousePos(this._el,t.touches[1]),n=e.sub(r);return{vec:n,center:e.add(r).div(2),scale:n.mag()/this._startVec.mag(),bearing:this._rotationDisabled?0:180n.angleWith(this._startVec)/Math.PI}},tn.prototype._onMove=function(e){if(2===e.touches.length){var r=this._getTouchEventData(e),n=r.vec,i=r.scale,a=r.bearing;if(!this._gestureIntent){var o=Math.abs(1-i)>.15;Math.abs(a)>10?this._gestureIntent=\"rotate\":o&&(this._gestureIntent=\"zoom\"),this._gestureIntent&&(this._map.fire(new t.Event(this._gestureIntent+\"start\",{originalEvent:e})),this._map.fire(new t.Event(\"movestart\",{originalEvent:e})),this._startVec=n)}this._lastTouchEvent=e,this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onTouchFrame)),e.preventDefault()}},tn.prototype._onTouchFrame=function(){this._frameId=null;var e=this._gestureIntent;if(e){var r=this._map.transform;this._startScale\|\|(this._startScale=r.scale,this._startBearing=r.bearing);var n=this._getTouchEventData(this._lastTouchEvent),i=n.center,o=n.bearing,s=n.scale,l=r.pointLocation(i),c=r.locationPoint(l);\"rotate\"===e&&(r.bearing=this._startBearing+o),r.zoom=r.scaleZoom(this._startScales),r.setLocationAtPoint(l,c),this._map.fire(new t.Event(e,{originalEvent:this._lastTouchEvent})),this._map.fire(new t.Event(\"move\",{originalEvent:this._lastTouchEvent})),this._drainInertiaBuffer(),this._inertia.push([a.now(),s,i])}},tn.prototype._onEnd=function(e){s.removeEventListener(t.default.document,\"touchmove\",this._onMove,{passive:!1}),s.removeEventListener(t.default.document,\"touchend\",this._onEnd);var r=this._gestureIntent,n=this._startScale;if(this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._gestureIntent,delete this._startScale,delete this._startBearing,delete this._lastTouchEvent,r){this._map.fire(new t.Event(r+\"end\",{originalEvent:e})),this._drainInertiaBuffer();var i=this._inertia,a=this._map;if(i.length<2)a.snapToNorth({},{originalEvent:e});else{var o=i[i.length-1],l=i[0],c=a.transform.scaleZoom(no[1]),u=a.transform.scaleZoom(nl[1]),f=c-u,h=(o[0]-l[0])/1e3,p=o[2];if(0!==h&&c!==u){var d=.15f/h;Math.abs(d)>2.5&&(d=d>0?2.5:-2.5);var g=1e3Math.abs(d/(12.15)),v=c+dg/2e3;v<0&&(v=0),a.easeTo({zoom:v,duration:g,easing:Qr,around:this._aroundCenter?a.getCenter():a.unproject(p),noMoveStart:!0},{originalEvent:e})}else a.snapToNorth({},{originalEvent:e})}}},tn.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>2&&e-t[0][0]>160;)t.shift()};var en={scrollZoom:Hr,boxZoom:Gr,dragRotate:Yr,dragPan:Zr,keyboard:$r,doubleClickZoom:Kr,touchZoomRotate:tn},rn=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return this.transform.center},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.default$1.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.fitBounds=function(e,r,n){if(\"number\"==typeof(r=t.extend({padding:{top:0,bottom:0,right:0,left:0},offset:[0,0],maxZoom:this.transform.maxZoom},r)).padding){var i=r.padding;r.padding={top:i,bottom:i,right:i,left:i}}if(!t.default$10(Object.keys(r.padding).sort(function(t,e){return t<e?-1:t>e?1:0}),[\"bottom\",\"left\",\"right\",\"top\"]))return t.warnOnce(\"options.padding must be a positive number, or an Object with keys 'bottom', 'left', 'right', 'top'\"),this;e=W.convert(e);var a=[(r.padding.left-r.padding.right)/2,(r.padding.top-r.padding.bottom)/2],o=Math.min(r.padding.right,r.padding.left),s=Math.min(r.padding.top,r.padding.bottom);r.offset=[r.offset[0]+a[0],r.offset[1]+a[1]];var l=t.default$1.convert(r.offset),c=this.transform,u=c.project(e.getNorthWest()),f=c.project(e.getSouthEast()),h=f.sub(u),p=(c.width-2o-2Math.abs(l.x))/h.x,d=(c.height-2s-2Math.abs(l.y))/h.y;return d<0\|\|p<0?(t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\"),this):(r.center=c.unproject(u.add(f).div(2)),r.zoom=Math.min(c.scaleZoom(c.scaleMath.min(p,d)),r.maxZoom),r.bearing=0,r.linear?this.easeTo(r,n):this.flyTo(r,n))},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=G.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this.stop(),!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=\"zoom\"in e?+e.zoom:a,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,u=\"pitch\"in e?+e.pitch:s,f=i.centerPoint.add(t.default$1.convert(e.offset)),h=i.pointLocation(f),p=G.convert(e.center\|\|h);this._normalizeCenter(p);var d,g,v=i.project(h),m=i.project(p).sub(v),y=i.zoomScale(l-a);return e.around&&(d=G.convert(e.around),g=i.locationPoint(d)),this._zooming=l!==a,this._rotating=o!==c,this._pitching=u!==s,this._prepareEase(r,e.noMoveStart),clearTimeout(this._easeEndTimeoutID),this._ease(function(e){if(n._zooming&&(i.zoom=t.number(a,l,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,u,e)),d)i.setLocationAtPoint(d,g);else{var h=i.zoomScale(i.zoom-a),p=l>a?Math.min(2,y):Math.max(.5,y),x=Math.pow(p,1-e),b=i.unproject(v.add(m.mult(ex)).mult(h));i.setLocationAtPoint(i.renderWorldCopies?b.wrap():b,f)}n._fireMoveEvents(r)},function(){e.delayEndEvents?n._easeEndTimeoutID=setTimeout(function(){return n._afterEase(r)},e.delayEndEvents):n._afterEase(r)},e),this},r.prototype._prepareEase=function(e,r){this._moving=!0,r\|\|this.fire(new t.Event(\"movestart\",e)),this._zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e){var r=this._zooming,n=this._rotating,i=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,r&&this.fire(new t.Event(\"zoomend\",e)),n&&this.fire(new t.Event(\"rotateend\",e)),i&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))},r.prototype.flyTo=function(e,r){var n=this;this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=\"zoom\"in e?t.clamp(+e.zoom,i.minZoom,i.maxZoom):a,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,u=\"pitch\"in e?+e.pitch:s,f=i.zoomScale(l-a),h=i.centerPoint.add(t.default$1.convert(e.offset)),p=i.pointLocation(h),d=G.convert(e.center\|\|p);this._normalizeCenter(d);var g=i.project(p),v=i.project(d).sub(g),m=e.curve,y=Math.max(i.width,i.height),x=y/f,b=v.mag();if(\"minZoom\"in e){var _=t.clamp(Math.min(e.minZoom,a,l),i.minZoom,i.maxZoom),w=y/i.zoomScale(_-a);m=Math.sqrt(w/b2)}var k=mm;function M(t){var e=(xx-yy+(t?-1:1)kkbb)/(2(t?x:y)kb);return Math.log(Math.sqrt(ee+1)-e)}function A(t){return(Math.exp(t)-Math.exp(-t))/2}function T(t){return(Math.exp(t)+Math.exp(-t))/2}var S=M(0),E=function(t){return T(S)/T(S+mt)},C=function(t){return y((T(S)(A(e=S+mt)/T(e))-A(S))/k)/b;var e},L=(M(1)-S)/m;if(Math.abs(b)<1e-6\|\|!isFinite(L)){if(Math.abs(y-x)<1e-6)return this.easeTo(e,r);var z=x<y?-1:1;L=Math.abs(Math.log(x/y))/m,C=function(){return 0},E=function(t){return Math.exp(zmt)}}if(\"duration\"in e)e.duration=+e.duration;else{var O=\"screenSpeed\"in e?+e.screenSpeed/m:+e.speed;e.duration=1e3L/O}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=o!==c,this._pitching=u!==s,this._prepareEase(r,!1),this._ease(function(e){var l=eL,f=1/E(l);i.zoom=a+i.scaleZoom(f),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,u,e));var p=i.unproject(g.add(v.mult(C(l))).mult(f));i.setLocationAtPoint(i.renderWorldCopies?p.wrap():p,h),n._fireMoveEvents(r)},function(){return n._afterEase(r)},e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var t=this._onEaseEnd;delete this._onEaseEnd,t.call(this)}return this},r.prototype._ease=function(t,e,r){!1===r.animate\|\|0===r.duration?(t(1),e()):(this._easeStart=a.now(),this._easeOptions=r,this._onEaseFrame=t,this._onEaseEnd=e,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var t=Math.min((a.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(t)),t<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),nn=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};nn.prototype.getDefaultPosition=function(){return\"bottom-right\"},nn.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=s.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},nn.prototype.onRemove=function(){s.remove(this._container),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0},nn.prototype._updateEditLink=function(){var t=this._editLink;t\|\|(t=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var e=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:v.ACCESS_TOKEN}];if(t){var r=e.reduce(function(t,r,n){return r.value&&(t+=r.key+\"=\"+r.value+(n<e.length-1?\"&\":\"\")),t},\"?\");t.href=\"https://www.mapbox.com/feedback/\"+r+(this._map._hash?this._map._hash.getHashString(!0):\"\")}},nn.prototype._updateData=function(t){t&&\"metadata\"===t.sourceDataType&&(this._updateAttributions(),this._updateEditLink())},nn.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n].getSource();i.attribution&&t.indexOf(i.attribution)<0&&t.push(i.attribution)}t.sort(function(t,e){return t.length-e.length}),(t=t.filter(function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0})).length?(this._container.innerHTML=t.join(\" \| \"),this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null}},nn.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var an=function(){t.bindAll([\"_updateLogo\"],this)};an.prototype.onAdd=function(t){this._map=t,this._container=s.create(\"div\",\"mapboxgl-ctrl\");var e=s.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",\"Mapbox logo\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._container},an.prototype.onRemove=function(){s.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo)},an.prototype.getDefaultPosition=function(){return\"bottom-left\"},an.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType\|\|(this._container.style.display=this._logoRequired()?\"block\":\"none\")},an.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}};var on=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};on.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},on.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},on.prototype.run=function(){var t=this._currentlyRunning=this._queue;this._queue=[];for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!n.cancelled&&(n.callback(),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},on.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var sn=t.default.HTMLImageElement,ln=t.default.HTMLElement,cn={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:0,maxZoom:22,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,transformRequest:null,fadeDuration:300},un=function(r){function n(e){if(null!=(e=t.extend({},cn,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than minZoom\");var n=new Fr(e.minZoom,e.maxZoom,e.renderWorldCopies);r.call(this,n,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new on;var i=e.transformRequest;if(this._transformRequest=i?function(t,e){return i(t,e)\|\|{url:t}}:function(t){return{url:t}},\"string\"==typeof e.container){var a=t.default.document.getElementById(e.container);if(!a)throw new Error(\"Container '\"+e.container+\"' not found.\");this._container=a}else{if(!(e.container instanceof ln))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\",\"_update\",\"_render\",\"_onData\",\"_onDataLoading\"],this),this._setupContainer(),this._setupPainter(),this.on(\"move\",this._update.bind(this,!1)),this.on(\"zoom\",this._update.bind(this,!0)),void 0!==t.default&&(t.default.addEventListener(\"online\",this._onWindowOnline,!1),t.default.addEventListener(\"resize\",this._onWindowResize,!1)),function(t,e){var r=t.getCanvasContainer(),n=null,i=!1;for(var a in en)t[a]=new en[a](t,e),e.interactive&&e[a]&&t[a].enable(e[a]);s.addEventListener(r,\"mouseout\",function(e){t.fire(new Vr(\"mouseout\",t,e))}),s.addEventListener(r,\"mousedown\",function(r){i=!0;var n=new Vr(\"mousedown\",t,r);t.fire(n),n.defaultPrevented\|\|(e.interactive&&!t.doubleClickZoom.isActive()&&t.stop(),t.boxZoom.onMouseDown(r),t.boxZoom.isActive()\|\|t.dragPan.isActive()\|\|t.dragRotate.onMouseDown(r),t.boxZoom.isActive()\|\|t.dragRotate.isActive()\|\|t.dragPan.onMouseDown(r))}),s.addEventListener(r,\"mouseup\",function(e){var r=t.dragRotate.isActive();n&&!r&&t.fire(new Vr(\"contextmenu\",t,n)),n=null,i=!1,t.fire(new Vr(\"mouseup\",t,e))}),s.addEventListener(r,\"mousemove\",function(e){if(!t.dragPan.isActive()&&!t.dragRotate.isActive()){for(var n=e.toElement\|\|e.target;n&&n!==r;)n=n.parentNode;n===r&&t.fire(new Vr(\"mousemove\",t,e))}}),s.addEventListener(r,\"mouseover\",function(e){for(var n=e.toElement\|\|e.target;n&&n!==r;)n=n.parentNode;n===r&&t.fire(new Vr(\"mouseover\",t,e))}),s.addEventListener(r,\"touchstart\",function(r){var n=new Ur(\"touchstart\",t,r);t.fire(n),n.defaultPrevented\|\|(e.interactive&&t.stop(),t.boxZoom.isActive()\|\|t.dragRotate.isActive()\|\|t.dragPan.onTouchStart(r),t.touchZoomRotate.onStart(r),t.doubleClickZoom.onTouchStart(n))},{passive:!1}),s.addEventListener(r,\"touchmove\",function(e){t.fire(new Ur(\"touchmove\",t,e))},{passive:!1}),s.addEventListener(r,\"touchend\",function(e){t.fire(new Ur(\"touchend\",t,e))}),s.addEventListener(r,\"touchcancel\",function(e){t.fire(new Ur(\"touchcancel\",t,e))}),s.addEventListener(r,\"click\",function(e){t.fire(new Vr(\"click\",t,e))}),s.addEventListener(r,\"dblclick\",function(e){var r=new Vr(\"dblclick\",t,e);t.fire(r),r.defaultPrevented\|\|t.doubleClickZoom.onDblClick(r)}),s.addEventListener(r,\"contextmenu\",function(e){var r=t.dragRotate.isActive();i\|\|r?i&&(n=e):t.fire(new Vr(\"contextmenu\",t,e)),e.preventDefault()}),s.addEventListener(r,\"wheel\",function(e){var r=new qr(\"wheel\",t,e);t.fire(r),r.defaultPrevented\|\|t.scrollZoom.onWheel(e)},{passive:!1})}(this,e),this._hash=e.hash&&(new jr).addTo(this),this._hash&&this._hash._onHashChange()\|\|this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),this.resize(),e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new nn),this.addControl(new an,e.logoPosition),this.on(\"style.load\",function(){this.transform.unmodified&&this.jumpTo(this.style.stylesheet)}),this.on(\"data\",this._onData),this.on(\"dataloading\",this._onDataLoading)}r&&(n.__proto__=r),n.prototype=Object.create(r&&r.prototype),n.prototype.constructor=n;var i={showTileBoundaries:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0}};return n.prototype.addControl=function(t,e){void 0===e&&t.getDefaultPosition&&(e=t.getDefaultPosition()),void 0===e&&(e=\"top-right\");var r=t.onAdd(this),n=this._controlPositions[e];return-1!==e.indexOf(\"bottom\")?n.insertBefore(r,n.firstChild):n.appendChild(r),this},n.prototype.removeControl=function(t){return t.onRemove(this),this},n.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];return this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e)).fire(new t.Event(\"resize\",e)).fire(new t.Event(\"moveend\",e))},n.prototype.getBounds=function(){var e=new W(this.transform.pointLocation(new t.default$1(0,this.transform.height)),this.transform.pointLocation(new t.default$1(this.transform.width,0)));return(this.transform.angle\|\|this.transform.pitch)&&(e.extend(this.transform.pointLocation(new t.default$1(this.transform.size.x,0))),e.extend(this.transform.pointLocation(new t.default$1(0,this.transform.size.y)))),e},n.prototype.getMaxBounds=function(){return this.transform.latRange&&2===this.transform.latRange.length&&this.transform.lngRange&&2===this.transform.lngRange.length?new W([this.transform.lngRange[0],this.transform.latRange[0]],[this.transform.lngRange[1],this.transform.latRange[1]]):null},n.prototype.setMaxBounds=function(t){if(t){var e=W.convert(t);this.transform.lngRange=[e.getWest(),e.getEast()],this.transform.latRange=[e.getSouth(),e.getNorth()],this.transform._constrain(),this._update()}else null==t&&(this.transform.lngRange=null,this.transform.latRange=null,this._update());return this},n.prototype.setMinZoom=function(t){if((t=null==t?0:t)>=0&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between 0 and the current maxZoom, inclusive\")},n.prototype.getMinZoom=function(){return this.transform.minZoom},n.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},n.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},n.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update(),this},n.prototype.getMaxZoom=function(){return this.transform.maxZoom},n.prototype.project=function(t){return this.transform.locationPoint(G.convert(t))},n.prototype.unproject=function(e){return this.transform.pointLocation(t.default$1.convert(e))},n.prototype.isMoving=function(){return this._moving\|\|this.dragPan.isActive()\|\|this.dragRotate.isActive()\|\|this.scrollZoom.isActive()},n.prototype.isZooming=function(){return this._zooming\|\|this.scrollZoom.isActive()},n.prototype.isRotating=function(){return this._rotating\|\|this.dragRotate.isActive()},n.prototype.on=function(t,e,n){var i,a=this;if(void 0===n)return r.prototype.on.call(this,t,e);var o=function(){if(\"mouseenter\"===t\|\|\"mouseover\"===t){var r=!1;return{layer:e,listener:n,delegates:{mousemove:function(i){var o=a.getLayer(e)?a.queryRenderedFeatures(i.point,{layers:[e]}):[];o.length?r\|\|(r=!0,n.call(a,new Vr(t,a,i.originalEvent,{features:o}))):r=!1},mouseout:function(){r=!1}}}}if(\"mouseleave\"===t\|\|\"mouseout\"===t){var o=!1;return{layer:e,listener:n,delegates:{mousemove:function(r){(a.getLayer(e)?a.queryRenderedFeatures(r.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,n.call(a,new Vr(t,a,r.originalEvent)))},mouseout:function(e){o&&(o=!1,n.call(a,new Vr(t,a,e.originalEvent)))}}}}return{layer:e,listener:n,delegates:(i={},i[t]=function(t){var r=a.getLayer(e)?a.queryRenderedFeatures(t.point,{layers:[e]}):[];r.length&&(t.features=r,n.call(a,t),delete t.features)},i)}}();for(var s in this._delegatedListeners=this._delegatedListeners\|\|{},this._delegatedListeners[t]=this._delegatedListeners[t]\|\|[],this._delegatedListeners[t].push(o),o.delegates)a.on(s,o.delegates[s]);return this},n.prototype.off=function(t,e,n){if(void 0===n)return r.prototype.off.call(this,t,e);if(this._delegatedListeners&&this._delegatedListeners[t])for(var i=this._delegatedListeners[t],a=0;a<i.length;a++){var o=i[a];if(o.layer===e&&o.listener===n){for(var s in o.delegates)this.off(s,o.delegates[s]);return i.splice(a,1),this}}return this},n.prototype.queryRenderedFeatures=function(e,r){var n;return 2===arguments.length?(e=arguments[0],r=arguments[1]):1===arguments.length&&((n=arguments[0])instanceof t.default$1\|\|Array.isArray(n))?(e=arguments[0],r={}):1===arguments.length?(e=void 0,r=arguments[0]):(e=void 0,r={}),this.style?this.style.queryRenderedFeatures(this._makeQueryGeometry(e),r,this.transform):[]},n.prototype._makeQueryGeometry=function(e){var r,n=this;if(void 0===e&&(e=[t.default$1.convert([0,0]),t.default$1.convert([this.transform.width,this.transform.height])]),e instanceof t.default$1\|\|\"number\"==typeof e[0])r=[t.default$1.convert(e)];else{var i=[t.default$1.convert(e[0]),t.default$1.convert(e[1])];r=[i[0],new t.default$1(i[1].x,i[0].y),i[1],new t.default$1(i[0].x,i[1].y),i[0]]}return{viewport:r,worldCoordinate:r.map(function(t){return n.transform.pointCoordinate(t)})}},n.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},n.prototype.setStyle=function(e,r){if((!r\|\|!1!==r.diff&&!r.localIdeographFontFamily)&&this.style&&e&&\"object\"==typeof e)try{return this.style.setState(e)&&this._update(!0),this}catch(e){t.warnOnce(\"Unable to perform style diff: \"+(e.message\|\|e.error\|\|e)+\". Rebuilding the style from scratch.\")}return this.style&&(this.style.setEventedParent(null),this.style._remove()),e?(this.style=new Je(this,r\|\|{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof e?this.style.loadURL(e):this.style.loadJSON(e),this):(delete this.style,this)},n.prototype.getStyle=function(){if(this.style)return this.style.serialize()},n.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},n.prototype.addSource=function(t,e){return this.style.addSource(t,e),this._update(!0),this},n.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},n.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},n.prototype.addSourceType=function(t,e,r){return this.style.addSourceType(t,e,r)},n.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0),this},n.prototype.getSource=function(t){return this.style.getSource(t)},n.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var o=n.sdf;if(void 0===o&&(o=!1),r instanceof sn){var s=a.getImageData(r),l=s.width,c=s.height,u=s.data;this.style.addImage(e,{data:new t.RGBAImage({width:l,height:c},u),pixelRatio:i,sdf:o})}else{if(void 0===r.width\|\|void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var f=r.width,h=r.height,p=r.data;this.style.addImage(e,{data:new t.RGBAImage({width:f,height:h},p.slice(0)),pixelRatio:i,sdf:o})}},n.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},n.prototype.removeImage=function(t){this.style.removeImage(t)},n.prototype.loadImage=function(e,r){t.getImage(this._transformRequest(e,t.ResourceType.Image),r)},n.prototype.addLayer=function(t,e){return this.style.addLayer(t,e),this._update(!0),this},n.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0),this},n.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0),this},n.prototype.getLayer=function(t){return this.style.getLayer(t)},n.prototype.setFilter=function(t,e){return this.style.setFilter(t,e),this._update(!0),this},n.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0),this},n.prototype.getFilter=function(t){return this.style.getFilter(t)},n.prototype.setPaintProperty=function(t,e,r){return this.style.setPaintProperty(t,e,r),this._update(!0),this},n.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},n.prototype.setLayoutProperty=function(t,e,r){return this.style.setLayoutProperty(t,e,r),this._update(!0),this},n.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},n.prototype.setLight=function(t){return this.style.setLight(t),this._update(!0),this},n.prototype.getLight=function(){return this.style.getLight()},n.prototype.getContainer=function(){return this._container},n.prototype.getCanvasContainer=function(){return this._canvasContainer},n.prototype.getCanvas=function(){return this._canvas},n.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.offsetWidth\|\|400,e=this._container.offsetHeight\|\|300),[t,e]},n.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSContainer=s.create(\"div\",\"mapboxgl-missing-css\",t)).innerHTML=\"Missing Mapbox GL JS CSS\";var e=this._canvasContainer=s.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=s.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.style.position=\"absolute\",this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var r=this._containerDimensions();this._resizeCanvas(r[0],r[1]);var n=this._controlContainer=s.create(\"div\",\"mapboxgl-control-container\",t),i=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(t){i[t]=s.create(\"div\",\"mapboxgl-ctrl-\"+t,n)})},n.prototype._resizeCanvas=function(e,r){var n=t.default.devicePixelRatio\|\|1;this._canvas.width=ne,this._canvas.height=nr,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},n.prototype._setupPainter=function(){var r=t.extend({failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer},e.webGLContextAttributes),n=this._canvas.getContext(\"webgl\",r)\|\|this._canvas.getContext(\"experimental-webgl\",r);n?this.painter=new zr(n,this.transform):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},n.prototype._contextLost=function(e){e.preventDefault(),this._frameId&&(a.cancelFrame(this._frameId),this._frameId=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},n.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},n.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!(!this.style\|\|!this.style.loaded())},n.prototype._update=function(t){this.style&&(this._styleDirty=this._styleDirty\|\|t,this._sourcesDirty=!0,this._rerender())},n.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},n.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},n.prototype._render=function(){this._renderTaskQueue.run();var e=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var r=this.transform.zoom,n=a.now();this.style.zoomHistory.update(r,n);var i=new t.default$16(r,{now:n,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),o=i.crossFadingFactor();1===o&&o===this._crossFadingFactor\|\|(e=!0,this._crossFadingFactor=o),this.style.update(i)}return this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),fadeDuration:this._fadeDuration}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()\|\|e)&&(this._styleDirty=!0),(this._sourcesDirty\|\|this._repaint\|\|this._styleDirty\|\|this._placementDirty)&&this._rerender(),this},n.prototype.remove=function(){this._hash&&this._hash.remove(),a.cancelFrame(this._frameId),this._renderTaskQueue.clear(),this._frameId=null,this.setStyle(null),void 0!==t.default&&(t.default.removeEventListener(\"resize\",this._onWindowResize,!1),t.default.removeEventListener(\"online\",this._onWindowOnline,!1));var e=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");e&&e.loseContext(),fn(this._canvasContainer),fn(this._controlContainer),fn(this._missingCSSContainer),this._container.classList.remove(\"mapboxgl-map\"),this.fire(new t.Event(\"remove\"))},n.prototype._rerender=function(){var t=this;this.style&&!this._frameId&&(this._frameId=a.frame(function(){t._frameId=null,t._render()}))},n.prototype._onWindowOnline=function(){this._update()},n.prototype._onWindowResize=function(){this._trackResize&&this.stop().resize()._update()},i.showTileBoundaries.get=function(){return!!this._showTileBoundaries},i.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},i.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},i.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},i.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},i.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},i.repaint.get=function(){return!!this._repaint},i.repaint.set=function(t){this._repaint=t,this._update()},i.vertices.get=function(){return!!this._vertices},i.vertices.set=function(t){this._vertices=t,this._update()},n.prototype._onData=function(e){this._update(\"style\"===e.dataType),this.fire(new t.Event(e.dataType+\"data\",e))},n.prototype._onDataLoading=function(e){this.fire(new t.Event(e.dataType+\"dataloading\",e))},Object.defineProperties(n.prototype,i),n}(rn);function fn(t){t.parentNode&&t.parentNode.removeChild(t)}var hn={showCompass:!0,showZoom:!0},pn=function(e){var r=this;this.options=t.extend({},hn,e),this._container=s.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",function(t){return t.preventDefault()}),this.options.showZoom&&(this._zoomInButton=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-zoom-in\",\"Zoom In\",function(){return r._map.zoomIn()}),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-zoom-out\",\"Zoom Out\",function(){return r._map.zoomOut()})),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-compass\",\"Reset North\",function(){return r._map.resetNorth()}),this._compassArrow=s.create(\"span\",\"mapboxgl-ctrl-compass-arrow\",this._compass))};function dn(t,e,r){if(t=new G(t.lng,t.lat),e){var n=new G(t.lng-360,t.lat),i=new G(t.lng+360,t.lat),a=r.locationPoint(t).distSqr(e);r.locationPoint(n).distSqr(e)<a?t=n:r.locationPoint(i).distSqr(e)<a&&(t=i)}for(;Math.abs(t.lng-r.center.lng)>180;){var o=r.locationPoint(t);if(o.x>=0&&o.y>=0&&o.x<=r.width&&o.y<=r.height)break;t.lng>r.center.lng?t.lng-=360:t.lng+=360}return t}pn.prototype._rotateCompassArrow=function(){var t=\"rotate(\"+this._map.transform.angle(180/Math.PI)+\"deg)\";this._compassArrow.style.transform=t},pn.prototype.onAdd=function(t){return this._map=t,this.options.showCompass&&(this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Yr(t,{button:\"left\",element:this._compass}),this._handler.enable()),this._container},pn.prototype.onRemove=function(){s.remove(this._container),this.options.showCompass&&(this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.disable(),delete this._handler),delete this._map},pn.prototype._createButton=function(t,e,r){var n=s.create(\"button\",t,this._container);return n.type=\"button\",n.setAttribute(\"aria-label\",e),n.addEventListener(\"click\",r),n};var gn={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function vn(t,e,r){var n=t.classList;for(var i in gn)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var mn=function(e){if((arguments[0]instanceof t.default.HTMLElement\|\|2===arguments.length)&&(e=t.extend({element:e},arguments[1])),t.bindAll([\"_update\",\"_onMapClick\"],this),this._anchor=e&&e.anchor\|\|\"center\",this._color=e&&e.color\|\|\"#3FB1CE\",e&&e.element)this._element=e.element,this._offset=t.default$1.convert(e&&e.offset\|\|[0,0]);else{this._defaultMarker=!0,this._element=s.create(\"div\");var r=s.createNS(\"http://www.w3.org/2000/svg\",\"svg\");r.setAttributeNS(null,\"height\",\"41px\"),r.setAttributeNS(null,\"width\",\"27px\"),r.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var n=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");n.setAttributeNS(null,\"stroke\",\"none\"),n.setAttributeNS(null,\"stroke-width\",\"1\"),n.setAttributeNS(null,\"fill\",\"none\"),n.setAttributeNS(null,\"fill-rule\",\"evenodd\");var i=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");i.setAttributeNS(null,\"fill-rule\",\"nonzero\");var a=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");a.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),a.setAttributeNS(null,\"fill\",\"#000000\");for(var o=0,l=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];o<l.length;o+=1){var c=l[o],u=s.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");u.setAttributeNS(null,\"opacity\",\"0.04\"),u.setAttributeNS(null,\"cx\",\"10.5\"),u.setAttributeNS(null,\"cy\",\"5.80029008\"),u.setAttributeNS(null,\"rx\",c.rx),u.setAttributeNS(null,\"ry\",c.ry),a.appendChild(u)}var f=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");f.setAttributeNS(null,\"fill\",this._color);var h=s.createNS(\"http://www.w3.org/2000/svg\",\"path\");h.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),f.appendChild(h);var p=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");p.setAttributeNS(null,\"opacity\",\"0.25\"),p.setAttributeNS(null,\"fill\",\"#000000\");var d=s.createNS(\"http://www.w3.org/2000/svg\",\"path\");d.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),p.appendChild(d);var g=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),g.setAttributeNS(null,\"fill\",\"#FFFFFF\");var v=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");v.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var m=s.createNS(\"http://www.w3.org/2000/svg\",\"circle\");m.setAttributeNS(null,\"fill\",\"#000000\"),m.setAttributeNS(null,\"opacity\",\"0.25\"),m.setAttributeNS(null,\"cx\",\"5.5\"),m.setAttributeNS(null,\"cy\",\"5.5\"),m.setAttributeNS(null,\"r\",\"5.4999962\");var y=s.createNS(\"http://www.w3.org/2000/svg\",\"circle\");y.setAttributeNS(null,\"fill\",\"#FFFFFF\"),y.setAttributeNS(null,\"cx\",\"5.5\"),y.setAttributeNS(null,\"cy\",\"5.5\"),y.setAttributeNS(null,\"r\",\"5.4999962\"),v.appendChild(m),v.appendChild(y),i.appendChild(a),i.appendChild(f),i.appendChild(p),i.appendChild(g),i.appendChild(v),r.appendChild(i),this._element.appendChild(r),this._offset=t.default$1.convert(e&&e.offset\|\|[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._popup=null};mn.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this._update(),this._map.on(\"click\",this._onMapClick),this},mn.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),delete this._map),s.remove(this._element),this._popup&&this._popup.remove(),this},mn.prototype.getLngLat=function(){return this._lngLat},mn.prototype.setLngLat=function(t){return this._lngLat=G.convert(t),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},mn.prototype.getElement=function(){return this._element},mn.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1(24.6+e)],\"bottom-right\":[-e,-1(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat)}return this},mn.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r\|\|r.contains(e))&&this.togglePopup()},mn.prototype.getPopup=function(){return this._popup},mn.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},mn.prototype._update=function(t){this._map&&(this._map.transform.renderWorldCopies&&(this._lngLat=dn(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset),t&&\"moveend\"!==t.type\|\|(this._pos=this._pos.round()),s.setTransform(this._element,gn[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px)\"),vn(this._element,this._anchor,\"marker\"))},mn.prototype.getOffset=function(){return this._offset},mn.prototype.setOffset=function(e){return this._offset=t.default$1.convert(e),this._update(),this};var yn,xn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showUserLocation:!0},bn=function(e){function r(r){e.call(this),this.options=t.extend({},xn,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(e){var r;return this._map=e,this._container=s.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),r=this._setupUI,void 0!==yn?r(yn):void 0!==t.default.navigator.permissions?t.default.navigator.permissions.query({name:\"geolocation\"}).then(function(t){yn=\"denied\"!==t.state,r(yn)}):(yn=!!t.default.navigator.geolocation,r(yn)),this._container},r.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.default.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker.remove(),s.remove(this._container),this._map=void 0},r.prototype._onSuccess=function(e){if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState\|\|this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()},r.prototype._updateCamera=function(t){var e=new G(t.coords.longitude,t.coords.latitude),r=t.coords.accuracy;this._map.fitBounds(e.toBounds(r),this.options.fitBoundsOptions,{geolocateSource:!0})},r.prototype._updateMarker=function(t){t?this._userLocationDotMarker.setLngLat([t.coords.longitude,t.coords.latitude]).addTo(this._map):this._userLocationDotMarker.remove()},r.prototype._onError=function(e){if(this.options.trackUserLocation)if(1===e.code)this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),void 0!==this._geolocationWatchID&&this._clearWatch();else switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()},r.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},r.prototype._setupUI=function(e){var r=this;!1!==e&&(this._container.addEventListener(\"contextmenu\",function(t){return t.preventDefault()}),this._geolocateButton=s.create(\"button\",\"mapboxgl-ctrl-icon mapboxgl-ctrl-geolocate\",this._container),this._geolocateButton.type=\"button\",this._geolocateButton.setAttribute(\"aria-label\",\"Geolocate\"),this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=s.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new mn(this._dotElement),this.options.trackUserLocation&&(this._watchState=\"OFF\")),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",function(e){e.geolocateSource\|\|\"ACTIVE_LOCK\"!==r._watchState\|\|(r._watchState=\"BACKGROUND\",r._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),r._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),r.fire(new t.Event(\"trackuserlocationend\")))}))},r.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID?this._clearWatch():void 0===this._geolocationWatchID&&(this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),this._geolocationWatchID=t.default.navigator.geolocation.watchPosition(this._onSuccess,this._onError,this.options.positionOptions))}else t.default.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},r.prototype._clearWatch=function(){t.default.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},r}(t.Evented),_n={maxWidth:100,unit:\"metric\"},wn=function(e){this.options=t.extend({},_n,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function kn(t,e,r){var n,i,a,o,s,l,c=r&&r.maxWidth\|\|100,u=t._container.clientHeight/2,f=(n=t.unproject([0,u]),i=t.unproject([c,u]),a=Math.PI/180,o=n.lata,s=i.lata,l=Math.sin(o)Math.sin(s)+Math.cos(o)Math.cos(s)Math.cos((i.lng-n.lng)a),6371e3Math.acos(Math.min(l,1)));if(r&&\"imperial\"===r.unit){var h=3.2808f;h>5280?Mn(e,c,h/5280,\"mi\"):Mn(e,c,h,\"ft\")}else r&&\"nautical\"===r.unit?Mn(e,c,f/1852,\"nm\"):Mn(e,c,f,\"m\")}function Mn(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))(o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:1)),l=s/r;\"m\"===n&&s>=1e3&&(s/=1e3,n=\"km\"),t.style.width=el+\"px\",t.innerHTML=s+n}wn.prototype.getDefaultPosition=function(){return\"bottom-left\"},wn.prototype._onMove=function(){kn(this._map,this._container,this.options)},wn.prototype.onAdd=function(t){return this._map=t,this._container=s.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},wn.prototype.onRemove=function(){s.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},wn.prototype.setUnit=function(t){this.options.unit=t,kn(this._map,this._container,this.options)};var An=function(){this._fullscreen=!1,t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.default.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.default.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.default.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.default.document&&(this._fullscreenchange=\"MSFullscreenChange\"),this._className=\"mapboxgl-ctrl\"};An.prototype.onAdd=function(e){return this._map=e,this._mapContainer=this._map.getContainer(),this._container=s.create(\"div\",this._className+\" mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._container.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._container},An.prototype.onRemove=function(){s.remove(this._container),this._map=null,t.default.document.removeEventListener(this._fullscreenchange,this._changeIcon)},An.prototype._checkFullscreenSupport=function(){return!!(t.default.document.fullscreenEnabled\|\|t.default.document.mozFullScreenEnabled\|\|t.default.document.msFullscreenEnabled\|\|t.default.document.webkitFullscreenEnabled)},An.prototype._setupUI=function(){var e=this._fullscreenButton=s.create(\"button\",this._className+\"-icon \"+this._className+\"-fullscreen\",this._container);e.setAttribute(\"aria-label\",\"Toggle fullscreen\"),e.type=\"button\",this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.default.document.addEventListener(this._fullscreenchange,this._changeIcon)},An.prototype._isFullscreen=function(){return this._fullscreen},An.prototype._changeIcon=function(){(t.default.document.fullscreenElement\|\|t.default.document.mozFullScreenElement\|\|t.default.document.webkitFullscreenElement\|\|t.default.document.msFullscreenElement)===this._mapContainer!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(this._className+\"-shrink\"),this._fullscreenButton.classList.toggle(this._className+\"-fullscreen\"))},An.prototype._onClickFullscreen=function(){this._isFullscreen()?t.default.document.exitFullscreen?t.default.document.exitFullscreen():t.default.document.mozCancelFullScreen?t.default.document.mozCancelFullScreen():t.default.document.msExitFullscreen?t.default.document.msExitFullscreen():t.default.document.webkitCancelFullScreen&&t.default.document.webkitCancelFullScreen():this._mapContainer.requestFullscreen?this._mapContainer.requestFullscreen():this._mapContainer.mozRequestFullScreen?this._mapContainer.mozRequestFullScreen():this._mapContainer.msRequestFullscreen?this._mapContainer.msRequestFullscreen():this._mapContainer.webkitRequestFullscreen&&this._mapContainer.webkitRequestFullscreen()};var Tn={closeButton:!0,closeOnClick:!0},Sn=function(e){function r(r){e.call(this),this.options=t.extend(Object.create(Tn),r),t.bindAll([\"_update\",\"_onClickClose\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.addTo=function(e){return this._map=e,this._map.on(\"move\",this._update),this.options.closeOnClick&&this._map.on(\"click\",this._onClickClose),this._update(),this.fire(new t.Event(\"open\")),this},r.prototype.isOpen=function(){return!!this._map},r.prototype.remove=function(){return this._content&&s.remove(this._content),this._container&&(s.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"click\",this._onClickClose),delete this._map),this.fire(new t.Event(\"close\")),this},r.prototype.getLngLat=function(){return this._lngLat},r.prototype.setLngLat=function(t){return this._lngLat=G.convert(t),this._pos=null,this._update(),this},r.prototype.setText=function(e){return this.setDOMContent(t.default.document.createTextNode(e))},r.prototype.setHTML=function(e){var r,n=t.default.document.createDocumentFragment(),i=t.default.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},r.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},r.prototype._createContent=function(){this._content&&s.remove(this._content),this._content=s.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=s.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClickClose))},r.prototype._update=function(){if(this._map&&this._lngLat&&this._content){this._container\|\|(this._container=s.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=s.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content)),this._map.transform.renderWorldCopies&&(this._lngLat=dn(this._lngLat,this._pos,this._map.transform));var e=this._pos=this._map.project(this._lngLat),r=this.options.anchor,n=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5Math.pow(r,2)));return{center:new t.default$1(0,0),top:new t.default$1(0,r),\"top-left\":new t.default$1(n,n),\"top-right\":new t.default$1(-n,n),bottom:new t.default$1(0,-r),\"bottom-left\":new t.default$1(n,-n),\"bottom-right\":new t.default$1(-n,-n),left:new t.default$1(r,0),right:new t.default$1(-r,0)}}if(r instanceof t.default$1\|\|Array.isArray(r)){var i=t.default$1.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.default$1.convert(r.center\|\|[0,0]),top:t.default$1.convert(r.top\|\|[0,0]),\"top-left\":t.default$1.convert(r[\"top-left\"]\|\|[0,0]),\"top-right\":t.default$1.convert(r[\"top-right\"]\|\|[0,0]),bottom:t.default$1.convert(r.bottom\|\|[0,0]),\"bottom-left\":t.default$1.convert(r[\"bottom-left\"]\|\|[0,0]),\"bottom-right\":t.default$1.convert(r[\"bottom-right\"]\|\|[0,0]),left:t.default$1.convert(r.left\|\|[0,0]),right:t.default$1.convert(r.right\|\|[0,0])}}return e(new t.default$1(0,0))}(this.options.offset);if(!r){var i,a=this._container.offsetWidth,o=this._container.offsetHeight;i=e.y+n.bottom.y<o?[\"top\"]:e.y>this._map.transform.height-o?[\"bottom\"]:[],e.x<a/2?i.push(\"left\"):e.x>this._map.transform.width-a/2&&i.push(\"right\"),r=0===i.length?\"bottom\":i.join(\"-\")}var l=e.add(n[r]).round();s.setTransform(this._container,gn[r]+\" translate(\"+l.x+\"px,\"+l.y+\"px)\"),vn(this._container,r,\"popup\")}},r.prototype._onClickClose=function(){this.remove()},r}(t.Evented),En={version:\"0.45.0\",supported:e,workerCount:Math.max(Math.floor(a.hardwareConcurrency/2),1),setRTLTextPlugin:t.setRTLTextPlugin,Map:un,NavigationControl:pn,GeolocateControl:bn,AttributionControl:nn,ScaleControl:wn,FullscreenControl:An,Popup:Sn,Marker:mn,Style:Je,LngLat:G,LngLatBounds:W,Point:t.default$1,Evented:t.Evented,config:v,get accessToken(){return v.ACCESS_TOKEN},set accessToken(t){v.ACCESS_TOKEN=t},workerUrl:\"\"};return En}),n})}).call(this,\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{}],410:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=1<<t+1,r=new Array(e),n=0;n<e;++n)r[n]=a(t,n);return r};var n=t(\"convex-hull\");function i(t,e,r){for(var n=new Array(t),i=0;i<t;++i)n[i]=0,i===e&&(n[i]+=.5),i===r&&(n[i]+=.5);return n}function a(t,e){if(0===e\|\|e===(1<<t+1)-1)return[];for(var r=[],a=[],o=0;o<=t;++o)if(e&1<<o){r.push(i(t,o-1,o-1)),a.push(null);for(var s=0;s<=t;++s)~e&1<<s&&(r.push(i(t,o-1,s-1)),a.push([o,s]))}var l=n(r),c=[];t:for(o=0;o<l.length;++o){var u=l[o],f=[];for(s=0;s<u.length;++s){if(!a[u[s]])continue t;f.push(a[u[s]].slice())}c.push(f)}return c}},{\"convex-hull\":118}],411:[function(t,e,r){var n=t(\"./normalize\"),i=t(\"gl-mat4/create\"),a=t(\"gl-mat4/clone\"),o=t(\"gl-mat4/determinant\"),s=t(\"gl-mat4/invert\"),l=t(\"gl-mat4/transpose\"),c={length:t(\"gl-vec3/length\"),normalize:t(\"gl-vec3/normalize\"),dot:t(\"gl-vec3/dot\"),cross:t(\"gl-vec3/cross\")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function g(t,e,r,n,i){t[0]=e[0]n+r[0]i,t[1]=e[1]n+r[1]i,t[2]=e[2]n+r[2]i}e.exports=function(t,e,r,i,v,m){if(e\|\|(e=[0,0,0]),r\|\|(r=[0,0,0]),i\|\|(i=[0,0,0]),v\|\|(v=[0,0,0,1]),m\|\|(m=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,k,M,A=u[3],T=u[7],S=u[11],E=u[12],C=u[13],L=u[14],z=u[15];if(0!==A\|\|0!==T\|\|0!==S){if(h[0]=A,h[1]=T,h[2]=S,h[3]=z,!s(f,f))return!1;l(f,f),y=v,b=f,_=(x=h)[0],w=x[1],k=x[2],M=x[3],y[0]=b[0]_+b[4]w+b[8]k+b[12]M,y[1]=b[1]_+b[5]w+b[9]k+b[13]M,y[2]=b[2]_+b[6]w+b[10]k+b[14]M,y[3]=b[3]_+b[7]w+b[11]k+b[15]M}else v[0]=v[1]=v[2]=0,v[3]=1;if(e[0]=E,e[1]=C,e[2]=L,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),g(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),g(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),g(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var O=0;O<3;O++)r[O]=-1,p[O][0]=-1,p[O][1]=-1,p[O][2]=-1;return 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nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) < length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) < length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha = min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha = min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a = alpha opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n + positionFract * scale + translateFract\\n + position * scaleFract\\n + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a = opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach(function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]e.viewport.width>v.precisionThreshold\|\|e.scale[1]e.viewport.height>v.precisionThreshold?t.shaders.rect(e):\"rect\"===e.join\|\|!e.join&&(e.thickness<=2\|\|e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))}),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)\|\|(t=[t]);var r=this.regl,o=this.gl;if(t.forEach(function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin join type mode\",miterLimit:\"miterlimit miterLimit\",dashes:\"dash dashes dasharray dash-array dashArray\",color:\"color colour stroke colors colours stroke-color strokeColor\",fill:\"fill fill-color fillColor\",opacity:\"alpha opacity\",overlay:\"overlay crease overlap intersect\",close:\"closed close closed-path closePath\",range:\"range dataBox\",viewport:\"viewport viewBox\",hole:\"holes hole hollow\"}),d\|\|(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:\"linear\",min:\"linear\"}),colorBuffer:r.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array}),positionBuffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=g(t.viewport)),d.viewport\|\|(d.viewport=g([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var m,y;if(t.positions.x&&t.positions.y){var x=t.positions.x,b=t.positions.y;y=d.count=Math.max(x.length,b.length),m=new Float64Array(2y);for(var _=0;_<y;_++)m[2_]=x[_],m[2_+1]=b[_]}else m=l(t.positions,\"float64\"),y=d.count=Math.floor(m.length/2);var w=d.bounds=i(m,2);if(d.fill){for(var k=[],M={},A=0,T=0,S=0,E=d.count;T<E;T++){var C=m[2T],L=m[2T+1];isNaN(C)\|\|isNaN(L)\|\|null==C\|\|null==L?(C=m[2A],L=m[2A+1],M[T]=A):A=T,k[S++]=C,k[S++]=L}for(var z=c(k,d.hole\|\|[]),O=0,I=z.length;O<I;O++)null!=M[z[O]]&&(z[O]=M[z[O]]);d.triangles=z}var P=new Float64Array(m);u(P,2,w);var D=new Float64Array(2y+6);d.close?m[0]===m[2y-2]&&m[1]===m[2y-1]?(D[0]=P[2y-4],D[1]=P[2y-3]):(D[0]=P[2y-2],D[1]=P[2y-1]):(D[0]=P[0],D[1]=P[1]),D.set(P,2),d.close?m[0]===m[2y-2]&&m[1]===m[2y-1]?(D[2y+2]=P[2],D[2y+3]=P[3],d.count-=1):(D[2y+2]=P[0],D[2y+3]=P[1],D[2y+4]=P[2],D[2y+5]=P[3]):(D[2y+2]=P[2y-2],D[2y+3]=P[2y-1],D[2y+4]=P[2y-2],D[2y+5]=P[2y-1]),d.positionBuffer(h(D)),d.positionFractBuffer(p(D))}if(t.range?d.range=t.range:d.range\|\|(d.range=d.bounds),(t.range\|\|t.positions)&&d.count){var R=d.bounds,B=R[2]-R[0],F=R[3]-R[1],N=d.range[2]-d.range[0],j=d.range[3]-d.range[1];d.scale=[B/N,F/j],d.translate=[-d.range[0]/N+R[0]/N\|\|0,-d.range[1]/j+R[1]/j\|\|0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var V,U=0;if(!t.dashes\|\|t.dashes.length<2)U=1,V=new Uint8Array([255,255,255,255,255,255,255,255]);else{U=0;for(var q=0;q<t.dashes.length;++q)U+=t.dashes[q];V=new Uint8Array(Uv.dashMult);for(var H=0,G=255,W=0;W<2;W++)for(var Y=0;Y<t.dashes.length;++Y){for(var X=0,Z=t.dashes[Y]v.dashMult.5;X<Z;++X)V[H++]=G;G^=255}}d.dashLength=U,d.dashTexture({channels:1,data:V,width:V.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var $=d.count,J=t.color;J\|\|(J=\"transparent\");var K=new Uint8Array(4$+4);if(Array.isArray(J)&&\"number\"!=typeof J[0]){for(var Q=0;Q<$;Q++){var tt=n(J[Q],\"uint8\");K.set(tt,4Q)}K.set(n(J[0],\"uint8\"),4$)}else for(var et=n(J,\"uint8\"),rt=0;rt<$+1;rt++)K.set(et,4rt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:K})}}else e.passes[f]=null}),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=e.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var m=[],y=0;y<this.passes.length;y++)null!==e.passes[y]&&m.push(e.passes[y]);return this.passes=m,this}},v.prototype.destroy=function(){return this.passes.forEach(function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()}),this.passes.length=0,this}},{\"array-bounds\":53,\"array-normalize\":54,\"color-normalize\":108,earcut:156,\"es6-weak-map\":209,\"flatten-vertex-data\":216,glslify:392,\"object-assign\":437,\"parse-rect\":442,\"pick-by-alias\":448,\"to-float32\":515}],475:[function(t,e,r){\"use strict\";var n=t(\"./scatter\"),i=t(\"object-assign\");e.exports=function(t,e){var r=new n(t,e),a=r.render.bind(r);return i(a,{render:a,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),a}},{\"./scatter\":476,\"object-assign\":437}],476:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"color-id\"),o=t(\"point-cluster\"),s=t(\"object-assign\"),l=t(\"glslify\"),c=t(\"pick-by-alias\"),u=t(\"update-diff\"),f=t(\"flatten-vertex-data\"),h=t(\"is-iexplorer\"),p=t(\"to-float32\"),d=t(\"parse-rect\");function g(t,e){var r=this;if(!(this instanceof g))return new g(t,e);\"function\"==typeof t?(e\|\|(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=h,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),s(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{pixelRatio:t.context(\"pixelRatio\"),palette:n,paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),opacity:t.prop(\"opacity\"),marker:t.prop(\"markerTexture\")},attributes:{x:function(t,e){return e.xAttr\|\|{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr\|\|{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4e.color,4e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4e.borderColor,4e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop(\"elements\"),count:t.prop(\"count\"),offset:t.prop(\"offset\"),primitive:\"points\"},c=s({},o);c.frag=l([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nuniform sampler2D marker;\\nuniform float pixelRatio, opacity;\\n\\nfloat smoothStep(float x, float y) {\\n return 1.0 / (1.0 + exp(50.0(x - y)));\\n}\\n\\nvoid main() {\\n float dist = texture2D(marker, gl_PointCoord).r, delta = fragWidth;\\n\\n // max-distance alpha\\n if (dist < 0.003) discard;\\n\\n // null-border case\\n if (fragBorderColorLevel == fragColorLevel \|\| fragBorderColor.a == 0.) {\\n float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\\n gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\\n }\\n else {\\n float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\\n float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\\n\\n vec4 color = fragBorderColor;\\n color.a = borderColorAmt;\\n color = mix(color, fragColor, colorAmt);\\n color.a = opacity;\\n\\n gl_FragColor = color;\\n }\\n\\n}\\n\"]),c.vert=l([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform float pixelRatio;\\nuniform sampler2D palette;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius,\\n fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nvec2 paletteCoord(float id) {\\n return vec2(\\n (mod(id, paletteSize.x) + .5) / paletteSize.x,\\n (floor(id / paletteSize.x) + .5) / paletteSize.y\\n );\\n}\\nvec2 paletteCoord(vec2 id) {\\n return vec2(\\n (id.x + .5) / paletteSize.x,\\n (id.y + .5) / paletteSize.y\\n );\\n}\\nvec4 getColor(vec4 id) {\\n // zero-palette means we deal with direct buffer\\n if (paletteSize.x == 0.) return id / 255.;\\n return texture2D(palette, paletteCoord(id.xy));\\n}\\n\\nvoid main() {\\n if (isActive == 0.) return;\\n\\n vec2 position = vec2(x, y);\\n vec2 positionFract = vec2(xFract, yFract);\\n\\n vec4 color = getColor(colorId);\\n vec4 borderColor = getColor(borderColorId);\\n\\n float size = size * maxSize / 255.;\\n float borderSize = borderSize * maxSize / 255.;\\n\\n gl_PointSize = 2. * size * pixelRatio;\\n fragPointSize = size * pixelRatio;\\n\\n vec2 pos = (position + translate) * scale\\n + (positionFract + translateFract) * scale\\n + (position + translate) * scaleFract\\n + (positionFract + translateFract) * scaleFract;\\n\\n gl_Position = vec4(pos * 2. - 1., 0, 1);\\n\\n fragColor = color;\\n fragBorderColor = borderColor;\\n fragWidth = 1. / gl_PointSize;\\n\\n fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\\n\"]),this.drawMarker=t(c);var u=s({},o);u.frag=l([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\n\\nuniform float opacity;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a = alpha opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),u.vert=l([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio;\\nuniform sampler2D palette;\\nuniform vec2 paletteSize;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nvec2 paletteCoord(float id) {\\n return vec2(\\n (mod(id, paletteSize.x) + .5) / paletteSize.x,\\n (floor(id / paletteSize.x) + .5) / paletteSize.y\\n );\\n}\\nvec2 paletteCoord(vec2 id) {\\n return vec2(\\n (id.x + .5) / paletteSize.x,\\n (id.y + .5) / paletteSize.y\\n );\\n}\\n\\nvec4 getColor(vec4 id) {\\n // zero-palette means we deal with direct buffer\\n if (paletteSize.x == 0.) return id / 255.;\\n return texture2D(palette, paletteCoord(id.xy));\\n}\\n\\nvoid main() {\\n // ignore inactive points\\n if (isActive == 0.) return;\\n\\n vec2 position = vec2(x, y);\\n vec2 positionFract = vec2(xFract, yFract);\\n\\n vec4 color = getColor(colorId);\\n vec4 borderColor = getColor(borderColorId);\\n\\n float size = size * maxSize / 255.;\\n float borderSize = borderSize * maxSize / 255.;\\n\\n gl_PointSize = (size + borderSize) * pixelRatio;\\n\\n vec2 pos = (position + translate) * scale\\n + (positionFract + translateFract) * scale\\n + (position + translate) * scaleFract\\n + (positionFract + translateFract) * scaleFract;\\n\\n gl_Position = vec4(pos * 2. - 1., 0, 1);\\n\\n fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n fragColor = color;\\n fragBorderColor = borderColor.a == 0. \|\| borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n fragWidth = 1. / gl_PointSize;\\n}\\n\"]),h&&(u.frag=u.frag.replace(\"smoothstep\",\"smoothStep\"),c.frag=c.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(u)}e.exports=g,g.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},g.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];return e.length&&(t=this).update.apply(t,e),this.draw(),this},g.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];var n=this.groups;if(1===e.length&&Array.isArray(e[0])&&(null===e[0][0]\|\|Array.isArray(e[0][0]))&&(e=e[0]),this.regl._refresh(),e.length)for(var i=0;i<e.length;i++)t.drawItem(i,e[i]);else n.forEach(function(e,r){t.drawItem(r)});return this},g.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var i=[],a=1;a<n.activation.length;a++)n.activation[a]&&(!0===n.activation[a]\|\|n.activation[a].data.length)&&i.push.apply(i,this.getMarkerDrawOptions(a,n,e));i.length&&this.drawMarker(i)}},g.prototype.getMarkerDrawOptions=function(t,e,r){var n=e.range,i=e.tree,a=e.viewport,o=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!i)return r?[s({},e,{markerTexture:this.markerTextures[t],activation:o[t],count:r.length,elements:r,offset:0})]:[s({},e,{markerTexture:this.markerTextures[t],activation:o[t],offset:0})];var u=[],f=i.range(n,{lod:!0,px:[(n[2]-n[0])/a.width,(n[3]-n[1])/a.height]});if(r){for(var h=o[t].data,p=new Uint8Array(c),d=0;d<r.length;d++){var g=r[d];p[g]=h?h[g]:1}l.subdata(p)}for(var v=f.length;v--;){var m=f[v],y=m[0],x=m[1];u.push(s({},e,{markerTexture:this.markerTextures[t],activation:r?l:o[t],offset:y,count:x-y}))}return u},g.prototype.update=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){1===e.length&&Array.isArray(e[0])&&(e=e[0]);var n=this.groups,a=this.gl,l=this.regl,h=this.maxSize,v=this.maxColors,m=this.palette;this.groups=n=e.map(function(e,r){var y=n[r];if(void 0===e)return y;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=c(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\"})).positions&&(e.positions=[]),y\|\|(n[r]=y={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:l.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:l.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:l.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:l.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:l.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=s({},g.defaults,e)),!e.positions\|\|\"marker\"in e\|\|(e.marker=y.marker,delete y.marker),!e.marker\|\|\"positions\"in e\|\|(e.positions=y.positions,delete y.positions);var x=0,b=0;if(u(y,e,[{snap:!0,size:function(t,e){return null==t&&(t=g.defaults.size),x+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=g.defaults.borderSize),x+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=g.defaults.color),e=t.updateColor(e),b++,e},borderColor:function(e,r){return null==e&&(e=g.defaults.borderColor),e=t.updateColor(e),b++,e},bounds:function(t,e,r){return\"range\"in r\|\|(r.range=null),t},positions:function(t,e,r){var n=e.snap,a=e.positionBuffer,s=e.positionFractBuffer,c=e.selectionBuffer;if(t.x\|\|t.y)return t.x.length?e.xAttr={buffer:l.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4t.x.offset\|\|0,stride:4(t.x.stride\|\|1),count:t.x.count},t.y.length?e.yAttr={buffer:l.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4t.y.offset\|\|0,stride:4(t.y.stride\|\|1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=f(t,\"float64\");var u=e.count=Math.floor(t.length/2),h=e.bounds=u?i(t,2):null;if(r.range\|\|e.range\|\|(delete e.range,r.range=h),r.marker\|\|e.marker\|\|(delete e.marker,r.marker=null),n&&(!0===n\|\|u>n)?e.tree=o(t,{bounds:h}):n&&n.length&&(e.tree=n),e.tree){var d={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(d):e.elements=l.elements(d)}return a({data:p.float(t),usage:\"dynamic\"}),s({data:p.fract(t),usage:\"dynamic\"}),c({data:new Uint8Array(u),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach(function(t){return t&&t.destroy&&t.destroy()}),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],o=0,s=Math.min(e.length,r.count);o<s;o++){var c=t.addMarker(e[o]);a[c]\|\|(a[c]=new Uint8Array(r.count)),a[c][o]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=l.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t\|\|(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=p.fract(e.scale),e.translateFract=p.fract(e.translate),t},viewport:function(t){return d(t\|\|[a.drawingBufferWidth,a.drawingBufferHeight])}}]),x){var _=y.count,w=y.size,k=y.borderSize,M=y.sizeBuffer,A=new Uint8Array(2_);if(w.length\|\|k.length)for(var T=0;T<_;T++)A[2T]=Math.round(255(null==w[T]?w:w[T])/h),A[2T+1]=Math.round(255(null==k[T]?k:k[T])/h);M({data:A,usage:\"dynamic\"})}if(b){var S,E=y.count,C=y.color,L=y.borderColor,z=y.colorBuffer;if(t.tooManyColors){if(C.length\|\|L.length){S=new Uint8Array(8E);for(var O=0;O<E;O++){var I=C[O];S[8O]=m[4I],S[8O+1]=m[4I+1],S[8O+2]=m[4I+2],S[8O+3]=m[4I+3];var P=L[O];S[8O+4]=m[4P],S[8O+5]=m[4P+1],S[8O+6]=m[4P+2],S[8O+7]=m[4P+3]}}}else if(C.length\|\|L.length){S=new Uint8Array(4E+2);for(var D=0;D<E;D++)null!=C[D]&&(S[4D]=C[D]%v,S[4D+1]=Math.floor(C[D]/v)),null!=L[D]&&(S[4D+2]=L[D]%v,S[4D+3]=Math.floor(L[D]/v))}z({data:S\|\|new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return y})}},g.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array\|\|t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},g.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,i=this.maxColors;Array.isArray(t)\|\|(t=[t]);var o=[];if(\"number\"==typeof t[0]){var s=[];if(Array.isArray(t))for(var l=0;l<t.length;l+=4)s.push(t.slice(l,l+4));else for(var c=0;c<t.length;c+=4)s.push(t.subarray(c,c+4));t=s}for(var u=0;u<t.length;u++){var f=t[u];f=n(f,\"uint8\");var h=a(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}o[u]=e[h]}return!this.tooManyColors&&r.length>ii4&&(this.tooManyColors=!0),this.updatePalette(r),1===o.length?o[0]:o},g.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25t.length/e);if(n>1)for(var i=.25(t=t.slice()).length%e;i<ne;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25t.length,e),height:n,data:t},0,0)}},g.prototype.destroy=function(){return this.groups.forEach(function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach(function(t){return t&&t.destroy&&t.destroy()}),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()}),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach(function(t){return t&&t.destroy&&t.destroy()}),this}},{\"array-bounds\":53,\"color-id\":106,\"color-normalize\":108,\"flatten-vertex-data\":216,glslify:392,\"is-iexplorer\":402,\"object-assign\":437,\"parse-rect\":442,\"pick-by-alias\":448,\"point-cluster\":452,\"to-float32\":515,\"update-diff\":526}],477:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d/scatter\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16\|(255&e)<<8\|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o(function(){e.draw(),e.dirty=!0,e.planned=null})):(this.draw(),this.dirty=!0,o(function(){e.dirty=!1})),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag 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u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var g=s.columns,v=s.count,m=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=m/g,w=y/g;s.passes=[];for(var k=0;k<g;k++)for(var M=0;M<g;M++)if((s.diagonal\|\|M!==k)&&(s.upper\|\|!(k>M))&&(s.lower\|\|!(k<M))){var A=f(s.id,k,M),T=this.passes[A]\|\|(this.passes[A]={});if(o.data&&(o.transpose?T.positions={x:{buffer:s.buffer,offset:M,count:v,stride:g},y:{buffer:s.buffer,offset:k,count:v,stride:g}}:T.positions={x:{buffer:s.buffer,offset:Mv,count:v},y:{buffer:s.buffer,offset:kv,count:v}},T.bounds=h(s.bounds,k,M)),o.domain\|\|o.viewport\|\|o.data){var S=d?h(s.padding,k,M):s.padding;if(s.domain){var 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Coptic\",jdEpoch:1825029.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Coptic\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Thout\",\"Paopi\",\"Hathor\",\"Koiak\",\"Tobi\",\"Meshir\",\"Paremhat\",\"Paremoude\",\"Pashons\",\"Paoni\",\"Epip\",\"Mesori\",\"Pi Kogi Enavot\"],monthNamesShort:[\"Tho\",\"Pao\",\"Hath\",\"Koi\",\"Tob\",\"Mesh\",\"Pat\",\"Pad\",\"Pash\",\"Pao\",\"Epi\",\"Meso\",\"PiK\"],dayNames:[\"Tkyriaka\",\"Pesnau\",\"Pshoment\",\"Peftoou\",\"Ptiou\",\"Psoou\",\"Psabbaton\"],dayNamesShort:[\"Tky\",\"Pes\",\"Psh\",\"Pef\",\"Pti\",\"Pso\",\"Psa\"],dayNamesMin:[\"Tk\",\"Pes\",\"Psh\",\"Pef\",\"Pt\",\"Pso\",\"Psa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3\|\|t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear\|\|n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30(i.month()-1)+365(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.coptic=a},{\"../main\":548,\"object-assign\":437}],536:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Discworld\",jdEpoch:1721425.5,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]\|\|\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32(e-2):0)+400(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":548,\"object-assign\":437}],537:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Ethiopian\",jdEpoch:1724220.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Ethiopian\",epochs:[\"BEE\",\"EE\"],monthNames:[\"Meskerem\",\"Tikemet\",\"Hidar\",\"Tahesas\",\"Tir\",\"Yekatit\",\"Megabit\",\"Miazia\",\"Genbot\",\"Sene\",\"Hamle\",\"Nehase\",\"Pagume\"],monthNamesShort:[\"Mes\",\"Tik\",\"Hid\",\"Tah\",\"Tir\",\"Yek\",\"Meg\",\"Mia\",\"Gen\",\"Sen\",\"Ham\",\"Neh\",\"Pag\"],dayNames:[\"Ehud\",\"Segno\",\"Maksegno\",\"Irob\",\"Hamus\",\"Arb\",\"Kidame\"],dayNamesShort:[\"Ehu\",\"Seg\",\"Mak\",\"Iro\",\"Ham\",\"Arb\",\"Kid\"],dayNamesMin:[\"Eh\",\"Se\",\"Ma\",\"Ir\",\"Ha\",\"Ar\",\"Ki\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3\|\|t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear\|\|n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30(i.month()-1)+365(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{\"../main\":548,\"object-assign\":437}],538:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}function o(t,e){return t-eMath.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)?30:8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235t-234)/19),r=12084+13753e,n=29e+Math.floor(r/25920);return o(3(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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e=Math.floor((30(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":548,\"object-assign\":437}],540:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new 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c=t-(s>9\|\|9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)\"undefined\"==typeof this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2000:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29,29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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}function o(t,e){return t-eMath.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Persian\",jdEpoch:1948320.5,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Persian\",epochs:[\"BP\",\"AP\"],monthNames:[\"Farvardin\",\"Ordibehesht\",\"Khordad\",\"Tir\",\"Mordad\",\"Shahrivar\",\"Mehr\",\"Aban\",\"Azar\",\"Day\",\"Bahman\",\"Esfand\"],monthNamesShort:[\"Far\",\"Ord\",\"Kho\",\"Tir\",\"Mor\",\"Sha\",\"Meh\",\"Aba\",\"Aza\",\"Day\",\"Bah\",\"Esf\"],dayNames:[\"Yekshambe\",\"Doshambe\",\"Seshambe\",\"Ch\\xe6harshambe\",\"Panjshambe\",\"Jom'e\",\"Shambe\"],dayNamesShort:[\"Yek\",\"Do\",\"Se\",\"Ch\\xe6\",\"Panj\",\"Jom\",\"Sha\"],dayNamesMin:[\"Ye\",\"Do\",\"Se\",\"Ch\",\"Pa\",\"Jo\",\"Sh\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31(e-1):30(e-1)+6)+Math.floor((682s-110)/2816)+365(s-1)+1029983Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134a+2816s+2815)/1028522)+a+1}var l=i+2820r+474;l=l<=0?l-1:l;var c=t-this.toJD(l,1,1)+1,u=c<=186?Math.ceil(c/31):Math.ceil((c-6)/30),f=t-this.toJD(l,u,1)+1;return this.newDate(l,u,f)}}),n.calendars.persian=a,n.calendars.jalali=a},{\"../main\":548,\"object-assign\":437}],545:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function o(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,i(o.prototype,{name:\"Taiwan\",jdEpoch:2419402.5,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":548,\"object-assign\":437}],546:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function o(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,i(o.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":548,\"object-assign\":437}],547:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276\|\|a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]\|\|this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t\|\|\"gregorian\").toLowerCase(),e=e\|\|\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar\|\|this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)\|\|this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,function(e){return t[e]})}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n\|\|t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars\|\|c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear\|\|c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear\|\|c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear\|\|c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth\|\|c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth\|\|c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear\|\|c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r\|\|\"w\"===r){var n=t.toJD()+e(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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u=S.x.head,f=S.y.head,h=S.x.tail+r,d=S.y.tail+n,m=S.x.text+r,y=S.y.text+n,x=o.rotationXYMatrix(E,m,y),w=o.apply2DTransform(x),T=o.apply2DTransform2(x),z=+B.attr(\"width\"),O=+B.attr(\"height\"),P=m-.5z,D=P+z,R=y-.5O,F=R+O,N=[[P,R,P,F],[P,F,D,F],[D,F,D,R],[D,R,P,R]].map(T);if(!N.reduce(function(t,e){return t^!!o.segmentsIntersect(u,f,u+1e6,f+1e6,e[0],e[1],e[2],e[3])},!1)){N.forEach(function(t){var e=o.segmentsIntersect(h,d,u,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,d=e.y)});var j=e.arrowwidth,V=e.arrowcolor,U=e.arrowside,q=C.append(\"g\").style({opacity:l.opacity(V)}).classed(\"annotation-arrow-g\",!0),H=q.append(\"path\").attr(\"d\",\"M\"+h+\",\"+d+\"L\"+u+\",\"+f).style(\"stroke-width\",j+\"px\").call(l.stroke,l.rgb(V));if(g(H,U,e),_.annotationPosition&&H.node().parentNode&&!a){var G=u,W=f;if(e.standoff){var Y=Math.sqrt(Math.pow(u-h,2)+Math.pow(f-d,2));G+=e.standoff(h-u)/Y,W+=e.standoff(d-f)/Y}var X,Z,$=q.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-G)+\",\"+(d-W),transform:\"translate(\"+G+\",\"+W+\")\"}).style(\"stroke-width\",j+6+\"px\").call(l.stroke,\"rgba(0,0,0,0)\").call(l.fill,\"rgba(0,0,0,0)\");p.init({element:$.node(),gd:t,prepFn:function(){var t=c.getTranslate(I);X=t.x,Z=t.y,s&&s.autorange&&k(s._name+\".autorange\",!0),v&&v.autorange&&k(v._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(X,Z),i=n[0]+t,a=n[1]+r;I.call(c.setTranslate,i,a),M(\"x\",s?s.p2r(s.r2p(e.x)+t):e.x+t/b.w),M(\"y\",v?v.p2r(v.r2p(e.y)+r):e.y-r/b.h),e.axref===e.xref&&M(\"ax\",s.p2r(s.r2p(e.ax)+t)),e.ayref===e.yref&&M(\"ay\",v.p2r(v.r2p(e.ay)+r)),q.attr(\"transform\",\"translate(\"+t+\",\"+r+\")\"),L.attr({transform:\"rotate(\"+E+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"relayout\",t,A());var 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e._module.eventData?a=e._module.eventData(a,t,e,n,i):(\"xVal\"in t?a.x=t.xVal:\"x\"in t&&(a.x=t.x),\"yVal\"in t?a.y=t.yVal:\"y\"in t&&(a.y=t.y),t.xa&&(a.xaxis=t.xa),t.ya&&(a.yaxis=t.ya),void 0!==t.zLabelVal&&(a.z=t.zLabelVal)),r.appendArrayPointValue(a,e,i),a},r.appendArrayPointValue=function(t,e,r){var i=e._arrayAttrs;if(i)for(var s=0;s<i.length;s++){var l=i[s],c=a(l);if(void 0===t[c]){var u=o(n.nestedProperty(e,l).get(),r);void 0!==u&&(t[c]=u)}}},r.appendArrayMultiPointValues=function(t,e,r){var i=e._arrayAttrs;if(i)for(var s=0;s<i.length;s++){var l=i[s],c=a(l);if(void 0===t[c]){for(var u=n.nestedProperty(e,l).get(),f=new Array(r.length),h=0;h<r.length;h++)f[h]=o(u,r[h]);t[c]=f}}};var i={ids:\"id\",locations:\"location\",labels:\"label\",values:\"value\",\"marker.colors\":\"color\"};function a(t){return i[t]\|\|t}function o(t,e){return Array.isArray(e)?Array.isArray(t)&&Array.isArray(t[e[0]])?t[e[0]][e[1]]:void 0:t[e]}},{\"../../lib\":696}],610:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"fast-isnumeric\"),a=t(\"tinycolor2\"),o=t(\"../../lib\"),s=t(\"../../lib/events\"),l=t(\"../../lib/svg_text_utils\"),c=t(\"../../lib/override_cursor\"),u=t(\"../drawing\"),f=t(\"../color\"),h=t(\"../dragelement\"),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../registry\"),g=t(\"./helpers\"),v=t(\"./constants\"),m=v.YANGLE,y=Math.PIm/180,x=1/Math.sin(y),b=Math.cos(y),_=Math.sin(y),w=v.HOVERARROWSIZE,k=v.HOVERTEXTPAD;function M(t,e,r){var i=e.hovermode,a=e.rotateLabels,s=e.bgColor,c=e.container,h=e.outerContainer,p=e.commonLabelOpts\|\|{},d=e.fontFamily\|\|v.HOVERFONT,g=e.fontSize\|\|v.HOVERFONTSIZE,y=t[0],x=y.xa,b=y.ya,_=\"y\"===i?\"yLabel\":\"xLabel\",M=y[_],A=(String(M)\|\|\"\").split(\" \")[0],T=h.node().getBoundingClientRect(),S=T.top,E=T.width,C=T.height,L=void 0!==M&&y.distance<=e.hoverdistance&&(\"x\"===i\|\|\"y\"===i);if(L){var z,O,I=!0;for(z=0;z<t.length;z++){I&&void 0===t[z].zLabel&&(I=!1),O=t[z].hoverinfo\|\|t[z].trace.hoverinfo;var P=Array.isArray(O)?O:O.split(\"+\");if(-1===P.indexOf(\"all\")&&-1===P.indexOf(i)){L=!1;break}}I&&(L=!1)}var D=c.selectAll(\"g.axistext\").data(L?[0]:[]);D.enter().append(\"g\").classed(\"axistext\",!0),D.exit().remove(),D.each(function(){var e=n.select(this),a=o.ensureSingle(e,\"path\",\"\",function(t){t.style({\"stroke-width\":\"1px\"})}),s=o.ensureSingle(e,\"text\",\"\",function(t){t.attr(\"data-notex\",1)}),c=p.bgcolor\|\|f.defaultLine,h=p.bordercolor\|\|f.contrast(c),v=f.contrast(c);a.style({fill:c,stroke:h}),s.text(M).call(u.font,p.font.family\|\|d,p.font.size\|\|g,p.font.color\|\|v).call(l.positionText,0,0).call(l.convertToTspans,r),e.attr(\"transform\",\"\");var m=s.node().getBoundingClientRect();if(\"x\"===i){s.attr(\"text-anchor\",\"middle\").call(l.positionText,0,\"top\"===x.side?S-m.bottom-w-k:S-m.top+w+k);var T=\"top\"===x.side?\"-\":\"\";a.attr(\"d\",\"M0,0L\"+w+\",\"+T+w+\"H\"+(k+m.width/2)+\"v\"+T+(2k+m.height)+\"H-\"+(k+m.width/2)+\"V\"+T+w+\"H-\"+w+\"Z\"),e.attr(\"transform\",\"translate(\"+(x._offset+(y.x0+y.x1)/2)+\",\"+(b._offset+(\"top\"===x.side?0:b._length))+\")\")}else{s.attr(\"text-anchor\",\"right\"===b.side?\"start\":\"end\").call(l.positionText,(\"right\"===b.side?1:-1)(k+w),S-m.top-m.height/2);var E=\"right\"===b.side?\"\":\"-\";a.attr(\"d\",\"M0,0L\"+E+w+\",\"+w+\"V\"+(k+m.height/2)+\"h\"+E+(2k+m.width)+\"V-\"+(k+m.height/2)+\"H\"+E+w+\"V-\"+w+\"Z\"),e.attr(\"transform\",\"translate(\"+(x._offset+(\"right\"===b.side?x._length:0))+\",\"+(b._offset+(y.y0+y.y1)/2)+\")\")}t=t.filter(function(t){return void 0!==t.zLabelVal\|\|(t[_]\|\|\"\").split(\" \")[0]===A})});var R=c.selectAll(\"g.hovertext\").data(t,function(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa,t.ya\|\|\"\"].join(\",\")});return R.enter().append(\"g\").classed(\"hovertext\",!0).each(function(){var t=n.select(this);t.append(\"rect\").call(f.fill,f.addOpacity(s,.8)),t.append(\"text\").classed(\"name\",!0),t.append(\"path\").style(\"stroke-width\",\"1px\"),t.append(\"text\").classed(\"nums\",!0).call(u.font,d,g)}),R.exit().remove(),R.each(function(t){var e=n.select(this).attr(\"transform\",\"\"),o=\"\",c=\"\",h=t.bgcolor\|\|t.color,p=f.combine(f.opacity(h)?h:f.defaultLine,s),v=f.combine(f.opacity(t.color)?t.color:f.defaultLine,s),y=t.borderColor\|\|f.contrast(p);if(void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name){o=l.plainText(t.name\|\|\"\");var x=Math.round(t.nameLength);x>-1&&o.length>x&&(o=x>3?o.substr(0,x-3)+\"...\":o.substr(0,x))}void 0!==t.zLabel?(void 0!==t.xLabel&&(c+=\"x: \"+t.xLabel+\"<br>\"),void 0!==t.yLabel&&(c+=\"y: \"+t.yLabel+\"<br>\"),c+=(c?\"z: \":\"\")+t.zLabel):L&&t[i+\"Label\"]===M?c=t[(\"x\"===i?\"y\":\"x\")+\"Label\"]\|\|\"\":void 0===t.xLabel?void 0!==t.yLabel&&(c=t.yLabel):c=void 0===t.yLabel?t.xLabel:\"(\"+t.xLabel+\", 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T,z,O=b.node().getBoundingClientRect(),I=t.xa._offset+(t.x0+t.x1)/2,P=t.ya._offset+(t.y0+t.y1)/2,D=Math.abs(t.x1-t.x0),R=Math.abs(t.y1-t.y0),B=O.width+w+k+A;t.ty0=S-O.top,t.bx=O.width+2k,t.by=O.height+2k,t.anchor=\"start\",t.txwidth=O.width,t.tx2width=A,t.offset=0,a?(t.pos=I,T=P+R/2+B<=C,z=P-R/2-B>=0,\"top\"!==t.idealAlign&&T\|\|!z?T?(P+=R/2,t.anchor=\"start\"):t.anchor=\"middle\":(P-=R/2,t.anchor=\"end\")):(t.pos=P,T=I+D/2+B<=E,z=I-D/2-B>=0,\"left\"!==t.idealAlign&&T\|\|!z?T?(I+=D/2,t.anchor=\"start\"):t.anchor=\"middle\":(I-=D/2,t.anchor=\"end\")),b.attr(\"text-anchor\",t.anchor),A&&_.attr(\"text-anchor\",t.anchor),e.attr(\"transform\",\"translate(\"+I+\",\"+P+\")\"+(a?\"rotate(\"+m+\")\":\"\"))}),R}function A(t,e){t.each(function(t){var r=n.select(this);if(t.del)r.remove();else{var i=\"end\"===t.anchor?-1:1,a=r.select(\"text.nums\"),o={start:1,end:-1,middle:0}[t.anchor],s=o(w+k),c=s+o(t.txwidth+k),f=0,h=t.offset;\"middle\"===t.anchor&&(s-=t.tx2width/2,c+=t.txwidth/2+k),e&&(h=-_,f=t.offsetb),r.select(\"path\").attr(\"d\",\"middle\"===t.anchor?\"M-\"+(t.bx/2+t.tx2width/2)+\",\"+(h-t.by/2)+\"h\"+t.bx+\"v\"+t.by+\"h-\"+t.bx+\"Z\":\"M0,0L\"+(iw+f)+\",\"+(w+h)+\"v\"+(t.by/2-w)+\"h\"+it.bx+\"v-\"+t.by+\"H\"+(iw+f)+\"V\"+(h-w)+\"Z\"),a.call(l.positionText,s+f,h+t.ty0-t.by/2+k),t.tx2width&&(r.select(\"text.name\").call(l.positionText,c+ok+f,h+t.ty0-t.by/2+k),r.select(\"rect\").call(u.setRect,c+(o-1)t.tx2width/2+f,h-t.by/2-1,t.tx2width,t.by+2))}})}function T(t,e){var r=t.index,n=t.trace\|\|{},i=t.cd[0],a=t.cd[r]\|\|{},s=Array.isArray(r)?function(t,e){return o.castOption(i,r,t)\|\|o.extractOption({},n,\"\",e)}:function(t,e){return o.extractOption(a,n,t,e)};function l(e,r,n){var i=s(r,n);i&&(t[e]=i)}if(l(\"hoverinfo\",\"hi\",\"hoverinfo\"),l(\"bgcolor\",\"hbg\",\"hoverlabel.bgcolor\"),l(\"borderColor\",\"hbc\",\"hoverlabel.bordercolor\"),l(\"fontFamily\",\"htf\",\"hoverlabel.font.family\"),l(\"fontSize\",\"hts\",\"hoverlabel.font.size\"),l(\"fontColor\",\"htc\",\"hoverlabel.font.color\"),l(\"nameLength\",\"hnl\",\"hoverlabel.namelength\"),t.posref=\"y\"===e?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel=\"xLabel\"in t?t.xLabel:p.hoverLabelText(t.xa,t.xLabelVal),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel=\"yLabel\"in t?t.yLabel:p.hoverLabelText(t.ya,t.yLabelVal),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)\|\|\"log\"===t.xa.type&&t.xerr<=0)){var c=p.tickText(t.xa,t.xa.c2l(t.xerr),\"hover\").text;void 0!==t.xerrneg?t.xLabel+=\" +\"+c+\" / -\"+p.tickText(t.xa,t.xa.c2l(t.xerrneg),\"hover\").text:t.xLabel+=\" \\xb1 \"+c,\"x\"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)\|\|\"log\"===t.ya.type&&t.yerr<=0)){var u=p.tickText(t.ya,t.ya.c2l(t.yerr),\"hover\").text;void 0!==t.yerrneg?t.yLabel+=\" +\"+u+\" / -\"+p.tickText(t.ya,t.ya.c2l(t.yerrneg),\"hover\").text:t.yLabel+=\" \\xb1 \"+u,\"y\"===e&&(t.distance+=1)}var f=t.hoverinfo\|\|t.trace.hoverinfo;return\"all\"!==f&&(-1===(f=Array.isArray(f)?f:f.split(\"+\")).indexOf(\"x\")&&(t.xLabel=void 0),-1===f.indexOf(\"y\")&&(t.yLabel=void 0),-1===f.indexOf(\"z\")&&(t.zLabel=void 0),-1===f.indexOf(\"text\")&&(t.text=void 0),-1===f.indexOf(\"name\")&&(t.name=void 0)),t}function S(t,e){var r,n,i=e.container,o=e.fullLayout,s=e.event,l=!!t.hLinePoint,c=!!t.vLinePoint;if(i.selectAll(\".spikeline\").remove(),c\|\|l){var h=f.combine(o.plot_bgcolor,o.paper_bgcolor);if(l){var p,d,g=t.hLinePoint;r=g&&g.xa,\"cursor\"===(n=g&&g.ya).spikesnap?(p=s.pointerX,d=s.pointerY):(p=r._offset+g.x,d=n._offset+g.y);var v,m,y=a.readability(g.color,h)<1.5?f.contrast(h):g.color,x=n.spikemode,b=n.spikethickness,_=n.spikecolor\|\|y,w=n._boundingBox,k=(w.left+w.right)/2<p?w.right:w.left;-1===x.indexOf(\"toaxis\")&&-1===x.indexOf(\"across\")\|\|(-1!==x.indexOf(\"toaxis\")&&(v=k,m=p),-1!==x.indexOf(\"across\")&&(v=n._counterSpan[0],m=n._counterSpan[1]),i.insert(\"line\",\":first-child\").attr({x1:v,x2:m,y1:d,y2:d,\"stroke-width\":b,stroke:_,\"stroke-dasharray\":u.dashStyle(n.spikedash,b)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),i.insert(\"line\",\":first-child\").attr({x1:v,x2:m,y1:d,y2:d,\"stroke-width\":b+2,stroke:h}).classed(\"spikeline\",!0).classed(\"crisp\",!0)),-1!==x.indexOf(\"marker\")&&i.insert(\"circle\",\":first-child\").attr({cx:k+(\"right\"!==n.side?b:-b),cy:d,r:b,fill:_}).classed(\"spikeline\",!0)}if(c){var M,A,T=t.vLinePoint;r=T&&T.xa,n=T&&T.ya,\"cursor\"===r.spikesnap?(M=s.pointerX,A=s.pointerY):(M=r._offset+T.x,A=n._offset+T.y);var S,E,C=a.readability(T.color,h)<1.5?f.contrast(h):T.color,L=r.spikemode,z=r.spikethickness,O=r.spikecolor\|\|C,I=r._boundingBox,P=(I.top+I.bottom)/2<A?I.bottom:I.top;-1===L.indexOf(\"toaxis\")&&-1===L.indexOf(\"across\")\|\|(-1!==L.indexOf(\"toaxis\")&&(S=P,E=A),-1!==L.indexOf(\"across\")&&(S=r._counterSpan[0],E=r._counterSpan[1]),i.insert(\"line\",\":first-child\").attr({x1:M,x2:M,y1:S,y2:E,\"stroke-width\":z,stroke:O,\"stroke-dasharray\":u.dashStyle(r.spikedash,z)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),i.insert(\"line\",\":first-child\").attr({x1:M,x2:M,y1:S,y2:E,\"stroke-width\":z+2,stroke:h}).classed(\"spikeline\",!0).classed(\"crisp\",!0)),-1!==L.indexOf(\"marker\")&&i.insert(\"circle\",\":first-child\").attr({cx:M,cy:P-(\"top\"!==r.side?z:-z),r:z,fill:O}).classed(\"spikeline\",!0)}}}function E(t,e){return!e\|\|(e.vLinePoint!==t._spikepoints.vLinePoint\|\|e.hLinePoint!==t._spikepoints.hLinePoint)}r.hover=function(t,e,r,a){t=o.getGraphDiv(t),o.throttle(t._fullLayout._uid+v.HOVERID,v.HOVERMINTIME,function(){!function(t,e,r,a){r\|\|(r=\"xy\");var l=Array.isArray(r)?r:[r],u=t._fullLayout,v=u._plots\|\|[],m=v[r],y=u._has(\"cartesian\");if(m){var b=m.overlays.map(function(t){return t.id});l=l.concat(b)}for(var _=l.length,w=new Array(_),k=new Array(_),C=!1,L=0;L<_;L++){var z=l[L],O=v[z];if(O)C=!0,w[L]=p.getFromId(t,O.xaxis._id),k[L]=p.getFromId(t,O.yaxis._id);else{var I=u[z]._subplot;w[L]=I.xaxis,k[L]=I.yaxis}}var P=e.hovermode\|\|u.hovermode;P&&!C&&(P=\"closest\");if(-1===[\"x\",\"y\",\"closest\"].indexOf(P)\|\|!t.calcdata\|\|t.querySelector(\".zoombox\")\|\|t._dragging)return h.unhoverRaw(t,e);var D,R,B,F,N,j,V,U,q,H,G,W,Y,X=-1===u.hoverdistance?1/0:u.hoverdistance,Z=-1===u.spikedistance?1/0:u.spikedistance,$=[],J=[],K={hLinePoint:null,vLinePoint:null},Q=!1;if(Array.isArray(e))for(P=\"array\",B=0;B<e.length;B++)N=t.calcdata[e[B].curveNumber\|\|0],j=N[0].trace,\"skip\"!==N[0].trace.hoverinfo&&(J.push(N),\"h\"===j.orientation&&(Q=!0));else{for(F=0;F<t.calcdata.length;F++)N=t.calcdata[F],\"skip\"!==(j=N[0].trace).hoverinfo&&g.isTraceInSubplots(j,l)&&(J.push(N),\"h\"===j.orientation&&(Q=!0));var tt,et,rt=!e.target;if(rt)tt=\"xpx\"in e?e.xpx:w[0]._length/2,et=\"ypx\"in e?e.ypx:k[0]._length/2;else{if(!1===s.triggerHandler(t,\"plotly_beforehover\",e))return;var nt=e.target.getBoundingClientRect();if(tt=e.clientX-nt.left,et=e.clientY-nt.top,tt<0\|\|tt>w[0]._length\|\|et<0\|\|et>k[0]._length)return h.unhoverRaw(t,e)}if(e.pointerX=tt+w[0]._offset,e.pointerY=et+k[0]._offset,D=\"xval\"in e?g.flat(l,e.xval):g.p2c(w,tt),R=\"yval\"in e?g.flat(l,e.yval):g.p2c(k,et),!i(D[0])\|\|!i(R[0]))return o.warn(\"Fx.hover failed\",e,t),h.unhoverRaw(t,e)}var it=1/0;for(F=0;F<J.length;F++)if((N=J[F])&&N[0]&&N[0].trace&&!0===N[0].trace.visible&&(j=N[0].trace,-1===[\"carpet\",\"contourcarpet\"].indexOf(j._module.name))){if(\"splom\"===j.type?V=l[U=0]:(V=g.getSubplot(j),U=l.indexOf(V)),q=P,W={cd:N,trace:j,xa:w[U],ya:k[U],maxHoverDistance:X,maxSpikeDistance:Z,index:!1,distance:Math.min(it,X),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:f.defaultLine,name:j.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[V]&&(W.subplot=u[V]._subplot),u._splomScenes&&u._splomScenes[j.uid]&&(W.scene=u._splomScenes[j.uid]),Y=$.length,\"array\"===q){var at=e[F];\"pointNumber\"in at?(W.index=at.pointNumber,q=\"closest\"):(q=\"\",\"xval\"in at&&(H=at.xval,q=\"x\"),\"yval\"in at&&(G=at.yval,q=q?\"closest\":\"y\"))}else H=D[U],G=R[U];if(0!==X)if(j._module&&j._module.hoverPoints){var ot=j._module.hoverPoints(W,H,G,q,u._hoverlayer);if(ot)for(var st,lt=0;lt<ot.length;lt++)st=ot[lt],i(st.x0)&&i(st.y0)&&$.push(T(st,P))}else o.log(\"Unrecognized trace type in hover:\",j);if(\"closest\"===P&&$.length>Y&&($.splice(0,Y),it=$[0].distance),y&&0!==Z&&0===$.length){W.distance=Z,W.index=!1;var ct=j._module.hoverPoints(W,H,G,\"closest\",u._hoverlayer);if(ct&&(ct=ct.filter(function(t){return t.spikeDistance<=Z})),ct&&ct.length){var ut,ft=ct.filter(function(t){return t.xa.showspikes});if(ft.length){var ht=ft[0];i(ht.x0)&&i(ht.y0)&&(ut=vt(ht),(!K.vLinePoint\|\|K.vLinePoint.spikeDistance>ut.spikeDistance)&&(K.vLinePoint=ut))}var pt=ct.filter(function(t){return t.ya.showspikes});if(pt.length){var dt=pt[0];i(dt.x0)&&i(dt.y0)&&(ut=vt(dt),(!K.hLinePoint\|\|K.hLinePoint.spikeDistance>ut.spikeDistance)&&(K.hLinePoint=ut))}}}}function gt(t,e){for(var r,n=null,i=1/0,a=0;a<t.length;a++)(r=t[a].spikeDistance)<i&&r<=e&&(n=t[a],i=r);return n}function vt(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 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Et=\"y\"===P&&(J.length>1\|\|$.length>1)\|\|\"closest\"===P&&Q&&$.length>1,Ct=f.combine(u.plot_bgcolor\|\|f.background,u.paper_bgcolor),Lt={hovermode:P,rotateLabels:Et,bgColor:Ct,container:u._hoverlayer,outerContainer:u._paperdiv,commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance},zt=M($,Lt,t);if(function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.map(function(t,n){var i=t[e],a=\"x\"===i._id.charAt(0),o=i.range;return!n&&o&&o[0]>o[1]!==a&&(f=-1),[{i:n,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by(a?x:1)/2,pmin:0,pmax:a?r.width:r.height}]}).sort(function(t,e){return t[0].posref-e[0].posref\|\|f(e[0].traceIndex-t[0].traceIndex)});function p(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var 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r={color:t.color\|\|f.defaultLine,x0:t.x0\|\|t.x\|\|0,x1:t.x1\|\|t.x\|\|0,y0:t.y0\|\|t.y\|\|0,y1:t.y1\|\|t.y\|\|0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,trace:{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0},i=n.select(e.container),a=e.outerContainer?n.select(e.outerContainer):i,o={hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor\|\|f.background,container:i,outerContainer:a},s=M([r],o,e.gd);return A(s,o.rotateLabels),s.node()},r.multiHovers=function(t,e){Array.isArray(t)\|\|(t=[t]);var r=t.map(function(t){return{color:t.color\|\|f.defaultLine,x0:t.x0\|\|t.x\|\|0,x1:t.x1\|\|t.x\|\|0,y0:t.y0\|\|t.y\|\|0,y1:t.y1\|\|t.y\|\|0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,trace:{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0}}),i=n.select(e.container),a=e.outerContainer?n.select(e.outerContainer):i,o={hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor\|\|f.background,container:i,outerContainer:a},s=M(r,o,e.gd),l=0;return s.sort(function(t,e){return t.y0-e.y0}).each(function(t){var 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n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../dragelement\"),o=t(\"./helpers\"),s=t(\"./layout_attributes\"),l=t(\"./hover\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:s},attributes:t(\"./attributes\"),layoutAttributes:s,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,\"hoverlabel.\"+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,\"hoverinfo\",function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)})},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,multiHovers:l.multiHovers,loneUnhover:function(t){var e=i.isD3Selection(t)?t:n.select(t);e.selectAll(\"g.hovertext\").remove(),e.selectAll(\".spikeline\").remove()},click:t(\"./click\")}},{\"../../lib\":696,\"../dragelement\":592,\"./attributes\":604,\"./calc\":605,\"./click\":606,\"./constants\":607,\"./defaults\":608,\"./helpers\":609,\"./hover\":610,\"./layout_attributes\":613,\"./layout_defaults\":614,\"./layout_global_defaults\":615,d3:148}],613:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"../../plots/font_attributes\")({editType:\"none\"});i.family.dflt=n.HOVERFONT,i.size.dflt=n.HOVERFONTSIZE,e.exports={clickmode:{valType:\"flaglist\",flags:[\"event\",\"select\"],dflt:\"event\",editType:\"plot\",extras:[\"none\"]},dragmode:{valType:\"enumerated\",values:[\"zoom\",\"pan\",\"select\",\"lasso\",\"orbit\",\"turntable\"],dflt:\"zoom\",editType:\"modebar\"},hovermode:{valType:\"enumerated\",values:[\"x\",\"y\",\"closest\",!1],editType:\"modebar\"},hoverdistance:{valType:\"integer\",min:-1,dflt:20,editType:\"none\"},spikedistance:{valType:\"integer\",min:-1,dflt:20,editType:\"none\"},hoverlabel:{bgcolor:{valType:\"color\",editType:\"none\"},bordercolor:{valType:\"color\",editType:\"none\"},font:i,namelength:{valType:\"integer\",min:-1,dflt:15,editType:\"none\"},editType:\"none\"},selectdirection:{valType:\"enumerated\",values:[\"h\",\"v\",\"d\",\"any\"],dflt:\"any\",editType:\"none\"}}},{\"../../plots/font_attributes\":771,\"./constants\":607}],614:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}var o,s=a(\"clickmode\");\"select\"===a(\"dragmode\")&&a(\"selectdirection\"),e._has(\"cartesian\")?s.indexOf(\"select\")>-1?o=\"closest\":(e._isHoriz=function(t){for(var e=!0,r=0;r<t.length;r++){var n=t[r];if(\"h\"!==n.orientation){e=!1;break}}return e}(r),o=e._isHoriz?\"y\":\"x\"):o=\"closest\",a(\"hovermode\",o)&&(a(\"hoverdistance\"),a(\"spikedistance\"));var l=e._has(\"mapbox\"),c=e._has(\"geo\"),u=e._basePlotModules.length;\"zoom\"===e.dragmode&&((l\|\|c)&&1===u\|\|l&&c&&2===u)&&(e.dragmode=\"pan\")}},{\"../../lib\":696,\"./layout_attributes\":613}],615:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./hoverlabel_defaults\"),a=t(\"./layout_attributes\");e.exports=function(t,e){i(t,e,function(r,i){return n.coerce(t,e,a,r,i)})}},{\"../../lib\":696,\"./hoverlabel_defaults\":611,\"./layout_attributes\":613}],616:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../lib/regex\").counter,a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/cartesian/constants\").idRegex,s=t(\"../../plot_api/plot_template\"),l={rows:{valType:\"integer\",min:1,editType:\"plot\"},roworder:{valType:\"enumerated\",values:[\"top to bottom\",\"bottom to top\"],dflt:\"top to 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i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n=r.pixelRatio,i=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case\"zoom\":if(e){var m=n/(f[2]-f[0])(l[2]-l[0])+l[0],y=i/(f[3]-f[1])(l[3]-l[1])+l[1];c.boxInited\|\|(c.boxStart[0]=m,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=m,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled\|\|c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]\|\|(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<g,b=Math.abs(c.dragStart[1]-i)<g;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()\|\|x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)_(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a\|\|s?(a&&(v(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(v(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning\|\|(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener(\"touchstart\",function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()},!!s&&{passive:!1}),e.addEventListener(\"touchmove\",function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()},!!s&&{passive:!1}),e.addEventListener(\"touchend\",function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()},!!s&&{passive:!1}),c.wheelListener=i(e,function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5n/(a[3]-a[1])),f=o/(a[2]-a[0])(i[2]-i[0])+i[0],h=s/(a[3]-a[1])(i[3]-i[1])+i[1];return i[0]=(i[0]-f)l+f,i[2]=(i[2]-f)l+f,i[1]=(i[1]-h)l+h,i[3]=(i[3]-h)l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0},!0),c}},{\"../cartesian/constants\":750,\"has-passive-events\":394,\"mouse-change\":418,\"mouse-event-offset\":419,\"mouse-wheel\":421}],783:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/html2unicode\"),a=t(\"../../lib/str2rgbarray\");function o(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=!1,this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var s=o.prototype,l=[\"xaxis\",\"yaxis\"];s.merge=function(t){var e,r,n,o,s,c,u,f,h,p,d;for(this.titleEnable=!1,this.backgroundColor=a(t.plot_bgcolor),p=0;p<2;++p){var g=(e=l[p]).charAt(0);for(n=(r=t[this.scene[e]._name]).title===this.scene.fullLayout._dfltTitle[g]?\"\":r.title,d=0;d<=2;d+=2)this.labelEnable[p+d]=!1,this.labels[p+d]=i(n),this.labelColor[p+d]=a(r.titlefont.color),this.labelFont[p+d]=r.titlefont.family,this.labelSize[p+d]=r.titlefont.size,this.labelPad[p+d]=this.getLabelPad(e,r),this.tickEnable[p+d]=!1,this.tickColor[p+d]=a((r.tickfont\|\|{}).color),this.tickAngle[p+d]=\"auto\"===r.tickangle?0:Math.PI-r.tickangle/180,this.tickPad[p+d]=this.getTickPad(r),this.tickMarkLength[p+d]=0,this.tickMarkWidth[p+d]=r.tickwidth\|\|0,this.tickMarkColor[p+d]=a(r.tickcolor),this.borderLineEnable[p+d]=!1,this.borderLineColor[p+d]=a(r.linecolor),this.borderLineWidth[p+d]=r.linewidth\|\|0;u=this.hasSharedAxis(r),s=this.hasAxisInDfltPos(e,r)&&!u,c=this.hasAxisInAltrPos(e,r)&&!u,o=r.mirror\|\|!1,f=u?-1!==String(o).indexOf(\"all\"):!!o,h=u?\"allticks\"===o:-1!==String(o).indexOf(\"ticks\"),s?this.labelEnable[p]=!0:c&&(this.labelEnable[p+2]=!0),s?this.tickEnable[p]=r.showticklabels:c&&(this.tickEnable[p+2]=r.showticklabels),(s\|\|f)&&(this.borderLineEnable[p]=r.showline),(c\|\|f)&&(this.borderLineEnable[p+2]=r.showline),(s\|\|h)&&(this.tickMarkLength[p]=this.getTickMarkLength(r)),(c\|\|h)&&(this.tickMarkLength[p+2]=this.getTickMarkLength(r)),this.gridLineEnable[p]=r.showgrid,this.gridLineColor[p]=a(r.gridcolor),this.gridLineWidth[p]=r.gridwidth,this.zeroLineEnable[p]=r.zeroline,this.zeroLineColor[p]=a(r.zerolinecolor),this.zeroLineWidth[p]=r.zerolinewidth}},s.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},s.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},s.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},s.getLabelPad=function(t,e){var r=e.titlefont.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r(1.5+(n?1:0))-10:r(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r(1.5+(n?1:.5)):10+r(1.5+(n?.5:0)):void 0},s.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},s.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new o(t)}},{\"../../lib/html2unicode\":694,\"../../lib/str2rgbarray\":719,\"../cartesian/axes\":744}],784:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")\|\|l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d\|\|[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d)0===u(t,\"gl2d\",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot\|\|l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":613,\"../../constants/xmlns_namespaces\":674,\"../../plot_api/edit_types\":727,\"../cartesian\":756,\"../cartesian/attributes\":742,\"../cartesian/constants\":750,\"../get_data\":781,\"../layout_attributes\":799,\"./scene2d\":785}],785:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"gl-plot2d\"),c=t(\"gl-spikes2d\"),u=t(\"gl-select-box\"),f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/html2unicode\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"../cartesian/constraints\"),m=v.enforce,y=v.clean,x=t(\"../cartesian/autorange\").doAutoRange,b=[\"xaxis\",\"yaxis\"],_=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function w(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio\|\|window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context.scrollZoom,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped\|\|(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=w;var k=w.prototype;k.makeFramework=function(){if(this.staticPlot){if(!(i\|\|(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return g(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r),r.className+=\" user-select-none\";var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",function(){l.isMouseOver=!1,l.unhover()}),o.addEventListener(\"mouseover\",function(){l.isMouseOver=!0})},k.toImage=function(t){t\|\|(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT\|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(ri4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4(ro+l)+c];a[4(ro+l)+c]=a[4(rs+l)+c],a[4(rs+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},k.updateSize=function(t){t\|\|(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0\|Math.ceil(en),o=0\|Math.ceil(ei);return t.width===a&&t.height===o\|\|(t.width=a,t.height=o),t},k.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=d(t[e][r].text+\"\");return t},k.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(_),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},k.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout;n.xaxis.autorange=e.autorange,n.xaxis.range=e.range.slice(0),n.yaxis.autorange=r.autorange,n.yaxis.range=r.range.slice(0);var i={lastInputTime:this.camera.lastInputTime};i[e._name]=e.range.slice(0),i[r._name]=r.range.slice(0),t.emit(\"plotly_relayout\",i)},k.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},k.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},k.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map(function(e){t[e].dispose(),delete t[e]}),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},k.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};y(s,this.xaxis),y(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]u.w,u.b+h[0]u.h,i-u.r-(1-f[1])u.w,a-u.t-(1-h[1])u.h],this.mouseContainer.style.width=u.w(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])u.h+\"px\",c=0;c<2;++c)(l=this[b[c]])._length=o.viewBox[c+2]-o.viewBox[c],x(this.graphDiv,l),l.setScale();m(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},k.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},k.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},k.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort(function(t,e){return t._trace.index-e._trace.index})},k.updateFx=function(t){\"lasso\"===t\|\|\"select\"===t?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},k.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},k.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.xt.pixelRatio,l=this.canvas.height-t.pixelRatior.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]f.w,l/t.pixelRatio-(f.t+(1-p[1])f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult\|\|this.lastPickResult.traceUid!==d.trace.uid\|\|this.lastPickResult.dataCoord[0]!==d.dataCoord[0]\|\|this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var v=this.fullData[g.trace.index]\|\|{},m=g.pointIndex,y=s.castHoverinfo(v,i,m);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(v,m,\"bgcolor\")\|\|g.color,borderColor:s.castHoverOption(v,m,\"bordercolor\"),fontFamily:s.castHoverOption(v,m,\"font.family\"),fontSize:s.castHoverOption(v,m,\"font.size\"),fontColor:s.castHoverOption(v,m,\"font.color\")},{container:this.svgContainer,gd:this.graphDiv})}}a\|\|this.unhover(),t.draw()}},k.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},k.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/fx\":612,\"../../lib/html2unicode\":694,\"../../lib/show_no_webgl_msg\":717,\"../../plots/cartesian/axes\":744,\"../../registry\":827,\"../cartesian/autorange\":743,\"../cartesian/constants\":750,\"../cartesian/constraints\":752,\"./camera\":782,\"./convert\":783,\"gl-plot2d\":275,\"gl-select-box\":286,\"gl-spikes2d\":295,\"webgl-context\":533}],786:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t\|\|document.body,e=e\|\|{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center\|\|[0,0,0],up:e.up\|\|[0,1,0],eye:e.eye\|\|[0,0,10],mode:e.mode\|\|\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",view:c,element:t,delay:e.delay\|\|16,rotateSpeed:e.rotateSpeed\|\|1,zoomSpeed:e.zoomSpeed\|\|1,translateSpeed:e.translateSpeed\|\|1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,tick:function(){var e=n(),r=this.delay,i=e-2r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",function(t){return t.preventDefault(),!1});var g=0,v=0,m={shift:!1,control:!1,alt:!1,meta:!1};function y(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,y=!!a.shift,x=!!(1&e),b=!!(2&e),_=!!(4&e),w=1/t.clientHeight,k=w(r-g),M=w(i-v),A=d.flipX?1:-1,T=d.flipY?1:-1,S=n(),E=Math.PId.rotateSpeed;if((s&&x&&!h&&!p&&!y\|\|x&&!h&&!p&&y)&&c.rotate(S,AEk,-TEM,0),(l&&x&&!h&&!p&&!y\|\|b\|\|x&&h&&!p&&!y)&&c.pan(S,-d.translateSpeedkf,d.translateSpeedMf,0),u&&x&&!h&&!p&&!y\|\|_\|\|x&&!h&&p&&!y){var C=-d.zoomSpeedM/window.innerHeight(S-c.lastT())100;c.pan(S,0,0,f(Math.exp(C)-1))}return g=r,v=i,m=a,!0}}return d.mouseListener=a(t,y),t.addEventListener(\"touchstart\",function(e){var r=s(e.changedTouches[0],t);y(0,r[0],r[1],m),y(1,r[0],r[1],m),e.preventDefault()},!!l&&{passive:!1}),t.addEventListener(\"touchmove\",function(e){var r=s(e.changedTouches[0],t);y(1,r[0],r[1],m),e.preventDefault()},!!l&&{passive:!1}),t.addEventListener(\"touchend\",function(t){y(0,g,v,m),t.preventDefault()},!!l&&{passive:!1}),d.wheelListener=o(t,function(t,e){if(!1!==d.keyBindingMode){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-trMath.PId.rotateSpeed/window.innerWidth);else{var o=-d.zoomSpeedie/window.innerHeight(a-c.lastT())/20;c.pan(a,0,0,f(Math.exp(o)-1))}}},!0),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":45,\"has-passive-events\":394,\"mouse-change\":418,\"mouse-event-offset\":419,\"mouse-wheel\":421,\"right-now\":480}],787:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var l=n[i],c=o(r,\"gl3d\",l),u=e[l],f=u._scene;f\|\|(f=new a({id:l,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),u._scene=f),f.cameraInitial\|\|(f.cameraInitial=s.extendDeep({},u.camera)),f.plot(c,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d\|\|[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.wo.x[0],y:n.t+n.h(1-o.y[1]),width:n.w(o.x[1]-o.x[0]),height:n.h(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":613,\"../../constants/xmlns_namespaces\":674,\"../../lib\":696,\"../../plot_api/edit_types\":727,\"../get_data\":781,\"./layout/attributes\":788,\"./layout/defaults\":792,\"./layout/layout_attributes\":793,\"./scene\":797}],788:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],789:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:i.title,titlefont:i.titlefont,type:i.type,autorange:i.autorange,rangemode:i.rangemode,range:i.range,tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth},\"plot\",\"from-root\")},{\"../../../components/color\":570,\"../../../lib/extend\":685,\"../../../plot_api/edit_types\":727,\"../../cartesian/layout_attributes\":757}],790:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]\|\|{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":696,\"../../../plot_api/plot_template\":734,\"../../cartesian/axis_defaults\":746,\"../../cartesian/type_defaults\":768,\"./axis_attributes\":789,tinycolor2:514}],791:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/html2unicode\"),i=t(\"../../../lib/str2rgbarray\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[a[e]];r.visible?(this.labels[e]=n(r.title),\"titlefont\"in r&&(r.titlefont.color&&(this.labelColor[e]=i(r.titlefont.color)),r.titlefont.family&&(this.labelFont[e]=r.titlefont.family),r.titlefont.size&&(this.labelSize[e]=r.titlefont.size)),\"showline\"in r&&(this.lineEnable[e]=r.showline),\"linecolor\"in r&&(this.lineColor[e]=i(r.linecolor)),\"linewidth\"in r&&(this.lineWidth[e]=r.linewidth),\"showgrid\"in r&&(this.gridEnable[e]=r.showgrid),\"gridcolor\"in r&&(this.gridColor[e]=i(r.gridcolor)),\"gridwidth\"in r&&(this.gridWidth[e]=r.gridwidth),\"log\"===r.type?this.zeroEnable[e]=!1:\"zeroline\"in r&&(this.zeroEnable[e]=r.zeroline),\"zerolinecolor\"in r&&(this.zeroLineColor[e]=i(r.zerolinecolor)),\"zerolinewidth\"in r&&(this.zeroLineWidth[e]=r.zerolinewidth),\"ticks\"in r&&r.ticks?this.lineTickEnable[e]=!0:this.lineTickEnable[e]=!1,\"ticklen\"in r&&(this.lineTickLength[e]=this._defaultLineTickLength[e]=r.ticklen),\"tickcolor\"in r&&(this.lineTickColor[e]=i(r.tickcolor)),\"tickwidth\"in r&&(this.lineTickWidth[e]=r.tickwidth),\"tickangle\"in r&&(this.tickAngle[e]=\"auto\"===r.tickangle?-3600:Math.PI-r.tickangle/180),\"showticklabels\"in r&&(this.tickEnable[e]=r.showticklabels),\"tickfont\"in r&&(r.tickfont.color&&(this.tickColor[e]=i(r.tickfont.color)),r.tickfont.family&&(this.tickFont[e]=r.tickfont.family),r.tickfont.size&&(this.tickSize[e]=r.tickfont.size)),\"mirror\"in r?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(r.mirror)?(this.lineTickMirror[e]=!0,this.lineMirror[e]=!0):!0===r.mirror?(this.lineTickMirror[e]=!1,this.lineMirror[e]=!0):(this.lineTickMirror[e]=!1,this.lineMirror[e]=!1):this.lineMirror[e]=!1,\"showbackground\"in r&&!1!==r.showbackground?(this.backgroundEnable[e]=!0,this.backgroundColor[e]=i(r.backgroundcolor)):this.backgroundEnable[e]=!1):(this.tickEnable[e]=!1,this.labelEnable[e]=!1,this.lineEnable[e]=!1,this.lineTickEnable[e]=!1,this.gridEnable[e]=!1,this.zeroEnable[e]=!1,this.backgroundEnable[e]=!1)}},e.exports=function(t){var e=new o;return e.merge(t),e}},{\"../../../lib/html2unicode\":694,\"../../../lib/str2rgbarray\":719}],792:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\");function c(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),c=[\"up\",\"center\",\"eye\"],u=0;u<c.length;u++)r(\"camera.\"+c[u]+\".x\"),r(\"camera.\"+c[u]+\".y\"),r(\"camera.\"+c[u]+\".z\");var f=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),h=r(\"aspectmode\",f?\"manual\":\"auto\");f\|\|(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===h&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode),s(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:l,calendar:n.calendar,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n),r(\"dragmode\",n.getDfltFromLayout(\"dragmode\")),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:c,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":570,\"../../../lib\":696,\"../../../registry\":827,\"../../subplot_defaults\":822,\"./axis_defaults\":790,\"./layout_attributes\":793}],793:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],dflt:\"turntable\",editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":696,\"../../../lib/extend\":685,\"../../domain\":770,\"./axis_attributes\":789}],794:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":719}],795:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,l=t.fullSceneLayout,c=[[],[],[]],u=0;u<3;++u){var f=l[o[u]];if(f._length=(r[u].hi-r[u].lo)r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(f._length)===1/0)c[u]=[];else{f._input_range=f.range.slice(),f.range[0]=r[u].lo/t.dataScale[u],f.range[1]=r[u].hi/t.dataScale[u],f._m=1/(t.dataScale[u]r[u].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var h=f.tickmode;if(\"auto\"===f.tickmode){f.tickmode=\"linear\";var p=f.nticks\|\|i.constrain(f._length/40,4,9);n.autoTicks(f,Math.abs(f.range[1]-f.range[0])/p)}for(var d=n.calcTicks(f),g=0;g<d.length;++g)d[g].x=d[g].xt.dataScale[u],d[g].text=a(d[g].text);c[u]=d,f.tickmode=h}}e.ticks=c;for(var u=0;u<3;++u){s[u]=.5(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]);for(var g=0;g<2;++g)e.bounds[g][u]=t.glplot.bounds[g][u]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(c)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=t(\"../../../lib/html2unicode\"),o=[\"xaxis\",\"yaxis\",\"zaxis\"],s=[0,0,0]},{\"../../../lib\":696,\"../../../lib/html2unicode\":694,\"../../cartesian/axes\":744}],796:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4r+n]e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],797:[function(t,e,r){\"use strict\";var n,i,a=t(\"gl-plot3d\"),o=t(\"webgl-context\"),s=t(\"has-passive-events\"),l=t(\"../../registry\"),c=t(\"../../lib\"),u=t(\"../../plots/cartesian/axes\"),f=t(\"../../components/fx\"),h=t(\"../../lib/str2rgbarray\"),p=t(\"../../lib/show_no_webgl_msg\"),d=t(\"./camera\"),g=t(\"./project\"),v=t(\"./layout/convert\"),m=t(\"./layout/spikes\"),y=t(\"./layout/tick_marks\");function x(t,e,r,l){var c=t.graphDiv,h={canvas:r,gl:l,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!(i\|\|(n=document.createElement(\"canvas\"),i=o({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");h.pixelRatio=t.pixelRatio,h.gl=i,h.canvas=n}try{t.glplot=a(h)}catch(e){return p(t)}var v=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+\".camera\"]=M(t.camera),t.saveCamera(c.layout),t.graphDiv.emit(\"plotly_relayout\",e)}};if(t.glplot.canvas.addEventListener(\"mouseup\",v.bind(null,t)),t.glplot.canvas.addEventListener(\"wheel\",v.bind(null,t),!!s&&{passive:!1}),t.staticMode\|\|t.glplot.canvas.addEventListener(\"webglcontextlost\",function(e){c&&c.emit&&c.emit(\"plotly_webglcontextlost\",{event:e,layer:t.id})},!1),!t.camera){var m=t.fullSceneLayout.camera;t.camera=d(t.container,{center:[m.center.x,m.center.y,m.center.z],eye:[m.eye.x,m.eye.y,m.eye.z],up:[m.up.x,m.up.y,m.up.z],zoomMin:.1,zoomMax:100,mode:\"orbit\"})}return t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(t){var e,r=t.svgContainer,n=t.container.getBoundingClientRect(),i=n.width,a=n.height;r.setAttributeNS(null,\"viewBox\",\"0 0 \"+i+\" \"+a),r.setAttributeNS(null,\"width\",i),r.setAttributeNS(null,\"height\",a),y(t),t.glplot.axes.update(t.axesOptions);for(var o,s=Object.keys(t.traces),l=null,c=t.glplot.selection,h=0;h<s.length;++h)\"skip\"!==(e=t.traces[s[h]]).data.hoverinfo&&e.handlePick(c)&&(l=e),e.setContourLevels&&e.setContourLevels();function p(e,r){var n=t.fullSceneLayout[e];return u.tickText(n,n.d2l(r),\"hover\").text}if(null!==l){var d=g(t.glplot.cameraParams,c.dataCoordinate);e=l.data;var v,m=c.index,x=f.castHoverinfo(e,t.fullLayout,m),b=x.split(\"+\"),_=\"all\"===x,w=p(\"xaxis\",c.traceCoordinate[0]),k=p(\"yaxis\",c.traceCoordinate[1]),M=p(\"zaxis\",c.traceCoordinate[2]);if(_\|\|(-1===b.indexOf(\"x\")&&(w=void 0),-1===b.indexOf(\"y\")&&(k=void 0),-1===b.indexOf(\"z\")&&(M=void 0),-1===b.indexOf(\"text\")&&(c.textLabel=void 0),-1===b.indexOf(\"name\")&&(l.name=void 0)),\"cone\"===e.type\|\|\"streamtube\"===e.type){var A=[];(_\|\|-1!==b.indexOf(\"u\"))&&A.push(\"u: \"+p(\"xaxis\",c.traceCoordinate[3])),(_\|\|-1!==b.indexOf(\"v\"))&&A.push(\"v: \"+p(\"yaxis\",c.traceCoordinate[4])),(_\|\|-1!==b.indexOf(\"w\"))&&A.push(\"w: \"+p(\"zaxis\",c.traceCoordinate[5])),(_\|\|-1!==b.indexOf(\"norm\"))&&A.push(\"norm: \"+c.traceCoordinate[6].toPrecision(3)),\"streamtube\"!==e.type\|\|!_&&-1===b.indexOf(\"divergence\")\|\|A.push(\"divergence: \"+c.traceCoordinate[7].toPrecision(3)),c.textLabel&&A.push(c.textLabel),v=A.join(\"<br>\")}else v=c.textLabel;t.fullSceneLayout.hovermode&&f.loneHover({x:(.5+.5d[0]/d[3])i,y:(.5-.5d[1]/d[3])a,xLabel:w,yLabel:k,zLabel:M,text:v,name:l.name,color:f.castHoverOption(e,m,\"bgcolor\")\|\|l.color,borderColor:f.castHoverOption(e,m,\"bordercolor\"),fontFamily:f.castHoverOption(e,m,\"font.family\"),fontSize:f.castHoverOption(e,m,\"font.size\"),fontColor:f.castHoverOption(e,m,\"font.color\")},{container:r,gd:t.graphDiv});var T={x:c.traceCoordinate[0],y:c.traceCoordinate[1],z:c.traceCoordinate[2],data:e._input,fullData:e,curveNumber:e.index,pointNumber:m};e._module.eventData&&(T=e._module.eventData(T,c,e,{},m)),f.appendArrayPointValue(T,e,m);var S={points:[T]};c.buttons&&c.distance<5?t.graphDiv.emit(\"plotly_click\",S):t.graphDiv.emit(\"plotly_hover\",S),o=S}else f.loneUnhover(r),t.graphDiv.emit(\"plotly_unhover\",o);t.drawAnnotations(t)}.bind(null,t),t.traces={},!0}function b(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id\|\|\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=v(e[this.id]),this.spikeOptions=m(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio\|\|2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=l.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=l.getComponentMethod(\"annotations3d\",\"draw\"),x(this)}var _=b.prototype;_.recoverContext=function(){var t=this,e=this.glplot.gl,r=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(function n(){e.isContextLost()?requestAnimationFrame(n):x(t,t.fullLayout,r,e)?t.plot.apply(t,t.plotArgs):c.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")})};var w=[\"xaxis\",\"yaxis\",\"zaxis\"];function k(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=w[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+\"calendar\"],f=e[\"_\"+o+\"length\"];if(c.isArrayOrTypedArray(l))for(var h,p=0;p<(f\|\|l.length);p++)if(c.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],f-1)}}function M(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}_.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];c.bgcolor?this.glplot.clearColor=h(c.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=c,this.glplotLayout=c,this.axesOptions.merge(c),this.spikeOptions.merge(c),this.setCamera(c.camera),this.updateFx(c.dragmode,c.hovermode),this.glplot.update({}),this.setConvert(s),t?Array.isArray(t)\|\|(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&k(this,n,f);var p=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?p[o]=1:p[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=p,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var d=Object.keys(this.traces);t:for(a=0;a<d.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===d[a]&&!0===t[o].visible)continue t;(i=this.traces[d[a]]).dispose(),delete this.traces[d[a]]}this.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var g=[[0,0,0],[0,0,0]],v=[],m={};for(a=0;a<3;++a){if((l=(s=c[w[a]]).type)in m?(m[l].acc=p[a],m[l].count+=1):m[l]={acc:p[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var y=this.glplot.objects,x=this.fullSceneLayout.annotations\|\|[],b=s._name.charAt(0);for(o=0;o<y.length;o++){var _=y[o],M=_.bounds,A=_._trace.data._pad\|\|0;\"ErrorBars\"===_.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],M[0][a]/p[a]-A),g[1][a]=Math.max(g[1][a],M[1][a]/p[a]+A)}for(o=0;o<x.length;o++){var T=x[o];if(T.visible){var S=s.r2l(T[b]);g[0][a]=Math.min(g[0][a],S),g[1][a]=Math.max(g[1][a],S)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var E=g[1][a]-g[0][a];g[0][a]-=E/32,g[1][a]+=E/32}if(\"reversed\"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var L=s.range;g[0][a]=s.r2l(L[0]),g[1][a]=s.r2l(L[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.bounds[0][a]=g[0][a]p[a],this.glplot.bounds[1][a]=g[1][a]p[a]}var z=[1,1,1];for(a=0;a<3;++a){var O=m[l=(s=c[w[a]]).type];z[a]=Math.pow(O.acc,1/O.count)/p[a]}var I;if(\"auto\"===c.aspectmode)I=Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1];else if(\"cube\"===c.aspectmode)I=[1,1,1];else if(\"data\"===c.aspectmode)I=z;else{if(\"manual\"!==c.aspectmode)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var P=c.aspectratio;I=[P.x,P.y,P.z]}c.aspectratio.x=u.aspectratio.x=I[0],c.aspectratio.y=u.aspectratio.y=I[1],c.aspectratio.z=u.aspectratio.z=I[2],this.glplot.aspect=I;var D=c.domain\|\|null,R=e._size\|\|null;if(D&&R){var B=this.container.style;B.position=\"absolute\",B.left=R.l+D.x[0]R.w+\"px\",B.top=R.t+(1-D.y[1])R.h+\"px\",B.width=R.w(D.x[1]-D.x[0])+\"px\",B.height=R.h(D.y[1]-D.y[0])+\"px\"}this.glplot.redraw()}},_.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=this.glplot.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},_.getCamera=function(){return this.glplot.camera.view.recalcMatrix(this.camera.view.lastT()),M(this.glplot.camera)},_.setCamera=function(t){var e;this.glplot.camera.lookAt.apply(this,[[(e=t).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]])},_.saveCamera=function(t){var e=this.getCamera(),r=c.nestedProperty(t,this.id+\".camera\"),n=r.get(),i=!1;function a(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}if(void 0===n)i=!0;else for(var o=0;o<3;o++)for(var s=0;s<3;s++)if(!a(e,n,o,s)){i=!0;break}return i&&r.set(e),i},_.updateFx=function(t,e){var r=this.camera;r&&(\"orbit\"===t?(r.mode=\"orbit\",r.keyBindingMode=\"rotate\"):\"turntable\"===t?(r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\"):r.keyBindingMode=t),this.fullSceneLayout.hovermode=e},_.toImage=function(t){t\|\|(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(ri4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4(ro+l)+c];a[4(ro+l)+c]=a[4(rs+l)+c],a[4(rs+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(n),h},_.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[w[t]];u.setConvert(e,this.fullLayout),e.setScale=c.noop}},e.exports=b},{\"../../components/fx\":612,\"../../lib\":696,\"../../lib/show_no_webgl_msg\":717,\"../../lib/str2rgbarray\":719,\"../../plots/cartesian/axes\":744,\"../../registry\":827,\"./camera\":786,\"./layout/convert\":791,\"./layout/spikes\":794,\"./layout/tick_marks\":795,\"./project\":796,\"gl-plot3d\":277,\"has-passive-events\":394,\"webgl-context\":533}],798:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){n=n\|\|t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],799:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"../components/color/attributes\"),a=n({editType:\"calc\"});a.family.dflt='\"Open Sans\", verdana, arial, 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a=e[r[i]],s=a.domain,l=a._subplot,c=l.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":c,x:n.l+n.ws.x[0],y:n.t+n.h(1-s.y[1]),width:n.w(s.x[1]-s.x[0]),height:n.h(s.y[1]-s.y[0]),preserveAspectRatio:\"none\"}),l.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.mapbox,n=0;n<r.length;n++){e[r[n]]._subplot.updateFx(e)}}},{\"../../constants/xmlns_namespaces\":674,\"../../lib\":696,\"../../plots/get_data\":781,\"./constants\":800,\"./layout_attributes\":804,\"./layout_defaults\":805,\"./mapbox\":806,\"mapbox-gl\":409}],803:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./convert_text_opts\");function a(t,e){this.mapbox=t,this.map=t.map,this.uid=t.uid+\"-layer\"+e,this.idSource=this.uid+\"-source\",this.idLayer=this.uid+\"-layer\",this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var o=a.prototype;function s(t){var e=t.source;return t.visible&&(n.isPlainObject(e)\|\|\"string\"==typeof e&&e.length>0)}function l(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var a=t.symbol,o=i(a.textposition,a.iconsize);n.extendFlat(e,{\"icon-image\":a.icon+\"-15\",\"icon-size\":a.iconsize/10,\"text-field\":a.text,\"text-size\":a.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":a.textfont.color,\"text-opacity\":t.opacity})}return{layout:e,paint:r}}o.update=function(t){this.visible?this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=s(t)},o.needsNewSource=function(t){return this.sourceType!==t.sourcetype\|\|this.source!==t.source\|\|this.layerType!==t.type},o.needsNewLayer=function(t){return this.layerType!==t.type\|\|this.below!==t.below},o.updateSource=function(t){var e=this.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,s(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,i={type:r};\"geojson\"===r?e=\"data\":\"vector\"===r&&(e=\"string\"==typeof n?\"url\":\"tiles\");return i[e]=n,i}(t);e.addSource(this.idSource,r)}},o.updateLayer=function(t){var e=this.map,r=l(t);this.removeLayer(),this.layerType=t.type,s(t)&&e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer\|\|\"\",type:t.type,layout:r.layout,paint:r.paint},t.below)},o.updateStyle=function(t){if(s(t)){var e=l(t);this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.mapbox.setOptions(this.idLayer,\"setPaintProperty\",e.paint)}},o.removeLayer=function(){var t=this.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},o.dispose=function(){var t=this.map;t.removeLayer(this.idLayer),t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new a(t,e);return n.update(r),n}},{\"../../lib\":696,\"./convert_text_opts\":801}],804:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../domain\").attributes,o=t(\"../font_attributes\"),s=t(\"../../traces/scatter/attributes\").textposition,l=t(\"../../plot_api/edit_types\").overrideAll,c=t(\"../../plot_api/plot_template\").templatedArray,u=o({});u.family.dflt=\"Open Sans Regular, Arial Unicode MS 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a=r(\"type\");r(\"below\"),r(\"color\"),r(\"opacity\"),\"circle\"===a&&r(\"circle.radius\"),\"line\"===a&&r(\"line.width\"),\"fill\"===a&&r(\"fill.outlinecolor\"),\"symbol\"===a&&(r(\"symbol.icon\"),r(\"symbol.iconsize\"),r(\"symbol.text\"),n.coerceFont(r,\"symbol.textfont\"),r(\"symbol.textposition\"))}}e.exports=function(t,e,r){i(t,e,r,{type:\"mapbox\",attributes:o,handleDefaults:s,partition:\"y\",accessToken:e._mapboxAccessToken})}},{\"../../lib\":696,\"../array_container_defaults\":740,\"../subplot_defaults\":822,\"./layout_attributes\":804}],806:[function(t,e,r){\"use strict\";var n=t(\"mapbox-gl\"),i=t(\"../../components/fx\"),a=t(\"../../lib\"),o=t(\"../../components/dragelement\"),s=t(\"../cartesian/select\").prepSelect,l=t(\"../cartesian/select\").selectOnClick,c=t(\"./constants\"),u=t(\"./layout_attributes\"),f=t(\"./layers\");function h(t){this.id=t.id,this.gd=t.gd,this.container=t.container,this.isStatic=t.staticPlot;var e=t.fullLayout;this.uid=e._uid+\"-\"+this.id,this.opts=e[this.id],this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(e),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[]}var p=h.prototype;function d(t){var e=u.style.values,r=u.style.dflt,n={};return a.isPlainObject(t)?(n.id=t.id,n.style=t):\"string\"==typeof t?(n.id=t,n.style=-1!==e.indexOf(t)?g(t):t):(n.id=r,n.style=g(r)),n.transition={duration:0,delay:0},n}function g(t){return c.styleUrlPrefix+t+\"-\"+c.styleUrlSuffix}function v(t){return[t.lon,t.lat]}e.exports=function(t){return new h(t)},p.plot=function(t,e,r){var n,i=this,a=i.opts=e[this.id];i.map&&a.accesstoken!==i.accessToken&&(i.map.remove(),i.map=null,i.styleObj=null,i.traceHash=[],i.layerList={}),n=i.map?new Promise(function(r,n){i.updateMap(t,e,r,n)}):new Promise(function(r,n){i.createMap(t,e,r,n)}),r.push(n)},p.createMap=function(t,e,r,a){var o=this,s=o.gd,u=o.opts,f=o.styleObj=d(u.style);o.accessToken=u.accesstoken;var h=o.map=new n.Map({container:o.div,style:f.style,center:v(u.center),zoom:u.zoom,bearing:u.bearing,pitch:u.pitch,interactive:!o.isStatic,preserveDrawingBuffer:o.isStatic,doubleClickZoom:!1,boxZoom:!1}),p=c.controlContainerClassName,g=o.div.getElementsByClassName(p)[0];if(o.div.removeChild(g),h._canvas.style.left=\"0px\",h._canvas.style.top=\"0px\",o.rejectOnError(a),h.once(\"load\",function(){o.updateData(t),o.updateLayout(e),o.resolveOnRender(r)}),!o.isStatic){var m=!1;h.on(\"moveend\",function(t){if(o.map){var e=o.getView();u._input.center=u.center=e.center,u._input.zoom=u.zoom=e.zoom,u._input.bearing=u.bearing=e.bearing,u._input.pitch=u.pitch=e.pitch,(t.originalEvent\|\|m)&&x(e),m=!1}}),h.on(\"wheel\",function(){m=!0}),h.on(\"mousemove\",function(t){var e=o.div.getBoundingClientRect();t.clientX=t.point.x+e.left,t.clientY=t.point.y+e.top,t.target.getBoundingClientRect=function(){return e},o.xaxis.p2c=function(){return t.lngLat.lng},o.yaxis.p2c=function(){return t.lngLat.lat},i.hover(s,t,o.id)}),h.on(\"dragstart\",y),h.on(\"zoomstart\",y),h.on(\"dblclick\",function(){s.emit(\"plotly_doubleclick\",null);var t=o.viewInitial;h.setCenter(v(t.center)),h.setZoom(t.zoom),h.setBearing(t.bearing),h.setPitch(t.pitch);var e=o.getView();u._input.center=u.center=e.center,u._input.zoom=u.zoom=e.zoom,u._input.bearing=u.bearing=e.bearing,u._input.pitch=u.pitch=e.pitch,x(e)}),o.clearSelect=function(){s._fullLayout._zoomlayer.selectAll(\".select-outline\").remove()},o.onClickInPanFn=function(t){return function(e){var r=s._fullLayout.clickmode;r.indexOf(\"select\")>-1&&l(e.originalEvent,s,[o.xaxis],[o.yaxis],o.id,t),r.indexOf(\"event\")>-1&&i.click(s,e.originalEvent)}}}function y(){i.loneUnhover(e._toppaper)}function x(t){var e=o.id,r={};for(var n in t)r[e+\".\"+n]=t[n];s.emit(\"plotly_relayout\",r)}},p.updateMap=function(t,e,r,n){var i=this,a=i.map;i.rejectOnError(n);var o=d(i.opts.style);i.styleObj.id!==o.id?(i.styleObj=o,a.setStyle(o.style),a.once(\"styledata\",function(){i.traceHash={},i.updateData(t),i.updateLayout(e),i.resolveOnRender(r)})):(i.updateData(t),i.updateLayout(e),i.resolveOnRender(r))},p.updateData=function(t){var e,r,n,i,a=this.traceHash;for(n=0;n<t.length;n++){var o=t[n];(e=a[(r=o[0].trace).uid])?e.update(o):r._module&&(a[r.uid]=r._module.plot(this,o))}var s=Object.keys(a);t:for(n=0;n<s.length;n++){var l=s[n];for(i=0;i<t.length;i++)if(l===(r=t[i][0].trace).uid)continue t;(e=a[l]).dispose(),delete a[l]}},p.updateLayout=function(t){var e=this.map,r=this.opts;e.setCenter(v(r.center)),e.setZoom(r.zoom),e.setBearing(r.bearing),e.setPitch(r.pitch),this.updateLayers(),this.updateFramework(t),this.updateFx(t),this.map.resize()},p.resolveOnRender=function(t){var e=this.map;e.on(\"render\",function r(){e.loaded()&&(e.off(\"render\",r),setTimeout(t,0))})},p.rejectOnError=function(t){var e=this.map;function r(){t(new Error(c.mapOnErrorMsg))}e.once(\"error\",r),e.once(\"style.error\",r),e.once(\"source.error\",r),e.once(\"tile.error\",r),e.once(\"layer.error\",r)},p.createFramework=function(t){var e=this,r=e.div=document.createElement(\"div\");r.id=e.uid,r.style.position=\"absolute\",e.container.appendChild(r),e.xaxis={_id:\"x\",c2p:function(t){return e.project(t).x}},e.yaxis={_id:\"y\",c2p:function(t){return e.project(t).y}},e.updateFramework(t)},p.updateFx=function(t){var e=this,r=e.map,n=e.gd;if(!e.isStatic){var i,l=t.dragmode;i=\"select\"===l?function(t,r){(t.range={})[e.id]=[u([r.xmin,r.ymin]),u([r.xmax,r.ymax])]}:function(t,r,n){(t.lassoPoints={})[e.id]=n.filtered.map(u)};var c=e.dragOptions;e.dragOptions=a.extendDeep(c\|\|{},{element:e.div,gd:n,plotinfo:{id:e.id,xaxis:e.xaxis,yaxis:e.yaxis,fillRangeItems:i},xaxes:[e.xaxis],yaxes:[e.yaxis],subplot:e.id}),r.off(\"click\",e.onClickInPanHandler),\"select\"===l\|\|\"lasso\"===l?(r.dragPan.disable(),r.on(\"zoomstart\",e.clearSelect),e.dragOptions.prepFn=function(t,r,n){s(t,r,n,e.dragOptions,l)},o.init(e.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",e.clearSelect),e.div.onmousedown=null,e.onClickInPanHandler=e.onClickInPanFn(e.dragOptions),r.on(\"click\",e.onClickInPanHandler))}function u(t){var r=e.map.unproject(t);return[r.lng,r.lat]}},p.updateFramework=function(t){var 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n=eMath.PI/180;return[e,t(n)]})},o.util.dataFromEquation=function(t,e,r){var i=e\|\|6,a=[],o=[];n.range(0,360+i,i).forEach(function(e,r){var n=eMath.PI/180,i=t(n);a.push(e),o.push(i)});var s={t:a,r:o};return r&&(s.name=r),s},o.util.ensureArray=function(t,e){if(\"undefined\"==typeof t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]\|\|r[0]})},o.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=o.util.ensureArray(t[e],r)}),t},o.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},o.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length\|\|objHasKeys(t[r],e))},o.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},o.util.arrayLast=function(t){return t[t.length-1]},o.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return-2===r},o.util.flattenArray=function(t){for(var e=[];!o.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},o.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},o.util.convertToCartesian=function(t,e){var r=eMath.PI/180;return[tMath.cos(r),tMath.sin(r)]},o.util.round=function(t,e){var r=e\|\|2,n=Math.pow(10,r);return Math.round(tn)/n},o.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],i=e[1],a={};return a.x=r,a.y=i,a.pos=e,a.angle=180(Math.atan2(i,r)+Math.PI)/Math.PI,a.radius=Math.sqrt(rr+ii),a},o.util.duplicatesCount=function(t){for(var e,r={},n={},i=0,a=t.length;i<a;i++)(e=t[i])in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},o.util.duplicates=function(t){return Object.keys(o.util.duplicatesCount(t))},o.util.translator=function(t,e,r,n){if(n){var i=r.slice();r=e,e=i}var a=e.reduce(function(t,e){if(\"undefined\"!=typeof t)return t[e]},t);\"undefined\"!=typeof a&&(e.reduce(function(t,r,n){if(\"undefined\"!=typeof t)return n===e.length-1&&delete 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o=r[2]?[r[0],r[1]+r[2]]:r,s=n.svg.symbol().size(t[a].data.dotSize).type(t[a].data.dotType)(r,i);n.select(this).attr({class:\"mark dot\",d:s,transform:function(t,r){var n,i,a,s=(n=function(t,r){var n=e.radialScale(t[1]),i=(e.angularScale(t[0])+e.orientation)Math.PI/180;return{r:n,t:i}}(o),i=n.rMath.cos(n.t),a=n.rMath.sin(n.t),{x:i,y:a});return\"translate(\"+[s.x,s.y]+\")\"}})};var u=n.svg.line.radial().interpolate(t[0].data.lineInterpolation).radius(function(t){return e.radialScale(t[1])}).angle(function(t){return e.angularScale(t[0])Math.PI/180});c.line=function(r,i,a){var s=r[2]?o[a].map(function(t,e){return[t[0],t[1]+t[2]]}):o[a];if(n.select(this).each(c.dot).style({opacity:function(e,r){return+t[a].data.dotVisible},fill:d.stroke(r,i,a)}).attr({class:\"mark dot\"}),!(i>0)){var l=n.select(this.parentNode).selectAll(\"path.line\").data([0]);l.enter().insert(\"path\"),l.attr({class:\"line\",d:u(s),transform:function(t,r){return\"rotate(\"+(e.orientation+90)+\")\"},\"pointer-events\":\"none\"}).style({fill:function(t,e){return d.fill(r,i,a)},\"fill-opacity\":0,stroke:function(t,e){return d.stroke(r,i,a)},\"stroke-width\":function(t,e){return d[\"stroke-width\"](r,i,a)},\"stroke-dasharray\":function(t,e){return d[\"stroke-dasharray\"](r,i,a)},opacity:function(t,e){return d.opacity(r,i,a)},display:function(t,e){return d.display(r,i,a)}})}};var f=e.angularScale.range(),h=Math.abs(f[1]-f[0])/o[0].lengthMath.PI/180,p=n.svg.arc().startAngle(function(t){return-h/2}).endAngle(function(t){return h/2}).innerRadius(function(t){return e.radialScale(l+(t[2]\|\|0))}).outerRadius(function(t){return e.radialScale(l+(t[2]\|\|0))+e.radialScale(t[1])});c.arc=function(t,r,i){n.select(this).attr({class:\"mark 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arguments.length?(e.forEach(function(e,r){t[r]\|\|(t[r]={}),i(t[r],o.PolyChart.defaultConfig()),i(t[r],e)}),this):t},a.getColorScale=function(){},n.rebind(a,e,\"on\"),a},o.PolyChart.defaultConfig=function(){return{data:{name:\"geom1\",t:[[1,2,3,4]],r:[[1,2,3,4]],dotType:\"circle\",dotSize:64,dotVisible:!1,barWidth:20,color:\"#ffa500\",strokeSize:1,strokeColor:\"silver\",strokeDash:\"solid\",opacity:1,index:0,visible:!0,visibleInLegend:!0},geometryConfig:{geometry:\"LinePlot\",geometryType:\"arc\",direction:\"clockwise\",orientation:0,container:\"body\",radialScale:null,angularScale:null,colorScale:n.scale.category20()}}},o.BarChart=function(){return o.PolyChart()},o.BarChart.defaultConfig=function(){return{geometryConfig:{geometryType:\"bar\"}}},o.AreaChart=function(){return o.PolyChart()},o.AreaChart.defaultConfig=function(){return{geometryConfig:{geometryType:\"arc\"}}},o.DotPlot=function(){return o.PolyChart()},o.DotPlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"dot\",dotType:\"circle\"}}},o.LinePlot=function(){return o.PolyChart()},o.LinePlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"line\"}}},o.Legend=function(){var t=o.Legend.defaultConfig(),e=n.dispatch(\"hover\");function r(){var e=t.legendConfig,a=t.data.map(function(t,r){return[].concat(t).map(function(t,n){var a=i({},e.elements[r]);return a.name=t,a.color=[].concat(e.elements[r].color)[n],a})}),o=n.merge(a);o=o.filter(function(t,r){return e.elements[r]&&(e.elements[r].visibleInLegend\|\|\"undefined\"==typeof e.elements[r].visibleInLegend)}),e.reverseOrder&&(o=o.reverse());var s=e.container;(\"string\"==typeof s\|\|s.nodeName)&&(s=n.select(s));var l=o.map(function(t,e){return t.color}),c=e.fontSize,u=null==e.isContinuous?\"number\"==typeof o[0]:e.isContinuous,f=u?e.height:co.length,h=s.classed(\"legend-group\",!0).selectAll(\"svg\").data([0]),p=h.enter().append(\"svg\").attr({width:300,height:f+c,xmlns:\"http://www.w3.org/2000/svg\",\"xmlns:xlink\":\"http://www.w3.org/1999/xlink\",version:\"1.1\"});p.append(\"g\").classed(\"legend-axis\",!0),p.append(\"g\").classed(\"legend-marks\",!0);var d=n.range(o.length),g=n.scale[u?\"linear\":\"ordinal\"]().domain(d).range(l),v=n.scale[u?\"linear\":\"ordinal\"]().domain(d)[u?\"range\":\"rangePoints\"]([0,f]);if(u){var m=h.select(\".legend-marks\").append(\"defs\").append(\"linearGradient\").attr({id:\"grad1\",x1:\"0%\",y1:\"0%\",x2:\"0%\",y2:\"100%\"}).selectAll(\"stop\").data(l);m.enter().append(\"stop\"),m.attr({offset:function(t,e){return e/(l.length-1)100+\"%\"}}).style({\"stop-color\":function(t,e){return t}}),h.append(\"rect\").classed(\"legend-mark\",!0).attr({height:e.height,width:e.colorBandWidth,fill:\"url(#grad1)\"})}else{var y=h.select(\".legend-marks\").selectAll(\"path.legend-mark\").data(o);y.enter().append(\"path\").classed(\"legend-mark\",!0),y.attr({transform:function(t,e){return\"translate(\"+[c/2,v(e)+c/2]+\")\"},d:function(t,e){var r,i,a,o=t.symbol;return a=3(i=c),\"line\"===(r=o)?\"M\"+[[-i/2,-i/12],[i/2,-i/12],[i/2,i/12],[-i/2,i/12]]+\"Z\":-1!=n.svg.symbolTypes.indexOf(r)?n.svg.symbol().type(r).size(a)():n.svg.symbol().type(\"square\").size(a)()},fill:function(t,e){return g(e)}}),y.exit().remove()}var x=n.svg.axis().scale(v).orient(\"right\"),b=h.select(\"g.legend-axis\").attr({transform:\"translate(\"+[u?e.colorBandWidth:c,c/2]+\")\"}).call(x);return b.selectAll(\".domain\").style({fill:\"none\",stroke:\"none\"}),b.selectAll(\"line\").style({fill:\"none\",stroke:u?e.textColor:\"none\"}),b.selectAll(\"text\").style({fill:e.textColor,\"font-size\":e.fontSize}).text(function(t,e){return o[e].name}),r}return r.config=function(e){return arguments.length?(i(t,e),this):t},n.rebind(r,e,\"on\"),r},o.Legend.defaultConfig=function(t,e){return{data:[\"a\",\"b\",\"c\"],legendConfig:{elements:[{symbol:\"line\",color:\"red\"},{symbol:\"square\",color:\"yellow\"},{symbol:\"diamond\",color:\"limegreen\"}],height:150,colorBandWidth:30,fontSize:12,container:\"body\",isContinuous:null,textColor:\"grey\",reverseOrder:!1}}},o.tooltipPanel=function(){var t,e,r,a={container:null,hasTick:!1,fontSize:12,color:\"white\",padding:5},s=\"tooltip-\"+o.tooltipPanel.uid++,l=10,c=function(){var n=(t=a.container.selectAll(\"g.\"+s).data([0])).enter().append(\"g\").classed(s,!0).style({\"pointer-events\":\"none\",display:\"none\"});return r=n.append(\"path\").style({fill:\"white\",\"fill-opacity\":.9}).attr({d:\"M0 0\"}),e=n.append(\"text\").attr({dx:a.padding+l,dy:.3+a.fontSize}),c};return c.text=function(i){var o=n.hsl(a.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",f=i\|\|\"\";e.style({fill:u,\"font-size\":a.fontSize+\"px\"}).text(f);var h=a.padding,p=e.node().getBBox(),d={fill:a.color,stroke:s,\"stroke-width\":\"2px\"},g=p.width+2h+l,v=p.height+2h;return r.attr({d:\"M\"+[[l,-v/2],[l,-v/4],[a.hasTick?0:l,0],[l,v/4],[l,v/2],[g,v/2],[g,-v/2]].join(\"L\")+\"Z\"}).style(d),t.attr({transform:\"translate(\"+[l,-v/2+2h]+\")\"}),t.style({display:\"block\"}),c},c.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),c},c.hide=function(){if(t)return t.style({display:\"none\"}),c},c.show=function(){if(t)return t.style({display:\"block\"}),c},c.config=function(t){return i(a,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map(function(t,r){var n=i({},t);return[[n,[\"marker\",\"color\"],[\"color\"]],[n,[\"marker\",\"opacity\"],[\"opacity\"]],[n,[\"marker\",\"line\",\"color\"],[\"strokeColor\"]],[n,[\"marker\",\"line\",\"dash\"],[\"strokeDash\"]],[n,[\"marker\",\"line\",\"width\"],[\"strokeSize\"]],[n,[\"marker\",\"symbol\"],[\"dotType\"]],[n,[\"marker\",\"size\"],[\"dotSize\"]],[n,[\"marker\",\"barWidth\"],[\"barWidth\"]],[n,[\"line\",\"interpolation\"],[\"lineInterpolation\"]],[n,[\"showlegend\"],[\"visibleInLegend\"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e\|\|delete n.marker,e&&delete n.groupId,e?(\"LinePlot\"===n.geometry?(n.type=\"scatter\",!0===n.dotVisible?(delete n.dotVisible,n.mode=\"lines+markers\"):n.mode=\"lines\"):\"DotPlot\"===n.geometry?(n.type=\"scatter\",n.mode=\"markers\"):\"AreaChart\"===n.geometry?n.type=\"area\":\"BarChart\"===n.geometry&&(n.type=\"bar\"),delete n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n}),!e&&t.layout&&\"stack\"===t.layout.barmode)){var a=o.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var n=a.indexOf(t.geometry);-1!=n&&(r.data[e].groupId=n)})}if(t.layout){var s=i({},t.layout);if([[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e?(\"undefined\"!=typeof s.tickLength&&(s.angularaxis.ticklen=s.tickLength,delete s.tickLength),s.tickColor&&(s.angularaxis.tickcolor=s.tickColor,delete s.tickColor)):(s.angularAxis&&\"undefined\"!=typeof s.angularAxis.ticklen&&(s.tickLength=s.angularAxis.ticklen),s.angularAxis&&\"undefined\"!=typeof s.angularAxis.tickcolor&&(s.tickColor=s.angularAxis.tickcolor)),s.legend&&\"boolean\"!=typeof s.legend.reverseOrder&&(s.legend.reverseOrder=\"normal\"!=s.legend.reverseOrder),s.legend&&\"boolean\"==typeof s.legend.traceorder&&(s.legend.traceorder=s.legend.traceorder?\"reversed\":\"normal\",delete s.legend.reverseOrder),s.margin&&\"undefined\"!=typeof s.margin.t){var l=[\"t\",\"r\",\"b\",\"l\",\"pad\"],c=[\"top\",\"right\",\"bottom\",\"left\",\"pad\"],u={};n.entries(s.margin).forEach(function(t,e){u[c[l.indexOf(t.key)]]=t.value}),s.margin=u}e&&(delete s.needsEndSpacing,delete s.minorTickColor,delete s.minorTicks,delete s.angularaxis.ticksCount,delete s.angularaxis.ticksCount,delete s.angularaxis.ticksStep,delete s.angularaxis.rewriteTicks,delete s.angularaxis.nticks,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksStep,delete s.radialaxis.rewriteTicks,delete s.radialaxis.nticks),r.layout=s}return r}};return t}},{\"../../../constants/alignment\":668,\"../../../lib\":696,d3:148}],818:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../../lib\"),a=t(\"../../../components/color\"),o=t(\"./micropolar\"),s=t(\"./undo_manager\"),l=i.extendDeepAll,c=e.exports={};c.framework=function(t){var e,r,i,a,u,f=new s;function h(r,s){return s&&(u=s),n.select(n.select(u).node().parentNode).selectAll(\".svg-container>:not(.chart-root)\").remove(),e=e?l(e,r):r,i\|\|(i=o.Axis()),a=o.adapter.plotly().convert(e),i.config(a).render(u),t.data=e.data,t.layout=e.layout,c.fillLayout(t),e}return h.isPolar=!0,h.svg=function(){return i.svg()},h.getConfig=function(){return e},h.getLiveConfig=function(){return o.adapter.plotly().convert(i.getLiveConfig(),!0)},h.getLiveScales=function(){return{t:i.angularScale(),r:i.radialScale()}},h.setUndoPoint=function(){var t,n,i=this,a=o.util.cloneJson(e);t=a,n=r,f.add({undo:function(){n&&i(n)},redo:function(){i(t)}}),r=o.util.cloneJson(a)},h.undo=function(){f.undo()},h.redo=function(){f.redo()},h},c.fillLayout=function(t){var e=n.select(t).selectAll(\".plot-container\"),r=e.selectAll(\".svg-container\"),i=t.framework&&t.framework.svg&&t.framework.svg(),o={width:800,height:600,paper_bgcolor:a.background,_container:e,_paperdiv:r,_paper:i};t._fullLayout=l(o,t.layout)}},{\"../../../components/color\":570,\"../../../lib\":696,\"./micropolar\":817,\"./undo_manager\":819,d3:148}],819:[function(t,e,r){\"use strict\";e.exports=function(){var t,e=[],r=-1,n=!1;function i(t,e){return t?(n=!0,t[e](),n=!1,this):this}return{add:function(t){return n?this:(e.splice(r+1,e.length-r),e.push(t),r=e.length-1,this)},setCallback:function(e){t=e},undo:function(){var n=e[r];return n?(i(n,\"undo\"),r-=1,t&&t(n.undo),this):this},redo:function(){var n=e[r+1];return n?(i(n,\"redo\"),r+=1,t&&t(n.redo),this):this},clear:function(){e=[],r=-1},hasUndo:function(){return-1!==r},hasRedo:function(){return r<e.length-1},getCommands:function(){return e},getPreviousCommand:function(){return e[r-1]},getIndex:function(){return r}}}},{}],820:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=t(\"../../components/color\"),l=t(\"../../components/drawing\"),c=t(\"../plots\"),u=t(\"../cartesian/set_convert\"),f=t(\"./set_convert\"),h=t(\"../cartesian/autorange\").doAutoRange,p=t(\"../cartesian/axes\").doTicksSingle,d=t(\"../cartesian/dragbox\"),g=t(\"../../components/dragelement\"),v=t(\"../../components/fx\"),m=t(\"../../components/titles\"),y=t(\"../cartesian/select\").prepSelect,x=t(\"../cartesian/select\").selectOnClick,b=t(\"../cartesian/select\").clearSelect,_=t(\"../../lib/setcursor\"),w=t(\"../../lib/clear_gl_canvases\"),k=t(\"../../plot_api/subroutines\").redrawReglTraces,M=t(\"../../constants/alignment\").MID_SHIFT,A=t(\"./constants\"),T=t(\"./helpers\"),S=o._,E=o.mod,C=o.deg2rad,L=o.rad2deg;function z(t,e){this.id=e,this.gd=t,this._hasClipOnAxisFalse=null,this.vangles=null,this.radialAxisAngle=null,this.traceHash={},this.layers={},this.clipPaths={},this.clipIds={},this.viewInitial={};var r=t._fullLayout,n=\"clip\"+r._uid+e;this.clipIds.forTraces=n+\"-for-traces\",this.clipPaths.forTraces=r._clips.append(\"clipPath\").attr(\"id\",this.clipIds.forTraces),this.clipPaths.forTraces.append(\"path\"),this.framework=r._polarlayer.append(\"g\").attr(\"class\",e),this.radialTickLayout=null,this.angularTickLayout=null}var O=z.prototype;function I(t){var e=t.ticks+String(t.ticklen)+String(t.showticklabels);return\"side\"in t&&(e+=t.side),e}function P(t,e){return e[o.findIndexOfMin(e,function(e){return o.angleDist(t,e)})]}function D(t,e,r){return e?(t.attr(\"display\",null),t.attr(r)):t&&t.attr(\"display\",\"none\"),t}function R(t,e){return\"translate(\"+t+\",\"+e+\")\"}function B(t){return\"rotate(\"+t+\")\"}function F(t){return Math.abs(t)<1e-10?0:t>0?1:-1}function N(t){return F(Math.cos(t))}function j(t){return F(Math.sin(t))}e.exports=function(t,e){return new z(t,e)},O.plot=function(t,e){var r=e[this.id];this._hasClipOnAxisFalse=!1;for(var n=0;n<t.length;n++){if(!1===t[n][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(e,r),this.updateLayout(e,r),c.generalUpdatePerTraceModule(this.gd,this,t,r),this.updateFx(e,r)},O.updateLayers=function(t,e){var r=this.layers,i=e.radialaxis,a=e.angularaxis,o=A.layerNames,s=o.indexOf(\"frontplot\"),l=o.slice(0,s),c=\"below traces\"===a.layer,u=\"below traces\"===i.layer;c&&l.push(\"angular-line\"),u&&l.push(\"radial-line\"),c&&l.push(\"angular-axis\"),u&&l.push(\"radial-axis\"),l.push(\"frontplot\"),c\|\|l.push(\"angular-line\"),u\|\|l.push(\"radial-line\"),c\|\|l.push(\"angular-axis\"),u\|\|l.push(\"radial-axis\");var f=this.framework.selectAll(\".polarsublayer\").data(l,String);f.enter().append(\"g\").attr(\"class\",function(t){return\"polarsublayer \"+t}).each(function(t){var e=r[t]=n.select(this);switch(t){case\"frontplot\":e.append(\"g\").classed(\"barlayer\",!0),e.append(\"g\").classed(\"scatterlayer\",!0);break;case\"backplot\":e.append(\"g\").classed(\"maplayer\",!0);break;case\"plotbg\":r.bg=e.append(\"path\");break;case\"radial-grid\":e.style(\"fill\",\"none\"),e.append(\"g\").classed(\"x\",1);break;case\"angular-grid\":e.style(\"fill\",\"none\"),e.append(\"g\").classed(\"angularaxis\",1);break;case\"radial-line\":e.append(\"line\").style(\"fill\",\"none\");break;case\"angular-line\":e.append(\"path\").style(\"fill\",\"none\")}}),f.order()},O.updateLayout=function(t,e){var r=this.layers,n=t._size,i=e.radialaxis,a=e.angularaxis,o=e.domain.x,c=e.domain.y;this.xOffset=n.l+n.wo[0],this.yOffset=n.t+n.h(1-c[1]);var u=this.xLength=n.w(o[1]-o[0]),f=this.yLength=n.h(c[1]-c[0]),h=e.sector;this.sectorInRad=h.map(C);var p,d,g,v,m,y=this.sectorBBox=function(t){var e,r,n,i,a=t[0],o=t[1]-a,s=E(a,360),l=s+o,c=Math.cos(C(s)),u=Math.sin(C(s)),f=Math.cos(C(l)),h=Math.sin(C(l));i=s<=90&&l>=90\|\|s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180\|\|s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270\|\|s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(h),x=y[2]-y[0],b=y[3]-y[1],_=f/u,w=Math.abs(b/x);_>w?(p=u,m=(f-(d=uw))/n.h/2,g=[o[0],o[1]],v=[c[0]+m,c[1]-m]):(d=f,m=(u-(p=f/w))/n.w/2,g=[o[0]+m,o[1]-m],v=[c[0],c[1]]),this.xLength2=p,this.yLength2=d,this.xDomain2=g,this.yDomain2=v;var k=this.xOffset2=n.l+n.wg[0],M=this.yOffset2=n.t+n.h(1-v[1]),A=this.radius=p/x,T=this.innerRadius=e.holeA,S=this.cx=k-Ay[0],L=this.cy=M+Ay[3],z=this.cxx=S-k,O=this.cyy=L-M;this.radialAxis=this.mockAxis(t,e,i,{_axislayer:r[\"radial-axis\"],_gridlayer:r[\"radial-grid\"],_id:\"x\",side:{counterclockwise:\"top\",clockwise:\"bottom\"}[i.side],domain:[T/n.w,A/n.w]}),this.angularAxis=this.mockAxis(t,e,a,{_axislayer:r[\"angular-axis\"],_gridlayer:r[\"angular-grid\"],side:\"right\",domain:[0,Math.PI],autorange:!1}),this.doAutoRange(t,e),this.updateAngularAxis(t,e),this.updateRadialAxis(t,e),this.updateRadialAxisTitle(t,e),this.xaxis=this.mockCartesianAxis(t,e,{_id:\"x\",domain:g}),this.yaxis=this.mockCartesianAxis(t,e,{_id:\"y\",domain:v});var I=this.pathSubplot();this.clipPaths.forTraces.select(\"path\").attr(\"d\",I).attr(\"transform\",R(z,O)),r.frontplot.attr(\"transform\",R(k,M)).call(l.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces),r.bg.attr(\"d\",I).attr(\"transform\",R(S,L)).call(s.fill,e.bgcolor),this.framework.selectAll(\".crisp\").classed(\"crisp\",0)},O.mockAxis=function(t,e,r,n){var i=o.extendFlat({anchor:\"free\",position:0,_pos:0,_counteraxis:!0,automargin:!1},r,n);return f(i,e,t),i},O.mockCartesianAxis=function(t,e,r){var n=this,i=r._id,a=o.extendFlat({type:\"linear\"},r);u(a,t);var s={x:[0,2],y:[1,3]};return a.setRange=function(){var t=n.sectorBBox,r=s[i],o=n.radialAxis._rl,l=(o[1]-o[0])/(1-e.hole);a.range=[t[r[0]]l,t[r[1]]l]},a.isPtWithinRange=\"x\"===i?function(t){return n.isPtInside(t)}:function(){return!0},a.setRange(),a.setScale(),a},O.doAutoRange=function(t,e){var r=this.gd,n=this.radialAxis,i=e.radialaxis;n.setScale(),h(r,n);var a=n.range;i.range=a.slice(),i._input.range=a.slice(),n._rl=[n.r2l(a[0],null,\"gregorian\"),n.r2l(a[1],null,\"gregorian\")]},O.updateRadialAxis=function(t,e){var r=this,n=r.gd,i=r.layers,a=r.radius,o=r.innerRadius,l=r.cx,c=r.cy,u=e.radialaxis,f=E(e.sector[0],360),h=r.radialAxis,d=o<a;r.fillViewInitialKey(\"radialaxis.angle\",u.angle),r.fillViewInitialKey(\"radialaxis.range\",h.range.slice()),h.setGeometry(),\"auto\"===h.tickangle&&f>90&&f<=270&&(h.tickangle=180),h._transfn=function(t){return\"translate(\"+(h.l2p(t.x)+o)+\",0)\"},h._gridpath=function(t){return r.pathArc(h.r2p(t.x)+o)};var g=I(u);r.radialTickLayout!==g&&(i[\"radial-axis\"].selectAll(\".xtick\").remove(),r.radialTickLayout=g),d&&(h.setScale(),p(n,h,!0));var v=r.radialAxisAngle=r.vangles?L(P(C(u.angle),r.vangles)):u.angle,m=R(l,c)+B(-v);D(i[\"radial-axis\"],d&&(u.showticklabels\|\|u.ticks),{transform:m}),D(i[\"radial-grid\"],d&&u.showgrid,{transform:R(l,c)}).selectAll(\"path\").attr(\"transform\",null),D(i[\"radial-line\"].select(\"line\"),d&&u.showline,{x1:o,y1:0,x2:a,y2:0,transform:m}).attr(\"stroke-width\",u.linewidth).call(s.stroke,u.linecolor)},O.updateRadialAxisTitle=function(t,e,r){var n=this.gd,i=this.radius,a=this.cx,o=this.cy,s=e.radialaxis,c=this.id+\"title\",u=void 0!==r?r:this.radialAxisAngle,f=C(u),h=Math.cos(f),p=Math.sin(f),d=0;if(s.title){var g=l.bBox(this.layers[\"radial-axis\"].node()).height,v=s.titlefont.size;d=\"counterclockwise\"===s.side?-g-.4v:g+.8v}this.layers[\"radial-axis-title\"]=m.draw(n,c,{propContainer:s,propName:this.id+\".radialaxis.title\",placeholder:S(n,\"Click to enter radial axis title\"),attributes:{x:a+i/2h+dp,y:o-i/2p+dh,\"text-anchor\":\"middle\"},transform:{rotate:-u}})},O.updateAngularAxis=function(t,e){var r=this,i=r.gd,a=r.layers,l=r.radius,c=r.innerRadius,u=r.cx,f=r.cy,h=e.angularaxis,d=r.angularAxis;r.fillViewInitialKey(\"angularaxis.rotation\",h.rotation),d.setGeometry();var g=function(t){return d.t2g(t.x)};\"linear\"===d.type&&\"radians\"===d.thetaunit&&(d.tick0=L(d.tick0),d.dtick=L(d.dtick)),\"category\"===d.type&&(d._tickFilter=function(t){return o.isAngleInsideSector(g(t),r.sectorInRad)}),d._transfn=function(t){var e=n.select(this),r=e&&e.node();if(r&&e.classed(\"angularaxisgrid\"))return\"\";var i=g(t),a=R(u+lMath.cos(i),f-lMath.sin(i));return r&&e.classed(\"ticks\")&&(a+=B(-L(i))),a},d._gridpath=function(t){var e=g(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[u+cr,f-cn]+\"L\"+[u+lr,f-ln]};var v=\"outside\"!==h.ticks?.7:.5;d._labelx=function(t){var e=g(t),r=d._labelStandoff,n=d._pad;return(0===j(e)?0:Math.cos(e)(r+n+vt.fontSize))+N(e)(t.dx+r+n)},d._labely=function(t){var e=g(t),r=d._labelStandoff,n=d._labelShift,i=d._pad;return t.dy+t.fontSizeM-n+-Math.sin(e)(r+i+vt.fontSize)},d._labelanchor=function(t,e){var r=g(e);return 0===j(r)?N(r)>0?\"start\":\"end\":\"middle\"};var m,y=I(h);r.angularTickLayout!==y&&(a[\"angular-axis\"].selectAll(\".\"+d._id+\"tick\").remove(),r.angularTickLayout=y),d.setScale(),p(i,d,!0),\"linear\"===e.gridshape?(m=d._vals.map(g),o.angleDelta(m[0],m[1])<0&&(m=m.slice().reverse())):m=null,r.vangles=m,D(a[\"angular-line\"].select(\"path\"),h.showline,{d:r.pathSubplot(),transform:R(u,f)}).attr(\"stroke-width\",h.linewidth).call(s.stroke,h.linecolor)},O.updateFx=function(t,e){this.gd._context.staticPlot\|\|(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1),this.updateMainDrag(t))},O.updateMainDrag=function(t){var e=this,r=e.gd,o=e.layers,s=t._zoomlayer,l=A.MINZOOM,c=A.OFFEDGE,u=e.radius,f=e.innerRadius,h=e.cx,p=e.cy,m=e.cxx,_=e.cyy,w=e.sectorInRad,k=e.vangles,M=e.radialAxis,S=T.clampTiny,E=T.findXYatLength,C=T.findEnclosingVertexAngles,L=A.cornerHalfWidth,z=A.cornerLen/2,O=d.makeDragger(o,\"path\",\"maindrag\",\"crosshair\");n.select(O).attr(\"d\",e.pathSubplot()).attr(\"transform\",R(h,p));var I,P,D,B,F,N,j,V,U,q={element:O,gd:r,subplot:e.id,plotinfo:{id:e.id,xaxis:e.xaxis,yaxis:e.yaxis},xaxes:[e.xaxis],yaxes:[e.yaxis]};function H(t,e){return Math.sqrt(tt+ee)}function G(t,e){return H(t-m,e-_)}function W(t,e){return Math.atan2(_-e,t-m)}function Y(t,e){return[tMath.cos(e),tMath.sin(-e)]}function X(t,r){if(0===t)return e.pathSector(2L);var n=z/t,i=r-n,a=r+n,o=Math.max(0,Math.min(t,u)),s=o-L,l=o+L;return\"M\"+Y(s,i)+\"A\"+[s,s]+\" 0,0,0 \"+Y(s,a)+\"L\"+Y(l,a)+\"A\"+[l,l]+\" 0,0,1 \"+Y(l,i)+\"Z\"}function Z(t,r,n){if(0===t)return e.pathSector(2L);var 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n={};n.attributes=t(\"./attributes\"),n.layoutAttributes=t(\"./layout_attributes\"),n.supplyDefaults=t(\"./defaults\").supplyDefaults,n.supplyLayoutDefaults=t(\"./layout_defaults\").supplyLayoutDefaults,n.calc=t(\"./calc\"),n.crossTraceCalc=t(\"./cross_trace_calc\").crossTraceCalc,n.plot=t(\"./plot\").plot,n.style=t(\"./style\").style,n.styleOnSelect=t(\"./style\").styleOnSelect,n.hoverPoints=t(\"./hover\").hoverPoints,n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"box\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"boxLayout\",\"zoomScale\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":756,\"./attributes\":859,\"./calc\":860,\"./cross_trace_calc\":861,\"./defaults\":862,\"./event_data\":863,\"./hover\":864,\"./layout_attributes\":866,\"./layout_defaults\":867,\"./plot\":868,\"./select\":869,\"./style\":870}],866:[function(t,e,r){\"use 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o=t._fullLayout,s=e.xaxis,f=e.yaxis,h=o._numBoxes,p=1-o.boxgap,d=\"group\"===o.boxmode&&h>1;i.makeTraceGroups(a,r,\"trace boxes\").each(function(t){var r=n.select(this),i=t[0],a=i.t,g=i.trace;e.isRangePlot\|\|(i.node3=r);var v,m,y=a.dPosp(1-o.boxgroupgap)/(d?h:1),x=d?2a.dPos((a.num+.5)/h-.5)p:0,b=yg.whiskerwidth;!0!==g.visible\|\|a.empty?r.remove():(\"h\"===g.orientation?(v=f,m=s):(v=s,m=f),a.bPos=x,a.bdPos=y,a.wdPos=b,a.wHover=a.dPos(d?p/h:1),l(r,{pos:v,val:m},g,a),c(r,{x:s,y:f},g,a),u(r,{pos:v,val:m},g,a))})},plotBoxAndWhiskers:l,plotPoints:c,plotBoxMean:u}},{\"../../components/drawing\":595,\"../../lib\":696,d3:148}],869:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r,n,i=t.cd,a=t.xaxis,o=t.yaxis,s=[];if(!1===e)for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts\|\|[]).length;n++)i[r].pts[n].selected=0;else for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts\|\|[]).length;n++){var l=i[r].pts[n],c=a.c2p(l.x),u=o.c2p(l.y);e.contains([c,u],null,l.i,t)?(s.push({pointNumber:l.i,x:a.c2d(l.x),y:o.c2d(l.y)}),l.selected=1):l.selected=0}return s}},{}],870:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/color\"),a=t(\"../../components/drawing\");e.exports={style:function(t,e){var r=e?e[0].node3:n.select(t).selectAll(\"g.trace.boxes\");r.style(\"opacity\",function(t){return t[0].trace.opacity}),r.each(function(e){var r=n.select(this),o=e[0].trace,s=o.line.width;function l(t,e,r,n){t.style(\"stroke-width\",e+\"px\").call(i.stroke,r).call(i.fill,n)}var c=r.selectAll(\"path.box\");if(\"candlestick\"===o.type)c.each(function(t){var e=n.select(this),r=o[t.dir];l(e,r.line.width,r.line.color,r.fillcolor),e.style(\"opacity\",o.selectedpoints&&!t.selected?.3:1)});else{l(c,s,o.line.color,o.fillcolor),r.selectAll(\"path.mean\").style({\"stroke-width\":s,\"stroke-dasharray\":2s+\"px,\"+s+\"px\"}).call(i.stroke,o.line.color);var u=r.selectAll(\"path.point\");a.pointStyle(u,o,t)}})},styleOnSelect:function(t,e){var r=e[0].node3,n=e[0].trace,i=r.selectAll(\"path.point\");n.selectedpoints?a.selectedPointStyle(i,n):a.pointStyle(i,n,t)}}},{\"../../components/color\":570,\"../../components/drawing\":595,d3:148}],871:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").extendFlat,i=t(\"../ohlc/attributes\"),a=t(\"../box/attributes\");function o(t){return{line:{color:n({},a.line.color,{dflt:t}),width:a.line.width,editType:\"style\"},fillcolor:a.fillcolor,editType:\"style\"}}e.exports={x:i.x,open:i.open,high:i.high,low:i.low,close:i.close,line:{width:n({},a.line.width,{}),editType:\"style\"},increasing:o(i.increasing.line.color.dflt),decreasing:o(i.decreasing.line.color.dflt),text:i.text,whiskerwidth:n({},a.whiskerwidth,{dflt:0}),hoverlabel:i.hoverlabel}},{\"../../lib\":696,\"../box/attributes\":859,\"../ohlc/attributes\":991}],872:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../ohlc/calc\").calcCommon;function o(t,e,r,n){return{min:r,q1:Math.min(t,n),med:n,q3:Math.max(t,n),max:e}}e.exports=function(t,e){var r=t._fullLayout,s=i.getFromId(t,e.xaxis),l=i.getFromId(t,e.yaxis),c=s.makeCalcdata(e,\"x\"),u=a(t,e,c,l,o);return u.length?(n.extendFlat(u[0].t,{num:r._numBoxes,dPos:n.distinctVals(c).minDiff/2,posLetter:\"x\",valLetter:\"y\"}),r._numBoxes++,u):[{t:{empty:!0}}]}},{\"../../lib\":696,\"../../plots/cartesian/axes\":744,\"../ohlc/calc\":992}],873:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../ohlc/ohlc_defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var a=r(n+\".line.color\");r(n+\".line.width\",e.line.width),r(n+\".fillcolor\",i.addOpacity(a,.5))}e.exports=function(t,e,r,i){function l(r,i){return 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n=t(\"../../components/colorscale/defaults\"),i=t(\"./label_defaults\");e.exports=function(t,e,r,a,o){var s,l=r(\"contours.coloring\"),c=\"\";\"fill\"===l&&(s=r(\"contours.showlines\")),!1!==s&&(\"lines\"!==l&&(c=r(\"line.color\",\"#000\")),r(\"line.width\",.5),r(\"line.dash\")),\"none\"!==l&&(!0!==t.showlegend&&(e.showlegend=!1),e._dfltShowLegend=!1,n(t,e,a,r,{prefix:\"\",cLetter:\"z\"})),r(\"line.smoothing\"),i(r,a,c,o)}},{\"../../components/colorscale/defaults\":580,\"./label_defaults\":931}],938:[function(t,e,r){\"use strict\";var 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g={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",g);var v={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",v)}},{\"../../components/colorscale/defaults\":580,\"../../components/colorscale/has_colorscale\":584,\"../../lib\":696,\"../../plots/array_container_defaults\":740,\"../../plots/domain\":770,\"../parcoords/merge_length\":1015,\"./attributes\":1e3}],1004:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.colorbar={container:\"line\",min:\"cmin\",max:\"cmax\"},n.moduleType=\"trace\",n.name=\"parcats\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"noOpacity\"],n.meta={},e.exports=n},{\"./attributes\":1e3,\"./base_plot\":1001,\"./calc\":1002,\"./defaults\":1003,\"./plot\":1006}],1005:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plot_api/plot_api\"),a=t(\"../../components/fx\"),o=t(\"../../lib\"),s=t(\"../../components/drawing\"),l=t(\"tinycolor2\"),c=t(\"../../lib/svg_text_utils\");function u(t,e,r,i){var a=t.map(function(t,e,r){var n,i=r[0],a=e.margin\|\|{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l(s.x[1]-s.x[0])),f=Math.floor(c(s.y[1]-s.y[0])),h=s.x[0]l+a.l,p=e.height-s.y[1]e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:o.hoverinfo.split(\"+\");var g={key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};i.dimensions&&(R(g),D(g));return g}.bind(0,e,r)),l=i.selectAll(\"g.parcatslayer\").data([null]);l.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var u=l.selectAll(\"g.trace.parcats\").data(a,f),v=u.enter().append(\"g\").attr(\"class\",\"trace parcats\");u.attr(\"transform\",function(t){return\"translate(\"+t.x+\", \"+t.y+\")\"}),v.append(\"g\").attr(\"class\",\"paths\");var x=u.select(\"g.paths\").selectAll(\"path.path\").data(function(t){return t.paths},f);x.attr(\"fill\",function(t){return t.model.color});var w=x.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.model.color}).attr(\"fill-opacity\",0);y(w),x.attr(\"d\",function(t){return t.svgD}),w.empty()\|\|x.sort(p),x.exit().remove(),x.on(\"mouseover\",d).on(\"mouseout\",g).on(\"click\",m),v.append(\"g\").attr(\"class\",\"dimensions\");var k=u.select(\"g.dimensions\").selectAll(\"g.dimension\").data(function(t){return t.dimensions},f);k.enter().append(\"g\").attr(\"class\",\"dimension\"),k.attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),k.exit().remove();var M=k.selectAll(\"g.category\").data(function(t){return t.categories},f),A=M.enter().append(\"g\").attr(\"class\",\"category\");M.attr(\"transform\",function(t){return\"translate(0, \"+t.y+\")\"}),A.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),M.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",function(t){return t.width}).attr(\"height\",function(t){return t.height}),b(A);var z=M.selectAll(\"rect.bandrect\").data(function(t){return t.bands},f);z.each(function(){o.raiseToTop(this)}),z.attr(\"fill\",function(t){return t.color});var O=z.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.color}).attr(\"fill-opacity\",0);z.attr(\"fill\",function(t){return t.color}).attr(\"width\",function(t){return t.width}).attr(\"height\",function(t){return t.height}).attr(\"y\",function(t){return t.y}).attr(\"cursor\",function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"}),_(O),z.exit().remove(),A.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var I=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",function(t){return h(t)?\"start\":\"end\"}).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",I+\" -1px 1px 2px, \"+I+\" 1px 1px 2px, \"+I+\" 1px -1px 2px, \"+I+\" -1px -1px 2px\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",function(t){return h(t)?t.width+5:-5}).attr(\"y\",function(t){return t.height/2}).text(function(t){return t.model.categoryLabel}).each(function(t){s.font(n.select(this),t.parcatsViewModel.categorylabelfont),c.convertToTspans(n.select(this),e)}),A.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"}).attr(\"x\",function(t){return t.width/2}).attr(\"y\",-5).text(function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null}).each(function(t){s.font(n.select(this),t.parcatsViewModel.labelfont)}),M.selectAll(\"rect.bandrect\").on(\"mouseover\",T).on(\"mouseout\",S),M.exit().remove(),k.call(n.behavior.drag().origin(function(t){return{x:t.x,y:0}}).on(\"dragstart\",E).on(\"drag\",C).on(\"dragend\",L)),u.each(function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")}),u.exit().remove()}function f(t){return t.key}function h(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function p(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function d(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){o.raiseToTop(this),x(n.select(this));var e=v(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var r,i,s,c=n.mouse(this)[0],u=t.parcatsViewModel.graphDiv,f=u._fullLayout,h=f._paperdiv.node().getBoundingClientRect(),p=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(s=0;s<t.leftXs.length-1;s++)if(t.leftXs[s]+t.dimWidths[s]-2<=c&&c<=t.leftXs[s+1]+2){var d=t.parcatsViewModel.dimensions[s],g=t.parcatsViewModel.dimensions[s+1];r=(d.x+d.width+g.x)/2,i=(t.topYs[s]+t.topYs[s+1]+t.height)/2;break}var m=t.parcatsViewModel.x+r,y=t.parcatsViewModel.y+i,b=l.mostReadable(t.model.color,[\"black\",\"white\"]),_=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&_.push([\"Count:\",t.model.count].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&_.push([\"P:\",(t.model.count/t.parcatsViewModel.model.count).toFixed(3)].join(\" \"));var w=_.join(\"<br>\"),k=n.mouse(u)[0];a.loneHover({x:m-h.left+p.left,y:y-h.top+p.top,text:w,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:b,idealAlign:k<m?\"right\":\"left\"},{container:f._hoverlayer.node(),outerContainer:f._paper.node(),gd:u})}}}function g(t){if(!t.parcatsViewModel.dragDimension&&(y(n.select(this)),a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(p),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=v(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event})}}function v(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function m(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=v(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event})}}function y(t){t.attr(\"fill\",function(t){return t.model.color}).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function x(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",function(t){return l.mostReadable(t.model.color,[\"black\",\"white\"])}).attr(\"stroke-width\",.3)}function b(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function _(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function w(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter(function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color})}function k(t,e,r){var i=n.select(t).datum().parcatsViewModel.graphDiv,a=n.select(t.parentNode).selectAll(\"rect.bandrect\"),o=[];a.each(function(t){w(t).each(function(t){Array.prototype.push.apply(o,v(t))})}),i.emit(e,{points:o,event:r})}function M(t,e,r){var i=n.select(t).datum(),a=i.parcatsViewModel.graphDiv,o=w(i),s=[];o.each(function(t){Array.prototype.push.apply(s,v(t))}),a.emit(e,{points:s,event:r})}function A(t,e){var r,i,a=n.select(e.parentNode).select(\"rect.catrect\"),o=a.node().getBoundingClientRect(),s=a.datum(),l=s.parcatsViewModel,c=l.model.dimensions[s.model.dimensionInd],u=o.top+o.height/2;l.dimensions.length>1&&c.displayInd===l.dimensions.length-1?(r=o.left,i=\"left\"):(r=o.left+o.width,i=\"right\");var f=[];-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&f.push([\"Count:\",s.model.count].join(\" \")),-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&f.push([\"P(\"+s.model.categoryLabel+\"):\",(s.model.count/s.parcatsViewModel.model.count).toFixed(3)].join(\" \"));var h=f.join(\"<br>\");return{x:r-t.left,y:u-t.top,text:h,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:i}}function T(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,s=i._paperdiv.node().getBoundingClientRect(),c=t.parcatsViewModel.hoveron;if(\"color\"===c?(!function(t){var e=n.select(t).datum(),r=w(e);x(r),r.each(function(){o.raiseToTop(this)}),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter(function(t){return t.color===e.color}).each(function(){o.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)})}(this),M(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each(function(t){var e=w(t);x(e),e.each(function(){o.raiseToTop(this)})}),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),k(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===c?e=A(s,this):\"color\"===c?e=function(t,e){var r,i,a=e.getBoundingClientRect(),o=n.select(e).datum(),s=o.categoryViewModel,c=s.parcatsViewModel,u=c.model.dimensions[s.model.dimensionInd],f=a.y+a.height/2;c.dimensions.length>1&&u.displayInd===c.dimensions.length-1?(r=a.left,i=\"left\"):(r=a.left+a.width,i=\"right\");var h=s.model.categoryLabel,p=o.parcatsViewModel.model.count,d=0;o.categoryViewModel.bands.forEach(function(t){t.color===o.color&&(d+=t.count)});var g=s.model.count,v=0;c.pathSelection.each(function(t){t.model.color===o.color&&(v+=t.model.count)});var m=[];if(-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&m.push([\"Count:\",d].join(\" \")),-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")){var y=\"P(color \\u2229 \"+h+\"): \"+(d/p).toFixed(3);m.push(y);var x=\"P(\"+h+\" \| color): \"+(d/v).toFixed(3);m.push(x);var b=\"P(color \| \"+h+\"): \"+(d/g).toFixed(3);m.push(b)}var _=m.join(\"<br>\"),w=l.mostReadable(o.color,[\"black\",\"white\"]);return{x:r-t.left,y:f-t.top,text:_,color:o.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:w,fontSize:10,idealAlign:i}}(s,this):\"dimension\"===c&&(e=function(t,e){var r=[];return n.select(e.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each(function(){r.push(A(t,this))}),r}(s,this)),e&&a.multiHovers(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function S(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(y(e.pathSelection),b(e.dimensionSelection.selectAll(\"g.category\")),_(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),a.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(p),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?M(this,\"plotly_unhover\",n.event):k(this,\"plotly_unhover\",n.event)}}function E(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each(function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map(function(t){return t.displayInd}),e.model.dragY=e.y,o.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each(function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)}))}),t.parcatsViewModel.dragDimension=t,a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function C(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd\|\|\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}R(t.parcatsViewModel),D(t.parcatsViewModel),I(t.parcatsViewModel),O(t.parcatsViewModel)}}function L(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),a=t.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),o=t.initialDragDimensionDisplayInds.some(function(t,e){return t!==a[e]});o&&a.forEach(function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r});var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map(function(t){return t.displayInd});if(s=t.initialDragCategoryDisplayInds.some(function(t,e){return t!==l[e]})){var c=t.model.categories.slice().sort(function(t,e){return t.displayInd-e.displayInd}),u=c.map(function(t){return t.categoryValue}),f=c.map(function(t){return t.categoryLabel});e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):k(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,R(t.parcatsViewModel),D(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each(function(){I(t.parcatsViewModel,!0),O(t.parcatsViewModel,!0)}).each(\"end\",function(){(o\|\|s)&&i.restyle(t.parcatsViewModel.graphDiv,e,[r])})}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function O(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data(function(t){return t.paths},f),(r=t.pathSelection,e?r.transition():r).attr(\"d\",function(t){return t.svgD})}function I(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data(function(t){return t.dimensions},f);var i=t.dimensionSelection.selectAll(\"g.category\").data(function(t){return t.categories},f);r(t.dimensionSelection).attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),r(i).attr(\"transform\",function(t){return\"translate(0, \"+t.y+\")\"}),i.select(\".dimlabel\").text(function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null}),i.select(\".catlabel\").attr(\"text-anchor\",function(t){return h(t)?\"start\":\"end\"}).attr(\"x\",function(t){return h(t)?t.width+5:-5}).each(function(t){var e,r;h(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)});var a=i.selectAll(\"rect.bandrect\").data(function(t){return t.bands},f),s=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.color}).attr(\"fill-opacity\",0);a.attr(\"fill\",function(t){return t.color}).attr(\"width\",function(t){return t.width}).attr(\"height\",function(t){return t.height}).attr(\"y\",function(t){return t.y}),_(s),a.each(function(){o.raiseToTop(this)}),a.exit().remove()}function P(t,e,r,i,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=n.interpolateNumber(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+i+\" \",u+=\"l -\"+r[r.length-1]+\",0 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../components/colorbar/attributes\"),a=t(\"../../plots/cartesian/layout_attributes\"),o=t(\"../../plots/font_attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:s({name:\"parcoords\",trace:!0,editType:\"calc\"}),hoverlabel:void 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a=Math.max(0,-r.newExtent[0]),o=Math.max(0,r.newExtent[1]-1);r.newExtent[0]+=a,r.newExtent[1]-=o,r.grabbingBar&&(r.newExtent[1]+=a,r.newExtent[0]-=o),t.brush.filterSpecified=!0,r.extent=r.stayingIntervals.concat([r.newExtent]),r.brushCallback(t),_(this.parentNode)}).on(\"dragend\",function(t){i.event.sourceEvent.stopPropagation();var e=t.brush,r=e.filter,n=e.svgBrush,a=n.grabbingBar;if(n.grabbingBar=!1,n.grabLocation=void 0,t.parent.inBrushDrag=!1,x(),!n.wasDragged)return n.wasDragged=void 0,n.clickableOrdinalRange?e.filterSpecified&&t.multiselect?n.extent.push(n.clickableOrdinalRange):(n.extent=[n.clickableOrdinalRange],e.filterSpecified=!0):a?(n.extent=n.stayingIntervals,0===n.extent.length&&A(e)):A(e),n.brushCallback(t),_(this.parentNode),void n.brushEndCallback(e.filterSpecified?r.getConsolidated():[]);var o=function(){r.set(r.getConsolidated())};if(t.ordinal){var s=t.unitTickvals;s[s.length-1]<s[0]&&s.reverse(),n.newExtent=[h(s,n.newExtent[0],n.stayingIntervals),p(s,n.newExtent[1],n.stayingIntervals)];var l=n.newExtent[1]>n.newExtent[0];n.extent=n.stayingIntervals.concat(l?[n.newExtent]:[]),n.extent.length\|\|A(e),n.brushCallback(t),l?_(this.parentNode,o):(o(),_(this.parentNode))}else o();n.brushEndCallback(e.filterSpecified?r.getConsolidated():[])}))}function M(t,e){return t[0]-e[0]}function A(t){t.filterSpecified=!1,t.svgBrush.extent=[[0,1]]}function T(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){r=n.map(function(t){return t.slice().sort(s)}).sort(M),t=T(r),e=r.reduce(function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]},[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return 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r=t.selectAll(\".highlight-shadow\").data(o);r.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",n.bar.strokeColor).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),r.attr(\"y1\",function(t){return t.height}).call(b);var i=t.selectAll(\".highlight\").data(o);i.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",function(t){return t.height}).call(b)}(e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map(function(t){return t.sort(s)}),t=e.multiselect?T(t.sort(M)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map(function(t){var e=[h(r,t[0],[]),p(r,t[1],[])];if(e[1]>e[0])return e}).filter(function(t){return t})).length)return}return 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t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.width,height:this.height})}),window.setTimeout(function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")},60)}},{\"../../constants/xmlns_namespaces\":674,\"../../plots/get_data\":781,\"./plot\":1017,d3:148}],1010:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/has_colorscale\"),i=t(\"../../components/colorscale/calc\"),a=t(\"../../lib\"),o=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r=!!e.line.colorscale&&a.isArrayOrTypedArray(e.line.color),s=r?e.line.color:function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),l=r?e.line.colorscale:[[0,e.line.color],[1,e.line.color]];return 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strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/has_colorscale\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"./attributes\"),c=t(\"./axisbrush\"),u=t(\"./constants\").maxDimensionCount,f=t(\"./merge_length\");function h(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r(\"values\"),a=r(\"visible\");if(i&&i.length\|\|(a=e.visible=!1),a){r(\"label\"),r(\"tickvals\"),r(\"ticktext\"),r(\"tickformat\"),r(\"range\"),r(\"multiselect\");var o=r(\"constraintrange\");o&&(e.constraintrange=c.cleanRanges(o,e))}}e.exports=function(t,e,r,c){function p(r,i){return n.coerce(t,e,l,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>u&&(n.log(\"parcoords traces support up to \"+u+\" dimensions at the moment\"),d.splice(u));var g=s(t,e,{name:\"dimensions\",handleItemDefaults:h}),v=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,c,p);o(e,c,p),Array.isArray(g)&&g.length\|\|(e.visible=!1),f(e,g,\"values\",v);var m={family:c.font.family,size:Math.round(c.font.size/1.2),color:c.font.color};n.coerceFont(p,\"labelfont\",m),n.coerceFont(p,\"tickfont\",m),n.coerceFont(p,\"rangefont\",m)}},{\"../../components/colorscale/defaults\":580,\"../../components/colorscale/has_colorscale\":584,\"../../lib\":696,\"../../plots/array_container_defaults\":740,\"../../plots/domain\":770,\"./attributes\":1007,\"./axisbrush\":1008,\"./constants\":1011,\"./merge_length\":1015}],1013:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.colorbar={container:\"line\",min:\"cmin\",max:\"cmax\"},n.moduleType=\"trace\",n.name=\"parcoords\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"gl\",\"regl\",\"noOpacity\"],n.meta={},e.exports=n},{\"./attributes\":1007,\"./base_plot\":1009,\"./calc\":1010,\"./defaults\":1012,\"./plot\":1017}],1014:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 p0, p1, p2, p3,\\n p4, p5, p6, p7,\\n p8, p9, pa, pb,\\n pc, pd, pe;\\n\\nattribute vec4 pf;\\n\\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution,\\n viewBoxPosition,\\n viewBoxSize;\\n\\nuniform sampler2D palette;\\nuniform sampler2D mask;\\nuniform float maskHeight;\\n\\nuniform vec2 colorClamp;\\n\\nvarying vec4 fragColor;\\n\\nvec4 unit_1 = vec4(1, 1, 1, 1);\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) unit_1, unit_1);\\n}\\n\\nfloat axisY(\\n float x,\\n mat4 d[4],\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\\n ) {\\n\\n float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\\n float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\\n return y1 * (1.0 - x) + y2 * x;\\n}\\n\\nconst int bitsPerByte = 8;\\n\\nint mod2(int a) {\\n return a - 2 * (a / 2);\\n}\\n\\nint mod8(int a) {\\n return a - 8 * (a / 8);\\n}\\n\\nvec4 zero = vec4(0, 0, 0, 0);\\nvec4 unit_0 = vec4(1, 1, 1, 1);\\nvec2 xyProjection = vec2(1, 1);\\n\\nmat4 mclamp(mat4 m, mat4 lo, mat4 hi) {\\n return mat4(clamp(m[0], lo[0], hi[0]),\\n clamp(m[1], lo[1], hi[1]),\\n clamp(m[2], lo[2], hi[2]),\\n clamp(m[3], lo[3], hi[3]));\\n}\\n\\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\\n return mclamp(p, lo, hi) == p;\\n}\\n\\nbool withinBoundingBox(\\n mat4 d[4],\\n mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD\\n ) {\\n\\n return mshow(d[0], loA, hiA) &&\\n mshow(d[1], loB, hiB) &&\\n mshow(d[2], loC, hiC) &&\\n mshow(d[3], loD, hiD);\\n}\\n\\nbool withinRasterMask(mat4 d[4], sampler2D mask, float height) {\\n bool result = true;\\n int bitInByteStepper;\\n float valY, valueY, scaleX;\\n int hit, bitmask, valX;\\n for(int i = 0; i < 4; i++) {\\n for(int j = 0; j < 4; j++) {\\n for(int k = 0; k < 4; k++) {\\n bitInByteStepper = mod8(j * 4 + k);\\n valX = i * 2 + j / 2;\\n valY = d[i][j][k];\\n valueY = valY * (height - 1.0) + 0.5;\\n scaleX = (float(valX) + 0.5) / 8.0;\\n hit = int(texture2D(mask, vec2(scaleX, (valueY + 0.5) / height))[3] * 255.0) / int(pow(2.0, float(bitInByteStepper)));\\n result = result && mod2(hit) == 1;\\n }\\n }\\n }\\n return result;\\n}\\n\\nvec4 position(\\n float depth,\\n vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\\n mat4 dims[4],\\n float signum,\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D,\\n mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD,\\n sampler2D mask, float maskHeight\\n ) {\\n\\n float x = 0.5 * signum + 0.5;\\n float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\\n\\n float show = float(\\n withinBoundingBox(dims, loA, hiA, loB, hiB, loC, hiC, loD, hiD)\\n && withinRasterMask(dims, mask, maskHeight)\\n );\\n\\n vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\\n float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n return vec4(\\n xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n depthOrHide,\\n 1.0\\n );\\n}\\n\\nvoid main() {\\n\\n float prominence = abs(pf[3]);\\n\\n mat4 p[4];\\n p[0] = mat4(p0, p1, p2, p3);\\n p[1] = mat4(p4, p5, p6, p7);\\n p[2] = mat4(p8, p9, pa, pb);\\n p[3] = mat4(pc, pd, pe, abs(pf));\\n\\n gl_Position = position(\\n 1.0 - prominence,\\n resolution, viewBoxPosition, viewBoxSize,\\n p,\\n sign(pf[3]),\\n dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n loA, hiA, loB, hiB, loC, hiC, loD, hiD,\\n mask, maskHeight\\n );\\n\\n float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\\n fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 p0, p1, p2, p3,\\n p4, p5, p6, p7,\\n p8, p9, pa, pb,\\n pc, pd, pe;\\n\\nattribute vec4 pf;\\n\\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D;\\n\\nuniform vec2 resolution,\\n viewBoxPosition,\\n viewBoxSize;\\n\\nuniform sampler2D palette;\\n\\nuniform vec2 colorClamp;\\n\\nvarying vec4 fragColor;\\n\\nvec2 xyProjection = vec2(1, 1);\\n\\nvec4 unit = vec4(1, 1, 1, 1);\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) * unit, unit);\\n}\\n\\nfloat axisY(\\n float x,\\n mat4 d[4],\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\\n ) {\\n\\n float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\\n float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\\n return y1 * (1.0 - x) + y2 * x;\\n}\\n\\nvec4 position(\\n float depth,\\n vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\\n mat4 dims[4],\\n float signum,\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\\n ) {\\n\\n float x = 0.5 * signum + 0.5;\\n float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\\n\\n vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\\n\\n return vec4(\\n xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n depth,\\n 1.0\\n );\\n}\\n\\nvoid main() {\\n\\n float prominence = abs(pf[3]);\\n\\n mat4 p[4];\\n p[0] = mat4(p0, p1, p2, p3);\\n p[1] = mat4(p4, p5, p6, p7);\\n p[2] = mat4(p8, p9, pa, pb);\\n p[3] = mat4(pc, pd, pe, abs(pf));\\n\\n gl_Position = position(\\n 1.0 - prominence,\\n resolution, viewBoxPosition, viewBoxSize,\\n p,\\n sign(pf[3]),\\n dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D\\n );\\n\\n float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\\n fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 p0, p1, p2, p3,\\n p4, p5, p6, p7,\\n p8, p9, pa, pb,\\n pc, pd, pe;\\n\\nattribute vec4 pf;\\n\\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution,\\n viewBoxPosition,\\n viewBoxSize;\\n\\nuniform sampler2D mask;\\nuniform float maskHeight;\\n\\nuniform vec2 colorClamp;\\n\\nvarying vec4 fragColor;\\n\\nvec4 unit_1 = vec4(1, 1, 1, 1);\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) * unit_1, unit_1);\\n}\\n\\nfloat axisY(\\n float x,\\n mat4 d[4],\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\\n ) {\\n\\n float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\\n float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\\n return y1 * (1.0 - x) + y2 * x;\\n}\\n\\nconst int bitsPerByte = 8;\\n\\nint mod2(int a) {\\n return a - 2 * (a / 2);\\n}\\n\\nint mod8(int a) {\\n return a - 8 * (a / 8);\\n}\\n\\nvec4 zero = vec4(0, 0, 0, 0);\\nvec4 unit_0 = vec4(1, 1, 1, 1);\\nvec2 xyProjection = vec2(1, 1);\\n\\nmat4 mclamp(mat4 m, mat4 lo, mat4 hi) {\\n return mat4(clamp(m[0], lo[0], hi[0]),\\n clamp(m[1], lo[1], hi[1]),\\n clamp(m[2], lo[2], hi[2]),\\n clamp(m[3], lo[3], hi[3]));\\n}\\n\\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\\n return mclamp(p, lo, hi) == p;\\n}\\n\\nbool withinBoundingBox(\\n mat4 d[4],\\n mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD\\n ) {\\n\\n return mshow(d[0], loA, hiA) &&\\n mshow(d[1], loB, hiB) &&\\n mshow(d[2], loC, hiC) &&\\n mshow(d[3], loD, hiD);\\n}\\n\\nbool withinRasterMask(mat4 d[4], sampler2D mask, float height) {\\n bool result = true;\\n int bitInByteStepper;\\n float valY, valueY, scaleX;\\n int hit, bitmask, valX;\\n for(int i = 0; i < 4; i++) {\\n for(int j = 0; j < 4; j++) {\\n for(int k = 0; k < 4; k++) {\\n bitInByteStepper = mod8(j * 4 + k);\\n valX = i * 2 + j / 2;\\n valY = d[i][j][k];\\n valueY = valY * (height - 1.0) + 0.5;\\n scaleX = (float(valX) + 0.5) / 8.0;\\n hit = int(texture2D(mask, vec2(scaleX, (valueY + 0.5) / height))[3] * 255.0) / int(pow(2.0, float(bitInByteStepper)));\\n result = result && mod2(hit) == 1;\\n }\\n }\\n }\\n return result;\\n}\\n\\nvec4 position(\\n float depth,\\n vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\\n mat4 dims[4],\\n float signum,\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D,\\n mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD,\\n sampler2D mask, float maskHeight\\n ) {\\n\\n float x = 0.5 * signum + 0.5;\\n float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\\n\\n float show = float(\\n withinBoundingBox(dims, loA, hiA, loB, hiB, loC, hiC, loD, hiD)\\n && withinRasterMask(dims, mask, maskHeight)\\n );\\n\\n vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\\n float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n return vec4(\\n xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n depthOrHide,\\n 1.0\\n );\\n}\\n\\nvoid main() {\\n\\n float prominence = abs(pf[3]);\\n\\n mat4 p[4];\\n p[0] = mat4(p0, p1, p2, p3);\\n p[1] = mat4(p4, p5, p6, p7);\\n p[2] = mat4(p8, p9, pa, pb);\\n p[3] = mat4(pc, pd, pe, abs(pf));\\n\\n gl_Position = position(\\n 1.0 - prominence,\\n resolution, viewBoxPosition, viewBoxSize,\\n p,\\n sign(pf[3]),\\n dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n loA, hiA, loB, hiB, loC, hiC, loD, hiD,\\n mask, maskHeight\\n );\\n\\n fragColor = vec4(pf.rgb, 1.0);\\n}\\n\"]),s=n([\"precision lowp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\\n\"]),l=t(\"../../lib\"),c=1e-6,u=1e-7,f=2048,h=64,p=2,d=4,g=8,v=h/g,m=[119,119,119],y=new Uint8Array(4),x=new Uint8Array(4),b={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function _(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function w(t,e,r,n,i,a){var o=a.key;r.drawCompleted\|\|(!function(t){t.read({x:0,y:0,width:1,height:1,data:y})}(t),r.drawCompleted=!0),function s(l){var c;c=Math.min(n,i-ln),a.offset=pln,a.count=pc,0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete 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O(t){r=t.model,n=t.viewModel,p=n.dimensions.slice(),d=p[0]?p[0].values.length:0;var e=r.lines,i=T?e.color.map(function(t,r){return r/e.color.length}):e.color,a=Math.max(1/255,Math.pow(1/i.length,1/3)),o=function(t,e,r){for(var n,i=e.length,a=[],o=0;o<t;o++)for(var s=0;s<h;s++)a.push(s<i?e[s].paddedUnitValues[o]:s===h-1?(n=r[o],Math.max(c,Math.min(1-c,n))):s>=h-4?k(o,h-2-s):.5);return a}(d,p,i);!function(t,e,r){for(var n=0;n<16;n++)t[\"p\"+n.toString(16)](M(e,r,n))}(C,d,o),L=S.texture(l.extendFlat({data:function(t,e,r){for(var n=[],i=0;i<256;i++){var a=t(i/255);n.push((e?m:a).concat(r))}return n}(r.unitToColor,A,Math.round(255(A?a:1)))},b))}var I=[0,1];var P=[];function D(t,e,n,i,a,o,s,c,u,f,h){var p,d,g,v,m=[t,e],y=[0,1].map(function(){return[0,1,2,3].map(function(){return new Float32Array(16)})});for(p=0;p<2;p++)for(v=m[p],d=0;d<4;d++)for(g=0;g<16;g++)y[p][d][g]=g+16d===v?1:0;var x=r.lines.canvasOverdrag,b=r.domain,_=r.canvasWidth,w=r.canvasHeight;return l.extendFlat({key:s,resolution:[_,w],viewBoxPosition:[n+x,i],viewBoxSize:[a,o],i:t,ii:e,dim1A:y[0][0],dim1B:y[0][1],dim1C:y[0][2],dim1D:y[0][3],dim2A:y[1][0],dim2B:y[1][1],dim2C:y[1][2],dim2D:y[1][3],colorClamp:I,scissorX:(c===u?0:n+x)+(r.pad.l-x)+r.layoutWidthb.x[0],scissorWidth:(c===f?_-n+x:a+.5)+(c===u?n+x:0),scissorY:i+r.pad.b+r.layoutHeightb.y[0],scissorHeight:o,viewportX:r.pad.l-x+r.layoutWidthb.x[0],viewportY:r.pad.b+r.layoutHeightb.y[0],viewportWidth:_,viewportHeight:w},h)}return{setColorDomain:function(t){I[0]=t[0],I[1]=t[1]},render:function(t,e,n){var i,a,o,s=t.length,l=1/0,c=-1/0;for(i=0;i<s;i++)t[i].dim2.canvasX>c&&(c=t[i].dim2.canvasX,o=i),t[i].dim1.canvasX<l&&(l=t[i].dim1.canvasX,a=i);0===s&&_(S,0,0,r.canvasWidth,r.canvasHeight);var h=A?{}:function(){var t,e,r,n=[0,1].map(function(){return[0,1,2,3].map(function(){return new Float32Array(16)})});for(t=0;t<2;t++)for(e=0;e<4;e++)for(r=0;r<16;r++){var i,a=r+16e;i=a<p.length?p[a].brush.filter.getBounds()[t]:t,n[t][e][r]=i+(2t-1)u}function o(t,e){var r=f-1;return[Math.max(0,Math.floor(e[0]r)),Math.min(r,Math.ceil(e[1]r))]}for(var s=Array.apply(null,new Array(fv)).map(function(){return 255}),l=0;l<p.length;l++){var c=l%g,h=(l-c)/g,d=Math.pow(2,c),m=p[l],x=m.brush.filter.get();if(!(x.length<2))for(var b=o(0,x[0])[1],_=1;_<x.length;_++){for(var w=o(0,x[_]),k=b+1;k<w[0];k++)s[kv+h]&=~d;b=Math.max(b,w[1])}}var M={shape:[v,f],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:s};return y?y(M):y=S.texture(M),{maskTexture:y,maskHeight:f,loA:n[0][0],loB:n[0][1],loC:n[0][2],loD:n[0][3],hiA:n[1][0],hiB:n[1][1],hiC:n[1][2],hiD:n[1][3]}}();for(i=0;i<s;i++){var m=t[i],x=m.dim1,b=x.crossfilterDimensionIndex,k=m.canvasX,M=m.canvasY,T=m.dim2.crossfilterDimensionIndex,C=m.panelSizeX,L=m.panelSizeY,O=k+C;if(e\|\|!P[b]\|\|P[b][0]!==k\|\|P[b][1]!==O){P[b]=[k,O];var I=D(b,T,k,M,C,L,x.crossfilterDimensionIndex,i,a,o,h);E.clearOnly=n,w(S,z,E,e?r.lines.blockLineCount:d,d,I)}}},readPixel:function(t,e){return S.read({x:t,y:e,width:1,height:1,data:x}),x},readPixels:function(t,e,r,n){var i=new Uint8Array(4rn);return S.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",L.destroy(),y&&y.destroy(),C)C[e].destroy()},update:O}}},{\"../../lib\":696,glslify:392}],1015:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){var i,a;for(n\|\|(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],1016:[function(t,e,r){\"use strict\";var n=t(\"./lines\"),i=t(\"./constants\"),a=t(\"../../lib\"),o=t(\"d3\"),s=t(\"../../components/drawing\"),l=t(\"../../lib/gup\"),c=l.keyFun,u=l.repeat,f=l.unwrap,h=t(\"./axisbrush\");function p(t){return!(\"visible\"in t)\|\|t.visible}function d(t){var e=t.range?t.range[0]:a.aggNums(Math.min,null,t.values,t._length),r=t.range?t.range[1]:a.aggNums(Math.max,null,t.values,t._length);return!isNaN(e)&&isFinite(e)\|\|(e=0),!isNaN(r)&&isFinite(r)\|\|(r=0),e===r&&(0===e?(e-=1,r+=1):(e=.9,r=1.1)),[e,r]}function g(t){return t.dimensions.some(function(t){return t.brush.filterSpecified})}function v(t,e,r){var n=f(e),s=n.trace,l=n.lineColor,c=n.cscale,u=s.line,h=s.domain,g=s.dimensions,v=t.width,m=s.labelfont,y=s.tickfont,x=s.rangefont,b=a.extendDeepNoArrays({},u,{color:l.map(o.scale.linear().domain(d({values:l,range:[u.cmin,u.cmax],_length:s._length}))),blockLineCount:i.blockLineCount,canvasOverdrag:i.overdragi.canvasPixelRatio}),_=Math.floor(v(h.x[1]-h.x[0])),w=Math.floor(t.height(h.y[1]-h.y[0])),k=t.margin\|\|{l:80,r:80,t:100,b:80},M=_,A=w;return{key:r,colCount:g.filter(p).length,dimensions:g,tickDistance:i.tickDistance,unitToColor:function(t){var e=t.map(function(t){return t[0]}),r=t.map(function(t){return o.rgb(t[1])}),n=\"rgb\".split(\"\").map(function(t){return o.scale.linear().clamp(!0).domain(e).range(r.map((n=t,function(t){return t[n]})));var n});return function(t){return n.map(function(e){return e(t)})}}(c),lines:b,labelFont:m,tickFont:y,rangeFont:x,layoutWidth:v,layoutHeight:t.height,domain:h,translateX:h.x[0]v,translateY:t.height-h.y[1]t.height,pad:k,canvasWidth:Mi.canvasPixelRatio+2b.canvasOverdrag,canvasHeight:Ai.canvasPixelRatio,width:M,height:A,canvasPixelRatio:i.canvasPixelRatio}}function m(t,e,r){var n=r.width,s=r.height,l=r.dimensions,c=r.canvasPixelRatio,u=function(t){return nt/Math.max(1,r.colCount-1)},f=i.verticalPadding/s,v=function(t,e){return o.scale.linear().range([e,t-e])}(s,i.verticalPadding),m={key:r.key,xScale:u,model:r,inBrushDrag:!1},y={};return m.dimensions=l.filter(p).map(function(n,l){var p=function(t,e){return o.scale.linear().domain(d(t)).range([e,1-e])}(n,f),x=y[n.label];y[n.label]=(x\|\|0)+1;var b=n.label+(x?\"__\"+x:\"\"),_=n.constraintrange,w=_&&_.length;w&&!Array.isArray(_[0])&&(_=[_]);var k=w?_.map(function(t){return t.map(p)}):[[0,1]],M=n.values;M.length>n._length&&(M=M.slice(0,n._length));var A,T=n.tickvals;function S(t,e){return{val:t,text:A[e]}}function E(t,e){return t.val-e.val}if(Array.isArray(T)&&T.length){A=n.ticktext,Array.isArray(A)&&A.length?A.length>T.length?A=A.slice(0,T.length):T.length>A.length&&(T=T.slice(0,A.length)):A=T.map(o.format(n.tickformat));for(var C=1;C<T.length;C++)if(T[C]<T[C-1]){for(var L=T.map(S).sort(E),z=0;z<T.length;z++)T[z]=L[z].val,A[z]=L[z].text;break}}else T=void 0;return{key:b,label:n.label,tickFormat:n.tickformat,tickvals:T,ticktext:A,ordinal:!!T,multiselect:n.multiselect,xIndex:l,crossfilterDimensionIndex:l,visibleIndex:n._index,height:s,values:M,paddedUnitValues:M.map(p),unitTickvals:T&&T.map(p),xScale:u,x:u(l),canvasX:u(l)c,unitToPaddedPx:v,domainScale:function(t,e,r,n,i){var a,s,l=d(r);return n?o.scale.ordinal().domain(n.map((a=o.format(r.tickformat),s=i,s?function(t,e){var r=s[e];return null==r?a(t):r}:a))).range(n.map(function(r){var n=(r-l[0])/(l[1]-l[0]);return t-e+n(2e-t)})):o.scale.linear().domain(l).range([t-e,e])}(s,i.verticalPadding,n,T,A),ordinalScale:function(t){if(t.tickvals){var e=d(t);return o.scale.ordinal().domain(t.tickvals).range(t.tickvals.map(function(t){return(t-e[0])/(e[1]-e[0])}))}}(n),parent:m,model:r,brush:h.makeBrush(t,w,k,function(){t.linePickActive(!1)},function(){var e=m;e.focusLayer&&e.focusLayer.render(e.panels,!0);var r=g(e);!t.contextShown()&&r?(e.contextLayer&&e.contextLayer.render(e.panels,!0),t.contextShown(!0)):t.contextShown()&&!r&&(e.contextLayer&&e.contextLayer.render(e.panels,!0,!0),t.contextShown(!1))},function(r){var i=m;if(i.focusLayer.render(i.panels,!0),i.pickLayer&&i.pickLayer.render(i.panels,!0),t.linePickActive(!0),e&&e.filterChanged){var o=p.invert,s=r.map(function(t){return t.map(o).sort(a.sorterAsc)}).sort(function(t,e){return t[0]-e[0]});e.filterChanged(i.key,n._index,s)}})}}),m}function y(t){t.classed(i.cn.axisExtentText,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"default\").style(\"user-select\",\"none\")}e.exports=function(t,e,r,l,p,d){var x,b,_=(x=!0,b=!1,{linePickActive:function(t){return arguments.length?x=!!t:x},contextShown:function(t){return arguments.length?b=!!t:b}}),w=l.filter(function(t){return f(t).trace.visible}).map(v.bind(0,p)).map(m.bind(0,_,d));r.each(function(t,e){return a.extendFlat(t,w[e])});var k=r.selectAll(\".gl-canvas\").each(function(t){t.viewModel=w[0],t.model=t.viewModel?t.viewModel.model:null}),M=null;k.filter(function(t){return t.pick}).style(\"pointer-events\",\"auto\").on(\"mousemove\",function(t){if(_.linePickActive()&&t.lineLayer&&d&&d.hover){var e=o.event,r=this.width,n=this.height,i=o.mouse(this),a=i[0],s=i[1];if(a<0\|\|s<0\|\|a>=r\|\|s>=n)return;var l=t.lineLayer.readPixel(a,n-1-s),c=0!==l[3],u=c?l[2]+256(l[1]+256l[0]):null,f={x:a,y:s,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:u};u!==M&&(c?d.hover(f):d.unhover&&d.unhover(f),M=u)}}),k.style(\"opacity\",function(t){return t.pick?.01:1}),e.style(\"background\",\"rgba(255, 255, 255, 0)\");var A=e.selectAll(\".\"+i.cn.parcoords).data(w,c);A.exit().remove(),A.enter().append(\"g\").classed(i.cn.parcoords,!0).style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\"),A.attr(\"transform\",function(t){return\"translate(\"+t.model.translateX+\",\"+t.model.translateY+\")\"});var T=A.selectAll(\".\"+i.cn.parcoordsControlView).data(u,c);T.enter().append(\"g\").classed(i.cn.parcoordsControlView,!0),T.attr(\"transform\",function(t){return\"translate(\"+t.model.pad.l+\",\"+t.model.pad.t+\")\"});var S=T.selectAll(\".\"+i.cn.yAxis).data(function(t){return t.dimensions},c);function E(t,e){for(var r=e.panels\|\|(e.panels=[]),n=t.data(),i=n.length-1,a=0;a<i;a++){var o=r[a]\|\|(r[a]={}),s=n[a],l=n[a+1];o.dim1=s,o.dim2=l,o.canvasX=s.canvasX,o.panelSizeX=l.canvasX-s.canvasX,o.panelSizeY=e.model.canvasHeight,o.y=0,o.canvasY=0}}S.enter().append(\"g\").classed(i.cn.yAxis,!0),T.each(function(t){E(S,t)}),k.each(function(t){if(t.viewModel){!t.lineLayer\|\|d?t.lineLayer=n(this,t):t.lineLayer.update(t),(t.key\|\|0===t.key)&&(t.viewModel[t.key]=t.lineLayer);var e=!t.context\|\|d;t.lineLayer.render(t.viewModel.panels,e)}}),S.attr(\"transform\",function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"}),S.call(o.behavior.drag().origin(function(t){return t}).on(\"drag\",function(t){var e=t.parent;_.linePickActive(!1),t.x=Math.max(-i.overdrag,Math.min(t.model.width+i.overdrag,o.event.x)),t.canvasX=t.xt.model.canvasPixelRatio,S.sort(function(t,e){return t.x-e.x}).each(function(e,r){e.xIndex=r,e.x=t===e?e.x:e.xScale(e.xIndex),e.canvasX=e.xe.model.canvasPixelRatio}),E(S,e),S.filter(function(e){return 0!==Math.abs(t.xIndex-e.xIndex)}).attr(\"transform\",function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"}),o.select(this).attr(\"transform\",\"translate(\"+t.x+\", 0)\"),S.each(function(r,n,i){i===t.parent.key&&(e.dimensions[n]=r)}),e.contextLayer&&e.contextLayer.render(e.panels,!1,!g(e)),e.focusLayer.render&&e.focusLayer.render(e.panels)}).on(\"dragend\",function(t){var e=t.parent;t.x=t.xScale(t.xIndex),t.canvasX=t.xt.model.canvasPixelRatio,E(S,e),o.select(this).attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),e.contextLayer&&e.contextLayer.render(e.panels,!1,!g(e)),e.focusLayer&&e.focusLayer.render(e.panels),e.pickLayer&&e.pickLayer.render(e.panels,!0),_.linePickActive(!0),d&&d.axesMoved&&d.axesMoved(e.key,e.dimensions.map(function(t){return t.crossfilterDimensionIndex}))})),S.exit().remove();var C=S.selectAll(\".\"+i.cn.axisOverlays).data(u,c);C.enter().append(\"g\").classed(i.cn.axisOverlays,!0),C.selectAll(\".\"+i.cn.axis).remove();var L=C.selectAll(\".\"+i.cn.axis).data(u,c);L.enter().append(\"g\").classed(i.cn.axis,!0),L.each(function(t){var e=t.model.height/t.model.tickDistance,r=t.domainScale,n=r.domain();o.select(this).call(o.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(e,t.tickFormat).tickValues(t.ordinal?n:null).tickFormat(t.ordinal?function(t){return t}:null).scale(r)),s.font(L.selectAll(\"text\"),t.model.tickFont)}),L.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),L.selectAll(\"text\").style(\"text-shadow\",\"1px 1px 1px #fff, -1px -1px 1px #fff, 1px -1px 1px #fff, -1px 1px 1px #fff\").style(\"cursor\",\"default\").style(\"user-select\",\"none\");var z=C.selectAll(\".\"+i.cn.axisHeading).data(u,c);z.enter().append(\"g\").classed(i.cn.axisHeading,!0);var O=z.selectAll(\".\"+i.cn.axisTitle).data(u,c);O.enter().append(\"text\").classed(i.cn.axisTitle,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"ew-resize\").style(\"user-select\",\"none\").style(\"pointer-events\",\"auto\"),O.attr(\"transform\",\"translate(0,\"+-i.axisTitleOffset+\")\").text(function(t){return t.label}).each(function(t){s.font(o.select(this),t.model.labelFont)});var I=C.selectAll(\".\"+i.cn.axisExtent).data(u,c);I.enter().append(\"g\").classed(i.cn.axisExtent,!0);var P=I.selectAll(\".\"+i.cn.axisExtentTop).data(u,c);P.enter().append(\"g\").classed(i.cn.axisExtentTop,!0),P.attr(\"transform\",\"translate(0,\"+-i.axisExtentOffset+\")\");var D=P.selectAll(\".\"+i.cn.axisExtentTopText).data(u,c);function R(t,e){if(t.ordinal)return\"\";var r=t.domainScale.domain();return o.format(t.tickFormat)(r[e?r.length-1:0])}D.enter().append(\"text\").classed(i.cn.axisExtentTopText,!0).call(y),D.text(function(t){return R(t,!0)}).each(function(t){s.font(o.select(this),t.model.rangeFont)});var B=I.selectAll(\".\"+i.cn.axisExtentBottom).data(u,c);B.enter().append(\"g\").classed(i.cn.axisExtentBottom,!0),B.attr(\"transform\",function(t){return\"translate(0,\"+(t.model.height+i.axisExtentOffset)+\")\"});var F=B.selectAll(\".\"+i.cn.axisExtentBottomText).data(u,c);F.enter().append(\"text\").classed(i.cn.axisExtentBottomText,!0).attr(\"dy\",\"0.75em\").call(y),F.text(function(t){return R(t)}).each(function(t){s.font(o.select(this),t.model.rangeFont)}),h.ensureAxisBrush(C)}},{\"../../components/drawing\":595,\"../../lib\":696,\"../../lib/gup\":693,\"./axisbrush\":1008,\"./constants\":1011,\"./lines\":1014,d3:148}],1017:[function(t,e,r){\"use strict\";var n=t(\"./parcoords\"),i=t(\"../../lib/prepare_regl\");e.exports=function(t,e){var r=t._fullLayout,a=r._toppaper,o=r._paperdiv,s=r._glcontainer;if(i(t)){var l={},c={},u=r._size;e.forEach(function(e,r){l[r]=t.data[r].dimensions,c[r]=t.data[r].dimensions.slice()});n(o,a,s,e,{width:u.w,height:u.h,margin:{t:u.t,r:u.r,b:u.b,l:u.l}},{filterChanged:function(e,r,n){var i=c[e][r],a=n.map(function(t){return t.slice()});a.length?(1===a.length&&(a=a[0]),i.constraintrange=a,a=[a]):(delete i.constraintrange,a=null);var o={};o[\"dimensions[\"+r+\"].constraintrange\"]=a,t.emit(\"plotly_restyle\",[o,[e]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){function n(t){return!(\"visible\"in t)\|\|t.visible}function i(t,e,r){var n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}var a=function(t){return function(e,n){return i(r,t,e)-i(r,t,n)}}(c[e].filter(n));l[e].sort(a),c[e].filter(function(t){return!n(t)}).sort(function(t){return c[e].indexOf(t)}).forEach(function(t){l[e].splice(l[e].indexOf(t),1),l[e].splice(c[e].indexOf(t),0,t)}),t.emit(\"plotly_restyle\")}})}}},{\"../../lib/prepare_regl\":709,\"./parcoords\":1016}],1018:[function(t,e,r){\"use strict\";var n=t(\"../../components/color/attributes\"),i=t(\"../../plots/font_attributes\"),a=t(\"../../plots/attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=i({editType:\"calc\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:n.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:s({},a.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"calc\"},textfont:s({},l,{}),insidetextfont:s({},l,{}),outsidetextfont:s({},l,{}),title:{valType:\"string\",dflt:\"\",editType:\"calc\"},titleposition:{valType:\"enumerated\",values:[\"top 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i=n._has&&n._has(\"pie\"),a=e._has&&e._has(\"pie\");i&&!a&&n._pielayer.selectAll(\"g.trace\").remove()}},{\"../../plots/get_data\":781,\"../../registry\":827}],1020:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\").isArrayOrTypedArray,a=t(\"tinycolor2\"),o=t(\"../../components/color\"),s=t(\"./helpers\");r.calc=function(t,e){var r,l,c,u,f,h=e.values,p=i(h)&&h.length,d=e.labels,g=e.marker.colors\|\|[],v=[],m=t._fullLayout,y=m._piecolormap,x={},b=0,_=m.hiddenlabels\|\|[];if(e.dlabel)for(d=new Array(h.length),r=0;r<h.length;r++)d[r]=String(e.label0+re.dlabel);function w(t,e){return!!t&&(!!(t=a(t)).isValid()&&(t=o.addOpacity(t,t.getAlpha()),y[e]\|\|(y[e]=t),t))}var k=(p?h:d).length;for(r=0;r<k;r++){if(p){if(l=h[r],!n(l))continue;if((l=+l)<0)continue}else l=1;void 0!==(c=d[r])&&\"\"!==c\|\|(c=r);var M=x[c=String(c)];void 0===M?(x[c]=v.length,(u=-1!==_.indexOf(c))\|\|(b+=l),v.push({v:l,label:c,color:w(g[r],c),i:r,pts:[r],hidden:u})):((f=v[M]).v+=l,f.pts.push(r),f.hidden\|\|(b+=l),!1===f.color&&g[r]&&(f.color=w(g[r],c)))}if(e.sort&&v.sort(function(t,e){return e.v-t.v}),v[0]&&(v[0].vTotal=b),e.textinfo&&\"none\"!==e.textinfo){var A,T=-1!==e.textinfo.indexOf(\"label\"),S=-1!==e.textinfo.indexOf(\"text\"),E=-1!==e.textinfo.indexOf(\"value\"),C=-1!==e.textinfo.indexOf(\"percent\"),L=m.separators;for(r=0;r<v.length;r++){if(f=v[r],A=T?[f.label]:[],S){var z=s.getFirstFilled(e.text,f.pts);z&&A.push(z)}E&&A.push(s.formatPieValue(f.v,L)),C&&A.push(s.formatPiePercent(f.v/b,L)),f.text=A.join(\"<br>\")}}return v},r.crossTraceCalc=function(t){var e=t._fullLayout,r=t.calcdata,n=e.piecolorway,i=e._piecolormap;e.extendpiecolors&&(n=function(t){var e,r=JSON.stringify(t),n=l[r];if(!n){for(n=t.slice(),e=0;e<t.length;e++)n.push(a(t[e]).lighten(20).toHexString());for(e=0;e<t.length;e++)n.push(a(t[e]).darken(20).toHexString());l[r]=n}return n}(n));var o,s,c,u,f=0;for(o=0;o<r.length;o++)if(\"pie\"===(c=r[o])[0].trace.type)for(s=0;s<c.length;s++)!1===(u=c[s]).color&&(i[u.label]?u.color=i[u.label]:(i[u.label]=u.color=n[f%n.length],f++))};var l={}},{\"../../components/color\":570,\"../../lib\":696,\"./helpers\":1023,\"fast-isnumeric\":214,tinycolor2:514}],1021:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults;e.exports=function(t,e,r,o){function s(r,a){return n.coerce(t,e,i,r,a)}var l,c=n.coerceFont,u=s(\"values\"),f=n.isArrayOrTypedArray(u),h=s(\"labels\");if(Array.isArray(h)?(l=h.length,f&&(l=Math.min(l,u.length))):f&&(l=u.length,s(\"label0\"),s(\"dlabel\")),l){e._length=l,s(\"marker.line.width\")&&s(\"marker.line.color\"),s(\"marker.colors\"),s(\"scalegroup\");var p=s(\"text\"),d=s(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\");if(s(\"hovertext\"),d&&\"none\"!==d){var g=s(\"textposition\"),v=Array.isArray(g)\|\|\"auto\"===g,m=v\|\|\"inside\"===g,y=v\|\|\"outside\"===g;if(m\|\|y){var x=c(s,\"textfont\",o.font);if(m){var b=n.extendFlat({},x);!(t.textfont&&t.textfont.color)&&delete b.color,c(s,\"insidetextfont\",b)}y&&c(s,\"outsidetextfont\",x)}}a(e,o,s);var _=s(\"hole\");if(s(\"title\")){var w=s(\"titleposition\",_?\"middle center\":\"top center\");_\|\|\"middle center\"!==w\|\|(e.titleposition=\"top center\"),c(s,\"titlefont\",o.font)}s(\"sort\"),s(\"direction\"),s(\"rotation\"),s(\"pull\")}else e.visible=!1}},{\"../../lib\":696,\"../../plots/domain\":770,\"./attributes\":1018}],1022:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),r}},{\"../../components/fx/helpers\":609}],1023:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");r.formatPiePercent=function(t,e){var r=(100t).toPrecision(3);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n\|\|0===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t\|\|void 0}},{\"../../lib\":696}],1024:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\");var i=t(\"./calc\");n.calc=i.calc,n.crossTraceCalc=i.crossTraceCalc,n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":1018,\"./base_plot\":1019,\"./calc\":1020,\"./defaults\":1021,\"./layout_attributes\":1025,\"./layout_defaults\":1026,\"./plot\":1027,\"./style\":1028,\"./style_one\":1029}],1025:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],1026:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":696,\"./layout_attributes\":1025}],1027:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/fx\"),a=t(\"../../components/color\"),o=t(\"../../components/drawing\"),s=t(\"../../lib\"),l=t(\"../../lib/svg_text_utils\"),c=t(\"./helpers\"),u=t(\"./event_data\");function f(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PIMath.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function h(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i=-1),n<0&&(a=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)(i>0?1:-1)/2,y:a/(1+rr/(nn)),outside:!0}}function p(t,e){var r=t.trace,n=e.h(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function d(t){var e,r=t.pull;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}e.exports=function(t,e){var r=t._fullLayout;!function(t,e){for(var r,n,i=0;i<t.length;i++)if(r=t[i][0],(n=r.trace).title){var a=o.tester.append(\"text\").attr(\"data-notex\",1).text(n.title).call(o.font,n.titlefont).call(l.convertToTspans,e),s=o.bBox(a.node(),!0);r.titleBox={width:s.width,height:s.height},a.remove()}}(e,t),function(t,e){var r,n,i,a,o,s,l,c,u,f=[];for(i=0;i<t.length;i++)o=t[i][0],s=o.trace,r=e.w(s.domain.x[1]-s.domain.x[0]),n=e.h(s.domain.y[1]-s.domain.y[0]),s.title&&\"middle center\"!==s.titleposition&&(n-=p(o,e)),l=d(s),o.r=Math.min(r,n)/(2+2l),o.cx=e.l+e.w(s.domain.x[1]+s.domain.x[0])/2,o.cy=e.t+e.h(1-s.domain.y[0])-n/2,s.title&&-1!==s.titleposition.indexOf(\"bottom\")&&(o.cy-=p(o,e)),s.scalegroup&&-1===f.indexOf(s.scalegroup)&&f.push(s.scalegroup);for(a=0;a<f.length;a++){for(u=1/0,c=f[a],i=0;i<t.length;i++)(o=t[i][0]).trace.scalegroup===c&&(u=Math.min(u,o.ro.r/o.vTotal));for(i=0;i<t.length;i++)(o=t[i][0]).trace.scalegroup===c&&(o.r=Math.sqrt(uo.vTotal))}}(e,r._size);var g=s.makeTraceGroups(r._pielayer,e,\"trace\").each(function(e){var g=n.select(this),v=e[0],m=v.trace;!function(t){var e,r,n,i=t[0],a=i.trace,o=a.rotationMath.PI/180,s=2Math.PI/i.vTotal,l=\"px0\",c=\"px1\";if(\"counterclockwise\"===a.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;o+=st[e].v,s=-1,l=\"px1\",c=\"px0\"}function u(t){return[i.rMath.sin(t),-i.rMath.cos(t)]}for(n=u(o),e=0;e<t.length;e++)(r=t[e]).hidden\|\|(r[l]=n,o+=sr.v/2,r.pxmid=u(o),r.midangle=o,o+=sr.v/2,n=u(o),r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0)}(e),g.attr(\"stroke-linejoin\",\"round\"),g.each(function(){var g=n.select(this).selectAll(\"g.slice\").data(e);g.enter().append(\"g\").classed(\"slice\",!0),g.exit().remove();var y=[[[],[]],[[],[]]],x=!1;g.each(function(e){if(e.hidden)n.select(this).selectAll(\"path,g\").remove();else{e.pointNumber=e.i,e.curveNumber=m.index,y[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var p=v.cx,d=v.cy,g=n.select(this),b=g.selectAll(\"path.surface\").data([e]),_=!1,w=!1;if(b.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),g.select(\"path.textline\").remove(),g.on(\"mouseover\",function(){var a=t._fullLayout,o=t._fullData[m.index];if(!t._dragging&&!1!==a.hovermode){var s=o.hoverinfo;if(Array.isArray(s)&&(s=i.castHoverinfo({hoverinfo:[c.castOption(s,e.pts)],_module:m._module},a,0)),\"all\"===s&&(s=\"label+text+value+percent+name\"),\"none\"!==s&&\"skip\"!==s&&s){var l=f(e,v),h=p+e.pxmid[0](1-l),g=d+e.pxmid[1](1-l),y=r.separators,x=[];if(-1!==s.indexOf(\"label\")&&x.push(e.label),-1!==s.indexOf(\"text\")){var b=c.castOption(o.hovertext\|\|o.text,e.pts);b&&x.push(b)}-1!==s.indexOf(\"value\")&&x.push(c.formatPieValue(e.v,y)),-1!==s.indexOf(\"percent\")&&x.push(c.formatPiePercent(e.v/v.vTotal,y));var k=m.hoverlabel,M=k.font;i.loneHover({x0:h-lv.r,x1:h+lv.r,y:g,text:x.join(\"<br>\"),name:-1!==s.indexOf(\"name\")?o.name:void 0,idealAlign:e.pxmid[0]<0?\"left\":\"right\",color:c.castOption(k.bgcolor,e.pts)\|\|e.color,borderColor:c.castOption(k.bordercolor,e.pts),fontFamily:c.castOption(M.family,e.pts),fontSize:c.castOption(M.size,e.pts),fontColor:c.castOption(M.color,e.pts)},{container:a._hoverlayer.node(),outerContainer:a._paper.node(),gd:t}),_=!0}t.emit(\"plotly_hover\",{points:[u(e,o)],event:n.event}),w=!0}}).on(\"mouseout\",function(r){var a=t._fullLayout,o=t._fullData[m.index];w&&(r.originalEvent=n.event,t.emit(\"plotly_unhover\",{points:[u(e,o)],event:n.event}),w=!1),_&&(i.loneUnhover(a._hoverlayer.node()),_=!1)}).on(\"click\",function(){var r=t._fullLayout,a=t._fullData[m.index];t._dragging\|\|!1===r.hovermode\|\|(t._hoverdata=[u(e,a)],i.click(t,n.event))}),m.pull){var k=+c.castOption(m.pull,e.pts)\|\|0;k>0&&(p+=ke.pxmid[0],d+=ke.pxmid[1])}e.cxFinal=p,e.cyFinal=d;var M=m.hole;if(e.v===v.vTotal){var A=\"M\"+(p+e.px0[0])+\",\"+(d+e.px0[1])+L(e.px0,e.pxmid,!0,1)+L(e.pxmid,e.px0,!0,1)+\"Z\";M?b.attr(\"d\",\"M\"+(p+Me.px0[0])+\",\"+(d+Me.px0[1])+L(e.px0,e.pxmid,!1,M)+L(e.pxmid,e.px0,!1,M)+\"Z\"+A):b.attr(\"d\",A)}else{var T=L(e.px0,e.px1,!0,1);if(M){var S=1-M;b.attr(\"d\",\"M\"+(p+Me.px1[0])+\",\"+(d+Me.px1[1])+L(e.px1,e.px0,!1,M)+\"l\"+Se.px0[0]+\",\"+Se.px0[1]+T+\"Z\")}else b.attr(\"d\",\"M\"+p+\",\"+d+\"l\"+e.px0[0]+\",\"+e.px0[1]+T+\"Z\")}var E=c.castOption(m.textposition,e.pts),C=g.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==E?[0]:[]);C.enter().append(\"g\").classed(\"slicetext\",!0),C.exit().remove(),C.each(function(){var r=s.ensureSingle(n.select(this),\"text\",\"\",function(t){t.attr(\"data-notex\",1)});r.text(e.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(o.font,\"outside\"===E?function(t,e,r){var n=c.castOption(t.outsidetextfont.color,e.pts)\|\|c.castOption(t.textfont.color,e.pts)\|\|r.color,i=c.castOption(t.outsidetextfont.family,e.pts)\|\|c.castOption(t.textfont.family,e.pts)\|\|r.family,a=c.castOption(t.outsidetextfont.size,e.pts)\|\|c.castOption(t.textfont.size,e.pts)\|\|r.size;return{color:n,family:i,size:a}}(m,e,t._fullLayout.font):function(t,e,r){var n=c.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=c.castOption(t._input.textfont.color,e.pts));var i=c.castOption(t.insidetextfont.family,e.pts)\|\|c.castOption(t.textfont.family,e.pts)\|\|r.family,o=c.castOption(t.insidetextfont.size,e.pts)\|\|c.castOption(t.textfont.size,e.pts)\|\|r.size;return{color:n\|\|a.contrast(e.color),family:i,size:o}}(m,e,t._fullLayout.font)).call(l.convertToTspans,t);var i,u=o.bBox(r.node());\"outside\"===E?i=h(u,e):(i=function(t,e,r){var n=Math.sqrt(t.widtht.width+t.heightt.height),i=t.width/t.height,a=Math.PIMath.min(e.v/r.vTotal,.5),o=1-r.trace.hole,s=f(e,r),l={scale:sr.r2/n,rCenter:1-s,rotate:0};if(l.scale>=1)return l;var c=i+1/(2Math.tan(a)),u=r.rMath.min(1/(Math.sqrt(cc+.5)+c),o/(Math.sqrt(ii+o/2)+i)),h={scale:2u/t.height,rCenter:Math.cos(u/r.r)-ui/r.r,rotate:(180/Math.PIe.midangle+720)%180-90},p=1/i,d=p+1/(2Math.tan(a)),g=r.rMath.min(1/(Math.sqrt(dd+.5)+d),o/(Math.sqrt(pp+o/2)+p)),v={scale:2g/t.width,rCenter:Math.cos(g/r.r)-g/i/r.r,rotate:(180/Math.PIe.midangle+810)%180-90},m=v.scale>h.scale?v:h;return l.scale<1&&m.scale>l.scale?m:l}(u,e,v),\"auto\"===E&&i.scale<1&&(r.call(o.font,m.outsidetextfont),m.outsidetextfont.family===m.insidetextfont.family&&m.outsidetextfont.size===m.insidetextfont.size\|\|(u=o.bBox(r.node())),i=h(u,e)));var g=p+e.pxmid[0]i.rCenter+(i.x\|\|0),y=d+e.pxmid[1]i.rCenter+(i.y\|\|0);i.outside&&(e.yLabelMin=y-u.height/2,e.yLabelMid=y,e.yLabelMax=y+u.height/2,e.labelExtraX=0,e.labelExtraY=0,x=!0),r.attr(\"transform\",\"translate(\"+g+\",\"+y+\")\"+(i.scale<1?\"scale(\"+i.scale+\")\":\"\")+(i.rotate?\"rotate(\"+i.rotate+\")\":\"\")+\"translate(\"+-(u.left+u.right)/2+\",\"+-(u.top+u.bottom)/2+\")\")})}function L(t,r,n,i){return\"a\"+iv.r+\",\"+iv.r+\" 0 \"+e.largeArc+(n?\" 1 \":\" 0 \")+i(r[0]-t[0])+\",\"+i(r[1]-t[1])}});var b=n.select(this).selectAll(\"g.titletext\").data(m.title?[0]:[]);b.enter().append(\"g\").classed(\"titletext\",!0),b.exit().remove(),b.each(function(){var e,i=s.ensureSingle(n.select(this),\"text\",\"\",function(t){t.attr(\"data-notex\",1)});i.text(m.title).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(o.font,m.titlefont).call(l.convertToTspans,t),e=\"middle center\"===m.titleposition?function(t){var e=Math.sqrt(t.titleBox.widtht.titleBox.width+t.titleBox.heightt.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.holet.r2/e,tx:0,ty:-t.titleBox.height/2+t.trace.titlefont.size}}(v):function(t,e){var r,n,i=1,a=1,o=t.trace,s={x:t.cx,y:t.cy},l={tx:0,ty:0};l.ty+=o.titlefont.size,n=d(o),-1!==o.titleposition.indexOf(\"top\")?(s.y-=(1+n)t.r,l.ty-=t.titleBox.height):-1!==o.titleposition.indexOf(\"bottom\")&&(s.y+=(1+n)t.r);-1!==o.titleposition.indexOf(\"left\")?(r=e.w(o.domain.x[1]-o.domain.x[0])/2+t.r,s.x-=(1+n)t.r,l.tx+=t.titleBox.width/2):-1!==o.titleposition.indexOf(\"center\")?r=e.w(o.domain.x[1]-o.domain.x[0]):-1!==o.titleposition.indexOf(\"right\")&&(r=e.w(o.domain.x[1]-o.domain.x[0])/2+t.r,s.x+=(1+n)t.r,l.tx-=t.titleBox.width/2);return 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n=t(\"gl-pointcloud2d\"),i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;(e.exports=c({hoverinfo:l({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),node:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:.5,arrayOk:!0}},pad:{valType:\"number\",arrayOk:!1,min:0,dflt:20},thickness:{valType:\"number\",arrayOk:!1,min:1,dflt:20},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel},link:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0}},source:{valType:\"data_array\",dflt:[]},target:{valType:\"data_array\",dflt:[]},value:{valType:\"data_array\",dflt:[]},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel}},\"calc\",\"nested\")).transforms=void 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../components/color\"),o=t(\"tinycolor2\"),s=t(\"../../plots/domain\").defaults,l=t(\"../../components/fx/hoverlabel_defaults\"),c=t(\"../../plot_api/plot_template\");e.exports=function(t,e,r,u){function f(r,a){return n.coerce(t,e,i,r,a)}var h=n.extendDeep(u.hoverlabel,t.hoverlabel),p=t.node,d=c.newContainer(e,\"node\");function g(t,e){return n.coerce(p,d,i.node,t,e)}g(\"label\"),g(\"pad\"),g(\"thickness\"),g(\"line.color\"),g(\"line.width\"),g(\"hoverinfo\",t.hoverinfo),l(p,d,g,h);var v=u.colorway;g(\"color\",d.label.map(function(t,e){return a.addOpacity(function(t){return v[t%v.length]}(e),.8)}));var m=t.link,y=c.newContainer(e,\"link\");function x(t,e){return n.coerce(m,y,i.link,t,e)}x(\"label\"),x(\"source\"),x(\"target\"),x(\"value\"),x(\"line.color\"),x(\"line.width\"),x(\"hoverinfo\",t.hoverinfo),l(m,y,x,h);var b=o(u.paper_bgcolor).getLuminance()<.333?\"rgba(255, 255, 255, 0.6)\":\"rgba(0, 0, 0, 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o=r.node;o.originalEvent=n.event,t._hoverdata=[o],n.select(e).call(m,r,i),a.click(t,{target:!0})}}})}},{\"../../components/color\":570,\"../../components/fx\":612,\"../../lib\":696,\"./constants\":1037,\"./render\":1041,d3:148}],1041:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"d3\"),a=t(\"tinycolor2\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"@plotly/d3-sankey\").sankey,c=t(\"d3-force\"),u=t(\"../../lib\"),f=u.isArrayOrTypedArray,h=u.isIndex,p=t(\"../../lib/gup\"),d=p.keyFun,g=p.repeat,v=p.unwrap;function m(t){t.lastDraggedX=t.x,t.lastDraggedY=t.y}function y(t){return function(e){return e.node.originalX===t.node.originalX}}function x(t){for(var e=0;e<t.length;e++)t[e].y=t[e].y+t[e].dy/2}function b(t){t.attr(\"transform\",function(t){return\"translate(\"+t.node.x.toFixed(3)+\", \"+(t.node.y-t.node.dy/2).toFixed(3)+\")\"})}function _(t){var e=t.sankey.nodes();!function(t){for(var e=0;e<t.length;e++)t[e].y=t[e].y-t[e].dy/2}(e);var r=t.sankey.link()(t.link);return x(e),r}function w(t){t.call(b)}function k(t,e){t.call(w),e.attr(\"d\",_)}function M(t){t.attr(\"width\",function(t){return t.visibleWidth}).attr(\"height\",function(t){return t.visibleHeight})}function A(t){return t.link.dy>1\|\|t.linkLineWidth>0}function T(t){return\"translate(\"+t.translateX+\",\"+t.translateY+\")\"+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function S(t){return\"translate(\"+(t.horizontal?0:t.labelY)+\" \"+(t.horizontal?t.labelY:0)+\")\"}function E(t){return i.svg.line()([[t.horizontal?t.left?-t.sizeAcross:t.visibleWidth+n.nodeTextOffsetHorizontal:n.nodeTextOffsetHorizontal,0],[t.horizontal?t.left?-n.nodeTextOffsetHorizontal:t.sizeAcross:t.visibleHeight-n.nodeTextOffsetHorizontal,0]])}function C(t){return t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\"}function L(t){return t.horizontal?\"scale(1 1)\":\"scale(-1 1)\"}function z(t){return t.darkBackground&&!t.horizontal?\"rgb(255,255,255)\":\"rgb(0,0,0)\"}function O(t){return t.horizontal&&t.left?\"100%\":\"0%\"}function I(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",function(t){t.interactionState.dragInProgress\|\|(r.hover(this,t,e),t.interactionState.hovered=[this,t])}).on(\"mousemove.basic\",function(t){t.interactionState.dragInProgress\|\|(r.follow(this,t),t.interactionState.hovered=[this,t])}).on(\"mouseout.basic\",function(t){t.interactionState.dragInProgress\|\|(r.unhover(this,t,e),t.interactionState.hovered=!1)}).on(\"click.basic\",function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress\|\|r.select(this,t,e)})}function P(t,e,r){var a=i.behavior.drag().origin(function(t){return t.node}).on(\"dragstart\",function(i){if(\"fixed\"!==i.arrangement&&(u.raiseToTop(this),i.interactionState.dragInProgress=i.node,m(i.node),i.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,i.interactionState.hovered),i.interactionState.hovered=!1),\"snap\"===i.arrangement)){var a=i.traceId+\"\|\"+Math.floor(i.node.originalX);i.forceLayouts[a]?i.forceLayouts[a].alpha(1):function(t,e,r){var i=r.sankey.nodes().filter(function(t){return t.originalX===r.node.originalX});r.forceLayouts[e]=c.forceSimulation(i).alphaDecay(0).force(\"collide\",c.forceCollide().radius(function(t){return t.dy/2+r.nodePad/2}).strength(1).iterations(n.forceIterations)).force(\"constrain\",function(t,e,r,i){return function(){for(var t=0,a=0;a<r.length;a++){var o=r[a];o===i.interactionState.dragInProgress?(o.x=o.lastDraggedX,o.y=o.lastDraggedY):(o.vx=(o.originalX-o.x)/n.forceTicksPerFrame,o.y=Math.min(i.size-o.dy/2,Math.max(o.dy/2,o.y))),t=Math.max(t,Math.abs(o.vx),Math.abs(o.vy))}!i.interactionState.dragInProgress&&t<.1&&i.forceLayouts[e].alpha()>0&&i.forceLayouts[e].alpha(0)}}(0,e,i,r)).stop()}(0,a,i),function(t,e,r,i){window.requestAnimationFrame(function a(){for(var o=0;o<n.forceTicksPerFrame;o++)r.forceLayouts[i].tick();r.sankey.relayout(),k(t.filter(y(r)),e),r.forceLayouts[i].alpha()>0&&window.requestAnimationFrame(a)})}(t,e,i,a)}}).on(\"drag\",function(r){if(\"fixed\"!==r.arrangement){var n=i.event.x,a=i.event.y;\"snap\"===r.arrangement?(r.node.x=n,r.node.y=a):(\"freeform\"===r.arrangement&&(r.node.x=n),r.node.y=Math.max(r.node.dy/2,Math.min(r.size-r.node.dy/2,a))),m(r.node),\"snap\"!==r.arrangement&&(r.sankey.relayout(),k(t.filter(y(r)),e))}}).on(\"dragend\",function(t){t.interactionState.dragInProgress=!1});t.on(\".drag\",null).call(a)}e.exports=function(t,e,r,i){var c=t.selectAll(\".\"+n.cn.sankey).data(e.filter(function(t){return v(t).trace.visible}).map(function(t,e,r){var i,a=v(e).trace,o=a.domain,s=a.node,c=a.link,p=a.arrangement,d=\"h\"===a.orientation,g=a.node.pad,m=a.node.thickness,y=a.node.line.color,b=a.node.line.width,_=a.link.line.color,w=a.link.line.width,k=a.valueformat,M=a.valuesuffix,A=a.textfont,T=t.width(o.x[1]-o.x[0]),S=t.height(o.y[1]-o.y[0]),E=[],C=f(c.color),L={},z=s.label.length;for(i=0;i<c.value.length;i++){var O=c.value[i],I=c.source[i],P=c.target[i];O>0&&h(I,z)&&h(P,z)&&(P=+P,L[I=+I]=L[P]=!0,E.push({pointNumber:i,label:c.label[i],color:C?c.color[i]:c.color,source:I,target:P,value:+O}))}var D=f(s.color),R=[],B=!1,F={};for(i=0;i<z;i++)if(L[i]){var N=s.label[i];F[i]=R.length,R.push({pointNumber:i,label:N,color:D?s.color[i]:s.color})}else B=!0;if(B)for(i=0;i<E.length;i++)E[i].source=F[E[i].source],E[i].target=F[E[i].target];var j=l().size(d?[T,S]:[S,T]).nodeWidth(m).nodePadding(g).nodes(R).links(E).layout(n.sankeyIterations);j.nodePadding()<g&&u.warn(\"node.pad was reduced to \",j.nodePadding(),\" to fit within the figure.\");for(var V,U=j.nodes(),q=0;q<U.length;q++)(V=U[q]).width=T,V.height=S;return 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t.sankey.links().filter(function(t){return t.value}).map(function(t,e,r){var n=a(r.color),i=r.source.label+\"\|\"+r.target.label,s=t[i];t[i]=(s\|\|0)+1;var l=i+\"__\"+t[i];return r.trace=e.trace,r.curveNumber=e.trace.index,{key:l,traceId:e.key,link:r,tinyColorHue:o.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkLineColor:e.linkLineColor,linkLineWidth:e.linkLineWidth,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,interactionState:e.interactionState}}.bind(null,{},t))},d);m.enter().append(\"path\").classed(n.cn.sankeyLink,!0).attr(\"d\",_).call(I,c,i.linkEvents),m.style(\"stroke\",function(t){return A(t)?o.tinyRGB(a(t.linkLineColor)):t.tinyColorHue}).style(\"stroke-opacity\",function(t){return A(t)?o.opacity(t.linkLineColor):t.tinyColorAlpha}).style(\"stroke-width\",function(t){return A(t)?t.linkLineWidth:1}).style(\"fill\",function(t){return t.tinyColorHue}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),m.transition().ease(n.ease).duration(n.duration).attr(\"d\",_),m.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var y=c.selectAll(\".\"+n.cn.sankeyNodeSet).data(g,d);y.enter().append(\"g\").classed(n.cn.sankeyNodeSet,!0),y.style(\"cursor\",function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}});var w=y.selectAll(\".\"+n.cn.sankeyNode).data(function(t){var e=t.sankey.nodes();return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=t[e].x,t[e].originalY=t[e].y,-1===r.indexOf(t[e].x)&&r.push(t[e].x);for(r.sort(function(t,e){return t-e}),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.filter(function(t){return t.value}).map(function(t,e,r){var i=a(r.color),s=n.nodePadAcross,l=e.nodePad/2,c=r.dx,u=Math.max(.5,r.dy),f=r.label,h=t[f];t[f]=(h\|\|0)+1;var p=f+\"__\"+t[f];return r.trace=e.trace,r.curveNumber=e.trace.index,{key:p,traceId:e.key,node:r,nodePad:e.nodePad,nodeLineColor:e.nodeLineColor,nodeLineWidth:e.nodeLineWidth,textFont:e.textFont,size:e.horizontal?e.height:e.width,visibleWidth:Math.ceil(c),visibleHeight:u,zoneX:-s,zoneY:-l,zoneWidth:c+2s,zoneHeight:u+2l,labelY:e.horizontal?r.dy/2+1:r.dx/2+1,left:1===r.originalLayer,sizeAcross:e.width,forceLayouts:e.forceLayouts,horizontal:e.horizontal,darkBackground:i.getBrightness()<=128,tinyColorHue:o.tinyRGB(i),tinyColorAlpha:i.getAlpha(),valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,arrangement:e.arrangement,uniqueNodeLabelPathId:[e.guid,e.key,p].join(\" \"),interactionState:e.interactionState}}.bind(null,{},t))},d);w.enter().append(\"g\").classed(n.cn.sankeyNode,!0).call(b).call(I,c,i.nodeEvents),w.call(P,m,i),w.transition().ease(n.ease).duration(n.duration).call(b),w.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var k=w.selectAll(\".\"+n.cn.nodeRect).data(g);k.enter().append(\"rect\").classed(n.cn.nodeRect,!0).call(M),k.style(\"stroke-width\",function(t){return t.nodeLineWidth}).style(\"stroke\",function(t){return o.tinyRGB(a(t.nodeLineColor))}).style(\"stroke-opacity\",function(t){return o.opacity(t.nodeLineColor)}).style(\"fill\",function(t){return t.tinyColorHue}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),k.transition().ease(n.ease).duration(n.duration).call(M);var D=w.selectAll(\".\"+n.cn.nodeCapture).data(g);D.enter().append(\"rect\").classed(n.cn.nodeCapture,!0).style(\"fill-opacity\",0),D.attr(\"x\",function(t){return t.zoneX}).attr(\"y\",function(t){return t.zoneY}).attr(\"width\",function(t){return t.zoneWidth}).attr(\"height\",function(t){return t.zoneHeight});var R=w.selectAll(\".\"+n.cn.nodeCentered).data(g);R.enter().append(\"g\").classed(n.cn.nodeCentered,!0).attr(\"transform\",S),R.transition().ease(n.ease).duration(n.duration).attr(\"transform\",S);var B=R.selectAll(\".\"+n.cn.nodeLabelGuide).data(g);B.enter().append(\"path\").classed(n.cn.nodeLabelGuide,!0).attr(\"id\",function(t){return t.uniqueNodeLabelPathId}).attr(\"d\",E).attr(\"transform\",C),B.transition().ease(n.ease).duration(n.duration).attr(\"d\",E).attr(\"transform\",C);var F=R.selectAll(\".\"+n.cn.nodeLabel).data(g);F.enter().append(\"text\").classed(n.cn.nodeLabel,!0).attr(\"transform\",L).style(\"user-select\",\"none\").style(\"cursor\",\"default\").style(\"fill\",\"black\"),F.style(\"text-shadow\",function(t){return t.horizontal?\"-1px 1px 1px #fff, 1px 1px 1px #fff, 1px -1px 1px #fff, -1px -1px 1px #fff\":\"none\"}).each(function(t){s.font(F,t.textFont)}),F.transition().ease(n.ease).duration(n.duration).attr(\"transform\",L);var N=F.selectAll(\".\"+n.cn.nodeLabelTextPath).data(g);N.enter().append(\"textPath\").classed(n.cn.nodeLabelTextPath,!0).attr(\"alignment-baseline\",\"middle\").attr(\"xlink:href\",function(t){return\"#\"+t.uniqueNodeLabelPathId}).attr(\"startOffset\",O).style(\"fill\",z),N.text(function(t){return t.horizontal\|\|t.node.dy>5?t.node.label:\"\"}).attr(\"text-anchor\",function(t){return t.horizontal&&t.left?\"end\":\"start\"}),N.transition().ease(n.ease).duration(n.duration).attr(\"startOffset\",O).style(\"fill\",z)}},{\"../../components/color\":570,\"../../components/drawing\":595,\"../../lib\":696,\"../../lib/gup\":693,\"./constants\":1037,\"@plotly/d3-sankey\":46,d3:148,\"d3-force\":144,tinycolor2:514}],1042:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var i=e.marker;if(i){n.mergeArray(i.size,t,\"ms\"),n.mergeArray(i.opacity,t,\"mo\"),n.mergeArray(i.symbol,t,\"mx\"),n.mergeArray(i.color,t,\"mc\");var a=i.line;i.line&&(n.mergeArray(a.color,t,\"mlc\"),n.mergeArray(a.width,t,\"mlw\"));var o=i.gradient;o&&\"none\"!==o.type&&(n.mergeArray(o.type,t,\"mgt\"),n.mergeArray(o.color,t,\"mgc\"))}}},{\"../../lib\":696}],1043:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../components/colorbar/attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../components/drawing\"),l=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},x0:{valType:\"any\",dflt:0,editType:\"calc+clearAxisTypes\"},dx:{valType:\"number\",dflt:1,editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y0:{valType:\"any\",dflt:0,editType:\"calc+clearAxisTypes\"},dy:{valType:\"number\",dflt:1,editType:\"calc\"},stackgroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],editType:\"calc\"},groupnorm:{valType:\"enumerated\",values:[\"\",\"fraction\",\"percent\"],dflt:\"\",editType:\"calc\"},stackgaps:{valType:\"enumerated\",values:[\"infer zero\",\"interpolate\"],dflt:\"infer 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n=t(\"../../registry\");e.exports=function(t,e,r,i){var a,o=i(\"x\"),s=i(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),o)s?a=Math.min(o.length,s.length):(a=o.length,i(\"y0\"),i(\"dy\"));else{if(!s)return 0;a=e.y.length,i(\"x0\"),i(\"dx\")}return e._length=a,a}},{\"../../registry\":827}],1070:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/attributes\"),o=t(\"../../constants/gl3d_dashes\"),s=t(\"../../constants/gl3d_markers\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,u=n.line,f=n.marker,h=f.line,p=l({width:u.width,dash:{valType:\"enumerated\",values:Object.keys(o),dflt:\"solid\"}},i(\"line\"));delete p.showscale,delete p.colorbar;var 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l=a(+t[s],s);if(\"log\"===i.type){var c=i.c2l(t[s]),u=t[s]-l[0],f=t[s]+l[1];if(o[s]=[(i.c2l(u,!0)-c)r,(i.c2l(f,!0)-c)r],u>0){var h=i.c2l(u);i._lowerLogErrorBound\|\|(i._lowerLogErrorBound=h),i._lowerErrorBound=Math.min(i._lowerLogErrorBound,h)}}else o[s]=[-l[0]r,l[1]r]}return o}e.exports=function(t,e,r){var n=[i(t.x,t.error_x,e[0],r.xaxis),i(t.y,t.error_y,e[1],r.yaxis),i(t.z,t.error_z,e[2],r.zaxis)],a=function(t){for(var e=0;e<t.length;e++)if(t[e])return t[e].length;return 0}(n);if(0===a)return null;for(var o=new Array(a),s=0;s<a;s++){for(var l=[[0,0,0],[0,0,0]],c=0;c<3;c++)if(n[c])for(var u=0;u<2;u++)l[u][c]=n[c][s][u];o[s]=l}return o}},{\"../../registry\":827}],1073:[function(t,e,r){\"use strict\";var n=t(\"gl-line3d\"),i=t(\"gl-scatter3d\"),a=t(\"gl-error3d\"),o=t(\"gl-mesh3d\"),s=t(\"delaunay-triangulate\"),l=t(\"../../lib\"),c=t(\"../../lib/str2rgbarray\"),u=t(\"../../lib/gl_format_color\").formatColor,f=t(\"../scatter/make_bubble_size_func\"),h=t(\"../../constants/gl3d_dashes\"),p=t(\"../../constants/gl3d_markers\"),d=t(\"./calc_errors\");function g(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode=\"\",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var v=g.prototype;function m(t,e){return e(4t)}function y(t){return p[t]}function x(t,e,r,n,i){var a=null;if(l.isArrayOrTypedArray(t)){a=[];for(var o=0;o<e;o++)void 0===t[o]?a[o]=n:a[o]=r(t[o],i)}else a=r(t,l.identity);return a}function b(t,e){var r,n,i,a,o,s,h,p,g=[],v=t.fullSceneLayout,b=t.dataScale,_=v.xaxis,w=v.yaxis,k=v.zaxis,M=e.marker,A=e.line,T=e.x\|\|[],S=e.y\|\|[],E=e.z\|\|[],C=T.length,L=e.xcalendar,z=e.ycalendar,O=e.zcalendar;for(n=0;n<C;n++)i=_.d2l(T[n],0,L)b[0],a=w.d2l(S[n],0,z)b[1],o=k.d2l(E[n],0,O)b[2],g[n]=[i,a,o];if(Array.isArray(e.text))s=e.text;else if(void 0!==e.text)for(s=new Array(C),n=0;n<C;n++)s[n]=e.text;if(r={position:g,mode:e.mode,text:s},\"line\"in e&&(r.lineColor=u(A,1,C),r.lineWidth=A.width,r.lineDashes=A.dash),\"marker\"in e){var I=f(e);r.scatterColor=u(M,1,C),r.scatterSize=x(M.size,C,m,20,I),r.scatterMarker=x(M.symbol,C,y,\"\\u25cf\"),r.scatterLineWidth=M.line.width,r.scatterLineColor=u(M.line,1,C),r.scatterAngle=0}\"textposition\"in e&&(r.textOffset=(h=e.textposition,p=[0,0],Array.isArray(h)?[0,-1]:(h.indexOf(\"bottom\")>=0&&(p[1]+=1),h.indexOf(\"top\")>=0&&(p[1]-=1),h.indexOf(\"left\")>=0&&(p[0]-=1),h.indexOf(\"right\")>=0&&(p[0]+=1),p)),r.textColor=u(e.textfont,1,C),r.textSize=x(e.textfont.size,C,l.identity,12),r.textFont=e.textfont.family,r.textAngle=0);var P=[\"x\",\"y\",\"z\"];for(r.project=[!1,!1,!1],r.projectScale=[1,1,1],r.projectOpacity=[1,1,1],n=0;n<3;++n){var D=e.projection[P[n]];(r.project[n]=D.show)&&(r.projectOpacity[n]=D.opacity,r.projectScale[n]=D.scale)}r.errorBounds=d(e,b,v);var R=function(t){for(var e=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[1,1,1],i=0;i<3;i++){var a=t[i];a&&!1!==a.copy_zstyle&&!1!==t[2].visible&&(a=t[2]),a&&a.visible&&(e[i]=a.width/2,r[i]=c(a.color),n[i]=a.thickness)}return{capSize:e,color:r,lineWidth:n}}([e.error_x,e.error_y,e.error_z]);return r.errorColor=R.color,r.errorLineWidth=R.lineWidth,r.errorCapSize=R.capSize,r.delaunayAxis=e.surfaceaxis,r.delaunayColor=c(e.surfacecolor),r}function _(t){if(Array.isArray(t)){var e=t[0];return Array.isArray(e)&&(t=e),\"rgb(\"+t.slice(0,3).map(function(t){return Math.round(255t)})+\")\"}return null}v.handlePick=function(t){if(t.object&&(t.object===this.linePlot\|\|t.object===this.delaunayMesh\|\|t.object===this.textMarkers\|\|t.object===this.scatterPlot)){var e=t.index=t.data.index;return t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel=\"\",this.textLabels&&(Array.isArray(this.textLabels)?(this.textLabels[e]\|\|0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},v.update=function(t){var e,r,l,c,u=this.scene.glplot.gl,f=h.solid;this.data=t;var p=b(this.scene,t);\"mode\"in 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d=t.opacity;if(t.marker&&t.marker.opacity&&(d=t.marker.opacity),r={gl:u,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf(\"markers\")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:u,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext\|\|t.text,-1!==this.mode.indexOf(\"text\")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=i(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:u,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var g=function(t,e,r){var n,i=(r+1)%3,a=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var c=t[n];!isNaN(c[i])&&isFinite(c[i])&&!isNaN(c[a])&&isFinite(c[a])&&(o.push([c[i],c[a]]),l.push(n))}var u=s(o);for(n=0;n<u.length;++n)for(var f=u[n],h=0;h<f.length;++h)f[h]=l[f[h]];return{positions:t,cells:u,meshColor:e}}(p.position,p.delaunayColor,p.delaunayAxis);g.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(g):(g.gl=u,this.delaunayMesh=o(g),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},v.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},e.exports=function(t,e){var r=new g(t,e.uid);return 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a}(t,e,f,u)){f(\"text\"),f(\"hovertext\"),f(\"mode\"),a.hasLines(e)&&(f(\"connectgaps\"),s(t,e,r,u,f)),a.hasMarkers(e)&&o(t,e,r,u,f,{noSelect:!0}),a.hasText(e)&&l(t,e,u,f,{noSelect:!0});var h=(e.line\|\|{}).color,p=(e.marker\|\|{}).color;f(\"surfaceaxis\")>=0&&f(\"surfacecolor\",h\|\|p);for(var d=[\"x\",\"y\",\"z\"],g=0;g<3;++g){var v=\"projection.\"+d[g];f(v+\".show\")&&(f(v+\".opacity\"),f(v+\".scale\"))}var m=n.getComponentMethod(\"errorbars\",\"supplyDefaults\");m(t,e,r,{axis:\"z\"}),m(t,e,r,{axis:\"y\",inherit:\"z\"}),m(t,e,r,{axis:\"x\",inherit:\"z\"})}else e.visible=!1}},{\"../../lib\":696,\"../../registry\":827,\"../scatter/line_defaults\":1056,\"../scatter/marker_defaults\":1062,\"../scatter/subtypes\":1067,\"../scatter/text_defaults\":1068,\"./attributes\":1070}],1075:[function(t,e,r){\"use strict\";var n={};n.plot=t(\"./convert\"),n.attributes=t(\"./attributes\"),n.markerSymbols=t(\"../../constants/gl3d_markers\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/marker_colorbar\"),n.calc=t(\"./calc\"),n.moduleType=\"trace\",n.name=\"scatter3d\",n.basePlotModule=t(\"../../plots/gl3d\"),n.categories=[\"gl3d\",\"symbols\",\"showLegend\"],n.meta={},e.exports=n},{\"../../constants/gl3d_markers\":671,\"../../plots/gl3d\":787,\"../scatter/marker_colorbar\":1061,\"./attributes\":1070,\"./calc\":1071,\"./convert\":1073,\"./defaults\":1074}],1076:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=n.marker,c=n.line,u=l.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),line:{color:c.color,width:c.width,dash:c.dash,shape:s({},c.shape,{values:[\"linear\",\"spline\"]}),smoothing:c.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:s({symbol:l.symbol,opacity:l.opacity,maxdisplayed:l.maxdisplayed,size:l.size,sizeref:l.sizeref,sizemin:l.sizemin,sizemode:l.sizemode,line:s({width:u.width,editType:\"calc\"},a(\"marker.line\")),gradient:l.gradient,editType:\"calc\"},a(\"marker\"),{colorbar:o}),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:s({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":571,\"../../components/colorscale/attributes\":577,\"../../lib/extend\":685,\"../../plots/attributes\":741,\"../scatter/attributes\":1043}],1077:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),g=p(\"b\"),v=Math.min(d.length,g.length);if(v){e._length=v,p(\"text\"),p(\"mode\",v<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var m=[];(a.hasMarkers(e)\|\|a.hasText(e))&&(p(\"marker.maxdisplayed\"),m.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)\|\|l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill\|\|m.push(\"fills\"),p(\"hoveron\",m.join(\"+\")\|\|\"points\"),n.coerceSelectionMarkerOpacity(e,p)}else 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/marker_colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"../scatter/style\").style,n.styleOnSelect=t(\"../scatter/style\").styleOnSelect,n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"../scatter/select\"),n.eventData=t(\"./event_data\"),n.moduleType=\"trace\",n.name=\"scattercarpet\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"svg\",\"carpet\",\"symbols\",\"showLegend\",\"carpetDependent\",\"zoomScale\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":756,\"../scatter/marker_colorbar\":1061,\"../scatter/select\":1064,\"../scatter/style\":1066,\"./attributes\":1076,\"./calc\":1077,\"./defaults\":1078,\"./event_data\":1079,\"./hover\":1080,\"./plot\":1082}],1082:[function(t,e,r){\"use strict\";var n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r,o){var s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis\|\|\"x\"),yaxis:i.getFromId(t,u.yaxis\|\|\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,u._clipPathId)}},{\"../../components/drawing\":595,\"../../plots/cartesian/axes\":744,\"../scatter/plot\":1063}],1083:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,c=n.marker,u=n.line,f=c.line;e.exports=l({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"fast-isnumeric\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/colorscale_calc\"),o=t(\"../scatter/arrays_to_calcdata\"),s=t(\"../scatter/calc_selection\"),l=t(\"../../lib\")._;e.exports=function(t,e){for(var r=Array.isArray(e.locations),c=r?e.locations.length:e._length,u=new Array(c),f=0;f<c;f++){var h=u[f]={};if(r){var p=e.locations[f];h.loc=\"string\"==typeof p?p:null}else{var d=e.lon[f],g=e.lat[f];n(d)&&n(g)?h.lonlat=[+d,+g]:h.lonlat=[i,i]}}return o(u,e),a(e),s(u,e),c&&(u[0].t={labels:{lat:l(t,\"lat:\")+\" \",lon:l(t,\"lon:\")+\" \"}}),u}},{\"../../constants/numerical\":673,\"../../lib\":696,\"../scatter/arrays_to_calcdata\":1042,\"../scatter/calc_selection\":1045,\"../scatter/colorscale_calc\":1046,\"fast-isnumeric\":214}],1085:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}!function(t,e,r){var n,i,a=0,o=r(\"locations\");if(o)return r(\"locationmode\"),a=o.length;return n=r(\"lon\")\|\|[],i=r(\"lat\")\|\|[],a=Math.min(n.length,i.length),e._length=a,a}(0,e,f)?e.visible=!1:(f(\"text\"),f(\"hovertext\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&s(t,e,u,f),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f))}},{\"../../lib\":696,\"../scatter/fillcolor_defaults\":1052,\"../scatter/line_defaults\":1056,\"../scatter/marker_defaults\":1062,\"../scatter/subtypes\":1067,\"../scatter/text_defaults\":1068,\"./attributes\":1083}],1086:[function(t,e,r){\"use 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a=e.hi\|\|t.hoverinfo,o=\"all\"===a?l.hoverinfo.flags:a.split(\"+\"),c=-1!==o.indexOf(\"location\")&&Array.isArray(t.locations),u=-1!==o.indexOf(\"lon\"),f=-1!==o.indexOf(\"lat\"),h=-1!==o.indexOf(\"text\"),p=[];function d(t){return i.tickText(r,r.c2l(t),\"hover\").text+\"\\xb0\"}c?p.push(e.loc):u&&f?p.push(\"(\"+d(e.lonlat[0])+\", \"+d(e.lonlat[1])+\")\"):u?p.push(n.lon+d(e.lonlat[0])):f&&p.push(n.lat+d(e.lonlat[1]));h&&s(e,t,p);return p.join(\"<br>\")}(u,v,p.mockAxis,c[0].t.labels),[t]}}},{\"../../components/fx\":612,\"../../constants/numerical\":673,\"../../plots/cartesian/axes\":744,\"../scatter/fill_hover_text\":1051,\"../scatter/get_trace_color\":1053,\"./attributes\":1083}],1088:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/marker_colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOnSelect=t(\"../scatter/style\").styleOnSelect,n.hoverPoints=t(\"./hover\"),n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergeo\",n.basePlotModule=t(\"../../plots/geo\"),n.categories=[\"geo\",\"symbols\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/geo\":775,\"../scatter/marker_colorbar\":1061,\"../scatter/style\":1066,\"./attributes\":1083,\"./calc\":1084,\"./defaults\":1085,\"./event_data\":1086,\"./hover\":1087,\"./plot\":1089,\"./select\":1090,\"./style\":1091}],1089:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../constants/numerical\").BADNUM,o=t(\"../../lib/topojson_utils\").getTopojsonFeatures,s=t(\"../../lib/geo_location_utils\").locationToFeature,l=t(\"../../lib/geojson_utils\"),c=t(\"../scatter/subtypes\"),u=t(\"./style\");function f(t,e){var r=t[0].trace;if(Array.isArray(r.locations))for(var n=o(r,e),i=r.locationmode,l=0;l<t.length;l++){var c=t[l],u=s(i,c.loc,n);c.lonlat=u?u.properties.ct:[a,a]}}e.exports=function(t,e,r){for(var o=0;o<r.length;o++)f(r[o],e.topojson);function s(t,e){t.lonlat[0]===a&&n.select(e).remove()}var h=e.layers.frontplot.select(\".scatterlayer\"),p=i.makeTraceGroups(h,r,\"trace scattergeo\");p.selectAll(\"\").remove(),p.each(function(e){var r=e[0].node3=n.select(this),a=e[0].trace;if(c.hasLines(a)\|\|\"none\"!==a.fill){var 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for(l=0;l<c.length;l++)(a=(r=c[l]).lonlat)[0]!==i&&(o=u.c2p(a),s=f.c2p(a),e.contains([o,s],null,l,t)?(h.push({pointNumber:l,lon:a[0],lat:a[1]}),r.selected=1):r.selected=0);return h}},{\"../../constants/numerical\":673,\"../scatter/subtypes\":1067}],1091:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/drawing\"),a=t(\"../../components/color\"),o=t(\"../scatter/style\"),s=o.stylePoints,l=o.styleText;e.exports=function(t,e){e&&function(t,e){var r=e[0].trace,o=e[0].node3;o.style(\"opacity\",e[0].trace.opacity),s(o,r,t),l(o,r,t),o.selectAll(\"path.js-line\").style(\"fill\",\"none\").each(function(t){var e=n.select(this),r=t.trace,o=r.line\|\|{};e.call(a.stroke,o.color).call(i.dashLine,o.dash\|\|\"\",o.width\|\|0),\"none\"!==r.fill&&e.call(a.fill,r.fillcolor)})}(t,e)}},{\"../../components/color\":570,\"../../components/drawing\":595,\"../scatter/style\":1066,d3:148}],1092:[function(t,e,r){\"use strict\";var n=t(\"../../plots/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../lib/extend\").extendFlat,s=t(\"../../plot_api/edit_types\").overrideAll,l=t(\"./constants\").DASHES,c=i.line,u=i.marker,f=u.line,h=e.exports=s({x:i.x,x0:i.x0,dx:i.dx,y:i.y,y0:i.y0,dy:i.dy,text:i.text,hovertext:i.hovertext,textposition:i.textposition,textfont:i.textfont,mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},line:{color:c.color,width:c.width,shape:{valType:\"enumerated\",values:[\"linear\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\",editType:\"plot\"},dash:{valType:\"enumerated\",values:Object.keys(l),dflt:\"solid\"}},marker:o({},a(\"marker\"),{symbol:u.symbol,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,opacity:u.opacity,colorbar:u.colorbar,line:o({},a(\"marker.line\"),{width:f.width})}),connectgaps:i.connectgaps,fill:o({},i.fill,{dflt:\"none\"}),fillcolor:i.fillcolor,selected:{marker:i.selected.marker,textfont:i.selected.textfont},unselected:{marker:i.unselected.marker,textfont:i.unselected.textfont},opacity:n.opacity},\"calc\",\"nested\");h.x.editType=h.y.editType=h.x0.editType=h.y0.editType=\"calc+clearAxisTypes\"},{\"../../components/colorscale/attributes\":577,\"../../lib/extend\":685,\"../../plot_api/edit_types\":727,\"../../plots/attributes\":741,\"../scatter/attributes\":1043,\"./constants\":1093}],1093:[function(t,e,r){\"use 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f=o[e].split(/\\s+/);switch(f[1]){case\"left\":u.align.push(\"right\");break;case\"right\":u.align.push(\"left\");break;default:u.align.push(f[1])}switch(f[0]){case\"top\":u.baseline.push(\"bottom\");break;case\"bottom\":u.baseline.push(\"top\");break;default:u.baseline.push(f[0])}}if(Array.isArray(s))for(u.color=new Array(r),e=0;e<r;e++)u.color[e]=s[e];else u.color=s;if(Array.isArray(l)\|\|Array.isArray(c))for(u.font=new Array(r),e=0;e<r;e++){var h=u.font[e]={};h.size=Array.isArray(l)?n(l[e])?l[e]:0:l,h.family=Array.isArray(c)?c[e]:c}else u.font={size:l,family:c};return u}function m(t){var e,r,n=t._length,i=t.marker,o={},l=Array.isArray(i.symbol),c=s.isArrayOrTypedArray(i.color),f=s.isArrayOrTypedArray(i.line.color),d=s.isArrayOrTypedArray(i.opacity),g=s.isArrayOrTypedArray(i.size),v=s.isArrayOrTypedArray(i.line.width);if(l\|\|(r=p.OPEN_RE.test(i.symbol)),l\|\|c\|\|f\|\|d){o.colors=new Array(n),o.borderColors=new Array(n);var m=u(i,i.opacity,n),y=u(i.line,i.opacity,n);if(!Array.isArray(y[0])){var x=y;for(y=Array(n),e=0;e<n;e++)y[e]=x}if(!Array.isArray(m[0])){var b=m;for(m=Array(n),e=0;e<n;e++)m[e]=b}for(o.colors=m,o.borderColors=y,e=0;e<n;e++){if(l){var _=i.symbol[e];r=p.OPEN_RE.test(_)}r&&(y[e]=m[e].slice(),m[e]=m[e].slice(),m[e][3]=0)}o.opacity=t.opacity}else r?(o.color=a(i.color,\"uint8\"),o.color[3]=0,o.borderColor=a(i.color,\"uint8\")):(o.color=a(i.color,\"uint8\"),o.borderColor=a(i.line.color,\"uint8\")),o.opacity=t.opacityi.opacity;if(l)for(o.markers=new Array(n),e=0;e<n;e++)o.markers[e]=T(i.symbol[e]);else o.marker=T(i.symbol);var w,k=h(t);if(g\|\|v){var M,A=o.sizes=new Array(n),S=o.borderSizes=new Array(n),E=0;if(g){for(e=0;e<n;e++)A[e]=k(i.size[e]),E+=A[e];M=E/n}else for(w=k(i.size),e=0;e<n;e++)A[e]=w;if(v)for(e=0;e<n;e++)S[e]=i.line.width[e]/2;else for(w=i.line.width/2,e=0;e<n;e++)S[e]=w;o.sizeAvg=M}else o.size=k(i&&i.size\|\|10),o.borderSizes=k(i.line.width);return o}function y(t,e){var r=t.marker,n={};return e?(e.marker&&e.marker.symbol?n=m(s.extendFlat({},r,e.marker)):e.marker&&(e.marker.size&&(n.size=e.marker.size/2),e.marker.color&&(n.colors=e.marker.color),void 0!==e.marker.opacity&&(n.opacity=e.marker.opacity)),n):n}function x(t,e){var r={};if(!e)return r;if(e.textfont){var n={opacity:1,text:t.text,textposition:t.textposition,textfont:s.extendFlat({},t.textfont)};e.textfont&&s.extendFlat(n.textfont,e.textfont),r=v(n)}return r}function b(t,e){var r={capSize:2e.width,lineWidth:e.thickness,color:e.color};return e.copy_ystyle&&(r=t.error_y),r}var _=p.SYMBOL_SDF_SIZE,w=p.SYMBOL_SIZE,k=p.SYMBOL_STROKE,M={},A=l.symbolFuncs[0](.05w);function T(t){if(\"circle\"===t)return null;var e,r,n=l.symbolNumber(t),a=l.symbolFuncs[n%100],o=!!l.symbolNoDot[n%100],s=!!l.symbolNoFill[n%100],c=p.DOT_RE.test(t);return M[t]?M[t]:(e=c&&!o?a(1.1w)+A:a(w),r=i(e,{w:_,h:_,viewBox:[-w,-w,w,w],stroke:s?k:-k}),M[t]=r,r\|\|null)}e.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0};if(!0!==e.visible)return n;if(f.hasText(e)&&(n.text=v(e),n.textSel=x(e,e.selected),n.textUnsel=x(e,e.unselected)),f.hasMarkers(e)&&(n.marker=m(e),n.markerSel=y(e,e.selected),n.markerUnsel=y(e,e.unselected),!e.unselected&&Array.isArray(e.marker.opacity))){var i=e.marker.opacity;for(n.markerUnsel.opacity=new Array(i.length),r=0;r<i.length;r++)n.markerUnsel.opacity[r]=di[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width,color:e.line.color,opacity:e.opacity};var a=(p.DASHES[e.line.dash]\|\|[1]).slice();for(r=0;r<a.length;++r)a[r]=e.line.width;n.line.dashes=a}return e.error_x&&e.error_x.visible&&(n.errorX=b(e,e.error_x)),e.error_y&&e.error_y.visible&&(n.errorY=b(e,e.error_y)),e.fill&&\"none\"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:m,markerSelection:y,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if(\"hv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2i])\|\|isNaN(r[2i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2i],r[2i+1]),isNaN(r[2i+2])\|\|isNaN(r[2i+3])?n.push(NaN,NaN):n.push(r[2i+2],r[2i+1]));n.push(r[a-2],r[a-1])}else if(\"hvh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2i])\|\|isNaN(r[2i+1])\|\|isNaN(r[2i+2])\|\|isNaN(r[2i+3]))isNaN(r[2i])\|\|isNaN(r[2i+1])?n.push(NaN,NaN):n.push(r[2i],r[2i+1]),n.push(NaN,NaN);else{var s=(r[2i]+r[2i+2])/2;n.push(r[2i],r[2i+1],s,r[2i+1],s,r[2i+3])}n.push(r[a-2],r[a-1])}else if(\"vhv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2i])\|\|isNaN(r[2i+1])\|\|isNaN(r[2i+2])\|\|isNaN(r[2i+3]))isNaN(r[2i])\|\|isNaN(r[2i+1])?n.push(NaN,NaN):n.push(r[2i],r[2i+1]),n.push(NaN,NaN);else{var l=(r[2i+1]+r[2i+3])/2;n.push(r[2i],r[2i+1],r[2i],l,r[2i+2],l)}n.push(r[a-2],r[a-1])}else if(\"vh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2i])\|\|isNaN(r[2i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2i],r[2i+1]),isNaN(r[2i+2])\|\|isNaN(r[2i+3])?n.push(NaN,NaN):n.push(r[2i],r[2i+3]));n.push(r[a-2],r[a-1])}else n=r;var c=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){c=!0;break}var u=c\|\|n.length>p.TOO_MANY_POINTS?\"rect\":f.hasMarkers(e)?\"rect\":\"round\";if(c&&e.connectgaps){var h=n[0],d=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])\|\|isNaN(n[i+1])?(n[i]=h,n[i+1]=d):(h=n[i],d=n[i+1])}return{join:u,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod(\"errorbars\",\"makeComputeError\"),l=c.getFromId(t,e.xaxis),u=c.getFromId(t,e.yaxis),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e[\"error_\"+a];if(o&&o.visible&&(\"linear\"===i.type\|\|\"log\"===i.type)){for(var l=s(o),c={x:0,y:1}[a],u={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4f),d=1/0,g=-1/0,v=0,m=0;v<f;v++,m+=4){var y=t[v];if(n(y)){var 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n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./attributes\"),o=t(\"../scatter/constants\"),s=t(\"../scatter/subtypes\"),l=t(\"../scatter/xy_defaults\"),c=t(\"../scatter/marker_defaults\"),u=t(\"../scatter/line_defaults\"),f=t(\"../scatter/fillcolor_defaults\"),h=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,p){function d(r,i){return n.coerce(t,e,a,r,i)}var g=!!t.marker&&/-open/.test(t.marker.symbol),v=s.isBubble(t),m=l(t,e,p,d);if(m){var y=m<o.PTS_LINESONLY?\"lines+markers\":\"lines\";d(\"text\"),d(\"hovertext\"),d(\"mode\",y),s.hasLines(e)&&(d(\"connectgaps\"),u(t,e,r,p,d),d(\"line.shape\")),s.hasMarkers(e)&&(c(t,e,r,p,d),d(\"marker.line.width\",g\|\|v?1:0)),s.hasText(e)&&h(t,e,p,d),d(\"fill\"),\"none\"!==e.fill&&f(t,e,r,d);var x=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");x(t,e,r,{axis:\"y\"}),x(t,e,r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,d)}else 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n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"point-cluster\"),s=t(\"array-range\"),l=t(\"gl-text\"),c=t(\"../../registry\"),u=t(\"../../lib\"),f=t(\"../../lib/prepare_regl\"),h=t(\"../../plots/cartesian/axis_ids\"),p=t(\"../../plots/cartesian/autorange\").findExtremes,d=t(\"../../components/color\"),g=t(\"../scatter/subtypes\"),v=t(\"../scatter/calc\"),m=v.calcMarkerSize,y=v.calcAxisExpansion,x=v.setFirstScatter,b=t(\"../scatter/colorscale_calc\"),_=t(\"../scatter/link_traces\"),w=t(\"../scatter/get_trace_color\"),k=t(\"../scatter/fill_hover_text\"),M=t(\"./convert\"),A=t(\"../../constants/numerical\").BADNUM,T=t(\"./constants\").TOO_MANY_POINTS,S=t(\"../../constants/interactions\").DESELECTDIM;function E(t,e,r){var n=t._extremes[e._id],i=p(e,r._bnds,{padded:!0});n.min=n.min.concat(i.min),n.max=n.max.concat(i.max)}function C(t,e){var r=e._scene,n={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[]},i={selectBatch:null,unselectBatch:null,fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:null};return e._scene\|\|((r=e._scene={}).init=function(){u.extendFlat(r,i,n)},r.init(),r.update=function(t){var e=u.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var n=0;n<r.count;n++)r.glText[n].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,n=r.error2d,i=r.line2d,a=r.scatter2d,o=r.glText,s=r.select2d,l=r.selectBatch,c=r.unselectBatch,u=0;u<t;u++)e&&r.fillOrder[u]&&e.draw(r.fillOrder[u]),i&&r.lineOptions[u]&&i.draw(u),n&&(r.errorXOptions[u]&&n.draw(u),r.errorYOptions[u]&&n.draw(u+t)),!a\|\|!r.markerOptions[u]\|\|l&&l[u]\|\|a.draw(u),o[u]&&r.textOptions[u]&&o[u].render();a&&s&&l&&(s.draw(l),a.draw(c)),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach(function(t){t.destroy&&t.destroy()}),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty\|\|u.extendFlat(r,n),r}function L(t,e,r,n){var i=t.xa,a=t.ya,o=t.distance,s=t.dxy,l=t.index,f={pointNumber:l,x:e[l],y:r[l]};f.tx=Array.isArray(n.text)?n.text[l]:n.text,f.htx=Array.isArray(n.hovertext)?n.hovertext[l]:n.hovertext,f.data=Array.isArray(n.customdata)?n.customdata[l]:n.customdata,f.tp=Array.isArray(n.textposition)?n.textposition[l]:n.textposition;var h=n.textfont;h&&(f.ts=Array.isArray(h.size)?h.size[l]:h.size,f.tc=Array.isArray(h.color)?h.color[l]:h.color,f.tf=Array.isArray(h.family)?h.family[l]:h.family);var p=n.marker;p&&(f.ms=u.isArrayOrTypedArray(p.size)?p.size[l]:p.size,f.mo=u.isArrayOrTypedArray(p.opacity)?p.opacity[l]:p.opacity,f.mx=Array.isArray(p.symbol)?p.symbol[l]:p.symbol,f.mc=u.isArrayOrTypedArray(p.color)?p.color[l]:p.color);var d=p&&p.line;d&&(f.mlc=Array.isArray(d.color)?d.color[l]:d.color,f.mlw=u.isArrayOrTypedArray(d.width)?d.width[l]:d.width);var g=p&&p.gradient;g&&\"none\"!==g.type&&(f.mgt=Array.isArray(g.type)?g.type[l]:g.type,f.mgc=Array.isArray(g.color)?g.color[l]:g.color);var v=i.c2p(f.x,!0),m=a.c2p(f.y,!0),y=f.mrc\|\|1,x=n.hoverlabel;x&&(f.hbg=Array.isArray(x.bgcolor)?x.bgcolor[l]:x.bgcolor,f.hbc=Array.isArray(x.bordercolor)?x.bordercolor[l]:x.bordercolor,f.hts=Array.isArray(x.font.size)?x.font.size[l]:x.font.size,f.htc=Array.isArray(x.font.color)?x.font.color[l]:x.font.color,f.htf=Array.isArray(x.font.family)?x.font.family[l]:x.font.family,f.hnl=Array.isArray(x.namelength)?x.namelength[l]:x.namelength);var b=n.hoverinfo;b&&(f.hi=Array.isArray(b)?b[l]:b);var _={};return _[t.index]=f,u.extendFlat(t,{color:w(n,f),x0:v-y,x1:v+y,xLabelVal:f.x,y0:m-y,y1:m+y,yLabelVal:f.y,cd:_,distance:o,spikeDistance:s}),f.htx?t.text=f.htx:f.tx?t.text=f.tx:n.text&&(t.text=n.text),k(f,n,t),c.getComponentMethod(\"errorbars\",\"hoverInfo\")(f,n,t),t}function z(t){var e,r,n=t[0],i=n.trace,a=n.t,o=a._scene,s=a.index,l=o.selectBatch[s],c=o.unselectBatch[s],f=o.textOptions[s],h=o.textSelectedOptions[s]\|\|{},p=o.textUnselectedOptions[s]\|\|{},g=u.extendFlat({},f);if(l&&c){var v=h.color,m=p.color,y=f.color,x=Array.isArray(y);for(g.color=new Array(i._length),e=0;e<l.length;e++)r=l[e],g.color[r]=v\|\|(x?y[r]:y);for(e=0;e<c.length;e++){r=c[e];var b=x?y[r]:y;g.color[r]=m\|\|(v?b:d.addOpacity(b,S))}}o.glText[s].update(g)}e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),calc:function(t,e){var r,n,i,a=t._fullLayout,s=h.getFromId(t,e.xaxis),l=h.getFromId(t,e.yaxis),c=a._plots[e.xaxis+e.yaxis],f=e._length,p=2f,d={},g=s.makeCalcdata(e,\"x\"),v=l.makeCalcdata(e,\"y\"),_=new 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i,a,o,s,l,c,u,f,h,p=t.cd,d=p[0].t,g=p[0].trace,v=t.xa,m=t.ya,y=d.x,x=d.y,b=v.c2p(e),_=m.c2p(r),w=t.distance;if(d.tree){var k=v.p2c(b-w),M=v.p2c(b+w),A=m.p2c(_-w),T=m.p2c(_+w);i=\"x\"===n?d.tree.range(Math.min(k,M),Math.min(m._rl[0],m._rl[1]),Math.max(k,M),Math.max(m._rl[0],m._rl[1])):d.tree.range(Math.min(k,M),Math.min(A,T),Math.max(k,M),Math.max(A,T))}else{if(!d.ids)return[t];i=d.ids}var S=w;if(\"x\"===n)for(l=0;l<i.length;l++)o=y[i[l]],(c=Math.abs(v.c2p(o)-b))<S&&(S=c,u=m.c2p(x[i[l]])-_,h=Math.sqrt(cc+uu),a=i[l]);else for(l=0;l<i.length;l++)o=y[i[l]],s=x[i[l]],c=v.c2p(o)-b,u=m.c2p(s)-_,(f=Math.sqrt(cc+uu))<S&&(S=h=f,a=i[l]);return t.index=a,t.distance=S,t.dxy=h,void 0===a?[t]:(L(t,y,x,g),[t])},selectPoints:function(t,e){var r=t.cd,n=[],i=r[0].trace,a=r[0].t,o=i._length,l=a.x,c=a.y,u=a._scene;if(!u)return n;var f=g.hasText(i),h=g.hasMarkers(i),p=!h&&!f;if(!0!==i.visible\|\|p)return n;var d,v=null,m=null;if(!1===e\|\|e.degenerate)m=s(o);else 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u.selectBatch[a.index]=v,u.unselectBatch[a.index]=m,f&&z(r),n},sceneUpdate:C,calcHover:L,meta:{}}},{\"../../components/color\":570,\"../../constants/interactions\":672,\"../../constants/numerical\":673,\"../../lib\":696,\"../../lib/prepare_regl\":709,\"../../plots/cartesian\":756,\"../../plots/cartesian/autorange\":743,\"../../plots/cartesian/axis_ids\":747,\"../../registry\":827,\"../scatter/calc\":1044,\"../scatter/colorscale_calc\":1046,\"../scatter/cross_trace_defaults\":1049,\"../scatter/fill_hover_text\":1051,\"../scatter/get_trace_color\":1053,\"../scatter/link_traces\":1059,\"../scatter/marker_colorbar\":1061,\"../scatter/subtypes\":1067,\"./attributes\":1092,\"./constants\":1093,\"./convert\":1094,\"./defaults\":1095,\"array-range\":55,\"gl-text\":304,\"point-cluster\":452,\"regl-error2d\":473,\"regl-line2d\":474,\"regl-scatter2d\":475}],1097:[function(t,e,r){\"use strict\";var n=t(\"../scattergeo/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/mapbox/layout_attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorbar/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,u=n.line,f=n.marker;e.exports=c({lon:n.lon,lat:n.lat,mode:l({},i.mode,{dflt:\"markers\"}),text:l({},i.text,{}),hovertext:l({},i.hovertext,{}),line:{color:u.color,width:u.width},connectgaps:i.connectgaps,marker:{symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},opacity:f.opacity,size:f.size,sizeref:f.sizeref,sizemin:f.sizemin,sizemode:f.sizemode,color:f.color,colorscale:f.colorscale,cauto:f.cauto,cmax:f.cmax,cmin:f.cmin,autocolorscale:f.autocolorscale,reversescale:f.reversescale,showscale:f.showscale,colorbar:s},fill:n.fill,fillcolor:i.fillcolor,textfont:a.layers.symbol.textfont,textposition:a.layers.symbol.textposition,selected:{marker:i.selected.marker},unselected:{marker:i.unselected.marker},hoverinfo:l({},o.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorbar/attributes\":571,\"../../lib/extend\":685,\"../../plot_api/edit_types\":727,\"../../plots/attributes\":741,\"../../plots/mapbox/layout_attributes\":804,\"../scatter/attributes\":1043,\"../scattergeo/attributes\":1083}],1098:[function(t,e,r){\"use 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T;if((v\|\|m)&&(e=o.calcTraceToLineCoords(t)),v&&(w.geojson=o.makePolygon(e),w.layout.visibility=\"visible\",i.extendFlat(w.paint,{\"fill-color\":r.fillcolor})),m&&(k.geojson=o.makeLine(e),k.layout.visibility=\"visible\",i.extendFlat(k.paint,{\"line-width\":r.line.width,\"line-color\":r.line.color,\"line-opacity\":r.opacity})),b){var S=function(t){var e,r,a,o,u=t[0].trace,f=u.marker,h=u.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),v=i.isArrayOrTypedArray(f.opacity);function m(t){return u.opacityt}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFunc(s.extractScale(f.colorscale,f.cmin,f.cmax)):i.identity);d&&(a=c(u));v&&(o=function(t){var e=n(t)?+i.constrain(t,0,1):0;return m(e)});var y,x=[];for(e=0;e<t.length;e++){var b=t[e],_=b.lonlat;if(!g(_)){var w={};r&&(w.mcc=b.mcc=r(b.mc)),a&&(w.mrc=b.mrc=a(b.ms)),o&&(w.mo=o(b.mo)),h&&(w.selected=b.selected\|\|0),x.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:_},properties:w})}}if(h)for(y=l.makeSelectedPointStyleFns(u),e=0;e<x.length;e++){var k=x[e].properties;y.selectedOpacityFn&&(k.mo=m(y.selectedOpacityFn(k))),y.selectedColorFn&&(k.mcc=y.selectedColorFn(k)),y.selectedSizeFn&&(k.mrc=y.selectedSizeFn(k))}return{geojson:{type:\"FeatureCollection\",features:x},mcc:p\|\|y&&y.selectedColorFn?{type:\"identity\",property:\"mcc\"}:f.color,mrc:d\|\|y&&y.selectedSizeFn?{type:\"identity\",property:\"mrc\"}:(M=f.size,M/2),mo:v\|\|y&&y.selectedOpacityFn?{type:\"identity\",property:\"mo\"}:m(f.opacity)};var M}(t);M.geojson=S.geojson,M.layout.visibility=\"visible\",i.extendFlat(M.paint,{\"circle-color\":S.mcc,\"circle-radius\":S.mrc,\"circle-opacity\":S.mo})}if((_\|\|x)&&(A.geojson=function(t){for(var e=t[0].trace,r=(e.marker\|\|{}).symbol,n=e.text,i=\"circle\"!==r?p(r):d,a=u.hasText(e)?p(n):d,o=[],s=0;s<t.length;s++){var l=t[s];g(l.lonlat)\|\|o.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:l.lonlat},properties:{symbol:i(l.mx),text:a(l.tx)}})}return{type:\"FeatureCollection\",features:o}}(t),i.extendFlat(A.layout,{visibility:\"visible\",\"icon-image\":\"{symbol}-15\",\"text-field\":\"{text}\"}),_&&(i.extendFlat(A.layout,{\"icon-size\":r.marker.size/10}),i.extendFlat(A.paint,{\"icon-opacity\":r.opacityr.marker.opacity,\"icon-color\":r.marker.color})),x)){var E=(r.marker\|\|{}).size,C=f(r.textposition,E);i.extendFlat(A.layout,{\"text-size\":r.textfont.size,\"text-anchor\":C.anchor,\"text-offset\":C.offset}),i.extendFlat(A.paint,{\"text-color\":r.textfont.color,\"text-opacity\":r.opacity})}return T}},{\"../../components/colorscale\":585,\"../../components/drawing\":595,\"../../constants/numerical\":673,\"../../lib\":696,\"../../lib/geojson_utils\":689,\"../../plots/mapbox/convert_text_opts\":801,\"../scatter/make_bubble_size_func\":1060,\"../scatter/subtypes\":1067,\"fast-isnumeric\":214}],1099:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}if(function(t,e,r){var n=r(\"lon\")\|\|[],i=r(\"lat\")\|\|[],a=Math.min(n.length,i.length);return e._length=a,a}(0,e,f)){if(f(\"text\"),f(\"hovertext\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f,{noDash:!0}),f(\"connectgaps\")),i.hasMarkers(e)){a(t,e,r,u,f,{noLine:!0});var h=e.marker;\"circle\"!==h.symbol&&(n.isArrayOrTypedArray(h.size)&&(h.size=h.size[0]),n.isArrayOrTypedArray(h.color)&&(h.color=h.color[0]))}i.hasText(e)&&s(t,e,u,f,{noSelect:!0}),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)}else e.visible=!1}},{\"../../lib\":696,\"../scatter/fillcolor_defaults\":1052,\"../scatter/line_defaults\":1056,\"../scatter/marker_defaults\":1062,\"../scatter/subtypes\":1067,\"../scatter/text_defaults\":1068,\"./attributes\":1097}],1100:[function(t,e,r){\"use strict\";e.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t}},{}],1101:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\"),o=t(\"../scatter/fill_hover_text\"),s=t(\"../../constants/numerical\").BADNUM;e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=360(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),d=e-p;if(n.getClosest(l,function(t){var e=t.lonlat;if(e[0]===s)return 1/0;var n=i.modHalf(e[0],360),a=e[1],o=h.project([n,a]),l=o.x-u.c2p([d,a]),c=o.y-f.c2p([n,r]),p=Math.max(3,t.mrc\|\|0);return Math.max(Math.sqrt(ll+cc)-p,1-3/p)},t),!1!==t.index){var g=l[t.index],v=g.lonlat,m=[i.modHalf(v[0],360)+p,v[1]],y=u.c2p(m),x=f.c2p(m),b=g.mrc\|\|1;return t.x0=y-b,t.x1=y+b,t.y0=x-b,t.y1=x+b,t.color=a(c,g),t.extraText=function(t,e,r){var n=(e.hi\|\|t.hoverinfo).split(\"+\"),i=-1!==n.indexOf(\"all\"),a=-1!==n.indexOf(\"lon\"),s=-1!==n.indexOf(\"lat\"),l=e.lonlat,c=[];function u(t){return t+\"\\xb0\"}i\|\|a&&s?c.push(\"(\"+u(l[0])+\", \"+u(l[1])+\")\"):a?c.push(r.lon+u(l[0])):s&&c.push(r.lat+u(l[1]));(i\|\|-1!==n.indexOf(\"text\"))&&o(e,t,c);return c.join(\"<br>\")}(c,g,l[0].t.labels),[t]}}},{\"../../components/fx\":612,\"../../constants/numerical\":673,\"../../lib\":696,\"../scatter/fill_hover_text\":1051,\"../scatter/get_trace_color\":1053}],1102:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/marker_colorbar\"),n.calc=t(\"../scattergeo/calc\"),n.plot=t(\"./plot\"),n.hoverPoints=t(\"./hover\"),n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.style=function(t,e){e&&e[0].trace._glTrace.update(e)},n.moduleType=\"trace\",n.name=\"scattermapbox\",n.basePlotModule=t(\"../../plots/mapbox\"),n.categories=[\"mapbox\",\"gl\",\"symbols\",\"showLegend\",\"scatterlike\"],n.meta={},e.exports=n},{\"../../plots/mapbox\":802,\"../scatter/marker_colorbar\":1061,\"../scattergeo/calc\":1084,\"./attributes\":1097,\"./defaults\":1099,\"./event_data\":1100,\"./hover\":1101,\"./plot\":1103,\"./select\":1104}],1103:[function(t,e,r){\"use strict\";var n=t(\"./convert\");function 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../components/drawing/attributes\").dash,l=t(\"../../lib/extend\").extendFlat,c=n.marker,u=n.line,f=c.line;e.exports={a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},c:{valType:\"data_array\",editType:\"calc\"},sum:{valType:\"number\",dflt:0,min:0,editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),hovertext:l({},n.hovertext,{}),line:{color:u.color,width:u.width,dash:s,shape:l({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,cliponaxis:n.cliponaxis,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:c.symbol,opacity:c.opacity,maxdisplayed:c.maxdisplayed,size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,line:l({width:f.width,editType:\"calc\"},a(\"marker.line\")),gradient:c.gradient,editType:\"calc\"},a(\"marker\"),{colorbar:o}),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"c\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":571,\"../../components/colorscale/attributes\":577,\"../../components/drawing/attributes\":594,\"../../lib/extend\":685,\"../../plots/attributes\":741,\"../scatter/attributes\":1043}],1115:[function(t,e,r){\"use 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c},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()\|\|(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]\|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative\|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative\|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative\|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0\|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26\|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]\|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0\|r.words[a])+(0\|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0\|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0\|r.words[o])-(0\|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0\|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0\|o[0],f=8191&u,h=u>>>13,p=0\|o[1],d=8191&p,g=p>>>13,v=0\|o[2],m=8191&v,y=v>>>13,x=0\|o[3],b=8191&x,_=x>>>13,w=0\|o[4],k=8191&w,M=w>>>13,A=0\|o[5],T=8191&A,S=A>>>13,E=0\|o[6],C=8191&E,L=E>>>13,z=0\|o[7],O=8191&z,I=z>>>13,P=0\|o[8],D=8191&P,R=P>>>13,B=0\|o[9],F=8191&B,N=B>>>13,j=0\|s[0],V=8191&j,U=j>>>13,q=0\|s[1],H=8191&q,G=q>>>13,W=0\|s[2],Y=8191&W,X=W>>>13,Z=0\|s[3],$=8191&Z,J=Z>>>13,K=0\|s[4],Q=8191&K,tt=K>>>13,et=0\|s[5],rt=8191&et,nt=et>>>13,it=0\|s[6],at=8191&it,ot=it>>>13,st=0\|s[7],lt=8191&st,ct=st>>>13,ut=0\|s[8],ft=8191&ut,ht=ut>>>13,pt=0\|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var vt=(c+(n=Math.imul(f,V))\|0)+((8191&(i=(i=Math.imul(f,U))+Math.imul(h,V)\|0))<<13)\|0;c=((a=Math.imul(h,U))+(i>>>13)\|0)+(vt>>>26)\|0,vt&=67108863,n=Math.imul(d,V),i=(i=Math.imul(d,U))+Math.imul(g,V)\|0,a=Math.imul(g,U);var mt=(c+(n=n+Math.imul(f,H)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,G)\|0)+Math.imul(h,H)\|0))<<13)\|0;c=((a=a+Math.imul(h,G)\|0)+(i>>>13)\|0)+(mt>>>26)\|0,mt&=67108863,n=Math.imul(m,V),i=(i=Math.imul(m,U))+Math.imul(y,V)\|0,a=Math.imul(y,U),n=n+Math.imul(d,H)\|0,i=(i=i+Math.imul(d,G)\|0)+Math.imul(g,H)\|0,a=a+Math.imul(g,G)\|0;var yt=(c+(n=n+Math.imul(f,Y)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,X)\|0)+Math.imul(h,Y)\|0))<<13)\|0;c=((a=a+Math.imul(h,X)\|0)+(i>>>13)\|0)+(yt>>>26)\|0,yt&=67108863,n=Math.imul(b,V),i=(i=Math.imul(b,U))+Math.imul(_,V)\|0,a=Math.imul(_,U),n=n+Math.imul(m,H)\|0,i=(i=i+Math.imul(m,G)\|0)+Math.imul(y,H)\|0,a=a+Math.imul(y,G)\|0,n=n+Math.imul(d,Y)\|0,i=(i=i+Math.imul(d,X)\|0)+Math.imul(g,Y)\|0,a=a+Math.imul(g,X)\|0;var xt=(c+(n=n+Math.imul(f,$)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,J)\|0)+Math.imul(h,$)\|0))<<13)\|0;c=((a=a+Math.imul(h,J)\|0)+(i>>>13)\|0)+(xt>>>26)\|0,xt&=67108863,n=Math.imul(k,V),i=(i=Math.imul(k,U))+Math.imul(M,V)\|0,a=Math.imul(M,U),n=n+Math.imul(b,H)\|0,i=(i=i+Math.imul(b,G)\|0)+Math.imul(_,H)\|0,a=a+Math.imul(_,G)\|0,n=n+Math.imul(m,Y)\|0,i=(i=i+Math.imul(m,X)\|0)+Math.imul(y,Y)\|0,a=a+Math.imul(y,X)\|0,n=n+Math.imul(d,$)\|0,i=(i=i+Math.imul(d,J)\|0)+Math.imul(g,$)\|0,a=a+Math.imul(g,J)\|0;var bt=(c+(n=n+Math.imul(f,Q)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,tt)\|0)+Math.imul(h,Q)\|0))<<13)\|0;c=((a=a+Math.imul(h,tt)\|0)+(i>>>13)\|0)+(bt>>>26)\|0,bt&=67108863,n=Math.imul(T,V),i=(i=Math.imul(T,U))+Math.imul(S,V)\|0,a=Math.imul(S,U),n=n+Math.imul(k,H)\|0,i=(i=i+Math.imul(k,G)\|0)+Math.imul(M,H)\|0,a=a+Math.imul(M,G)\|0,n=n+Math.imul(b,Y)\|0,i=(i=i+Math.imul(b,X)\|0)+Math.imul(_,Y)\|0,a=a+Math.imul(_,X)\|0,n=n+Math.imul(m,$)\|0,i=(i=i+Math.imul(m,J)\|0)+Math.imul(y,$)\|0,a=a+Math.imul(y,J)\|0,n=n+Math.imul(d,Q)\|0,i=(i=i+Math.imul(d,tt)\|0)+Math.imul(g,Q)\|0,a=a+Math.imul(g,tt)\|0;var _t=(c+(n=n+Math.imul(f,rt)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,nt)\|0)+Math.imul(h,rt)\|0))<<13)\|0;c=((a=a+Math.imul(h,nt)\|0)+(i>>>13)\|0)+(_t>>>26)\|0,_t&=67108863,n=Math.imul(C,V),i=(i=Math.imul(C,U))+Math.imul(L,V)\|0,a=Math.imul(L,U),n=n+Math.imul(T,H)\|0,i=(i=i+Math.imul(T,G)\|0)+Math.imul(S,H)\|0,a=a+Math.imul(S,G)\|0,n=n+Math.imul(k,Y)\|0,i=(i=i+Math.imul(k,X)\|0)+Math.imul(M,Y)\|0,a=a+Math.imul(M,X)\|0,n=n+Math.imul(b,$)\|0,i=(i=i+Math.imul(b,J)\|0)+Math.imul(_,$)\|0,a=a+Math.imul(_,J)\|0,n=n+Math.imul(m,Q)\|0,i=(i=i+Math.imul(m,tt)\|0)+Math.imul(y,Q)\|0,a=a+Math.imul(y,tt)\|0,n=n+Math.imul(d,rt)\|0,i=(i=i+Math.imul(d,nt)\|0)+Math.imul(g,rt)\|0,a=a+Math.imul(g,nt)\|0;var wt=(c+(n=n+Math.imul(f,at)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,ot)\|0)+Math.imul(h,at)\|0))<<13)\|0;c=((a=a+Math.imul(h,ot)\|0)+(i>>>13)\|0)+(wt>>>26)\|0,wt&=67108863,n=Math.imul(O,V),i=(i=Math.imul(O,U))+Math.imul(I,V)\|0,a=Math.imul(I,U),n=n+Math.imul(C,H)\|0,i=(i=i+Math.imul(C,G)\|0)+Math.imul(L,H)\|0,a=a+Math.imul(L,G)\|0,n=n+Math.imul(T,Y)\|0,i=(i=i+Math.imul(T,X)\|0)+Math.imul(S,Y)\|0,a=a+Math.imul(S,X)\|0,n=n+Math.imul(k,$)\|0,i=(i=i+Math.imul(k,J)\|0)+Math.imul(M,$)\|0,a=a+Math.imul(M,J)\|0,n=n+Math.imul(b,Q)\|0,i=(i=i+Math.imul(b,tt)\|0)+Math.imul(_,Q)\|0,a=a+Math.imul(_,tt)\|0,n=n+Math.imul(m,rt)\|0,i=(i=i+Math.imul(m,nt)\|0)+Math.imul(y,rt)\|0,a=a+Math.imul(y,nt)\|0,n=n+Math.imul(d,at)\|0,i=(i=i+Math.imul(d,ot)\|0)+Math.imul(g,at)\|0,a=a+Math.imul(g,ot)\|0;var kt=(c+(n=n+Math.imul(f,lt)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,ct)\|0)+Math.imul(h,lt)\|0))<<13)\|0;c=((a=a+Math.imul(h,ct)\|0)+(i>>>13)\|0)+(kt>>>26)\|0,kt&=67108863,n=Math.imul(D,V),i=(i=Math.imul(D,U))+Math.imul(R,V)\|0,a=Math.imul(R,U),n=n+Math.imul(O,H)\|0,i=(i=i+Math.imul(O,G)\|0)+Math.imul(I,H)\|0,a=a+Math.imul(I,G)\|0,n=n+Math.imul(C,Y)\|0,i=(i=i+Math.imul(C,X)\|0)+Math.imul(L,Y)\|0,a=a+Math.imul(L,X)\|0,n=n+Math.imul(T,$)\|0,i=(i=i+Math.imul(T,J)\|0)+Math.imul(S,$)\|0,a=a+Math.imul(S,J)\|0,n=n+Math.imul(k,Q)\|0,i=(i=i+Math.imul(k,tt)\|0)+Math.imul(M,Q)\|0,a=a+Math.imul(M,tt)\|0,n=n+Math.imul(b,rt)\|0,i=(i=i+Math.imul(b,nt)\|0)+Math.imul(_,rt)\|0,a=a+Math.imul(_,nt)\|0,n=n+Math.imul(m,at)\|0,i=(i=i+Math.imul(m,ot)\|0)+Math.imul(y,at)\|0,a=a+Math.imul(y,ot)\|0,n=n+Math.imul(d,lt)\|0,i=(i=i+Math.imul(d,ct)\|0)+Math.imul(g,lt)\|0,a=a+Math.imul(g,ct)\|0;var Mt=(c+(n=n+Math.imul(f,ft)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,ht)\|0)+Math.imul(h,ft)\|0))<<13)\|0;c=((a=a+Math.imul(h,ht)\|0)+(i>>>13)\|0)+(Mt>>>26)\|0,Mt&=67108863,n=Math.imul(F,V),i=(i=Math.imul(F,U))+Math.imul(N,V)\|0,a=Math.imul(N,U),n=n+Math.imul(D,H)\|0,i=(i=i+Math.imul(D,G)\|0)+Math.imul(R,H)\|0,a=a+Math.imul(R,G)\|0,n=n+Math.imul(O,Y)\|0,i=(i=i+Math.imul(O,X)\|0)+Math.imul(I,Y)\|0,a=a+Math.imul(I,X)\|0,n=n+Math.imul(C,$)\|0,i=(i=i+Math.imul(C,J)\|0)+Math.imul(L,$)\|0,a=a+Math.imul(L,J)\|0,n=n+Math.imul(T,Q)\|0,i=(i=i+Math.imul(T,tt)\|0)+Math.imul(S,Q)\|0,a=a+Math.imul(S,tt)\|0,n=n+Math.imul(k,rt)\|0,i=(i=i+Math.imul(k,nt)\|0)+Math.imul(M,rt)\|0,a=a+Math.imul(M,nt)\|0,n=n+Math.imul(b,at)\|0,i=(i=i+Math.imul(b,ot)\|0)+Math.imul(_,at)\|0,a=a+Math.imul(_,ot)\|0,n=n+Math.imul(m,lt)\|0,i=(i=i+Math.imul(m,ct)\|0)+Math.imul(y,lt)\|0,a=a+Math.imul(y,ct)\|0,n=n+Math.imul(d,ft)\|0,i=(i=i+Math.imul(d,ht)\|0)+Math.imul(g,ft)\|0,a=a+Math.imul(g,ht)\|0;var At=(c+(n=n+Math.imul(f,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(f,gt)\|0)+Math.imul(h,dt)\|0))<<13)\|0;c=((a=a+Math.imul(h,gt)\|0)+(i>>>13)\|0)+(At>>>26)\|0,At&=67108863,n=Math.imul(F,H),i=(i=Math.imul(F,G))+Math.imul(N,H)\|0,a=Math.imul(N,G),n=n+Math.imul(D,Y)\|0,i=(i=i+Math.imul(D,X)\|0)+Math.imul(R,Y)\|0,a=a+Math.imul(R,X)\|0,n=n+Math.imul(O,$)\|0,i=(i=i+Math.imul(O,J)\|0)+Math.imul(I,$)\|0,a=a+Math.imul(I,J)\|0,n=n+Math.imul(C,Q)\|0,i=(i=i+Math.imul(C,tt)\|0)+Math.imul(L,Q)\|0,a=a+Math.imul(L,tt)\|0,n=n+Math.imul(T,rt)\|0,i=(i=i+Math.imul(T,nt)\|0)+Math.imul(S,rt)\|0,a=a+Math.imul(S,nt)\|0,n=n+Math.imul(k,at)\|0,i=(i=i+Math.imul(k,ot)\|0)+Math.imul(M,at)\|0,a=a+Math.imul(M,ot)\|0,n=n+Math.imul(b,lt)\|0,i=(i=i+Math.imul(b,ct)\|0)+Math.imul(_,lt)\|0,a=a+Math.imul(_,ct)\|0,n=n+Math.imul(m,ft)\|0,i=(i=i+Math.imul(m,ht)\|0)+Math.imul(y,ft)\|0,a=a+Math.imul(y,ht)\|0;var Tt=(c+(n=n+Math.imul(d,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(d,gt)\|0)+Math.imul(g,dt)\|0))<<13)\|0;c=((a=a+Math.imul(g,gt)\|0)+(i>>>13)\|0)+(Tt>>>26)\|0,Tt&=67108863,n=Math.imul(F,Y),i=(i=Math.imul(F,X))+Math.imul(N,Y)\|0,a=Math.imul(N,X),n=n+Math.imul(D,$)\|0,i=(i=i+Math.imul(D,J)\|0)+Math.imul(R,$)\|0,a=a+Math.imul(R,J)\|0,n=n+Math.imul(O,Q)\|0,i=(i=i+Math.imul(O,tt)\|0)+Math.imul(I,Q)\|0,a=a+Math.imul(I,tt)\|0,n=n+Math.imul(C,rt)\|0,i=(i=i+Math.imul(C,nt)\|0)+Math.imul(L,rt)\|0,a=a+Math.imul(L,nt)\|0,n=n+Math.imul(T,at)\|0,i=(i=i+Math.imul(T,ot)\|0)+Math.imul(S,at)\|0,a=a+Math.imul(S,ot)\|0,n=n+Math.imul(k,lt)\|0,i=(i=i+Math.imul(k,ct)\|0)+Math.imul(M,lt)\|0,a=a+Math.imul(M,ct)\|0,n=n+Math.imul(b,ft)\|0,i=(i=i+Math.imul(b,ht)\|0)+Math.imul(_,ft)\|0,a=a+Math.imul(_,ht)\|0;var St=(c+(n=n+Math.imul(m,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(m,gt)\|0)+Math.imul(y,dt)\|0))<<13)\|0;c=((a=a+Math.imul(y,gt)\|0)+(i>>>13)\|0)+(St>>>26)\|0,St&=67108863,n=Math.imul(F,$),i=(i=Math.imul(F,J))+Math.imul(N,$)\|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)\|0,i=(i=i+Math.imul(D,tt)\|0)+Math.imul(R,Q)\|0,a=a+Math.imul(R,tt)\|0,n=n+Math.imul(O,rt)\|0,i=(i=i+Math.imul(O,nt)\|0)+Math.imul(I,rt)\|0,a=a+Math.imul(I,nt)\|0,n=n+Math.imul(C,at)\|0,i=(i=i+Math.imul(C,ot)\|0)+Math.imul(L,at)\|0,a=a+Math.imul(L,ot)\|0,n=n+Math.imul(T,lt)\|0,i=(i=i+Math.imul(T,ct)\|0)+Math.imul(S,lt)\|0,a=a+Math.imul(S,ct)\|0,n=n+Math.imul(k,ft)\|0,i=(i=i+Math.imul(k,ht)\|0)+Math.imul(M,ft)\|0,a=a+Math.imul(M,ht)\|0;var Et=(c+(n=n+Math.imul(b,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(b,gt)\|0)+Math.imul(_,dt)\|0))<<13)\|0;c=((a=a+Math.imul(_,gt)\|0)+(i>>>13)\|0)+(Et>>>26)\|0,Et&=67108863,n=Math.imul(F,Q),i=(i=Math.imul(F,tt))+Math.imul(N,Q)\|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)\|0,i=(i=i+Math.imul(D,nt)\|0)+Math.imul(R,rt)\|0,a=a+Math.imul(R,nt)\|0,n=n+Math.imul(O,at)\|0,i=(i=i+Math.imul(O,ot)\|0)+Math.imul(I,at)\|0,a=a+Math.imul(I,ot)\|0,n=n+Math.imul(C,lt)\|0,i=(i=i+Math.imul(C,ct)\|0)+Math.imul(L,lt)\|0,a=a+Math.imul(L,ct)\|0,n=n+Math.imul(T,ft)\|0,i=(i=i+Math.imul(T,ht)\|0)+Math.imul(S,ft)\|0,a=a+Math.imul(S,ht)\|0;var Ct=(c+(n=n+Math.imul(k,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(k,gt)\|0)+Math.imul(M,dt)\|0))<<13)\|0;c=((a=a+Math.imul(M,gt)\|0)+(i>>>13)\|0)+(Ct>>>26)\|0,Ct&=67108863,n=Math.imul(F,rt),i=(i=Math.imul(F,nt))+Math.imul(N,rt)\|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)\|0,i=(i=i+Math.imul(D,ot)\|0)+Math.imul(R,at)\|0,a=a+Math.imul(R,ot)\|0,n=n+Math.imul(O,lt)\|0,i=(i=i+Math.imul(O,ct)\|0)+Math.imul(I,lt)\|0,a=a+Math.imul(I,ct)\|0,n=n+Math.imul(C,ft)\|0,i=(i=i+Math.imul(C,ht)\|0)+Math.imul(L,ft)\|0,a=a+Math.imul(L,ht)\|0;var Lt=(c+(n=n+Math.imul(T,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(T,gt)\|0)+Math.imul(S,dt)\|0))<<13)\|0;c=((a=a+Math.imul(S,gt)\|0)+(i>>>13)\|0)+(Lt>>>26)\|0,Lt&=67108863,n=Math.imul(F,at),i=(i=Math.imul(F,ot))+Math.imul(N,at)\|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)\|0,i=(i=i+Math.imul(D,ct)\|0)+Math.imul(R,lt)\|0,a=a+Math.imul(R,ct)\|0,n=n+Math.imul(O,ft)\|0,i=(i=i+Math.imul(O,ht)\|0)+Math.imul(I,ft)\|0,a=a+Math.imul(I,ht)\|0;var zt=(c+(n=n+Math.imul(C,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(C,gt)\|0)+Math.imul(L,dt)\|0))<<13)\|0;c=((a=a+Math.imul(L,gt)\|0)+(i>>>13)\|0)+(zt>>>26)\|0,zt&=67108863,n=Math.imul(F,lt),i=(i=Math.imul(F,ct))+Math.imul(N,lt)\|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)\|0,i=(i=i+Math.imul(D,ht)\|0)+Math.imul(R,ft)\|0,a=a+Math.imul(R,ht)\|0;var Ot=(c+(n=n+Math.imul(O,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(O,gt)\|0)+Math.imul(I,dt)\|0))<<13)\|0;c=((a=a+Math.imul(I,gt)\|0)+(i>>>13)\|0)+(Ot>>>26)\|0,Ot&=67108863,n=Math.imul(F,ft),i=(i=Math.imul(F,ht))+Math.imul(N,ft)\|0,a=Math.imul(N,ht);var It=(c+(n=n+Math.imul(D,dt)\|0)\|0)+((8191&(i=(i=i+Math.imul(D,gt)\|0)+Math.imul(R,dt)\|0))<<13)\|0;c=((a=a+Math.imul(R,gt)\|0)+(i>>>13)\|0)+(It>>>26)\|0,It&=67108863;var Pt=(c+(n=Math.imul(F,dt))\|0)+((8191&(i=(i=Math.imul(F,gt))+Math.imul(N,dt)\|0))<<13)\|0;return c=((a=Math.imul(N,gt))+(i>>>13)\|0)+(Pt>>>26)\|0,Pt&=67108863,l[0]=vt,l[1]=mt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=kt,l[8]=Mt,l[9]=At,l[10]=Tt,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=zt,l[16]=Ot,l[17]=It,l[18]=Pt,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul\|\|(p=h),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?h(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0\|t.words[u])(0\|e.words[c]),h=67108863&f;s=67108863&(h=h+s\|0),i+=(o=(o=o+(f/67108864\|0)\|0)+(h>>>26)\|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t\|\|t===r-1)return t;for(var n=0,i=0;i<e;i++)n\|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2Math.PI/s),c=Math.sin(2Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],v=r[u+p+o],m=n[u+p+o],y=fv-hm;m=fm+hv,v=y,r[u+p]=d+v,n[u+p]=g+m,r[u+p+o]=d-v,n[u+p+o]=g-m,p!==s&&(y=lf-ch,h=lh+cf,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1\|Math.max(e,t),n=1&r,i=0;for(r=r/2\|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192Math.round(t[2n+1]/e)+Math.round(t[2n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864\|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0\|t[o],r[2o]=8191&a,a>>>=13,r[2o+1]=8191&a,a>>>=13;for(o=2e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]u[p]-l[p]f[p];l[p]=s[p]f[p]+l[p]u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0\|this.words[r])t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864\|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26\|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0\|this.words[e])-s<<r;this.words[e]=l\|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u\|\|c>=i);c--){var f=0\|this.words[c];this.words[c]=u<<26-a\|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0\|this.words[0])<t?(this.words[0]=t-(0\|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0\|this.words[i+r])+s;var l=(0\|t.words[i])e;s=((a-=67108863&l)>>26)-(l/67108864\|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0\|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0\|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0\|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0\|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864(0\|n.words[i.length+f])+(0\|n.words[i.length+f-1]);for(h=Math.min(h/o\|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()\|\|(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s\|\|null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length\|\|this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0\|\|1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(er+(0\|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0\|this.words[r])+67108864e;this.words[r]=i/t\|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()\|\|o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()\|\|l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i\|\|0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]\|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0\|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0\|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0\|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0\|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0\|this.words[r],i=0\|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function x(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function _(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function w(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(y,m),y.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0\|t.words[n];t.words[n-10]=(4194303&a)<<4\|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},y.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0\|t.words[r];e+=977n,t.words[r]=67108863&e,e=64n+(e/67108864\|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(x,m),i(b,m),i(_,m),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19(0\|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new y;else if(\"p224\"===t)e=new x;else if(\"p192\"===t)e=new b;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new _}return v[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},w.prototype._verify2=function(t,e){n(0==(t.negative\|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2uu).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var g=p,v=0;0!==g.cmp(s);v++)g=g.redSqr();n(v<d);var m=this.pow(f,new a(1).iushln(d-v-1));h=h.redMul(m),f=m.redSqr(),p=p.redMul(f),d=v}return h},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f\|\|0!==o?(o<<=1,o\|=f,(4===++s\|\|0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},w.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},w.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,w),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()\|\|e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()\|\|e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(\"undefined\"==typeof e\|\|e,this)},{buffer:91}],83:[function(t,e,r){\"use strict\";e.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e<i;++e)a+=t[e].length;var o=new Array(a),s=0;for(e=0;e<i;++e){var l=t[e],c=l.length;for(r=0;r<c;++r){var u=o[s++]=new Array(c-1),f=0;for(n=0;n<c;++n)n!==r&&(u[f++]=l[n]);if(1&r){var h=u[1];u[1]=u[0],u[0]=h}}}return o}},{}],84:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){switch(arguments.length){case 1:return n=[],c(i=t,i,u,!0),n;case 2:return\"function\"==typeof e?c(t,t,e,!0):function(t,e){return n=[],c(t,e,u,!1),n}(t,e);case 3:return c(t,e,r,!1);default:throw new Error(\"box-intersect: Invalid arguments\")}var i};var 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n=t(\"./partition\")(\"lo<p0\",[\"p0\"]),i=8;function a(t,e,r,n,i,a){for(var o=2t,s=o(r+1)+e,l=r+1;l<n;++l,s+=o)for(var c=i[s],u=l,f=o(l-1);u>r&&i[f+e]>c;--u,f-=o){for(var h=f,p=f+o,d=0;d<o;++d,++h,++p){var g=i[h];i[h]=i[p],i[p]=g}var v=a[u];a[u]=a[u-1],a[u-1]=v}}},{\"./partition\":88}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=\"abcdef\".split(\"\").concat(e),i=[];t.indexOf(\"lo\")>=0&&i.push(\"lo=e[k+n]\");t.indexOf(\"hi\")>=0&&i.push(\"hi=e[k+o]\");return r.push(n.replace(\"_\",i.join()).replace(\"$\",t)),Function.apply(void 0,r)};var n=\"for(var j=2a,k=jc,l=k,m=c,n=b,o=a+b,p=c;d>p;++p,k+=j){var _;if($)if(m===p)m+=1,l+=j;else{for(var s=0;j>s;++s){var t=e[k+s];e[k+s]=e[l],e[l++]=t}var u=f[p];f[p]=f[m],f[m++]=u}}return m\"},{}],89:[function(t,e,r){\"use strict\";e.exports=function(t,e){e<=4n?i(0,e-1,t):function t(e,r,f){var 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e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new f(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new f(this,[])},u.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new f(this,n)},u.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new f(this,n)},u.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new f(this,n)},u.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new f(this,n)},u.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new 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c=(a=r.right=o(a)).right=o(a.right),r.right=a.left,a.left=r,a.right=c,a._color=r._color,e._color=i,r._color=i,c._color=i,l(r),l(a),u>1&&((f=t[u-2]).left===r?f.left=a:f.right=a),void(t[u-1]=a);if(a.left&&a.left._color===n)return c=(a=r.right=o(a)).left=o(a.left),r.right=c.left,a.left=c.right,c.left=r,c.right=a,c._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(c),u>1&&((f=t[u-2]).left===r?f.left=c:f.right=c),void(t[u-1]=c);if(a._color===i){if(r._color===n)return r._color=i,void(r.right=s(n,a));r.right=s(n,a);continue}a=o(a),r.right=a.left,a.left=r,a._color=r._color,r._color=n,l(r),l(a),u>1&&((f=t[u-2]).left===r?f.left=a:f.right=a),t[u-1]=a,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}else{if((a=r.left).left&&a.left._color===n)return c=(a=r.left=o(a)).left=o(a.left),r.left=a.right,a.right=r,a.left=c,a._color=r._color,e._color=i,r._color=i,c._color=i,l(r),l(a),u>1&&((f=t[u-2]).right===r?f.right=a:f.left=a),void(t[u-1]=a);if(a.right&&a.right._color===n)return c=(a=r.left=o(a)).right=o(a.right),r.left=c.right,a.right=c.left,c.right=r,c.left=a,c._color=r._color,r._color=i,a._color=i,e._color=i,l(r),l(a),l(c),u>1&&((f=t[u-2]).right===r?f.right=c:f.left=c),void(t[u-1]=c);if(a._color===i){if(r._color===n)return r._color=i,void(r.left=s(n,a));r.left=s(n,a);continue}var f;a=o(a),r.left=a.right,a.right=r,a._color=r._color,r._color=n,l(r),l(a),u>1&&((f=t[u-2]).right===r?f.right=a:f.left=a),t[u-1]=a,t[u]=r,u+1<t.length?t[u+1]=e:t.push(e),u+=2}}}(e),g.left===r?g.left=null:g.right=null,new c(this.tree._compare,e[0])},Object.defineProperty(h,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(h,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(h,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return 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c(this.tree._compare,r[0])},h.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],220:[function(t,e,r){var n=[.9999999999998099,676.5203681218851,-1259.1392167224028,771.3234287776531,-176.6150291621406,12.507343278686905,-.13857109526572012,9984369578019572e-21,1.5056327351493116e-7],i=607/128,a=[.9999999999999971,57.15623566586292,-59.59796035547549,14.136097974741746,-.4919138160976202,3399464998481189e-20,4652362892704858e-20,-9837447530487956e-20,.0001580887032249125,-.00021026444172410488,.00021743961811521265,-.0001643181065367639,8441822398385275e-20,-26190838401581408e-21,36899182659531625e-22];function o(t){if(t<0)return Number(\"0/0\");for(var e=a[0],r=a.length-1;r>0;--r)e+=a[r]/(t+r);var n=t+i+.5;return.5Math.log(2Math.PI)+(t+.5)Math.log(n)-n+Math.log(e)-Math.log(t)}e.exports=function t(e){if(e<.5)return Math.PI/(Math.sin(Math.PIe)t(1-e));if(e>100)return Math.exp(o(e));e-=1;for(var r=n[0],i=1;i<9;i++)r+=n[i]/(e+i);var a=e+7+.5;return Math.sqrt(2Math.PI)Math.pow(a,e+.5)Math.exp(-a)r},e.exports.log=o},{}],221:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e\|\|{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas\|\|document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf(\"webgl\")&&a.push(\"experimental-\"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n\|\|null}},{}],222:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this._tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this._labelAngle=[0,0,0],this._labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t\|\|{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]}),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t\|\|\"tickSpacing\"in t\|\|(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort(function(t,e){return t.x-e.x});s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(g\|\|u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines\|\|(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var g=[0,0,0],v={model:l,view:l,projection:l};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var m=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t\|\|v;for(var e=this.gl,r=t.model\|\|l,n=t.view\|\|l,i=t.projection\|\|l,a=this.bounds,s=o(r,n,i,a),u=s.cubeEdges,f=s.axis,h=n[12],b=n[13],_=n[14],w=n[15],k=this.pixelRatio(i[3]h+i[7]b+i[11]_+i[15]w)/e.drawingBufferHeight,M=0;M<3;++M)this.lastCubeProps.cubeEdges[M]=u[M],this.lastCubeProps.axis[M]=f[M];var A=p;for(M=0;M<3;++M)d(p[M],M,this.bounds,u,f);e=this.gl;var T,S=g;for(M=0;M<3;++M)this.backgroundEnable[M]?S[M]=f[M]:S[M]=0;this._background.draw(r,n,i,a,S,this.backgroundColor),this._lines.bind(r,n,i,this);for(M=0;M<3;++M){var E=[0,0,0];f[M]>0?E[M]=a[1][M]:E[M]=a[0][M];for(var C=0;C<2;++C){var L=(M+1+C)%3,z=(M+1+(1^C))%3;this.gridEnable[L]&&this._lines.drawGrid(L,z,this.bounds,E,this.gridColor[L],this.gridWidth[L]this.pixelRatio)}for(C=0;C<2;++C){L=(M+1+C)%3,z=(M+1+(1^C))%3;this.zeroEnable[z]&&Math.min(a[0][z],a[1][z])<=0&&Math.max(a[0][z],a[1][z])>=0&&this._lines.drawZero(L,z,this.bounds,E,this.zeroLineColor[z],this.zeroLineWidth[z]this.pixelRatio)}}for(M=0;M<3;++M){this.lineEnable[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].primalOffset,this.lineColor[M],this.lineWidth[M]this.pixelRatio),this.lineMirror[M]&&this._lines.drawAxisLine(M,this.bounds,A[M].mirrorOffset,this.lineColor[M],this.lineWidth[M]this.pixelRatio);var O=c(m,A[M].primalMinor),I=c(y,A[M].mirrorMinor),P=this.lineTickLength;for(C=0;C<3;++C){var D=k/r[5C];O[C]=P[C]D,I[C]=P[C]D}this.lineTickEnable[M]&&this._lines.drawAxisTicks(M,A[M].primalOffset,O,this.lineTickColor[M],this.lineTickWidth[M]this.pixelRatio),this.lineTickMirror[M]&&this._lines.drawAxisTicks(M,A[M].mirrorOffset,I,this.lineTickColor[M],this.lineTickWidth[M]this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var R,B;function F(t){(B=[0,0,0])[t]=1}function N(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0?F(n):a>0&&l<0?F(n):a<0&&l>0?F(n):a<0&&l<0?F(n):o>0&&s>0?F(i):o>0&&s<0?F(i):o<0&&s>0?F(i):o<0&&s<0&&F(i)}for(M=0;M<3;++M){var j=A[M].primalMinor,V=A[M].mirrorMinor,U=c(x,A[M].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[M]&&(U[C]+=kj[C]Math.max(this.lineTickLength[C],0)/r[5C]);var q=[0,0,0];if(q[M]=1,this.tickEnable[M]){-3600===this.tickAngle[M]?(this.tickAngle[M]=0,this._tickAlign[M]=\"auto\"):this._tickAlign[M]=-1,R=1,\"auto\"===(T=[this._tickAlign[M],.5,R])[0]?T[0]=0:T[0]=parseInt(\"\"+T[0]),B=[0,0,0],N(M,j,V);for(C=0;C<3;++C)U[C]+=kj[C]this.tickPad[C]/r[5C];this._text.drawTicks(M,this.tickSize[M],this.tickAngle[M],U,this.tickColor[M],q,B,T)}if(this.labelEnable[M]){R=0,B=[0,0,0],this.labels[M].length>4&&(F(M),R=1),\"auto\"===(T=[this._labelAlign[M],.5,R])[0]?T[0]=0:T[0]=parseInt(\"\"+T[0]);for(C=0;C<3;++C)U[C]+=kj[C]this.labelPad[C]/r[5C];U[M]+=.5(a[0][M]+a[1][M]),this._text.drawLabel(M,this.labelSize[M],this._labelAngle[M],U,this.labelColor[M],[0,0,0],B,T)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":223,\"./lib/cube.js\":224,\"./lib/lines.js\":225,\"./lib/text.js\":227,\"./lib/ticks.js\":228}],223:[function(t,e,r){\"use 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l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":226,\"gl-buffer\":230,\"gl-vao\":310}],224:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a){i(s,e,t),i(s,r,s);for(var p=0,y=0;y<2;++y){u[2]=a[y][2];for(var x=0;x<2;++x){u[1]=a[x][1];for(var b=0;b<2;++b)u[0]=a[b][0],h(l[p],u,s),p+=1}}for(var _=-1,y=0;y<8;++y){for(var w=l[y][3],k=0;k<3;++k)c[y][k]=l[y][k]/w;w<0&&(_<0?_=y:c[y][2]<c[_][2]&&(_=y))}if(_<0){_=0;for(var M=0;M<3;++M){for(var A=(M+2)%3,T=(M+1)%3,S=-1,E=-1,C=0;C<2;++C){var L=C<<M,z=L+(C<<A)+(1-C<<T),O=L+(1-C<<A)+(C<<T);o(c[L],c[z],c[O],f)<0\|\|(C?S=1:E=1)}if(S<0\|\|E<0)E>S&&(_\|=1<<M);else{for(var C=0;C<2;++C){var L=C<<M,z=L+(C<<A)+(1-C<<T),O=L+(1-C<<A)+(C<<T),I=d([l[L],l[z],l[O],l[L+(1<<A)+(1<<T)]]);C?S=I:E=I}E>S&&(_\|=1<<M)}}}for(var P=7^_,D=-1,y=0;y<8;++y)y!==_&&y!==P&&(D<0?D=y:c[D][1]>c[y][1]&&(D=y));for(var R=-1,y=0;y<3;++y){var B=D^1<<y;if(B!==_&&B!==P){R<0&&(R=B);var T=c[B];T[0]<c[R][0]&&(R=B)}}for(var F=-1,y=0;y<3;++y){var B=D^1<<y;if(B!==_&&B!==P&&B!==R){F<0&&(F=B);var T=c[B];T[0]>c[F][0]&&(F=B)}}var N=g;N[0]=N[1]=N[2]=0,N[n.log2(R^D)]=D&R,N[n.log2(D^F)]=D&F;var j=7^F;j===_\|\|j===P?(j=7^R,N[n.log2(F^j)]=j&F):N[n.log2(R^j)]=j&R;for(var V=v,U=_,M=0;M<3;++M)V[M]=U&1<<M?-1:1;return m};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=(t(\"gl-mat4/invert\"),t(\"split-polygon\")),o=t(\"robust-orientation\"),s=new Array(16),l=(new Array(16),new Array(8)),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]r[4i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(ch-uf)}return o}var g=[1,1,1],v=[0,0,0],m={cubeEdges:g,axis:v}},{\"bit-twiddle\":80,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"robust-orientation\":486,\"split-polygon\":503}],225:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3\|0,d=0;d<r[f].length;++d){var g=+r[f][d].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3\|0;s[f]=h,l[f]=v-h;for(var h=o.length/3\|0,m=0;m<r[f].length;++m){var g=+r[f][m].x;o.push(g,0,1,g,1,1,g,0,-1,g,0,-1,g,1,1,g,1,-1)}var v=o.length/3\|0;c[f]=h,u[f]=v-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var g=f(l);g[t]=1,this.shader.uniforms.screenAxis=g,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":226,\"gl-buffer\":230,\"gl-vao\":310}],226:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"#define GLSLIFY 1\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n vec4 pp = projection * view * model * vec4(p, 1.0);\\n return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n vec3 major = position.x * majorAxis;\\n vec3 minor = position.y * minorAxis;\\n\\n vec3 vPosition = major + minor + offset;\\n vec3 pPosition = project(vPosition);\\n vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"#define GLSLIFY 1\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n vec4 pp = projection * view * model * vec4(p, 1.0);\\n return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n vec3 A = project(a);\\n vec3 B = project(b);\\n\\n return atan(\\n (B.y - A.y) * resolution.y,\\n (B.x - A.x) * resolution.x\\n );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio = alignOpt.y;\\nbool enableAlign = (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n return mod_angle((a < 0.0) ?\\n a + TWO_PI :\\n a\\n );\\n}\\n\\nfloat look_upwards(float a) {\\n float b = positive_angle(a);\\n return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n b - PI :\\n b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n // if ratio is set to 0.5 then it is 50%, 50%.\\n // when using a higher ratio e.g. 0.75 the result would\\n // likely be more horizontal than vertical.\\n\\n float b = positive_angle(a);\\n\\n return\\n (b < ( ratio) * HALF_PI) ? 0.0 :\\n (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n 0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n float b = positive_angle(a);\\n float div = TWO_PI / float(n);\\n float c = roundTo(b, div);\\n return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n return\\n (option > 2) ? look_round_n_directions(rawAngle + delta, option) : // option 3-n: round to n directions\\n (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n (option == 1) ? rawAngle + delta : // use free angle, and flip to align with one direction of the axis\\n (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n (option ==-1) ? 0.0 : // useful for backward compatibility, all texts remains horizontal\\n rawAngle; // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n (axis.y == 0.0) &&\\n (axis.z == 0.0);\\n\\nvoid main() {\\n //Compute world offset\\n float axisDistance = position.z;\\n vec3 dataPosition = axisDistance * axis + offset;\\n\\n float beta = angle; // i.e. user defined attributes for each tick\\n\\n float axisAngle;\\n float clipAngle;\\n float flip;\\n\\n if (enableAlign) {\\n axisAngle = (isAxisTitle) ? HALF_PI :\\n computeViewAngle(dataPosition, dataPosition + axis);\\n clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n beta += applyAlignOption(clipAngle, flip * PI);\\n }\\n\\n //Compute plane offset\\n vec2 planeCoord = position.xy * pixelScale;\\n\\n mat2 planeXform = scale * mat2(\\n cos(beta), sin(beta),\\n -sin(beta), cos(beta)\\n );\\n\\n vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n //Compute clip position\\n vec3 clipPosition = project(dataPosition);\\n\\n //Apply text offset in clip coordinates\\n clipPosition += vec3(viewOffset, 0.0);\\n\\n //Done\\n gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"#define GLSLIFY 1\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n vec3 realNormal = signAxis * normal;\\n\\n if(dot(realNormal, enable) > 0.0) {\\n vec3 minRange = min(bounds[0], bounds[1]);\\n vec3 maxRange = max(bounds[0], bounds[1]);\\n vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n } else {\\n gl_Position = vec4(0,0,0,0);\\n }\\n\\n colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n gl_FragColor = colorChannel.x * colors[0] +\\n colorChannel.y * colors[1] +\\n colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":288,glslify:392}],227:[function(t,e,r){(function(r){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window\|\|r.global\|\|{},l=s.__TEXT_CACHE\|\|{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){this.gl;var o=[];function s(t,e,r,n){var i=l[r];i\|\|(i=l[r]={});var s=i[e];s\|\|(s=i[e]=function(t,e){try{return a(t,e)}catch(t){return console.warn(\"error vectorizing 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h=e.model\|\|c,p=e.view\|\|c,m=e.projection\|\|c,y=t.bounds,x=(f=f\|\|a(h,p,m,y)).axis;f.edges;o(u,p,h),o(u,m,u);for(var b=g,_=0;_<3;++_)b[_].lo=1/0,b[_].hi=-1/0,b[_].pixelsPerDataUnit=1/0;var w=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var M=(k+1)%3,A=(k+2)%3,T=v;t:for(var _=0;_<2;++_){var S=[];if(x[k]<0!=!!_){T[k]=y[_][k];for(var E=0;E<2;++E){T[M]=y[E^_][M];for(var C=0;C<2;++C)T[A]=y[C^E^_][A],S.push(T.slice())}for(var E=0;E<w.length;++E){if(0===S.length)continue t;S=i.positive(S,w[E])}for(var E=0;E<S.length;++E)for(var A=S[E],L=d(v,u,A,r,l),C=0;C<3;++C)b[C].lo=Math.min(b[C].lo,A[C]),b[C].hi=Math.max(b[C].hi,A[C]),C!==k&&(b[C].pixelsPerDataUnit=Math.min(b[C].pixelsPerDataUnit,Math.abs(L[C])))}}}return b};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])n,f=(o[1]/o[3]-s[1]/s[3])i;t[a]=.25Math.sqrt(uu+ff)}return t}var g=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],v=[0,0,0]},{\"./lib/cube.js\":224,\"extract-frustum-planes\":213,\"gl-mat4/multiply\":256,\"gl-mat4/transpose\":264,\"gl-vec4/transformMat4\":381,\"split-polygon\":503}],230:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.lengthi.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&\"undefined\"!=typeof t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t\|=0)<=0&&(t=1),this.gl.bufferData(this.type,0\|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r\|\|t.ARRAY_BUFFER,n=n\|\|t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:433,\"ndarray-ops\":427,\"typedarray-pool\":522}],231:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=(t(\"gl-vec4\"),function(t,e){for(var r=0;r<t.length;r++)if(t[r]>=e)return r-1;return r}),a=n.create(),o=n.create(),s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t,e,r,l){var c=t[0],u=t[1],f=t[2],h=r[0].length,p=r[1].length,d=r[2].length,g=i(r[0],c),v=i(r[1],u),m=i(r[2],f),y=g+1,x=v+1,b=m+1;if(l&&(g=s(g,0,h-1),y=s(y,0,h-1),v=s(v,0,p-1),x=s(x,0,p-1),m=s(m,0,d-1),b=s(b,0,d-1)),g<0\|\|v<0\|\|m<0\|\|y>=h\|\|x>=p\|\|b>=d)return n.create();var _=(c-r[0][g])/(r[0][y]-r[0][g]),w=(u-r[1][v])/(r[1][x]-r[1][v]),k=(f-r[2][m])/(r[2][b]-r[2][m]);(_<0\|\|_>1\|\|isNaN(_))&&(_=0),(w<0\|\|w>1\|\|isNaN(w))&&(w=0),(k<0\|\|k>1\|\|isNaN(k))&&(k=0);var M=mhp,A=bhp,T=vh,S=xh,E=g,C=y,L=e[T+M+E],z=e[T+M+C],O=e[S+M+E],I=e[S+M+C],P=e[T+A+E],D=e[T+A+C],R=e[S+A+E],B=e[S+A+C],F=n.create();return n.lerp(F,L,z,_),n.lerp(a,O,I,_),n.lerp(F,F,a,w),n.lerp(a,P,D,_),n.lerp(o,R,B,_),n.lerp(a,a,o,w),n.lerp(F,F,a,k),F};e.exports=function(t,e){var r;r=t.positions?t.positions:function(t){for(var e=t[0],r=t[1],n=t[2],i=[],a=0;a<n.length;a++)for(var o=0;o<r.length;o++)for(var s=0;s<e.length;s++)i.push([n[a],r[o],e[s]]);return i}(t.meshgrid);var i=t.meshgrid,a=t.vectors,o={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vertexNormals:[],vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),o;for(var s=0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=1/0,d=-1/0,g=null,v=null,m=[],y=1/0,x=0;x<r.length;x++){var b,_=r[x];c=Math.min(_[0],c),u=Math.max(_[0],u),f=Math.min(_[1],f),h=Math.max(_[1],h),p=Math.min(_[2],p),d=Math.max(_[2],d),b=i?l(_,a,i,!0):a[x],n.length(b)>s&&(s=n.length(b)),x&&(y=Math.min(y,2n.distance(g,_)/(n.length(v)+n.length(b)))),g=_,v=b,m.push(b)}var w=[c,f,p],k=[u,h,d];e&&(e[0]=w,e[1]=k),0===s&&(s=1);var M=1/s;isFinite(y)&&!isNaN(y)\|\|(y=1),o.vectorScale=y;var A=function(t,e,r){var i=n.create();return void 0!==t&&n.set(i,t,e,r),i}(0,1,0),T=t.coneSize\|\|.5;t.absoluteConeSize&&(T=t.absoluteConeSizeM),o.coneScale=T;x=0;for(var S=0;x<r.length;x++)for(var E=(_=r[x])[0],C=_[1],L=_[2],z=m[x],O=n.length(z)M,I=0;I<8;I++){o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.positions.push([E,C,L,S++]),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vectors.push(z),o.vertexIntensity.push(O,O,O),o.vertexIntensity.push(O,O,O),o.vertexNormals.push(A,A,A),o.vertexNormals.push(A,A,A);var P=o.positions.length;o.cells.push([P-6,P-5,P-4],[P-3,P-2,P-1])}return o},e.exports.createConeMesh=t(\"./lib/conemesh\")},{\"./lib/conemesh\":233,\"gl-vec3\":329,\"gl-vec4\":365}],232:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4n+i]e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5i[0](1+o[0]),.5i[1](1-o[1])]}e.exports=function(t,e,r,a,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;u<t.length;++u)c[u]=o(t[u],r,a,s,l);for(var f=0,h=1/0,u=0;u<c.length;++u){for(var p=0,d=0;d<2;++d)p+=Math.pow(c[u][d]-e[d],2);p<h&&(h=p,f=u)}for(var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),v=0,u=0;u<3;++u){if(g[u]<-.001\|\|g[u]>1.0001)return null;v+=g[u]}if(Math.abs(v-1)>.001)return null;return[f,function(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=ai[o];return r}(t,g),g]}},{barycentric:61,\"polytope-closest-point/lib/closest_point_2d.js\":464}],233:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./shaders\"),g=(t(\"./closest-point\"),d.meshShader),v=d.pickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,v,y,x,b,_,w,k,M,A,T,S,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=c,this.triangleColors=f,this.triangleNormals=p,this.triangleUVs=h,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=x,this.edgeIds=v,this.edgeVAO=b,this.edgeCount=0,this.pointPositions=_,this.pointColors=k,this.pointUVs=M,this.pointSizes=A,this.pointIds=w,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=S,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1]}var x=y.prototype;function b(t){var e=n(t,v.vertex,v.fragment,null,v.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(t){this.pickId=t},x.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=vy[b]+mx[b]}this.contourCount=s/3\|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},x.update=function(t){t=t\|\|{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4n+a]=i[a];r[4n+3]=255i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],l=[],c=[],h=[],p=[],d=[],g=[],v=[],m=[],y=[],x=[],b=[],_=[],w=[],k=[];this.cells=r,this.positions=n;var M=t.vertexNormals,A=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,S)),A\|\|M\|\|(M=s.vertexNormals(r,n,T));var E=t.vertexColors,C=t.cellColors,L=t.meshColor\|\|[1,1,1,1],z=t.vertexUVs,O=t.vertexIntensity,I=t.cellUVs,P=t.cellIntensity,D=1/0,R=-1/0;if(!z&&!I)if(O)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],R=+t.vertexIntensityBounds[1];else for(var B=0;B<O.length;++B){var F=O[B];D=Math.min(D,F),R=Math.max(R,F)}else if(P)for(B=0;B<P.length;++B){F=P[B];D=Math.min(D,F),R=Math.max(R,F)}else for(B=0;B<n.length;++B){F=n[B][2];D=Math.min(D,F),R=Math.max(R,F)}this.intensity=O\|\|(P?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,P):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var N=t.pointSizes,j=t.pointSize\|\|1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(B=0;B<n.length;++B)for(var V=n[B],U=0;U<3;++U)!isNaN(V[U])&&isFinite(V[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],V[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],V[U]));var q=0,H=0,G=0;t:for(B=0;B<r.length;++B){var W=r[B];switch(W.length){case 1:for(V=n[X=W[0]],U=0;U<3;++U)if(isNaN(V[U])\|\|!isFinite(V[U]))continue t;x.push(V[0],V[1],V[2],V[3]),3===(Z=E?E[X]:C?C[B]:L).length?b.push(Z[0],Z[1],Z[2],1):b.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],_.push($[0],$[1]),N?w.push(N[X]):w.push(j),k.push(B),G+=1;break;case 2:for(U=0;U<2;++U){V=n[X=W[U]];for(var Y=0;Y<3;++Y)if(isNaN(V[Y])\|\|!isFinite(V[Y]))continue t}for(U=0;U<2;++U){V=n[X=W[U]];g.push(V[0],V[1],V[2]),3===(Z=E?E[X]:C?C[B]:L).length?v.push(Z[0],Z[1],Z[2],1):v.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],m.push($[0],$[1]),y.push(B)}H+=1;break;case 3:for(U=0;U<3;++U)for(V=n[X=W[U]],Y=0;Y<3;++Y)if(isNaN(V[Y])\|\|!isFinite(V[Y]))continue t;for(U=0;U<3;++U){var X;V=n[X=W[U]];a.push(V[0],V[1],V[2],V[3]);var Z,$,J,K=i[X];l.push(K[0],K[1],K[2]),3===(Z=E?E[X]:C?C[B]:L).length?c.push(Z[0],Z[1],Z[2],1):c.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],p.push($[0],$[1]),J=M?M[X]:A[B],h.push(J[0],J[1],J[2]),d.push(B)}q+=1}}this.pointCount=G,this.edgeCount=H,this.triangleCount=q,this.pointPositions.update(x),this.pointColors.update(b),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(g),this.edgeColors.update(v),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(c),this.triangleUVs.update(p),this.triangleNormals.update(h),this.triangleIds.update(new Uint32Array(d))}},x.drawTransparent=x.draw=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|m,n=t.view\|\|m,i=t.projection\|\|m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]u[4o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4d+o]this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},x.drawPick=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|m,n=t.view\|\|m,i=t.projection\|\|m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},x.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256t.value[1]+65536t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:Math.floor(r[1]/48),position:n,dataCoordinate:n}},x.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader\|\|function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=b(t),l=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var c=i(t),f=i(t),h=i(t),p=i(t),d=i(t),v=i(t),m=a(t,[{buffer:c,type:t.FLOAT,size:4},{buffer:v,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:h,type:t.FLOAT,size:4},{buffer:p,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:3},{buffer:f,type:t.FLOAT,size:3}]),x=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:x,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),A=i(t),T=i(t),S=i(t),E=i(t),C=i(t),L=a(t,[{buffer:A,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2},{buffer:E,type:t.FLOAT,size:1}]),z=i(t),O=new y(t,l,r,null,null,s,null,null,c,f,v,h,p,d,m,x,k,_,w,M,A,C,T,S,E,L,z,a(t,[{buffer:z,type:t.FLOAT,size:3}]));return O.update(e),O}},{\"./closest-point\":232,\"./shaders\":234,colormap:114,\"gl-buffer\":230,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"gl-shader\":288,\"gl-texture2d\":305,\"gl-vao\":310,ndarray:433,normals:436,\"simplicial-complex-contour\":494,\"typedarray-pool\":522}],234:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat inverse(float m) {\\n return 1.0 / m;\\n}\\n\\nmat2 inverse(mat2 m) {\\n return mat2(m[1][1],-m[0][1],\\n -m[1][0], m[0][0]) / (m[0][0]m[1][1] - m[0][1]m[1][0]);\\n}\\n\\nmat3 inverse(mat3 m) {\\n float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\\n float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\\n float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\\n\\n float b01 = a22 * a11 - a12 * a21;\\n float b11 = -a22 * a10 + a12 * a20;\\n float b21 = a21 * a10 - a11 * a20;\\n\\n float det = a00 * b01 + a01 * b11 + a02 * b21;\\n\\n return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\\n b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\\n b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\\n}\\n\\nmat4 inverse(mat4 m) {\\n float\\n a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\\n a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\\n a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\\n a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\\n\\n b00 = a00 * a11 - a01 * a10,\\n b01 = a00 * a12 - a02 * a10,\\n b02 = a00 * a13 - a03 * a10,\\n b03 = a01 * a12 - a02 * a11,\\n b04 = a01 * a13 - a03 * a11,\\n b05 = a02 * a13 - a03 * a12,\\n b06 = a20 * a31 - a21 * a30,\\n b07 = a20 * a32 - a22 * a30,\\n b08 = a20 * a33 - a23 * a30,\\n b09 = a21 * a32 - a22 * a31,\\n b10 = a21 * a33 - a23 * a31,\\n b11 = a22 * a33 - a23 * a32,\\n\\n det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\\n\\n return mat4(\\n a11 * b11 - a12 * b10 + a13 * b09,\\n a02 * b10 - a01 * b11 - a03 * b09,\\n a31 * b05 - a32 * b04 + a33 * b03,\\n a22 * b04 - a21 * b05 - a23 * b03,\\n a12 * b08 - a10 * b11 - a13 * b07,\\n a00 * b11 - a02 * b08 + a03 * b07,\\n a32 * b02 - a30 * b05 - a33 * b01,\\n a20 * b05 - a22 * b02 + a23 * b01,\\n a10 * b10 - a11 * b08 + a13 * b06,\\n a01 * b08 - a00 * b10 - a03 * b06,\\n a30 * b04 - a31 * b02 + a33 * b00,\\n a21 * b02 - a20 * b04 - a23 * b00,\\n a11 * b07 - a10 * b09 - a12 * b06,\\n a00 * b09 - a01 * b07 + a02 * b06,\\n a31 * b01 - a30 * b03 - a32 * b00,\\n a20 * b03 - a21 * b01 + a22 * b00) / det;\\n}\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have \|\|a\|\| > 0 U \|\|b\|\| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a-b + ba + c0 = -ba + ba + 0 = 0\\n if (v.xv.x > v.zv.z \|\| v.yv.y > v.zv.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2pi (segment/segmentCount)\\n// To go from index to segment index, index - (segment6)\\n//\\nvec3 getConePosition(vec3 d, float index, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n index = mod(index, segmentCount 6.0);\\n\\n float segment = floor(index/6.0);\\n float segmentIndex = index - (segment6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex == 3.0) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n // angle = 2pi ((segment + ((segmentIndex == 1.0 \|\| segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\\n float nextAngle = float(segmentIndex == 1.0 \|\| segmentIndex == 5.0);\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)0.25;\\n vec3 y = v sin(angle) * length(d)0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex <= 2.0) {\\n vec3 tx = u sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\nuniform float vectorScale;\\nuniform float coneScale;\\n\\nuniform float coneOffset;\\n\\nuniform mat4 model\\n , view\\n , projection;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data\\n , f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n normal = normalize(normal * inverse(mat3(model)));\\n\\n // vec4 m_position = model * vec4(conePosition, 1.0);\\n vec4 t_position = view * conePosition;\\n gl_Position = projection * t_position;\\n f_color = color; //vec4(position.w, color.r, 0, 0);\\n f_normal = normal;\\n f_data = conePosition.xyz;\\n f_position = position.xyz;\\n f_eyeDirection = eyePosition - conePosition.xyz;\\n f_lightDirection = lightPosition - conePosition.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data\\n , f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(!gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have \|\|a\|\| > 0 U \|\|b\|\| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a-b + ba + c0 = -ba + ba + 0 = 0\\n if (v.xv.x > v.zv.z \|\| v.yv.y > v.zv.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2pi (segment/segmentCount)\\n// To go from index to segment index, index - (segment6)\\n//\\nvec3 getConePosition(vec3 d, float index, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n index = mod(index, segmentCount 6.0);\\n\\n float segment = floor(index/6.0);\\n float segmentIndex = index - (segment6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex == 3.0) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n // angle = 2pi ((segment + ((segmentIndex == 1.0 \|\| segmentIndex == 5.0) ? 1.0 : 0.0)) / segmentCount)\\n float nextAngle = float(segmentIndex == 1.0 \|\| segmentIndex == 5.0);\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)0.25;\\n vec3 y = v sin(angle) * length(d)0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex <= 2.0) {\\n vec3 tx = u sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nuniform float vectorScale;\\nuniform float coneScale;\\nuniform float coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n gl_Position = projection * view * conePosition;\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:392}],235:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34000:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],236:[function(t,e,r){var n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":235}],237:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return this.opacity>=1},l.isTransparent=function(){return this.opacity<1},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view\|\|o,i=r.projection=t.projection\|\|o;r.model=t.model\|\|o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=this.pixelRatio(i[3]a+i[7]s+i[11]l+i[15]c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]),r.capSize=this.capSize[f]u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){\"lineWidth\"in(t=t\|\|{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)\|\|(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)\|\|(this.capSize=[this.capSize,this.capSize,this.capSize])),\"opacity\"in t&&(this.opacity=t.opacity);var e=t.color\|\|[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])\|\|(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])\|\|!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length&&(d=[d[0],d[1],d[2],1]),!isNaN(p[0][s])&&!isNaN(p[1][s])){var g;if(p[0][s]<0)(g=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+f(i,g,d,s);if(p[1][s]>0)(g=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+f(i,g,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":238,\"gl-buffer\":230,\"gl-vao\":310}],238:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = model * vec4(position, 1.0);\\n worldPosition = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n gl_Position = projection * view * worldPosition;\\n fragColor = color;\\n fragPosition = position;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], fragPosition)) discard;\\n\\n gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":288,glslify:392}],239:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i\|\|(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0\|e[1],e=0\|e[0]);if(\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0\|\|e>u\|\|r<0\|\|r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n\|\|{})){if((f=Math.max(0\|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var g=!0;\"depth\"in n&&(g=!!n.depth);var v=!1;\"stencil\"in n&&(v=!!n.stencil);return new d(t,e,r,h,f,g,v,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0\|e,0\|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var g=this,v=[0\|e,0\|r];Object.defineProperties(v,{0:{get:function(){return g._shape[0]},set:function(t){return g.width=t}},1:{get:function(){return g._shape[1]},set:function(t){return g.height=t}}}),this._shapeVector=v,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,g=t._useDepth,v=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var m=0;m<o;++m)t.color[m]=h(r,i,a,v,r.RGBA,r.COLOR_ATTACHMENT0+m);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),m=0;m<t.color.length;++m)t.color[m].dispose(),t.color[m]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var g=d.prototype;function v(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e\|\|t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0\|\|e>i\|\|r<0\|\|r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(g,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)\|\|(t=[0\|t,0\|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0\|t[0],r=0\|t[1];return v(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return v(this,t\|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t\|=0,v(this,this._shape[0],t),t},enumerable:!1}}),g.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},g.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":305}],240:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)\|\|\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var g=parseInt(d.split(\":\")[2]);if(isNaN(g))throw new Error(n(\"Could not parse error: %s\",d));h[g]=d}}for(var v=o(e).split(\"\\n\"),p=0;p<v.length;p++)if(h[p+3]\|\|h[p+2]\|\|h[p+1]){var m=v[p];if(c+=m+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":49,\"gl-constants/lookup\":236,\"glsl-shader-name\":384,\"sprintf-js\":504}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2o/l,u[4]=2s/c,u[6]=2(r[0]-a[0])/l-1,u[7]=2(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2c/f,t[4]=2u/h,t[6]=2(a[0]-l[0])/f-1,t[7]=2(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]this.shape[1];if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]\|0]]}},f.update=function(t){var e=(t=t\|\|{}).shape\|\|[0,0],r=t.x\|\|i(e[0]),o=t.y\|\|i(e[1]),s=t.z\|\|new Float32Array(e[0]e[1]);this.xData=r,this.yData=o;var l=t.colorLevels\|\|[0],c=t.colorValues\|\|[0,0,0,1],u=l.length,f=this.bounds,p=f[0]=r[0],d=f[1]=o[0],g=1/((f[2]=r[r.length-1])-p),v=1/((f[3]=o[o.length-1])-d),m=e[0],y=e[1];this.shape=[m,y];var x=(m-1)(y-1)(h.length>>>1);this.numVertices=x;for(var b=a.mallocUint8(4x),_=a.mallocFloat32(2x),w=a.mallocUint8(2x),k=a.mallocUint32(x),M=0,A=0;A<y-1;++A)for(var T=v(o[A]-d),S=v(o[A+1]-d),E=0;E<m-1;++E)for(var C=g(r[E]-p),L=g(r[E+1]-p),z=0;z<h.length;z+=2){var O,I,P,D,R=h[z],B=h[z+1],F=s[(A+B)m+(E+R)],N=n.le(l,F);if(N<0)O=c[0],I=c[1],P=c[2],D=c[3];else if(N===u-1)O=c[4u-4],I=c[4u-3],P=c[4u-2],D=c[4u-1];else{var j=(F-l[N])/(l[N+1]-l[N]),V=1-j,U=4N,q=4(N+1);O=Vc[U]+jc[q],I=Vc[U+1]+jc[q+1],P=Vc[U+2]+jc[q+2],D=Vc[U+3]+jc[q+3]}b[4M]=255O,b[4M+1]=255I,b[4M+2]=255P,b[4M+3]=255D,_[2M]=.5C+.5L,_[2M+1]=.5T+.5S,w[2M]=R,w[2M+1]=B,k[M]=Am+E,M+=1}this.positionBuffer.update(_),this.weightBuffer.update(w),this.colorBuffer.update(b),this.idBuffer.update(k),a.free(_),a.free(b),a.free(w),a.free(k)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":242,\"binary-search-bounds\":243,\"gl-buffer\":230,\"gl-shader\":288,\"iota-array\":399,\"typedarray-pool\":522}],242:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = vec4(fragColor.rgb fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n fragColor = color;\\n gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n vec2 d = step(.5, vWeight);\\n vec4 id = fragId + pickOffset;\\n id.x += d.x + d.yshape.x;\\n\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n vWeight = weight;\\n\\n fragId = pickId;\\n\\n vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:392}],243:[function(t,e,r){arguments[4][99][0].apply(r,arguments)},{dup:99}],244:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n vec4 startPoint = project(position);\\n vec4 endPoint = project(nextPosition);\\n\\n vec2 A = startPoint.xy / startPoint.w;\\n vec2 B = endPoint.xy / endPoint.w;\\n\\n float clipAngle = atan(\\n (B.y - A.y) * screenShape.y,\\n (B.x - A.x) * screenShape.x\\n );\\n\\n vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n sin(clipAngle),\\n -cos(clipAngle)\\n ) / screenShape;\\n\\n gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n worldPosition = position;\\n pixelArcLength = arcLength;\\n fragColor = color;\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\nlowp vec4 encode_float_1540259130(highp float v) {\\n highp float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n highp vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n highp float e = floor(log2(av));\\n highp float m = av * pow(2.0, -e) - 1.0;\\n \\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n \\n //Unpack exponent\\n highp float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0; \\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n gl_FragColor = vec4(pickId/255.0, encode_float_1540259130(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":288,glslify:392}],245:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl\|\|t.scene&&t.scene.gl,r=u(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),c=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),h=l(new Array(1024),[256,1,4]),p=0;p<1024;++p)h.data[p]=255;var d=a(e,h);d.wrap=e.REPEAT;var g=new v(e,r,o,s,c,d);return g.update(t),g};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=t(\"glsl-read-float\"),s=t(\"binary-search-bounds\"),l=t(\"ndarray\"),c=t(\"./lib/shaders\"),u=c.createShader,f=c.createPickShader,h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function p(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=ii}return Math.sqrt(r)}function d(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}var m=v.prototype;m.isTransparent=function(){return this.opacity<1},m.isOpaque=function(){return this.opacity>=1},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model\|\|h,view:t.view\|\|h,projection:t.projection\|\|h,clipBounds:d(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model\|\|h,view:t.view\|\|h,projection:t.projection\|\|h,pickId:this.pickId,clipBounds:d(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),\"opacity\"in t&&(this.opacity=+t.opacity);var i=[],a=[],o=[],c=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position\|\|t.positions;if(h){var d=t.color\|\|t.colors\|\|[0,0,0,1],g=t.lineWidth\|\|1,v=!1;t:for(e=1;e<h.length;++e){var m,y,x,b=h[e-1],_=h[e];for(a.push(c),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])\|\|isNaN(_[r])\|\|!isFinite(b[r])\|\|!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,v=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(d[0])?(m=d.length>e-1?d[e-1]:d.length>0?d[d.length-1]:[0,0,0,1],y=d.length>e?d[e]:d.length>0?d[d.length-1]:[0,0,0,1]):m=y=d,3===m.length&&(m=[m[0],m[1],m[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var k=c;if(c+=p(b,_),v){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,m[0],m[1],m[2],m[3]);u+=2,v=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],k,x,m[0],m[1],m[2],m[3],b[0],b[1],b[2],_[0],_[1],_[2],k,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],c,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],c,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(c),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var M=t.dashes.slice();for(M.unshift(0),e=1;e<M.length;++e)M[e]=M[e-1]+M[e];var A=l(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&s.le(M,M[M.length-1]e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=o(t.value[0],t.value[1],t.value[2],0),r=s.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),l=1-a,c=[0,0,0],u=0;u<3;++u)c[u]=ln[u]+ai[u];var f=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,c,f,this.points[f])}},{\"./lib/shaders\":244,\"binary-search-bounds\":79,\"gl-buffer\":230,\"gl-texture2d\":305,\"gl-vao\":310,\"glsl-read-float\":383,ndarray:433}],246:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=ra-in;return o?(o=1/o,t[0]=ao,t[1]=-no,t[2]=-io,t[3]=ro,t):null}},{}],247:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=uo-sc,h=-ua+sl,p=ca-ol,d=rf+nh+ip;return d?(d=1/d,t[0]=fd,t[1]=(-un+ic)d,t[2]=(sn-io)d,t[3]=hd,t[4]=(ur-il)d,t[5]=(-sr+ia)d,t[6]=pd,t[7]=(-cr+nl)d,t[8]=(or-na)d,t):null}},{}],248:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],249:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],250:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],g=t[14],v=t[15];return(eo-ra)(fv-hg)-(es-na)(uv-hd)+(el-ia)(ug-fd)+(rs-no)(cv-hp)-(rl-io)(cg-fp)+(nl-is)(cd-up)}},{}],251:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=ro,u=no,f=ns,h=io,p=is,d=il,g=ao,v=as,m=al;return t[0]=1-f-d,t[1]=u+m,t[2]=h-v,t[3]=0,t[4]=u-m,t[5]=1-c-d,t[6]=p+g,t[7]=0,t[8]=h+v,t[9]=p-g,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],252:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=ns,f=nl,h=nc,p=il,d=ic,g=ac,v=os,m=ol,y=oc;return t[0]=1-(p+g),t[1]=f+y,t[2]=h-m,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=d+v,t[7]=0,t[8]=h+m,t[9]=d-v,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],253:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],254:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],g=e[13],v=e[14],m=e[15],y=rs-no,x=rl-io,b=rc-ao,_=nl-is,w=nc-as,k=ic-al,M=ug-fd,A=uv-hd,T=um-pd,S=fv-hg,E=fm-pg,C=hm-pv,L=yC-xE+bS+_T-wA+kM;if(!L)return null;return L=1/L,t[0]=(sC-lE+cS)L,t[1]=(iE-nC-aS)L,t[2]=(gk-vw+m_)L,t[3]=(hw-fk-p_)L,t[4]=(lT-oC-cA)L,t[5]=(rC-iT+aA)L,t[6]=(vb-dk-mx)L,t[7]=(uk-hb+px)L,t[8]=(oE-sT+cM)L,t[9]=(nT-rE-aM)L,t[10]=(dw-gb+my)L,t[11]=(fb-uw-py)L,t[12]=(sA-oS-lM)L,t[13]=(rS-nA+iM)L,t[14]=(gx-d_-vy)L,t[15]=(u_-fx+hy)L,t}},{}],255:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,g=e[0],v=e[1],m=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],k=r[2];if(Math.abs(g-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(m-k)<1e-6)return n(t);f=g-_,h=v-w,p=m-k,d=1/Math.sqrt(ff+hh+pp),a=x(p=d)-b(h=d),o=b(f=d)-yp,s=yh-xf,(d=Math.sqrt(aa+oo+ss))?(a=d=1/d,o=d,s=d):(a=0,o=0,s=0);l=hs-po,c=pa-fs,u=fo-ha,(d=Math.sqrt(ll+cc+uu))?(l=d=1/d,c=d,u=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(ag+ov+sm),t[13]=-(lg+cv+um),t[14]=-(fg+hv+pm),t[15]=1,t}},{\"./identity\":253}],256:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],g=e[12],v=e[13],m=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=xn+bs+_f+wg,t[1]=xi+bl+_h+wv,t[2]=xa+bc+_p+wm,t[3]=xo+bu+_d+wy,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=xn+bs+_f+wg,t[5]=xi+bl+_h+wv,t[6]=xa+bc+_p+wm,t[7]=xo+bu+_d+wy,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=xn+bs+_f+wg,t[9]=xi+bl+_h+wv,t[10]=xa+bc+_p+wm,t[11]=xo+bu+_d+wy,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=xn+bs+_f+wg,t[13]=xi+bl+_h+wv,t[14]=xa+bc+_p+wm,t[15]=xo+bu+_d+wy,t}},{}],257:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2ino,t[15]=0,t}},{}],258:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,g,v,m,y,x,b,_,w,k,M,A,T,S,E=n[0],C=n[1],L=n[2],z=Math.sqrt(EE+CC+LL);if(Math.abs(z)<1e-6)return null;E=z=1/z,C=z,L=z,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],g=e[8],v=e[9],m=e[10],y=e[11],x=EEo+a,b=CEo+Li,_=LEo-Ci,w=ECo-Li,k=CCo+a,M=LCo+Ei,A=ELo+Ci,T=CLo-Ei,S=LLo+a,t[0]=sx+fb+g_,t[1]=lx+hb+v_,t[2]=cx+pb+m_,t[3]=ux+db+y_,t[4]=sw+fk+gM,t[5]=lw+hk+vM,t[6]=cw+pk+mM,t[7]=uw+dk+yM,t[8]=sA+fT+gS,t[9]=lA+hT+vS,t[10]=cA+pT+mS,t[11]=uA+dT+yS,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],259:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=ai+cn,t[5]=oi+un,t[6]=si+fn,t[7]=li+hn,t[8]=ci-an,t[9]=ui-on,t[10]=fi-sn,t[11]=hi-ln,t}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=ai-cn,t[1]=oi-un,t[2]=si-fn,t[3]=li-hn,t[8]=an+ci,t[9]=on+ui,t[10]=sn+fi,t[11]=ln+hi,t}},{}],261:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=ai+cn,t[1]=oi+un,t[2]=si+fn,t[3]=li+hn,t[4]=ci-an,t[5]=ui-on,t[6]=fi-sn,t[7]=hi-ln,t}},{}],262:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]n,t[1]=e[1]n,t[2]=e[2]n,t[3]=e[3]n,t[4]=e[4]i,t[5]=e[5]i,t[6]=e[6]i,t[7]=e[7]i,t[8]=e[8]a,t[9]=e[9]a,t[10]=e[10]a,t[11]=e[11]a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],263:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,g=r[0],v=r[1],m=r[2];e===t?(t[12]=e[0]g+e[4]v+e[8]m+e[12],t[13]=e[1]g+e[5]v+e[9]m+e[13],t[14]=e[2]g+e[6]v+e[10]m+e[14],t[15]=e[3]g+e[7]v+e[11]m+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=ng+sv+fm+e[12],t[13]=ig+lv+hm+e[13],t[14]=ag+cv+pm+e[14],t[15]=og+uv+dm+e[15]);return t}},{}],264:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],265:[function(t,e,r){\"use strict\";e.exports=function(t,e){switch(e.length){case 0:break;case 1:t[0]=1/e[0];break;case 4:n(t,e);break;case 9:i(t,e);break;case 16:a(t,e);break;default:throw new Error(\"currently supports matrices up to 4x4\")}return t};var n=t(\"gl-mat2/invert\"),i=t(\"gl-mat3/invert\"),a=t(\"gl-mat4/invert\")},{\"gl-mat2/invert\":246,\"gl-mat3/invert\":247,\"gl-mat4/invert\":254}],266:[function(t,e,r){arguments[4][232][0].apply(r,arguments)},{barycentric:61,dup:232,\"polytope-closest-point/lib/closest_point_2d.js\":464}],267:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec4 m_position = model vec4(position, 1.0);\\n vec4 t_position = view * m_position;\\n gl_Position = projection * t_position;\\n f_color = color;\\n f_normal = normal;\\n f_data = position;\\n f_eyeDirection = eyePosition - position;\\n f_lightDirection = lightPosition - position;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nvec3 normals(vec3 pos) {\\n vec3 fdx = dFdx(pos);\\n vec3 fdy = dFdy(pos);\\n return normalize(cross(fdx, fdy));\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n vec3 normal = normals(f_data);\\n\\n if (dot(N, normal) < 0.0) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\"]),s=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\"]),c=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\"]),f=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\"]),p=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor,1);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:392}],268:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),v=d.meshShader,m=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function k(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,v,m,y,x,b,_,k,M,A,T,S){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=v,this.edgeUVs=m,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=k,this.pointSizes=M,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=T,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var M=k.prototype;function A(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function T(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function S(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function E(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}M.isOpaque=function(){return this.opacity>=1},M.isTransparent=function(){return this.opacity<1},M.pickSlots=1,M.setPickBase=function(t){this.pickId=t},M.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=vy[b]+mx[b]}this.contourCount=s/3\|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},M.update=function(t){t=t\|\|{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4n+a]=i[a];r[4n+3]=255i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],g=[],v=[],m=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,k=t.cellNormals,M=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,A=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=s.faceNormals(r,n,A)),k\|\|w\|\|(w=s.vertexNormals(r,n,M));var T=t.vertexColors,S=t.cellColors,E=t.meshColor\|\|[1,1,1,1],C=t.vertexUVs,L=t.vertexIntensity,z=t.cellUVs,O=t.cellIntensity,I=1/0,P=-1/0;if(!C&&!z)if(L)if(t.vertexIntensityBounds)I=+t.vertexIntensityBounds[0],P=+t.vertexIntensityBounds[1];else for(var D=0;D<L.length;++D){var R=L[D];I=Math.min(I,R),P=Math.max(P,R)}else if(O)for(D=0;D<O.length;++D){R=O[D];I=Math.min(I,R),P=Math.max(P,R)}else for(D=0;D<n.length;++D){R=n[D][2];I=Math.min(I,R),P=Math.max(P,R)}this.intensity=L\|\|(O?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,O):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var B=t.pointSizes,F=t.pointSize\|\|1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(D=0;D<n.length;++D)for(var N=n[D],j=0;j<3;++j)!isNaN(N[j])&&isFinite(N[j])&&(this.bounds[0][j]=Math.min(this.bounds[0][j],N[j]),this.bounds[1][j]=Math.max(this.bounds[1][j],N[j]));var V=0,U=0,q=0;t:for(D=0;D<r.length;++D){var H=r[D];switch(H.length){case 1:for(N=n[W=H[0]],j=0;j<3;++j)if(isNaN(N[j])\|\|!isFinite(N[j]))continue t;m.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?y.push(Y[0],Y[1],Y[2],1):y.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(P-I),0]:z?z[D]:O?[(O[D]-I)/(P-I),0]:[(N[2]-I)/(P-I),0],x.push(X[0],X[1]),B?b.push(B[W]):b.push(F),_.push(D),q+=1;break;case 2:for(j=0;j<2;++j){N=n[W=H[j]];for(var G=0;G<3;++G)if(isNaN(N[G])\|\|!isFinite(N[G]))continue t}for(j=0;j<2;++j){N=n[W=H[j]];p.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?d.push(Y[0],Y[1],Y[2],1):d.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(P-I),0]:z?z[D]:O?[(O[D]-I)/(P-I),0]:[(N[2]-I)/(P-I),0],g.push(X[0],X[1]),v.push(D)}U+=1;break;case 3:for(j=0;j<3;++j)for(N=n[W=H[j]],G=0;G<3;++G)if(isNaN(N[G])\|\|!isFinite(N[G]))continue t;for(j=0;j<3;++j){var W,Y,X,Z;N=n[W=H[j]];i.push(N[0],N[1],N[2]),3===(Y=T?T[W]:S?S[D]:E).length?a.push(Y[0],Y[1],Y[2],1):a.push(Y[0],Y[1],Y[2],Y[3]),X=C?C[W]:L?[(L[W]-I)/(P-I),0]:z?z[D]:O?[(O[D]-I)/(P-I),0]:[(N[2]-I)/(P-I),0],c.push(X[0],X[1]),Z=w?w[W]:k[D],l.push(Z[0],Z[1],Z[2]),h.push(D)}V+=1}}this.pointCount=q,this.edgeCount=U,this.triangleCount=V,this.pointPositions.update(m),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(v)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},M.drawTransparent=M.draw=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|w,n=t.view\|\|w,i=t.projection\|\|w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]u[4o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4d+o]this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},M.drawPick=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|w,n=t.view\|\|w,i=t.projection\|\|w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},M.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256t.value[1]+65536t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=g(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;var s=o[2],l=0;for(a=0;a<r.length;++a)l+=s[a]this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},M.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension(\"OES_standard_derivatives\")\|\|t.getExtension(\"MOZ_OES_standard_derivatives\")\|\|t.getExtension(\"WEBKIT_OES_standard_derivatives\")))throw new Error(\"derivatives not supported\");var r=function(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=A(t),c=T(t),f=S(t),h=E(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),g=i(t),y=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:y,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),M=i(t),C=i(t),L=i(t),z=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:M,type:t.FLOAT,size:4},{buffer:C,type:t.FLOAT,size:2}]),O=i(t),I=i(t),P=i(t),D=i(t),R=i(t),B=a(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:P,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),F=i(t),N=new k(t,p,r,s,l,c,f,h,d,b,g,y,x,_,w,L,M,C,z,O,R,I,P,D,B,F,a(t,[{buffer:F,type:t.FLOAT,size:3}]));return N.update(e),N}},{\"./lib/closest-point\":266,\"./lib/shaders\":267,colormap:114,\"gl-buffer\":230,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"gl-shader\":288,\"gl-texture2d\":305,\"gl-vao\":310,ndarray:433,normals:436,\"simplicial-complex-contour\":494,\"typedarray-pool\":522}],269:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":272,\"gl-buffer\":230,\"gl-shader\":288}],270:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,g=0;g<2;++g){var v=a[g],m=a[g+2]-v,y=.5(o[g+2]+o[g]),x=o[g+2]-o[g];u[g]=2m/x,c[g]=2(v-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(g=0;g<2;++g){f[0]=f[1]=0,f[g]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[g]/(s[g+2]-s[g])d,r.uniforms.color=h[g];var _=6n[g].length;p[g]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,g=s.viewBox,v=s.pixelRatio,m=s.screenBox,y=m[2]-m[0],x=m[3]-m[1],b=g[2]-g[0],_=g[3]-g[1],w=0;w<2;++w){var k=p[w],M=p[w+2]-k,A=.5(d[w+2]+d[w]),T=d[w+2]-d[w];e[w]=2M/T,t[w]=2(k-A)/T}e[0]=b/y,t[0]=b/y,e[1]=_/x,t[1]=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,C=s.tickMarkWidth,L=s.tickMarkColor,z=6f[0].length,O=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),I=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),P=0+6O,D=6Math.max(0,I-O),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),B=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),F=z+6R,N=6Math.max(0,B-R);i[0]=2(g[0]-E[1])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[1]v/y,o[1]=C[1]v/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,F,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2(g[1]-E[0])/x-1,o[0]=C[0]v/y,o[1]=E[0]v/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,P,D)),i[0]=2(g[2]+E[3])/y-1,i[1]=(g[3]+g[1])/x-1,o[0]=E[3]v/y,o[1]=C[3]v/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,F,N)),i[0]=(g[2]+g[0])/y-1,i[1]=2(g[3]+E[2])/x-1,o[0]=C[2]v/y,o[1]=E[2]v/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,P,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var g=0;g<6;++g)n[i++]=d,n[i++]=h[g],n[i++]=p[g]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":272,\"binary-search-bounds\":274,\"gl-buffer\":230,\"gl-shader\":288}],271:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=io.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":272,\"gl-buffer\":230,\"gl-shader\":288}],272:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n vec2 delta = normalize(perp(start - end));\\n vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n float dataOffset = textCoordinate.z;\\n vec2 glyphOffset = textCoordinate.xy;\\n mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n glyphMatrix * glyphOffset * textScale + screenOffset;\\n gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n gl_Position = vec4(pos + tickScaledataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:392}],273:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,g=l.prototype;g.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,g=e.tickPad,v=e.tickColor,m=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],k=this.labelCount[t],M=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var T=2/h[2+(1^t)]-h[1^t];u[1^t]=Ts[1^t]-1,d[t]&&(u[1^t]-=Tpg[t],M<A&&i[A]>i[M]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=v[t],r.uniforms.angle=m[t],a.drawArrays(a.TRIANGLES,i[M],i[A]-i[M]))),y[t]&&k&&(u[1^t]-=Tpx[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,k)),u[1^t]=Ts[2+(1^t)]-1,d[t+2]&&(u[1^t]+=Tpg[t+2],M<A&&i[A]>i[M]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=v[t+2],r.uniforms.angle=m[t+2],a.drawArrays(a.TRIANGLES,i[M],i[A]-i[M]))),y[t+2]&&k&&(u[1^t]+=Tpx[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,k))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2(o[u]c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],g=a[o+2]-f,v=i[o],m=i[o+2]-v;p[o]=2l/ug/m,h[o]=2(s-c)/ug/m}d[1]=2t.pixelRatio/(i[3]-i[1]),d[0]=d[1](i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,g=p.text,v=p.font\|\|\"sans-serif\";i=p.fontSize\|\|12;for(var m=1/(c[o+2]-c[o]),y=c[o],x=g.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(v,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]i,-n[r+1]i-bi1.2,(d-y)m);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]i,-n[e+1]i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]i,-n[e+1]i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},g.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":272,\"binary-search-bounds\":274,\"gl-buffer\":230,\"gl-shader\":288,\"text-cache\":513}],274:[function(t,e,r){arguments[4][99][0].apply(r,arguments)},{dup:99}],275:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]c[3],c[1]c[3],c[2]c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT\|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]u[3],u[1]u[3],u[2]u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]\|\|f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var g=e[d]-n[d](e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],h[d]):o.drawLine(e[0],g,e[2],g,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var v=this.borderLineEnable,m=this.borderLineWidth,y=this.borderLineColor;for(v[1]&&o.drawLine(r[0],r[1]-.5m[1]i,r[0],r[3]+.5m[3]i,m[1],y[1]),v[0]&&o.drawLine(r[0]-.5m[0]i,r[1],r[2]+.5m[2]i,r[1],m[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5m[1]i,r[2],r[3]+.5m[3]i,m[3],y[3]),v[2]&&o.drawLine(r[0]-.5m[0]i,r[3],r[2]+.5m[2]i,r[3],m[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0\|Math.round((t-i[0]/r)n),o=0\|Math.round((e-i[1]/r)n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0\|Math.round(t[0]r),e[1]=0\|Math.round(t[1]r),e[2]=0\|Math.round(t[2]r),e[3]=0\|Math.round(t[3]r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]\|\|e[1]!==t[1]\|\|e[2]!==t[2]\|\|e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0\|Math.round(t[0]e),r[1]=0\|Math.round(t[1]e),r[2]=0\|Math.round(t[2]e),r[3]=0\|Math.round(t[3]e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0\|Math.round((t[2]-t[0])n),0\|Math.round((t[3]-t[1])n)],this.setDirty()},c.update=function(t){t=t\|\|{};var e=this.gl;this.pixelRatio=t.pixelRatio\|\|1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox\|\|[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox\|\|[.125(this.screenBox[2]-this.screenBox[0])/r,.125(this.screenBox[3]-this.screenBox[1])/r,.875(this.screenBox[2]-this.screenBox[0])/r,.875(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox\|\|[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor\|\|[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor\|\|[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable\|\|[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth\|\|[1,1]).slice(),this.gridLineColor=u(t.gridLineColor\|\|[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable\|\|[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth\|\|[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor\|\|[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength\|\|[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth\|\|[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter\|\|[.5(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t&&!t.titleEnable),this.titleAngle=t.titleAngle\|\|0,this.titleColor=(t.titleColor\|\|[0,0,0,1]).slice(),this.labelPad=(t.labelPad\|\|[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle\|\|[0,Math.PI/2,0,3Math.PI/2]).slice(),this.labelEnable=(t.labelEnable\|\|[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad\|\|[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle\|\|[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable\|\|[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable\|\|[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth\|\|[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks\|\|[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels\|\|[\"x\",\"y\"],labelSize:t.labelSize\|\|[12,12],labelFont:t.labelFont\|\|[\"sans-serif\",\"sans-serif\"],title:t.title\|\|\"\",titleSize:t.titleSize\|\|18,titleFont:t.titleFont\|\|\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":269,\"./lib/grid\":270,\"./lib/line\":271,\"./lib/text\":273,\"gl-select-static\":287}],276:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":288,glslify:392}],277:[function(t,e,r){\"use strict\";e.exports=function(t){var e=!1,r=((t=t\|\|{}).pixelRatio\|\|parseFloat(window.devicePixelRatio),t.canvas);if(!r)if(r=document.createElement(\"canvas\"),t.container){var m=t.container;m.appendChild(r)}else document.body.appendChild(r);var y=t.gl;y\|\|(y=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))\|\|(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(r,t.glOptions\|\|{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:p}));if(!y)throw new Error(\"webgl not supported\");var x=t.bounds\|\|[[-10,-10,-10],[10,10,10]],b=new d,_=l(y,[y.drawingBufferWidth,y.drawingBufferHeight],{preferFloat:!p}),w=h(y),k=t.camera\|\|{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:\"turntable\"},M=t.axes\|\|{},A=i(y,M);A.enable=!M.disable;var T=t.spikes\|\|{},S=o(y,T),E=[],C=[],L=[],z=[],O=!0,I=!0,P=new Array(16),D=new Array(16),R={view:null,projection:P,model:D},I=!0,B=[y.drawingBufferWidth,y.drawingBufferHeight],F={gl:y,contextLost:!1,pixelRatio:t.pixelRatio\|\|parseFloat(window.devicePixelRatio),canvas:r,selection:b,camera:n(r,k),axes:A,axesPixels:null,spikes:S,bounds:x,objects:E,shape:B,aspect:t.aspectRatio\|\|[1,1,1],pickRadius:t.pickRadius\|\|10,zNear:t.zNear\|\|.01,zFar:t.zFar\|\|1e3,fovy:t.fovy\|\|Math.PI/4,clearColor:t.clearColor\|\|[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect\|\|null,onrender:t.onrender\|\|null,onclick:t.onclick\|\|null,cameraParams:R,oncontextloss:null,mouseListener:null},N=[y.drawingBufferWidth/F.pixelRatio\|0,y.drawingBufferHeight/F.pixelRatio\|0];function j(){if(!e&&F.autoResize){var t=r.parentNode,n=1,i=1;t&&t!==document.body?(n=t.clientWidth,i=t.clientHeight):(n=window.innerWidth,i=window.innerHeight);var a=0\|Math.ceil(nF.pixelRatio),o=0\|Math.ceil(iF.pixelRatio);if(a!==r.width\|\|o!==r.height){r.width=a,r.height=o;var s=r.style;s.position=s.position\|\|\"absolute\",s.left=\"0px\",s.top=\"0px\",s.width=n+\"px\",s.height=i+\"px\",O=!0}}}F.autoResize&&j();function V(){for(var t=E.length,e=z.length,r=0;r<e;++r)L[r]=0;t:for(var r=0;r<t;++r){var n=E[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(L[a]+i<255){C[r]=a,n.setPickBase(L[a]+1),L[a]+=i;continue t}var o=s(y,B);C[r]=e,z.push(o),L.push(i),n.setPickBase(1),e+=1}else C[r]=-1}for(;e>0&&0===L[e-1];)L.pop(),z.pop().dispose()}window.addEventListener(\"resize\",j),F.update=function(t){e\|\|(t=t\|\|{},O=!0,I=!0)},F.add=function(t){e\|\|(t.axes=A,E.push(t),C.push(-1),O=!0,I=!0,V())},F.remove=function(t){if(!e){var r=E.indexOf(t);r<0\|\|(E.splice(r,1),C.pop(),O=!0,I=!0,V())}},F.dispose=function(){if(!e&&(e=!0,window.removeEventListener(\"resize\",j),r.removeEventListener(\"webglcontextlost\",H),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();_.dispose();for(var t=0;t<z.length;++t)z[t].dispose();w.dispose(),y=null,A=null,S=null,E=[]}};var U=!1,q=0;function H(){if(F.contextLost)return!0;y.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.mouseListener=u(r,function(t,r,n){if(!e){var i=z.length,a=E.length,o=b.object;b.distance=1/0,b.mouse[0]=r,b.mouse[1]=n,b.object=null,b.screen=null,b.dataCoordinate=b.dataPosition=null;var s=!1;if(t&&q)U=!0;else{U&&(I=!0),U=!1;for(var l=0;l<i;++l){var c=z[l].query(r,N[1]-n-1,F.pickRadius);if(c){if(c.distance>b.distance)continue;for(var u=0;u<a;++u){var f=E[u];if(C[u]===l){var h=f.pick(c);h&&(b.buttons=t,b.screen=c.coord,b.distance=c.distance,b.object=f,b.index=h.distance,b.dataPosition=h.position,b.dataCoordinate=h.dataCoordinate,b.data=h,s=!0)}}}}}o&&o!==b.object&&(o.highlight&&o.highlight(null),O=!0),b.object&&(b.object.highlight&&b.object.highlight(b.data),O=!0),(s=s\|\|b.object!==o)&&F.onselect&&F.onselect(b),1&t&&!(1&q)&&F.onclick&&F.onclick(b),q=t}}),r.addEventListener(\"webglcontextlost\",H);var G=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],W=[G[0].slice(),G[1].slice()];function Y(){if(!H()){j();var t=F.camera.tick();R.view=F.camera.matrix,O=O\|\|t,I=I\|\|t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,r=G[0],n=G[1];r[0]=r[1]=r[2]=1/0,n[0]=n[1]=n[2]=-1/0;for(var i=0;i<e;++i){var o=E[i];o.pixelRatio=F.pixelRatio,o.axes=F.axes,O=O\|\|!!o.dirty,I=I\|\|!!o.dirty;var s=o.bounds;if(s)for(var l=s[0],u=s[1],h=0;h<3;++h)r[h]=Math.min(r[h],l[h]),n[h]=Math.max(n[h],u[h])}var p=F.bounds;if(F.autoBounds)for(var h=0;h<3;++h){if(n[h]<r[h])r[h]=-1,n[h]=1;else{r[h]===n[h]&&(r[h]-=1,n[h]+=1);var d=.05(n[h]-r[h]);r[h]=r[h]-d,n[h]=n[h]+d}p[0][h]=r[h],p[1][h]=n[h]}for(var v=!1,h=0;h<3;++h)v=v\|\|W[0][h]!==p[0][h]\|\|W[1][h]!==p[1][h],W[0][h]=p[0][h],W[1][h]=p[1][h];if(I=I\|\|v,O=O\|\|v){if(v){for(var m=[0,0,0],i=0;i<3;++i)m[i]=g((p[1][i]-p[0][i])/10);A.autoTicks?A.update({bounds:p,tickSpacing:m}):A.update({bounds:p})}var x=y.drawingBufferWidth,k=y.drawingBufferHeight;B[0]=x,B[1]=k,N[0]=0\|Math.max(x/F.pixelRatio,1),N[1]=0\|Math.max(k/F.pixelRatio,1),f(P,F.fovy,x/k,F.zNear,F.zFar);for(var i=0;i<16;++i)D[i]=0;D[15]=1;for(var M=0,i=0;i<3;++i)M=Math.max(M,p[1][i]-p[0][i]);for(var i=0;i<3;++i)F.autoScale?D[5i]=F.aspect[i]/(p[1][i]-p[0][i]):D[5i]=1/M,F.autoCenter&&(D[12+i]=.5-D[5i](p[0][i]+p[1][i]));for(var i=0;i<e;++i){var o=E[i];o.axesBounds=p,F.clipToBounds&&(o.clipBounds=p)}b.object&&(F.snapToData?S.position=b.dataCoordinate:S.position=b.dataPosition,S.bounds=p),I&&(I=!1,function(){if(H())return;y.colorMask(!0,!0,!0,!0),y.depthMask(!0),y.disable(y.BLEND),y.enable(y.DEPTH_TEST);for(var t=E.length,e=z.length,r=0;r<e;++r){var n=z[r];n.shape=N,n.begin();for(var i=0;i<t;++i)if(C[i]===r){var a=E[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(R))}n.end()}}()),F.axesPixels=a(F.axes,R,x,k),F.onrender&&F.onrender(),y.bindFramebuffer(y.FRAMEBUFFER,null),y.viewport(0,0,x,k);var T=F.clearColor;y.clearColor(T[0],T[1],T[2],T[3]),y.clear(y.COLOR_BUFFER_BIT\|y.DEPTH_BUFFER_BIT),y.depthMask(!0),y.colorMask(!0,!0,!0,!0),y.enable(y.DEPTH_TEST),y.depthFunc(y.LEQUAL),y.disable(y.BLEND),y.disable(y.CULL_FACE);var L=!1;A.enable&&(L=L\|\|A.isTransparent(),A.draw(R)),S.axes=A,b.object&&S.draw(R),y.disable(y.CULL_FACE);for(var i=0;i<e;++i){var o=E[i];o.axes=A,o.pixelRatio=F.pixelRatio,o.isOpaque&&o.isOpaque()&&o.draw(R),o.isTransparent&&o.isTransparent()&&(L=!0)}if(L){_.shape=B,_.bind(),y.clear(y.DEPTH_BUFFER_BIT),y.colorMask(!1,!1,!1,!1),y.depthMask(!0),y.depthFunc(y.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(var i=0;i<e;++i){var o=E[i];o.isOpaque&&o.isOpaque()&&o.draw(R)}y.enable(y.BLEND),y.blendEquation(y.FUNC_ADD),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.colorMask(!0,!0,!0,!0),y.depthMask(!1),y.clearColor(0,0,0,0),y.clear(y.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(var i=0;i<e;++i){var o=E[i];o.isTransparent&&o.isTransparent()&&o.drawTransparent(R)}y.bindFramebuffer(y.FRAMEBUFFER,null),y.blendFunc(y.ONE,y.ONE_MINUS_SRC_ALPHA),y.disable(y.DEPTH_TEST),w.bind(),_.color[0].bind(0),w.uniforms.accumBuffer=0,c(y),y.disable(y.BLEND)}O=!1;for(var i=0;i<e;++i)E[i].dirty=!1}}}return function t(){e\|\|F.contextLost\|\|(Y(),requestAnimationFrame(t))}(),F.redraw=function(){e\|\|(O=!0,Y())},F};var n=t(\"3d-view-controls\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"./lib/shader\"),p=t(\"is-mobile\")({tablet:!0});function d(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(tr)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t\|\|t}},{\"./lib/shader\":276,\"3d-view-controls\":44,\"a-big-triangle\":47,\"gl-axes3d\":222,\"gl-axes3d/properties\":229,\"gl-fbo\":239,\"gl-mat4/perspective\":257,\"gl-select-static\":287,\"gl-spikes3d\":297,\"is-mobile\":403,\"mouse-change\":418}],278:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n highp float a = 12.9898;\\n highp float b = 78.233;\\n highp float c = 43758.5453;\\n highp float d = dot(co.xy, vec2(a, b));\\n highp float e = mod(d, 3.14);\\n return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n // if we don't jitter the point size a bit, overall point cloud\\n // saturation 'jumps' on zooming, which is disturbing and confusing\\n gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n if(pointCloud != 0.0) { // pointCloud is truthy\\n // get the same square surface as circle would be\\n gl_PointSize = 0.886;\\n }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n float radius;\\n vec4 baseColor;\\n if(pointCloud != 0.0) { // pointCloud is truthy\\n if(centerFraction == 1.0) {\\n gl_FragColor = color;\\n } else {\\n gl_FragColor = mix(borderColor, color, centerFraction);\\n }\\n } else {\\n radius = length(2.0 gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:392}],279:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t\|\|{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i\|\|l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i\|\|a.free(l),o\|\|a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2r],o=t[2r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatioMath.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2i[0]/a-1,l[7]=-2i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2a],o[2a+1]]}}},{\"./lib/shader\":278,\"gl-buffer\":230,\"gl-shader\":288,\"typedarray-pool\":522}],280:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],g=r[2],v=r[3];(a=cp+ud+fg+hv)<0&&(a=-a,p=-p,d=-d,g=-g,v=-v);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)i)/o,l=Math.sin(ni)/o):(s=1-n,l=n);return t[0]=sc+lp,t[1]=su+ld,t[2]=sf+lg,t[3]=sh+lv,t}},{}],281:[function(t,e,r){\"use strict\";e.exports=function(t){return t\|\|0===t?t.toString():\"\"}},{}],282:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e){var r=i[e];r\|\|(r=i[e]={});if(t in r)return r[t];for(var a=n(t,{textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),o=n(t,{triangles:!0,textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<a.positions.length;++l)for(var c=a.positions[l],u=0;u<2;++u)s[0][u]=Math.min(s[0][u],c[u]),s[1][u]=Math.max(s[1][u],c[u]);return r[t]=[o,a,s]};var i={}},{\"vectorize-text\":527}],283:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),o=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n\\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),s=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale = highlightScale;\\n }\\n\\n vec4 clipCenter = projection view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5lscale(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\"]),l=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},v={vertex:s,fragment:c,attributes:u};function m(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return m(t,f)},r.createOrtho=function(t){return m(t,h)},r.createProject=function(t){return m(t,p)},r.createPickPerspective=function(t){return m(t,d)},r.createPickOrtho=function(t){return m(t,g)},r.createPickProject=function(t){return m(t,v)}},{\"gl-shader\":288,glslify:392}],284:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]r+e[4]n+e[8]i+e[12]a,t[1]=e[1]r+e[5]n+e[9]i+e[13]a,t[2]=e[2]r+e[6]n+e[10]i+e[14]a,t[3]=e[3]r+e[7]n+e[11]i+e[15]a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),m=new g(e,r,n,o,f,h,p,d,v,s,c,u);return m.update(t),m};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},v.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var m=[0,0],y=[0,0,0],x=[0,0,0],b=[0,0,0,1],_=[0,0,0,1],w=f.slice(),k=[0,0,0],M=[[0,0,0],[0,0,0]];function A(t){return t[0]=t[1]=t[2]=0,t}function T(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function S(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function E(t,e,r,n,i){var a,o=e.axesProject,l=e.gl,c=t.uniforms,u=r.model\|\|f,h=r.view\|\|f,d=r.projection\|\|f,g=e.axesBounds,v=function(t){for(var e=M,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);a=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,t.bind(),c.view=h,c.projection=d,c.screenSize=m,c.highlightId=e.highlightId,c.highlightScale=e.highlightScale,c.clipBounds=v,c.pickGroup=e.pickId/255,c.pixelRatio=e.pixelRatio;for(var E=0;E<3;++E)if(o[E]&&e.projectOpacity[E]<1===n){c.scale=e.projectScale[E],c.opacity=e.projectOpacity[E];for(var C=w,L=0;L<16;++L)C[L]=0;for(L=0;L<4;++L)C[5L]=1;C[5E]=0,a[E]<0?C[12+E]=g[0][E]:C[12+E]=g[1][E],s(C,u,C),c.model=C;var z=(E+1)%3,O=(E+2)%3,I=A(y),P=A(x);I[z]=1,P[O]=1;var D=p(0,0,0,T(b,I)),R=p(0,0,0,T(_,P));if(Math.abs(D[1])>Math.abs(R[1])){var B=D;D=R,R=B,B=I,I=P,P=B;var F=z;z=O,O=F}D[0]<0&&(I[z]=-1),R[1]>0&&(P[O]=-1);var N=0,j=0;for(L=0;L<4;++L)N+=Math.pow(u[4z+L],2),j+=Math.pow(u[4O+L],2);I[z]/=Math.sqrt(N),P[O]/=Math.sqrt(j),c.axes[0]=I,c.axes[1]=P,c.fragClipBounds[0]=S(k,v[0],E,-1e8),c.fragClipBounds[1]=S(k,v[1],E,1e8),e.vao.draw(l.TRIANGLES,e.vertexCount),e.lineWidth>0&&(l.lineWidth(e.lineWidth),e.vao.draw(l.LINES,e.lineVertexCount,e.vertexCount))}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function L(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1\|\|a){t.bind();var s=t.uniforms;s.model=n.model\|\|f,s.view=n.view\|\|f,s.projection=n.projection\|\|f,m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,s.screenSize=m,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=C,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}E(e,r,n,i),r.vao.unbind()}function z(t,e,r){var i;i=Array.isArray(t)?e<t.length?t[e]:void 0:t,i=u(i);var a=!0;n(i)&&(i=\"\\u25bc\",a=!1);var o=c(i,r);return{mesh:o[0],lines:o[1],bounds:o[2],visible:a}}v.draw=function(t){L(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},v.drawTransparent=function(t){L(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},v.drawPick=function(t){L(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount\|\|e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t\|\|{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(\"projectOpacity\"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}\"opacity\"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position,i=t.font\|\|\"normal\",a=t.alignment\|\|[0,0],s=[1/0,1/0,1/0],l=[-1/0,-1/0,-1/0],c=t.glyph,u=t.color,f=t.size,h=t.angle,p=t.lineColor,d=-1,g=0,v=0,m=0;if(n.length){m=n.length;t:for(var y=0;y<m;++y){for(var x=n[y],b=0;b<3;++b)if(isNaN(x[b])\|\|!isFinite(x[b]))continue t;var _=(R=z(c,y,i)).mesh,w=R.lines,k=R.bounds;g+=3_.cells.length,v+=2w.edges.length}}var M=g+v,A=o.mallocFloat(3M),T=o.mallocFloat(4M),S=o.mallocFloat(2M),E=o.mallocUint32(M);if(M>0){var C=0,L=g,O=[0,0,0,1],I=[0,0,0,1],P=Array.isArray(u)&&Array.isArray(u[0]),D=Array.isArray(p)&&Array.isArray(p[0]);t:for(y=0;y<m;++y){d+=1;for(x=n[y],b=0;b<3;++b){if(isNaN(x[b])\|\|!isFinite(x[b]))continue t;l[b]=Math.max(l[b],x[b]),s[b]=Math.min(s[b],x[b])}_=(R=z(c,y,i)).mesh,w=R.lines,k=R.bounds;var R,B=R.visible;if(B)if(Array.isArray(u)){if(3===(F=P?y<u.length?u[y]:[0,0,0,0]:u).length){for(b=0;b<3;++b)O[b]=F[b];O[3]=1}else if(4===F.length)for(b=0;b<4;++b)O[b]=F[b]}else O[0]=O[1]=O[2]=0,O[3]=1;else O=[1,1,1,0];if(B)if(Array.isArray(p)){var F;if(3===(F=D?y<p.length?p[y]:[0,0,0,0]:p).length){for(b=0;b<3;++b)I[b]=F[b];I[b]=1}else if(4===F.length)for(b=0;b<4;++b)I[b]=F[b]}else I[0]=I[1]=I[2]=0,I[3]=1;else I=[1,1,1,0];var N=.5;B?Array.isArray(f)?N=y<f.length?+f[y]:12:f?N=+f:this.useOrtho&&(N=12):N=0;var j=0;Array.isArray(h)?j=y<h.length?+h[y]:0:h&&(j=+h);var V=Math.cos(j),U=Math.sin(j);for(x=n[y],b=0;b<3;++b)l[b]=Math.max(l[b],x[b]),s[b]=Math.min(s[b],x[b]);var 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r=this.gl,a=this.shaders[t===r.FRAGMENT_SHADER\|0],l=a[e];if(l&&r.isShader(l.shader))l.count+=1;else{var c=function(t,e,r){var a=t.createShader(e);if(t.shaderSource(a,r),t.compileShader(a),!t.getShaderParameter(a,t.COMPILE_STATUS)){var o=t.getShaderInfoLog(a);try{var s=i(o,r,e)}catch(t){throw console.warn(\"Failed to format compiler error: \"+t),new n(o,\"Error compiling shader:\\n\"+o)}throw new n(o,s.short,s.long)}return a}(r,t,e);l=a[e]=new s(o++,e,t,c,[],1,this)}return l},c.getProgram=function(t,e,r,i){var a=[t.id,e.id,r.join(\":\"),i.join(\":\")].join(\"@\"),o=this.programs[a];return o&&this.gl.isProgram(o)\|\|(this.programs[a]=o=function(t,e,r,i,a){var o=t.createProgram();t.attachShader(o,e),t.attachShader(o,r);for(var s=0;s<i.length;++s)t.bindAttribLocation(o,a[s],i[s]);if(t.linkProgram(o),!t.getProgramParameter(o,t.LINK_STATUS)){var l=t.getProgramInfoLog(o);throw new n(l,\"Error linking program: \"+l)}return 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l=s[0]+(n[0]-o[0])/(o[2]-o[0])(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],296:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vertexPosition = mix(coordinates[0],\\n mix(coordinates[2], coordinates[1], 0.5 (position + 1.0)), abs(position));\\n\\n vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n vec2 delta = weight * clipOffset 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s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model\|\|o,s=t.view\|\|o,l=t.projection\|\|o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":296,\"gl-buffer\":230,\"gl-vao\":310}],298:[function(t,e,r){arguments[4][232][0].apply(r,arguments)},{barycentric:61,dup:232,\"polytope-closest-point/lib/closest_point_2d.js\":464}],299:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat inverse(float m) {\\n return 1.0 / m;\\n}\\n\\nmat2 inverse(mat2 m) {\\n return mat2(m[1][1],-m[0][1],\\n -m[1][0], m[0][0]) / (m[0][0]m[1][1] - m[0][1]m[1][0]);\\n}\\n\\nmat3 inverse(mat3 m) {\\n float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\\n float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\\n float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\\n\\n float b01 = a22 a11 - a12 * a21;\\n float b11 = -a22 * a10 + a12 * a20;\\n float b21 = a21 * a10 - a11 * a20;\\n\\n float det = a00 * b01 + a01 * b11 + a02 * b21;\\n\\n return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\\n b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\\n b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\\n}\\n\\nmat4 inverse(mat4 m) {\\n float\\n a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\\n a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\\n a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\\n a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\\n\\n b00 = a00 * a11 - a01 * a10,\\n b01 = a00 * a12 - a02 * a10,\\n b02 = a00 * a13 - a03 * a10,\\n b03 = a01 * a12 - a02 * a11,\\n b04 = a01 * a13 - a03 * a11,\\n b05 = a02 * a13 - a03 * a12,\\n b06 = a20 * a31 - a21 * a30,\\n b07 = a20 * a32 - a22 * a30,\\n b08 = a20 * a33 - a23 * a30,\\n b09 = a21 * a32 - a22 * a31,\\n b10 = a21 * a33 - a23 * a31,\\n b11 = a22 * a33 - a23 * a32,\\n\\n det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\\n\\n return mat4(\\n a11 * b11 - a12 * b10 + a13 * b09,\\n a02 * b10 - a01 * b11 - a03 * b09,\\n a31 * b05 - a32 * b04 + a33 * b03,\\n a22 * b04 - a21 * b05 - a23 * b03,\\n a12 * b08 - a10 * b11 - a13 * b07,\\n a00 * b11 - a02 * b08 + a03 * b07,\\n a32 * b02 - a30 * b05 - a33 * b01,\\n a20 * b05 - a22 * b02 + a23 * b01,\\n a10 * b10 - a11 * b08 + a13 * b06,\\n a01 * b08 - a00 * b10 - a03 * b06,\\n a30 * b04 - a31 * b02 + a33 * b00,\\n a21 * b02 - a20 * b04 - a23 * b00,\\n a11 * b07 - a10 * b09 - a12 * b06,\\n a00 * b09 - a01 * b07 + a02 * b06,\\n a31 * b01 - a30 * b03 - a32 * b00,\\n a20 * b03 - a21 * b01 + a22 * b00) / det;\\n}\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have \|\|a\|\| > 0 U \|\|b\|\| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a-b + ba + c0 = -ba + ba + 0 = 0\\n if (v.xv.x > v.zv.z \|\| v.yv.y > v.zv.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\nuniform float tubeScale;\\n\\nuniform mat4 model\\n , view\\n , projection;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data\\n , f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n normal = normalize(normal * inverse(mat3(model)));\\n\\n gl_Position = projection * view * tubePosition;\\n f_color = color;\\n f_normal = normal;\\n f_data = tubePosition.xyz;\\n f_position = position.xyz;\\n f_eyeDirection = eyePosition - tubePosition.xyz;\\n f_lightDirection = lightPosition - tubePosition.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data\\n , f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(!gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have \|\|a\|\| > 0 U \|\|b\|\| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a-b + ba + c0 = -ba + ba + 0 = 0\\n if (v.xv.x > v.zv.z \|\| v.yv.y > v.zv.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n gl_Position = projection * view * tubePosition;\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:392}],300:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./shaders\"),g=(t(\"./closest-point\"),d.meshShader),v=d.pickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function y(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,g,v,y,x,b,_,w,k,M,A,T,S,E){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleVectors=c,this.triangleColors=f,this.triangleNormals=p,this.triangleUVs=h,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=g,this.edgeColors=y,this.edgeUVs=x,this.edgeIds=v,this.edgeVAO=b,this.edgeCount=0,this.pointPositions=_,this.pointColors=k,this.pointUVs=M,this.pointSizes=A,this.pointIds=w,this.pointVAO=T,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=S,this.contourVAO=E,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!1,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.tubeScale=1,this._model=m,this._view=m,this._projection=m,this._resolution=[1,1]}var x=y.prototype;function b(t){var e=n(t,v.vertex,v.fragment,null,v.attributes);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.vector.location=5,e}x.isOpaque=function(){return this.opacity>=1},x.isTransparent=function(){return this.opacity<1},x.pickSlots=1,x.setPickBase=function(t){this.pickId=t},x.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],g=n[f][1],v=i[f],m=1-v,y=this.positions[d],x=this.positions[g],b=0;b<3;++b)o[s++]=vy[b]+mx[b]}this.contourCount=s/3\|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},x.update=function(t){t=t\|\|{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=f({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4n+a]=i[a];r[4n+3]=255i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale);var a=[],l=[],c=[],h=[],p=[],d=[],g=[],v=[],m=[],y=[],x=[],b=[],_=[],w=[],k=[];this.cells=r,this.positions=n,this.vectors=i;var M=t.vertexNormals,A=t.cellNormals,T=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!A&&(A=s.faceNormals(r,n,S)),A\|\|M\|\|(M=s.vertexNormals(r,n,T));var E=t.vertexColors,C=t.cellColors,L=t.meshColor\|\|[1,1,1,1],z=t.vertexUVs,O=t.vertexIntensity,I=t.cellUVs,P=t.cellIntensity,D=1/0,R=-1/0;if(!z&&!I)if(O)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],R=+t.vertexIntensityBounds[1];else for(var B=0;B<O.length;++B){var F=O[B];D=Math.min(D,F),R=Math.max(R,F)}else if(P)for(B=0;B<P.length;++B){F=P[B];D=Math.min(D,F),R=Math.max(R,F)}else for(B=0;B<n.length;++B){F=n[B][2];D=Math.min(D,F),R=Math.max(R,F)}this.intensity=O\|\|(P?function(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;var a=t.length;for(i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}(r,n.length,P):function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n));var N=t.pointSizes,j=t.pointSize\|\|1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(B=0;B<n.length;++B)for(var V=n[B],U=0;U<3;++U)!isNaN(V[U])&&isFinite(V[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],V[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],V[U]));var q=0,H=0,G=0;t:for(B=0;B<r.length;++B){var W=r[B];switch(W.length){case 1:for(V=n[X=W[0]],U=0;U<3;++U)if(isNaN(V[U])\|\|!isFinite(V[U]))continue t;x.push(V[0],V[1],V[2],V[3]),3===(Z=E?E[X]:C?C[B]:L).length?b.push(Z[0],Z[1],Z[2],1):b.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],_.push($[0],$[1]),N?w.push(N[X]):w.push(j),k.push(B),G+=1;break;case 2:for(U=0;U<2;++U){V=n[X=W[U]];for(var Y=0;Y<3;++Y)if(isNaN(V[Y])\|\|!isFinite(V[Y]))continue t}for(U=0;U<2;++U){V=n[X=W[U]];g.push(V[0],V[1],V[2]),3===(Z=E?E[X]:C?C[B]:L).length?v.push(Z[0],Z[1],Z[2],1):v.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],m.push($[0],$[1]),y.push(B)}H+=1;break;case 3:for(U=0;U<3;++U)for(V=n[X=W[U]],Y=0;Y<3;++Y)if(isNaN(V[Y])\|\|!isFinite(V[Y]))continue t;for(U=0;U<3;++U){var X;V=n[X=W[U]];a.push(V[0],V[1],V[2],V[3]);var Z,$,J,K=i[X];l.push(K[0],K[1],K[2],K[3]),3===(Z=E?E[X]:C?C[B]:L).length?c.push(Z[0],Z[1],Z[2],1):c.push(Z[0],Z[1],Z[2],Z[3]),$=z?z[X]:O?[(O[X]-D)/(R-D),0]:I?I[B]:P?[(P[B]-D)/(R-D),0]:[(V[2]-D)/(R-D),0],p.push($[0],$[1]),J=M?M[X]:A[B],h.push(J[0],J[1],J[2]),d.push(B)}q+=1}}this.pointCount=G,this.edgeCount=H,this.triangleCount=q,this.pointPositions.update(x),this.pointColors.update(b),this.pointUVs.update(_),this.pointSizes.update(w),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(g),this.edgeColors.update(v),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(y)),this.trianglePositions.update(a),this.triangleVectors.update(l),this.triangleColors.update(c),this.triangleUVs.update(p),this.triangleNormals.update(h),this.triangleIds.update(new Uint32Array(d))}},x.drawTransparent=x.draw=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|m,n=t.view\|\|m,i=t.projection\|\|m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,contourColor:this.contourColor,texture:0};this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]u[4o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4d+o]this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},x.drawPick=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|m,n=t.view\|\|m,i=t.projection\|\|m,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},x.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256t.value[1]+65536t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3);return{index:e,position:n,intensity:this.intensity[r[1]],velocity:this.vectors[r[1]].slice(0,3),divergence:this.vectors[r[1]][3],dataCoordinate:n}},x.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=e.triShader\|\|function(t){var e=n(t,g.vertex,g.fragment,null,g.attributes);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.vector.location=5,e}(t),s=b(t),l=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));l.generateMipmap(),l.minFilter=t.LINEAR_MIPMAP_LINEAR,l.magFilter=t.LINEAR;var c=i(t),f=i(t),h=i(t),p=i(t),d=i(t),v=i(t),m=a(t,[{buffer:c,type:t.FLOAT,size:4},{buffer:v,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:h,type:t.FLOAT,size:4},{buffer:p,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:3},{buffer:f,type:t.FLOAT,size:4}]),x=i(t),_=i(t),w=i(t),k=i(t),M=a(t,[{buffer:x,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),A=i(t),T=i(t),S=i(t),E=i(t),C=i(t),L=a(t,[{buffer:A,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:T,type:t.FLOAT,size:4},{buffer:S,type:t.FLOAT,size:2},{buffer:E,type:t.FLOAT,size:1}]),z=i(t),O=new y(t,l,r,null,null,s,null,null,c,f,v,h,p,d,m,x,k,_,w,M,A,C,T,S,E,L,z,a(t,[{buffer:z,type:t.FLOAT,size:3}]));return O.update(e),O}},{\"./closest-point\":298,\"./shaders\":299,colormap:114,\"gl-buffer\":230,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"gl-shader\":288,\"gl-texture2d\":305,\"gl-vao\":310,ndarray:433,normals:436,\"simplicial-complex-contour\":494,\"typedarray-pool\":522}],301:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++){var u=n.length(l[c]);u>o&&(o=u)}var f=t.map(function(t){return function(t,e,r,a){var o,s,l,c=t.points,u=t.velocities,f=t.divergences;n.set(n.create(),0,1,0),n.create(),n.create();n.create();for(var h=[],p=[],d=[],g=[],v=[],m=[],y=0,x=0,b=i.create(),_=i.create(),w=0;w<c.length;w++){o=c[w],s=u[w],l=f[w],0===e&&(l=.05r),x=n.length(s)/a,b=i.create(),n.copy(b,s),b[3]=l;for(var k=0;k<8;k++)v[k]=[o[0],o[1],o[2],k];if(g.length>0)for(k=0;k<8;k++){var M=(k+1)%8;h.push(g[k],v[k],v[M],v[M],g[M],g[k]),d.push(_,b,b,b,_,_),m.push(y,x,x,x,y,y),p.push([h.length-6,h.length-5,h.length-4],[h.length-3,h.length-2,h.length-1])}var A=g;g=v,v=A,A=_,_=b,b=A,A=y,y=x,x=A}return{positions:h,cells:p,vectors:d,vertexIntensity:m}}(t,r,a,o)}),h=[],p=[],d=[],g=[];for(s=0;s<f.length;s++){var v=f[s],m=h.length;h=h.concat(v.positions),d=d.concat(v.vectors),g=g.concat(v.vertexIntensity);for(c=0;c<v.cells.length;c++){var y=v.cells[c],x=[];p.push(x);for(var b=0;b<y.length;b++)x.push(y[b]+m)}}return{positions:h,cells:p,vectors:d,vertexIntensity:g,colormap:e}},o=function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=this.getVelocity(r);n.subtract(a,a,e),n.scale(a,a,1e4),n.add(r,t,[0,i,0]);var o=this.getVelocity(r);n.subtract(o,o,e),n.scale(o,o,1e4),n.add(r,t,[0,0,i]);var s=this.getVelocity(r);return n.subtract(s,s,e),n.scale(s,s,1e4),n.add(r,a,o),n.add(r,r,s),r},s=function(t){return h(t,this.vectors,this.meshgrid,this.clampBorders)},l=function(t,e){for(var r=0;r<t.length;r++){var n=t[r];if(n===e)return r;if(n>e)return r-1}return r},c=n.create(),u=n.create(),f=function(t,e,r){return t<e?e:t>r?r:t},h=function(t,e,r,i){var a=t[0],o=t[1],s=t[2],h=r[0].length,p=r[1].length,d=r[2].length,g=l(r[0],a),v=l(r[1],o),m=l(r[2],s),y=g+1,x=v+1,b=m+1;if(r[0][g]===a&&(y=g),r[1][v]===o&&(x=v),r[2][m]===s&&(b=m),i&&(g=f(g,0,h-1),y=f(y,0,h-1),v=f(v,0,p-1),x=f(x,0,p-1),m=f(m,0,d-1),b=f(b,0,d-1)),g<0\|\|v<0\|\|m<0\|\|y>=h\|\|x>=p\|\|b>=d)return n.create();var _=(a-r[0][g])/(r[0][y]-r[0][g]),w=(o-r[1][v])/(r[1][x]-r[1][v]),k=(s-r[2][m])/(r[2][b]-r[2][m]);(_<0\|\|_>1\|\|isNaN(_))&&(_=0),(w<0\|\|w>1\|\|isNaN(w))&&(w=0),(k<0\|\|k>1\|\|isNaN(k))&&(k=0);var M=mhp,A=bhp,T=vh,S=xh,E=g,C=y,L=e[T+M+E],z=e[T+M+C],O=e[S+M+E],I=e[S+M+C],P=e[T+A+E],D=e[T+A+C],R=e[S+A+E],B=e[S+A+C],F=n.create();return n.lerp(F,L,z,_),n.lerp(c,O,I,_),n.lerp(F,F,c,w),n.lerp(c,P,D,_),n.lerp(u,R,B,_),n.lerp(c,c,u,w),n.lerp(F,F,c,k),F},p=function(t){var e=1/0;t.sort(function(t,e){return t-e});for(var r=1;r<t.length;r++){var n=Math.abs(t[r]-t[r-1]);n<e&&(e=n)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength\|\|1e3,l=t.tubeSize\|\|1,c=t.absoluteTubeSize;t.getDivergence\|\|(t.getDivergence=o),t.getVelocity\|\|(t.getVelocity=s),void 0===t.clampBorders&&(t.clampBorders=!0);var u=[],f=e[0][0],h=e[0][1],d=e[0][2],g=e[1][0],v=e[1][1],m=e[1][2],y=function(t,e){var r=e[0],n=e[1],i=e[2];return r>=f&&r<=g&&n>=h&&n<=v&&i>=d&&i<=m},x=10n.distance(e[0],e[1])/i,b=xx,_=1,w=0;n.create();r.length>=2&&(_=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=0;s<t.length;s++){var l=t[s],c=l[0],u=l[1],f=l[2];i[c]\|\|(e.push(c),i[c]=!0),a[u]\|\|(r.push(u),a[u]=!0),o[f]\|\|(n.push(f),o[f]=!0)}var h=p(e),d=p(r),g=p(n),v=Math.min(h,d,g);return isFinite(v)?v:1}(r));for(var k=0;k<r.length;k++){var M=n.create();n.copy(M,r[k]);var A=[M],T=[],S=t.getVelocity(M),E=M;T.push(S);var C=[],L=t.getDivergence(M,S);(P=n.length(L))>w&&!isNaN(P)&&isFinite(P)&&(w=P),C.push(P),u.push({points:A,velocities:T,divergences:C});for(var z=0;z<100i&&A.length<i&&y(0,M);){z++;var O=n.clone(S),I=n.squaredLength(O);if(0===I)break;if(I>b&&n.scale(O,O,x/Math.sqrt(I)),n.add(O,O,M),S=t.getVelocity(O),n.squaredDistance(E,O)-b>-1e-4b){A.push(O),E=O,T.push(S);L=t.getDivergence(O,S);(P=n.length(L))>w&&!isNaN(P)&&isFinite(P)&&(w=P),C.push(P)}M=O}}for(k=0;k<C.length;k++){var P=C[k];!isNaN(P)&&isFinite(P)\|\|(C[k]=w)}var D=a(u,t.colormap,w,_);return c?D.tubeScale=c:(0===w&&(w=1),D.tubeScale=.5l_/w),D},e.exports.createTubeMesh=t(\"./lib/tubemesh\")},{\"./lib/tubemesh\":300,\"gl-vec3\":329,\"gl-vec4\":365}],302:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n worldCoordinate = vec3(uv.zw, f.x);\\n vec4 worldPosition = model vec4(worldCoordinate, 1.0);\\n vec4 clipPosition = projection * view * worldPosition;\\n gl_Position = clipPosition;\\n kill = f.y;\\n value = f.z;\\n planeCoordinate = uv.xy;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * worldPosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n lightDirection = lightPosition - cameraCoordinate.xyz;\\n eyeDirection = eyePosition - cameraCoordinate.xyz;\\n surfaceNormal = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness) {\\n return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n if ((kill > 0.0) \|\|\\n (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n vec3 N = normalize(surfaceNormal);\\n vec3 V = normalize(eyeDirection);\\n vec3 L = normalize(lightDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n //decide how to interpolate color \\u2014 in vertex or in fragment\\n vec4 surfaceColor = step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) + step(.5, vertexColor) * vColor;\\n\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * view * worldPosition;\\n clipPosition.z = clipPosition.z + zOffset;\\n\\n gl_Position = clipPosition;\\n value = f;\\n kill = -1.0;\\n worldCoordinate = dataCoordinate;\\n planeCoordinate = uv.zw;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) \|\| \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) \|\|\\n outOfRange(a.y, b.y, p.y) \|\|\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if ((kill > 0.0) \|\|\\n (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":288,glslify:392}],303:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:w,offset:0},{buffer:c,size:3,stride:w,offset:16},{buffer:c,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var v=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,f,h,p,d),m={levels:[[],[],[]]};for(var k in t)m[k]=t[k];return m.colormap=m.colormap\|\|\"jet\",v.update(m),v};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),v=t(\"ndarray-gradient\"),m=t(\"./lib/shaders\"),y=m.createShader,x=m.createContourShader,b=m.createPickShader,_=m.createPickContourShader,w=40,k=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],M=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function T(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,c,u,h,p,d,g){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new T([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var C=E.prototype;C.isTransparent=function(){return this.opacity<1},C.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0\|\|this._dynamicCounts[t]>0)return!0;return!1},C.pickSlots=1,C.setPickBase=function(t){this.pickId=t};var L=[0,0,0],z={showSurface:!1,showContour:!1,projections:[k.slice(),k.slice(),k.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis\|\|L,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o\|\|e.surfaceProject[r],n=0;n<3;++n)s=s\|\|e.contourProject[r][n];for(r=0;r<3;++r){var l=z.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5n]=1;l[5r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=z.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return z.showSurface=o,z.showContour=s,z}var I={model:k,view:k,projection:k,inverseModel:k.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},P=k.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t\|\|{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model\|\|k,n.view=t.view\|\|k,n.projection=t.projection\|\|k,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]\|\|this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]\|\|this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=P;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]s[4i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4o+i]this.lightPosition[o];n.lightPosition[i]=c/l}var u=O(n,this);if(u.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]\|\|(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]);for(var g=0;g<this.contourLevels[o].length;++g)g===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==g&&g-1!==this.highlightLevel[o]\|\|(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),f.uniforms.height=this.contourLevels[o][g],h.draw(r.LINES,this._contourCounts[o][g],this._contourOffsets[o][g])}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}C.draw=function(t){return R.call(this,t,!1)},C.drawTransparent=function(t){return R.call(this,t,!0)};var B={model:k,view:k,projection:k,inverseModel:k,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function F(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function N(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function j(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function V(t){if(Array.isArray(t)){if(Array.isArray(t))return[j(t[0]),j(t[1]),j(t[2])];var e=j(t);return[e.slice(),e.slice(),e.slice()]}}C.drawPick=function(t){t=t\|\|{};var e=this.gl;e.disable(e.CULL_FACE);var r=B;r.model=t.model\|\|k,r.view=t.view\|\|k,r.projection=t.projection\|\|k,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=D;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=O(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},C.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0](t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1](t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,v=f(h?l:1-l),m=0;m<3;++m)c[m]+=this._field[m].get(p,d)v;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],m=0;m<3;++m)r.dataCoordinate[m]=this._field[m].get(r.index[0],r.index[1]);return r},C.update=function(t){t=t\|\|{},this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=N(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=N(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=N(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=V(t.contourColor)),\"contourProject\"in t&&(this.contourProject=N(t.contourProject,function(t){return N(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=V(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=N(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=N(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field\|\|t.coords&&t.coords[2]\|\|null,r=!1;if(e\|\|(e=this._field[2].shape[0]\|\|this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t\|\|\"coords\"in t){var i=(e.shape[0]+2)(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),F(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var p=t.coords;if(!Array.isArray(p)\|\|3!==p.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var d=p[o];for(b=0;b<2;++b)if(d.shape[b]!==a[b])throw new Error(\"gl-surface: coords have incorrect shape\");F(this._field[o],d)}}else if(t.ticks){var g=t.ticks;if(!Array.isArray(g)\|\|2!==g.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var m=g[o];if((Array.isArray(m)\|\|m.length)&&(m=f(m)),m.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var y=f(m.data,a);y.stride[o]=m.stride[0],y.stride[1^o]=0,F(this._field[o],y)}}else{for(o=0;o<2;++o){var x=[0,0];x[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],x,0)}this._field[0].set(0,0,0);for(var b=0;b<a[0];++b)this._field[0].set(b+1,0,b);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),b=0;b<a[1];++b)this._field[1].set(0,b+1,b);this._field[1].set(0,a[1]+1,a[1]-1)}var _=this._field,w=f(s.mallocFloat(3_[2].size2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)v(w.pick(o),_[o],\"mirror\");var k=f(s.mallocFloat(3_[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(b=0;b<a[1]+2;++b){var A=w.get(0,o,b,0),T=w.get(0,o,b,1),E=w.get(1,o,b,0),C=w.get(1,o,b,1),L=w.get(2,o,b,0),z=w.get(2,o,b,1),O=Ez-CL,I=LT-zA,P=AC-TE,D=Math.sqrt(OO+II+PP);D<1e-8?(D=Math.max(Math.abs(O),Math.abs(I),Math.abs(P)))<1e-8?(P=1,I=O=0,D=1):D=1/D:D=1/Math.sqrt(D),k.set(o,b,0,OD),k.set(o,b,1,ID),k.set(o,b,2,PD)}s.free(w.data);var R=[1/0,1/0,1/0],B=[-1/0,-1/0,-1/0],j=1/0,U=-1/0,q=(a[0]-1)(a[1]-1)6,H=s.mallocFloat(n.nextPow2(10q)),G=0,W=0;for(o=0;o<a[0]-1;++o)t:for(b=0;b<a[1]-1;++b){for(var Y=0;Y<2;++Y)for(var X=0;X<2;++X)for(var Z=0;Z<3;++Z){var $=this._field[Z].get(1+o+Y,1+b+X);if(isNaN($)\|\|!isFinite($))continue t}for(Z=0;Z<6;++Z){var J=o+M[Z][0],K=b+M[Z][1],Q=this._field[0].get(J+1,K+1),tt=this._field[1].get(J+1,K+1),et=$=this._field[2].get(J+1,K+1);O=k.get(J+1,K+1,0),I=k.get(J+1,K+1,1),P=k.get(J+1,K+1,2),t.intensity&&(et=t.intensity.get(J,K)),H[G++]=J,H[G++]=K,H[G++]=Q,H[G++]=tt,H[G++]=$,H[G++]=0,H[G++]=et,H[G++]=O,H[G++]=I,H[G++]=P,R[0]=Math.min(R[0],Q),R[1]=Math.min(R[1],tt),R[2]=Math.min(R[2],$),j=Math.min(j,et),B[0]=Math.max(B[0],Q),B[1]=Math.max(B[1],tt),B[2]=Math.max(B[2],$),U=Math.max(U,et),W+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],U=+t.intensityBounds[1]),o=6;o<G;o+=10)H[o]=(H[o]-j)/(U-j);this._vertexCount=W,this._coordinateBuffer.update(H.subarray(0,G)),s.freeFloat(H),s.free(k.data),this.bounds=[R,B],this.intensity=t.intensity\|\|this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===U\|\|(r=!0),this.intensityBounds=[j,U]}if(\"levels\"in t){var rt=t.levels;for(rt=Array.isArray(rt[0])?rt.slice():[[],[],rt],o=0;o<3;++o)rt[o]=rt[o].slice(),rt.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if(rt[o].length!==this.contourLevels[o].length){r=!0;break}for(b=0;b<rt[o].length;++b)if(rt[o][b]!==this.contourLevels[o][b]){r=!0;break t}}this.contourLevels=rt}if(r){_=this._field,a=this.shape;for(var nt=[],it=0;it<3;++it){rt=this.contourLevels[it];var at=[],ot=[],st=[0,0,0];for(o=0;o<rt.length;++o){var lt=h(this._field[it],rt[o]);at.push(nt.length/5\|0),W=0;t:for(b=0;b<lt.cells.length;++b){var ct=lt.cells[b];for(Z=0;Z<2;++Z){var ut=lt.positions[ct[Z]],ft=ut[0],ht=0\|Math.floor(ft),pt=ft-ht,dt=ut[1],gt=0\|Math.floor(dt),vt=dt-gt,mt=!1;e:for(var yt=0;yt<3;++yt){st[yt]=0;var xt=(it+yt+1)%3;for(Y=0;Y<2;++Y){var bt=Y?pt:1-pt;for(J=0\|Math.min(Math.max(ht+Y,0),a[0]),X=0;X<2;++X){var _t=X?vt:1-vt;if(K=0\|Math.min(Math.max(gt+X,0),a[1]),$=yt<2?this._field[xt].get(J,K):(this.intensity.get(J,K)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite($)\|\|isNaN($)){mt=!0;break e}var wt=bt_t;st[yt]+=wt$}}}if(mt){if(Z>0){for(var kt=0;kt<5;++kt)nt.pop();W-=1}continue t}nt.push(st[0],st[1],ut[0],ut[1],st[2]),W+=1}}ot.push(W)}this._contourOffsets[it]=at,this._contourCounts[it]=ot}var Mt=s.mallocFloat(nt.length);for(o=0;o<nt.length;++o)Mt[o]=nt[o];this._contourBuffer.update(Mt),s.freeFloat(Mt)}t.colormap&&this._colorMap.setPixels(function(t){var e=u([l({colormap:t,nshades:S,format:\"rgba\"}).map(function(t){return[t[0],t[1],t[2],255t[3]]})]);return c.divseq(e,255),e}(t.colormap))},C.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},C.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]\|\|this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]\|\|this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12i[0]i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=(this.intensity,h(u,r[o])),g=d.cells,v=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<g.length;++e)for(var m=g[e],y=0;y<2;++y){var x=v[m[y]],b=+x[0],_=0\|b,w=0\|Math.min(_+1,i[0]),k=b-_,M=1-k,A=+x[1],T=0\|A,S=0\|Math.min(T+1,i[1]),E=A-T,C=1-E,L=MC,z=ME,O=kC,I=kE,P=Lf.get(_,T)+zf.get(_,S)+Of.get(w,T)+If.get(w,S),D=Lp.get(_,T)+zp.get(_,S)+Op.get(w,T)+Ip.get(w,S);if(isNaN(P)\|\|isNaN(D)){y&&(n-=1);break}a[2n+0]=P,a[2n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2n)),s.freeFloat(a)}}},{\"./lib/shaders\":302,\"binary-search-bounds\":79,\"bit-twiddle\":80,colormap:114,\"gl-buffer\":230,\"gl-mat4/invert\":254,\"gl-mat4/multiply\":256,\"gl-texture2d\":305,\"gl-vao\":310,ndarray:433,\"ndarray-gradient\":424,\"ndarray-ops\":427,\"ndarray-pack\":428,\"surface-nets\":508,\"typedarray-pool\":522}],304:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),g=t(\"detect-kerning\"),v=t(\"object-assign\"),m=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var k=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl\|\|a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader\|\|(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};k.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\t\"+(k.normalViewport?\"\":\"vec2 positionOffset = vec2(positionOffset.x,- positionOffset.y);\")+\"\\n\\n\\t\\t\\t\\tvec2 offset = floor(em (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ positionOffset))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\t\"+(k.normalViewport?\"position.y = 1. - position.y;\":\"\")+\"\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth \|\|\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a = maskY;\\n\\t\\t\\t\\tcolor.a = opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},k.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map(function(t){return parseFloat(t)}):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),k.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale\|\|(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate\|\|(this.translate=[0,0]),this.font.length\|\|t.font\|\|(t.font=k.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach(function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(k.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:k.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString\|\|(a=!0,e.font[r]=k.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:m(c,{origin:\"top\",fontSize:k.baseFontSize,fontStyle:u.join(\" \")})},k.fonts[i]=e.font[r]}}),(a\|\|o)&&this.font.forEach(function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2Math.ceil(e.fontSize[i]o.bottom.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)}),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text\|\|a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)e.textOffsets[b]=e.textOffsets[b-1]+t.text[b-1].length,e.count+=t.text[b].length,e.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach(function(t,n){k.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=k.atlasContext.measureText(o).width/k.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);v(t.kerning,g(t.family,{pairs:s}))}}})}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,M=u.mallocFloat(2this.count),A=0,T=0;A<this.counts.length;A++){var S=e.counts[A];if(w)for(var E=0;E<S;E++)M[T++]=t.position[2A],M[T++]=t.position[2A+1];else for(var C=0;C<S;C++)M[T++]=t.position[A][0],M[T++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:M}):this.position=this.regl.buffer({type:\"float\",data:M}),u.freeFloat(M)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text\|\|a){var L=u.mallocUint8(this.count),z=u.mallocFloat(2this.count);this.textWidth=[];for(var O=0,I=0;O<this.counts.length;O++){for(var P=e.counts[O],D=e.font[O]\|\|e.font[0],R=e.fontAtlas[O]\|\|e.fontAtlas[0],B=0;B<P;B++){var F=e.text.charAt(I),N=e.text.charAt(I-1);if(L[I]=R.ids[F],z[2I]=D.width[F],B){var j=z[2I-2],V=z[2I],U=z[2I-1]+.5j+.5V;if(e.kerning){var q=D.kerning[N+F];q&&(U+=.001q)}z[2I+1]=U}else z[2I+1]=.5z[2I];I++}e.textWidth.push(z.length?.5z[2I-2]+z[2I-1]:0)}t.align\|\|(t.align=this.align),this.charBuffer({data:L,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:z,type:\"float\",usage:\"stream\"}),u.freeUint8(L),u.freeFloat(z),r.length&&this.font.forEach(function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(k.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(oi),u=x(si);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:k.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})})}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map(function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5-t}return 0})),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)\|\|(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map(function(t,r){var n=(e.font[r]\|\|e.font[0]).metrics,i=0;return i+=.5n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],k.normalViewport\|\|(i=-1),i})),null!=t.color)if(t.color\|\|(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=u.mallocUint8(G);for(var W=(t.color.subarray\|\|t.color.slice).bind(t.color),Y=0;Y<G;Y+=4)H.set(l(W(Y,Y+4),\"uint8\"),Y)}else{var X=t.color.length;H=u.mallocUint8(4X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]\|\|0,\"uint8\"),4Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position\|\|t.text\|\|t.color\|\|t.baseline\|\|t.align\|\|t.font\|\|t.offset\|\|t.opacity)if(this.color.length>4\|\|this.baselineOffset.length>1\|\|this.align&&this.align.length>1\|\|this.fontAtlas.length>1\|\|this.positionOffset.length>2){var $=Math.max(.5this.position.length\|\|0,.25this.color.length\|\|0,this.baselineOffset.length\|\|0,this.alignOffset.length\|\|0,this.font.length\|\|0,this.opacity.length\|\|0,.5this.positionOffset.length\|\|0);this.batch=Array($);for(var J=0;J<this.batch.length;J++)e.batch[J]={count:e.counts.length>1?e.counts[J]:e.counts[0],offset:e.textOffsets.length>1?e.textOffsets[J]:e.textOffsets[0],color:e.color?e.color.length<=4?e.color:e.color.subarray(4J,4J+4):[0,0,0,255],opacity:Array.isArray(e.opacity)?e.opacity[J]:e.opacity,baseline:null!=e.baselineOffset[J]?e.baselineOffset[J]:e.baselineOffset[0],align:e.align?null!=e.alignOffset[J]?e.alignOffset[J]:e.alignOffset[0]:0,atlas:e.fontAtlas[J]\|\|e.fontAtlas[0],positionOffset:e.positionOffset.length>2?e.positionOffset.subarray(2J,2J+2):e.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color\|\|[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},k.prototype.destroy=function(){},k.prototype.kerning=!0,k.prototype.position={constant:new Float32Array(2)},k.prototype.translate=null,k.prototype.scale=null,k.prototype.font=null,k.prototype.text=\"\",k.prototype.positionOffset=[0,0],k.prototype.opacity=1,k.prototype.color=new Uint8Array([0,0,0,255]),k.prototype.alignOffset=[0,0],k.normalViewport=!1,k.maxAtlasSize=1024,k.atlasCanvas=document.createElement(\"canvas\"),k.atlasContext=k.atlasCanvas.getContext(\"2d\",{alpha:!1}),k.baseFontSize=64,k.fonts={},e.exports=k},{\"bit-twiddle\":80,\"color-normalize\":108,\"css-font\":127,\"detect-kerning\":151,\"es6-weak-map\":209,\"flatten-vertex-data\":216,\"font-atlas\":217,\"font-measure\":218,\"gl-util/context\":306,\"is-plain-obj\":405,\"object-assign\":437,\"parse-rect\":442,\"parse-unit\":444,\"pick-by-alias\":448,regl:478,\"to-px\":516,\"typedarray-pool\":522}],305:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o\|\|function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]\|\|t.RGBA,arguments[4]\|\|t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0\|arguments[1][0],0\|arguments[1][1],arguments[2]\|\|t.RGBA,arguments[3]\|\|t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=c(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0\|e.width,0\|e.height,arguments[2]\|\|t.RGBA,arguments[3]\|\|t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0\|\|o[0]>s\|\|o[1]<0\|\|o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=d(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var f,p,v=0;if(2===o.length)v=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])v=t.ALPHA;else if(2===o[2])v=t.LUMINANCE_ALPHA;else if(3===o[2])v=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");v=t.RGBA}}c!==t.FLOAT\|\|t.getExtension(\"OES_texture_float\")\|\|(c=t.UNSIGNED_BYTE,l=!1);var m=e.size;if(l)f=0===e.offset&&e.data.length===m?e.data:e.data.subarray(e.offset,e.offset+m);else{var y=[o[2],o[2]o[0],1];p=a.malloc(m,r);var x=n(p,o,y,0);\"float32\"!==r&&\"float64\"!==r\|\|c!==t.UNSIGNED_BYTE?i.assign(x,e):u(x,e),f=p.subarray(0,m)}var b=g(t);t.texImage2D(t.TEXTURE_2D,0,v,o[0],o[1],0,v,c,f),l\|\|a.free(p);return new h(t,b,o[0],o[1],v,c)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement\|\|\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement\|\|\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement\|\|\"undefined\"!=typeof ImageData&&t instanceof ImageData}var u=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0\|\|e>i\|\|r<0\|\|r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0\|\|e>a\|\|r<0\|\|r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")\|\|(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return 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t=0;t<this._attribs.length;++t)this._attribs[t].bind(this.gl)},a.prototype.unbind=function(){this._ext.bindVertexArrayOES(null)},a.prototype.dispose=function(){this._ext.deleteVertexArrayOES(this.handle)},a.prototype.update=function(t,e,r){if(this.bind(),n(this.gl,e,t),this.unbind(),this._attribs.length=0,t)for(var a=0;a<t.length;++a){var o=t[a];\"number\"==typeof o?this._attribs.push(new i(a,1,o)):Array.isArray(o)&&this._attribs.push(new i(a,o.length,o[0],o[1],o[2],o[3]))}this._useElements=!!e,this._elementsType=r\|\|this.gl.UNSIGNED_SHORT},a.prototype.draw=function(t,e,r){r=r\|\|0;var n=this.gl;this._useElements?n.drawElements(t,e,this._elementsType,r):n.drawArrays(t,r,e)},e.exports=function(t,e){return new a(t,e,e.createVertexArrayOES())}},{\"./do-bind.js\":307}],310:[function(t,e,r){\"use strict\";var n=t(\"./lib/vao-native.js\"),i=t(\"./lib/vao-emulated.js\");function 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l=i[cr+n.length],c=i[cr+n.length+1];this.keys=i.subarray(l,c),this.bboxes=i.subarray(c),this.insert=this._insertReadonly}else{this.d=e+2r;for(var u=0;u<this.dthis.d;u++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var f=r/et;this.min=-f,this.max=t+f}ur.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},ur.prototype._insertReadonly=function(){throw\"Cannot insert into a GridIndex created from an ArrayBuffer.\"},ur.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},ur.prototype.query=function(t,e,r,n){var i=this.min,a=this.max;if(t<=i&&e<=i&&a<=r&&a<=n)return Array.prototype.slice.call(this.keys);var o=[];return this._forEachCell(t,e,r,n,this._queryCell,o,{}),o},ur.prototype._queryCell=function(t,e,r,n,i,a,o){var s=this.cells[i];if(null!==s)for(var l=this.keys,c=this.bboxes,u=0;u<s.length;u++){var f=s[u];if(void 0===o[f]){var h=4f;t<=c[h+2]&&e<=c[h+3]&&r>=c[h+0]&&n>=c[h+1]?(o[f]=!0,a.push(l[f])):o[f]=!1}}},ur.prototype._forEachCell=function(t,e,r,n,i,a,o){for(var s=this._convertToCellCoord(t),l=this._convertToCellCoord(e),c=this._convertToCellCoord(r),u=this._convertToCellCoord(n),f=s;f<=c;f++)for(var h=l;h<=u;h++){var p=this.dh+f;if(i.call(this,t,e,r,n,p,a,o))return}},ur.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(tthis.scale)+this.padding))},ur.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=cr+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[cr+o]=a,i.set(s,a),a+=s.length}return i[cr+t.length]=a,i.set(this.keys,a),a+=this.keys.length,i[cr+t.length+1]=a,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var fr=self.ImageData,hr={};function pr(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,\"_classRegistryKey\",{value:t,writeable:!1}),hr[t]={klass:e,omit:r.omit\|\|[],shallow:r.shallow\|\|[]}}for(var dr in pr(\"Object\",Object),lr.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),r},lr.deserialize=function(t){return new lr(t)},pr(\"Grid\",lr),pr(\"Color\",tt),pr(\"Error\",Error),pr(\"StylePropertyFunction\",Te),pr(\"StyleExpression\",be,{omit:[\"_evaluator\"]}),pr(\"ZoomDependentExpression\",Me),pr(\"ZoomConstantExpression\",ke),pr(\"CompoundExpression\",gt,{omit:[\"_evaluate\"]}),Rt)Rt[dr]._classRegistryKey\|\|pr(\"Expression_\"+dr,Rt[dr]);function gr(t,e){if(null==t\|\|\"boolean\"==typeof t\|\|\"number\"==typeof t\|\|\"string\"==typeof t\|\|t instanceof Boolean\|\|t instanceof Number\|\|t instanceof String\|\|t instanceof Date\|\|t instanceof RegExp)return t;if(t instanceof ArrayBuffer)return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof fr)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1){var o=a[i];n.push(gr(o,e))}return n}if(\"object\"==typeof t){var s=t.constructor,l=s._classRegistryKey;if(!l)throw new Error(\"can't serialize object of unregistered class\");var c={};if(s.serialize)c._serialized=s.serialize(t,e);else{for(var u in t)if(t.hasOwnProperty(u)&&!(hr[l].omit.indexOf(u)>=0)){var f=t[u];c[u]=hr[l].shallow.indexOf(u)>=0?f:gr(f,e)}t instanceof Error&&(c.message=t.message)}return{name:l,properties:c}}throw new Error(\"can't serialize object of type \"+typeof t)}function vr(t){if(null==t\|\|\"boolean\"==typeof t\|\|\"number\"==typeof t\|\|\"string\"==typeof t\|\|t instanceof Boolean\|\|t instanceof Number\|\|t instanceof String\|\|t instanceof Date\|\|t instanceof RegExp\|\|t instanceof ArrayBuffer\|\|ArrayBuffer.isView(t)\|\|t instanceof fr)return t;if(Array.isArray(t))return t.map(function(t){return vr(t)});if(\"object\"==typeof t){var e=t,r=e.name,n=e.properties;if(!r)throw new Error(\"can't deserialize object of anonymous class\");var i=hr[r].klass;if(!i)throw new Error(\"can't deserialize unregistered class \"+r);if(i.deserialize)return i.deserialize(n._serialized);for(var a=Object.create(i.prototype),o=0,s=Object.keys(n);o<s.length;o+=1){var l=s[o];a[l]=hr[r].shallow.indexOf(l)>=0?n[l]:vr(n[l])}return a}throw new Error(\"can't deserialize object of type \"+typeof t)}var mr=function(){this.first=!0};mr.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))};var yr={\"Latin-1 Supplement\":function(t){return t>=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic Supplement\":function(t){return t>=1872&&t<=1919},\"Arabic Extended-A\":function(t){return t>=2208&&t<=2303},\"Hangul Jamo\":function(t){return t>=4352&&t<=4607},\"Unified Canadian Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram Symbols\":function(t){return t>=19904&&t<=19967},\"CJK Unified Ideographs\":function(t){return t>=19968&&t<=40959},\"Yi Syllables\":function(t){return t>=40960&&t<=42127},\"Yi Radicals\":function(t){return t>=42128&&t<=42191},\"Hangul Jamo Extended-A\":function(t){return t>=43360&&t<=43391},\"Hangul Syllables\":function(t){return t>=44032&&t<=55215},\"Hangul Jamo Extended-B\":function(t){return t>=55216&&t<=55295},\"Private Use Area\":function(t){return t>=57344&&t<=63743},\"CJK Compatibility Ideographs\":function(t){return t>=63744&&t<=64255},\"Arabic Presentation Forms-A\":function(t){return t>=64336&&t<=65023},\"Vertical Forms\":function(t){return t>=65040&&t<=65055},\"CJK Compatibility Forms\":function(t){return t>=65072&&t<=65103},\"Small Form Variants\":function(t){return t>=65104&&t<=65135},\"Arabic Presentation Forms-B\":function(t){return t>=65136&&t<=65279},\"Halfwidth and Fullwidth Forms\":function(t){return t>=65280&&t<=65519}};function xr(t){for(var e=0,r=t;e<r.length;e+=1)if(_r(r[e].charCodeAt(0)))return!0;return!1}function br(t){return!(yr.Arabic(t)\|\|yr[\"Arabic Supplement\"](t)\|\|yr[\"Arabic Extended-A\"](t)\|\|yr[\"Arabic Presentation Forms-A\"](t)\|\|yr[\"Arabic Presentation Forms-B\"](t))}function _r(t){return!!(746===t\|\|747===t\|\|!(t<4352)&&(yr[\"Bopomofo Extended\"](t)\|\|yr.Bopomofo(t)\|\|yr[\"CJK Compatibility Forms\"](t)&&!(t>=65097&&t<=65103)\|\|yr[\"CJK Compatibility Ideographs\"](t)\|\|yr[\"CJK Compatibility\"](t)\|\|yr[\"CJK Radicals Supplement\"](t)\|\|yr[\"CJK Strokes\"](t)\|\|!(!yr[\"CJK Symbols 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Ir=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};Ir.prototype.getValue=function(t){return x(this._values[t].value.value)},Ir.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)\|\|(this._values[t]=new Or(this._values[t].property)),this._values[t].value=new zr(this._values[t].property,null===e?void 0:x(e))},Ir.prototype.getTransition=function(t){return x(this._values[t].transition)},Ir.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)\|\|(this._values[t]=new Or(this._values[t].property)),this._values[t].transition=x(e)\|\|void 0},Ir.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},Ir.prototype.transitioned=function(t,e){for(var r=new Dr(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var 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Dr=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Dr.prototype.possiblyEvaluate=function(t){for(var e=new Fr(this._properties),r=0,n=Object.keys(this._values);r<n.length;r+=1){var i=n[r];e._values[i]=this._values[i].possiblyEvaluate(t)}return e},Dr.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var Rr=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};Rr.prototype.getValue=function(t){return x(this._values[t].value)},Rr.prototype.setValue=function(t,e){this._values[t]=new zr(this._values[t].property,null===e?void 0:x(e))},Rr.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},Rr.prototype.possiblyEvaluate=function(t){for(var e=new Fr(this._properties),r=0,n=Object.keys(this._values);r<n.length;r+=1){var 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Br(this,t.expression,e)},jr.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind\|\|\"constant\"!==e.value.kind)return t;if(void 0===t.value.value\|\|void 0===e.value.value)return new Br(this,{kind:\"constant\",value:void 0},t.globals);var n=kt[this.specification.type];return n?new Br(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.globals):t},jr.prototype.evaluate=function(t,e,r){return\"constant\"===t.kind?t.value:t.evaluate(e,r)};var Vr=function(t){this.specification=t};Vr.prototype.possiblyEvaluate=function(t,e){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var r=t.expression.evaluate(e);return this._calculate(r,r,r,e)}return this._calculate(t.expression.evaluate(new Lr(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Lr(Math.floor(e.zoom),e)),t.expression.evaluate(new Lr(Math.floor(e.zoom+1),e)),e)}},Vr.prototype._calculate=function(t,e,r,n){var i=n.zoom,a=i-Math.floor(i),o=n.crossFadingFactor();return 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Zr(t,e){return Math.ceil(t/e)e}Yr.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Yr.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Yr.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.lengththis.bytesPerElement),this._refreshViews())},Yr.prototype.clear=function(){this.length=0},Yr.prototype.resize=function(t){this.reserve(t),this.length=t},Yr.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacitythis.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Yr.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete 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this.int16[a+0]=t,this.int16[a+1]=e,this.int16[a+2]=r,this.int16[a+3]=n,i},e}(Yr);Jr.prototype.bytesPerElement=8,pr(\"StructArrayLayout4i8\",Jr);var Kr=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;this.resize(o+1);var s=6o;return this.int16[s+0]=t,this.int16[s+1]=e,this.int16[s+2]=r,this.int16[s+3]=n,this.int16[s+4]=i,this.int16[s+5]=a,o},e}(Yr);Kr.prototype.bytesPerElement=12,pr(\"StructArrayLayout2i4i12\",Kr);var Qr=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s){var l=this.length;this.resize(l+1);var c=6l,u=12l;return this.int16[c+0]=t,this.int16[c+1]=e,this.int16[c+2]=r,this.int16[c+3]=n,this.uint8[u+8]=i,this.uint8[u+9]=a,this.uint8[u+10]=o,this.uint8[u+11]=s,l},e}(Yr);Qr.prototype.bytesPerElement=12,pr(\"StructArrayLayout4i4ub12\",Qr);var tn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s){var l=this.length;this.resize(l+1);var c=8l;return this.int16[c+0]=t,this.int16[c+1]=e,this.int16[c+2]=r,this.int16[c+3]=n,this.uint16[c+4]=i,this.uint16[c+5]=a,this.uint16[c+6]=o,this.uint16[c+7]=s,l},e}(Yr);tn.prototype.bytesPerElement=16,pr(\"StructArrayLayout4i4ui16\",tn);var en=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3n;return this.float32[i+0]=t,this.float32[i+1]=e,this.float32[i+2]=r,n},e}(Yr);en.prototype.bytesPerElement=12,pr(\"StructArrayLayout3f12\",en);var rn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;this.resize(e+1);var r=1e;return this.uint32[r+0]=t,e},e}(Yr);rn.prototype.bytesPerElement=4,pr(\"StructArrayLayout1ul4\",rn);var nn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.length;this.resize(f+1);var h=12f,p=6f;return this.int16[h+0]=t,this.int16[h+1]=e,this.int16[h+2]=r,this.int16[h+3]=n,this.int16[h+4]=i,this.int16[h+5]=a,this.uint32[p+3]=o,this.uint16[h+8]=s,this.uint16[h+9]=l,this.int16[h+10]=c,this.int16[h+11]=u,f},e}(Yr);nn.prototype.bytesPerElement=24,pr(\"StructArrayLayout6i1ul2ui2i24\",nn);var an=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;this.resize(o+1);var s=6o;return this.int16[s+0]=t,this.int16[s+1]=e,this.int16[s+2]=r,this.int16[s+3]=n,this.int16[s+4]=i,this.int16[s+5]=a,o},e}(Yr);an.prototype.bytesPerElement=12,pr(\"StructArrayLayout2i2i2i12\",an);var on=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=4r;return this.uint8[n+0]=t,this.uint8[n+1]=e,r},e}(Yr);on.prototype.bytesPerElement=4,pr(\"StructArrayLayout2ub4\",on);var sn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p){var d=this.length;this.resize(d+1);var g=20d,v=10d,m=40d;return this.int16[g+0]=t,this.int16[g+1]=e,this.uint16[g+2]=r,this.uint16[g+3]=n,this.uint32[v+2]=i,this.uint32[v+3]=a,this.uint32[v+4]=o,this.uint16[g+10]=s,this.uint16[g+11]=l,this.uint16[g+12]=c,this.float32[v+7]=u,this.float32[v+8]=f,this.uint8[m+36]=h,this.uint8[m+37]=p,d},e}(Yr);sn.prototype.bytesPerElement=40,pr(\"StructArrayLayout2i2ui3ul3ui2f2ub40\",sn);var ln=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;this.resize(e+1);var r=1e;return this.float32[r+0]=t,e},e}(Yr);ln.prototype.bytesPerElement=4,pr(\"StructArrayLayout1f4\",ln);var cn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3n;return this.int16[i+0]=t,this.int16[i+1]=e,this.int16[i+2]=r,n},e}(Yr);cn.prototype.bytesPerElement=6,pr(\"StructArrayLayout3i6\",cn);var un=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=2n,a=4n;return this.uint32[i+0]=t,this.uint16[a+2]=e,this.uint16[a+3]=r,n},e}(Yr);un.prototype.bytesPerElement=8,pr(\"StructArrayLayout1ul2ui8\",un);var fn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;this.resize(n+1);var i=3n;return this.uint16[i+0]=t,this.uint16[i+1]=e,this.uint16[i+2]=r,n},e}(Yr);fn.prototype.bytesPerElement=6,pr(\"StructArrayLayout3ui6\",fn);var hn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=2r;return this.uint16[n+0]=t,this.uint16[n+1]=e,r},e}(Yr);hn.prototype.bytesPerElement=4,pr(\"StructArrayLayout2ui4\",hn);var pn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;this.resize(r+1);var n=2r;return this.float32[n+0]=t,this.float32[n+1]=e,r},e}(Yr);pn.prototype.bytesPerElement=8,pr(\"StructArrayLayout2f8\",pn);var dn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;this.resize(i+1);var a=4i;return this.float32[a+0]=t,this.float32[a+1]=e,this.float32[a+2]=r,this.float32[a+3]=n,i},e}(Yr);dn.prototype.bytesPerElement=16,pr(\"StructArrayLayout4f16\",dn);var gn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},radius:{configurable:!0},signedDistanceFromAnchor:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointX.set=function(t){this._structArray.int16[this._pos2+0]=t},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.anchorPointY.set=function(t){this._structArray.int16[this._pos2+1]=t},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.x1.set=function(t){this._structArray.int16[this._pos2+2]=t},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.y1.set=function(t){this._structArray.int16[this._pos2+3]=t},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.x2.set=function(t){this._structArray.int16[this._pos2+4]=t},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.y2.set=function(t){this._structArray.int16[this._pos2+5]=t},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.featureIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.sourceLayerIndex.set=function(t){this._structArray.uint16[this._pos2+8]=t},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.bucketIndex.set=function(t){this._structArray.uint16[this._pos2+9]=t},r.radius.get=function(){return this._structArray.int16[this._pos2+10]},r.radius.set=function(t){this._structArray.int16[this._pos2+10]=t},r.signedDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+11]},r.signedDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+11]=t},r.anchorPoint.get=function(){return new l(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Wr);gn.prototype.size=24;var vn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new gn(this,t)},e}(nn);pr(\"CollisionBoxArray\",vn);var mn=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},hidden:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorX.set=function(t){this._structArray.int16[this._pos2+0]=t},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.anchorY.set=function(t){this._structArray.int16[this._pos2+1]=t},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.glyphStartIndex.set=function(t){this._structArray.uint16[this._pos2+2]=t},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.numGlyphs.set=function(t){this._structArray.uint16[this._pos2+3]=t},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.vertexStartIndex.set=function(t){this._structArray.uint32[this._pos4+2]=t},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineStartIndex.set=function(t){this._structArray.uint32[this._pos4+3]=t},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.lineLength.set=function(t){this._structArray.uint32[this._pos4+4]=t},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.segment.set=function(t){this._structArray.uint16[this._pos2+10]=t},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.lowerSize.set=function(t){this._structArray.uint16[this._pos2+11]=t},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.upperSize.set=function(t){this._structArray.uint16[this._pos2+12]=t},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetX.set=function(t){this._structArray.float32[this._pos4+7]=t},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.lineOffsetY.set=function(t){this._structArray.float32[this._pos4+8]=t},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.writingMode.set=function(t){this._structArray.uint8[this._pos1+36]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+37]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+37]=t},Object.defineProperties(e.prototype,r),e}(Wr);mn.prototype.size=40;var yn=function(t){function e(){t.apply(this,arguments)}return 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e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={x:{configurable:!0},y:{configurable:!0},tileUnitDistanceFromAnchor:{configurable:!0}};return r.x.get=function(){return this._structArray.int16[this._pos2+0]},r.x.set=function(t){this._structArray.int16[this._pos2+0]=t},r.y.get=function(){return this._structArray.int16[this._pos2+1]},r.y.set=function(t){this._structArray.int16[this._pos2+1]=t},r.tileUnitDistanceFromAnchor.get=function(){return this._structArray.int16[this._pos2+2]},r.tileUnitDistanceFromAnchor.set=function(t){this._structArray.int16[this._pos2+2]=t},Object.defineProperties(e.prototype,r),e}(Wr);_n.prototype.size=6;var wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3t+0]},e.prototype.gety=function(t){return 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Nn=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map(function(t){return t.id}),this.index=t.index,this.layoutVertexArray=new $r,this.indexArray=new fn,this.segments=new Tn,this.programConfigurations=new In(An,t.layers,t.zoom)};function jn(t,e,r){for(var n=0;n<t.length;n++){var i=t[n];if(Zn(i,e))return!0;if(Wn(e,i,r))return!0}return!1}function Vn(t,e){if(1===t.length&&1===t[0].length)return Xn(e,t[0][0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Xn(t,n[i]))return!0;for(var a=0;a<t.length;a++){for(var o=t[a],s=0;s<o.length;s++)if(Xn(e,o[s]))return!0;for(var l=0;l<e.length;l++)if(Hn(o,e[l]))return!0}return!1}function Un(t,e,r){for(var n=0;n<e.length;n++)for(var i=e[n],a=0;a<t.length;a++){var o=t[a];if(o.length>=3)for(var s=0;s<i.length;s++)if(Zn(o,i[s]))return!0;if(qn(o,i,r))return!0}return!1}function qn(t,e,r){if(t.length>1){if(Hn(t,e))return!0;for(var n=0;n<e.length;n++)if(Wn(e[n],t,r))return!0}for(var 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h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var fa=ha;function ha(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(pa,this,e),this.length=this._features.length}function pa(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function da(t,e,r){if(3===t){var n=new fa(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}ha.prototype.feature=function(t){if(t<0\|\|t>=this._features.length)throw new Error(\"feature index out of 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i=n[r],a=i.feature,o=i.index,s=i.sourceLayerIndex;if(this.layers[0]._featureFilter(new Lr(this.zoom),a)){var l=Bn(a);this.addFeature(a,l),e.featureIndex.insert(a,l,o,s,this.index)}}},wa.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},wa.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,oa),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.programConfigurations.upload(t)},wa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},wa.prototype.addFeature=function(t,e){for(var r=this.layers[0].layout,n=r.get(\"line-join\").evaluate(t),i=r.get(\"line-cap\"),a=r.get(\"line-miter-limit\"),o=r.get(\"line-round-limit\"),s=0,l=e;s<l.length;s+=1){var c=l[s];this.addLine(c,t,n,i,a,o)}},wa.prototype.addLine=function(t,e,r,n,i,a){var o=null;e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")&&(o={start:e.properties.mapbox_clip_start,end:e.properties.mapbox_clip_end,tileTotal:void 0});for(var s=\"Polygon\"===va[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){o&&(o.tileTotal=function(t,e,r){for(var n,i,a=0,o=c;o<r-1;o++)n=t[o],i=t[o+1],a+=n.dist(i);return a}(t,0,l)),\"bevel\"===r&&(i=1.05);var u=Dn/(512this.overscaling)15,f=t[c],h=this.segments.prepareSegment(10l,this.layoutVertexArray,this.indexArray);this.distance=0;var p,d,g,v=n,m=s?\"butt\":n,y=!0,x=void 0,b=void 0,_=void 0,w=void 0;this.e1=this.e2=this.e3=-1,s&&(p=t[l-2],w=f.sub(p)._unit()._perp());for(var k=c;k<l;k++)if(!(b=s&&k===l-1?t[c+1]:t[k+1])\|\|!t[k].equals(b)){w&&(_=w),p&&(x=p),p=t[k],w=b?b.sub(p)._unit()._perp():_;var M=(_=_\|\|w).add(w);0===M.x&&0===M.y\|\|M._unit();var A=M.xw.x+M.yw.y,T=0!==A?1/A:1/0,S=A<ya&&x&&b;if(S&&k>c){var E=p.dist(x);if(E>2u){var C=p.sub(p.sub(x)._mult(u/E)._round());this.distance+=C.dist(x),this.addCurrentVertex(C,this.distance,_.mult(1),0,0,!1,h,o),x=C}}var L=x&&b,z=L?r:b?v:m;if(L&&\"round\"===z&&(T<a?z=\"miter\":T<=2&&(z=\"fakeround\")),\"miter\"===z&&T>i&&(z=\"bevel\"),\"bevel\"===z&&(T>2&&(z=\"flipbevel\"),T<i&&(z=\"miter\")),x&&(this.distance+=p.dist(x)),\"miter\"===z)M._mult(T),this.addCurrentVertex(p,this.distance,M,0,0,!1,h,o);else if(\"flipbevel\"===z){if(T>100)M=w.clone().mult(-1);else{var O=_.xw.y-_.yw.x>0?-1:1,I=T_.add(w).mag()/_.sub(w).mag();M._perp()._mult(IO)}this.addCurrentVertex(p,this.distance,M,0,0,!1,h,o),this.addCurrentVertex(p,this.distance,M.mult(-1),0,0,!1,h,o)}else if(\"bevel\"===z\|\|\"fakeround\"===z){var P=_.xw.y-_.yw.x>0,D=-Math.sqrt(TT-1);if(P?(g=0,d=D):(d=0,g=D),y\|\|this.addCurrentVertex(p,this.distance,_,d,g,!1,h,o),\"fakeround\"===z){for(var R=Math.floor(8(.5-(A-.5))),B=void 0,F=0;F<R;F++)B=w.mult((F+1)/(R+1))._add(_)._unit(),this.addPieSliceVertex(p,this.distance,B,P,h,o);this.addPieSliceVertex(p,this.distance,M,P,h,o);for(var N=R-1;N>=0;N--)B=_.mult((N+1)/(R+1))._add(w)._unit(),this.addPieSliceVertex(p,this.distance,B,P,h,o)}b&&this.addCurrentVertex(p,this.distance,w,-d,-g,!1,h,o)}else\"butt\"===z?(y\|\|this.addCurrentVertex(p,this.distance,_,0,0,!1,h,o),b&&this.addCurrentVertex(p,this.distance,w,0,0,!1,h,o)):\"square\"===z?(y\|\|(this.addCurrentVertex(p,this.distance,_,1,1,!1,h,o),this.e1=this.e2=-1),b&&this.addCurrentVertex(p,this.distance,w,-1,-1,!1,h,o)):\"round\"===z&&(y\|\|(this.addCurrentVertex(p,this.distance,_,0,0,!1,h,o),this.addCurrentVertex(p,this.distance,_,1,1,!0,h,o),this.e1=this.e2=-1),b&&(this.addCurrentVertex(p,this.distance,w,-1,-1,!0,h,o),this.addCurrentVertex(p,this.distance,w,0,0,!1,h,o)));if(S&&k<l-1){var j=p.dist(b);if(j>2u){var V=p.add(b.sub(p)._mult(u/j)._round());this.distance+=V.dist(p),this.addCurrentVertex(V,this.distance,w.mult(1),0,0,!1,h,o),p=V}}y=!1}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,e)}},wa.prototype.addCurrentVertex=function(t,e,r,n,i,a,o,s){var l,c=this.layoutVertexArray,u=this.indexArray;s&&(e=ka(e,s)),l=r.clone(),n&&l._sub(r.perp()._mult(n)),_a(c,t,l,a,!1,n,e),this.e3=o.vertexLength++,this.e1>=0&&this.e2>=0&&(u.emplaceBack(this.e1,this.e2,this.e3),o.primitiveLength++),this.e1=this.e2,this.e2=this.e3,l=r.mult(-1),i&&l._sub(r.perp()._mult(i)),_a(c,t,l,a,!0,-i,e),this.e3=o.vertexLength++,this.e1>=0&&this.e2>=0&&(u.emplaceBack(this.e1,this.e2,this.e3),o.primitiveLength++),this.e1=this.e2,this.e2=this.e3,e>ba/2&&!s&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,a,o))},wa.prototype.addPieSliceVertex=function(t,e,r,n,i,a){r=r.mult(n?-1:1);var o=this.layoutVertexArray,s=this.indexArray;a&&(e=ka(e,a)),_a(o,t,r,!1,n,0,e),this.e3=i.vertexLength++,this.e1>=0&&this.e2>=0&&(s.emplaceBack(this.e1,this.e2,this.e3),i.primitiveLength++),n?this.e2=this.e3:this.e1=this.e3},pr(\"LineBucket\",wa,{omit:[\"layers\"]});var Ma=new qr({\"line-cap\":new Nr(I.layout_line[\"line-cap\"]),\"line-join\":new jr(I.layout_line[\"line-join\"]),\"line-miter-limit\":new Nr(I.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Nr(I.layout_line[\"line-round-limit\"])}),Aa={paint:new qr({\"line-opacity\":new jr(I.paint_line[\"line-opacity\"]),\"line-color\":new jr(I.paint_line[\"line-color\"]),\"line-translate\":new Nr(I.paint_line[\"line-translate\"]),\"line-translate-anchor\":new Nr(I.paint_line[\"line-translate-anchor\"]),\"line-width\":new jr(I.paint_line[\"line-width\"]),\"line-gap-width\":new jr(I.paint_line[\"line-gap-width\"]),\"line-offset\":new jr(I.paint_line[\"line-offset\"]),\"line-blur\":new jr(I.paint_line[\"line-blur\"]),\"line-dasharray\":new Vr(I.paint_line[\"line-dasharray\"]),\"line-pattern\":new Vr(I.paint_line[\"line-pattern\"]),\"line-gradient\":new Ur(I.paint_line[\"line-gradient\"])}),layout:Ma},Ta=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new Lr(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n){return r=p({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n)},e}(jr))(Aa.paint.properties[\"line-width\"].specification);Ta.useIntegerZoom=!0;var Sa=function(t){function e(e){t.call(this,e,Aa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setPaintProperty=function(e,r,n){t.prototype.setPaintProperty.call(this,e,r,n),\"line-gradient\"===e&&this._updateGradient()},e.prototype._updateGradient=function(){var t=this._transitionablePaint._values[\"line-gradient\"].value.expression;this.gradient=hi(t,\"lineProgress\"),this.gradientTexture=null},e.prototype.recalculate=function(e){t.prototype.recalculate.call(this,e),this.paint._values[\"line-floorwidth\"]=Ta.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,e)},e.prototype.createBucket=function(t){return new wa(t)},e.prototype.queryRadius=function(t){var e=t,r=Ea($n(\"line-width\",this,e),$n(\"line-gap-width\",this,e)),n=$n(\"line-offset\",this,e);return r/2+Math.abs(n)+Jn(this.paint.get(\"line-translate\"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,a){var o=Kn(t,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),i.angle,a),s=a/2Ea(this.paint.get(\"line-width\").evaluate(e),this.paint.get(\"line-gap-width\").evaluate(e)),c=this.paint.get(\"line-offset\").evaluate(e);return c&&(r=function(t,e){for(var r=[],n=new l(0,0),i=0;i<t.length;i++){for(var a=t[i],o=[],s=0;s<a.length;s++){var c=a[s-1],u=a[s],f=a[s+1],h=0===s?n:u.sub(c)._unit()._perp(),p=s===a.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),g=d.xp.x+d.yp.y;d._mult(1/g),o.push(d._mult(e)._add(u))}r.push(o)}return r}(r,ca)),Un(o,r,s)},e}(Hr);function Ea(t,e){return e>0?e+2t:t}var Ca=Xr([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"}]),La=Xr([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4),za=(Xr([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4),Xr([{name:\"a_placed\",components:2,type:\"Uint8\"}],4)),Oa=(Xr([{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"},{type:\"Int16\",name:\"radius\"},{type:\"Int16\",name:\"signedDistanceFromAnchor\"}]),Xr([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4)),Ia=Xr([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4);function Pa(t,e,r){var n=e.layout.get(\"text-transform\").evaluate(r);return\"uppercase\"===n?t=t.toLocaleUpperCase():\"lowercase\"===n&&(t=t.toLocaleLowerCase()),Cr.applyArabicShaping&&(t=Cr.applyArabicShaping(t)),t}Xr([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Uint16\",name:\"glyphStartIndex\"},{type:\"Uint16\",name:\"numGlyphs\"},{type:\"Uint32\",name:\"vertexStartIndex\"},{type:\"Uint32\",name:\"lineStartIndex\"},{type:\"Uint32\",name:\"lineLength\"},{type:\"Uint16\",name:\"segment\"},{type:\"Uint16\",name:\"lowerSize\"},{type:\"Uint16\",name:\"upperSize\"},{type:\"Float32\",name:\"lineOffsetX\"},{type:\"Float32\",name:\"lineOffsetY\"},{type:\"Uint8\",name:\"writingMode\"},{type:\"Uint8\",name:\"hidden\"}]),Xr([{type:\"Float32\",name:\"offsetX\"}]),Xr([{type:\"Int16\",name:\"x\"},{type:\"Int16\",name:\"y\"},{type:\"Int16\",name:\"tileUnitDistanceFromAnchor\"}]);var Da={\"!\":\"\\ufe15\",\"#\":\"\\uff03\",$:\"\\uff04\",\"%\":\"\\uff05\",\"&\":\"\\uff06\",\"(\":\"\\ufe35\",\")\":\"\\ufe36\",\"\":\"\\uff0a\",\"+\":\"\\uff0b\",\",\":\"\\ufe10\",\"-\":\"\\ufe32\",\".\":\"\\u30fb\",\"/\":\"\\uff0f\",\":\":\"\\ufe13\",\";\":\"\\ufe14\",\"<\":\"\\ufe3f\",\"=\":\"\\uff1d\",\">\":\"\\ufe40\",\"?\":\"\\ufe16\",\"@\":\"\\uff20\",\"[\":\"\\ufe47\",\"\\\\\":\"\\uff3c\",\"]\":\"\\ufe48\",\"^\":\"\\uff3e\",_:\"\\ufe33\",\"`\":\"\\uff40\",\"{\":\"\\ufe37\",\"\|\":\"\\u2015\",\"}\":\"\\ufe38\",\"~\":\"\\uff5e\",\"\\xa2\":\"\\uffe0\",\"\\xa3\":\"\\uffe1\",\"\\xa5\":\"\\uffe5\",\"\\xa6\":\"\\uffe4\",\"\\xac\":\"\\uffe2\",\"\\xaf\":\"\\uffe3\",\"\\u2013\":\"\\ufe32\",\"\\u2014\":\"\\ufe31\",\"\\u2018\":\"\\ufe43\",\"\\u2019\":\"\\ufe44\",\"\\u201c\":\"\\ufe41\",\"\\u201d\":\"\\ufe42\",\"\\u2026\":\"\\ufe19\",\"\\u2027\":\"\\u30fb\",\"\\u20a9\":\"\\uffe6\",\"\\u3001\":\"\\ufe11\",\"\\u3002\":\"\\ufe12\",\"\\u3008\":\"\\ufe3f\",\"\\u3009\":\"\\ufe40\",\"\\u300a\":\"\\ufe3d\",\"\\u300b\":\"\\ufe3e\",\"\\u300c\":\"\\ufe41\",\"\\u300d\":\"\\ufe42\",\"\\u300e\":\"\\ufe43\",\"\\u300f\":\"\\ufe44\",\"\\u3010\":\"\\ufe3b\",\"\\u3011\":\"\\ufe3c\",\"\\u3014\":\"\\ufe39\",\"\\u3015\":\"\\ufe3a\",\"\\u3016\":\"\\ufe17\",\"\\u3017\":\"\\ufe18\",\"\\uff01\":\"\\ufe15\",\"\\uff08\":\"\\ufe35\",\"\\uff09\":\"\\ufe36\",\"\\uff0c\":\"\\ufe10\",\"\\uff0d\":\"\\ufe32\",\"\\uff0e\":\"\\u30fb\",\"\\uff1a\":\"\\ufe13\",\"\\uff1b\":\"\\ufe14\",\"\\uff1c\":\"\\ufe3f\",\"\\uff1e\":\"\\ufe40\",\"\\uff1f\":\"\\ufe16\",\"\\uff3b\":\"\\ufe47\",\"\\uff3d\":\"\\ufe48\",\"\\uff3f\":\"\\ufe33\",\"\\uff5b\":\"\\ufe37\",\"\\uff5c\":\"\\u2015\",\"\\uff5d\":\"\\ufe38\",\"\\uff5f\":\"\\ufe35\",\"\\uff60\":\"\\ufe36\",\"\\uff61\":\"\\ufe12\",\"\\uff62\":\"\\ufe41\",\"\\uff63\":\"\\ufe42\"},Ra=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(l);function Ba(t,e){var r=e.expression;if(\"constant\"===r.kind)return{functionType:\"constant\",layoutSize:r.evaluate(new Lr(t+1))};if(\"source\"===r.kind)return{functionType:\"source\"};for(var n=r.zoomStops,i=0;i<n.length&&n[i]<=t;)i++;for(var a=i=Math.max(0,i-1);a<n.length&&n[a]<t+1;)a++;a=Math.min(n.length-1,a);var o={min:n[i],max:n[a]};return\"composite\"===r.kind?{functionType:\"composite\",zoomRange:o,propertyValue:e.value}:{functionType:\"camera\",layoutSize:r.evaluate(new Lr(t+1)),zoomRange:o,sizeRange:{min:r.evaluate(new Lr(o.min)),max:r.evaluate(new Lr(o.max))},propertyValue:e.value}}pr(\"Anchor\",Ra);var Fa=ga.VectorTileFeature.types,Na=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function ja(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,Math.round(32n),Math.round(32i),a,o,s?s[0]:0,s?s[1]:0)}function Va(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}var Ua=function(t){this.layoutVertexArray=new tn,this.indexArray=new fn,this.programConfigurations=t,this.segments=new Tn,this.dynamicLayoutVertexArray=new en,this.opacityVertexArray=new rn,this.placedSymbolArray=new yn};Ua.prototype.upload=function(t,e){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Ca.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.programConfigurations.upload(t),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,La.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,Na,!0),this.opacityVertexBuffer.itemSize=1},Ua.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},pr(\"SymbolBuffers\",Ua);var qa=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Tn,this.collisionVertexArray=new on};qa.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,za.members,!0)},qa.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},pr(\"CollisionBuffers\",qa);var Ha=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map(function(t){return t.id}),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex;var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Ba(this.zoom,e[\"text-size\"]),this.iconSizeData=Ba(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout;this.sortFeaturesByY=r.get(\"text-allow-overlap\")\|\|r.get(\"icon-allow-overlap\")\|\|r.get(\"text-ignore-placement\")\|\|r.get(\"icon-ignore-placement\")};Ha.prototype.createArrays=function(){this.text=new Ua(new In(Ca.members,this.layers,this.zoom,function(t){return/^text/.test(t)})),this.icon=new Ua(new In(Ca.members,this.layers,this.zoom,function(t){return/^icon/.test(t)})),this.collisionBox=new qa(an,Oa.members,hn),this.collisionCircle=new qa(an,Ia.members,fn),this.glyphOffsetArray=new bn,this.lineVertexArray=new wn},Ha.prototype.populate=function(t,e){var r=this.layers[0],n=r.layout,i=n.get(\"text-font\"),a=n.get(\"text-field\"),o=n.get(\"icon-image\"),s=(\"constant\"!==a.value.kind\|\|a.value.value.length>0)&&(\"constant\"!==i.value.kind\|\|i.value.value.length>0),l=\"constant\"!==o.value.kind\|\|o.value.value&&o.value.value.length>0;if(this.features=[],s\|\|l){for(var c=e.iconDependencies,u=e.glyphDependencies,f=new Lr(this.zoom),h=0,p=t;h<p.length;h+=1){var d=p[h],g=d.feature,v=d.index,m=d.sourceLayerIndex;if(r._featureFilter(f,g)){var y=void 0;s&&(y=Pa(y=r.getValueAndResolveTokens(\"text-field\",g),r,g));var x=void 0;if(l&&(x=r.getValueAndResolveTokens(\"icon-image\",g)),y\|\|x){var b={text:y,icon:x,index:v,sourceLayerIndex:m,geometry:Bn(g),properties:g.properties,type:Fa[g.type]};if(void 0!==g.id&&(b.id=g.id),this.features.push(b),x&&(c[x]=!0),y)for(var _=i.evaluate(g).join(\",\"),w=u[_]=u[_]\|\|{},k=\"map\"===n.get(\"text-rotation-alignment\")&&\"line\"===n.get(\"symbol-placement\"),M=xr(y),A=0;A<y.length;A++)if(w[y.charCodeAt(A)]=!0,k&&M){var T=Da[y.charAt(A)];T&&(w[T.charCodeAt(0)]=!0)}}}}\"line\"===n.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var g=s(p,d,f),v=o(p,d,n[g].geometry);delete e[p],delete r[d],r[l(h,n[v].geometry,!0)]=v,n[g].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter(function(t){return t.geometry})}(this.features))}},Ha.prototype.isEmpty=function(){return 0===this.symbolInstances.length},Ha.prototype.upload=function(t){this.text.upload(t,this.sortFeaturesByY),this.icon.upload(t,this.sortFeaturesByY),this.collisionBox.upload(t),this.collisionCircle.upload(t)},Ha.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.collisionBox.destroy(),this.collisionCircle.destroy()},Ha.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment\|\|0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Ha.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c){for(var u=t.indexArray,f=t.layoutVertexArray,h=t.dynamicLayoutVertexArray,p=t.segments.prepareSegment(4e.length,t.layoutVertexArray,t.indexArray),d=this.glyphOffsetArray.length,g=p.vertexLength,v=0,m=e;v<m.length;v+=1){var y=m[v],x=y.tl,b=y.tr,_=y.bl,w=y.br,k=y.tex,M=p.vertexLength,A=y.glyphOffset[1];ja(f,s.x,s.y,x.x,A+x.y,k.x,k.y,r),ja(f,s.x,s.y,b.x,A+b.y,k.x+k.w,k.y,r),ja(f,s.x,s.y,_.x,A+_.y,k.x,k.y+k.h,r),ja(f,s.x,s.y,w.x,A+w.y,k.x+k.w,k.y+k.h,r),Va(h,s,0),u.emplaceBack(M,M+1,M+2),u.emplaceBack(M+1,M+2,M+3),p.vertexLength+=4,p.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(y.glyphOffset[0])}t.placedSymbolArray.emplaceBack(s.x,s.y,d,this.glyphOffsetArray.length-d,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,!1),t.programConfigurations.populatePaintArrays(t.layoutVertexArray.length,a)},Ha.prototype._addCollisionDebugVertex=function(t,e,r,n,i){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n.x,n.y,Math.round(i.x),Math.round(i.y))},Ha.prototype.addCollisionDebugVertices=function(t,e,r,n,i,a,o,s){var c=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=c.vertexLength,f=i.layoutVertexArray,h=i.collisionVertexArray;if(this._addCollisionDebugVertex(f,h,a,o.anchor,new l(t,e)),this._addCollisionDebugVertex(f,h,a,o.anchor,new l(r,e)),this._addCollisionDebugVertex(f,h,a,o.anchor,new l(r,n)),this._addCollisionDebugVertex(f,h,a,o.anchor,new l(t,n)),c.vertexLength+=4,s){var p=i.indexArray;p.emplaceBack(u,u+1,u+2),p.emplaceBack(u,u+2,u+3),c.primitiveLength+=2}else{var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),c.primitiveLength+=4}},Ha.prototype.generateCollisionDebugBuffers=function(){for(var t=0,e=this.symbolInstances;t<e.length;t+=1){var r=e[t];r.textCollisionFeature={boxStartIndex:r.textBoxStartIndex,boxEndIndex:r.textBoxEndIndex},r.iconCollisionFeature={boxStartIndex:r.iconBoxStartIndex,boxEndIndex:r.iconBoxEndIndex};for(var n=0;n<2;n++){var i=r[0===n?\"textCollisionFeature\":\"iconCollisionFeature\"];if(i)for(var a=i.boxStartIndex;a<i.boxEndIndex;a++){var o=this.collisionBoxArray.get(a),s=o.x1,l=o.y1,c=o.x2,u=o.y2,f=o.radius>0;this.addCollisionDebugVertices(s,l,c,u,f?this.collisionCircle:this.collisionBox,o.anchorPoint,r,f)}}}},Ha.prototype.deserializeCollisionBoxes=function(t,e,r,n,i){for(var a={},o=e;o<r;o++){var s=t.get(o);if(0===s.radius){a.textBox={x1:s.x1,y1:s.y1,x2:s.x2,y2:s.y2,anchorPointX:s.anchorPointX,anchorPointY:s.anchorPointY},a.textFeatureIndex=s.featureIndex;break}a.textCircles\|\|(a.textCircles=[],a.textFeatureIndex=s.featureIndex),a.textCircles.push(s.anchorPointX,s.anchorPointY,s.radius,s.signedDistanceFromAnchor,1)}for(var l=n;l<i;l++){var c=t.get(l);if(0===c.radius){a.iconBox={x1:c.x1,y1:c.y1,x2:c.x2,y2:c.y2,anchorPointX:c.anchorPointX,anchorPointY:c.anchorPointY},a.iconFeatureIndex=c.featureIndex;break}}return a},Ha.prototype.hasTextData=function(){return this.text.segments.get().length>0},Ha.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Ha.prototype.hasCollisionBoxData=function(){return this.collisionBox.segments.get().length>0},Ha.prototype.hasCollisionCircleData=function(){return this.collisionCircle.segments.get().length>0},Ha.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&(this.sortedAngle=t,!(this.text.segments.get().length>1\|\|this.icon.segments.get().length>1))){for(var r=[],n=0;n<this.symbolInstances.length;n++)r.push(n);var i=Math.sin(t),a=Math.cos(t);r.sort(function(t,r){var n=e.symbolInstances[t],o=e.symbolInstances[r];return(in.anchor.x+an.anchor.y\|0)-(io.anchor.x+ao.anchor.y\|0)\|\|o.featureIndex-n.featureIndex}),this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var o=0,s=r;o<s.length;o+=1){var l=s[o],c=e.symbolInstances[l];e.featureSortOrder.push(c.featureIndex);for(var u=0,f=c.placedTextSymbolIndices;u<f.length;u+=1)for(var h=f[u],p=e.text.placedSymbolArray.get(h),d=p.vertexStartIndex+4p.numGlyphs,g=p.vertexStartIndex;g<d;g+=4)e.text.indexArray.emplaceBack(g,g+1,g+2),e.text.indexArray.emplaceBack(g+1,g+2,g+3);var v=e.icon.placedSymbolArray.get(l);if(v.numGlyphs){var m=v.vertexStartIndex;e.icon.indexArray.emplaceBack(m,m+1,m+2),e.icon.indexArray.emplaceBack(m+1,m+2,m+3)}}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},pr(\"SymbolBucket\",Ha,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"],shallow:[\"symbolInstances\"]}),Ha.MAX_GLYPHS=65535,Ha.addDynamicAttributes=Va;var Ga=new qr({\"symbol-placement\":new Nr(I.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Nr(I.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Nr(I.layout_symbol[\"symbol-avoid-edges\"]),\"icon-allow-overlap\":new Nr(I.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new Nr(I.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Nr(I.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Nr(I.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new jr(I.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Nr(I.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Nr(I.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new jr(I.layout_symbol[\"icon-image\"]),\"icon-rotate\":new jr(I.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new Nr(I.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Nr(I.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new jr(I.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new jr(I.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Nr(I.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Nr(I.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Nr(I.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new jr(I.layout_symbol[\"text-field\"]),\"text-font\":new jr(I.layout_symbol[\"text-font\"]),\"text-size\":new jr(I.layout_symbol[\"text-size\"]),\"text-max-width\":new jr(I.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Nr(I.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new jr(I.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new jr(I.layout_symbol[\"text-justify\"]),\"text-anchor\":new jr(I.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Nr(I.layout_symbol[\"text-max-angle\"]),\"text-rotate\":new jr(I.layout_symbol[\"text-rotate\"]),\"text-padding\":new Nr(I.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Nr(I.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new jr(I.layout_symbol[\"text-transform\"]),\"text-offset\":new jr(I.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Nr(I.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new Nr(I.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Nr(I.layout_symbol[\"text-optional\"])}),Wa={paint:new qr({\"icon-opacity\":new jr(I.paint_symbol[\"icon-opacity\"]),\"icon-color\":new jr(I.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new jr(I.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new jr(I.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new jr(I.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Nr(I.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Nr(I.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new jr(I.paint_symbol[\"text-opacity\"]),\"text-color\":new jr(I.paint_symbol[\"text-color\"]),\"text-halo-color\":new jr(I.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new jr(I.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new jr(I.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Nr(I.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Nr(I.paint_symbol[\"text-translate-anchor\"])}),layout:Ga},Ya=function(t){function e(e){t.call(this,e,Wa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e){t.prototype.recalculate.call(this,e),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"line\"===this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"line\"===this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\"))},e.prototype.getValueAndResolveTokens=function(t,e){var r,n=this.layout.get(t).evaluate(e),i=this._unevaluatedLayout._values[t];return i.isDataDriven()\|\|_e(i.value)?n:(r=e.properties,n.replace(/{([^{}]+)}/g,function(t,e){return e in r?String(r[e]):\"\"}))},e.prototype.createBucket=function(t){return new Ha(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e}(Hr),Xa={paint:new qr({\"background-color\":new Nr(I.paint_background[\"background-color\"]),\"background-pattern\":new Vr(I.paint_background[\"background-pattern\"]),\"background-opacity\":new Nr(I.paint_background[\"background-opacity\"])})},Za=function(t){function e(e){t.call(this,e,Xa)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Hr),$a={paint:new qr({\"raster-opacity\":new Nr(I.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new Nr(I.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new Nr(I.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new Nr(I.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new Nr(I.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new Nr(I.paint_raster[\"raster-contrast\"]),\"raster-fade-duration\":new Nr(I.paint_raster[\"raster-fade-duration\"])})},Ja={circle:ni,heatmap:pi,hillshade:gi,fill:Ji,\"fill-extrusion\":aa,line:Sa,symbol:Ya,background:Za,raster:function(t){function e(e){t.call(this,e,$a)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Hr)},Ka=i(function(t,e){t.exports=function(){function t(t,e,r){r=r\|\|{},this.w=t\|\|64,this.h=e\|\|64,this.autoResize=!!r.autoResize,this.shelves=[],this.freebins=[],this.stats={},this.bins={},this.maxId=0}function e(t,e,r){this.x=0,this.y=t,this.w=this.free=e,this.h=r}return t.prototype.pack=function(t,e){t=[].concat(t),e=e\|\|{};for(var r,n,i,a,o=[],s=0;s<t.length;s++)if(r=t[s].w\|\|t[s].width,n=t[s].h\|\|t[s].height,i=t[s].id,r&&n){if(!(a=this.packOne(r,n,i)))continue;e.inPlace&&(t[s].x=a.x,t[s].y=a.y,t[s].id=a.id),o.push(a)}return this.shrink(),o},t.prototype.packOne=function(t,r,n){var i,a,o,s,l,c,u,f,h={freebin:-1,shelf:-1,waste:1/0},p=0;if(\"string\"==typeof n\|\|\"number\"==typeof n){if(i=this.getBin(n))return this.ref(i),i;\"number\"==typeof n&&(this.maxId=Math.max(n,this.maxId))}else n=++this.maxId;for(s=0;s<this.freebins.length;s++){if(r===(i=this.freebins[s]).maxh&&t===i.maxw)return this.allocFreebin(s,t,r,n);r>i.maxh\|\|t>i.maxw\|\|r<=i.maxh&&t<=i.maxw&&(o=i.maxwi.maxh-tr)<h.waste&&(h.waste=o,h.freebin=s)}for(s=0;s<this.shelves.length;s++)if(p+=(a=this.shelves[s]).h,!(t>a.free)){if(r===a.h)return this.allocShelf(s,t,r,n);r>a.h\|\|r<a.h&&(o=(a.h-r)t)<h.waste&&(h.freebin=-1,h.waste=o,h.shelf=s)}return-1!==h.freebin?this.allocFreebin(h.freebin,t,r,n):-1!==h.shelf?this.allocShelf(h.shelf,t,r,n):r<=this.h-p&&t<=this.w?(a=new e(p,this.w,r),this.allocShelf(this.shelves.push(a)-1,t,r,n)):this.autoResize?(l=c=this.h,((u=f=this.w)<=l\|\|t>u)&&(f=2Math.max(t,u)),(l<u\|\|r>l)&&(c=2Math.max(r,l)),this.resize(f,c),this.packOne(t,r,n)):null},t.prototype.allocFreebin=function(t,e,r,n){var i=this.freebins.splice(t,1)[0];return i.id=n,i.w=e,i.h=r,i.refcount=0,this.bins[n]=i,this.ref(i),i},t.prototype.allocShelf=function(t,e,r,n){var i=this.shelves[t].alloc(e,r,n);return this.bins[n]=i,this.ref(i),i},t.prototype.shrink=function(){if(this.shelves.length>0){for(var t=0,e=0,r=0;r<this.shelves.length;r++){var n=this.shelves[r];e+=n.h,t=Math.max(n.w-n.free,t)}this.resize(t,e)}},t.prototype.getBin=function(t){return this.bins[t]},t.prototype.ref=function(t){if(1==++t.refcount){var e=t.h;this.stats[e]=1+(0\|this.stats[e])}return t.refcount},t.prototype.unref=function(t){return 0===t.refcount?0:(0==--t.refcount&&(this.stats[t.h]--,delete this.bins[t.id],this.freebins.push(t)),t.refcount)},t.prototype.clear=function(){this.shelves=[],this.freebins=[],this.stats={},this.bins={},this.maxId=0},t.prototype.resize=function(t,e){this.w=t,this.h=e;for(var r=0;r<this.shelves.length;r++)this.shelves[r].resize(t);return!0},e.prototype.alloc=function(t,e,r){if(t>this.free\|\|e>this.h)return null;var n=this.x;return this.x+=t,this.free-=t,new function(t,e,r,n,i,a,o){this.id=t,this.x=e,this.y=r,this.w=n,this.h=i,this.maxw=a\|\|n,this.maxh=o\|\|i,this.refcount=0}(r,n,this.y,t,e,t,this.h)},e.prototype.resize=function(t){return this.free+=t-this.w,this.w=t,!0},t}()}),Qa=function(t,e){var r=e.pixelRatio;this.paddedRect=t,this.pixelRatio=r},to={tl:{configurable:!0},br:{configurable:!0},displaySize:{configurable:!0}};to.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},to.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},to.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(Qa.prototype,to);var eo=function(t){var e=new ui({width:0,height:0}),r={},n=new Ka(0,0,{autoResize:!0});for(var i in t){var a=t[i],o=n.packOne(a.data.width+2,a.data.height+2);e.resize({width:n.w,height:n.h}),ui.copy(a.data,e,{x:0,y:0},{x:o.x+1,y:o.y+1},a.data),r[i]=new Qa(o,a)}n.shrink(),e.resize({width:n.w,height:n.h}),this.image=e,this.positions=r};pr(\"ImagePosition\",Qa),pr(\"ImageAtlas\",eo);var ro=function(t,e,r,n,i){var 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u=a/2,f=Math.floor(i/u),h=1+.4Math.log(c)/Math.LN2,p=Math.floor(fh/2),d=-a/2,g=r,v=n+1,m=d,y=-i/2,x=y-i/4;do{if(--v<0){if(m>y)return;v=0;break}m-=e[v].dist(g),g=e[v]}while(m>x);for(var b=e[v].dist(e[v+1]),_=-p;_<f+p;_++){var w=_u,k=y+w;if(w<0&&(k+=w),w>i&&(k+=w-i),!(k<m)){for(;m+b<k;){if(m+=b,++v+1>=e.length)return;b=e[v].dist(e[v+1])}var M=k-m,A=e[v],T=e[v+1].sub(A)._unit()._mult(M)._add(A)._round(),S=Math.abs(k-d)<u?0:.8(k-d);t.emplaceBack(T.x,T.y,-a/2,-a/2,a/2,a/2,o,s,l,a/2,S)}}};var l=u,c=u;function u(t,e){if(!(this instanceof u))return new u(t,e);if(this.data=t\|\|[],this.length=this.data.length,this.compare=e\|\|f,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)}function f(t,e){return t<e?-1:t>e?1:0}function h(e,r,n){void 0===r&&(r=1),void 0===n&&(n=!1);for(var i=1/0,a=1/0,o=-1/0,s=-1/0,c=e[0],u=0;u<c.length;u++){var f=c[u];(!u\|\|f.x<i)&&(i=f.x),(!u\|\|f.y<a)&&(a=f.y),(!u\|\|f.x>o)&&(o=f.x),(!u\|\|f.y>s)&&(s=f.y)}var h=o-i,g=s-a,v=Math.min(h,g),m=v/2,y=new 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e,r,n}function ut(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return pt(pt({},t.properties),{cluster:!0,cluster_id:t.id,point_count:e,point_count_abbreviated:r})}function ft(t){return t/360+.5}function ht(t){var e=Math.sin(tMath.PI/180),r=.5-.25Math.log((1+e)/(1-e))/Math.PI;return r<0?0:r>1?1:r}function pt(t,e){for(var r in e)t[r]=e[r];return t}function dt(t){return t.x}function gt(t){return t.y}function vt(t,e,r,n,i,a){var o=i-r,s=a-n;if(0!==o\|\|0!==s){var l=((t-r)o+(e-n)s)/(oo+ss);l>1?(r=i,n=a):l>0&&(r+=ol,n+=sl)}return(o=t-r)o+(s=e-n)s}function mt(t,e,r,n){var i={id:t\|\|null,type:e,geometry:r,tags:n,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return function(t){var e=t.geometry,r=t.type;if(\"Point\"===r\|\|\"MultiPoint\"===r\|\|\"LineString\"===r)yt(t,e);else if(\"Polygon\"===r\|\|\"MultiLineString\"===r)for(var n=0;n<e.length;n++)yt(t,e[n]);else if(\"MultiPolygon\"===r)for(n=0;n<e.length;n++)for(var i=0;i<e[n].length;i++)yt(t,e[n][i])}(i),i}function yt(t,e){for(var r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function xt(t,e,r){if(e.geometry){var n=e.geometry.coordinates,i=e.geometry.type,a=Math.pow(r.tolerance/((1<<r.maxZoom)r.extent),2),o=[];if(\"Point\"===i)bt(n,o);else if(\"MultiPoint\"===i)for(var s=0;s<n.length;s++)bt(n[s],o);else if(\"LineString\"===i)_t(n,o,a,!1);else if(\"MultiLineString\"===i)if(r.lineMetrics)for(s=0;s<n.length;s++)return o=[],_t(n[s],o,a,!1),void t.push(mt(e.id,\"LineString\",o,e.properties));else wt(n,o,a,!1);else if(\"Polygon\"===i)wt(n,o,a,!0);else{if(\"MultiPolygon\"!==i){if(\"GeometryCollection\"===i){for(s=0;s<e.geometry.geometries.length;s++)xt(t,{id:e.id,geometry:e.geometry.geometries[s],properties:e.properties},r);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(s=0;s<n.length;s++){var l=[];wt(n[s],l,a,!0),o.push(l)}}t.push(mt(e.id,i,o,e.properties))}}function bt(t,e){e.push(kt(t[0])),e.push(Mt(t[1])),e.push(0)}function _t(t,e,r,n){for(var i,a,o=0,s=0;s<t.length;s++){var l=kt(t[s][0]),c=Mt(t[s][1]);e.push(l),e.push(c),e.push(0),s>0&&(o+=n?(ic-la)/2:Math.sqrt(Math.pow(l-i,2)+Math.pow(c-a,2))),i=l,a=c}var u=e.length-3;e[2]=1,function t(e,r,n,i){for(var a,o=i,s=e[r],l=e[r+1],c=e[n],u=e[n+1],f=r+3;f<n;f+=3){var h=vt(e[f],e[f+1],s,l,c,u);h>o&&(a=f,o=h)}o>i&&(a-r>3&&t(e,r,a,i),e[a+2]=o,n-a>3&&t(e,a,n,i))}(e,0,u,r),e[u+2]=1,e.size=Math.abs(o),e.start=0,e.end=e.size}function wt(t,e,r,n){for(var i=0;i<t.length;i++){var a=[];_t(t[i],a,r,n),e.push(a)}}function kt(t){return t/360+.5}function Mt(t){var e=Math.sin(tMath.PI/180),r=.5-.25Math.log((1+e)/(1-e))/Math.PI;return r<0?0:r>1?1:r}function At(t,e,r,n,i,a,o,s){if(n/=e,a>=(r/=e)&&o<=n)return t;if(a>n\|\|o<r)return null;for(var l=[],c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<=n)l.push(u);else if(!(p>n\|\|d<r)){var g=[];if(\"Point\"===h\|\|\"MultiPoint\"===h)Tt(f,g,r,n,i);else if(\"LineString\"===h)St(f,g,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)Ct(f,g,r,n,i,!1);else if(\"Polygon\"===h)Ct(f,g,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var v=0;v<f.length;v++){var m=[];Ct(f[v],m,r,n,i,!0),m.length&&g.push(m)}if(g.length){if(s.lineMetrics&&\"LineString\"===h){for(v=0;v<g.length;v++)l.push(mt(u.id,h,g[v],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h\|\|(1===g.length?(h=\"LineString\",g=g[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h\|\|(h=3===g.length?\"Point\":\"MultiPoint\"),l.push(mt(u.id,h,g,u.tags))}}}return l.length?l:null}function Tt(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function St(t,e,r,n,i,a,o){for(var s,l,c=Et(t),u=0===i?zt:Ot,f=t.start,h=0;h<t.length-3;h+=3){var p=t[h],d=t[h+1],g=t[h+2],v=t[h+3],m=t[h+4],y=0===i?p:d,x=0===i?v:m,b=!1;o&&(s=Math.sqrt(Math.pow(p-v,2)+Math.pow(d-m,2))),y<r?x>=r&&(l=u(c,p,d,v,m,r),o&&(c.start=f+sl)):y>n?x<=n&&(l=u(c,p,d,v,m,n),o&&(c.start=f+sl)):Lt(c,p,d,g),x<r&&y>=r&&(l=u(c,p,d,v,m,r),b=!0),x>n&&y<=n&&(l=u(c,p,d,v,m,n),b=!0),!a&&b&&(o&&(c.end=f+sl),e.push(c),c=Et(t)),o&&(f+=s)}var _=t.length-3;p=t[_],d=t[_+1],g=t[_+2],(y=0===i?p:d)>=r&&y<=n&&Lt(c,p,d,g),_=c.length-3,a&&_>=3&&(c[_]!==c[0]\|\|c[_+1]!==c[1])&&Lt(c,c[0],c[1],c[2]),c.length&&e.push(c)}function Et(t){var e=[];return e.size=t.size,e.start=t.start,e.end=t.end,e}function Ct(t,e,r,n,i,a){for(var o=0;o<t.length;o++)St(t[o],e,r,n,i,a,!1)}function Lt(t,e,r,n){t.push(e),t.push(r),t.push(n)}function zt(t,e,r,n,i,a){var o=(a-e)/(n-e);return t.push(a),t.push(r+(i-r)o),t.push(1),o}function Ot(t,e,r,n,i,a){var o=(a-r)/(i-r);return t.push(e+(n-e)o),t.push(a),t.push(1),o}function It(t,e){for(var r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o\|\|\"MultiPoint\"===o\|\|\"LineString\"===o)i=Pt(a.geometry,e);else if(\"MultiLineString\"===o\|\|\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(Pt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(Pt(a.geometry[s][c],e));i.push(l)}r.push(mt(a.id,o,i,a.tags))}return r}function Pt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function Dt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(Rt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(Rt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function Rt(t,e,r,n,i,a){return[Math.round(r(tn-i)),Math.round(r(en-a))]}function Bt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,Ft(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function Ft(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a\|\|\"MultiPoint\"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if(\"LineString\"===a)Nt(o,i,t,r,!1,!1);else if(\"MultiLineString\"===a\|\|\"Polygon\"===a)for(s=0;s<i.length;s++)Nt(o,i[s],t,r,\"Polygon\"===a,0===s);else if(\"MultiPolygon\"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)Nt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags\|\|null;if(\"LineString\"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a\|\|\"MultiPolygon\"===a?3:\"LineString\"===a\|\|\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function Nt(t,e,r,n,i,a){var o=nn;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n\|\|e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function jt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0\|\|e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)xt(r,t.features[n],e);else\"Feature\"===t.type?xt(r,t,e):xt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=At(t,1,-1-r,r,0,-1,2,e),a=At(t,1,1-r,2+r,0,-1,2,e);return(i\|\|a)&&(n=At(t,1,-r,1+r,0,-1,2,e)\|\|[],i&&(n=It(i,1).concat(n)),a&&(n=n.concat(It(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function Vt(t,e,r){return 32((1<<t)r+e)+t}function Ut(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new R(n.features),a=V(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength\|\|(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}ot.prototype={options:{minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,reduce:null,initial:function(){return{}},map:function(t){return t}},load:function(t){var e=this.options.log;e&&console.time(\"total time\");var r=\"prepare \"+t.length+\" points\";e&&console.time(r),this.points=t;var n=t.map(lt);e&&console.timeEnd(r);for(var i=this.options.maxZoom;i>=this.options.minZoom;i--){var a=+Date.now();this.trees[i+1]=rt(n,dt,gt,this.options.nodeSize,Float32Array),n=this._cluster(n,i),e&&console.log(\"z%d: %d clusters in %dms\",i,n.length,+Date.now()-a)}return this.trees[this.options.minZoom]=rt(n,dt,gt,this.options.nodeSize,Float32Array),e&&console.timeEnd(\"total time\"),this},getClusters:function(t,e){for(var r=this.trees[this._limitZoom(e)],n=r.range(ft(t[0]),ht(t[3]),ft(t[2]),ht(t[1])),i=[],a=0;a<n.length;a++){var o=r.points[n[a]];i.push(o.numPoints?ct(o):this.points[o.id])}return i},getChildren:function(t,e){for(var r=this.trees[e+1].points[t],n=this.options.radius/(this.options.extentMath.pow(2,e)),i=this.trees[e+1].within(r.x,r.y,n),a=[],o=0;o<i.length;o++){var s=this.trees[e+1].points[i[o]];s.parentId===t&&a.push(s.numPoints?ct(s):this.points[s.id])}return a},getLeaves:function(t,e,r,n){r=r\|\|10,n=n\|\|0;var i=[];return this._appendLeaves(i,t,e,r,n,0),i},getTile:function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options.extent,o=this.options.radius/a,s=(r-o)/i,l=(r+1+o)/i,c={features:[]};return this._addTileFeatures(n.range((e-o)/i,s,(e+1+o)/i,l),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-o/i,s,1,l),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,s,o/i,l),n.points,-1,r,i,c),c.features.length?c:null},getClusterExpansionZoom:function(t,e){for(;e<this.options.maxZoom;){var r=this.getChildren(t,e);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},_appendLeaves:function(t,e,r,n,i,a){for(var o=this.getChildren(e,r),s=0;s<o.length;s++){var l=o[s].properties;if(l.cluster?a+l.point_count<=i?a+=l.point_count:a=this._appendLeaves(t,l.cluster_id,r+1,n,i,a):a<i?a++:t.push(o[s]),t.length===n)break}return a},_addTileFeatures:function(t,e,r,n,i,a){for(var o=0;o<t.length;o++){var s=e[t[o]];a.features.push({type:1,geometry:[[Math.round(this.options.extent(s.xi-r)),Math.round(this.options.extent(s.yi-n))]],tags:s.numPoints?ut(s):this.points[s.id].properties})}},_limitZoom:function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},_cluster:function(t,e){for(var r=[],n=this.options.radius/(this.options.extentMath.pow(2,e)),i=0;i<t.length;i++){var a=t[i];if(!(a.zoom<=e)){a.zoom=e;var o=this.trees[e+1],s=o.within(a.x,a.y,n),l=a.numPoints\|\|1,c=a.xl,u=a.yl,f=null;this.options.reduce&&(f=this.options.initial(),this._accumulate(f,a));for(var h=0;h<s.length;h++){var p=o.points[s[h]];if(e<p.zoom){var d=p.numPoints\|\|1;p.zoom=e,c+=p.xd,u+=p.yd,l+=d,p.parentId=i,this.options.reduce&&this._accumulate(f,p)}}1===l?r.push(a):(a.parentId=i,r.push(st(c/l,u/l,l,i,f)))}}return r},_accumulate:function(t,e){var r=e.numPoints?e.properties:this.options.map(this.points[e.id].properties);this.options.reduce(t,r)}},jt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,debug:0},jt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=Vt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=Bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]\|\|0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom\|\|e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)\|\|n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom\|\|h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var g,v,m,y,x,b,_=.5l.buffer/l.extent,w=.5-_,k=.5+_,M=1+_;g=v=m=y=null,x=At(t,u,r-_,r+k,0,h.minX,h.maxX,l),b=At(t,u,r+w,r+M,0,h.minX,h.maxX,l),t=null,x&&(g=At(x,u,n-_,n+k,1,h.minY,h.maxY,l),v=At(x,u,n+w,n+M,1,h.minY,h.maxY,l),x=null),b&&(m=At(b,u,n-_,n+k,1,h.minY,h.maxY,l),y=At(b,u,n+w,n+M,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g\|\|[],e+1,2r,2n),s.push(v\|\|[],e+1,2r,2n+1),s.push(m\|\|[],e+1,2r+1,2n),s.push(y\|\|[],e+1,2r+1,2n+1)}}},jt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0\|\|t>24)return null;var o=1<<t,s=Vt(t,e=(e%o+o)%o,r);if(this.tiles[s])return Dt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[Vt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?Dt(this.tiles[s],i):null):null};var qt=function(e){function r(t,r,n){e.call(this,t,r,Ut),n&&(this.loadGeoJSON=n)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var t=this;if(this._pendingCallback&&this._pendingLoadDataParams){var e=this._pendingCallback,r=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams,this.loadGeoJSON(r,function(n,i){if(n\|\|!i)return e(n);if(\"object\"!=typeof i)return e(new Error(\"Input data is not a valid GeoJSON object.\"));!function t(e,r){switch(e&&e.type\|\|null){case\"FeatureCollection\":return e.features=e.features.map(z(t,r)),e;case\"Feature\":return e.geometry=t(e.geometry,r),e;case\"Polygon\":case\"MultiPolygon\":return function(t,e){return\"Polygon\"===t.type?t.coordinates=O(t.coordinates,e):\"MultiPolygon\"===t.type&&(t.coordinates=t.coordinates.map(z(O,e))),t}(e,r);default:return e}}(i,!0);try{t._geoJSONIndex=r.cluster?function(t){return new ot(t)}(r.superclusterOptions).load(i.features):new jt(i,r.geojsonVtOptions)}catch(n){return e(n)}t.loaded={};var a={};if(r.request&&r.request.collectResourceTiming){var o=w(r.request.url);o&&(a.resourceTiming={},a.resourceTiming[r.source]=JSON.parse(JSON.stringify(o)))}e(null,a)})}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r}(M),Ht=function(e){var r=this;this.self=e,this.actor=new t.default$7(e,this),this.layerIndexes={},this.workerSourceTypes={vector:M,geojson:qt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isLoaded())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText}};return Ht.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},Ht.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},Ht.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},Ht.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},Ht.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},Ht.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},Ht.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},Ht.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},Ht.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},Ht.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},Ht.prototype.loadRTLTextPlugin=function(e,r,n){try{t.plugin.isLoaded()\|\|(this.self.importScripts(r),n(t.plugin.isLoaded()?null:new Error(\"RTL Text Plugin failed to import scripts from \"+r)))}catch(t){n(t.toString())}},Ht.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e\|\|(e=this.layerIndexes[t]=new n),e},Ht.prototype.getWorkerSource=function(t,e,r){var n=this;if(this.workerSources[t]\|\|(this.workerSources[t]={}),this.workerSources[t][e]\|\|(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){var i={send:function(e,r,i){n.actor.send(e,r,i,t)}};this.workerSources[t][e][r]=new this.workerSourceTypes[e](i,this.getLayerIndex(t))}return this.workerSources[t][e][r]},Ht.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]\|\|(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]\|\|(this.demWorkerSources[t][e]=new A),this.demWorkerSources[t][e]},\"undefined\"!=typeof WorkerGlobalScope&&\"undefined\"!=typeof self&&self instanceof WorkerGlobalScope&&new Ht(self),Ht}),i(0,function(t){var e=t.createCommonjsModule(function(t){function e(t){return!!(\"undefined\"!=typeof window&&\"undefined\"!=typeof document&&Array.prototype&&Array.prototype.every&&Array.prototype.filter&&Array.prototype.forEach&&Array.prototype.indexOf&&Array.prototype.lastIndexOf&&Array.prototype.map&&Array.prototype.some&&Array.prototype.reduce&&Array.prototype.reduceRight&&Array.isArray&&Function.prototype&&Function.prototype.bind&&Object.keys&&Object.create&&Object.getPrototypeOf&&Object.getOwnPropertyNames&&Object.isSealed&&Object.isFrozen&&Object.isExtensible&&Object.getOwnPropertyDescriptor&&Object.defineProperty&&Object.defineProperties&&Object.seal&&Object.freeze&&Object.preventExtensions&&\"JSON\"in window&&\"parse\"in JSON&&\"stringify\"in JSON&&function(){if(!(\"Worker\"in window&&\"Blob\"in window&&\"URL\"in window))return!1;var t,e,r=new Blob([\"\"],{type:\"text/javascript\"}),n=URL.createObjectURL(r);try{e=new Worker(n),t=!0}catch(e){t=!1}return e&&e.terminate(),URL.revokeObjectURL(n),t}()&&\"Uint8ClampedArray\"in window&&function(t){return void 0===r[t]&&(r[t]=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)\|\|r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)\|\|r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)\|\|r.getContext(\"experimental-webgl\",n)}(t)),r[t]}(t&&t.failIfMajorPerformanceCaveat))}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl\|\|{},window.mapboxgl.supported=e);var r={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}}),r=t.default.performance&&t.default.performance.now?t.default.performance.now.bind(t.default.performance):Date.now.bind(Date),n=t.default.requestAnimationFrame\|\|t.default.mozRequestAnimationFrame\|\|t.default.webkitRequestAnimationFrame\|\|t.default.msRequestAnimationFrame,i=t.default.cancelAnimationFrame\|\|t.default.mozCancelAnimationFrame\|\|t.default.webkitCancelAnimationFrame\|\|t.default.msCancelAnimationFrame,a={now:r,frame:function(t){return n(t)},cancelFrame:function(t){return i(t)},getImageData:function(e){var r=t.default.document.createElement(\"canvas\"),n=r.getContext(\"2d\");if(!n)throw new Error(\"failed to create canvas 2d context\");return r.width=e.width,r.height=e.height,n.drawImage(e,0,0,e.width,e.height),n.getImageData(0,0,e.width,e.height)},hardwareConcurrency:t.default.navigator.hardwareConcurrency\|\|4,get devicePixelRatio(){return t.default.devicePixelRatio},supportsWebp:!1};if(t.default.document){var o=t.default.document.createElement(\"img\");o.onload=function(){a.supportsWebp=!0},o.src=\"data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=\"}var s={create:function(e,r,n){var i=t.default.document.createElement(e);return r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.default.document.createElementNS(e,r)}},l=t.default.document?t.default.document.documentElement.style:null;function c(t){if(!l)return null;for(var e=0;e<t.length;e++)if(t[e]in l)return t[e];return t[0]}var u,f=c([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]);s.disableDrag=function(){l&&f&&(u=l[f],l[f]=\"none\")},s.enableDrag=function(){l&&f&&(l[f]=u)};var h=c([\"transform\",\"WebkitTransform\"]);s.setTransform=function(t,e){t.style[h]=e};var p=!1;try{var d=Object.defineProperty({},\"passive\",{get:function(){p=!0}});t.default.addEventListener(\"test\",d,d),t.default.removeEventListener(\"test\",d,d)}catch(t){p=!1}s.addEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&p?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},s.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&p?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var g=function(e){e.preventDefault(),e.stopPropagation(),t.default.removeEventListener(\"click\",g,!0)};s.suppressClick=function(){t.default.addEventListener(\"click\",g,!0),t.default.setTimeout(function(){t.default.removeEventListener(\"click\",g,!0)},0)},s.mousePos=function(e,r){var n=e.getBoundingClientRect();return r=r.touches?r.touches[0]:r,new t.default$1(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},s.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=\"touchend\"===r.type?r.changedTouches:r.touches,o=0;o<a.length;o++)i.push(new t.default$1(a[o].clientX-n.left-e.clientLeft,a[o].clientY-n.top-e.clientTop));return i},s.mouseButton=function(e){return void 0!==t.default.InstallTrigger&&2===e.button&&e.ctrlKey&&t.default.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},s.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var v={API_URL:\"https://api.mapbox.com\",REQUIRE_ACCESS_TOKEN:!0,ACCESS_TOKEN:null},m=\"See https://www.mapbox.com/api-documentation/#access-tokens\";function y(t,e){var r=A(v.API_URL);if(t.protocol=r.protocol,t.authority=r.authority,\"/\"!==r.path&&(t.path=\"\"+r.path+t.path),!v.REQUIRE_ACCESS_TOKEN)return T(t);if(!(e=e\|\|v.ACCESS_TOKEN))throw new Error(\"An API access token is required to use Mapbox GL. \"+m);if(\"s\"===e[0])throw new Error(\"Use a public access token (pk.) with Mapbox GL, not a secret access token (sk.). \"+m);return t.params.push(\"access_token=\"+e),T(t)}function x(t){return 0===t.indexOf(\"mapbox:\")}var b=function(t,e){if(!x(t))return t;var r=A(t);return r.path=\"/v4/\"+r.authority+\".json\",r.params.push(\"secure\"),y(r,e)},_=function(t,e,r,n){var i=A(t);return x(t)?(i.path=\"/styles/v1\"+i.path+\"/sprite\"+e+r,y(i,n)):(i.path+=\"\"+e+r,T(i))},w=/(\\.(png\|jpg)\\d)(?=$)/,k=function(t,e,r){if(!e\|\|!x(e))return t;var n=A(t),i=a.devicePixelRatio>=2\|\|512===r?\"@2x\":\"\",o=a.supportsWebp?\".webp\":\"$1\";return n.path=n.path.replace(w,\"\"+i+o),function(t){for(var e=0;e<t.length;e++)0===t[e].indexOf(\"access_token=tk.\")&&(t[e]=\"access_token=\"+(v.ACCESS_TOKEN\|\|\"\"))}(n.params),T(n)},M=/^(\\w+):\\/\\/([^\\/?])(\\/[^?]+)?\\??(.+)?/;function A(t){var e=t.match(M);if(!e)throw new Error(\"Unable to parse URL object\");return{protocol:e[1],authority:e[2],path:e[3]\|\|\"/\",params:e[4]?e[4].split(\"&\"):[]}}function T(t){var e=t.params.length?\"?\"+t.params.join(\"&\"):\"\";return t.protocol+\"://\"+t.authority+t.path+e}var S=t.default.HTMLImageElement,E=t.default.HTMLCanvasElement,C=t.default.HTMLVideoElement,L=t.default.ImageData,z=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};z.prototype.update=function(t,e){var r=t.width,n=t.height,i=!this.size\|\|this.size[0]!==r\|\|this.size[1]!==n,a=this.context,o=a.gl;this.useMipmap=Boolean(e&&e.useMipmap),o.bindTexture(o.TEXTURE_2D,this.texture),i?(this.size=[r,n],a.pixelStoreUnpack.set(1),this.format!==o.RGBA\|\|e&&!1===e.premultiply\|\|a.pixelStoreUnpackPremultiplyAlpha.set(!0),t instanceof S\|\|t instanceof E\|\|t instanceof C\|\|t instanceof L?o.texImage2D(o.TEXTURE_2D,0,this.format,this.format,o.UNSIGNED_BYTE,t):o.texImage2D(o.TEXTURE_2D,0,this.format,r,n,0,this.format,o.UNSIGNED_BYTE,t.data)):t instanceof S\|\|t instanceof E\|\|t instanceof C\|\|t instanceof L?o.texSubImage2D(o.TEXTURE_2D,0,0,0,o.RGBA,o.UNSIGNED_BYTE,t):o.texSubImage2D(o.TEXTURE_2D,0,0,0,r,n,o.RGBA,o.UNSIGNED_BYTE,t.data),this.useMipmap&&this.isSizePowerOfTwo()&&o.generateMipmap(o.TEXTURE_2D)},z.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST\|\|this.isSizePowerOfTwo()\|\|(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r\|\|t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},z.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},z.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var O=function(){this.images={},this.loaded=!1,this.requestors=[],this.shelfPack=new t.default$2(64,64,{autoResize:!0}),this.patterns={},this.atlasImage=new t.RGBAImage({width:64,height:64}),this.dirty=!0};O.prototype.isLoaded=function(){return this.loaded},O.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},O.prototype.getImage=function(t){return this.images[t]},O.prototype.addImage=function(t,e){this.images[t]=e},O.prototype.removeImage=function(t){delete this.images[t];var e=this.patterns[t];e&&(this.shelfPack.unref(e.bin),delete this.patterns[t])},O.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]\|\|(r=!1)}this.isLoaded()\|\|r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},O.prototype._notify=function(t,e){for(var r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=this.images[a];o&&(r[a]={data:o.data.clone(),pixelRatio:o.pixelRatio,sdf:o.sdf})}e(null,r)},O.prototype.getPixelSize=function(){return{width:this.shelfPack.w,height:this.shelfPack.h}},O.prototype.getPattern=function(e){var r=this.patterns[e];if(r)return r.position;var n=this.getImage(e);if(!n)return null;var i=n.data.width+2,a=n.data.height+2,o=this.shelfPack.packOne(i,a);if(!o)return null;this.atlasImage.resize(this.getPixelSize());var s=n.data,l=this.atlasImage,c=o.x+1,u=o.y+1,f=s.width,h=s.height;t.RGBAImage.copy(s,l,{x:0,y:0},{x:c,y:u},{width:f,height:h}),t.RGBAImage.copy(s,l,{x:0,y:h-1},{x:c,y:u-1},{width:f,height:1}),t.RGBAImage.copy(s,l,{x:0,y:0},{x:c,y:u+h},{width:f,height:1}),t.RGBAImage.copy(s,l,{x:f-1,y:0},{x:c-1,y:u},{width:1,height:h}),t.RGBAImage.copy(s,l,{x:0,y:0},{x:c+f,y:u},{width:1,height:h}),this.dirty=!0;var p=new t.ImagePosition(o,n);return this.patterns[e]={bin:o,position:p},p},O.prototype.bind=function(t){var e=t.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new z(t,this.atlasImage,e.RGBA),this.atlasTexture.bind(e.LINEAR,e.CLAMP_TO_EDGE)};var I=D,P=1e20;function D(t,e,r,n,i,a){this.fontSize=t\|\|24,this.buffer=void 0===e?3:e,this.cutoff=n\|\|.25,this.fontFamily=i\|\|\"sans-serif\",this.fontWeight=a\|\|\"normal\",this.radius=r\|\|8;var o=this.size=this.fontSize+2this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(oo),this.gridInner=new Float64Array(oo),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function R(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[le+s];for(B(n,i,a,o,r),l=0;l<r;l++)t[le+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[le+s];for(B(n,i,a,o,e),s=0;s<e;s++)t[le+s]=Math.sqrt(i[s])}}function B(t,e,r,n,i){r[0]=0,n[0]=-P,n[1]=+P;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+aa-(t[r[o]]+r[o]r[o]))/(2a-2r[o]);s<=n[o];)o--,s=(t[a]+aa-(t[r[o]]+r[o]r[o]))/(2a-2r[o]);r[++o]=a,n[o]=s,n[o+1]=+P}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])(a-r[o])+t[r[o]]}}D.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.sizethis.size),n=0;n<this.sizethis.size;n++){var i=e.data[4n+3]/255;this.gridOuter[n]=1===i?0:0===i?P:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?P:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(R(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),R(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.sizethis.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255(a/this.radius+this.cutoff))))}return r};var F=function(t,e){this.requestTransform=t,this.localIdeographFontFamily=e,this.entries={}};F.prototype.setURL=function(t){this.url=t},F.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a\|\|(a=n.entries[r]={glyphs:{},requests:{}});var o=a.glyphs[i];if(void 0===o)if(o=n._tinySDF(a,r,i))e(null,{stack:r,id:i,glyph:o});else{var s=Math.floor(i/256);if(256s>65535)e(new Error(\"glyphs > 65535 not supported\"));else{var l=a.requests[s];l\|\|(l=a.requests[s]=[],F.loadGlyphRange(r,s,n.url,n.requestTransform,function(t,e){if(e)for(var r in e)a.glyphs[+r]=e[+r];for(var n=0,i=l;n<i.length;n+=1)(0,i[n])(t,e);delete a.requests[s]})),l.push(function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]\|\|null})})}}else e(null,{stack:r,id:i,glyph:o})},function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]\|\|(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}})},F.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&(t.default$4[\"CJK Unified Ideographs\"](n)\|\|t.default$4[\"Hangul Syllables\"](n))){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new F.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},F.loadGlyphRange=function(e,r,n,i,a){var o=256r,s=o+255,l=i(function(t,e){if(!x(t))return t;var r=A(t);return r.path=\"/fonts/v1\"+r.path,y(r,e)}(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.default$3(r.data);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}})},F.TinySDF=I;var N=function(){this.specification=t.default$5.light.position};N.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},N.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var j=new t.Properties({anchor:new t.DataConstantProperty(t.default$5.light.anchor),position:new N,color:new t.DataConstantProperty(t.default$5.light.color),intensity:new t.DataConstantProperty(t.default$5.light.intensity)}),V=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(j),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e){if(!this._validate(t.validateLight,e))for(var r in e){var n=e[r];t.endsWith(r,\"-transition\")?this._transitionable.setTransition(r.slice(0,-\"-transition\".length),n):this._transitionable.setValue(r,n)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r){return t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.default$5})))},r}(t.Evented),U=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.widththis.heightthis.bytes),this.positions={}};U.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.positions[r]\|\|(this.positions[r]=this.addDash(t,e)),this.positions[r]},U.prototype.addDash=function(e,r){var n=r?7:0,i=2n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];for(var s=this.width/a,l=s/2,c=e.length%2==1,u=-n;u<=n;u++)for(var f=this.nextRow+n+u,h=this.widthf,p=c?-e[e.length-1]:0,d=e[0],g=1,v=0;v<this.width;v++){for(;d<v/s;)p=d,d+=e[g],c&&g===e.length-1&&(d+=e[0]),g++;var m=Math.abs(v-ps),y=Math.abs(v-ds),x=Math.min(m,y),b=g%2==1,_=void 0;if(r){var w=n?u/n(l+1):0;if(b){var k=l-Math.abs(w);_=Math.sqrt(xx+kk)}else _=l-Math.sqrt(xx+ww)}else _=(b?1:-1)x;this.data[3+4(h+v)]=Math.max(0,Math.min(255,_+128))}var M={y:(this.nextRow+n+.5)/this.height,height:2n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,M},U.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.RGBA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,this.width,this.height,0,e.RGBA,e.UNSIGNED_BYTE,this.data))};var q=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function H(e,r,n){var i=function(e,r){if(e)return n(e);if(r){var i=t.pick(r,[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\"]);r.vector_layers&&(i.vectorLayers=r.vector_layers,i.vectorLayerIds=i.vectorLayers.map(function(t){return t.id})),n(null,i)}};e.url?t.getJSON(r(b(e.url),t.ResourceType.Source),i):a.frame(function(){return i(null,e)})}q.prototype.broadcast=function(e,r,n){n=n\|\|function(){},t.asyncAll(this.actors,function(t,n){t.send(e,r,n)},n)},q.prototype.send=function(t,e,r,n){return(\"number\"!=typeof n\|\|isNaN(n))&&(n=this.currentActor=(this.currentActor+1)%this.actors.length),this.actors[n].send(t,e,r),n},q.prototype.remove=function(){this.actors.forEach(function(t){t.remove()}),this.actors=[],this.workerPool.release(this.id)},q.Actor=t.default$7;var G=function(t,e){if(isNaN(t)\|\|isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90\|\|this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};G.prototype.wrap=function(){return new G(t.wrap(this.lng,-180,180),this.lat)},G.prototype.toArray=function(){return[this.lng,this.lat]},G.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},G.prototype.toBounds=function(t){var e=360t/40075017,r=e/Math.cos(Math.PI/180this.lat);return new W(new G(this.lng-r,this.lat-e),new G(this.lng+r,this.lat+e))},G.convert=function(t){if(t instanceof G)return t;if(Array.isArray(t)&&(2===t.length\|\|3===t.length))return new G(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new G(Number(t.lng),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var W=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};W.prototype.setNorthEast=function(t){return this._ne=t instanceof G?new G(t.lng,t.lat):G.convert(t),this},W.prototype.setSouthWest=function(t){return this._sw=t instanceof G?new G(t.lng,t.lat):G.convert(t),this},W.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof G)e=t,r=t;else{if(!(t instanceof W))return Array.isArray(t)?t.every(Array.isArray)?this.extend(W.convert(t)):this.extend(G.convert(t)):this;if(e=t._sw,r=t._ne,!e\|\|!r)return this}return n\|\|i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new G(e.lng,e.lat),this._ne=new G(r.lng,r.lat)),this},W.prototype.getCenter=function(){return new G((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},W.prototype.getSouthWest=function(){return this._sw},W.prototype.getNorthEast=function(){return this._ne},W.prototype.getNorthWest=function(){return new G(this.getWest(),this.getNorth())},W.prototype.getSouthEast=function(){return new G(this.getEast(),this.getSouth())},W.prototype.getWest=function(){return this._sw.lng},W.prototype.getSouth=function(){return this._sw.lat},W.prototype.getEast=function(){return this._ne.lng},W.prototype.getNorth=function(){return this._ne.lat},W.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},W.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},W.prototype.isEmpty=function(){return!(this._sw&&this._ne)},W.convert=function(t){return!t\|\|t instanceof W?t:new W(t)};var Y=function(t,e,r){this.bounds=W.convert(this.validateBounds(t)),this.minzoom=e\|\|0,this.maxzoom=r\|\|24};Y.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},Y.prototype.contains=function(t){var e=Math.floor(this.lngX(this.bounds.getWest(),t.z)),r=Math.floor(this.latY(this.bounds.getNorth(),t.z)),n=Math.ceil(this.lngX(this.bounds.getEast(),t.z)),i=Math.ceil(this.latY(this.bounds.getSouth(),t.z));return t.x>=e&&t.x<n&&t.y>=r&&t.y<i},Y.prototype.lngX=function(t,e){return(t+180)(Math.pow(2,e)/360)},Y.prototype.latY=function(e,r){var n=t.clamp(Math.sin(Math.PI/180e),-.9999,.9999),i=Math.pow(2,r)/(2Math.PI);return Math.pow(2,r-1)+.5Math.log((1+n)/(1-n))-i};var X=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),H(this._options,this.map._transformRequest,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new Y(n.bounds,e.minzoom,e.maxzoom)),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},r.prototype.hasTile=function(t){return!this.tileBounds\|\|this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url),i={request:this.map._transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSizee.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:a.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes};function o(t,n){return e.aborted?r(null):t?r(t):(n&&n.resourceTiming&&(e.resourceTiming=n.resourceTiming),this.map._refreshExpiredTiles&&e.setExpiryData(n),e.loadVectorData(n,this.map.painter),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,void 0===e.workerID\|\|\"expired\"===e.state?e.workerID=this.dispatcher.send(\"loadTile\",i,o.bind(this)):\"loading\"===e.state?e.reloadCallback=r:this.dispatcher.send(\"reloadTile\",i,o.bind(this),e.workerID)},r.prototype.abortTile=function(t){this.dispatcher.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0,t.workerID)},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.dispatcher.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0,t.workerID)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),Z=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),H(this._options,this.map._transformRequest,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new Y(n.bounds,e.minzoom,e.maxzoom)),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds\|\|this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url,this.tileSize);e.request=t.getImage(this.map._transformRequest(i,t.ResourceType.Tile),function(t,i){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(t)e.state=\"errored\",r(t);else if(i){n.map._refreshExpiredTiles&&e.setExpiryData(i),delete i.cacheControl,delete i.expires;var a=n.map.painter.context,o=a.gl;e.texture=n.map.painter.getTileTexture(i.width),e.texture?e.texture.update(i,{useMipmap:!0}):(e.texture=new z(a,i,o.RGBA,{useMipmap:!0}),e.texture.bind(o.LINEAR,o.CLAMP_TO_EDGE,o.LINEAR_MIPMAP_NEAREST),a.extTextureFilterAnisotropic&&o.texParameterf(o.TEXTURE_2D,a.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,a.extTextureFilterAnisotropicMax)),e.state=\"loaded\",r(null)}})},r.prototype.abortTile=function(t,e){t.request&&(t.request.abort(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),$=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({},n),this.encoding=n.encoding\|\|\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=k(e.tileID.canonical.url(this.tiles,this.scheme),this.url,this.tileSize);e.request=t.getImage(this.map._transformRequest(n,t.ResourceType.Tile),function(t,n){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(t)e.state=\"errored\",r(t);else if(n){this.map._refreshExpiredTiles&&e.setExpiryData(n),delete n.cacheControl,delete n.expires;var i=a.getImageData(n),o={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:i,encoding:this.encoding};e.workerID&&\"expired\"!==e.state\|\|(e.workerID=this.dispatcher.send(\"loadDEMTile\",o,function(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",this.dispatcher.send(\"removeDEMTile\",{uid:t.uid,source:this.id},void 0,t.workerID)},r}(Z),J=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this.dispatcher=i,this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type);var o=t.default$8/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster\|\|!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)o,extent:t.default$8,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics\|\|!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.default$8,radius:(n.clusterRadius\|\|50)o,log:!1}},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData(function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}})},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData(function(e){if(e)return r.fire(new t.ErrorEvent(e));var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}),this},r.prototype._updateWorkerData=function(e){var r,n,i=this,a=t.extend({},this.workerOptions),o=this._data;\"string\"==typeof o?(a.request=this.map._transformRequest((r=o,(n=t.default.document.createElement(\"a\")).href=r,n.href),t.ResourceType.Source),a.request.collectResourceTiming=this._collectResourceTiming):a.data=JSON.stringify(o),this.workerID=this.dispatcher.send(this.type+\".\"+a.source+\".loadData\",a,function(t,r){i._removed\|\|r&&r.abandoned\|\|(i._loaded=!0,r&&r.resourceTiming&&r.resourceTiming[i.id]&&(i._resourceTiming=r.resourceTiming[i.id].slice(0)),i.dispatcher.send(i.type+\".\"+a.source+\".coalesce\",null,null,i.workerID),e(t))},this.workerID)},r.prototype.loadTile=function(t,e){var r=this,n=void 0===t.workerID?\"loadTile\":\"reloadTile\",i={type:this.type,uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:a.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID=this.dispatcher.send(n,i,function(i,a){return t.unloadVectorData(),t.aborted?e(null):i?e(i):(t.loadVectorData(a,r.map.painter,\"reloadTile\"===n),e(null))},this.workerID)},r.prototype.abortTile=function(t){t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.dispatcher.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},null,t.workerID)},r.prototype.onRemove=function(){this._removed=!0,this.dispatcher.send(\"removeSource\",{type:this.type,source:this.id},null,this.workerID)},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),K=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),Q=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};Q.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao\|\|this.boundProgram!==e\|\|this.boundLayoutVertexBuffer!==r\|\|l\|\|this.boundIndexBuffer!==i\|\|this.boundVertexOffset!==a\|\|this.boundDynamicVertexBuffer!==o\|\|this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject\|\|u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Q.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes\|\|0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(u,t);a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,g=r;d<g.length;d+=1){var v=g[d];v.bind(),v.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},Q.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var tt=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._transformRequest(this.url,t.ResourceType.Image),function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(e.image=a.getImageData(n),e._finishLoading())})},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){this.coordinates=e;var r=this.map,n=e.map(function(t){return r.transform.locationCoordinate(G.convert(t)).zoomTo(0)}),i=this.centerCoord=t.getCoordinatesCenter(n);i.column=Math.floor(i.column),i.row=Math.floor(i.row),this.tileID=new t.CanonicalTileID(i.zoom,i.column,i.row),this.minzoom=this.maxzoom=i.zoom;var a=n.map(function(e){var r=e.zoomTo(i.zoom);return new t.default$1(Math.round((r.column-i.column)t.default$8),Math.round((r.row-i.row)t.default$8))});return this._boundsArray=new t.RasterBoundsArray,this._boundsArray.emplaceBack(a[0].x,a[0].y,0,0),this._boundsArray.emplaceBack(a[1].x,a[1].y,t.default$8,0),this._boundsArray.emplaceBack(a[3].x,a[3].y,0,t.default$8),this._boundsArray.emplaceBack(a[2].x,a[2].y,t.default$8,t.default$8),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var t=this.map.painter.context,e=t.gl;for(var r in this.boundsBuffer\|\|(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO\|\|(this.boundsVAO=new Q),this.texture\|\|(this.texture=new z(t,this.image,e.RGBA),this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE)),this.tiles){var n=this.tiles[r];\"loaded\"!==n.state&&(n.state=\"loaded\",n.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented),et=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this,r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];e.urls.push(e.map._transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,function(r,n){r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",function(){e.map._rerender()}),e.map&&e.video.play(),e._finishLoading())})},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map\|\|(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length\|\|this.video.readyState<2)){var t=this.map.painter.context,e=t.gl;for(var r in this.boundsBuffer\|\|(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO\|\|(this.boundsVAO=new Q),this.texture?this.video.paused\|\|(this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE),e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,this.video)):(this.texture=new z(t,this.video,e.RGBA),this.texture.bind(e.LINEAR,e.CLAMP_TO_EDGE)),this.tiles){var n=this.tiles[r];\"loaded\"!==n.state&&(n.state=\"loaded\",n.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(tt),rt=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some(function(t){return!Array.isArray(t)\|\|2!==t.length\|\|t.some(function(t){return\"number\"!=typeof t})})\|\|this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas\|\|n.canvas instanceof t.default.HTMLCanvasElement\|\|this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.default$9(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate\|\|n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this.canvas\|\|(this.canvas=this.options.canvas instanceof t.default.HTMLCanvasElement?this.options.canvas:t.default.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map._rerender()},this.pause=function(){this._playing=!1},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var t=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,t=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,t=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer\|\|(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,K.members)),this.boundsVAO\|\|(this.boundsVAO=new Q),this.texture?t?this.texture.update(this.canvas):this._playing&&(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.canvas)):(this.texture=new z(e,this.canvas,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)\|\|r<=0)return!0}return!1},r}(tt),nt={vector:X,raster:Z,\"raster-dem\":$,geojson:J,video:et,image:tt,canvas:rt},it=function(e,r,n,i){var a=new nt[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a};function at(t,e,r,n,i){var a=i.maxPitchScaleFactor(),o=t.tilesIn(r,a);o.sort(ot);for(var s=[],l=0,c=o;l<c.length;l+=1){var u=c[l];s.push({wrappedTileID:u.tileID.wrapped().key,queryResults:u.tile.queryRenderedFeatures(e,u.queryGeometry,u.scale,n,i,a,t.transform.calculatePosMatrix(u.tileID.toUnwrapped()))})}return function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]\|\|{};for(var c in o)for(var u=o[c],f=l[c]=l[c]\|\|{},h=e[c]=e[c]\|\|[],p=0,d=u;p<d.length;p+=1){var g=d[p];f[g.featureIndex]\|\|(f[g.featureIndex]=!0,h.push(g.feature))}}return e}(s)}function ot(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ\|\|r.canonical.y-n.canonical.y\|\|r.wrap-n.wrap\|\|r.canonical.x-n.canonical.x}var st=function(e,r){this.tileID=e,this.uid=t.uniqueId(),this.uses=0,this.tileSize=r,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.expiredRequestCount=0,this.state=\"loading\"};st.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<a.now()\|\|this.fadeEndTime&&e<this.fadeEndTime\|\|(this.fadeEndTime=e)},st.prototype.wasRequested=function(){return\"errored\"===this.state\|\|\"loaded\"===this.state\|\|\"reloading\"===this.state},st.prototype.loadVectorData=function(e,r,n){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",e){if(e.featureIndex&&(this.latestFeatureIndex=e.featureIndex,e.rawTileData?(this.latestRawTileData=e.rawTileData,this.latestFeatureIndex.rawTileData=e.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=e.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.layerIds.map(function(t){return e.getLayer(t)}).filter(Boolean);if(0!==o.length){a.layers=o;for(var s=0,l=o;s<l.length;s+=1)r[l[s].id]=a}}return r}(e.buckets,r.style),n)for(var i in this.buckets){var a=this.buckets[i];a instanceof t.default$14&&(a.justReloaded=!0)}for(var o in this.queryPadding=0,this.buckets){var s=this.buckets[o];this.queryPadding=Math.max(this.queryPadding,r.style.getLayer(s.layerIds[0]).queryRadius(s))}e.iconAtlasImage&&(this.iconAtlasImage=e.iconAtlasImage),e.glyphAtlasImage&&(this.glyphAtlasImage=e.glyphAtlasImage)}else this.collisionBoxArray=new t.CollisionBoxArray},st.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.iconAtlasTexture&&this.iconAtlasTexture.destroy(),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},st.prototype.unloadDEMData=function(){this.dem=null,this.neighboringTiles=null,this.state=\"unloaded\"},st.prototype.getBucket=function(t){return this.buckets[t.id]},st.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploaded\|\|(r.upload(t),r.uploaded=!0)}var n=t.gl;this.iconAtlasImage&&(this.iconAtlasTexture=new z(t,this.iconAtlasImage,n.RGBA),this.iconAtlasImage=null),this.glyphAtlasImage&&(this.glyphAtlasTexture=new z(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},st.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:e,scale:r,tileSize:this.tileSize,posMatrix:o,transform:i,params:n,queryPadding:this.queryPaddinga},t):{}},st.prototype.querySourceFeatures=function(e,r){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData){var n=this.latestFeatureIndex.loadVTLayers(),i=r?r.sourceLayer:\"\",a=n._geojsonTileLayer\|\|n[i];if(a)for(var o=t.default$13(r&&r.filter),s={z:this.tileID.overscaledZ,x:this.tileID.canonical.x,y:this.tileID.canonical.y},l=0;l<a.length;l++){var c=a.feature(l);if(o(new t.default$16(this.tileID.overscaledZ),c)){var u=new t.default$12(c,s.z,s.x,s.y);u.tile=s,e.push(u)}}}},st.prototype.clearMask=function(){this.segments&&(this.segments.destroy(),delete this.segments),this.maskedBoundsBuffer&&(this.maskedBoundsBuffer.destroy(),delete this.maskedBoundsBuffer),this.maskedIndexBuffer&&(this.maskedIndexBuffer.destroy(),delete this.maskedIndexBuffer)},st.prototype.setMask=function(e,r){if(!t.default$10(this.mask,e)&&(this.mask=e,this.clearMask(),!t.default$10(e,{0:!0}))){var n=new t.RasterBoundsArray,i=new t.TriangleIndexArray;this.segments=new t.default$15,this.segments.prepareSegment(0,n,i);for(var a=Object.keys(e),o=0;o<a.length;o++){var s=e[a[o]],l=t.default$8>>s.z,c=new t.default$1(s.xl,s.yl),u=new t.default$1(c.x+l,c.y+l),f=this.segments.prepareSegment(4,n,i);n.emplaceBack(c.x,c.y,c.x,c.y),n.emplaceBack(u.x,c.y,u.x,c.y),n.emplaceBack(c.x,u.y,c.x,u.y),n.emplaceBack(u.x,u.y,u.x,u.y);var h=f.vertexLength;i.emplaceBack(h,h+1,h+2),i.emplaceBack(h+1,h+2,h+3),f.vertexLength+=4,f.primitiveLength+=2}this.maskedBoundsBuffer=r.createVertexBuffer(n,K.members),this.maskedIndexBuffer=r.createIndexBuffer(i)}},st.prototype.hasData=function(){return\"loaded\"===this.state\|\|\"reloading\"===this.state\|\|\"expired\"===this.state},st.prototype.setExpiryData=function(e){var r=this.expirationTime;if(e.cacheControl){var n=t.parseCacheControl(e.cacheControl);n[\"max-age\"]&&(this.expirationTime=Date.now()+1e3n[\"max-age\"])}else e.expires&&(this.expirationTime=new Date(e.expires).getTime());if(this.expirationTime){var i=Date.now(),a=!1;if(this.expirationTime>i)a=!1;else if(r)if(this.expirationTime<r)a=!0;else{var o=this.expirationTime-r;o?this.expirationTime=i+Math.max(o,3e4):a=!0}else a=!0;a?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},st.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)};var lt=function(t,e){this.max=t,this.onRemove=e,this.reset()};lt.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},lt.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout(function(){n.remove(t,a)},r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},lt.prototype.has=function(t){return t.wrapped().key in this.data},lt.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},lt.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},lt.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},lt.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},lt.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this};var ct=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw\|\|delete e.arrayBuffer};ct.prototype.unbindVAO=function(){this.context.extVertexArrayObject&&this.context.bindVertexArrayOES.set(null)},ct.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},ct.prototype.updateData=function(t){var e=this.context.gl;this.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},ct.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var ut={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},ft=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw\|\|delete e.arrayBuffer};ft.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},ft.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},ft.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},ft.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[ut[i.type]],!1,this.itemSize,i.offset+this.itemSize(r\|\|0))}},ft.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var ht=function(e){this.context=e,this.current=t.default$6.transparent};ht.prototype.get=function(){return this.current},ht.prototype.set=function(t){var e=this.current;t.r===e.r&&t.g===e.g&&t.b===e.b&&t.a===e.a\|\|(this.context.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t)};var pt=function(t){this.context=t,this.current=1};pt.prototype.get=function(){return this.current},pt.prototype.set=function(t){this.current!==t&&(this.context.gl.clearDepth(t),this.current=t)};var dt=function(t){this.context=t,this.current=0};dt.prototype.get=function(){return this.current},dt.prototype.set=function(t){this.current!==t&&(this.context.gl.clearStencil(t),this.current=t)};var gt=function(t){this.context=t,this.current=[!0,!0,!0,!0]};gt.prototype.get=function(){return this.current},gt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]\|\|(this.context.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t)};var vt=function(t){this.context=t,this.current=!0};vt.prototype.get=function(){return this.current},vt.prototype.set=function(t){this.current!==t&&(this.context.gl.depthMask(t),this.current=t)};var mt=function(t){this.context=t,this.current=255};mt.prototype.get=function(){return this.current},mt.prototype.set=function(t){this.current!==t&&(this.context.gl.stencilMask(t),this.current=t)};var yt=function(t){this.context=t,this.current={func:t.gl.ALWAYS,ref:0,mask:255}};yt.prototype.get=function(){return this.current},yt.prototype.set=function(t){var e=this.current;t.func===e.func&&t.ref===e.ref&&t.mask===e.mask\|\|(this.context.gl.stencilFunc(t.func,t.ref,t.mask),this.current=t)};var xt=function(t){this.context=t;var e=this.context.gl;this.current=[e.KEEP,e.KEEP,e.KEEP]};xt.prototype.get=function(){return this.current},xt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]\|\|(this.context.gl.stencilOp(t[0],t[1],t[2]),this.current=t)};var bt=function(t){this.context=t,this.current=!1};bt.prototype.get=function(){return this.current},bt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.STENCIL_TEST):e.disable(e.STENCIL_TEST),this.current=t}};var _t=function(t){this.context=t,this.current=[0,1]};_t.prototype.get=function(){return this.current},_t.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]\|\|(this.context.gl.depthRange(t[0],t[1]),this.current=t)};var wt=function(t){this.context=t,this.current=!1};wt.prototype.get=function(){return this.current},wt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.DEPTH_TEST):e.disable(e.DEPTH_TEST),this.current=t}};var kt=function(t){this.context=t,this.current=t.gl.LESS};kt.prototype.get=function(){return this.current},kt.prototype.set=function(t){this.current!==t&&(this.context.gl.depthFunc(t),this.current=t)};var Mt=function(t){this.context=t,this.current=!1};Mt.prototype.get=function(){return this.current},Mt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;t?e.enable(e.BLEND):e.disable(e.BLEND),this.current=t}};var At=function(t){this.context=t;var e=this.context.gl;this.current=[e.ONE,e.ZERO]};At.prototype.get=function(){return this.current},At.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]\|\|(this.context.gl.blendFunc(t[0],t[1]),this.current=t)};var Tt=function(e){this.context=e,this.current=t.default$6.transparent};Tt.prototype.get=function(){return this.current},Tt.prototype.set=function(t){var e=this.current;t.r===e.r&&t.g===e.g&&t.b===e.b&&t.a===e.a\|\|(this.context.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t)};var St=function(t){this.context=t,this.current=null};St.prototype.get=function(){return this.current},St.prototype.set=function(t){this.current!==t&&(this.context.gl.useProgram(t),this.current=t)};var Et=function(t){this.context=t,this.current=1};Et.prototype.get=function(){return this.current},Et.prototype.set=function(e){var r=this.context.lineWidthRange,n=t.clamp(e,r[0],r[1]);this.current!==n&&(this.context.gl.lineWidth(n),this.current=e)};var Ct=function(t){this.context=t,this.current=t.gl.TEXTURE0};Ct.prototype.get=function(){return this.current},Ct.prototype.set=function(t){this.current!==t&&(this.context.gl.activeTexture(t),this.current=t)};var Lt=function(t){this.context=t;var e=this.context.gl;this.current=[0,0,e.drawingBufferWidth,e.drawingBufferHeight]};Lt.prototype.get=function(){return this.current},Lt.prototype.set=function(t){var e=this.current;t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]\|\|(this.context.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t)};var zt=function(t){this.context=t,this.current=null};zt.prototype.get=function(){return this.current},zt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t}};var Ot=function(t){this.context=t,this.current=null};Ot.prototype.get=function(){return this.current},Ot.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t}};var It=function(t){this.context=t,this.current=null};It.prototype.get=function(){return this.current},It.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t}};var Pt=function(t){this.context=t,this.current=null};Pt.prototype.get=function(){return this.current},Pt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t}};var Dt=function(t){this.context=t,this.current=null};Dt.prototype.get=function(){return this.current},Dt.prototype.set=function(t){var e=this.context.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t};var Rt=function(t){this.context=t,this.current=null};Rt.prototype.get=function(){return this.current},Rt.prototype.set=function(t){this.current!==t&&this.context.extVertexArrayObject&&(this.context.extVertexArrayObject.bindVertexArrayOES(t),this.current=t)};var Bt=function(t){this.context=t,this.current=4};Bt.prototype.get=function(){return this.current},Bt.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t}};var Ft=function(t){this.context=t,this.current=!1};Ft.prototype.get=function(){return this.current},Ft.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t}};var Nt=function(t,e){this.context=t,this.current=null,this.parent=e};Nt.prototype.get=function(){return this.current};var jt=function(t){function e(e,r){t.call(this,e,r),this.dirty=!1}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(this.dirty\|\|this.current!==t){var e=this.context.gl;this.context.bindFramebuffer.set(this.parent),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e.prototype.setDirty=function(){this.dirty=!0},e}(Nt),Vt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(this.current!==t){var e=this.context.gl;this.context.bindFramebuffer.set(this.parent),e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t}},e}(Nt),Ut=function(t,e,r){this.context=t,this.width=e,this.height=r;var n=t.gl,i=this.framebuffer=n.createFramebuffer();this.colorAttachment=new jt(t,i),this.depthAttachment=new Vt(t,i)};Ut.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();e&&t.deleteTexture(e);var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r),t.deleteFramebuffer(this.framebuffer)};var qt=function(t,e,r){this.func=t,this.mask=e,this.range=r};qt.ReadOnly=!1,qt.ReadWrite=!0,qt.disabled=new qt(519,qt.ReadOnly,[0,1]);var Ht=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Ht.disabled=new Ht({func:519,mask:0},0,0,7680,7680,7680);var Gt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Gt.disabled=new Gt(Gt.Replace=[1,0],t.default$6.transparent,[!1,!1,!1,!1]),Gt.unblended=new Gt(Gt.Replace,t.default$6.transparent,[!0,!0,!0,!0]),Gt.alphaBlended=new Gt([1,771],t.default$6.transparent,[!0,!0,!0,!0]);var Wt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.lineWidthRange=t.getParameter(t.ALIASED_LINE_WIDTH_RANGE),this.clearColor=new ht(this),this.clearDepth=new pt(this),this.clearStencil=new dt(this),this.colorMask=new gt(this),this.depthMask=new vt(this),this.stencilMask=new mt(this),this.stencilFunc=new yt(this),this.stencilOp=new xt(this),this.stencilTest=new bt(this),this.depthRange=new _t(this),this.depthTest=new wt(this),this.depthFunc=new kt(this),this.blend=new Mt(this),this.blendFunc=new At(this),this.blendColor=new Tt(this),this.program=new St(this),this.lineWidth=new Et(this),this.activeTexture=new Ct(this),this.viewport=new Lt(this),this.bindFramebuffer=new zt(this),this.bindRenderbuffer=new Ot(this),this.bindTexture=new It(this),this.bindVertexBuffer=new Pt(this),this.bindElementBuffer=new Dt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new Rt(this),this.pixelStoreUnpack=new Bt(this),this.pixelStoreUnpackPremultiplyAlpha=new Ft(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")\|\|t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")\|\|t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&t.getExtension(\"OES_texture_half_float_linear\")};Wt.prototype.createIndexBuffer=function(t,e){return new ct(this,t,e)},Wt.prototype.createVertexBuffer=function(t,e,r){return new ft(this,t,e,r)},Wt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Wt.prototype.createFramebuffer=function(t,e){return new Ut(this,t,e)},Wt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i\|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i\|=n.DEPTH_BUFFER_BIT,this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Wt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS\|\|t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Wt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS\|\|t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Wt.prototype.setColorMode=function(e){t.default$10(e.blendFunction,Gt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)};var Yt=function(e){function r(t,r,n){var i=this;e.call(this),this.id=t,this.dispatcher=n,this.on(\"data\",function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(i._sourceLoaded=!0),i._sourceLoaded&&!i._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(i.reload(),i.transform&&i.update(i.transform))}),this.on(\"error\",function(){i._sourceErrored=!0}),this._source=it(t,r,n,this),this._tiles={},this._cache=new lt(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._isIdRenderable=this._isIdRenderable.bind(this),this._coveredTiles={}}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,function(){})},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,function(){})},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._tiles)this._tiles[e].upload(t)},r.prototype.getIds=function(){var e=this;return Object.keys(this._tiles).map(Number).sort(function(r,n){var i=e._tiles[r].tileID,a=e._tiles[n].tileID,o=new t.default$1(i.canonical.x,i.canonical.y).rotate(e.transform.angle),s=new t.default$1(a.canonical.x,a.canonical.y).rotate(e.transform.angle);return i.overscaledZ-a.overscaledZ\|\|s.y-o.y\|\|s.x-o.x})},r.prototype.getRenderableIds=function(){return this.getIds().filter(this._isIdRenderable)},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0,{});return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=a.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID})),this.map&&(this.map.painter.tileExtentVAO.vao=null)},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n\|\|Math.abs(n)>1\|\|(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype.getZoom=function(t){return t.zoom+t.scaleZoom(t.tileSize/this._source.tileSize)},r.prototype._findLoadedChildren=function(t,e,r){var n=!1;for(var i in this._tiles){var a=this._tiles[i];if(!(r[i]\|\|!a.hasData()\|\|a.tileID.overscaledZ<=t.overscaledZ\|\|a.tileID.overscaledZ>e)){var o=Math.pow(2,a.tileID.canonical.z-t.canonical.z);if(Math.floor(a.tileID.canonical.x/o)===t.canonical.x&&Math.floor(a.tileID.canonical.y/o)===t.canonical.y)for(r[i]=a.tileID,n=!0;a&&a.tileID.overscaledZ-1>t.overscaledZ;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);if(!s)break;(a=this._tiles[s.key])&&a.hasData()&&(delete r[i],r[s.key]=s)}}}return n},r.prototype.findLoadedParent=function(t,e,r){for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n);if(!i)return;var a=String(i.key),o=this._tiles[a];if(o&&o.hasData())return r[a]=i,o;if(this._cache.has(i))return r[a]=i,this._cache.get(i)}},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map(function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)}):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter(function(t){return n._source.hasTile(t)}))):i=[];var o,s=(this._source.roundZoom?Math.round:Math.floor)(this.getZoom(e)),l=Math.max(s-r.maxOverzooming,this._source.minzoom),c=Math.max(s+r.maxUnderzooming,this._source.minzoom),u=this._updateRetainedTiles(i,s),f={};if(Zt(this._source.type))for(var h=Object.keys(u),p=0;p<h.length;p++){var d=h[p],g=u[d],v=n._tiles[d];if(v&&(void 0===v.fadeEndTime\|\|v.fadeEndTime>=a.now())){n._findLoadedChildren(g,c,u)&&(u[d]=g);var m=n.findLoadedParent(g,l,f);m&&n._addTile(m.tileID)}}for(o in f)u[o]\|\|(n._coveredTiles[o]=!0);for(o in f)u[o]=f[o];for(var y=t.keysDifference(this._tiles,u),x=0;x<y.length;x++)n._removeTile(y[x])}},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s=0;s<t.length;s++){var l=t[s],c=this._addTile(l),u=!1;if(c.hasData())n[l.key]=l;else{u=c.wasRequested(),n[l.key]=l;var f=!0;if(e+1>this._source.maxzoom){var h=l.children(this._source.maxzoom)[0],p=this.getTile(h);p&&p.hasData()?n[h.key]=h:f=!1}else{this._findLoadedChildren(l,o,n);for(var d=l.children(this._source.maxzoom),g=0;g<d.length;g++)if(!n[d[g].key]){f=!1;break}}if(!f)for(var v=l.overscaledZ-1;v>=a;--v){var m=l.scaledTo(v);if(i[m.key])break;if(i[m.key]=!0,!(c=this.getTile(m))&&u&&(c=this._addTile(m)),c&&(n[m.key]=m,u=c.wasRequested(),c.hasData()))break}}}return n},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e);var n=Boolean(r);return n\|\|(r=new st(e,this._source.tileSizee.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n\|\|this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout(function(){r._reloadTile(t,\"expired\"),delete r._timers[t]},n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0\|\|(e.hasData()?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r){for(var n=[],i=this.getIds(),a=1/0,o=1/0,s=-1/0,l=-1/0,c=e[0].zoom,u=0;u<e.length;u++){var f=e[u];a=Math.min(a,f.column),o=Math.min(o,f.row),s=Math.max(s,f.column),l=Math.max(l,f.row)}for(var h=0;h<i.length;h++){var p=this._tiles[i[h]],d=p.tileID,g=Math.pow(2,this.transform.zoom-p.tileID.overscaledZ),v=rp.queryPaddingt.default$8/p.tileSize/g,m=[Xt(d,new t.default$17(a,o,c)),Xt(d,new t.default$17(s,l,c))];if(m[0].x-v<t.default$8&&m[0].y-v<t.default$8&&m[1].x+v>=0&&m[1].y+v>=0){for(var y=[],x=0;x<e.length;x++)y.push(Xt(d,e[x]));n.push({tile:p,tileID:d,queryGeometry:[y],scale:g})}}return n},r.prototype.getVisibleCoordinates=function(){for(var t=this,e=this.getRenderableIds().map(function(e){return t._tiles[e].tileID}),r=0,n=e;r<n.length;r+=1){var i=n[r];i.posMatrix=t.transform.calculatePosMatrix(i.toUnwrapped())}return e},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Zt(this._source.type))for(var t in this._tiles){var e=this._tiles[t];if(void 0!==e.fadeEndTime&&e.fadeEndTime>=a.now())return!0}return!1},r}(t.Evented);function Xt(e,r){var n=r.zoomTo(e.canonical.z);return new t.default$1((n.column-(e.canonical.x+e.wrapMath.pow(2,e.canonical.z)))t.default$8,(n.row-e.canonical.y)t.default$8)}function Zt(t){return\"raster\"===t\|\|\"image\"===t\|\|\"video\"===t}function $t(){return new t.default.Worker(En.workerUrl)}Yt.maxOverzooming=10,Yt.maxUnderzooming=3;var Jt,Kt=function(){this.active={}};function Qt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.default$18.forEach(function(t){t in r&&(n[t]=r[t])}),n}function te(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Qt(t[n],e[t[n].ref]));return t}Kt.prototype.acquire=function(t){if(!this.workers){var e=En.workerCount;for(this.workers=[];this.workers.length<e;)this.workers.push(new $t)}return this.active[t]=!0,this.workers.slice()},Kt.prototype.release=function(t){delete this.active[t],0===Object.keys(this.active).length&&(this.workers.forEach(function(t){t.terminate()}),this.workers=null)};var ee={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function re(t,e,r){r.push({command:ee.addSource,args:[t,e[t]]})}function ne(t,e,r){e.push({command:ee.removeSource,args:[t]}),r[t]=!0}function ie(t,e,r,n){ne(t,r,n),re(t,e,r)}function ae(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.default$10(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.default$10(e[n][i],r[n][i]))return!1;return!0}function oe(e,r,n,i,a,o){var s;for(s in r=r\|\|{},e=e\|\|{})e.hasOwnProperty(s)&&(t.default$10(e[s],r[s])\|\|n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.default$10(e[s],r[s])\|\|n.push({command:o,args:[i,s,r[s],a]}))}function se(t){return t.id}function le(t,e){return t[e.id]=e,t}var ce=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCountthis.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};ce.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},ce.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},ce.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},ce.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},ce.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},ce.prototype._query=function(t,e,r,n,i){if(r<0\|\|t>this.width\|\|n<0\|\|e>this.height)return!i&&[];var a=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var o=0;o<this.boxKeys.length;o++)a.push({key:this.boxKeys[o],x1:this.bboxes[4o],y1:this.bboxes[4o+1],x2:this.bboxes[4o+2],y2:this.bboxes[4o+3]});for(var s=0;s<this.circleKeys.length;s++){var l=this.circles[3s],c=this.circles[3s+1],u=this.circles[3s+2];a.push({key:this.circleKeys[s],x1:l-u,y1:c-u,x2:l+u,y2:c+u})}}else{var f={hitTest:i,seenUids:{box:{},circle:{}}};this._forEachCell(t,e,r,n,this._queryCell,a,f)}return i?a.length>0:a},ce.prototype._queryCircle=function(t,e,r,n){var i=t-r,a=t+r,o=e-r,s=e+r;if(a<0\|\|i>this.width\|\|s<0\|\|o>this.height)return!n&&[];var l=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(i,o,a,s,this._queryCellCircle,l,c),n?l.length>0:l},ce.prototype.query=function(t,e,r,n){return this._query(t,e,r,n,!1)},ce.prototype.hitTest=function(t,e,r,n){return this._query(t,e,r,n,!0)},ce.prototype.hitTestCircle=function(t,e,r){return this._queryCircle(t,e,r,!0)},ce.prototype._queryCell=function(t,e,r,n,i,a,o){var s=o.seenUids,l=this.boxCells[i];if(null!==l)for(var c=this.bboxes,u=0,f=l;u<f.length;u+=1){var h=f[u];if(!s.box[h]){s.box[h]=!0;var p=4h;if(t<=c[p+2]&&e<=c[p+3]&&r>=c[p+0]&&n>=c[p+1]){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[h],x1:c[p],y1:c[p+1],x2:c[p+2],y2:c[p+3]})}}}var d=this.circleCells[i];if(null!==d)for(var g=this.circles,v=0,m=d;v<m.length;v+=1){var y=m[v];if(!s.circle[y]){s.circle[y]=!0;var x=3y;if(this._circleAndRectCollide(g[x],g[x+1],g[x+2],t,e,r,n)){if(o.hitTest)return a.push(!0),!0;var b=g[x],_=g[x+1],w=g[x+2];a.push({key:this.circleKeys[y],x1:b-w,y1:_-w,x2:b+w,y2:_+w})}}}},ce.prototype._queryCellCircle=function(t,e,r,n,i,a,o){var s=o.circle,l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4p;if(this._circleAndRectCollide(s.x,s.y,s.radius,u[d+0],u[d+1],u[d+2],u[d+3]))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,m=0,y=g;m<y.length;m+=1){var x=y[m];if(!l.circle[x]){l.circle[x]=!0;var b=3x;if(this._circlesCollide(v[b],v[b+1],v[b+2],s.x,s.y,s.radius))return a.push(!0),!0}}},ce.prototype._forEachCell=function(t,e,r,n,i,a,o){for(var s=this._convertToXCellCoord(t),l=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),u=this._convertToYCellCoord(n),f=s;f<=c;f++)for(var h=l;h<=u;h++){var p=this.xCellCounth+f;if(i.call(this,t,e,r,n,p,a,o))return}},ce.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(tthis.xScale)))},ce.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(tthis.yScale)))},ce.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return ll>oo+ss},ce.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s\|\|u<=c)return!0;var f=l-s,h=u-c;return ff+hh<=rr};var ue=t.default$19.layout;function fe(e,r,n,i,a){var o=t.mat4.identity(new Float32Array(16));return r?(t.mat4.identity(o),t.mat4.scale(o,o,[1/a,1/a,1]),n\|\|t.mat4.rotateZ(o,o,i.angle)):(t.mat4.scale(o,o,[i.width/2,-i.height/2,1]),t.mat4.translate(o,o,[1,-1,0]),t.mat4.multiply(o,o,e)),o}function he(e,r,n,i,a){var o=t.mat4.identity(new Float32Array(16));return r?(t.mat4.multiply(o,o,e),t.mat4.scale(o,o,[a,a,1]),n\|\|t.mat4.rotateZ(o,o,-i.angle)):(t.mat4.scale(o,o,[1,-1,1]),t.mat4.translate(o,o,[-1,-1,0]),t.mat4.scale(o,o,[2/i.width,2/i.height,1])),o}function pe(e,r){var n=[e.x,e.y,0,1];ke(n,n,r);var i=n[3];return{point:new t.default$1(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function de(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ge(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom,ue.properties[i?\"text-size\":\"icon-size\"]),f=[256/n.width2+1,256/n.height2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,v=!1,m=0;m<d.length;m++){var y=d.get(m);if(y.hidden\|\|y.writingMode===t.WritingMode.vertical&&!v)we(y.numGlyphs,h);else{v=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.vec4.transformMat4(x,x,r),de(x,f)){var b=.5+x[3]/n.transform.cameraToCenterDistance.5,_=t.evaluateSizeForFeature(c,u,y),w=s?_b:_/b,k=new t.default$1(y.anchorX,y.anchorY),M=pe(k,a).point,A={},T=ye(y,w,!1,l,r,a,o,e.glyphOffsetArray,p,h,M,k,A,g);v=T.useVertical,(T.notEnoughRoom\|\|v\|\|T.needsFlipping&&ye(y,w,!0,l,r,a,o,e.glyphOffsetArray,p,h,M,k,A,g).notEnoughRoom)&&we(y.numGlyphs,h)}else we(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ve(t,e,r,n,i,a,o,s,l,c,u,f){var h=s.glyphStartIndex+s.numGlyphs,p=s.lineStartIndex,d=s.lineStartIndex+s.lineLength,g=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(h-1),m=be(tg,r,n,i,a,o,s.segment,p,d,l,c,u,f);if(!m)return null;var y=be(tv,r,n,i,a,o,s.segment,p,d,l,c,u,f);return y?{first:m,last:y}:null}function me(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function ye(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var g,v=r/24,m=e.lineOffsetXr,y=e.lineOffsetYr;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ve(v,l,m,y,n,f,h,e,c,o,p,!1);if(!w)return{notEnoughRoom:!0};var k=pe(w.first.point,s).point,M=pe(w.last.point,s).point;if(i&&!n){var A=me(e.writingMode,k,M,d);if(A)return A}g=[w.first];for(var T=e.glyphStartIndex+1;T<x-1;T++)g.push(be(vl.getoffsetX(T),m,y,n,f,h,e.segment,b,_,c,o,p,!1));g.push(w.last)}else{if(i&&!n){var S=pe(h,a).point,E=e.lineStartIndex+e.segment+1,C=new t.default$1(c.getx(E),c.gety(E)),L=pe(C,a),z=L.signedDistanceFromCamera>0?L.point:xe(h,C,S,1,a),O=me(e.writingMode,S,z,d);if(O)return O}var I=be(vl.getoffsetX(e.glyphStartIndex),m,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p,!1);if(!I)return{notEnoughRoom:!0};g=[I]}for(var P=0,D=g;P<D.length;P+=1){var R=D[P];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function xe(t,e,r,n,i){var a=pe(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function be(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=i?e-r:e+r,g=d>0?1:-1,v=0;i&&(g=-1,v=Math.PI),g<0&&(v+=Math.PI);for(var m=g>0?l+s:l+s+1,y=m,x=a,b=a,_=0,w=0,k=Math.abs(d);_+w<=k;){if((m+=g)<l\|\|m>=c)return null;if(b=x,void 0===(x=h[m])){var M=new t.default$1(u.getx(m),u.gety(m)),A=pe(M,f);if(A.signedDistanceFromCamera>0)x=h[m]=A.point;else{var T=m-g;x=xe(0===_?o:new t.default$1(u.getx(T),u.gety(T)),M,b,k-_+1,f)}}_+=w,w=b.dist(x)}var S=(k-_)/w,E=x.sub(b),C=E.mult(S)._add(b);return C._add(E._unit()._perp()._mult(ng)),{point:C,angle:v+Math.atan2(x.y-b.y,x.x-b.x),tileDistance:p?{prevTileDistance:m-g===y?0:u.gettileUnitDistanceFromAnchor(m-g),lastSegmentViewportDistance:k-_}:null}}var _e=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function we(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(_e,3n)}}function ke(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]n+r[4]i+r[12],t[1]=r[1]n+r[5]i+r[13],t[3]=r[3]n+r[7]i+r[15],t}t.default$20.mat4;var Me=function(t,e,r){void 0===e&&(e=new ce(t.width+200,t.height+200,25)),void 0===r&&(r=new ce(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100};function Ae(t,e,r){t[e+4]=r?1:0}function Te(e,r,n){return r(t.default$8/(e.tileSizeMath.pow(2,n-e.tileID.overscaledZ)))}Me.prototype.placeCollisionBox=function(t,e,r,n){var i=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),a=ri.perspectiveRatio,o=t.x1a+i.point.x,s=t.y1a+i.point.y,l=t.x2a+i.point.x,c=t.y2a+i.point.y;return!e&&this.grid.hitTest(o,s,l,c)?{box:[],offscreen:!1}:{box:[o,s,l,c],offscreen:this.isOffscreen(o,s,l,c)}},Me.prototype.approximateTileDistance=function(t,e,r,n,i){var a=i?1:n/this.pitchfactor,o=t.lastSegmentViewportDistancer;return t.prevTileDistance+o+(a-1)oMath.abs(Math.sin(e))},Me.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],g=this.projectAnchor(u,o.anchorX,o.anchorY),v=c/24,m=o.lineOffsetXc,y=o.lineOffsetYc,x=new t.default$1(o.anchorX,o.anchorY),b=ve(v,l,m,y,!1,pe(x,f).point,x,o,s,f,{},!0),_=!1,w=!0,k=g.perspectiveRatioi,M=1/(in),A=0,T=0;b&&(A=this.approximateTileDistance(b.first.tileDistance,b.first.angle,M,g.cameraDistance,p),T=this.approximateTileDistance(b.last.tileDistance,b.last.angle,M,g.cameraDistance,p));for(var S=0;S<e.length;S+=5){var E=e[S],C=e[S+1],L=e[S+2],z=e[S+3];if(!b\|\|z<-A\|\|z>T)Ae(e,S,!1);else{var O=this.projectPoint(u,E,C),I=Lk;if(d.length>0){var P=O.x-d[d.length-4],D=O.y-d[d.length-3];if(II2>PP+DD&&S+8<e.length){var R=e[S+8];if(R>-A&&R<T){Ae(e,S,!1);continue}}}var B=S/5;if(d.push(O.x,O.y,I,B),Ae(e,S,!0),w=w&&this.isOffscreen(O.x-I,O.y-I,O.x+I,O.y+I),!r&&this.grid.hitTestCircle(O.x,O.y,I)){if(!h)return{circles:[],offscreen:!1};_=!0}}}return{circles:_?[]:d,offscreen:w}},Me.prototype.queryRenderedSymbols=function(e){if(0===e.length\|\|0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.default$1(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var g=d[p],v=g.key;if(void 0===f[v.bucketInstanceId]&&(f[v.bucketInstanceId]={}),!f[v.bucketInstanceId][v.featureIndex]){var m=[new t.default$1(g.x1,g.y1),new t.default$1(g.x2,g.y1),new t.default$1(g.x2,g.y2),new t.default$1(g.x1,g.y2)];t.polygonIntersectsPolygon(r,m)&&(f[v.bucketInstanceId][v.featureIndex]=!0,void 0===h[v.bucketInstanceId]&&(h[v.bucketInstanceId]=[]),h[v.bucketInstanceId].push(v.featureIndex))}}return h},Me.prototype.insertCollisionBox=function(t,e,r,n){var i={bucketInstanceId:r,featureIndex:n};(e?this.ignoredGrid:this.grid).insert(i,t[0],t[1],t[2],t[3])},Me.prototype.insertCollisionCircles=function(t,e,r,n){for(var i=e?this.ignoredGrid:this.grid,a={bucketInstanceId:r,featureIndex:n},o=0;o<t.length;o+=4)i.insertCircle(a,t[o],t[o+1],t[o+2])},Me.prototype.projectAnchor=function(t,e,r){var n=[e,r,0,1];return ke(n,n,t),{perspectiveRatio:.5+this.transform.cameraToCenterDistance/n[3].5,cameraDistance:n[3]}},Me.prototype.projectPoint=function(e,r,n){var i=[r,n,0,1];return ke(i,i,e),new t.default$1((i[0]/i[3]+1)/2this.transform.width+100,(-i[1]/i[3]+1)/2this.transform.height+100)},Me.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return ke(i,i,e),{point:new t.default$1((i[0]/i[3]+1)/2this.transform.width+100,(-i[1]/i[3]+1)/2this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3].5}},Me.prototype.isOffscreen=function(t,e,r,n){return r<100\|\|t>=this.screenRightBoundary\|\|n<100\|\|e>this.screenBottomBoundary};var Se=t.default$19.layout,Ee=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};Ee.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var Ce=function(t,e,r,n,i){this.text=new Ee(t?t.text:null,e,r,i),this.icon=new Ee(t?t.icon:null,e,n,i)};Ce.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var Le=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ze=function(t,e){this.transform=t.clone(),this.collisionIndex=new Me(this.transform),this.placements={},this.opacities={},this.stale=!1,this.fadeDuration=e,this.retainedQueryData={}};function Oe(t,e,r){t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0),t.emplaceBack(e?1:0,r?1:0)}ze.prototype.placeLayerTile=function(e,r,n,i){var a=r.getBucket(e),o=r.latestFeatureIndex;if(a&&o&&e.id===a.layerIds[0]){var s=r.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-r.tileID.overscaledZ),u=r.tileSize/t.default$8,f=this.transform.calculatePosMatrix(r.tileID.toUnwrapped()),h=fe(f,\"map\"===l.get(\"text-pitch-alignment\"),\"map\"===l.get(\"text-rotation-alignment\"),this.transform,Te(r,1,this.transform.zoom)),p=fe(f,\"map\"===l.get(\"icon-pitch-alignment\"),\"map\"===l.get(\"icon-rotation-alignment\"),this.transform,Te(r,1,this.transform.zoom));this.retainedQueryData[a.bucketInstanceId]=new function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i}(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,r.tileID),this.placeLayerBucket(a,f,h,p,c,u,n,i,s)}},ze.prototype.placeLayerBucket=function(e,r,n,i,a,o,s,l,c){for(var u=e.layers[0].layout,f=t.evaluateSizeForZoom(e.textSizeData,this.transform.zoom,Se.properties[\"text-size\"]),h=!e.hasTextData()\|\|u.get(\"text-optional\"),p=!e.hasIconData()\|\|u.get(\"icon-optional\"),d=0,g=e.symbolInstances;d<g.length;d+=1){var v=g[d];if(!l[v.crossTileID]){var m=void 0!==v.feature.text,y=void 0!==v.feature.icon,x=!0,b=null,_=null,w=null,k=0,M=0;v.collisionArrays\|\|(v.collisionArrays=e.deserializeCollisionBoxes(c,v.textBoxStartIndex,v.textBoxEndIndex,v.iconBoxStartIndex,v.iconBoxEndIndex)),v.collisionArrays.textFeatureIndex&&(k=v.collisionArrays.textFeatureIndex),v.collisionArrays.textBox&&(m=(b=this.collisionIndex.placeCollisionBox(v.collisionArrays.textBox,u.get(\"text-allow-overlap\"),o,r)).box.length>0,x=x&&b.offscreen);var A=v.collisionArrays.textCircles;if(A){var T=e.text.placedSymbolArray.get(v.placedTextSymbolIndices[0]),S=t.evaluateSizeForFeature(e.textSizeData,f,T);_=this.collisionIndex.placeCollisionCircles(A,u.get(\"text-allow-overlap\"),a,o,v.key,T,e.lineVertexArray,e.glyphOffsetArray,S,r,n,s,\"map\"===u.get(\"text-pitch-alignment\")),m=u.get(\"text-allow-overlap\")\|\|_.circles.length>0,x=x&&_.offscreen}v.collisionArrays.iconFeatureIndex&&(M=v.collisionArrays.iconFeatureIndex),v.collisionArrays.iconBox&&(y=(w=this.collisionIndex.placeCollisionBox(v.collisionArrays.iconBox,u.get(\"icon-allow-overlap\"),o,r)).box.length>0,x=x&&w.offscreen),h\|\|p?p?h\|\|(y=y&&m):m=y&&m:y=m=y&&m,m&&b&&this.collisionIndex.insertCollisionBox(b.box,u.get(\"text-ignore-placement\"),e.bucketInstanceId,k),y&&w&&this.collisionIndex.insertCollisionBox(w.box,u.get(\"icon-ignore-placement\"),e.bucketInstanceId,M),m&&_&&this.collisionIndex.insertCollisionCircles(_.circles,u.get(\"text-ignore-placement\"),e.bucketInstanceId,k),this.placements[v.crossTileID]=new Le(m,y,x\|\|e.justReloaded),l[v.crossTileID]=!0}}e.justReloaded=!1},ze.prototype.commit=function(t,e){this.commitTime=e;var r=!1,n=t&&0!==this.fadeDuration?(this.commitTime-t.commitTime)/this.fadeDuration:1,i=t?t.opacities:{};for(var a in this.placements){var o=this.placements[a],s=i[a];s?(this.opacities[a]=new Ce(s,n,o.text,o.icon),r=r\|\|o.text!==s.text.placed\|\|o.icon!==s.icon.placed):(this.opacities[a]=new Ce(null,n,o.text,o.icon,o.skipFade),r=r\|\|o.text\|\|o.icon)}for(var l in i){var c=i[l];if(!this.opacities[l]){var u=new Ce(c,n,!1,!1);u.isHidden()\|\|(this.opacities[l]=u,r=r\|\|c.text.placed\|\|c.icon.placed)}}r?this.lastPlacementChangeTime=e:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=t?t.lastPlacementChangeTime:e)},ze.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},ze.prototype.updateBucketOpacities=function(t,e,r){t.hasTextData()&&t.text.opacityVertexArray.clear(),t.hasIconData()&&t.icon.opacityVertexArray.clear(),t.hasCollisionBoxData()&&t.collisionBox.collisionVertexArray.clear(),t.hasCollisionCircleData()&&t.collisionCircle.collisionVertexArray.clear();for(var n=t.layers[0].layout,i=new Ce(null,0,!1,!1,!0),a=new Ce(null,0,n.get(\"text-allow-overlap\"),n.get(\"icon-allow-overlap\"),!0),o=0;o<t.symbolInstances.length;o++){var s=t.symbolInstances[o],l=e[s.crossTileID],c=this.opacities[s.crossTileID];l?c=i:c\|\|(c=a,this.opacities[s.crossTileID]=c),e[s.crossTileID]=!0;var u=s.numGlyphVertices>0\|\|s.numVerticalGlyphVertices>0,f=s.numIconVertices>0;if(u){for(var h=je(c.text),p=(s.numGlyphVertices+s.numVerticalGlyphVertices)/4,d=0;d<p;d++)t.text.opacityVertexArray.emplaceBack(h);for(var g=0,v=s.placedTextSymbolIndices;g<v.length;g+=1){var m=v[g];t.text.placedSymbolArray.get(m).hidden=c.text.isHidden()}}if(f){for(var y=je(c.icon),x=0;x<s.numIconVertices/4;x++)t.icon.opacityVertexArray.emplaceBack(y);t.icon.placedSymbolArray.get(o).hidden=c.icon.isHidden()}s.collisionArrays\|\|(s.collisionArrays=t.deserializeCollisionBoxes(r,s.textBoxStartIndex,s.textBoxEndIndex,s.iconBoxStartIndex,s.iconBoxEndIndex));var b=s.collisionArrays;if(b){b.textBox&&t.hasCollisionBoxData()&&Oe(t.collisionBox.collisionVertexArray,c.text.placed,!1),b.iconBox&&t.hasCollisionBoxData()&&Oe(t.collisionBox.collisionVertexArray,c.icon.placed,!1);var _=b.textCircles;if(_&&t.hasCollisionCircleData())for(var w=0;w<_.length;w+=5){var k=l\|\|0===_[w+4];Oe(t.collisionCircle.collisionVertexArray,c.text.placed,k)}}}t.sortFeatures(this.transform.angle),this.retainedQueryData[t.bucketInstanceId]&&(this.retainedQueryData[t.bucketInstanceId].featureSortOrder=t.featureSortOrder),t.hasTextData()&&t.text.opacityVertexBuffer&&t.text.opacityVertexBuffer.updateData(t.text.opacityVertexArray),t.hasIconData()&&t.icon.opacityVertexBuffer&&t.icon.opacityVertexBuffer.updateData(t.icon.opacityVertexArray),t.hasCollisionBoxData()&&t.collisionBox.collisionVertexBuffer&&t.collisionBox.collisionVertexBuffer.updateData(t.collisionBox.collisionVertexArray),t.hasCollisionCircleData()&&t.collisionCircle.collisionVertexBuffer&&t.collisionCircle.collisionVertexBuffer.updateData(t.collisionCircle.collisionVertexArray)},ze.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration},ze.prototype.hasTransitions=function(t){return this.stale\|\|t-this.lastPlacementChangeTime<this.fadeDuration},ze.prototype.stillRecent=function(t){return\"undefined\"!==this.commitTime&&this.commitTime+this.fadeDuration>t},ze.prototype.setStale=function(){this.stale=!0};var Ie=Math.pow(2,25),Pe=Math.pow(2,24),De=Math.pow(2,17),Re=Math.pow(2,16),Be=Math.pow(2,9),Fe=Math.pow(2,8),Ne=Math.pow(2,1);function je(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127t.opacity);return rIe+ePe+rDe+eRe+rBe+eFe+rNe+e}var Ve=function(){this._currentTileIndex=0,this._seenCrossTileIDs={}};Ve.prototype.continuePlacement=function(t,e,r,n,i){for(;this._currentTileIndex<t.length;){var a=t[this._currentTileIndex];if(e.placeLayerTile(n,a,r,this._seenCrossTileIDs),this._currentTileIndex++,i())return!0}};var Ue=function(t,e,r,n,i){this.placement=new ze(t,i),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Ue.prototype.isDone=function(){return this._done},Ue.prototype.continuePlacement=function(t,e,r){for(var n=this,i=a.now(),o=function(){var t=a.now()-i;return!n._forceFullPlacement&&t>2};this._currentPlacementIndex>=0;){var s=e[t[n._currentPlacementIndex]],l=n.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom\|\|s.minzoom<=l)&&(!s.maxzoom\|\|s.maxzoom>l)){if(n._inProgressLayer\|\|(n._inProgressLayer=new Ve),n._inProgressLayer.continuePlacement(r[s.source],n.placement,n._showCollisionBoxes,s,o))return;delete n._inProgressLayer}n._currentPlacementIndex--}this._done=!0},Ue.prototype.commit=function(t,e){return this.placement.commit(t,e),this.placement};var qe=512/t.default$8/2,He=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.key;this.indexedSymbolInstances[o]\|\|(this.indexedSymbolInstances[o]=[]),this.indexedSymbolInstances[o].push({crossTileID:a.crossTileID,coord:this.getScaledCoordinates(a,t)})}};He.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=qe/Math.pow(2,n),a=e.anchor;return{x:Math.floor((r.canonical.xt.default$8+a.x)i),y:Math.floor((r.canonical.yt.default$8+a.y)i)}},He.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0,a=t;i<a.length;i+=1){var o=a[i];if(!o.crossTileID){var s=this.indexedSymbolInstances[o.key];if(s)for(var l=this.getScaledCoordinates(o,e),c=0,u=s;c<u.length;c+=1){var f=u[c];if(Math.abs(f.coord.x-l.x)<=n&&Math.abs(f.coord.y-l.y)<=n&&!r[f.crossTileID]){r[f.crossTileID]=!0,o.crossTileID=f.crossTileID;break}}}}};var Ge=function(){this.maxCrossTileID=0};Ge.prototype.generate=function(){return++this.maxCrossTileID};var We=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};We.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},We.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0,i=e.symbolInstances;n<i.length;n+=1)i[n].crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]\|\|(this.usedCrossTileIDs[t.overscaledZ]={});var a=this.usedCrossTileIDs[t.overscaledZ];for(var o in this.indexes){var s=this.indexes[o];if(Number(o)>t.overscaledZ)for(var l in s){var c=s[l];c.tileID.isChildOf(t)&&c.findMatches(e.symbolInstances,t,a)}else{var u=s[t.scaledTo(Number(o)).key];u&&u.findMatches(e.symbolInstances,t,a)}}for(var f=0,h=e.symbolInstances;f<h.length;f+=1){var p=h[f];p.crossTileID\|\|(p.crossTileID=r.generate(),a[p.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new He(t,e.symbolInstances,e.bucketInstanceId),!0},We.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},We.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]\|\|(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ye=function(){this.layerIndexes={},this.crossTileIDs=new Ge,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ye.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new We);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId\|\|(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ye.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach(function(t){e[t]=!0}),this.layerIndexes)e[r]\|\|delete this.layerIndexes[r]};var Xe=function(e,r){return t.emitValidationErrors(e,r&&r.filter(function(t){return\"source.canvas\"!==t.identifier}))},Ze=t.pick(ee,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),$e=t.pick(ee,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Je=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new q((Jt\|\|(Jt=new Kt),Jt),this),this.imageManager=new O,this.glyphManager=new F(n._transformRequest,i.localIdeographFontFamily),this.lineAtlas=new U(256,512),this.crossTileSymbolIndex=new Ye,this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.default$23,this._loaded=!1,this._resetUpdates();var o=this;this._rtlTextPluginCallback=r.registerForPluginAvailability(function(t){for(var e in o.dispatcher.broadcast(\"loadRTLTextPlugin\",t.pluginURL,t.completionCallback),o.sourceCaches)o.sourceCaches[e].reload()}),this.on(\"data\",function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}})}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!x(e);e=function(t,e){if(!x(t))return t;var r=A(t);return r.path=\"/styles/v1\"+r.path,y(r,e)}(e,r.accessToken);var a=this.map._transformRequest(e,t.ResourceType.Style);t.getJSON(a,function(e,r){e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)})},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),a.frame(function(){n._load(e,!1!==r.validate)})},r.prototype._load=function(e,r){var n=this;if(!r\|\|!Xe(this,t.validateStyle(e))){for(var i in this._loaded=!0,this.stylesheet=e,e.sources)n.addSource(i,e.sources[i],{validate:!1});e.sprite?function(e,r,n){var i,o,s,l=a.devicePixelRatio>1?\"@2x\":\"\";function c(){if(s)n(s);else if(i&&o){var e=a.getImageData(o),r={};for(var l in i){var c=i[l],u=c.width,f=c.height,h=c.x,p=c.y,d=c.sdf,g=c.pixelRatio,v=new t.RGBAImage({width:u,height:f});t.RGBAImage.copy(e,v,{x:h,y:p},{x:0,y:0},{width:u,height:f}),r[l]={data:v,pixelRatio:g,sdf:d}}n(null,r)}}t.getJSON(r(_(e,l,\".json\"),t.ResourceType.SpriteJSON),function(t,e){s\|\|(s=t,i=e,c())}),t.getImage(r(_(e,l,\".png\"),t.ResourceType.SpriteImage),function(t,e){s\|\|(s=t,o=e,c())})}(e.sprite,this.map._transformRequest,function(e,r){if(e)n.fire(new t.ErrorEvent(e));else if(r)for(var i in r)n.imageManager.addImage(i,r[i]);n.imageManager.setLoaded(!0),n.fire(new t.Event(\"data\",{dataType:\"style\"}))}):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var o=te(this.stylesheet.layers);this._order=o.map(function(t){return t.id}),this._layers={};for(var s=0,l=o;s<l.length;s+=1){var c=l[s];(c=t.default$22(c)).setEventedParent(n,{layer:{id:c.id}}),n._layers[c.id]=c}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new V(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type\|\|i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){var e=this;return t.map(function(t){return e._layers[t].serialize()})},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){if(this._changed){var r=Object.keys(this._updatedLayers),n=Object.keys(this._removedLayers);for(var i in(r.length\|\|n.length)&&this._updateWorkerLayers(r,n),this._updatedSources){var a=this._updatedSources[i];\"reload\"===a?this._reloadSource(i):\"clear\"===a&&this._clearSource(i)}for(var o in this._updatedPaintProps)this._layers[o].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates(),this.fire(new t.Event(\"data\",{dataType:\"style\"}))}for(var s in this.sourceCaches)this.sourceCaches[s].used=!1;for(var l=0,c=this._order;l<c.length;l+=1){var u=c[l],f=this._layers[u];f.recalculate(e),!f.isHidden(e.zoom)&&f.source&&(this.sourceCaches[f.source].used=!0)}this.light.recalculate(e),this.z=e.zoom}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),Xe(this,t.validateStyle(e)))return!1;(e=t.clone(e)).layers=te(e.layers);var n=function(e,r){if(!e)return[{command:ee.setStyle,args:[r]}];var n=[];try{if(!t.default$10(e.version,r.version))return[{command:ee.setStyle,args:[r]}];t.default$10(e.center,r.center)\|\|n.push({command:ee.setCenter,args:[r.center]}),t.default$10(e.zoom,r.zoom)\|\|n.push({command:ee.setZoom,args:[r.zoom]}),t.default$10(e.bearing,r.bearing)\|\|n.push({command:ee.setBearing,args:[r.bearing]}),t.default$10(e.pitch,r.pitch)\|\|n.push({command:ee.setPitch,args:[r.pitch]}),t.default$10(e.sprite,r.sprite)\|\|n.push({command:ee.setSprite,args:[r.sprite]}),t.default$10(e.glyphs,r.glyphs)\|\|n.push({command:ee.setGlyphs,args:[r.glyphs]}),t.default$10(e.transition,r.transition)\|\|n.push({command:ee.setTransition,args:[r.transition]}),t.default$10(e.light,r.light)\|\|n.push({command:ee.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r\|\|{},e=e\|\|{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)\|\|ne(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.default$10(e[a],r[a])\|\|(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&ae(e,r,a)?n.push({command:ee.setGeoJSONSourceData,args:[a,r[a].data]}):ie(a,r,n,i)):re(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach(function(t){i[t.source]?n.push({command:ee.removeLayer,args:[t.id]}):o.push(t)}),n=n.concat(a),function(e,r,n){r=r\|\|[];var i,a,o,s,l,c,u,f=(e=e\|\|[]).map(se),h=r.map(se),p=e.reduce(le,{}),d=r.reduce(le,{}),g=f.slice(),v=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:ee.removeLayer,args:[o]}),g.splice(g.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],g[g.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:ee.removeLayer,args:[o]}),g.splice(g.lastIndexOf(o,g.length-a),1)):a++,c=g[g.length-i],n.push({command:ee.addLayer,args:[d[o],c]}),g.splice(g.length-i,0,o),v[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!v[o]&&!t.default$10(s,l))if(t.default$10(s.source,l.source)&&t.default$10(s[\"source-layer\"],l[\"source-layer\"])&&t.default$10(s.type,l.type)){for(u in oe(s.layout,l.layout,n,o,null,ee.setLayoutProperty),oe(s.paint,l.paint,n,o,null,ee.setPaintProperty),t.default$10(s.filter,l.filter)\|\|n.push({command:ee.setFilter,args:[o,l.filter]}),t.default$10(s.minzoom,l.minzoom)&&t.default$10(s.maxzoom,l.maxzoom)\|\|n.push({command:ee.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?oe(s[u],l[u],n,o,u.slice(6),ee.setPaintProperty):t.default$10(s[u],l[u])\|\|n.push({command:ee.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?oe(s[u],l[u],n,o,u.slice(6),ee.setPaintProperty):t.default$10(s[u],l[u])\|\|n.push({command:ee.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:ee.removeLayer,args:[o]}),c=g[g.lastIndexOf(o)+1],n.push({command:ee.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:ee.setStyle,args:[r]}]}return n}(this.serialize(),e).filter(function(t){return!(t.command in $e)});if(0===n.length)return!1;var i=n.filter(function(t){return!(t.command in Ze)});if(i.length>0)throw new Error(\"Unimplemented: \"+i.map(function(t){return t.command}).join(\", \")+\".\");return n.forEach(function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)}),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.addSource=function(e,r,n){var i=this;if(this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Yt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}}),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone(e),e=t.extend(e,{source:i})),!this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n)){var a=t.default$22(e);this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}});var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e]}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n\|\|(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r){this._checkLoaded();var n=this.getLayer(e);if(n){if(!t.default$10(n.filter,r))return null==r?(n.filter=void 0,void this._updateLayer(n)):void(this._validate(t.validateStyle.filter,\"layers.\"+n.id+\".filter\",r)\|\|(n.filter=t.clone(r),this._updateLayer(n)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?t.default$10(i.getLayoutProperty(r),n)\|\|(i.setLayoutProperty(r,n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(t,e){return this.getLayer(t).getLayoutProperty(e)},r.prototype.setPaintProperty=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.default$10(i.getPaintProperty(r),n)){var a=i._transitionablePaint._values[r].value.isDataDriven();i.setPaintProperty(r,n),(i._transitionablePaint._values[r].value.isDataDriven()\|\|a)&&this._updateLayer(i),this._changed=!0,this._updatedPaintProps[e]=!0}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){var e=this;return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,function(t){return t.serialize()}),layers:this._order.map(function(t){return e._layers[t].serialize()})},function(t){return void 0!==t})},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenRenderedFeatures=function(t){for(var e=[],r=this._order.length-1;r>=0;r--)for(var n=this._order[r],i=0,a=t;i<a.length;i+=1){var o=a[i][n];if(o)for(var s=0,l=o;s<l.length;s+=1){var c=l[s];e.push(c)}}return e},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in this.sourceCaches)r.layers&&!i[u]\|\|c.push(at(this.sourceCaches[u],this._layers,e.worldCoordinate,r,n));return this.placement&&c.push(function(t,e,r,n,i){for(var a={},o=n.queryRenderedSymbols(e),s=[],l=0,c=Object.keys(o).map(Number);l<c.length;l+=1){var u=c[l];s.push(i[u])}s.sort(ot);for(var f=function(){var e=p[h],n=e.featureIndex.lookupSymbolFeatures(o[e.bucketInstanceId],e.bucketIndex,e.sourceLayerIndex,r.filter,r.layers,t);for(var i in n){var s=a[i]=a[i]\|\|[],l=n[i];l.sort(function(t,r){var n=e.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(r.featureIndex)-i}return r.featureIndex-t.featureIndex});for(var c=0,u=l;c<u.length;c+=1){var f=u[c];s.push(f.feature)}}},h=0,p=s;h<p.length;h+=1)f();return a}(this._layers,e.viewport,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map(function(e){return t.getTileByID(e)}),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]\|\|(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e){this._checkLoaded();var r=this.light.getLight(),n=!1;for(var i in e)if(!t.default$10(e[i],r[i])){n=!0;break}if(n){var o={now:a.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return(!a\|\|!1!==a.validate)&&Xe(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.default$5},i)))},r.prototype._remove=function(){for(var e in t.evented.off(\"pluginAvailable\",this._rtlTextPluginCallback),this.sourceCaches)this.sourceCaches[e].clearTiles();this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(t,e,r){for(var n=!1,i=!1,o={},s=0,l=this._order;s<l.length;s+=1){var c=l[s],u=this._layers[c];if(\"symbol\"===u.type){if(!o[u.source]){var f=this.sourceCaches[u.source];o[u.source]=f.getRenderableIds().map(function(t){return f.getTileByID(t)}).sort(function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ\|\|(t.tileID.isLessThan(e.tileID)?-1:1)})}var h=this.crossTileSymbolIndex.addLayer(u,o[u.source],t.center.lng);n=n\|\|h}}this.crossTileSymbolIndex.pruneUnusedLayers(this._order);var p=this._layerOrderChanged;if((p\|\|!this.pauseablePlacement\|\|this.pauseablePlacement.isDone()&&!this.placement.stillRecent(a.now()))&&(this.pauseablePlacement=new Ue(t,this._order,p,e,r),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,o),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(this.placement,a.now()),i=!0),n&&this.pauseablePlacement.placement.setStale()),i\|\|n)for(var d=0,g=this._order;d<g.length;d+=1){var v=g[d],m=this._layers[v];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,o[m.source])}return!this.pauseablePlacement.isDone()\|\|this.placement.hasTransitions(a.now())},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r}(t.Evented);Je.getSourceType=function(t){return nt[t]},Je.setSourceType=function(t,e){nt[t]=e},Je.registerForPluginAvailability=t.registerForPluginAvailability;var Ke=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Qe={prelude:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n\\n#if !defined(highp)\\n#define highp\\n#endif\\n\\n#endif\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n\\n#if !defined(highp)\\n#define highp\\n#endif\\n\\n#endif\\n\\n// Unpack a pair of values that have been packed into a single float.\\n// The packed values are assumed to be 8-bit unsigned integers, and are\\n// packed like so:\\n// packedValue = floor(input[0]) 256 + input[1],\\nvec2 unpack_float(const float packedValue) {\\n int packedIntValue = int(packedValue);\\n int v0 = packedIntValue / 256;\\n return vec2(v0, packedIntValue - v0 * 256);\\n}\\n\\nvec2 unpack_opacity(const float packedOpacity) {\\n int intOpacity = int(packedOpacity) / 2;\\n return vec2(float(intOpacity) / 127.0, mod(packedOpacity, 2.0));\\n}\\n\\n// To minimize the number of attributes needed, we encode a 4-component\\n// color into a pair of floats (i.e. a vec2) as follows:\\n// [ floor(color.r * 255) * 256 + color.g * 255,\\n// floor(color.b * 255) * 256 + color.g * 255 ]\\nvec4 decode_color(const vec2 encodedColor) {\\n return vec4(\\n unpack_float(encodedColor[0]) / 255.0,\\n unpack_float(encodedColor[1]) / 255.0\\n );\\n}\\n\\n// Unpack a pair of paint values and interpolate between them.\\nfloat unpack_mix_vec2(const vec2 packedValue, const float t) {\\n return mix(packedValue[0], packedValue[1], t);\\n}\\n\\n// Unpack a pair of paint values and interpolate between them.\\nvec4 unpack_mix_vec4(const vec4 packedColors, const float t) {\\n vec4 minColor = decode_color(vec2(packedColors[0], packedColors[1]));\\n vec4 maxColor = decode_color(vec2(packedColors[2], packedColors[3]));\\n return mix(minColor, maxColor, t);\\n}\\n\\n// The offset depends on how many pixels are between the world origin and the edge of the tile:\\n// vec2 offset = mod(pixel_coord, size)\\n//\\n// At high zoom levels there are a ton of pixels between the world origin and the edge of the tile.\\n// The glsl spec only guarantees 16 bits of precision for highp floats. We need more than that.\\n//\\n// The pixel_coord is passed in as two 16 bit values:\\n// pixel_coord_upper = floor(pixel_coord / 2^16)\\n// pixel_coord_lower = mod(pixel_coord, 2^16)\\n//\\n// The offset is calculated in a series of steps that should preserve this precision:\\nvec2 get_pattern_pos(const vec2 pixel_coord_upper, const vec2 pixel_coord_lower,\\n const vec2 pattern_size, const float tile_units_to_pixels, const vec2 pos) {\\n\\n vec2 offset = mod(mod(mod(pixel_coord_upper, pattern_size) * 256.0, pattern_size) * 256.0 + pixel_coord_lower, pattern_size);\\n return (tile_units_to_pixels * pos + offset) / pattern_size;\\n}\\n\"},background:{fragmentSource:\"uniform vec4 u_color;\\nuniform float u_opacity;\\n\\nvoid main() {\\n gl_FragColor = u_color * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},backgroundPattern:{fragmentSource:\"uniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_mix;\\nuniform float u_opacity;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n gl_FragColor = mix(color1, color2, u_mix) * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n\\n v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\\n v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\\n}\\n\"},circle:{fragmentSource:\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\n\\nvarying vec3 v_data;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize mediump float radius\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize highp vec4 stroke_color\\n #pragma mapbox: initialize mediump float stroke_width\\n #pragma mapbox: initialize lowp float stroke_opacity\\n\\n vec2 extrude = v_data.xy;\\n float extrude_length = length(extrude);\\n\\n lowp float antialiasblur = v_data.z;\\n float antialiased_blur = -max(blur, antialiasblur);\\n\\n float opacity_t = smoothstep(0.0, antialiased_blur, extrude_length - 1.0);\\n\\n float color_t = stroke_width < 0.01 ? 0.0 : smoothstep(\\n antialiased_blur,\\n 0.0,\\n extrude_length - radius / (radius + stroke_width)\\n );\\n\\n gl_FragColor = opacity_t * mix(color * opacity, stroke_color * stroke_opacity, color_t);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform bool u_scale_with_map;\\nuniform bool u_pitch_with_map;\\nuniform vec2 u_extrude_scale;\\nuniform highp float u_camera_to_center_distance;\\n\\nattribute vec2 a_pos;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\n\\nvarying vec3 v_data;\\n\\nvoid main(void) {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize mediump float radius\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize highp vec4 stroke_color\\n #pragma mapbox: initialize mediump float stroke_width\\n #pragma mapbox: initialize lowp float stroke_opacity\\n\\n // unencode the extrusion vector that we snuck into the a_pos vector\\n vec2 extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\\n\\n // multiply a_pos by 0.5, since we had it * 2 in order to sneak\\n // in extrusion data\\n vec2 circle_center = floor(a_pos * 0.5);\\n if (u_pitch_with_map) {\\n vec2 corner_position = circle_center;\\n if (u_scale_with_map) {\\n corner_position += extrude * (radius + stroke_width) * u_extrude_scale;\\n } else {\\n // Pitching the circle with the map effectively scales it with the map\\n // To counteract the effect for pitch-scale: viewport, we rescale the\\n // whole circle based on the pitch scaling effect at its central point\\n vec4 projected_center = u_matrix * vec4(circle_center, 0, 1);\\n corner_position += extrude * (radius + stroke_width) * u_extrude_scale * (projected_center.w / u_camera_to_center_distance);\\n }\\n\\n gl_Position = u_matrix * vec4(corner_position, 0, 1);\\n } else {\\n gl_Position = u_matrix * vec4(circle_center, 0, 1);\\n\\n if (u_scale_with_map) {\\n gl_Position.xy += extrude * (radius + stroke_width) * u_extrude_scale * u_camera_to_center_distance;\\n } else {\\n gl_Position.xy += extrude * (radius + stroke_width) * u_extrude_scale * gl_Position.w;\\n }\\n }\\n\\n // This is a minimum blur distance that serves as a faux-antialiasing for\\n // the circle. since blur is a ratio of the circle's size and the intent is\\n // to keep the blur at roughly 1px, the two are inversely related.\\n lowp float antialiasblur = 1.0 / DEVICE_PIXEL_RATIO / (radius + stroke_width);\\n\\n v_data = vec3(extrude.x, extrude.y, antialiasblur);\\n}\\n\"},clippingMask:{fragmentSource:\"void main() {\\n gl_FragColor = vec4(1.0);\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},heatmap:{fragmentSource:\"#pragma mapbox: define highp float weight\\n\\nuniform highp float u_intensity;\\nvarying vec2 v_extrude;\\n\\n// Gaussian kernel coefficient: 1 / sqrt(2 * PI)\\n#define GAUSS_COEF 0.3989422804014327\\n\\nvoid main() {\\n #pragma mapbox: initialize highp float weight\\n\\n // Kernel density estimation with a Gaussian kernel of size 5x5\\n float d = -0.5 * 3.0 * 3.0 * dot(v_extrude, v_extrude);\\n float val = weight * u_intensity * GAUSS_COEF * exp(d);\\n\\n gl_FragColor = vec4(val, 1.0, 1.0, 1.0);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\n\\nuniform mat4 u_matrix;\\nuniform float u_extrude_scale;\\nuniform float u_opacity;\\nuniform float u_intensity;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_extrude;\\n\\n// Effective \\\"0\\\" in the kernel density texture to adjust the kernel size to;\\n// this empirically chosen number minimizes artifacts on overlapping kernels\\n// for typical heatmap cases (assuming clustered source)\\nconst highp float ZERO = 1.0 / 255.0 / 16.0;\\n\\n// Gaussian kernel coefficient: 1 / sqrt(2 * PI)\\n#define GAUSS_COEF 0.3989422804014327\\n\\nvoid main(void) {\\n #pragma mapbox: initialize highp float weight\\n #pragma mapbox: initialize mediump float radius\\n\\n // unencode the extrusion vector that we snuck into the a_pos vector\\n vec2 unscaled_extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\\n\\n // This 'extrude' comes in ranging from [-1, -1], to [1, 1]. We'll use\\n // it to produce the vertices of a square mesh framing the point feature\\n // we're adding to the kernel density texture. We'll also pass it as\\n // a varying, so that the fragment shader can determine the distance of\\n // each fragment from the point feature.\\n // Before we do so, we need to scale it up sufficiently so that the\\n // kernel falls effectively to zero at the edge of the mesh.\\n // That is, we want to know S such that\\n // weight * u_intensity * GAUSS_COEF * exp(-0.5 * 3.0^2 * S^2) == ZERO\\n // Which solves to:\\n // S = sqrt(-2.0 * log(ZERO / (weight * u_intensity * GAUSS_COEF))) / 3.0\\n float S = sqrt(-2.0 * log(ZERO / weight / u_intensity / GAUSS_COEF)) / 3.0;\\n\\n // Pass the varying in units of radius\\n v_extrude = S * unscaled_extrude;\\n\\n // Scale by radius and the zoom-based scale factor to produce actual\\n // mesh position\\n vec2 extrude = v_extrude * radius * u_extrude_scale;\\n\\n // multiply a_pos by 0.5, since we had it * 2 in order to sneak\\n // in extrusion data\\n vec4 pos = vec4(floor(a_pos * 0.5) + extrude, 0, 1);\\n\\n gl_Position = u_matrix * pos;\\n}\\n\"},heatmapTexture:{fragmentSource:\"uniform sampler2D u_image;\\nuniform sampler2D u_color_ramp;\\nuniform float u_opacity;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n float t = texture2D(u_image, v_pos).r;\\n vec4 color = texture2D(u_color_ramp, vec2(t, 0.5));\\n gl_FragColor = color * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(0.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_world;\\nattribute vec2 a_pos;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos * u_world, 0, 1);\\n\\n v_pos.x = a_pos.x;\\n v_pos.y = 1.0 - a_pos.y;\\n}\\n\"},collisionBox:{fragmentSource:\"\\nvarying float v_placed;\\nvarying float v_notUsed;\\n\\nvoid main() {\\n\\n float alpha = 0.5;\\n\\n // Red = collision, hide label\\n gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\\n\\n // Blue = no collision, label is showing\\n if (v_placed > 0.5) {\\n gl_FragColor = vec4(0.0, 0.0, 1.0, 0.5) * alpha;\\n }\\n\\n if (v_notUsed > 0.5) {\\n // This box not used, fade it out\\n gl_FragColor = .1;\\n }\\n}\",vertexSource:\"attribute vec2 a_pos;\\nattribute vec2 a_anchor_pos;\\nattribute vec2 a_extrude;\\nattribute vec2 a_placed;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_extrude_scale;\\nuniform float u_camera_to_center_distance;\\n\\nvarying float v_placed;\\nvarying float v_notUsed;\\n\\nvoid main() {\\n vec4 projectedPoint = u_matrix vec4(a_anchor_pos, 0, 1);\\n highp float camera_to_anchor_distance = projectedPoint.w;\\n highp float collision_perspective_ratio = clamp(\\n 0.5 + 0.5 * (u_camera_to_center_distance / camera_to_anchor_distance),\\n 0.0, // Prevents oversized near-field boxes in pitched/overzoomed tiles\\n 4.0);\\n\\n gl_Position = u_matrix * vec4(a_pos, 0.0, 1.0);\\n gl_Position.xy += a_extrude * u_extrude_scale * gl_Position.w * collision_perspective_ratio;\\n\\n v_placed = a_placed.x;\\n v_notUsed = a_placed.y;\\n}\\n\"},collisionCircle:{fragmentSource:\"uniform float u_overscale_factor;\\n\\nvarying float v_placed;\\nvarying float v_notUsed;\\nvarying float v_radius;\\nvarying vec2 v_extrude;\\nvarying vec2 v_extrude_scale;\\n\\nvoid main() {\\n float alpha = 0.5;\\n\\n // Red = collision, hide label\\n vec4 color = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\\n\\n // Blue = no collision, label is showing\\n if (v_placed > 0.5) {\\n color = vec4(0.0, 0.0, 1.0, 0.5) * alpha;\\n }\\n\\n if (v_notUsed > 0.5) {\\n // This box not used, fade it out\\n color = .2;\\n }\\n\\n float extrude_scale_length = length(v_extrude_scale);\\n float extrude_length = length(v_extrude) extrude_scale_length;\\n float stroke_width = 15.0 * extrude_scale_length / u_overscale_factor;\\n float radius = v_radius * extrude_scale_length;\\n\\n float distance_to_edge = abs(extrude_length - radius);\\n float opacity_t = smoothstep(-stroke_width, 0.0, -distance_to_edge);\\n\\n gl_FragColor = opacity_t * color;\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\nattribute vec2 a_anchor_pos;\\nattribute vec2 a_extrude;\\nattribute vec2 a_placed;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_extrude_scale;\\nuniform float u_camera_to_center_distance;\\n\\nvarying float v_placed;\\nvarying float v_notUsed;\\nvarying float v_radius;\\n\\nvarying vec2 v_extrude;\\nvarying vec2 v_extrude_scale;\\n\\nvoid main() {\\n vec4 projectedPoint = u_matrix * vec4(a_anchor_pos, 0, 1);\\n highp float camera_to_anchor_distance = projectedPoint.w;\\n highp float collision_perspective_ratio = clamp(\\n 0.5 + 0.5 * (u_camera_to_center_distance / camera_to_anchor_distance),\\n 0.0, // Prevents oversized near-field circles in pitched/overzoomed tiles\\n 4.0);\\n\\n gl_Position = u_matrix * vec4(a_pos, 0.0, 1.0);\\n\\n highp float padding_factor = 1.2; // Pad the vertices slightly to make room for anti-alias blur\\n gl_Position.xy += a_extrude * u_extrude_scale * padding_factor * gl_Position.w * collision_perspective_ratio;\\n\\n v_placed = a_placed.x;\\n v_notUsed = a_placed.y;\\n v_radius = abs(a_extrude.y); // We don't pitch the circles, so both units of the extrusion vector are equal in magnitude to the radius\\n\\n v_extrude = a_extrude * padding_factor;\\n v_extrude_scale = u_extrude_scale * u_camera_to_center_distance * collision_perspective_ratio;\\n}\\n\"},debug:{fragmentSource:\"uniform highp vec4 u_color;\\n\\nvoid main() {\\n gl_FragColor = u_color;\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},fill:{fragmentSource:\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_FragColor = color * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},fillOutline:{fragmentSource:\"#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 outline_color\\n #pragma mapbox: initialize lowp float opacity\\n\\n float dist = length(v_pos - gl_FragCoord.xy);\\n float alpha = 1.0 - smoothstep(0.0, 1.0, dist);\\n gl_FragColor = outline_color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"attribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 outline_color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},fillOutlinePattern:{fragmentSource:\"uniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n // find distance to outline for alpha interpolation\\n\\n float dist = length(v_pos - gl_FragCoord.xy);\\n float alpha = 1.0 - smoothstep(0.0, 1.0, dist);\\n\\n\\n gl_FragColor = mix(color1, color2, u_mix) * alpha * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_world;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n\\n v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\\n v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\\n\\n v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},fillPattern:{fragmentSource:\"uniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n gl_FragColor = mix(color1, color2, u_mix) * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n\\n v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, a_pos);\\n v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, a_pos);\\n}\\n\"},fillExtrusion:{fragmentSource:\"varying vec4 v_color;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define highp vec4 color\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float base\\n #pragma mapbox: initialize lowp float height\\n #pragma mapbox: initialize highp vec4 color\\n\\n gl_FragColor = v_color;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec3 u_lightcolor;\\nuniform lowp vec3 u_lightpos;\\nuniform lowp float u_lightintensity;\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_normal_ed;\\n\\nvarying vec4 v_color;\\n\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n\\n#pragma mapbox: define highp vec4 color\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float base\\n #pragma mapbox: initialize lowp float height\\n #pragma mapbox: initialize highp vec4 color\\n\\n vec3 normal = a_normal_ed.xyz;\\n\\n base = max(0.0, base);\\n height = max(0.0, height);\\n\\n float t = mod(normal.x, 2.0);\\n\\n gl_Position = u_matrix * vec4(a_pos, t > 0.0 ? height : base, 1);\\n\\n // Relative luminance (how dark/bright is the surface color?)\\n float colorvalue = color.r * 0.2126 + color.g * 0.7152 + color.b * 0.0722;\\n\\n v_color = vec4(0.0, 0.0, 0.0, 1.0);\\n\\n // Add slight ambient lighting so no extrusions are totally black\\n vec4 ambientlight = vec4(0.03, 0.03, 0.03, 1.0);\\n color += ambientlight;\\n\\n // Calculate cos(theta), where theta is the angle between surface normal and diffuse light ray\\n float directional = clamp(dot(normal / 16384.0, u_lightpos), 0.0, 1.0);\\n\\n // Adjust directional so that\\n // the range of values for highlight/shading is narrower\\n // with lower light intensity\\n // and with lighter/brighter surface colors\\n directional = mix((1.0 - u_lightintensity), max((1.0 - colorvalue + u_lightintensity), 1.0), directional);\\n\\n // Add gradient along z axis of side surfaces\\n if (normal.y != 0.0) {\\n directional = clamp((t + base) pow(height / 150.0, 0.5), mix(0.7, 0.98, 1.0 - u_lightintensity), 1.0);\\n }\\n\\n // Assign final color based on surface + ambient light color, diffuse light directional, and light color\\n // with lower bounds adjusted to hue of light\\n // so that shading is tinted with the complementary (opposite) color to the light color\\n v_color.r += clamp(color.r * directional * u_lightcolor.r, mix(0.0, 0.3, 1.0 - u_lightcolor.r), 1.0);\\n v_color.g += clamp(color.g * directional * u_lightcolor.g, mix(0.0, 0.3, 1.0 - u_lightcolor.g), 1.0);\\n v_color.b += clamp(color.b * directional * u_lightcolor.b, mix(0.0, 0.3, 1.0 - u_lightcolor.b), 1.0);\\n}\\n\"},fillExtrusionPattern:{fragmentSource:\"uniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec4 v_lighting;\\n\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float base\\n #pragma mapbox: initialize lowp float height\\n\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n vec4 mixedColor = mix(color1, color2, u_mix);\\n\\n gl_FragColor = mixedColor * v_lighting;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\nuniform float u_height_factor;\\n\\nuniform vec3 u_lightcolor;\\nuniform lowp vec3 u_lightpos;\\nuniform lowp float u_lightintensity;\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_normal_ed;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec4 v_lighting;\\nvarying float v_directional;\\n\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float base\\n #pragma mapbox: initialize lowp float height\\n\\n vec3 normal = a_normal_ed.xyz;\\n float edgedistance = a_normal_ed.w;\\n\\n base = max(0.0, base);\\n height = max(0.0, height);\\n\\n float t = mod(normal.x, 2.0);\\n float z = t > 0.0 ? height : base;\\n\\n gl_Position = u_matrix * vec4(a_pos, z, 1);\\n\\n vec2 pos = normal.x == 1.0 && normal.y == 0.0 && normal.z == 16384.0\\n ? a_pos // extrusion top\\n : vec2(edgedistance, z * u_height_factor); // extrusion side\\n\\n v_pos_a = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_a * u_pattern_size_a, u_tile_units_to_pixels, pos);\\n v_pos_b = get_pattern_pos(u_pixel_coord_upper, u_pixel_coord_lower, u_scale_b * u_pattern_size_b, u_tile_units_to_pixels, pos);\\n\\n v_lighting = vec4(0.0, 0.0, 0.0, 1.0);\\n float directional = clamp(dot(normal / 16383.0, u_lightpos), 0.0, 1.0);\\n directional = mix((1.0 - u_lightintensity), max((0.5 + u_lightintensity), 1.0), directional);\\n\\n if (normal.y != 0.0) {\\n directional = clamp((t + base) pow(height / 150.0, 0.5), mix(0.7, 0.98, 1.0 - u_lightintensity), 1.0);\\n }\\n\\n v_lighting.rgb += clamp(directional * u_lightcolor, mix(vec3(0.0), vec3(0.3), 1.0 - u_lightcolor), vec3(1.0));\\n}\\n\"},extrusionTexture:{fragmentSource:\"uniform sampler2D u_image;\\nuniform float u_opacity;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_FragColor = texture2D(u_image, v_pos) * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(0.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_world;\\nattribute vec2 a_pos;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos * u_world, 0, 1);\\n\\n v_pos.x = a_pos.x;\\n v_pos.y = 1.0 - a_pos.y;\\n}\\n\"},hillshadePrepare:{fragmentSource:\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\n\\nuniform sampler2D u_image;\\nvarying vec2 v_pos;\\nuniform vec2 u_dimension;\\nuniform float u_zoom;\\nuniform float u_maxzoom;\\n\\nfloat getElevation(vec2 coord, float bias) {\\n // Convert encoded elevation value to meters\\n vec4 data = texture2D(u_image, coord) * 255.0;\\n return (data.r + data.g * 256.0 + data.b * 256.0 * 256.0) / 4.0;\\n}\\n\\nvoid main() {\\n vec2 epsilon = 1.0 / u_dimension;\\n\\n // queried pixels:\\n // +-----------+\\n // \| \| \| \|\\n // \| a \| b \| c \|\\n // \| \| \| \|\\n // +-----------+\\n // \| \| \| \|\\n // \| d \| e \| f \|\\n // \| \| \| \|\\n // +-----------+\\n // \| \| \| \|\\n // \| g \| h \| i \|\\n // \| \| \| \|\\n // +-----------+\\n\\n float a = getElevation(v_pos + vec2(-epsilon.x, -epsilon.y), 0.0);\\n float b = getElevation(v_pos + vec2(0, -epsilon.y), 0.0);\\n float c = getElevation(v_pos + vec2(epsilon.x, -epsilon.y), 0.0);\\n float d = getElevation(v_pos + vec2(-epsilon.x, 0), 0.0);\\n float e = getElevation(v_pos, 0.0);\\n float f = getElevation(v_pos + vec2(epsilon.x, 0), 0.0);\\n float g = getElevation(v_pos + vec2(-epsilon.x, epsilon.y), 0.0);\\n float h = getElevation(v_pos + vec2(0, epsilon.y), 0.0);\\n float i = getElevation(v_pos + vec2(epsilon.x, epsilon.y), 0.0);\\n\\n // here we divide the x and y slopes by 8 * pixel size\\n // where pixel size (aka meters/pixel) is:\\n // circumference of the world / (pixels per tile * number of tiles)\\n // which is equivalent to: 8 * 40075016.6855785 / (512 * pow(2, u_zoom))\\n // which can be reduced to: pow(2, 19.25619978527 - u_zoom)\\n // we want to vertically exaggerate the hillshading though, because otherwise\\n // it is barely noticeable at low zooms. to do this, we multiply this by some\\n // scale factor pow(2, (u_zoom - u_maxzoom) * a) where a is an arbitrary value\\n // Here we use a=0.3 which works out to the expression below. see \\n // nickidlugash's awesome breakdown for more info\\n // https://github.com/mapbox/mapbox-gl-js/pull/5286#discussion_r148419556\\n float exaggeration = u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;\\n\\n vec2 deriv = vec2(\\n (c + f + f + i) - (a + d + d + g),\\n (g + h + h + i) - (a + b + b + c)\\n ) / pow(2.0, (u_zoom - u_maxzoom) * exaggeration + 19.2562 - u_zoom);\\n\\n gl_FragColor = clamp(vec4(\\n deriv.x / 2.0 + 0.5,\\n deriv.y / 2.0 + 0.5,\\n 1.0,\\n 1.0), 0.0, 1.0);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos = (a_texture_pos / 8192.0) / 2.0 + 0.25;\\n}\\n\"},hillshade:{fragmentSource:\"uniform sampler2D u_image;\\nvarying vec2 v_pos;\\n\\nuniform vec2 u_latrange;\\nuniform vec2 u_light;\\nuniform vec4 u_shadow;\\nuniform vec4 u_highlight;\\nuniform vec4 u_accent;\\n\\n#define PI 3.141592653589793\\n\\nvoid main() {\\n vec4 pixel = texture2D(u_image, v_pos);\\n\\n vec2 deriv = ((pixel.rg * 2.0) - 1.0);\\n\\n // We divide the slope by a scale factor based on the cosin of the pixel's approximate latitude\\n // to account for mercator projection distortion. see #4807 for details\\n float scaleFactor = cos(radians((u_latrange[0] - u_latrange[1]) * (1.0 - v_pos.y) + u_latrange[1]));\\n // We also multiply the slope by an arbitrary z-factor of 1.25\\n float slope = atan(1.25 * length(deriv) / scaleFactor);\\n float aspect = deriv.x != 0.0 ? atan(deriv.y, -deriv.x) : PI / 2.0 * (deriv.y > 0.0 ? 1.0 : -1.0);\\n\\n float intensity = u_light.x;\\n // We add PI to make this property match the global light object, which adds PI/2 to the light's azimuthal\\n // position property to account for 0deg corresponding to north/the top of the viewport in the style spec\\n // and the original shader was written to accept (-illuminationDirection - 90) as the azimuthal.\\n float azimuth = u_light.y + PI;\\n\\n // We scale the slope exponentially based on intensity, using a calculation similar to\\n // the exponential interpolation function in the style spec:\\n // https://github.com/mapbox/mapbox-gl-js/blob/master/src/style-spec/expression/definitions/interpolate.js#L217-L228\\n // so that higher intensity values create more opaque hillshading.\\n float base = 1.875 - intensity * 1.75;\\n float maxValue = 0.5 * PI;\\n float scaledSlope = intensity != 0.5 ? ((pow(base, slope) - 1.0) / (pow(base, maxValue) - 1.0)) * maxValue : slope;\\n\\n // The accent color is calculated with the cosine of the slope while the shade color is calculated with the sine\\n // so that the accent color's rate of change eases in while the shade color's eases out.\\n float accent = cos(scaledSlope);\\n // We multiply both the accent and shade color by a clamped intensity value\\n // so that intensities >= 0.5 do not additionally affect the color values\\n // while intensity values < 0.5 make the overall color more transparent.\\n vec4 accent_color = (1.0 - accent) * u_accent * clamp(intensity * 2.0, 0.0, 1.0);\\n float shade = abs(mod((aspect + azimuth) / PI + 0.5, 2.0) - 1.0);\\n vec4 shade_color = mix(u_shadow, u_highlight, shade) * sin(scaledSlope) * clamp(intensity * 2.0, 0.0, 1.0);\\n gl_FragColor = accent_color * (1.0 - shade_color.a) + shade_color;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos = a_texture_pos / 8192.0;\\n}\\n\"},line:{fragmentSource:\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_width2;\\nvarying vec2 v_normal;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_width2.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_width2.t) or when fading out\\n // (v_width2.s)\\n float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\\n\\n gl_FragColor = color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"\\n\\n// the distance over which the line edge fades out.\\n// Retina devices need a smaller distance to avoid aliasing.\\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\nattribute vec4 a_pos_normal;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform vec2 u_gl_units_to_pixels;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying float v_gamma_scale;\\nvarying highp float v_linesofar;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize mediump float gapwidth\\n #pragma mapbox: initialize lowp float offset\\n #pragma mapbox: initialize mediump float width\\n\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n\\n v_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * 2.0;\\n\\n vec2 pos = a_pos_normal.xy;\\n\\n // x is 1 if it's a round cap, 0 otherwise\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = a_pos_normal.zw;\\n v_normal = normal;\\n\\n // these transformations used to be applied in the JS and native code bases.\\n // moved them into the shader for clarity and simplicity.\\n gapwidth = gapwidth / 2.0;\\n float halfwidth = width / 2.0;\\n offset = -1.0 * offset;\\n\\n float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\\n float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\\n gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\\n\\n // calculate how much the perspective view squishes or stretches the extrude\\n float extrude_length_without_perspective = length(dist);\\n float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\\n v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\\n\\n v_width2 = vec2(outset, inset);\\n}\\n\"},lineGradient:{fragmentSource:\"\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_width2;\\nvarying vec2 v_normal;\\nvarying float v_gamma_scale;\\nvarying highp float v_lineprogress;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_width2.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_width2.t) or when fading out\\n // (v_width2.s)\\n float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\\n\\n // For gradient lines, v_lineprogress is the ratio along the entire line,\\n // scaled to [0, 2^15), and the gradient ramp is stored in a texture.\\n vec4 color = texture2D(u_image, vec2(v_lineprogress, 0.5));\\n\\n gl_FragColor = color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"\\n// the attribute conveying progress along a line is scaled to [0, 2^15)\\n#define MAX_LINE_DISTANCE 32767.0\\n\\n// the distance over which the line edge fades out.\\n// Retina devices need a smaller distance to avoid aliasing.\\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\nattribute vec4 a_pos_normal;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform vec2 u_gl_units_to_pixels;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying float v_gamma_scale;\\nvarying highp float v_lineprogress;\\n\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize mediump float gapwidth\\n #pragma mapbox: initialize lowp float offset\\n #pragma mapbox: initialize mediump float width\\n\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n\\n v_lineprogress = (floor(a_data.z / 4.0) + a_data.w * 64.0) * 2.0 / MAX_LINE_DISTANCE;\\n\\n vec2 pos = a_pos_normal.xy;\\n\\n // x is 1 if it's a round cap, 0 otherwise\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = a_pos_normal.zw;\\n v_normal = normal;\\n\\n // these transformations used to be applied in the JS and native code bases.\\n // moved them into the shader for clarity and simplicity.\\n gapwidth = gapwidth / 2.0;\\n float halfwidth = width / 2.0;\\n offset = -1.0 * offset;\\n\\n float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\\n float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\\n gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\\n\\n // calculate how much the perspective view squishes or stretches the extrude\\n float extrude_length_without_perspective = length(dist);\\n float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\\n v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\\n\\n v_width2 = vec2(outset, inset);\\n}\\n\"},linePattern:{fragmentSource:\"uniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform vec2 u_texsize;\\nuniform float u_fade;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_width2.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_width2.t) or when fading out\\n // (v_width2.s)\\n float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\\n\\n float x_a = mod(v_linesofar / u_pattern_size_a.x, 1.0);\\n float x_b = mod(v_linesofar / u_pattern_size_b.x, 1.0);\\n\\n // v_normal.y is 0 at the midpoint of the line, -1 at the lower edge, 1 at the upper edge\\n // we clamp the line width outset to be between 0 and half the pattern height plus padding (2.0)\\n // to ensure we don't sample outside the designated symbol on the sprite sheet.\\n // 0.5 is added to shift the component to be bounded between 0 and 1 for interpolation of\\n // the texture coordinate\\n float y_a = 0.5 + (v_normal.y * clamp(v_width2.s, 0.0, (u_pattern_size_a.y + 2.0) / 2.0) / u_pattern_size_a.y);\\n float y_b = 0.5 + (v_normal.y * clamp(v_width2.s, 0.0, (u_pattern_size_b.y + 2.0) / 2.0) / u_pattern_size_b.y);\\n vec2 pos_a = mix(u_pattern_tl_a / u_texsize, u_pattern_br_a / u_texsize, vec2(x_a, y_a));\\n vec2 pos_b = mix(u_pattern_tl_b / u_texsize, u_pattern_br_b / u_texsize, vec2(x_b, y_b));\\n\\n vec4 color = mix(texture2D(u_image, pos_a), texture2D(u_image, pos_b), u_fade);\\n\\n gl_FragColor = color * alpha * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\n// the distance over which the line edge fades out.\\n// Retina devices need a smaller distance to avoid aliasing.\\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\\n\\nattribute vec4 a_pos_normal;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform vec2 u_gl_units_to_pixels;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize lowp float offset\\n #pragma mapbox: initialize mediump float gapwidth\\n #pragma mapbox: initialize mediump float width\\n\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n vec2 pos = a_pos_normal.xy;\\n\\n // x is 1 if it's a round cap, 0 otherwise\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = a_pos_normal.zw;\\n v_normal = normal;\\n\\n // these transformations used to be applied in the JS and native code bases.\\n // moved them into the shader for clarity and simplicity.\\n gapwidth = gapwidth / 2.0;\\n float halfwidth = width / 2.0;\\n offset = -1.0 * offset;\\n\\n float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\\n float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\\n gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\\n\\n // calculate how much the perspective view squishes or stretches the extrude\\n float extrude_length_without_perspective = length(dist);\\n float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\\n v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\\n\\n v_linesofar = a_linesofar;\\n v_width2 = vec2(outset, inset);\\n}\\n\"},lineSDF:{fragmentSource:\"\\nuniform sampler2D u_image;\\nuniform float u_sdfgamma;\\nuniform float u_mix;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize mediump float width\\n #pragma mapbox: initialize lowp float floorwidth\\n\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_width2.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_width2.t) or when fading out\\n // (v_width2.s)\\n float blur2 = (blur + 1.0 / DEVICE_PIXEL_RATIO) * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_width2.t - blur2), v_width2.s - dist) / blur2, 0.0, 1.0);\\n\\n float sdfdist_a = texture2D(u_image, v_tex_a).a;\\n float sdfdist_b = texture2D(u_image, v_tex_b).a;\\n float sdfdist = mix(sdfdist_a, sdfdist_b, u_mix);\\n alpha = smoothstep(0.5 - u_sdfgamma / floorwidth, 0.5 + u_sdfgamma / floorwidth, sdfdist);\\n\\n gl_FragColor = color (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\n// the distance over which the line edge fades out.\\n// Retina devices need a smaller distance to avoid aliasing.\\n#define ANTIALIASING 1.0 / DEVICE_PIXEL_RATIO / 2.0\\n\\nattribute vec4 a_pos_normal;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform vec2 u_patternscale_a;\\nuniform float u_tex_y_a;\\nuniform vec2 u_patternscale_b;\\nuniform float u_tex_y_b;\\nuniform vec2 u_gl_units_to_pixels;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_width2;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize mediump float gapwidth\\n #pragma mapbox: initialize lowp float offset\\n #pragma mapbox: initialize mediump float width\\n #pragma mapbox: initialize lowp float floorwidth\\n\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n vec2 pos = a_pos_normal.xy;\\n\\n // x is 1 if it's a round cap, 0 otherwise\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = a_pos_normal.zw;\\n v_normal = normal;\\n\\n // these transformations used to be applied in the JS and native code bases.\\n // moved them into the shader for clarity and simplicity.\\n gapwidth = gapwidth / 2.0;\\n float halfwidth = width / 2.0;\\n offset = -1.0 * offset;\\n\\n float inset = gapwidth + (gapwidth > 0.0 ? ANTIALIASING : 0.0);\\n float outset = gapwidth + halfwidth * (gapwidth > 0.0 ? 2.0 : 1.0) + ANTIALIASING;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist =outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset2 = offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n vec4 projected_extrude = u_matrix * vec4(dist / u_ratio, 0.0, 0.0);\\n gl_Position = u_matrix * vec4(pos + offset2 / u_ratio, 0.0, 1.0) + projected_extrude;\\n\\n // calculate how much the perspective view squishes or stretches the extrude\\n float extrude_length_without_perspective = length(dist);\\n float extrude_length_with_perspective = length(projected_extrude.xy / gl_Position.w * u_gl_units_to_pixels);\\n v_gamma_scale = extrude_length_without_perspective / extrude_length_with_perspective;\\n\\n v_tex_a = vec2(a_linesofar * u_patternscale_a.x / floorwidth, normal.y * u_patternscale_a.y + u_tex_y_a);\\n v_tex_b = vec2(a_linesofar * u_patternscale_b.x / floorwidth, normal.y * u_patternscale_b.y + u_tex_y_b);\\n\\n v_width2 = vec2(outset, inset);\\n}\\n\"},raster:{fragmentSource:\"uniform float u_fade_t;\\nuniform float u_opacity;\\nuniform sampler2D u_image0;\\nuniform sampler2D u_image1;\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nuniform float u_brightness_low;\\nuniform float u_brightness_high;\\n\\nuniform float u_saturation_factor;\\nuniform float u_contrast_factor;\\nuniform vec3 u_spin_weights;\\n\\nvoid main() {\\n\\n // read and cross-fade colors from the main and parent tiles\\n vec4 color0 = texture2D(u_image0, v_pos0);\\n vec4 color1 = texture2D(u_image1, v_pos1);\\n if (color0.a > 0.0) {\\n color0.rgb = color0.rgb / color0.a;\\n }\\n if (color1.a > 0.0) {\\n color1.rgb = color1.rgb / color1.a;\\n }\\n vec4 color = mix(color0, color1, u_fade_t);\\n color.a = u_opacity;\\n vec3 rgb = color.rgb;\\n\\n // spin\\n rgb = vec3(\\n dot(rgb, u_spin_weights.xyz),\\n dot(rgb, u_spin_weights.zxy),\\n dot(rgb, u_spin_weights.yzx));\\n\\n // saturation\\n float average = (color.r + color.g + color.b) / 3.0;\\n rgb += (average - rgb) u_saturation_factor;\\n\\n // contrast\\n rgb = (rgb - 0.5) * u_contrast_factor + 0.5;\\n\\n // brightness\\n vec3 u_high_vec = vec3(u_brightness_low, u_brightness_low, u_brightness_low);\\n vec3 u_low_vec = vec3(u_brightness_high, u_brightness_high, u_brightness_high);\\n\\n gl_FragColor = vec4(mix(u_high_vec, u_low_vec, rgb) * color.a, color.a);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"uniform mat4 u_matrix;\\nuniform vec2 u_tl_parent;\\nuniform float u_scale_parent;\\nuniform float u_buffer_scale;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n // We are using Int16 for texture position coordinates to give us enough precision for\\n // fractional coordinates. We use 8192 to scale the texture coordinates in the buffer\\n // as an arbitrarily high number to preserve adequate precision when rendering.\\n // This is also the same value as the EXTENT we are using for our tile buffer pos coordinates,\\n // so math for modifying either is consistent.\\n v_pos0 = (((a_texture_pos / 8192.0) - 0.5) / u_buffer_scale ) + 0.5;\\n v_pos1 = (v_pos0 * u_scale_parent) + u_tl_parent;\\n}\\n\"},symbolIcon:{fragmentSource:\"uniform sampler2D u_texture;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_tex;\\nvarying float v_fade_opacity;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n lowp float alpha = opacity * v_fade_opacity;\\n gl_FragColor = texture2D(u_texture, v_tex) * alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"const float PI = 3.141592653589793;\\n\\nattribute vec4 a_pos_offset;\\nattribute vec4 a_data;\\nattribute vec3 a_projected_pos;\\nattribute float a_fade_opacity;\\n\\nuniform bool u_is_size_zoom_constant;\\nuniform bool u_is_size_feature_constant;\\nuniform highp float u_size_t; // used to interpolate between zoom stops when size is a composite function\\nuniform highp float u_size; // used when size is both zoom and feature constant\\nuniform highp float u_camera_to_center_distance;\\nuniform highp float u_pitch;\\nuniform bool u_rotate_symbol;\\nuniform highp float u_aspect_ratio;\\nuniform float u_fade_change;\\n\\n#pragma mapbox: define lowp float opacity\\n\\nuniform mat4 u_matrix;\\nuniform mat4 u_label_plane_matrix;\\nuniform mat4 u_gl_coord_matrix;\\n\\nuniform bool u_is_text;\\nuniform bool u_pitch_with_map;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying float v_fade_opacity;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp float opacity\\n\\n vec2 a_pos = a_pos_offset.xy;\\n vec2 a_offset = a_pos_offset.zw;\\n\\n vec2 a_tex = a_data.xy;\\n vec2 a_size = a_data.zw;\\n\\n highp float segment_angle = -a_projected_pos[2];\\n\\n float size;\\n if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {\\n size = mix(a_size[0], a_size[1], u_size_t) / 10.0;\\n } else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {\\n size = a_size[0] / 10.0;\\n } else if (!u_is_size_zoom_constant && u_is_size_feature_constant) {\\n size = u_size;\\n } else {\\n size = u_size;\\n }\\n\\n vec4 projectedPoint = u_matrix * vec4(a_pos, 0, 1);\\n highp float camera_to_anchor_distance = projectedPoint.w;\\n // See comments in symbol_sdf.vertex\\n highp float distance_ratio = u_pitch_with_map ?\\n camera_to_anchor_distance / u_camera_to_center_distance :\\n u_camera_to_center_distance / camera_to_anchor_distance;\\n highp float perspective_ratio = clamp(\\n 0.5 + 0.5 * distance_ratio,\\n 0.0, // Prevents oversized near-field symbols in pitched/overzoomed tiles\\n 4.0);\\n\\n size = perspective_ratio;\\n\\n float fontScale = u_is_text ? size / 24.0 : size;\\n\\n highp float symbol_rotation = 0.0;\\n if (u_rotate_symbol) {\\n // See comments in symbol_sdf.vertex\\n vec4 offsetProjectedPoint = u_matrix vec4(a_pos + vec2(1, 0), 0, 1);\\n\\n vec2 a = projectedPoint.xy / projectedPoint.w;\\n vec2 b = offsetProjectedPoint.xy / offsetProjectedPoint.w;\\n\\n symbol_rotation = atan((b.y - a.y) / u_aspect_ratio, b.x - a.x);\\n }\\n\\n highp float angle_sin = sin(segment_angle + symbol_rotation);\\n highp float angle_cos = cos(segment_angle + symbol_rotation);\\n mat2 rotation_matrix = mat2(angle_cos, -1.0 * angle_sin, angle_sin, angle_cos);\\n\\n vec4 projected_pos = u_label_plane_matrix * vec4(a_projected_pos.xy, 0.0, 1.0);\\n gl_Position = u_gl_coord_matrix * vec4(projected_pos.xy / projected_pos.w + rotation_matrix * (a_offset / 32.0 * fontScale), 0.0, 1.0);\\n\\n v_tex = a_tex / u_texsize;\\n vec2 fade_opacity = unpack_opacity(a_fade_opacity);\\n float fade_change = fade_opacity[1] > 0.5 ? u_fade_change : -u_fade_change;\\n v_fade_opacity = max(0.0, min(1.0, fade_opacity[0] + fade_change));\\n}\\n\"},symbolSDF:{fragmentSource:\"#define SDF_PX 8.0\\n#define EDGE_GAMMA 0.105/DEVICE_PIXEL_RATIO\\n\\nuniform bool u_is_halo;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\n\\nuniform sampler2D u_texture;\\nuniform highp float u_gamma_scale;\\nuniform bool u_is_text;\\n\\nvarying vec2 v_data0;\\nvarying vec3 v_data1;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 fill_color\\n #pragma mapbox: initialize highp vec4 halo_color\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize lowp float halo_width\\n #pragma mapbox: initialize lowp float halo_blur\\n\\n vec2 tex = v_data0.xy;\\n float gamma_scale = v_data1.x;\\n float size = v_data1.y;\\n float fade_opacity = v_data1[2];\\n\\n float fontScale = u_is_text ? size / 24.0 : size;\\n\\n lowp vec4 color = fill_color;\\n highp float gamma = EDGE_GAMMA / (fontScale * u_gamma_scale);\\n lowp float buff = (256.0 - 64.0) / 256.0;\\n if (u_is_halo) {\\n color = halo_color;\\n gamma = (halo_blur * 1.19 / SDF_PX + EDGE_GAMMA) / (fontScale * u_gamma_scale);\\n buff = (6.0 - halo_width / fontScale) / SDF_PX;\\n }\\n\\n lowp float dist = texture2D(u_texture, tex).a;\\n highp float gamma_scaled = gamma * gamma_scale;\\n highp float alpha = smoothstep(buff - gamma_scaled, buff + gamma_scaled, dist);\\n\\n gl_FragColor = color * (alpha * opacity * fade_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"const float PI = 3.141592653589793;\\n\\nattribute vec4 a_pos_offset;\\nattribute vec4 a_data;\\nattribute vec3 a_projected_pos;\\nattribute float a_fade_opacity;\\n\\n// contents of a_size vary based on the type of property value\\n// used for {text,icon}-size.\\n// For constants, a_size is disabled.\\n// For source functions, we bind only one value per vertex: the value of {text,icon}-size evaluated for the current feature.\\n// For composite functions:\\n// [ text-size(lowerZoomStop, feature),\\n// text-size(upperZoomStop, feature) ]\\nuniform bool u_is_size_zoom_constant;\\nuniform bool u_is_size_feature_constant;\\nuniform highp float u_size_t; // used to interpolate between zoom stops when size is a composite function\\nuniform highp float u_size; // used when size is both zoom and feature constant\\n\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\n\\nuniform mat4 u_matrix;\\nuniform mat4 u_label_plane_matrix;\\nuniform mat4 u_gl_coord_matrix;\\n\\nuniform bool u_is_text;\\nuniform bool u_pitch_with_map;\\nuniform highp float u_pitch;\\nuniform bool u_rotate_symbol;\\nuniform highp float u_aspect_ratio;\\nuniform highp float u_camera_to_center_distance;\\nuniform float u_fade_change;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_data0;\\nvarying vec3 v_data1;\\n\\nvoid main() {\\n #pragma mapbox: initialize highp vec4 fill_color\\n #pragma mapbox: initialize highp vec4 halo_color\\n #pragma mapbox: initialize lowp float opacity\\n #pragma mapbox: initialize lowp float halo_width\\n #pragma mapbox: initialize lowp float halo_blur\\n\\n vec2 a_pos = a_pos_offset.xy;\\n vec2 a_offset = a_pos_offset.zw;\\n\\n vec2 a_tex = a_data.xy;\\n vec2 a_size = a_data.zw;\\n\\n highp float segment_angle = -a_projected_pos[2];\\n float size;\\n\\n if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {\\n size = mix(a_size[0], a_size[1], u_size_t) / 10.0;\\n } else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {\\n size = a_size[0] / 10.0;\\n } else if (!u_is_size_zoom_constant && u_is_size_feature_constant) {\\n size = u_size;\\n } else {\\n size = u_size;\\n }\\n\\n vec4 projectedPoint = u_matrix * vec4(a_pos, 0, 1);\\n highp float camera_to_anchor_distance = projectedPoint.w;\\n // If the label is pitched with the map, layout is done in pitched space,\\n // which makes labels in the distance smaller relative to viewport space.\\n // We counteract part of that effect by multiplying by the perspective ratio.\\n // If the label isn't pitched with the map, we do layout in viewport space,\\n // which makes labels in the distance larger relative to the features around\\n // them. We counteract part of that effect by dividing by the perspective ratio.\\n highp float distance_ratio = u_pitch_with_map ?\\n camera_to_anchor_distance / u_camera_to_center_distance :\\n u_camera_to_center_distance / camera_to_anchor_distance;\\n highp float perspective_ratio = clamp(\\n 0.5 + 0.5 * distance_ratio,\\n 0.0, // Prevents oversized near-field symbols in pitched/overzoomed tiles\\n 4.0);\\n\\n size = perspective_ratio;\\n\\n float fontScale = u_is_text ? size / 24.0 : size;\\n\\n highp float symbol_rotation = 0.0;\\n if (u_rotate_symbol) {\\n // Point labels with 'rotation-alignment: map' are horizontal with respect to tile units\\n // To figure out that angle in projected space, we draw a short horizontal line in tile\\n // space, project it, and measure its angle in projected space.\\n vec4 offsetProjectedPoint = u_matrix vec4(a_pos + vec2(1, 0), 0, 1);\\n\\n vec2 a = projectedPoint.xy / projectedPoint.w;\\n vec2 b = offsetProjectedPoint.xy / offsetProjectedPoint.w;\\n\\n symbol_rotation = atan((b.y - a.y) / u_aspect_ratio, b.x - a.x);\\n }\\n\\n highp float angle_sin = sin(segment_angle + symbol_rotation);\\n highp float angle_cos = cos(segment_angle + symbol_rotation);\\n mat2 rotation_matrix = mat2(angle_cos, -1.0 * angle_sin, angle_sin, angle_cos);\\n\\n vec4 projected_pos = u_label_plane_matrix * vec4(a_projected_pos.xy, 0.0, 1.0);\\n gl_Position = u_gl_coord_matrix * vec4(projected_pos.xy / projected_pos.w + rotation_matrix * (a_offset / 32.0 * fontScale), 0.0, 1.0);\\n float gamma_scale = gl_Position.w;\\n\\n vec2 tex = a_tex / u_texsize;\\n vec2 fade_opacity = unpack_opacity(a_fade_opacity);\\n float fade_change = fade_opacity[1] > 0.5 ? u_fade_change : -u_fade_change;\\n float interpolated_fade_opacity = max(0.0, min(1.0, fade_opacity[0] + fade_change));\\n\\n v_data0 = vec2(tex.x, tex.y);\\n v_data1 = vec3(gamma_scale, size, interpolated_fade_opacity);\\n}\\n\"}},tr=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,er=function(t){var e=Qe[t],r={};e.fragmentSource=e.fragmentSource.replace(tr,function(t,e,n,i,a){return r[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+n+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+n+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n \"+n+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}),e.vertexSource=e.vertexSource.replace(tr,function(t,e,n,i,a){var o=\"float\"===i?\"vec2\":\"vec4\";return r[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float a_\"+a+\"_t;\\nattribute \"+n+\" \"+o+\" a_\"+a+\";\\nvarying \"+n+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+n+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+a+\" = unpack_mix_\"+o+\"(a_\"+a+\", a_\"+a+\"_t);\\n#else\\n \"+n+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float a_\"+a+\"_t;\\nattribute \"+n+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+n+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+n+\" \"+i+\" \"+a+\" = unpack_mix_\"+o+\"(a_\"+a+\", a_\"+a+\"_t);\\n#else\\n \"+n+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})};for(var rr in Qe)er(rr);var nr=Qe,ir=function(t,e,r,n){var i=t.gl;this.program=i.createProgram();var o=r.defines().concat(\"#define DEVICE_PIXEL_RATIO \"+a.devicePixelRatio.toFixed(1));n&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(nr.prelude.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(nr.prelude.vertexSource,e.vertexSource).join(\"\\n\"),c=i.createShader(i.FRAGMENT_SHADER);i.shaderSource(c,s),i.compileShader(c),i.attachShader(this.program,c);var u=i.createShader(i.VERTEX_SHADER);i.shaderSource(u,l),i.compileShader(u),i.attachShader(this.program,u);for(var f=r.layoutAttributes\|\|[],h=0;h<f.length;h++)i.bindAttribLocation(this.program,h,f[h].name);i.linkProgram(this.program),this.numAttributes=i.getProgramParameter(this.program,i.ACTIVE_ATTRIBUTES),this.attributes={},this.uniforms={};for(var p=0;p<this.numAttributes;p++){var d=i.getActiveAttrib(this.program,p);d&&(this.attributes[d.name]=i.getAttribLocation(this.program,d.name))}for(var g=i.getProgramParameter(this.program,i.ACTIVE_UNIFORMS),v=0;v<g;v++){var m=i.getActiveUniform(this.program,v);m&&(this.uniforms[m.name]=i.getUniformLocation(this.program,m.name))}};function ar(e,r,n,i,a){for(var o=0;o<n.length;o++){var s=n[o];if(i.isLessThan(s.tileID))break;if(r.key===s.tileID.key)return;if(s.tileID.isChildOf(r)){for(var l=r.children(1/0),c=0;c<l.length;c++)ar(e,l[c],n.slice(o),i,a);return}}var u=r.overscaledZ-e.overscaledZ,f=new t.CanonicalTileID(u,r.canonical.x-(e.canonical.x<<u),r.canonical.y-(e.canonical.y<<u));a[f.key]=a[f.key]\|\|f}function or(t,e,r,n,i){var a=t.context,o=a.gl,s=i?t.useProgram(\"collisionCircle\"):t.useProgram(\"collisionBox\");a.setDepthMode(qt.disabled),a.setStencilMode(Ht.disabled),a.setColorMode(t.colorModeForRenderPass());for(var l=0;l<n.length;l++){var c=n[l],u=e.getTile(c),f=u.getBucket(r);if(f){var h=i?f.collisionCircle:f.collisionBox;if(h){o.uniformMatrix4fv(s.uniforms.u_matrix,!1,c.posMatrix),i\|\|a.lineWidth.set(1),o.uniform1f(s.uniforms.u_camera_to_center_distance,t.transform.cameraToCenterDistance);var p=Te(u,1,t.transform.zoom),d=Math.pow(2,t.transform.zoom-u.tileID.overscaledZ);o.uniform1f(s.uniforms.u_pixels_to_tile_units,p),o.uniform2f(s.uniforms.u_extrude_scale,t.transform.pixelsToGLUnits[0]/(pd),t.transform.pixelsToGLUnits[1]/(pd)),o.uniform1f(s.uniforms.u_overscale_factor,u.tileID.overscaleFactor()),s.draw(a,i?o.TRIANGLES:o.LINES,r.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,null,h.collisionVertexBuffer,null)}}}}ir.prototype.draw=function(t,e,r,n,i,a,o,s,l){for(var c,u=t.gl,f=(c={},c[u.LINES]=2,c[u.TRIANGLES]=3,c)[e],h=0,p=a.get();h<p.length;h+=1){var d=p[h],g=d.vaos\|\|(d.vaos={});(g[r]\|\|(g[r]=new Q)).bind(t,this,n,o?o.getPaintVertexBuffers():[],i,d.vertexOffset,s,l),u.drawElements(e,d.primitiveLengthf,u.UNSIGNED_SHORT,d.primitiveOffsetf2)}};var sr=t.mat4.identity(new Float32Array(16)),lr=t.default$19.layout;function cr(t,e,r,n,i,a,o,s,l,c){var u,f=t.context,h=f.gl,p=t.transform,d=\"map\"===s,g=\"map\"===l,v=d&&\"line\"===r.layout.get(\"symbol-placement\"),m=d&&!g&&!v,y=g;f.setDepthMode(y?t.depthModeForSublayer(0,qt.ReadOnly):qt.disabled);for(var x=0,b=n;x<b.length;x+=1){var _=b[x],w=e.getTile(_),k=w.getBucket(r);if(k){var M=i?k.text:k.icon;if(M&&M.segments.get().length){var A=M.programConfigurations.get(r.id),T=i\|\|k.sdfIcons,S=i?k.textSizeData:k.iconSizeData;if(u\|\|(u=t.useProgram(T?\"symbolSDF\":\"symbolIcon\",A),A.setUniforms(t.context,u,r.paint,{zoom:t.transform.zoom}),ur(u,t,r,i,m,g,S)),f.activeTexture.set(h.TEXTURE0),h.uniform1i(u.uniforms.u_texture,0),i)w.glyphAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),h.uniform2fv(u.uniforms.u_texsize,w.glyphAtlasTexture.size);else{var E=1!==r.layout.get(\"icon-size\").constantOr(0)\|\|k.iconsNeedLinear,C=g\|\|0!==p.pitch;w.iconAtlasTexture.bind(T\|\|t.options.rotating\|\|t.options.zooming\|\|E\|\|C?h.LINEAR:h.NEAREST,h.CLAMP_TO_EDGE),h.uniform2fv(u.uniforms.u_texsize,w.iconAtlasTexture.size)}h.uniformMatrix4fv(u.uniforms.u_matrix,!1,t.translatePosMatrix(_.posMatrix,w,a,o));var L=Te(w,1,t.transform.zoom),z=fe(_.posMatrix,g,d,t.transform,L),O=he(_.posMatrix,g,d,t.transform,L);h.uniformMatrix4fv(u.uniforms.u_gl_coord_matrix,!1,t.translatePosMatrix(O,w,a,o,!0)),v?(h.uniformMatrix4fv(u.uniforms.u_label_plane_matrix,!1,sr),ge(k,_.posMatrix,t,i,z,O,g,c)):h.uniformMatrix4fv(u.uniforms.u_label_plane_matrix,!1,z),h.uniform1f(u.uniforms.u_fade_change,t.options.fadeDuration?t.symbolFadeChange:1),fr(u,A,t,r,w,M,i,T,g)}}}}function ur(e,r,n,i,a,o,s){var l=r.context.gl,c=r.transform;l.uniform1i(e.uniforms.u_pitch_with_map,o?1:0),l.uniform1f(e.uniforms.u_is_text,i?1:0),l.uniform1f(e.uniforms.u_pitch,c.pitch/3602Math.PI);var u=\"constant\"===s.functionType\|\|\"source\"===s.functionType,f=\"constant\"===s.functionType\|\|\"camera\"===s.functionType;l.uniform1i(e.uniforms.u_is_size_zoom_constant,u?1:0),l.uniform1i(e.uniforms.u_is_size_feature_constant,f?1:0),l.uniform1f(e.uniforms.u_camera_to_center_distance,c.cameraToCenterDistance);var h=t.evaluateSizeForZoom(s,c.zoom,lr.properties[i?\"text-size\":\"icon-size\"]);void 0!==h.uSizeT&&l.uniform1f(e.uniforms.u_size_t,h.uSizeT),void 0!==h.uSize&&l.uniform1f(e.uniforms.u_size,h.uSize),l.uniform1f(e.uniforms.u_aspect_ratio,c.width/c.height),l.uniform1i(e.uniforms.u_rotate_symbol,a?1:0)}function fr(t,e,r,n,i,a,o,s,l){var c=r.context,u=c.gl,f=r.transform;if(s){var h=0!==n.paint.get(o?\"text-halo-width\":\"icon-halo-width\").constantOr(1),p=l?Math.cos(f._pitch)f.cameraToCenterDistance:1;u.uniform1f(t.uniforms.u_gamma_scale,p),h&&(u.uniform1f(t.uniforms.u_is_halo,1),hr(a,n,c,t)),u.uniform1f(t.uniforms.u_is_halo,0)}hr(a,n,c,t)}function hr(t,e,r,n){n.draw(r,r.gl.TRIANGLES,e.id,t.layoutVertexBuffer,t.indexBuffer,t.segments,t.programConfigurations.get(e.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function pr(t,e,r,n,i,o,s,l,c){var u,f,h,p,d=e.context,g=d.gl,v=i.paint.get(\"line-dasharray\"),m=i.paint.get(\"line-pattern\");if(l\|\|c){var y=1/Te(r,1,e.transform.tileZoom);if(v){u=e.lineAtlas.getDash(v.from,\"round\"===i.layout.get(\"line-cap\")),f=e.lineAtlas.getDash(v.to,\"round\"===i.layout.get(\"line-cap\"));var x=u.widthv.fromScale,b=f.widthv.toScale;g.uniform2f(t.uniforms.u_patternscale_a,y/x,-u.height/2),g.uniform2f(t.uniforms.u_patternscale_b,y/b,-f.height/2),g.uniform1f(t.uniforms.u_sdfgamma,e.lineAtlas.width/(256Math.min(x,b)a.devicePixelRatio)/2)}else if(m){if(h=e.imageManager.getPattern(m.from),p=e.imageManager.getPattern(m.to),!h\|\|!p)return;g.uniform2f(t.uniforms.u_pattern_size_a,h.displaySize[0]m.fromScale/y,h.displaySize[1]),g.uniform2f(t.uniforms.u_pattern_size_b,p.displaySize[0]m.toScale/y,p.displaySize[1]);var _=e.imageManager.getPixelSize(),w=_.width,k=_.height;g.uniform2fv(t.uniforms.u_texsize,[w,k])}g.uniform2f(t.uniforms.u_gl_units_to_pixels,1/e.transform.pixelsToGLUnits[0],1/e.transform.pixelsToGLUnits[1])}l&&(v?(g.uniform1i(t.uniforms.u_image,0),d.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(d),g.uniform1f(t.uniforms.u_tex_y_a,u.y),g.uniform1f(t.uniforms.u_tex_y_b,f.y),g.uniform1f(t.uniforms.u_mix,v.t)):m&&(g.uniform1i(t.uniforms.u_image,0),d.activeTexture.set(g.TEXTURE0),e.imageManager.bind(d),g.uniform2fv(t.uniforms.u_pattern_tl_a,h.tl),g.uniform2fv(t.uniforms.u_pattern_br_a,h.br),g.uniform2fv(t.uniforms.u_pattern_tl_b,p.tl),g.uniform2fv(t.uniforms.u_pattern_br_b,p.br),g.uniform1f(t.uniforms.u_fade,m.t))),d.setStencilMode(e.stencilModeForClipping(o));var M=e.translatePosMatrix(o.posMatrix,r,i.paint.get(\"line-translate\"),i.paint.get(\"line-translate-anchor\"));if(g.uniformMatrix4fv(t.uniforms.u_matrix,!1,M),g.uniform1f(t.uniforms.u_ratio,1/Te(r,1,e.transform.zoom)),i.paint.get(\"line-gradient\")){d.activeTexture.set(g.TEXTURE0);var A=i.gradientTexture;if(!i.gradient)return;A\|\|(A=i.gradientTexture=new z(d,i.gradient,g.RGBA)),A.bind(g.LINEAR,g.CLAMP_TO_EDGE),g.uniform1i(t.uniforms.u_image,0)}t.draw(d,g.TRIANGLES,i.id,n.layoutVertexBuffer,n.indexBuffer,n.segments,s)}var dr=function(t,e){if(!t)return!1;var r=e.imageManager.getPattern(t.from),n=e.imageManager.getPattern(t.to);return!r\|\|!n},gr=function(t,e,r){var n=e.context,i=n.gl,a=e.imageManager.getPattern(t.from),o=e.imageManager.getPattern(t.to);i.uniform1i(r.uniforms.u_image,0),i.uniform2fv(r.uniforms.u_pattern_tl_a,a.tl),i.uniform2fv(r.uniforms.u_pattern_br_a,a.br),i.uniform2fv(r.uniforms.u_pattern_tl_b,o.tl),i.uniform2fv(r.uniforms.u_pattern_br_b,o.br);var s=e.imageManager.getPixelSize(),l=s.width,c=s.height;i.uniform2fv(r.uniforms.u_texsize,[l,c]),i.uniform1f(r.uniforms.u_mix,t.t),i.uniform2fv(r.uniforms.u_pattern_size_a,a.displaySize),i.uniform2fv(r.uniforms.u_pattern_size_b,o.displaySize),i.uniform1f(r.uniforms.u_scale_a,t.fromScale),i.uniform1f(r.uniforms.u_scale_b,t.toScale),n.activeTexture.set(i.TEXTURE0),e.imageManager.bind(e.context)},vr=function(t,e,r){var n=e.context.gl;n.uniform1f(r.uniforms.u_tile_units_to_pixels,1/Te(t,1,e.transform.tileZoom));var i=Math.pow(2,t.tileID.overscaledZ),a=t.tileSizeMath.pow(2,e.transform.tileZoom)/i,o=a(t.tileID.canonical.x+t.tileID.wrapi),s=at.tileID.canonical.y;n.uniform2f(r.uniforms.u_pixel_coord_upper,o>>16,s>>16),n.uniform2f(r.uniforms.u_pixel_coord_lower,65535&o,65535&s)};function mr(t,e,r,n,i){if(!dr(r.paint.get(\"fill-pattern\"),t))for(var a=!0,o=0,s=n;o<s.length;o+=1){var l=s[o],c=e.getTile(l),u=c.getBucket(r);u&&(t.context.setStencilMode(t.stencilModeForClipping(l)),i(t,e,r,c,l,u,a),a=!1)}}function yr(t,e,r,n,i,a,o){var s=t.context.gl,l=a.programConfigurations.get(r.id);br(\"fill\",r.paint.get(\"fill-pattern\"),t,l,r,n,i,o).draw(t.context,s.TRIANGLES,r.id,a.layoutVertexBuffer,a.indexBuffer,a.segments,l)}function xr(t,e,r,n,i,a,o){var s=t.context.gl,l=a.programConfigurations.get(r.id),c=br(\"fillOutline\",r.getPaintProperty(\"fill-outline-color\")?null:r.paint.get(\"fill-pattern\"),t,l,r,n,i,o);s.uniform2f(c.uniforms.u_world,s.drawingBufferWidth,s.drawingBufferHeight),c.draw(t.context,s.LINES,r.id,a.layoutVertexBuffer,a.indexBuffer2,a.segments2,l)}function br(t,e,r,n,i,a,o,s){var l,c=r.context.program.get();return e?(l=r.useProgram(t+\"Pattern\",n),(s\|\|l.program!==c)&&(n.setUniforms(r.context,l,i.paint,{zoom:r.transform.zoom}),gr(e,r,l)),vr(a,r,l)):(l=r.useProgram(t,n),(s\|\|l.program!==c)&&n.setUniforms(r.context,l,i.paint,{zoom:r.transform.zoom})),r.context.gl.uniformMatrix4fv(l.uniforms.u_matrix,!1,r.translatePosMatrix(o.posMatrix,a,i.paint.get(\"fill-translate\"),i.paint.get(\"fill-translate-anchor\"))),l}var _r=t.default$20.mat3,wr=t.default$20.mat4,kr=t.default$20.vec3;function Mr(t,e,r,n,i,a,o){var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=t.context.program.get(),f=a.programConfigurations.get(r.id),h=t.useProgram(c?\"fillExtrusionPattern\":\"fillExtrusion\",f);if((o\|\|h.program!==u)&&f.setUniforms(s,h,r.paint,{zoom:t.transform.zoom}),c){if(dr(c,t))return;gr(c,t,h),vr(n,t,h),l.uniform1f(h.uniforms.u_height_factor,-Math.pow(2,i.overscaledZ)/n.tileSize/8)}t.context.gl.uniformMatrix4fv(h.uniforms.u_matrix,!1,t.translatePosMatrix(i.posMatrix,n,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\"))),function(t,e){var r=e.context.gl,n=e.style.light,i=n.properties.get(\"position\"),a=[i.x,i.y,i.z],o=_r.create();\"viewport\"===n.properties.get(\"anchor\")&&_r.fromRotation(o,-e.transform.angle),kr.transformMat3(a,a,o);var s=n.properties.get(\"color\");r.uniform3fv(t.uniforms.u_lightpos,a),r.uniform1f(t.uniforms.u_lightintensity,n.properties.get(\"intensity\")),r.uniform3f(t.uniforms.u_lightcolor,s.r,s.g,s.b)}(h,t),h.draw(s,l.TRIANGLES,r.id,a.layoutVertexBuffer,a.indexBuffer,a.segments,f)}function Ar(e,r,n){var i=e.context,a=i.gl,o=r.fbo;if(o){var s=e.useProgram(\"hillshade\"),l=e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),!0);!function(t,e,r){var n=r.paint.get(\"hillshade-illumination-direction\")(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(n-=e.transform.angle),e.context.gl.uniform2f(t.uniforms.u_light,r.paint.get(\"hillshade-exaggeration\"),n)}(s,e,n);var c=function(e,r){var n=r.toCoordinate(),i=new t.default$17(n.column,n.row+1,n.zoom);return[e.transform.coordinateLocation(n).lat,e.transform.coordinateLocation(i).lat]}(e,r.tileID);i.activeTexture.set(a.TEXTURE0),a.bindTexture(a.TEXTURE_2D,o.colorAttachment.get()),a.uniformMatrix4fv(s.uniforms.u_matrix,!1,l),a.uniform2fv(s.uniforms.u_latrange,c),a.uniform1i(s.uniforms.u_image,0);var u=n.paint.get(\"hillshade-shadow-color\");a.uniform4f(s.uniforms.u_shadow,u.r,u.g,u.b,u.a);var f=n.paint.get(\"hillshade-highlight-color\");a.uniform4f(s.uniforms.u_highlight,f.r,f.g,f.b,f.a);var h=n.paint.get(\"hillshade-accent-color\");if(a.uniform4f(s.uniforms.u_accent,h.r,h.g,h.b,h.a),r.maskedBoundsBuffer&&r.maskedIndexBuffer&&r.segments)s.draw(i,a.TRIANGLES,n.id,r.maskedBoundsBuffer,r.maskedIndexBuffer,r.segments);else{var p=e.rasterBoundsBuffer;e.rasterBoundsVAO.bind(i,s,p,[]),a.drawArrays(a.TRIANGLE_STRIP,0,p.length)}}}function Tr(e,r,n){var i=e.context,a=i.gl;if(r.dem&&r.dem.level){var o=r.dem.level.dim,s=r.dem.getPixels();if(i.activeTexture.set(a.TEXTURE1),i.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture\|\|e.getTileTexture(r.tileSize),r.demTexture){var l=r.demTexture;l.update(s,{premultiply:!1}),l.bind(a.NEAREST,a.CLAMP_TO_EDGE)}else r.demTexture=new z(i,s,a.RGBA,{premultiply:!1}),r.demTexture.bind(a.NEAREST,a.CLAMP_TO_EDGE);i.activeTexture.set(a.TEXTURE0);var c=r.fbo;if(!c){var u=new z(i,{width:o,height:o,data:null},a.RGBA);u.bind(a.LINEAR,a.CLAMP_TO_EDGE),(c=r.fbo=i.createFramebuffer(o,o)).colorAttachment.set(u.texture)}i.bindFramebuffer.set(c.framebuffer),i.viewport.set([0,0,o,o]);var f=t.mat4.create();t.mat4.ortho(f,0,t.default$8,-t.default$8,0,0,1),t.mat4.translate(f,f,[0,-t.default$8,0]);var h=e.useProgram(\"hillshadePrepare\");a.uniformMatrix4fv(h.uniforms.u_matrix,!1,f),a.uniform1f(h.uniforms.u_zoom,r.tileID.overscaledZ),a.uniform2fv(h.uniforms.u_dimension,[2o,2o]),a.uniform1i(h.uniforms.u_image,1),a.uniform1f(h.uniforms.u_maxzoom,n);var p=e.rasterBoundsBuffer;e.rasterBoundsVAO.bind(i,h,p,[]),a.drawArrays(a.TRIANGLE_STRIP,0,p.length),r.needsHillshadePrepare=!1}}function Sr(e,r,n,i,o){var s=i.paint.get(\"raster-fade-duration\");if(s>0){var l=a.now(),c=(l-e.timeAdded)/s,u=r?(l-r.timeAdded)/s:-1,f=n.getSource(),h=o.coveringZoomLevel({tileSize:f.tileSize,roundZoom:f.roundZoom}),p=!r\|\|Math.abs(r.tileID.overscaledZ-h)>Math.abs(e.tileID.overscaledZ-h),d=p&&e.refreshedUponExpiration?1:t.clamp(p?c:1-u,0,1);return e.refreshedUponExpiration&&c>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-d}:{opacity:d,mix:0}}return{opacity:1,mix:0}}function Er(e,r,n){var i=e.context,o=i.gl;i.lineWidth.set(1a.devicePixelRatio);var s=n.posMatrix,l=e.useProgram(\"debug\");i.setDepthMode(qt.disabled),i.setStencilMode(Ht.disabled),i.setColorMode(e.colorModeForRenderPass()),o.uniformMatrix4fv(l.uniforms.u_matrix,!1,s),o.uniform4f(l.uniforms.u_color,1,0,0,1),e.debugVAO.bind(i,l,e.debugBuffer,[]),o.drawArrays(o.LINE_STRIP,0,e.debugBuffer.length);for(var c=function(t,e,r,n){n=n\|\|1;var i,a,o,s,l,c,u,f,h=[];for(i=0,a=t.length;i<a;i++)if(l=Cr[t[i]]){for(f=null,o=0,s=l[1].length;o<s;o+=2)-1===l[1][o]&&-1===l[1][o+1]?f=null:(c=e+l[1][o]n,u=200-l[1][o+1]n,f&&h.push(f.x,f.y,c,u),f={x:c,y:u});e+=l[0]n}return h}(n.toString(),50,0,5),u=new t.PosArray,f=0;f<c.length;f+=2)u.emplaceBack(c[f],c[f+1]);var h=i.createVertexBuffer(u,Ke.members);(new Q).bind(i,l,h,[]),o.uniform4f(l.uniforms.u_color,1,1,1,1);for(var p=r.getTile(n).tileSize,d=t.default$8/(Math.pow(2,e.transform.zoom-n.overscaledZ)p),g=[[-1,-1],[-1,1],[1,-1],[1,1]],v=0;v<g.length;v++){var m=g[v];o.uniformMatrix4fv(l.uniforms.u_matrix,!1,t.mat4.translate([],s,[dm[0],dm[1],0])),o.drawArrays(o.LINES,0,h.length)}o.uniform4f(l.uniforms.u_color,0,0,0,1),o.uniformMatrix4fv(l.uniforms.u_matrix,!1,s),o.drawArrays(o.LINES,0,h.length)}var Cr={\" 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a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4),function t(e,r,n,i){var a=e.gl;a.texImage2D(a.TEXTURE_2D,0,a.RGBA,r.width/4,r.height/4,0,a.RGBA,e.extTextureHalfFloat?e.extTextureHalfFloat.HALF_FLOAT_OES:a.UNSIGNED_BYTE,null),i.colorAttachment.set(n),e.extTextureHalfFloat&&a.checkFramebufferStatus(a.FRAMEBUFFER)!==a.FRAMEBUFFER_COMPLETE&&(e.extTextureHalfFloat=null,i.colorAttachment.setDirty(),t(e,r,n,i))}(t,e,a,i)}}(a,e,n),a.clear({color:t.default$6.transparent}),a.setColorMode(new Gt([o.ONE,o.ONE],t.default$6.transparent,[!0,!0,!0,!0]));for(var s=!0,l=0;l<i.length;l++){var c=i[l];if(!r.hasRenderableParent(c)){var u=r.getTile(c),f=u.getBucket(n);if(f){var h=e.context.program.get(),p=f.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),g=e.transform.zoom;(s\|\|d.program!==h)&&(p.setUniforms(e.context,d,n.paint,{zoom:g}),s=!1),o.uniform1f(d.uniforms.u_extrude_scale,Te(u,1,g)),o.uniform1f(d.uniforms.u_intensity,n.paint.get(\"heatmap-intensity\")),o.uniformMatrix4fv(d.uniforms.u_matrix,!1,c.posMatrix),d.draw(a,o.TRIANGLES,n.id,f.layoutVertexBuffer,f.indexBuffer,f.segments,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o\|\|(o=r.colorRampTexture=new z(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),n.setDepthMode(qt.disabled);var s=e.useProgram(\"heatmapTexture\"),l=r.paint.get(\"heatmap-opacity\");i.uniform1f(s.uniforms.u_opacity,l),i.uniform1i(s.uniforms.u_image,0),i.uniform1i(s.uniforms.u_color_ramp,1);var c=t.mat4.create();t.mat4.ortho(c,0,e.width,e.height,0,0,1),i.uniformMatrix4fv(s.uniforms.u_matrix,!1,c),i.uniform2f(s.uniforms.u_world,i.drawingBufferWidth,i.drawingBufferHeight),e.viewportVAO.bind(e.context,s,e.viewportBuffer,[]),i.drawArrays(i.TRIANGLE_STRIP,0,4)}}(e,n))},line:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"line-opacity\").constantOr(1)){var i=t.context;i.setDepthMode(t.depthModeForSublayer(0,qt.ReadOnly)),i.setColorMode(t.colorModeForRenderPass());for(var a,o=r.paint.get(\"line-dasharray\")?\"lineSDF\":r.paint.get(\"line-pattern\")?\"linePattern\":r.paint.get(\"line-gradient\")?\"lineGradient\":\"line\",s=!0,l=0,c=n;l<c.length;l+=1){var u=c[l],f=e.getTile(u),h=f.getBucket(r);if(h){var p=h.programConfigurations.get(r.id),d=t.context.program.get(),g=t.useProgram(o,p),v=s\|\|g.program!==d,m=a!==f.tileID.overscaledZ;v&&p.setUniforms(t.context,g,r.paint,{zoom:t.transform.zoom}),pr(g,t,f,h,r,u,p,v,m),a=f.tileID.overscaledZ,s=!1}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.context;s.setColorMode(e.colorModeForRenderPass());var l=n.paint.get(\"fill-pattern\")\|\|1!==a.constantOr(t.default$6.transparent).a\|\|1!==o.constantOr(0)?\"translucent\":\"opaque\";e.renderPass===l&&(s.setDepthMode(e.depthModeForSublayer(1,\"opaque\"===e.renderPass?qt.ReadWrite:qt.ReadOnly)),mr(e,r,n,i,yr)),\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")&&(s.lineWidth.set(2),s.setDepthMode(e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,qt.ReadOnly)),mr(e,r,n,i,xr))}},\"fill-extrusion\":function(e,r,n,i){if(0!==n.paint.get(\"fill-extrusion-opacity\"))if(\"offscreen\"===e.renderPass){!function(e,r){var n=e.context,i=n.gl,a=r.viewportFrame;if(e.depthRboNeedsClear&&e.setupOffscreenDepthRenderbuffer(),!a){var o=new z(n,{width:e.width,height:e.height,data:null},i.RGBA);o.bind(i.LINEAR,i.CLAMP_TO_EDGE),(a=r.viewportFrame=n.createFramebuffer(e.width,e.height)).colorAttachment.set(o.texture)}n.bindFramebuffer.set(a.framebuffer),a.depthAttachment.set(e.depthRbo),e.depthRboNeedsClear&&(n.clear({depth:1}),e.depthRboNeedsClear=!1),n.clear({color:t.default$6.transparent}),n.setStencilMode(Ht.disabled),n.setDepthMode(new qt(i.LEQUAL,qt.ReadWrite,[0,1])),n.setColorMode(e.colorModeForRenderPass())}(e,n);for(var a=!0,o=0,s=i;o<s.length;o+=1){var l=s[o],c=r.getTile(l),u=c.getBucket(n);u&&(Mr(e,0,n,c,l,u,a),a=!1)}}else\"translucent\"===e.renderPass&&function(t,e){var r=e.viewportFrame;if(r){var n=t.context,i=n.gl,a=t.useProgram(\"extrusionTexture\");n.setStencilMode(Ht.disabled),n.setDepthMode(qt.disabled),n.setColorMode(t.colorModeForRenderPass()),n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,r.colorAttachment.get()),i.uniform1f(a.uniforms.u_opacity,e.paint.get(\"fill-extrusion-opacity\")),i.uniform1i(a.uniforms.u_image,0);var o=wr.create();wr.ortho(o,0,t.width,t.height,0,0,1),i.uniformMatrix4fv(a.uniforms.u_matrix,!1,o),i.uniform2f(a.uniforms.u_world,i.drawingBufferWidth,i.drawingBufferHeight),t.viewportVAO.bind(n,a,t.viewportBuffer,[]),i.drawArrays(i.TRIANGLE_STRIP,0,4)}}(e,n)},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass\|\|\"translucent\"===t.renderPass){var i=t.context,a=e.getSource().maxzoom;i.setDepthMode(t.depthModeForSublayer(0,qt.ReadOnly)),i.setStencilMode(Ht.disabled),i.setColorMode(t.colorModeForRenderPass());for(var o=0,s=n;o<s.length;o+=1){var l=s[o],c=e.getTile(l);c.needsHillshadePrepare&&\"offscreen\"===t.renderPass?Tr(t,c,a):\"translucent\"===t.renderPass&&Ar(t,c,r)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")){var i,a,o=t.context,s=o.gl,l=e.getSource(),c=t.useProgram(\"raster\");o.setStencilMode(Ht.disabled),o.setColorMode(t.colorModeForRenderPass()),s.uniform1f(c.uniforms.u_brightness_low,r.paint.get(\"raster-brightness-min\")),s.uniform1f(c.uniforms.u_brightness_high,r.paint.get(\"raster-brightness-max\")),s.uniform1f(c.uniforms.u_saturation_factor,(i=r.paint.get(\"raster-saturation\"))>0?1-1/(1.001-i):-i),s.uniform1f(c.uniforms.u_contrast_factor,(a=r.paint.get(\"raster-contrast\"))>0?1/(1-a):1+a),s.uniform3fv(c.uniforms.u_spin_weights,function(t){t=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2r+1)/3,(-Math.sqrt(3)e-r+1)/3,(Math.sqrt(3)e-r+1)/3]}(r.paint.get(\"raster-hue-rotate\"))),s.uniform1f(c.uniforms.u_buffer_scale,1),s.uniform1i(c.uniforms.u_image0,0),s.uniform1i(c.uniforms.u_image1,1);for(var u=n.length&&n[0].overscaledZ,f=0,h=n;f<h.length;f+=1){var p=h[f];o.setDepthMode(t.depthModeForSublayer(p.overscaledZ-u,1===r.paint.get(\"raster-opacity\")?qt.ReadWrite:qt.ReadOnly,s.LESS));var d=e.getTile(p),g=t.transform.calculatePosMatrix(p.toUnwrapped(),!0);d.registerFadeDuration(r.paint.get(\"raster-fade-duration\")),s.uniformMatrix4fv(c.uniforms.u_matrix,!1,g);var v=e.findLoadedParent(p,0,{}),m=Sr(d,v,e,r,t.transform),y=void 0,x=void 0;if(o.activeTexture.set(s.TEXTURE0),d.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.activeTexture.set(s.TEXTURE1),v?(v.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),y=Math.pow(2,v.tileID.overscaledZ-d.tileID.overscaledZ),x=[d.tileID.canonical.xy%1,d.tileID.canonical.yy%1]):d.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),s.uniform2fv(c.uniforms.u_tl_parent,x\|\|[0,0]),s.uniform1f(c.uniforms.u_scale_parent,y\|\|1),s.uniform1f(c.uniforms.u_fade_t,m.mix),s.uniform1f(c.uniforms.u_opacity,m.opacityr.paint.get(\"raster-opacity\")),l instanceof tt){var b=l.boundsBuffer;l.boundsVAO.bind(o,c,b,[]),s.drawArrays(s.TRIANGLE_STRIP,0,b.length)}else if(d.maskedBoundsBuffer&&d.maskedIndexBuffer&&d.segments)c.draw(o,s.TRIANGLES,r.id,d.maskedBoundsBuffer,d.maskedIndexBuffer,d.segments);else{var _=t.rasterBoundsBuffer;t.rasterBoundsVAO.bind(o,c,_,[]),s.drawArrays(s.TRIANGLE_STRIP,0,_.length)}}}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\"),u=c\|\|1!==n.a\|\|1!==i?\"translucent\":\"opaque\";if(t.renderPass===u){var f;if(a.setStencilMode(Ht.disabled),a.setDepthMode(t.depthModeForSublayer(0,\"opaque\"===u?qt.ReadWrite:qt.ReadOnly)),a.setColorMode(t.colorModeForRenderPass()),c){if(dr(c,t))return;f=t.useProgram(\"backgroundPattern\"),gr(c,t,f),t.tileExtentPatternVAO.bind(a,f,t.tileExtentBuffer,[])}else f=t.useProgram(\"background\"),o.uniform4fv(f.uniforms.u_color,[n.r,n.g,n.b,n.a]),t.tileExtentVAO.bind(a,f,t.tileExtentBuffer,[]);o.uniform1f(f.uniforms.u_opacity,i);for(var h=0,p=s.coveringTiles({tileSize:l});h<p.length;h+=1){var d=p[h];c&&vr({tileID:d,tileSize:l},t,f),o.uniformMatrix4fv(f.uniforms.u_matrix,!1,t.transform.calculatePosMatrix(d.toUnwrapped())),o.drawArrays(o.TRIANGLE_STRIP,0,t.tileExtentBuffer.length)}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)Er(t,e,r[n])}},zr=function(e,r){this.context=new Wt(e),this.transform=r,this._tileTextures={},this.setup(),this.numSublayers=Yt.maxUnderzooming+Yt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.depthRboNeedsClear=!0,this.emptyProgramConfiguration=new t.default$24,this.crossTileSymbolIndex=new Ye};function Or(t,e){if(t.row>e.row){var r=t;t=e,e=r}return{x0:t.column,y0:t.row,x1:e.column,y1:e.row,dx:e.column-t.column,dy:e.row-t.row}}function Ir(t,e,r,n,i){var a=Math.max(r,Math.floor(e.y0)),o=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dyt.dx<e.x1:t.x1-e.dy/t.dyt.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,c=e.dx/e.dy,u=t.dx>0,f=e.dx<0,h=a;h<o;h++){var p=lMath.max(0,Math.min(t.dy,h+u-t.y0))+t.x0,d=cMath.max(0,Math.min(e.dy,h+f-e.y0))+e.x0;i(Math.floor(d),Math.ceil(p),h)}}function Pr(t,e,r,n,i,a){var o,s=Or(t,e),l=Or(e,r),c=Or(r,t);s.dy>l.dy&&(o=s,s=l,l=o),s.dy>c.dy&&(o=s,s=c,c=o),l.dy>c.dy&&(o=l,l=c,c=o),s.dy&&Ir(c,s,n,i,a),l.dy&&Ir(c,l,n,i,a)}zr.prototype.resize=function(t,e){var r=this.context.gl;if(this.width=ta.devicePixelRatio,this.height=ea.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var o=i[n];this.style._layers[o].resize()}this.depthRbo&&(r.deleteRenderbuffer(this.depthRbo),this.depthRbo=null)},zr.prototype.setup=function(){var e=this.context,r=new t.PosArray;r.emplaceBack(0,0),r.emplaceBack(t.default$8,0),r.emplaceBack(0,t.default$8),r.emplaceBack(t.default$8,t.default$8),this.tileExtentBuffer=e.createVertexBuffer(r,Ke.members),this.tileExtentVAO=new Q,this.tileExtentPatternVAO=new Q;var n=new t.PosArray;n.emplaceBack(0,0),n.emplaceBack(t.default$8,0),n.emplaceBack(t.default$8,t.default$8),n.emplaceBack(0,t.default$8),n.emplaceBack(0,0),this.debugBuffer=e.createVertexBuffer(n,Ke.members),this.debugVAO=new Q;var i=new t.RasterBoundsArray;i.emplaceBack(0,0,0,0),i.emplaceBack(t.default$8,0,t.default$8,0),i.emplaceBack(0,t.default$8,0,t.default$8),i.emplaceBack(t.default$8,t.default$8,t.default$8,t.default$8),this.rasterBoundsBuffer=e.createVertexBuffer(i,K.members),this.rasterBoundsVAO=new Q;var a=new t.PosArray;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ke.members),this.viewportVAO=new Q},zr.prototype.clearStencil=function(){var e=this.context,r=e.gl;e.setColorMode(Gt.disabled),e.setDepthMode(qt.disabled),e.setStencilMode(new Ht({func:r.ALWAYS,mask:0},0,255,r.ZERO,r.ZERO,r.ZERO));var n=t.mat4.create();t.mat4.ortho(n,0,this.width,this.height,0,0,1),t.mat4.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]);var i=this.useProgram(\"clippingMask\");r.uniformMatrix4fv(i.uniforms.u_matrix,!1,n),this.viewportVAO.bind(e,i,this.viewportBuffer,[]),r.drawArrays(r.TRIANGLE_STRIP,0,4)},zr.prototype._renderTileClippingMasks=function(t){var e=this.context,r=e.gl;e.setColorMode(Gt.disabled),e.setDepthMode(qt.disabled);var n=1;this._tileClippingMaskIDs={};for(var i=0,a=t;i<a.length;i+=1){var o=a[i],s=this._tileClippingMaskIDs[o.key]=n++;e.setStencilMode(new Ht({func:r.ALWAYS,mask:0},s,255,r.KEEP,r.KEEP,r.REPLACE));var l=this.useProgram(\"clippingMask\");r.uniformMatrix4fv(l.uniforms.u_matrix,!1,o.posMatrix),this.tileExtentVAO.bind(this.context,l,this.tileExtentBuffer,[]),r.drawArrays(r.TRIANGLE_STRIP,0,this.tileExtentBuffer.length)}},zr.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Ht({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},zr.prototype.colorModeForRenderPass=function(){var e=this.context.gl;return this._showOverdrawInspector?new Gt([e.CONSTANT_COLOR,e.ONE],new t.default$6(1/8,1/8,1/8,0),[!0,!0,!0,!0]):\"opaque\"===this.renderPass?Gt.unblended:Gt.alphaBlended},zr.prototype.depthModeForSublayer=function(t,e,r){var n=1-((1+this.currentLayer)this.numSublayers+t)this.depthEpsilon,i=n-1+this.depthRange;return new qt(r\|\|this.context.gl.LEQUAL,e,[i,n])},zr.prototype.render=function(e,r){var n=this;for(var i in this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(a.now()),e.sourceCaches){var o=n.style.sourceCaches[i];o.used&&o.prepare(n.context)}var s=this.style._order,l=t.filterObject(this.style.sourceCaches,function(t){return\"raster\"===t.getSource().type\|\|\"raster-dem\"===t.getSource().type}),c=function(e){var r=l[e];!function(e,r){for(var n=e.sort(function(t,e){return t.tileID.isLessThan(e.tileID)?-1:e.tileID.isLessThan(t.tileID)?1:0}),i=0;i<n.length;i++){var a={},o=n[i],s=n.slice(i+1);ar(o.tileID.wrapped(),o.tileID,s,new t.OverscaledTileID(0,o.tileID.wrap+1,0,0,0),a),o.setMask(a,r)}}(r.getVisibleCoordinates().map(function(t){return r.getTile(t)}),n.context)};for(var u in l)c(u);this.renderPass=\"offscreen\";var f,h=[];this.depthRboNeedsClear=!0;for(var p=0;p<s.length;p++){var d=n.style._layers[s[p]];d.hasOffscreenPass()&&!d.isHidden(n.transform.zoom)&&(d.source!==(f&&f.id)&&(h=[],(f=n.style.sourceCaches[d.source])&&(h=f.getVisibleCoordinates()).reverse()),h.length&&n.renderLayer(n,f,d,h))}this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.default$6.black:t.default$6.transparent,depth:1}),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRange=(e._order.length+2)this.numSublayersthis.depthEpsilon,this.renderPass=\"opaque\";var g,v=[];for(this.currentLayer=s.length-1,this.currentLayer;this.currentLayer>=0;this.currentLayer--){var m=n.style._layers[s[n.currentLayer]];m.source!==(g&&g.id)&&(v=[],(g=n.style.sourceCaches[m.source])&&(n.clearStencil(),v=g.getVisibleCoordinates(),g.getSource().isTileClipped&&n._renderTileClippingMasks(v))),n.renderLayer(n,g,m,v)}this.renderPass=\"translucent\";var y,x=[];for(this.currentLayer=0,this.currentLayer;this.currentLayer<s.length;this.currentLayer++){var b=n.style._layers[s[n.currentLayer]];b.source!==(y&&y.id)&&(x=[],(y=n.style.sourceCaches[b.source])&&(n.clearStencil(),x=y.getVisibleCoordinates(),y.getSource().isTileClipped&&n._renderTileClippingMasks(x)),x.reverse()),n.renderLayer(n,y,b,x)}if(this.options.showTileBoundaries){var _=this.style.sourceCaches[Object.keys(this.style.sourceCaches)[0]];_&&Lr.debug(this,_,_.getVisibleCoordinates())}},zr.prototype.setupOffscreenDepthRenderbuffer=function(){var t=this.context;this.depthRbo\|\|(this.depthRbo=t.createRenderbuffer(t.gl.DEPTH_COMPONENT16,this.width,this.height))},zr.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)\|\|(\"background\"===r.type\|\|n.length)&&(this.id=r.id,Lr[r.type](t,e,r,n))},zr.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]l-n[1]s,n[0]s+n[1]l]}var c=[a?n[0]:Te(r,n[0],this.transform.zoom),a?n[1]:Te(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.mat4.translate(u,e,c),u},zr.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},zr.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},zr.prototype._createProgramCached=function(t,e){this.cache=this.cache\|\|{};var r=\"\"+t+(e.cacheKey\|\|\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]\|\|(this.cache[r]=new ir(this.context,nr[t],e,this._showOverdrawInspector)),this.cache[r]},zr.prototype.useProgram=function(t,e){var r=this._createProgramCached(t,e\|\|this.emptyProgramConfiguration);return this.context.program.set(r.program),r};var Dr=t.default$20.vec4,Rr=t.default$20.mat4,Br=t.default$20.mat2,Fr=function(t,e,r){this.tileSize=512,this._renderWorldCopies=void 0===r\|\|r,this._minZoom=t\|\|0,this._maxZoom=e\|\|22,this.latRange=[-85.05113,85.05113],this.width=0,this.height=0,this._center=new G(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._posMatrixCache={},this._alignedPosMatrixCache={}},Nr={minZoom:{configurable:!0},maxZoom:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerPoint:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},unmodified:{configurable:!0},x:{configurable:!0},y:{configurable:!0},point:{configurable:!0}};Fr.prototype.clone=function(){var t=new Fr(this._minZoom,this._maxZoom,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._calcMatrices(),t},Nr.minZoom.get=function(){return this._minZoom},Nr.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Nr.maxZoom.get=function(){return this._maxZoom},Nr.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Nr.renderWorldCopies.get=function(){return this._renderWorldCopies},Nr.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Nr.worldSize.get=function(){return this.tileSizethis.scale},Nr.centerPoint.get=function(){return this.size._div(2)},Nr.size.get=function(){return new t.default$1(this.width,this.height)},Nr.bearing.get=function(){return-this.angle/Math.PI180},Nr.bearing.set=function(e){var r=-t.wrap(e,-180,180)Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=Br.create(),Br.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Nr.pitch.get=function(){return this._pitch/Math.PI180},Nr.pitch.set=function(e){var r=t.clamp(e,0,60)/180Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Nr.fov.get=function(){return this._fov/Math.PI180},Nr.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180Math.PI,this._calcMatrices())},Nr.zoom.get=function(){return this._zoom},Nr.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Nr.center.get=function(){return this._center},Nr.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng\|\|(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Fr.prototype.coveringZoomLevel=function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},Fr.prototype.getVisibleUnwrappedCoordinates=function(e){var r=this.pointCoordinate(new t.default$1(0,0),0),n=this.pointCoordinate(new t.default$1(this.width,0),0),i=Math.floor(r.column),a=Math.floor(n.column),o=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var s=i;s<=a;s++)0!==s&&o.push(new t.UnwrappedTileID(s,e));return o},Fr.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=this.pointCoordinate(this.centerPoint,r),a=new t.default$1(i.column-.5,i.row-.5);return function(e,r,n,i){void 0===i&&(i=!0);var a=1<<e,o={};function s(r,s,l){var c,u,f,h;if(l>=0&&l<=a)for(c=r;c<s;c++)u=Math.floor(c/a),f=(c%a+a)%a,0!==u&&!0!==i\|\|(h=new t.OverscaledTileID(n,u,e,f,l),o[h.key]=h)}return Pr(r[0],r[1],r[2],0,a,s),Pr(r[2],r[3],r[0],0,a,s),Object.keys(o).map(function(t){return o[t]})}(r,[this.pointCoordinate(new t.default$1(0,0),r),this.pointCoordinate(new t.default$1(this.width,0),r),this.pointCoordinate(new t.default$1(this.width,this.height),r),this.pointCoordinate(new t.default$1(0,this.height),r)],e.reparseOverscaled?n:r,this._renderWorldCopies).sort(function(t,e){return a.dist(t.canonical)-a.dist(e.canonical)})},Fr.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Nr.unmodified.get=function(){return this._unmodified},Fr.prototype.zoomScale=function(t){return Math.pow(2,t)},Fr.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Fr.prototype.project=function(e){return new t.default$1(this.lngX(e.lng),this.latY(e.lat))},Fr.prototype.unproject=function(t){return new G(this.xLng(t.x),this.yLat(t.y))},Nr.x.get=function(){return this.lngX(this.center.lng)},Nr.y.get=function(){return this.latY(this.center.lat)},Nr.point.get=function(){return new t.default$1(this.x,this.y)},Fr.prototype.lngX=function(t){return(180+t)this.worldSize/360},Fr.prototype.latY=function(t){return(180-180/Math.PIMath.log(Math.tan(Math.PI/4+tMath.PI/360)))this.worldSize/360},Fr.prototype.xLng=function(t){return 360t/this.worldSize-180},Fr.prototype.yLat=function(t){var e=180-360t/this.worldSize;return 360/Math.PIMath.atan(Math.exp(eMath.PI/180))-90},Fr.prototype.setLocationAtPoint=function(t,e){var r=this.pointCoordinate(e)._sub(this.pointCoordinate(this.centerPoint));this.center=this.coordinateLocation(this.locationCoordinate(t)._sub(r)),this._renderWorldCopies&&(this.center=this.center.wrap())},Fr.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Fr.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Fr.prototype.locationCoordinate=function(e){return new t.default$17(this.lngX(e.lng)/this.tileSize,this.latY(e.lat)/this.tileSize,this.zoom).zoomTo(this.tileZoom)},Fr.prototype.coordinateLocation=function(t){var e=t.zoomTo(this.zoom);return new G(this.xLng(e.columnthis.tileSize),this.yLat(e.rowthis.tileSize))},Fr.prototype.pointCoordinate=function(e,r){void 0===r&&(r=this.tileZoom);var n=[e.x,e.y,0,1],i=[e.x,e.y,1,1];Dr.transformMat4(n,n,this.pixelMatrixInverse),Dr.transformMat4(i,i,this.pixelMatrixInverse);var a=n[3],o=i[3],s=n[0]/a,l=i[0]/o,c=n[1]/a,u=i[1]/o,f=n[2]/a,h=i[2]/o,p=f===h?0:(0-f)/(h-f);return new t.default$17(t.number(s,l,p)/this.tileSize,t.number(c,u,p)/this.tileSize,this.zoom)._zoomTo(r)},Fr.prototype.coordinatePoint=function(e){var r=e.zoomTo(this.zoom),n=[r.columnthis.tileSize,r.rowthis.tileSize,0,1];return Dr.transformMat4(n,n,this.pixelMatrix),new t.default$1(n[0]/n[3],n[1]/n[3])},Fr.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)e.wrap,l=Rr.identity(new Float64Array(16));return Rr.translate(l,l,[so,a.yo,0]),Rr.scale(l,l,[o/t.default$8,o/t.default$8,1]),Rr.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Fr.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=this.latY(f[1]),e=(o=this.latY(f[0]))-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=this.lngX(h[0]),r=(l=this.lngX(h[1]))-s<c.x?c.x/(l-s):0}var p=Math.max(r\|\|0,e\|\|0);if(p)return this.center=this.unproject(new t.default$1(r?(l+s)/2:this.x,e?(o+a)/2:this.y)),this.zoom+=this.scaleZoom(p),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var d=this.y,g=c.y/2;d-g<a&&(i=a+g),d+g>o&&(i=o-g)}if(this.lngRange){var v=this.x,m=c.x/2;v-m<s&&(n=s+m),v+m>l&&(n=l-m)}void 0===n&&void 0===i\|\|(this.center=this.unproject(new t.default$1(void 0!==n?n:this.x,void 0!==i?i:this.y))),this._unmodified=u,this._constraining=!1}},Fr.prototype._calcMatrices=function(){if(this.height){this.cameraToCenterDistance=.5/Math.tan(this._fov/2)this.height;var t=this._fov/2,e=Math.PI/2+this._pitch,r=Math.sin(t)this.cameraToCenterDistance/Math.sin(Math.PI-e-t),n=this.x,i=this.y,a=1.01(Math.cos(Math.PI/2-this._pitch)r+this.cameraToCenterDistance),o=new Float64Array(16);Rr.perspective(o,this._fov,this.width/this.height,1,a),Rr.scale(o,o,[1,-1,1]),Rr.translate(o,o,[0,0,-this.cameraToCenterDistance]),Rr.rotateX(o,o,this._pitch),Rr.rotateZ(o,o,this.angle),Rr.translate(o,o,[-n,-i,0]);var s=this.worldSize/(2Math.PI6378137Math.abs(Math.cos(this.center.lat(Math.PI/180))));Rr.scale(o,o,[1,1,s,1]),this.projMatrix=o;var l=this.width%2/2,c=this.height%2/2,u=Math.cos(this.angle),f=Math.sin(this.angle),h=n-Math.round(n)+ul+fc,p=i-Math.round(i)+uc+fl,d=new Float64Array(o);if(Rr.translate(d,d,[h>.5?h-1:h,p>.5?p-1:p,0]),this.alignedProjMatrix=d,o=Rr.create(),Rr.scale(o,o,[this.width/2,-this.height/2,1]),Rr.translate(o,o,[1,-1,0]),this.pixelMatrix=Rr.multiply(new Float64Array(16),o,this.projMatrix),!(o=Rr.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=o,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Fr.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.default$1(0,0)).zoomTo(this.zoom),r=[e.columnthis.tileSize,e.rowthis.tileSize,0,1];return Dr.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Object.defineProperties(Fr.prototype,Nr);var jr=function(){var e,r,n,i;t.bindAll([\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(e=this._updateHashUnthrottled.bind(this),300,r=!1,n=0,i=function(){n=0,r&&(e(),n=setTimeout(i,300),r=!1)},function(){return r=!0,n\|\|i(),n})};jr.prototype.addTo=function(e){return this._map=e,t.default.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},jr.prototype.remove=function(){return t.default.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},jr.prototype.getHashString=function(t){var e=this._map.getCenter(),r=Math.round(100this._map.getZoom())/100,n=Math.ceil((rMath.LN2+Math.log(512/360/.5))/Math.LN10),i=Math.pow(10,n),a=Math.round(e.lngi)/i,o=Math.round(e.lati)/i,s=this._map.getBearing(),l=this._map.getPitch(),c=\"\";return c+=t?\"#/\"+a+\"/\"+o+\"/\"+r:\"#\"+r+\"/\"+o+\"/\"+a,(s\|\|l)&&(c+=\"/\"+Math.round(10s)/10),l&&(c+=\"/\"+Math.round(l)),c},jr.prototype._onHashChange=function(){var e=t.default.location.hash.replace(\"#\",\"\").split(\"/\");return e.length>=3&&(this._map.jumpTo({center:[+e[2],+e[1]],zoom:+e[0],bearing:+(e[3]\|\|0),pitch:+(e[4]\|\|0)}),!0)},jr.prototype._updateHashUnthrottled=function(){var e=this.getHashString();t.default.history.replaceState(t.default.history.state,\"\",e)};var Vr=function(e){function r(r,n,i,a){void 0===a&&(a={});var o=s.mousePos(n.getCanvasContainer(),i),l=n.unproject(o);e.call(this,r,t.extend({point:o,lngLat:l,originalEvent:i},a)),this._defaultPrevented=!1,this.target=n}e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r;var n={defaultPrevented:{configurable:!0}};return r.prototype.preventDefault=function(){this._defaultPrevented=!0},n.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(r.prototype,n),r}(t.Event),Ur=function(e){function r(r,n,i){var a=s.touchPos(n.getCanvasContainer(),i),o=a.map(function(t){return n.unproject(t)}),l=a.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new t.default$1(0,0)),c=n.unproject(l);e.call(this,r,{points:a,point:l,lngLats:o,lngLat:c,originalEvent:i}),this._defaultPrevented=!1}e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r;var n={defaultPrevented:{configurable:!0}};return r.prototype.preventDefault=function(){this._defaultPrevented=!0},n.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(r.prototype,n),r}(t.Event),qr=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Hr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._delta=0,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};Hr.prototype.isEnabled=function(){return!!this._enabled},Hr.prototype.isActive=function(){return!!this._active},Hr.prototype.enable=function(t){this.isEnabled()\|\|(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},Hr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Hr.prototype.onWheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.default.WheelEvent.DOM_DELTA_LINE?40e.deltaY:e.deltaY,n=a.now(),i=n-(this._lastWheelEventTime\|\|0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type\|\|(this._type=Math.abs(ir)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this.isActive()\|\|this._start(e)),e.preventDefault()}},Hr.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this.isActive()\|\|this._start(t)},Hr.prototype._start=function(e){if(this._delta){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),this._active=!0,this._map.fire(new t.Event(\"movestart\",{originalEvent:e})),this._map.fire(new t.Event(\"zoomstart\",{originalEvent:e})),this._finishTimeout&&clearTimeout(this._finishTimeout);var r=s.mousePos(this._el,e);this._around=G.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(r)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onScrollFrame))}},Hr.prototype._onScrollFrame=function(){var e=this;if(this._frameId=null,this.isActive()){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?1/450:.01,i=2/(1+Math.exp(-Math.abs(this._deltan)));this._delta<0&&0!==i&&(i=1/i);var o=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(oi))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var s=!1;if(\"wheel\"===this._type){var l=Math.min((a.now()-this._lastWheelEventTime)/200,1),c=this._easing(l);r.zoom=t.number(this._startZoom,this._targetZoom,c),l<1?this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onScrollFrame)):s=!0}else r.zoom=this._targetZoom,s=!0;r.setLocationAtPoint(this._around,this._aroundPoint),this._map.fire(new t.Event(\"move\",{originalEvent:this._lastWheelEvent})),this._map.fire(new t.Event(\"zoom\",{originalEvent:this._lastWheelEvent})),s&&(this._active=!1,this._finishTimeout=setTimeout(function(){e._map.fire(new t.Event(\"zoomend\",{originalEvent:e._lastWheelEvent})),e._map.fire(new t.Event(\"moveend\",{originalEvent:e._lastWheelEvent})),delete e._targetZoom},200))}},Hr.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(a.now()-n.start)/n.duration,o=n.easing(i+.01)-n.easing(i),s=.27/Math.sqrt(oo+1e-4).01,l=Math.sqrt(.0729-ss);r=t.bezier(s,l,.25,1)}return this._prevEase={start:a.now(),duration:e,easing:r},r};var Gr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._container=e.getContainer(),t.bindAll([\"_onMouseMove\",\"_onMouseUp\",\"_onKeyDown\"],this)};Gr.prototype.isEnabled=function(){return!!this._enabled},Gr.prototype.isActive=function(){return!!this._active},Gr.prototype.enable=function(){this.isEnabled()\|\|(this._enabled=!0)},Gr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Gr.prototype.onMouseDown=function(e){this.isEnabled()&&e.shiftKey&&0===e.button&&(t.default.document.addEventListener(\"mousemove\",this._onMouseMove,!1),t.default.document.addEventListener(\"keydown\",this._onKeyDown,!1),t.default.document.addEventListener(\"mouseup\",this._onMouseUp,!1),s.disableDrag(),this._startPos=s.mousePos(this._el,e),this._active=!0)},Gr.prototype._onMouseMove=function(t){var e=this._startPos,r=s.mousePos(this._el,t);this._box\|\|(this._box=s.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var n=Math.min(e.x,r.x),i=Math.max(e.x,r.x),a=Math.min(e.y,r.y),o=Math.max(e.y,r.y);s.setTransform(this._box,\"translate(\"+n+\"px,\"+a+\"px)\"),this._box.style.width=i-n+\"px\",this._box.style.height=o-a+\"px\"},Gr.prototype._onMouseUp=function(e){if(0===e.button){var r=this._startPos,n=s.mousePos(this._el,e),i=(new W).extend(this._map.unproject(r)).extend(this._map.unproject(n));this._finish(),s.suppressClick(),r.x===n.x&&r.y===n.y?this._fireEvent(\"boxzoomcancel\",e):this._map.fitBounds(i,{linear:!0}).fire(new t.Event(\"boxzoomend\",{originalEvent:e,boxZoomBounds:i}))}},Gr.prototype._onKeyDown=function(t){27===t.keyCode&&(this._finish(),this._fireEvent(\"boxzoomcancel\",t))},Gr.prototype._finish=function(){this._active=!1,t.default.document.removeEventListener(\"mousemove\",this._onMouseMove,!1),t.default.document.removeEventListener(\"keydown\",this._onKeyDown,!1),t.default.document.removeEventListener(\"mouseup\",this._onMouseUp,!1),this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(s.remove(this._box),this._box=null),s.enableDrag()},Gr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Wr=t.bezier(0,0,.25,1),Yr=function(e,r){this._map=e,this._el=r.element\|\|e.getCanvasContainer(),this._state=\"disabled\",this._button=r.button\|\|\"right\",this._bearingSnap=r.bearingSnap\|\|0,this._pitchWithRotate=!1!==r.pitchWithRotate,t.bindAll([\"_onMouseMove\",\"_onMouseUp\",\"_onBlur\",\"_onDragFrame\"],this)};Yr.prototype.isEnabled=function(){return\"disabled\"!==this._state},Yr.prototype.isActive=function(){return\"active\"===this._state},Yr.prototype.enable=function(){this.isEnabled()\|\|(this._state=\"enabled\")},Yr.prototype.disable=function(){if(this.isEnabled())switch(this._state){case\"active\":this._state=\"disabled\",this._unbind(),this._deactivate(),this._fireEvent(\"rotateend\"),this._pitchWithRotate&&this._fireEvent(\"pitchend\"),this._fireEvent(\"moveend\");break;case\"pending\":this._state=\"disabled\",this._unbind();break;default:this._state=\"disabled\"}},Yr.prototype.onMouseDown=function(e){if(\"enabled\"===this._state){if(\"right\"===this._button){if(this._eventButton=s.mouseButton(e),this._eventButton!==(e.ctrlKey?0:2))return}else{if(e.ctrlKey\|\|0!==s.mouseButton(e))return;this._eventButton=0}s.disableDrag(),t.default.document.addEventListener(\"mousemove\",this._onMouseMove,{capture:!0}),t.default.document.addEventListener(\"mouseup\",this._onMouseUp),t.default.addEventListener(\"blur\",this._onBlur),this._state=\"pending\",this._inertia=[[a.now(),this._map.getBearing()]],this._previousPos=s.mousePos(this._el,e),this._center=this._map.transform.centerPoint,e.preventDefault()}},Yr.prototype._onMouseMove=function(t){this._lastMoveEvent=t,this._pos=s.mousePos(this._el,t),\"pending\"===this._state&&(this._state=\"active\",this._fireEvent(\"rotatestart\",t),this._fireEvent(\"movestart\",t),this._pitchWithRotate&&this._fireEvent(\"pitchstart\",t)),this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onDragFrame))},Yr.prototype._onDragFrame=function(){this._frameId=null;var t=this._lastMoveEvent;if(t){var e=this._map.transform,r=this._previousPos,n=this._pos,i=.8(r.x-n.x),o=-.5(r.y-n.y),s=e.bearing-i,l=e.pitch-o,c=this._inertia,u=c[c.length-1];this._drainInertiaBuffer(),c.push([a.now(),this._map._normalizeBearing(s,u[1])]),e.bearing=s,this._pitchWithRotate&&(this._fireEvent(\"pitch\",t),e.pitch=l),this._fireEvent(\"rotate\",t),this._fireEvent(\"move\",t),delete this._lastMoveEvent,this._previousPos=this._pos}},Yr.prototype._onMouseUp=function(t){if(s.mouseButton(t)===this._eventButton)switch(this._state){case\"active\":this._state=\"enabled\",s.suppressClick(),this._unbind(),this._deactivate(),this._inertialRotate(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Yr.prototype._onBlur=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._fireEvent(\"rotateend\",t),this._pitchWithRotate&&this._fireEvent(\"pitchend\",t),this._fireEvent(\"moveend\",t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Yr.prototype._unbind=function(){t.default.document.removeEventListener(\"mousemove\",this._onMouseMove,{capture:!0}),t.default.document.removeEventListener(\"mouseup\",this._onMouseUp),t.default.removeEventListener(\"blur\",this._onBlur),s.enableDrag()},Yr.prototype._deactivate=function(){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._lastMoveEvent,delete this._previousPos},Yr.prototype._inertialRotate=function(t){var e=this;this._fireEvent(\"rotateend\",t),this._drainInertiaBuffer();var r=this._map,n=r.getBearing(),i=this._inertia,a=function(){Math.abs(n)<e._bearingSnap?r.resetNorth({noMoveStart:!0},{originalEvent:t}):e._fireEvent(\"moveend\",t),e._pitchWithRotate&&e._fireEvent(\"pitchend\",t)};if(i.length<2)a();else{var o=i[0],s=i[i.length-1],l=i[i.length-2],c=r._normalizeBearing(n,l[1]),u=s[1]-o[1],f=u<0?-1:1,h=(s[0]-o[0])/1e3;if(0!==u&&0!==h){var p=Math.abs(u(.25/h));p>180&&(p=180);var d=p/180;c+=fp(d/2),Math.abs(r._normalizeBearing(c,0))<this._bearingSnap&&(c=r._normalizeBearing(0,c)),r.rotateTo(c,{duration:1e3d,easing:Wr,noMoveStart:!0},{originalEvent:t})}else a()}},Yr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Yr.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>0&&e-t[0][0]>160;)t.shift()};var Xr=t.bezier(0,0,.3,1),Zr=function(e){this._map=e,this._el=e.getCanvasContainer(),this._state=\"disabled\",t.bindAll([\"_onMove\",\"_onMouseUp\",\"_onTouchEnd\",\"_onBlur\",\"_onDragFrame\"],this)};Zr.prototype.isEnabled=function(){return\"disabled\"!==this._state},Zr.prototype.isActive=function(){return\"active\"===this._state},Zr.prototype.enable=function(){this.isEnabled()\|\|(this._el.classList.add(\"mapboxgl-touch-drag-pan\"),this._state=\"enabled\")},Zr.prototype.disable=function(){if(this.isEnabled())switch(this._el.classList.remove(\"mapboxgl-touch-drag-pan\"),this._state){case\"active\":this._state=\"disabled\",this._unbind(),this._deactivate(),this._fireEvent(\"dragend\"),this._fireEvent(\"moveend\");break;case\"pending\":this._state=\"disabled\",this._unbind();break;default:this._state=\"disabled\"}},Zr.prototype.onMouseDown=function(e){\"enabled\"===this._state&&(e.ctrlKey\|\|0!==s.mouseButton(e)\|\|(s.addEventListener(t.default.document,\"mousemove\",this._onMove,{capture:!0}),s.addEventListener(t.default.document,\"mouseup\",this._onMouseUp),this._start(e)))},Zr.prototype.onTouchStart=function(e){\"enabled\"===this._state&&(e.touches.length>1\|\|(s.addEventListener(t.default.document,\"touchmove\",this._onMove,{capture:!0,passive:!1}),s.addEventListener(t.default.document,\"touchend\",this._onTouchEnd),this._start(e)))},Zr.prototype._start=function(e){t.default.addEventListener(\"blur\",this._onBlur),this._state=\"pending\",this._previousPos=s.mousePos(this._el,e),this._inertia=[[a.now(),this._previousPos]]},Zr.prototype._onMove=function(t){this._lastMoveEvent=t,t.preventDefault(),this._pos=s.mousePos(this._el,t),this._drainInertiaBuffer(),this._inertia.push([a.now(),this._pos]),\"pending\"===this._state&&(this._state=\"active\",this._fireEvent(\"dragstart\",t),this._fireEvent(\"movestart\",t)),this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onDragFrame))},Zr.prototype._onDragFrame=function(){this._frameId=null;var t=this._lastMoveEvent;if(t){var e=this._map.transform;e.setLocationAtPoint(e.pointLocation(this._previousPos),this._pos),this._fireEvent(\"drag\",t),this._fireEvent(\"move\",t),this._previousPos=this._pos,delete this._lastMoveEvent}},Zr.prototype._onMouseUp=function(t){if(0===s.mouseButton(t))switch(this._state){case\"active\":this._state=\"enabled\",s.suppressClick(),this._unbind(),this._deactivate(),this._inertialPan(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Zr.prototype._onTouchEnd=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._inertialPan(t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Zr.prototype._onBlur=function(t){switch(this._state){case\"active\":this._state=\"enabled\",this._unbind(),this._deactivate(),this._fireEvent(\"dragend\",t),this._fireEvent(\"moveend\",t);break;case\"pending\":this._state=\"enabled\",this._unbind()}},Zr.prototype._unbind=function(){s.removeEventListener(t.default.document,\"touchmove\",this._onMove,{capture:!0,passive:!1}),s.removeEventListener(t.default.document,\"touchend\",this._onTouchEnd),s.removeEventListener(t.default.document,\"mousemove\",this._onMove,{capture:!0}),s.removeEventListener(t.default.document,\"mouseup\",this._onMouseUp),s.removeEventListener(t.default,\"blur\",this._onBlur)},Zr.prototype._deactivate=function(){this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._lastMoveEvent,delete this._previousPos,delete this._pos},Zr.prototype._inertialPan=function(t){this._fireEvent(\"dragend\",t),this._drainInertiaBuffer();var e=this._inertia;if(e.length<2)this._fireEvent(\"moveend\",t);else{var r=e[e.length-1],n=e[0],i=r[1].sub(n[1]),a=(r[0]-n[0])/1e3;if(0===a\|\|r[1].equals(n[1]))this._fireEvent(\"moveend\",t);else{var o=i.mult(.3/a),s=o.mag();s>1400&&(s=1400,o._unit()._mult(s));var l=s/750,c=o.mult(-l/2);this._map.panBy(c,{duration:1e3l,easing:Xr,noMoveStart:!0},{originalEvent:t})}}},Zr.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Zr.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>0&&e-t[0][0]>160;)t.shift()};var $r=function(e){this._map=e,this._el=e.getCanvasContainer(),t.bindAll([\"_onKeyDown\"],this)};function Jr(t){return t(2-t)}$r.prototype.isEnabled=function(){return!!this._enabled},$r.prototype.enable=function(){this.isEnabled()\|\|(this._el.addEventListener(\"keydown\",this._onKeyDown,!1),this._enabled=!0)},$r.prototype.disable=function(){this.isEnabled()&&(this._el.removeEventListener(\"keydown\",this._onKeyDown),this._enabled=!1)},$r.prototype._onKeyDown=function(t){if(!(t.altKey\|\|t.ctrlKey\|\|t.metaKey)){var e=0,r=0,n=0,i=0,a=0;switch(t.keyCode){case 61:case 107:case 171:case 187:e=1;break;case 189:case 109:case 173:e=-1;break;case 37:t.shiftKey?r=-1:(t.preventDefault(),i=-1);break;case 39:t.shiftKey?r=1:(t.preventDefault(),i=1);break;case 38:t.shiftKey?n=1:(t.preventDefault(),a=-1);break;case 40:t.shiftKey?n=-1:(a=1,t.preventDefault());break;default:return}var o=this._map,s=o.getZoom(),l={duration:300,delayEndEvents:500,easing:Jr,zoom:e?Math.round(s)+e(t.shiftKey?2:1):s,bearing:o.getBearing()+15r,pitch:o.getPitch()+10n,offset:[100-i,100-a],center:o.getCenter()};o.easeTo(l,{originalEvent:t})}};var Kr=function(e){this._map=e,t.bindAll([\"_onDblClick\",\"_onZoomEnd\"],this)};Kr.prototype.isEnabled=function(){return!!this._enabled},Kr.prototype.isActive=function(){return!!this._active},Kr.prototype.enable=function(){this.isEnabled()\|\|(this._enabled=!0)},Kr.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Kr.prototype.onTouchStart=function(t){var e=this;this.isEnabled()&&(t.points.length>1\|\|(this._tapped?(clearTimeout(this._tapped),this._tapped=null,this._zoom(t)):this._tapped=setTimeout(function(){e._tapped=null},300)))},Kr.prototype.onDblClick=function(t){this.isEnabled()&&(t.originalEvent.preventDefault(),this._zoom(t))},Kr.prototype._zoom=function(t){this._active=!0,this._map.on(\"zoomend\",this._onZoomEnd),this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)},Kr.prototype._onZoomEnd=function(){this._active=!1,this._map.off(\"zoomend\",this._onZoomEnd)};var Qr=t.bezier(0,0,.15,1),tn=function(e){this._map=e,this._el=e.getCanvasContainer(),t.bindAll([\"_onMove\",\"_onEnd\",\"_onTouchFrame\"],this)};tn.prototype.isEnabled=function(){return!!this._enabled},tn.prototype.enable=function(t){this.isEnabled()\|\|(this._el.classList.add(\"mapboxgl-touch-zoom-rotate\"),this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around)},tn.prototype.disable=function(){this.isEnabled()&&(this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\"),this._enabled=!1)},tn.prototype.disableRotation=function(){this._rotationDisabled=!0},tn.prototype.enableRotation=function(){this._rotationDisabled=!1},tn.prototype.onStart=function(e){if(this.isEnabled()&&2===e.touches.length){var r=s.mousePos(this._el,e.touches[0]),n=s.mousePos(this._el,e.touches[1]);this._startVec=r.sub(n),this._gestureIntent=void 0,this._inertia=[],s.addEventListener(t.default.document,\"touchmove\",this._onMove,{passive:!1}),s.addEventListener(t.default.document,\"touchend\",this._onEnd)}},tn.prototype._getTouchEventData=function(t){var e=s.mousePos(this._el,t.touches[0]),r=s.mousePos(this._el,t.touches[1]),n=e.sub(r);return{vec:n,center:e.add(r).div(2),scale:n.mag()/this._startVec.mag(),bearing:this._rotationDisabled?0:180n.angleWith(this._startVec)/Math.PI}},tn.prototype._onMove=function(e){if(2===e.touches.length){var r=this._getTouchEventData(e),n=r.vec,i=r.scale,a=r.bearing;if(!this._gestureIntent){var o=Math.abs(1-i)>.15;Math.abs(a)>10?this._gestureIntent=\"rotate\":o&&(this._gestureIntent=\"zoom\"),this._gestureIntent&&(this._map.fire(new t.Event(this._gestureIntent+\"start\",{originalEvent:e})),this._map.fire(new t.Event(\"movestart\",{originalEvent:e})),this._startVec=n)}this._lastTouchEvent=e,this._frameId\|\|(this._frameId=this._map._requestRenderFrame(this._onTouchFrame)),e.preventDefault()}},tn.prototype._onTouchFrame=function(){this._frameId=null;var e=this._gestureIntent;if(e){var r=this._map.transform;this._startScale\|\|(this._startScale=r.scale,this._startBearing=r.bearing);var n=this._getTouchEventData(this._lastTouchEvent),i=n.center,o=n.bearing,s=n.scale,l=r.pointLocation(i),c=r.locationPoint(l);\"rotate\"===e&&(r.bearing=this._startBearing+o),r.zoom=r.scaleZoom(this._startScales),r.setLocationAtPoint(l,c),this._map.fire(new t.Event(e,{originalEvent:this._lastTouchEvent})),this._map.fire(new t.Event(\"move\",{originalEvent:this._lastTouchEvent})),this._drainInertiaBuffer(),this._inertia.push([a.now(),s,i])}},tn.prototype._onEnd=function(e){s.removeEventListener(t.default.document,\"touchmove\",this._onMove,{passive:!1}),s.removeEventListener(t.default.document,\"touchend\",this._onEnd);var r=this._gestureIntent,n=this._startScale;if(this._frameId&&(this._map._cancelRenderFrame(this._frameId),this._frameId=null),delete this._gestureIntent,delete this._startScale,delete this._startBearing,delete this._lastTouchEvent,r){this._map.fire(new t.Event(r+\"end\",{originalEvent:e})),this._drainInertiaBuffer();var i=this._inertia,a=this._map;if(i.length<2)a.snapToNorth({},{originalEvent:e});else{var o=i[i.length-1],l=i[0],c=a.transform.scaleZoom(no[1]),u=a.transform.scaleZoom(nl[1]),f=c-u,h=(o[0]-l[0])/1e3,p=o[2];if(0!==h&&c!==u){var d=.15f/h;Math.abs(d)>2.5&&(d=d>0?2.5:-2.5);var g=1e3Math.abs(d/(12.15)),v=c+dg/2e3;v<0&&(v=0),a.easeTo({zoom:v,duration:g,easing:Qr,around:this._aroundCenter?a.getCenter():a.unproject(p),noMoveStart:!0},{originalEvent:e})}else a.snapToNorth({},{originalEvent:e})}}},tn.prototype._drainInertiaBuffer=function(){for(var t=this._inertia,e=a.now();t.length>2&&e-t[0][0]>160;)t.shift()};var en={scrollZoom:Hr,boxZoom:Gr,dragRotate:Yr,dragPan:Zr,keyboard:$r,doubleClickZoom:Kr,touchZoomRotate:tn},rn=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return this.transform.center},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.default$1.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.fitBounds=function(e,r,n){if(\"number\"==typeof(r=t.extend({padding:{top:0,bottom:0,right:0,left:0},offset:[0,0],maxZoom:this.transform.maxZoom},r)).padding){var i=r.padding;r.padding={top:i,bottom:i,right:i,left:i}}if(!t.default$10(Object.keys(r.padding).sort(function(t,e){return t<e?-1:t>e?1:0}),[\"bottom\",\"left\",\"right\",\"top\"]))return t.warnOnce(\"options.padding must be a positive number, or an Object with keys 'bottom', 'left', 'right', 'top'\"),this;e=W.convert(e);var a=[(r.padding.left-r.padding.right)/2,(r.padding.top-r.padding.bottom)/2],o=Math.min(r.padding.right,r.padding.left),s=Math.min(r.padding.top,r.padding.bottom);r.offset=[r.offset[0]+a[0],r.offset[1]+a[1]];var l=t.default$1.convert(r.offset),c=this.transform,u=c.project(e.getNorthWest()),f=c.project(e.getSouthEast()),h=f.sub(u),p=(c.width-2o-2Math.abs(l.x))/h.x,d=(c.height-2s-2Math.abs(l.y))/h.y;return d<0\|\|p<0?(t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\"),this):(r.center=c.unproject(u.add(f).div(2)),r.zoom=Math.min(c.scaleZoom(c.scaleMath.min(p,d)),r.maxZoom),r.bearing=0,r.linear?this.easeTo(r,n):this.flyTo(r,n))},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=G.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this.stop(),!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=\"zoom\"in e?+e.zoom:a,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,u=\"pitch\"in e?+e.pitch:s,f=i.centerPoint.add(t.default$1.convert(e.offset)),h=i.pointLocation(f),p=G.convert(e.center\|\|h);this._normalizeCenter(p);var d,g,v=i.project(h),m=i.project(p).sub(v),y=i.zoomScale(l-a);return e.around&&(d=G.convert(e.around),g=i.locationPoint(d)),this._zooming=l!==a,this._rotating=o!==c,this._pitching=u!==s,this._prepareEase(r,e.noMoveStart),clearTimeout(this._easeEndTimeoutID),this._ease(function(e){if(n._zooming&&(i.zoom=t.number(a,l,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,u,e)),d)i.setLocationAtPoint(d,g);else{var h=i.zoomScale(i.zoom-a),p=l>a?Math.min(2,y):Math.max(.5,y),x=Math.pow(p,1-e),b=i.unproject(v.add(m.mult(ex)).mult(h));i.setLocationAtPoint(i.renderWorldCopies?b.wrap():b,f)}n._fireMoveEvents(r)},function(){e.delayEndEvents?n._easeEndTimeoutID=setTimeout(function(){return n._afterEase(r)},e.delayEndEvents):n._afterEase(r)},e),this},r.prototype._prepareEase=function(e,r){this._moving=!0,r\|\|this.fire(new t.Event(\"movestart\",e)),this._zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e){var r=this._zooming,n=this._rotating,i=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,r&&this.fire(new t.Event(\"zoomend\",e)),n&&this.fire(new t.Event(\"rotateend\",e)),i&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))},r.prototype.flyTo=function(e,r){var n=this;this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=\"zoom\"in e?t.clamp(+e.zoom,i.minZoom,i.maxZoom):a,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,u=\"pitch\"in e?+e.pitch:s,f=i.zoomScale(l-a),h=i.centerPoint.add(t.default$1.convert(e.offset)),p=i.pointLocation(h),d=G.convert(e.center\|\|p);this._normalizeCenter(d);var g=i.project(p),v=i.project(d).sub(g),m=e.curve,y=Math.max(i.width,i.height),x=y/f,b=v.mag();if(\"minZoom\"in e){var _=t.clamp(Math.min(e.minZoom,a,l),i.minZoom,i.maxZoom),w=y/i.zoomScale(_-a);m=Math.sqrt(w/b2)}var k=mm;function M(t){var e=(xx-yy+(t?-1:1)kkbb)/(2(t?x:y)kb);return Math.log(Math.sqrt(ee+1)-e)}function A(t){return(Math.exp(t)-Math.exp(-t))/2}function T(t){return(Math.exp(t)+Math.exp(-t))/2}var S=M(0),E=function(t){return T(S)/T(S+mt)},C=function(t){return y((T(S)(A(e=S+mt)/T(e))-A(S))/k)/b;var e},L=(M(1)-S)/m;if(Math.abs(b)<1e-6\|\|!isFinite(L)){if(Math.abs(y-x)<1e-6)return this.easeTo(e,r);var z=x<y?-1:1;L=Math.abs(Math.log(x/y))/m,C=function(){return 0},E=function(t){return Math.exp(zmt)}}if(\"duration\"in e)e.duration=+e.duration;else{var O=\"screenSpeed\"in e?+e.screenSpeed/m:+e.speed;e.duration=1e3L/O}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=o!==c,this._pitching=u!==s,this._prepareEase(r,!1),this._ease(function(e){var l=eL,f=1/E(l);i.zoom=a+i.scaleZoom(f),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,u,e));var p=i.unproject(g.add(v.mult(C(l))).mult(f));i.setLocationAtPoint(i.renderWorldCopies?p.wrap():p,h),n._fireMoveEvents(r)},function(){return n._afterEase(r)},e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var t=this._onEaseEnd;delete this._onEaseEnd,t.call(this)}return this},r.prototype._ease=function(t,e,r){!1===r.animate\|\|0===r.duration?(t(1),e()):(this._easeStart=a.now(),this._easeOptions=r,this._onEaseFrame=t,this._onEaseEnd=e,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var t=Math.min((a.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(t)),t<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),nn=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};nn.prototype.getDefaultPosition=function(){return\"bottom-right\"},nn.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=s.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},nn.prototype.onRemove=function(){s.remove(this._container),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0},nn.prototype._updateEditLink=function(){var t=this._editLink;t\|\|(t=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var e=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:v.ACCESS_TOKEN}];if(t){var r=e.reduce(function(t,r,n){return r.value&&(t+=r.key+\"=\"+r.value+(n<e.length-1?\"&\":\"\")),t},\"?\");t.href=\"https://www.mapbox.com/feedback/\"+r+(this._map._hash?this._map._hash.getHashString(!0):\"\")}},nn.prototype._updateData=function(t){t&&\"metadata\"===t.sourceDataType&&(this._updateAttributions(),this._updateEditLink())},nn.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n].getSource();i.attribution&&t.indexOf(i.attribution)<0&&t.push(i.attribution)}t.sort(function(t,e){return t.length-e.length}),(t=t.filter(function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0})).length?(this._container.innerHTML=t.join(\" \| \"),this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null}},nn.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var an=function(){t.bindAll([\"_updateLogo\"],this)};an.prototype.onAdd=function(t){this._map=t,this._container=s.create(\"div\",\"mapboxgl-ctrl\");var e=s.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",\"Mapbox logo\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._container},an.prototype.onRemove=function(){s.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo)},an.prototype.getDefaultPosition=function(){return\"bottom-left\"},an.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType\|\|(this._container.style.display=this._logoRequired()?\"block\":\"none\")},an.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}};var on=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};on.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},on.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},on.prototype.run=function(){var t=this._currentlyRunning=this._queue;this._queue=[];for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!n.cancelled&&(n.callback(),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},on.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var sn=t.default.HTMLImageElement,ln=t.default.HTMLElement,cn={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:0,maxZoom:22,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,transformRequest:null,fadeDuration:300},un=function(r){function n(e){if(null!=(e=t.extend({},cn,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than minZoom\");var n=new Fr(e.minZoom,e.maxZoom,e.renderWorldCopies);r.call(this,n,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new on;var i=e.transformRequest;if(this._transformRequest=i?function(t,e){return i(t,e)\|\|{url:t}}:function(t){return{url:t}},\"string\"==typeof e.container){var a=t.default.document.getElementById(e.container);if(!a)throw new Error(\"Container '\"+e.container+\"' not found.\");this._container=a}else{if(!(e.container instanceof ln))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\",\"_update\",\"_render\",\"_onData\",\"_onDataLoading\"],this),this._setupContainer(),this._setupPainter(),this.on(\"move\",this._update.bind(this,!1)),this.on(\"zoom\",this._update.bind(this,!0)),void 0!==t.default&&(t.default.addEventListener(\"online\",this._onWindowOnline,!1),t.default.addEventListener(\"resize\",this._onWindowResize,!1)),function(t,e){var r=t.getCanvasContainer(),n=null,i=!1;for(var a in en)t[a]=new en[a](t,e),e.interactive&&e[a]&&t[a].enable(e[a]);s.addEventListener(r,\"mouseout\",function(e){t.fire(new Vr(\"mouseout\",t,e))}),s.addEventListener(r,\"mousedown\",function(r){i=!0;var n=new Vr(\"mousedown\",t,r);t.fire(n),n.defaultPrevented\|\|(e.interactive&&!t.doubleClickZoom.isActive()&&t.stop(),t.boxZoom.onMouseDown(r),t.boxZoom.isActive()\|\|t.dragPan.isActive()\|\|t.dragRotate.onMouseDown(r),t.boxZoom.isActive()\|\|t.dragRotate.isActive()\|\|t.dragPan.onMouseDown(r))}),s.addEventListener(r,\"mouseup\",function(e){var r=t.dragRotate.isActive();n&&!r&&t.fire(new Vr(\"contextmenu\",t,n)),n=null,i=!1,t.fire(new Vr(\"mouseup\",t,e))}),s.addEventListener(r,\"mousemove\",function(e){if(!t.dragPan.isActive()&&!t.dragRotate.isActive()){for(var n=e.toElement\|\|e.target;n&&n!==r;)n=n.parentNode;n===r&&t.fire(new Vr(\"mousemove\",t,e))}}),s.addEventListener(r,\"mouseover\",function(e){for(var n=e.toElement\|\|e.target;n&&n!==r;)n=n.parentNode;n===r&&t.fire(new Vr(\"mouseover\",t,e))}),s.addEventListener(r,\"touchstart\",function(r){var n=new Ur(\"touchstart\",t,r);t.fire(n),n.defaultPrevented\|\|(e.interactive&&t.stop(),t.boxZoom.isActive()\|\|t.dragRotate.isActive()\|\|t.dragPan.onTouchStart(r),t.touchZoomRotate.onStart(r),t.doubleClickZoom.onTouchStart(n))},{passive:!1}),s.addEventListener(r,\"touchmove\",function(e){t.fire(new Ur(\"touchmove\",t,e))},{passive:!1}),s.addEventListener(r,\"touchend\",function(e){t.fire(new Ur(\"touchend\",t,e))}),s.addEventListener(r,\"touchcancel\",function(e){t.fire(new Ur(\"touchcancel\",t,e))}),s.addEventListener(r,\"click\",function(e){t.fire(new Vr(\"click\",t,e))}),s.addEventListener(r,\"dblclick\",function(e){var r=new Vr(\"dblclick\",t,e);t.fire(r),r.defaultPrevented\|\|t.doubleClickZoom.onDblClick(r)}),s.addEventListener(r,\"contextmenu\",function(e){var r=t.dragRotate.isActive();i\|\|r?i&&(n=e):t.fire(new Vr(\"contextmenu\",t,e)),e.preventDefault()}),s.addEventListener(r,\"wheel\",function(e){var r=new qr(\"wheel\",t,e);t.fire(r),r.defaultPrevented\|\|t.scrollZoom.onWheel(e)},{passive:!1})}(this,e),this._hash=e.hash&&(new jr).addTo(this),this._hash&&this._hash._onHashChange()\|\|this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),this.resize(),e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new nn),this.addControl(new an,e.logoPosition),this.on(\"style.load\",function(){this.transform.unmodified&&this.jumpTo(this.style.stylesheet)}),this.on(\"data\",this._onData),this.on(\"dataloading\",this._onDataLoading)}r&&(n.__proto__=r),n.prototype=Object.create(r&&r.prototype),n.prototype.constructor=n;var i={showTileBoundaries:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0}};return n.prototype.addControl=function(t,e){void 0===e&&t.getDefaultPosition&&(e=t.getDefaultPosition()),void 0===e&&(e=\"top-right\");var r=t.onAdd(this),n=this._controlPositions[e];return-1!==e.indexOf(\"bottom\")?n.insertBefore(r,n.firstChild):n.appendChild(r),this},n.prototype.removeControl=function(t){return t.onRemove(this),this},n.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];return this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e)).fire(new t.Event(\"resize\",e)).fire(new t.Event(\"moveend\",e))},n.prototype.getBounds=function(){var e=new W(this.transform.pointLocation(new t.default$1(0,this.transform.height)),this.transform.pointLocation(new t.default$1(this.transform.width,0)));return(this.transform.angle\|\|this.transform.pitch)&&(e.extend(this.transform.pointLocation(new t.default$1(this.transform.size.x,0))),e.extend(this.transform.pointLocation(new t.default$1(0,this.transform.size.y)))),e},n.prototype.getMaxBounds=function(){return this.transform.latRange&&2===this.transform.latRange.length&&this.transform.lngRange&&2===this.transform.lngRange.length?new W([this.transform.lngRange[0],this.transform.latRange[0]],[this.transform.lngRange[1],this.transform.latRange[1]]):null},n.prototype.setMaxBounds=function(t){if(t){var e=W.convert(t);this.transform.lngRange=[e.getWest(),e.getEast()],this.transform.latRange=[e.getSouth(),e.getNorth()],this.transform._constrain(),this._update()}else null==t&&(this.transform.lngRange=null,this.transform.latRange=null,this._update());return this},n.prototype.setMinZoom=function(t){if((t=null==t?0:t)>=0&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between 0 and the current maxZoom, inclusive\")},n.prototype.getMinZoom=function(){return this.transform.minZoom},n.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},n.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},n.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update(),this},n.prototype.getMaxZoom=function(){return this.transform.maxZoom},n.prototype.project=function(t){return this.transform.locationPoint(G.convert(t))},n.prototype.unproject=function(e){return this.transform.pointLocation(t.default$1.convert(e))},n.prototype.isMoving=function(){return this._moving\|\|this.dragPan.isActive()\|\|this.dragRotate.isActive()\|\|this.scrollZoom.isActive()},n.prototype.isZooming=function(){return this._zooming\|\|this.scrollZoom.isActive()},n.prototype.isRotating=function(){return this._rotating\|\|this.dragRotate.isActive()},n.prototype.on=function(t,e,n){var i,a=this;if(void 0===n)return r.prototype.on.call(this,t,e);var o=function(){if(\"mouseenter\"===t\|\|\"mouseover\"===t){var r=!1;return{layer:e,listener:n,delegates:{mousemove:function(i){var o=a.getLayer(e)?a.queryRenderedFeatures(i.point,{layers:[e]}):[];o.length?r\|\|(r=!0,n.call(a,new Vr(t,a,i.originalEvent,{features:o}))):r=!1},mouseout:function(){r=!1}}}}if(\"mouseleave\"===t\|\|\"mouseout\"===t){var o=!1;return{layer:e,listener:n,delegates:{mousemove:function(r){(a.getLayer(e)?a.queryRenderedFeatures(r.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,n.call(a,new Vr(t,a,r.originalEvent)))},mouseout:function(e){o&&(o=!1,n.call(a,new Vr(t,a,e.originalEvent)))}}}}return{layer:e,listener:n,delegates:(i={},i[t]=function(t){var r=a.getLayer(e)?a.queryRenderedFeatures(t.point,{layers:[e]}):[];r.length&&(t.features=r,n.call(a,t),delete t.features)},i)}}();for(var s in this._delegatedListeners=this._delegatedListeners\|\|{},this._delegatedListeners[t]=this._delegatedListeners[t]\|\|[],this._delegatedListeners[t].push(o),o.delegates)a.on(s,o.delegates[s]);return this},n.prototype.off=function(t,e,n){if(void 0===n)return r.prototype.off.call(this,t,e);if(this._delegatedListeners&&this._delegatedListeners[t])for(var i=this._delegatedListeners[t],a=0;a<i.length;a++){var o=i[a];if(o.layer===e&&o.listener===n){for(var s in o.delegates)this.off(s,o.delegates[s]);return i.splice(a,1),this}}return this},n.prototype.queryRenderedFeatures=function(e,r){var n;return 2===arguments.length?(e=arguments[0],r=arguments[1]):1===arguments.length&&((n=arguments[0])instanceof t.default$1\|\|Array.isArray(n))?(e=arguments[0],r={}):1===arguments.length?(e=void 0,r=arguments[0]):(e=void 0,r={}),this.style?this.style.queryRenderedFeatures(this._makeQueryGeometry(e),r,this.transform):[]},n.prototype._makeQueryGeometry=function(e){var r,n=this;if(void 0===e&&(e=[t.default$1.convert([0,0]),t.default$1.convert([this.transform.width,this.transform.height])]),e instanceof t.default$1\|\|\"number\"==typeof e[0])r=[t.default$1.convert(e)];else{var i=[t.default$1.convert(e[0]),t.default$1.convert(e[1])];r=[i[0],new t.default$1(i[1].x,i[0].y),i[1],new t.default$1(i[0].x,i[1].y),i[0]]}return{viewport:r,worldCoordinate:r.map(function(t){return n.transform.pointCoordinate(t)})}},n.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},n.prototype.setStyle=function(e,r){if((!r\|\|!1!==r.diff&&!r.localIdeographFontFamily)&&this.style&&e&&\"object\"==typeof e)try{return this.style.setState(e)&&this._update(!0),this}catch(e){t.warnOnce(\"Unable to perform style diff: \"+(e.message\|\|e.error\|\|e)+\". Rebuilding the style from scratch.\")}return this.style&&(this.style.setEventedParent(null),this.style._remove()),e?(this.style=new Je(this,r\|\|{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof e?this.style.loadURL(e):this.style.loadJSON(e),this):(delete this.style,this)},n.prototype.getStyle=function(){if(this.style)return this.style.serialize()},n.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},n.prototype.addSource=function(t,e){return this.style.addSource(t,e),this._update(!0),this},n.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},n.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},n.prototype.addSourceType=function(t,e,r){return this.style.addSourceType(t,e,r)},n.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0),this},n.prototype.getSource=function(t){return this.style.getSource(t)},n.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var o=n.sdf;if(void 0===o&&(o=!1),r instanceof sn){var s=a.getImageData(r),l=s.width,c=s.height,u=s.data;this.style.addImage(e,{data:new t.RGBAImage({width:l,height:c},u),pixelRatio:i,sdf:o})}else{if(void 0===r.width\|\|void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var f=r.width,h=r.height,p=r.data;this.style.addImage(e,{data:new t.RGBAImage({width:f,height:h},p.slice(0)),pixelRatio:i,sdf:o})}},n.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},n.prototype.removeImage=function(t){this.style.removeImage(t)},n.prototype.loadImage=function(e,r){t.getImage(this._transformRequest(e,t.ResourceType.Image),r)},n.prototype.addLayer=function(t,e){return this.style.addLayer(t,e),this._update(!0),this},n.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0),this},n.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0),this},n.prototype.getLayer=function(t){return this.style.getLayer(t)},n.prototype.setFilter=function(t,e){return this.style.setFilter(t,e),this._update(!0),this},n.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0),this},n.prototype.getFilter=function(t){return this.style.getFilter(t)},n.prototype.setPaintProperty=function(t,e,r){return this.style.setPaintProperty(t,e,r),this._update(!0),this},n.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},n.prototype.setLayoutProperty=function(t,e,r){return this.style.setLayoutProperty(t,e,r),this._update(!0),this},n.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},n.prototype.setLight=function(t){return this.style.setLight(t),this._update(!0),this},n.prototype.getLight=function(){return this.style.getLight()},n.prototype.getContainer=function(){return this._container},n.prototype.getCanvasContainer=function(){return this._canvasContainer},n.prototype.getCanvas=function(){return this._canvas},n.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.offsetWidth\|\|400,e=this._container.offsetHeight\|\|300),[t,e]},n.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSContainer=s.create(\"div\",\"mapboxgl-missing-css\",t)).innerHTML=\"Missing Mapbox GL JS CSS\";var e=this._canvasContainer=s.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=s.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.style.position=\"absolute\",this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var r=this._containerDimensions();this._resizeCanvas(r[0],r[1]);var n=this._controlContainer=s.create(\"div\",\"mapboxgl-control-container\",t),i=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(t){i[t]=s.create(\"div\",\"mapboxgl-ctrl-\"+t,n)})},n.prototype._resizeCanvas=function(e,r){var n=t.default.devicePixelRatio\|\|1;this._canvas.width=ne,this._canvas.height=nr,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},n.prototype._setupPainter=function(){var r=t.extend({failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer},e.webGLContextAttributes),n=this._canvas.getContext(\"webgl\",r)\|\|this._canvas.getContext(\"experimental-webgl\",r);n?this.painter=new zr(n,this.transform):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},n.prototype._contextLost=function(e){e.preventDefault(),this._frameId&&(a.cancelFrame(this._frameId),this._frameId=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},n.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},n.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!(!this.style\|\|!this.style.loaded())},n.prototype._update=function(t){this.style&&(this._styleDirty=this._styleDirty\|\|t,this._sourcesDirty=!0,this._rerender())},n.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},n.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},n.prototype._render=function(){this._renderTaskQueue.run();var e=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var r=this.transform.zoom,n=a.now();this.style.zoomHistory.update(r,n);var i=new t.default$16(r,{now:n,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),o=i.crossFadingFactor();1===o&&o===this._crossFadingFactor\|\|(e=!0,this._crossFadingFactor=o),this.style.update(i)}return this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),fadeDuration:this._fadeDuration}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()\|\|e)&&(this._styleDirty=!0),(this._sourcesDirty\|\|this._repaint\|\|this._styleDirty\|\|this._placementDirty)&&this._rerender(),this},n.prototype.remove=function(){this._hash&&this._hash.remove(),a.cancelFrame(this._frameId),this._renderTaskQueue.clear(),this._frameId=null,this.setStyle(null),void 0!==t.default&&(t.default.removeEventListener(\"resize\",this._onWindowResize,!1),t.default.removeEventListener(\"online\",this._onWindowOnline,!1));var e=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");e&&e.loseContext(),fn(this._canvasContainer),fn(this._controlContainer),fn(this._missingCSSContainer),this._container.classList.remove(\"mapboxgl-map\"),this.fire(new t.Event(\"remove\"))},n.prototype._rerender=function(){var t=this;this.style&&!this._frameId&&(this._frameId=a.frame(function(){t._frameId=null,t._render()}))},n.prototype._onWindowOnline=function(){this._update()},n.prototype._onWindowResize=function(){this._trackResize&&this.stop().resize()._update()},i.showTileBoundaries.get=function(){return!!this._showTileBoundaries},i.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},i.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},i.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},i.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},i.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},i.repaint.get=function(){return!!this._repaint},i.repaint.set=function(t){this._repaint=t,this._update()},i.vertices.get=function(){return!!this._vertices},i.vertices.set=function(t){this._vertices=t,this._update()},n.prototype._onData=function(e){this._update(\"style\"===e.dataType),this.fire(new t.Event(e.dataType+\"data\",e))},n.prototype._onDataLoading=function(e){this.fire(new t.Event(e.dataType+\"dataloading\",e))},Object.defineProperties(n.prototype,i),n}(rn);function fn(t){t.parentNode&&t.parentNode.removeChild(t)}var hn={showCompass:!0,showZoom:!0},pn=function(e){var r=this;this.options=t.extend({},hn,e),this._container=s.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",function(t){return t.preventDefault()}),this.options.showZoom&&(this._zoomInButton=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-zoom-in\",\"Zoom In\",function(){return r._map.zoomIn()}),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-zoom-out\",\"Zoom Out\",function(){return r._map.zoomOut()})),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-icon mapboxgl-ctrl-compass\",\"Reset North\",function(){return r._map.resetNorth()}),this._compassArrow=s.create(\"span\",\"mapboxgl-ctrl-compass-arrow\",this._compass))};function dn(t,e,r){if(t=new G(t.lng,t.lat),e){var n=new G(t.lng-360,t.lat),i=new G(t.lng+360,t.lat),a=r.locationPoint(t).distSqr(e);r.locationPoint(n).distSqr(e)<a?t=n:r.locationPoint(i).distSqr(e)<a&&(t=i)}for(;Math.abs(t.lng-r.center.lng)>180;){var o=r.locationPoint(t);if(o.x>=0&&o.y>=0&&o.x<=r.width&&o.y<=r.height)break;t.lng>r.center.lng?t.lng-=360:t.lng+=360}return t}pn.prototype._rotateCompassArrow=function(){var t=\"rotate(\"+this._map.transform.angle(180/Math.PI)+\"deg)\";this._compassArrow.style.transform=t},pn.prototype.onAdd=function(t){return this._map=t,this.options.showCompass&&(this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Yr(t,{button:\"left\",element:this._compass}),this._handler.enable()),this._container},pn.prototype.onRemove=function(){s.remove(this._container),this.options.showCompass&&(this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.disable(),delete this._handler),delete this._map},pn.prototype._createButton=function(t,e,r){var n=s.create(\"button\",t,this._container);return n.type=\"button\",n.setAttribute(\"aria-label\",e),n.addEventListener(\"click\",r),n};var gn={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function vn(t,e,r){var n=t.classList;for(var i in gn)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var mn=function(e){if((arguments[0]instanceof t.default.HTMLElement\|\|2===arguments.length)&&(e=t.extend({element:e},arguments[1])),t.bindAll([\"_update\",\"_onMapClick\"],this),this._anchor=e&&e.anchor\|\|\"center\",this._color=e&&e.color\|\|\"#3FB1CE\",e&&e.element)this._element=e.element,this._offset=t.default$1.convert(e&&e.offset\|\|[0,0]);else{this._defaultMarker=!0,this._element=s.create(\"div\");var r=s.createNS(\"http://www.w3.org/2000/svg\",\"svg\");r.setAttributeNS(null,\"height\",\"41px\"),r.setAttributeNS(null,\"width\",\"27px\"),r.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var n=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");n.setAttributeNS(null,\"stroke\",\"none\"),n.setAttributeNS(null,\"stroke-width\",\"1\"),n.setAttributeNS(null,\"fill\",\"none\"),n.setAttributeNS(null,\"fill-rule\",\"evenodd\");var i=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");i.setAttributeNS(null,\"fill-rule\",\"nonzero\");var a=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");a.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),a.setAttributeNS(null,\"fill\",\"#000000\");for(var o=0,l=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];o<l.length;o+=1){var c=l[o],u=s.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");u.setAttributeNS(null,\"opacity\",\"0.04\"),u.setAttributeNS(null,\"cx\",\"10.5\"),u.setAttributeNS(null,\"cy\",\"5.80029008\"),u.setAttributeNS(null,\"rx\",c.rx),u.setAttributeNS(null,\"ry\",c.ry),a.appendChild(u)}var f=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");f.setAttributeNS(null,\"fill\",this._color);var h=s.createNS(\"http://www.w3.org/2000/svg\",\"path\");h.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),f.appendChild(h);var p=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");p.setAttributeNS(null,\"opacity\",\"0.25\"),p.setAttributeNS(null,\"fill\",\"#000000\");var d=s.createNS(\"http://www.w3.org/2000/svg\",\"path\");d.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),p.appendChild(d);var g=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),g.setAttributeNS(null,\"fill\",\"#FFFFFF\");var v=s.createNS(\"http://www.w3.org/2000/svg\",\"g\");v.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var m=s.createNS(\"http://www.w3.org/2000/svg\",\"circle\");m.setAttributeNS(null,\"fill\",\"#000000\"),m.setAttributeNS(null,\"opacity\",\"0.25\"),m.setAttributeNS(null,\"cx\",\"5.5\"),m.setAttributeNS(null,\"cy\",\"5.5\"),m.setAttributeNS(null,\"r\",\"5.4999962\");var y=s.createNS(\"http://www.w3.org/2000/svg\",\"circle\");y.setAttributeNS(null,\"fill\",\"#FFFFFF\"),y.setAttributeNS(null,\"cx\",\"5.5\"),y.setAttributeNS(null,\"cy\",\"5.5\"),y.setAttributeNS(null,\"r\",\"5.4999962\"),v.appendChild(m),v.appendChild(y),i.appendChild(a),i.appendChild(f),i.appendChild(p),i.appendChild(g),i.appendChild(v),r.appendChild(i),this._element.appendChild(r),this._offset=t.default$1.convert(e&&e.offset\|\|[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._popup=null};mn.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this._update(),this._map.on(\"click\",this._onMapClick),this},mn.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),delete this._map),s.remove(this._element),this._popup&&this._popup.remove(),this},mn.prototype.getLngLat=function(){return this._lngLat},mn.prototype.setLngLat=function(t){return this._lngLat=G.convert(t),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},mn.prototype.getElement=function(){return this._element},mn.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1(24.6+e)],\"bottom-right\":[-e,-1(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat)}return this},mn.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r\|\|r.contains(e))&&this.togglePopup()},mn.prototype.getPopup=function(){return this._popup},mn.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},mn.prototype._update=function(t){this._map&&(this._map.transform.renderWorldCopies&&(this._lngLat=dn(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset),t&&\"moveend\"!==t.type\|\|(this._pos=this._pos.round()),s.setTransform(this._element,gn[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px)\"),vn(this._element,this._anchor,\"marker\"))},mn.prototype.getOffset=function(){return this._offset},mn.prototype.setOffset=function(e){return this._offset=t.default$1.convert(e),this._update(),this};var yn,xn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showUserLocation:!0},bn=function(e){function r(r){e.call(this),this.options=t.extend({},xn,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(e){var r;return this._map=e,this._container=s.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),r=this._setupUI,void 0!==yn?r(yn):void 0!==t.default.navigator.permissions?t.default.navigator.permissions.query({name:\"geolocation\"}).then(function(t){yn=\"denied\"!==t.state,r(yn)}):(yn=!!t.default.navigator.geolocation,r(yn)),this._container},r.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.default.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker.remove(),s.remove(this._container),this._map=void 0},r.prototype._onSuccess=function(e){if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState\|\|this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()},r.prototype._updateCamera=function(t){var e=new G(t.coords.longitude,t.coords.latitude),r=t.coords.accuracy;this._map.fitBounds(e.toBounds(r),this.options.fitBoundsOptions,{geolocateSource:!0})},r.prototype._updateMarker=function(t){t?this._userLocationDotMarker.setLngLat([t.coords.longitude,t.coords.latitude]).addTo(this._map):this._userLocationDotMarker.remove()},r.prototype._onError=function(e){if(this.options.trackUserLocation)if(1===e.code)this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),void 0!==this._geolocationWatchID&&this._clearWatch();else switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()},r.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},r.prototype._setupUI=function(e){var r=this;!1!==e&&(this._container.addEventListener(\"contextmenu\",function(t){return t.preventDefault()}),this._geolocateButton=s.create(\"button\",\"mapboxgl-ctrl-icon mapboxgl-ctrl-geolocate\",this._container),this._geolocateButton.type=\"button\",this._geolocateButton.setAttribute(\"aria-label\",\"Geolocate\"),this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=s.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new mn(this._dotElement),this.options.trackUserLocation&&(this._watchState=\"OFF\")),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",function(e){e.geolocateSource\|\|\"ACTIVE_LOCK\"!==r._watchState\|\|(r._watchState=\"BACKGROUND\",r._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),r._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),r.fire(new t.Event(\"trackuserlocationend\")))}))},r.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID?this._clearWatch():void 0===this._geolocationWatchID&&(this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),this._geolocationWatchID=t.default.navigator.geolocation.watchPosition(this._onSuccess,this._onError,this.options.positionOptions))}else t.default.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},r.prototype._clearWatch=function(){t.default.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},r}(t.Evented),_n={maxWidth:100,unit:\"metric\"},wn=function(e){this.options=t.extend({},_n,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function kn(t,e,r){var n,i,a,o,s,l,c=r&&r.maxWidth\|\|100,u=t._container.clientHeight/2,f=(n=t.unproject([0,u]),i=t.unproject([c,u]),a=Math.PI/180,o=n.lata,s=i.lata,l=Math.sin(o)Math.sin(s)+Math.cos(o)Math.cos(s)Math.cos((i.lng-n.lng)a),6371e3Math.acos(Math.min(l,1)));if(r&&\"imperial\"===r.unit){var h=3.2808f;h>5280?Mn(e,c,h/5280,\"mi\"):Mn(e,c,h,\"ft\")}else r&&\"nautical\"===r.unit?Mn(e,c,f/1852,\"nm\"):Mn(e,c,f,\"m\")}function Mn(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))(o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:1)),l=s/r;\"m\"===n&&s>=1e3&&(s/=1e3,n=\"km\"),t.style.width=el+\"px\",t.innerHTML=s+n}wn.prototype.getDefaultPosition=function(){return\"bottom-left\"},wn.prototype._onMove=function(){kn(this._map,this._container,this.options)},wn.prototype.onAdd=function(t){return this._map=t,this._container=s.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},wn.prototype.onRemove=function(){s.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},wn.prototype.setUnit=function(t){this.options.unit=t,kn(this._map,this._container,this.options)};var An=function(){this._fullscreen=!1,t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.default.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.default.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.default.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.default.document&&(this._fullscreenchange=\"MSFullscreenChange\"),this._className=\"mapboxgl-ctrl\"};An.prototype.onAdd=function(e){return this._map=e,this._mapContainer=this._map.getContainer(),this._container=s.create(\"div\",this._className+\" mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._container.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._container},An.prototype.onRemove=function(){s.remove(this._container),this._map=null,t.default.document.removeEventListener(this._fullscreenchange,this._changeIcon)},An.prototype._checkFullscreenSupport=function(){return!!(t.default.document.fullscreenEnabled\|\|t.default.document.mozFullScreenEnabled\|\|t.default.document.msFullscreenEnabled\|\|t.default.document.webkitFullscreenEnabled)},An.prototype._setupUI=function(){var e=this._fullscreenButton=s.create(\"button\",this._className+\"-icon \"+this._className+\"-fullscreen\",this._container);e.setAttribute(\"aria-label\",\"Toggle fullscreen\"),e.type=\"button\",this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.default.document.addEventListener(this._fullscreenchange,this._changeIcon)},An.prototype._isFullscreen=function(){return this._fullscreen},An.prototype._changeIcon=function(){(t.default.document.fullscreenElement\|\|t.default.document.mozFullScreenElement\|\|t.default.document.webkitFullscreenElement\|\|t.default.document.msFullscreenElement)===this._mapContainer!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(this._className+\"-shrink\"),this._fullscreenButton.classList.toggle(this._className+\"-fullscreen\"))},An.prototype._onClickFullscreen=function(){this._isFullscreen()?t.default.document.exitFullscreen?t.default.document.exitFullscreen():t.default.document.mozCancelFullScreen?t.default.document.mozCancelFullScreen():t.default.document.msExitFullscreen?t.default.document.msExitFullscreen():t.default.document.webkitCancelFullScreen&&t.default.document.webkitCancelFullScreen():this._mapContainer.requestFullscreen?this._mapContainer.requestFullscreen():this._mapContainer.mozRequestFullScreen?this._mapContainer.mozRequestFullScreen():this._mapContainer.msRequestFullscreen?this._mapContainer.msRequestFullscreen():this._mapContainer.webkitRequestFullscreen&&this._mapContainer.webkitRequestFullscreen()};var Tn={closeButton:!0,closeOnClick:!0},Sn=function(e){function r(r){e.call(this),this.options=t.extend(Object.create(Tn),r),t.bindAll([\"_update\",\"_onClickClose\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.addTo=function(e){return this._map=e,this._map.on(\"move\",this._update),this.options.closeOnClick&&this._map.on(\"click\",this._onClickClose),this._update(),this.fire(new t.Event(\"open\")),this},r.prototype.isOpen=function(){return!!this._map},r.prototype.remove=function(){return this._content&&s.remove(this._content),this._container&&(s.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"click\",this._onClickClose),delete this._map),this.fire(new t.Event(\"close\")),this},r.prototype.getLngLat=function(){return this._lngLat},r.prototype.setLngLat=function(t){return this._lngLat=G.convert(t),this._pos=null,this._update(),this},r.prototype.setText=function(e){return this.setDOMContent(t.default.document.createTextNode(e))},r.prototype.setHTML=function(e){var r,n=t.default.document.createDocumentFragment(),i=t.default.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},r.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},r.prototype._createContent=function(){this._content&&s.remove(this._content),this._content=s.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=s.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClickClose))},r.prototype._update=function(){if(this._map&&this._lngLat&&this._content){this._container\|\|(this._container=s.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=s.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content)),this._map.transform.renderWorldCopies&&(this._lngLat=dn(this._lngLat,this._pos,this._map.transform));var e=this._pos=this._map.project(this._lngLat),r=this.options.anchor,n=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5Math.pow(r,2)));return{center:new t.default$1(0,0),top:new t.default$1(0,r),\"top-left\":new t.default$1(n,n),\"top-right\":new t.default$1(-n,n),bottom:new t.default$1(0,-r),\"bottom-left\":new t.default$1(n,-n),\"bottom-right\":new t.default$1(-n,-n),left:new t.default$1(r,0),right:new t.default$1(-r,0)}}if(r instanceof t.default$1\|\|Array.isArray(r)){var i=t.default$1.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.default$1.convert(r.center\|\|[0,0]),top:t.default$1.convert(r.top\|\|[0,0]),\"top-left\":t.default$1.convert(r[\"top-left\"]\|\|[0,0]),\"top-right\":t.default$1.convert(r[\"top-right\"]\|\|[0,0]),bottom:t.default$1.convert(r.bottom\|\|[0,0]),\"bottom-left\":t.default$1.convert(r[\"bottom-left\"]\|\|[0,0]),\"bottom-right\":t.default$1.convert(r[\"bottom-right\"]\|\|[0,0]),left:t.default$1.convert(r.left\|\|[0,0]),right:t.default$1.convert(r.right\|\|[0,0])}}return e(new t.default$1(0,0))}(this.options.offset);if(!r){var i,a=this._container.offsetWidth,o=this._container.offsetHeight;i=e.y+n.bottom.y<o?[\"top\"]:e.y>this._map.transform.height-o?[\"bottom\"]:[],e.x<a/2?i.push(\"left\"):e.x>this._map.transform.width-a/2&&i.push(\"right\"),r=0===i.length?\"bottom\":i.join(\"-\")}var l=e.add(n[r]).round();s.setTransform(this._container,gn[r]+\" translate(\"+l.x+\"px,\"+l.y+\"px)\"),vn(this._container,r,\"popup\")}},r.prototype._onClickClose=function(){this.remove()},r}(t.Evented),En={version:\"0.45.0\",supported:e,workerCount:Math.max(Math.floor(a.hardwareConcurrency/2),1),setRTLTextPlugin:t.setRTLTextPlugin,Map:un,NavigationControl:pn,GeolocateControl:bn,AttributionControl:nn,ScaleControl:wn,FullscreenControl:An,Popup:Sn,Marker:mn,Style:Je,LngLat:G,LngLatBounds:W,Point:t.default$1,Evented:t.Evented,config:v,get accessToken(){return v.ACCESS_TOKEN},set accessToken(t){v.ACCESS_TOKEN=t},workerUrl:\"\"};return En}),n})}).call(this,\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{}],410:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=1<<t+1,r=new Array(e),n=0;n<e;++n)r[n]=a(t,n);return r};var n=t(\"convex-hull\");function i(t,e,r){for(var n=new Array(t),i=0;i<t;++i)n[i]=0,i===e&&(n[i]+=.5),i===r&&(n[i]+=.5);return n}function a(t,e){if(0===e\|\|e===(1<<t+1)-1)return[];for(var r=[],a=[],o=0;o<=t;++o)if(e&1<<o){r.push(i(t,o-1,o-1)),a.push(null);for(var s=0;s<=t;++s)~e&1<<s&&(r.push(i(t,o-1,s-1)),a.push([o,s]))}var l=n(r),c=[];t:for(o=0;o<l.length;++o){var u=l[o],f=[];for(s=0;s<u.length;++s){if(!a[u[s]])continue t;f.push(a[u[s]].slice())}c.push(f)}return c}},{\"convex-hull\":118}],411:[function(t,e,r){var n=t(\"./normalize\"),i=t(\"gl-mat4/create\"),a=t(\"gl-mat4/clone\"),o=t(\"gl-mat4/determinant\"),s=t(\"gl-mat4/invert\"),l=t(\"gl-mat4/transpose\"),c={length:t(\"gl-vec3/length\"),normalize:t(\"gl-vec3/normalize\"),dot:t(\"gl-vec3/dot\"),cross:t(\"gl-vec3/cross\")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function g(t,e,r,n,i){t[0]=e[0]n+r[0]i,t[1]=e[1]n+r[1]i,t[2]=e[2]n+r[2]i}e.exports=function(t,e,r,i,v,m){if(e\|\|(e=[0,0,0]),r\|\|(r=[0,0,0]),i\|\|(i=[0,0,0]),v\|\|(v=[0,0,0,1]),m\|\|(m=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,k,M,A=u[3],T=u[7],S=u[11],E=u[12],C=u[13],L=u[14],z=u[15];if(0!==A\|\|0!==T\|\|0!==S){if(h[0]=A,h[1]=T,h[2]=S,h[3]=z,!s(f,f))return!1;l(f,f),y=v,b=f,_=(x=h)[0],w=x[1],k=x[2],M=x[3],y[0]=b[0]_+b[4]w+b[8]k+b[12]M,y[1]=b[1]_+b[5]w+b[9]k+b[13]M,y[2]=b[2]_+b[6]w+b[10]k+b[14]M,y[3]=b[3]_+b[7]w+b[11]k+b[15]M}else v[0]=v[1]=v[2]=0,v[3]=1;if(e[0]=E,e[1]=C,e[2]=L,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),g(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),g(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),g(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var O=0;O<3;O++)r[O]=-1,p[O][0]=-1,p[O][1]=-1,p[O][2]=-1;return 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nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) < length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) < length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha = min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha = min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a = alpha opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n + positionFract * scale + translateFract\\n + position * scaleFract\\n + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a = opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach(function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]e.viewport.width>v.precisionThreshold\|\|e.scale[1]e.viewport.height>v.precisionThreshold?t.shaders.rect(e):\"rect\"===e.join\|\|!e.join&&(e.thickness<=2\|\|e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))}),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)\|\|(t=[t]);var r=this.regl,o=this.gl;if(t.forEach(function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin join type mode\",miterLimit:\"miterlimit miterLimit\",dashes:\"dash dashes dasharray dash-array dashArray\",color:\"color colour stroke colors colours stroke-color strokeColor\",fill:\"fill fill-color fillColor\",opacity:\"alpha opacity\",overlay:\"overlay crease overlap intersect\",close:\"closed close closed-path closePath\",range:\"range dataBox\",viewport:\"viewport viewBox\",hole:\"holes hole hollow\"}),d\|\|(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:\"linear\",min:\"linear\"}),colorBuffer:r.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array}),positionBuffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=g(t.viewport)),d.viewport\|\|(d.viewport=g([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var m,y;if(t.positions.x&&t.positions.y){var x=t.positions.x,b=t.positions.y;y=d.count=Math.max(x.length,b.length),m=new Float64Array(2y);for(var _=0;_<y;_++)m[2_]=x[_],m[2_+1]=b[_]}else m=l(t.positions,\"float64\"),y=d.count=Math.floor(m.length/2);var w=d.bounds=i(m,2);if(d.fill){for(var k=[],M={},A=0,T=0,S=0,E=d.count;T<E;T++){var C=m[2T],L=m[2T+1];isNaN(C)\|\|isNaN(L)\|\|null==C\|\|null==L?(C=m[2A],L=m[2A+1],M[T]=A):A=T,k[S++]=C,k[S++]=L}for(var z=c(k,d.hole\|\|[]),O=0,I=z.length;O<I;O++)null!=M[z[O]]&&(z[O]=M[z[O]]);d.triangles=z}var P=new Float64Array(m);u(P,2,w);var D=new Float64Array(2y+6);d.close?m[0]===m[2y-2]&&m[1]===m[2y-1]?(D[0]=P[2y-4],D[1]=P[2y-3]):(D[0]=P[2y-2],D[1]=P[2y-1]):(D[0]=P[0],D[1]=P[1]),D.set(P,2),d.close?m[0]===m[2y-2]&&m[1]===m[2y-1]?(D[2y+2]=P[2],D[2y+3]=P[3],d.count-=1):(D[2y+2]=P[0],D[2y+3]=P[1],D[2y+4]=P[2],D[2y+5]=P[3]):(D[2y+2]=P[2y-2],D[2y+3]=P[2y-1],D[2y+4]=P[2y-2],D[2y+5]=P[2y-1]),d.positionBuffer(h(D)),d.positionFractBuffer(p(D))}if(t.range?d.range=t.range:d.range\|\|(d.range=d.bounds),(t.range\|\|t.positions)&&d.count){var R=d.bounds,B=R[2]-R[0],F=R[3]-R[1],N=d.range[2]-d.range[0],j=d.range[3]-d.range[1];d.scale=[B/N,F/j],d.translate=[-d.range[0]/N+R[0]/N\|\|0,-d.range[1]/j+R[1]/j\|\|0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var V,U=0;if(!t.dashes\|\|t.dashes.length<2)U=1,V=new Uint8Array([255,255,255,255,255,255,255,255]);else{U=0;for(var q=0;q<t.dashes.length;++q)U+=t.dashes[q];V=new Uint8Array(Uv.dashMult);for(var H=0,G=255,W=0;W<2;W++)for(var Y=0;Y<t.dashes.length;++Y){for(var X=0,Z=t.dashes[Y]v.dashMult.5;X<Z;++X)V[H++]=G;G^=255}}d.dashLength=U,d.dashTexture({channels:1,data:V,width:V.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var $=d.count,J=t.color;J\|\|(J=\"transparent\");var K=new Uint8Array(4$+4);if(Array.isArray(J)&&\"number\"!=typeof J[0]){for(var Q=0;Q<$;Q++){var tt=n(J[Q],\"uint8\");K.set(tt,4Q)}K.set(n(J[0],\"uint8\"),4$)}else for(var et=n(J,\"uint8\"),rt=0;rt<$+1;rt++)K.set(et,4rt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:K})}}else e.passes[f]=null}),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=e.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var m=[],y=0;y<this.passes.length;y++)null!==e.passes[y]&&m.push(e.passes[y]);return this.passes=m,this}},v.prototype.destroy=function(){return this.passes.forEach(function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()}),this.passes.length=0,this}},{\"array-bounds\":53,\"array-normalize\":54,\"color-normalize\":108,earcut:156,\"es6-weak-map\":209,\"flatten-vertex-data\":216,glslify:392,\"object-assign\":437,\"parse-rect\":442,\"pick-by-alias\":448,\"to-float32\":515}],475:[function(t,e,r){\"use strict\";var n=t(\"./scatter\"),i=t(\"object-assign\");e.exports=function(t,e){var r=new n(t,e),a=r.render.bind(r);return i(a,{render:a,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),a}},{\"./scatter\":476,\"object-assign\":437}],476:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"color-id\"),o=t(\"point-cluster\"),s=t(\"object-assign\"),l=t(\"glslify\"),c=t(\"pick-by-alias\"),u=t(\"update-diff\"),f=t(\"flatten-vertex-data\"),h=t(\"is-iexplorer\"),p=t(\"to-float32\"),d=t(\"parse-rect\");function g(t,e){var r=this;if(!(this instanceof g))return new g(t,e);\"function\"==typeof t?(e\|\|(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=h,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),s(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{pixelRatio:t.context(\"pixelRatio\"),palette:n,paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),opacity:t.prop(\"opacity\"),marker:t.prop(\"markerTexture\")},attributes:{x:function(t,e){return e.xAttr\|\|{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr\|\|{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4e.color,4e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4e.borderColor,4e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop(\"elements\"),count:t.prop(\"count\"),offset:t.prop(\"offset\"),primitive:\"points\"},c=s({},o);c.frag=l([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nuniform sampler2D marker;\\nuniform float pixelRatio, opacity;\\n\\nfloat smoothStep(float x, float y) {\\n return 1.0 / (1.0 + exp(50.0(x - y)));\\n}\\n\\nvoid main() {\\n float dist = texture2D(marker, gl_PointCoord).r, delta = fragWidth;\\n\\n // max-distance alpha\\n if (dist < 0.003) discard;\\n\\n // null-border case\\n if (fragBorderColorLevel == fragColorLevel \|\| fragBorderColor.a == 0.) {\\n float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\\n gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\\n }\\n else {\\n float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\\n float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\\n\\n vec4 color = fragBorderColor;\\n color.a = borderColorAmt;\\n color = mix(color, fragColor, colorAmt);\\n color.a = opacity;\\n\\n gl_FragColor = color;\\n }\\n\\n}\\n\"]),c.vert=l([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform float pixelRatio;\\nuniform sampler2D palette;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius,\\n fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nvec2 paletteCoord(float id) {\\n return vec2(\\n (mod(id, paletteSize.x) + .5) / paletteSize.x,\\n (floor(id / paletteSize.x) + .5) / paletteSize.y\\n );\\n}\\nvec2 paletteCoord(vec2 id) {\\n return vec2(\\n (id.x + .5) / paletteSize.x,\\n (id.y + .5) / paletteSize.y\\n );\\n}\\nvec4 getColor(vec4 id) {\\n // zero-palette means we deal with direct buffer\\n if (paletteSize.x == 0.) return id / 255.;\\n return texture2D(palette, paletteCoord(id.xy));\\n}\\n\\nvoid main() {\\n if (isActive == 0.) return;\\n\\n vec2 position = vec2(x, y);\\n vec2 positionFract = vec2(xFract, yFract);\\n\\n vec4 color = getColor(colorId);\\n vec4 borderColor = getColor(borderColorId);\\n\\n float size = size * maxSize / 255.;\\n float borderSize = borderSize * maxSize / 255.;\\n\\n gl_PointSize = 2. * size * pixelRatio;\\n fragPointSize = size * pixelRatio;\\n\\n vec2 pos = (position + translate) * scale\\n + (positionFract + translateFract) * scale\\n + (position + translate) * scaleFract\\n + (positionFract + translateFract) * scaleFract;\\n\\n gl_Position = vec4(pos * 2. - 1., 0, 1);\\n\\n fragColor = color;\\n fragBorderColor = borderColor;\\n fragWidth = 1. / gl_PointSize;\\n\\n fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\\n\"]),this.drawMarker=t(c);var u=s({},o);u.frag=l([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\n\\nuniform float opacity;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a = alpha opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),u.vert=l([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio;\\nuniform sampler2D palette;\\nuniform vec2 paletteSize;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nvec2 paletteCoord(float id) {\\n return vec2(\\n (mod(id, paletteSize.x) + .5) / paletteSize.x,\\n (floor(id / paletteSize.x) + .5) / paletteSize.y\\n );\\n}\\nvec2 paletteCoord(vec2 id) {\\n return vec2(\\n (id.x + .5) / paletteSize.x,\\n (id.y + .5) / paletteSize.y\\n );\\n}\\n\\nvec4 getColor(vec4 id) {\\n // zero-palette means we deal with direct buffer\\n if (paletteSize.x == 0.) return id / 255.;\\n return texture2D(palette, paletteCoord(id.xy));\\n}\\n\\nvoid main() {\\n // ignore inactive points\\n if (isActive == 0.) return;\\n\\n vec2 position = vec2(x, y);\\n vec2 positionFract = vec2(xFract, yFract);\\n\\n vec4 color = getColor(colorId);\\n vec4 borderColor = getColor(borderColorId);\\n\\n float size = size * maxSize / 255.;\\n float borderSize = borderSize * maxSize / 255.;\\n\\n gl_PointSize = (size + borderSize) * pixelRatio;\\n\\n vec2 pos = (position + translate) * scale\\n + (positionFract + translateFract) * scale\\n + (position + translate) * scaleFract\\n + (positionFract + translateFract) * scaleFract;\\n\\n gl_Position = vec4(pos * 2. - 1., 0, 1);\\n\\n fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n fragColor = color;\\n fragBorderColor = borderColor.a == 0. \|\| borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n fragWidth = 1. / gl_PointSize;\\n}\\n\"]),h&&(u.frag=u.frag.replace(\"smoothstep\",\"smoothStep\"),c.frag=c.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(u)}e.exports=g,g.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},g.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];return e.length&&(t=this).update.apply(t,e),this.draw(),this},g.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];var n=this.groups;if(1===e.length&&Array.isArray(e[0])&&(null===e[0][0]\|\|Array.isArray(e[0][0]))&&(e=e[0]),this.regl._refresh(),e.length)for(var i=0;i<e.length;i++)t.drawItem(i,e[i]);else n.forEach(function(e,r){t.drawItem(r)});return this},g.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var i=[],a=1;a<n.activation.length;a++)n.activation[a]&&(!0===n.activation[a]\|\|n.activation[a].data.length)&&i.push.apply(i,this.getMarkerDrawOptions(a,n,e));i.length&&this.drawMarker(i)}},g.prototype.getMarkerDrawOptions=function(t,e,r){var n=e.range,i=e.tree,a=e.viewport,o=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!i)return r?[s({},e,{markerTexture:this.markerTextures[t],activation:o[t],count:r.length,elements:r,offset:0})]:[s({},e,{markerTexture:this.markerTextures[t],activation:o[t],offset:0})];var u=[],f=i.range(n,{lod:!0,px:[(n[2]-n[0])/a.width,(n[3]-n[1])/a.height]});if(r){for(var h=o[t].data,p=new Uint8Array(c),d=0;d<r.length;d++){var g=r[d];p[g]=h?h[g]:1}l.subdata(p)}for(var v=f.length;v--;){var m=f[v],y=m[0],x=m[1];u.push(s({},e,{markerTexture:this.markerTextures[t],activation:r?l:o[t],offset:y,count:x-y}))}return u},g.prototype.update=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){1===e.length&&Array.isArray(e[0])&&(e=e[0]);var n=this.groups,a=this.gl,l=this.regl,h=this.maxSize,v=this.maxColors,m=this.palette;this.groups=n=e.map(function(e,r){var y=n[r];if(void 0===e)return y;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=c(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\"})).positions&&(e.positions=[]),y\|\|(n[r]=y={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:l.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:l.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:l.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:l.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:l.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=s({},g.defaults,e)),!e.positions\|\|\"marker\"in e\|\|(e.marker=y.marker,delete y.marker),!e.marker\|\|\"positions\"in e\|\|(e.positions=y.positions,delete y.positions);var x=0,b=0;if(u(y,e,[{snap:!0,size:function(t,e){return null==t&&(t=g.defaults.size),x+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=g.defaults.borderSize),x+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=g.defaults.color),e=t.updateColor(e),b++,e},borderColor:function(e,r){return null==e&&(e=g.defaults.borderColor),e=t.updateColor(e),b++,e},bounds:function(t,e,r){return\"range\"in r\|\|(r.range=null),t},positions:function(t,e,r){var n=e.snap,a=e.positionBuffer,s=e.positionFractBuffer,c=e.selectionBuffer;if(t.x\|\|t.y)return t.x.length?e.xAttr={buffer:l.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4t.x.offset\|\|0,stride:4(t.x.stride\|\|1),count:t.x.count},t.y.length?e.yAttr={buffer:l.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4t.y.offset\|\|0,stride:4(t.y.stride\|\|1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=f(t,\"float64\");var u=e.count=Math.floor(t.length/2),h=e.bounds=u?i(t,2):null;if(r.range\|\|e.range\|\|(delete e.range,r.range=h),r.marker\|\|e.marker\|\|(delete e.marker,r.marker=null),n&&(!0===n\|\|u>n)?e.tree=o(t,{bounds:h}):n&&n.length&&(e.tree=n),e.tree){var d={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(d):e.elements=l.elements(d)}return a({data:p.float(t),usage:\"dynamic\"}),s({data:p.fract(t),usage:\"dynamic\"}),c({data:new Uint8Array(u),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach(function(t){return t&&t.destroy&&t.destroy()}),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],o=0,s=Math.min(e.length,r.count);o<s;o++){var c=t.addMarker(e[o]);a[c]\|\|(a[c]=new Uint8Array(r.count)),a[c][o]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=l.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t\|\|(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=p.fract(e.scale),e.translateFract=p.fract(e.translate),t},viewport:function(t){return d(t\|\|[a.drawingBufferWidth,a.drawingBufferHeight])}}]),x){var _=y.count,w=y.size,k=y.borderSize,M=y.sizeBuffer,A=new Uint8Array(2_);if(w.length\|\|k.length)for(var T=0;T<_;T++)A[2T]=Math.round(255(null==w[T]?w:w[T])/h),A[2T+1]=Math.round(255(null==k[T]?k:k[T])/h);M({data:A,usage:\"dynamic\"})}if(b){var S,E=y.count,C=y.color,L=y.borderColor,z=y.colorBuffer;if(t.tooManyColors){if(C.length\|\|L.length){S=new Uint8Array(8E);for(var O=0;O<E;O++){var I=C[O];S[8O]=m[4I],S[8O+1]=m[4I+1],S[8O+2]=m[4I+2],S[8O+3]=m[4I+3];var P=L[O];S[8O+4]=m[4P],S[8O+5]=m[4P+1],S[8O+6]=m[4P+2],S[8O+7]=m[4P+3]}}}else if(C.length\|\|L.length){S=new Uint8Array(4E+2);for(var D=0;D<E;D++)null!=C[D]&&(S[4D]=C[D]%v,S[4D+1]=Math.floor(C[D]/v)),null!=L[D]&&(S[4D+2]=L[D]%v,S[4D+3]=Math.floor(L[D]/v))}z({data:S\|\|new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return y})}},g.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array\|\|t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},g.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,i=this.maxColors;Array.isArray(t)\|\|(t=[t]);var o=[];if(\"number\"==typeof t[0]){var s=[];if(Array.isArray(t))for(var l=0;l<t.length;l+=4)s.push(t.slice(l,l+4));else for(var c=0;c<t.length;c+=4)s.push(t.subarray(c,c+4));t=s}for(var u=0;u<t.length;u++){var f=t[u];f=n(f,\"uint8\");var h=a(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}o[u]=e[h]}return!this.tooManyColors&&r.length>ii4&&(this.tooManyColors=!0),this.updatePalette(r),1===o.length?o[0]:o},g.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25t.length/e);if(n>1)for(var i=.25(t=t.slice()).length%e;i<ne;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25t.length,e),height:n,data:t},0,0)}},g.prototype.destroy=function(){return this.groups.forEach(function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach(function(t){return t&&t.destroy&&t.destroy()}),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()}),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach(function(t){return t&&t.destroy&&t.destroy()}),this}},{\"array-bounds\":53,\"color-id\":106,\"color-normalize\":108,\"flatten-vertex-data\":216,glslify:392,\"is-iexplorer\":402,\"object-assign\":437,\"parse-rect\":442,\"pick-by-alias\":448,\"point-cluster\":452,\"to-float32\":515,\"update-diff\":526}],477:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d/scatter\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16\|(255&e)<<8\|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o(function(){e.draw(),e.dirty=!0,e.planned=null})):(this.draw(),this.dirty=!0,o(function(){e.dirty=!1})),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag 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u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var g=s.columns,v=s.count,m=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=m/g,w=y/g;s.passes=[];for(var k=0;k<g;k++)for(var M=0;M<g;M++)if((s.diagonal\|\|M!==k)&&(s.upper\|\|!(k>M))&&(s.lower\|\|!(k<M))){var A=f(s.id,k,M),T=this.passes[A]\|\|(this.passes[A]={});if(o.data&&(o.transpose?T.positions={x:{buffer:s.buffer,offset:M,count:v,stride:g},y:{buffer:s.buffer,offset:k,count:v,stride:g}}:T.positions={x:{buffer:s.buffer,offset:Mv,count:v},y:{buffer:s.buffer,offset:kv,count:v}},T.bounds=h(s.bounds,k,M)),o.domain\|\|o.viewport\|\|o.data){var S=d?h(s.padding,k,M):s.padding;if(s.domain){var 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Coptic\",jdEpoch:1825029.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Coptic\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Thout\",\"Paopi\",\"Hathor\",\"Koiak\",\"Tobi\",\"Meshir\",\"Paremhat\",\"Paremoude\",\"Pashons\",\"Paoni\",\"Epip\",\"Mesori\",\"Pi Kogi Enavot\"],monthNamesShort:[\"Tho\",\"Pao\",\"Hath\",\"Koi\",\"Tob\",\"Mesh\",\"Pat\",\"Pad\",\"Pash\",\"Pao\",\"Epi\",\"Meso\",\"PiK\"],dayNames:[\"Tkyriaka\",\"Pesnau\",\"Pshoment\",\"Peftoou\",\"Ptiou\",\"Psoou\",\"Psabbaton\"],dayNamesShort:[\"Tky\",\"Pes\",\"Psh\",\"Pef\",\"Pti\",\"Pso\",\"Psa\"],dayNamesMin:[\"Tk\",\"Pes\",\"Psh\",\"Pef\",\"Pt\",\"Pso\",\"Psa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3\|\|t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear\|\|n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30(i.month()-1)+365(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.coptic=a},{\"../main\":548,\"object-assign\":437}],536:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Discworld\",jdEpoch:1721425.5,daysPerMonth:[16,32,32,32,32,32,32,32,32,32,32,32,32],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Discworld\",epochs:[\"BUC\",\"UC\"],monthNames:[\"Ick\",\"Offle\",\"February\",\"March\",\"April\",\"May\",\"June\",\"Grune\",\"August\",\"Spune\",\"Sektober\",\"Ember\",\"December\"],monthNamesShort:[\"Ick\",\"Off\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Gru\",\"Aug\",\"Spu\",\"Sek\",\"Emb\",\"Dec\"],dayNames:[\"Sunday\",\"Octeday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Oct\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Oc\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:2,isRTL:!1}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]\|\|\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32(e-2):0)+400(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":548,\"object-assign\":437}],537:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Ethiopian\",jdEpoch:1724220.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Ethiopian\",epochs:[\"BEE\",\"EE\"],monthNames:[\"Meskerem\",\"Tikemet\",\"Hidar\",\"Tahesas\",\"Tir\",\"Yekatit\",\"Megabit\",\"Miazia\",\"Genbot\",\"Sene\",\"Hamle\",\"Nehase\",\"Pagume\"],monthNamesShort:[\"Mes\",\"Tik\",\"Hid\",\"Tah\",\"Tir\",\"Yek\",\"Meg\",\"Mia\",\"Gen\",\"Sen\",\"Ham\",\"Neh\",\"Pag\"],dayNames:[\"Ehud\",\"Segno\",\"Maksegno\",\"Irob\",\"Hamus\",\"Arb\",\"Kidame\"],dayNamesShort:[\"Ehu\",\"Seg\",\"Mak\",\"Iro\",\"Ham\",\"Arb\",\"Kid\"],dayNamesMin:[\"Eh\",\"Se\",\"Ma\",\"Ir\",\"Ha\",\"Ar\",\"Ki\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3\|\|t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear\|\|n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30(i.month()-1)+365(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{\"../main\":548,\"object-assign\":437}],538:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}function o(t,e){return t-eMath.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)?30:8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235t-234)/19),r=12084+13753e,n=29e+Math.floor(r/25920);return o(3(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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e=Math.floor((30(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":548,\"object-assign\":437}],540:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new 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c=t-(s>9\|\|9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)\"undefined\"==typeof this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2000:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29,29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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}function o(t,e){return t-eMath.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Persian\",jdEpoch:1948320.5,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Persian\",epochs:[\"BP\",\"AP\"],monthNames:[\"Farvardin\",\"Ordibehesht\",\"Khordad\",\"Tir\",\"Mordad\",\"Shahrivar\",\"Mehr\",\"Aban\",\"Azar\",\"Day\",\"Bahman\",\"Esfand\"],monthNamesShort:[\"Far\",\"Ord\",\"Kho\",\"Tir\",\"Mor\",\"Sha\",\"Meh\",\"Aba\",\"Aza\",\"Day\",\"Bah\",\"Esf\"],dayNames:[\"Yekshambe\",\"Doshambe\",\"Seshambe\",\"Ch\\xe6harshambe\",\"Panjshambe\",\"Jom'e\",\"Shambe\"],dayNamesShort:[\"Yek\",\"Do\",\"Se\",\"Ch\\xe6\",\"Panj\",\"Jom\",\"Sha\"],dayNamesMin:[\"Ye\",\"Do\",\"Se\",\"Ch\",\"Pa\",\"Jo\",\"Sh\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31(e-1):30(e-1)+6)+Math.floor((682s-110)/2816)+365(s-1)+1029983Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134a+2816s+2815)/1028522)+a+1}var l=i+2820r+474;l=l<=0?l-1:l;var c=t-this.toJD(l,1,1)+1,u=c<=186?Math.ceil(c/31):Math.ceil((c-6)/30),f=t-this.toJD(l,u,1)+1;return this.newDate(l,u,f)}}),n.calendars.persian=a,n.calendars.jalali=a},{\"../main\":548,\"object-assign\":437}],545:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function o(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,i(o.prototype,{name:\"Taiwan\",jdEpoch:2419402.5,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":548,\"object-assign\":437}],546:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function o(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,i(o.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":548,\"object-assign\":437}],547:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276\|\|a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]\|\|this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t\|\|\"gregorian\").toLowerCase(),e=e\|\|\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar\|\|this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)\|\|this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,function(e){return t[e]})}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n\|\|t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars\|\|c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear\|\|c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear\|\|c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear\|\|c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth\|\|c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth\|\|c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear\|\|c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate\|\|c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r\|\|\"w\"===r){var n=t.toJD()+e(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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u=S.x.head,f=S.y.head,h=S.x.tail+r,d=S.y.tail+n,m=S.x.text+r,y=S.y.text+n,x=o.rotationXYMatrix(E,m,y),w=o.apply2DTransform(x),T=o.apply2DTransform2(x),z=+B.attr(\"width\"),O=+B.attr(\"height\"),P=m-.5z,D=P+z,R=y-.5O,F=R+O,N=[[P,R,P,F],[P,F,D,F],[D,F,D,R],[D,R,P,R]].map(T);if(!N.reduce(function(t,e){return t^!!o.segmentsIntersect(u,f,u+1e6,f+1e6,e[0],e[1],e[2],e[3])},!1)){N.forEach(function(t){var e=o.segmentsIntersect(h,d,u,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,d=e.y)});var j=e.arrowwidth,V=e.arrowcolor,U=e.arrowside,q=C.append(\"g\").style({opacity:l.opacity(V)}).classed(\"annotation-arrow-g\",!0),H=q.append(\"path\").attr(\"d\",\"M\"+h+\",\"+d+\"L\"+u+\",\"+f).style(\"stroke-width\",j+\"px\").call(l.stroke,l.rgb(V));if(g(H,U,e),_.annotationPosition&&H.node().parentNode&&!a){var G=u,W=f;if(e.standoff){var Y=Math.sqrt(Math.pow(u-h,2)+Math.pow(f-d,2));G+=e.standoff(h-u)/Y,W+=e.standoff(d-f)/Y}var X,Z,$=q.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-G)+\",\"+(d-W),transform:\"translate(\"+G+\",\"+W+\")\"}).style(\"stroke-width\",j+6+\"px\").call(l.stroke,\"rgba(0,0,0,0)\").call(l.fill,\"rgba(0,0,0,0)\");p.init({element:$.node(),gd:t,prepFn:function(){var t=c.getTranslate(I);X=t.x,Z=t.y,s&&s.autorange&&k(s._name+\".autorange\",!0),v&&v.autorange&&k(v._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(X,Z),i=n[0]+t,a=n[1]+r;I.call(c.setTranslate,i,a),M(\"x\",s?s.p2r(s.r2p(e.x)+t):e.x+t/b.w),M(\"y\",v?v.p2r(v.r2p(e.y)+r):e.y-r/b.h),e.axref===e.xref&&M(\"ax\",s.p2r(s.r2p(e.ax)+t)),e.ayref===e.yref&&M(\"ay\",v.p2r(v.r2p(e.ay)+r)),q.attr(\"transform\",\"translate(\"+t+\",\"+r+\")\"),L.attr({transform:\"rotate(\"+E+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"relayout\",t,A());var 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e._module.eventData?a=e._module.eventData(a,t,e,n,i):(\"xVal\"in t?a.x=t.xVal:\"x\"in t&&(a.x=t.x),\"yVal\"in t?a.y=t.yVal:\"y\"in t&&(a.y=t.y),t.xa&&(a.xaxis=t.xa),t.ya&&(a.yaxis=t.ya),void 0!==t.zLabelVal&&(a.z=t.zLabelVal)),r.appendArrayPointValue(a,e,i),a},r.appendArrayPointValue=function(t,e,r){var i=e._arrayAttrs;if(i)for(var s=0;s<i.length;s++){var l=i[s],c=a(l);if(void 0===t[c]){var u=o(n.nestedProperty(e,l).get(),r);void 0!==u&&(t[c]=u)}}},r.appendArrayMultiPointValues=function(t,e,r){var i=e._arrayAttrs;if(i)for(var s=0;s<i.length;s++){var l=i[s],c=a(l);if(void 0===t[c]){for(var u=n.nestedProperty(e,l).get(),f=new Array(r.length),h=0;h<r.length;h++)f[h]=o(u,r[h]);t[c]=f}}};var i={ids:\"id\",locations:\"location\",labels:\"label\",values:\"value\",\"marker.colors\":\"color\"};function a(t){return i[t]\|\|t}function o(t,e){return Array.isArray(e)?Array.isArray(t)&&Array.isArray(t[e[0]])?t[e[0]][e[1]]:void 0:t[e]}},{\"../../lib\":696}],610:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"fast-isnumeric\"),a=t(\"tinycolor2\"),o=t(\"../../lib\"),s=t(\"../../lib/events\"),l=t(\"../../lib/svg_text_utils\"),c=t(\"../../lib/override_cursor\"),u=t(\"../drawing\"),f=t(\"../color\"),h=t(\"../dragelement\"),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../registry\"),g=t(\"./helpers\"),v=t(\"./constants\"),m=v.YANGLE,y=Math.PIm/180,x=1/Math.sin(y),b=Math.cos(y),_=Math.sin(y),w=v.HOVERARROWSIZE,k=v.HOVERTEXTPAD;function M(t,e,r){var i=e.hovermode,a=e.rotateLabels,s=e.bgColor,c=e.container,h=e.outerContainer,p=e.commonLabelOpts\|\|{},d=e.fontFamily\|\|v.HOVERFONT,g=e.fontSize\|\|v.HOVERFONTSIZE,y=t[0],x=y.xa,b=y.ya,_=\"y\"===i?\"yLabel\":\"xLabel\",M=y[_],A=(String(M)\|\|\"\").split(\" \")[0],T=h.node().getBoundingClientRect(),S=T.top,E=T.width,C=T.height,L=void 0!==M&&y.distance<=e.hoverdistance&&(\"x\"===i\|\|\"y\"===i);if(L){var z,O,I=!0;for(z=0;z<t.length;z++){I&&void 0===t[z].zLabel&&(I=!1),O=t[z].hoverinfo\|\|t[z].trace.hoverinfo;var P=Array.isArray(O)?O:O.split(\"+\");if(-1===P.indexOf(\"all\")&&-1===P.indexOf(i)){L=!1;break}}I&&(L=!1)}var D=c.selectAll(\"g.axistext\").data(L?[0]:[]);D.enter().append(\"g\").classed(\"axistext\",!0),D.exit().remove(),D.each(function(){var e=n.select(this),a=o.ensureSingle(e,\"path\",\"\",function(t){t.style({\"stroke-width\":\"1px\"})}),s=o.ensureSingle(e,\"text\",\"\",function(t){t.attr(\"data-notex\",1)}),c=p.bgcolor\|\|f.defaultLine,h=p.bordercolor\|\|f.contrast(c),v=f.contrast(c);a.style({fill:c,stroke:h}),s.text(M).call(u.font,p.font.family\|\|d,p.font.size\|\|g,p.font.color\|\|v).call(l.positionText,0,0).call(l.convertToTspans,r),e.attr(\"transform\",\"\");var m=s.node().getBoundingClientRect();if(\"x\"===i){s.attr(\"text-anchor\",\"middle\").call(l.positionText,0,\"top\"===x.side?S-m.bottom-w-k:S-m.top+w+k);var T=\"top\"===x.side?\"-\":\"\";a.attr(\"d\",\"M0,0L\"+w+\",\"+T+w+\"H\"+(k+m.width/2)+\"v\"+T+(2k+m.height)+\"H-\"+(k+m.width/2)+\"V\"+T+w+\"H-\"+w+\"Z\"),e.attr(\"transform\",\"translate(\"+(x._offset+(y.x0+y.x1)/2)+\",\"+(b._offset+(\"top\"===x.side?0:b._length))+\")\")}else{s.attr(\"text-anchor\",\"right\"===b.side?\"start\":\"end\").call(l.positionText,(\"right\"===b.side?1:-1)(k+w),S-m.top-m.height/2);var E=\"right\"===b.side?\"\":\"-\";a.attr(\"d\",\"M0,0L\"+E+w+\",\"+w+\"V\"+(k+m.height/2)+\"h\"+E+(2k+m.width)+\"V-\"+(k+m.height/2)+\"H\"+E+w+\"V-\"+w+\"Z\"),e.attr(\"transform\",\"translate(\"+(x._offset+(\"right\"===b.side?x._length:0))+\",\"+(b._offset+(y.y0+y.y1)/2)+\")\")}t=t.filter(function(t){return void 0!==t.zLabelVal\|\|(t[_]\|\|\"\").split(\" \")[0]===A})});var R=c.selectAll(\"g.hovertext\").data(t,function(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa,t.ya\|\|\"\"].join(\",\")});return R.enter().append(\"g\").classed(\"hovertext\",!0).each(function(){var t=n.select(this);t.append(\"rect\").call(f.fill,f.addOpacity(s,.8)),t.append(\"text\").classed(\"name\",!0),t.append(\"path\").style(\"stroke-width\",\"1px\"),t.append(\"text\").classed(\"nums\",!0).call(u.font,d,g)}),R.exit().remove(),R.each(function(t){var e=n.select(this).attr(\"transform\",\"\"),o=\"\",c=\"\",h=t.bgcolor\|\|t.color,p=f.combine(f.opacity(h)?h:f.defaultLine,s),v=f.combine(f.opacity(t.color)?t.color:f.defaultLine,s),y=t.borderColor\|\|f.contrast(p);if(void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name){o=l.plainText(t.name\|\|\"\");var x=Math.round(t.nameLength);x>-1&&o.length>x&&(o=x>3?o.substr(0,x-3)+\"...\":o.substr(0,x))}void 0!==t.zLabel?(void 0!==t.xLabel&&(c+=\"x: \"+t.xLabel+\"<br>\"),void 0!==t.yLabel&&(c+=\"y: \"+t.yLabel+\"<br>\"),c+=(c?\"z: \":\"\")+t.zLabel):L&&t[i+\"Label\"]===M?c=t[(\"x\"===i?\"y\":\"x\")+\"Label\"]\|\|\"\":void 0===t.xLabel?void 0!==t.yLabel&&(c=t.yLabel):c=void 0===t.yLabel?t.xLabel:\"(\"+t.xLabel+\", 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T,z,O=b.node().getBoundingClientRect(),I=t.xa._offset+(t.x0+t.x1)/2,P=t.ya._offset+(t.y0+t.y1)/2,D=Math.abs(t.x1-t.x0),R=Math.abs(t.y1-t.y0),B=O.width+w+k+A;t.ty0=S-O.top,t.bx=O.width+2k,t.by=O.height+2k,t.anchor=\"start\",t.txwidth=O.width,t.tx2width=A,t.offset=0,a?(t.pos=I,T=P+R/2+B<=C,z=P-R/2-B>=0,\"top\"!==t.idealAlign&&T\|\|!z?T?(P+=R/2,t.anchor=\"start\"):t.anchor=\"middle\":(P-=R/2,t.anchor=\"end\")):(t.pos=P,T=I+D/2+B<=E,z=I-D/2-B>=0,\"left\"!==t.idealAlign&&T\|\|!z?T?(I+=D/2,t.anchor=\"start\"):t.anchor=\"middle\":(I-=D/2,t.anchor=\"end\")),b.attr(\"text-anchor\",t.anchor),A&&_.attr(\"text-anchor\",t.anchor),e.attr(\"transform\",\"translate(\"+I+\",\"+P+\")\"+(a?\"rotate(\"+m+\")\":\"\"))}),R}function A(t,e){t.each(function(t){var r=n.select(this);if(t.del)r.remove();else{var i=\"end\"===t.anchor?-1:1,a=r.select(\"text.nums\"),o={start:1,end:-1,middle:0}[t.anchor],s=o(w+k),c=s+o(t.txwidth+k),f=0,h=t.offset;\"middle\"===t.anchor&&(s-=t.tx2width/2,c+=t.txwidth/2+k),e&&(h=-_,f=t.offsetb),r.select(\"path\").attr(\"d\",\"middle\"===t.anchor?\"M-\"+(t.bx/2+t.tx2width/2)+\",\"+(h-t.by/2)+\"h\"+t.bx+\"v\"+t.by+\"h-\"+t.bx+\"Z\":\"M0,0L\"+(iw+f)+\",\"+(w+h)+\"v\"+(t.by/2-w)+\"h\"+it.bx+\"v-\"+t.by+\"H\"+(iw+f)+\"V\"+(h-w)+\"Z\"),a.call(l.positionText,s+f,h+t.ty0-t.by/2+k),t.tx2width&&(r.select(\"text.name\").call(l.positionText,c+ok+f,h+t.ty0-t.by/2+k),r.select(\"rect\").call(u.setRect,c+(o-1)t.tx2width/2+f,h-t.by/2-1,t.tx2width,t.by+2))}})}function T(t,e){var r=t.index,n=t.trace\|\|{},i=t.cd[0],a=t.cd[r]\|\|{},s=Array.isArray(r)?function(t,e){return o.castOption(i,r,t)\|\|o.extractOption({},n,\"\",e)}:function(t,e){return o.extractOption(a,n,t,e)};function l(e,r,n){var i=s(r,n);i&&(t[e]=i)}if(l(\"hoverinfo\",\"hi\",\"hoverinfo\"),l(\"bgcolor\",\"hbg\",\"hoverlabel.bgcolor\"),l(\"borderColor\",\"hbc\",\"hoverlabel.bordercolor\"),l(\"fontFamily\",\"htf\",\"hoverlabel.font.family\"),l(\"fontSize\",\"hts\",\"hoverlabel.font.size\"),l(\"fontColor\",\"htc\",\"hoverlabel.font.color\"),l(\"nameLength\",\"hnl\",\"hoverlabel.namelength\"),t.posref=\"y\"===e?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel=\"xLabel\"in t?t.xLabel:p.hoverLabelText(t.xa,t.xLabelVal),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel=\"yLabel\"in t?t.yLabel:p.hoverLabelText(t.ya,t.yLabelVal),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)\|\|\"log\"===t.xa.type&&t.xerr<=0)){var c=p.tickText(t.xa,t.xa.c2l(t.xerr),\"hover\").text;void 0!==t.xerrneg?t.xLabel+=\" +\"+c+\" / -\"+p.tickText(t.xa,t.xa.c2l(t.xerrneg),\"hover\").text:t.xLabel+=\" \\xb1 \"+c,\"x\"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)\|\|\"log\"===t.ya.type&&t.yerr<=0)){var u=p.tickText(t.ya,t.ya.c2l(t.yerr),\"hover\").text;void 0!==t.yerrneg?t.yLabel+=\" +\"+u+\" / -\"+p.tickText(t.ya,t.ya.c2l(t.yerrneg),\"hover\").text:t.yLabel+=\" \\xb1 \"+u,\"y\"===e&&(t.distance+=1)}var f=t.hoverinfo\|\|t.trace.hoverinfo;return\"all\"!==f&&(-1===(f=Array.isArray(f)?f:f.split(\"+\")).indexOf(\"x\")&&(t.xLabel=void 0),-1===f.indexOf(\"y\")&&(t.yLabel=void 0),-1===f.indexOf(\"z\")&&(t.zLabel=void 0),-1===f.indexOf(\"text\")&&(t.text=void 0),-1===f.indexOf(\"name\")&&(t.name=void 0)),t}function S(t,e){var r,n,i=e.container,o=e.fullLayout,s=e.event,l=!!t.hLinePoint,c=!!t.vLinePoint;if(i.selectAll(\".spikeline\").remove(),c\|\|l){var h=f.combine(o.plot_bgcolor,o.paper_bgcolor);if(l){var p,d,g=t.hLinePoint;r=g&&g.xa,\"cursor\"===(n=g&&g.ya).spikesnap?(p=s.pointerX,d=s.pointerY):(p=r._offset+g.x,d=n._offset+g.y);var v,m,y=a.readability(g.color,h)<1.5?f.contrast(h):g.color,x=n.spikemode,b=n.spikethickness,_=n.spikecolor\|\|y,w=n._boundingBox,k=(w.left+w.right)/2<p?w.right:w.left;-1===x.indexOf(\"toaxis\")&&-1===x.indexOf(\"across\")\|\|(-1!==x.indexOf(\"toaxis\")&&(v=k,m=p),-1!==x.indexOf(\"across\")&&(v=n._counterSpan[0],m=n._counterSpan[1]),i.insert(\"line\",\":first-child\").attr({x1:v,x2:m,y1:d,y2:d,\"stroke-width\":b,stroke:_,\"stroke-dasharray\":u.dashStyle(n.spikedash,b)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),i.insert(\"line\",\":first-child\").attr({x1:v,x2:m,y1:d,y2:d,\"stroke-width\":b+2,stroke:h}).classed(\"spikeline\",!0).classed(\"crisp\",!0)),-1!==x.indexOf(\"marker\")&&i.insert(\"circle\",\":first-child\").attr({cx:k+(\"right\"!==n.side?b:-b),cy:d,r:b,fill:_}).classed(\"spikeline\",!0)}if(c){var M,A,T=t.vLinePoint;r=T&&T.xa,n=T&&T.ya,\"cursor\"===r.spikesnap?(M=s.pointerX,A=s.pointerY):(M=r._offset+T.x,A=n._offset+T.y);var S,E,C=a.readability(T.color,h)<1.5?f.contrast(h):T.color,L=r.spikemode,z=r.spikethickness,O=r.spikecolor\|\|C,I=r._boundingBox,P=(I.top+I.bottom)/2<A?I.bottom:I.top;-1===L.indexOf(\"toaxis\")&&-1===L.indexOf(\"across\")\|\|(-1!==L.indexOf(\"toaxis\")&&(S=P,E=A),-1!==L.indexOf(\"across\")&&(S=r._counterSpan[0],E=r._counterSpan[1]),i.insert(\"line\",\":first-child\").attr({x1:M,x2:M,y1:S,y2:E,\"stroke-width\":z,stroke:O,\"stroke-dasharray\":u.dashStyle(r.spikedash,z)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),i.insert(\"line\",\":first-child\").attr({x1:M,x2:M,y1:S,y2:E,\"stroke-width\":z+2,stroke:h}).classed(\"spikeline\",!0).classed(\"crisp\",!0)),-1!==L.indexOf(\"marker\")&&i.insert(\"circle\",\":first-child\").attr({cx:M,cy:P-(\"top\"!==r.side?z:-z),r:z,fill:O}).classed(\"spikeline\",!0)}}}function E(t,e){return!e\|\|(e.vLinePoint!==t._spikepoints.vLinePoint\|\|e.hLinePoint!==t._spikepoints.hLinePoint)}r.hover=function(t,e,r,a){t=o.getGraphDiv(t),o.throttle(t._fullLayout._uid+v.HOVERID,v.HOVERMINTIME,function(){!function(t,e,r,a){r\|\|(r=\"xy\");var l=Array.isArray(r)?r:[r],u=t._fullLayout,v=u._plots\|\|[],m=v[r],y=u._has(\"cartesian\");if(m){var b=m.overlays.map(function(t){return t.id});l=l.concat(b)}for(var _=l.length,w=new Array(_),k=new Array(_),C=!1,L=0;L<_;L++){var z=l[L],O=v[z];if(O)C=!0,w[L]=p.getFromId(t,O.xaxis._id),k[L]=p.getFromId(t,O.yaxis._id);else{var I=u[z]._subplot;w[L]=I.xaxis,k[L]=I.yaxis}}var P=e.hovermode\|\|u.hovermode;P&&!C&&(P=\"closest\");if(-1===[\"x\",\"y\",\"closest\"].indexOf(P)\|\|!t.calcdata\|\|t.querySelector(\".zoombox\")\|\|t._dragging)return h.unhoverRaw(t,e);var D,R,B,F,N,j,V,U,q,H,G,W,Y,X=-1===u.hoverdistance?1/0:u.hoverdistance,Z=-1===u.spikedistance?1/0:u.spikedistance,$=[],J=[],K={hLinePoint:null,vLinePoint:null},Q=!1;if(Array.isArray(e))for(P=\"array\",B=0;B<e.length;B++)N=t.calcdata[e[B].curveNumber\|\|0],j=N[0].trace,\"skip\"!==N[0].trace.hoverinfo&&(J.push(N),\"h\"===j.orientation&&(Q=!0));else{for(F=0;F<t.calcdata.length;F++)N=t.calcdata[F],\"skip\"!==(j=N[0].trace).hoverinfo&&g.isTraceInSubplots(j,l)&&(J.push(N),\"h\"===j.orientation&&(Q=!0));var tt,et,rt=!e.target;if(rt)tt=\"xpx\"in e?e.xpx:w[0]._length/2,et=\"ypx\"in e?e.ypx:k[0]._length/2;else{if(!1===s.triggerHandler(t,\"plotly_beforehover\",e))return;var nt=e.target.getBoundingClientRect();if(tt=e.clientX-nt.left,et=e.clientY-nt.top,tt<0\|\|tt>w[0]._length\|\|et<0\|\|et>k[0]._length)return h.unhoverRaw(t,e)}if(e.pointerX=tt+w[0]._offset,e.pointerY=et+k[0]._offset,D=\"xval\"in e?g.flat(l,e.xval):g.p2c(w,tt),R=\"yval\"in e?g.flat(l,e.yval):g.p2c(k,et),!i(D[0])\|\|!i(R[0]))return o.warn(\"Fx.hover failed\",e,t),h.unhoverRaw(t,e)}var it=1/0;for(F=0;F<J.length;F++)if((N=J[F])&&N[0]&&N[0].trace&&!0===N[0].trace.visible&&(j=N[0].trace,-1===[\"carpet\",\"contourcarpet\"].indexOf(j._module.name))){if(\"splom\"===j.type?V=l[U=0]:(V=g.getSubplot(j),U=l.indexOf(V)),q=P,W={cd:N,trace:j,xa:w[U],ya:k[U],maxHoverDistance:X,maxSpikeDistance:Z,index:!1,distance:Math.min(it,X),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:f.defaultLine,name:j.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[V]&&(W.subplot=u[V]._subplot),u._splomScenes&&u._splomScenes[j.uid]&&(W.scene=u._splomScenes[j.uid]),Y=$.length,\"array\"===q){var at=e[F];\"pointNumber\"in at?(W.index=at.pointNumber,q=\"closest\"):(q=\"\",\"xval\"in at&&(H=at.xval,q=\"x\"),\"yval\"in at&&(G=at.yval,q=q?\"closest\":\"y\"))}else H=D[U],G=R[U];if(0!==X)if(j._module&&j._module.hoverPoints){var ot=j._module.hoverPoints(W,H,G,q,u._hoverlayer);if(ot)for(var st,lt=0;lt<ot.length;lt++)st=ot[lt],i(st.x0)&&i(st.y0)&&$.push(T(st,P))}else o.log(\"Unrecognized trace type in hover:\",j);if(\"closest\"===P&&$.length>Y&&($.splice(0,Y),it=$[0].distance),y&&0!==Z&&0===$.length){W.distance=Z,W.index=!1;var ct=j._module.hoverPoints(W,H,G,\"closest\",u._hoverlayer);if(ct&&(ct=ct.filter(function(t){return t.spikeDistance<=Z})),ct&&ct.length){var ut,ft=ct.filter(function(t){return t.xa.showspikes});if(ft.length){var ht=ft[0];i(ht.x0)&&i(ht.y0)&&(ut=vt(ht),(!K.vLinePoint\|\|K.vLinePoint.spikeDistance>ut.spikeDistance)&&(K.vLinePoint=ut))}var pt=ct.filter(function(t){return t.ya.showspikes});if(pt.length){var dt=pt[0];i(dt.x0)&&i(dt.y0)&&(ut=vt(dt),(!K.hLinePoint\|\|K.hLinePoint.spikeDistance>ut.spikeDistance)&&(K.hLinePoint=ut))}}}}function gt(t,e){for(var r,n=null,i=1/0,a=0;a<t.length;a++)(r=t[a].spikeDistance)<i&&r<=e&&(n=t[a],i=r);return n}function vt(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 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Et=\"y\"===P&&(J.length>1\|\|$.length>1)\|\|\"closest\"===P&&Q&&$.length>1,Ct=f.combine(u.plot_bgcolor\|\|f.background,u.paper_bgcolor),Lt={hovermode:P,rotateLabels:Et,bgColor:Ct,container:u._hoverlayer,outerContainer:u._paperdiv,commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance},zt=M($,Lt,t);if(function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.map(function(t,n){var i=t[e],a=\"x\"===i._id.charAt(0),o=i.range;return!n&&o&&o[0]>o[1]!==a&&(f=-1),[{i:n,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by(a?x:1)/2,pmin:0,pmax:a?r.width:r.height}]}).sort(function(t,e){return t[0].posref-e[0].posref\|\|f(e[0].traceIndex-t[0].traceIndex)});function p(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var 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r={color:t.color\|\|f.defaultLine,x0:t.x0\|\|t.x\|\|0,x1:t.x1\|\|t.x\|\|0,y0:t.y0\|\|t.y\|\|0,y1:t.y1\|\|t.y\|\|0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,trace:{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0},i=n.select(e.container),a=e.outerContainer?n.select(e.outerContainer):i,o={hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor\|\|f.background,container:i,outerContainer:a},s=M([r],o,e.gd);return A(s,o.rotateLabels),s.node()},r.multiHovers=function(t,e){Array.isArray(t)\|\|(t=[t]);var r=t.map(function(t){return{color:t.color\|\|f.defaultLine,x0:t.x0\|\|t.x\|\|0,x1:t.x1\|\|t.x\|\|0,y0:t.y0\|\|t.y\|\|0,y1:t.y1\|\|t.y\|\|0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,trace:{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0}}),i=n.select(e.container),a=e.outerContainer?n.select(e.outerContainer):i,o={hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor\|\|f.background,container:i,outerContainer:a},s=M(r,o,e.gd),l=0;return s.sort(function(t,e){return t.y0-e.y0}).each(function(t){var 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n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../dragelement\"),o=t(\"./helpers\"),s=t(\"./layout_attributes\"),l=t(\"./hover\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:s},attributes:t(\"./attributes\"),layoutAttributes:s,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,\"hoverlabel.\"+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,\"hoverinfo\",function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)})},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,multiHovers:l.multiHovers,loneUnhover:function(t){var e=i.isD3Selection(t)?t:n.select(t);e.selectAll(\"g.hovertext\").remove(),e.selectAll(\".spikeline\").remove()},click:t(\"./click\")}},{\"../../lib\":696,\"../dragelement\":592,\"./attributes\":604,\"./calc\":605,\"./click\":606,\"./constants\":607,\"./defaults\":608,\"./helpers\":609,\"./hover\":610,\"./layout_attributes\":613,\"./layout_defaults\":614,\"./layout_global_defaults\":615,d3:148}],613:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"../../plots/font_attributes\")({editType:\"none\"});i.family.dflt=n.HOVERFONT,i.size.dflt=n.HOVERFONTSIZE,e.exports={clickmode:{valType:\"flaglist\",flags:[\"event\",\"select\"],dflt:\"event\",editType:\"plot\",extras:[\"none\"]},dragmode:{valType:\"enumerated\",values:[\"zoom\",\"pan\",\"select\",\"lasso\",\"orbit\",\"turntable\"],dflt:\"zoom\",editType:\"modebar\"},hovermode:{valType:\"enumerated\",values:[\"x\",\"y\",\"closest\",!1],editType:\"modebar\"},hoverdistance:{valType:\"integer\",min:-1,dflt:20,editType:\"none\"},spikedistance:{valType:\"integer\",min:-1,dflt:20,editType:\"none\"},hoverlabel:{bgcolor:{valType:\"color\",editType:\"none\"},bordercolor:{valType:\"color\",editType:\"none\"},font:i,namelength:{valType:\"integer\",min:-1,dflt:15,editType:\"none\"},editType:\"none\"},selectdirection:{valType:\"enumerated\",values:[\"h\",\"v\",\"d\",\"any\"],dflt:\"any\",editType:\"none\"}}},{\"../../plots/font_attributes\":771,\"./constants\":607}],614:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}var o,s=a(\"clickmode\");\"select\"===a(\"dragmode\")&&a(\"selectdirection\"),e._has(\"cartesian\")?s.indexOf(\"select\")>-1?o=\"closest\":(e._isHoriz=function(t){for(var e=!0,r=0;r<t.length;r++){var n=t[r];if(\"h\"!==n.orientation){e=!1;break}}return e}(r),o=e._isHoriz?\"y\":\"x\"):o=\"closest\",a(\"hovermode\",o)&&(a(\"hoverdistance\"),a(\"spikedistance\"));var l=e._has(\"mapbox\"),c=e._has(\"geo\"),u=e._basePlotModules.length;\"zoom\"===e.dragmode&&((l\|\|c)&&1===u\|\|l&&c&&2===u)&&(e.dragmode=\"pan\")}},{\"../../lib\":696,\"./layout_attributes\":613}],615:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./hoverlabel_defaults\"),a=t(\"./layout_attributes\");e.exports=function(t,e){i(t,e,function(r,i){return n.coerce(t,e,a,r,i)})}},{\"../../lib\":696,\"./hoverlabel_defaults\":611,\"./layout_attributes\":613}],616:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../lib/regex\").counter,a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/cartesian/constants\").idRegex,s=t(\"../../plot_api/plot_template\"),l={rows:{valType:\"integer\",min:1,editType:\"plot\"},roworder:{valType:\"enumerated\",values:[\"top to bottom\",\"bottom to top\"],dflt:\"top to 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i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n=r.pixelRatio,i=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case\"zoom\":if(e){var m=n/(f[2]-f[0])(l[2]-l[0])+l[0],y=i/(f[3]-f[1])(l[3]-l[1])+l[1];c.boxInited\|\|(c.boxStart[0]=m,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=m,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled\|\|c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]\|\|(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<g,b=Math.abs(c.dragStart[1]-i)<g;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()\|\|x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)_(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a\|\|s?(a&&(v(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(v(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning\|\|(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener(\"touchstart\",function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()},!!s&&{passive:!1}),e.addEventListener(\"touchmove\",function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()},!!s&&{passive:!1}),e.addEventListener(\"touchend\",function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()},!!s&&{passive:!1}),c.wheelListener=i(e,function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5n/(a[3]-a[1])),f=o/(a[2]-a[0])(i[2]-i[0])+i[0],h=s/(a[3]-a[1])(i[3]-i[1])+i[1];return i[0]=(i[0]-f)l+f,i[2]=(i[2]-f)l+f,i[1]=(i[1]-h)l+h,i[3]=(i[3]-h)l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0},!0),c}},{\"../cartesian/constants\":750,\"has-passive-events\":394,\"mouse-change\":418,\"mouse-event-offset\":419,\"mouse-wheel\":421}],783:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/html2unicode\"),a=t(\"../../lib/str2rgbarray\");function o(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=!1,this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var s=o.prototype,l=[\"xaxis\",\"yaxis\"];s.merge=function(t){var e,r,n,o,s,c,u,f,h,p,d;for(this.titleEnable=!1,this.backgroundColor=a(t.plot_bgcolor),p=0;p<2;++p){var g=(e=l[p]).charAt(0);for(n=(r=t[this.scene[e]._name]).title===this.scene.fullLayout._dfltTitle[g]?\"\":r.title,d=0;d<=2;d+=2)this.labelEnable[p+d]=!1,this.labels[p+d]=i(n),this.labelColor[p+d]=a(r.titlefont.color),this.labelFont[p+d]=r.titlefont.family,this.labelSize[p+d]=r.titlefont.size,this.labelPad[p+d]=this.getLabelPad(e,r),this.tickEnable[p+d]=!1,this.tickColor[p+d]=a((r.tickfont\|\|{}).color),this.tickAngle[p+d]=\"auto\"===r.tickangle?0:Math.PI-r.tickangle/180,this.tickPad[p+d]=this.getTickPad(r),this.tickMarkLength[p+d]=0,this.tickMarkWidth[p+d]=r.tickwidth\|\|0,this.tickMarkColor[p+d]=a(r.tickcolor),this.borderLineEnable[p+d]=!1,this.borderLineColor[p+d]=a(r.linecolor),this.borderLineWidth[p+d]=r.linewidth\|\|0;u=this.hasSharedAxis(r),s=this.hasAxisInDfltPos(e,r)&&!u,c=this.hasAxisInAltrPos(e,r)&&!u,o=r.mirror\|\|!1,f=u?-1!==String(o).indexOf(\"all\"):!!o,h=u?\"allticks\"===o:-1!==String(o).indexOf(\"ticks\"),s?this.labelEnable[p]=!0:c&&(this.labelEnable[p+2]=!0),s?this.tickEnable[p]=r.showticklabels:c&&(this.tickEnable[p+2]=r.showticklabels),(s\|\|f)&&(this.borderLineEnable[p]=r.showline),(c\|\|f)&&(this.borderLineEnable[p+2]=r.showline),(s\|\|h)&&(this.tickMarkLength[p]=this.getTickMarkLength(r)),(c\|\|h)&&(this.tickMarkLength[p+2]=this.getTickMarkLength(r)),this.gridLineEnable[p]=r.showgrid,this.gridLineColor[p]=a(r.gridcolor),this.gridLineWidth[p]=r.gridwidth,this.zeroLineEnable[p]=r.zeroline,this.zeroLineColor[p]=a(r.zerolinecolor),this.zeroLineWidth[p]=r.zerolinewidth}},s.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},s.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},s.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},s.getLabelPad=function(t,e){var r=e.titlefont.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r(1.5+(n?1:0))-10:r(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r(1.5+(n?1:.5)):10+r(1.5+(n?.5:0)):void 0},s.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},s.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new o(t)}},{\"../../lib/html2unicode\":694,\"../../lib/str2rgbarray\":719,\"../cartesian/axes\":744}],784:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")\|\|l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d\|\|[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d)0===u(t,\"gl2d\",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot\|\|l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":613,\"../../constants/xmlns_namespaces\":674,\"../../plot_api/edit_types\":727,\"../cartesian\":756,\"../cartesian/attributes\":742,\"../cartesian/constants\":750,\"../get_data\":781,\"../layout_attributes\":799,\"./scene2d\":785}],785:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"gl-plot2d\"),c=t(\"gl-spikes2d\"),u=t(\"gl-select-box\"),f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/html2unicode\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"../cartesian/constraints\"),m=v.enforce,y=v.clean,x=t(\"../cartesian/autorange\").doAutoRange,b=[\"xaxis\",\"yaxis\"],_=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function w(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio\|\|window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context.scrollZoom,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped\|\|(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=w;var k=w.prototype;k.makeFramework=function(){if(this.staticPlot){if(!(i\|\|(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return g(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r),r.className+=\" user-select-none\";var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",function(){l.isMouseOver=!1,l.unhover()}),o.addEventListener(\"mouseover\",function(){l.isMouseOver=!0})},k.toImage=function(t){t\|\|(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT\|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(ri4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4(ro+l)+c];a[4(ro+l)+c]=a[4(rs+l)+c],a[4(rs+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},k.updateSize=function(t){t\|\|(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0\|Math.ceil(en),o=0\|Math.ceil(ei);return t.width===a&&t.height===o\|\|(t.width=a,t.height=o),t},k.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=d(t[e][r].text+\"\");return t},k.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(_),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},k.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout;n.xaxis.autorange=e.autorange,n.xaxis.range=e.range.slice(0),n.yaxis.autorange=r.autorange,n.yaxis.range=r.range.slice(0);var i={lastInputTime:this.camera.lastInputTime};i[e._name]=e.range.slice(0),i[r._name]=r.range.slice(0),t.emit(\"plotly_relayout\",i)},k.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},k.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},k.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map(function(e){t[e].dispose(),delete t[e]}),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},k.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};y(s,this.xaxis),y(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]u.w,u.b+h[0]u.h,i-u.r-(1-f[1])u.w,a-u.t-(1-h[1])u.h],this.mouseContainer.style.width=u.w(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])u.h+\"px\",c=0;c<2;++c)(l=this[b[c]])._length=o.viewBox[c+2]-o.viewBox[c],x(this.graphDiv,l),l.setScale();m(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},k.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},k.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},k.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort(function(t,e){return t._trace.index-e._trace.index})},k.updateFx=function(t){\"lasso\"===t\|\|\"select\"===t?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},k.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},k.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.xt.pixelRatio,l=this.canvas.height-t.pixelRatior.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]f.w,l/t.pixelRatio-(f.t+(1-p[1])f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult\|\|this.lastPickResult.traceUid!==d.trace.uid\|\|this.lastPickResult.dataCoord[0]!==d.dataCoord[0]\|\|this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var g=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),g.screenCoord=[((t.viewBox[2]-t.viewBox[0])(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var v=this.fullData[g.trace.index]\|\|{},m=g.pointIndex,y=s.castHoverinfo(v,i,m);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(g.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(g.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(g.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(g.textLabel=void 0),-1===x.indexOf(\"name\")&&(g.name=void 0)}s.loneHover({x:g.screenCoord[0],y:g.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",g.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",g.traceCoord[1]),zLabel:g.traceCoord[2],text:g.textLabel,name:g.name,color:s.castHoverOption(v,m,\"bgcolor\")\|\|g.color,borderColor:s.castHoverOption(v,m,\"bordercolor\"),fontFamily:s.castHoverOption(v,m,\"font.family\"),fontSize:s.castHoverOption(v,m,\"font.size\"),fontColor:s.castHoverOption(v,m,\"font.color\")},{container:this.svgContainer,gd:this.graphDiv})}}a\|\|this.unhover(),t.draw()}},k.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},k.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/fx\":612,\"../../lib/html2unicode\":694,\"../../lib/show_no_webgl_msg\":717,\"../../plots/cartesian/axes\":744,\"../../registry\":827,\"../cartesian/autorange\":743,\"../cartesian/constants\":750,\"../cartesian/constraints\":752,\"./camera\":782,\"./convert\":783,\"gl-plot2d\":275,\"gl-select-box\":286,\"gl-spikes2d\":295,\"webgl-context\":533}],786:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t\|\|document.body,e=e\|\|{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center\|\|[0,0,0],up:e.up\|\|[0,1,0],eye:e.eye\|\|[0,0,10],mode:e.mode\|\|\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",view:c,element:t,delay:e.delay\|\|16,rotateSpeed:e.rotateSpeed\|\|1,zoomSpeed:e.zoomSpeed\|\|1,translateSpeed:e.translateSpeed\|\|1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,tick:function(){var e=n(),r=this.delay,i=e-2r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",function(t){return t.preventDefault(),!1});var g=0,v=0,m={shift:!1,control:!1,alt:!1,meta:!1};function y(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,y=!!a.shift,x=!!(1&e),b=!!(2&e),_=!!(4&e),w=1/t.clientHeight,k=w(r-g),M=w(i-v),A=d.flipX?1:-1,T=d.flipY?1:-1,S=n(),E=Math.PId.rotateSpeed;if((s&&x&&!h&&!p&&!y\|\|x&&!h&&!p&&y)&&c.rotate(S,AEk,-TEM,0),(l&&x&&!h&&!p&&!y\|\|b\|\|x&&h&&!p&&!y)&&c.pan(S,-d.translateSpeedkf,d.translateSpeedMf,0),u&&x&&!h&&!p&&!y\|\|_\|\|x&&!h&&p&&!y){var C=-d.zoomSpeedM/window.innerHeight(S-c.lastT())100;c.pan(S,0,0,f(Math.exp(C)-1))}return g=r,v=i,m=a,!0}}return d.mouseListener=a(t,y),t.addEventListener(\"touchstart\",function(e){var r=s(e.changedTouches[0],t);y(0,r[0],r[1],m),y(1,r[0],r[1],m),e.preventDefault()},!!l&&{passive:!1}),t.addEventListener(\"touchmove\",function(e){var r=s(e.changedTouches[0],t);y(1,r[0],r[1],m),e.preventDefault()},!!l&&{passive:!1}),t.addEventListener(\"touchend\",function(t){y(0,g,v,m),t.preventDefault()},!!l&&{passive:!1}),d.wheelListener=o(t,function(t,e){if(!1!==d.keyBindingMode){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-trMath.PId.rotateSpeed/window.innerWidth);else{var o=-d.zoomSpeedie/window.innerHeight(a-c.lastT())/20;c.pan(a,0,0,f(Math.exp(o)-1))}}},!0),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":45,\"has-passive-events\":394,\"mouse-change\":418,\"mouse-event-offset\":419,\"mouse-wheel\":421,\"right-now\":480}],787:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var l=n[i],c=o(r,\"gl3d\",l),u=e[l],f=u._scene;f\|\|(f=new a({id:l,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),u._scene=f),f.cameraInitial\|\|(f.cameraInitial=s.extendDeep({},u.camera)),f.plot(c,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d\|\|[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.wo.x[0],y:n.t+n.h(1-o.y[1]),width:n.w(o.x[1]-o.x[0]),height:n.h(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":613,\"../../constants/xmlns_namespaces\":674,\"../../lib\":696,\"../../plot_api/edit_types\":727,\"../get_data\":781,\"./layout/attributes\":788,\"./layout/defaults\":792,\"./layout/layout_attributes\":793,\"./scene\":797}],788:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],789:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:i.title,titlefont:i.titlefont,type:i.type,autorange:i.autorange,rangemode:i.rangemode,range:i.range,tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth},\"plot\",\"from-root\")},{\"../../../components/color\":570,\"../../../lib/extend\":685,\"../../../plot_api/edit_types\":727,\"../../cartesian/layout_attributes\":757}],790:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]\|\|{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":696,\"../../../plot_api/plot_template\":734,\"../../cartesian/axis_defaults\":746,\"../../cartesian/type_defaults\":768,\"./axis_attributes\":789,tinycolor2:514}],791:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/html2unicode\"),i=t(\"../../../lib/str2rgbarray\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[a[e]];r.visible?(this.labels[e]=n(r.title),\"titlefont\"in r&&(r.titlefont.color&&(this.labelColor[e]=i(r.titlefont.color)),r.titlefont.family&&(this.labelFont[e]=r.titlefont.family),r.titlefont.size&&(this.labelSize[e]=r.titlefont.size)),\"showline\"in r&&(this.lineEnable[e]=r.showline),\"linecolor\"in r&&(this.lineColor[e]=i(r.linecolor)),\"linewidth\"in r&&(this.lineWidth[e]=r.linewidth),\"showgrid\"in r&&(this.gridEnable[e]=r.showgrid),\"gridcolor\"in r&&(this.gridColor[e]=i(r.gridcolor)),\"gridwidth\"in r&&(this.gridWidth[e]=r.gridwidth),\"log\"===r.type?this.zeroEnable[e]=!1:\"zeroline\"in r&&(this.zeroEnable[e]=r.zeroline),\"zerolinecolor\"in r&&(this.zeroLineColor[e]=i(r.zerolinecolor)),\"zerolinewidth\"in r&&(this.zeroLineWidth[e]=r.zerolinewidth),\"ticks\"in r&&r.ticks?this.lineTickEnable[e]=!0:this.lineTickEnable[e]=!1,\"ticklen\"in r&&(this.lineTickLength[e]=this._defaultLineTickLength[e]=r.ticklen),\"tickcolor\"in r&&(this.lineTickColor[e]=i(r.tickcolor)),\"tickwidth\"in r&&(this.lineTickWidth[e]=r.tickwidth),\"tickangle\"in r&&(this.tickAngle[e]=\"auto\"===r.tickangle?-3600:Math.PI-r.tickangle/180),\"showticklabels\"in r&&(this.tickEnable[e]=r.showticklabels),\"tickfont\"in r&&(r.tickfont.color&&(this.tickColor[e]=i(r.tickfont.color)),r.tickfont.family&&(this.tickFont[e]=r.tickfont.family),r.tickfont.size&&(this.tickSize[e]=r.tickfont.size)),\"mirror\"in r?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(r.mirror)?(this.lineTickMirror[e]=!0,this.lineMirror[e]=!0):!0===r.mirror?(this.lineTickMirror[e]=!1,this.lineMirror[e]=!0):(this.lineTickMirror[e]=!1,this.lineMirror[e]=!1):this.lineMirror[e]=!1,\"showbackground\"in r&&!1!==r.showbackground?(this.backgroundEnable[e]=!0,this.backgroundColor[e]=i(r.backgroundcolor)):this.backgroundEnable[e]=!1):(this.tickEnable[e]=!1,this.labelEnable[e]=!1,this.lineEnable[e]=!1,this.lineTickEnable[e]=!1,this.gridEnable[e]=!1,this.zeroEnable[e]=!1,this.backgroundEnable[e]=!1)}},e.exports=function(t){var e=new o;return e.merge(t),e}},{\"../../../lib/html2unicode\":694,\"../../../lib/str2rgbarray\":719}],792:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\");function c(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),c=[\"up\",\"center\",\"eye\"],u=0;u<c.length;u++)r(\"camera.\"+c[u]+\".x\"),r(\"camera.\"+c[u]+\".y\"),r(\"camera.\"+c[u]+\".z\");var f=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),h=r(\"aspectmode\",f?\"manual\":\"auto\");f\|\|(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===h&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode),s(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:l,calendar:n.calendar,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n),r(\"dragmode\",n.getDfltFromLayout(\"dragmode\")),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:c,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":570,\"../../../lib\":696,\"../../../registry\":827,\"../../subplot_defaults\":822,\"./axis_defaults\":790,\"./layout_attributes\":793}],793:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],dflt:\"turntable\",editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":696,\"../../../lib/extend\":685,\"../../domain\":770,\"./axis_attributes\":789}],794:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":719}],795:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,l=t.fullSceneLayout,c=[[],[],[]],u=0;u<3;++u){var f=l[o[u]];if(f._length=(r[u].hi-r[u].lo)r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(f._length)===1/0)c[u]=[];else{f._input_range=f.range.slice(),f.range[0]=r[u].lo/t.dataScale[u],f.range[1]=r[u].hi/t.dataScale[u],f._m=1/(t.dataScale[u]r[u].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var h=f.tickmode;if(\"auto\"===f.tickmode){f.tickmode=\"linear\";var p=f.nticks\|\|i.constrain(f._length/40,4,9);n.autoTicks(f,Math.abs(f.range[1]-f.range[0])/p)}for(var d=n.calcTicks(f),g=0;g<d.length;++g)d[g].x=d[g].xt.dataScale[u],d[g].text=a(d[g].text);c[u]=d,f.tickmode=h}}e.ticks=c;for(var u=0;u<3;++u){s[u]=.5(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]);for(var g=0;g<2;++g)e.bounds[g][u]=t.glplot.bounds[g][u]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(c)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=t(\"../../../lib/html2unicode\"),o=[\"xaxis\",\"yaxis\",\"zaxis\"],s=[0,0,0]},{\"../../../lib\":696,\"../../../lib/html2unicode\":694,\"../../cartesian/axes\":744}],796:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4r+n]e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],797:[function(t,e,r){\"use strict\";var n,i,a=t(\"gl-plot3d\"),o=t(\"webgl-context\"),s=t(\"has-passive-events\"),l=t(\"../../registry\"),c=t(\"../../lib\"),u=t(\"../../plots/cartesian/axes\"),f=t(\"../../components/fx\"),h=t(\"../../lib/str2rgbarray\"),p=t(\"../../lib/show_no_webgl_msg\"),d=t(\"./camera\"),g=t(\"./project\"),v=t(\"./layout/convert\"),m=t(\"./layout/spikes\"),y=t(\"./layout/tick_marks\");function x(t,e,r,l){var c=t.graphDiv,h={canvas:r,gl:l,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!(i\|\|(n=document.createElement(\"canvas\"),i=o({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");h.pixelRatio=t.pixelRatio,h.gl=i,h.canvas=n}try{t.glplot=a(h)}catch(e){return p(t)}var v=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+\".camera\"]=M(t.camera),t.saveCamera(c.layout),t.graphDiv.emit(\"plotly_relayout\",e)}};if(t.glplot.canvas.addEventListener(\"mouseup\",v.bind(null,t)),t.glplot.canvas.addEventListener(\"wheel\",v.bind(null,t),!!s&&{passive:!1}),t.staticMode\|\|t.glplot.canvas.addEventListener(\"webglcontextlost\",function(e){c&&c.emit&&c.emit(\"plotly_webglcontextlost\",{event:e,layer:t.id})},!1),!t.camera){var m=t.fullSceneLayout.camera;t.camera=d(t.container,{center:[m.center.x,m.center.y,m.center.z],eye:[m.eye.x,m.eye.y,m.eye.z],up:[m.up.x,m.up.y,m.up.z],zoomMin:.1,zoomMax:100,mode:\"orbit\"})}return t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(t){var e,r=t.svgContainer,n=t.container.getBoundingClientRect(),i=n.width,a=n.height;r.setAttributeNS(null,\"viewBox\",\"0 0 \"+i+\" \"+a),r.setAttributeNS(null,\"width\",i),r.setAttributeNS(null,\"height\",a),y(t),t.glplot.axes.update(t.axesOptions);for(var o,s=Object.keys(t.traces),l=null,c=t.glplot.selection,h=0;h<s.length;++h)\"skip\"!==(e=t.traces[s[h]]).data.hoverinfo&&e.handlePick(c)&&(l=e),e.setContourLevels&&e.setContourLevels();function p(e,r){var n=t.fullSceneLayout[e];return u.tickText(n,n.d2l(r),\"hover\").text}if(null!==l){var d=g(t.glplot.cameraParams,c.dataCoordinate);e=l.data;var v,m=c.index,x=f.castHoverinfo(e,t.fullLayout,m),b=x.split(\"+\"),_=\"all\"===x,w=p(\"xaxis\",c.traceCoordinate[0]),k=p(\"yaxis\",c.traceCoordinate[1]),M=p(\"zaxis\",c.traceCoordinate[2]);if(_\|\|(-1===b.indexOf(\"x\")&&(w=void 0),-1===b.indexOf(\"y\")&&(k=void 0),-1===b.indexOf(\"z\")&&(M=void 0),-1===b.indexOf(\"text\")&&(c.textLabel=void 0),-1===b.indexOf(\"name\")&&(l.name=void 0)),\"cone\"===e.type\|\|\"streamtube\"===e.type){var A=[];(_\|\|-1!==b.indexOf(\"u\"))&&A.push(\"u: \"+p(\"xaxis\",c.traceCoordinate[3])),(_\|\|-1!==b.indexOf(\"v\"))&&A.push(\"v: \"+p(\"yaxis\",c.traceCoordinate[4])),(_\|\|-1!==b.indexOf(\"w\"))&&A.push(\"w: \"+p(\"zaxis\",c.traceCoordinate[5])),(_\|\|-1!==b.indexOf(\"norm\"))&&A.push(\"norm: \"+c.traceCoordinate[6].toPrecision(3)),\"streamtube\"!==e.type\|\|!_&&-1===b.indexOf(\"divergence\")\|\|A.push(\"divergence: \"+c.traceCoordinate[7].toPrecision(3)),c.textLabel&&A.push(c.textLabel),v=A.join(\"<br>\")}else v=c.textLabel;t.fullSceneLayout.hovermode&&f.loneHover({x:(.5+.5d[0]/d[3])i,y:(.5-.5d[1]/d[3])a,xLabel:w,yLabel:k,zLabel:M,text:v,name:l.name,color:f.castHoverOption(e,m,\"bgcolor\")\|\|l.color,borderColor:f.castHoverOption(e,m,\"bordercolor\"),fontFamily:f.castHoverOption(e,m,\"font.family\"),fontSize:f.castHoverOption(e,m,\"font.size\"),fontColor:f.castHoverOption(e,m,\"font.color\")},{container:r,gd:t.graphDiv});var T={x:c.traceCoordinate[0],y:c.traceCoordinate[1],z:c.traceCoordinate[2],data:e._input,fullData:e,curveNumber:e.index,pointNumber:m};e._module.eventData&&(T=e._module.eventData(T,c,e,{},m)),f.appendArrayPointValue(T,e,m);var S={points:[T]};c.buttons&&c.distance<5?t.graphDiv.emit(\"plotly_click\",S):t.graphDiv.emit(\"plotly_hover\",S),o=S}else f.loneUnhover(r),t.graphDiv.emit(\"plotly_unhover\",o);t.drawAnnotations(t)}.bind(null,t),t.traces={},!0}function b(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id\|\|\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=v(e[this.id]),this.spikeOptions=m(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio\|\|2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=l.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=l.getComponentMethod(\"annotations3d\",\"draw\"),x(this)}var _=b.prototype;_.recoverContext=function(){var t=this,e=this.glplot.gl,r=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(function n(){e.isContextLost()?requestAnimationFrame(n):x(t,t.fullLayout,r,e)?t.plot.apply(t,t.plotArgs):c.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")})};var w=[\"xaxis\",\"yaxis\",\"zaxis\"];function k(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=w[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+\"calendar\"],f=e[\"_\"+o+\"length\"];if(c.isArrayOrTypedArray(l))for(var h,p=0;p<(f\|\|l.length);p++)if(c.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],f-1)}}function M(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}_.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];c.bgcolor?this.glplot.clearColor=h(c.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=c,this.glplotLayout=c,this.axesOptions.merge(c),this.spikeOptions.merge(c),this.setCamera(c.camera),this.updateFx(c.dragmode,c.hovermode),this.glplot.update({}),this.setConvert(s),t?Array.isArray(t)\|\|(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&k(this,n,f);var p=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?p[o]=1:p[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=p,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var d=Object.keys(this.traces);t:for(a=0;a<d.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===d[a]&&!0===t[o].visible)continue t;(i=this.traces[d[a]]).dispose(),delete this.traces[d[a]]}this.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var g=[[0,0,0],[0,0,0]],v=[],m={};for(a=0;a<3;++a){if((l=(s=c[w[a]]).type)in m?(m[l].acc=p[a],m[l].count+=1):m[l]={acc:p[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var y=this.glplot.objects,x=this.fullSceneLayout.annotations\|\|[],b=s._name.charAt(0);for(o=0;o<y.length;o++){var _=y[o],M=_.bounds,A=_._trace.data._pad\|\|0;\"ErrorBars\"===_.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],M[0][a]/p[a]-A),g[1][a]=Math.max(g[1][a],M[1][a]/p[a]+A)}for(o=0;o<x.length;o++){var T=x[o];if(T.visible){var S=s.r2l(T[b]);g[0][a]=Math.min(g[0][a],S),g[1][a]=Math.max(g[1][a],S)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var E=g[1][a]-g[0][a];g[0][a]-=E/32,g[1][a]+=E/32}if(\"reversed\"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var L=s.range;g[0][a]=s.r2l(L[0]),g[1][a]=s.r2l(L[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.bounds[0][a]=g[0][a]p[a],this.glplot.bounds[1][a]=g[1][a]p[a]}var z=[1,1,1];for(a=0;a<3;++a){var O=m[l=(s=c[w[a]]).type];z[a]=Math.pow(O.acc,1/O.count)/p[a]}var I;if(\"auto\"===c.aspectmode)I=Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1];else if(\"cube\"===c.aspectmode)I=[1,1,1];else if(\"data\"===c.aspectmode)I=z;else{if(\"manual\"!==c.aspectmode)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var P=c.aspectratio;I=[P.x,P.y,P.z]}c.aspectratio.x=u.aspectratio.x=I[0],c.aspectratio.y=u.aspectratio.y=I[1],c.aspectratio.z=u.aspectratio.z=I[2],this.glplot.aspect=I;var D=c.domain\|\|null,R=e._size\|\|null;if(D&&R){var B=this.container.style;B.position=\"absolute\",B.left=R.l+D.x[0]R.w+\"px\",B.top=R.t+(1-D.y[1])R.h+\"px\",B.width=R.w(D.x[1]-D.x[0])+\"px\",B.height=R.h(D.y[1]-D.y[0])+\"px\"}this.glplot.redraw()}},_.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=this.glplot.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},_.getCamera=function(){return this.glplot.camera.view.recalcMatrix(this.camera.view.lastT()),M(this.glplot.camera)},_.setCamera=function(t){var e;this.glplot.camera.lookAt.apply(this,[[(e=t).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]])},_.saveCamera=function(t){var e=this.getCamera(),r=c.nestedProperty(t,this.id+\".camera\"),n=r.get(),i=!1;function a(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}if(void 0===n)i=!0;else for(var o=0;o<3;o++)for(var s=0;s<3;s++)if(!a(e,n,o,s)){i=!0;break}return i&&r.set(e),i},_.updateFx=function(t,e){var r=this.camera;r&&(\"orbit\"===t?(r.mode=\"orbit\",r.keyBindingMode=\"rotate\"):\"turntable\"===t?(r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\"):r.keyBindingMode=t),this.fullSceneLayout.hovermode=e},_.toImage=function(t){t\|\|(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(ri4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4(ro+l)+c];a[4(ro+l)+c]=a[4(rs+l)+c],a[4(rs+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(n),h},_.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[w[t]];u.setConvert(e,this.fullLayout),e.setScale=c.noop}},e.exports=b},{\"../../components/fx\":612,\"../../lib\":696,\"../../lib/show_no_webgl_msg\":717,\"../../lib/str2rgbarray\":719,\"../../plots/cartesian/axes\":744,\"../../registry\":827,\"./camera\":786,\"./layout/convert\":791,\"./layout/spikes\":794,\"./layout/tick_marks\":795,\"./project\":796,\"gl-plot3d\":277,\"has-passive-events\":394,\"webgl-context\":533}],798:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){n=n\|\|t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],799:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"../components/color/attributes\"),a=n({editType:\"calc\"});a.family.dflt='\"Open Sans\", verdana, arial, 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a=e[r[i]],s=a.domain,l=a._subplot,c=l.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":c,x:n.l+n.ws.x[0],y:n.t+n.h(1-s.y[1]),width:n.w(s.x[1]-s.x[0]),height:n.h(s.y[1]-s.y[0]),preserveAspectRatio:\"none\"}),l.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.mapbox,n=0;n<r.length;n++){e[r[n]]._subplot.updateFx(e)}}},{\"../../constants/xmlns_namespaces\":674,\"../../lib\":696,\"../../plots/get_data\":781,\"./constants\":800,\"./layout_attributes\":804,\"./layout_defaults\":805,\"./mapbox\":806,\"mapbox-gl\":409}],803:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./convert_text_opts\");function a(t,e){this.mapbox=t,this.map=t.map,this.uid=t.uid+\"-layer\"+e,this.idSource=this.uid+\"-source\",this.idLayer=this.uid+\"-layer\",this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var o=a.prototype;function s(t){var e=t.source;return t.visible&&(n.isPlainObject(e)\|\|\"string\"==typeof e&&e.length>0)}function l(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var a=t.symbol,o=i(a.textposition,a.iconsize);n.extendFlat(e,{\"icon-image\":a.icon+\"-15\",\"icon-size\":a.iconsize/10,\"text-field\":a.text,\"text-size\":a.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":a.textfont.color,\"text-opacity\":t.opacity})}return{layout:e,paint:r}}o.update=function(t){this.visible?this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=s(t)},o.needsNewSource=function(t){return this.sourceType!==t.sourcetype\|\|this.source!==t.source\|\|this.layerType!==t.type},o.needsNewLayer=function(t){return this.layerType!==t.type\|\|this.below!==t.below},o.updateSource=function(t){var e=this.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,s(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,i={type:r};\"geojson\"===r?e=\"data\":\"vector\"===r&&(e=\"string\"==typeof n?\"url\":\"tiles\");return i[e]=n,i}(t);e.addSource(this.idSource,r)}},o.updateLayer=function(t){var e=this.map,r=l(t);this.removeLayer(),this.layerType=t.type,s(t)&&e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer\|\|\"\",type:t.type,layout:r.layout,paint:r.paint},t.below)},o.updateStyle=function(t){if(s(t)){var e=l(t);this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.mapbox.setOptions(this.idLayer,\"setPaintProperty\",e.paint)}},o.removeLayer=function(){var t=this.map;t.getLayer(this.idLayer)&&t.removeLayer(this.idLayer)},o.dispose=function(){var t=this.map;t.removeLayer(this.idLayer),t.removeSource(this.idSource)},e.exports=function(t,e,r){var n=new a(t,e);return n.update(r),n}},{\"../../lib\":696,\"./convert_text_opts\":801}],804:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../domain\").attributes,o=t(\"../font_attributes\"),s=t(\"../../traces/scatter/attributes\").textposition,l=t(\"../../plot_api/edit_types\").overrideAll,c=t(\"../../plot_api/plot_template\").templatedArray,u=o({});u.family.dflt=\"Open Sans Regular, Arial Unicode MS 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a=r(\"type\");r(\"below\"),r(\"color\"),r(\"opacity\"),\"circle\"===a&&r(\"circle.radius\"),\"line\"===a&&r(\"line.width\"),\"fill\"===a&&r(\"fill.outlinecolor\"),\"symbol\"===a&&(r(\"symbol.icon\"),r(\"symbol.iconsize\"),r(\"symbol.text\"),n.coerceFont(r,\"symbol.textfont\"),r(\"symbol.textposition\"))}}e.exports=function(t,e,r){i(t,e,r,{type:\"mapbox\",attributes:o,handleDefaults:s,partition:\"y\",accessToken:e._mapboxAccessToken})}},{\"../../lib\":696,\"../array_container_defaults\":740,\"../subplot_defaults\":822,\"./layout_attributes\":804}],806:[function(t,e,r){\"use strict\";var n=t(\"mapbox-gl\"),i=t(\"../../components/fx\"),a=t(\"../../lib\"),o=t(\"../../components/dragelement\"),s=t(\"../cartesian/select\").prepSelect,l=t(\"../cartesian/select\").selectOnClick,c=t(\"./constants\"),u=t(\"./layout_attributes\"),f=t(\"./layers\");function h(t){this.id=t.id,this.gd=t.gd,this.container=t.container,this.isStatic=t.staticPlot;var e=t.fullLayout;this.uid=e._uid+\"-\"+this.id,this.opts=e[this.id],this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(e),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[]}var p=h.prototype;function d(t){var e=u.style.values,r=u.style.dflt,n={};return a.isPlainObject(t)?(n.id=t.id,n.style=t):\"string\"==typeof t?(n.id=t,n.style=-1!==e.indexOf(t)?g(t):t):(n.id=r,n.style=g(r)),n.transition={duration:0,delay:0},n}function g(t){return c.styleUrlPrefix+t+\"-\"+c.styleUrlSuffix}function v(t){return[t.lon,t.lat]}e.exports=function(t){return new h(t)},p.plot=function(t,e,r){var n,i=this,a=i.opts=e[this.id];i.map&&a.accesstoken!==i.accessToken&&(i.map.remove(),i.map=null,i.styleObj=null,i.traceHash=[],i.layerList={}),n=i.map?new Promise(function(r,n){i.updateMap(t,e,r,n)}):new Promise(function(r,n){i.createMap(t,e,r,n)}),r.push(n)},p.createMap=function(t,e,r,a){var o=this,s=o.gd,u=o.opts,f=o.styleObj=d(u.style);o.accessToken=u.accesstoken;var h=o.map=new n.Map({container:o.div,style:f.style,center:v(u.center),zoom:u.zoom,bearing:u.bearing,pitch:u.pitch,interactive:!o.isStatic,preserveDrawingBuffer:o.isStatic,doubleClickZoom:!1,boxZoom:!1}),p=c.controlContainerClassName,g=o.div.getElementsByClassName(p)[0];if(o.div.removeChild(g),h._canvas.style.left=\"0px\",h._canvas.style.top=\"0px\",o.rejectOnError(a),h.once(\"load\",function(){o.updateData(t),o.updateLayout(e),o.resolveOnRender(r)}),!o.isStatic){var m=!1;h.on(\"moveend\",function(t){if(o.map){var e=o.getView();u._input.center=u.center=e.center,u._input.zoom=u.zoom=e.zoom,u._input.bearing=u.bearing=e.bearing,u._input.pitch=u.pitch=e.pitch,(t.originalEvent\|\|m)&&x(e),m=!1}}),h.on(\"wheel\",function(){m=!0}),h.on(\"mousemove\",function(t){var e=o.div.getBoundingClientRect();t.clientX=t.point.x+e.left,t.clientY=t.point.y+e.top,t.target.getBoundingClientRect=function(){return e},o.xaxis.p2c=function(){return t.lngLat.lng},o.yaxis.p2c=function(){return t.lngLat.lat},i.hover(s,t,o.id)}),h.on(\"dragstart\",y),h.on(\"zoomstart\",y),h.on(\"dblclick\",function(){s.emit(\"plotly_doubleclick\",null);var t=o.viewInitial;h.setCenter(v(t.center)),h.setZoom(t.zoom),h.setBearing(t.bearing),h.setPitch(t.pitch);var e=o.getView();u._input.center=u.center=e.center,u._input.zoom=u.zoom=e.zoom,u._input.bearing=u.bearing=e.bearing,u._input.pitch=u.pitch=e.pitch,x(e)}),o.clearSelect=function(){s._fullLayout._zoomlayer.selectAll(\".select-outline\").remove()},o.onClickInPanFn=function(t){return function(e){var r=s._fullLayout.clickmode;r.indexOf(\"select\")>-1&&l(e.originalEvent,s,[o.xaxis],[o.yaxis],o.id,t),r.indexOf(\"event\")>-1&&i.click(s,e.originalEvent)}}}function y(){i.loneUnhover(e._toppaper)}function x(t){var e=o.id,r={};for(var n in t)r[e+\".\"+n]=t[n];s.emit(\"plotly_relayout\",r)}},p.updateMap=function(t,e,r,n){var i=this,a=i.map;i.rejectOnError(n);var o=d(i.opts.style);i.styleObj.id!==o.id?(i.styleObj=o,a.setStyle(o.style),a.once(\"styledata\",function(){i.traceHash={},i.updateData(t),i.updateLayout(e),i.resolveOnRender(r)})):(i.updateData(t),i.updateLayout(e),i.resolveOnRender(r))},p.updateData=function(t){var e,r,n,i,a=this.traceHash;for(n=0;n<t.length;n++){var o=t[n];(e=a[(r=o[0].trace).uid])?e.update(o):r._module&&(a[r.uid]=r._module.plot(this,o))}var s=Object.keys(a);t:for(n=0;n<s.length;n++){var l=s[n];for(i=0;i<t.length;i++)if(l===(r=t[i][0].trace).uid)continue t;(e=a[l]).dispose(),delete a[l]}},p.updateLayout=function(t){var e=this.map,r=this.opts;e.setCenter(v(r.center)),e.setZoom(r.zoom),e.setBearing(r.bearing),e.setPitch(r.pitch),this.updateLayers(),this.updateFramework(t),this.updateFx(t),this.map.resize()},p.resolveOnRender=function(t){var e=this.map;e.on(\"render\",function r(){e.loaded()&&(e.off(\"render\",r),setTimeout(t,0))})},p.rejectOnError=function(t){var e=this.map;function r(){t(new Error(c.mapOnErrorMsg))}e.once(\"error\",r),e.once(\"style.error\",r),e.once(\"source.error\",r),e.once(\"tile.error\",r),e.once(\"layer.error\",r)},p.createFramework=function(t){var e=this,r=e.div=document.createElement(\"div\");r.id=e.uid,r.style.position=\"absolute\",e.container.appendChild(r),e.xaxis={_id:\"x\",c2p:function(t){return e.project(t).x}},e.yaxis={_id:\"y\",c2p:function(t){return e.project(t).y}},e.updateFramework(t)},p.updateFx=function(t){var e=this,r=e.map,n=e.gd;if(!e.isStatic){var i,l=t.dragmode;i=\"select\"===l?function(t,r){(t.range={})[e.id]=[u([r.xmin,r.ymin]),u([r.xmax,r.ymax])]}:function(t,r,n){(t.lassoPoints={})[e.id]=n.filtered.map(u)};var c=e.dragOptions;e.dragOptions=a.extendDeep(c\|\|{},{element:e.div,gd:n,plotinfo:{id:e.id,xaxis:e.xaxis,yaxis:e.yaxis,fillRangeItems:i},xaxes:[e.xaxis],yaxes:[e.yaxis],subplot:e.id}),r.off(\"click\",e.onClickInPanHandler),\"select\"===l\|\|\"lasso\"===l?(r.dragPan.disable(),r.on(\"zoomstart\",e.clearSelect),e.dragOptions.prepFn=function(t,r,n){s(t,r,n,e.dragOptions,l)},o.init(e.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",e.clearSelect),e.div.onmousedown=null,e.onClickInPanHandler=e.onClickInPanFn(e.dragOptions),r.on(\"click\",e.onClickInPanHandler))}function u(t){var r=e.map.unproject(t);return[r.lng,r.lat]}},p.updateFramework=function(t){var 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n=eMath.PI/180;return[e,t(n)]})},o.util.dataFromEquation=function(t,e,r){var i=e\|\|6,a=[],o=[];n.range(0,360+i,i).forEach(function(e,r){var n=eMath.PI/180,i=t(n);a.push(e),o.push(i)});var s={t:a,r:o};return r&&(s.name=r),s},o.util.ensureArray=function(t,e){if(\"undefined\"==typeof t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]\|\|r[0]})},o.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=o.util.ensureArray(t[e],r)}),t},o.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},o.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length\|\|objHasKeys(t[r],e))},o.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},o.util.arrayLast=function(t){return t[t.length-1]},o.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return-2===r},o.util.flattenArray=function(t){for(var e=[];!o.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},o.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},o.util.convertToCartesian=function(t,e){var r=eMath.PI/180;return[tMath.cos(r),tMath.sin(r)]},o.util.round=function(t,e){var r=e\|\|2,n=Math.pow(10,r);return Math.round(tn)/n},o.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],i=e[1],a={};return a.x=r,a.y=i,a.pos=e,a.angle=180(Math.atan2(i,r)+Math.PI)/Math.PI,a.radius=Math.sqrt(rr+ii),a},o.util.duplicatesCount=function(t){for(var e,r={},n={},i=0,a=t.length;i<a;i++)(e=t[i])in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},o.util.duplicates=function(t){return Object.keys(o.util.duplicatesCount(t))},o.util.translator=function(t,e,r,n){if(n){var i=r.slice();r=e,e=i}var a=e.reduce(function(t,e){if(\"undefined\"!=typeof t)return t[e]},t);\"undefined\"!=typeof a&&(e.reduce(function(t,r,n){if(\"undefined\"!=typeof t)return n===e.length-1&&delete 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o=r[2]?[r[0],r[1]+r[2]]:r,s=n.svg.symbol().size(t[a].data.dotSize).type(t[a].data.dotType)(r,i);n.select(this).attr({class:\"mark dot\",d:s,transform:function(t,r){var n,i,a,s=(n=function(t,r){var n=e.radialScale(t[1]),i=(e.angularScale(t[0])+e.orientation)Math.PI/180;return{r:n,t:i}}(o),i=n.rMath.cos(n.t),a=n.rMath.sin(n.t),{x:i,y:a});return\"translate(\"+[s.x,s.y]+\")\"}})};var u=n.svg.line.radial().interpolate(t[0].data.lineInterpolation).radius(function(t){return e.radialScale(t[1])}).angle(function(t){return e.angularScale(t[0])Math.PI/180});c.line=function(r,i,a){var s=r[2]?o[a].map(function(t,e){return[t[0],t[1]+t[2]]}):o[a];if(n.select(this).each(c.dot).style({opacity:function(e,r){return+t[a].data.dotVisible},fill:d.stroke(r,i,a)}).attr({class:\"mark dot\"}),!(i>0)){var l=n.select(this.parentNode).selectAll(\"path.line\").data([0]);l.enter().insert(\"path\"),l.attr({class:\"line\",d:u(s),transform:function(t,r){return\"rotate(\"+(e.orientation+90)+\")\"},\"pointer-events\":\"none\"}).style({fill:function(t,e){return d.fill(r,i,a)},\"fill-opacity\":0,stroke:function(t,e){return d.stroke(r,i,a)},\"stroke-width\":function(t,e){return d[\"stroke-width\"](r,i,a)},\"stroke-dasharray\":function(t,e){return d[\"stroke-dasharray\"](r,i,a)},opacity:function(t,e){return d.opacity(r,i,a)},display:function(t,e){return d.display(r,i,a)}})}};var f=e.angularScale.range(),h=Math.abs(f[1]-f[0])/o[0].lengthMath.PI/180,p=n.svg.arc().startAngle(function(t){return-h/2}).endAngle(function(t){return h/2}).innerRadius(function(t){return e.radialScale(l+(t[2]\|\|0))}).outerRadius(function(t){return e.radialScale(l+(t[2]\|\|0))+e.radialScale(t[1])});c.arc=function(t,r,i){n.select(this).attr({class:\"mark 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arguments.length?(e.forEach(function(e,r){t[r]\|\|(t[r]={}),i(t[r],o.PolyChart.defaultConfig()),i(t[r],e)}),this):t},a.getColorScale=function(){},n.rebind(a,e,\"on\"),a},o.PolyChart.defaultConfig=function(){return{data:{name:\"geom1\",t:[[1,2,3,4]],r:[[1,2,3,4]],dotType:\"circle\",dotSize:64,dotVisible:!1,barWidth:20,color:\"#ffa500\",strokeSize:1,strokeColor:\"silver\",strokeDash:\"solid\",opacity:1,index:0,visible:!0,visibleInLegend:!0},geometryConfig:{geometry:\"LinePlot\",geometryType:\"arc\",direction:\"clockwise\",orientation:0,container:\"body\",radialScale:null,angularScale:null,colorScale:n.scale.category20()}}},o.BarChart=function(){return o.PolyChart()},o.BarChart.defaultConfig=function(){return{geometryConfig:{geometryType:\"bar\"}}},o.AreaChart=function(){return o.PolyChart()},o.AreaChart.defaultConfig=function(){return{geometryConfig:{geometryType:\"arc\"}}},o.DotPlot=function(){return o.PolyChart()},o.DotPlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"dot\",dotType:\"circle\"}}},o.LinePlot=function(){return o.PolyChart()},o.LinePlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"line\"}}},o.Legend=function(){var t=o.Legend.defaultConfig(),e=n.dispatch(\"hover\");function r(){var e=t.legendConfig,a=t.data.map(function(t,r){return[].concat(t).map(function(t,n){var a=i({},e.elements[r]);return a.name=t,a.color=[].concat(e.elements[r].color)[n],a})}),o=n.merge(a);o=o.filter(function(t,r){return e.elements[r]&&(e.elements[r].visibleInLegend\|\|\"undefined\"==typeof e.elements[r].visibleInLegend)}),e.reverseOrder&&(o=o.reverse());var s=e.container;(\"string\"==typeof s\|\|s.nodeName)&&(s=n.select(s));var l=o.map(function(t,e){return t.color}),c=e.fontSize,u=null==e.isContinuous?\"number\"==typeof o[0]:e.isContinuous,f=u?e.height:co.length,h=s.classed(\"legend-group\",!0).selectAll(\"svg\").data([0]),p=h.enter().append(\"svg\").attr({width:300,height:f+c,xmlns:\"http://www.w3.org/2000/svg\",\"xmlns:xlink\":\"http://www.w3.org/1999/xlink\",version:\"1.1\"});p.append(\"g\").classed(\"legend-axis\",!0),p.append(\"g\").classed(\"legend-marks\",!0);var d=n.range(o.length),g=n.scale[u?\"linear\":\"ordinal\"]().domain(d).range(l),v=n.scale[u?\"linear\":\"ordinal\"]().domain(d)[u?\"range\":\"rangePoints\"]([0,f]);if(u){var m=h.select(\".legend-marks\").append(\"defs\").append(\"linearGradient\").attr({id:\"grad1\",x1:\"0%\",y1:\"0%\",x2:\"0%\",y2:\"100%\"}).selectAll(\"stop\").data(l);m.enter().append(\"stop\"),m.attr({offset:function(t,e){return e/(l.length-1)100+\"%\"}}).style({\"stop-color\":function(t,e){return t}}),h.append(\"rect\").classed(\"legend-mark\",!0).attr({height:e.height,width:e.colorBandWidth,fill:\"url(#grad1)\"})}else{var y=h.select(\".legend-marks\").selectAll(\"path.legend-mark\").data(o);y.enter().append(\"path\").classed(\"legend-mark\",!0),y.attr({transform:function(t,e){return\"translate(\"+[c/2,v(e)+c/2]+\")\"},d:function(t,e){var r,i,a,o=t.symbol;return a=3(i=c),\"line\"===(r=o)?\"M\"+[[-i/2,-i/12],[i/2,-i/12],[i/2,i/12],[-i/2,i/12]]+\"Z\":-1!=n.svg.symbolTypes.indexOf(r)?n.svg.symbol().type(r).size(a)():n.svg.symbol().type(\"square\").size(a)()},fill:function(t,e){return g(e)}}),y.exit().remove()}var x=n.svg.axis().scale(v).orient(\"right\"),b=h.select(\"g.legend-axis\").attr({transform:\"translate(\"+[u?e.colorBandWidth:c,c/2]+\")\"}).call(x);return b.selectAll(\".domain\").style({fill:\"none\",stroke:\"none\"}),b.selectAll(\"line\").style({fill:\"none\",stroke:u?e.textColor:\"none\"}),b.selectAll(\"text\").style({fill:e.textColor,\"font-size\":e.fontSize}).text(function(t,e){return o[e].name}),r}return r.config=function(e){return arguments.length?(i(t,e),this):t},n.rebind(r,e,\"on\"),r},o.Legend.defaultConfig=function(t,e){return{data:[\"a\",\"b\",\"c\"],legendConfig:{elements:[{symbol:\"line\",color:\"red\"},{symbol:\"square\",color:\"yellow\"},{symbol:\"diamond\",color:\"limegreen\"}],height:150,colorBandWidth:30,fontSize:12,container:\"body\",isContinuous:null,textColor:\"grey\",reverseOrder:!1}}},o.tooltipPanel=function(){var t,e,r,a={container:null,hasTick:!1,fontSize:12,color:\"white\",padding:5},s=\"tooltip-\"+o.tooltipPanel.uid++,l=10,c=function(){var n=(t=a.container.selectAll(\"g.\"+s).data([0])).enter().append(\"g\").classed(s,!0).style({\"pointer-events\":\"none\",display:\"none\"});return r=n.append(\"path\").style({fill:\"white\",\"fill-opacity\":.9}).attr({d:\"M0 0\"}),e=n.append(\"text\").attr({dx:a.padding+l,dy:.3+a.fontSize}),c};return c.text=function(i){var o=n.hsl(a.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",f=i\|\|\"\";e.style({fill:u,\"font-size\":a.fontSize+\"px\"}).text(f);var h=a.padding,p=e.node().getBBox(),d={fill:a.color,stroke:s,\"stroke-width\":\"2px\"},g=p.width+2h+l,v=p.height+2h;return r.attr({d:\"M\"+[[l,-v/2],[l,-v/4],[a.hasTick?0:l,0],[l,v/4],[l,v/2],[g,v/2],[g,-v/2]].join(\"L\")+\"Z\"}).style(d),t.attr({transform:\"translate(\"+[l,-v/2+2h]+\")\"}),t.style({display:\"block\"}),c},c.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),c},c.hide=function(){if(t)return t.style({display:\"none\"}),c},c.show=function(){if(t)return t.style({display:\"block\"}),c},c.config=function(t){return i(a,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map(function(t,r){var n=i({},t);return[[n,[\"marker\",\"color\"],[\"color\"]],[n,[\"marker\",\"opacity\"],[\"opacity\"]],[n,[\"marker\",\"line\",\"color\"],[\"strokeColor\"]],[n,[\"marker\",\"line\",\"dash\"],[\"strokeDash\"]],[n,[\"marker\",\"line\",\"width\"],[\"strokeSize\"]],[n,[\"marker\",\"symbol\"],[\"dotType\"]],[n,[\"marker\",\"size\"],[\"dotSize\"]],[n,[\"marker\",\"barWidth\"],[\"barWidth\"]],[n,[\"line\",\"interpolation\"],[\"lineInterpolation\"]],[n,[\"showlegend\"],[\"visibleInLegend\"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e\|\|delete n.marker,e&&delete n.groupId,e?(\"LinePlot\"===n.geometry?(n.type=\"scatter\",!0===n.dotVisible?(delete n.dotVisible,n.mode=\"lines+markers\"):n.mode=\"lines\"):\"DotPlot\"===n.geometry?(n.type=\"scatter\",n.mode=\"markers\"):\"AreaChart\"===n.geometry?n.type=\"area\":\"BarChart\"===n.geometry&&(n.type=\"bar\"),delete n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n}),!e&&t.layout&&\"stack\"===t.layout.barmode)){var a=o.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var n=a.indexOf(t.geometry);-1!=n&&(r.data[e].groupId=n)})}if(t.layout){var s=i({},t.layout);if([[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach(function(t,r){o.util.translator.apply(null,t.concat(e))}),e?(\"undefined\"!=typeof s.tickLength&&(s.angularaxis.ticklen=s.tickLength,delete s.tickLength),s.tickColor&&(s.angularaxis.tickcolor=s.tickColor,delete s.tickColor)):(s.angularAxis&&\"undefined\"!=typeof s.angularAxis.ticklen&&(s.tickLength=s.angularAxis.ticklen),s.angularAxis&&\"undefined\"!=typeof s.angularAxis.tickcolor&&(s.tickColor=s.angularAxis.tickcolor)),s.legend&&\"boolean\"!=typeof s.legend.reverseOrder&&(s.legend.reverseOrder=\"normal\"!=s.legend.reverseOrder),s.legend&&\"boolean\"==typeof s.legend.traceorder&&(s.legend.traceorder=s.legend.traceorder?\"reversed\":\"normal\",delete s.legend.reverseOrder),s.margin&&\"undefined\"!=typeof s.margin.t){var l=[\"t\",\"r\",\"b\",\"l\",\"pad\"],c=[\"top\",\"right\",\"bottom\",\"left\",\"pad\"],u={};n.entries(s.margin).forEach(function(t,e){u[c[l.indexOf(t.key)]]=t.value}),s.margin=u}e&&(delete s.needsEndSpacing,delete s.minorTickColor,delete s.minorTicks,delete s.angularaxis.ticksCount,delete s.angularaxis.ticksCount,delete s.angularaxis.ticksStep,delete s.angularaxis.rewriteTicks,delete s.angularaxis.nticks,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksStep,delete s.radialaxis.rewriteTicks,delete s.radialaxis.nticks),r.layout=s}return r}};return t}},{\"../../../constants/alignment\":668,\"../../../lib\":696,d3:148}],818:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../../lib\"),a=t(\"../../../components/color\"),o=t(\"./micropolar\"),s=t(\"./undo_manager\"),l=i.extendDeepAll,c=e.exports={};c.framework=function(t){var e,r,i,a,u,f=new s;function h(r,s){return s&&(u=s),n.select(n.select(u).node().parentNode).selectAll(\".svg-container>:not(.chart-root)\").remove(),e=e?l(e,r):r,i\|\|(i=o.Axis()),a=o.adapter.plotly().convert(e),i.config(a).render(u),t.data=e.data,t.layout=e.layout,c.fillLayout(t),e}return h.isPolar=!0,h.svg=function(){return i.svg()},h.getConfig=function(){return e},h.getLiveConfig=function(){return o.adapter.plotly().convert(i.getLiveConfig(),!0)},h.getLiveScales=function(){return{t:i.angularScale(),r:i.radialScale()}},h.setUndoPoint=function(){var t,n,i=this,a=o.util.cloneJson(e);t=a,n=r,f.add({undo:function(){n&&i(n)},redo:function(){i(t)}}),r=o.util.cloneJson(a)},h.undo=function(){f.undo()},h.redo=function(){f.redo()},h},c.fillLayout=function(t){var e=n.select(t).selectAll(\".plot-container\"),r=e.selectAll(\".svg-container\"),i=t.framework&&t.framework.svg&&t.framework.svg(),o={width:800,height:600,paper_bgcolor:a.background,_container:e,_paperdiv:r,_paper:i};t._fullLayout=l(o,t.layout)}},{\"../../../components/color\":570,\"../../../lib\":696,\"./micropolar\":817,\"./undo_manager\":819,d3:148}],819:[function(t,e,r){\"use strict\";e.exports=function(){var t,e=[],r=-1,n=!1;function i(t,e){return t?(n=!0,t[e](),n=!1,this):this}return{add:function(t){return n?this:(e.splice(r+1,e.length-r),e.push(t),r=e.length-1,this)},setCallback:function(e){t=e},undo:function(){var n=e[r];return n?(i(n,\"undo\"),r-=1,t&&t(n.undo),this):this},redo:function(){var n=e[r+1];return n?(i(n,\"redo\"),r+=1,t&&t(n.redo),this):this},clear:function(){e=[],r=-1},hasUndo:function(){return-1!==r},hasRedo:function(){return r<e.length-1},getCommands:function(){return e},getPreviousCommand:function(){return e[r-1]},getIndex:function(){return r}}}},{}],820:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=t(\"../../components/color\"),l=t(\"../../components/drawing\"),c=t(\"../plots\"),u=t(\"../cartesian/set_convert\"),f=t(\"./set_convert\"),h=t(\"../cartesian/autorange\").doAutoRange,p=t(\"../cartesian/axes\").doTicksSingle,d=t(\"../cartesian/dragbox\"),g=t(\"../../components/dragelement\"),v=t(\"../../components/fx\"),m=t(\"../../components/titles\"),y=t(\"../cartesian/select\").prepSelect,x=t(\"../cartesian/select\").selectOnClick,b=t(\"../cartesian/select\").clearSelect,_=t(\"../../lib/setcursor\"),w=t(\"../../lib/clear_gl_canvases\"),k=t(\"../../plot_api/subroutines\").redrawReglTraces,M=t(\"../../constants/alignment\").MID_SHIFT,A=t(\"./constants\"),T=t(\"./helpers\"),S=o._,E=o.mod,C=o.deg2rad,L=o.rad2deg;function z(t,e){this.id=e,this.gd=t,this._hasClipOnAxisFalse=null,this.vangles=null,this.radialAxisAngle=null,this.traceHash={},this.layers={},this.clipPaths={},this.clipIds={},this.viewInitial={};var r=t._fullLayout,n=\"clip\"+r._uid+e;this.clipIds.forTraces=n+\"-for-traces\",this.clipPaths.forTraces=r._clips.append(\"clipPath\").attr(\"id\",this.clipIds.forTraces),this.clipPaths.forTraces.append(\"path\"),this.framework=r._polarlayer.append(\"g\").attr(\"class\",e),this.radialTickLayout=null,this.angularTickLayout=null}var O=z.prototype;function I(t){var e=t.ticks+String(t.ticklen)+String(t.showticklabels);return\"side\"in t&&(e+=t.side),e}function P(t,e){return e[o.findIndexOfMin(e,function(e){return o.angleDist(t,e)})]}function D(t,e,r){return e?(t.attr(\"display\",null),t.attr(r)):t&&t.attr(\"display\",\"none\"),t}function R(t,e){return\"translate(\"+t+\",\"+e+\")\"}function B(t){return\"rotate(\"+t+\")\"}function F(t){return Math.abs(t)<1e-10?0:t>0?1:-1}function N(t){return F(Math.cos(t))}function j(t){return F(Math.sin(t))}e.exports=function(t,e){return new z(t,e)},O.plot=function(t,e){var r=e[this.id];this._hasClipOnAxisFalse=!1;for(var n=0;n<t.length;n++){if(!1===t[n][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(e,r),this.updateLayout(e,r),c.generalUpdatePerTraceModule(this.gd,this,t,r),this.updateFx(e,r)},O.updateLayers=function(t,e){var r=this.layers,i=e.radialaxis,a=e.angularaxis,o=A.layerNames,s=o.indexOf(\"frontplot\"),l=o.slice(0,s),c=\"below traces\"===a.layer,u=\"below traces\"===i.layer;c&&l.push(\"angular-line\"),u&&l.push(\"radial-line\"),c&&l.push(\"angular-axis\"),u&&l.push(\"radial-axis\"),l.push(\"frontplot\"),c\|\|l.push(\"angular-line\"),u\|\|l.push(\"radial-line\"),c\|\|l.push(\"angular-axis\"),u\|\|l.push(\"radial-axis\");var f=this.framework.selectAll(\".polarsublayer\").data(l,String);f.enter().append(\"g\").attr(\"class\",function(t){return\"polarsublayer \"+t}).each(function(t){var e=r[t]=n.select(this);switch(t){case\"frontplot\":e.append(\"g\").classed(\"barlayer\",!0),e.append(\"g\").classed(\"scatterlayer\",!0);break;case\"backplot\":e.append(\"g\").classed(\"maplayer\",!0);break;case\"plotbg\":r.bg=e.append(\"path\");break;case\"radial-grid\":e.style(\"fill\",\"none\"),e.append(\"g\").classed(\"x\",1);break;case\"angular-grid\":e.style(\"fill\",\"none\"),e.append(\"g\").classed(\"angularaxis\",1);break;case\"radial-line\":e.append(\"line\").style(\"fill\",\"none\");break;case\"angular-line\":e.append(\"path\").style(\"fill\",\"none\")}}),f.order()},O.updateLayout=function(t,e){var r=this.layers,n=t._size,i=e.radialaxis,a=e.angularaxis,o=e.domain.x,c=e.domain.y;this.xOffset=n.l+n.wo[0],this.yOffset=n.t+n.h(1-c[1]);var u=this.xLength=n.w(o[1]-o[0]),f=this.yLength=n.h(c[1]-c[0]),h=e.sector;this.sectorInRad=h.map(C);var p,d,g,v,m,y=this.sectorBBox=function(t){var e,r,n,i,a=t[0],o=t[1]-a,s=E(a,360),l=s+o,c=Math.cos(C(s)),u=Math.sin(C(s)),f=Math.cos(C(l)),h=Math.sin(C(l));i=s<=90&&l>=90\|\|s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180\|\|s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270\|\|s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(h),x=y[2]-y[0],b=y[3]-y[1],_=f/u,w=Math.abs(b/x);_>w?(p=u,m=(f-(d=uw))/n.h/2,g=[o[0],o[1]],v=[c[0]+m,c[1]-m]):(d=f,m=(u-(p=f/w))/n.w/2,g=[o[0]+m,o[1]-m],v=[c[0],c[1]]),this.xLength2=p,this.yLength2=d,this.xDomain2=g,this.yDomain2=v;var k=this.xOffset2=n.l+n.wg[0],M=this.yOffset2=n.t+n.h(1-v[1]),A=this.radius=p/x,T=this.innerRadius=e.holeA,S=this.cx=k-Ay[0],L=this.cy=M+Ay[3],z=this.cxx=S-k,O=this.cyy=L-M;this.radialAxis=this.mockAxis(t,e,i,{_axislayer:r[\"radial-axis\"],_gridlayer:r[\"radial-grid\"],_id:\"x\",side:{counterclockwise:\"top\",clockwise:\"bottom\"}[i.side],domain:[T/n.w,A/n.w]}),this.angularAxis=this.mockAxis(t,e,a,{_axislayer:r[\"angular-axis\"],_gridlayer:r[\"angular-grid\"],side:\"right\",domain:[0,Math.PI],autorange:!1}),this.doAutoRange(t,e),this.updateAngularAxis(t,e),this.updateRadialAxis(t,e),this.updateRadialAxisTitle(t,e),this.xaxis=this.mockCartesianAxis(t,e,{_id:\"x\",domain:g}),this.yaxis=this.mockCartesianAxis(t,e,{_id:\"y\",domain:v});var I=this.pathSubplot();this.clipPaths.forTraces.select(\"path\").attr(\"d\",I).attr(\"transform\",R(z,O)),r.frontplot.attr(\"transform\",R(k,M)).call(l.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces),r.bg.attr(\"d\",I).attr(\"transform\",R(S,L)).call(s.fill,e.bgcolor),this.framework.selectAll(\".crisp\").classed(\"crisp\",0)},O.mockAxis=function(t,e,r,n){var i=o.extendFlat({anchor:\"free\",position:0,_pos:0,_counteraxis:!0,automargin:!1},r,n);return f(i,e,t),i},O.mockCartesianAxis=function(t,e,r){var n=this,i=r._id,a=o.extendFlat({type:\"linear\"},r);u(a,t);var s={x:[0,2],y:[1,3]};return a.setRange=function(){var t=n.sectorBBox,r=s[i],o=n.radialAxis._rl,l=(o[1]-o[0])/(1-e.hole);a.range=[t[r[0]]l,t[r[1]]l]},a.isPtWithinRange=\"x\"===i?function(t){return n.isPtInside(t)}:function(){return!0},a.setRange(),a.setScale(),a},O.doAutoRange=function(t,e){var r=this.gd,n=this.radialAxis,i=e.radialaxis;n.setScale(),h(r,n);var a=n.range;i.range=a.slice(),i._input.range=a.slice(),n._rl=[n.r2l(a[0],null,\"gregorian\"),n.r2l(a[1],null,\"gregorian\")]},O.updateRadialAxis=function(t,e){var r=this,n=r.gd,i=r.layers,a=r.radius,o=r.innerRadius,l=r.cx,c=r.cy,u=e.radialaxis,f=E(e.sector[0],360),h=r.radialAxis,d=o<a;r.fillViewInitialKey(\"radialaxis.angle\",u.angle),r.fillViewInitialKey(\"radialaxis.range\",h.range.slice()),h.setGeometry(),\"auto\"===h.tickangle&&f>90&&f<=270&&(h.tickangle=180),h._transfn=function(t){return\"translate(\"+(h.l2p(t.x)+o)+\",0)\"},h._gridpath=function(t){return r.pathArc(h.r2p(t.x)+o)};var g=I(u);r.radialTickLayout!==g&&(i[\"radial-axis\"].selectAll(\".xtick\").remove(),r.radialTickLayout=g),d&&(h.setScale(),p(n,h,!0));var v=r.radialAxisAngle=r.vangles?L(P(C(u.angle),r.vangles)):u.angle,m=R(l,c)+B(-v);D(i[\"radial-axis\"],d&&(u.showticklabels\|\|u.ticks),{transform:m}),D(i[\"radial-grid\"],d&&u.showgrid,{transform:R(l,c)}).selectAll(\"path\").attr(\"transform\",null),D(i[\"radial-line\"].select(\"line\"),d&&u.showline,{x1:o,y1:0,x2:a,y2:0,transform:m}).attr(\"stroke-width\",u.linewidth).call(s.stroke,u.linecolor)},O.updateRadialAxisTitle=function(t,e,r){var n=this.gd,i=this.radius,a=this.cx,o=this.cy,s=e.radialaxis,c=this.id+\"title\",u=void 0!==r?r:this.radialAxisAngle,f=C(u),h=Math.cos(f),p=Math.sin(f),d=0;if(s.title){var g=l.bBox(this.layers[\"radial-axis\"].node()).height,v=s.titlefont.size;d=\"counterclockwise\"===s.side?-g-.4v:g+.8v}this.layers[\"radial-axis-title\"]=m.draw(n,c,{propContainer:s,propName:this.id+\".radialaxis.title\",placeholder:S(n,\"Click to enter radial axis title\"),attributes:{x:a+i/2h+dp,y:o-i/2p+dh,\"text-anchor\":\"middle\"},transform:{rotate:-u}})},O.updateAngularAxis=function(t,e){var r=this,i=r.gd,a=r.layers,l=r.radius,c=r.innerRadius,u=r.cx,f=r.cy,h=e.angularaxis,d=r.angularAxis;r.fillViewInitialKey(\"angularaxis.rotation\",h.rotation),d.setGeometry();var g=function(t){return d.t2g(t.x)};\"linear\"===d.type&&\"radians\"===d.thetaunit&&(d.tick0=L(d.tick0),d.dtick=L(d.dtick)),\"category\"===d.type&&(d._tickFilter=function(t){return o.isAngleInsideSector(g(t),r.sectorInRad)}),d._transfn=function(t){var e=n.select(this),r=e&&e.node();if(r&&e.classed(\"angularaxisgrid\"))return\"\";var i=g(t),a=R(u+lMath.cos(i),f-lMath.sin(i));return r&&e.classed(\"ticks\")&&(a+=B(-L(i))),a},d._gridpath=function(t){var e=g(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[u+cr,f-cn]+\"L\"+[u+lr,f-ln]};var v=\"outside\"!==h.ticks?.7:.5;d._labelx=function(t){var e=g(t),r=d._labelStandoff,n=d._pad;return(0===j(e)?0:Math.cos(e)(r+n+vt.fontSize))+N(e)(t.dx+r+n)},d._labely=function(t){var e=g(t),r=d._labelStandoff,n=d._labelShift,i=d._pad;return t.dy+t.fontSizeM-n+-Math.sin(e)(r+i+vt.fontSize)},d._labelanchor=function(t,e){var r=g(e);return 0===j(r)?N(r)>0?\"start\":\"end\":\"middle\"};var m,y=I(h);r.angularTickLayout!==y&&(a[\"angular-axis\"].selectAll(\".\"+d._id+\"tick\").remove(),r.angularTickLayout=y),d.setScale(),p(i,d,!0),\"linear\"===e.gridshape?(m=d._vals.map(g),o.angleDelta(m[0],m[1])<0&&(m=m.slice().reverse())):m=null,r.vangles=m,D(a[\"angular-line\"].select(\"path\"),h.showline,{d:r.pathSubplot(),transform:R(u,f)}).attr(\"stroke-width\",h.linewidth).call(s.stroke,h.linecolor)},O.updateFx=function(t,e){this.gd._context.staticPlot\|\|(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1),this.updateMainDrag(t))},O.updateMainDrag=function(t){var e=this,r=e.gd,o=e.layers,s=t._zoomlayer,l=A.MINZOOM,c=A.OFFEDGE,u=e.radius,f=e.innerRadius,h=e.cx,p=e.cy,m=e.cxx,_=e.cyy,w=e.sectorInRad,k=e.vangles,M=e.radialAxis,S=T.clampTiny,E=T.findXYatLength,C=T.findEnclosingVertexAngles,L=A.cornerHalfWidth,z=A.cornerLen/2,O=d.makeDragger(o,\"path\",\"maindrag\",\"crosshair\");n.select(O).attr(\"d\",e.pathSubplot()).attr(\"transform\",R(h,p));var I,P,D,B,F,N,j,V,U,q={element:O,gd:r,subplot:e.id,plotinfo:{id:e.id,xaxis:e.xaxis,yaxis:e.yaxis},xaxes:[e.xaxis],yaxes:[e.yaxis]};function H(t,e){return Math.sqrt(tt+ee)}function G(t,e){return H(t-m,e-_)}function W(t,e){return Math.atan2(_-e,t-m)}function Y(t,e){return[tMath.cos(e),tMath.sin(-e)]}function X(t,r){if(0===t)return e.pathSector(2L);var n=z/t,i=r-n,a=r+n,o=Math.max(0,Math.min(t,u)),s=o-L,l=o+L;return\"M\"+Y(s,i)+\"A\"+[s,s]+\" 0,0,0 \"+Y(s,a)+\"L\"+Y(l,a)+\"A\"+[l,l]+\" 0,0,1 \"+Y(l,i)+\"Z\"}function Z(t,r,n){if(0===t)return e.pathSector(2L);var 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n={};n.attributes=t(\"./attributes\"),n.layoutAttributes=t(\"./layout_attributes\"),n.supplyDefaults=t(\"./defaults\").supplyDefaults,n.supplyLayoutDefaults=t(\"./layout_defaults\").supplyLayoutDefaults,n.calc=t(\"./calc\"),n.crossTraceCalc=t(\"./cross_trace_calc\").crossTraceCalc,n.plot=t(\"./plot\").plot,n.style=t(\"./style\").style,n.styleOnSelect=t(\"./style\").styleOnSelect,n.hoverPoints=t(\"./hover\").hoverPoints,n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"box\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"boxLayout\",\"zoomScale\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":756,\"./attributes\":859,\"./calc\":860,\"./cross_trace_calc\":861,\"./defaults\":862,\"./event_data\":863,\"./hover\":864,\"./layout_attributes\":866,\"./layout_defaults\":867,\"./plot\":868,\"./select\":869,\"./style\":870}],866:[function(t,e,r){\"use 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o=t._fullLayout,s=e.xaxis,f=e.yaxis,h=o._numBoxes,p=1-o.boxgap,d=\"group\"===o.boxmode&&h>1;i.makeTraceGroups(a,r,\"trace boxes\").each(function(t){var r=n.select(this),i=t[0],a=i.t,g=i.trace;e.isRangePlot\|\|(i.node3=r);var v,m,y=a.dPosp(1-o.boxgroupgap)/(d?h:1),x=d?2a.dPos((a.num+.5)/h-.5)p:0,b=yg.whiskerwidth;!0!==g.visible\|\|a.empty?r.remove():(\"h\"===g.orientation?(v=f,m=s):(v=s,m=f),a.bPos=x,a.bdPos=y,a.wdPos=b,a.wHover=a.dPos(d?p/h:1),l(r,{pos:v,val:m},g,a),c(r,{x:s,y:f},g,a),u(r,{pos:v,val:m},g,a))})},plotBoxAndWhiskers:l,plotPoints:c,plotBoxMean:u}},{\"../../components/drawing\":595,\"../../lib\":696,d3:148}],869:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r,n,i=t.cd,a=t.xaxis,o=t.yaxis,s=[];if(!1===e)for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts\|\|[]).length;n++)i[r].pts[n].selected=0;else for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts\|\|[]).length;n++){var l=i[r].pts[n],c=a.c2p(l.x),u=o.c2p(l.y);e.contains([c,u],null,l.i,t)?(s.push({pointNumber:l.i,x:a.c2d(l.x),y:o.c2d(l.y)}),l.selected=1):l.selected=0}return s}},{}],870:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/color\"),a=t(\"../../components/drawing\");e.exports={style:function(t,e){var r=e?e[0].node3:n.select(t).selectAll(\"g.trace.boxes\");r.style(\"opacity\",function(t){return t[0].trace.opacity}),r.each(function(e){var r=n.select(this),o=e[0].trace,s=o.line.width;function l(t,e,r,n){t.style(\"stroke-width\",e+\"px\").call(i.stroke,r).call(i.fill,n)}var c=r.selectAll(\"path.box\");if(\"candlestick\"===o.type)c.each(function(t){var e=n.select(this),r=o[t.dir];l(e,r.line.width,r.line.color,r.fillcolor),e.style(\"opacity\",o.selectedpoints&&!t.selected?.3:1)});else{l(c,s,o.line.color,o.fillcolor),r.selectAll(\"path.mean\").style({\"stroke-width\":s,\"stroke-dasharray\":2s+\"px,\"+s+\"px\"}).call(i.stroke,o.line.color);var u=r.selectAll(\"path.point\");a.pointStyle(u,o,t)}})},styleOnSelect:function(t,e){var r=e[0].node3,n=e[0].trace,i=r.selectAll(\"path.point\");n.selectedpoints?a.selectedPointStyle(i,n):a.pointStyle(i,n,t)}}},{\"../../components/color\":570,\"../../components/drawing\":595,d3:148}],871:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").extendFlat,i=t(\"../ohlc/attributes\"),a=t(\"../box/attributes\");function o(t){return{line:{color:n({},a.line.color,{dflt:t}),width:a.line.width,editType:\"style\"},fillcolor:a.fillcolor,editType:\"style\"}}e.exports={x:i.x,open:i.open,high:i.high,low:i.low,close:i.close,line:{width:n({},a.line.width,{}),editType:\"style\"},increasing:o(i.increasing.line.color.dflt),decreasing:o(i.decreasing.line.color.dflt),text:i.text,whiskerwidth:n({},a.whiskerwidth,{dflt:0}),hoverlabel:i.hoverlabel}},{\"../../lib\":696,\"../box/attributes\":859,\"../ohlc/attributes\":991}],872:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../ohlc/calc\").calcCommon;function o(t,e,r,n){return{min:r,q1:Math.min(t,n),med:n,q3:Math.max(t,n),max:e}}e.exports=function(t,e){var r=t._fullLayout,s=i.getFromId(t,e.xaxis),l=i.getFromId(t,e.yaxis),c=s.makeCalcdata(e,\"x\"),u=a(t,e,c,l,o);return u.length?(n.extendFlat(u[0].t,{num:r._numBoxes,dPos:n.distinctVals(c).minDiff/2,posLetter:\"x\",valLetter:\"y\"}),r._numBoxes++,u):[{t:{empty:!0}}]}},{\"../../lib\":696,\"../../plots/cartesian/axes\":744,\"../ohlc/calc\":992}],873:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../ohlc/ohlc_defaults\"),o=t(\"./attributes\");function s(t,e,r,n){var a=r(n+\".line.color\");r(n+\".line.width\",e.line.width),r(n+\".fillcolor\",i.addOpacity(a,.5))}e.exports=function(t,e,r,i){function l(r,i){return 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n=t(\"../../components/colorscale/defaults\"),i=t(\"./label_defaults\");e.exports=function(t,e,r,a,o){var s,l=r(\"contours.coloring\"),c=\"\";\"fill\"===l&&(s=r(\"contours.showlines\")),!1!==s&&(\"lines\"!==l&&(c=r(\"line.color\",\"#000\")),r(\"line.width\",.5),r(\"line.dash\")),\"none\"!==l&&(!0!==t.showlegend&&(e.showlegend=!1),e._dfltShowLegend=!1,n(t,e,a,r,{prefix:\"\",cLetter:\"z\"})),r(\"line.smoothing\"),i(r,a,c,o)}},{\"../../components/colorscale/defaults\":580,\"./label_defaults\":931}],938:[function(t,e,r){\"use strict\";var 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g={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",g);var v={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",v)}},{\"../../components/colorscale/defaults\":580,\"../../components/colorscale/has_colorscale\":584,\"../../lib\":696,\"../../plots/array_container_defaults\":740,\"../../plots/domain\":770,\"../parcoords/merge_length\":1015,\"./attributes\":1e3}],1004:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.colorbar={container:\"line\",min:\"cmin\",max:\"cmax\"},n.moduleType=\"trace\",n.name=\"parcats\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"noOpacity\"],n.meta={},e.exports=n},{\"./attributes\":1e3,\"./base_plot\":1001,\"./calc\":1002,\"./defaults\":1003,\"./plot\":1006}],1005:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plot_api/plot_api\"),a=t(\"../../components/fx\"),o=t(\"../../lib\"),s=t(\"../../components/drawing\"),l=t(\"tinycolor2\"),c=t(\"../../lib/svg_text_utils\");function u(t,e,r,i){var a=t.map(function(t,e,r){var n,i=r[0],a=e.margin\|\|{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l(s.x[1]-s.x[0])),f=Math.floor(c(s.y[1]-s.y[0])),h=s.x[0]l+a.l,p=e.height-s.y[1]e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:o.hoverinfo.split(\"+\");var g={key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};i.dimensions&&(R(g),D(g));return g}.bind(0,e,r)),l=i.selectAll(\"g.parcatslayer\").data([null]);l.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var u=l.selectAll(\"g.trace.parcats\").data(a,f),v=u.enter().append(\"g\").attr(\"class\",\"trace parcats\");u.attr(\"transform\",function(t){return\"translate(\"+t.x+\", \"+t.y+\")\"}),v.append(\"g\").attr(\"class\",\"paths\");var x=u.select(\"g.paths\").selectAll(\"path.path\").data(function(t){return t.paths},f);x.attr(\"fill\",function(t){return t.model.color});var w=x.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.model.color}).attr(\"fill-opacity\",0);y(w),x.attr(\"d\",function(t){return t.svgD}),w.empty()\|\|x.sort(p),x.exit().remove(),x.on(\"mouseover\",d).on(\"mouseout\",g).on(\"click\",m),v.append(\"g\").attr(\"class\",\"dimensions\");var k=u.select(\"g.dimensions\").selectAll(\"g.dimension\").data(function(t){return t.dimensions},f);k.enter().append(\"g\").attr(\"class\",\"dimension\"),k.attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),k.exit().remove();var M=k.selectAll(\"g.category\").data(function(t){return t.categories},f),A=M.enter().append(\"g\").attr(\"class\",\"category\");M.attr(\"transform\",function(t){return\"translate(0, \"+t.y+\")\"}),A.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),M.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",function(t){return t.width}).attr(\"height\",function(t){return t.height}),b(A);var z=M.selectAll(\"rect.bandrect\").data(function(t){return t.bands},f);z.each(function(){o.raiseToTop(this)}),z.attr(\"fill\",function(t){return t.color});var O=z.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.color}).attr(\"fill-opacity\",0);z.attr(\"fill\",function(t){return t.color}).attr(\"width\",function(t){return t.width}).attr(\"height\",function(t){return t.height}).attr(\"y\",function(t){return t.y}).attr(\"cursor\",function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"}),_(O),z.exit().remove(),A.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var I=e._fullLayout.paper_bgcolor;M.select(\"text.catlabel\").attr(\"text-anchor\",function(t){return h(t)?\"start\":\"end\"}).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",I+\" -1px 1px 2px, \"+I+\" 1px 1px 2px, \"+I+\" 1px -1px 2px, \"+I+\" -1px -1px 2px\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",function(t){return h(t)?t.width+5:-5}).attr(\"y\",function(t){return t.height/2}).text(function(t){return t.model.categoryLabel}).each(function(t){s.font(n.select(this),t.parcatsViewModel.categorylabelfont),c.convertToTspans(n.select(this),e)}),A.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"}).attr(\"x\",function(t){return t.width/2}).attr(\"y\",-5).text(function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null}).each(function(t){s.font(n.select(this),t.parcatsViewModel.labelfont)}),M.selectAll(\"rect.bandrect\").on(\"mouseover\",T).on(\"mouseout\",S),M.exit().remove(),k.call(n.behavior.drag().origin(function(t){return{x:t.x,y:0}}).on(\"dragstart\",E).on(\"drag\",C).on(\"dragend\",L)),u.each(function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")}),u.exit().remove()}function f(t){return t.key}function h(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function p(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function d(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){o.raiseToTop(this),x(n.select(this));var e=v(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var r,i,s,c=n.mouse(this)[0],u=t.parcatsViewModel.graphDiv,f=u._fullLayout,h=f._paperdiv.node().getBoundingClientRect(),p=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(s=0;s<t.leftXs.length-1;s++)if(t.leftXs[s]+t.dimWidths[s]-2<=c&&c<=t.leftXs[s+1]+2){var d=t.parcatsViewModel.dimensions[s],g=t.parcatsViewModel.dimensions[s+1];r=(d.x+d.width+g.x)/2,i=(t.topYs[s]+t.topYs[s+1]+t.height)/2;break}var m=t.parcatsViewModel.x+r,y=t.parcatsViewModel.y+i,b=l.mostReadable(t.model.color,[\"black\",\"white\"]),_=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&_.push([\"Count:\",t.model.count].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&_.push([\"P:\",(t.model.count/t.parcatsViewModel.model.count).toFixed(3)].join(\" \"));var w=_.join(\"<br>\"),k=n.mouse(u)[0];a.loneHover({x:m-h.left+p.left,y:y-h.top+p.top,text:w,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:b,idealAlign:k<m?\"right\":\"left\"},{container:f._hoverlayer.node(),outerContainer:f._paper.node(),gd:u})}}}function g(t){if(!t.parcatsViewModel.dragDimension&&(y(n.select(this)),a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(p),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=v(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event})}}function v(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function m(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=v(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event})}}function y(t){t.attr(\"fill\",function(t){return t.model.color}).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function x(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",function(t){return l.mostReadable(t.model.color,[\"black\",\"white\"])}).attr(\"stroke-width\",.3)}function b(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function _(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function w(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter(function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color})}function k(t,e,r){var i=n.select(t).datum().parcatsViewModel.graphDiv,a=n.select(t.parentNode).selectAll(\"rect.bandrect\"),o=[];a.each(function(t){w(t).each(function(t){Array.prototype.push.apply(o,v(t))})}),i.emit(e,{points:o,event:r})}function M(t,e,r){var i=n.select(t).datum(),a=i.parcatsViewModel.graphDiv,o=w(i),s=[];o.each(function(t){Array.prototype.push.apply(s,v(t))}),a.emit(e,{points:s,event:r})}function A(t,e){var r,i,a=n.select(e.parentNode).select(\"rect.catrect\"),o=a.node().getBoundingClientRect(),s=a.datum(),l=s.parcatsViewModel,c=l.model.dimensions[s.model.dimensionInd],u=o.top+o.height/2;l.dimensions.length>1&&c.displayInd===l.dimensions.length-1?(r=o.left,i=\"left\"):(r=o.left+o.width,i=\"right\");var f=[];-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&f.push([\"Count:\",s.model.count].join(\" \")),-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&f.push([\"P(\"+s.model.categoryLabel+\"):\",(s.model.count/s.parcatsViewModel.model.count).toFixed(3)].join(\" \"));var h=f.join(\"<br>\");return{x:r-t.left,y:u-t.top,text:h,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:i}}function T(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,s=i._paperdiv.node().getBoundingClientRect(),c=t.parcatsViewModel.hoveron;if(\"color\"===c?(!function(t){var e=n.select(t).datum(),r=w(e);x(r),r.each(function(){o.raiseToTop(this)}),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter(function(t){return t.color===e.color}).each(function(){o.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)})}(this),M(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each(function(t){var e=w(t);x(e),e.each(function(){o.raiseToTop(this)})}),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),k(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===c?e=A(s,this):\"color\"===c?e=function(t,e){var r,i,a=e.getBoundingClientRect(),o=n.select(e).datum(),s=o.categoryViewModel,c=s.parcatsViewModel,u=c.model.dimensions[s.model.dimensionInd],f=a.y+a.height/2;c.dimensions.length>1&&u.displayInd===c.dimensions.length-1?(r=a.left,i=\"left\"):(r=a.left+a.width,i=\"right\");var h=s.model.categoryLabel,p=o.parcatsViewModel.model.count,d=0;o.categoryViewModel.bands.forEach(function(t){t.color===o.color&&(d+=t.count)});var g=s.model.count,v=0;c.pathSelection.each(function(t){t.model.color===o.color&&(v+=t.model.count)});var m=[];if(-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&m.push([\"Count:\",d].join(\" \")),-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")){var y=\"P(color \\u2229 \"+h+\"): \"+(d/p).toFixed(3);m.push(y);var x=\"P(\"+h+\" \| color): \"+(d/v).toFixed(3);m.push(x);var b=\"P(color \| \"+h+\"): \"+(d/g).toFixed(3);m.push(b)}var _=m.join(\"<br>\"),w=l.mostReadable(o.color,[\"black\",\"white\"]);return{x:r-t.left,y:f-t.top,text:_,color:o.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:w,fontSize:10,idealAlign:i}}(s,this):\"dimension\"===c&&(e=function(t,e){var r=[];return n.select(e.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each(function(){r.push(A(t,this))}),r}(s,this)),e&&a.multiHovers(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function S(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(y(e.pathSelection),b(e.dimensionSelection.selectAll(\"g.category\")),_(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),a.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(p),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?M(this,\"plotly_unhover\",n.event):k(this,\"plotly_unhover\",n.event)}}function E(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each(function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map(function(t){return t.displayInd}),e.model.dragY=e.y,o.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each(function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)}))}),t.parcatsViewModel.dragDimension=t,a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function C(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd\|\|\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}R(t.parcatsViewModel),D(t.parcatsViewModel),I(t.parcatsViewModel),O(t.parcatsViewModel)}}function L(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),a=t.parcatsViewModel.model.dimensions.map(function(t){return t.displayInd}),o=t.initialDragDimensionDisplayInds.some(function(t,e){return t!==a[e]});o&&a.forEach(function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r});var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map(function(t){return t.displayInd});if(s=t.initialDragCategoryDisplayInds.some(function(t,e){return t!==l[e]})){var c=t.model.categories.slice().sort(function(t,e){return t.displayInd-e.displayInd}),u=c.map(function(t){return t.categoryValue}),f=c.map(function(t){return t.categoryLabel});e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):k(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,R(t.parcatsViewModel),D(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each(function(){I(t.parcatsViewModel,!0),O(t.parcatsViewModel,!0)}).each(\"end\",function(){(o\|\|s)&&i.restyle(t.parcatsViewModel.graphDiv,e,[r])})}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function O(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data(function(t){return t.paths},f),(r=t.pathSelection,e?r.transition():r).attr(\"d\",function(t){return t.svgD})}function I(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data(function(t){return t.dimensions},f);var i=t.dimensionSelection.selectAll(\"g.category\").data(function(t){return t.categories},f);r(t.dimensionSelection).attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),r(i).attr(\"transform\",function(t){return\"translate(0, \"+t.y+\")\"}),i.select(\".dimlabel\").text(function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null}),i.select(\".catlabel\").attr(\"text-anchor\",function(t){return h(t)?\"start\":\"end\"}).attr(\"x\",function(t){return h(t)?t.width+5:-5}).each(function(t){var e,r;h(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)});var a=i.selectAll(\"rect.bandrect\").data(function(t){return t.bands},f),s=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",function(t){return t.color}).attr(\"fill-opacity\",0);a.attr(\"fill\",function(t){return t.color}).attr(\"width\",function(t){return t.width}).attr(\"height\",function(t){return t.height}).attr(\"y\",function(t){return t.y}),_(s),a.each(function(){o.raiseToTop(this)}),a.exit().remove()}function P(t,e,r,i,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=n.interpolateNumber(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+i+\" \",u+=\"l -\"+r[r.length-1]+\",0 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../components/colorbar/attributes\"),a=t(\"../../plots/cartesian/layout_attributes\"),o=t(\"../../plots/font_attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:s({name:\"parcoords\",trace:!0,editType:\"calc\"}),hoverlabel:void 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a=Math.max(0,-r.newExtent[0]),o=Math.max(0,r.newExtent[1]-1);r.newExtent[0]+=a,r.newExtent[1]-=o,r.grabbingBar&&(r.newExtent[1]+=a,r.newExtent[0]-=o),t.brush.filterSpecified=!0,r.extent=r.stayingIntervals.concat([r.newExtent]),r.brushCallback(t),_(this.parentNode)}).on(\"dragend\",function(t){i.event.sourceEvent.stopPropagation();var e=t.brush,r=e.filter,n=e.svgBrush,a=n.grabbingBar;if(n.grabbingBar=!1,n.grabLocation=void 0,t.parent.inBrushDrag=!1,x(),!n.wasDragged)return n.wasDragged=void 0,n.clickableOrdinalRange?e.filterSpecified&&t.multiselect?n.extent.push(n.clickableOrdinalRange):(n.extent=[n.clickableOrdinalRange],e.filterSpecified=!0):a?(n.extent=n.stayingIntervals,0===n.extent.length&&A(e)):A(e),n.brushCallback(t),_(this.parentNode),void n.brushEndCallback(e.filterSpecified?r.getConsolidated():[]);var o=function(){r.set(r.getConsolidated())};if(t.ordinal){var s=t.unitTickvals;s[s.length-1]<s[0]&&s.reverse(),n.newExtent=[h(s,n.newExtent[0],n.stayingIntervals),p(s,n.newExtent[1],n.stayingIntervals)];var l=n.newExtent[1]>n.newExtent[0];n.extent=n.stayingIntervals.concat(l?[n.newExtent]:[]),n.extent.length\|\|A(e),n.brushCallback(t),l?_(this.parentNode,o):(o(),_(this.parentNode))}else o();n.brushEndCallback(e.filterSpecified?r.getConsolidated():[])}))}function M(t,e){return t[0]-e[0]}function A(t){t.filterSpecified=!1,t.svgBrush.extent=[[0,1]]}function T(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){r=n.map(function(t){return t.slice().sort(s)}).sort(M),t=T(r),e=r.reduce(function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]},[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return 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r=t.selectAll(\".highlight-shadow\").data(o);r.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",n.bar.strokeColor).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),r.attr(\"y1\",function(t){return t.height}).call(b);var i=t.selectAll(\".highlight\").data(o);i.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",function(t){return t.height}).call(b)}(e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map(function(t){return t.sort(s)}),t=e.multiselect?T(t.sort(M)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map(function(t){var e=[h(r,t[0],[]),p(r,t[1],[])];if(e[1]>e[0])return e}).filter(function(t){return t})).length)return}return 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t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.width,height:this.height})}),window.setTimeout(function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")},60)}},{\"../../constants/xmlns_namespaces\":674,\"../../plots/get_data\":781,\"./plot\":1017,d3:148}],1010:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/has_colorscale\"),i=t(\"../../components/colorscale/calc\"),a=t(\"../../lib\"),o=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r=!!e.line.colorscale&&a.isArrayOrTypedArray(e.line.color),s=r?e.line.color:function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),l=r?e.line.colorscale:[[0,e.line.color],[1,e.line.color]];return 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strict\";var n=t(\"../../lib\"),i=t(\"../../components/colorscale/has_colorscale\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"../../plots/domain\").defaults,s=t(\"../../plots/array_container_defaults\"),l=t(\"./attributes\"),c=t(\"./axisbrush\"),u=t(\"./constants\").maxDimensionCount,f=t(\"./merge_length\");function h(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r(\"values\"),a=r(\"visible\");if(i&&i.length\|\|(a=e.visible=!1),a){r(\"label\"),r(\"tickvals\"),r(\"ticktext\"),r(\"tickformat\"),r(\"range\"),r(\"multiselect\");var o=r(\"constraintrange\");o&&(e.constraintrange=c.cleanRanges(o,e))}}e.exports=function(t,e,r,c){function p(r,i){return n.coerce(t,e,l,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>u&&(n.log(\"parcoords traces support up to \"+u+\" dimensions at the moment\"),d.splice(u));var g=s(t,e,{name:\"dimensions\",handleItemDefaults:h}),v=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,c,p);o(e,c,p),Array.isArray(g)&&g.length\|\|(e.visible=!1),f(e,g,\"values\",v);var m={family:c.font.family,size:Math.round(c.font.size/1.2),color:c.font.color};n.coerceFont(p,\"labelfont\",m),n.coerceFont(p,\"tickfont\",m),n.coerceFont(p,\"rangefont\",m)}},{\"../../components/colorscale/defaults\":580,\"../../components/colorscale/has_colorscale\":584,\"../../lib\":696,\"../../plots/array_container_defaults\":740,\"../../plots/domain\":770,\"./attributes\":1007,\"./axisbrush\":1008,\"./constants\":1011,\"./merge_length\":1015}],1013:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.colorbar={container:\"line\",min:\"cmin\",max:\"cmax\"},n.moduleType=\"trace\",n.name=\"parcoords\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"gl\",\"regl\",\"noOpacity\"],n.meta={},e.exports=n},{\"./attributes\":1007,\"./base_plot\":1009,\"./calc\":1010,\"./defaults\":1012,\"./plot\":1017}],1014:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 p0, p1, p2, p3,\\n p4, p5, p6, p7,\\n p8, p9, pa, pb,\\n pc, pd, pe;\\n\\nattribute vec4 pf;\\n\\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution,\\n viewBoxPosition,\\n viewBoxSize;\\n\\nuniform sampler2D palette;\\nuniform sampler2D mask;\\nuniform float maskHeight;\\n\\nuniform vec2 colorClamp;\\n\\nvarying vec4 fragColor;\\n\\nvec4 unit_1 = vec4(1, 1, 1, 1);\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) unit_1, unit_1);\\n}\\n\\nfloat axisY(\\n float x,\\n mat4 d[4],\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\\n ) {\\n\\n float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\\n float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\\n return y1 * (1.0 - x) + y2 * x;\\n}\\n\\nconst int bitsPerByte = 8;\\n\\nint mod2(int a) {\\n return a - 2 * (a / 2);\\n}\\n\\nint mod8(int a) {\\n return a - 8 * (a / 8);\\n}\\n\\nvec4 zero = vec4(0, 0, 0, 0);\\nvec4 unit_0 = vec4(1, 1, 1, 1);\\nvec2 xyProjection = vec2(1, 1);\\n\\nmat4 mclamp(mat4 m, mat4 lo, mat4 hi) {\\n return mat4(clamp(m[0], lo[0], hi[0]),\\n clamp(m[1], lo[1], hi[1]),\\n clamp(m[2], lo[2], hi[2]),\\n clamp(m[3], lo[3], hi[3]));\\n}\\n\\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\\n return mclamp(p, lo, hi) == p;\\n}\\n\\nbool withinBoundingBox(\\n mat4 d[4],\\n mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD\\n ) {\\n\\n return mshow(d[0], loA, hiA) &&\\n mshow(d[1], loB, hiB) &&\\n mshow(d[2], loC, hiC) &&\\n mshow(d[3], loD, hiD);\\n}\\n\\nbool withinRasterMask(mat4 d[4], sampler2D mask, float height) {\\n bool result = true;\\n int bitInByteStepper;\\n float valY, valueY, scaleX;\\n int hit, bitmask, valX;\\n for(int i = 0; i < 4; i++) {\\n for(int j = 0; j < 4; j++) {\\n for(int k = 0; k < 4; k++) {\\n bitInByteStepper = mod8(j * 4 + k);\\n valX = i * 2 + j / 2;\\n valY = d[i][j][k];\\n valueY = valY * (height - 1.0) + 0.5;\\n scaleX = (float(valX) + 0.5) / 8.0;\\n hit = int(texture2D(mask, vec2(scaleX, (valueY + 0.5) / height))[3] * 255.0) / int(pow(2.0, float(bitInByteStepper)));\\n result = result && mod2(hit) == 1;\\n }\\n }\\n }\\n return result;\\n}\\n\\nvec4 position(\\n float depth,\\n vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\\n mat4 dims[4],\\n float signum,\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D,\\n mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD,\\n sampler2D mask, float maskHeight\\n ) {\\n\\n float x = 0.5 * signum + 0.5;\\n float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\\n\\n float show = float(\\n withinBoundingBox(dims, loA, hiA, loB, hiB, loC, hiC, loD, hiD)\\n && withinRasterMask(dims, mask, maskHeight)\\n );\\n\\n vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\\n float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n return vec4(\\n xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n depthOrHide,\\n 1.0\\n );\\n}\\n\\nvoid main() {\\n\\n float prominence = abs(pf[3]);\\n\\n mat4 p[4];\\n p[0] = mat4(p0, p1, p2, p3);\\n p[1] = mat4(p4, p5, p6, p7);\\n p[2] = mat4(p8, p9, pa, pb);\\n p[3] = mat4(pc, pd, pe, abs(pf));\\n\\n gl_Position = position(\\n 1.0 - prominence,\\n resolution, viewBoxPosition, viewBoxSize,\\n p,\\n sign(pf[3]),\\n dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n loA, hiA, loB, hiB, loC, hiC, loD, hiD,\\n mask, maskHeight\\n );\\n\\n float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\\n fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 p0, p1, p2, p3,\\n p4, p5, p6, p7,\\n p8, p9, pa, pb,\\n pc, pd, pe;\\n\\nattribute vec4 pf;\\n\\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D;\\n\\nuniform vec2 resolution,\\n viewBoxPosition,\\n viewBoxSize;\\n\\nuniform sampler2D palette;\\n\\nuniform vec2 colorClamp;\\n\\nvarying vec4 fragColor;\\n\\nvec2 xyProjection = vec2(1, 1);\\n\\nvec4 unit = vec4(1, 1, 1, 1);\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) * unit, unit);\\n}\\n\\nfloat axisY(\\n float x,\\n mat4 d[4],\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\\n ) {\\n\\n float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\\n float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\\n return y1 * (1.0 - x) + y2 * x;\\n}\\n\\nvec4 position(\\n float depth,\\n vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\\n mat4 dims[4],\\n float signum,\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\\n ) {\\n\\n float x = 0.5 * signum + 0.5;\\n float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\\n\\n vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\\n\\n return vec4(\\n xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n depth,\\n 1.0\\n );\\n}\\n\\nvoid main() {\\n\\n float prominence = abs(pf[3]);\\n\\n mat4 p[4];\\n p[0] = mat4(p0, p1, p2, p3);\\n p[1] = mat4(p4, p5, p6, p7);\\n p[2] = mat4(p8, p9, pa, pb);\\n p[3] = mat4(pc, pd, pe, abs(pf));\\n\\n gl_Position = position(\\n 1.0 - prominence,\\n resolution, viewBoxPosition, viewBoxSize,\\n p,\\n sign(pf[3]),\\n dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D\\n );\\n\\n float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\\n fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 p0, p1, p2, p3,\\n p4, p5, p6, p7,\\n p8, p9, pa, pb,\\n pc, pd, pe;\\n\\nattribute vec4 pf;\\n\\nuniform mat4 dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution,\\n viewBoxPosition,\\n viewBoxSize;\\n\\nuniform sampler2D mask;\\nuniform float maskHeight;\\n\\nuniform vec2 colorClamp;\\n\\nvarying vec4 fragColor;\\n\\nvec4 unit_1 = vec4(1, 1, 1, 1);\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) * unit_1, unit_1);\\n}\\n\\nfloat axisY(\\n float x,\\n mat4 d[4],\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D\\n ) {\\n\\n float y1 = val(d[0], dim1A) + val(d[1], dim1B) + val(d[2], dim1C) + val(d[3], dim1D);\\n float y2 = val(d[0], dim2A) + val(d[1], dim2B) + val(d[2], dim2C) + val(d[3], dim2D);\\n return y1 * (1.0 - x) + y2 * x;\\n}\\n\\nconst int bitsPerByte = 8;\\n\\nint mod2(int a) {\\n return a - 2 * (a / 2);\\n}\\n\\nint mod8(int a) {\\n return a - 8 * (a / 8);\\n}\\n\\nvec4 zero = vec4(0, 0, 0, 0);\\nvec4 unit_0 = vec4(1, 1, 1, 1);\\nvec2 xyProjection = vec2(1, 1);\\n\\nmat4 mclamp(mat4 m, mat4 lo, mat4 hi) {\\n return mat4(clamp(m[0], lo[0], hi[0]),\\n clamp(m[1], lo[1], hi[1]),\\n clamp(m[2], lo[2], hi[2]),\\n clamp(m[3], lo[3], hi[3]));\\n}\\n\\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\\n return mclamp(p, lo, hi) == p;\\n}\\n\\nbool withinBoundingBox(\\n mat4 d[4],\\n mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD\\n ) {\\n\\n return mshow(d[0], loA, hiA) &&\\n mshow(d[1], loB, hiB) &&\\n mshow(d[2], loC, hiC) &&\\n mshow(d[3], loD, hiD);\\n}\\n\\nbool withinRasterMask(mat4 d[4], sampler2D mask, float height) {\\n bool result = true;\\n int bitInByteStepper;\\n float valY, valueY, scaleX;\\n int hit, bitmask, valX;\\n for(int i = 0; i < 4; i++) {\\n for(int j = 0; j < 4; j++) {\\n for(int k = 0; k < 4; k++) {\\n bitInByteStepper = mod8(j * 4 + k);\\n valX = i * 2 + j / 2;\\n valY = d[i][j][k];\\n valueY = valY * (height - 1.0) + 0.5;\\n scaleX = (float(valX) + 0.5) / 8.0;\\n hit = int(texture2D(mask, vec2(scaleX, (valueY + 0.5) / height))[3] * 255.0) / int(pow(2.0, float(bitInByteStepper)));\\n result = result && mod2(hit) == 1;\\n }\\n }\\n }\\n return result;\\n}\\n\\nvec4 position(\\n float depth,\\n vec2 resolution, vec2 viewBoxPosition, vec2 viewBoxSize,\\n mat4 dims[4],\\n float signum,\\n mat4 dim1A, mat4 dim2A, mat4 dim1B, mat4 dim2B, mat4 dim1C, mat4 dim2C, mat4 dim1D, mat4 dim2D,\\n mat4 loA, mat4 hiA, mat4 loB, mat4 hiB, mat4 loC, mat4 hiC, mat4 loD, mat4 hiD,\\n sampler2D mask, float maskHeight\\n ) {\\n\\n float x = 0.5 * signum + 0.5;\\n float y = axisY(x, dims, dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D);\\n\\n float show = float(\\n withinBoundingBox(dims, loA, hiA, loB, hiB, loC, hiC, loD, hiD)\\n && withinRasterMask(dims, mask, maskHeight)\\n );\\n\\n vec2 viewBoxXY = viewBoxPosition + viewBoxSize * vec2(x, y);\\n float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n return vec4(\\n xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n depthOrHide,\\n 1.0\\n );\\n}\\n\\nvoid main() {\\n\\n float prominence = abs(pf[3]);\\n\\n mat4 p[4];\\n p[0] = mat4(p0, p1, p2, p3);\\n p[1] = mat4(p4, p5, p6, p7);\\n p[2] = mat4(p8, p9, pa, pb);\\n p[3] = mat4(pc, pd, pe, abs(pf));\\n\\n gl_Position = position(\\n 1.0 - prominence,\\n resolution, viewBoxPosition, viewBoxSize,\\n p,\\n sign(pf[3]),\\n dim1A, dim2A, dim1B, dim2B, dim1C, dim2C, dim1D, dim2D,\\n loA, hiA, loB, hiB, loC, hiC, loD, hiD,\\n mask, maskHeight\\n );\\n\\n fragColor = vec4(pf.rgb, 1.0);\\n}\\n\"]),s=n([\"precision lowp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\\n\"]),l=t(\"../../lib\"),c=1e-6,u=1e-7,f=2048,h=64,p=2,d=4,g=8,v=h/g,m=[119,119,119],y=new Uint8Array(4),x=new Uint8Array(4),b={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function _(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function w(t,e,r,n,i,a){var o=a.key;r.drawCompleted\|\|(!function(t){t.read({x:0,y:0,width:1,height:1,data:y})}(t),r.drawCompleted=!0),function s(l){var c;c=Math.min(n,i-ln),a.offset=pln,a.count=pc,0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete 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O(t){r=t.model,n=t.viewModel,p=n.dimensions.slice(),d=p[0]?p[0].values.length:0;var e=r.lines,i=T?e.color.map(function(t,r){return r/e.color.length}):e.color,a=Math.max(1/255,Math.pow(1/i.length,1/3)),o=function(t,e,r){for(var n,i=e.length,a=[],o=0;o<t;o++)for(var s=0;s<h;s++)a.push(s<i?e[s].paddedUnitValues[o]:s===h-1?(n=r[o],Math.max(c,Math.min(1-c,n))):s>=h-4?k(o,h-2-s):.5);return a}(d,p,i);!function(t,e,r){for(var n=0;n<16;n++)t[\"p\"+n.toString(16)](M(e,r,n))}(C,d,o),L=S.texture(l.extendFlat({data:function(t,e,r){for(var n=[],i=0;i<256;i++){var a=t(i/255);n.push((e?m:a).concat(r))}return n}(r.unitToColor,A,Math.round(255(A?a:1)))},b))}var I=[0,1];var P=[];function D(t,e,n,i,a,o,s,c,u,f,h){var p,d,g,v,m=[t,e],y=[0,1].map(function(){return[0,1,2,3].map(function(){return new Float32Array(16)})});for(p=0;p<2;p++)for(v=m[p],d=0;d<4;d++)for(g=0;g<16;g++)y[p][d][g]=g+16d===v?1:0;var x=r.lines.canvasOverdrag,b=r.domain,_=r.canvasWidth,w=r.canvasHeight;return l.extendFlat({key:s,resolution:[_,w],viewBoxPosition:[n+x,i],viewBoxSize:[a,o],i:t,ii:e,dim1A:y[0][0],dim1B:y[0][1],dim1C:y[0][2],dim1D:y[0][3],dim2A:y[1][0],dim2B:y[1][1],dim2C:y[1][2],dim2D:y[1][3],colorClamp:I,scissorX:(c===u?0:n+x)+(r.pad.l-x)+r.layoutWidthb.x[0],scissorWidth:(c===f?_-n+x:a+.5)+(c===u?n+x:0),scissorY:i+r.pad.b+r.layoutHeightb.y[0],scissorHeight:o,viewportX:r.pad.l-x+r.layoutWidthb.x[0],viewportY:r.pad.b+r.layoutHeightb.y[0],viewportWidth:_,viewportHeight:w},h)}return{setColorDomain:function(t){I[0]=t[0],I[1]=t[1]},render:function(t,e,n){var i,a,o,s=t.length,l=1/0,c=-1/0;for(i=0;i<s;i++)t[i].dim2.canvasX>c&&(c=t[i].dim2.canvasX,o=i),t[i].dim1.canvasX<l&&(l=t[i].dim1.canvasX,a=i);0===s&&_(S,0,0,r.canvasWidth,r.canvasHeight);var h=A?{}:function(){var t,e,r,n=[0,1].map(function(){return[0,1,2,3].map(function(){return new Float32Array(16)})});for(t=0;t<2;t++)for(e=0;e<4;e++)for(r=0;r<16;r++){var i,a=r+16e;i=a<p.length?p[a].brush.filter.getBounds()[t]:t,n[t][e][r]=i+(2t-1)u}function o(t,e){var r=f-1;return[Math.max(0,Math.floor(e[0]r)),Math.min(r,Math.ceil(e[1]r))]}for(var s=Array.apply(null,new Array(fv)).map(function(){return 255}),l=0;l<p.length;l++){var c=l%g,h=(l-c)/g,d=Math.pow(2,c),m=p[l],x=m.brush.filter.get();if(!(x.length<2))for(var b=o(0,x[0])[1],_=1;_<x.length;_++){for(var w=o(0,x[_]),k=b+1;k<w[0];k++)s[kv+h]&=~d;b=Math.max(b,w[1])}}var M={shape:[v,f],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:s};return y?y(M):y=S.texture(M),{maskTexture:y,maskHeight:f,loA:n[0][0],loB:n[0][1],loC:n[0][2],loD:n[0][3],hiA:n[1][0],hiB:n[1][1],hiC:n[1][2],hiD:n[1][3]}}();for(i=0;i<s;i++){var m=t[i],x=m.dim1,b=x.crossfilterDimensionIndex,k=m.canvasX,M=m.canvasY,T=m.dim2.crossfilterDimensionIndex,C=m.panelSizeX,L=m.panelSizeY,O=k+C;if(e\|\|!P[b]\|\|P[b][0]!==k\|\|P[b][1]!==O){P[b]=[k,O];var I=D(b,T,k,M,C,L,x.crossfilterDimensionIndex,i,a,o,h);E.clearOnly=n,w(S,z,E,e?r.lines.blockLineCount:d,d,I)}}},readPixel:function(t,e){return S.read({x:t,y:e,width:1,height:1,data:x}),x},readPixels:function(t,e,r,n){var i=new Uint8Array(4rn);return S.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",L.destroy(),y&&y.destroy(),C)C[e].destroy()},update:O}}},{\"../../lib\":696,glslify:392}],1015:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){var i,a;for(n\|\|(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],1016:[function(t,e,r){\"use strict\";var n=t(\"./lines\"),i=t(\"./constants\"),a=t(\"../../lib\"),o=t(\"d3\"),s=t(\"../../components/drawing\"),l=t(\"../../lib/gup\"),c=l.keyFun,u=l.repeat,f=l.unwrap,h=t(\"./axisbrush\");function p(t){return!(\"visible\"in t)\|\|t.visible}function d(t){var e=t.range?t.range[0]:a.aggNums(Math.min,null,t.values,t._length),r=t.range?t.range[1]:a.aggNums(Math.max,null,t.values,t._length);return!isNaN(e)&&isFinite(e)\|\|(e=0),!isNaN(r)&&isFinite(r)\|\|(r=0),e===r&&(0===e?(e-=1,r+=1):(e=.9,r=1.1)),[e,r]}function g(t){return t.dimensions.some(function(t){return t.brush.filterSpecified})}function v(t,e,r){var n=f(e),s=n.trace,l=n.lineColor,c=n.cscale,u=s.line,h=s.domain,g=s.dimensions,v=t.width,m=s.labelfont,y=s.tickfont,x=s.rangefont,b=a.extendDeepNoArrays({},u,{color:l.map(o.scale.linear().domain(d({values:l,range:[u.cmin,u.cmax],_length:s._length}))),blockLineCount:i.blockLineCount,canvasOverdrag:i.overdragi.canvasPixelRatio}),_=Math.floor(v(h.x[1]-h.x[0])),w=Math.floor(t.height(h.y[1]-h.y[0])),k=t.margin\|\|{l:80,r:80,t:100,b:80},M=_,A=w;return{key:r,colCount:g.filter(p).length,dimensions:g,tickDistance:i.tickDistance,unitToColor:function(t){var e=t.map(function(t){return t[0]}),r=t.map(function(t){return o.rgb(t[1])}),n=\"rgb\".split(\"\").map(function(t){return o.scale.linear().clamp(!0).domain(e).range(r.map((n=t,function(t){return t[n]})));var n});return function(t){return n.map(function(e){return e(t)})}}(c),lines:b,labelFont:m,tickFont:y,rangeFont:x,layoutWidth:v,layoutHeight:t.height,domain:h,translateX:h.x[0]v,translateY:t.height-h.y[1]t.height,pad:k,canvasWidth:Mi.canvasPixelRatio+2b.canvasOverdrag,canvasHeight:Ai.canvasPixelRatio,width:M,height:A,canvasPixelRatio:i.canvasPixelRatio}}function m(t,e,r){var n=r.width,s=r.height,l=r.dimensions,c=r.canvasPixelRatio,u=function(t){return nt/Math.max(1,r.colCount-1)},f=i.verticalPadding/s,v=function(t,e){return o.scale.linear().range([e,t-e])}(s,i.verticalPadding),m={key:r.key,xScale:u,model:r,inBrushDrag:!1},y={};return m.dimensions=l.filter(p).map(function(n,l){var p=function(t,e){return o.scale.linear().domain(d(t)).range([e,1-e])}(n,f),x=y[n.label];y[n.label]=(x\|\|0)+1;var b=n.label+(x?\"__\"+x:\"\"),_=n.constraintrange,w=_&&_.length;w&&!Array.isArray(_[0])&&(_=[_]);var k=w?_.map(function(t){return t.map(p)}):[[0,1]],M=n.values;M.length>n._length&&(M=M.slice(0,n._length));var A,T=n.tickvals;function S(t,e){return{val:t,text:A[e]}}function E(t,e){return t.val-e.val}if(Array.isArray(T)&&T.length){A=n.ticktext,Array.isArray(A)&&A.length?A.length>T.length?A=A.slice(0,T.length):T.length>A.length&&(T=T.slice(0,A.length)):A=T.map(o.format(n.tickformat));for(var C=1;C<T.length;C++)if(T[C]<T[C-1]){for(var L=T.map(S).sort(E),z=0;z<T.length;z++)T[z]=L[z].val,A[z]=L[z].text;break}}else T=void 0;return{key:b,label:n.label,tickFormat:n.tickformat,tickvals:T,ticktext:A,ordinal:!!T,multiselect:n.multiselect,xIndex:l,crossfilterDimensionIndex:l,visibleIndex:n._index,height:s,values:M,paddedUnitValues:M.map(p),unitTickvals:T&&T.map(p),xScale:u,x:u(l),canvasX:u(l)c,unitToPaddedPx:v,domainScale:function(t,e,r,n,i){var a,s,l=d(r);return n?o.scale.ordinal().domain(n.map((a=o.format(r.tickformat),s=i,s?function(t,e){var r=s[e];return null==r?a(t):r}:a))).range(n.map(function(r){var n=(r-l[0])/(l[1]-l[0]);return t-e+n(2e-t)})):o.scale.linear().domain(l).range([t-e,e])}(s,i.verticalPadding,n,T,A),ordinalScale:function(t){if(t.tickvals){var e=d(t);return o.scale.ordinal().domain(t.tickvals).range(t.tickvals.map(function(t){return(t-e[0])/(e[1]-e[0])}))}}(n),parent:m,model:r,brush:h.makeBrush(t,w,k,function(){t.linePickActive(!1)},function(){var e=m;e.focusLayer&&e.focusLayer.render(e.panels,!0);var r=g(e);!t.contextShown()&&r?(e.contextLayer&&e.contextLayer.render(e.panels,!0),t.contextShown(!0)):t.contextShown()&&!r&&(e.contextLayer&&e.contextLayer.render(e.panels,!0,!0),t.contextShown(!1))},function(r){var i=m;if(i.focusLayer.render(i.panels,!0),i.pickLayer&&i.pickLayer.render(i.panels,!0),t.linePickActive(!0),e&&e.filterChanged){var o=p.invert,s=r.map(function(t){return t.map(o).sort(a.sorterAsc)}).sort(function(t,e){return t[0]-e[0]});e.filterChanged(i.key,n._index,s)}})}}),m}function y(t){t.classed(i.cn.axisExtentText,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"default\").style(\"user-select\",\"none\")}e.exports=function(t,e,r,l,p,d){var x,b,_=(x=!0,b=!1,{linePickActive:function(t){return arguments.length?x=!!t:x},contextShown:function(t){return arguments.length?b=!!t:b}}),w=l.filter(function(t){return f(t).trace.visible}).map(v.bind(0,p)).map(m.bind(0,_,d));r.each(function(t,e){return a.extendFlat(t,w[e])});var k=r.selectAll(\".gl-canvas\").each(function(t){t.viewModel=w[0],t.model=t.viewModel?t.viewModel.model:null}),M=null;k.filter(function(t){return t.pick}).style(\"pointer-events\",\"auto\").on(\"mousemove\",function(t){if(_.linePickActive()&&t.lineLayer&&d&&d.hover){var e=o.event,r=this.width,n=this.height,i=o.mouse(this),a=i[0],s=i[1];if(a<0\|\|s<0\|\|a>=r\|\|s>=n)return;var l=t.lineLayer.readPixel(a,n-1-s),c=0!==l[3],u=c?l[2]+256(l[1]+256l[0]):null,f={x:a,y:s,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:u};u!==M&&(c?d.hover(f):d.unhover&&d.unhover(f),M=u)}}),k.style(\"opacity\",function(t){return t.pick?.01:1}),e.style(\"background\",\"rgba(255, 255, 255, 0)\");var A=e.selectAll(\".\"+i.cn.parcoords).data(w,c);A.exit().remove(),A.enter().append(\"g\").classed(i.cn.parcoords,!0).style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\"),A.attr(\"transform\",function(t){return\"translate(\"+t.model.translateX+\",\"+t.model.translateY+\")\"});var T=A.selectAll(\".\"+i.cn.parcoordsControlView).data(u,c);T.enter().append(\"g\").classed(i.cn.parcoordsControlView,!0),T.attr(\"transform\",function(t){return\"translate(\"+t.model.pad.l+\",\"+t.model.pad.t+\")\"});var S=T.selectAll(\".\"+i.cn.yAxis).data(function(t){return t.dimensions},c);function E(t,e){for(var r=e.panels\|\|(e.panels=[]),n=t.data(),i=n.length-1,a=0;a<i;a++){var o=r[a]\|\|(r[a]={}),s=n[a],l=n[a+1];o.dim1=s,o.dim2=l,o.canvasX=s.canvasX,o.panelSizeX=l.canvasX-s.canvasX,o.panelSizeY=e.model.canvasHeight,o.y=0,o.canvasY=0}}S.enter().append(\"g\").classed(i.cn.yAxis,!0),T.each(function(t){E(S,t)}),k.each(function(t){if(t.viewModel){!t.lineLayer\|\|d?t.lineLayer=n(this,t):t.lineLayer.update(t),(t.key\|\|0===t.key)&&(t.viewModel[t.key]=t.lineLayer);var e=!t.context\|\|d;t.lineLayer.render(t.viewModel.panels,e)}}),S.attr(\"transform\",function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"}),S.call(o.behavior.drag().origin(function(t){return t}).on(\"drag\",function(t){var e=t.parent;_.linePickActive(!1),t.x=Math.max(-i.overdrag,Math.min(t.model.width+i.overdrag,o.event.x)),t.canvasX=t.xt.model.canvasPixelRatio,S.sort(function(t,e){return t.x-e.x}).each(function(e,r){e.xIndex=r,e.x=t===e?e.x:e.xScale(e.xIndex),e.canvasX=e.xe.model.canvasPixelRatio}),E(S,e),S.filter(function(e){return 0!==Math.abs(t.xIndex-e.xIndex)}).attr(\"transform\",function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"}),o.select(this).attr(\"transform\",\"translate(\"+t.x+\", 0)\"),S.each(function(r,n,i){i===t.parent.key&&(e.dimensions[n]=r)}),e.contextLayer&&e.contextLayer.render(e.panels,!1,!g(e)),e.focusLayer.render&&e.focusLayer.render(e.panels)}).on(\"dragend\",function(t){var e=t.parent;t.x=t.xScale(t.xIndex),t.canvasX=t.xt.model.canvasPixelRatio,E(S,e),o.select(this).attr(\"transform\",function(t){return\"translate(\"+t.x+\", 0)\"}),e.contextLayer&&e.contextLayer.render(e.panels,!1,!g(e)),e.focusLayer&&e.focusLayer.render(e.panels),e.pickLayer&&e.pickLayer.render(e.panels,!0),_.linePickActive(!0),d&&d.axesMoved&&d.axesMoved(e.key,e.dimensions.map(function(t){return t.crossfilterDimensionIndex}))})),S.exit().remove();var C=S.selectAll(\".\"+i.cn.axisOverlays).data(u,c);C.enter().append(\"g\").classed(i.cn.axisOverlays,!0),C.selectAll(\".\"+i.cn.axis).remove();var L=C.selectAll(\".\"+i.cn.axis).data(u,c);L.enter().append(\"g\").classed(i.cn.axis,!0),L.each(function(t){var e=t.model.height/t.model.tickDistance,r=t.domainScale,n=r.domain();o.select(this).call(o.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(e,t.tickFormat).tickValues(t.ordinal?n:null).tickFormat(t.ordinal?function(t){return t}:null).scale(r)),s.font(L.selectAll(\"text\"),t.model.tickFont)}),L.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),L.selectAll(\"text\").style(\"text-shadow\",\"1px 1px 1px #fff, -1px -1px 1px #fff, 1px -1px 1px #fff, -1px 1px 1px #fff\").style(\"cursor\",\"default\").style(\"user-select\",\"none\");var z=C.selectAll(\".\"+i.cn.axisHeading).data(u,c);z.enter().append(\"g\").classed(i.cn.axisHeading,!0);var O=z.selectAll(\".\"+i.cn.axisTitle).data(u,c);O.enter().append(\"text\").classed(i.cn.axisTitle,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"ew-resize\").style(\"user-select\",\"none\").style(\"pointer-events\",\"auto\"),O.attr(\"transform\",\"translate(0,\"+-i.axisTitleOffset+\")\").text(function(t){return t.label}).each(function(t){s.font(o.select(this),t.model.labelFont)});var I=C.selectAll(\".\"+i.cn.axisExtent).data(u,c);I.enter().append(\"g\").classed(i.cn.axisExtent,!0);var P=I.selectAll(\".\"+i.cn.axisExtentTop).data(u,c);P.enter().append(\"g\").classed(i.cn.axisExtentTop,!0),P.attr(\"transform\",\"translate(0,\"+-i.axisExtentOffset+\")\");var D=P.selectAll(\".\"+i.cn.axisExtentTopText).data(u,c);function R(t,e){if(t.ordinal)return\"\";var r=t.domainScale.domain();return o.format(t.tickFormat)(r[e?r.length-1:0])}D.enter().append(\"text\").classed(i.cn.axisExtentTopText,!0).call(y),D.text(function(t){return R(t,!0)}).each(function(t){s.font(o.select(this),t.model.rangeFont)});var B=I.selectAll(\".\"+i.cn.axisExtentBottom).data(u,c);B.enter().append(\"g\").classed(i.cn.axisExtentBottom,!0),B.attr(\"transform\",function(t){return\"translate(0,\"+(t.model.height+i.axisExtentOffset)+\")\"});var F=B.selectAll(\".\"+i.cn.axisExtentBottomText).data(u,c);F.enter().append(\"text\").classed(i.cn.axisExtentBottomText,!0).attr(\"dy\",\"0.75em\").call(y),F.text(function(t){return R(t)}).each(function(t){s.font(o.select(this),t.model.rangeFont)}),h.ensureAxisBrush(C)}},{\"../../components/drawing\":595,\"../../lib\":696,\"../../lib/gup\":693,\"./axisbrush\":1008,\"./constants\":1011,\"./lines\":1014,d3:148}],1017:[function(t,e,r){\"use strict\";var n=t(\"./parcoords\"),i=t(\"../../lib/prepare_regl\");e.exports=function(t,e){var r=t._fullLayout,a=r._toppaper,o=r._paperdiv,s=r._glcontainer;if(i(t)){var l={},c={},u=r._size;e.forEach(function(e,r){l[r]=t.data[r].dimensions,c[r]=t.data[r].dimensions.slice()});n(o,a,s,e,{width:u.w,height:u.h,margin:{t:u.t,r:u.r,b:u.b,l:u.l}},{filterChanged:function(e,r,n){var i=c[e][r],a=n.map(function(t){return t.slice()});a.length?(1===a.length&&(a=a[0]),i.constraintrange=a,a=[a]):(delete i.constraintrange,a=null);var o={};o[\"dimensions[\"+r+\"].constraintrange\"]=a,t.emit(\"plotly_restyle\",[o,[e]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){function n(t){return!(\"visible\"in t)\|\|t.visible}function i(t,e,r){var n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}var a=function(t){return function(e,n){return i(r,t,e)-i(r,t,n)}}(c[e].filter(n));l[e].sort(a),c[e].filter(function(t){return!n(t)}).sort(function(t){return c[e].indexOf(t)}).forEach(function(t){l[e].splice(l[e].indexOf(t),1),l[e].splice(c[e].indexOf(t),0,t)}),t.emit(\"plotly_restyle\")}})}}},{\"../../lib/prepare_regl\":709,\"./parcoords\":1016}],1018:[function(t,e,r){\"use strict\";var n=t(\"../../components/color/attributes\"),i=t(\"../../plots/font_attributes\"),a=t(\"../../plots/attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=i({editType:\"calc\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:n.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:s({},a.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"calc\"},textfont:s({},l,{}),insidetextfont:s({},l,{}),outsidetextfont:s({},l,{}),title:{valType:\"string\",dflt:\"\",editType:\"calc\"},titleposition:{valType:\"enumerated\",values:[\"top 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i=n._has&&n._has(\"pie\"),a=e._has&&e._has(\"pie\");i&&!a&&n._pielayer.selectAll(\"g.trace\").remove()}},{\"../../plots/get_data\":781,\"../../registry\":827}],1020:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\").isArrayOrTypedArray,a=t(\"tinycolor2\"),o=t(\"../../components/color\"),s=t(\"./helpers\");r.calc=function(t,e){var r,l,c,u,f,h=e.values,p=i(h)&&h.length,d=e.labels,g=e.marker.colors\|\|[],v=[],m=t._fullLayout,y=m._piecolormap,x={},b=0,_=m.hiddenlabels\|\|[];if(e.dlabel)for(d=new Array(h.length),r=0;r<h.length;r++)d[r]=String(e.label0+re.dlabel);function w(t,e){return!!t&&(!!(t=a(t)).isValid()&&(t=o.addOpacity(t,t.getAlpha()),y[e]\|\|(y[e]=t),t))}var k=(p?h:d).length;for(r=0;r<k;r++){if(p){if(l=h[r],!n(l))continue;if((l=+l)<0)continue}else l=1;void 0!==(c=d[r])&&\"\"!==c\|\|(c=r);var M=x[c=String(c)];void 0===M?(x[c]=v.length,(u=-1!==_.indexOf(c))\|\|(b+=l),v.push({v:l,label:c,color:w(g[r],c),i:r,pts:[r],hidden:u})):((f=v[M]).v+=l,f.pts.push(r),f.hidden\|\|(b+=l),!1===f.color&&g[r]&&(f.color=w(g[r],c)))}if(e.sort&&v.sort(function(t,e){return e.v-t.v}),v[0]&&(v[0].vTotal=b),e.textinfo&&\"none\"!==e.textinfo){var A,T=-1!==e.textinfo.indexOf(\"label\"),S=-1!==e.textinfo.indexOf(\"text\"),E=-1!==e.textinfo.indexOf(\"value\"),C=-1!==e.textinfo.indexOf(\"percent\"),L=m.separators;for(r=0;r<v.length;r++){if(f=v[r],A=T?[f.label]:[],S){var z=s.getFirstFilled(e.text,f.pts);z&&A.push(z)}E&&A.push(s.formatPieValue(f.v,L)),C&&A.push(s.formatPiePercent(f.v/b,L)),f.text=A.join(\"<br>\")}}return v},r.crossTraceCalc=function(t){var e=t._fullLayout,r=t.calcdata,n=e.piecolorway,i=e._piecolormap;e.extendpiecolors&&(n=function(t){var e,r=JSON.stringify(t),n=l[r];if(!n){for(n=t.slice(),e=0;e<t.length;e++)n.push(a(t[e]).lighten(20).toHexString());for(e=0;e<t.length;e++)n.push(a(t[e]).darken(20).toHexString());l[r]=n}return n}(n));var o,s,c,u,f=0;for(o=0;o<r.length;o++)if(\"pie\"===(c=r[o])[0].trace.type)for(s=0;s<c.length;s++)!1===(u=c[s]).color&&(i[u.label]?u.color=i[u.label]:(i[u.label]=u.color=n[f%n.length],f++))};var l={}},{\"../../components/color\":570,\"../../lib\":696,\"./helpers\":1023,\"fast-isnumeric\":214,tinycolor2:514}],1021:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults;e.exports=function(t,e,r,o){function s(r,a){return n.coerce(t,e,i,r,a)}var l,c=n.coerceFont,u=s(\"values\"),f=n.isArrayOrTypedArray(u),h=s(\"labels\");if(Array.isArray(h)?(l=h.length,f&&(l=Math.min(l,u.length))):f&&(l=u.length,s(\"label0\"),s(\"dlabel\")),l){e._length=l,s(\"marker.line.width\")&&s(\"marker.line.color\"),s(\"marker.colors\"),s(\"scalegroup\");var p=s(\"text\"),d=s(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\");if(s(\"hovertext\"),d&&\"none\"!==d){var g=s(\"textposition\"),v=Array.isArray(g)\|\|\"auto\"===g,m=v\|\|\"inside\"===g,y=v\|\|\"outside\"===g;if(m\|\|y){var x=c(s,\"textfont\",o.font);if(m){var b=n.extendFlat({},x);!(t.textfont&&t.textfont.color)&&delete b.color,c(s,\"insidetextfont\",b)}y&&c(s,\"outsidetextfont\",x)}}a(e,o,s);var _=s(\"hole\");if(s(\"title\")){var w=s(\"titleposition\",_?\"middle center\":\"top center\");_\|\|\"middle center\"!==w\|\|(e.titleposition=\"top center\"),c(s,\"titlefont\",o.font)}s(\"sort\"),s(\"direction\"),s(\"rotation\"),s(\"pull\")}else e.visible=!1}},{\"../../lib\":696,\"../../plots/domain\":770,\"./attributes\":1018}],1022:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),r}},{\"../../components/fx/helpers\":609}],1023:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");r.formatPiePercent=function(t,e){var r=(100t).toPrecision(3);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n\|\|0===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t\|\|void 0}},{\"../../lib\":696}],1024:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\");var i=t(\"./calc\");n.calc=i.calc,n.crossTraceCalc=i.crossTraceCalc,n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":1018,\"./base_plot\":1019,\"./calc\":1020,\"./defaults\":1021,\"./layout_attributes\":1025,\"./layout_defaults\":1026,\"./plot\":1027,\"./style\":1028,\"./style_one\":1029}],1025:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],1026:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":696,\"./layout_attributes\":1025}],1027:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/fx\"),a=t(\"../../components/color\"),o=t(\"../../components/drawing\"),s=t(\"../../lib\"),l=t(\"../../lib/svg_text_utils\"),c=t(\"./helpers\"),u=t(\"./event_data\");function f(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PIMath.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function h(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i=-1),n<0&&(a=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)(i>0?1:-1)/2,y:a/(1+rr/(nn)),outside:!0}}function p(t,e){var r=t.trace,n=e.h(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function d(t){var e,r=t.pull;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}e.exports=function(t,e){var r=t._fullLayout;!function(t,e){for(var r,n,i=0;i<t.length;i++)if(r=t[i][0],(n=r.trace).title){var a=o.tester.append(\"text\").attr(\"data-notex\",1).text(n.title).call(o.font,n.titlefont).call(l.convertToTspans,e),s=o.bBox(a.node(),!0);r.titleBox={width:s.width,height:s.height},a.remove()}}(e,t),function(t,e){var r,n,i,a,o,s,l,c,u,f=[];for(i=0;i<t.length;i++)o=t[i][0],s=o.trace,r=e.w(s.domain.x[1]-s.domain.x[0]),n=e.h(s.domain.y[1]-s.domain.y[0]),s.title&&\"middle center\"!==s.titleposition&&(n-=p(o,e)),l=d(s),o.r=Math.min(r,n)/(2+2l),o.cx=e.l+e.w(s.domain.x[1]+s.domain.x[0])/2,o.cy=e.t+e.h(1-s.domain.y[0])-n/2,s.title&&-1!==s.titleposition.indexOf(\"bottom\")&&(o.cy-=p(o,e)),s.scalegroup&&-1===f.indexOf(s.scalegroup)&&f.push(s.scalegroup);for(a=0;a<f.length;a++){for(u=1/0,c=f[a],i=0;i<t.length;i++)(o=t[i][0]).trace.scalegroup===c&&(u=Math.min(u,o.ro.r/o.vTotal));for(i=0;i<t.length;i++)(o=t[i][0]).trace.scalegroup===c&&(o.r=Math.sqrt(uo.vTotal))}}(e,r._size);var g=s.makeTraceGroups(r._pielayer,e,\"trace\").each(function(e){var g=n.select(this),v=e[0],m=v.trace;!function(t){var e,r,n,i=t[0],a=i.trace,o=a.rotationMath.PI/180,s=2Math.PI/i.vTotal,l=\"px0\",c=\"px1\";if(\"counterclockwise\"===a.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;o+=st[e].v,s=-1,l=\"px1\",c=\"px0\"}function u(t){return[i.rMath.sin(t),-i.rMath.cos(t)]}for(n=u(o),e=0;e<t.length;e++)(r=t[e]).hidden\|\|(r[l]=n,o+=sr.v/2,r.pxmid=u(o),r.midangle=o,o+=sr.v/2,n=u(o),r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0)}(e),g.attr(\"stroke-linejoin\",\"round\"),g.each(function(){var g=n.select(this).selectAll(\"g.slice\").data(e);g.enter().append(\"g\").classed(\"slice\",!0),g.exit().remove();var y=[[[],[]],[[],[]]],x=!1;g.each(function(e){if(e.hidden)n.select(this).selectAll(\"path,g\").remove();else{e.pointNumber=e.i,e.curveNumber=m.index,y[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var p=v.cx,d=v.cy,g=n.select(this),b=g.selectAll(\"path.surface\").data([e]),_=!1,w=!1;if(b.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),g.select(\"path.textline\").remove(),g.on(\"mouseover\",function(){var a=t._fullLayout,o=t._fullData[m.index];if(!t._dragging&&!1!==a.hovermode){var s=o.hoverinfo;if(Array.isArray(s)&&(s=i.castHoverinfo({hoverinfo:[c.castOption(s,e.pts)],_module:m._module},a,0)),\"all\"===s&&(s=\"label+text+value+percent+name\"),\"none\"!==s&&\"skip\"!==s&&s){var l=f(e,v),h=p+e.pxmid[0](1-l),g=d+e.pxmid[1](1-l),y=r.separators,x=[];if(-1!==s.indexOf(\"label\")&&x.push(e.label),-1!==s.indexOf(\"text\")){var b=c.castOption(o.hovertext\|\|o.text,e.pts);b&&x.push(b)}-1!==s.indexOf(\"value\")&&x.push(c.formatPieValue(e.v,y)),-1!==s.indexOf(\"percent\")&&x.push(c.formatPiePercent(e.v/v.vTotal,y));var k=m.hoverlabel,M=k.font;i.loneHover({x0:h-lv.r,x1:h+lv.r,y:g,text:x.join(\"<br>\"),name:-1!==s.indexOf(\"name\")?o.name:void 0,idealAlign:e.pxmid[0]<0?\"left\":\"right\",color:c.castOption(k.bgcolor,e.pts)\|\|e.color,borderColor:c.castOption(k.bordercolor,e.pts),fontFamily:c.castOption(M.family,e.pts),fontSize:c.castOption(M.size,e.pts),fontColor:c.castOption(M.color,e.pts)},{container:a._hoverlayer.node(),outerContainer:a._paper.node(),gd:t}),_=!0}t.emit(\"plotly_hover\",{points:[u(e,o)],event:n.event}),w=!0}}).on(\"mouseout\",function(r){var a=t._fullLayout,o=t._fullData[m.index];w&&(r.originalEvent=n.event,t.emit(\"plotly_unhover\",{points:[u(e,o)],event:n.event}),w=!1),_&&(i.loneUnhover(a._hoverlayer.node()),_=!1)}).on(\"click\",function(){var r=t._fullLayout,a=t._fullData[m.index];t._dragging\|\|!1===r.hovermode\|\|(t._hoverdata=[u(e,a)],i.click(t,n.event))}),m.pull){var k=+c.castOption(m.pull,e.pts)\|\|0;k>0&&(p+=ke.pxmid[0],d+=ke.pxmid[1])}e.cxFinal=p,e.cyFinal=d;var M=m.hole;if(e.v===v.vTotal){var A=\"M\"+(p+e.px0[0])+\",\"+(d+e.px0[1])+L(e.px0,e.pxmid,!0,1)+L(e.pxmid,e.px0,!0,1)+\"Z\";M?b.attr(\"d\",\"M\"+(p+Me.px0[0])+\",\"+(d+Me.px0[1])+L(e.px0,e.pxmid,!1,M)+L(e.pxmid,e.px0,!1,M)+\"Z\"+A):b.attr(\"d\",A)}else{var T=L(e.px0,e.px1,!0,1);if(M){var S=1-M;b.attr(\"d\",\"M\"+(p+Me.px1[0])+\",\"+(d+Me.px1[1])+L(e.px1,e.px0,!1,M)+\"l\"+Se.px0[0]+\",\"+Se.px0[1]+T+\"Z\")}else b.attr(\"d\",\"M\"+p+\",\"+d+\"l\"+e.px0[0]+\",\"+e.px0[1]+T+\"Z\")}var E=c.castOption(m.textposition,e.pts),C=g.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==E?[0]:[]);C.enter().append(\"g\").classed(\"slicetext\",!0),C.exit().remove(),C.each(function(){var r=s.ensureSingle(n.select(this),\"text\",\"\",function(t){t.attr(\"data-notex\",1)});r.text(e.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(o.font,\"outside\"===E?function(t,e,r){var n=c.castOption(t.outsidetextfont.color,e.pts)\|\|c.castOption(t.textfont.color,e.pts)\|\|r.color,i=c.castOption(t.outsidetextfont.family,e.pts)\|\|c.castOption(t.textfont.family,e.pts)\|\|r.family,a=c.castOption(t.outsidetextfont.size,e.pts)\|\|c.castOption(t.textfont.size,e.pts)\|\|r.size;return{color:n,family:i,size:a}}(m,e,t._fullLayout.font):function(t,e,r){var n=c.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=c.castOption(t._input.textfont.color,e.pts));var i=c.castOption(t.insidetextfont.family,e.pts)\|\|c.castOption(t.textfont.family,e.pts)\|\|r.family,o=c.castOption(t.insidetextfont.size,e.pts)\|\|c.castOption(t.textfont.size,e.pts)\|\|r.size;return{color:n\|\|a.contrast(e.color),family:i,size:o}}(m,e,t._fullLayout.font)).call(l.convertToTspans,t);var i,u=o.bBox(r.node());\"outside\"===E?i=h(u,e):(i=function(t,e,r){var n=Math.sqrt(t.widtht.width+t.heightt.height),i=t.width/t.height,a=Math.PIMath.min(e.v/r.vTotal,.5),o=1-r.trace.hole,s=f(e,r),l={scale:sr.r2/n,rCenter:1-s,rotate:0};if(l.scale>=1)return l;var c=i+1/(2Math.tan(a)),u=r.rMath.min(1/(Math.sqrt(cc+.5)+c),o/(Math.sqrt(ii+o/2)+i)),h={scale:2u/t.height,rCenter:Math.cos(u/r.r)-ui/r.r,rotate:(180/Math.PIe.midangle+720)%180-90},p=1/i,d=p+1/(2Math.tan(a)),g=r.rMath.min(1/(Math.sqrt(dd+.5)+d),o/(Math.sqrt(pp+o/2)+p)),v={scale:2g/t.width,rCenter:Math.cos(g/r.r)-g/i/r.r,rotate:(180/Math.PIe.midangle+810)%180-90},m=v.scale>h.scale?v:h;return l.scale<1&&m.scale>l.scale?m:l}(u,e,v),\"auto\"===E&&i.scale<1&&(r.call(o.font,m.outsidetextfont),m.outsidetextfont.family===m.insidetextfont.family&&m.outsidetextfont.size===m.insidetextfont.size\|\|(u=o.bBox(r.node())),i=h(u,e)));var g=p+e.pxmid[0]i.rCenter+(i.x\|\|0),y=d+e.pxmid[1]i.rCenter+(i.y\|\|0);i.outside&&(e.yLabelMin=y-u.height/2,e.yLabelMid=y,e.yLabelMax=y+u.height/2,e.labelExtraX=0,e.labelExtraY=0,x=!0),r.attr(\"transform\",\"translate(\"+g+\",\"+y+\")\"+(i.scale<1?\"scale(\"+i.scale+\")\":\"\")+(i.rotate?\"rotate(\"+i.rotate+\")\":\"\")+\"translate(\"+-(u.left+u.right)/2+\",\"+-(u.top+u.bottom)/2+\")\")})}function L(t,r,n,i){return\"a\"+iv.r+\",\"+iv.r+\" 0 \"+e.largeArc+(n?\" 1 \":\" 0 \")+i(r[0]-t[0])+\",\"+i(r[1]-t[1])}});var b=n.select(this).selectAll(\"g.titletext\").data(m.title?[0]:[]);b.enter().append(\"g\").classed(\"titletext\",!0),b.exit().remove(),b.each(function(){var e,i=s.ensureSingle(n.select(this),\"text\",\"\",function(t){t.attr(\"data-notex\",1)});i.text(m.title).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(o.font,m.titlefont).call(l.convertToTspans,t),e=\"middle center\"===m.titleposition?function(t){var e=Math.sqrt(t.titleBox.widtht.titleBox.width+t.titleBox.heightt.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.holet.r2/e,tx:0,ty:-t.titleBox.height/2+t.trace.titlefont.size}}(v):function(t,e){var r,n,i=1,a=1,o=t.trace,s={x:t.cx,y:t.cy},l={tx:0,ty:0};l.ty+=o.titlefont.size,n=d(o),-1!==o.titleposition.indexOf(\"top\")?(s.y-=(1+n)t.r,l.ty-=t.titleBox.height):-1!==o.titleposition.indexOf(\"bottom\")&&(s.y+=(1+n)t.r);-1!==o.titleposition.indexOf(\"left\")?(r=e.w(o.domain.x[1]-o.domain.x[0])/2+t.r,s.x-=(1+n)t.r,l.tx+=t.titleBox.width/2):-1!==o.titleposition.indexOf(\"center\")?r=e.w(o.domain.x[1]-o.domain.x[0]):-1!==o.titleposition.indexOf(\"right\")&&(r=e.w(o.domain.x[1]-o.domain.x[0])/2+t.r,s.x+=(1+n)t.r,l.tx-=t.titleBox.width/2);return 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n=t(\"gl-pointcloud2d\"),i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var 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n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;(e.exports=c({hoverinfo:l({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\"},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),node:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:.5,arrayOk:!0}},pad:{valType:\"number\",arrayOk:!1,min:0,dflt:20},thickness:{valType:\"number\",arrayOk:!1,min:1,dflt:20},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel},link:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0}},source:{valType:\"data_array\",dflt:[]},target:{valType:\"data_array\",dflt:[]},value:{valType:\"data_array\",dflt:[]},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel}},\"calc\",\"nested\")).transforms=void 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../components/color\"),o=t(\"tinycolor2\"),s=t(\"../../plots/domain\").defaults,l=t(\"../../components/fx/hoverlabel_defaults\"),c=t(\"../../plot_api/plot_template\");e.exports=function(t,e,r,u){function f(r,a){return n.coerce(t,e,i,r,a)}var h=n.extendDeep(u.hoverlabel,t.hoverlabel),p=t.node,d=c.newContainer(e,\"node\");function g(t,e){return n.coerce(p,d,i.node,t,e)}g(\"label\"),g(\"pad\"),g(\"thickness\"),g(\"line.color\"),g(\"line.width\"),g(\"hoverinfo\",t.hoverinfo),l(p,d,g,h);var v=u.colorway;g(\"color\",d.label.map(function(t,e){return a.addOpacity(function(t){return v[t%v.length]}(e),.8)}));var m=t.link,y=c.newContainer(e,\"link\");function x(t,e){return n.coerce(m,y,i.link,t,e)}x(\"label\"),x(\"source\"),x(\"target\"),x(\"value\"),x(\"line.color\"),x(\"line.width\"),x(\"hoverinfo\",t.hoverinfo),l(m,y,x,h);var b=o(u.paper_bgcolor).getLuminance()<.333?\"rgba(255, 255, 255, 0.6)\":\"rgba(0, 0, 0, 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o=r.node;o.originalEvent=n.event,t._hoverdata=[o],n.select(e).call(m,r,i),a.click(t,{target:!0})}}})}},{\"../../components/color\":570,\"../../components/fx\":612,\"../../lib\":696,\"./constants\":1037,\"./render\":1041,d3:148}],1041:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"d3\"),a=t(\"tinycolor2\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"@plotly/d3-sankey\").sankey,c=t(\"d3-force\"),u=t(\"../../lib\"),f=u.isArrayOrTypedArray,h=u.isIndex,p=t(\"../../lib/gup\"),d=p.keyFun,g=p.repeat,v=p.unwrap;function m(t){t.lastDraggedX=t.x,t.lastDraggedY=t.y}function y(t){return function(e){return e.node.originalX===t.node.originalX}}function x(t){for(var e=0;e<t.length;e++)t[e].y=t[e].y+t[e].dy/2}function b(t){t.attr(\"transform\",function(t){return\"translate(\"+t.node.x.toFixed(3)+\", \"+(t.node.y-t.node.dy/2).toFixed(3)+\")\"})}function _(t){var e=t.sankey.nodes();!function(t){for(var e=0;e<t.length;e++)t[e].y=t[e].y-t[e].dy/2}(e);var r=t.sankey.link()(t.link);return x(e),r}function w(t){t.call(b)}function k(t,e){t.call(w),e.attr(\"d\",_)}function M(t){t.attr(\"width\",function(t){return t.visibleWidth}).attr(\"height\",function(t){return t.visibleHeight})}function A(t){return t.link.dy>1\|\|t.linkLineWidth>0}function T(t){return\"translate(\"+t.translateX+\",\"+t.translateY+\")\"+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function S(t){return\"translate(\"+(t.horizontal?0:t.labelY)+\" \"+(t.horizontal?t.labelY:0)+\")\"}function E(t){return i.svg.line()([[t.horizontal?t.left?-t.sizeAcross:t.visibleWidth+n.nodeTextOffsetHorizontal:n.nodeTextOffsetHorizontal,0],[t.horizontal?t.left?-n.nodeTextOffsetHorizontal:t.sizeAcross:t.visibleHeight-n.nodeTextOffsetHorizontal,0]])}function C(t){return t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\"}function L(t){return t.horizontal?\"scale(1 1)\":\"scale(-1 1)\"}function z(t){return t.darkBackground&&!t.horizontal?\"rgb(255,255,255)\":\"rgb(0,0,0)\"}function O(t){return t.horizontal&&t.left?\"100%\":\"0%\"}function I(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",function(t){t.interactionState.dragInProgress\|\|(r.hover(this,t,e),t.interactionState.hovered=[this,t])}).on(\"mousemove.basic\",function(t){t.interactionState.dragInProgress\|\|(r.follow(this,t),t.interactionState.hovered=[this,t])}).on(\"mouseout.basic\",function(t){t.interactionState.dragInProgress\|\|(r.unhover(this,t,e),t.interactionState.hovered=!1)}).on(\"click.basic\",function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress\|\|r.select(this,t,e)})}function P(t,e,r){var a=i.behavior.drag().origin(function(t){return t.node}).on(\"dragstart\",function(i){if(\"fixed\"!==i.arrangement&&(u.raiseToTop(this),i.interactionState.dragInProgress=i.node,m(i.node),i.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,i.interactionState.hovered),i.interactionState.hovered=!1),\"snap\"===i.arrangement)){var a=i.traceId+\"\|\"+Math.floor(i.node.originalX);i.forceLayouts[a]?i.forceLayouts[a].alpha(1):function(t,e,r){var i=r.sankey.nodes().filter(function(t){return t.originalX===r.node.originalX});r.forceLayouts[e]=c.forceSimulation(i).alphaDecay(0).force(\"collide\",c.forceCollide().radius(function(t){return t.dy/2+r.nodePad/2}).strength(1).iterations(n.forceIterations)).force(\"constrain\",function(t,e,r,i){return function(){for(var t=0,a=0;a<r.length;a++){var o=r[a];o===i.interactionState.dragInProgress?(o.x=o.lastDraggedX,o.y=o.lastDraggedY):(o.vx=(o.originalX-o.x)/n.forceTicksPerFrame,o.y=Math.min(i.size-o.dy/2,Math.max(o.dy/2,o.y))),t=Math.max(t,Math.abs(o.vx),Math.abs(o.vy))}!i.interactionState.dragInProgress&&t<.1&&i.forceLayouts[e].alpha()>0&&i.forceLayouts[e].alpha(0)}}(0,e,i,r)).stop()}(0,a,i),function(t,e,r,i){window.requestAnimationFrame(function a(){for(var o=0;o<n.forceTicksPerFrame;o++)r.forceLayouts[i].tick();r.sankey.relayout(),k(t.filter(y(r)),e),r.forceLayouts[i].alpha()>0&&window.requestAnimationFrame(a)})}(t,e,i,a)}}).on(\"drag\",function(r){if(\"fixed\"!==r.arrangement){var n=i.event.x,a=i.event.y;\"snap\"===r.arrangement?(r.node.x=n,r.node.y=a):(\"freeform\"===r.arrangement&&(r.node.x=n),r.node.y=Math.max(r.node.dy/2,Math.min(r.size-r.node.dy/2,a))),m(r.node),\"snap\"!==r.arrangement&&(r.sankey.relayout(),k(t.filter(y(r)),e))}}).on(\"dragend\",function(t){t.interactionState.dragInProgress=!1});t.on(\".drag\",null).call(a)}e.exports=function(t,e,r,i){var c=t.selectAll(\".\"+n.cn.sankey).data(e.filter(function(t){return v(t).trace.visible}).map(function(t,e,r){var i,a=v(e).trace,o=a.domain,s=a.node,c=a.link,p=a.arrangement,d=\"h\"===a.orientation,g=a.node.pad,m=a.node.thickness,y=a.node.line.color,b=a.node.line.width,_=a.link.line.color,w=a.link.line.width,k=a.valueformat,M=a.valuesuffix,A=a.textfont,T=t.width(o.x[1]-o.x[0]),S=t.height(o.y[1]-o.y[0]),E=[],C=f(c.color),L={},z=s.label.length;for(i=0;i<c.value.length;i++){var O=c.value[i],I=c.source[i],P=c.target[i];O>0&&h(I,z)&&h(P,z)&&(P=+P,L[I=+I]=L[P]=!0,E.push({pointNumber:i,label:c.label[i],color:C?c.color[i]:c.color,source:I,target:P,value:+O}))}var D=f(s.color),R=[],B=!1,F={};for(i=0;i<z;i++)if(L[i]){var N=s.label[i];F[i]=R.length,R.push({pointNumber:i,label:N,color:D?s.color[i]:s.color})}else B=!0;if(B)for(i=0;i<E.length;i++)E[i].source=F[E[i].source],E[i].target=F[E[i].target];var j=l().size(d?[T,S]:[S,T]).nodeWidth(m).nodePadding(g).nodes(R).links(E).layout(n.sankeyIterations);j.nodePadding()<g&&u.warn(\"node.pad was reduced to \",j.nodePadding(),\" to fit within the figure.\");for(var V,U=j.nodes(),q=0;q<U.length;q++)(V=U[q]).width=T,V.height=S;return 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t.sankey.links().filter(function(t){return t.value}).map(function(t,e,r){var n=a(r.color),i=r.source.label+\"\|\"+r.target.label,s=t[i];t[i]=(s\|\|0)+1;var l=i+\"__\"+t[i];return r.trace=e.trace,r.curveNumber=e.trace.index,{key:l,traceId:e.key,link:r,tinyColorHue:o.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkLineColor:e.linkLineColor,linkLineWidth:e.linkLineWidth,valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,interactionState:e.interactionState}}.bind(null,{},t))},d);m.enter().append(\"path\").classed(n.cn.sankeyLink,!0).attr(\"d\",_).call(I,c,i.linkEvents),m.style(\"stroke\",function(t){return A(t)?o.tinyRGB(a(t.linkLineColor)):t.tinyColorHue}).style(\"stroke-opacity\",function(t){return A(t)?o.opacity(t.linkLineColor):t.tinyColorAlpha}).style(\"stroke-width\",function(t){return A(t)?t.linkLineWidth:1}).style(\"fill\",function(t){return t.tinyColorHue}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),m.transition().ease(n.ease).duration(n.duration).attr(\"d\",_),m.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var y=c.selectAll(\".\"+n.cn.sankeyNodeSet).data(g,d);y.enter().append(\"g\").classed(n.cn.sankeyNodeSet,!0),y.style(\"cursor\",function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}});var w=y.selectAll(\".\"+n.cn.sankeyNode).data(function(t){var e=t.sankey.nodes();return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=t[e].x,t[e].originalY=t[e].y,-1===r.indexOf(t[e].x)&&r.push(t[e].x);for(r.sort(function(t,e){return t-e}),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.filter(function(t){return t.value}).map(function(t,e,r){var i=a(r.color),s=n.nodePadAcross,l=e.nodePad/2,c=r.dx,u=Math.max(.5,r.dy),f=r.label,h=t[f];t[f]=(h\|\|0)+1;var p=f+\"__\"+t[f];return r.trace=e.trace,r.curveNumber=e.trace.index,{key:p,traceId:e.key,node:r,nodePad:e.nodePad,nodeLineColor:e.nodeLineColor,nodeLineWidth:e.nodeLineWidth,textFont:e.textFont,size:e.horizontal?e.height:e.width,visibleWidth:Math.ceil(c),visibleHeight:u,zoneX:-s,zoneY:-l,zoneWidth:c+2s,zoneHeight:u+2l,labelY:e.horizontal?r.dy/2+1:r.dx/2+1,left:1===r.originalLayer,sizeAcross:e.width,forceLayouts:e.forceLayouts,horizontal:e.horizontal,darkBackground:i.getBrightness()<=128,tinyColorHue:o.tinyRGB(i),tinyColorAlpha:i.getAlpha(),valueFormat:e.valueFormat,valueSuffix:e.valueSuffix,sankey:e.sankey,arrangement:e.arrangement,uniqueNodeLabelPathId:[e.guid,e.key,p].join(\" \"),interactionState:e.interactionState}}.bind(null,{},t))},d);w.enter().append(\"g\").classed(n.cn.sankeyNode,!0).call(b).call(I,c,i.nodeEvents),w.call(P,m,i),w.transition().ease(n.ease).duration(n.duration).call(b),w.exit().transition().ease(n.ease).duration(n.duration).style(\"opacity\",0).remove();var k=w.selectAll(\".\"+n.cn.nodeRect).data(g);k.enter().append(\"rect\").classed(n.cn.nodeRect,!0).call(M),k.style(\"stroke-width\",function(t){return t.nodeLineWidth}).style(\"stroke\",function(t){return o.tinyRGB(a(t.nodeLineColor))}).style(\"stroke-opacity\",function(t){return o.opacity(t.nodeLineColor)}).style(\"fill\",function(t){return t.tinyColorHue}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),k.transition().ease(n.ease).duration(n.duration).call(M);var D=w.selectAll(\".\"+n.cn.nodeCapture).data(g);D.enter().append(\"rect\").classed(n.cn.nodeCapture,!0).style(\"fill-opacity\",0),D.attr(\"x\",function(t){return t.zoneX}).attr(\"y\",function(t){return t.zoneY}).attr(\"width\",function(t){return t.zoneWidth}).attr(\"height\",function(t){return t.zoneHeight});var R=w.selectAll(\".\"+n.cn.nodeCentered).data(g);R.enter().append(\"g\").classed(n.cn.nodeCentered,!0).attr(\"transform\",S),R.transition().ease(n.ease).duration(n.duration).attr(\"transform\",S);var B=R.selectAll(\".\"+n.cn.nodeLabelGuide).data(g);B.enter().append(\"path\").classed(n.cn.nodeLabelGuide,!0).attr(\"id\",function(t){return t.uniqueNodeLabelPathId}).attr(\"d\",E).attr(\"transform\",C),B.transition().ease(n.ease).duration(n.duration).attr(\"d\",E).attr(\"transform\",C);var F=R.selectAll(\".\"+n.cn.nodeLabel).data(g);F.enter().append(\"text\").classed(n.cn.nodeLabel,!0).attr(\"transform\",L).style(\"user-select\",\"none\").style(\"cursor\",\"default\").style(\"fill\",\"black\"),F.style(\"text-shadow\",function(t){return t.horizontal?\"-1px 1px 1px #fff, 1px 1px 1px #fff, 1px -1px 1px #fff, -1px -1px 1px #fff\":\"none\"}).each(function(t){s.font(F,t.textFont)}),F.transition().ease(n.ease).duration(n.duration).attr(\"transform\",L);var N=F.selectAll(\".\"+n.cn.nodeLabelTextPath).data(g);N.enter().append(\"textPath\").classed(n.cn.nodeLabelTextPath,!0).attr(\"alignment-baseline\",\"middle\").attr(\"xlink:href\",function(t){return\"#\"+t.uniqueNodeLabelPathId}).attr(\"startOffset\",O).style(\"fill\",z),N.text(function(t){return t.horizontal\|\|t.node.dy>5?t.node.label:\"\"}).attr(\"text-anchor\",function(t){return t.horizontal&&t.left?\"end\":\"start\"}),N.transition().ease(n.ease).duration(n.duration).attr(\"startOffset\",O).style(\"fill\",z)}},{\"../../components/color\":570,\"../../components/drawing\":595,\"../../lib\":696,\"../../lib/gup\":693,\"./constants\":1037,\"@plotly/d3-sankey\":46,d3:148,\"d3-force\":144,tinycolor2:514}],1042:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var i=e.marker;if(i){n.mergeArray(i.size,t,\"ms\"),n.mergeArray(i.opacity,t,\"mo\"),n.mergeArray(i.symbol,t,\"mx\"),n.mergeArray(i.color,t,\"mc\");var a=i.line;i.line&&(n.mergeArray(a.color,t,\"mlc\"),n.mergeArray(a.width,t,\"mlw\"));var o=i.gradient;o&&\"none\"!==o.type&&(n.mergeArray(o.type,t,\"mgt\"),n.mergeArray(o.color,t,\"mgc\"))}}},{\"../../lib\":696}],1043:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../components/colorbar/attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../components/drawing\"),l=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},x0:{valType:\"any\",dflt:0,editType:\"calc+clearAxisTypes\"},dx:{valType:\"number\",dflt:1,editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y0:{valType:\"any\",dflt:0,editType:\"calc+clearAxisTypes\"},dy:{valType:\"number\",dflt:1,editType:\"calc\"},stackgroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],editType:\"calc\"},groupnorm:{valType:\"enumerated\",values:[\"\",\"fraction\",\"percent\"],dflt:\"\",editType:\"calc\"},stackgaps:{valType:\"enumerated\",values:[\"infer zero\",\"interpolate\"],dflt:\"infer 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n=t(\"../../registry\");e.exports=function(t,e,r,i){var a,o=i(\"x\"),s=i(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),o)s?a=Math.min(o.length,s.length):(a=o.length,i(\"y0\"),i(\"dy\"));else{if(!s)return 0;a=e.y.length,i(\"x0\"),i(\"dx\")}return e._length=a,a}},{\"../../registry\":827}],1070:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/attributes\"),o=t(\"../../constants/gl3d_dashes\"),s=t(\"../../constants/gl3d_markers\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,u=n.line,f=n.marker,h=f.line,p=l({width:u.width,dash:{valType:\"enumerated\",values:Object.keys(o),dflt:\"solid\"}},i(\"line\"));delete p.showscale,delete p.colorbar;var 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l=a(+t[s],s);if(\"log\"===i.type){var c=i.c2l(t[s]),u=t[s]-l[0],f=t[s]+l[1];if(o[s]=[(i.c2l(u,!0)-c)r,(i.c2l(f,!0)-c)r],u>0){var h=i.c2l(u);i._lowerLogErrorBound\|\|(i._lowerLogErrorBound=h),i._lowerErrorBound=Math.min(i._lowerLogErrorBound,h)}}else o[s]=[-l[0]r,l[1]r]}return o}e.exports=function(t,e,r){var n=[i(t.x,t.error_x,e[0],r.xaxis),i(t.y,t.error_y,e[1],r.yaxis),i(t.z,t.error_z,e[2],r.zaxis)],a=function(t){for(var e=0;e<t.length;e++)if(t[e])return t[e].length;return 0}(n);if(0===a)return null;for(var o=new Array(a),s=0;s<a;s++){for(var l=[[0,0,0],[0,0,0]],c=0;c<3;c++)if(n[c])for(var u=0;u<2;u++)l[u][c]=n[c][s][u];o[s]=l}return o}},{\"../../registry\":827}],1073:[function(t,e,r){\"use strict\";var n=t(\"gl-line3d\"),i=t(\"gl-scatter3d\"),a=t(\"gl-error3d\"),o=t(\"gl-mesh3d\"),s=t(\"delaunay-triangulate\"),l=t(\"../../lib\"),c=t(\"../../lib/str2rgbarray\"),u=t(\"../../lib/gl_format_color\").formatColor,f=t(\"../scatter/make_bubble_size_func\"),h=t(\"../../constants/gl3d_dashes\"),p=t(\"../../constants/gl3d_markers\"),d=t(\"./calc_errors\");function g(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode=\"\",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var v=g.prototype;function m(t,e){return e(4t)}function y(t){return p[t]}function x(t,e,r,n,i){var a=null;if(l.isArrayOrTypedArray(t)){a=[];for(var o=0;o<e;o++)void 0===t[o]?a[o]=n:a[o]=r(t[o],i)}else a=r(t,l.identity);return a}function b(t,e){var r,n,i,a,o,s,h,p,g=[],v=t.fullSceneLayout,b=t.dataScale,_=v.xaxis,w=v.yaxis,k=v.zaxis,M=e.marker,A=e.line,T=e.x\|\|[],S=e.y\|\|[],E=e.z\|\|[],C=T.length,L=e.xcalendar,z=e.ycalendar,O=e.zcalendar;for(n=0;n<C;n++)i=_.d2l(T[n],0,L)b[0],a=w.d2l(S[n],0,z)b[1],o=k.d2l(E[n],0,O)b[2],g[n]=[i,a,o];if(Array.isArray(e.text))s=e.text;else if(void 0!==e.text)for(s=new Array(C),n=0;n<C;n++)s[n]=e.text;if(r={position:g,mode:e.mode,text:s},\"line\"in e&&(r.lineColor=u(A,1,C),r.lineWidth=A.width,r.lineDashes=A.dash),\"marker\"in e){var I=f(e);r.scatterColor=u(M,1,C),r.scatterSize=x(M.size,C,m,20,I),r.scatterMarker=x(M.symbol,C,y,\"\\u25cf\"),r.scatterLineWidth=M.line.width,r.scatterLineColor=u(M.line,1,C),r.scatterAngle=0}\"textposition\"in e&&(r.textOffset=(h=e.textposition,p=[0,0],Array.isArray(h)?[0,-1]:(h.indexOf(\"bottom\")>=0&&(p[1]+=1),h.indexOf(\"top\")>=0&&(p[1]-=1),h.indexOf(\"left\")>=0&&(p[0]-=1),h.indexOf(\"right\")>=0&&(p[0]+=1),p)),r.textColor=u(e.textfont,1,C),r.textSize=x(e.textfont.size,C,l.identity,12),r.textFont=e.textfont.family,r.textAngle=0);var P=[\"x\",\"y\",\"z\"];for(r.project=[!1,!1,!1],r.projectScale=[1,1,1],r.projectOpacity=[1,1,1],n=0;n<3;++n){var D=e.projection[P[n]];(r.project[n]=D.show)&&(r.projectOpacity[n]=D.opacity,r.projectScale[n]=D.scale)}r.errorBounds=d(e,b,v);var R=function(t){for(var e=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[1,1,1],i=0;i<3;i++){var a=t[i];a&&!1!==a.copy_zstyle&&!1!==t[2].visible&&(a=t[2]),a&&a.visible&&(e[i]=a.width/2,r[i]=c(a.color),n[i]=a.thickness)}return{capSize:e,color:r,lineWidth:n}}([e.error_x,e.error_y,e.error_z]);return r.errorColor=R.color,r.errorLineWidth=R.lineWidth,r.errorCapSize=R.capSize,r.delaunayAxis=e.surfaceaxis,r.delaunayColor=c(e.surfacecolor),r}function _(t){if(Array.isArray(t)){var e=t[0];return Array.isArray(e)&&(t=e),\"rgb(\"+t.slice(0,3).map(function(t){return Math.round(255t)})+\")\"}return null}v.handlePick=function(t){if(t.object&&(t.object===this.linePlot\|\|t.object===this.delaunayMesh\|\|t.object===this.textMarkers\|\|t.object===this.scatterPlot)){var e=t.index=t.data.index;return t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel=\"\",this.textLabels&&(Array.isArray(this.textLabels)?(this.textLabels[e]\|\|0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},v.update=function(t){var e,r,l,c,u=this.scene.glplot.gl,f=h.solid;this.data=t;var p=b(this.scene,t);\"mode\"in 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d=t.opacity;if(t.marker&&t.marker.opacity&&(d=t.marker.opacity),r={gl:u,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf(\"markers\")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:u,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext\|\|t.text,-1!==this.mode.indexOf(\"text\")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=i(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:u,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var g=function(t,e,r){var n,i=(r+1)%3,a=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var c=t[n];!isNaN(c[i])&&isFinite(c[i])&&!isNaN(c[a])&&isFinite(c[a])&&(o.push([c[i],c[a]]),l.push(n))}var u=s(o);for(n=0;n<u.length;++n)for(var f=u[n],h=0;h<f.length;++h)f[h]=l[f[h]];return{positions:t,cells:u,meshColor:e}}(p.position,p.delaunayColor,p.delaunayAxis);g.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(g):(g.gl=u,this.delaunayMesh=o(g),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},v.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},e.exports=function(t,e){var r=new g(t,e.uid);return 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a}(t,e,f,u)){f(\"text\"),f(\"hovertext\"),f(\"mode\"),a.hasLines(e)&&(f(\"connectgaps\"),s(t,e,r,u,f)),a.hasMarkers(e)&&o(t,e,r,u,f,{noSelect:!0}),a.hasText(e)&&l(t,e,u,f,{noSelect:!0});var h=(e.line\|\|{}).color,p=(e.marker\|\|{}).color;f(\"surfaceaxis\")>=0&&f(\"surfacecolor\",h\|\|p);for(var d=[\"x\",\"y\",\"z\"],g=0;g<3;++g){var v=\"projection.\"+d[g];f(v+\".show\")&&(f(v+\".opacity\"),f(v+\".scale\"))}var m=n.getComponentMethod(\"errorbars\",\"supplyDefaults\");m(t,e,r,{axis:\"z\"}),m(t,e,r,{axis:\"y\",inherit:\"z\"}),m(t,e,r,{axis:\"x\",inherit:\"z\"})}else e.visible=!1}},{\"../../lib\":696,\"../../registry\":827,\"../scatter/line_defaults\":1056,\"../scatter/marker_defaults\":1062,\"../scatter/subtypes\":1067,\"../scatter/text_defaults\":1068,\"./attributes\":1070}],1075:[function(t,e,r){\"use strict\";var n={};n.plot=t(\"./convert\"),n.attributes=t(\"./attributes\"),n.markerSymbols=t(\"../../constants/gl3d_markers\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/marker_colorbar\"),n.calc=t(\"./calc\"),n.moduleType=\"trace\",n.name=\"scatter3d\",n.basePlotModule=t(\"../../plots/gl3d\"),n.categories=[\"gl3d\",\"symbols\",\"showLegend\"],n.meta={},e.exports=n},{\"../../constants/gl3d_markers\":671,\"../../plots/gl3d\":787,\"../scatter/marker_colorbar\":1061,\"./attributes\":1070,\"./calc\":1071,\"./convert\":1073,\"./defaults\":1074}],1076:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=n.marker,c=n.line,u=l.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),line:{color:c.color,width:c.width,dash:c.dash,shape:s({},c.shape,{values:[\"linear\",\"spline\"]}),smoothing:c.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:s({symbol:l.symbol,opacity:l.opacity,maxdisplayed:l.maxdisplayed,size:l.size,sizeref:l.sizeref,sizemin:l.sizemin,sizemode:l.sizemode,line:s({width:u.width,editType:\"calc\"},a(\"marker.line\")),gradient:l.gradient,editType:\"calc\"},a(\"marker\"),{colorbar:o}),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:s({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":571,\"../../components/colorscale/attributes\":577,\"../../lib/extend\":685,\"../../plots/attributes\":741,\"../scatter/attributes\":1043}],1077:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),g=p(\"b\"),v=Math.min(d.length,g.length);if(v){e._length=v,p(\"text\"),p(\"mode\",v<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var m=[];(a.hasMarkers(e)\|\|a.hasText(e))&&(p(\"marker.maxdisplayed\"),m.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)\|\|l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill\|\|m.push(\"fills\"),p(\"hoveron\",m.join(\"+\")\|\|\"points\"),n.coerceSelectionMarkerOpacity(e,p)}else 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/marker_colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"../scatter/style\").style,n.styleOnSelect=t(\"../scatter/style\").styleOnSelect,n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"../scatter/select\"),n.eventData=t(\"./event_data\"),n.moduleType=\"trace\",n.name=\"scattercarpet\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"svg\",\"carpet\",\"symbols\",\"showLegend\",\"carpetDependent\",\"zoomScale\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":756,\"../scatter/marker_colorbar\":1061,\"../scatter/select\":1064,\"../scatter/style\":1066,\"./attributes\":1076,\"./calc\":1077,\"./defaults\":1078,\"./event_data\":1079,\"./hover\":1080,\"./plot\":1082}],1082:[function(t,e,r){\"use strict\";var n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r,o){var s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis\|\|\"x\"),yaxis:i.getFromId(t,u.yaxis\|\|\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,u._clipPathId)}},{\"../../components/drawing\":595,\"../../plots/cartesian/axes\":744,\"../scatter/plot\":1063}],1083:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,c=n.marker,u=n.line,f=c.line;e.exports=l({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"fast-isnumeric\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/colorscale_calc\"),o=t(\"../scatter/arrays_to_calcdata\"),s=t(\"../scatter/calc_selection\"),l=t(\"../../lib\")._;e.exports=function(t,e){for(var r=Array.isArray(e.locations),c=r?e.locations.length:e._length,u=new Array(c),f=0;f<c;f++){var h=u[f]={};if(r){var p=e.locations[f];h.loc=\"string\"==typeof p?p:null}else{var d=e.lon[f],g=e.lat[f];n(d)&&n(g)?h.lonlat=[+d,+g]:h.lonlat=[i,i]}}return o(u,e),a(e),s(u,e),c&&(u[0].t={labels:{lat:l(t,\"lat:\")+\" \",lon:l(t,\"lon:\")+\" \"}}),u}},{\"../../constants/numerical\":673,\"../../lib\":696,\"../scatter/arrays_to_calcdata\":1042,\"../scatter/calc_selection\":1045,\"../scatter/colorscale_calc\":1046,\"fast-isnumeric\":214}],1085:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}!function(t,e,r){var n,i,a=0,o=r(\"locations\");if(o)return r(\"locationmode\"),a=o.length;return n=r(\"lon\")\|\|[],i=r(\"lat\")\|\|[],a=Math.min(n.length,i.length),e._length=a,a}(0,e,f)?e.visible=!1:(f(\"text\"),f(\"hovertext\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&s(t,e,u,f),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f))}},{\"../../lib\":696,\"../scatter/fillcolor_defaults\":1052,\"../scatter/line_defaults\":1056,\"../scatter/marker_defaults\":1062,\"../scatter/subtypes\":1067,\"../scatter/text_defaults\":1068,\"./attributes\":1083}],1086:[function(t,e,r){\"use 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a=e.hi\|\|t.hoverinfo,o=\"all\"===a?l.hoverinfo.flags:a.split(\"+\"),c=-1!==o.indexOf(\"location\")&&Array.isArray(t.locations),u=-1!==o.indexOf(\"lon\"),f=-1!==o.indexOf(\"lat\"),h=-1!==o.indexOf(\"text\"),p=[];function d(t){return i.tickText(r,r.c2l(t),\"hover\").text+\"\\xb0\"}c?p.push(e.loc):u&&f?p.push(\"(\"+d(e.lonlat[0])+\", \"+d(e.lonlat[1])+\")\"):u?p.push(n.lon+d(e.lonlat[0])):f&&p.push(n.lat+d(e.lonlat[1]));h&&s(e,t,p);return p.join(\"<br>\")}(u,v,p.mockAxis,c[0].t.labels),[t]}}},{\"../../components/fx\":612,\"../../constants/numerical\":673,\"../../plots/cartesian/axes\":744,\"../scatter/fill_hover_text\":1051,\"../scatter/get_trace_color\":1053,\"./attributes\":1083}],1088:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/marker_colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOnSelect=t(\"../scatter/style\").styleOnSelect,n.hoverPoints=t(\"./hover\"),n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergeo\",n.basePlotModule=t(\"../../plots/geo\"),n.categories=[\"geo\",\"symbols\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/geo\":775,\"../scatter/marker_colorbar\":1061,\"../scatter/style\":1066,\"./attributes\":1083,\"./calc\":1084,\"./defaults\":1085,\"./event_data\":1086,\"./hover\":1087,\"./plot\":1089,\"./select\":1090,\"./style\":1091}],1089:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../constants/numerical\").BADNUM,o=t(\"../../lib/topojson_utils\").getTopojsonFeatures,s=t(\"../../lib/geo_location_utils\").locationToFeature,l=t(\"../../lib/geojson_utils\"),c=t(\"../scatter/subtypes\"),u=t(\"./style\");function f(t,e){var r=t[0].trace;if(Array.isArray(r.locations))for(var n=o(r,e),i=r.locationmode,l=0;l<t.length;l++){var c=t[l],u=s(i,c.loc,n);c.lonlat=u?u.properties.ct:[a,a]}}e.exports=function(t,e,r){for(var o=0;o<r.length;o++)f(r[o],e.topojson);function s(t,e){t.lonlat[0]===a&&n.select(e).remove()}var h=e.layers.frontplot.select(\".scatterlayer\"),p=i.makeTraceGroups(h,r,\"trace scattergeo\");p.selectAll(\"\").remove(),p.each(function(e){var r=e[0].node3=n.select(this),a=e[0].trace;if(c.hasLines(a)\|\|\"none\"!==a.fill){var 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for(l=0;l<c.length;l++)(a=(r=c[l]).lonlat)[0]!==i&&(o=u.c2p(a),s=f.c2p(a),e.contains([o,s],null,l,t)?(h.push({pointNumber:l,lon:a[0],lat:a[1]}),r.selected=1):r.selected=0);return h}},{\"../../constants/numerical\":673,\"../scatter/subtypes\":1067}],1091:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../components/drawing\"),a=t(\"../../components/color\"),o=t(\"../scatter/style\"),s=o.stylePoints,l=o.styleText;e.exports=function(t,e){e&&function(t,e){var r=e[0].trace,o=e[0].node3;o.style(\"opacity\",e[0].trace.opacity),s(o,r,t),l(o,r,t),o.selectAll(\"path.js-line\").style(\"fill\",\"none\").each(function(t){var e=n.select(this),r=t.trace,o=r.line\|\|{};e.call(a.stroke,o.color).call(i.dashLine,o.dash\|\|\"\",o.width\|\|0),\"none\"!==r.fill&&e.call(a.fill,r.fillcolor)})}(t,e)}},{\"../../components/color\":570,\"../../components/drawing\":595,\"../scatter/style\":1066,d3:148}],1092:[function(t,e,r){\"use strict\";var n=t(\"../../plots/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../lib/extend\").extendFlat,s=t(\"../../plot_api/edit_types\").overrideAll,l=t(\"./constants\").DASHES,c=i.line,u=i.marker,f=u.line,h=e.exports=s({x:i.x,x0:i.x0,dx:i.dx,y:i.y,y0:i.y0,dy:i.dy,text:i.text,hovertext:i.hovertext,textposition:i.textposition,textfont:i.textfont,mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},line:{color:c.color,width:c.width,shape:{valType:\"enumerated\",values:[\"linear\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\",editType:\"plot\"},dash:{valType:\"enumerated\",values:Object.keys(l),dflt:\"solid\"}},marker:o({},a(\"marker\"),{symbol:u.symbol,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,opacity:u.opacity,colorbar:u.colorbar,line:o({},a(\"marker.line\"),{width:f.width})}),connectgaps:i.connectgaps,fill:o({},i.fill,{dflt:\"none\"}),fillcolor:i.fillcolor,selected:{marker:i.selected.marker,textfont:i.selected.textfont},unselected:{marker:i.unselected.marker,textfont:i.unselected.textfont},opacity:n.opacity},\"calc\",\"nested\");h.x.editType=h.y.editType=h.x0.editType=h.y0.editType=\"calc+clearAxisTypes\"},{\"../../components/colorscale/attributes\":577,\"../../lib/extend\":685,\"../../plot_api/edit_types\":727,\"../../plots/attributes\":741,\"../scatter/attributes\":1043,\"./constants\":1093}],1093:[function(t,e,r){\"use 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f=o[e].split(/\\s+/);switch(f[1]){case\"left\":u.align.push(\"right\");break;case\"right\":u.align.push(\"left\");break;default:u.align.push(f[1])}switch(f[0]){case\"top\":u.baseline.push(\"bottom\");break;case\"bottom\":u.baseline.push(\"top\");break;default:u.baseline.push(f[0])}}if(Array.isArray(s))for(u.color=new Array(r),e=0;e<r;e++)u.color[e]=s[e];else u.color=s;if(Array.isArray(l)\|\|Array.isArray(c))for(u.font=new Array(r),e=0;e<r;e++){var h=u.font[e]={};h.size=Array.isArray(l)?n(l[e])?l[e]:0:l,h.family=Array.isArray(c)?c[e]:c}else u.font={size:l,family:c};return u}function m(t){var e,r,n=t._length,i=t.marker,o={},l=Array.isArray(i.symbol),c=s.isArrayOrTypedArray(i.color),f=s.isArrayOrTypedArray(i.line.color),d=s.isArrayOrTypedArray(i.opacity),g=s.isArrayOrTypedArray(i.size),v=s.isArrayOrTypedArray(i.line.width);if(l\|\|(r=p.OPEN_RE.test(i.symbol)),l\|\|c\|\|f\|\|d){o.colors=new Array(n),o.borderColors=new Array(n);var m=u(i,i.opacity,n),y=u(i.line,i.opacity,n);if(!Array.isArray(y[0])){var x=y;for(y=Array(n),e=0;e<n;e++)y[e]=x}if(!Array.isArray(m[0])){var b=m;for(m=Array(n),e=0;e<n;e++)m[e]=b}for(o.colors=m,o.borderColors=y,e=0;e<n;e++){if(l){var _=i.symbol[e];r=p.OPEN_RE.test(_)}r&&(y[e]=m[e].slice(),m[e]=m[e].slice(),m[e][3]=0)}o.opacity=t.opacity}else r?(o.color=a(i.color,\"uint8\"),o.color[3]=0,o.borderColor=a(i.color,\"uint8\")):(o.color=a(i.color,\"uint8\"),o.borderColor=a(i.line.color,\"uint8\")),o.opacity=t.opacityi.opacity;if(l)for(o.markers=new Array(n),e=0;e<n;e++)o.markers[e]=T(i.symbol[e]);else o.marker=T(i.symbol);var w,k=h(t);if(g\|\|v){var M,A=o.sizes=new Array(n),S=o.borderSizes=new Array(n),E=0;if(g){for(e=0;e<n;e++)A[e]=k(i.size[e]),E+=A[e];M=E/n}else for(w=k(i.size),e=0;e<n;e++)A[e]=w;if(v)for(e=0;e<n;e++)S[e]=i.line.width[e]/2;else for(w=i.line.width/2,e=0;e<n;e++)S[e]=w;o.sizeAvg=M}else o.size=k(i&&i.size\|\|10),o.borderSizes=k(i.line.width);return o}function y(t,e){var r=t.marker,n={};return e?(e.marker&&e.marker.symbol?n=m(s.extendFlat({},r,e.marker)):e.marker&&(e.marker.size&&(n.size=e.marker.size/2),e.marker.color&&(n.colors=e.marker.color),void 0!==e.marker.opacity&&(n.opacity=e.marker.opacity)),n):n}function x(t,e){var r={};if(!e)return r;if(e.textfont){var n={opacity:1,text:t.text,textposition:t.textposition,textfont:s.extendFlat({},t.textfont)};e.textfont&&s.extendFlat(n.textfont,e.textfont),r=v(n)}return r}function b(t,e){var r={capSize:2e.width,lineWidth:e.thickness,color:e.color};return e.copy_ystyle&&(r=t.error_y),r}var _=p.SYMBOL_SDF_SIZE,w=p.SYMBOL_SIZE,k=p.SYMBOL_STROKE,M={},A=l.symbolFuncs[0](.05w);function T(t){if(\"circle\"===t)return null;var e,r,n=l.symbolNumber(t),a=l.symbolFuncs[n%100],o=!!l.symbolNoDot[n%100],s=!!l.symbolNoFill[n%100],c=p.DOT_RE.test(t);return M[t]?M[t]:(e=c&&!o?a(1.1w)+A:a(w),r=i(e,{w:_,h:_,viewBox:[-w,-w,w,w],stroke:s?k:-k}),M[t]=r,r\|\|null)}e.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0};if(!0!==e.visible)return n;if(f.hasText(e)&&(n.text=v(e),n.textSel=x(e,e.selected),n.textUnsel=x(e,e.unselected)),f.hasMarkers(e)&&(n.marker=m(e),n.markerSel=y(e,e.selected),n.markerUnsel=y(e,e.unselected),!e.unselected&&Array.isArray(e.marker.opacity))){var i=e.marker.opacity;for(n.markerUnsel.opacity=new Array(i.length),r=0;r<i.length;r++)n.markerUnsel.opacity[r]=di[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width,color:e.line.color,opacity:e.opacity};var a=(p.DASHES[e.line.dash]\|\|[1]).slice();for(r=0;r<a.length;++r)a[r]=e.line.width;n.line.dashes=a}return e.error_x&&e.error_x.visible&&(n.errorX=b(e,e.error_x)),e.error_y&&e.error_y.visible&&(n.errorY=b(e,e.error_y)),e.fill&&\"none\"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:m,markerSelection:y,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if(\"hv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2i])\|\|isNaN(r[2i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2i],r[2i+1]),isNaN(r[2i+2])\|\|isNaN(r[2i+3])?n.push(NaN,NaN):n.push(r[2i+2],r[2i+1]));n.push(r[a-2],r[a-1])}else if(\"hvh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2i])\|\|isNaN(r[2i+1])\|\|isNaN(r[2i+2])\|\|isNaN(r[2i+3]))isNaN(r[2i])\|\|isNaN(r[2i+1])?n.push(NaN,NaN):n.push(r[2i],r[2i+1]),n.push(NaN,NaN);else{var s=(r[2i]+r[2i+2])/2;n.push(r[2i],r[2i+1],s,r[2i+1],s,r[2i+3])}n.push(r[a-2],r[a-1])}else if(\"vhv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2i])\|\|isNaN(r[2i+1])\|\|isNaN(r[2i+2])\|\|isNaN(r[2i+3]))isNaN(r[2i])\|\|isNaN(r[2i+1])?n.push(NaN,NaN):n.push(r[2i],r[2i+1]),n.push(NaN,NaN);else{var l=(r[2i+1]+r[2i+3])/2;n.push(r[2i],r[2i+1],r[2i],l,r[2i+2],l)}n.push(r[a-2],r[a-1])}else if(\"vh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2i])\|\|isNaN(r[2i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2i],r[2i+1]),isNaN(r[2i+2])\|\|isNaN(r[2i+3])?n.push(NaN,NaN):n.push(r[2i],r[2i+3]));n.push(r[a-2],r[a-1])}else n=r;var c=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){c=!0;break}var u=c\|\|n.length>p.TOO_MANY_POINTS?\"rect\":f.hasMarkers(e)?\"rect\":\"round\";if(c&&e.connectgaps){var h=n[0],d=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])\|\|isNaN(n[i+1])?(n[i]=h,n[i+1]=d):(h=n[i],d=n[i+1])}return{join:u,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod(\"errorbars\",\"makeComputeError\"),l=c.getFromId(t,e.xaxis),u=c.getFromId(t,e.yaxis),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e[\"error_\"+a];if(o&&o.visible&&(\"linear\"===i.type\|\|\"log\"===i.type)){for(var l=s(o),c={x:0,y:1}[a],u={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4f),d=1/0,g=-1/0,v=0,m=0;v<f;v++,m+=4){var y=t[v];if(n(y)){var 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n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./attributes\"),o=t(\"../scatter/constants\"),s=t(\"../scatter/subtypes\"),l=t(\"../scatter/xy_defaults\"),c=t(\"../scatter/marker_defaults\"),u=t(\"../scatter/line_defaults\"),f=t(\"../scatter/fillcolor_defaults\"),h=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,p){function d(r,i){return n.coerce(t,e,a,r,i)}var g=!!t.marker&&/-open/.test(t.marker.symbol),v=s.isBubble(t),m=l(t,e,p,d);if(m){var y=m<o.PTS_LINESONLY?\"lines+markers\":\"lines\";d(\"text\"),d(\"hovertext\"),d(\"mode\",y),s.hasLines(e)&&(d(\"connectgaps\"),u(t,e,r,p,d),d(\"line.shape\")),s.hasMarkers(e)&&(c(t,e,r,p,d),d(\"marker.line.width\",g\|\|v?1:0)),s.hasText(e)&&h(t,e,p,d),d(\"fill\"),\"none\"!==e.fill&&f(t,e,r,d);var x=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");x(t,e,r,{axis:\"y\"}),x(t,e,r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,d)}else 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n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"point-cluster\"),s=t(\"array-range\"),l=t(\"gl-text\"),c=t(\"../../registry\"),u=t(\"../../lib\"),f=t(\"../../lib/prepare_regl\"),h=t(\"../../plots/cartesian/axis_ids\"),p=t(\"../../plots/cartesian/autorange\").findExtremes,d=t(\"../../components/color\"),g=t(\"../scatter/subtypes\"),v=t(\"../scatter/calc\"),m=v.calcMarkerSize,y=v.calcAxisExpansion,x=v.setFirstScatter,b=t(\"../scatter/colorscale_calc\"),_=t(\"../scatter/link_traces\"),w=t(\"../scatter/get_trace_color\"),k=t(\"../scatter/fill_hover_text\"),M=t(\"./convert\"),A=t(\"../../constants/numerical\").BADNUM,T=t(\"./constants\").TOO_MANY_POINTS,S=t(\"../../constants/interactions\").DESELECTDIM;function E(t,e,r){var n=t._extremes[e._id],i=p(e,r._bnds,{padded:!0});n.min=n.min.concat(i.min),n.max=n.max.concat(i.max)}function C(t,e){var r=e._scene,n={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[]},i={selectBatch:null,unselectBatch:null,fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:null};return e._scene\|\|((r=e._scene={}).init=function(){u.extendFlat(r,i,n)},r.init(),r.update=function(t){var e=u.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var n=0;n<r.count;n++)r.glText[n].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,n=r.error2d,i=r.line2d,a=r.scatter2d,o=r.glText,s=r.select2d,l=r.selectBatch,c=r.unselectBatch,u=0;u<t;u++)e&&r.fillOrder[u]&&e.draw(r.fillOrder[u]),i&&r.lineOptions[u]&&i.draw(u),n&&(r.errorXOptions[u]&&n.draw(u),r.errorYOptions[u]&&n.draw(u+t)),!a\|\|!r.markerOptions[u]\|\|l&&l[u]\|\|a.draw(u),o[u]&&r.textOptions[u]&&o[u].render();a&&s&&l&&(s.draw(l),a.draw(c)),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach(function(t){t.destroy&&t.destroy()}),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty\|\|u.extendFlat(r,n),r}function L(t,e,r,n){var i=t.xa,a=t.ya,o=t.distance,s=t.dxy,l=t.index,f={pointNumber:l,x:e[l],y:r[l]};f.tx=Array.isArray(n.text)?n.text[l]:n.text,f.htx=Array.isArray(n.hovertext)?n.hovertext[l]:n.hovertext,f.data=Array.isArray(n.customdata)?n.customdata[l]:n.customdata,f.tp=Array.isArray(n.textposition)?n.textposition[l]:n.textposition;var h=n.textfont;h&&(f.ts=Array.isArray(h.size)?h.size[l]:h.size,f.tc=Array.isArray(h.color)?h.color[l]:h.color,f.tf=Array.isArray(h.family)?h.family[l]:h.family);var p=n.marker;p&&(f.ms=u.isArrayOrTypedArray(p.size)?p.size[l]:p.size,f.mo=u.isArrayOrTypedArray(p.opacity)?p.opacity[l]:p.opacity,f.mx=Array.isArray(p.symbol)?p.symbol[l]:p.symbol,f.mc=u.isArrayOrTypedArray(p.color)?p.color[l]:p.color);var d=p&&p.line;d&&(f.mlc=Array.isArray(d.color)?d.color[l]:d.color,f.mlw=u.isArrayOrTypedArray(d.width)?d.width[l]:d.width);var g=p&&p.gradient;g&&\"none\"!==g.type&&(f.mgt=Array.isArray(g.type)?g.type[l]:g.type,f.mgc=Array.isArray(g.color)?g.color[l]:g.color);var v=i.c2p(f.x,!0),m=a.c2p(f.y,!0),y=f.mrc\|\|1,x=n.hoverlabel;x&&(f.hbg=Array.isArray(x.bgcolor)?x.bgcolor[l]:x.bgcolor,f.hbc=Array.isArray(x.bordercolor)?x.bordercolor[l]:x.bordercolor,f.hts=Array.isArray(x.font.size)?x.font.size[l]:x.font.size,f.htc=Array.isArray(x.font.color)?x.font.color[l]:x.font.color,f.htf=Array.isArray(x.font.family)?x.font.family[l]:x.font.family,f.hnl=Array.isArray(x.namelength)?x.namelength[l]:x.namelength);var b=n.hoverinfo;b&&(f.hi=Array.isArray(b)?b[l]:b);var _={};return _[t.index]=f,u.extendFlat(t,{color:w(n,f),x0:v-y,x1:v+y,xLabelVal:f.x,y0:m-y,y1:m+y,yLabelVal:f.y,cd:_,distance:o,spikeDistance:s}),f.htx?t.text=f.htx:f.tx?t.text=f.tx:n.text&&(t.text=n.text),k(f,n,t),c.getComponentMethod(\"errorbars\",\"hoverInfo\")(f,n,t),t}function z(t){var e,r,n=t[0],i=n.trace,a=n.t,o=a._scene,s=a.index,l=o.selectBatch[s],c=o.unselectBatch[s],f=o.textOptions[s],h=o.textSelectedOptions[s]\|\|{},p=o.textUnselectedOptions[s]\|\|{},g=u.extendFlat({},f);if(l&&c){var v=h.color,m=p.color,y=f.color,x=Array.isArray(y);for(g.color=new Array(i._length),e=0;e<l.length;e++)r=l[e],g.color[r]=v\|\|(x?y[r]:y);for(e=0;e<c.length;e++){r=c[e];var b=x?y[r]:y;g.color[r]=m\|\|(v?b:d.addOpacity(b,S))}}o.glText[s].update(g)}e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),calc:function(t,e){var r,n,i,a=t._fullLayout,s=h.getFromId(t,e.xaxis),l=h.getFromId(t,e.yaxis),c=a._plots[e.xaxis+e.yaxis],f=e._length,p=2f,d={},g=s.makeCalcdata(e,\"x\"),v=l.makeCalcdata(e,\"y\"),_=new 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i,a,o,s,l,c,u,f,h,p=t.cd,d=p[0].t,g=p[0].trace,v=t.xa,m=t.ya,y=d.x,x=d.y,b=v.c2p(e),_=m.c2p(r),w=t.distance;if(d.tree){var k=v.p2c(b-w),M=v.p2c(b+w),A=m.p2c(_-w),T=m.p2c(_+w);i=\"x\"===n?d.tree.range(Math.min(k,M),Math.min(m._rl[0],m._rl[1]),Math.max(k,M),Math.max(m._rl[0],m._rl[1])):d.tree.range(Math.min(k,M),Math.min(A,T),Math.max(k,M),Math.max(A,T))}else{if(!d.ids)return[t];i=d.ids}var S=w;if(\"x\"===n)for(l=0;l<i.length;l++)o=y[i[l]],(c=Math.abs(v.c2p(o)-b))<S&&(S=c,u=m.c2p(x[i[l]])-_,h=Math.sqrt(cc+uu),a=i[l]);else for(l=0;l<i.length;l++)o=y[i[l]],s=x[i[l]],c=v.c2p(o)-b,u=m.c2p(s)-_,(f=Math.sqrt(cc+uu))<S&&(S=h=f,a=i[l]);return t.index=a,t.distance=S,t.dxy=h,void 0===a?[t]:(L(t,y,x,g),[t])},selectPoints:function(t,e){var r=t.cd,n=[],i=r[0].trace,a=r[0].t,o=i._length,l=a.x,c=a.y,u=a._scene;if(!u)return n;var f=g.hasText(i),h=g.hasMarkers(i),p=!h&&!f;if(!0!==i.visible\|\|p)return n;var d,v=null,m=null;if(!1===e\|\|e.degenerate)m=s(o);else 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u.selectBatch[a.index]=v,u.unselectBatch[a.index]=m,f&&z(r),n},sceneUpdate:C,calcHover:L,meta:{}}},{\"../../components/color\":570,\"../../constants/interactions\":672,\"../../constants/numerical\":673,\"../../lib\":696,\"../../lib/prepare_regl\":709,\"../../plots/cartesian\":756,\"../../plots/cartesian/autorange\":743,\"../../plots/cartesian/axis_ids\":747,\"../../registry\":827,\"../scatter/calc\":1044,\"../scatter/colorscale_calc\":1046,\"../scatter/cross_trace_defaults\":1049,\"../scatter/fill_hover_text\":1051,\"../scatter/get_trace_color\":1053,\"../scatter/link_traces\":1059,\"../scatter/marker_colorbar\":1061,\"../scatter/subtypes\":1067,\"./attributes\":1092,\"./constants\":1093,\"./convert\":1094,\"./defaults\":1095,\"array-range\":55,\"gl-text\":304,\"point-cluster\":452,\"regl-error2d\":473,\"regl-line2d\":474,\"regl-scatter2d\":475}],1097:[function(t,e,r){\"use strict\";var n=t(\"../scattergeo/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/mapbox/layout_attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorbar/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,u=n.line,f=n.marker;e.exports=c({lon:n.lon,lat:n.lat,mode:l({},i.mode,{dflt:\"markers\"}),text:l({},i.text,{}),hovertext:l({},i.hovertext,{}),line:{color:u.color,width:u.width},connectgaps:i.connectgaps,marker:{symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},opacity:f.opacity,size:f.size,sizeref:f.sizeref,sizemin:f.sizemin,sizemode:f.sizemode,color:f.color,colorscale:f.colorscale,cauto:f.cauto,cmax:f.cmax,cmin:f.cmin,autocolorscale:f.autocolorscale,reversescale:f.reversescale,showscale:f.showscale,colorbar:s},fill:n.fill,fillcolor:i.fillcolor,textfont:a.layers.symbol.textfont,textposition:a.layers.symbol.textposition,selected:{marker:i.selected.marker},unselected:{marker:i.unselected.marker},hoverinfo:l({},o.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorbar/attributes\":571,\"../../lib/extend\":685,\"../../plot_api/edit_types\":727,\"../../plots/attributes\":741,\"../../plots/mapbox/layout_attributes\":804,\"../scatter/attributes\":1043,\"../scattergeo/attributes\":1083}],1098:[function(t,e,r){\"use 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T;if((v\|\|m)&&(e=o.calcTraceToLineCoords(t)),v&&(w.geojson=o.makePolygon(e),w.layout.visibility=\"visible\",i.extendFlat(w.paint,{\"fill-color\":r.fillcolor})),m&&(k.geojson=o.makeLine(e),k.layout.visibility=\"visible\",i.extendFlat(k.paint,{\"line-width\":r.line.width,\"line-color\":r.line.color,\"line-opacity\":r.opacity})),b){var S=function(t){var e,r,a,o,u=t[0].trace,f=u.marker,h=u.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),v=i.isArrayOrTypedArray(f.opacity);function m(t){return u.opacityt}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFunc(s.extractScale(f.colorscale,f.cmin,f.cmax)):i.identity);d&&(a=c(u));v&&(o=function(t){var e=n(t)?+i.constrain(t,0,1):0;return m(e)});var y,x=[];for(e=0;e<t.length;e++){var b=t[e],_=b.lonlat;if(!g(_)){var w={};r&&(w.mcc=b.mcc=r(b.mc)),a&&(w.mrc=b.mrc=a(b.ms)),o&&(w.mo=o(b.mo)),h&&(w.selected=b.selected\|\|0),x.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:_},properties:w})}}if(h)for(y=l.makeSelectedPointStyleFns(u),e=0;e<x.length;e++){var k=x[e].properties;y.selectedOpacityFn&&(k.mo=m(y.selectedOpacityFn(k))),y.selectedColorFn&&(k.mcc=y.selectedColorFn(k)),y.selectedSizeFn&&(k.mrc=y.selectedSizeFn(k))}return{geojson:{type:\"FeatureCollection\",features:x},mcc:p\|\|y&&y.selectedColorFn?{type:\"identity\",property:\"mcc\"}:f.color,mrc:d\|\|y&&y.selectedSizeFn?{type:\"identity\",property:\"mrc\"}:(M=f.size,M/2),mo:v\|\|y&&y.selectedOpacityFn?{type:\"identity\",property:\"mo\"}:m(f.opacity)};var M}(t);M.geojson=S.geojson,M.layout.visibility=\"visible\",i.extendFlat(M.paint,{\"circle-color\":S.mcc,\"circle-radius\":S.mrc,\"circle-opacity\":S.mo})}if((_\|\|x)&&(A.geojson=function(t){for(var e=t[0].trace,r=(e.marker\|\|{}).symbol,n=e.text,i=\"circle\"!==r?p(r):d,a=u.hasText(e)?p(n):d,o=[],s=0;s<t.length;s++){var l=t[s];g(l.lonlat)\|\|o.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:l.lonlat},properties:{symbol:i(l.mx),text:a(l.tx)}})}return{type:\"FeatureCollection\",features:o}}(t),i.extendFlat(A.layout,{visibility:\"visible\",\"icon-image\":\"{symbol}-15\",\"text-field\":\"{text}\"}),_&&(i.extendFlat(A.layout,{\"icon-size\":r.marker.size/10}),i.extendFlat(A.paint,{\"icon-opacity\":r.opacityr.marker.opacity,\"icon-color\":r.marker.color})),x)){var E=(r.marker\|\|{}).size,C=f(r.textposition,E);i.extendFlat(A.layout,{\"text-size\":r.textfont.size,\"text-anchor\":C.anchor,\"text-offset\":C.offset}),i.extendFlat(A.paint,{\"text-color\":r.textfont.color,\"text-opacity\":r.opacity})}return T}},{\"../../components/colorscale\":585,\"../../components/drawing\":595,\"../../constants/numerical\":673,\"../../lib\":696,\"../../lib/geojson_utils\":689,\"../../plots/mapbox/convert_text_opts\":801,\"../scatter/make_bubble_size_func\":1060,\"../scatter/subtypes\":1067,\"fast-isnumeric\":214}],1099:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}if(function(t,e,r){var n=r(\"lon\")\|\|[],i=r(\"lat\")\|\|[],a=Math.min(n.length,i.length);return e._length=a,a}(0,e,f)){if(f(\"text\"),f(\"hovertext\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f,{noDash:!0}),f(\"connectgaps\")),i.hasMarkers(e)){a(t,e,r,u,f,{noLine:!0});var h=e.marker;\"circle\"!==h.symbol&&(n.isArrayOrTypedArray(h.size)&&(h.size=h.size[0]),n.isArrayOrTypedArray(h.color)&&(h.color=h.color[0]))}i.hasText(e)&&s(t,e,u,f,{noSelect:!0}),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)}else e.visible=!1}},{\"../../lib\":696,\"../scatter/fillcolor_defaults\":1052,\"../scatter/line_defaults\":1056,\"../scatter/marker_defaults\":1062,\"../scatter/subtypes\":1067,\"../scatter/text_defaults\":1068,\"./attributes\":1097}],1100:[function(t,e,r){\"use strict\";e.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t}},{}],1101:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\"),o=t(\"../scatter/fill_hover_text\"),s=t(\"../../constants/numerical\").BADNUM;e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=360(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),d=e-p;if(n.getClosest(l,function(t){var e=t.lonlat;if(e[0]===s)return 1/0;var n=i.modHalf(e[0],360),a=e[1],o=h.project([n,a]),l=o.x-u.c2p([d,a]),c=o.y-f.c2p([n,r]),p=Math.max(3,t.mrc\|\|0);return Math.max(Math.sqrt(ll+cc)-p,1-3/p)},t),!1!==t.index){var g=l[t.index],v=g.lonlat,m=[i.modHalf(v[0],360)+p,v[1]],y=u.c2p(m),x=f.c2p(m),b=g.mrc\|\|1;return t.x0=y-b,t.x1=y+b,t.y0=x-b,t.y1=x+b,t.color=a(c,g),t.extraText=function(t,e,r){var n=(e.hi\|\|t.hoverinfo).split(\"+\"),i=-1!==n.indexOf(\"all\"),a=-1!==n.indexOf(\"lon\"),s=-1!==n.indexOf(\"lat\"),l=e.lonlat,c=[];function u(t){return t+\"\\xb0\"}i\|\|a&&s?c.push(\"(\"+u(l[0])+\", \"+u(l[1])+\")\"):a?c.push(r.lon+u(l[0])):s&&c.push(r.lat+u(l[1]));(i\|\|-1!==n.indexOf(\"text\"))&&o(e,t,c);return c.join(\"<br>\")}(c,g,l[0].t.labels),[t]}}},{\"../../components/fx\":612,\"../../constants/numerical\":673,\"../../lib\":696,\"../scatter/fill_hover_text\":1051,\"../scatter/get_trace_color\":1053}],1102:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/marker_colorbar\"),n.calc=t(\"../scattergeo/calc\"),n.plot=t(\"./plot\"),n.hoverPoints=t(\"./hover\"),n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.style=function(t,e){e&&e[0].trace._glTrace.update(e)},n.moduleType=\"trace\",n.name=\"scattermapbox\",n.basePlotModule=t(\"../../plots/mapbox\"),n.categories=[\"mapbox\",\"gl\",\"symbols\",\"showLegend\",\"scatterlike\"],n.meta={},e.exports=n},{\"../../plots/mapbox\":802,\"../scatter/marker_colorbar\":1061,\"../scattergeo/calc\":1084,\"./attributes\":1097,\"./defaults\":1099,\"./event_data\":1100,\"./hover\":1101,\"./plot\":1103,\"./select\":1104}],1103:[function(t,e,r){\"use strict\";var n=t(\"./convert\");function 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n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../components/drawing/attributes\").dash,l=t(\"../../lib/extend\").extendFlat,c=n.marker,u=n.line,f=c.line;e.exports={a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},c:{valType:\"data_array\",editType:\"calc\"},sum:{valType:\"number\",dflt:0,min:0,editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),hovertext:l({},n.hovertext,{}),line:{color:u.color,width:u.width,dash:s,shape:l({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,cliponaxis:n.cliponaxis,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:c.symbol,opacity:c.opacity,maxdisplayed:c.maxdisplayed,size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,line:l({width:f.width,editType:\"calc\"},a(\"marker.line\")),gradient:c.gradient,editType:\"calc\"},a(\"marker\"),{colorbar:o}),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"c\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":571,\"../../components/colorscale/attributes\":577,\"../../components/drawing/attributes\":594,\"../../lib/extend\":685,\"../../plots/attributes\":741,\"../scatter/attributes\":1043}],1115:[function(t,e,r){\"use 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"display_data" } ], "source": [ "import plotly.offline as py\n", "import plotly.graph_objs as go\n", "\n", "py.init_notebook_mode()\n", "\n", "def plot_difference(mdiff, title=\"\", annotation=None):\n", " \"\"\"\n", " Helper function for plot difference between models\n", " \"\"\"\n", " annotation_html = None\n", " if annotation is not None:\n", " annotation_html = [\n", " [\n", " \"+++ {}<br>--- {}\".format(\", \".join(int_tokens), \", \".join(diff_tokens)) \n", " for (int_tokens, diff_tokens) in row\n", " ] \n", " for row in annotation\n", " ]\n", " \n", " data = go.Heatmap(z=mdiff, colorscale='RdBu', text=annotation_html)\n", " layout = go.Layout(width=950, height=950, title=title, xaxis=dict(title=\"topic\"), yaxis=dict(title=\"topic\"))\n", " py.iplot(dict(data=[data], layout=layout))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In gensim, you can visualise topic different with matrix and annotation. For this purposes, you can use method `diff` from LdaModel.\n", "\n", "This function return matrix with distances <span style=\"color:green\">mdiff</span> and matrix with annotations <span style=\"color:green\">annotation</span>. Read the docstring for more detailed info.\n", "\n", "In cells <span style=\"color:green\">mdiff[i][j]</span> we can see a distance between <span style=\"color:green\">topic_i</span> from the first model and <span style=\"color:green\">topic_j</span> from the second model.\n", "\n", "In cells <span style=\"color:green\">annotation[i][j]</span> we can see <span style=\"color:green\">[tokens from intersection, tokens from difference]</span> between <span style=\"color:green\">topic_i</span> from first model and <span style=\"color:green\">topic_j</span> from the second model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "LdaMulticore.diff?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Case 1: How topics in ONE model correlate with each other." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Short description:\n", "- x-axis - topic;\n", "- y-axis - topic;\n", "- <span style=\"color:red\">almost red cell</span> - strongly decorrelated topics;\n", "- <span style=\"color:blue\">almost blue cell</span> - strongly correlated topics.\n", "\n", "In an ideal world, we would like to see different topics decorrelated between themselves. In this case, our matrix would look like this:\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "colorscale": "RdBu", "type": "heatmap", "uid": "973157fc-4482-42a4-9058-906b13c21939", "z": [ [ 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ], [ 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ], [ 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ], [ 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ], [ 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ], [ 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 ], [ 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1 ], [ 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1 ], [ 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 ], [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1 ], [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1 ], [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1 ], [ 1, 1, 1, 1, 1, 1, 1, 1, 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\"topic\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "mdiff = np.ones((num_topics, num_topics))\n", "np.fill_diagonal(mdiff, 0.)\n", " \n", "plot_difference(mdiff, title=\"Topic difference (one model) in ideal world\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, in real life, not everything is so good, and the matrix looks different." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Short description (annotations):\n", "- +++ make, world, well - words from the intersection of topics;\n", "- --- money, day, still - words from the symmetric difference of topics." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "colorscale": "RdBu", "text": [ [ "+++ said, well, two, american, right, arab, say, palestinian, mani, want<br>--- ", "+++ even, could, make, well, time, question, right, also, say, want<br>--- two, mani, give, thing, sgi, chip, world, need, 000, jewish", "+++ could, time, make, also, want<br>--- well, two, right, say, mani, thank, give, speed, simm, chip", "+++ even, world, could, peopl, make, well, time, right, say, want<br>--- car, two, give, thing, also, happen, need, 000, jewish, question", "+++ want, time, think, make<br>--- car, well, two, c8v, right, say, mani, b8f, okz, give", "+++ even, world, could, said, kill, well, time, two, peopl, right<br>--- turkey, went, mani, give, also, serdar, need, number, 000, jewish", "+++ even, said, make, well, time, two, right, also, say, think<br>--- hockey, presid, mani, give, stephanopoulo, nhl, score, world, 000, jewish", "+++ could, make, well, time, two, question, also, want, year<br>--- current, right, say, mani, give, sale, world, need, 000, jewish", "+++ make, time, also, want, call, way<br>--- server, client, well, two, right, say, mani, resourc, thank, display", "+++ way, world, peopl, make, well, time, two, question, right, also<br>--- mani, thank, access, give, opinion, great, 000, jewish, dod, much", "+++ even, way, peopl, could, make, well, time, two, right, also<br>--- car, give, thing, world, number, 000, jewish, question, amend, polici", "+++ want, also, time<br>--- well, two, right, say, mani, thank, give, sale, inform, world", "+++ said, well, two, say, mani, want, even, make, also, see<br>--- point, law, right, arab, american, palestinian, live, believ, argument, govern", "+++ said, well, right, say, mani, want, even, make, also, see<br>--- atheist, realli, two, point, must, arab, palestinian, live, believ, object", "+++ could, peopl, make, time, two, right, also, think, govern, year<br>--- well, presid, say, mani, develop, access, give, inform, chip, world" ], [ "+++ even, could, make, well, time, question, right, also, say, want<br>--- two, mani, give, thing, sgi, chip, world, need, 000, jewish", "+++ realli, well, law, right, say, indiana, want, run, disk, state<br>--- ", "+++ want, run, disk, tri, make, also, new, problem, version, chip<br>--- realli, well, right, say, mac, thank, ide, thing, speed, simm", "+++ realli, well, right, say, want, state, tri, even, make, thing<br>--- car, point, law, repli, indiana, mani, run, disk, system, got", "+++ need, make, time, new, look, distribut, want, good, think, anyon<br>--- car, well, c8v, right, say, b8f, okz, thing, also, sgi", "+++ even, could, need, time, well, right, new, say, look, want<br>--- two, turkey, went, thing, also, serdar, sgi, chip, world, number", "+++ even, make, well, time, right, new, say, also, look, think<br>--- hockey, two, presid, stephanopoulo, nhl, thing, sgi, score, chip, need", "+++ could, need, make, well, time, question, new, also, look, distribut<br>--- current, two, right, say, thing, sale, sgi, chip, much, softwar", "+++ need, make, time, bit, also, look, distribut, want, run, problem<br>--- server, client, well, right, say, resourc, thank, display, thing, sourc", "+++ well, right, say, want, run, state, tri, make, new, also<br>--- realli, two, thank, access, opinion, thing, sgi, chip, world, need", "+++ well, law, right, say, state, even, make, thing, new, also<br>--- car, realli, two, file, weapon, mani, run, health, crime, sgi", "+++ need, time, drive, new, also, look, distribut, want, run, system<br>--- well, right, say, thank, thing, sale, sgi, inform, chip, question", "+++ well, law, say, want, even, make, thing, also, new, see<br>--- realli, two, right, mani, run, church, sgi, claim, chip, world", "+++ realli, well, right, say, want, tri, even, make, thing, also<br>--- atheist, said, point, law, must, indiana, mani, run, believ, object", "+++ could, need, make, time, key, law, new, right, also, distribut<br>--- well, two, presid, say, develop, access, thing, sgi, inform, clipper" ], [ "+++ could, time, make, also, want<br>--- well, two, right, say, mani, thank, give, speed, simm, chip", "+++ want, run, disk, tri, system, make, also, new, problem, version<br>--- realli, well, law, right, say, repli, indiana, monitor, mac, color", "+++ repli, monitor, want, mac, run, thank, disk, mous, ide, color<br>--- ", "+++ could, need, make, time, anyon, look, repli, distribut, want, problem<br>--- car, well, right, say, mani, thank, thing, also, speed, simm", "+++ need, make, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, speed, simm, uchicago, chip", "+++ could, need, time, new, look, want, work<br>--- well, two, turkey, right, say, went, thank, also, speed, serdar", "+++ make, time, also, new, look, run<br>--- hockey, well, two, presid, right, say, thank, stephanopoulo, nhl, speed", "+++ could, need, make, time, new, also, repli, look, distribut, want<br>--- well, current, two, thank, speed, simm, sale, chip, question, nasa", "+++ repli, want, color, run, thank, set, tri, make, help, also<br>--- server, client, motif, applic, monitor, mous, resourc, mac, data, disk", "+++ make, time, new, also, repli, look, distribut, want, run, thank<br>--- well, two, right, say, access, opinion, speed, simm, chip, world", "+++ could, make, time, also, new, problem, scsi, control, system<br>--- car, well, two, right, say, mani, thank, thing, speed, simm", "+++ repli, want, mac, run, thank, system, help, new, also, version<br>--- imag, price, monitor, color, mous, netcom, disk, data, ide, instal", "+++ could, make, time, new, also, want<br>--- well, two, say, mani, thank, thing, speed, simm, chip, world", "+++ could, make, time, also, want, system, problem, tri<br>--- well, right, say, mani, thank, thing, speed, simm, chip, world", "+++ could, need, make, time, comput, also, new, distribut, work, system<br>--- two, presid, right, thank, develop, access, speed, simm, inform, clipper" ], [ "+++ even, world, could, peopl, make, well, time, right, say, mani<br>--- car, two, give, thing, also, happen, need, 000, jewish, question", "+++ realli, well, right, say, want, state, tri, even, make, thing<br>--- car, point, law, first, repli, indiana, mani, run, disk, got", "+++ could, need, make, time, anyon, look, repli, distribut, want, problem<br>--- car, well, right, say, mani, thank, thing, also, speed, simm", "+++ car, realli, well, point, first, right, say, repli, want, mani<br>--- ", "+++ need, car, make, time, look, repli, distribut, want, good, think<br>--- well, c8v, right, say, mani, b8f, okz, thing, uchicago, happen", "+++ even, world, could, need, time, well, peopl, right, look, say<br>--- car, two, turkey, went, mani, thing, serdar, happen, number, dod", "+++ even, make, well, time, point, right, look, say, good, think<br>--- car, hockey, two, presid, mani, stephanopoulo, nhl, also, thing, score", "+++ could, need, gov, well, make, time, point, look, repli, sun<br>--- car, current, two, right, say, mani, thing, also, sale, happen", "+++ need, make, time, look, repli, distribut, want, problem, tri, way<br>--- server, client, car, well, right, say, mani, resourc, thank, display", "+++ well, right, say, repli, want, state, tri, make, problem, year<br>--- car, realli, two, point, first, mani, run, thank, net, bnr", "+++ car, well, right, say, mani, state, even, make, thing, problem<br>--- realli, two, point, law, repli, weapon, want, health, system, case", "+++ need, time, look, repli, distribut, want, anyon<br>--- car, well, right, say, mani, thank, thing, also, sale, inform", "+++ well, point, say, mani, want, even, make, thing, see, world<br>--- said, car, realli, two, first, law, right, repli, believ, argument", "+++ realli, well, point, right, say, mani, want, tri, even, make<br>--- atheist, said, car, first, must, repli, believ, object, state, got", "+++ could, need, make, time, peopl, right, distribut, think, year, state<br>--- car, well, two, presid, say, mani, develop, access, thing, also" ], [ "+++ want, time, think, make<br>--- car, well, two, right, c8v, say, mani, b8f, okz, give", "+++ need, make, time, new, look, distribut, want, good, think, anyon<br>--- car, well, right, c8v, say, b8f, okz, thing, also, sgi", "+++ need, make, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, speed, simm, uchicago, chip", "+++ need, car, make, time, look, repli, distribut, want, good, think<br>--- well, right, c8v, say, mani, b8f, okz, thing, uchicago, happen", "+++ 2tm, 1eq, car, c8v, 2di, repli, want, b8f, 1d9, 7ey<br>--- ", "+++ need, time, new, look, want, think<br>--- car, well, two, turkey, right, c8v, say, went, b8f, okz", "+++ time, make, new, look, good, think<br>--- car, hockey, well, two, presid, right, c8v, say, b8f, okz", "+++ need, make, time, new, look, repli, distribut, want, good, engin<br>--- car, well, current, two, c8v, b8f, okz, also, sale, uchicago", "+++ need, make, time, look, repli, distribut, want<br>--- server, client, car, c8v, b8f, resourc, thank, okz, display, also", "+++ make, time, new, look, repli, usa, want, distribut, good, think<br>--- car, well, two, right, c8v, say, b8f, thank, okz, access", "+++ car, time, make, new, good, think<br>--- well, two, right, c8v, say, mani, b8f, okz, thing, also", "+++ need, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, sale, uchicago, inform, softwar", "+++ time, make, new, want, good, think<br>--- car, well, two, c8v, say, mani, b8f, okz, thing, also", "+++ time, make, want, good, think<br>--- car, well, right, c8v, say, mani, b8f, okz, thing, also", "+++ need, time, make, new, distribut, think<br>--- car, two, presid, right, c8v, b8f, develop, okz, access, also" ], [ "+++ even, world, could, said, kill, well, time, two, peopl, right<br>--- turkey, went, mani, give, also, serdar, need, number, 000, jewish", "+++ even, could, need, time, well, right, new, say, look, want<br>--- two, turkey, went, thing, also, serdar, sgi, chip, world, number", "+++ could, need, time, new, look, want, work<br>--- well, two, turkey, right, say, went, thank, also, speed, serdar", "+++ even, world, could, need, time, well, peopl, right, look, say<br>--- car, two, turkey, went, mani, thing, serdar, happen, number, dod", "+++ need, time, new, look, want, think<br>--- car, well, two, turkey, c8v, right, say, went, b8f, okz", "+++ said, armenia, well, two, turkey, right, say, went, want, live<br>--- ", "+++ even, said, time, well, two, right, new, say, look, think<br>--- hockey, presid, turkey, went, stephanopoulo, nhl, also, serdar, score, world", "+++ could, need, time, well, two, new, look, want, year, work<br>--- current, turkey, right, say, went, also, serdar, sale, world, number", "+++ program, need, time, number, work, look, want, name, file, call<br>--- server, client, well, two, turkey, right, say, went, resourc, thank", "+++ world, peopl, time, well, two, right, new, say, look, want<br>--- turkey, went, thank, access, opinion, also, serdar, need, great, number", "+++ even, peopl, could, time, well, number, two, right, new, say<br>--- car, turkey, went, mani, thing, also, serdar, world, need, much", "+++ program, need, time, new, look, want, file, work<br>--- well, two, turkey, right, say, went, thank, also, serdar, sale", "+++ even, world, could, said, time, well, two, new, say, want<br>--- turkey, right, went, mani, thing, also, serdar, need, number, question", "+++ even, world, could, said, time, well, right, say, want, think<br>--- two, turkey, went, mani, thing, also, serdar, need, number, question", "+++ program, could, need, time, peopl, two, new, right, year, think<br>--- well, presid, turkey, say, went, develop, access, also, serdar, inform" ], [ "+++ even, said, make, well, time, two, right, also, say, think<br>--- hockey, presid, mani, give, stephanopoulo, nhl, score, world, 000, jewish", "+++ even, make, well, time, 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right, say, mani, thing, also, sale, happen\", \"+++ need, make, time, look, repli, distribut, want, problem, tri, way<br>--- server, client, car, well, right, say, mani, resourc, thank, display\", \"+++ well, right, say, repli, want, state, tri, make, problem, year<br>--- car, realli, two, point, first, mani, run, thank, net, bnr\", \"+++ car, well, right, say, mani, state, even, make, thing, problem<br>--- realli, two, point, law, repli, weapon, want, health, system, case\", \"+++ need, time, look, repli, distribut, want, anyon<br>--- car, well, right, say, mani, thank, thing, also, sale, inform\", \"+++ well, point, say, mani, want, even, make, thing, see, world<br>--- said, car, realli, two, first, law, right, repli, believ, argument\", \"+++ realli, well, point, right, say, mani, want, tri, even, make<br>--- atheist, said, car, first, must, repli, believ, object, state, got\", \"+++ could, need, make, time, peopl, right, distribut, think, year, state<br>--- car, well, two, presid, say, mani, develop, access, thing, also\"], [\"+++ want, time, think, make<br>--- car, well, two, right, c8v, say, mani, b8f, okz, give\", \"+++ need, make, time, new, look, distribut, want, good, think, anyon<br>--- car, well, right, c8v, say, b8f, okz, thing, also, sgi\", \"+++ need, make, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, speed, simm, uchicago, chip\", \"+++ need, car, make, time, look, repli, distribut, want, good, think<br>--- well, right, c8v, say, mani, b8f, okz, thing, uchicago, happen\", \"+++ 2tm, 1eq, car, c8v, 2di, repli, want, b8f, 1d9, 7ey<br>--- \", \"+++ need, time, new, look, want, think<br>--- car, well, two, turkey, right, c8v, say, went, b8f, okz\", \"+++ time, make, new, look, good, think<br>--- car, hockey, well, two, presid, right, c8v, say, b8f, okz\", \"+++ need, make, time, new, look, repli, distribut, want, good, engin<br>--- car, well, current, two, c8v, b8f, okz, also, sale, uchicago\", \"+++ need, make, 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0.8353596757852078, 0.9043748505857041, 0.9043748505857041, 0.91725768321513, 0.0]], \"type\": \"heatmap\", \"uid\": \"554e3414-6cb8-445f-aa34-35e83307fe5f\"}], {\"height\": 950, \"title\": \"Topic difference (one model) [jaccard distance]\", \"width\": 950, \"xaxis\": {\"title\": \"topic\"}, \"yaxis\": {\"title\": \"topic\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mdiff, annotation = lda_fst.diff(lda_fst, distance='jaccard', num_words=50)\n", "plot_difference(mdiff, title=\"Topic difference (one model) [jaccard distance]\", annotation=annotation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you compare a model with itself, you want to see as many red elements as possible (except diagonal). With this picture, you can look at the not very red elements and understand which topics in the model are very similar and why (you can read annotation if you move your pointer to cell).\n", "\n", "\n", "Jaccard is stable and robust distance function, but this function not enough sensitive for some purposes. Let's try to use Hellinger distance now." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "colorscale": "RdBu", "text": [ [ "+++ said, well, two, american, right, arab, say, palestinian, mani, want<br>--- ", "+++ even, could, make, well, time, question, right, also, say, want<br>--- two, mani, give, thing, sgi, chip, world, need, 000, jewish", "+++ could, time, make, also, want<br>--- well, two, right, say, mani, thank, give, speed, simm, chip", "+++ even, world, could, peopl, make, well, time, right, say, want<br>--- car, two, give, thing, also, happen, need, 000, jewish, question", "+++ want, time, think, make<br>--- car, well, two, c8v, right, say, mani, b8f, okz, give", "+++ even, world, could, said, kill, well, time, two, peopl, right<br>--- turkey, went, mani, give, also, serdar, need, number, 000, jewish", "+++ even, said, make, well, time, two, right, also, say, think<br>--- hockey, presid, mani, give, stephanopoulo, nhl, score, world, 000, jewish", "+++ could, make, well, time, two, question, also, want, year<br>--- current, right, say, mani, give, sale, world, need, 000, jewish", "+++ make, time, also, want, call, way<br>--- server, client, well, two, right, say, mani, resourc, thank, display", "+++ way, world, peopl, make, well, time, two, question, right, also<br>--- mani, thank, access, give, opinion, great, 000, jewish, dod, much", "+++ even, way, peopl, could, make, well, time, two, right, also<br>--- car, give, thing, world, number, 000, jewish, question, amend, polici", "+++ want, also, time<br>--- well, two, right, say, mani, thank, give, sale, inform, world", "+++ said, well, two, say, mani, want, even, make, also, see<br>--- point, law, right, arab, american, palestinian, live, believ, argument, govern", "+++ said, well, right, say, mani, want, even, 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world, number", "+++ even, make, well, time, right, new, say, also, look, think<br>--- hockey, two, presid, stephanopoulo, nhl, thing, sgi, score, chip, need", "+++ could, need, make, well, time, question, new, also, look, distribut<br>--- current, two, right, say, thing, sale, sgi, chip, much, softwar", "+++ need, make, time, bit, also, look, distribut, want, run, problem<br>--- server, client, well, right, say, resourc, thank, display, thing, sourc", "+++ well, right, say, want, run, state, tri, make, new, also<br>--- realli, two, thank, access, opinion, thing, sgi, chip, world, need", "+++ well, law, right, say, state, even, make, thing, new, also<br>--- car, realli, two, file, weapon, mani, run, health, crime, sgi", "+++ need, time, drive, new, also, look, distribut, want, run, system<br>--- well, right, say, thank, thing, sale, sgi, inform, chip, question", "+++ well, law, say, want, even, make, thing, also, new, see<br>--- realli, two, right, mani, run, church, sgi, claim, chip, world", "+++ realli, well, right, say, want, tri, even, make, thing, also<br>--- atheist, said, point, law, must, indiana, mani, run, believ, object", "+++ could, need, make, time, key, law, new, right, also, distribut<br>--- well, two, presid, say, develop, access, thing, sgi, inform, clipper" ], [ "+++ could, time, make, also, want<br>--- well, two, right, say, mani, thank, give, speed, simm, chip", "+++ want, run, disk, tri, system, make, also, new, problem, version<br>--- realli, well, law, right, say, repli, indiana, monitor, mac, color", "+++ repli, monitor, want, mac, run, thank, disk, mous, ide, color<br>--- ", "+++ could, need, make, time, anyon, look, repli, distribut, want, problem<br>--- car, well, right, say, mani, thank, thing, also, speed, simm", "+++ need, make, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, speed, simm, uchicago, chip", "+++ could, need, time, new, look, want, work<br>--- well, two, turkey, right, say, went, thank, also, speed, serdar", "+++ make, time, also, new, look, run<br>--- hockey, well, two, presid, right, say, thank, stephanopoulo, nhl, speed", "+++ could, need, make, time, new, also, repli, look, distribut, want<br>--- well, current, two, thank, speed, simm, sale, chip, question, nasa", "+++ repli, want, color, run, thank, set, tri, make, help, also<br>--- server, client, motif, applic, monitor, mous, resourc, mac, data, disk", "+++ make, time, new, also, repli, look, distribut, want, run, thank<br>--- well, two, right, say, access, opinion, speed, simm, chip, world", "+++ could, make, time, also, new, problem, scsi, control, system<br>--- car, well, two, right, say, mani, thank, thing, speed, simm", "+++ repli, want, mac, run, thank, system, help, new, also, version<br>--- imag, price, monitor, color, mous, netcom, disk, data, ide, instal", "+++ could, make, time, new, also, want<br>--- well, two, say, mani, thank, thing, speed, simm, chip, world", "+++ could, make, time, also, want, system, problem, tri<br>--- well, right, say, mani, thank, thing, speed, simm, chip, world", "+++ could, need, make, time, comput, also, new, distribut, work, system<br>--- two, presid, right, thank, develop, access, speed, simm, inform, clipper" ], [ "+++ even, world, could, peopl, make, well, time, right, say, mani<br>--- car, two, give, thing, also, happen, need, 000, jewish, question", "+++ realli, well, right, say, want, state, tri, even, make, thing<br>--- car, point, law, first, repli, indiana, mani, run, disk, got", "+++ could, need, make, time, anyon, look, repli, distribut, want, problem<br>--- car, well, right, say, mani, thank, thing, also, speed, simm", "+++ car, realli, well, point, first, right, say, repli, want, mani<br>--- ", "+++ need, car, make, time, look, repli, distribut, want, good, think<br>--- well, c8v, right, say, mani, b8f, okz, thing, uchicago, happen", "+++ even, world, could, need, time, well, peopl, right, look, say<br>--- car, two, turkey, went, mani, thing, serdar, happen, number, dod", "+++ even, make, well, time, point, right, look, say, good, think<br>--- car, hockey, two, presid, mani, stephanopoulo, nhl, also, thing, score", "+++ could, need, gov, well, make, time, point, look, repli, sun<br>--- car, current, two, right, say, mani, thing, also, sale, happen", "+++ need, make, time, look, repli, distribut, want, problem, tri, way<br>--- server, client, car, well, right, say, mani, resourc, thank, display", "+++ well, right, say, repli, want, state, tri, make, problem, year<br>--- car, realli, two, point, first, mani, run, thank, net, bnr", "+++ car, well, right, say, mani, state, even, make, thing, problem<br>--- realli, two, point, law, repli, weapon, want, health, system, case", "+++ need, time, look, repli, distribut, want, anyon<br>--- car, well, right, say, mani, thank, thing, also, sale, inform", "+++ well, point, say, mani, want, even, make, thing, see, world<br>--- said, car, realli, two, first, law, right, repli, believ, argument", "+++ realli, well, point, right, say, mani, want, tri, even, make<br>--- atheist, said, car, first, must, repli, believ, object, state, got", "+++ could, need, make, time, peopl, right, distribut, think, year, state<br>--- car, well, two, presid, say, mani, develop, access, thing, also" ], [ "+++ want, time, think, make<br>--- car, well, two, right, c8v, say, mani, b8f, okz, give", "+++ need, make, time, new, look, distribut, want, good, think, anyon<br>--- car, well, right, c8v, say, b8f, okz, thing, also, sgi", "+++ need, make, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, speed, simm, uchicago, chip", "+++ need, car, make, time, look, repli, distribut, want, good, think<br>--- well, right, c8v, say, mani, b8f, okz, thing, uchicago, happen", "+++ 2tm, 1eq, car, c8v, 2di, repli, want, b8f, 1d9, 7ey<br>--- ", "+++ need, time, new, look, want, think<br>--- car, well, two, turkey, right, c8v, say, went, b8f, okz", "+++ time, make, new, look, good, think<br>--- car, hockey, well, two, presid, right, c8v, say, b8f, okz", "+++ need, make, time, new, look, repli, distribut, want, good, engin<br>--- car, well, current, two, c8v, b8f, okz, also, sale, uchicago", "+++ need, make, time, look, repli, distribut, want<br>--- server, client, car, c8v, b8f, resourc, thank, okz, display, also", "+++ make, time, new, look, repli, usa, want, distribut, good, think<br>--- car, well, two, right, c8v, say, b8f, thank, okz, access", "+++ car, time, make, new, good, think<br>--- well, two, right, c8v, say, mani, b8f, okz, thing, also", "+++ need, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, sale, uchicago, inform, softwar", "+++ time, make, new, want, good, think<br>--- car, well, two, c8v, say, mani, b8f, okz, thing, also", "+++ time, make, want, good, think<br>--- car, well, right, c8v, say, mani, b8f, okz, thing, also", "+++ need, time, make, new, distribut, think<br>--- car, two, presid, right, c8v, b8f, develop, okz, access, also" ], [ "+++ even, world, could, said, kill, well, time, two, peopl, right<br>--- turkey, went, mani, give, also, serdar, need, number, 000, jewish", "+++ even, could, need, time, well, right, new, say, look, want<br>--- two, turkey, went, thing, also, serdar, sgi, chip, world, number", "+++ could, need, time, new, look, want, work<br>--- well, two, turkey, right, say, went, thank, also, speed, serdar", "+++ even, world, could, need, time, well, peopl, right, look, say<br>--- car, two, turkey, went, mani, thing, serdar, happen, number, dod", "+++ need, time, new, look, want, think<br>--- car, well, two, turkey, c8v, right, say, went, b8f, okz", "+++ said, armenia, well, two, turkey, right, say, went, want, live<br>--- ", "+++ even, said, time, well, two, right, new, say, look, think<br>--- hockey, presid, turkey, went, stephanopoulo, nhl, also, serdar, score, world", "+++ could, need, time, well, two, new, look, want, year, work<br>--- current, turkey, right, say, went, also, serdar, sale, world, number", "+++ program, need, time, number, work, look, want, name, file, call<br>--- server, client, well, two, turkey, right, say, went, resourc, thank", "+++ world, peopl, time, well, two, right, new, say, look, want<br>--- turkey, went, thank, access, opinion, also, serdar, need, great, number", "+++ even, peopl, could, time, well, number, two, right, new, say<br>--- car, turkey, went, mani, thing, also, serdar, world, need, much", "+++ program, need, time, new, look, want, file, work<br>--- well, two, turkey, right, say, went, thank, also, serdar, sale", "+++ even, world, could, said, time, well, two, new, say, want<br>--- turkey, right, went, mani, thing, also, serdar, need, number, question", "+++ even, world, could, said, time, well, right, say, want, think<br>--- two, turkey, went, mani, thing, also, serdar, need, number, question", "+++ program, could, need, time, peopl, two, new, right, year, think<br>--- well, presid, turkey, say, went, develop, access, also, serdar, inform" ], [ "+++ even, said, make, well, time, two, right, also, say, think<br>--- hockey, presid, mani, give, stephanopoulo, nhl, score, world, 000, jewish", "+++ even, make, well, time, right, also, say, new, look, think<br>--- hockey, two, presid, stephanopoulo, thing, nhl, sgi, score, chip, need", "+++ make, time, also, look, new, run<br>--- hockey, well, two, presid, right, say, thank, stephanopoulo, nhl, speed", "+++ even, make, well, time, point, right, look, say, good, think<br>--- car, hockey, two, presid, mani, stephanopoulo, thing, nhl, also, score", "+++ time, make, new, look, good, think<br>--- car, hockey, well, two, presid, c8v, right, say, b8f, okz", "+++ even, said, time, well, two, right, new, say, look, think<br>--- hockey, presid, turkey, went, stephanopoulo, nhl, also, serdar, score, world", "+++ game, said, play, hockey, well, two, point, presid, hit, right<br>--- ", "+++ make, well, time, two, point, new, also, look, good, run<br>--- hockey, current, presid, right, say, stephanopoulo, nhl, sale, score, need", "+++ make, time, also, look, run<br>--- server, client, hockey, well, two, presid, right, say, resourc, thank", "+++ make, well, time, two, right, new, say, also, look, good<br>--- hockey, presid, thank, access, opinion, stephanopoulo, nhl, score, world, great", "+++ day, even, make, well, time, two, right, new, say, also<br>--- car, hockey, presid, mani, stephanopoulo, thing, nhl, score, number, season", "+++ time, also, new, look, run<br>--- hockey, well, two, presid, right, say, thank, stephanopoulo, nhl, sale", "+++ day, even, said, make, well, time, two, point, also, new<br>--- hockey, presid, right, mani, stephanopoulo, thing, nhl, score, world, question", "+++ even, said, make, well, time, point, right, also, say, think<br>--- hockey, two, presid, mani, stephanopoulo, thing, nhl, score, world, question", "+++ make, time, two, presid, right, new, also, year, think, first<br>--- hockey, well, say, develop, access, stephanopoulo, nhl, inform, chip, score" ], [ "+++ could, make, well, time, two, question, also, want, year<br>--- current, right, say, mani, give, sale, world, need, 000, jewish", "+++ could, need, make, well, time, question, also, new, look, distribut<br>--- current, two, right, say, thing, sale, sgi, chip, much, softwar", "+++ could, need, make, time, also, new, repli, look, distribut, want<br>--- well, current, two, thank, speed, simm, sale, chip, question, nasa", "+++ could, need, make, well, gov, time, point, look, repli, sun<br>--- car, current, two, right, say, mani, thing, also, sale, happen", "+++ need, make, time, new, look, repli, distribut, want, good, engin<br>--- car, well, current, two, c8v, b8f, okz, also, sale, uchicago", "+++ could, need, time, well, two, new, look, want, year, work<br>--- current, turkey, right, say, went, also, serdar, sale, world, number", "+++ make, well, time, two, point, new, also, look, good, run<br>--- hockey, current, presid, right, say, stephanopoulo, nhl, sale, score, need", "+++ well, imag, two, point, current, wire, repli, want, pitt, run<br>--- ", "+++ need, make, time, also, look, repli, includ, want, distribut, run<br>--- server, client, well, current, two, resourc, thank, display, sourc, sale", "+++ make, well, time, two, question, new, also, repli, look, distribut<br>--- current, right, say, thank, access, opinion, sale, world, need, great", "+++ could, make, well, time, two, first, new, also, good, year<br>--- car, current, right, say, mani, thing, sale, need, number, question", "+++ need, time, imag, new, also, repli, look, includ, want, interest<br>--- well, current, two, thank, inform, question, softwar, address, list, nasa", "+++ could, make, well, scienc, two, point, question, also, new, time<br>--- current, say, mani, thing, sale, world, need, much, work, nasa", "+++ could, make, well, time, point, question, also, want, good, system<br>--- current, two, right, say, mani, thing, sale, world, need, work", "+++ could, need, make, time, two, also, new, space, distribut, nasa<br>--- well, current, presid, right, develop, access, sale, inform, chip, clipper" ], [ "+++ make, time, also, want, call, way<br>--- server, client, well, two, right, say, mani, resourc, thank, display", "+++ need, make, time, bit, also, look, distribut, want, run, problem<br>--- server, client, well, right, say, resourc, thank, display, thing, sourc", "+++ repli, want, color, run, thank, set, tri, make, help, also<br>--- server, client, motif, applic, file, monitor, mac, disk, mous, ide", "+++ need, make, time, look, repli, distribut, want, problem, tri, way<br>--- server, client, car, well, right, say, mani, resourc, thank, display", "+++ need, make, time, look, repli, distribut, want<br>--- server, client, car, c8v, b8f, resourc, thank, okz, display, also", "+++ program, need, time, number, work, look, want, name, file, call<br>--- server, client, well, two, turkey, right, say, went, resourc, thank", "+++ make, time, also, look, run<br>--- server, client, hockey, well, two, presid, right, say, resourc, thank", "+++ need, make, time, also, look, repli, includ, want, distribut, run<br>--- server, client, well, current, two, resourc, thank, display, sourc, sale", "+++ server, client, motif, applic, repli, want, color, run, thank, resourc<br>--- ", "+++ make, time, also, look, repli, distribut, want, run, thank, problem<br>--- server, client, well, two, right, say, resourc, display, access, opinion", "+++ make, time, number, also, problem, file, system, way<br>--- server, client, car, well, two, right, say, mani, resourc, thank", "+++ repli, want, run, thank, data, program, help, also, avail, version<br>--- server, client, imag, price, motif, applic, mac, resourc, netcom, color", "+++ make, time, also, want, may, way<br>--- server, client, well, two, say, mani, resourc, thank, display, thing", "+++ make, time, also, want, system, problem, may, tri, way<br>--- server, client, well, right, say, mani, resourc, thank, display, thing", "+++ way, program, need, make, time, also, distribut, may, data, work<br>--- server, client, two, presid, right, resourc, thank, develop, display, access" ], [ "+++ world, peopl, make, well, time, two, question, right, also, say<br>--- mani, thank, give, access, opinion, great, 000, jewish, dod, much", "+++ well, right, say, want, run, state, tri, make, also, new<br>--- realli, two, thank, access, opinion, thing, sgi, chip, world, need", "+++ make, time, also, new, repli, tri, look, want, distribut, run<br>--- well, two, right, say, access, opinion, speed, simm, chip, world", "+++ well, right, say, repli, want, state, tri, make, problem, year<br>--- car, realli, two, mani, run, thank, got, access, put, opinion", "+++ make, time, new, look, repli, usa, want, distribut, good, think<br>--- car, well, two, c8v, right, say, b8f, thank, okz, access", "+++ world, peopl, time, well, two, right, new, say, look, want<br>--- turkey, went, thank, access, opinion, also, serdar, need, number, great", "+++ make, well, time, two, right, new, say, also, look, good<br>--- hockey, presid, thank, access, opinion, stephanopoulo, nhl, score, world, great", "+++ make, well, time, two, question, new, also, repli, look, distribut<br>--- current, right, say, thank, access, opinion, sale, world, need, great", "+++ make, time, also, look, repli, distribut, want, run, thank, problem<br>--- server, client, well, two, right, say, resourc, display, access, opinion", "+++ well, two, right, say, repli, want, run, thank, net, state<br>--- ", "+++ peopl, make, well, time, two, right, new, say, also, good<br>--- car, mani, thank, access, opinion, thing, world, number, great, question", "+++ time, new, also, repli, look, distribut, want, run, thank, pleas<br>--- well, two, right, say, access, opinion, sale, inform, world, need", "+++ world, peopl, make, well, time, two, question, also, new, say<br>--- right, mani, thank, access, opinion, thing, great, dod, much, bmw", "+++ world, peopl, make, well, time, question, right, also, say, human<br>--- realli, two, mani, run, object, thank, access, opinion, thing, christ", "+++ access, peopl, make, time, two, right, new, also, propos, distribut<br>--- well, presid, say, thank, develop, opinion, inform, chip, world, clipper" ], [ "+++ even, way, could, peopl, make, well, time, two, right, also<br>--- car, arab, weapon, health, give, crime, kill, thing, new, control", "+++ well, law, right, say, state, even, make, thing, also, new<br>--- car, realli, two, weapon, mani, run, health, crime, sgi, control", "+++ could, make, time, new, also, problem, scsi, control, system<br>--- car, well, two, right, say, mani, thank, thing, speed, simm", "+++ car, well, right, say, mani, state, even, make, thing, problem<br>--- realli, two, weapon, health, got, put, crime, gov, new, also", "+++ car, time, make, new, good, think<br>--- well, two, c8v, right, say, mani, b8f, okz, thing, also", "+++ even, year, could, peopl, time, well, number, two, right, new<br>--- car, turkey, went, mani, thing, also, serdar, world, need, much", "+++ day, even, make, well, time, two, right, new, say, also<br>--- car, hockey, presid, mani, stephanopoulo, nhl, thing, score, number, season", "+++ could, make, well, time, two, new, also, good, system, year<br>--- car, current, right, say, mani, thing, sale, need, number, question", "+++ make, time, number, also, problem, file, system, way<br>--- server, client, car, well, two, right, say, mani, resourc, thank", "+++ peopl, make, well, time, two, right, new, say, also, good<br>--- car, weapon, mani, run, thank, health, access, crime, opinion, thing", "+++ car, well, two, first, law, right, file, say, weapon, mani<br>--- ", "+++ time, also, new, file, system<br>--- car, well, two, right, say, mani, thank, thing, sale, inform", "+++ well, two, law, say, mani, even, make, thing, also, new<br>--- said, car, point, first, right, 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\"+++ realli, well, point, right, say, mani, want, tri, even, make<br>--- atheist, said, car, first, must, repli, believ, object, state, got\", \"+++ could, need, make, time, peopl, right, distribut, think, year, state<br>--- car, well, two, presid, say, mani, develop, access, thing, also\"], [\"+++ want, time, think, make<br>--- car, well, two, right, c8v, say, mani, b8f, okz, give\", \"+++ need, make, time, new, look, distribut, want, good, think, anyon<br>--- car, well, right, c8v, say, b8f, okz, thing, also, sgi\", \"+++ need, make, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, speed, simm, uchicago, chip\", \"+++ need, car, make, time, look, repli, distribut, want, good, think<br>--- well, right, c8v, say, mani, b8f, okz, thing, uchicago, happen\", \"+++ 2tm, 1eq, car, c8v, 2di, repli, want, b8f, 1d9, 7ey<br>--- \", \"+++ need, time, new, look, want, think<br>--- car, well, two, turkey, right, c8v, say, went, b8f, okz\", \"+++ time, make, new, look, good, think<br>--- car, hockey, well, two, presid, right, c8v, say, b8f, okz\", \"+++ need, make, time, new, look, repli, distribut, want, good, engin<br>--- car, well, current, two, c8v, b8f, okz, also, sale, uchicago\", \"+++ need, make, time, look, repli, distribut, want<br>--- server, client, car, c8v, b8f, resourc, thank, okz, display, also\", \"+++ make, time, new, look, repli, usa, want, distribut, good, think<br>--- car, well, two, right, c8v, say, b8f, thank, okz, access\", \"+++ car, time, make, new, good, think<br>--- well, two, right, c8v, say, mani, b8f, okz, thing, also\", \"+++ need, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, sale, uchicago, inform, softwar\", \"+++ time, make, new, want, good, think<br>--- car, well, two, c8v, say, mani, b8f, okz, thing, also\", \"+++ time, make, want, good, think<br>--- car, well, right, c8v, say, mani, b8f, okz, thing, also\", \"+++ need, time, make, new, distribut, think<br>--- car, two, presid, right, c8v, b8f, develop, okz, access, also\"], [\"+++ even, world, could, said, kill, well, time, two, peopl, right<br>--- turkey, went, mani, give, also, serdar, need, number, 000, jewish\", \"+++ even, could, need, time, well, right, new, say, look, want<br>--- two, turkey, went, thing, also, serdar, sgi, chip, world, number\", \"+++ could, need, time, new, look, want, work<br>--- well, two, turkey, right, say, went, thank, also, speed, serdar\", \"+++ even, world, could, need, time, well, peopl, right, look, say<br>--- car, two, turkey, went, mani, thing, serdar, happen, number, dod\", \"+++ need, time, new, look, want, think<br>--- car, well, two, turkey, c8v, right, say, went, b8f, okz\", \"+++ said, armenia, well, two, turkey, right, say, went, want, live<br>--- \", \"+++ even, said, time, well, two, right, new, say, look, think<br>--- hockey, presid, turkey, went, stephanopoulo, nhl, also, serdar, score, world\", \"+++ could, need, time, well, two, new, look, want, year, work<br>--- current, turkey, right, say, went, also, serdar, sale, world, number\", \"+++ program, need, time, number, work, look, want, name, file, call<br>--- server, client, well, two, turkey, right, say, went, resourc, thank\", \"+++ world, peopl, time, well, two, right, new, say, look, want<br>--- turkey, went, thank, access, opinion, also, serdar, need, great, number\", \"+++ even, peopl, could, time, well, number, two, right, new, say<br>--- car, turkey, went, mani, thing, also, serdar, world, need, much\", \"+++ program, need, time, new, look, want, file, work<br>--- well, two, turkey, right, say, went, thank, also, serdar, sale\", \"+++ even, world, could, said, time, well, two, new, say, want<br>--- turkey, right, went, mani, thing, also, serdar, need, number, question\", \"+++ even, world, could, said, time, well, right, say, want, think<br>--- two, turkey, went, mani, thing, also, serdar, need, number, question\", \"+++ program, could, need, time, peopl, two, new, right, year, think<br>--- well, presid, turkey, say, went, develop, access, also, serdar, inform\"], [\"+++ even, said, make, well, time, two, right, also, say, think<br>--- hockey, presid, mani, give, stephanopoulo, nhl, score, world, 000, jewish\", \"+++ even, make, well, time, right, also, say, new, look, think<br>--- hockey, two, presid, stephanopoulo, thing, nhl, sgi, score, chip, need\", \"+++ make, time, also, look, new, run<br>--- hockey, well, two, presid, right, say, thank, stephanopoulo, nhl, speed\", \"+++ even, make, well, time, point, right, look, say, good, think<br>--- car, hockey, two, presid, mani, stephanopoulo, thing, nhl, also, score\", \"+++ time, make, new, look, good, think<br>--- car, hockey, well, two, presid, c8v, right, say, b8f, okz\", \"+++ even, said, time, well, two, right, new, say, look, think<br>--- hockey, presid, turkey, went, stephanopoulo, nhl, also, serdar, score, world\", \"+++ game, said, play, hockey, well, two, point, presid, hit, right<br>--- \", \"+++ make, well, time, two, point, new, also, look, good, run<br>--- hockey, current, presid, right, say, stephanopoulo, nhl, sale, score, need\", \"+++ make, time, also, look, run<br>--- server, client, hockey, well, two, presid, right, say, resourc, thank\", \"+++ make, well, time, two, right, new, say, also, look, good<br>--- hockey, presid, thank, access, opinion, stephanopoulo, nhl, score, world, great\", \"+++ day, even, make, well, time, two, right, new, say, also<br>--- car, hockey, presid, mani, stephanopoulo, thing, nhl, score, number, season\", \"+++ time, also, new, look, run<br>--- hockey, well, two, presid, right, say, thank, stephanopoulo, nhl, sale\", \"+++ day, even, said, make, well, time, two, point, also, new<br>--- hockey, presid, right, mani, stephanopoulo, thing, nhl, score, world, question\", \"+++ even, said, make, well, time, point, right, also, say, think<br>--- hockey, two, presid, mani, stephanopoulo, thing, nhl, score, world, question\", \"+++ make, time, two, presid, right, new, also, year, think, first<br>--- hockey, well, say, develop, access, stephanopoulo, nhl, inform, chip, score\"], [\"+++ could, make, well, time, two, question, also, want, year<br>--- current, right, say, mani, give, sale, world, need, 000, jewish\", \"+++ could, need, make, well, time, question, also, new, look, distribut<br>--- current, two, right, say, thing, sale, sgi, chip, much, softwar\", \"+++ could, need, make, time, also, new, repli, look, distribut, want<br>--- well, current, two, thank, speed, simm, sale, chip, question, nasa\", \"+++ could, need, make, well, gov, time, point, look, repli, sun<br>--- car, current, two, right, say, mani, thing, also, sale, happen\", \"+++ need, make, time, new, look, repli, distribut, want, good, engin<br>--- car, well, current, two, c8v, b8f, okz, also, sale, uchicago\", \"+++ could, need, time, well, two, new, look, want, year, work<br>--- current, turkey, right, say, went, also, serdar, sale, world, number\", \"+++ make, well, time, two, point, new, also, look, good, run<br>--- hockey, current, presid, right, say, stephanopoulo, nhl, sale, score, need\", \"+++ well, imag, two, point, current, wire, repli, want, pitt, run<br>--- \", \"+++ need, make, time, also, look, repli, includ, want, distribut, run<br>--- server, client, well, current, two, resourc, thank, display, sourc, sale\", \"+++ make, well, time, two, question, new, also, repli, look, distribut<br>--- current, right, say, thank, access, opinion, sale, world, need, great\", \"+++ could, make, well, time, two, first, new, also, good, year<br>--- car, current, right, say, mani, thing, sale, need, number, question\", \"+++ need, time, imag, new, also, repli, look, includ, want, interest<br>--- well, current, two, thank, inform, question, softwar, address, list, nasa\", \"+++ could, make, well, scienc, two, point, question, also, new, time<br>--- current, say, mani, thing, sale, world, need, much, work, nasa\", \"+++ could, make, well, time, point, question, also, want, good, system<br>--- current, two, right, say, mani, thing, sale, world, need, work\", \"+++ could, need, make, time, two, also, new, space, distribut, nasa<br>--- well, current, presid, right, develop, access, sale, inform, chip, clipper\"], [\"+++ make, time, also, want, call, way<br>--- server, client, well, two, right, say, mani, resourc, thank, display\", \"+++ need, make, time, bit, also, look, distribut, want, run, problem<br>--- server, client, well, right, say, resourc, thank, display, thing, sourc\", \"+++ repli, want, color, run, thank, set, tri, make, help, also<br>--- server, client, motif, applic, file, monitor, mac, disk, mous, ide\", \"+++ need, make, time, look, repli, distribut, want, problem, tri, way<br>--- server, client, car, well, right, say, mani, resourc, thank, display\", \"+++ need, make, time, look, repli, distribut, want<br>--- server, client, car, c8v, b8f, resourc, thank, okz, display, also\", \"+++ program, need, time, number, work, look, want, name, file, call<br>--- server, client, well, two, turkey, right, say, went, resourc, thank\", \"+++ make, time, also, look, run<br>--- server, client, hockey, well, two, presid, right, say, resourc, thank\", \"+++ need, make, time, also, look, repli, includ, want, distribut, run<br>--- server, client, well, current, two, resourc, thank, display, sourc, sale\", \"+++ server, client, motif, applic, repli, want, color, run, thank, resourc<br>--- \", \"+++ make, time, also, look, repli, distribut, want, run, thank, problem<br>--- server, client, well, two, right, say, resourc, display, access, opinion\", \"+++ make, time, number, also, problem, file, system, way<br>--- server, client, car, well, two, right, say, mani, resourc, thank\", \"+++ repli, want, run, thank, data, program, help, also, avail, version<br>--- server, client, imag, price, motif, applic, mac, resourc, netcom, color\", \"+++ make, time, also, want, may, way<br>--- server, client, well, two, say, mani, resourc, thank, display, thing\", \"+++ make, time, also, want, system, problem, may, tri, way<br>--- server, client, well, right, say, mani, resourc, thank, display, thing\", \"+++ way, program, need, make, time, also, distribut, may, data, work<br>--- server, client, two, presid, right, resourc, thank, develop, display, access\"], [\"+++ world, peopl, make, well, time, two, question, right, also, say<br>--- mani, thank, give, access, opinion, great, 000, jewish, dod, much\", \"+++ well, right, say, want, run, state, tri, make, also, new<br>--- realli, two, thank, access, opinion, thing, sgi, chip, world, need\", \"+++ make, time, also, new, repli, tri, look, want, distribut, run<br>--- well, two, right, say, access, opinion, speed, simm, chip, world\", \"+++ well, right, say, repli, want, state, tri, make, problem, year<br>--- car, realli, two, mani, run, thank, got, access, put, opinion\", \"+++ make, time, new, look, repli, usa, want, distribut, good, think<br>--- car, well, two, c8v, right, say, b8f, thank, okz, access\", \"+++ world, peopl, time, well, two, right, new, say, look, want<br>--- turkey, went, thank, access, opinion, also, serdar, need, number, great\", \"+++ make, well, time, two, right, new, say, also, look, good<br>--- hockey, presid, thank, access, opinion, stephanopoulo, nhl, score, world, great\", \"+++ make, well, time, two, question, new, also, repli, look, distribut<br>--- current, right, say, thank, access, opinion, sale, world, need, great\", \"+++ make, time, also, look, repli, distribut, want, run, thank, problem<br>--- server, client, well, two, right, say, resourc, display, access, opinion\", \"+++ well, two, right, say, repli, want, run, thank, net, state<br>--- \", \"+++ peopl, make, well, time, two, right, new, say, also, good<br>--- car, mani, thank, access, opinion, thing, world, number, great, question\", \"+++ time, new, also, repli, look, distribut, want, run, thank, pleas<br>--- well, two, right, say, access, opinion, sale, inform, world, need\", \"+++ world, peopl, make, well, time, two, question, also, new, say<br>--- right, mani, thank, access, opinion, thing, great, dod, much, bmw\", \"+++ world, peopl, make, well, time, question, right, also, say, human<br>--- realli, two, mani, run, object, thank, access, opinion, thing, christ\", \"+++ access, peopl, make, time, two, right, new, also, propos, distribut<br>--- well, presid, say, thank, develop, opinion, inform, chip, world, clipper\"], [\"+++ even, way, could, peopl, make, well, time, two, right, also<br>--- car, arab, weapon, health, give, crime, kill, thing, new, control\", \"+++ well, law, right, say, state, even, make, thing, also, new<br>--- car, realli, two, weapon, mani, run, health, crime, sgi, control\", \"+++ could, make, time, new, also, problem, scsi, control, system<br>--- car, well, two, right, say, mani, thank, thing, speed, simm\", \"+++ car, well, right, say, mani, state, even, make, thing, problem<br>--- realli, two, weapon, health, got, put, crime, gov, new, also\", \"+++ car, time, make, new, good, think<br>--- well, two, c8v, right, say, mani, b8f, okz, thing, also\", \"+++ even, year, could, peopl, time, well, number, two, right, new<br>--- car, turkey, went, mani, thing, also, serdar, world, need, much\", \"+++ day, even, make, well, time, two, right, new, say, also<br>--- car, hockey, presid, mani, stephanopoulo, nhl, thing, score, number, season\", \"+++ could, make, well, time, two, new, also, good, system, year<br>--- car, current, right, say, mani, thing, sale, need, number, question\", \"+++ make, time, number, also, problem, file, system, way<br>--- server, client, car, well, two, right, say, mani, resourc, thank\", \"+++ peopl, make, well, time, two, right, new, say, also, good<br>--- car, weapon, mani, run, thank, health, access, crime, opinion, thing\", \"+++ car, well, two, first, law, right, file, say, weapon, mani<br>--- \", \"+++ time, also, new, file, system<br>--- car, well, two, right, say, mani, thank, thing, sale, inform\", \"+++ well, two, law, say, mani, even, make, thing, also, new<br>--- said, car, point, first, right, weapon, want, believ, argument, health\", \"+++ well, right, say, mani, even, make, thing, also, problem, see<br>--- car, realli, two, weapon, object, health, crime, christ, new, control\", \"+++ could, peopl, make, time, two, public, law, right, new, also<br>--- car, well, presid, say, mani, develop, access, thing, inform, chip\"], [\"+++ want, also, time<br>--- well, two, right, say, mani, thank, give, sale, inform, world\", \"+++ need, time, drive, also, new, look, distribut, want, run, system<br>--- well, right, say, thank, thing, sale, sgi, inform, chip, question\", \"+++ repli, want, mac, run, thank, help, also, new, version, window<br>--- imag, price, monitor, netcom, disk, mous, ide, color, instal, set\", \"+++ need, time, look, repli, distribut, want, anyon<br>--- car, well, right, say, mani, thank, thing, also, sale, inform\", \"+++ need, time, new, look, repli, distribut, want, anyon<br>--- car, c8v, b8f, thank, okz, also, sale, uchicago, inform, softwar\", \"+++ program, need, time, new, look, want, file, work<br>--- well, two, turkey, right, say, went, thank, also, serdar, sale\", \"+++ time, also, new, look, run<br>--- hockey, well, two, presid, right, say, thank, stephanopoulo, nhl, sale\", \"+++ need, time, imag, new, also, repli, look, includ, want, interest<br>--- well, current, two, thank, inform, question, softwar, address, list, nasa\", \"+++ repli, want, run, thank, data, program, help, also, avail, version<br>--- server, client, imag, motif, price, applic, color, netcom, resourc, mac\", \"+++ time, new, also, repli, look, distribut, want, run, thank, pleas<br>--- well, two, right, say, access, opinion, sale, inform, world, need\", \"+++ time, also, new, file, system<br>--- car, well, two, right, say, mani, thank, thing, sale, inform\", \"+++ imag, price, file, repli, want, mac, run, thank, netcom, data<br>--- \", \"+++ want, new, also, time<br>--- well, two, say, mani, thank, thing, sale, inform, world, need\", \"+++ want, system, also, time<br>--- well, right, say, mani, thank, thing, sale, inform, world, need\", \"+++ program, need, time, also, new, distribut, data, work, system, inform<br>--- two, presid, right, thank, develop, access, sale, chip, clipper, softwar\"], [\"+++ said, well, two, say, mani, want, even, make, also, see<br>--- point, law, right, arab, palestinian, live, govern, believ, argument, state\", \"+++ well, law, say, want, even, make, thing, also, new, see<br>--- said, realli, two, point, right, indiana, mani, run, believ, disk\", \"+++ could, make, time, new, also, want<br>--- well, two, say, mani, thank, thing, speed, simm, chip, world\", \"+++ well, point, say, want, mani, even, make, thing, see, world<br>--- car, realli, two, right, got, put, gov, also, new, sun\", \"+++ time, make, new, want, good, think<br>--- car, well, two, c8v, say, mani, b8f, okz, thing, also\", \"+++ even, world, could, said, time, well, two, new, say, want<br>--- turkey, right, went, mani, thing, also, serdar, need, number, question\", \"+++ day, even, said, make, well, time, two, point, new, also<br>--- hockey, presid, right, mani, stephanopoulo, nhl, thing, score, world, question\", \"+++ could, make, scienc, well, two, point, question, new, also, time<br>--- current, say, mani, thing, sale, world, need, much, work, nasa\", \"+++ make, time, also, want, may, way<br>--- server, client, well, two, say, mani, resourc, thank, display, thing\", \"+++ world, peopl, make, well, time, two, question, new, also, say<br>--- right, mani, thank, access, opinion, thing, great, dod, much, bmw\", \"+++ well, two, law, say, mani, even, make, thing, new, also<br>--- car, said, point, right, weapon, want, believ, argument, health, state\", \"+++ want, also, new, time<br>--- well, two, say, mani, thank, thing, sale, inform, world, need\", \"+++ said, well, two, point, law, say, mani, want, believ, argument<br>--- \", \"+++ said, well, point, say, mani, want, believ, come, even, make<br>--- atheist, realli, two, law, right, must, argument, object, tri, scienc\", \"+++ way, could, make, time, two, law, also, new, peopl, think<br>--- well, presid, right, say, mani, develop, access, thing, inform, chip\"], [\"+++ said, well, right, say, mani, want, even, make, also, see<br>--- atheist, realli, two, point, must, arab, palestinian, live, govern, believ\", \"+++ realli, well, right, say, want, tri, even, make, thing, also<br>--- atheist, said, point, law, must, indiana, mani, run, believ, disk\", \"+++ could, make, time, also, tri, want, problem, system<br>--- well, right, say, mani, thank, thing, speed, simm, chip, world\", \"+++ realli, well, point, right, say, want, mani, tri, even, make<br>--- atheist, car, said, must, repli, believ, object, state, system, got\", \"+++ time, make, want, good, think<br>--- car, well, c8v, right, 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0.5398098970501584, 0.0, 0.7854428745787172], [0.8038717033978985, 0.6964932824724144, 0.8796770147813993, 0.8428560212776922, 0.9862908354943184, 0.8247767768912454, 0.8746609598118593, 0.760409513739808, 0.7982346696285947, 0.8371214921530137, 0.7233383725539672, 0.7915239430267434, 0.7811421666690115, 0.7854428745787172, 0.0]], \"type\": \"heatmap\", \"uid\": \"f0b1a7a2-2641-4e85-85b1-b6a96dc2f0a7\"}], {\"height\": 950, \"title\": \"Topic difference (one model)[hellinger distance]\", \"width\": 950, \"xaxis\": {\"title\": \"topic\"}, \"yaxis\": {\"title\": \"topic\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mdiff, annotation = lda_fst.diff(lda_fst, distance='hellinger', num_words=50)\n", "plot_difference(mdiff, title=\"Topic difference (one model)[hellinger distance]\", annotation=annotation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You see that everything has become worse, but remember that everything depends on the task.\n", "\n", "You need to choose the function with which your personal point of view about topics similarity and your task (from my experience, Jaccard is fine)." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Case 2: How topics from DIFFERENT models correlate with each other." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, we want to look at the patterns between two different models and compare them. \n", "\n", "You can do this by constructing a matrix with the difference." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "colorscale": "RdBu", "text": [ [ "+++ world, could, peopl, make, well, time, two, nazi, right, also<br>--- car, azeri, turkey, say, mani, give, serdar, german, 000, question", "+++ <br>--- well, two, c8v, right, say, mani, b8f, okz, give, qtm", "+++ want, time, also, call<br>--- server, well, two, right, say, mani, thank, display, give, inform", "+++ well, two, right, say, mani, want, govern, state, even, make<br>--- atheist, said, evid, point, must, law, arab, american, live, palestinian", "+++ even, could, said, kill, well, make, two, time, peopl, right<br>--- presid, went, mani, give, made, stephanopoulo, thing, happen, world, 000", "+++ even, world, could, make, well, time, right, say, mani, think<br>--- two, give, opinion, thing, also, sgi, 000, jewish, question, much", "+++ world, could, time, also, nation, year<br>--- well, two, right, say, mani, develop, lunar, news, give, project", "+++ even, could, make, time, two, also, say, mani, govern, peopl<br>--- well, right, develop, give, inform, chip, world, clipper, need, number", "+++ even, make, well, time, two, right, say, think, much, year<br>--- hockey, mani, give, nhl, also, score, world, 000, jewish, question", "+++ two, time, also, want<br>--- modem, well, floppi, right, say, mani, thank, access, give, speed", "+++ jewish, year<br>--- well, two, right, check, say, mani, give, char, also, sourc", "+++ right, arab, say, live, mani, want, state, israel, even, fact<br>--- said, well, two, point, law, american, palestinian, believ, govern, case", "+++ even, could, make, well, time, two, right, also, say, want<br>--- car, mani, give, thing, speed, world, need, 000, jewish, question", "+++ year, world, time, 000, also, nation, peopl, state<br>--- armi, well, two, turkey, right, say, mani, kent, sandvik, give", "+++ world, could, make, time, also, columbia, want, peopl, insur<br>--- server, cunixb, well, two, right, say, newsgroup, mani, thank, news" ], [ "+++ could, make, well, time, key, law, right, new, also, look<br>--- car, two, azeri, turkey, say, thing, serdar, sgi, german, chip", "+++ <br>--- well, c8v, right, say, b8f, okz, qtm, wm4u, thing, also", "+++ need, time, bit, anyon, also, new, look, distribut, want, run<br>--- server, well, right, say, thank, display, thing, sgi, inform, chip", "+++ well, law, right, say, want, state, tri, even, make, thing<br>--- atheist, realli, two, evid, point, must, indiana, mani, run, govern", "+++ well, right, say, want, state, tri, even, make, thing, also<br>--- said, realli, two, presid, law, went, indiana, run, believ, disk", "+++ realli, well, right, say, state, tri, even, make, thing, sgi<br>--- atheist, caltech, optilink, law, keith, show, indiana, mani, gay, want", "+++ could, time, new, also, distribut, problem, softwar, work, system<br>--- well, right, say, develop, lunar, news, project, thing, moon, sgi", "+++ law, say, even, make, new, also, chip, need, good, work<br>--- realli, well, two, right, mani, netcom, run, data, develop, thing", "+++ even, make, well, time, take, right, new, say, look, think<br>--- hockey, two, nhl, thing, also, sgi, score, chip, need, question", "+++ need, time, drive, also, new, look, distribut, want, system, problem<br>--- modem, well, two, floppi, right, say, thank, access, thing, speed", "+++ read, need, tri, may<br>--- well, right, check, say, char, thing, also, sourc, sgi, inform", "+++ law, right, say, want, state, even, make, thing, also, see<br>--- realli, well, point, arab, live, mani, indiana, run, believ, disk", "+++ realli, well, right, say, want, run, state, tri, even, make<br>--- car, two, first, law, wire, indiana, turn, disk, system, got", "+++ time, also, new, org, peopl, state, work<br>--- armi, well, turkey, right, say, kent, sandvik, thing, sourc, serdar", "+++ could, make, time, also, distribut, want, org, run, system, peopl<br>--- server, cunixb, well, right, say, newsgroup, thank, news, thing, sgi" ], [ "+++ could, make, time, also, new, repli, look, distribut, want, work<br>--- car, well, two, azeri, turkey, right, thank, speed, serdar, simm", "+++ <br>--- c8v, b8f, thank, okz, qtm, wm4u, also, speed, simm, chip", "+++ want, color, run, thank, mac, mous, set, tri, system, help<br>--- server, imag, applic, repli, monitor, vga, disk, ide, data, instal", "+++ time, make, also, want, tri<br>--- well, two, right, say, mani, thank, without, thing, speed, simm", "+++ could, make, time, also, look, want, work, tri<br>--- well, two, presid, right, say, went, thank, made, stephanopoulo, thing", "+++ could, time, make, system, tri<br>--- well, right, say, mani, thank, opinion, thing, also, speed, simm", "+++ could, time, new, also, distribut, problem, scsi, work, system<br>--- thank, develop, lunar, news, project, speed, simm, moon, inform, chip", "+++ could, need, make, time, comput, bit, also, new, work, system<br>--- two, say, mani, thank, develop, speed, simm, inform, clipper, number", "+++ make, time, new, look, run<br>--- hockey, well, two, right, say, thank, nhl, also, speed, simm", "+++ repli, monitor, want, thank, disk, ide, system, pin, new, also<br>--- 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right, say, mani, thank, display, thing, also", "+++ well, point, right, say, mani, want, state, tri, even, make<br>--- atheist, car, realli, two, evid, first, must, repli, law, govern", "+++ well, right, say, want, state, tri, got, even, make, thing<br>--- said, car, realli, two, presid, point, repli, went, mani, believ", "+++ realli, well, right, say, mani, state, tri, even, make, thing<br>--- car, show, gay, frank, object, got, cramer, put, opinion, gov", "+++ world, could, gov, time, distribut, problem, year, first<br>--- car, well, right, say, mani, develop, lunar, news, project, thing", "+++ even, could, need, make, time, peopl, say, mani, good, think<br>--- car, well, two, right, develop, thing, also, inform, chip, happen", "+++ well, point, right, say, even, make, year, see, better, look<br>--- period, game, car, play, hockey, two, realli, hit, repli, weapon", "+++ need, time, point, look, repli, distribut, want, problem, good, anyon<br>--- modem, car, well, two, floppi, right, say, mani, thank, access", "+++ need, tri, year, read, first<br>--- car, well, right, check, say, mani, char, thing, sourc, inform", "+++ point, right, say, mani, want, state, even, make, thing, see<br>--- car, realli, well, law, first, arab, repli, live, believ, tri", "+++ car, realli, well, right, say, want, state, tri, got, even<br>--- two, point, wire, repli, mani, turn, run, put, gov, new", "+++ year, world, time, repli, peopl, state, first<br>--- armi, car, well, turkey, right, say, mani, kent, sandvik, thing", "+++ world, could, make, time, repli, distribut, want, peopl, anyon<br>--- server, car, cunixb, well, right, say, newsgroup, mani, thank, news" ], [ "+++ car, make, time, new, look, repli, distribut, want, think, engin<br>--- well, two, azeri, turkey, right, c8v, b8f, okz, also, serdar", "+++ 2tm, 1eq, c8v, 2di, b8f, 1d9, 7ey, max, okz, b8e<br>--- dyer, car, 1d9l, repli, want, 7ez, r8f, f9d, brake, mile", "+++ need, time, new, look, distribut, want, anyon<br>--- server, car, c8v, b8f, thank, okz, display, also, uchicago, inform", "+++ time, make, want, good, think<br>--- car, well, two, right, c8v, say, mani, b8f, without, okz", "+++ time, make, look, want, good, think<br>--- car, well, two, presid, right, c8v, say, went, b8f, okz", "+++ time, think, make, good<br>--- car, well, right, c8v, say, mani, b8f, okz, opinion, thing", "+++ time, new, engin, distribut<br>--- car, c8v, b8f, develop, okz, lunar, news, project, also, moon", "+++ need, time, make, new, good, think<br>--- car, two, c8v, say, mani, b8f, develop, okz, also, uchicago", "+++ time, make, new, look, good, think<br>--- car, hockey, well, two, right, c8v, say, b8f, okz, nhl", "+++ need, time, new, look, repli, usa, want, distribut, good, anyon<br>--- modem, car, two, floppi, c8v, b8f, thank, okz, access, also", "+++ need<br>--- car, c8v, check, b8f, okz, char, sourc, uchicago, inform, number", "+++ time, make, want, good, think<br>--- car, right, c8v, say, mani, b8f, okz, thing, also, uchicago", "+++ car, make, need, time, new, look, usa, want, distribut, good<br>--- well, two, right, c8v, say, b8f, okz, thing, also, speed", "+++ time, new, repli, umd, eng<br>--- armi, car, turkey, c8v, b8f, kent, sandvik, okz, also, sourc", "+++ time, make, repli, distribut, want, anyon<br>--- server, car, cunixb, c8v, newsgroup, b8f, thank, okz, news, also" ], [ "+++ armenia, well, two, turkey, right, greek, want, turk, new, serdar<br>--- car, said, azeri, law, file, repli, ingr, pass, live, say", "+++ <br>--- well, two, turkey, c8v, right, say, went, b8f, okz, qtm", "+++ program, need, time, work, new, look, want, file, call<br>--- server, well, two, turkey, right, say, went, thank, display, also", "+++ even, time, well, two, right, say, want, think, peopl, see<br>--- turkey, went, mani, without, thing, also, serdar, world, need, number", "+++ said, well, two, right, say, went, want, start, come, even<br>--- armenia, presid, turkey, file, azerbaijani, live, greek, believ, state, return", "+++ even, world, could, time, well, right, say, think, someth, peopl<br>--- two, turkey, went, mani, opinion, thing, serdar, sgi, need, number", "+++ program, world, could, time, new, year, work, first<br>--- well, two, turkey, right, say, went, develop, lunar, news, project", "+++ even, could, need, time, number, two, work, new, say, peopl<br>--- well, turkey, right, went, mani, develop, also, serdar, inform, chip", "+++ even, time, well, two, right, new, say, look, think, start<br>--- hockey, turkey, went, nhl, serdar, score, world, need, number, season", "+++ need, time, two, new, look, want, work<br>--- modem, well, turkey, floppi, right, say, went, thank, access, also", "+++ rule, program, need, number, output, file, build, name, year, return<br>--- well, two, turkey, right, check, say, went, char, sourc, serdar", "+++ even, world, could, kill, time, right, say, live, want, think<br>--- well, two, turkey, went, mani, thing, also, serdar, need, number", "+++ even, could, need, time, well, two, right, new, say, look<br>--- car, turkey, went, thing, also, speed, serdar, world, number, dod", "+++ armenia, turkey, file, muslim, turk, new, serdar, year, world, number<br>--- armi, david, well, two, right, say, went, greek, popul, kent", "+++ world, could, time, file, want, name, peopl<br>--- server, cunixb, well, two, turkey, right, say, newsgroup, went, thank" ], [ "+++ make, well, time, two, new, right, also, look, year, think<br>--- car, hockey, azeri, turkey, presid, say, stephanopoulo, nhl, serdar, german", "+++ <br>--- hockey, well, two, presid, c8v, right, say, b8f, okz, qtm", "+++ time, also, look, new, run<br>--- server, hockey, well, two, presid, right, say, thank, display, stephanopoulo", "+++ even, make, well, time, two, point, right, also, say, think<br>--- hockey, presid, mani, without, stephanopoulo, thing, nhl, score, question, constitut", "+++ said, well, two, presid, right, say, start, got, come, even<br>--- game, play, hockey, point, hit, went, want, win, run, believ", "+++ even, make, well, time, right, say, think, good, see<br>--- hockey, two, presid, mani, opinion, stephanopoulo, thing, nhl, also, sgi", "+++ time, new, also, year, first<br>--- hockey, well, two, presid, right, say, develop, lunar, news, project", "+++ even, make, time, two, new, also, say, good, think<br>--- hockey, well, presid, right, mani, develop, stephanopoulo, nhl, inform, chip", "+++ game, play, hockey, well, two, point, right, hit, say, win<br>--- period, said, presid, weapon, next, got, come, stephanopoulo, also, day", "+++ time, two, point, new, look, also, good<br>--- modem, hockey, well, presid, floppi, right, say, thank, access, stephanopoulo", "+++ first, year<br>--- hockey, well, two, presid, right, check, say, char, stephanopoulo, nhl", "+++ day, even, make, time, point, right, also, say, think, good<br>--- hockey, well, two, presid, mani, stephanopoulo, thing, nhl, score, world", "+++ well, two, right, say, run, got, even, make, new, also<br>--- game, car, realli, said, play, wire, hockey, point, want, turn", "+++ time, also, new, toronto, year, first<br>--- armi, hockey, well, two, presid, turkey, right, say, kent, sandvik", "+++ also, run, make, time<br>--- server, cunixb, hockey, well, two, presid, right, say, newsgroup, thank" ], [ "+++ could, make, well, time, two, new, also, repli, sun, look<br>--- car, current, azeri, turkey, right, serdar, sale, german, world, need", "+++ <br>--- well, current, two, c8v, b8f, okz, qtm, wm4u, also, sale", "+++ need, time, imag, also, new, look, includ, want, distribut, run<br>--- server, well, current, two, thank, display, sale, inform, question, softwar", "+++ make, well, time, two, point, question, also, want, good, may<br>--- current, right, say, mani, without, thing, sale, need, constitut, much", "+++ could, make, well, time, two, also, look, want, good, year<br>--- current, presid, right, say, went, made, stephanopoulo, thing, sale, happen", "+++ could, time, make, well, good, system<br>--- current, two, right, say, mani, opinion, thing, also, sale, sgi", "+++ imag, system, gov, scienc, also, new, year, space, center, includ<br>--- well, fund, current, two, point, wire, satellit, repli, 1993, want", "+++ could, need, make, scienc, time, two, work, new, also, pitt<br>--- well, current, say, mani, develop, sale, inform, chip, clipper, number", "+++ make, well, time, two, point, new, look, good, run, year<br>--- hockey, current, right, say, nhl, also, sale, score, need, question", "+++ two, point, repli, want, offer, new, also, power, sale, need<br>--- modem, imag, current, price, first, well, floppi, motherboard, wire, monitor", "+++ need, includ, year, first, may, comput<br>--- well, current, two, check, char, also, sourc, sale, inform, number", "+++ could, make, time, point, question, also, want, book, good, may<br>--- well, current, two, right, say, mani, thing, sale, world, need", "+++ well, two, wire, want, run, make, new, also, year, need<br>--- car, realli, imag, current, right, say, point, repli, turn, pitt", "+++ time, also, new, repli, center, year, work, first<br>--- armi, well, current, two, turkey, kent, sandvik, sourc, serdar, sale", "+++ could, make, time, also, repli, distribut, want, run, system, may<br>--- server, cunixb, current, two, well, newsgroup, thank, news, sale, inform" ], [ "+++ make, time, also, look, repli, distribut, want, work, way<br>--- server, client, car, well, two, azeri, turkey, right, resourc, thank", "+++ <br>--- server, client, c8v, b8f, resourc, thank, okz, display, qtm, wm4u", "+++ server, applic, want, color, run, thank, data, set, tri, display<br>--- client, imag, motif, repli, resourc, mac, mous, make, graphic, new", "+++ way, make, time, also, want, tri, may<br>--- server, client, well, two, right, say, mani, resourc, thank, without", "+++ make, time, also, look, want, work, tri, way<br>--- server, client, well, two, presid, right, say, went, resourc, thank", "+++ time, system, tri, make<br>--- server, client, well, right, say, mani, resourc, thank, display, opinion", "+++ program, time, softwar, also, group, includ, distribut, avail, problem, data<br>--- server, client, resourc, thank, develop, display, lunar, news, project, sourc", "+++ need, make, time, number, bit, work, also, may, data, call<br>--- server, client, two, say, mani, resourc, thank, develop, display, sourc", "+++ time, make, look, run, way<br>--- server, client, hockey, well, two, right, say, resourc, thank, display", "+++ need, time, also, look, repli, includ, want, distribut, thank, problem<br>--- modem, server, client, two, floppi, resourc, display, access, sourc, speed", "+++ program, need, help, number, sourc, includ, name, mail, may, file<br>--- server, client, check, resourc, thank, display, char, also, lib, jewish", "+++ way, make, time, also, want, may<br>--- server, client, right, say, mani, resourc, thank, display, thing, sourc", "+++ way, need, make, time, also, look, distribut, want, run, problem<br>--- server, client, car, well, two, right, say, resourc, thank, display", "+++ time, number, also, sourc, repli, file, work<br>--- armi, server, client, turkey, resourc, thank, kent, sandvik, display, serdar", "+++ server, repli, want, run, thank, make, help, also, inform, window<br>--- client, anonym, cunixb, motif, applic, newsgroup, color, resourc, privaci, data" ], [ "+++ well, two, right, repli, want, state, make, new, also, convex<br>--- car, armenia, azeri, law, turkey, say, ingr, pass, greek, run", "+++ <br>--- well, two, c8v, right, say, b8f, thank, okz, access, opinion", "+++ time, also, new, look, distribut, want, run, thank, problem, pleas<br>--- server, well, two, right, say, display, access, opinion, inform, world", "+++ peopl, make, well, time, two, question, right, also, say, human<br>--- mani, thank, without, access, opinion, thing, world, great, constitut, dod", "+++ peopl, make, well, time, two, right, also, say, look, want<br>--- presid, went, run, thank, ask, got, tax, access, kill, opinion", "+++ world, peopl, opinion, make, well, time, right, say, think, system<br>--- two, mani, thank, access, thing, also, sgi, great, question, dod", "+++ year, world, time, new, also, distribut, problem, base, system<br>--- well, two, right, say, thank, develop, lunar, news, access, opinion", "+++ peopl, make, time, two, also, new, say, good, think, system<br>--- well, right, mani, thank, develop, access, opinion, inform, chip, world", "+++ make, well, time, two, right, new, say, look, good, run<br>--- hockey, thank, access, opinion, nhl, also, score, world, great, question", "+++ access, time, two, new, also, repli, look, usa, want, distribut<br>--- modem, well, floppi, right, say, opinion, speed, sale, world, need", "+++ comput, pleas, tri, year<br>--- well, two, right, check, say, thank, access, char, opinion, also", "+++ world, peopl, make, time, question, right, also, say, human, want<br>--- well, two, mani, thank, access, opinion, thing, great, jewish, dod", "+++ well, two, right, say, want, run, state, tri, make, new<br>--- car, realli, first, wire, repli, turn, thank, net, bnr, pyron", "+++ world, peopl, time, also, new, repli, org, year, state<br>--- armi, well, two, turkey, right, say, thank, kent, sandvik, access", "+++ world, make, time, also, repli, distribut, want, org, run, thank<br>--- server, cunixb, well, two, right, say, newsgroup, news, access, opinion" ], [ "+++ crime, could, car, make, well, time, two, peopl, law, right<br>--- armenia, azeri, turkey, file, say, pass, weapon, greek, mani, health", "+++ <br>--- car, well, two, c8v, right, say, mani, b8f, okz, qtm", "+++ time, new, also, problem, file, system<br>--- server, car, well, two, right, say, mani, thank, display, thing", "+++ well, two, law, right, say, mani, state, even, make, thing<br>--- atheist, car, evid, point, first, must, file, weapon, want, govern", "+++ well, two, right, say, state, even, make, thing, also, year<br>--- car, presid, weapon, went, mani, health, ask, got, tax, crime", "+++ even, could, peopl, make, well, time, person, thing, right, say<br>--- car, realli, two, show, weapon, gay, frank, object, health, cramer", "+++ could, time, new, also, system, problem, scsi, year, first<br>--- car, well, two, right, say, mani, develop, lunar, news, project", "+++ two, law, say, mani, case, even, make, new, also, number<br>--- car, well, right, weapon, netcom, data, develop, health, crime, thing", "+++ even, make, well, firearm, two, time, gun, right, new, say<br>--- car, hockey, file, mani, run, health, crime, nhl, also, thing", "+++ time, two, also, new, problem, scsi, good, control, system<br>--- modem, car, well, floppi, right, say, mani, thank, access, thing", "+++ file, first, number, year<br>--- car, well, two, right, check, say, mani, char, thing, also", "+++ day, even, could, peopl, make, time, person, law, right, thing<br>--- car, well, two, arab, weapon, health, crime, kill, christ, new", "+++ car, well, two, right, say, state, even, make, thing, new<br>--- realli, law, wire, file, weapon, want, turn, mani, run, health", "+++ peopl, report, time, number, public, also, new, file, year, state<br>--- armi, car, well, two, turkey, right, say, mani, kent, sandvik", "+++ could, time, make, also, file, peopl, system<br>--- server, car, cunixb, well, two, right, say, newsgroup, mani, thank" ], [ "+++ time, also, new, look, repli, distribut, want, work, comput<br>--- car, well, two, azeri, turkey, right, thank, serdar, sale, german", "+++ <br>--- c8v, b8f, thank, okz, qtm, wm4u, also, sale, inform, 3di", "+++ imag, file, want, mac, run, thank, data, system, program, help<br>--- server, price, applic, repli, color, netcom, mous, set, display, tri", "+++ want, also, time<br>--- well, two, right, say, mani, thank, without, thing, sale, inform", "+++ time, also, look, want, work<br>--- well, two, presid, right, say, went, thank, made, stephanopoulo, thing", "+++ system, time<br>--- well, right, say, mani, thank, opinion, thing, also, sale, sgi", "+++ program, time, imag, softwar, new, also, graphic, includ, distribut, avail<br>--- thank, develop, lunar, news, project, sale, moon, world, need, group", "+++ need, time, also, new, netcom, data, work, system, inform, comput<br>--- two, say, mani, thank, develop, sale, chip, clipper, number, softwar", "+++ new, run, look, time<br>--- hockey, well, two, right, say, thank, nhl, also, sale, score", "+++ price, repli, want, thank, data, system, new, also, sale, need<br>--- modem, imag, two, point, floppi, motherboard, file, monitor, origin, mac", "+++ info, program, need, address, list, help, includ, send, pleas, mail<br>--- check, thank, char, also, sourc, sale, number, lib, jewish, titl", "+++ want, also, time<br>--- right, say, mani, thank, thing, sale, inform, world, need, question", "+++ need, time, drive, new, also, look, distribut, want, run, work<br>--- car, well, two, right, say, thank, thing, speed, sale, inform", "+++ time, also, new, repli, file, work<br>--- armi, turkey, thank, kent, sandvik, sourc, serdar, sale, inform, world", "+++ repli, want, run, thank, system, help, also, inform, window, pleas<br>--- server, anonym, cunixb, imag, price, newsgroup, mac, netcom, privaci, data" ], [ "+++ world, could, make, well, time, two, law, new, also, want<br>--- car, azeri, turkey, right, say, mani, thing, serdar, german, question", "+++ <br>--- well, two, c8v, say, mani, b8f, okz, qtm, wm4u, thing", "+++ want, new, also, time<br>--- server, well, two, say, mani, thank, display, thing, inform, world", "+++ well, two, point, law, say, mani, want, believ, argument, even<br>--- atheist, said, evid, right, must, govern, state, without, tri, come", "+++ said, well, two, say, want, believ, come, even, make, thing<br>--- presid, point, right, law, went, mani, argument, start, state, seem", "+++ well, say, mani, believ, even, make, thing, see, world, god<br>--- atheist, said, caltech, optilink, realli, two, point, right, keith, show", "+++ world, could, time, scienc, new, also<br>--- well, two, say, mani, develop, lunar, news, project, thing, moon", "+++ even, could, peopl, make, scienc, time, two, law, new, also<br>--- well, develop, thing, inform, chip, world, clipper, need, number, question", "+++ even, make, well, time, two, point, new, say, think, much<br>--- hockey, right, mani, nhl, thing, also, score, world, question, season", "+++ time, two, point, new, also, want, good<br>--- modem, well, floppi, say, mani, thank, access, thing, speed, sale", "+++ read, may<br>--- well, two, check, say, mani, char, thing, also, sourc, inform", "+++ point, law, say, mani, want, believ, come, even, make, thing<br>--- said, well, two, right, arab, live, argument, state, israel, fact", "+++ day, even, way, could, make, well, time, two, thing, new<br>--- car, realli, said, first, point, right, wire, law, turn, mani", "+++ world, time, also, new, peopl<br>--- armi, well, two, turkey, say, mani, kent, sandvik, thing, sourc", "+++ world, could, make, time, also, want, peopl, may<br>--- server, cunixb, well, two, say, newsgroup, mani, thank, news, thing" ], [ "+++ world, could, make, well, time, also, right, want, peopl, think<br>--- car, two, azeri, turkey, say, mani, thing, serdar, german, question", "+++ <br>--- well, c8v, right, say, mani, b8f, okz, qtm, wm4u, thing", "+++ time, also, want, system, problem, tri<br>--- server, well, right, say, mani, thank, display, thing, inform, world", "+++ atheist, well, point, right, must, say, mani, want, believ, tri<br>--- said, realli, two, evid, law, govern, object, argument, state, without", "+++ said, well, right, say, want, believ, tri, come, even, make<br>--- atheist, realli, two, presid, point, must, went, mani, object, start", "+++ atheist, realli, well, right, say, mani, object, believ, tri, even<br>--- said, caltech, optilink, point, must, keith, show, want, gay, frank", "+++ world, could, time, also, problem, system<br>--- well, right, say, mani, develop, lunar, news, project, thing, moon", "+++ even, could, peopl, make, time, also, say, mani, good, think<br>--- well, two, right, develop, thing, inform, chip, world, clipper, need", "+++ even, make, well, time, take, point, right, say, think, good<br>--- hockey, two, mani, nhl, thing, also, score, world, question, season", "+++ time, point, also, want, problem, good, system<br>--- modem, well, two, floppi, right, say, mani, thank, access, thing", "+++ must, tri, may, follow<br>--- well, right, check, say, mani, char, thing, also, sourc, inform", "+++ point, right, say, mani, want, believ, come, even, make, thing<br>--- atheist, said, realli, well, law, must, arab, live, object, state", "+++ realli, well, right, say, want, tri, even, make, thing, also<br>--- atheist, car, said, two, first, point, must, wire, turn, mani", "+++ world, also, time, peopl<br>--- armi, well, turkey, right, say, mani, kent, sandvik, thing, sourc", "+++ world, could, make, find, time, also, want, peopl, system, may<br>--- server, cunixb, well, right, say, newsgroup, mani, thank, news, thing" ], [ "+++ could, peopl, make, time, key, two, law, right, new, also<br>--- car, well, azeri, turkey, presid, develop, access, serdar, german, inform", "+++ <br>--- two, presid, c8v, right, b8f, develop, okz, access, qtm, wm4u", "+++ program, need, time, new, also, distribut, data, work, system, inform<br>--- server, two, presid, right, thank, develop, display, access, chip, clipper", "+++ way, make, time, two, law, right, also, think, govern, peopl<br>--- well, presid, say, mani, develop, without, access, thing, inform, chip", "+++ could, peopl, make, time, two, presid, right, also, think, year<br>--- well, say, went, develop, access, made, stephanopoulo, thing, inform, chip", "+++ could, time, make, right, think, peopl, state, system<br>--- well, two, presid, say, mani, develop, access, opinion, thing, also", "+++ year, program, could, time, launch, also, data, new, technolog, space<br>--- two, presid, right, lunar, news, access, project, moon, chip, world", "+++ encrypt, commun, two, law, govern, develop, data, escrow, secret, make<br>--- effect, presid, first, right, say, mani, clinton, pitt, netcom, privaci", "+++ make, time, two, new, right, year, think, first, way<br>--- hockey, well, presid, say, develop, access, nhl, also, score, inform", "+++ access, need, time, two, new, also, space, distribut, data, work<br>--- modem, presid, floppi, right, thank, develop, speed, sale, inform, chip", "+++ program, need, inform, year, first, may, comput<br>--- two, presid, right, check, develop, access, char, also, sourc, chip", "+++ could, make, time, law, also, right, think, may, peopl, state<br>--- two, presid, say, mani, develop, access, thing, inform, chip, world", "+++ way, could, need, make, time, two, right, new, also, distribut<br>--- car, well, presid, say, develop, access, thing, speed, inform, chip", "+++ year, time, public, also, new, peopl, state, work, first<br>--- armi, two, presid, turkey, right, develop, kent, sandvik, access, sourc", "+++ could, make, time, also, messag, distribut, privaci, may, peopl, system<br>--- server, cunixb, two, presid, right, newsgroup, thank, develop, news, access" ] ], "type": "heatmap", "uid": "a3ce4eba-5101-4544-850d-7b2963eb6250", "z": [ [ 0.8095238095238095, 1, 0.9583333333333334, 0.75, 0.7804878048780488, 0.8235294117647058, 0.9361702127659575, 0.8505747126436781, 0.8636363636363636, 0.9583333333333334, 0.9795918367346939, 0.6486486486486487, 0.8095238095238095, 0.9130434782608696, 0.9010989010989011 ], [ 0.8095238095238095, 1, 0.8095238095238095, 0.7341772151898734, 0.7341772151898734, 0.717948717948718, 0.9010989010989011, 0.7654320987654322, 0.8372093023255813, 0.8235294117647058, 0.9583333333333334, 0.7341772151898734, 0.6301369863013699, 0.9247311827956989, 0.8636363636363636 ], [ 0.8764044943820225, 1, 0.6111111111111112, 0.9473684210526316, 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function(Plotly) { window.PLOTLYENV=window.PLOTLYENV \|\| {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e658128a-8e00-426f-b3a2-ee96583500cc\", [{\"colorscale\": \"RdBu\", \"text\": [[\"+++ world, could, peopl, make, well, time, two, nazi, right, also<br>--- car, azeri, turkey, say, mani, give, serdar, german, 000, question\", \"+++ <br>--- well, two, c8v, right, say, mani, b8f, okz, give, qtm\", \"+++ want, time, also, call<br>--- server, well, two, right, say, mani, thank, display, give, inform\", \"+++ well, two, right, say, mani, want, govern, state, even, make<br>--- atheist, said, evid, point, must, law, arab, american, live, palestinian\", \"+++ even, could, said, kill, well, make, two, time, peopl, right<br>--- presid, went, mani, give, made, stephanopoulo, thing, happen, world, 000\", \"+++ even, world, could, make, well, time, right, say, mani, think<br>--- two, give, opinion, thing, also, sgi, 000, jewish, question, much\", \"+++ world, could, time, also, nation, year<br>--- well, two, right, say, mani, develop, lunar, news, give, project\", \"+++ even, could, make, time, two, also, say, mani, govern, peopl<br>--- well, right, develop, give, inform, chip, world, clipper, need, number\", \"+++ even, make, well, time, two, right, say, think, much, year<br>--- hockey, mani, give, nhl, also, score, world, 000, jewish, question\", \"+++ two, time, also, want<br>--- modem, well, floppi, right, say, mani, thank, access, give, speed\", \"+++ jewish, year<br>--- well, two, right, check, say, mani, give, char, also, sourc\", \"+++ right, arab, say, live, mani, want, state, israel, even, fact<br>--- said, well, two, point, law, american, palestinian, believ, govern, case\", \"+++ even, could, make, well, time, two, right, also, say, want<br>--- car, mani, give, thing, speed, world, need, 000, jewish, question\", \"+++ year, world, time, 000, also, nation, peopl, state<br>--- armi, well, two, turkey, right, say, mani, kent, sandvik, give\", \"+++ world, could, make, time, also, columbia, want, peopl, insur<br>--- server, cunixb, well, two, right, say, newsgroup, mani, thank, news\"], [\"+++ could, make, well, time, key, law, right, new, also, look<br>--- car, two, azeri, turkey, say, thing, serdar, sgi, german, chip\", \"+++ <br>--- well, c8v, right, say, b8f, okz, qtm, wm4u, thing, also\", \"+++ need, time, bit, anyon, also, new, look, distribut, want, run<br>--- server, well, right, say, thank, display, thing, sgi, inform, chip\", \"+++ well, law, right, say, want, state, tri, even, make, thing<br>--- atheist, realli, two, evid, point, must, indiana, mani, run, govern\", \"+++ well, right, say, want, state, tri, even, make, thing, also<br>--- said, realli, two, presid, law, went, indiana, run, believ, disk\", \"+++ realli, well, right, say, state, tri, even, make, thing, sgi<br>--- atheist, caltech, optilink, law, keith, show, indiana, mani, gay, want\", \"+++ could, time, new, also, distribut, problem, softwar, work, system<br>--- well, right, say, develop, lunar, news, project, thing, moon, sgi\", \"+++ law, say, even, make, new, also, chip, need, good, work<br>--- realli, well, two, right, mani, netcom, run, data, develop, thing\", \"+++ even, make, well, time, take, right, new, say, look, think<br>--- hockey, two, nhl, thing, also, sgi, score, chip, need, question\", \"+++ need, time, drive, also, new, look, distribut, want, system, problem<br>--- modem, well, two, floppi, right, say, thank, access, thing, speed\", \"+++ read, need, tri, may<br>--- well, right, check, say, char, thing, also, sourc, sgi, inform\", \"+++ law, right, say, want, state, even, make, thing, also, see<br>--- realli, well, point, arab, live, mani, indiana, run, believ, disk\", \"+++ realli, well, right, say, want, run, state, tri, even, make<br>--- car, two, first, law, wire, indiana, turn, disk, system, got\", \"+++ time, also, new, org, peopl, state, work<br>--- armi, well, turkey, right, say, kent, sandvik, thing, sourc, serdar\", \"+++ could, make, time, also, distribut, want, org, run, system, peopl<br>--- server, cunixb, well, right, say, newsgroup, thank, news, thing, sgi\"], [\"+++ could, make, time, also, new, repli, look, distribut, want, work<br>--- car, well, two, azeri, turkey, right, thank, speed, serdar, simm\", \"+++ <br>--- c8v, b8f, thank, okz, qtm, wm4u, also, speed, simm, chip\", \"+++ want, color, run, thank, mac, mous, set, tri, system, help<br>--- server, imag, applic, repli, monitor, vga, disk, ide, data, instal\", \"+++ time, make, also, want, tri<br>--- well, two, right, say, mani, thank, without, thing, speed, simm\", \"+++ could, make, time, also, look, want, work, tri<br>--- well, two, presid, right, say, went, thank, made, stephanopoulo, thing\", \"+++ could, time, make, system, tri<br>--- well, right, say, mani, thank, opinion, thing, also, speed, simm\", \"+++ could, time, new, also, distribut, problem, scsi, work, system<br>--- thank, develop, lunar, news, project, speed, simm, moon, inform, chip\", \"+++ could, need, make, time, comput, bit, also, new, work, system<br>--- two, say, mani, thank, develop, speed, simm, inform, clipper, number\", \"+++ make, time, new, look, run<br>--- hockey, well, two, right, say, thank, nhl, also, speed, simm\", \"+++ repli, monitor, want, thank, disk, ide, system, pin, new, also<br>--- modem, price, two, point, floppi, motherboard, origin, mac, run, vga\", \"+++ need, help, pleas, tri, comput<br>--- check, thank, char, also, sourc, speed, simm, inform, chip, number\", \"+++ could, time, make, also, want<br>--- right, say, mani, thank, thing, speed, simm, chip, world, need\", \"+++ could, need, make, time, drive, new, also, speed, look, distribut<br>--- car, well, two, right, say, thank, thing, simm, chip, dod\", \"+++ time, appl, also, new, repli, work<br>--- armi, turkey, thank, kent, sandvik, sourc, speed, serdar, simm, chip\", \"+++ window, could, make, time, help, also, repli, distribut, want, run<br>--- server, cunixb, newsgroup, news, speed, simm, inform, chip, world, need\"], [\"+++ world, could, car, make, well, time, peopl, right, look, repli<br>--- armenia, realli, two, azeri, law, turkey, ingr, pass, greek, mani\", \"+++ <br>--- car, well, c8v, right, say, mani, b8f, okz, qtm, wm4u\", \"+++ need, time, anyon, look, distribut, want, problem, tri<br>--- server, car, well, right, say, mani, thank, display, thing, also\", \"+++ well, point, right, say, mani, want, state, tri, even, make<br>--- atheist, car, realli, two, evid, first, must, repli, law, govern\", \"+++ well, right, say, want, state, tri, got, even, make, thing<br>--- said, car, realli, two, presid, point, repli, went, mani, believ\", \"+++ realli, well, right, say, mani, state, tri, even, make, thing<br>--- car, show, gay, frank, object, got, cramer, put, opinion, gov\", \"+++ world, could, gov, time, distribut, problem, year, first<br>--- car, well, right, say, mani, develop, lunar, news, project, thing\", \"+++ even, could, need, make, time, peopl, say, mani, good, think<br>--- car, well, two, right, develop, thing, also, inform, chip, happen\", \"+++ well, point, right, say, even, make, year, see, better, look<br>--- period, game, car, play, hockey, two, realli, hit, repli, weapon\", \"+++ need, time, point, look, repli, distribut, want, problem, good, anyon<br>--- modem, car, well, two, floppi, right, say, mani, thank, access\", \"+++ need, tri, year, read, first<br>--- car, well, right, check, say, mani, char, thing, sourc, inform\", \"+++ point, right, say, mani, want, state, even, make, thing, see<br>--- car, realli, well, law, first, arab, repli, live, believ, tri\", \"+++ car, realli, well, right, say, want, state, tri, got, even<br>--- two, point, wire, repli, mani, turn, run, put, gov, new\", \"+++ year, world, time, repli, peopl, state, first<br>--- armi, car, well, turkey, right, say, mani, kent, sandvik, thing\", \"+++ world, could, make, time, repli, distribut, want, peopl, anyon<br>--- server, car, cunixb, well, right, say, newsgroup, mani, thank, news\"], [\"+++ car, make, time, new, look, repli, distribut, want, think, engin<br>--- well, two, azeri, turkey, right, c8v, b8f, okz, also, serdar\", \"+++ 2tm, 1eq, c8v, 2di, b8f, 1d9, 7ey, max, okz, b8e<br>--- dyer, car, 1d9l, repli, want, 7ez, r8f, f9d, brake, mile\", \"+++ need, time, new, look, distribut, want, anyon<br>--- server, car, c8v, b8f, thank, okz, display, also, uchicago, inform\", \"+++ time, make, want, good, think<br>--- car, well, two, right, c8v, say, mani, b8f, without, okz\", \"+++ time, make, look, want, good, think<br>--- car, well, two, presid, right, c8v, say, went, b8f, okz\", \"+++ time, think, make, good<br>--- car, well, right, c8v, say, mani, b8f, okz, opinion, thing\", \"+++ time, new, engin, distribut<br>--- car, c8v, b8f, develop, okz, lunar, news, project, also, moon\", \"+++ need, time, make, new, good, think<br>--- car, two, c8v, say, mani, b8f, develop, okz, also, uchicago\", \"+++ time, make, new, look, good, think<br>--- car, hockey, well, two, right, c8v, say, b8f, okz, nhl\", \"+++ need, time, new, look, repli, usa, want, distribut, good, anyon<br>--- modem, car, two, floppi, c8v, b8f, thank, okz, access, also\", \"+++ need<br>--- car, c8v, check, b8f, okz, char, sourc, uchicago, inform, number\", \"+++ time, make, want, good, think<br>--- car, right, c8v, say, mani, b8f, okz, thing, also, uchicago\", \"+++ car, make, need, time, new, look, usa, want, distribut, good<br>--- well, two, right, c8v, say, b8f, okz, thing, also, speed\", \"+++ time, new, repli, umd, eng<br>--- armi, car, turkey, c8v, b8f, kent, sandvik, okz, also, sourc\", \"+++ time, make, repli, distribut, want, anyon<br>--- server, car, cunixb, c8v, newsgroup, b8f, thank, okz, news, also\"], [\"+++ armenia, well, two, turkey, right, greek, want, turk, new, serdar<br>--- car, said, azeri, law, file, repli, ingr, pass, live, say\", \"+++ <br>--- well, two, turkey, c8v, right, say, went, b8f, okz, qtm\", \"+++ program, need, time, work, new, look, want, file, call<br>--- server, well, two, turkey, right, say, went, thank, display, also\", \"+++ even, time, well, two, right, say, want, think, peopl, see<br>--- turkey, went, mani, without, thing, also, serdar, world, need, number\", \"+++ said, well, two, right, say, went, want, start, come, even<br>--- armenia, presid, turkey, file, azerbaijani, live, greek, believ, state, return\", \"+++ even, world, could, time, well, right, say, think, someth, peopl<br>--- two, turkey, went, mani, opinion, thing, serdar, sgi, need, number\", \"+++ program, world, could, time, new, year, work, first<br>--- well, two, turkey, right, say, went, develop, lunar, news, project\", \"+++ even, could, need, time, number, two, work, new, say, peopl<br>--- well, turkey, right, went, mani, develop, also, serdar, inform, chip\", \"+++ even, time, well, two, right, new, say, look, think, start<br>--- hockey, turkey, went, nhl, serdar, score, world, need, number, season\", \"+++ need, time, two, new, look, want, work<br>--- modem, well, turkey, floppi, right, say, went, thank, access, also\", \"+++ rule, program, need, number, output, file, build, name, year, return<br>--- well, two, turkey, right, check, say, went, char, sourc, serdar\", \"+++ even, world, could, kill, time, right, say, live, want, think<br>--- well, two, turkey, went, mani, thing, also, serdar, need, number\", \"+++ even, could, need, time, well, two, right, new, say, look<br>--- car, turkey, went, thing, also, speed, serdar, world, number, dod\", \"+++ armenia, turkey, file, muslim, turk, new, serdar, year, world, number<br>--- armi, david, well, two, right, say, went, greek, popul, kent\", \"+++ world, could, time, file, want, name, peopl<br>--- server, cunixb, well, two, turkey, right, say, newsgroup, went, thank\"], [\"+++ make, well, time, two, new, right, also, look, year, think<br>--- car, hockey, azeri, turkey, presid, say, stephanopoulo, 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said, presid, weapon, next, got, come, stephanopoulo, also, day\", \"+++ time, two, point, new, look, also, good<br>--- modem, hockey, well, presid, floppi, right, say, thank, access, stephanopoulo\", \"+++ first, year<br>--- hockey, well, two, presid, right, check, say, char, stephanopoulo, nhl\", \"+++ day, even, make, time, point, right, also, say, think, good<br>--- hockey, well, two, presid, mani, stephanopoulo, thing, nhl, score, world\", \"+++ well, two, right, say, run, got, even, make, new, also<br>--- game, car, realli, said, play, wire, hockey, point, want, turn\", \"+++ time, also, new, toronto, year, first<br>--- armi, hockey, well, two, presid, turkey, right, say, kent, sandvik\", \"+++ also, run, make, time<br>--- server, cunixb, hockey, well, two, presid, right, say, newsgroup, thank\"], [\"+++ could, make, well, time, two, new, also, repli, sun, look<br>--- car, current, azeri, turkey, right, serdar, sale, german, world, need\", \"+++ <br>--- well, current, two, c8v, b8f, okz, qtm, wm4u, also, sale\", \"+++ need, time, imag, also, new, look, includ, want, distribut, run<br>--- server, well, current, two, thank, display, sale, inform, question, softwar\", \"+++ make, well, time, two, point, question, also, want, good, may<br>--- current, right, say, mani, without, thing, sale, need, constitut, much\", \"+++ could, make, well, time, two, also, look, want, good, year<br>--- current, presid, right, say, went, made, stephanopoulo, thing, sale, happen\", \"+++ could, time, make, well, good, system<br>--- current, two, right, say, mani, opinion, thing, also, sale, sgi\", \"+++ imag, system, gov, scienc, also, new, year, space, center, includ<br>--- well, fund, current, two, point, wire, satellit, repli, 1993, want\", \"+++ could, need, make, scienc, time, two, work, new, also, pitt<br>--- well, current, say, mani, develop, sale, inform, chip, clipper, number\", \"+++ make, well, time, two, point, new, look, good, run, year<br>--- hockey, current, right, 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azeri, turkey, right, resourc, thank\", \"+++ <br>--- server, client, c8v, b8f, resourc, thank, okz, display, qtm, wm4u\", \"+++ server, applic, want, color, run, thank, data, set, tri, display<br>--- client, imag, motif, repli, resourc, mac, mous, make, graphic, new\", \"+++ way, make, time, also, want, tri, may<br>--- server, client, well, two, right, say, mani, resourc, thank, without\", \"+++ make, time, also, look, want, work, tri, way<br>--- server, client, well, two, presid, right, say, went, resourc, thank\", \"+++ time, system, tri, make<br>--- server, client, well, right, say, mani, resourc, thank, display, opinion\", \"+++ program, time, softwar, also, group, includ, distribut, avail, problem, data<br>--- server, client, resourc, thank, develop, display, lunar, news, project, sourc\", \"+++ need, make, time, number, bit, work, also, may, data, call<br>--- server, client, two, say, mani, resourc, thank, develop, display, sourc\", \"+++ time, make, look, run, way<br>--- server, client, hockey, well, two, right, say, resourc, thank, display\", \"+++ need, time, also, look, repli, includ, want, distribut, thank, problem<br>--- modem, server, client, two, floppi, resourc, display, access, sourc, speed\", \"+++ program, need, help, number, sourc, includ, name, mail, may, file<br>--- server, client, check, resourc, thank, display, char, also, lib, jewish\", \"+++ way, make, time, also, want, may<br>--- server, client, right, say, mani, resourc, thank, display, thing, sourc\", \"+++ way, need, make, time, also, look, distribut, want, run, problem<br>--- server, client, car, well, two, right, say, resourc, thank, display\", \"+++ time, number, also, sourc, repli, file, work<br>--- armi, server, client, turkey, resourc, thank, kent, sandvik, display, serdar\", \"+++ server, repli, want, run, thank, make, help, also, inform, window<br>--- client, anonym, cunixb, motif, applic, newsgroup, color, resourc, privaci, data\"], [\"+++ well, two, right, repli, want, 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news\"], [\"+++ car, make, time, new, look, repli, distribut, want, think, engin<br>--- well, two, azeri, turkey, right, c8v, b8f, okz, also, serdar\", \"+++ 2tm, 1eq, c8v, 2di, b8f, 1d9, 7ey, max, okz, b8e<br>--- dyer, car, 1d9l, repli, want, 7ez, r8f, f9d, brake, mile\", \"+++ need, time, new, look, distribut, want, anyon<br>--- server, car, c8v, b8f, thank, okz, display, also, uchicago, inform\", \"+++ time, make, want, good, think<br>--- car, well, two, right, c8v, say, mani, b8f, without, okz\", \"+++ time, make, look, want, good, think<br>--- car, well, two, presid, right, c8v, say, went, b8f, okz\", \"+++ time, think, make, good<br>--- car, well, right, c8v, say, mani, b8f, okz, opinion, thing\", \"+++ time, new, engin, distribut<br>--- car, c8v, b8f, develop, okz, lunar, news, project, also, moon\", \"+++ need, time, make, new, good, think<br>--- car, two, c8v, say, mani, b8f, develop, okz, also, uchicago\", \"+++ time, make, new, look, good, think<br>--- car, hockey, well, 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resourc, thank, okz, display, qtm, wm4u\", \"+++ server, applic, want, color, run, thank, data, set, tri, display<br>--- client, imag, motif, repli, resourc, mac, mous, make, graphic, new\", \"+++ way, make, time, also, want, tri, may<br>--- server, client, well, two, right, say, mani, resourc, thank, without\", \"+++ make, time, also, look, want, work, tri, way<br>--- server, client, well, two, presid, right, say, went, resourc, thank\", \"+++ time, system, tri, make<br>--- server, client, well, right, say, mani, resourc, thank, display, opinion\", \"+++ program, time, softwar, also, group, includ, distribut, avail, problem, data<br>--- server, client, resourc, thank, develop, display, lunar, news, project, sourc\", \"+++ need, make, time, number, bit, work, also, may, data, call<br>--- server, client, two, say, mani, resourc, thank, develop, display, sourc\", \"+++ time, make, look, run, way<br>--- server, client, hockey, well, two, right, say, resourc, thank, display\", \"+++ need, 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pass, greek, run\", \"+++ <br>--- well, two, c8v, right, say, b8f, thank, okz, access, opinion\", \"+++ time, also, new, look, distribut, want, run, thank, problem, pleas<br>--- server, well, two, right, say, display, access, opinion, inform, world\", \"+++ peopl, make, well, time, two, question, right, also, say, human<br>--- mani, thank, without, access, opinion, thing, world, great, constitut, dod\", \"+++ peopl, make, well, time, two, right, also, say, look, want<br>--- presid, went, run, thank, ask, got, tax, access, kill, opinion\", \"+++ world, peopl, opinion, make, well, time, right, say, think, system<br>--- two, mani, thank, access, thing, also, sgi, great, question, dod\", \"+++ year, world, time, new, also, distribut, problem, base, system<br>--- well, two, right, say, thank, develop, lunar, news, access, opinion\", \"+++ peopl, make, time, two, also, new, say, good, think, system<br>--- well, right, mani, thank, develop, access, opinion, inform, chip, world\", \"+++ make, 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time, two, peopl, law, right<br>--- armenia, azeri, turkey, file, say, pass, weapon, greek, mani, health\", \"+++ <br>--- car, well, two, c8v, right, say, mani, b8f, okz, qtm\", \"+++ time, new, also, problem, file, system<br>--- server, car, well, two, right, say, mani, thank, display, thing\", \"+++ well, two, law, right, say, mani, state, even, make, thing<br>--- atheist, car, evid, point, first, must, file, weapon, want, govern\", \"+++ well, two, right, say, state, even, make, thing, also, year<br>--- car, presid, weapon, went, mani, health, ask, got, tax, crime\", \"+++ even, could, peopl, make, well, time, person, thing, right, say<br>--- car, realli, two, show, weapon, gay, frank, object, health, cramer\", \"+++ could, time, new, also, system, problem, scsi, year, first<br>--- car, well, two, right, say, mani, develop, lunar, news, project\", \"+++ two, law, say, mani, case, even, make, new, also, number<br>--- car, well, right, weapon, netcom, data, develop, health, crime, 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0.7804878048780488, 0.5507246376811594, 0.9010989010989011, 0.8636363636363636, 0.9247311827956989, 0.8764044943820225, 0.8095238095238095, 0.9010989010989011, 0.8636363636363636]], \"type\": \"heatmap\", \"uid\": \"2bfdeb0a-21a9-47d4-a766-a8375b14fc14\"}], {\"height\": 950, \"title\": \"Topic difference (two models)[jaccard distance]\", \"width\": 950, \"xaxis\": {\"title\": \"topic\"}, \"yaxis\": {\"title\": \"topic\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mdiff, annotation = lda_fst.diff(lda_snd, distance='jaccard', num_words=50)\n", "plot_difference(mdiff, title=\"Topic difference (two models)[jaccard distance]\", annotation=annotation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at this matrix, you can find similar and different topics (and relevant tokens which describe the intersection and difference)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	6,259,678	Python	.py	2,291	2,721.787866	2,954,347	0.645215	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,924	summarization_tutorial.ipynb	piskvorky_gensim/docs/notebooks/summarization_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,925	Wordrank_comparisons.ipynb	piskvorky_gensim/docs/notebooks/Wordrank_comparisons.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of WordRank, Word2Vec and FastText\n", "\n", "[Wordrank](https://arxiv.org/pdf/1506.02761v3.pdf) is a fresh new approach to the word embeddings, which formulates it as a ranking problem. That is, given a word w, it aims to output an ordered list (c1, c2, · · ·) of context words such that words that co-occur with w appear at the top of the list. This formulation fits naturally to popular word embedding tasks such as word similarity/analogy since instead of the likelihood of each word, we are interested in finding the most relevant words in a given context<sup>[1]</sup>.\n", "\n", "This notebook accompanies a more theoretical blog post [here](https://rare-technologies.com/wordrank-embedding-crowned-is-most-similar-to-king-not-word2vecs-canute/).\n", "\n", "Gensim is used to train and evaluate the word2vec models. Analogical reasoning and Word Similarity tasks are used for comparing the models. Word2vec and FastText embeddings are trained using the skipgram architecture here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Download and preprocess data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package brown to /home/misha/nltk_data...\n", "[nltk_data] Package brown is already up-to-date!\n" ] } ], "source": [ "import nltk\n", "from smart_open import smart_open\n", "from gensim.parsing.preprocessing import strip_punctuation, strip_multiple_whitespaces\n", "\n", "# Only the brown corpus is needed in case you don't have it.\n", "nltk.download('brown') \n", "\n", "# Generate brown corpus text file\n", "with smart_open('brown_corp.txt', 'w+') as f:\n", " for word in nltk.corpus.brown.words():\n", " f.write('{word} '.format(word=word))\n", " f.seek(0)\n", " brown = f.read()\n", "\n", "# Preprocess brown corpus\n", "with smart_open('proc_brown_corp.txt', 'w') as f:\n", " proc_brown = strip_punctuation(brown)\n", " proc_brown = strip_multiple_whitespaces(proc_brown).lower()\n", " f.write(proc_brown)\n", "\n", "# Set WR_HOME and FT_HOME to respective directory root\n", "WR_HOME = 'wordrank/'\n", "FT_HOME = 'fastText/'\n", "\n", "# download the text8 corpus (a 100 MB sample of preprocessed wikipedia text)\n", "import os.path\n", "if not os.path.isfile('text8'):\n", " !wget -c https://mattmahoney.net/dc/text8.zip\n", " !unzip text8.zip" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train Models\n", "For training the models yourself, you'll need to have Gensim, FastText and Wordrank set up on your machine." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training word2vec on proc_brown_corp.txt corpus..\n", "CPU times: user 44.6 s, sys: 85.5 ms, total: 44.7 s\n", "Wall time: 15.2 s\n" ] }, { "ename": "DeprecationWarning", "evalue": "Deprecated. Use model.wv.save_word2vec_format instead.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mDeprecationWarning\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-2-658d85b3cabf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\nUsing existing model file {:s}.vec'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0mtrain_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorpus_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'proc_brown_corp.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'brown'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-2-658d85b3cabf>\u001b[0m in \u001b[0;36mtrain_models\u001b[0;34m(corpus_file, output_name)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;31m# Text8Corpus class for reading space-separated words file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gs_model = Word2Vec(Text8Corpus(corpus_file), w2v_params); gs_model'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gs_model'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_word2vec_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMODELS_DIR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'{:s}.vec'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\nSaved gensim model as {:s}.vec'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/git/gensim/gensim/models/word2vec.py\u001b[0m in \u001b[0;36msave_word2vec_format\u001b[0;34m(self, fname, fvocab, binary)\u001b[0m\n\u001b[1;32m 1305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1306\u001b[0m \"\"\"\n\u001b[0;32m-> 1307\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mDeprecationWarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Deprecated. Use model.wv.save_word2vec_format instead.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1309\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDeprecationWarning\u001b[0m: Deprecated. Use model.wv.save_word2vec_format instead." ] } ], "source": [ "MODELS_DIR = 'models/'\n", "!mkdir -p {MODELS_DIR}\n", "\n", "from gensim.models import Word2Vec\n", "from gensim.models.wrappers import Wordrank\n", "from gensim.models.word2vec import Text8Corpus\n", "\n", "# fasttext params\n", "lr = 0.05\n", "dim = 100\n", "ws = 5\n", "epoch = 5\n", "minCount = 5\n", "neg = 5\n", "loss = 'ns'\n", "t = 1e-4\n", "\n", "w2v_params = {\n", " 'alpha': 0.025,\n", " 'size': 100,\n", " 'window': 15,\n", " 'iter': 5,\n", " 'min_count': 5,\n", " 'sample': t,\n", " 'sg': 1,\n", " 'hs': 0,\n", " 'negative': 5\n", "}\n", "\n", "wr_params = {\n", " 'size': 100,\n", " 'window': 15,\n", " 'iter': 91,\n", " 'min_count': 5\n", "}\n", "\n", "def train_models(corpus_file, output_name):\n", " # Train using word2vec\n", " output_file = '{:s}_gs'.format(output_name)\n", " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", " print('\\nTraining word2vec on {:s} corpus..'.format(corpus_file))\n", " # Text8Corpus class for reading space-separated words file\n", " %time gs_model = Word2Vec(Text8Corpus(corpus_file), w2v_params); gs_model\n", " locals()['gs_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", " print('\\nSaved gensim model as {:s}.vec'.format(output_file))\n", " else:\n", " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", "\n", " # Train using fasttext\n", " output_file = '{:s}_ft'.format(output_name)\n", " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", " print('Training fasttext on {:s} corpus..'.format(corpus_file))\n", " %time !{FT_HOME}fasttext skipgram -input {corpus_file} -output {MODELS_DIR+output_file} -lr {lr} -dim {dim} -ws {ws} -epoch {epoch} -minCount {minCount} -neg {neg} -loss {loss} -t {t}\n", " else:\n", " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", " \n", " # Train using wordrank\n", " output_file = '{:s}_wr'.format(output_name)\n", " output_dir = 'model' # directory to save embeddings and metadata to\n", " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", " print('\\nTraining wordrank on {:s} corpus..'.format(corpus_file))\n", " %time wr_model = Wordrank.train(WR_HOME, corpus_file, output_dir, *wr_params); wr_model\n", " locals()['wr_model'].save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", " print('\\nSaved wordrank model as {:s}.vec'.format(output_file))\n", " else:\n", " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", " \n", " # Loading ensemble embeddings\n", " output_file = '{:s}_wr_ensemble'.format(output_name)\n", " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", " print('\\nLoading ensemble embeddings (vector combination of word and context embeddings)..')\n", " %time wr_model = Wordrank.load_wordrank_model(os.path.join(WR_HOME, 'model/wordrank.words'), os.path.join(WR_HOME, 'model/meta/vocab.txt'), os.path.join(WR_HOME, 'model/wordrank.contexts'), sorted_vocab=1, ensemble=1); wr_model\n", " locals()['wr_model'].wv.save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", " print('\\nSaved wordrank (ensemble) model as {:s}.vec'.format(output_file))\n", " else:\n", " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", " \n", "train_models(corpus_file='proc_brown_corp.txt', output_name='brown')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_models(corpus_file='text8', output_name='text8')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we train wordrank model using ensemble in second case as it is known to give a small performance boost in some cases. So we'll test accuracy for both the cases." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparisons" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import logging\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", "\n", "def print_analogy_accuracy(model, questions_file):\n", " acc = model.accuracy(questions_file)\n", "\n", " sem_correct = sum((len(acc[i]['correct']) for i in range(5)))\n", " sem_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5))\n", " sem_acc = 100float(sem_correct)/sem_total\n", " print('\\nSemantic: {:d}/{:d}, Accuracy: {:.2f}%'.format(sem_correct, sem_total, sem_acc))\n", " \n", " syn_correct = sum((len(acc[i]['correct']) for i in range(5, len(acc)-1)))\n", " syn_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5,len(acc)-1))\n", " syn_acc = 100float(syn_correct)/syn_total\n", " print('Syntactic: {:d}/{:d}, Accuracy: {:.2f}%\\n'.format(syn_correct, syn_total, syn_acc))\n", " \n", "def print_similarity_accuracy(model, similarity_file):\n", " acc = model.evaluate_word_pairs(similarity_file)\n", " print('Pearson correlation coefficient: {:.2f}'.format(acc[0][0]))\n", " print('Spearman rank correlation coefficient: {:.2f}'.format(acc[1][0]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "MODELS_DIR = 'models/'\n", "word_analogies_file = './datasets/questions-words.txt'\n", "simlex_file = '../../gensim/test/test_data/simlex999.txt'\n", "wordsim_file = '../../gensim/test/test_data/wordsim353.tsv'\n", "\n", "print('\\nLoading Gensim embeddings')\n", "brown_gs = KeyedVectors.load_word2vec_format(MODELS_DIR + 'brown_gs.vec')\n", "print('Accuracy for Word2Vec:')\n", "print_analogy_accuracy(brown_gs, word_analogies_file)\n", "print('SimLex-999 similarity')\n", "print_similarity_accuracy(brown_gs, simlex_file)\n", "print('\\nWordSim-353 similarity')\n", "print_similarity_accuracy(brown_gs, wordsim_file)\n", "\n", "print('\\nLoading FastText embeddings')\n", "brown_ft = KeyedVectors.load_word2vec_format(MODELS_DIR + 'brown_ft.vec')\n", "print('Accuracy for FastText:')\n", "print_analogy_accuracy(brown_ft, word_analogies_file)\n", "print('SimLex-999 similarity')\n", "print_similarity_accuracy(brown_ft, simlex_file)\n", "print('\\nWordSim-353 similarity')\n", "print_similarity_accuracy(brown_ft, wordsim_file)\n", "\n", "print('\\nLoading Wordrank embeddings')\n", "brown_wr = KeyedVectors.load_word2vec_format(MODELS_DIR + 'brown_wr.vec')\n", "print('Accuracy for Wordrank:')\n", "print_analogy_accuracy(brown_wr, word_analogies_file)\n", "print('SimLex-999 similarity')\n", "print_similarity_accuracy(brown_wr, simlex_file)\n", "print('\\nWordSim-353 similarity')\n", "print_similarity_accuracy(brown_wr, wordsim_file)\n", "\n", "print('\\nLoading Wordrank ensemble embeddings')\n", "brown_wr_ensemble = KeyedVectors.load_word2vec_format(MODELS_DIR + 'brown_wr_ensemble.vec')\n", "print('Accuracy for Wordrank:')\n", "print_analogy_accuracy(brown_wr_ensemble, word_analogies_file)\n", "print('SimLex-999 similarity')\n", "print_similarity_accuracy(brown_wr_ensemble, simlex_file)\n", "print('\\nWordSim-353 similarity')\n", "print_similarity_accuracy(brown_wr_ensemble, wordsim_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As evident from the above outputs, WordRank performs significantly better in Semantic analogies, whereas, FastText on Syntactic analogies. Also ensemble embeddings gives a small performance boost in WordRank's case.\n", "\n", "Wordrank's effectiveness in Semantic analogies is possibly due to it's focused attention on getting most relevant words right at the top using the ranking approach.\n", "And as fasttext is designed to incorporate morphological information about words, it results in it's performance boost in Syntactic analogies, as most of the Syntactic analogies are morphology based<sup>[2]</sup>.\n", "\n", "And for the Word Similarity, Word2Vec performed better on SimLex-999 test data, whereas, WordRank on WS-353. This is probably due to the different types of similarities these datasets address. SimLex-999 provides a measure of how well the two words are interchangeable in similar contexts, and WS-353 tries to estimate the relatedness or co-occurrence of two words. Also, ensemble embeddings doesn't help in the Word Similarity task<sup>[1]</sup>, which is evident from the results above so we'll use just the Word Embeddings for it. \n", "\n", "Now lets evaluate on a larger corpus, text8, and see how it effects the performance of different embedding models. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('Loading Gensim embeddings')\n", "text8_gs = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text8_gs.vec')\n", "print('Accuracy for word2vec:')\n", "print_analogy_accuracy(text8_gs, word_analogies_file)\n", "print('SimLex-999 similarity')\n", "print_similarity_accuracy(text8_gs, simlex_file)\n", "print('\\nWordSim-353 similarity')\n", "print_similarity_accuracy(text8_gs, wordsim_file)\n", "\n", "print('Loading FastText embeddings')\n", "text8_ft = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text8_ft.vec')\n", "print('Accuracy for FastText (with n-grams):')\n", "print_analogy_accuracy(text8_ft, word_analogies_file)\n", "print('SimLex-999 similarity')\n", "print_similarity_accuracy(text8_ft, simlex_file)\n", "print('\\nWordSim-353 similarity')\n", "print_similarity_accuracy(text8_ft, wordsim_file)\n", "\n", "print('\\nLoading Wordrank embeddings')\n", "text8_wr = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text8_wr.vec')\n", "print('Accuracy for Wordrank:')\n", "print_analogy_accuracy(text8_wr, word_analogies_file)\n", "print('SimLex-999 similarity')\n", "print_similarity_accuracy(text8_wr, simlex_file)\n", "print('\\nWordSim-353 similarity')\n", "print_similarity_accuracy(text8_wr, wordsim_file)\n", "\n", "print('\\nLoading Wordrank ensemble embeddings')\n", "text8_wr_ensemble = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text8_wr_ensemble.vec')\n", "print('Accuracy for Wordrank:')\n", "print_analogy_accuracy(text8_wr_ensemble, word_analogies_file)\n", "print('SimLex-999 similarity')\n", "print_similarity_accuracy(text8_wr_ensemble, simlex_file)\n", "print('\\nWordSim-353 similarity')\n", "print_similarity_accuracy(text8_wr_ensemble, wordsim_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a larger corpus, we observe similar patterns in the accuracies. Here also, WordRank dominates the Semantic analogies and FastText Syntactic ones. Word2Vec again performs better on SimLex-999 dataset and WordRank on WordSim-353.\n", "Though we observe a little performance decrease in WordRank in case of ensemble embeddings here, so it's good to try both the cases for evaluations.\n", "\n", "# Word Frequency and Model Performance\n", "\n", "In this section, we'll see if the frequency of a word has any effect on embedding model's performance in Analogy task. Accuracy vs. Frequency graph is used to analyze this effect. The mean frequency of four words involved in each analogy is computed, and then bucketed with other analogies having similar mean frequencies. Each bucket has six percent of the total analogies involved in the particular task. You can go to this [repo](https://github.com/parulsethi/EmbeddingVisData/tree/master/WordAnalogyFreq) if you want to inspect about what analogies(with their sorted frequencies) were used for each of the plot." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from __future__ import division\n", "import matplotlib.pyplot as plt\n", "import copy\n", "import multiprocessing\n", "import numpy as np\n", "from smart_open import smart_open\n", "\n", "\n", "def compute_accuracies(model, freq):\n", " # mean_freq will contain analogies together with the mean frequency of 4 words involved\n", " mean_freq = {}\n", " with smart_open(word_analogies_file, 'r') as r:\n", " for i, line in enumerate(r):\n", " if ':' not in line:\n", " analogy = tuple(line.split())\n", " else:\n", " continue\n", " try:\n", " mfreq = sum([int(freq[x.lower()]) for x in analogy])/4\n", " mean_freq['a%d'%i] = [analogy, mfreq]\n", " except KeyError:\n", " continue\n", " \n", " # compute model's accuracy\n", " model = KeyedVectors.load_word2vec_format(model)\n", " acc = model.accuracy(word_analogies_file)\n", " \n", " sem_correct = [acc[i]['correct'] for i in range(5)]\n", " sem_total = [acc[i]['correct'] + acc[i]['incorrect'] for i in range(5)]\n", " syn_correct = [acc[i]['correct'] for i in range(5, len(acc)-1)]\n", " syn_total = [acc[i]['correct'] + acc[i]['incorrect'] for i in range(5, len(acc)-1)]\n", " total_correct = sem_correct + syn_correct\n", " total_total = sem_total + syn_total\n", "\n", " sem_x, sem_y = calc_axis(sem_correct, sem_total, mean_freq)\n", " syn_x, syn_y = calc_axis(syn_correct, syn_total, mean_freq)\n", " total_x, total_y = calc_axis(total_correct, total_total, mean_freq)\n", " return ((sem_x, sem_y), (syn_x, syn_y), (total_x, total_y))\n", "\n", "def calc_axis(correct, total, mean_freq):\n", " # make flat lists\n", " correct_analogies = []\n", " for i in range(len(correct)):\n", " for analogy in correct[i]:\n", " correct_analogies.append(analogy) \n", " total_analogies = []\n", " for i in range(len(total)):\n", " for analogy in total[i]:\n", " total_analogies.append(analogy)\n", "\n", " copy_mean_freq = copy.deepcopy(mean_freq)\n", " # delete other case's analogy from total analogies \n", " for key, value in copy_mean_freq.items():\n", " value[0] = tuple(x.upper() for x in value[0])\n", " if value[0] not in total_analogies:\n", " del copy_mean_freq[key]\n", "\n", " # append 0 or 1 for incorrect or correct analogy\n", " for key, value in copy_mean_freq.iteritems():\n", " value[0] = tuple(x.upper() for x in value[0])\n", " if value[0] in correct_analogies:\n", " copy_mean_freq[key].append(1)\n", " else:\n", " copy_mean_freq[key].append(0)\n", "\n", " x = []\n", " y = []\n", " bucket_size = int(len(copy_mean_freq) 0.06)\n", " # sort analogies according to their mean frequences \n", " copy_mean_freq = sorted(copy_mean_freq.items(), key=lambda x: x[1][1])\n", " # prepare analogies buckets according to given size\n", " for centre_p in range(bucket_size//2, len(copy_mean_freq), bucket_size):\n", " bucket = copy_mean_freq[centre_p-bucket_size//2:centre_p+bucket_size//2]\n", " b_acc = 0\n", " # calculate current bucket accuracy with b_acc count\n", " for analogy in bucket:\n", " if analogy[1][2]==1:\n", " b_acc+=1\n", " y.append(b_acc/bucket_size)\n", " x.append(np.log(copy_mean_freq[centre_p][1][1]))\n", " return x, y\n", "\n", "# a sample model using gensim's Word2Vec for getting vocab counts\n", "corpus = Text8Corpus('proc_brown_corp.txt')\n", "model = Word2Vec(min_count=5)\n", "model.build_vocab(corpus)\n", "freq = {}\n", "for word in model.wv.index2word:\n", " freq[word] = model.wv.vocab[word].count\n", "\n", "# plot results\n", "word2vec = compute_accuracies('brown_gs.vec', freq)\n", "wordrank = compute_accuracies('brown_wr_ensemble.vec', freq)\n", "fasttext = compute_accuracies('brown_ft.vec', freq)\n", "\n", "fig = plt.figure(figsize=(7,15))\n", "\n", "for i, subplot, title in zip([0, 1, 2], ['311', '312', '313'], ['Semantic Analogies', 'Syntactic Analogies', 'Total Analogy']):\n", " ax = fig.add_subplot(subplot)\n", " ax.plot(word2vec[i][0], word2vec[i][1], 'r-', label='Word2Vec')\n", " ax.plot(wordrank[i][0], wordrank[i][1], 'g--', label='WordRank')\n", " ax.plot(fasttext[i][0], fasttext[i][1], 'b:', label='FastText')\n", " ax.set_ylabel('Average accuracy')\n", " ax.set_xlabel('Log mean frequency')\n", " ax.set_title(title)\n", " ax.legend(loc='upper right', prop={'size':10})\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This graph show the results trained over Brown corpus(1 million tokens).\n", "\n", "The main observations that can be drawn here are-\n", "1. In Semantic Analogies, all the models perform poorly for rare words as compared to their performance at more frequent words.\n", "2. In Syntactic Analogies, FastText performance is way better than Word2Vec and WordRank.\n", "3. If we go through the frequency range in Syntactic Analogies plot, FastText performance drops significantly at highly frequent words, whereas, for Word2Vec and WordRank there is no significant difference over the whole frequency range.\n", "4. End plot shows the results of combined Semantic and Syntactic Analogies. It has more resemblance to the Syntactic Analogy's plot because the total no. of Syntactic Analogies(=5461) is much greater than the total no. of Semantic ones(=852). So it's bound to trace the Syntactic's results as they have more weightage in the total analogies considered.\n", "\n", "Now, let’s see if a larger corpus creates any difference in this pattern of model's performance over different frequencies." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a sample model using gensim's Word2Vec for getting vocab counts\n", "corpus = Text8Corpus('text8')\n", "model = Word2Vec(min_count=5)\n", "model.build_vocab(corpus)\n", "freq = {}\n", "for word in model.wv.index2word:\n", " freq[word] = model.wv.vocab[word].count\n", " \n", "word2vec = compute_accuracies('text8_gs.vec', freq)\n", "wordrank = compute_accuracies('text8_wr.vec', freq)\n", "fasttext = compute_accuracies('text8_ft.vec', freq)\n", "\n", "fig = plt.figure(figsize=(7,15))\n", "\n", "for i, subplot, title in zip([0, 1, 2], ['311', '312', '313'], ['Semantic Analogies', 'Syntactic Analogies', 'Total Analogy']):\n", " ax = fig.add_subplot(subplot)\n", " ax.plot(word2vec[i][0], word2vec[i][1], 'r-', label='Word2Vec')\n", " ax.plot(wordrank[i][0], wordrank[i][1], 'g--', label='WordRank')\n", " ax.plot(fasttext[i][0], fasttext[i][1], 'b:', label='FastText')\n", " ax.set_ylabel('Average accuracy')\n", " ax.set_xlabel('Log mean frequency')\n", " ax.set_title(title)\n", " ax.legend(loc='upper right', prop={'size':10})\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows the results for text8(17 million tokens). Following points can be observed in this case-\n", "\n", "1. For Semantic analogies, all the models perform comparatively poor on rare words and also when the word frequency is high towards the end.\n", "2. For Syntactic Analogies, FastText performance is fairly well on rare words but then falls steeply at highly frequent words.\n", "3. WordRank and Word2Vec perform very similar with low accuracy for rare and highly frequent words in Syntactic Analogies.\n", "4. FastText is again better in total analogies case due to the same reason described previously. Here the total no. of Semantic analogies is 7416 and Syntactic Analogies is 10411.\n", "\n", "These graphs also conclude that WordRank is the best suited method for Semantic Analogies, and FastText for Syntactic Analogies for all the frequency ranges and over different corpus sizes, though all the embedding methods could become very competitive as the corpus size increases largerly<sup>[2]</sup>. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusions\n", "\n", "\n", "The experiments here conclude two main points from comparing Word embeddings. Firstly, there is no single global embedding model we could rely on for different types of NLP applications. For example, in Word Similarity, WordRank performed better than the other two algorithms for WS-353 test data whereas, Word2Vec performed better on SimLex-999. This is probably due to the different type of similarities these datasets address<sup>[3]</sup>. And in Word Analogy task, WordRank performed better for Semantic Analogies and FastText for Syntactic Analogies. This basically tells us that we need to choose the embedding method carefully according to our final use-case.\n", "\n", "Secondly, our query words do matter apart from the generalized model performance. As we observed in Accuracy vs. Frequency graphs that models perform differently depending on the frequency of question analogy words in training corpus. For example, we are likely to get poor results if our query words are all highly frequent.\n", "\n", "__Note__: WordRank can sometimes produce NaN values during model evaluation, when the embedding vector values get too diverged at some iterations, but it dumps embedding vectors after every few iterations, so you could just load embeddings from a different iteration’s text file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References\n", "1. [WordRank: Learning Word Embeddings via Robust Ranking](https://arxiv.org/pdf/1506.02761v3.pdf)\n", "2. [Word2Vec and FastText comparison notebook](Word2Vec_FastText_Comparison.ipynb)\n", "3. [Similarity test data](https://www.cl.cam.ac.uk/~fh295/simlex.html)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	31,069	Python	.py	579	48.955095	1,898	0.637733	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,926	atmodel_prediction_tutorial.ipynb	piskvorky_gensim/docs/notebooks/atmodel_prediction_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Authorship prediction with the author-topic model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, you will learn how to use the author-topic model in Gensim for authorship prediction, based on the topic distributions and mesuring their similarity.\n", "We will train the author-topic model on a Reuters dataset, which contains 50 authors, each with 50 documents for trianing and another 50 documents for testing: https://archive.ics.uci.edu/ml/datasets/Reuter_50_50 ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you wish to learn more about the Author-topic model and LDA and how to train them, you should check out these tutorials beforehand. A lot of the preprocessing and configuration here has been done using their example:\n", "* [LDA training tips](https://nbviewer.jupyter.org/github/rare-technologies/gensim/blob/develop/docs/notebooks/lda_training_tips.ipynb)\n", "* [Training the author-topic model](https://nbviewer.jupyter.org/github/rare-technologies/gensim/blob/develop/docs/notebooks/atmodel_tutorial.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> NOTE:\n", ">\n", "> To run this tutorial on your own, install Jupyter, Gensim, SpaCy, Scikit-Learn, Bokeh and Pandas, e.g. using pip:\n", ">\n", "> `pip install jupyter gensim spacy sklearn bokeh pandas`\n", ">\n", "> Note that you need to download some data for SpaCy using `python -m spacy.en.download`.\n", ">\n", "> Download the notebook at https://github.com/RaRe-Technologies/gensim/tree/develop/docs/notebooks/atmodel_prediction_tutorial.ipynb.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Predicting the author of a document is a difficult task, where current approaches usually turn to neural networks. These base a lot of their predictions on learing stylistic and syntactic preferences of the authors and also other features which help rather identify the author. \n", "\n", "In our case, we first model the domain knowledge of a certain author, based on what the author writes about. We do this by calculating the topic distributions for each author using the author-topic model.\n", "After that, we perform the [new author inference](https://github.com/RaRe-Technologies/gensim/pull/1766) on the held-out subset. This again calculates a topic distribution for this new unknown author. \n", "In order to perform the prediction, we find out of all known authors, the most similar one to the new unknown. Mathematically speaking, we find the author, whose topic distribution is the closest to the topic distribution of the new author, by a certrain distrance function or metric. \n", "Here we explore the [Hellinger distance](https://en.wikipedia.org/wiki/Hellinger_distance) for the measuring the distance between two discrete multinomial topic distributions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start off by downloading the dataset. You can do it manually using the aforementioned link, or run the following code cell." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2018-03-25 17:24:26-- https://archive.ics.uci.edu/ml/machine-learning-databases/00217/C50.zip\n", "Resolving archive.ics.uci.edu... 128.195.10.249\n", "Connecting to archive.ics.uci.edu\|128.195.10.249\|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 8194031 (7.8M) [application/zip]\n", "Saving to: 'STDOUT'\n", "\n", "- 100%[===================>] 7.81M 2.30MB/s in 3.4s \n", "\n", "2018-03-25 17:24:31 (2.30 MB/s) - written to stdout [8194031/8194031]\n", "\n" ] } ], "source": [ "!wget -O - \"https://archive.ics.uci.edu/ml/machine-learning-databases/00217/C50.zip\" > /tmp/C50.zip" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import logging\n", "logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', level=logging.DEBUG, datefmt='%I:%M:%S')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import zipfile\n", "\n", "filename = '/tmp/C50.zip'\n", "\n", "zip_ref = zipfile.ZipFile(filename, 'r')\n", "zip_ref.extractall(\"/tmp/\")\n", "zip_ref.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We wrap all the preprocessing steps, that you can find more about in the [author-topic notebook](https://nbviewer.jupyter.org/github/rare-technologies/gensim/blob/develop/docs/notebooks/atmodel_tutorial.ipynb) , in one fucntion so that we are able to iterate over different preprocessing parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, re, io\n", "def preprocess_docs(data_dir):\n", " doc_ids = []\n", " author2doc = {}\n", " docs = []\n", " \n", " folders = os.listdir(data_dir) # List of filenames.\n", " for authorname in folders:\n", " files = file = os.listdir(data_dir + '/' + authorname)\n", " for filen in files:\n", " (idx1, idx2) = re.search('[0-9]+', filen).span() # Matches the indexes of the start end end of the ID.\n", " if not author2doc.get(authorname):\n", " # This is a new author.\n", " author2doc[authorname] = []\n", " doc_id = str(int(filen[idx1:idx2]))\n", " doc_ids.append(doc_id)\n", " author2doc[authorname].extend([doc_id])\n", "\n", " # Read document text.\n", " # Note: ignoring characters that cause encoding errors.\n", " with io.open(data_dir + '/' + authorname + '/' + filen, errors='ignore', encoding='utf-8') as fid:\n", " txt = fid.read()\n", "\n", " # Replace any whitespace (newline, tabs, etc.) by a single space.\n", " txt = re.sub('\\s', ' ', txt)\n", " docs.append(txt)\n", " \n", " doc_id_dict = dict(zip(doc_ids, range(len(doc_ids))))\n", " # Replace dataset IDs by integer IDs.\n", " for a, a_doc_ids in author2doc.items():\n", " for i, doc_id in enumerate(a_doc_ids):\n", " author2doc[a][i] = doc_id_dict[doc_id]\n", " import spacy\n", " nlp = spacy.load('en')\n", " \n", " %%time\n", " processed_docs = []\n", " for doc in nlp.pipe(docs, n_threads=4, batch_size=100):\n", " # Process document using Spacy NLP pipeline.\n", "\n", " ents = doc.ents # Named entities.\n", "\n", " # Keep only words (no numbers, no punctuation).\n", " # Lemmatize tokens, remove punctuation and remove stopwords.\n", " doc = [token.lemma_ for token in doc if token.is_alpha and not token.is_stop]\n", "\n", " # Remove common words from a stopword list.\n", " #doc = [token for token in doc if token not in STOPWORDS]\n", "\n", " # Add named entities, but only if they are a compound of more than word.\n", " doc.extend([str(entity) for entity in ents if len(entity) > 1])\n", " processed_docs.append(doc)\n", " docs = processed_docs\n", " del processed_docs\n", " \n", " # Compute bigrams.\n", "\n", " from gensim.models import Phrases\n", "\n", " # Add bigrams and trigrams to docs (only ones that appear 20 times or more).\n", " bigram = Phrases(docs, min_count=20)\n", " for idx in range(len(docs)):\n", " for token in bigram[docs[idx]]:\n", " if '_' in token:\n", " # Token is a bigram, add to document.\n", " docs[idx].append(token)\n", " return docs, author2doc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create the corpus of the train and test data using two separate functions, since each corpus is tied to a certain dictionary which maps the words to their ids. Also in order to create the test corpus, we use the dictionary from the train data, since the trained model has have the same id2word reference as the new test data. Otherwise token with id 1 from the test data wont't mean the same as the trained upon token with id 1 in the model." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_corpus_dictionary(docs, max_freq=0.5, min_wordcount=20):\n", " # Create a dictionary representation of the documents, and filter out frequent and rare words.\n", " from gensim.corpora import Dictionary\n", " dictionary = Dictionary(docs)\n", "\n", " # Remove rare and common tokens.\n", " # Filter out words that occur too frequently or too rarely.\n", " max_freq = max_freq\n", " min_wordcount = min_wordcount\n", " dictionary.filter_extremes(no_below=min_wordcount, no_above=max_freq)\n", "\n", " _ = dictionary[0] # This sort of \"initializes\" dictionary.id2token.\n", "\n", " # Vectorize data.\n", " # Bag-of-words representation of the documents.\n", " corpus = [dictionary.doc2bow(doc) for doc in docs]\n", "\n", " return corpus, dictionary\n", "\n", "def create_test_corpus(train_dictionary, docs):\n", " # Create test corpus using the dictionary from the train data.\n", " return [train_dictionary.doc2bow(doc) for doc in docs]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our first training, we specify that we want the parameters max_freq and min_wordcoun to be 50 and 20, as proposed by the original notebook tutorial. We will find out if this configuration is good enough for us." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "05:24:36 DEBUG:Registered VCS backend: git\n", "05:24:36 DEBUG:Registered VCS backend: hg\n", "05:24:36 DEBUG:Registered VCS backend: svn\n", "05:24:36 DEBUG:Registered VCS backend: bzr\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n", "Wall time: 7.15 µs\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:26:17 INFO:'pattern' package not found; tag filters are not available for English\n", "05:26:17 INFO:collecting all words and their counts\n", "05:26:17 INFO:PROGRESS: at sentence #0, processed 0 words and 0 word types\n", "05:26:19 INFO:collected 437598 word types from a corpus of 746622 words (unigram + bigrams) and 2500 sentences\n", "05:26:19 INFO:using 437598 counts as vocab in Phrases<0 vocab, min_count=20, threshold=10.0, max_vocab_size=40000000>\n", "/Users/martin/Projects/bachelor/gensim/gensim/models/phrases.py:490: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n", " warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n", "05:26:24 INFO:adding document #0 to Dictionary(0 unique tokens: [])\n", "05:26:25 INFO:built Dictionary(46905 unique tokens: ['$83.4 million', 'boarder', '$2.72 billion', 'checking', 'suzuki']...) from 2500 documents (total 786032 corpus positions)\n", "05:26:25 INFO:discarding 42991 tokens: [('$1.4 billion', 11), ('$15', 3), ('$17.25', 1), ('$380 million', 2), ('12.5 cents', 7), ('Big B', 3), ('Big B Inc.', 2), (\"Big B's\", 3), ('Big B. I', 1), ('Dwayne Hoven', 1)]...\n", "05:26:25 INFO:keeping 3914 tokens which were in no less than 20 and no more than 1250 (=50.0%) documents\n", "05:26:25 DEBUG:rebuilding dictionary, shrinking gaps\n", "05:26:25 INFO:resulting dictionary: Dictionary(3914 unique tokens: ['chris_patten', 'online', 'loss', 'hub', 'sound']...)\n" ] } ], "source": [ "traindata_dir = \"/tmp/C50train\"\n", "train_docs, train_author2doc = preprocess_docs(traindata_dir)\n", "train_corpus_50_20, train_dictionary_50_20 = create_corpus_dictionary(train_docs, 0.5, 20)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique tokens: 3914\n" ] } ], "source": [ "print('Number of unique tokens: %d' % len(train_dictionary_50_20))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs\n", "Wall time: 15 µs\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:28:06 INFO:collecting all words and their counts\n", "05:28:06 INFO:PROGRESS: at sentence #0, processed 0 words and 0 word types\n", "05:28:08 INFO:collected 448895 word types from a corpus of 758070 words (unigram + bigrams) and 2500 sentences\n", "05:28:08 INFO:using 448895 counts as vocab in Phrases<0 vocab, min_count=20, threshold=10.0, max_vocab_size=40000000>\n", "/Users/martin/Projects/bachelor/gensim/gensim/models/phrases.py:490: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n", " warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n" ] } ], "source": [ "testdata_dir = \"/tmp/C50test\"\n", "test_docs, test_author2doc = preprocess_docs(testdata_dir)\n", "test_corpus_50_20 = create_test_corpus(train_dictionary_50_20, test_docs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We wrap the model training also in a function, in order to, again, be able to iterate over different parametrizations." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train_model(corpus, author2doc, dictionary, num_topics=20, eval_every=0, iterations=50, passes=20):\n", " from gensim.models import AuthorTopicModel\n", " \n", " model = AuthorTopicModel(corpus=corpus, num_topics=num_topics, id2word=dictionary.id2token, \\\n", " author2doc=author2doc, chunksize=2500, passes=passes, \\\n", " eval_every=eval_every, iterations=iterations, random_state=1)\n", " top_topics = model.top_topics(corpus)\n", " tc = sum([t[1] for t in top_topics]) \n", " print(tc / num_topics)\n", " return model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# NOTE: Author of the logic of this function is the Olavur Mortensen, from his notebook tutorial.\n", "\n", "def predict_author(new_doc, atmodel, top_n=10, smallest_author=1):\n", " from gensim import matutils\n", " import pandas as pd\n", "\n", " def similarity(vec1, vec2):\n", " '''Get similarity between two vectors'''\n", " dist = matutils.hellinger(matutils.sparse2full(vec1, atmodel.num_topics), \\\n", " matutils.sparse2full(vec2, atmodel.num_topics))\n", " sim = 1.0 / (1.0 + dist)\n", " return sim\n", "\n", " def get_sims(vec):\n", " '''Get similarity of vector to all authors.'''\n", " sims = [similarity(vec, vec2) for vec2 in author_vecs]\n", " return sims\n", "\n", " author_vecs = [atmodel.get_author_topics(author) for author in atmodel.id2author.values()]\n", " new_doc_topics = atmodel.get_new_author_topics(new_doc)\n", " # Get similarities.\n", " sims = get_sims(new_doc_topics)\n", "\n", " # Arrange author names, similarities, and author sizes in a list of tuples.\n", " table = []\n", " for elem in enumerate(sims):\n", " author_name = atmodel.id2author[elem[0]]\n", " sim = elem[1]\n", " author_size = len(atmodel.author2doc[author_name])\n", " if author_size >= smallest_author:\n", " table.append((author_name, sim, author_size))\n", "\n", " # Make dataframe and retrieve top authors.\n", " df = pd.DataFrame(table, columns=['Author', 'Score', 'Size'])\n", " df = df.sort_values('Score', ascending=False)[:top_n]\n", "\n", " return df\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define a custom function, which measures the prediction accuracy, following the [precision at k](https://en.wikipedia.org/wiki/Information_retrieval#Precision_at_K) principle. We parametrize the accuracy by a parameter k, k=1 meaning we need an exact match in order to be accurate, k=5 meaning our prediction has be in the top 5 results, ordered by similarity." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def prediction_accuracy(test_author2doc, test_corpus, model, k=5):\n", "\n", " print(\"Precision@k: top_n={}\".format(k))\n", " matches=0\n", " tries = 0\n", " for author in test_author2doc:\n", " author_id = model.author2id[author]\n", " for doc_id in test_author2doc[author]:\n", " predicted_authors = predict_author(test_corpus[doc_id:doc_id+1], atmodel=model, top_n=k)\n", " tries = tries+1\n", " if author_id in predicted_authors[\"Author\"]:\n", " matches=matches+1\n", "\n", " accuracy = matches/tries\n", " print(\"Prediction accuracy: {}\".format(accuracy))\n", " return accuracy, k" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_accuracy(scores1, label1, scores2=None, label2=None):\n", " \n", " import matplotlib.pyplot as plt\n", " s = [score100 for score in scores1.values()]\n", " t = list(scores1.keys())\n", "\n", " plt.plot(t, s, \"b-\", label=label1)\n", " plt.plot(t, s, \"r^\", label=label1+\" data points\")\n", " \n", " if scores2 is not None:\n", " s2 = [score100 for score in scores2.values()]\n", " plt.plot(t, s2, label=label2)\n", " plt.plot(t, s2, \"o\", label=label2+\" data points\")\n", " \n", " plt.legend(loc=\"lower right\")\n", "\n", " plt.xlabel('parameter k')\n", " plt.ylabel('prediction accuracy')\n", " plt.title('Precision at k')\n", " plt.xticks(t)\n", " plt.grid(True)\n", " plt.yticks([30,40,50,60,70,80,90,100])\n", " plt.axis([0, 11, 30, 100])\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We calculate the accuracy for a range of values for k=[1,2,3,4,5,6,8,10] and plot how exactly the prediction accuracy naturally rises with higher k." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "05:28:14 INFO:Vocabulary consists of 3914 words.\n", "05:28:14 INFO:using symmetric alpha at 0.05\n", "05:28:14 INFO:using symmetric eta at 0.05\n", "05:28:14 INFO:running online author-topic training, 20 topics, 50 authors, 20 passes over the supplied corpus of 2500 documents, updating model once every 2500 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", "05:28:14 INFO:PROGRESS: pass 0, at document #2500/2500\n", "05:28:14 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:22 DEBUG:3/2500 documents converged within 50 iterations\n", "05:28:22 DEBUG:updating topics\n", "05:28:22 INFO:topic #11 (0.050): 0.028\"gm\" + 0.013\"plant\" + 0.012\"strike\" + 0.009\"worker\" + 0.009\"uaw\" + 0.007\"automaker\" + 0.007\"share\" + 0.007\"union\" + 0.006\"truck\" + 0.006\"analyst\"\n", "05:28:22 INFO:topic #17 (0.050): 0.018\"apple\" + 0.008\"computer\" + 0.008\"software\" + 0.008\"share\" + 0.008\"analyst\" + 0.007\"quarter\" + 0.006\"microsoft\" + 0.006\"service\" + 0.006\"base\" + 0.005\"plan\"\n", "05:28:22 INFO:topic #15 (0.050): 0.009\"analyst\" + 0.008\"computer\" + 0.008\"stock\" + 0.007\"billion\" + 0.007\"quarter\" + 0.007\"share\" + 0.006\"industry\" + 0.005\"software\" + 0.005\"oil\" + 0.005\"sale\"\n", "05:28:22 INFO:topic #9 (0.050): 0.009\"analyst\" + 0.006\"share\" + 0.006\"china\" + 0.006\"gold\" + 0.006\"chinese\" + 0.005\"price\" + 0.005\"government\" + 0.005\"stock\" + 0.004\"base\" + 0.004\"drug\"\n", "05:28:22 INFO:topic #14 (0.050): 0.010\"pound\" + 0.009\"share\" + 0.008\"profit\" + 0.007\"billion\" + 0.007\"analyst\" + 0.007\"group\" + 0.007\"bank\" + 0.006\"business\" + 0.005\"million_pound\" + 0.005\"price\"\n", "05:28:22 INFO:topic diff=2.864277, rho=1.000000\n", "05:28:22 INFO:PROGRESS: pass 1, at document #2500/2500\n", "05:28:22 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:25 DEBUG:2491/2500 documents converged within 50 iterations\n", "05:28:25 DEBUG:updating topics\n", "05:28:25 INFO:topic #0 (0.050): 0.011\"bank\" + 0.009\"analyst\" + 0.005\"share\" + 0.005\"billion\" + 0.005\"government\" + 0.004\"news\" + 0.004\"business\" + 0.004\"rule\" + 0.004\"profit\" + 0.004\"group\"\n", "05:28:25 INFO:topic #14 (0.050): 0.011\"pound\" + 0.010\"share\" + 0.009\"profit\" + 0.008\"group\" + 0.008\"analyst\" + 0.008\"billion\" + 0.007\"bank\" + 0.006\"business\" + 0.006\"million_pound\" + 0.005\"penny\"\n", "05:28:25 INFO:topic #15 (0.050): 0.010\"analyst\" + 0.009\"stock\" + 0.008\"share\" + 0.008\"billion\" + 0.007\"quarter\" + 0.007\"computer\" + 0.006\"oil\" + 0.006\"bank\" + 0.005\"industry\" + 0.005\"high\"\n", "05:28:25 INFO:topic #1 (0.050): 0.014\"bank\" + 0.010\"china\" + 0.009\"hong_kong\" + 0.009\"kong\" + 0.008\"hong\" + 0.008\"billion\" + 0.007\"Hong Kong\" + 0.006\"analyst\" + 0.006\"stock\" + 0.006\"fund\"\n", "05:28:25 INFO:topic #7 (0.050): 0.007\"analyst\" + 0.007\"sale\" + 0.007\"share\" + 0.006\"group\" + 0.005\"business\" + 0.005\"price\" + 0.004\"profit\" + 0.004\"industry\" + 0.004\"pound\" + 0.004\"billion\"\n", "05:28:25 INFO:topic diff=1.147566, rho=0.577350\n", "05:28:25 INFO:PROGRESS: pass 2, at document #2500/2500\n", "05:28:25 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:27 DEBUG:2498/2500 documents converged within 50 iterations\n", "05:28:27 DEBUG:updating topics\n", "05:28:27 INFO:topic #9 (0.050): 0.011\"drug\" + 0.009\"colombia\" + 0.007\"analyst\" + 0.006\"government\" + 0.006\"sale\" + 0.005\"share\" + 0.005\"base\" + 0.004\"price\" + 0.004\"stock\" + 0.004\"united\"\n", "05:28:27 INFO:topic #15 (0.050): 0.010\"stock\" + 0.009\"analyst\" + 0.008\"share\" + 0.008\"billion\" + 0.007\"bank\" + 0.007\"oil\" + 0.006\"quarter\" + 0.006\"canada\" + 0.006\"toronto\" + 0.005\"high\"\n", "05:28:27 INFO:topic #8 (0.050): 0.026\"bre\" + 0.024\"gold\" + 0.024\"bre_x\" + 0.024\"x\" + 0.018\"Bre-X\" + 0.015\"barrick\" + 0.011\"analyst\" + 0.010\"busang\" + 0.010\"indonesian\" + 0.008\"government\"\n", "05:28:27 INFO:topic #19 (0.050): 0.019\"hong\" + 0.018\"kong\" + 0.018\"hong_kong\" + 0.014\"china\" + 0.012\"Hong Kong\" + 0.006\"chinese\" + 0.005\"price\" + 0.005\"british\" + 0.005\"tell\" + 0.004\"tung\"\n", "05:28:27 INFO:topic #10 (0.050): 0.009\"billion\" + 0.008\"bank\" + 0.005\"loan\" + 0.005\"tonne\" + 0.005\"yen\" + 0.005\"price\" + 0.005\"exporter\" + 0.004\"real_estate\" + 0.004\"analyst\" + 0.004\"real\"\n", "05:28:27 INFO:topic diff=1.010061, rho=0.500000\n", "05:28:27 INFO:PROGRESS: pass 3, at document #2500/2500\n", "05:28:27 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:29 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:29 DEBUG:updating topics\n", "05:28:29 INFO:topic #13 (0.050): 0.017\"china\" + 0.014\"wang\" + 0.012\"beijing\" + 0.011\"taiwan\" + 0.009\"court\" + 0.009\"party\" + 0.008\"chinese\" + 0.008\"government\" + 0.007\"official\" + 0.007\"communist\"\n", "05:28:29 INFO:topic #19 (0.050): 0.022\"hong\" + 0.021\"kong\" + 0.021\"hong_kong\" + 0.015\"china\" + 0.014\"Hong Kong\" + 0.006\"chinese\" + 0.005\"airbus\" + 0.005\"tung\" + 0.005\"british\" + 0.005\"Hong Kong's\"\n", "05:28:29 INFO:topic #12 (0.050): 0.012\"czech\" + 0.007\"bank\" + 0.007\"crown\" + 0.006\"government\" + 0.006\"klaus\" + 0.005\"billion\" + 0.005\"price\" + 0.005\"party\" + 0.005\"prague\" + 0.005\"foreign\"\n", "05:28:29 INFO:topic #17 (0.050): 0.041\"apple\" + 0.026\"computer\" + 0.022\"software\" + 0.020\"quarter\" + 0.013\"microsoft\" + 0.013\"analyst\" + 0.010\"share\" + 0.009\"sale\" + 0.009\"macintosh\" + 0.008\"pc\"\n", "05:28:29 INFO:topic #6 (0.050): 0.020\"share\" + 0.017\"analyst\" + 0.010\"bank\" + 0.010\"shanghai\" + 0.009\"stock\" + 0.007\"sale\" + 0.007\"b\" + 0.006\"quarter\" + 0.006\"base\" + 0.005\"business\"\n", "05:28:29 INFO:topic diff=0.877566, rho=0.447214\n", "05:28:29 INFO:PROGRESS: pass 4, at document #2500/2500\n", "05:28:29 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:31 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:31 DEBUG:updating topics\n", "05:28:31 INFO:topic #14 (0.050): 0.012\"pound\" + 0.010\"profit\" + 0.010\"share\" + 0.009\"group\" + 0.008\"analyst\" + 0.008\"billion\" + 0.007\"business\" + 0.007\"bank\" + 0.006\"million_pound\" + 0.005\"british\"\n", "05:28:31 INFO:topic #9 (0.050): 0.014\"drug\" + 0.011\"colombia\" + 0.006\"government\" + 0.005\"analyst\" + 0.005\"sale\" + 0.005\"united\" + 0.005\"colombian\" + 0.004\"guerrilla\" + 0.004\"base\" + 0.004\"force\"\n", "05:28:31 INFO:topic #13 (0.050): 0.018\"china\" + 0.015\"wang\" + 0.013\"beijing\" + 0.012\"taiwan\" + 0.009\"court\" + 0.009\"party\" + 0.009\"chinese\" + 0.008\"government\" + 0.007\"communist\" + 0.007\"official\"\n", "05:28:31 INFO:topic #2 (0.050): 0.010\"share\" + 0.009\"analyst\" + 0.008\"service\" + 0.007\"billion\" + 0.007\"deal\" + 0.006\"offer\" + 0.006\"stock\" + 0.006\"corp\" + 0.006\"industry\" + 0.005\"business\"\n", "05:28:31 INFO:topic #15 (0.050): 0.010\"stock\" + 0.009\"analyst\" + 0.008\"billion\" + 0.008\"share\" + 0.008\"bank\" + 0.007\"oil\" + 0.007\"canada\" + 0.007\"toronto\" + 0.006\"russia\" + 0.005\"high\"\n", "05:28:31 INFO:topic diff=0.761073, rho=0.408248\n", "05:28:31 INFO:PROGRESS: pass 5, at document #2500/2500\n", "05:28:31 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:33 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:33 DEBUG:updating topics\n", "05:28:33 INFO:topic #15 (0.050): 0.010\"stock\" + 0.009\"analyst\" + 0.009\"bank\" + 0.008\"billion\" + 0.008\"share\" + 0.007\"oil\" + 0.007\"canada\" + 0.007\"toronto\" + 0.006\"russia\" + 0.006\"tonne\"\n", "05:28:33 INFO:topic #2 (0.050): 0.011\"share\" + 0.009\"analyst\" + 0.008\"service\" + 0.007\"billion\" + 0.007\"deal\" + 0.006\"offer\" + 0.006\"stock\" + 0.006\"corp\" + 0.006\"industry\" + 0.005\"business\"\n", "05:28:33 INFO:topic #13 (0.050): 0.018\"china\" + 0.015\"wang\" + 0.013\"beijing\" + 0.012\"taiwan\" + 0.009\"court\" + 0.009\"chinese\" + 0.009\"party\" + 0.008\"government\" + 0.007\"communist\" + 0.007\"official\"\n", "05:28:33 INFO:topic #8 (0.050): 0.027\"gold\" + 0.026\"bre\" + 0.026\"bre_x\" + 0.026\"x\" + 0.019\"Bre-X\" + 0.015\"barrick\" + 0.012\"analyst\" + 0.011\"busang\" + 0.010\"indonesian\" + 0.009\"government\"\n", "05:28:33 INFO:topic #1 (0.050): 0.017\"bank\" + 0.010\"fund\" + 0.010\"china\" + 0.009\"billion\" + 0.008\"hong_kong\" + 0.008\"kong\" + 0.008\"hong\" + 0.007\"financial\" + 0.007\"japan\" + 0.006\"Hong Kong\"\n", "05:28:33 INFO:topic diff=0.658823, rho=0.377964\n", "05:28:33 INFO:PROGRESS: pass 6, at document #2500/2500\n", "05:28:33 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:35 DEBUG:2500/2500 documents converged within 50 iterations\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:28:35 DEBUG:updating topics\n", "05:28:35 INFO:topic #17 (0.050): 0.038\"apple\" + 0.027\"computer\" + 0.022\"software\" + 0.021\"quarter\" + 0.014\"analyst\" + 0.013\"microsoft\" + 0.010\"sale\" + 0.010\"share\" + 0.008\"pc\" + 0.008\"macintosh\"\n", "05:28:35 INFO:topic #9 (0.050): 0.015\"drug\" + 0.012\"colombia\" + 0.006\"government\" + 0.005\"united\" + 0.005\"sale\" + 0.005\"colombian\" + 0.005\"guerrilla\" + 0.005\"analyst\" + 0.004\"force\" + 0.004\"week\"\n", "05:28:35 INFO:topic #15 (0.050): 0.010\"stock\" + 0.009\"bank\" + 0.009\"analyst\" + 0.008\"billion\" + 0.008\"share\" + 0.007\"oil\" + 0.007\"canada\" + 0.007\"toronto\" + 0.006\"russia\" + 0.006\"tonne\"\n", "05:28:35 INFO:topic #13 (0.050): 0.018\"china\" + 0.015\"wang\" + 0.014\"beijing\" + 0.012\"taiwan\" + 0.009\"court\" + 0.009\"chinese\" + 0.009\"party\" + 0.008\"government\" + 0.007\"communist\" + 0.007\"official\"\n", "05:28:35 INFO:topic #16 (0.050): 0.016\"franc\" + 0.015\"french\" + 0.015\"air\" + 0.014\"france\" + 0.011\"thomson\" + 0.010\"billion\" + 0.009\"group\" + 0.007\"government\" + 0.007\"plan\" + 0.007\"bid\"\n", "05:28:35 INFO:topic diff=0.568497, rho=0.353553\n", "05:28:35 INFO:PROGRESS: pass 7, at document #2500/2500\n", "05:28:35 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:36 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:36 DEBUG:updating topics\n", "05:28:37 INFO:topic #17 (0.050): 0.037\"apple\" + 0.027\"computer\" + 0.022\"software\" + 0.021\"quarter\" + 0.014\"analyst\" + 0.013\"microsoft\" + 0.010\"sale\" + 0.010\"share\" + 0.008\"pc\" + 0.008\"macintosh\"\n", "05:28:37 INFO:topic #2 (0.050): 0.011\"share\" + 0.010\"analyst\" + 0.007\"service\" + 0.007\"billion\" + 0.007\"deal\" + 0.006\"offer\" + 0.006\"stock\" + 0.006\"corp\" + 0.006\"industry\" + 0.005\"business\"\n", "05:28:37 INFO:topic #3 (0.050): 0.027\"bt\" + 0.017\"telecom\" + 0.015\"mci\" + 0.013\"pound\" + 0.011\"billion\" + 0.011\"analyst\" + 0.011\"deal\" + 0.010\"british\" + 0.010\"share\" + 0.010\"group\"\n", "05:28:37 INFO:topic #13 (0.050): 0.018\"china\" + 0.016\"wang\" + 0.014\"beijing\" + 0.012\"taiwan\" + 0.009\"court\" + 0.009\"chinese\" + 0.009\"party\" + 0.008\"government\" + 0.007\"communist\" + 0.007\"official\"\n", "05:28:37 INFO:topic #4 (0.050): 0.018\"china\" + 0.011\"official\" + 0.009\"state\" + 0.008\"beijing\" + 0.008\"tibet\" + 0.007\"chinese\" + 0.007\"government\" + 0.007\"wang\" + 0.006\"people\" + 0.005\"dissident\"\n", "05:28:37 INFO:topic diff=0.488932, rho=0.333333\n", "05:28:37 INFO:PROGRESS: pass 8, at document #2500/2500\n", "05:28:37 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:38 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:38 DEBUG:updating topics\n", "05:28:38 INFO:topic #17 (0.050): 0.037\"apple\" + 0.027\"computer\" + 0.022\"software\" + 0.021\"quarter\" + 0.014\"analyst\" + 0.013\"microsoft\" + 0.010\"sale\" + 0.010\"share\" + 0.008\"pc\" + 0.008\"macintosh\"\n", "05:28:38 INFO:topic #5 (0.050): 0.032\"china\" + 0.016\"chinese\" + 0.013\"beijing\" + 0.012\"official\" + 0.009\"tonne\" + 0.007\"hong\" + 0.007\"hong_kong\" + 0.007\"kong\" + 0.007\"trade\" + 0.006\"state\"\n", "05:28:38 INFO:topic #13 (0.050): 0.018\"china\" + 0.016\"wang\" + 0.014\"beijing\" + 0.012\"taiwan\" + 0.009\"chinese\" + 0.009\"court\" + 0.009\"party\" + 0.008\"government\" + 0.007\"communist\" + 0.007\"official\"\n", "05:28:38 INFO:topic #14 (0.050): 0.012\"pound\" + 0.011\"profit\" + 0.010\"share\" + 0.009\"analyst\" + 0.009\"group\" + 0.008\"billion\" + 0.007\"bank\" + 0.007\"business\" + 0.006\"million_pound\" + 0.005\"british\"\n", "05:28:38 INFO:topic #6 (0.050): 0.019\"share\" + 0.016\"analyst\" + 0.012\"shanghai\" + 0.011\"bank\" + 0.009\"stock\" + 0.007\"b\" + 0.007\"sale\" + 0.006\"exchange\" + 0.006\"base\" + 0.006\"quarter\"\n", "05:28:38 INFO:topic diff=0.419457, rho=0.316228\n", "05:28:38 INFO:PROGRESS: pass 9, at document #2500/2500\n", "05:28:38 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:40 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:40 DEBUG:updating topics\n", "05:28:40 INFO:topic #3 (0.050): 0.027\"bt\" + 0.017\"telecom\" + 0.015\"mci\" + 0.013\"pound\" + 0.011\"billion\" + 0.011\"analyst\" + 0.011\"deal\" + 0.010\"british\" + 0.010\"share\" + 0.010\"group\"\n", "05:28:40 INFO:topic #1 (0.050): 0.018\"bank\" + 0.011\"fund\" + 0.009\"billion\" + 0.008\"china\" + 0.008\"financial\" + 0.007\"japan\" + 0.007\"kong\" + 0.007\"hong_kong\" + 0.007\"hong\" + 0.006\"analyst\"\n", "05:28:40 INFO:topic #15 (0.050): 0.010\"bank\" + 0.009\"stock\" + 0.008\"analyst\" + 0.008\"billion\" + 0.008\"share\" + 0.008\"oil\" + 0.007\"canada\" + 0.007\"toronto\" + 0.006\"tonne\" + 0.006\"russia\"\n", "05:28:40 INFO:topic #19 (0.050): 0.029\"hong\" + 0.028\"kong\" + 0.028\"hong_kong\" + 0.019\"Hong Kong\" + 0.019\"china\" + 0.008\"chinese\" + 0.007\"tung\" + 0.007\"Hong Kong's\" + 0.006\"beijing\" + 0.006\"airbus\"\n", "05:28:40 INFO:topic #8 (0.050): 0.028\"gold\" + 0.026\"bre\" + 0.026\"bre_x\" + 0.026\"x\" + 0.019\"Bre-X\" + 0.015\"barrick\" + 0.012\"analyst\" + 0.011\"busang\" + 0.010\"indonesian\" + 0.009\"government\"\n", "05:28:40 INFO:topic diff=0.359320, rho=0.301511\n", "05:28:40 INFO:PROGRESS: pass 10, at document #2500/2500\n", "05:28:40 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:41 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:41 DEBUG:updating topics\n", "05:28:41 INFO:topic #6 (0.050): 0.019\"share\" + 0.016\"analyst\" + 0.013\"shanghai\" + 0.011\"bank\" + 0.009\"stock\" + 0.008\"b\" + 0.007\"sale\" + 0.007\"exchange\" + 0.006\"china\" + 0.006\"base\"\n", "05:28:41 INFO:topic #11 (0.050): 0.042\"gm\" + 0.028\"plant\" + 0.016\"uaw\" + 0.016\"strike\" + 0.015\"worker\" + 0.011\"automaker\" + 0.010\"local\" + 0.010\"truck\" + 0.009\"part\" + 0.008\"ford\"\n", "05:28:41 INFO:topic #2 (0.050): 0.011\"share\" + 0.010\"analyst\" + 0.007\"service\" + 0.007\"billion\" + 0.007\"deal\" + 0.006\"offer\" + 0.006\"stock\" + 0.006\"corp\" + 0.006\"industry\" + 0.005\"business\"\n", "05:28:41 INFO:topic #1 (0.050): 0.019\"bank\" + 0.011\"fund\" + 0.009\"billion\" + 0.008\"financial\" + 0.008\"china\" + 0.008\"japan\" + 0.007\"kong\" + 0.007\"hong_kong\" + 0.007\"hong\" + 0.006\"analyst\"\n", "05:28:41 INFO:topic #12 (0.050): 0.014\"czech\" + 0.008\"crown\" + 0.008\"bank\" + 0.007\"klaus\" + 0.007\"government\" + 0.006\"billion\" + 0.006\"prague\" + 0.005\"price\" + 0.005\"foreign\" + 0.005\"party\"\n", "05:28:41 INFO:topic diff=0.307661, rho=0.288675\n", "05:28:41 INFO:PROGRESS: pass 11, at document #2500/2500\n", "05:28:41 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:43 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:43 DEBUG:updating topics\n", "05:28:43 INFO:topic #15 (0.050): 0.011\"bank\" + 0.009\"stock\" + 0.008\"billion\" + 0.008\"analyst\" + 0.008\"share\" + 0.008\"oil\" + 0.007\"canada\" + 0.007\"toronto\" + 0.006\"russia\" + 0.006\"tonne\"\n", "05:28:43 INFO:topic #2 (0.050): 0.011\"share\" + 0.010\"analyst\" + 0.007\"service\" + 0.007\"billion\" + 0.007\"deal\" + 0.006\"offer\" + 0.006\"stock\" + 0.006\"corp\" + 0.006\"industry\" + 0.005\"business\"\n", "05:28:43 INFO:topic #5 (0.050): 0.032\"china\" + 0.016\"chinese\" + 0.013\"beijing\" + 0.012\"official\" + 0.009\"tonne\" + 0.007\"hong\" + 0.007\"hong_kong\" + 0.007\"kong\" + 0.007\"trade\" + 0.006\"state\"\n", "05:28:43 INFO:topic #7 (0.050): 0.009\"sale\" + 0.009\"analyst\" + 0.007\"share\" + 0.007\"group\" + 0.006\"profit\" + 0.006\"business\" + 0.005\"pound\" + 0.005\"price\" + 0.005\"billion\" + 0.005\"executive\"\n", "05:28:43 INFO:topic #19 (0.050): 0.029\"hong\" + 0.029\"kong\" + 0.029\"hong_kong\" + 0.019\"Hong Kong\" + 0.019\"china\" + 0.008\"chinese\" + 0.008\"tung\" + 0.007\"Hong Kong's\" + 0.006\"beijing\" + 0.006\"airbus\"\n", "05:28:43 INFO:topic diff=0.263525, rho=0.277350\n", "05:28:43 INFO:PROGRESS: pass 12, at document #2500/2500\n", "05:28:43 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:44 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:44 DEBUG:updating topics\n", "05:28:45 INFO:topic #11 (0.050): 0.042\"gm\" + 0.028\"plant\" + 0.016\"uaw\" + 0.016\"strike\" + 0.015\"worker\" + 0.011\"automaker\" + 0.010\"local\" + 0.010\"truck\" + 0.009\"part\" + 0.008\"ford\"\n", "05:28:45 INFO:topic #13 (0.050): 0.018\"china\" + 0.016\"wang\" + 0.014\"beijing\" + 0.012\"taiwan\" + 0.009\"chinese\" + 0.009\"court\" + 0.008\"party\" + 0.008\"government\" + 0.007\"official\" + 0.007\"communist\"\n", "05:28:45 INFO:topic #4 (0.050): 0.021\"china\" + 0.012\"official\" + 0.010\"beijing\" + 0.009\"chinese\" + 0.009\"wang\" + 0.008\"tibet\" + 0.007\"state\" + 0.007\"government\" + 0.006\"people\" + 0.006\"dissident\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:28:45 INFO:topic #16 (0.050): 0.020\"franc\" + 0.018\"french\" + 0.017\"air\" + 0.017\"france\" + 0.014\"thomson\" + 0.012\"billion\" + 0.010\"group\" + 0.008\"billion_franc\" + 0.008\"telecom\" + 0.007\"plan\"\n", "05:28:45 INFO:topic #18 (0.050): 0.014\"analyst\" + 0.011\"computer\" + 0.010\"quarter\" + 0.010\"internet\" + 0.008\"share\" + 0.008\"business\" + 0.008\"service\" + 0.008\"stock\" + 0.007\"industry\" + 0.007\"software\"\n", "05:28:45 INFO:topic diff=0.226015, rho=0.267261\n", "05:28:45 INFO:PROGRESS: pass 13, at document #2500/2500\n", "05:28:45 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:46 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:46 DEBUG:updating topics\n", "05:28:46 INFO:topic #4 (0.050): 0.021\"china\" + 0.012\"official\" + 0.010\"beijing\" + 0.009\"wang\" + 0.009\"chinese\" + 0.008\"tibet\" + 0.007\"state\" + 0.007\"government\" + 0.006\"people\" + 0.006\"dissident\"\n", "05:28:46 INFO:topic #3 (0.050): 0.027\"bt\" + 0.017\"telecom\" + 0.015\"mci\" + 0.013\"pound\" + 0.011\"billion\" + 0.011\"analyst\" + 0.011\"deal\" + 0.010\"british\" + 0.010\"share\" + 0.010\"group\"\n", "05:28:46 INFO:topic #12 (0.050): 0.015\"czech\" + 0.009\"crown\" + 0.008\"bank\" + 0.007\"klaus\" + 0.007\"government\" + 0.006\"prague\" + 0.006\"billion\" + 0.005\"foreign\" + 0.005\"party\" + 0.005\"price\"\n", "05:28:46 INFO:topic #19 (0.050): 0.030\"hong\" + 0.030\"kong\" + 0.030\"hong_kong\" + 0.020\"Hong Kong\" + 0.020\"china\" + 0.008\"chinese\" + 0.008\"tung\" + 0.007\"Hong Kong's\" + 0.007\"beijing\" + 0.006\"airbus\"\n", "05:28:46 INFO:topic #5 (0.050): 0.032\"china\" + 0.016\"chinese\" + 0.013\"beijing\" + 0.012\"official\" + 0.010\"tonne\" + 0.007\"hong\" + 0.007\"kong\" + 0.007\"hong_kong\" + 0.007\"trade\" + 0.006\"state\"\n", "05:28:46 INFO:topic diff=0.194260, rho=0.258199\n", "05:28:46 INFO:PROGRESS: pass 14, at document #2500/2500\n", "05:28:46 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:48 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:48 DEBUG:updating topics\n", "05:28:48 INFO:topic #5 (0.050): 0.033\"china\" + 0.016\"chinese\" + 0.013\"beijing\" + 0.012\"official\" + 0.010\"tonne\" + 0.008\"hong\" + 0.007\"kong\" + 0.007\"hong_kong\" + 0.007\"trade\" + 0.007\"state\"\n", "05:28:48 INFO:topic #13 (0.050): 0.018\"china\" + 0.016\"wang\" + 0.014\"beijing\" + 0.012\"taiwan\" + 0.009\"chinese\" + 0.009\"court\" + 0.008\"party\" + 0.008\"government\" + 0.007\"official\" + 0.007\"communist\"\n", "05:28:48 INFO:topic #2 (0.050): 0.011\"share\" + 0.010\"analyst\" + 0.007\"service\" + 0.007\"billion\" + 0.007\"deal\" + 0.006\"offer\" + 0.006\"stock\" + 0.006\"corp\" + 0.006\"industry\" + 0.006\"business\"\n", "05:28:48 INFO:topic #0 (0.050): 0.011\"bank\" + 0.009\"internet\" + 0.009\"fcc\" + 0.008\"service\" + 0.008\"phone\" + 0.007\"rule\" + 0.006\"local\" + 0.006\"tv\" + 0.006\"court\" + 0.006\"law\"\n", "05:28:48 INFO:topic #16 (0.050): 0.020\"franc\" + 0.019\"french\" + 0.018\"air\" + 0.017\"france\" + 0.014\"thomson\" + 0.013\"billion\" + 0.010\"group\" + 0.008\"billion_franc\" + 0.008\"telecom\" + 0.007\"plan\"\n", "05:28:48 INFO:topic diff=0.167433, rho=0.250000\n", "05:28:48 INFO:PROGRESS: pass 15, at document #2500/2500\n", "05:28:48 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:49 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:49 DEBUG:updating topics\n", "05:28:49 INFO:topic #3 (0.050): 0.027\"bt\" + 0.017\"telecom\" + 0.015\"mci\" + 0.013\"pound\" + 0.011\"analyst\" + 0.011\"billion\" + 0.011\"deal\" + 0.010\"british\" + 0.010\"share\" + 0.010\"group\"\n", "05:28:49 INFO:topic #10 (0.050): 0.001\"billion\" + 0.000\"bank\" + 0.000\"loan\" + 0.000\"tonne\" + 0.000\"yen\" + 0.000\"price\" + 0.000\"exporter\" + 0.000\"real_estate\" + 0.000\"analyst\" + 0.000\"real\"\n", "05:28:49 INFO:topic #5 (0.050): 0.033\"china\" + 0.017\"chinese\" + 0.013\"beijing\" + 0.012\"official\" + 0.010\"tonne\" + 0.008\"hong\" + 0.008\"kong\" + 0.007\"hong_kong\" + 0.007\"trade\" + 0.007\"state\"\n", "05:28:49 INFO:topic #4 (0.050): 0.021\"china\" + 0.012\"official\" + 0.011\"beijing\" + 0.009\"wang\" + 0.009\"chinese\" + 0.008\"tibet\" + 0.007\"state\" + 0.007\"government\" + 0.006\"people\" + 0.006\"dissident\"\n", "05:28:49 INFO:topic #0 (0.050): 0.010\"bank\" + 0.009\"internet\" + 0.009\"fcc\" + 0.008\"service\" + 0.008\"phone\" + 0.007\"rule\" + 0.007\"local\" + 0.007\"tv\" + 0.006\"court\" + 0.006\"law\"\n", "05:28:49 INFO:topic diff=0.144777, rho=0.242536\n", "05:28:49 INFO:PROGRESS: pass 16, at document #2500/2500\n", "05:28:49 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:51 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:51 DEBUG:updating topics\n", "05:28:51 INFO:topic #2 (0.050): 0.011\"share\" + 0.010\"analyst\" + 0.007\"service\" + 0.007\"billion\" + 0.007\"deal\" + 0.006\"offer\" + 0.006\"stock\" + 0.006\"corp\" + 0.006\"industry\" + 0.006\"business\"\n", "05:28:51 INFO:topic #9 (0.050): 0.016\"drug\" + 0.013\"colombia\" + 0.006\"government\" + 0.006\"united\" + 0.005\"colombian\" + 0.005\"guerrilla\" + 0.005\"force\" + 0.004\"oil\" + 0.004\"country\" + 0.004\"police\"\n", "05:28:51 INFO:topic #7 (0.050): 0.009\"sale\" + 0.008\"analyst\" + 0.007\"share\" + 0.007\"group\" + 0.007\"profit\" + 0.006\"business\" + 0.005\"pound\" + 0.005\"price\" + 0.005\"billion\" + 0.005\"executive\"\n", "05:28:51 INFO:topic #4 (0.050): 0.021\"china\" + 0.012\"official\" + 0.011\"beijing\" + 0.009\"wang\" + 0.009\"chinese\" + 0.008\"tibet\" + 0.007\"state\" + 0.007\"government\" + 0.006\"people\" + 0.006\"dissident\"\n", "05:28:51 INFO:topic #13 (0.050): 0.018\"china\" + 0.016\"wang\" + 0.014\"beijing\" + 0.012\"taiwan\" + 0.009\"chinese\" + 0.009\"court\" + 0.008\"party\" + 0.008\"government\" + 0.007\"official\" + 0.007\"communist\"\n", "05:28:51 INFO:topic diff=0.125646, rho=0.235702\n", "05:28:51 INFO:PROGRESS: pass 17, at document #2500/2500\n", "05:28:51 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:52 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:52 DEBUG:updating topics\n", "05:28:52 INFO:topic #6 (0.050): 0.019\"share\" + 0.016\"analyst\" + 0.013\"shanghai\" + 0.012\"bank\" + 0.009\"stock\" + 0.008\"china\" + 0.007\"b\" + 0.007\"sale\" + 0.007\"exchange\" + 0.006\"base\"\n", "05:28:52 INFO:topic #14 (0.050): 0.011\"pound\" + 0.011\"profit\" + 0.010\"share\" + 0.009\"analyst\" + 0.009\"group\" + 0.008\"billion\" + 0.007\"bank\" + 0.007\"business\" + 0.006\"million_pound\" + 0.005\"british\"\n", "05:28:52 INFO:topic #17 (0.050): 0.036\"apple\" + 0.026\"computer\" + 0.021\"software\" + 0.021\"quarter\" + 0.014\"analyst\" + 0.013\"microsoft\" + 0.010\"sale\" + 0.010\"share\" + 0.008\"pc\" + 0.008\"technology\"\n", "05:28:52 INFO:topic #7 (0.050): 0.009\"sale\" + 0.008\"analyst\" + 0.007\"share\" + 0.007\"group\" + 0.007\"profit\" + 0.006\"business\" + 0.005\"pound\" + 0.005\"price\" + 0.005\"billion\" + 0.005\"executive\"\n", "05:28:52 INFO:topic #8 (0.050): 0.028\"gold\" + 0.026\"bre\" + 0.026\"bre_x\" + 0.026\"x\" + 0.019\"Bre-X\" + 0.016\"barrick\" + 0.012\"analyst\" + 0.011\"busang\" + 0.010\"indonesian\" + 0.009\"government\"\n", "05:28:52 INFO:topic diff=0.109484, rho=0.229416\n", "05:28:52 INFO:PROGRESS: pass 18, at document #2500/2500\n", "05:28:52 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:54 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:28:54 DEBUG:updating topics\n", "05:28:54 INFO:topic #2 (0.050): 0.011\"share\" + 0.010\"analyst\" + 0.007\"service\" + 0.007\"billion\" + 0.007\"deal\" + 0.006\"offer\" + 0.006\"stock\" + 0.006\"corp\" + 0.006\"industry\" + 0.006\"business\"\n", "05:28:54 INFO:topic #15 (0.050): 0.012\"bank\" + 0.009\"stock\" + 0.008\"billion\" + 0.008\"analyst\" + 0.008\"oil\" + 0.007\"canada\" + 0.007\"share\" + 0.007\"toronto\" + 0.007\"russia\" + 0.006\"tonne\"\n", "05:28:54 INFO:topic #0 (0.050): 0.010\"bank\" + 0.009\"internet\" + 0.009\"fcc\" + 0.009\"service\" + 0.008\"phone\" + 0.007\"rule\" + 0.007\"local\" + 0.007\"tv\" + 0.007\"court\" + 0.006\"law\"\n", "05:28:54 INFO:topic #7 (0.050): 0.009\"sale\" + 0.008\"analyst\" + 0.007\"share\" + 0.007\"group\" + 0.007\"profit\" + 0.006\"business\" + 0.005\"pound\" + 0.005\"price\" + 0.005\"billion\" + 0.005\"executive\"\n", "05:28:54 INFO:topic #18 (0.050): 0.014\"analyst\" + 0.011\"computer\" + 0.010\"quarter\" + 0.010\"internet\" + 0.008\"share\" + 0.008\"business\" + 0.008\"stock\" + 0.008\"service\" + 0.007\"industry\" + 0.007\"software\"\n", "05:28:54 INFO:topic diff=0.095805, rho=0.223607\n", "05:28:54 INFO:PROGRESS: pass 19, at document #2500/2500\n", "05:28:54 DEBUG:performing inference on a chunk of 2500 documents\n", "05:28:55 DEBUG:2500/2500 documents converged within 50 iterations\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:28:55 DEBUG:updating topics\n", "05:28:55 INFO:topic #7 (0.050): 0.009\"sale\" + 0.008\"analyst\" + 0.007\"share\" + 0.007\"group\" + 0.007\"profit\" + 0.006\"business\" + 0.005\"pound\" + 0.005\"price\" + 0.005\"billion\" + 0.005\"executive\"\n", "05:28:55 INFO:topic #4 (0.050): 0.022\"china\" + 0.012\"official\" + 0.011\"beijing\" + 0.010\"wang\" + 0.009\"chinese\" + 0.008\"tibet\" + 0.007\"state\" + 0.007\"government\" + 0.007\"people\" + 0.006\"dissident\"\n", "05:28:55 INFO:topic #19 (0.050): 0.032\"hong\" + 0.031\"kong\" + 0.031\"hong_kong\" + 0.021\"Hong Kong\" + 0.021\"china\" + 0.009\"chinese\" + 0.008\"tung\" + 0.008\"Hong Kong's\" + 0.007\"beijing\" + 0.007\"airbus\"\n", "05:28:55 INFO:topic #3 (0.050): 0.027\"bt\" + 0.018\"telecom\" + 0.015\"mci\" + 0.013\"pound\" + 0.011\"deal\" + 0.011\"analyst\" + 0.011\"billion\" + 0.011\"british\" + 0.010\"share\" + 0.010\"group\"\n", "05:28:55 INFO:topic #8 (0.050): 0.028\"gold\" + 0.026\"bre\" + 0.026\"bre_x\" + 0.026\"x\" + 0.019\"Bre-X\" + 0.016\"barrick\" + 0.012\"analyst\" + 0.011\"busang\" + 0.010\"indonesian\" + 0.009\"government\"\n", "05:28:55 INFO:topic diff=0.084200, rho=0.218218\n", "05:28:55 DEBUG:Setting topics to those of the model: AuthorTopicModel(num_terms=3914, num_topics=20, num_authors=50, decay=0.5, chunksize=2500)\n", "05:28:55 INFO:CorpusAccumulator accumulated stats from 1000 documents\n", "05:28:55 INFO:CorpusAccumulator accumulated stats from 2000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "-1.50354141347\n" ] } ], "source": [ "atmodel_standard = train_model(train_corpus_50_20, train_author2doc, train_dictionary_50_20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We run our first training and observe that the passes and iterations parameters are set high enough, so that the model converges.\n", "\n", "07:47:24 INFO:PROGRESS: pass 15, at document #2500/2500\n", "\n", "07:47:24 DEBUG:performing inference on a chunk of 2500 documents \n", "\n", "07:47:27 DEBUG:2500/2500 documents converged within 50 iterations \n", "\n", "Tells us that the model indeed conveges well." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precision@k: top_n=1\n", "Prediction accuracy: 0.3548\n", "Precision@k: top_n=2\n", "Prediction accuracy: 0.5228\n", "Precision@k: top_n=3\n", "Prediction accuracy: 0.6456\n", "Precision@k: top_n=4\n", "Prediction accuracy: 0.7208\n", "Precision@k: top_n=5\n", "Prediction accuracy: 0.7748\n", "Precision@k: top_n=6\n", "Prediction accuracy: 0.8188\n", "Precision@k: top_n=8\n", "Prediction accuracy: 0.8576\n", "Precision@k: top_n=10\n", "Prediction accuracy: 0.8936\n" ] }, { "data": { "image/png": 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VhTa/AI43s73DJNMHWATkAueExwwGJlXhs0VSZuZMOPdcOOywYKrCgAHBZLepU6FvXyUH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\nzsx+SDhpzszOIZg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"text/plain": [ "<matplotlib.figure.Figure at 0x1190ddb70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "accuracy_scores_20topic={}\n", "for i in [1,2,3,4,5,6,8,10]:\n", " accuracy, k = prediction_accuracy(test_author2doc, test_corpus_50_20, atmodel_standard, k=i)\n", " accuracy_scores_20topic[k] = accuracy\n", " \n", "plot_accuracy(scores1=accuracy_scores_20topic, label1=\"20 topics\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a rather poor accuracy performace. We increase the number of topic to 100." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "05:31:51 INFO:Vocabulary consists of 3914 words.\n", "05:31:51 INFO:using symmetric alpha at 0.01\n", "05:31:51 INFO:using symmetric eta at 0.01\n", "05:31:53 INFO:running online author-topic training, 100 topics, 50 authors, 10 passes over the supplied corpus of 2500 documents, updating model once every 2500 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", "05:31:53 INFO:PROGRESS: pass 0, at document #2500/2500\n", "05:31:53 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:05 DEBUG:5/2500 documents converged within 50 iterations\n", "05:32:05 DEBUG:updating topics\n", "05:32:05 INFO:topic #18 (0.010): 0.007\"analyst\" + 0.007\"business\" + 0.005\"billion\" + 0.005\"stock\" + 0.005\"boeing\" + 0.004\"quarter\" + 0.004\"industry\" + 0.004\"share\" + 0.004\"shareholder\" + 0.004\"sale\"\n", "05:32:05 INFO:topic #71 (0.010): 0.015\"fcc\" + 0.015\"phone\" + 0.011\"local\" + 0.011\"carrier\" + 0.010\"service\" + 0.009\"rule\" + 0.008\"court\" + 0.008\"distance\" + 0.008\"long\" + 0.007\"tv\"\n", "05:32:05 INFO:topic #79 (0.010): 0.011\"china\" + 0.010\"beijing\" + 0.007\"official\" + 0.006\"chinese\" + 0.006\"lama\" + 0.006\"tibet\" + 0.006\"share\" + 0.005\"analyst\" + 0.005\"region\" + 0.005\"billion\"\n", "05:32:05 INFO:topic #93 (0.010): 0.015\"ibm\" + 0.011\"analyst\" + 0.011\"computer\" + 0.010\"pc\" + 0.009\"sale\" + 0.009\"quarter\" + 0.008\"industry\" + 0.008\"price\" + 0.007\"consumer\" + 0.007\"service\"\n", "05:32:05 INFO:topic #99 (0.010): 0.008\"world\" + 0.008\"czech\" + 0.007\"analyst\" + 0.006\"stock\" + 0.006\"win\" + 0.005\"billion\" + 0.005\"team\" + 0.005\"game\" + 0.005\"bank\" + 0.005\"second\"\n", "05:32:05 INFO:topic diff=25.070898, rho=1.000000\n", "05:32:05 INFO:PROGRESS: pass 1, at document #2500/2500\n", "05:32:05 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:12 DEBUG:2492/2500 documents converged within 50 iterations\n", "05:32:12 DEBUG:updating topics\n", "05:32:12 INFO:topic #70 (0.010): 0.019\"shanghai\" + 0.018\"share\" + 0.017\"china\" + 0.011\"stock\" + 0.011\"beijing\" + 0.010\"b\" + 0.010\"foreign\" + 0.010\"exchange\" + 0.009\"analyst\" + 0.008\"investor\"\n", "05:32:12 INFO:topic #2 (0.010): 0.020\"mci\" + 0.012\"long\" + 0.012\"service\" + 0.010\"distance\" + 0.010\"analyst\" + 0.010\"sprint\" + 0.010\"billion\" + 0.010\"corp\" + 0.008\"local\" + 0.008\"deal\"\n", "05:32:12 INFO:topic #57 (0.010): 0.024\"china\" + 0.013\"beijing\" + 0.011\"chinese\" + 0.010\"wang\" + 0.010\"hong_kong\" + 0.009\"hong\" + 0.008\"kong\" + 0.008\"official\" + 0.007\"Hong Kong\" + 0.006\"people\"\n", "05:32:12 INFO:topic #45 (0.010): 0.021\"time\" + 0.016\"executive\" + 0.013\"cable\" + 0.011\"rise\" + 0.011\"sale\" + 0.010\"billion\" + 0.009\"quarter\" + 0.008\"share\" + 0.008\"group\" + 0.007\"analyst\"\n", "05:32:12 INFO:topic #18 (0.010): 0.007\"analyst\" + 0.006\"business\" + 0.005\"billion\" + 0.004\"stock\" + 0.004\"boeing\" + 0.004\"quarter\" + 0.004\"industry\" + 0.004\"share\" + 0.004\"shareholder\" + 0.003\"sale\"\n", "05:32:12 INFO:topic diff=7.998665, rho=0.577350\n", "05:32:12 INFO:PROGRESS: pass 2, at document #2500/2500\n", "05:32:12 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:19 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:32:19 DEBUG:updating topics\n", "05:32:19 INFO:topic #70 (0.010): 0.021\"share\" + 0.021\"shanghai\" + 0.019\"china\" + 0.012\"b\" + 0.011\"foreign\" + 0.011\"stock\" + 0.011\"bank\" + 0.011\"analyst\" + 0.010\"beijing\" + 0.010\"exchange\"\n", "05:32:19 INFO:topic #71 (0.010): 0.020\"fcc\" + 0.015\"phone\" + 0.013\"carrier\" + 0.013\"tv\" + 0.012\"local\" + 0.010\"service\" + 0.010\"rule\" + 0.010\"long\" + 0.009\"distance\" + 0.008\"long_distance\"\n", "05:32:19 INFO:topic #22 (0.010): 0.033\"bank\" + 0.010\"rate\" + 0.010\"cut\" + 0.009\"analyst\" + 0.008\"day\" + 0.008\"merger\" + 0.007\"profit\" + 0.007\"australia\" + 0.007\"financial\" + 0.006\"ltd\"\n", "05:32:19 INFO:topic #18 (0.010): 0.006\"analyst\" + 0.005\"business\" + 0.004\"billion\" + 0.004\"stock\" + 0.004\"boeing\" + 0.003\"quarter\" + 0.003\"industry\" + 0.003\"share\" + 0.003\"shareholder\" + 0.003\"sale\"\n", "05:32:19 INFO:topic #5 (0.010): 0.018\"china\" + 0.009\"beijing\" + 0.008\"tonne\" + 0.007\"chinese\" + 0.006\"official\" + 0.005\"trade\" + 0.005\"price\" + 0.005\"chen\" + 0.005\"trader\" + 0.004\"million_tonne\"\n", "05:32:19 INFO:topic diff=7.090922, rho=0.500000\n", "05:32:19 INFO:PROGRESS: pass 3, at document #2500/2500\n", "05:32:19 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:25 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:32:25 DEBUG:updating topics\n", "05:32:26 INFO:topic #9 (0.010): 0.004\"analyst\" + 0.003\"government\" + 0.002\"share\" + 0.002\"china\" + 0.002\"cost\" + 0.002\"sale\" + 0.002\"right\" + 0.002\"stock\" + 0.002\"big\" + 0.002\"end\"\n", "05:32:26 INFO:topic #20 (0.010): 0.016\"gold\" + 0.016\"bre\" + 0.015\"x\" + 0.015\"bre_x\" + 0.010\"barrick\" + 0.009\"Bre-X\" + 0.008\"gm\" + 0.008\"price\" + 0.008\"analyst\" + 0.008\"plant\"\n", "05:32:26 INFO:topic #4 (0.010): 0.015\"franc\" + 0.015\"thomson\" + 0.014\"french\" + 0.009\"group\" + 0.009\"share\" + 0.008\"france\" + 0.008\"government\" + 0.008\"plan\" + 0.008\"lagardere\" + 0.007\"billion\"\n", "05:32:26 INFO:topic #87 (0.010): 0.017\"analyst\" + 0.011\"sale\" + 0.010\"share\" + 0.009\"business\" + 0.008\"quarter\" + 0.008\"price\" + 0.007\"add\" + 0.007\"chemical\" + 0.006\"stock\" + 0.006\"earning\"\n", "05:32:26 INFO:topic #62 (0.010): 0.022\"profit\" + 0.014\"pound\" + 0.011\"sale\" + 0.011\"rise\" + 0.010\"analyst\" + 0.010\"stg\" + 0.010\"group\" + 0.009\"business\" + 0.009\"half\" + 0.009\"million_stg\"\n", "05:32:26 INFO:topic diff=6.178695, rho=0.447214\n", "05:32:26 INFO:PROGRESS: pass 4, at document #2500/2500\n", "05:32:26 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:31 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:32:31 DEBUG:updating topics\n", "05:32:32 INFO:topic #47 (0.010): 0.012\"gold\" + 0.008\"oil\" + 0.008\"share\" + 0.007\"stock\" + 0.006\"analyst\" + 0.006\"government\" + 0.006\"price\" + 0.006\"colombia\" + 0.005\"rise\" + 0.005\"issue\"\n", "05:32:32 INFO:topic #86 (0.010): 0.014\"cargo\" + 0.010\"service\" + 0.010\"kong\" + 0.009\"hong\" + 0.009\"air\" + 0.009\"airline\" + 0.008\"hong_kong\" + 0.007\"Hong Kong\" + 0.007\"route\" + 0.006\"rate\"\n", "05:32:32 INFO:topic #25 (0.010): 0.010\"boeing\" + 0.009\"share\" + 0.009\"analyst\" + 0.007\"billion\" + 0.006\"service\" + 0.006\"mci\" + 0.006\"business\" + 0.005\"stock\" + 0.005\"jet\" + 0.005\"growth\"\n", "05:32:32 INFO:topic #74 (0.010): 0.043\"china\" + 0.020\"chinese\" + 0.014\"official\" + 0.014\"beijing\" + 0.010\"trade\" + 0.009\"state\" + 0.007\"states\" + 0.006\"united\" + 0.006\"united_states\" + 0.006\"import\"\n", "05:32:32 INFO:topic #53 (0.010): 0.032\"fund\" + 0.012\"investment\" + 0.011\"hong_kong\" + 0.011\"hong\" + 0.010\"stock\" + 0.010\"management\" + 0.010\"week\" + 0.009\"manager\" + 0.009\"billion\" + 0.009\"kong\"\n", "05:32:32 INFO:topic diff=5.327576, rho=0.408248\n", "05:32:32 INFO:PROGRESS: pass 5, at document #2500/2500\n", "05:32:32 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:36 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:32:36 DEBUG:updating topics\n", "05:32:37 INFO:topic #60 (0.010): 0.002\"financial\" + 0.002\"official\" + 0.002\"stock\" + 0.002\"policy\" + 0.002\"group\" + 0.002\"share\" + 0.002\"china\" + 0.001\"chinese\" + 0.001\"beijing\" + 0.001\"bank\"\n", "05:32:37 INFO:topic #77 (0.010): 0.006\"computer\" + 0.005\"internet\" + 0.005\"quarter\" + 0.005\"analyst\" + 0.004\"business\" + 0.004\"share\" + 0.004\"service\" + 0.003\"profit\" + 0.003\"industry\" + 0.003\"system\"\n", "05:32:37 INFO:topic #43 (0.010): 0.003\"bre_x\" + 0.003\"bre\" + 0.002\"analyst\" + 0.002\"gold\" + 0.002\"barrick\" + 0.002\"government\" + 0.002\"Bre-X\" + 0.002\"x\" + 0.002\"share\" + 0.002\"stock\"\n", "05:32:37 INFO:topic #10 (0.010): 0.002\"billion\" + 0.001\"investment\" + 0.001\"tonne\" + 0.001\"quarter\" + 0.001\"venture\" + 0.001\"industry\" + 0.001\"price\" + 0.001\"cocoa\" + 0.001\"coast\" + 0.001\"month\"\n", "05:32:37 INFO:topic #99 (0.010): 0.005\"world\" + 0.005\"czech\" + 0.004\"analyst\" + 0.004\"stock\" + 0.004\"win\" + 0.003\"billion\" + 0.003\"team\" + 0.003\"game\" + 0.003\"bank\" + 0.003\"second\"\n", "05:32:37 INFO:topic diff=4.560862, rho=0.377964\n", "05:32:37 INFO:PROGRESS: pass 6, at document #2500/2500\n", "05:32:37 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:41 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:32:41 DEBUG:updating topics\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:32:41 INFO:topic #38 (0.010): 0.015\"analyst\" + 0.014\"australian\" + 0.013\"ltd\" + 0.012\"share\" + 0.011\"australia\" + 0.011\"profit\" + 0.010\"sydney\" + 0.009\"group\" + 0.009\"news\" + 0.008\"corp\"\n", "05:32:41 INFO:topic #46 (0.010): 0.004\"hong\" + 0.004\"kong\" + 0.003\"china\" + 0.002\"Hong Kong\" + 0.002\"official\" + 0.002\"hong_kong\" + 0.002\"chinese\" + 0.002\"united\" + 0.002\"singapore\" + 0.001\"month\"\n", "05:32:41 INFO:topic #97 (0.010): 0.021\"internet\" + 0.017\"bank\" + 0.008\"law\" + 0.008\"court\" + 0.008\"congress\" + 0.007\"service\" + 0.007\"credit\" + 0.007\"allow\" + 0.007\"bill\" + 0.006\"policy\"\n", "05:32:41 INFO:topic #75 (0.010): 0.028\"bank\" + 0.016\"japan\" + 0.015\"billion\" + 0.014\"yen\" + 0.014\"financial\" + 0.012\"loan\" + 0.011\"japanese\" + 0.010\"problem\" + 0.010\"analyst\" + 0.009\"firm\"\n", "05:32:41 INFO:topic #11 (0.010): 0.063\"gm\" + 0.032\"plant\" + 0.024\"strike\" + 0.021\"automaker\" + 0.021\"worker\" + 0.017\"uaw\" + 0.013\"truck\" + 0.013\"local\" + 0.013\"union\" + 0.012\"chrysler\"\n", "05:32:41 INFO:topic diff=3.882969, rho=0.353553\n", "05:32:41 INFO:PROGRESS: pass 7, at document #2500/2500\n", "05:32:41 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:46 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:32:46 DEBUG:updating topics\n", "05:32:46 INFO:topic #82 (0.010): 0.003\"quarter\" + 0.003\"executive\" + 0.003\"internet\" + 0.003\"high\" + 0.003\"share\" + 0.002\"loss\" + 0.002\"technology\" + 0.002\"high_tech\" + 0.002\"stock\" + 0.002\"software\"\n", "05:32:46 INFO:topic #38 (0.010): 0.015\"analyst\" + 0.014\"australian\" + 0.013\"ltd\" + 0.012\"share\" + 0.011\"australia\" + 0.011\"profit\" + 0.010\"sydney\" + 0.009\"group\" + 0.009\"news\" + 0.008\"corp\"\n", "05:32:46 INFO:topic #9 (0.010): 0.001\"analyst\" + 0.001\"government\" + 0.001\"share\" + 0.001\"china\" + 0.001\"cost\" + 0.001\"sale\" + 0.001\"right\" + 0.001\"stock\" + 0.001\"big\" + 0.001\"end\"\n", "05:32:46 INFO:topic #13 (0.010): 0.001\"china\" + 0.001\"share\" + 0.001\"official\" + 0.001\"analyst\" + 0.001\"group\" + 0.001\"sale\" + 0.001\"beijing\" + 0.001\"party\" + 0.001\"month\" + 0.001\"billion\"\n", "05:32:46 INFO:topic #76 (0.010): 0.024\"cocoa\" + 0.019\"exporter\" + 0.019\"tonne\" + 0.012\"ivory\" + 0.012\"coast\" + 0.012\"ivory_coast\" + 0.011\"crop\" + 0.011\"price\" + 0.010\"buyer\" + 0.009\"export\"\n", "05:32:46 INFO:topic diff=3.291750, rho=0.333333\n", "05:32:46 INFO:PROGRESS: pass 8, at document #2500/2500\n", "05:32:46 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:50 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:32:50 DEBUG:updating topics\n", "05:32:50 INFO:topic #18 (0.010): 0.001\"analyst\" + 0.001\"business\" + 0.001\"billion\" + 0.001\"stock\" + 0.001\"boeing\" + 0.001\"quarter\" + 0.001\"industry\" + 0.001\"share\" + 0.001\"shareholder\" + 0.001\"sale\"\n", "05:32:50 INFO:topic #61 (0.010): 0.014\"analyst\" + 0.014\"microsoft\" + 0.010\"share\" + 0.009\"software\" + 0.009\"quarter\" + 0.009\"boeing\" + 0.009\"office\" + 0.008\"computer\" + 0.008\"worker\" + 0.008\"fiscal\"\n", "05:32:50 INFO:topic #2 (0.010): 0.019\"mci\" + 0.013\"analyst\" + 0.011\"long\" + 0.011\"share\" + 0.011\"service\" + 0.010\"distance\" + 0.010\"long_distance\" + 0.010\"billion\" + 0.010\"corp\" + 0.008\"local\"\n", "05:32:50 INFO:topic #98 (0.010): 0.031\"tonne\" + 0.030\"china\" + 0.019\"trader\" + 0.018\"chinese\" + 0.018\"price\" + 0.016\"hong_kong\" + 0.016\"hong\" + 0.016\"kong\" + 0.013\"source\" + 0.013\"import\"\n", "05:32:50 INFO:topic #71 (0.010): 0.019\"fcc\" + 0.014\"tv\" + 0.014\"phone\" + 0.013\"carrier\" + 0.011\"local\" + 0.010\"service\" + 0.010\"long\" + 0.009\"rule\" + 0.009\"distance\" + 0.009\"long_distance\"\n", "05:32:50 INFO:topic diff=2.781235, rho=0.316228\n", "05:32:50 INFO:PROGRESS: pass 9, at document #2500/2500\n", "05:32:50 DEBUG:performing inference on a chunk of 2500 documents\n", "05:32:54 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:32:54 DEBUG:updating topics\n", "05:32:55 INFO:topic #99 (0.010): 0.002\"world\" + 0.002\"czech\" + 0.002\"analyst\" + 0.002\"stock\" + 0.002\"win\" + 0.001\"billion\" + 0.001\"team\" + 0.001\"game\" + 0.001\"bank\" + 0.001\"second\"\n", "05:32:55 INFO:topic #26 (0.010): 0.003\"business\" + 0.002\"analyst\" + 0.002\"gm\" + 0.001\"share\" + 0.001\"internet\" + 0.001\"billion\" + 0.001\"stock\" + 0.001\"access\" + 0.001\"chemical\" + 0.001\"service\"\n", "05:32:55 INFO:topic #80 (0.010): 0.012\"analyst\" + 0.012\"computer\" + 0.009\"stock\" + 0.009\"internet\" + 0.008\"quarter\" + 0.008\"technology\" + 0.008\"service\" + 0.007\"software\" + 0.007\"share\" + 0.007\"business\"\n", "05:32:55 INFO:topic #67 (0.010): 0.045\"gm\" + 0.033\"plant\" + 0.021\"uaw\" + 0.017\"strike\" + 0.017\"worker\" + 0.012\"part\" + 0.011\"local\" + 0.010\"truck\" + 0.010\"automaker\" + 0.010\"contract\"\n", "05:32:55 INFO:topic #97 (0.010): 0.021\"internet\" + 0.017\"bank\" + 0.008\"law\" + 0.008\"court\" + 0.008\"congress\" + 0.007\"service\" + 0.007\"credit\" + 0.007\"allow\" + 0.007\"bill\" + 0.006\"policy\"\n", "05:32:55 INFO:topic diff=2.344407, rho=0.301511\n", "05:32:55 DEBUG:Setting topics to those of the model: AuthorTopicModel(num_terms=3914, num_topics=100, num_authors=50, decay=0.5, chunksize=2500)\n", "05:32:55 INFO:CorpusAccumulator accumulated stats from 1000 documents\n", "05:32:55 INFO:CorpusAccumulator accumulated stats from 2000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "-1.89056657258\n" ] } ], "source": [ "atmodel_100topics = train_model(train_corpus_50_20, train_author2doc, train_dictionary_50_20, num_topics=100, eval_every=0, iterations=50, passes=10)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precision@k: top_n=1\n", "Prediction accuracy: 0.5808\n", "Precision@k: top_n=2\n", "Prediction accuracy: 0.7472\n", "Precision@k: top_n=3\n", "Prediction accuracy: 0.8252\n", "Precision@k: top_n=4\n", "Prediction accuracy: 0.8732\n", "Precision@k: top_n=5\n", "Prediction accuracy: 0.8956\n", "Precision@k: top_n=6\n", "Prediction accuracy: 0.9072\n", "Precision@k: top_n=8\n", "Prediction accuracy: 0.9276\n", "Precision@k: top_n=10\n", "Prediction accuracy: 0.9412\n" ] }, { "data": { "image/png": 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rCK8TzN3DOrsdjl/t2QNDhpwowX1J80WITdxjjKu8ucXURFUTcxZU9TDQtKwN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\n4lyltAO2A6NV9XBFt12VZGf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"text/plain": [ "<matplotlib.figure.Figure at 0x15f425eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "accuracy_scores_100topic={}\n", "for i in [1,2,3,4,5,6,8,10]:\n", " accuracy, k = prediction_accuracy(test_author2doc, test_corpus_50_20, atmodel_100topics, k=i)\n", " accuracy_scores_100topic[k] = accuracy\n", " \n", "plot_accuracy(scores1=accuracy_scores_20topic, label1=\"20 topics\", scores2=accuracy_scores_100topic, label2=\"100 topics\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 100-topic model is much more accurate than the 20-topic model. We continue to increase the topic until convergence." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "05:36:37 INFO:Vocabulary consists of 3914 words.\n", "05:36:37 INFO:using symmetric alpha at 0.006666666666666667\n", "05:36:37 INFO:using symmetric eta at 0.006666666666666667\n", "05:36:40 INFO:running online author-topic training, 150 topics, 50 authors, 15 passes over the supplied corpus of 2500 documents, updating model once every 2500 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", "05:36:40 INFO:PROGRESS: pass 0, at document #2500/2500\n", "05:36:40 DEBUG:performing inference on a chunk of 2500 documents\n", "05:36:55 DEBUG:15/2500 documents converged within 50 iterations\n", "05:36:55 DEBUG:updating topics\n", "05:36:56 INFO:topic #51 (0.007): 0.015\"profit\" + 0.012\"price\" + 0.012\"group\" + 0.012\"analyst\" + 0.009\"share\" + 0.009\"steel\" + 0.008\"tell\" + 0.008\"australian\" + 0.007\"month\" + 0.007\"forecast\"\n", "05:36:56 INFO:topic #86 (0.007): 0.011\"china\" + 0.008\"kong\" + 0.007\"hong\" + 0.007\"cargo\" + 0.006\"hong_kong\" + 0.006\"service\" + 0.006\"Hong Kong\" + 0.005\"profit\" + 0.005\"analyst\" + 0.005\"month\"\n", "05:36:56 INFO:topic #125 (0.007): 0.009\"analyst\" + 0.007\"share\" + 0.007\"bank\" + 0.005\"problem\" + 0.004\"billion\" + 0.004\"sale\" + 0.004\"loan\" + 0.004\"plant\" + 0.004\"gm\" + 0.004\"corp\"\n", "05:36:56 INFO:topic #4 (0.007): 0.020\"franc\" + 0.018\"thomson\" + 0.016\"french\" + 0.011\"group\" + 0.011\"share\" + 0.010\"government\" + 0.009\"plan\" + 0.009\"france\" + 0.009\"lagardere\" + 0.009\"billion\"\n", "05:36:56 INFO:topic #114 (0.007): 0.006\"analyst\" + 0.006\"sale\" + 0.005\"chairman\" + 0.005\"business\" + 0.004\"social\" + 0.004\"party\" + 0.004\"month\" + 0.004\"industry\" + 0.003\"share\" + 0.003\"government\"\n", "05:36:56 INFO:topic diff=43.566047, rho=1.000000\n", "05:36:56 INFO:PROGRESS: pass 1, at document #2500/2500\n", "05:36:56 DEBUG:performing inference on a chunk of 2500 documents\n", "05:37:04 DEBUG:2493/2500 documents converged within 50 iterations\n", "05:37:04 DEBUG:updating topics\n", "05:37:04 INFO:topic #72 (0.007): 0.024\"gold\" + 0.024\"bre_x\" + 0.023\"x\" + 0.023\"bre\" + 0.020\"Bre-X\" + 0.013\"barrick\" + 0.010\"government\" + 0.010\"indonesian\" + 0.010\"busang\" + 0.010\"analyst\"\n", "05:37:04 INFO:topic #133 (0.007): 0.013\"group\" + 0.012\"pound\" + 0.011\"share\" + 0.009\"billion\" + 0.006\"bt\" + 0.006\"business\" + 0.006\"analyst\" + 0.005\"british\" + 0.005\"profit\" + 0.005\"britain\"\n", "05:37:04 INFO:topic #19 (0.007): 0.007\"billion\" + 0.006\"group\" + 0.006\"airbus\" + 0.005\"state\" + 0.005\"profit\" + 0.005\"industry\" + 0.005\"tobacco\" + 0.005\"tell\" + 0.004\"price\" + 0.004\"cost\"\n", "05:37:04 INFO:topic #90 (0.007): 0.029\"bank\" + 0.017\"canadian\" + 0.016\"billion\" + 0.014\"canada\" + 0.010\"toronto\" + 0.009\"analyst\" + 0.008\"stock\" + 0.008\"fund\" + 0.008\"share\" + 0.007\"high\"\n", "05:37:04 INFO:topic #91 (0.007): 0.008\"analyst\" + 0.007\"bre\" + 0.005\"bre_x\" + 0.005\"x\" + 0.005\"gm\" + 0.005\"billion\" + 0.005\"Bre-X\" + 0.005\"stock\" + 0.004\"sale\" + 0.004\"share\"\n", "05:37:04 INFO:topic diff=12.489199, rho=0.577350\n", "05:37:04 INFO:PROGRESS: pass 2, at document #2500/2500\n", "05:37:04 DEBUG:performing inference on a chunk of 2500 documents\n", "05:37:12 DEBUG:2497/2500 documents converged within 50 iterations\n", "05:37:12 DEBUG:updating topics\n", "05:37:12 INFO:topic #58 (0.007): 0.011\"shanghai\" + 0.010\"china\" + 0.005\"bank\" + 0.005\"chinese\" + 0.004\"city\" + 0.004\"stock\" + 0.004\"chen\" + 0.003\"beijing\" + 0.003\"analyst\" + 0.003\"modern\"\n", "05:37:12 INFO:topic #26 (0.007): 0.010\"business\" + 0.005\"analyst\" + 0.005\"share\" + 0.004\"billion\" + 0.004\"stock\" + 0.004\"continue\" + 0.003\"states\" + 0.003\"chemical\" + 0.003\"united\" + 0.003\"internet\"\n", "05:37:12 INFO:topic #61 (0.007): 0.027\"boeing\" + 0.014\"analyst\" + 0.013\"billion\" + 0.012\"microsoft\" + 0.012\"jet\" + 0.010\"airbus\" + 0.009\"share\" + 0.009\"order\" + 0.008\"mcdonnell\" + 0.007\"revenue\"\n", "05:37:12 INFO:topic #149 (0.007): 0.018\"china\" + 0.010\"official\" + 0.010\"chinese\" + 0.008\"beijing\" + 0.006\"trade\" + 0.006\"world\" + 0.005\"foreign\" + 0.005\"united_states\" + 0.005\"drug\" + 0.005\"metre\"\n", "05:37:12 INFO:topic #74 (0.007): 0.044\"china\" + 0.022\"chinese\" + 0.012\"official\" + 0.012\"tonne\" + 0.011\"beijing\" + 0.008\"trade\" + 0.008\"import\" + 0.008\"trader\" + 0.007\"price\" + 0.007\"state\"\n", "05:37:12 INFO:topic diff=10.945011, rho=0.500000\n", "05:37:12 INFO:PROGRESS: pass 3, at document #2500/2500\n", "05:37:12 DEBUG:performing inference on a chunk of 2500 documents\n", "05:37:19 DEBUG:2499/2500 documents converged within 50 iterations\n", "05:37:19 DEBUG:updating topics\n", "05:37:19 INFO:topic #125 (0.007): 0.004\"analyst\" + 0.003\"share\" + 0.003\"bank\" + 0.002\"problem\" + 0.002\"billion\" + 0.002\"sale\" + 0.002\"loan\" + 0.002\"plant\" + 0.002\"gm\" + 0.002\"corp\"\n", "05:37:19 INFO:topic #95 (0.007): 0.038\"bank\" + 0.018\"billion\" + 0.016\"society\" + 0.010\"analyst\" + 0.009\"debt\" + 0.009\"eurotunnel\" + 0.008\"banking\" + 0.008\"pound\" + 0.008\"member\" + 0.007\"convert\"\n", "05:37:19 INFO:topic #19 (0.007): 0.007\"billion\" + 0.006\"state\" + 0.005\"group\" + 0.005\"airbus\" + 0.005\"loss\" + 0.005\"cost\" + 0.005\"profit\" + 0.005\"industry\" + 0.005\"sale\" + 0.004\"executive\"\n", "05:37:19 INFO:topic #115 (0.007): 0.003\"share\" + 0.002\"stock\" + 0.002\"billion\" + 0.002\"analyst\" + 0.002\"china\" + 0.002\"industry\" + 0.001\"month\" + 0.001\"rise\" + 0.001\"big\" + 0.001\"deal\"\n", "05:37:20 INFO:topic #29 (0.007): 0.018\"czech\" + 0.008\"klaus\" + 0.007\"government\" + 0.007\"crown\" + 0.007\"party\" + 0.007\"bank\" + 0.007\"prague\" + 0.005\"country\" + 0.005\"foreign\" + 0.005\"election\"\n", "05:37:20 INFO:topic diff=9.415271, rho=0.447214\n", "05:37:20 INFO:PROGRESS: pass 4, at document #2500/2500\n", "05:37:20 DEBUG:performing inference on a chunk of 2500 documents\n", "05:37:26 DEBUG:2499/2500 documents converged within 50 iterations\n", "05:37:26 DEBUG:updating topics\n", "05:37:27 INFO:topic #31 (0.007): 0.010\"franc\" + 0.009\"french\" + 0.008\"china\" + 0.008\"billion\" + 0.006\"shanghai\" + 0.006\"analyst\" + 0.006\"share\" + 0.005\"government\" + 0.005\"plan\" + 0.005\"exchange\"\n", "05:37:27 INFO:topic #76 (0.007): 0.007\"china\" + 0.006\"hong_kong\" + 0.006\"price\" + 0.005\"kong\" + 0.005\"hong\" + 0.004\"tonne\" + 0.004\"world\" + 0.004\"analyst\" + 0.003\"chinese\" + 0.003\"Hong Kong\"\n", "05:37:27 INFO:topic #36 (0.007): 0.024\"bid\" + 0.021\"penny\" + 0.020\"analyst\" + 0.017\"share\" + 0.015\"electric\" + 0.013\"electricity\" + 0.012\"offer\" + 0.012\"price\" + 0.011\"northern\" + 0.010\"water\"\n", "05:37:27 INFO:topic #144 (0.007): 0.008\"computer\" + 0.007\"software\" + 0.006\"technology\" + 0.006\"internet\" + 0.005\"web\" + 0.004\"site\" + 0.004\"people\" + 0.004\"quarter\" + 0.004\"industry\" + 0.004\"base\"\n", "05:37:27 INFO:topic #96 (0.007): 0.013\"tv\" + 0.011\"industry\" + 0.010\"group\" + 0.010\"system\" + 0.008\"television\" + 0.008\"plan\" + 0.008\"service\" + 0.007\"rating\" + 0.006\"american\" + 0.006\"long\"\n", "05:37:27 INFO:topic diff=8.020445, rho=0.408248\n", "05:37:27 INFO:PROGRESS: pass 5, at document #2500/2500\n", "05:37:27 DEBUG:performing inference on a chunk of 2500 documents\n", "05:37:33 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:37:33 DEBUG:updating topics\n", "05:37:33 INFO:topic #128 (0.007): 0.019\"ford\" + 0.017\"gm\" + 0.015\"sale\" + 0.015\"plant\" + 0.011\"car\" + 0.011\"vehicle\" + 0.008\"chrysler\" + 0.008\"worker\" + 0.008\"automaker\" + 0.007\"truck\"\n", "05:37:33 INFO:topic #82 (0.007): 0.006\"china\" + 0.004\"tonne\" + 0.004\"chinese\" + 0.003\"trader\" + 0.003\"copper\" + 0.002\"price\" + 0.002\"source\" + 0.002\"kong\" + 0.002\"shanghai\" + 0.002\"metal\"\n", "05:37:33 INFO:topic #96 (0.007): 0.013\"tv\" + 0.011\"industry\" + 0.010\"group\" + 0.010\"system\" + 0.008\"plan\" + 0.008\"television\" + 0.008\"service\" + 0.007\"rating\" + 0.007\"american\" + 0.006\"long\"\n", "05:37:33 INFO:topic #41 (0.007): 0.016\"australian\" + 0.014\"bank\" + 0.013\"profit\" + 0.013\"share\" + 0.013\"news\" + 0.013\"sydney\" + 0.013\"australia\" + 0.011\"ltd\" + 0.011\"analyst\" + 0.011\"corp\"\n", "05:37:33 INFO:topic #79 (0.007): 0.006\"china\" + 0.006\"beijing\" + 0.004\"official\" + 0.004\"lama\" + 0.004\"tibet\" + 0.004\"chinese\" + 0.003\"region\" + 0.003\"dalai_lama\" + 0.003\"share\" + 0.003\"analyst\"\n", "05:37:33 INFO:topic diff=6.797042, rho=0.377964\n", "05:37:33 INFO:PROGRESS: pass 6, at document #2500/2500\n", "05:37:33 DEBUG:performing inference on a chunk of 2500 documents\n", "05:37:40 DEBUG:2500/2500 documents converged within 50 iterations\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:37:40 DEBUG:updating topics\n", "05:37:40 INFO:topic #76 (0.007): 0.005\"china\" + 0.004\"hong_kong\" + 0.004\"price\" + 0.003\"kong\" + 0.003\"hong\" + 0.003\"tonne\" + 0.003\"world\" + 0.003\"analyst\" + 0.002\"chinese\" + 0.002\"Hong Kong\"\n", "05:37:40 INFO:topic #31 (0.007): 0.008\"franc\" + 0.007\"french\" + 0.006\"china\" + 0.006\"billion\" + 0.005\"shanghai\" + 0.005\"analyst\" + 0.004\"share\" + 0.004\"government\" + 0.004\"plan\" + 0.004\"exchange\"\n", "05:37:40 INFO:topic #140 (0.007): 0.026\"china\" + 0.020\"beijing\" + 0.016\"chinese\" + 0.013\"official\" + 0.010\"wang\" + 0.006\"foreign\" + 0.005\"right\" + 0.005\"human\" + 0.005\"washington\" + 0.005\"state\"\n", "05:37:40 INFO:topic #46 (0.007): 0.004\"hong\" + 0.004\"kong\" + 0.003\"china\" + 0.003\"Hong Kong\" + 0.002\"official\" + 0.002\"hong_kong\" + 0.002\"chinese\" + 0.002\"singapore\" + 0.002\"united\" + 0.002\"plan\"\n", "05:37:40 INFO:topic #148 (0.007): 0.001\"network\" + 0.001\"analyst\" + 0.001\"stock\" + 0.001\"share\" + 0.001\"price\" + 0.001\"remote\" + 0.001\"recent\" + 0.001\"industry\" + 0.001\"chinese\" + 0.001\"billion\"\n", "05:37:40 INFO:topic diff=5.743502, rho=0.353553\n", "05:37:40 INFO:PROGRESS: pass 7, at document #2500/2500\n", "05:37:40 DEBUG:performing inference on a chunk of 2500 documents\n", "05:37:46 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:37:46 DEBUG:updating topics\n", "05:37:47 INFO:topic #99 (0.007): 0.003\"czech\" + 0.003\"world\" + 0.003\"team\" + 0.002\"win\" + 0.002\"game\" + 0.002\"play\" + 0.002\"second\" + 0.002\"stock\" + 0.002\"billion\" + 0.002\"end\"\n", "05:37:47 INFO:topic #81 (0.007): 0.003\"pound\" + 0.002\"share\" + 0.002\"profit\" + 0.002\"million_pound\" + 0.002\"sale\" + 0.001\"business\" + 0.001\"analyst\" + 0.001\"rise\" + 0.001\"group\" + 0.001\"fall\"\n", "05:37:47 INFO:topic #97 (0.007): 0.001\"analyst\" + 0.001\"business\" + 0.001\"china\" + 0.001\"internet\" + 0.001\"stock\" + 0.001\"sale\" + 0.001\"service\" + 0.001\"chairman\" + 0.001\"continue\" + 0.001\"base\"\n", "05:37:47 INFO:topic #28 (0.007): 0.000\"large\" + 0.000\"share\" + 0.000\"stock\" + 0.000\"property\" + 0.000\"analyst\" + 0.000\"taiwan\" + 0.000\"china\" + 0.000\"bank\" + 0.000\"news\" + 0.000\"billion\"\n", "05:37:47 INFO:topic #93 (0.007): 0.020\"ibm\" + 0.018\"internet\" + 0.016\"computer\" + 0.013\"pc\" + 0.012\"service\" + 0.011\"analyst\" + 0.009\"industry\" + 0.009\"software\" + 0.009\"quarter\" + 0.009\"consumer\"\n", "05:37:47 INFO:topic diff=4.844379, rho=0.333333\n", "05:37:47 INFO:PROGRESS: pass 8, at document #2500/2500\n", "05:37:47 DEBUG:performing inference on a chunk of 2500 documents\n", "05:37:53 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:37:53 DEBUG:updating topics\n", "05:37:54 INFO:topic #97 (0.007): 0.001\"analyst\" + 0.001\"business\" + 0.001\"china\" + 0.001\"internet\" + 0.001\"stock\" + 0.001\"sale\" + 0.001\"service\" + 0.001\"chairman\" + 0.001\"continue\" + 0.001\"base\"\n", "05:37:54 INFO:topic #77 (0.007): 0.002\"computer\" + 0.002\"internet\" + 0.002\"quarter\" + 0.002\"business\" + 0.002\"service\" + 0.002\"analyst\" + 0.001\"share\" + 0.001\"system\" + 0.001\"cost\" + 0.001\"industry\"\n", "05:37:54 INFO:topic #70 (0.007): 0.024\"share\" + 0.023\"shanghai\" + 0.021\"china\" + 0.015\"bank\" + 0.013\"b\" + 0.013\"analyst\" + 0.012\"foreign\" + 0.010\"exchange\" + 0.010\"investor\" + 0.010\"stock\"\n", "05:37:54 INFO:topic #26 (0.007): 0.003\"business\" + 0.002\"analyst\" + 0.001\"share\" + 0.001\"billion\" + 0.001\"stock\" + 0.001\"continue\" + 0.001\"states\" + 0.001\"chemical\" + 0.001\"united\" + 0.001\"internet\"\n", "05:37:54 INFO:topic #145 (0.007): 0.019\"analyst\" + 0.015\"sale\" + 0.013\"share\" + 0.012\"quarter\" + 0.009\"business\" + 0.008\"base\" + 0.007\"earning\" + 0.007\"stock\" + 0.007\"drug\" + 0.006\"amp\"\n", "05:37:54 INFO:topic diff=4.081138, rho=0.316228\n", "05:37:54 INFO:PROGRESS: pass 9, at document #2500/2500\n", "05:37:54 DEBUG:performing inference on a chunk of 2500 documents\n", "05:38:00 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:38:00 DEBUG:updating topics\n", "05:38:00 INFO:topic #116 (0.007): 0.003\"x\" + 0.002\"bre\" + 0.002\"analyst\" + 0.002\"Bre-X\" + 0.002\"bre_x\" + 0.001\"government\" + 0.001\"barrick\" + 0.001\"gold\" + 0.001\"mining\" + 0.001\"indonesian\"\n", "05:38:00 INFO:topic #67 (0.007): 0.002\"hong\" + 0.002\"china\" + 0.002\"kong\" + 0.001\"hong_kong\" + 0.001\"Hong Kong\" + 0.001\"beijing\" + 0.001\"legislature\" + 0.001\"rule\" + 0.001\"chinese\" + 0.001\"plan\"\n", "05:38:00 INFO:topic #69 (0.007): 0.001\"tibet\" + 0.001\"chen\" + 0.001\"dalai_lama\" + 0.001\"china\" + 0.001\"beijing\" + 0.001\"group\" + 0.001\"dalai\" + 0.000\"lama\" + 0.000\"billion\" + 0.000\"region\"\n", "05:38:00 INFO:topic #146 (0.007): 0.001\"hong_kong\" + 0.001\"analyst\" + 0.000\"share\" + 0.000\"hong\" + 0.000\"kong\" + 0.000\"china\" + 0.000\"news\" + 0.000\"Hong Kong\" + 0.000\"billion\" + 0.000\"price\"\n", "05:38:00 INFO:topic #66 (0.007): 0.001\"bank\" + 0.001\"china\" + 0.000\"hong\" + 0.000\"government\" + 0.000\"hong_kong\" + 0.000\"plan\" + 0.000\"bre\" + 0.000\"x\" + 0.000\"kong\" + 0.000\"financial\"\n", "05:38:00 INFO:topic diff=3.435844, rho=0.301511\n", "05:38:00 INFO:PROGRESS: pass 10, at document #2500/2500\n", "05:38:00 DEBUG:performing inference on a chunk of 2500 documents\n", "05:38:06 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:38:06 DEBUG:updating topics\n", "05:38:06 INFO:topic #80 (0.007): 0.019\"analyst\" + 0.015\"microsoft\" + 0.013\"quarter\" + 0.011\"business\" + 0.010\"computer\" + 0.009\"sale\" + 0.008\"revenue\" + 0.008\"windows\" + 0.008\"share\" + 0.008\"system\"\n", "05:38:06 INFO:topic #44 (0.007): 0.001\"sale\" + 0.001\"china\" + 0.001\"analyst\" + 0.001\"share\" + 0.001\"service\" + 0.000\"plan\" + 0.000\"bank\" + 0.000\"deal\" + 0.000\"billion\" + 0.000\"world\"\n", "05:38:06 INFO:topic #58 (0.007): 0.001\"shanghai\" + 0.001\"china\" + 0.001\"bank\" + 0.001\"chinese\" + 0.000\"city\" + 0.000\"stock\" + 0.000\"chen\" + 0.000\"beijing\" + 0.000\"analyst\" + 0.000\"modern\"\n", "05:38:06 INFO:topic #36 (0.007): 0.022\"penny\" + 0.022\"bid\" + 0.021\"analyst\" + 0.018\"share\" + 0.014\"electric\" + 0.012\"price\" + 0.012\"electricity\" + 0.012\"offer\" + 0.012\"pound\" + 0.011\"northern\"\n", "05:38:06 INFO:topic #143 (0.007): 0.018\"mci\" + 0.012\"analyst\" + 0.012\"allen\" + 0.011\"long\" + 0.011\"billion\" + 0.011\"distance\" + 0.010\"long_distance\" + 0.009\"stock\" + 0.009\"share\" + 0.009\"executive\"\n", "05:38:06 INFO:topic diff=2.892181, rho=0.288675\n", "05:38:06 INFO:PROGRESS: pass 11, at document #2500/2500\n", "05:38:06 DEBUG:performing inference on a chunk of 2500 documents\n", "05:38:12 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:38:12 DEBUG:updating topics\n", "05:38:13 INFO:topic #141 (0.007): 0.006\"internet\" + 0.005\"bank\" + 0.003\"law\" + 0.003\"congress\" + 0.003\"court\" + 0.002\"service\" + 0.002\"export\" + 0.002\"security\" + 0.002\"member\" + 0.002\"credit\"\n", "05:38:13 INFO:topic #33 (0.007): 0.000\"billion\" + 0.000\"service\" + 0.000\"plan\" + 0.000\"industry\" + 0.000\"china\" + 0.000\"internet\" + 0.000\"tonne\" + 0.000\"price\" + 0.000\"chinese\" + 0.000\"share\"\n", "05:38:13 INFO:topic #49 (0.007): 0.001\"eurotunnel\" + 0.001\"service\" + 0.000\"billion\" + 0.000\"share\" + 0.000\"fire\" + 0.000\"pound\" + 0.000\"tunnel\" + 0.000\"debt\" + 0.000\"group\" + 0.000\"financial\"\n", "05:38:13 INFO:topic #116 (0.007): 0.002\"x\" + 0.001\"bre\" + 0.001\"analyst\" + 0.001\"Bre-X\" + 0.001\"bre_x\" + 0.001\"government\" + 0.001\"barrick\" + 0.001\"gold\" + 0.001\"mining\" + 0.001\"indonesian\"\n", "05:38:13 INFO:topic #83 (0.007): 0.001\"beijing\" + 0.001\"chinese\" + 0.001\"billion\" + 0.001\"profit\" + 0.001\"tell\" + 0.001\"china\" + 0.001\"bank\" + 0.001\"analyst\" + 0.001\"australian\" + 0.001\"share\"\n", "05:38:13 INFO:topic diff=2.435570, rho=0.277350\n", "05:38:13 INFO:PROGRESS: pass 12, at document #2500/2500\n", "05:38:13 DEBUG:performing inference on a chunk of 2500 documents\n", "05:38:19 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:38:19 DEBUG:updating topics\n", "05:38:19 INFO:topic #138 (0.007): 0.000\"china\" + 0.000\"beijing\" + 0.000\"share\" + 0.000\"states\" + 0.000\"news\" + 0.000\"analyst\" + 0.000\"long\" + 0.000\"chinese\" + 0.000\"trade\" + 0.000\"the United States\"\n", "05:38:19 INFO:topic #125 (0.007): 0.000\"analyst\" + 0.000\"share\" + 0.000\"bank\" + 0.000\"problem\" + 0.000\"billion\" + 0.000\"sale\" + 0.000\"loan\" + 0.000\"plant\" + 0.000\"gm\" + 0.000\"corp\"\n", "05:38:19 INFO:topic #21 (0.007): 0.024\"stock\" + 0.021\"toronto\" + 0.018\"share\" + 0.017\"bank\" + 0.016\"canada\" + 0.013\"gold\" + 0.012\"billion\" + 0.012\"index\" + 0.011\"close\" + 0.010\"point\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:38:19 INFO:topic #5 (0.007): 0.002\"china\" + 0.001\"beijing\" + 0.001\"chen\" + 0.001\"official\" + 0.001\"trade\" + 0.001\"economic\" + 0.001\"chinese\" + 0.001\"party\" + 0.001\"survey\" + 0.001\"month\"\n", "05:38:19 INFO:topic #31 (0.007): 0.002\"franc\" + 0.002\"french\" + 0.002\"china\" + 0.002\"billion\" + 0.001\"shanghai\" + 0.001\"analyst\" + 0.001\"share\" + 0.001\"government\" + 0.001\"plan\" + 0.001\"exchange\"\n", "05:38:19 INFO:topic diff=2.053082, rho=0.267261\n", "05:38:19 INFO:PROGRESS: pass 13, at document #2500/2500\n", "05:38:19 DEBUG:performing inference on a chunk of 2500 documents\n", "05:38:26 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:38:26 DEBUG:updating topics\n", "05:38:26 INFO:topic #3 (0.007): 0.000\"pound\" + 0.000\"billion\" + 0.000\"share\" + 0.000\"group\" + 0.000\"british\" + 0.000\"deal\" + 0.000\"bank\" + 0.000\"sale\" + 0.000\"mci\" + 0.000\"service\"\n", "05:38:26 INFO:topic #42 (0.007): 0.000\"technology\" + 0.000\"russia\" + 0.000\"computer\" + 0.000\"industry\" + 0.000\"russian\" + 0.000\"internet\" + 0.000\"analyst\" + 0.000\"world\" + 0.000\"price\" + 0.000\"software\"\n", "05:38:26 INFO:topic #104 (0.007): 0.044\"cent\" + 0.043\"bank\" + 0.028\"cent_share\" + 0.022\"league\" + 0.019\"football\" + 0.016\"share\" + 0.014\"card\" + 0.013\"earning\" + 0.012\"cos\" + 0.011\"canada\"\n", "05:38:26 INFO:topic #69 (0.007): 0.000\"tibet\" + 0.000\"chen\" + 0.000\"dalai_lama\" + 0.000\"china\" + 0.000\"beijing\" + 0.000\"group\" + 0.000\"dalai\" + 0.000\"lama\" + 0.000\"billion\" + 0.000\"region\"\n", "05:38:26 INFO:topic #115 (0.007): 0.000\"share\" + 0.000\"stock\" + 0.000\"billion\" + 0.000\"analyst\" + 0.000\"china\" + 0.000\"industry\" + 0.000\"month\" + 0.000\"rise\" + 0.000\"big\" + 0.000\"deal\"\n", "05:38:26 INFO:topic diff=1.733322, rho=0.258199\n", "05:38:26 INFO:PROGRESS: pass 14, at document #2500/2500\n", "05:38:26 DEBUG:performing inference on a chunk of 2500 documents\n", "05:38:32 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:38:32 DEBUG:updating topics\n", "05:38:33 INFO:topic #82 (0.007): 0.001\"china\" + 0.000\"tonne\" + 0.000\"chinese\" + 0.000\"trader\" + 0.000\"copper\" + 0.000\"price\" + 0.000\"source\" + 0.000\"kong\" + 0.000\"shanghai\" + 0.000\"metal\"\n", "05:38:33 INFO:topic #122 (0.007): 0.000\"share\" + 0.000\"analyst\" + 0.000\"quarter\" + 0.000\"pc\" + 0.000\"computer\" + 0.000\"ibm\" + 0.000\"profit\" + 0.000\"service\" + 0.000\"industry\" + 0.000\"compaq\"\n", "05:38:33 INFO:topic #126 (0.007): 0.000\"group\" + 0.000\"europe\" + 0.000\"plan\" + 0.000\"air\" + 0.000\"model\" + 0.000\"pound\" + 0.000\"hong\" + 0.000\"month\" + 0.000\"japan\" + 0.000\"Hong Kong\"\n", "05:38:33 INFO:topic #1 (0.007): 0.005\"bank\" + 0.004\"stock\" + 0.004\"billion\" + 0.004\"japan\" + 0.003\"analyst\" + 0.003\"financial\" + 0.003\"asset\" + 0.003\"japanese\" + 0.002\"big\" + 0.002\"yen\"\n", "05:38:33 INFO:topic #120 (0.007): 0.022\"stiff\" + 0.019\"court\" + 0.018\"rating\" + 0.018\"frequently\" + 0.012\"mercury\" + 0.012\"williams\" + 0.012\"remove\" + 0.011\"judge\" + 0.011\"armed\" + 0.009\"ford\"\n", "05:38:33 INFO:topic diff=1.466340, rho=0.250000\n", "05:38:33 DEBUG:Setting topics to those of the model: AuthorTopicModel(num_terms=3914, num_topics=150, num_authors=50, decay=0.5, chunksize=2500)\n", "05:38:33 INFO:CorpusAccumulator accumulated stats from 1000 documents\n", "05:38:33 INFO:CorpusAccumulator accumulated stats from 2000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "-1.90810257282\n" ] } ], "source": [ "atmodel_150topics = train_model(train_corpus_50_20, train_author2doc, train_dictionary_50_20, num_topics=150, eval_every=0, iterations=50, passes=15)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precision@k: top_n=1\n", "Prediction accuracy: 0.6004\n", "Precision@k: top_n=2\n", "Prediction accuracy: 0.7632\n", "Precision@k: top_n=3\n", "Prediction accuracy: 0.8452\n", "Precision@k: top_n=4\n", "Prediction accuracy: 0.8796\n", "Precision@k: top_n=5\n", "Prediction accuracy: 0.8988\n", "Precision@k: top_n=6\n", "Prediction accuracy: 0.914\n", "Precision@k: top_n=8\n", "Prediction accuracy: 0.9324\n", "Precision@k: top_n=10\n", "Prediction accuracy: 0.9464\n" ] }, { "data": { "image/png": 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W6mgRkcm4ElR4eHjPBQsWlDSUYklOTqZmzZrF/v76Q1m8uDGdP7UPpIN/KDfe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\n/+jZsyc//fQT9erVK3YcVZk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"text/plain": [ "<matplotlib.figure.Figure at 0x14de666a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "accuracy_scores_150topic={}\n", "for i in [1,2,3,4,5,6,8,10]:\n", " accuracy, k = prediction_accuracy(test_author2doc, test_corpus_50_20, atmodel_150topics, k=i)\n", " accuracy_scores_150topic[k] = accuracy\n", " \n", "plot_accuracy(scores1=accuracy_scores_100topic, label1=\"100 topics\", scores2=accuracy_scores_150topic, label2=\"150 topics\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 150-topic model is also slightly better, especially in the lower end of k. But we clearly see convergence. We try with 200 topic to be sure." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "05:43:01 INFO:Vocabulary consists of 3914 words.\n", "05:43:01 INFO:using symmetric alpha at 0.005\n", "05:43:01 INFO:using symmetric eta at 0.005\n", "05:43:05 INFO:running online author-topic training, 200 topics, 50 authors, 15 passes over the supplied corpus of 2500 documents, updating model once every 2500 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", "05:43:05 INFO:PROGRESS: pass 0, at document #2500/2500\n", "05:43:05 DEBUG:performing inference on a chunk of 2500 documents\n", "05:43:25 DEBUG:2/2500 documents converged within 50 iterations\n", "05:43:25 DEBUG:updating topics\n", "05:43:26 INFO:topic #198 (0.005): 0.006\"plant\" + 0.006\"analyst\" + 0.006\"gm\" + 0.005\"sale\" + 0.005\"group\" + 0.005\"service\" + 0.004\"share\" + 0.004\"internet\" + 0.004\"plan\" + 0.004\"uaw\"\n", "05:43:26 INFO:topic #186 (0.005): 0.009\"pound\" + 0.007\"quarter\" + 0.007\"analyst\" + 0.007\"share\" + 0.006\"group\" + 0.006\"business\" + 0.005\"million_pound\" + 0.005\"software\" + 0.005\"sale\" + 0.005\"industry\"\n", "05:43:26 INFO:topic #188 (0.005): 0.011\"share\" + 0.010\"analyst\" + 0.008\"billion\" + 0.007\"sale\" + 0.007\"stock\" + 0.006\"mci\" + 0.006\"business\" + 0.005\"british\" + 0.005\"quarter\" + 0.005\"deal\"\n", "05:43:26 INFO:topic #162 (0.005): 0.012\"analyst\" + 0.008\"business\" + 0.007\"quarter\" + 0.006\"share\" + 0.006\"industry\" + 0.006\"sale\" + 0.005\"base\" + 0.005\"billion\" + 0.005\"high\" + 0.005\"price\"\n", "05:43:26 INFO:topic #22 (0.005): 0.039\"bank\" + 0.011\"rate\" + 0.011\"day\" + 0.011\"cut\" + 0.010\"analyst\" + 0.008\"australia\" + 0.008\"profit\" + 0.008\"financial\" + 0.007\"ltd\" + 0.007\"merger\"\n", "05:43:26 INFO:topic diff=65.500588, rho=1.000000\n", "05:43:26 INFO:PROGRESS: pass 1, at document #2500/2500\n", "05:43:26 DEBUG:performing inference on a chunk of 2500 documents\n", "05:43:36 DEBUG:2494/2500 documents converged within 50 iterations\n", "05:43:36 DEBUG:updating topics\n", "05:43:37 INFO:topic #77 (0.005): 0.013\"internet\" + 0.011\"computer\" + 0.009\"business\" + 0.009\"quarter\" + 0.009\"service\" + 0.008\"revenue\" + 0.007\"analyst\" + 0.007\"cost\" + 0.007\"industry\" + 0.006\"compaq\"\n", "05:43:37 INFO:topic #25 (0.005): 0.010\"share\" + 0.007\"analyst\" + 0.007\"service\" + 0.007\"business\" + 0.006\"growth\" + 0.006\"mci\" + 0.006\"billion\" + 0.006\"long\" + 0.005\"distance\" + 0.005\"stock\"\n", "05:43:37 INFO:topic #133 (0.005): 0.011\"group\" + 0.011\"share\" + 0.010\"pound\" + 0.009\"billion\" + 0.007\"profit\" + 0.007\"business\" + 0.006\"sale\" + 0.005\"good\" + 0.005\"bank\" + 0.005\"analyst\"\n", "05:43:37 INFO:topic #180 (0.005): 0.011\"billion\" + 0.006\"venture\" + 0.006\"quarter\" + 0.005\"investment\" + 0.005\"industry\" + 0.005\"analyst\" + 0.004\"price\" + 0.003\"group\" + 0.003\"high\" + 0.003\"rise\"\n", "05:43:37 INFO:topic #1 (0.005): 0.018\"japan\" + 0.014\"japanese\" + 0.013\"billion\" + 0.011\"yen\" + 0.011\"stock\" + 0.011\"bank\" + 0.010\"life\" + 0.010\"financial\" + 0.010\"big\" + 0.008\"profit\"\n", "05:43:37 INFO:topic diff=17.080447, rho=0.577350\n", "05:43:37 INFO:PROGRESS: pass 2, at document #2500/2500\n", "05:43:37 DEBUG:performing inference on a chunk of 2500 documents\n", "05:43:46 DEBUG:2499/2500 documents converged within 50 iterations\n", "05:43:46 DEBUG:updating topics\n", "05:43:47 INFO:topic #92 (0.005): 0.013\"analyst\" + 0.011\"share\" + 0.007\"sale\" + 0.006\"profit\" + 0.005\"pound\" + 0.004\"high\" + 0.004\"revenue\" + 0.004\"quarter\" + 0.004\"billion\" + 0.004\"cent\"\n", "05:43:47 INFO:topic #117 (0.005): 0.015\"access\" + 0.012\"local\" + 0.011\"internet\" + 0.011\"fee\" + 0.010\"distance\" + 0.010\"long\" + 0.008\"long_distance\" + 0.008\"service\" + 0.007\"issue\" + 0.006\"provider\"\n", "05:43:47 INFO:topic #81 (0.005): 0.011\"pound\" + 0.010\"profit\" + 0.009\"share\" + 0.008\"sale\" + 0.007\"million_pound\" + 0.006\"analyst\" + 0.006\"business\" + 0.006\"rise\" + 0.005\"group\" + 0.005\"fall\"\n", "05:43:47 INFO:topic #97 (0.005): 0.025\"internet\" + 0.018\"bill\" + 0.014\"administration\" + 0.014\"product\" + 0.012\"key\" + 0.011\"policy\" + 0.011\"export\" + 0.010\"law\" + 0.008\"access\" + 0.008\"bank\"\n", "05:43:47 INFO:topic #24 (0.005): 0.005\"crop\" + 0.005\"price\" + 0.005\"share\" + 0.004\"tonne\" + 0.004\"analyst\" + 0.004\"exporter\" + 0.004\"cocoa\" + 0.003\"ivory_coast\" + 0.003\"government\" + 0.003\"reuters\"\n", "05:43:47 INFO:topic diff=14.773285, rho=0.500000\n", "05:43:47 INFO:PROGRESS: pass 3, at document #2500/2500\n", "05:43:47 DEBUG:performing inference on a chunk of 2500 documents\n", "05:43:56 DEBUG:2499/2500 documents converged within 50 iterations\n", "05:43:56 DEBUG:updating topics\n", "05:43:56 INFO:topic #16 (0.005): 0.003\"group\" + 0.003\"billion\" + 0.002\"gm\" + 0.002\"hong_kong\" + 0.002\"pound\" + 0.002\"kong\" + 0.002\"china\" + 0.002\"bid\" + 0.002\"analyst\" + 0.002\"hong\"\n", "05:43:56 INFO:topic #133 (0.005): 0.012\"group\" + 0.011\"pound\" + 0.010\"share\" + 0.008\"billion\" + 0.007\"business\" + 0.006\"profit\" + 0.005\"good\" + 0.005\"sale\" + 0.005\"add\" + 0.005\"cost\"\n", "05:43:56 INFO:topic #23 (0.005): 0.017\"boeing\" + 0.010\"billion\" + 0.009\"analyst\" + 0.006\"share\" + 0.006\"microsoft\" + 0.006\"industry\" + 0.005\"quarter\" + 0.005\"jet\" + 0.005\"windows\" + 0.005\"mcdonnell\"\n", "05:43:56 INFO:topic #156 (0.005): 0.005\"analyst\" + 0.004\"bank\" + 0.003\"share\" + 0.003\"service\" + 0.003\"internet\" + 0.002\"china\" + 0.002\"plan\" + 0.002\"profit\" + 0.002\"billion\" + 0.002\"cost\"\n", "05:43:56 INFO:topic #131 (0.005): 0.024\"analyst\" + 0.017\"share\" + 0.013\"price\" + 0.011\"business\" + 0.011\"penny\" + 0.009\"bid\" + 0.007\"electric\" + 0.006\"offer\" + 0.006\"add\" + 0.006\"northern\"\n", "05:43:56 INFO:topic diff=12.542799, rho=0.447214\n", "05:43:56 INFO:PROGRESS: pass 4, at document #2500/2500\n", "05:43:56 DEBUG:performing inference on a chunk of 2500 documents\n", "05:44:05 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:44:05 DEBUG:updating topics\n", "05:44:05 INFO:topic #92 (0.005): 0.009\"analyst\" + 0.008\"share\" + 0.005\"sale\" + 0.004\"profit\" + 0.003\"pound\" + 0.003\"high\" + 0.003\"revenue\" + 0.003\"quarter\" + 0.003\"billion\" + 0.003\"cent\"\n", "05:44:05 INFO:topic #86 (0.005): 0.029\"cargo\" + 0.021\"kong\" + 0.020\"hong\" + 0.020\"hong_kong\" + 0.016\"air\" + 0.015\"Hong Kong\" + 0.015\"airline\" + 0.009\"service\" + 0.009\"route\" + 0.009\"airport\"\n", "05:44:05 INFO:topic #80 (0.005): 0.017\"analyst\" + 0.014\"microsoft\" + 0.013\"quarter\" + 0.010\"computer\" + 0.010\"business\" + 0.009\"windows\" + 0.008\"revenue\" + 0.008\"internet\" + 0.007\"system\" + 0.007\"sale\"\n", "05:44:05 INFO:topic #23 (0.005): 0.015\"boeing\" + 0.009\"billion\" + 0.008\"analyst\" + 0.006\"share\" + 0.005\"microsoft\" + 0.005\"industry\" + 0.005\"quarter\" + 0.005\"jet\" + 0.004\"windows\" + 0.004\"mcdonnell\"\n", "05:44:05 INFO:topic #177 (0.005): 0.011\"investment\" + 0.010\"pound\" + 0.010\"group\" + 0.008\"cable\" + 0.008\"british\" + 0.007\"fleming\" + 0.007\"management\" + 0.006\"fund\" + 0.006\"share\" + 0.006\"merger\"\n", "05:44:05 INFO:topic diff=10.561281, rho=0.408248\n", "05:44:05 INFO:PROGRESS: pass 5, at document #2500/2500\n", "05:44:05 DEBUG:performing inference on a chunk of 2500 documents\n", "05:44:14 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:44:14 DEBUG:updating topics\n", "05:44:15 INFO:topic #61 (0.005): 0.038\"boeing\" + 0.017\"billion\" + 0.016\"jet\" + 0.013\"analyst\" + 0.012\"mcdonnell\" + 0.011\"microsoft\" + 0.010\"airbus\" + 0.010\"order\" + 0.010\"douglas\" + 0.009\"share\"\n", "05:44:15 INFO:topic #73 (0.005): 0.001\"bank\" + 0.001\"china\" + 0.001\"group\" + 0.001\"big\" + 0.001\"analyst\" + 0.001\"sale\" + 0.001\"shanghai\" + 0.001\"deal\" + 0.001\"gm\" + 0.001\"pound\"\n", "05:44:15 INFO:topic #147 (0.005): 0.012\"czech\" + 0.011\"crown\" + 0.010\"week\" + 0.009\"analyst\" + 0.009\"point\" + 0.008\"investor\" + 0.007\"round\" + 0.007\"prague\" + 0.007\"billion\" + 0.006\"second\"\n", "05:44:15 INFO:topic #50 (0.005): 0.006\"british\" + 0.006\"telecom\" + 0.006\"deal\" + 0.005\"analyst\" + 0.005\"drug\" + 0.004\"share\" + 0.004\"mci\" + 0.004\"billion\" + 0.004\"group\" + 0.003\"sale\"\n", "05:44:15 INFO:topic #99 (0.005): 0.003\"stock\" + 0.002\"business\" + 0.002\"share\" + 0.002\"analyst\" + 0.002\"end\" + 0.002\"day\" + 0.002\"sale\" + 0.002\"world\" + 0.002\"billion\" + 0.001\"quarter\"\n", "05:44:15 INFO:topic diff=8.863923, rho=0.377964\n", "05:44:15 INFO:PROGRESS: pass 6, at document #2500/2500\n", "05:44:15 DEBUG:performing inference on a chunk of 2500 documents\n", "05:44:23 DEBUG:2500/2500 documents converged within 50 iterations\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:44:23 DEBUG:updating topics\n", "05:44:24 INFO:topic #70 (0.005): 0.011\"stock\" + 0.010\"shanghai\" + 0.008\"share\" + 0.008\"exchange\" + 0.007\"trading\" + 0.007\"china\" + 0.007\"bank\" + 0.006\"future\" + 0.006\"beijing\" + 0.005\"index\"\n", "05:44:24 INFO:topic #177 (0.005): 0.011\"investment\" + 0.010\"pound\" + 0.010\"group\" + 0.008\"cable\" + 0.008\"british\" + 0.007\"fleming\" + 0.007\"management\" + 0.007\"share\" + 0.006\"fund\" + 0.006\"merger\"\n", "05:44:24 INFO:topic #37 (0.005): 0.015\"bank\" + 0.012\"czech\" + 0.008\"crown\" + 0.006\"prague\" + 0.005\"foreign\" + 0.005\"billion\" + 0.004\"state\" + 0.004\"deficit\" + 0.004\"communist\" + 0.004\"central\"\n", "05:44:24 INFO:topic #56 (0.005): 0.032\"kong\" + 0.031\"hong\" + 0.031\"hong_kong\" + 0.022\"Hong Kong\" + 0.016\"china\" + 0.007\"fund\" + 0.007\"Hong Kong's\" + 0.006\"chinese\" + 0.005\"tung\" + 0.005\"british\"\n", "05:44:24 INFO:topic #32 (0.005): 0.004\"china\" + 0.003\"beijing\" + 0.002\"taiwan\" + 0.002\"bre\" + 0.002\"bre_x\" + 0.002\"share\" + 0.002\"x\" + 0.002\"chinese\" + 0.002\"analyst\" + 0.002\"party\"\n", "05:44:24 INFO:topic diff=7.433677, rho=0.353553\n", "05:44:24 INFO:PROGRESS: pass 7, at document #2500/2500\n", "05:44:24 DEBUG:performing inference on a chunk of 2500 documents\n", "05:44:32 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:44:32 DEBUG:updating topics\n", "05:44:33 INFO:topic #194 (0.005): 0.024\"cocoa\" + 0.020\"tonne\" + 0.019\"exporter\" + 0.012\"ivory\" + 0.012\"ivory_coast\" + 0.012\"coast\" + 0.011\"crop\" + 0.011\"price\" + 0.010\"buyer\" + 0.009\"export\"\n", "05:44:33 INFO:topic #116 (0.005): 0.004\"x\" + 0.003\"analyst\" + 0.003\"bre\" + 0.003\"Bre-X\" + 0.002\"bre_x\" + 0.002\"share\" + 0.002\"government\" + 0.002\"bank\" + 0.002\"billion\" + 0.002\"sale\"\n", "05:44:33 INFO:topic #174 (0.005): 0.001\"quarter\" + 0.001\"venture\" + 0.001\"china\" + 0.001\"billion\" + 0.001\"beijing\" + 0.000\"investment\" + 0.000\"share\" + 0.000\"level\" + 0.000\"chinese\" + 0.000\"official\"\n", "05:44:33 INFO:topic #169 (0.005): 0.001\"china\" + 0.001\"tell\" + 0.001\"service\" + 0.001\"hong_kong\" + 0.001\"billion\" + 0.001\"share\" + 0.001\"beijing\" + 0.001\"group\" + 0.001\"kong\" + 0.001\"analyst\"\n", "05:44:33 INFO:topic #88 (0.005): 0.024\"franc\" + 0.023\"french\" + 0.022\"air\" + 0.021\"france\" + 0.017\"thomson\" + 0.014\"billion\" + 0.011\"group\" + 0.010\"telecom\" + 0.010\"billion_franc\" + 0.009\"government\"\n", "05:44:33 INFO:topic diff=6.234229, rho=0.333333\n", "05:44:33 INFO:PROGRESS: pass 8, at document #2500/2500\n", "05:44:33 DEBUG:performing inference on a chunk of 2500 documents\n", "05:44:41 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:44:41 DEBUG:updating topics\n", "05:44:42 INFO:topic #149 (0.005): 0.010\"china\" + 0.005\"chinese\" + 0.005\"official\" + 0.004\"beijing\" + 0.004\"metre\" + 0.003\"world\" + 0.003\"trade\" + 0.003\"foreign\" + 0.003\"united_states\" + 0.003\"united\"\n", "05:44:42 INFO:topic #62 (0.005): 0.002\"property\" + 0.002\"increase\" + 0.002\"month\" + 0.002\"klaus\" + 0.001\"social\" + 0.001\"commission\" + 0.001\"pound\" + 0.001\"analyst\" + 0.001\"large\" + 0.001\"party\"\n", "05:44:42 INFO:topic #38 (0.005): 0.016\"analyst\" + 0.014\"australian\" + 0.014\"ltd\" + 0.013\"share\" + 0.012\"australia\" + 0.011\"profit\" + 0.011\"sydney\" + 0.009\"news\" + 0.009\"group\" + 0.009\"corp\"\n", "05:44:42 INFO:topic #155 (0.005): 0.002\"china\" + 0.001\"fund\" + 0.001\"stock\" + 0.001\"billion\" + 0.001\"economic\" + 0.001\"hong\" + 0.001\"bank\" + 0.001\"group\" + 0.001\"kong\" + 0.001\"canada\"\n", "05:44:42 INFO:topic #130 (0.005): 0.019\"mci\" + 0.012\"analyst\" + 0.011\"service\" + 0.011\"share\" + 0.011\"long\" + 0.010\"billion\" + 0.009\"long_distance\" + 0.009\"distance\" + 0.009\"corp\" + 0.008\"deal\"\n", "05:44:42 INFO:topic diff=5.231049, rho=0.316228\n", "05:44:42 INFO:PROGRESS: pass 9, at document #2500/2500\n", "05:44:42 DEBUG:performing inference on a chunk of 2500 documents\n", "05:44:50 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:44:51 DEBUG:updating topics\n", "05:44:51 INFO:topic #166 (0.005): 0.001\"oil\" + 0.001\"russian\" + 0.001\"russia\" + 0.001\"internet\" + 0.001\"export\" + 0.001\"world\" + 0.001\"service\" + 0.001\"tonne\" + 0.001\"analyst\" + 0.001\"output\"\n", "05:44:51 INFO:topic #187 (0.005): 0.033\"china\" + 0.011\"beijing\" + 0.011\"official\" + 0.010\"chinese\" + 0.008\"state\" + 0.008\"foreign\" + 0.008\"trade\" + 0.006\"united\" + 0.005\"united_states\" + 0.005\"states\"\n", "05:44:51 INFO:topic #139 (0.005): 0.013\"drug\" + 0.012\"group\" + 0.010\"pound\" + 0.010\"sale\" + 0.009\"plc\" + 0.009\"british\" + 0.009\"share\" + 0.008\"product\" + 0.008\"profit\" + 0.008\"analyst\"\n", "05:44:51 INFO:topic #43 (0.005): 0.001\"tonne\" + 0.001\"cocoa\" + 0.001\"china\" + 0.000\"share\" + 0.000\"bank\" + 0.000\"government\" + 0.000\"exporter\" + 0.000\"stock\" + 0.000\"plan\" + 0.000\"close\"\n", "05:44:51 INFO:topic #71 (0.005): 0.025\"fcc\" + 0.018\"phone\" + 0.016\"carrier\" + 0.015\"local\" + 0.012\"rule\" + 0.011\"long\" + 0.011\"service\" + 0.011\"distance\" + 0.010\"tv\" + 0.010\"long_distance\"\n", "05:44:51 INFO:topic diff=4.391602, rho=0.301511\n", "05:44:51 INFO:PROGRESS: pass 10, at document #2500/2500\n", "05:44:51 DEBUG:performing inference on a chunk of 2500 documents\n", "05:44:59 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:44:59 DEBUG:updating topics\n", "05:45:00 INFO:topic #156 (0.005): 0.001\"analyst\" + 0.001\"bank\" + 0.001\"share\" + 0.001\"service\" + 0.000\"internet\" + 0.000\"china\" + 0.000\"plan\" + 0.000\"profit\" + 0.000\"billion\" + 0.000\"cost\"\n", "05:45:00 INFO:topic #63 (0.005): 0.037\"oil\" + 0.029\"russia\" + 0.026\"russian\" + 0.016\"tonne\" + 0.016\"aluminium\" + 0.015\"smelter\" + 0.014\"output\" + 0.012\"world\" + 0.012\"export\" + 0.010\"western\"\n", "05:45:00 INFO:topic #83 (0.005): 0.001\"profit\" + 0.001\"bank\" + 0.001\"australian\" + 0.001\"analyst\" + 0.001\"billion\" + 0.001\"australia\" + 0.001\"share\" + 0.001\"tell\" + 0.001\"ltd\" + 0.001\"beijing\"\n", "05:45:00 INFO:topic #144 (0.005): 0.001\"computer\" + 0.001\"software\" + 0.001\"site\" + 0.001\"quarter\" + 0.001\"technology\" + 0.001\"internet\" + 0.001\"industry\" + 0.001\"web\" + 0.001\"product\" + 0.001\"high\"\n", "05:45:00 INFO:topic #84 (0.005): 0.015\"klaus\" + 0.014\"czech\" + 0.014\"bank\" + 0.011\"billion\" + 0.011\"crown\" + 0.009\"state\" + 0.009\"price\" + 0.008\"minister\" + 0.007\"tell\" + 0.007\"low\"\n", "05:45:00 INFO:topic diff=3.689287, rho=0.288675\n", "05:45:00 INFO:PROGRESS: pass 11, at document #2500/2500\n", "05:45:00 DEBUG:performing inference on a chunk of 2500 documents\n", "05:45:08 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:45:08 DEBUG:updating topics\n", "05:45:09 INFO:topic #50 (0.005): 0.001\"british\" + 0.001\"telecom\" + 0.001\"deal\" + 0.001\"analyst\" + 0.001\"drug\" + 0.001\"share\" + 0.001\"mci\" + 0.001\"billion\" + 0.001\"group\" + 0.001\"sale\"\n", "05:45:09 INFO:topic #194 (0.005): 0.024\"cocoa\" + 0.020\"tonne\" + 0.020\"exporter\" + 0.012\"ivory\" + 0.012\"coast\" + 0.012\"ivory_coast\" + 0.011\"crop\" + 0.011\"price\" + 0.010\"buyer\" + 0.009\"export\"\n", "05:45:09 INFO:topic #138 (0.005): 0.001\"china\" + 0.000\"beijing\" + 0.000\"share\" + 0.000\"news\" + 0.000\"states\" + 0.000\"chinese\" + 0.000\"analyst\" + 0.000\"month\" + 0.000\"long\" + 0.000\"the United States\"\n", "05:45:09 INFO:topic #100 (0.005): 0.001\"chinese\" + 0.001\"china\" + 0.001\"beijing\" + 0.000\"hong\" + 0.000\"hong_kong\" + 0.000\"official\" + 0.000\"tibet\" + 0.000\"magazine\" + 0.000\"kong\" + 0.000\"lama\"\n", "05:45:09 INFO:topic #94 (0.005): 0.000\"share\" + 0.000\"stock\" + 0.000\"election\" + 0.000\"analyst\" + 0.000\"low\" + 0.000\"bank\" + 0.000\"havel\" + 0.000\"government\" + 0.000\"high\" + 0.000\"large\"\n", "05:45:09 INFO:topic diff=3.102101, rho=0.277350\n", "05:45:09 INFO:PROGRESS: pass 12, at document #2500/2500\n", "05:45:09 DEBUG:performing inference on a chunk of 2500 documents\n", "05:45:17 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:45:17 DEBUG:updating topics\n", "05:45:18 INFO:topic #122 (0.005): 0.000\"share\" + 0.000\"analyst\" + 0.000\"plant\" + 0.000\"gm\" + 0.000\"industry\" + 0.000\"quarter\" + 0.000\"service\" + 0.000\"law\" + 0.000\"month\" + 0.000\"large\"\n", "05:45:18 INFO:topic #20 (0.005): 0.029\"gold\" + 0.019\"bre\" + 0.019\"x\" + 0.018\"bre_x\" + 0.014\"price\" + 0.014\"analyst\" + 0.011\"Bre-X\" + 0.010\"busang\" + 0.010\"barrick\" + 0.010\"toronto\"\n", "05:45:18 INFO:topic #107 (0.005): 0.031\"russia\" + 0.027\"oil\" + 0.016\"russian\" + 0.014\"export\" + 0.012\"output\" + 0.010\"moscow\" + 0.009\"tonne\" + 0.009\"domestic\" + 0.009\"world\" + 0.008\"western\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "05:45:18 INFO:topic #130 (0.005): 0.019\"mci\" + 0.012\"analyst\" + 0.011\"service\" + 0.011\"share\" + 0.010\"long\" + 0.010\"billion\" + 0.009\"long_distance\" + 0.009\"distance\" + 0.009\"corp\" + 0.008\"deal\"\n", "05:45:18 INFO:topic #151 (0.005): 0.012\"billion\" + 0.008\"sale\" + 0.007\"computer\" + 0.007\"industry\" + 0.006\"good\" + 0.006\"analyst\" + 0.006\"product\" + 0.006\"quarter\" + 0.005\"forecast\" + 0.005\"internet\"\n", "05:45:18 INFO:topic diff=2.611657, rho=0.267261\n", "05:45:18 INFO:PROGRESS: pass 13, at document #2500/2500\n", "05:45:18 DEBUG:performing inference on a chunk of 2500 documents\n", "05:45:28 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:45:28 DEBUG:updating topics\n", "05:45:28 INFO:topic #74 (0.005): 0.036\"china\" + 0.029\"tonne\" + 0.021\"chinese\" + 0.021\"trader\" + 0.018\"price\" + 0.014\"import\" + 0.013\"source\" + 0.011\"copper\" + 0.010\"official\" + 0.010\"million_tonne\"\n", "05:45:28 INFO:topic #138 (0.005): 0.000\"china\" + 0.000\"beijing\" + 0.000\"share\" + 0.000\"news\" + 0.000\"states\" + 0.000\"chinese\" + 0.000\"analyst\" + 0.000\"month\" + 0.000\"long\" + 0.000\"the United States\"\n", "05:45:28 INFO:topic #60 (0.005): 0.000\"half\" + 0.000\"financial\" + 0.000\"northern\" + 0.000\"policy\" + 0.000\"official\" + 0.000\"group\" + 0.000\"product\" + 0.000\"draft\" + 0.000\"administration\" + 0.000\"stock\"\n", "05:45:28 INFO:topic #42 (0.005): 0.000\"russia\" + 0.000\"russian\" + 0.000\"industry\" + 0.000\"technology\" + 0.000\"oil\" + 0.000\"world\" + 0.000\"export\" + 0.000\"price\" + 0.000\"diamond\" + 0.000\"analyst\"\n", "05:45:28 INFO:topic #36 (0.005): 0.026\"bid\" + 0.025\"penny\" + 0.016\"analyst\" + 0.015\"electric\" + 0.015\"share\" + 0.014\"electricity\" + 0.013\"pound\" + 0.013\"offer\" + 0.011\"northern\" + 0.011\"british\"\n", "05:45:28 INFO:topic diff=2.202422, rho=0.258199\n", "05:45:28 INFO:PROGRESS: pass 14, at document #2500/2500\n", "05:45:28 DEBUG:performing inference on a chunk of 2500 documents\n", "05:45:36 DEBUG:2500/2500 documents converged within 50 iterations\n", "05:45:36 DEBUG:updating topics\n", "05:45:36 INFO:topic #89 (0.005): 0.001\"bre_x\" + 0.001\"x\" + 0.001\"bre\" + 0.001\"analyst\" + 0.001\"barrick\" + 0.001\"Bre-X\" + 0.001\"government\" + 0.001\"gold\" + 0.001\"indonesian\" + 0.001\"billion\"\n", "05:45:36 INFO:topic #121 (0.005): 0.000\"time\" + 0.000\"share\" + 0.000\"second\" + 0.000\"tobacco\" + 0.000\"group\" + 0.000\"industry\" + 0.000\"action\" + 0.000\"month\" + 0.000\"plan\" + 0.000\"hand\"\n", "05:45:36 INFO:topic #188 (0.005): 0.015\"sale\" + 0.013\"analyst\" + 0.011\"share\" + 0.008\"mercury\" + 0.008\"bank\" + 0.007\"stock\" + 0.007\"billion\" + 0.006\"amp\" + 0.006\"think\" + 0.006\"base\"\n", "05:45:36 INFO:topic #80 (0.005): 0.016\"microsoft\" + 0.015\"analyst\" + 0.013\"quarter\" + 0.010\"windows\" + 0.010\"computer\" + 0.009\"business\" + 0.009\"revenue\" + 0.009\"sale\" + 0.008\"system\" + 0.008\"software\"\n", "05:45:36 INFO:topic #56 (0.005): 0.033\"hong\" + 0.033\"kong\" + 0.032\"hong_kong\" + 0.023\"Hong Kong\" + 0.017\"china\" + 0.007\"Hong Kong's\" + 0.006\"chinese\" + 0.006\"tung\" + 0.006\"british\" + 0.004\"government\"\n", "05:45:36 INFO:topic diff=1.861192, rho=0.250000\n", "05:45:36 DEBUG:Setting topics to those of the model: AuthorTopicModel(num_terms=3914, num_topics=200, num_authors=50, decay=0.5, chunksize=2500)\n", "05:45:37 INFO:CorpusAccumulator accumulated stats from 1000 documents\n", "05:45:37 INFO:CorpusAccumulator accumulated stats from 2000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "-1.93149366596\n" ] } ], "source": [ "atmodel_200topics = train_model(train_corpus_50_20, train_author2doc, train_dictionary_50_20, num_topics=200, eval_every=0, iterations=50, passes=15)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precision@k: top_n=1\n", "Prediction accuracy: 0.6232\n", "Precision@k: top_n=2\n", "Prediction accuracy: 0.7664\n", "Precision@k: top_n=3\n", "Prediction accuracy: 0.8456\n", "Precision@k: top_n=4\n", "Prediction accuracy: 0.8816\n", "Precision@k: top_n=5\n", "Prediction accuracy: 0.9032\n", "Precision@k: top_n=6\n", "Prediction accuracy: 0.9164\n", "Precision@k: top_n=8\n", "Prediction accuracy: 0.9368\n", "Precision@k: top_n=10\n", "Prediction accuracy: 0.9464\n" ] }, { "data": { "image/png": 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3xJGTFcJ998HgwXDLLV5HZ4wxFRfIFcQinA7q1TjzMJliCh8f+vCFvejZvhn3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\nW9esWaM7duxQQFesWKGqqjf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"text/plain": [ "<matplotlib.figure.Figure at 0x117a975c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "accuracy_scores_200topic={}\n", "for i in [1,2,3,4,5,6,8,10]:\n", " accuracy, k = prediction_accuracy(test_author2doc, test_corpus_50_20, atmodel_200topics, k=i)\n", " accuracy_scores_200topic[k] = accuracy\n", " \n", "plot_accuracy(scores1=accuracy_scores_150topic, label1=\"150 topics\", scores2=accuracy_scores_200topic, label2=\"200 topics\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The 200-topic seems to be performing a bit better for lower k, might be due to a slight overrepresentation with high topic number. So let us stop here with the topic number increase and focus some more on the dictionary. We choose either one of the models.\n", "Currently we are filtering out tokens, that appear in more 50% of all documents and no more than 20 times overall, which drastically decreaces the size of our dictionary. \n", "We know about this dataset, that the underlying topic are not so diverse and are structed around corporate/industrial topic class. Thus it makes sense to increase the dictionary by filtering less tokens." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set the parameters set max_freq=25%, min_wordcount=10" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "06:18:50 INFO:adding document #0 to Dictionary(0 unique tokens: [])\n", "06:18:51 INFO:built Dictionary(46905 unique tokens: ['$83.4 million', 'boarder', '$2.72 billion', 'checking', 'suzuki']...) from 2500 documents (total 786032 corpus positions)\n", "06:18:51 INFO:discarding 40690 tokens: [('$15', 3), ('$17.25', 1), ('$380 million', 2), ('12.5 cents', 7), ('Big B', 3), ('Big B Inc.', 2), (\"Big B's\", 3), ('Big B. I', 1), ('Dwayne Hoven', 1), ('Eckerd Corp.', 1)]...\n", "06:18:51 INFO:keeping 6215 tokens which were in no less than 10 and no more than 625 (=25.0%) documents\n", "06:18:51 DEBUG:rebuilding dictionary, shrinking gaps\n", "06:18:51 INFO:resulting dictionary: Dictionary(6215 unique tokens: ['offshoot', 'shore', 'loss', 'merger', 'disappointing']...)\n" ] } ], "source": [ "train_corpus_25_10, train_dictionary_25_10 = create_corpus_dictionary(train_docs, 0.25, 10)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_corpus_25_10 = create_test_corpus(train_dictionary_25_10, test_docs)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of unique tokens: 6215\n" ] } ], "source": [ "print('Number of unique tokens: %d' % len(train_dictionary_25_10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have now nearly doubled the tokens. Let's train and evaluate." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "06:18:53 INFO:Vocabulary consists of 6215 words.\n", "06:18:53 INFO:using symmetric alpha at 0.006666666666666667\n", "06:18:53 INFO:using symmetric eta at 0.006666666666666667\n", "06:18:57 INFO:running online author-topic training, 150 topics, 50 authors, 15 passes over the supplied corpus of 2500 documents, updating model once every 2500 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", "06:18:57 INFO:PROGRESS: pass 0, at document #2500/2500\n", "06:18:57 DEBUG:performing inference on a chunk of 2500 documents\n", "06:19:11 DEBUG:17/2500 documents converged within 50 iterations\n", "06:19:11 DEBUG:updating topics\n", "06:19:12 INFO:topic #141 (0.007): 0.031\"gm\" + 0.016\"plant\" + 0.011\"worker\" + 0.010\"uaw\" + 0.009\"strike\" + 0.009\"truck\" + 0.008\"local\" + 0.007\"automaker\" + 0.006\"part\" + 0.005\"contract\"\n", "06:19:12 INFO:topic #105 (0.007): 0.013\"china\" + 0.010\"tonne\" + 0.009\"chinese\" + 0.008\"trader\" + 0.007\"copper\" + 0.007\"product\" + 0.005\"drug\" + 0.005\"hong_kong\" + 0.004\"soybean\" + 0.004\"hong\"\n", "06:19:12 INFO:topic #15 (0.007): 0.006\"china\" + 0.004\"network\" + 0.003\"drug\" + 0.003\"trade\" + 0.003\"united\" + 0.003\"states\" + 0.003\"boeing\" + 0.003\"chinese\" + 0.003\"beijing\" + 0.002\"product\"\n", "06:19:12 INFO:topic #30 (0.007): 0.010\"amp\" + 0.009\"bank\" + 0.005\"ernst\" + 0.005\"claim\" + 0.005\"bre\" + 0.004\"bre_x\" + 0.004\"gold\" + 0.003\"rate\" + 0.003\"x\" + 0.003\"pay\"\n", "06:19:12 INFO:topic #114 (0.007): 0.019\"bank\" + 0.010\"japan\" + 0.009\"pound\" + 0.008\"problem\" + 0.008\"loan\" + 0.007\"financial\" + 0.006\"yen\" + 0.005\"bt\" + 0.005\"million_pound\" + 0.005\"japanese\"\n", "06:19:12 INFO:topic diff=61.971494, rho=1.000000\n", "06:19:12 INFO:PROGRESS: pass 1, at document #2500/2500\n", "06:19:12 DEBUG:performing inference on a chunk of 2500 documents\n", "06:19:19 DEBUG:2491/2500 documents converged within 50 iterations\n", "06:19:19 DEBUG:updating topics\n", "06:19:19 INFO:topic #45 (0.007): 0.006\"property\" + 0.004\"china\" + 0.003\"holding\" + 0.003\"survey\" + 0.003\"sector\" + 0.002\"bank\" + 0.002\"gold\" + 0.002\"fall\" + 0.002\"debt\" + 0.002\"air\"\n", "06:19:19 INFO:topic #139 (0.007): 0.024\"colombia\" + 0.021\"drug\" + 0.008\"guerrilla\" + 0.008\"colombian\" + 0.007\"police\" + 0.006\"extradition\" + 0.005\"late\" + 0.005\"anti\" + 0.005\"congress\" + 0.005\"contract\"\n", "06:19:19 INFO:topic #15 (0.007): 0.005\"china\" + 0.003\"network\" + 0.002\"drug\" + 0.002\"trade\" + 0.002\"united\" + 0.002\"states\" + 0.002\"boeing\" + 0.002\"chinese\" + 0.002\"beijing\" + 0.002\"product\"\n", "06:19:19 INFO:topic #2 (0.007): 0.004\"bre_x\" + 0.004\"x\" + 0.003\"bid\" + 0.003\"bre\" + 0.003\"product\" + 0.003\"Bre-X\" + 0.003\"drug\" + 0.003\"gold\" + 0.002\"mining\" + 0.002\"pound\"\n", "06:19:19 INFO:topic #116 (0.007): 0.007\"china\" + 0.006\"bank\" + 0.004\"tonne\" + 0.004\"problem\" + 0.004\"hong_kong\" + 0.003\"trader\" + 0.003\"chinese\" + 0.003\"loan\" + 0.003\"kong\" + 0.003\"hong\"\n", "06:19:19 INFO:topic diff=11.411593, rho=0.577350\n", "06:19:19 INFO:PROGRESS: pass 2, at document #2500/2500\n", "06:19:19 DEBUG:performing inference on a chunk of 2500 documents\n", "06:19:26 DEBUG:2499/2500 documents converged within 50 iterations\n", "06:19:26 DEBUG:updating topics\n", "06:19:26 INFO:topic #116 (0.007): 0.005\"china\" + 0.005\"bank\" + 0.003\"tonne\" + 0.003\"problem\" + 0.003\"hong_kong\" + 0.002\"trader\" + 0.002\"chinese\" + 0.002\"loan\" + 0.002\"kong\" + 0.002\"hong\"\n", "06:19:26 INFO:topic #79 (0.007): 0.030\"china\" + 0.020\"beijing\" + 0.014\"chinese\" + 0.009\"taiwan\" + 0.008\"trade\" + 0.008\"wang\" + 0.007\"foreign\" + 0.006\"united\" + 0.006\"washington\" + 0.006\"states\"\n", "06:19:26 INFO:topic #58 (0.007): 0.006\"pound\" + 0.004\"million_pound\" + 0.003\"hong_kong\" + 0.002\"hong\" + 0.002\"kong\" + 0.002\"pay\" + 0.002\"china\" + 0.002\"Hong Kong\" + 0.002\"shareholder\" + 0.002\"service\"\n", "06:19:26 INFO:topic #19 (0.007): 0.036\"bre\" + 0.035\"x\" + 0.033\"bre_x\" + 0.031\"gold\" + 0.026\"Bre-X\" + 0.019\"barrick\" + 0.015\"busang\" + 0.013\"indonesian\" + 0.011\"mining\" + 0.009\"deposit\"\n", "06:19:26 INFO:topic #112 (0.007): 0.005\"bank\" + 0.003\"russia\" + 0.002\"x\" + 0.002\"diamond\" + 0.002\"bre\" + 0.002\"bre_x\" + 0.002\"canada\" + 0.002\"export\" + 0.002\"canadian\" + 0.002\"Bre-X\"\n", "06:19:26 INFO:topic diff=9.522079, rho=0.500000\n", "06:19:26 INFO:PROGRESS: pass 3, at document #2500/2500\n", "06:19:26 DEBUG:performing inference on a chunk of 2500 documents\n", "06:19:32 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:19:32 DEBUG:updating topics\n", "06:19:33 INFO:topic #38 (0.007): 0.003\"block\" + 0.002\"quarter\" + 0.002\"service\" + 0.002\"compuserve\" + 0.002\"china\" + 0.002\"pound\" + 0.002\"loss\" + 0.001\"chinese\" + 0.001\"time_warner\" + 0.001\"cent\"\n", "06:19:33 INFO:topic #148 (0.007): 0.018\"franc\" + 0.015\"french\" + 0.014\"airbus\" + 0.014\"france\" + 0.013\"thomson\" + 0.009\"air\" + 0.009\"billion_franc\" + 0.007\"boeing\" + 0.007\"state\" + 0.007\"air_france\"\n", "06:19:33 INFO:topic #9 (0.007): 0.023\"shanghai\" + 0.021\"china\" + 0.018\"bank\" + 0.014\"b\" + 0.014\"foreign\" + 0.011\"investor\" + 0.011\"exchange\" + 0.011\"b_share\" + 0.010\"beijing\" + 0.010\"shenzhen\"\n", "06:19:33 INFO:topic #11 (0.007): 0.013\"tobacco\" + 0.010\"florida\" + 0.009\"quick\" + 0.008\"state\" + 0.007\"car\" + 0.007\"amp\" + 0.007\"trial\" + 0.006\"cigarette\" + 0.006\"television\" + 0.006\"maker\"\n", "06:19:33 INFO:topic #116 (0.007): 0.004\"china\" + 0.003\"bank\" + 0.002\"tonne\" + 0.002\"problem\" + 0.002\"hong_kong\" + 0.002\"trader\" + 0.002\"chinese\" + 0.002\"loan\" + 0.001\"kong\" + 0.001\"hong\"\n", "06:19:33 INFO:topic diff=7.935955, rho=0.447214\n", "06:19:33 INFO:PROGRESS: pass 4, at document #2500/2500\n", "06:19:33 DEBUG:performing inference on a chunk of 2500 documents\n", "06:19:39 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:19:39 DEBUG:updating topics\n", "06:19:39 INFO:topic #118 (0.007): 0.003\"tonne\" + 0.002\"cocoa\" + 0.002\"exporter\" + 0.001\"chad\" + 0.001\"bank\" + 0.001\"coast\" + 0.001\"ivory\" + 0.001\"crop\" + 0.001\"ivory_coast\" + 0.001\"cable\"\n", "06:19:39 INFO:topic #134 (0.007): 0.038\"bank\" + 0.020\"canada\" + 0.017\"canadian\" + 0.011\"toronto\" + 0.009\"fund\" + 0.008\"cent\" + 0.007\"molson\" + 0.006\"earning\" + 0.005\"royal_bank\" + 0.005\"royal\"\n", "06:19:39 INFO:topic #7 (0.007): 0.002\"soybean\" + 0.002\"china\" + 0.002\"monsanto\" + 0.002\"director\" + 0.002\"adm\" + 0.002\"hong\" + 0.002\"crop\" + 0.001\"hong_kong\" + 0.001\"united\" + 0.001\"equipment\"\n", "06:19:39 INFO:topic #93 (0.007): 0.005\"earning\" + 0.005\"point\" + 0.004\"quarter\" + 0.004\"investor\" + 0.004\"fund\" + 0.004\"growth\" + 0.003\"exchange\" + 0.003\"investment\" + 0.003\"strong\" + 0.003\"trade\"\n", "06:19:39 INFO:topic #26 (0.007): 0.012\"bank\" + 0.010\"yen\" + 0.008\"billion_yen\" + 0.005\"financial\" + 0.005\"affiliate\" + 0.004\"daiwa\" + 0.004\"non\" + 0.004\"non_bank\" + 0.004\"half\" + 0.004\"post\"\n", "06:19:39 INFO:topic diff=6.627219, rho=0.408248\n", "06:19:39 INFO:PROGRESS: pass 5, at document #2500/2500\n", "06:19:39 DEBUG:performing inference on a chunk of 2500 documents\n", "06:19:45 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:19:45 DEBUG:updating topics\n", "06:19:46 INFO:topic #16 (0.007): 0.030\"toronto\" + 0.020\"index\" + 0.019\"bank\" + 0.018\"canada\" + 0.016\"point\" + 0.015\"gold\" + 0.012\"canadian\" + 0.011\"toronto_stock\" + 0.011\"fall\" + 0.010\"gain\"\n", "06:19:46 INFO:topic #114 (0.007): 0.010\"bank\" + 0.005\"japan\" + 0.005\"pound\" + 0.004\"problem\" + 0.004\"loan\" + 0.003\"financial\" + 0.003\"yen\" + 0.003\"bt\" + 0.003\"million_pound\" + 0.003\"japanese\"\n", "06:19:46 INFO:topic #52 (0.007): 0.019\"bank\" + 0.017\"airbus\" + 0.008\"canada\" + 0.006\"fund\" + 0.006\"canadian\" + 0.006\"service\" + 0.005\"boeing\" + 0.004\"aircraft\" + 0.004\"aerospace\" + 0.004\"office\"\n", "06:19:46 INFO:topic #146 (0.007): 0.007\"china\" + 0.005\"party\" + 0.004\"pound\" + 0.003\"british\" + 0.003\"plc\" + 0.003\"stg\" + 0.003\"drug\" + 0.002\"million_pound\" + 0.002\"country\" + 0.002\"technology\"\n", "06:19:46 INFO:topic #47 (0.007): 0.009\"tonne\" + 0.008\"smelter\" + 0.007\"oil\" + 0.007\"aluminium\" + 0.006\"state\" + 0.006\"plant\" + 0.006\"russia\" + 0.006\"trader\" + 0.006\"source\" + 0.005\"metal\"\n", "06:19:46 INFO:topic diff=5.554374, rho=0.377964\n", "06:19:46 INFO:PROGRESS: pass 6, at document #2500/2500\n", "06:19:46 DEBUG:performing inference on a chunk of 2500 documents\n", "06:19:51 DEBUG:2500/2500 documents converged within 50 iterations\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "06:19:51 DEBUG:updating topics\n", "06:19:52 INFO:topic #44 (0.007): 0.007\"internet\" + 0.004\"committee\" + 0.003\"proposal\" + 0.003\"address\" + 0.003\"trade\" + 0.003\"china\" + 0.003\"congress\" + 0.002\"member\" + 0.002\"financial\" + 0.002\"name\"\n", "06:19:52 INFO:topic #131 (0.007): 0.009\"bank\" + 0.008\"internet\" + 0.007\"court\" + 0.005\"exchange\" + 0.004\"foreign\" + 0.004\"currency\" + 0.004\"trading\" + 0.004\"policy\" + 0.003\"law\" + 0.003\"security\"\n", "06:19:52 INFO:topic #112 (0.007): 0.001\"bank\" + 0.001\"russia\" + 0.001\"x\" + 0.001\"diamond\" + 0.001\"bre\" + 0.001\"bre_x\" + 0.001\"canada\" + 0.001\"export\" + 0.001\"canadian\" + 0.001\"Bre-X\"\n", "06:19:52 INFO:topic #49 (0.007): 0.008\"bid\" + 0.008\"penny\" + 0.005\"pound\" + 0.004\"northern\" + 0.004\"electric\" + 0.003\"midlands\" + 0.003\"offer\" + 0.003\"sector\" + 0.003\"electricity\" + 0.003\"east\"\n", "06:19:52 INFO:topic #61 (0.007): 0.008\"china\" + 0.008\"tibet\" + 0.005\"chinese\" + 0.005\"beijing\" + 0.005\"foreign\" + 0.004\"wang\" + 0.004\"hong_kong\" + 0.004\"kong\" + 0.003\"hong\" + 0.003\"region\"\n", "06:19:52 INFO:topic diff=4.666072, rho=0.353553\n", "06:19:52 INFO:PROGRESS: pass 7, at document #2500/2500\n", "06:19:52 DEBUG:performing inference on a chunk of 2500 documents\n", "06:19:58 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:19:58 DEBUG:updating topics\n", "06:19:58 INFO:topic #8 (0.007): 0.001\"french\" + 0.001\"bank\" + 0.001\"service\" + 0.001\"financial\" + 0.001\"internet\" + 0.001\"china\" + 0.000\"mfs\" + 0.000\"sell\" + 0.000\"state\" + 0.000\"product\"\n", "06:19:58 INFO:topic #45 (0.007): 0.001\"property\" + 0.000\"china\" + 0.000\"holding\" + 0.000\"survey\" + 0.000\"sector\" + 0.000\"bank\" + 0.000\"gold\" + 0.000\"fall\" + 0.000\"debt\" + 0.000\"air\"\n", "06:19:58 INFO:topic #22 (0.007): 0.016\"pound\" + 0.012\"drug\" + 0.011\"plc\" + 0.011\"british\" + 0.011\"million_pound\" + 0.008\"product\" + 0.007\"penny\" + 0.006\"cancer\" + 0.006\"stg\" + 0.005\"biotech\"\n", "06:19:58 INFO:topic #10 (0.007): 0.023\"bank\" + 0.015\"pound\" + 0.008\"society\" + 0.006\"banking\" + 0.006\"fund\" + 0.006\"shareholder\" + 0.005\"investment\" + 0.005\"eurotunnel\" + 0.005\"lloyds\" + 0.005\"debt\"\n", "06:19:58 INFO:topic #11 (0.007): 0.013\"tobacco\" + 0.012\"florida\" + 0.009\"quick\" + 0.009\"state\" + 0.008\"car\" + 0.007\"amp\" + 0.007\"trial\" + 0.007\"television\" + 0.006\"news\" + 0.006\"maker\"\n", "06:19:58 INFO:topic diff=3.925478, rho=0.333333\n", "06:19:58 INFO:PROGRESS: pass 8, at document #2500/2500\n", "06:19:58 DEBUG:performing inference on a chunk of 2500 documents\n", "06:20:04 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:20:04 DEBUG:updating topics\n", "06:20:04 INFO:topic #9 (0.007): 0.024\"shanghai\" + 0.022\"china\" + 0.018\"bank\" + 0.014\"b\" + 0.014\"foreign\" + 0.011\"investor\" + 0.011\"exchange\" + 0.011\"b_share\" + 0.010\"beijing\" + 0.010\"shenzhen\"\n", "06:20:04 INFO:topic #42 (0.007): 0.001\"news\" + 0.000\"china\" + 0.000\"corp\" + 0.000\"net\" + 0.000\"property\" + 0.000\"news_corp\" + 0.000\"value\" + 0.000\"shareholder\" + 0.000\"bre_x\" + 0.000\"x\"\n", "06:20:04 INFO:topic #131 (0.007): 0.006\"bank\" + 0.005\"internet\" + 0.004\"court\" + 0.003\"exchange\" + 0.003\"foreign\" + 0.003\"currency\" + 0.002\"trading\" + 0.002\"policy\" + 0.002\"law\" + 0.002\"security\"\n", "06:20:04 INFO:topic #99 (0.007): 0.011\"mci\" + 0.007\"digital\" + 0.007\"camera\" + 0.006\"rockwell\" + 0.005\"technology\" + 0.005\"kong\" + 0.005\"hand\" + 0.005\"hong_kong\" + 0.005\"system\" + 0.004\"trade\"\n", "06:20:04 INFO:topic #5 (0.007): 0.018\"bt\" + 0.013\"telecom\" + 0.011\"pound\" + 0.010\"british\" + 0.008\"mci\" + 0.007\"service\" + 0.006\"merger\" + 0.005\"penny\" + 0.005\"britain\" + 0.005\"ntt\"\n", "06:20:04 INFO:topic diff=3.304076, rho=0.316228\n", "06:20:04 INFO:PROGRESS: pass 9, at document #2500/2500\n", "06:20:04 DEBUG:performing inference on a chunk of 2500 documents\n", "06:20:10 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:20:10 DEBUG:updating topics\n", "06:20:10 INFO:topic #108 (0.007): 0.005\"gm\" + 0.004\"computer\" + 0.004\"quarter\" + 0.004\"ibm\" + 0.004\"car\" + 0.004\"technology\" + 0.004\"france\" + 0.003\"thomson\" + 0.003\"plant\" + 0.003\"service\"\n", "06:20:11 INFO:topic #111 (0.007): 0.008\"computer\" + 0.006\"software\" + 0.005\"apple\" + 0.005\"quarter\" + 0.004\"microsoft\" + 0.003\"technology\" + 0.003\"design\" + 0.003\"pc\" + 0.002\"oracle\" + 0.002\"financial\"\n", "06:20:11 INFO:topic #82 (0.007): 0.003\"china\" + 0.002\"shanghai\" + 0.002\"future\" + 0.002\"exchange\" + 0.001\"b\" + 0.001\"index\" + 0.001\"authority\" + 0.001\"investor\" + 0.001\"trading\" + 0.001\"foreign\"\n", "06:20:11 INFO:topic #89 (0.007): 0.016\"internet\" + 0.015\"computer\" + 0.014\"technology\" + 0.010\"quarter\" + 0.010\"software\" + 0.009\"product\" + 0.008\"microsoft\" + 0.008\"sun\" + 0.007\"netscape\" + 0.007\"web\"\n", "06:20:11 INFO:topic #101 (0.007): 0.001\"china\" + 0.001\"kong\" + 0.001\"hong\" + 0.001\"hong_kong\" + 0.000\"Hong Kong\" + 0.000\"macau\" + 0.000\"tung\" + 0.000\"chinese\" + 0.000\"formula\" + 0.000\"beijing\"\n", "06:20:11 INFO:topic diff=2.781140, rho=0.301511\n", "06:20:11 INFO:PROGRESS: pass 10, at document #2500/2500\n", "06:20:11 DEBUG:performing inference on a chunk of 2500 documents\n", "06:20:16 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:20:16 DEBUG:updating topics\n", "06:20:17 INFO:topic #60 (0.007): 0.011\"oil\" + 0.009\"colombia\" + 0.008\"colombian\" + 0.008\"paramilitary\" + 0.008\"country\" + 0.008\"drug\" + 0.008\"police\" + 0.007\"attack\" + 0.007\"force\" + 0.007\"medellin\"\n", "06:20:17 INFO:topic #99 (0.007): 0.010\"mci\" + 0.008\"digital\" + 0.007\"camera\" + 0.007\"rockwell\" + 0.005\"technology\" + 0.005\"hand\" + 0.005\"system\" + 0.005\"agreement\" + 0.005\"personal\" + 0.005\"trade\"\n", "06:20:17 INFO:topic #109 (0.007): 0.025\"pound\" + 0.016\"million_pound\" + 0.012\"life\" + 0.011\"insurance\" + 0.011\"scotam\" + 0.009\"offer\" + 0.009\"abbey\" + 0.007\"policyholder\" + 0.007\"british\" + 0.006\"scottish\"\n", "06:20:17 INFO:topic #55 (0.007): 0.028\"internet\" + 0.027\"court\" + 0.019\"foreign\" + 0.017\"exchange\" + 0.017\"currency\" + 0.014\"case\" + 0.014\"foreign_currency\" + 0.014\"trading\" + 0.012\"amendment\" + 0.012\"address\"\n", "06:20:17 INFO:topic #9 (0.007): 0.024\"shanghai\" + 0.022\"china\" + 0.018\"bank\" + 0.014\"b\" + 0.014\"foreign\" + 0.011\"investor\" + 0.011\"exchange\" + 0.011\"b_share\" + 0.010\"beijing\" + 0.010\"shenzhen\"\n", "06:20:17 INFO:topic diff=2.340896, rho=0.288675\n", "06:20:17 INFO:PROGRESS: pass 11, at document #2500/2500\n", "06:20:17 DEBUG:performing inference on a chunk of 2500 documents\n", "06:20:22 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:20:22 DEBUG:updating topics\n", "06:20:23 INFO:topic #20 (0.007): 0.000\"china\" + 0.000\"de\" + 0.000\"russia\" + 0.000\"chinese\" + 0.000\"beijing\" + 0.000\"diamond\" + 0.000\"kong\" + 0.000\"export\" + 0.000\"oil\" + 0.000\"service\"\n", "06:20:23 INFO:topic #7 (0.007): 0.000\"soybean\" + 0.000\"china\" + 0.000\"monsanto\" + 0.000\"director\" + 0.000\"adm\" + 0.000\"hong\" + 0.000\"crop\" + 0.000\"hong_kong\" + 0.000\"united\" + 0.000\"equipment\"\n", "06:20:23 INFO:topic #24 (0.007): 0.024\"czech\" + 0.011\"crown\" + 0.011\"bank\" + 0.010\"prague\" + 0.010\"klaus\" + 0.008\"party\" + 0.006\"havel\" + 0.006\"foreign\" + 0.006\"country\" + 0.006\"election\"\n", "06:20:23 INFO:topic #80 (0.007): 0.025\"king\" + 0.021\"silver\" + 0.013\"network\" + 0.012\"station\" + 0.012\"shopping\" + 0.012\"home_shopping\" + 0.012\"television\" + 0.009\"latin\" + 0.009\"news\" + 0.009\"home\"\n", "06:20:23 INFO:topic #82 (0.007): 0.001\"china\" + 0.001\"shanghai\" + 0.001\"future\" + 0.001\"exchange\" + 0.001\"b\" + 0.001\"index\" + 0.001\"authority\" + 0.001\"investor\" + 0.001\"trading\" + 0.001\"foreign\"\n", "06:20:23 INFO:topic diff=1.970765, rho=0.277350\n", "06:20:23 INFO:PROGRESS: pass 12, at document #2500/2500\n", "06:20:23 DEBUG:performing inference on a chunk of 2500 documents\n", "06:20:29 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:20:29 DEBUG:updating topics\n", "06:20:30 INFO:topic #123 (0.007): 0.021\"china\" + 0.013\"wang\" + 0.011\"beijing\" + 0.010\"chinese\" + 0.007\"tibet\" + 0.006\"dissident\" + 0.006\"state\" + 0.006\"party\" + 0.005\"communist\" + 0.005\"court\"\n", "06:20:30 INFO:topic #86 (0.007): 0.015\"internet\" + 0.014\"computer\" + 0.014\"ibm\" + 0.012\"quarter\" + 0.011\"service\" + 0.009\"pc\" + 0.008\"software\" + 0.007\"system\" + 0.007\"consumer\" + 0.006\"network\"\n", "06:20:30 INFO:topic #50 (0.007): 0.034\"hong_kong\" + 0.033\"kong\" + 0.033\"hong\" + 0.029\"china\" + 0.021\"Hong Kong\" + 0.018\"tung\" + 0.013\"beijing\" + 0.012\"chinese\" + 0.012\"Hong Kong's\" + 0.010\"britain\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "06:20:30 INFO:topic #9 (0.007): 0.025\"shanghai\" + 0.022\"china\" + 0.018\"bank\" + 0.014\"b\" + 0.014\"foreign\" + 0.011\"investor\" + 0.011\"exchange\" + 0.011\"b_share\" + 0.010\"beijing\" + 0.010\"shenzhen\"\n", "06:20:30 INFO:topic #24 (0.007): 0.024\"czech\" + 0.011\"crown\" + 0.011\"bank\" + 0.010\"prague\" + 0.009\"klaus\" + 0.008\"party\" + 0.006\"havel\" + 0.006\"foreign\" + 0.006\"country\" + 0.006\"election\"\n", "06:20:30 INFO:topic diff=1.660230, rho=0.267261\n", "06:20:30 INFO:PROGRESS: pass 13, at document #2500/2500\n", "06:20:30 DEBUG:performing inference on a chunk of 2500 documents\n", "06:20:37 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:20:37 DEBUG:updating topics\n", "06:20:38 INFO:topic #108 (0.007): 0.003\"gm\" + 0.002\"computer\" + 0.002\"quarter\" + 0.002\"ibm\" + 0.002\"car\" + 0.002\"technology\" + 0.002\"france\" + 0.002\"thomson\" + 0.002\"plant\" + 0.002\"service\"\n", "06:20:38 INFO:topic #116 (0.007): 0.000\"china\" + 0.000\"bank\" + 0.000\"tonne\" + 0.000\"problem\" + 0.000\"hong_kong\" + 0.000\"trader\" + 0.000\"chinese\" + 0.000\"loan\" + 0.000\"kong\" + 0.000\"hong\"\n", "06:20:38 INFO:topic #59 (0.007): 0.000\"pound\" + 0.000\"lloyds\" + 0.000\"bank\" + 0.000\"pension\" + 0.000\"insurance\" + 0.000\"amp\" + 0.000\"bhp\" + 0.000\"claim\" + 0.000\"million_pound\" + 0.000\"scottish\"\n", "06:20:38 INFO:topic #19 (0.007): 0.034\"bre\" + 0.033\"x\" + 0.032\"bre_x\" + 0.031\"gold\" + 0.025\"Bre-X\" + 0.018\"barrick\" + 0.015\"busang\" + 0.013\"indonesian\" + 0.011\"mining\" + 0.008\"exploration\"\n", "06:20:38 INFO:topic #112 (0.007): 0.000\"bank\" + 0.000\"russia\" + 0.000\"x\" + 0.000\"diamond\" + 0.000\"bre\" + 0.000\"bre_x\" + 0.000\"canada\" + 0.000\"export\" + 0.000\"canadian\" + 0.000\"Bre-X\"\n", "06:20:38 INFO:topic diff=1.400248, rho=0.258199\n", "06:20:38 INFO:PROGRESS: pass 14, at document #2500/2500\n", "06:20:38 DEBUG:performing inference on a chunk of 2500 documents\n", "06:20:47 DEBUG:2500/2500 documents converged within 50 iterations\n", "06:20:47 DEBUG:updating topics\n", "06:20:47 INFO:topic #43 (0.007): 0.001\"czech\" + 0.001\"party\" + 0.001\"klaus\" + 0.001\"coalition\" + 0.001\"election\" + 0.001\"havel\" + 0.000\"house\" + 0.000\"crown\" + 0.000\"prague\" + 0.000\"parliament\"\n", "06:20:47 INFO:topic #49 (0.007): 0.001\"bid\" + 0.001\"penny\" + 0.001\"pound\" + 0.001\"northern\" + 0.001\"electric\" + 0.000\"midlands\" + 0.000\"offer\" + 0.000\"sector\" + 0.000\"electricity\" + 0.000\"east\"\n", "06:20:47 INFO:topic #122 (0.007): 0.000\"wang\" + 0.000\"china\" + 0.000\"beijing\" + 0.000\"law\" + 0.000\"trial\" + 0.000\"death\" + 0.000\"dissident\" + 0.000\"pound\" + 0.000\"hong\" + 0.000\"sentence\"\n", "06:20:47 INFO:topic #132 (0.007): 0.001\"bank\" + 0.000\"crown\" + 0.000\"klaus\" + 0.000\"czech\" + 0.000\"social\" + 0.000\"banka\" + 0.000\"party\" + 0.000\"minister\" + 0.000\"state\" + 0.000\"billion_crown\"\n", "06:20:47 INFO:topic #16 (0.007): 0.030\"toronto\" + 0.020\"index\" + 0.019\"bank\" + 0.019\"canada\" + 0.017\"gold\" + 0.016\"point\" + 0.012\"canadian\" + 0.011\"toronto_stock\" + 0.011\"fall\" + 0.010\"gain\"\n", "06:20:47 INFO:topic diff=1.182972, rho=0.250000\n", "06:20:47 DEBUG:Setting topics to those of the model: AuthorTopicModel(num_terms=6215, num_topics=150, num_authors=50, decay=0.5, chunksize=2500)\n", "06:20:47 INFO:CorpusAccumulator accumulated stats from 1000 documents\n", "06:20:48 INFO:CorpusAccumulator accumulated stats from 2000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "-2.83261288295\n" ] } ], "source": [ "atmodel_150topics_25_10 = train_model(train_corpus_25_10, train_author2doc, train_dictionary_25_10, num_topics=150, eval_every=0, iterations=50, passes=15)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precision@k: top_n=1\n", "Prediction accuracy: 0.6176\n", "Precision@k: top_n=2\n", "Prediction accuracy: 0.7712\n", "Precision@k: top_n=3\n", "Prediction accuracy: 0.8268\n", "Precision@k: top_n=4\n", "Prediction accuracy: 0.8656\n", "Precision@k: top_n=5\n", "Prediction accuracy: 0.8916\n", "Precision@k: top_n=6\n", "Prediction accuracy: 0.9112\n", "Precision@k: top_n=8\n", "Prediction accuracy: 0.9308\n", "Precision@k: top_n=10\n", "Prediction accuracy: 0.9408\n" ] }, { "data": { "image/png": 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GGD+gD/BljtmvAX2MMXuA3o7pcuPZZyE6Gl6alsi/f9jPrW2DaN+oururpZRS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\nIHQ8iMLoeBCWsjIexPfff89//vMfAO6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"text/plain": [ "<matplotlib.figure.Figure at 0x11713ec18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "accuracy_scores_150topic_25_10={}\n", "for i in [1,2,3,4,5,6,8,10]:\n", " accuracy, k = prediction_accuracy(test_author2doc, test_corpus_25_10, atmodel_150topics_25_10, k=i)\n", " accuracy_scores_150topic_25_10[k] = accuracy\n", " \n", "plot_accuracy(scores1=accuracy_scores_150topic_25_10, label1=\"150 topics, max_freq=25%, min_wordcount=10\", scores2=accuracy_scores_150topic, label2=\"150 topics, standard\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results seem rather ambigious and do not show a clear trend. Which is why we would stop here for the iterations." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	267,682	Python	.py	1,844	139.32321	27,938	0.740392	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,927	FastText_Tutorial.ipynb	piskvorky_gensim/docs/notebooks/FastText_Tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using FastText via Gensim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial is about using [fastText](https://github.com/facebookresearch/fastText) model in Gensim. There are two ways you can use fastText in Gensim - Gensim's native implementation of fastText and Gensim wrapper for fastText's original C++ code. Here, we'll learn to work with fastText library for training word-embedding models, saving & loading them and performing similarity operations & vector lookups analogous to Word2Vec." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## When to use FastText?\n", "The main principle behind fastText is that the morphological structure of a word carries important information about the meaning of the word, which is not taken into account by traditional word embeddings, which train a unique word embedding for every individual word. This is especially significant for morphologically rich languages (German, Turkish) in which a single word can have a large number of morphological forms, each of which might occur rarely, thus making it hard to train good word embeddings. \n", "fastText attempts to solve this by treating each word as the aggregation of its subwords. For the sake of simplicity and language-independence, subwords are taken to be the character ngrams of the word. The vector for a word is simply taken to be the sum of all vectors of its component char-ngrams. \n", "According to a detailed comparison of Word2Vec and FastText in [this notebook](Word2Vec_FastText_Comparison.ipynb), fastText does significantly better on syntactic tasks as compared to the original Word2Vec, especially when the size of the training corpus is small. Word2Vec slightly outperforms FastText on semantic tasks though. The differences grow smaller as the size of training corpus increases.\n", "Training time for fastText is significantly higher than the Gensim version of Word2Vec (`15min 42s` vs `6min 42s` on text8, 17 mil tokens, 5 epochs, and a vector size of 100). \n", "fastText can be used to obtain vectors for out-of-vocabulary (OOV) words, by summing up vectors for its component char-ngrams, provided at least one of the char-ngrams was present in the training data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the following examples, we'll use the Lee Corpus (which you already have if you've installed gensim) for training our model.\n", "\n", "For using the wrapper for fastText, you need to have fastText setup locally to be able to train models. See [installation instructions for fastText](https://github.com/facebookresearch/fastText/#requirements) if you don't have fastText installed already." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using Gensim's implementation of fastText" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FastText(vocab=1762, size=100, alpha=0.025)\n" ] } ], "source": [ "from gensim.models.fasttext import FastText as FT_gensim\n", "from gensim.test.utils import datapath\n", "\n", "# Set file names for train and test data\n", "corpus_file = datapath('lee_background.cor')\n", "\n", "model_gensim = FT_gensim(size=100)\n", "\n", "# build the vocabulary\n", "model_gensim.build_vocab(corpus_file=corpus_file)\n", "\n", "# train the model\n", "model_gensim.train(\n", " corpus_file=corpus_file, epochs=model_gensim.epochs,\n", " total_examples=model_gensim.corpus_count, total_words=model_gensim.corpus_total_words\n", ")\n", "\n", "print(model_gensim)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using wrapper for fastText's C++ code" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FastText(vocab=1763, size=100, alpha=0.025)\n" ] } ], "source": [ "from gensim.models.wrappers.fasttext import FastText as FT_wrapper\n", "\n", "# Set FastText home to the path to the FastText executable\n", "ft_home = '/home/misha/src/fastText-0.1.0/fasttext'\n", "\n", "# train the model\n", "model_wrapper = FT_wrapper.train(ft_home, corpus_file)\n", "\n", "print(model_wrapper)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training hyperparameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hyperparameters for training the model follow the same pattern as Word2Vec. FastText supports the following parameters from the original word2vec - \n", " - model: Training architecture. Allowed values: `cbow`, `skipgram` (Default `cbow`)\n", " - size: Size of embeddings to be learnt (Default 100)\n", " - alpha: Initial learning rate (Default 0.025)\n", " - window: Context window size (Default 5)\n", " - min_count: Ignore words with number of occurrences below this (Default 5)\n", " - loss: Training objective. Allowed values: `ns`, `hs`, `softmax` (Default `ns`)\n", " - sample: Threshold for downsampling higher-frequency words (Default 0.001)\n", " - negative: Number of negative words to sample, for `ns` (Default 5)\n", " - iter: Number of epochs (Default 5)\n", " - sorted_vocab: Sort vocab by descending frequency (Default 1)\n", " - threads: Number of threads to use (Default 12)\n", " \n", "In addition, FastText has three additional parameters - \n", " - min_n: min length of char ngrams (Default 3)\n", " - max_n: max length of char ngrams (Default 6)\n", " - bucket: number of buckets used for hashing ngrams (Default 2000000)\n", "Parameters `min_n` and `max_n` control the lengths of character ngrams that each word is broken down into while training and looking up embeddings. If `max_n` is set to 0, or to be lesser than `min_n`, no character ngrams are used, and the model effectively reduces to Word2Vec.\n", "\n", "To bound the memory requirements of the model being trained, a hashing function is used that maps ngrams to integers in 1 to K. For hashing these character sequences, the [Fowler-Noll-Vo hashing function](http://www.isthe.com/chongo/tech/comp/fnv) (FNV-1a variant) is employed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: As in the case of Word2Vec, you can continue to train your model while using Gensim's native implementation of fastText." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving/loading models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Models can be saved and loaded via the `load` and `save` methods." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FastText(vocab=1762, size=100, alpha=0.025)\n", "FastText(vocab=1763, size=100, alpha=0.025)\n" ] } ], "source": [ "# saving a model trained via Gensim's fastText implementation\n", "model_gensim.save('saved_model_gensim')\n", "loaded_model = FT_gensim.load('saved_model_gensim')\n", "print(loaded_model)\n", "\n", "# saving a model trained via fastText wrapper\n", "model_wrapper.save('saved_model_wrapper')\n", "loaded_model = FT_wrapper.load('saved_model_wrapper')\n", "print(loaded_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `save_word2vec_method` causes the vectors for ngrams to be lost. As a result, a model loaded in this way will behave as a regular word2vec model. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Word vector lookup\n", "Note: Operations like word vector lookups and similarity queries can be performed in exactly the same manner for both the implementations of fastText so they have been demonstrated using only the fastText wrapper here.\n", "\n", "FastText models support vector lookups for out-of-vocabulary words by summing up character ngrams belonging to the word." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "False\n", "[ 0.6988306 0.7962038 0.04964953 0.11940945 0.45962736 0.02930021\n", " -0.148752 0.06151627 -0.09016804 0.28291386 0.47263005 -0.07578029\n", " -0.6577542 -0.27169365 -0.16403204 -0.01813792 0.16956581 -0.4356721\n", " -0.5964832 0.736336 -0.30984706 0.3427041 0.15378788 -0.5059883\n", " -0.15675616 -0.5815964 -0.07495894 -0.41970676 0.36809948 -0.26071918\n", " -0.46826273 0.15298584 -0.04599814 0.6263372 0.52292955 -0.02519639\n", " 0.65333563 -0.1163021 0.22651657 0.3189898 -0.07596255 -0.22332282\n", " 0.15195839 -0.39055198 -0.19732887 -0.2747241 0.04130132 -0.63913506\n", " 0.18559387 -0.15314367 0.26563224 0.71324116 -0.04440507 0.05624504\n", " -0.11758088 -0.3253568 -0.22081989 0.6120215 -0.35792083 -0.333798\n", " -0.11835586 -0.0503935 0.3735655 0.49913588 -0.02427495 0.17332387\n", " -0.6308407 -0.3670084 -0.23201697 0.1555578 -0.3275684 0.0828054\n", " -0.01972355 -0.27277097 -0.25400817 -0.50337344 0.12651777 0.01878418\n", " 0.21467368 -0.30219504 0.72938025 1.2315444 0.34624976 -0.7114608\n", " 0.36338523 0.06543703 0.01345754 -0.15920149 0.13876723 -0.5582751\n", " 0.38154316 0.18617174 0.4476739 -0.02872563 0.11876874 -0.02085596\n", " -0.64908224 0.03067067 0.14303452 0.33201975]\n", "[ 0.6146148 0.70105475 0.04316702 0.10669094 0.4011963 0.02504568\n", " -0.13186908 0.05575202 -0.07716817 0.24856749 0.41678062 -0.06665172\n", " -0.5781625 -0.2382541 -0.14530264 -0.01657776 0.1496157 -0.38340995\n", " -0.52317756 0.64602286 -0.27437162 0.30193132 0.13466597 -0.4432936\n", " -0.13953276 -0.51243937 -0.06671739 -0.36839843 0.323204 -0.23012711\n", " -0.4134057 0.13342045 -0.03989897 0.5513306 0.46034276 -0.02355763\n", " 0.5749811 -0.10196284 0.19741252 0.28229755 -0.06662108 -0.19657284\n", " 0.1323219 -0.34543604 -0.17333041 -0.24169934 0.03771086 -0.563315\n", " 0.16434433 -0.13390124 0.2337911 0.6275974 -0.03961363 0.04971414\n", " -0.10379436 -0.28675792 -0.19387211 0.5369245 -0.3134195 -0.29489765\n", " -0.10219254 -0.04510017 0.32892984 0.43901965 -0.02051542 0.15215051\n", " -0.5549378 -0.32053903 -0.20249408 0.1375998 -0.28648633 0.07105823\n", " -0.01473362 -0.24162163 -0.2248782 -0.44446456 0.11056102 0.01639792\n", " 0.1877773 -0.2670066 0.6425655 1.083377 0.30128938 -0.6256704\n", " 0.32030573 0.05688732 0.00961584 -0.1400448 0.12174114 -0.49109733\n", " 0.3353805 0.16405603 0.39405888 -0.02574553 0.10243315 -0.0189576\n", " -0.5711417 0.02725987 0.12675735 0.29079968]\n" ] } ], "source": [ "print('night' in model_wrapper.wv.vocab)\n", "print('nights' in model_wrapper.wv.vocab)\n", "print(model_wrapper['night'])\n", "print(model_wrapper['nights'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The word vector lookup operation only works if at least one of the component character ngrams is present in the training corpus. For example -" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Raises a KeyError since none of the character ngrams of the word `axe` are present in the training data\n", "try:\n", " model_wrapper['axe']\n", "except KeyError:\n", " #\n", " # trap the error here so it does not interfere\n", " # with the execution of the cells below\n", " #\n", " pass\n", "else:\n", " assert False, 'the above code should have raised a KeyError'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `in` operation works slightly differently from the original word2vec. It tests whether a vector for the given word exists or not, not whether the word is present in the word vocabulary. To test whether a word is present in the training word vocabulary -" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n", "True\n" ] } ], "source": [ "# Tests if word present in vocab\n", "print(\"word\" in model_wrapper.wv.vocab)\n", "# Tests if vector present for word\n", "print(\"word\" in model_wrapper)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Similarity operations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarity operations work the same way as word2vec. Out-of-vocabulary words can also be used, provided they have at least one character ngram present in the training data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n", "True\n" ] }, { "data": { "text/plain": [ "0.9999938" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"nights\" in model_wrapper.wv.vocab)\n", "print(\"night\" in model_wrapper.wv.vocab)\n", "model_wrapper.similarity(\"night\", \"nights\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Syntactically similar words generally have high similarity in fastText models, since a large number of the component char-ngrams will be the same. As a result, fastText generally does better at syntactic tasks than Word2Vec. A detailed comparison is provided [here](Word2Vec_FastText_Comparison.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other similarity operations" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('night', 0.9999542236328125),\n", " ('flights', 0.9999502897262573),\n", " ('rights', 0.9999481439590454),\n", " ('night.', 0.9999478459358215),\n", " ('night,', 0.999945878982544),\n", " ('eight', 0.9999404549598694),\n", " ('quarter', 0.9999394416809082),\n", " ('hearing', 0.9999383091926575),\n", " ('light', 0.9999381303787231),\n", " ('during', 0.9999378323554993)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The example training corpus is a toy corpus, results are not expected to be good, for proof-of-concept only\n", "model_wrapper.most_similar(\"nights\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.99997056" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_wrapper.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant'])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'dinner'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_wrapper.doesnt_match(\"breakfast cereal dinner lunch\".split())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('side', 0.999753475189209),\n", " ('inside', 0.9997509717941284),\n", " ('suicide', 0.9997484683990479),\n", " ('administrators', 0.9997476935386658),\n", " ('administration', 0.9997475743293762),\n", " ('Alliance', 0.9997474551200867),\n", " ('Three', 0.9997437000274658),\n", " ('Police', 0.9997435212135315),\n", " ('Minister,', 0.9997434616088867),\n", " ('end', 0.9997432827949524)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_wrapper.most_similar(positive=['baghdad', 'england'], negative=['london'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'section': 'capital-common-countries', 'correct': [], 'incorrect': []},\n", " {'section': 'capital-world', 'correct': [], 'incorrect': []},\n", " {'section': 'currency', 'correct': [], 'incorrect': []},\n", " {'section': 'city-in-state', 'correct': [], 'incorrect': []},\n", " {'section': 'family',\n", " 'correct': [],\n", " 'incorrect': [('HE', 'SHE', 'HIS', 'HER'), ('HIS', 'HER', 'HE', 'SHE')]},\n", " {'section': 'gram1-adjective-to-adverb', 'correct': [], 'incorrect': []},\n", " {'section': 'gram2-opposite', 'correct': [], 'incorrect': []},\n", " {'section': 'gram3-comparative',\n", " 'correct': [('GREAT', 'GREATER', 'LOW', 'LOWER'),\n", " ('LONG', 'LONGER', 'LOW', 'LOWER'),\n", " ('LOW', 'LOWER', 'GREAT', 'GREATER')],\n", " 'incorrect': [('GOOD', 'BETTER', 'GREAT', 'GREATER'),\n", " ('GOOD', 'BETTER', 'LONG', 'LONGER'),\n", " ('GOOD', 'BETTER', 'LOW', 'LOWER'),\n", " ('GREAT', 'GREATER', 'LONG', 'LONGER'),\n", " ('GREAT', 'GREATER', 'GOOD', 'BETTER'),\n", " ('LONG', 'LONGER', 'GOOD', 'BETTER'),\n", " ('LONG', 'LONGER', 'GREAT', 'GREATER'),\n", " ('LOW', 'LOWER', 'GOOD', 'BETTER'),\n", " ('LOW', 'LOWER', 'LONG', 'LONGER')]},\n", " {'section': 'gram4-superlative',\n", " 'correct': [('GOOD', 'BEST', 'GREAT', 'GREATEST'),\n", " ('GOOD', 'BEST', 'LARGE', 'LARGEST'),\n", " ('GOOD', 'BEST', 'BIG', 'BIGGEST'),\n", " ('GREAT', 'GREATEST', 'LARGE', 'LARGEST'),\n", " ('GREAT', 'GREATEST', 'BIG', 'BIGGEST'),\n", " ('LARGE', 'LARGEST', 'BIG', 'BIGGEST'),\n", " ('LARGE', 'LARGEST', 'GREAT', 'GREATEST')],\n", " 'incorrect': [('BIG', 'BIGGEST', 'GOOD', 'BEST'),\n", " ('BIG', 'BIGGEST', 'GREAT', 'GREATEST'),\n", " ('BIG', 'BIGGEST', 'LARGE', 'LARGEST'),\n", " ('GREAT', 'GREATEST', 'GOOD', 'BEST'),\n", " ('LARGE', 'LARGEST', 'GOOD', 'BEST')]},\n", " {'section': 'gram5-present-participle',\n", " 'correct': [('GO', 'GOING', 'LOOK', 'LOOKING'),\n", " ('GO', 'GOING', 'SAY', 'SAYING'),\n", " ('PLAY', 'PLAYING', 'SAY', 'SAYING'),\n", " ('PLAY', 'PLAYING', 'LOOK', 'LOOKING'),\n", " ('SAY', 'SAYING', 'LOOK', 'LOOKING'),\n", " ('SAY', 'SAYING', 'PLAY', 'PLAYING')],\n", " 'incorrect': [('GO', 'GOING', 'PLAY', 'PLAYING'),\n", " ('GO', 'GOING', 'RUN', 'RUNNING'),\n", " ('LOOK', 'LOOKING', 'PLAY', 'PLAYING'),\n", " ('LOOK', 'LOOKING', 'RUN', 'RUNNING'),\n", " ('LOOK', 'LOOKING', 'SAY', 'SAYING'),\n", " ('LOOK', 'LOOKING', 'GO', 'GOING'),\n", " ('PLAY', 'PLAYING', 'RUN', 'RUNNING'),\n", " ('PLAY', 'PLAYING', 'GO', 'GOING'),\n", " ('RUN', 'RUNNING', 'SAY', 'SAYING'),\n", " ('RUN', 'RUNNING', 'GO', 'GOING'),\n", " ('RUN', 'RUNNING', 'LOOK', 'LOOKING'),\n", " ('RUN', 'RUNNING', 'PLAY', 'PLAYING'),\n", " ('SAY', 'SAYING', 'GO', 'GOING'),\n", " ('SAY', 'SAYING', 'RUN', 'RUNNING')]},\n", " {'section': 'gram6-nationality-adjective',\n", " 'correct': [('AUSTRALIA', 'AUSTRALIAN', 'INDIA', 'INDIAN'),\n", " ('AUSTRALIA', 'AUSTRALIAN', 'ISRAEL', 'ISRAELI'),\n", " ('INDIA', 'INDIAN', 'AUSTRALIA', 'AUSTRALIAN')],\n", " 'incorrect': [('AUSTRALIA', 'AUSTRALIAN', 'FRANCE', 'FRENCH'),\n", " ('AUSTRALIA', 'AUSTRALIAN', 'SWITZERLAND', 'SWISS'),\n", " ('FRANCE', 'FRENCH', 'INDIA', 'INDIAN'),\n", " ('FRANCE', 'FRENCH', 'ISRAEL', 'ISRAELI'),\n", " ('FRANCE', 'FRENCH', 'SWITZERLAND', 'SWISS'),\n", " ('FRANCE', 'FRENCH', 'AUSTRALIA', 'AUSTRALIAN'),\n", " ('INDIA', 'INDIAN', 'ISRAEL', 'ISRAELI'),\n", " ('INDIA', 'INDIAN', 'SWITZERLAND', 'SWISS'),\n", " ('INDIA', 'INDIAN', 'FRANCE', 'FRENCH'),\n", " ('ISRAEL', 'ISRAELI', 'SWITZERLAND', 'SWISS'),\n", " ('ISRAEL', 'ISRAELI', 'AUSTRALIA', 'AUSTRALIAN'),\n", " ('ISRAEL', 'ISRAELI', 'FRANCE', 'FRENCH'),\n", " ('ISRAEL', 'ISRAELI', 'INDIA', 'INDIAN'),\n", " ('SWITZERLAND', 'SWISS', 'AUSTRALIA', 'AUSTRALIAN'),\n", " ('SWITZERLAND', 'SWISS', 'FRANCE', 'FRENCH'),\n", " ('SWITZERLAND', 'SWISS', 'INDIA', 'INDIAN'),\n", " ('SWITZERLAND', 'SWISS', 'ISRAEL', 'ISRAELI')]},\n", " {'section': 'gram7-past-tense',\n", " 'correct': [('PAYING', 'PAID', 'SAYING', 'SAID')],\n", " 'incorrect': [('GOING', 'WENT', 'PAYING', 'PAID'),\n", " ('GOING', 'WENT', 'PLAYING', 'PLAYED'),\n", " ('GOING', 'WENT', 'SAYING', 'SAID'),\n", " ('GOING', 'WENT', 'TAKING', 'TOOK'),\n", " ('PAYING', 'PAID', 'PLAYING', 'PLAYED'),\n", " ('PAYING', 'PAID', 'TAKING', 'TOOK'),\n", " ('PAYING', 'PAID', 'GOING', 'WENT'),\n", " ('PLAYING', 'PLAYED', 'SAYING', 'SAID'),\n", " ('PLAYING', 'PLAYED', 'TAKING', 'TOOK'),\n", " ('PLAYING', 'PLAYED', 'GOING', 'WENT'),\n", " ('PLAYING', 'PLAYED', 'PAYING', 'PAID'),\n", " ('SAYING', 'SAID', 'TAKING', 'TOOK'),\n", " ('SAYING', 'SAID', 'GOING', 'WENT'),\n", " ('SAYING', 'SAID', 'PAYING', 'PAID'),\n", " ('SAYING', 'SAID', 'PLAYING', 'PLAYED'),\n", " ('TAKING', 'TOOK', 'GOING', 'WENT'),\n", " ('TAKING', 'TOOK', 'PAYING', 'PAID'),\n", " ('TAKING', 'TOOK', 'PLAYING', 'PLAYED'),\n", " ('TAKING', 'TOOK', 'SAYING', 'SAID')]},\n", " {'section': 'gram8-plural',\n", " 'correct': [('MAN', 'MEN', 'CAR', 'CARS')],\n", " 'incorrect': [('BUILDING', 'BUILDINGS', 'CAR', 'CARS'),\n", " ('BUILDING', 'BUILDINGS', 'CHILD', 'CHILDREN'),\n", " ('BUILDING', 'BUILDINGS', 'MAN', 'MEN'),\n", " ('CAR', 'CARS', 'CHILD', 'CHILDREN'),\n", " ('CAR', 'CARS', 'MAN', 'MEN'),\n", " ('CAR', 'CARS', 'BUILDING', 'BUILDINGS'),\n", " ('CHILD', 'CHILDREN', 'MAN', 'MEN'),\n", " ('CHILD', 'CHILDREN', 'BUILDING', 'BUILDINGS'),\n", " ('CHILD', 'CHILDREN', 'CAR', 'CARS'),\n", " ('MAN', 'MEN', 'BUILDING', 'BUILDINGS'),\n", " ('MAN', 'MEN', 'CHILD', 'CHILDREN')]},\n", " {'section': 'gram9-plural-verbs', 'correct': [], 'incorrect': []},\n", " {'section': 'total',\n", " 'correct': [('GREAT', 'GREATER', 'LOW', 'LOWER'),\n", " ('LONG', 'LONGER', 'LOW', 'LOWER'),\n", " ('LOW', 'LOWER', 'GREAT', 'GREATER'),\n", " ('GOOD', 'BEST', 'GREAT', 'GREATEST'),\n", " ('GOOD', 'BEST', 'LARGE', 'LARGEST'),\n", " ('GOOD', 'BEST', 'BIG', 'BIGGEST'),\n", " ('GREAT', 'GREATEST', 'LARGE', 'LARGEST'),\n", " ('GREAT', 'GREATEST', 'BIG', 'BIGGEST'),\n", " ('LARGE', 'LARGEST', 'BIG', 'BIGGEST'),\n", " ('LARGE', 'LARGEST', 'GREAT', 'GREATEST'),\n", " ('GO', 'GOING', 'LOOK', 'LOOKING'),\n", " ('GO', 'GOING', 'SAY', 'SAYING'),\n", " ('PLAY', 'PLAYING', 'SAY', 'SAYING'),\n", " ('PLAY', 'PLAYING', 'LOOK', 'LOOKING'),\n", " ('SAY', 'SAYING', 'LOOK', 'LOOKING'),\n", " ('SAY', 'SAYING', 'PLAY', 'PLAYING'),\n", " ('AUSTRALIA', 'AUSTRALIAN', 'INDIA', 'INDIAN'),\n", " ('AUSTRALIA', 'AUSTRALIAN', 'ISRAEL', 'ISRAELI'),\n", " ('INDIA', 'INDIAN', 'AUSTRALIA', 'AUSTRALIAN'),\n", " ('PAYING', 'PAID', 'SAYING', 'SAID'),\n", " ('MAN', 'MEN', 'CAR', 'CARS')],\n", " 'incorrect': [('HE', 'SHE', 'HIS', 'HER'),\n", " ('HIS', 'HER', 'HE', 'SHE'),\n", " ('GOOD', 'BETTER', 'GREAT', 'GREATER'),\n", " ('GOOD', 'BETTER', 'LONG', 'LONGER'),\n", " ('GOOD', 'BETTER', 'LOW', 'LOWER'),\n", " ('GREAT', 'GREATER', 'LONG', 'LONGER'),\n", " ('GREAT', 'GREATER', 'GOOD', 'BETTER'),\n", " ('LONG', 'LONGER', 'GOOD', 'BETTER'),\n", " ('LONG', 'LONGER', 'GREAT', 'GREATER'),\n", " ('LOW', 'LOWER', 'GOOD', 'BETTER'),\n", " ('LOW', 'LOWER', 'LONG', 'LONGER'),\n", " ('BIG', 'BIGGEST', 'GOOD', 'BEST'),\n", " ('BIG', 'BIGGEST', 'GREAT', 'GREATEST'),\n", " ('BIG', 'BIGGEST', 'LARGE', 'LARGEST'),\n", " ('GREAT', 'GREATEST', 'GOOD', 'BEST'),\n", " ('LARGE', 'LARGEST', 'GOOD', 'BEST'),\n", " ('GO', 'GOING', 'PLAY', 'PLAYING'),\n", " ('GO', 'GOING', 'RUN', 'RUNNING'),\n", " ('LOOK', 'LOOKING', 'PLAY', 'PLAYING'),\n", " ('LOOK', 'LOOKING', 'RUN', 'RUNNING'),\n", " ('LOOK', 'LOOKING', 'SAY', 'SAYING'),\n", " ('LOOK', 'LOOKING', 'GO', 'GOING'),\n", " ('PLAY', 'PLAYING', 'RUN', 'RUNNING'),\n", " ('PLAY', 'PLAYING', 'GO', 'GOING'),\n", " ('RUN', 'RUNNING', 'SAY', 'SAYING'),\n", " ('RUN', 'RUNNING', 'GO', 'GOING'),\n", " ('RUN', 'RUNNING', 'LOOK', 'LOOKING'),\n", " ('RUN', 'RUNNING', 'PLAY', 'PLAYING'),\n", " ('SAY', 'SAYING', 'GO', 'GOING'),\n", " ('SAY', 'SAYING', 'RUN', 'RUNNING'),\n", " ('AUSTRALIA', 'AUSTRALIAN', 'FRANCE', 'FRENCH'),\n", " ('AUSTRALIA', 'AUSTRALIAN', 'SWITZERLAND', 'SWISS'),\n", " ('FRANCE', 'FRENCH', 'INDIA', 'INDIAN'),\n", " ('FRANCE', 'FRENCH', 'ISRAEL', 'ISRAELI'),\n", " ('FRANCE', 'FRENCH', 'SWITZERLAND', 'SWISS'),\n", " ('FRANCE', 'FRENCH', 'AUSTRALIA', 'AUSTRALIAN'),\n", " ('INDIA', 'INDIAN', 'ISRAEL', 'ISRAELI'),\n", " ('INDIA', 'INDIAN', 'SWITZERLAND', 'SWISS'),\n", " ('INDIA', 'INDIAN', 'FRANCE', 'FRENCH'),\n", " ('ISRAEL', 'ISRAELI', 'SWITZERLAND', 'SWISS'),\n", " ('ISRAEL', 'ISRAELI', 'AUSTRALIA', 'AUSTRALIAN'),\n", " ('ISRAEL', 'ISRAELI', 'FRANCE', 'FRENCH'),\n", " ('ISRAEL', 'ISRAELI', 'INDIA', 'INDIAN'),\n", " ('SWITZERLAND', 'SWISS', 'AUSTRALIA', 'AUSTRALIAN'),\n", " ('SWITZERLAND', 'SWISS', 'FRANCE', 'FRENCH'),\n", " ('SWITZERLAND', 'SWISS', 'INDIA', 'INDIAN'),\n", " ('SWITZERLAND', 'SWISS', 'ISRAEL', 'ISRAELI'),\n", " ('GOING', 'WENT', 'PAYING', 'PAID'),\n", " ('GOING', 'WENT', 'PLAYING', 'PLAYED'),\n", " ('GOING', 'WENT', 'SAYING', 'SAID'),\n", " ('GOING', 'WENT', 'TAKING', 'TOOK'),\n", " ('PAYING', 'PAID', 'PLAYING', 'PLAYED'),\n", " ('PAYING', 'PAID', 'TAKING', 'TOOK'),\n", " ('PAYING', 'PAID', 'GOING', 'WENT'),\n", " ('PLAYING', 'PLAYED', 'SAYING', 'SAID'),\n", " ('PLAYING', 'PLAYED', 'TAKING', 'TOOK'),\n", " ('PLAYING', 'PLAYED', 'GOING', 'WENT'),\n", " ('PLAYING', 'PLAYED', 'PAYING', 'PAID'),\n", " ('SAYING', 'SAID', 'TAKING', 'TOOK'),\n", " ('SAYING', 'SAID', 'GOING', 'WENT'),\n", " ('SAYING', 'SAID', 'PAYING', 'PAID'),\n", " ('SAYING', 'SAID', 'PLAYING', 'PLAYED'),\n", " ('TAKING', 'TOOK', 'GOING', 'WENT'),\n", " ('TAKING', 'TOOK', 'PAYING', 'PAID'),\n", " ('TAKING', 'TOOK', 'PLAYING', 'PLAYED'),\n", " ('TAKING', 'TOOK', 'SAYING', 'SAID'),\n", " ('BUILDING', 'BUILDINGS', 'CAR', 'CARS'),\n", " ('BUILDING', 'BUILDINGS', 'CHILD', 'CHILDREN'),\n", " ('BUILDING', 'BUILDINGS', 'MAN', 'MEN'),\n", " ('CAR', 'CARS', 'CHILD', 'CHILDREN'),\n", " ('CAR', 'CARS', 'MAN', 'MEN'),\n", " ('CAR', 'CARS', 'BUILDING', 'BUILDINGS'),\n", " ('CHILD', 'CHILDREN', 'MAN', 'MEN'),\n", " ('CHILD', 'CHILDREN', 'BUILDING', 'BUILDINGS'),\n", " ('CHILD', 'CHILDREN', 'CAR', 'CARS'),\n", " ('MAN', 'MEN', 'BUILDING', 'BUILDINGS'),\n", " ('MAN', 'MEN', 'CHILD', 'CHILDREN')]}]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_wrapper.accuracy(questions=datapath('questions-words.txt'))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.1133396301045417" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Word Movers distance\n", "sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()\n", "sentence_president = 'The president greets the press in Chicago'.lower().split()\n", "\n", "# Remove their stopwords.\n", "from nltk.corpus import stopwords\n", "stopwords = stopwords.words('english')\n", "sentence_obama = [w for w in sentence_obama if w not in stopwords]\n", "sentence_president = [w for w in sentence_president if w not in stopwords]\n", "\n", "# Compute WMD.\n", "distance = model_wrapper.wmdistance(sentence_obama, sentence_president)\n", "distance" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	31,037	Python	.py	773	34.476067	519	0.547416	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,928	atmodel_tutorial.ipynb	piskvorky_gensim/docs/notebooks/atmodel_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The author-topic model: LDA with metadata\n", "\n", "In this tutorial, you will learn how to use the author-topic model in Gensim. We will apply it to a corpus consisting of scientific papers, to get insight about the authors of the papers.\n", "\n", "The author-topic model is an extension of Latent Dirichlet Allocation (LDA), that allows us to learn topic representations of authors in a corpus. The model can be applied to any kinds of labels on documents, such as tags on posts on the web. The model can be used as a novel way of data exploration, as features in machine learning pipelines, for author (or tag) prediction, or to simply leverage your topic model with existing metadata.\n", "\n", "To learn about the theoretical side of the author-topic model, see [Rosen-Zvi and co-authors 2004](https://mimno.infosci.cornell.edu/info6150/readings/398.pdf), for example. A report on the algorithm used in the Gensim implementation will be available soon.\n", "\n", "Naturally, familiarity with topic modelling, LDA and Gensim is assumed in this tutorial. If you are not familiar with either LDA, or its Gensim implementation, I would recommend starting there. Consider some of these resources:\n", "* Gentle introduction to the LDA model: http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/\n", "* Gensim's LDA API documentation: https://radimrehurek.com/gensim/models/ldamodel.html\n", "* Topic modelling in Gensim: https://radimrehurek.com/topic_modeling_tutorial/2%20-%20Topic%20Modeling.html\n", "* [Pre-processing and training LDA](lda_training_tips.ipynb)\n", "\n", "\n", "> NOTE:\n", ">\n", "> To run this tutorial on your own, install Jupyter, Gensim, SpaCy, Scikit-Learn, Bokeh and Pandas, e.g. using pip:\n", ">\n", "> `pip install jupyter gensim spacy sklearn bokeh pandas`\n", ">\n", "> Note that you need to download some data for SpaCy using `python -m spacy.en.download`.\n", ">\n", "> Download the notebook at https://github.com/RaRe-Technologies/gensim/tree/develop/docs/notebooks/atmodel_tutorial.ipynb.\n", "\n", "In this tutorial, we will learn how to prepare data for the model, how to train it, and how to explore the resulting representation in different ways. We will inspect the topic representation of some well known authors like Geoffrey Hinton and Yann LeCun, and compare authors by plotting them in reduced dimensionality and performing similarity queries.\n", "\n", "## Analyzing scientific papers\n", "\n", "The data we will be using consists of scientific papers about machine learning, from the Neural Information Processing Systems conference (NIPS). It is the same dataset used in the [Pre-processing and training LDA](lda_training_tips.ipynb) tutorial, mentioned earlier.\n", "\n", "We will be performing qualitative analysis of the model, and at times this will require an understanding of the subject matter of the data. If you try running this tutorial on your own, consider applying it on a dataset with subject matter that you are familiar with. For example, try one of the [StackExchange datadump datasets](https://archive.org/details/stackexchange).\n", "\n", "You can download the data from Sam Roweis' website (http://www.cs.nyu.edu/~roweis/data.html). Or just run the cell below, and it will be downloaded and extracted into your `tmp." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2017-01-16 12:29:12-- http://www.cs.nyu.edu/~roweis/data/nips12raw_str602.tgz\n", "Resolving www.cs.nyu.edu (www.cs.nyu.edu)... 128.122.49.30\n", "Connecting to www.cs.nyu.edu (www.cs.nyu.edu)\|128.122.49.30\|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 12851423 (12M) [application/x-gzip]\n", "Saving to: ‘STDOUT’\n", "\n", "- 100%[===================>] 12.26M 3.33MB/s in 4.9s \n", "\n", "2017-01-16 12:29:18 (2.49 MB/s) - written to stdout [12851423/12851423]\n", "\n" ] } ], "source": [ "!wget -O - 'http://www.cs.nyu.edu/~roweis/data/nips12raw_str602.tgz' > /tmp/nips12raw_str602.tgz" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import tarfile\n", "\n", "filename = '/tmp/nips12raw_str602.tgz'\n", "tar = tarfile.open(filename, 'r:gz')\n", "for item in tar:\n", " tar.extract(item, path='/tmp')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following sections we will load the data, pre-process it, train the model, and explore the results using some of the implementation's functionality. Feel free to skip the loading and pre-processing for now, if you are familiar with the process.\n", "\n", "### Loading the data\n", "\n", "In the cell below, we crawl the folders and files in the dataset, and read the files into memory." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import os, re\n", "from smart_open import smart_open\n", "\n", "# Folder containing all NIPS papers.\n", "data_dir = '/tmp/nipstxt/' # Set this path to the data on your machine.\n", "\n", "# Folders containin individual NIPS papers.\n", "yrs = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']\n", "dirs = ['nips' + yr for yr in yrs]\n", "\n", "# Get all document texts and their corresponding IDs.\n", "docs = []\n", "doc_ids = []\n", "for yr_dir in dirs:\n", " files = os.listdir(data_dir + yr_dir) # List of filenames.\n", " for filen in files:\n", " # Get document ID.\n", " (idx1, idx2) = re.search('[0-9]+', filen).span() # Matches the indexes of the start end end of the ID.\n", " doc_ids.append(yr_dir[4:] + '_' + str(int(filen[idx1:idx2])))\n", " \n", " # Read document text.\n", " # Note: ignoring characters that cause encoding errors.\n", " with smart_open(data_dir + yr_dir + '/' + filen, encoding='utf-8', 'rb') as fid:\n", " txt = fid.read()\n", " \n", " # Replace any whitespace (newline, tabs, etc.) by a single space.\n", " txt = re.sub('\\s', ' ', txt)\n", " \n", " docs.append(txt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct a mapping from author names to document IDs." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from smart_open import smart_open\n", "filenames = [data_dir + 'idx/a' + yr + '.txt' for yr in yrs] # Using the years defined in previous cell.\n", "\n", "# Get all author names and their corresponding document IDs.\n", "author2doc = dict()\n", "i = 0\n", "for yr in yrs:\n", " # The files \"a00.txt\" and so on contain the author-document mappings.\n", " filename = data_dir + 'idx/a' + yr + '.txt'\n", " for line in smart_open(filename, errors='ignore', encoding='utf-8', 'rb'):\n", " # Each line corresponds to one author.\n", " contents = re.split(',', line)\n", " author_name = (contents[1] + contents[0]).strip()\n", " # Remove any whitespace to reduce redundant author names.\n", " author_name = re.sub('\\s', '', author_name)\n", " # Get document IDs for author.\n", " ids = [c.strip() for c in contents[2:]]\n", " if not author2doc.get(author_name):\n", " # This is a new author.\n", " author2doc[author_name] = []\n", " i += 1\n", " \n", " # Add document IDs to author.\n", " author2doc[author_name].extend([yr + '_' + id for id in ids])\n", "\n", "# Use an integer ID in author2doc, instead of the IDs provided in the NIPS dataset.\n", "# Mapping from ID of document in NIPS datast, to an integer ID.\n", "doc_id_dict = dict(zip(doc_ids, range(len(doc_ids))))\n", "# Replace NIPS IDs by integer IDs.\n", "for a, a_doc_ids in author2doc.items():\n", " for i, doc_id in enumerate(a_doc_ids):\n", " author2doc[a][i] = doc_id_dict[doc_id]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pre-processing text\n", "\n", "The text will be pre-processed using the following steps:\n", "* Tokenize text.\n", "* Replace all whitespace by single spaces.\n", "* Remove all punctuation and numbers.\n", "* Remove stopwords.\n", "* Lemmatize words.\n", "* Add multi-word named entities.\n", "* Add frequent bigrams.\n", "* Remove frequent and rare words.\n", "\n", "A lot of the heavy lifting will be done by the great package, Spacy. Spacy markets itself as \"industrial-strength natural language processing\", is fast, enables multiprocessing, and is easy to use. First, let's import it and load the NLP pipline in english." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import spacy\n", "nlp = spacy.load('en')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the code below, Spacy takes care of tokenization, removing non-alphabetic characters, removal of stopwords, lemmatization and named entity recognition.\n", "\n", "Note that we only keep named entities that consist of more than one word, as single word named entities are already there." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 9min 6s, sys: 276 ms, total: 9min 7s\n", "Wall time: 2min 52s\n" ] } ], "source": [ "%%time\n", "processed_docs = [] \n", "for doc in nlp.pipe(docs, n_threads=4, batch_size=100):\n", " # Process document using Spacy NLP pipeline.\n", " \n", " ents = doc.ents # Named entities.\n", "\n", " # Keep only words (no numbers, no punctuation).\n", " # Lemmatize tokens, remove punctuation and remove stopwords.\n", " doc = [token.lemma_ for token in doc if token.is_alpha and not token.is_stop]\n", "\n", " # Remove common words from a stopword list.\n", " #doc = [token for token in doc if token not in STOPWORDS]\n", "\n", " # Add named entities, but only if they are a compound of more than word.\n", " doc.extend([str(entity) for entity in ents if len(entity) > 1])\n", " \n", " processed_docs.append(doc)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "docs = processed_docs\n", "del processed_docs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we use a Gensim model to add bigrams. Note that this achieves the same goal as named entity recognition, that is, finding adjacent words that have some particular significance." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/olavur/Dropbox/my_folder/workstuff/DTU/thesis/code/gensim/gensim/models/phrases.py:248: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n", " warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n" ] } ], "source": [ "# Compute bigrams.\n", "from gensim.models import Phrases\n", "# Add bigrams and trigrams to docs (only ones that appear 20 times or more).\n", "bigram = Phrases(docs, min_count=20)\n", "for idx in range(len(docs)):\n", " for token in bigram[docs[idx]]:\n", " if '_' in token:\n", " # Token is a bigram, add to document.\n", " docs[idx].append(token)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to construct a dictionary, as our vocabulary is finalized. We then remove common words (occurring $> 50\\%$ of the time), and rare words (occur $< 20$ times in total)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Create a dictionary representation of the documents, and filter out frequent and rare words.\n", "\n", "from gensim.corpora import Dictionary\n", "dictionary = Dictionary(docs)\n", "\n", "# Remove rare and common tokens.\n", "# Filter out words that occur too frequently or too rarely.\n", "max_freq = 0.5\n", "min_wordcount = 20\n", "dictionary.filter_extremes(no_below=min_wordcount, no_above=max_freq)\n", "\n", "_ = dictionary[0] # This sort of \"initializes\" dictionary.id2token." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We produce the vectorized representation of the documents, to supply the author-topic model with, by computing the bag-of-words." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Vectorize data.\n", "\n", "# Bag-of-words representation of the documents.\n", "corpus = [dictionary.doc2bow(doc) for doc in docs]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's inspect the dimensionality of our data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of authors: 2479\n", "Number of unique tokens: 6996\n", "Number of documents: 1740\n" ] } ], "source": [ "print('Number of authors: %d' % len(author2doc))\n", "print('Number of unique tokens: %d' % len(dictionary))\n", "print('Number of documents: %d' % len(corpus))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train and use model\n", "\n", "We train the author-topic model on the data prepared in the previous sections. \n", "\n", "The interface to the author-topic model is very similar to that of LDA in Gensim. In addition to a corpus, ID to word mapping (`id2word`) and number of topics (`num_topics`), the author-topic model requires either an author to document ID mapping (`author2doc`), or the reverse (`doc2author`).\n", "\n", "Below, we have also (this can be skipped for now):\n", "* Increased the number of `passes` over the dataset (to improve the convergence of the optimization problem).\n", "* Decreased the number of `iterations` over each document (related to the above).\n", "* Specified the mini-batch size (`chunksize`) (primarily to speed up training).\n", "* Turned off bound evaluation (`eval_every`) (as it takes a long time to compute).\n", "* Turned on automatic learning of the `alpha` and `eta` priors (to improve the convergence of the optimization problem).\n", "* Set the random state (`random_state`) of the random number generator (to make these experiments reproducible).\n", "\n", "We load the model, and train it." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.56 s, sys: 316 ms, total: 3.87 s\n", "Wall time: 3.65 s\n" ] } ], "source": [ "from gensim.models import AuthorTopicModel\n", "%time model = AuthorTopicModel(corpus=corpus, num_topics=10, id2word=dictionary.id2token, \\\n", " author2doc=author2doc, chunksize=2000, passes=1, eval_every=0, \\\n", " iterations=1, random_state=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you believe your model hasn't converged, you can continue training using `model.update()`. If you have additional documents and/or authors call `model.update(corpus, author2doc)`.\n", "\n", "Before we explore the model, let's try to improve upon it. To do this, we will train several models with different random initializations, by giving different seeds for the random number generator (`random_state`). We evaluate the topic coherence of the model using the [top_topics](https://radimrehurek.com/gensim/models/ldamodel.html#gensim.models.ldamodel.LdaModel.top_topics) method, and pick the model with the highest topic coherence.\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 11min 59s, sys: 2min 14s, total: 14min 13s\n", "Wall time: 11min 41s\n" ] } ], "source": [ "%%time\n", "model_list = []\n", "for i in range(5):\n", " model = AuthorTopicModel(corpus=corpus, num_topics=10, id2word=dictionary.id2token, \\\n", " author2doc=author2doc, chunksize=2000, passes=100, gamma_threshold=1e-10, \\\n", " eval_every=0, iterations=1, random_state=i)\n", " top_topics = model.top_topics(corpus)\n", " tc = sum([t[1] for t in top_topics])\n", " model_list.append((model, tc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Choose the model with the highest topic coherence." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Topic coherence: -1.847e+03\n" ] } ], "source": [ "model, tc = max(model_list, key=lambda x: x[1])\n", "print('Topic coherence: %.3e' %tc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We save the model, to avoid having to train it again, and also show how to load it again." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Save model.\n", "model.save('/tmp/model.atmodel')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Load model.\n", "model = AuthorTopicModel.load('/tmp/model.atmodel')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Explore author-topic representation\n", "\n", "Now that we have trained a model, we can start exploring the authors and the topics.\n", "\n", "First, let's simply print the most important words in the topics. Below we have printed topic 0. As we can see, each topic is associated with a set of words, and each word has a probability of being expressed under that topic." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('chip', 0.014645100754555081),\n", " ('circuit', 0.011967493386263996),\n", " ('analog', 0.011466032752399413),\n", " ('control', 0.010067258628938444),\n", " ('implementation', 0.0078096719430403956),\n", " ('design', 0.0072620826472022419),\n", " ('implement', 0.0063648695668359189),\n", " ('signal', 0.0063389759280913392),\n", " ('vlsi', 0.0059415519461153785),\n", " ('processor', 0.0056545823226162124)]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.show_topic(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we have given each topic a label based on what each topic seems to be about intuitively. " ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "topic_labels = ['Circuits', 'Neuroscience', 'Numerical optimization', 'Object recognition', \\\n", " 'Math/general', 'Robotics', 'Character recognition', \\\n", " 'Reinforcement learning', 'Speech recognition', 'Bayesian modelling']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rather than just calling `model.show_topics(num_topics=10)`, we format the output a bit so it is easier to get an overview." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Label: Circuits\n", "Words: chip circuit analog control implementation design implement signal vlsi processor \n", "\n", "Label: Neuroscience\n", "Words: neuron cell spike response synaptic activity frequency stimulus synapse signal \n", "\n", "Label: Numerical optimization\n", "Words: gradient noise prediction w optimal nonlinear matrix approximation series variance \n", "\n", "Label: Object recognition\n", "Words: image visual object motion field direction representation map position orientation \n", "\n", "Label: Math/general\n", "Words: bound f generalization class let w p theorem y threshold \n", "\n", "Label: Robotics\n", "Words: dynamic control field trajectory neuron motor net forward l movement \n", "\n", "Label: Character recognition\n", "Words: node distance character layer recognition matrix image sequence p code \n", "\n", "Label: Reinforcement learning\n", "Words: action policy q reinforcement rule control optimal representation environment sequence \n", "\n", "Label: Speech recognition\n", "Words: recognition speech word layer classifier net classification hidden class context \n", "\n", "Label: Bayesian modelling\n", "Words: mixture gaussian likelihood prior data bayesian density sample cluster posterior \n", "\n" ] } ], "source": [ "for topic in model.show_topics(num_topics=10):\n", " print('Label: ' + topic_labels[topic[0]])\n", " words = ''\n", " for word, prob in model.show_topic(topic[0]):\n", " words += word + ' '\n", " print('Words: ' + words)\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These topics are by no means perfect. They have problems such as chained topics, intruded words, random topics, and unbalanced topics (see [Mimno and co-authors 2011](https://people.cs.umass.edu/~wallach/publications/mimno11optimizing.pdf)). They will do for the purposes of this tutorial, however.\n", "\n", "Below, we use the `model[name]` syntax to retrieve the topic distribution for an author. Each topic has a probability of being expressed given the particular author, but only the ones above a certain threshold are shown." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(6, 0.99976720177983869)]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model['YannLeCun']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's print the top topics of some authors. First, we make a function to help us do this more easily." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pprint import pprint\n", "\n", "def show_author(name):\n", " print('\\n%s' % name)\n", " print('Docs:', model.author2doc[name])\n", " print('Topics:')\n", " pprint([(topic_labels[topic[0]], topic[1]) for topic in model[name]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we print some high profile researchers and inspect them. Three of these, Yann LeCun, Geoffrey E. Hinton and Christof Koch, are spot on. \n", "\n", "Terrence J. Sejnowski's results are surprising, however. He is a neuroscientist, so we would expect him to get the \"neuroscience\" label. This may indicate that Sejnowski works with the neuroscience aspects of visual perception, or perhaps that we have labeled the topic incorrectly, or perhaps that this topic simply is not very informative." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "YannLeCun\n", "Docs: [143, 406, 370, 495, 456, 449, 595, 616, 760, 752, 1532]\n", "Topics:\n", "[('Character recognition', 0.99976720177983869)]\n" ] } ], "source": [ "show_author('YannLeCun')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "GeoffreyE.Hinton\n", "Docs: [56, 143, 284, 230, 197, 462, 463, 430, 688, 784, 826, 848, 869, 1387, 1684, 1728]\n", "Topics:\n", "[('Object recognition', 0.42128917017624745),\n", " ('Math/general', 0.043249835412857811),\n", " ('Robotics', 0.11149925993091593),\n", " ('Bayesian modelling', 0.42388500261455564)]\n" ] } ], "source": [ "show_author('GeoffreyE.Hinton')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "TerrenceJ.Sejnowski\n", "Docs: [513, 530, 539, 468, 611, 581, 600, 594, 703, 711, 849, 981, 944, 865, 850, 883, 881, 1221, 1137, 1224, 1146, 1282, 1248, 1179, 1424, 1359, 1528, 1484, 1571, 1727, 1732]\n", "Topics:\n", "[('Object recognition', 0.99992379088787087)]\n" ] } ], "source": [ "show_author('TerrenceJ.Sejnowski')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ChristofKoch\n", "Docs: [9, 221, 266, 272, 349, 411, 337, 371, 450, 483, 653, 663, 754, 712, 778, 921, 1212, 1285, 1254, 1533, 1489, 1580, 1441, 1657]\n", "Topics:\n", "[('Neuroscience', 0.99989393011046035)]\n" ] } ], "source": [ "show_author('ChristofKoch')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Simple model evaluation methods\n", "\n", "We can compute the per-word bound, which is a measure of the model's predictive performance (you could also say that it is the reconstruction error).\n", "\n", "To do that, we need the `doc2author` dictionary, which we can build automatically." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from gensim.models import atmodel\n", "doc2author = atmodel.construct_doc2author(model.corpus, model.author2doc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's evaluate the per-word bound." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-6.9955968712\n" ] } ], "source": [ "# Compute the per-word bound.\n", "# Number of words in corpus.\n", "corpus_words = sum(cnt for document in model.corpus for _, cnt in document)\n", "\n", "# Compute bound and divide by number of words.\n", "perwordbound = model.bound(model.corpus, author2doc=model.author2doc, \\\n", " doc2author=model.doc2author) / corpus_words\n", "print(perwordbound)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can evaluate the quality of the topics by computing the topic coherence, as in the LDA class. Use this to e.g. find out which of the topics are poor quality, or as a metric for model selection." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 15.6 s, sys: 4 ms, total: 15.6 s\n", "Wall time: 15.6 s\n" ] } ], "source": [ "%time top_topics = model.top_topics(model.corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting the authors\n", "\n", "Now we're going to produce the kind of pacific archipelago looking plot below. The goal of this plot is to give you a way to explore the author-topic representation in an intuitive manner.\n", "\n", "We take all the author-topic distributions (stored in `model.state.gamma`) and embed them in a 2D space. To do this, we reduce the dimensionality of this data using t-SNE. \n", "\n", "t-SNE is a method that attempts to reduce the dimensionality of a dataset, while maintaining the distances between the points. That means that if two authors are close together in the plot below, then their topic distributions are similar.\n", "\n", "In the cell below, we transform the author-topic representation into the t-SNE space. You can increase the `smallest_author` value if you do not want to view all the authors with few documents." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 35.4 s, sys: 1.16 s, total: 36.5 s\n", "Wall time: 36.4 s\n" ] } ], "source": [ "%%time\n", "from sklearn.manifold import TSNE\n", "tsne = TSNE(n_components=2, random_state=0)\n", "smallest_author = 0 # Ignore authors with documents less than this.\n", "authors = [model.author2id[a] for a in model.author2id.keys() if len(model.author2doc[a]) >= smallest_author]\n", "_ = tsne.fit_transform(model.state.gamma[authors, :]) # Result stored in tsne.embedding_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now ready to make the plot.\n", "\n", "Note that if you run this notebook yourself, you will see a different graph. The random initialization of the model will be different, and the result will thus be different to some degree. You may find an entirely different representation of the data, or it may show the same interpretation slightly differently.\n", "\n", "If you can't see the plot, you are probably viewing this tutorial in a Jupyter Notebook. View it in an nbviewer instead at http://nbviewer.jupyter.org/github/rare-technologies/gensim/blob/develop/docs/notebooks/atmodel_tutorial.ipynb." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"c8922b96-b8ff-4ac3-b6c6-882014f91988\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = \"1\";\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" \|\| force !== \"\") {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", "\n", "\n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" \|\| force !== \"\") {\n", " window._bokeh_timeout = Date.now() + 5000;\n", " window._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. 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i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === \"1\") {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (!force) {\n", " var cell = $(\"#c8922b96-b8ff-4ac3-b6c6-882014f91988\").parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(this));" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Tell Bokeh to display plots inside the notebook.\n", "from bokeh.io import output_notebook\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"bk-root\">\n", " <div class=\"plotdiv\" id=\"84a208a9-32ce-4111-b9e4-776e60455d02\"></div>\n", " </div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " var force = \"\";\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" \|\| force !== \"\") {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", " \n", " \n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" \|\| force !== \"\") {\n", " window._bokeh_timeout = Date.now() + 0;\n", " window._bokeh_failed_load = false;\n", " }\n", " \n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. 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Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", " \n", " function display_loaded() {\n", " if (window.Bokeh !== undefined) {\n", " Bokeh.$(\"#84a208a9-32ce-4111-b9e4-776e60455d02\").text(\"BokehJS successfully loaded.\");\n", " } else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " 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Application\",\"version\":\"0.12.3\"}};\n", " var render_items = [{\"docid\":\"e64bc00d-c5e8-48c6-85d7-9d719e821b4d\",\"elementid\":\"84a208a9-32ce-4111-b9e4-776e60455d02\",\"modelid\":\"5fca9abb-8d94-4900-a388-d2a3b67dc489\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " });\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " \n", " if ((window.Bokeh !== undefined) \|\| (force === \"1\")) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === \"1\") {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (!force) {\n", " var cell = $(\"#84a208a9-32ce-4111-b9e4-776e60455d02\").parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " \n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.models import HoverTool\n", "from bokeh.plotting import figure, show, ColumnDataSource\n", "\n", "x = tsne.embedding_[:, 0]\n", "y = tsne.embedding_[:, 1]\n", "author_names = [model.id2author[a] for a in authors]\n", "\n", "# Radius of each point corresponds to the number of documents attributed to that author.\n", "scale = 0.1\n", "author_sizes = [len(model.author2doc[a]) for a in author_names]\n", "radii = [size * scale for size in author_sizes]\n", "\n", "source = ColumnDataSource(\n", " data=dict(\n", " x=x,\n", " y=y,\n", " author_names=author_names,\n", " author_sizes=author_sizes,\n", " radii=radii,\n", " )\n", " )\n", "\n", "# Add author names and sizes to mouse-over info.\n", "hover = HoverTool(\n", " tooltips=[\n", " (\"author\", \"@author_names\"),\n", " (\"size\", \"@author_sizes\"),\n", " ]\n", " )\n", "\n", "p = figure(tools=[hover, 'crosshair,pan,wheel_zoom,box_zoom,reset,save,lasso_select'])\n", "p.scatter('x', 'y', radius='radii', source=source, fill_alpha=0.6, line_color=None)\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The circles in the plot above are individual authors, and their sizes represent the number of documents attributed to the corresponding author. Hovering your mouse over the circles will tell you the name of the authors and their sizes. Large clusters of authors tend to reflect some overlap in interest. \n", "\n", "We see that the model tends to put duplicate authors close together. For example, Terrence J. Sejnowki and T. J. Sejnowski are the same person, and their vectors end up in the same place (see about $(-10, -10)$ in the plot).\n", "\n", "At about $(-15, -10)$ we have a cluster of neuroscientists like Christof Koch and James M. Bower. \n", "\n", "As discussed earlier, the \"object recognition\" topic was assigned to Sejnowski. If we get the topics of the other authors in Sejnoski's neighborhood, like Peter Dayan, we also get this same topic. Furthermore, we see that this cluster is close to the \"neuroscience\" cluster discussed above, which is further indication that this topic is about visual perception in the brain.\n", "\n", "Other clusters include a reinforcement learning cluster at about $(-5, 8)$, and a Bayesian modelling cluster at about $(8, -12)$.\n", "\n", "#### Similarity queries\n", "\n", "In this section, we are going to set up a system that takes the name of an author and yields the authors that are most similar. This functionality can be used as a component in an information retrieval (i.e. a search engine of some kind), or in an author prediction system, i.e. a system that takes an unlabelled document and predicts the author(s) that wrote it.\n", "\n", "We simply need to search for the closest vector in the author-topic space. In this sense, the approach is similar to the t-SNE plot above.\n", "\n", "Below we illustrate a similarity query using a built-in similarity framework in Gensim." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from gensim.similarities import MatrixSimilarity\n", "\n", "# Generate a similarity object for the transformed corpus.\n", "index = MatrixSimilarity(model[list(model.id2author.values())])\n", "\n", "# Get similarities to some author.\n", "author_name = 'YannLeCun'\n", "sims = index[model[author_name]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, this framework uses the cosine distance, but we want to use the Hellinger distance. The Hellinger distance is a natural way of measuring the distance (i.e. dis-similarity) between two probability distributions. Its discrete version is defined as\n", "$$\n", "H(p, q) = \\frac{1}{\\sqrt{2}} \\sqrt{\\sum_{i=1}^K (\\sqrt{p_i} - \\sqrt{q_i})^2},\n", "$$\n", "\n", "where $p$ and $q$ are both topic distributions for two different authors. We define the similarity as\n", "$$\n", "S(p, q) = \\frac{1}{1 + H(p, q)}.\n", "$$\n", "\n", "In the cell below, we prepare everything we need to perform similarity queries based on the Hellinger distance." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Make a function that returns similarities based on the Hellinger distance.\n", "\n", "from gensim import matutils\n", "import pandas as pd\n", "\n", "# Make a list of all the author-topic distributions.\n", "author_vecs = [model.get_author_topics(author) for author in model.id2author.values()]\n", "\n", "def similarity(vec1, vec2):\n", " '''Get similarity between two vectors'''\n", " dist = matutils.hellinger(matutils.sparse2full(vec1, model.num_topics), \\\n", " matutils.sparse2full(vec2, model.num_topics))\n", " sim = 1.0 / (1.0 + dist)\n", " return sim\n", "\n", "def get_sims(vec):\n", " '''Get similarity of vector to all authors.'''\n", " sims = [similarity(vec, vec2) for vec2 in author_vecs]\n", " return sims\n", "\n", "def get_table(name, top_n=10, smallest_author=1):\n", " '''\n", " Get table with similarities, author names, and author sizes.\n", " Return `top_n` authors as a dataframe.\n", " \n", " '''\n", " \n", " # Get similarities.\n", " sims = get_sims(model.get_author_topics(name))\n", "\n", " # Arrange author names, similarities, and author sizes in a list of tuples.\n", " table = []\n", " for elem in enumerate(sims):\n", " author_name = model.id2author[elem[0]]\n", " sim = elem[1]\n", " author_size = len(model.author2doc[author_name])\n", " if author_size >= smallest_author:\n", " table.append((author_name, sim, author_size))\n", " \n", " # Make dataframe and retrieve top authors.\n", " df = pd.DataFrame(table, columns=['Author', 'Score', 'Size'])\n", " df = df.sort_values('Score', ascending=False)[:top_n]\n", " \n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can find the most similar authors to some particular author. We use the Pandas library to print the results in a nice looking tables." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Author</th>\n", " <th>Score</th>\n", " <th>Size</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2422</th>\n", " <td>YannLeCun</td>\n", " <td>1.000000</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>1717</th>\n", " <td>PatriceSimard</td>\n", " <td>0.999977</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>986</th>\n", " <td>J.S.Denker</td>\n", " <td>0.999581</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2425</th>\n", " <td>YaserAbu-Mostafa</td>\n", " <td>0.998040</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1160</th>\n", " <td>JohnS.Denker</td>\n", " <td>0.903560</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>187</th>\n", " <td>AntoninaStarita</td>\n", " <td>0.901699</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1718</th>\n", " <td>PatriceY.Simard</td>\n", " <td>0.899005</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>560</th>\n", " <td>DiegoSona</td>\n", " <td>0.876237</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>612</th>\n", " <td>EduardSackinger</td>\n", " <td>0.870400</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2413</th>\n", " <td>Y.LeCun</td>\n", " <td>0.868843</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Author Score Size\n", "2422 YannLeCun 1.000000 11\n", "1717 PatriceSimard 0.999977 8\n", "986 J.S.Denker 0.999581 3\n", "2425 YaserAbu-Mostafa 0.998040 5\n", "1160 JohnS.Denker 0.903560 6\n", "187 AntoninaStarita 0.901699 1\n", "1718 PatriceY.Simard 0.899005 4\n", "560 DiegoSona 0.876237 1\n", "612 EduardSackinger 0.870400 3\n", "2413 Y.LeCun 0.868843 2" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_table('YannLeCun')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, we can specify the minimum author size." ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Author</th>\n", " <th>Score</th>\n", " <th>Size</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>118</th>\n", " <td>JamesM.Bower</td>\n", " <td>1.000000</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>ChristofKoch</td>\n", " <td>0.999967</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>182</th>\n", " <td>MatthewA.Wilson</td>\n", " <td>0.999879</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>157</th>\n", " <td>L.F.Abbott</td>\n", " <td>0.999872</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>256</th>\n", " <td>StephenP.DeWeerth</td>\n", " <td>0.999869</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td>EveMarder</td>\n", " <td>0.999828</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>GirishN.Patel</td>\n", " <td>0.856874</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>ChdstofKoch</td>\n", " <td>0.788195</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>291</th>\n", " <td>WilliamBialek</td>\n", " <td>0.786987</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>247</th>\n", " <td>Shih-ChiiLiu</td>\n", " <td>0.781643</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Author Score Size\n", "118 JamesM.Bower 1.000000 10\n", "44 ChristofKoch 0.999967 24\n", "182 MatthewA.Wilson 0.999879 3\n", "157 L.F.Abbott 0.999872 4\n", "256 StephenP.DeWeerth 0.999869 5\n", "82 EveMarder 0.999828 3\n", "96 GirishN.Patel 0.856874 3\n", "43 ChdstofKoch 0.788195 3\n", "291 WilliamBialek 0.786987 4\n", "247 Shih-ChiiLiu 0.781643 3" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_table('JamesM.Bower', smallest_author=3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Serialized corpora\n", "\n", "The `AuthorTopicModel` class accepts serialized corpora, that is, corpora that are stored on the hard-drive rather than in memory. This is usually done when the corpus is too big to fit in memory. There are, however, some caveats to this functionality, which we will discuss here. As these caveats make this functionality less than ideal, it may be improved in the future.\n", "\n", "It is not necessary to read this section if you don't intend to use serialized corpora.\n", "\n", "In the following, an explanation, followed by an example and a summarization will be given.\n", "\n", "If the corpus is serialized, the user must specify `serialized=True`. Any input corpus can then be any type of iterable or generator.\n", "\n", "The model will then take the input corpus and serialize it in the `MmCorpus` format, which is [supported in Gensim](https://radimrehurek.com/gensim/corpora/mmcorpus.html).\n", "\n", "The user must specify the path where the model should serialize all input documents, for example `serialization_path='/tmp/model_serializer.mm'`. To avoid accidentally overwriting some important data, the model will raise an error if there already exists a file at `serialization_path`; in this case, either choose another path, or delete the old file.\n", "\n", "When you want to train on new data, and call `model.update(corpus, author2doc)`, all the old data and the new data have to be re-serialized. This can of course be quite computationally demanding, so it is recommended that you do this only when necessary; that is, wait until you have as much new data as possible to update, rather than updating the model for every new document." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 17.6 s, sys: 540 ms, total: 18.1 s\n", "Wall time: 17.7 s\n" ] } ], "source": [ "%time model_ser = AuthorTopicModel(corpus=corpus, num_topics=10, id2word=dictionary.id2token, \\\n", " author2doc=author2doc, random_state=1, serialized=True, \\\n", " serialization_path='/tmp/model_serialization.mm')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# Delete the file, once you're done using it.\n", "import os\n", "os.remove('/tmp/model_serialization.mm')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In summary, when using serialized corpora:\n", "* Set `serialized=True`.\n", "* Set `serialization_path` to a path that doesn't already contain a file.\n", "* Wait until you have lots of data before you call `model.update(corpus, author2doc)`.\n", "* When done, delete the file at `serialization_path` if it's not needed anymore." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What to try next\n", "\n", "Try the model on one of the datasets in the [StackExchange data dump](https://archive.org/details/stackexchange). You can treat the tags on the posts as authors and train a \"tag-topic\" model. There are many different categories, from statistics to cooking to philosophy, so you can pick on that you like. You can even try your hand at a [Kaggle competition](https://www.kaggle.com/c/transfer-learning-on-stack-exchange-tags) that uses tags in this dataset.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	225,681	Python	.py	1,691	127.881727	162,926	0.712862	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,929	lda_training_tips.ipynb	piskvorky_gensim/docs/notebooks/lda_training_tips.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,930	doc2vec-wikipedia.ipynb	piskvorky_gensim/docs/notebooks/doc2vec-wikipedia.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training Doc2Vec on Wikipedia articles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook replicates the Document Embedding with Paragraph Vectors paper, http://arxiv.org/abs/1507.07998.\n", "\n", "In that paper, the authors only showed results from the DBOW (\"distributed bag of words\") mode, trained on the English Wikipedia. Here we replicate this experiment using not only DBOW, but also the DM (\"distributed memory\") mode of the Paragraph Vector algorithm aka Doc2Vec." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's import the necessary modules and set up logging. The code below assumes Python 3.7+ and Gensim 4.0+." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import logging\n", "import multiprocessing\n", "from pprint import pprint\n", "\n", "import smart_open\n", "from gensim.corpora.wikicorpus import WikiCorpus, tokenize\n", "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n", "\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing the corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, download the dump of all Wikipedia articles from [here](http://download.wikimedia.org/enwiki/latest). You want the file named `enwiki-latest-pages-articles.xml.bz2`.\n", "\n", "Second, convert that Wikipedia article dump from the arcane Wikimedia XML format into a plain text file. This will make the subsequent training faster and also allow easy inspection of the data = \"input eyeballing\".\n", "\n", "We'll preprocess each article at the same time, normalizing its text to lowercase, splitting into tokens, etc. Below I use a regexp tokenizer that simply looks for alphabetic sequences as tokens. But feel free to adapt the text preprocessing to your own domain. High quality preprocessing is often critical for the final pipeline accuracy – garbage in, garbage out!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-04-16 11:23:20,663 : INFO : processing article #0: 'Anarchism' (6540 tokens)\n", "2022-04-16 11:30:53,798 : INFO : processing article #500000: 'Onward Muslim Soldiers' (517 tokens)\n", "2022-04-16 11:36:14,662 : INFO : processing article #1000000: 'Push Upstairs' (354 tokens)\n", "2022-04-16 11:40:59,785 : INFO : processing article #1500000: 'Small nucleolar RNA Z278' (113 tokens)\n", "2022-04-16 11:45:58,630 : INFO : processing article #2000000: '1925–26 Boston Bruins season' (556 tokens)\n", "2022-04-16 11:51:03,737 : INFO : processing article #2500000: 'Tessier, Saskatchewan' (119 tokens)\n", "2022-04-16 11:56:20,254 : INFO : processing article #3000000: 'Sebezhsky District' (908 tokens)\n", "2022-04-16 12:01:59,089 : INFO : processing article #3500000: 'Niko Peleshi' (248 tokens)\n", "2022-04-16 12:07:23,184 : INFO : processing article #4000000: 'Kudoa gunterae' (109 tokens)\n", "2022-04-16 12:13:08,024 : INFO : processing article #4500000: 'Danko (singer)' (699 tokens)\n", "2022-04-16 12:19:33,734 : INFO : processing article #5000000: 'Lada West Togliatti' (253 tokens)\n", "2022-04-16 12:22:20,928 : INFO : finished iterating over Wikipedia corpus of 5205168 documents with 3016298486 positions (total 21961341 articles, 3093120544 positions before pruning articles shorter than 50 words)\n" ] } ], "source": [ "wiki = WikiCorpus(\n", " \"enwiki-latest-pages-articles.xml.bz2\", # path to the file you downloaded above\n", " tokenizer_func=tokenize, # simple regexp; plug in your own tokenizer here\n", " metadata=True, # also return the article titles and ids when parsing\n", " dictionary={}, # don't start processing the data yet\n", ")\n", "\n", "with smart_open.open(\"wiki.txt.gz\", \"w\", encoding='utf8') as fout:\n", " for article_no, (content, (page_id, title)) in enumerate(wiki.get_texts()):\n", " title = ' '.join(title.split())\n", " if article_no % 500000 == 0:\n", " logging.info(\"processing article #%i: %r (%i tokens)\", article_no, title, len(content))\n", " fout.write(f\"{title}\\t{' '.join(content)}\\n\") # title_of_article [TAB] words of the article" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above took about 1 hour and created a new ~5.8 GB file named `wiki.txt.gz`. Note the output text was transparently compressed into `.gz` (GZIP) right away, using the [smart_open](https://github.com/RaRe-Technologies/smart_open) library, to save on disk space.\n", "\n", "Next we'll set up a document stream to load the preprocessed articles from `wiki.txt.gz` one by one, in the format expected by Doc2Vec, ready for training. We don't want to load everything into RAM at once, because that would blow up the memory. And it is not necessary – Gensim can handle streamed input training data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class TaggedWikiCorpus:\n", " def __init__(self, wiki_text_path):\n", " self.wiki_text_path = wiki_text_path\n", " \n", " def __iter__(self):\n", " for line in smart_open.open(self.wiki_text_path, encoding='utf8'):\n", " title, words = line.split('\\t')\n", " yield TaggedDocument(words=words.split(), tags=[title])\n", "\n", "documents = TaggedWikiCorpus('wiki.txt.gz') # A streamed iterable; nothing in RAM yet." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Anarchism'] : anarchism is political philosophy and movement that is sceptical of authority and rejects all involuntary coercive forms of hierarchy anarchism calls for the abolition of the state which it holds to be unnecessary undesirable and harmful as historically left wing movement placed on the farthest left of the political spectrum ……… criticism of philosophical anarchism defence of philosophical anarchism stating that both kinds of anarchism philosophical and political anarchism are philosophical and political claims anarchistic popular fiction novel an argument for philosophical anarchism external links anarchy archives anarchy archives is an online research center on the history and theory of anarchism\n" ] } ], "source": [ "# Load and print the first preprocessed Wikipedia document, as a sanity check = \"input eyeballing\".\n", "first_doc = next(iter(documents))\n", "print(first_doc.tags, ': ', ' '.join(first_doc.words[:50] + ['………'] + first_doc.words[-50:]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The document seems legit so let's move on to finally training some Doc2vec models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training Doc2Vec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The original paper had a vocabulary size of 915,715 word types, so we'll try to match it by setting `max_final_vocab` to 1,000,000 in the Doc2vec constructor.\n", "\n", "Other critical parameters were left unspecified in the paper, so we'll go with a window size of eight (a prediction window of 8 tokens to either side). It looks like the authors tried vector dimensionality of 100, 300, 1,000 & 10,000 in the paper (with 10k dims performing the best), but I'll only train with 200 dimensions here, to keep the RAM in check on my laptop.\n", "\n", "Feel free to tinker with these values yourself if you like:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-04-18 12:05:46,344 : INFO : Doc2Vec lifecycle event {'params': 'Doc2Vec<dbow+w,d200,n5,w8,mc5,s0.001,t20>', 'datetime': '2022-04-18T12:05:46.344471', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'created'}\n", "2022-04-18 12:05:46,345 : INFO : Doc2Vec lifecycle event {'params': 'Doc2Vec<dm/m,d200,n5,w8,mc5,s0.001,t20>', 'datetime': '2022-04-18T12:05:46.345716', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'created'}\n" ] } ], "source": [ "workers = 20 # multiprocessing.cpu_count() - 1 # leave one core for the OS & other stuff\n", "\n", "# PV-DBOW: paragraph vector in distributed bag of words mode\n", "model_dbow = Doc2Vec(\n", " dm=0, dbow_words=1, # dbow_words=1 to train word vectors at the same time too, not only DBOW\n", " vector_size=200, window=8, epochs=10, workers=workers, max_final_vocab=1000000,\n", ")\n", "\n", "# PV-DM: paragraph vector in distributed memory mode\n", "model_dm = Doc2Vec(\n", " dm=1, dm_mean=1, # use average of context word vectors to train DM\n", " vector_size=200, window=8, epochs=10, workers=workers, max_final_vocab=1000000,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run one pass through the Wikipedia corpus, to collect the 1M vocabulary and initialize the doc2vec models:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-04-18 12:05:47,311 : INFO : collecting all words and their counts\n", "2022-04-18 12:05:47,313 : INFO : PROGRESS: at example #0, processed 0 words (0 words/s), 0 word types, 0 tags\n", "2022-04-18 12:07:35,880 : INFO : PROGRESS: at example #500000, processed 656884578 words (6050478 words/s), 3221051 word types, 500000 tags\n", "2022-04-18 12:08:38,784 : INFO : PROGRESS: at example #1000000, processed 1021477892 words (5796084 words/s), 4478830 word types, 1000000 tags\n", "2022-04-18 12:09:29,607 : INFO : PROGRESS: at example #1500000, processed 1308608477 words (5649726 words/s), 5419923 word types, 1500000 tags\n", "2022-04-18 12:10:13,477 : INFO : PROGRESS: at example #2000000, processed 1554211349 words (5598537 words/s), 6190970 word types, 2000000 tags\n", "2022-04-18 12:10:56,549 : INFO : PROGRESS: at example #2500000, processed 1794853915 words (5587147 words/s), 6943275 word types, 2500000 tags\n", "2022-04-18 12:11:39,668 : INFO : PROGRESS: at example #3000000, processed 2032520202 words (5511955 words/s), 7668721 word types, 3000000 tags\n", "2022-04-18 12:12:23,192 : INFO : PROGRESS: at example #3500000, processed 2268859232 words (5430192 words/s), 8352590 word types, 3500000 tags\n", "2022-04-18 12:13:02,526 : INFO : PROGRESS: at example #4000000, processed 2493668037 words (5715482 words/s), 8977844 word types, 4000000 tags\n", "2022-04-18 12:13:42,550 : INFO : PROGRESS: at example #4500000, processed 2709484503 words (5392235 words/s), 9612299 word types, 4500000 tags\n", "2022-04-18 12:14:21,813 : INFO : PROGRESS: at example #5000000, processed 2932680226 words (5684768 words/s), 10226832 word types, 5000000 tags\n", "2022-04-18 12:14:51,346 : INFO : collected 10469247 word types and 5205168 unique tags from a corpus of 5205168 examples and 3016298486 words\n", "2022-04-18 12:14:55,076 : INFO : Doc2Vec lifecycle event {'msg': 'max_final_vocab=1000000 and min_count=5 resulted in calc_min_count=23, effective_min_count=23', 'datetime': '2022-04-18T12:14:55.076153', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'prepare_vocab'}\n", "2022-04-18 12:14:55,076 : INFO : Creating a fresh vocabulary\n", "2022-04-18 12:14:58,906 : INFO : Doc2Vec lifecycle event {'msg': 'effective_min_count=23 retains 996522 unique words (9.52% of original 10469247, drops 9472725)', 'datetime': '2022-04-18T12:14:58.906148', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'prepare_vocab'}\n", "2022-04-18 12:14:58,906 : INFO : Doc2Vec lifecycle event {'msg': 'effective_min_count=23 leaves 2988436691 word corpus (99.08% of original 3016298486, drops 27861795)', 'datetime': '2022-04-18T12:14:58.906730', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'prepare_vocab'}\n", "2022-04-18 12:15:01,747 : INFO : deleting the raw counts dictionary of 10469247 items\n", "2022-04-18 12:15:01,860 : INFO : sample=0.001 downsamples 23 most-common words\n", "2022-04-18 12:15:01,861 : INFO : Doc2Vec lifecycle event {'msg': 'downsampling leaves estimated 2431447874.2898555 word corpus (81.4%% of prior 2988436691)', 'datetime': '2022-04-18T12:15:01.861332', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'prepare_vocab'}\n", "2022-04-18 12:15:07,001 : INFO : estimated required memory for 996522 words and 200 dimensions: 7297864200 bytes\n", "2022-04-18 12:15:07,002 : INFO : resetting layer weights\n", "2022-04-18 12:15:10,247 : INFO : resetting layer weights\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doc2Vec<dbow+w,d200,n5,w8,mc5,s0.001,t20>\n", "Doc2Vec<dm/m,d200,n5,w8,mc5,s0.001,t20>\n" ] } ], "source": [ "model_dbow.build_vocab(documents, progress_per=500000)\n", "print(model_dbow)\n", "\n", "# Save some time by copying the vocabulary structures from the DBOW model to the DM model.\n", "# Both models are built on top of exactly the same data, so there's no need to repeat the vocab-building step.\n", "model_dm.reset_from(model_dbow)\n", "print(model_dm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we’re ready to train Doc2Vec on the entirety of the English Wikipedia. Warning! Training this DBOW model takes ~14 hours, and DM ~6 hours, on my 2020 Linux machine." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-04-18 12:15:13,503 : INFO : Doc2Vec lifecycle event {'msg': 'training model with 20 workers on 996522 vocabulary and 200 features, using sg=1 hs=0 sample=0.001 negative=5 window=8 shrink_windows=True', 'datetime': '2022-04-18T12:15:13.503265', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'train'}\n", "2022-04-18 12:15:14,566 : INFO : EPOCH 0 - PROGRESS: at 0.00% examples, 299399 words/s, in_qsize 38, out_qsize 1\n", "2022-04-18 12:45:14,574 : INFO : EPOCH 0 - PROGRESS: at 20.47% examples, 469454 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 13:15:14,578 : INFO : EPOCH 0 - PROGRESS: at 61.04% examples, 470927 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 13:40:53,256 : INFO : EPOCH 0: training on 3016298486 raw words (2421756111 effective words) took 5139.7s, 471184 effective words/s\n", "2022-04-18 13:40:54,274 : INFO : EPOCH 1 - PROGRESS: at 0.00% examples, 401497 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 14:10:54,283 : INFO : EPOCH 1 - PROGRESS: at 21.90% examples, 488616 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 14:40:54,290 : INFO : EPOCH 1 - PROGRESS: at 63.73% examples, 485374 words/s, in_qsize 40, out_qsize 0\n", "2022-04-18 15:04:11,566 : INFO : EPOCH 1: training on 3016298486 raw words (2421755370 effective words) took 4998.3s, 484515 effective words/s\n", "2022-04-18 15:04:12,590 : INFO : EPOCH 2 - PROGRESS: at 0.00% examples, 413109 words/s, in_qsize 38, out_qsize 2\n", "2022-04-18 15:34:12,592 : INFO : EPOCH 2 - PROGRESS: at 21.94% examples, 489186 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 16:04:12,595 : INFO : EPOCH 2 - PROGRESS: at 64.02% examples, 487045 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 16:27:13,124 : INFO : EPOCH 2: training on 3016298486 raw words (2421749843 effective words) took 4981.6s, 486143 effective words/s\n", "2022-04-18 16:27:14,132 : INFO : EPOCH 3 - PROGRESS: at 0.00% examples, 425720 words/s, in_qsize 37, out_qsize 0\n", "2022-04-18 16:57:14,170 : INFO : EPOCH 3 - PROGRESS: at 22.16% examples, 492364 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 17:27:14,181 : INFO : EPOCH 3 - PROGRESS: at 64.36% examples, 489039 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 17:49:58,875 : INFO : EPOCH 3: training on 3016298486 raw words (2421759041 effective words) took 4965.7s, 487693 effective words/s\n", "2022-04-18 17:49:59,888 : INFO : EPOCH 4 - PROGRESS: at 0.00% examples, 405295 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 18:19:59,893 : INFO : EPOCH 4 - PROGRESS: at 21.95% examples, 489379 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 18:49:59,917 : INFO : EPOCH 4 - PROGRESS: at 63.77% examples, 485582 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 19:13:19,358 : INFO : EPOCH 4: training on 3016298486 raw words (2421753794 effective words) took 5000.5s, 484304 effective words/s\n", "2022-04-18 19:13:20,362 : INFO : EPOCH 5 - PROGRESS: at 0.00% examples, 417569 words/s, in_qsize 38, out_qsize 1\n", "2022-04-18 19:43:20,366 : INFO : EPOCH 5 - PROGRESS: at 22.18% examples, 492529 words/s, in_qsize 40, out_qsize 0\n", "2022-04-18 20:13:20,367 : INFO : EPOCH 5 - PROGRESS: at 64.36% examples, 489058 words/s, in_qsize 39, out_qsize 1\n", "2022-04-18 20:36:01,806 : INFO : EPOCH 5: training on 3016298486 raw words (2421774390 effective words) took 4962.4s, 488021 effective words/s\n", "2022-04-18 20:36:02,845 : INFO : EPOCH 6 - PROGRESS: at 0.00% examples, 376602 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 21:06:02,845 : INFO : EPOCH 6 - PROGRESS: at 21.77% examples, 486989 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 21:36:02,858 : INFO : EPOCH 6 - PROGRESS: at 63.44% examples, 483745 words/s, in_qsize 40, out_qsize 0\n", "2022-04-18 21:59:40,920 : INFO : EPOCH 6: training on 3016298486 raw words (2421753569 effective words) took 5019.1s, 482507 effective words/s\n", "2022-04-18 21:59:41,945 : INFO : EPOCH 7 - PROGRESS: at 0.00% examples, 410164 words/s, in_qsize 38, out_qsize 1\n", "2022-04-18 22:29:41,989 : INFO : EPOCH 7 - PROGRESS: at 22.09% examples, 491334 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 22:59:42,000 : INFO : EPOCH 7 - PROGRESS: at 64.16% examples, 487826 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 23:22:40,504 : INFO : EPOCH 7: training on 3016298486 raw words (2421770259 effective words) took 4979.6s, 486340 effective words/s\n", "2022-04-18 23:22:41,509 : INFO : EPOCH 8 - PROGRESS: at 0.00% examples, 294981 words/s, in_qsize 39, out_qsize 0\n", "2022-04-18 23:52:41,532 : INFO : EPOCH 8 - PROGRESS: at 21.64% examples, 485279 words/s, in_qsize 40, out_qsize 0\n", "2022-04-19 00:22:41,533 : INFO : EPOCH 8 - PROGRESS: at 63.05% examples, 481687 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 00:46:43,879 : INFO : EPOCH 8: training on 3016298486 raw words (2421753439 effective words) took 5043.4s, 480185 effective words/s\n", "2022-04-19 00:46:44,905 : INFO : EPOCH 9 - PROGRESS: at 0.00% examples, 383709 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 01:16:44,926 : INFO : EPOCH 9 - PROGRESS: at 21.82% examples, 487579 words/s, in_qsize 40, out_qsize 0\n", "2022-04-19 01:46:44,928 : INFO : EPOCH 9 - PROGRESS: at 63.44% examples, 483731 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 02:10:25,029 : INFO : EPOCH 9: training on 3016298486 raw words (2421762745 effective words) took 5021.1s, 482313 effective words/s\n", "2022-04-19 02:10:25,030 : INFO : Doc2Vec lifecycle event {'msg': 'training on 30162984860 raw words (24217588561 effective words) took 50111.5s, 483274 effective words/s', 'datetime': '2022-04-19T02:10:25.030386', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'train'}\n" ] } ], "source": [ "# Train DBOW doc2vec incl. word vectors.\n", "# Report progress every ½ hour.\n", "model_dbow.train(documents, total_examples=model_dbow.corpus_count, epochs=model_dbow.epochs, report_delay=3060)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-04-19 02:10:25,033 : INFO : Doc2Vec lifecycle event {'msg': 'training model with 20 workers on 996522 vocabulary and 200 features, using sg=0 hs=0 sample=0.001 negative=5 window=8 shrink_windows=True', 'datetime': '2022-04-19T02:10:25.033682', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'train'}\n", "2022-04-19 02:10:26,039 : INFO : EPOCH 0 - PROGRESS: at 0.01% examples, 1154750 words/s, in_qsize 0, out_qsize 2\n", "2022-04-19 02:40:26,040 : INFO : EPOCH 0 - PROGRESS: at 83.97% examples, 1182619 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 02:44:58,625 : INFO : EPOCH 0: training on 3016298486 raw words (2421749575 effective words) took 2073.6s, 1167903 effective words/s\n", "2022-04-19 02:44:59,635 : INFO : EPOCH 1 - PROGRESS: at 0.01% examples, 1565065 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 03:14:59,636 : INFO : EPOCH 1 - PROGRESS: at 84.22% examples, 1185115 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 03:19:27,814 : INFO : EPOCH 1: training on 3016298486 raw words (2421738810 effective words) took 2069.2s, 1170383 effective words/s\n", "2022-04-19 03:19:28,819 : INFO : EPOCH 2 - PROGRESS: at 0.01% examples, 1582102 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 03:49:28,822 : INFO : EPOCH 2 - PROGRESS: at 84.33% examples, 1186338 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 03:53:55,901 : INFO : EPOCH 2: training on 3016298486 raw words (2421754027 effective words) took 2068.1s, 1171014 effective words/s\n", "2022-04-19 03:53:56,905 : INFO : EPOCH 3 - PROGRESS: at 0.01% examples, 1586215 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 04:23:56,914 : INFO : EPOCH 3 - PROGRESS: at 84.30% examples, 1186028 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 04:28:23,932 : INFO : EPOCH 3: training on 3016298486 raw words (2421734506 effective words) took 2068.0s, 1171036 effective words/s\n", "2022-04-19 04:28:24,943 : INFO : EPOCH 4 - PROGRESS: at 0.01% examples, 1594202 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 04:58:24,946 : INFO : EPOCH 4 - PROGRESS: at 84.53% examples, 1188348 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 05:02:49,190 : INFO : EPOCH 4: training on 3016298486 raw words (2421739011 effective words) took 2065.3s, 1172611 effective words/s\n", "2022-04-19 05:02:50,203 : INFO : EPOCH 5 - PROGRESS: at 0.01% examples, 1590285 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 05:32:50,205 : INFO : EPOCH 5 - PROGRESS: at 84.51% examples, 1188165 words/s, in_qsize 38, out_qsize 0\n", "2022-04-19 05:37:12,922 : INFO : EPOCH 5: training on 3016298486 raw words (2421759651 effective words) took 2063.7s, 1173488 effective words/s\n", "2022-04-19 05:37:13,928 : INFO : EPOCH 6 - PROGRESS: at 0.01% examples, 1574494 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 06:07:13,930 : INFO : EPOCH 6 - PROGRESS: at 84.61% examples, 1189231 words/s, in_qsize 40, out_qsize 0\n", "2022-04-19 06:11:35,588 : INFO : EPOCH 6: training on 3016298486 raw words (2421751669 effective words) took 2062.7s, 1174090 effective words/s\n", "2022-04-19 06:11:36,605 : INFO : EPOCH 7 - PROGRESS: at 0.01% examples, 1584768 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 06:41:36,617 : INFO : EPOCH 7 - PROGRESS: at 84.50% examples, 1188066 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 06:46:00,286 : INFO : EPOCH 7: training on 3016298486 raw words (2421751802 effective words) took 2064.7s, 1172935 effective words/s\n", "2022-04-19 06:46:01,290 : INFO : EPOCH 8 - PROGRESS: at 0.01% examples, 1610826 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 07:16:01,295 : INFO : EPOCH 8 - PROGRESS: at 84.71% examples, 1190249 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 07:20:20,193 : INFO : EPOCH 8: training on 3016298486 raw words (2421731383 effective words) took 2059.9s, 1175653 effective words/s\n", "2022-04-19 07:20:21,198 : INFO : EPOCH 9 - PROGRESS: at 0.01% examples, 1591209 words/s, in_qsize 0, out_qsize 0\n", "2022-04-19 07:50:21,200 : INFO : EPOCH 9 - PROGRESS: at 84.65% examples, 1189549 words/s, in_qsize 39, out_qsize 0\n", "2022-04-19 07:54:42,812 : INFO : EPOCH 9: training on 3016298486 raw words (2421765551 effective words) took 2062.6s, 1174124 effective words/s\n", "2022-04-19 07:54:42,813 : INFO : Doc2Vec lifecycle event {'msg': 'training on 30162984860 raw words (24217475985 effective words) took 20657.8s, 1172317 effective words/s', 'datetime': '2022-04-19T07:54:42.813436', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'train'}\n" ] } ], "source": [ "# Train DM doc2vec.\n", "model_dm.train(documents, total_examples=model_dm.corpus_count, epochs=model_dm.epochs, report_delay=3060)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finding similar documents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After that, let's test both models! The DBOW model shows similar results as the original paper.\n", "\n", "First, calculate the most similar Wikipedia articles to the \"Machine learning\" article. The calculated word vectors and document vectors are stored separately, in `model.wv` and `model.dv` respectively:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doc2Vec<dbow+w,d200,n5,w8,mc5,s0.001,t20>\n", "[('Supervised learning', 0.7491602301597595),\n", " ('Pattern recognition', 0.7462332844734192),\n", " ('Artificial neural network', 0.7142727971076965),\n", " ('Data mining', 0.6930587887763977),\n", " ('Computer mathematics', 0.686907947063446),\n", " ('Deep learning', 0.6868096590042114),\n", " ('Multi-task learning', 0.6859176158905029),\n", " ('Outline of computer science', 0.6858125925064087),\n", " ('Boosting (machine learning)', 0.6807966828346252),\n", " ('Linear classifier', 0.6807013154029846),\n", " ('Learning classifier system', 0.679194450378418),\n", " ('Knowledge retrieval', 0.6765366196632385),\n", " ('Perceptron', 0.675654947757721),\n", " ('Incremental learning', 0.6712607741355896),\n", " ('Support-vector machine', 0.6711161136627197),\n", " ('Feature selection', 0.6696343421936035),\n", " ('Image segmentation', 0.6688867211341858),\n", " ('Neural network', 0.6670624017715454),\n", " ('Reinforcement learning', 0.6666402220726013),\n", " ('Feature extraction', 0.6657401323318481)]\n", "Doc2Vec<dm/m,d200,n5,w8,mc5,s0.001,t20>\n", "[('Pattern recognition', 0.7151365280151367),\n", " ('Supervised learning', 0.7006939053535461),\n", " ('Multi-task learning', 0.6899284720420837),\n", " ('Semi-supervised learning', 0.674682080745697),\n", " ('Statistical classification', 0.6649825572967529),\n", " ('Deep learning', 0.6647047400474548),\n", " ('Artificial neural network', 0.66275954246521),\n", " ('Feature selection', 0.6612880825996399),\n", " ('Statistical learning theory', 0.6528184413909912),\n", " ('Naive Bayes classifier', 0.6506016850471497),\n", " ('Automatic image annotation', 0.6491228342056274),\n", " ('Regularization (mathematics)', 0.6452057957649231),\n", " ('Early stopping', 0.6439507007598877),\n", " ('Support-vector machine', 0.64285808801651),\n", " ('Meta learning (computer science)', 0.6418778300285339),\n", " ('Linear classifier', 0.6391816735267639),\n", " ('Empirical risk minimization', 0.6339778900146484),\n", " ('Anomaly detection', 0.6328380703926086),\n", " ('Predictive Model Markup Language', 0.6314322352409363),\n", " ('Learning classifier system', 0.6307871341705322)]\n" ] } ], "source": [ "for model in [model_dbow, model_dm]:\n", " print(model)\n", " pprint(model.dv.most_similar(positive=[\"Machine learning\"], topn=20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both results seem similar and match the results from the paper's Table 1, although not exactly. This is because we don't know the exact parameters of the original implementation (see above). And also because we're training the model 7 years later and the Wikipedia content has changed in the meantime.\n", "\n", "Now following the paper's Table 2a), let's calculate the most similar Wikipedia entries to \"Lady Gaga\" using Paragraph Vector:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doc2Vec<dbow+w,d200,n5,w8,mc5,s0.001,t20>\n", "[('Katy Perry', 0.7450265884399414),\n", " ('Miley Cyrus', 0.7275323867797852),\n", " ('Ariana Grande', 0.7223592400550842),\n", " ('Adele', 0.6982873678207397),\n", " ('Taylor Swift', 0.6901045441627502),\n", " ('Demi Lovato', 0.6819911003112793),\n", " ('Adam Lambert', 0.6552075147628784),\n", " ('Nicki Minaj', 0.6513625383377075),\n", " ('Selena Gomez', 0.6427122354507446),\n", " ('Rihanna', 0.6323978304862976)]\n", "Doc2Vec<dm/m,d200,n5,w8,mc5,s0.001,t20>\n", "[('Born This Way (album)', 0.6612793803215027),\n", " ('Artpop', 0.6428781747817993),\n", " ('Beautiful, Dirty, Rich', 0.6408763527870178),\n", " ('Lady Gaga videography', 0.6143141388893127),\n", " ('Lady Gaga discography', 0.6102882027626038),\n", " ('Katy Perry', 0.6046711802482605),\n", " ('Beyoncé', 0.6015700697898865),\n", " ('List of Lady Gaga live performances', 0.5977909564971924),\n", " ('Artpop (song)', 0.5930275917053223),\n", " ('Born This Way (song)', 0.5911758542060852)]\n" ] } ], "source": [ "for model in [model_dbow, model_dm]:\n", " print(model)\n", " pprint(model.dv.most_similar(positive=[\"Lady Gaga\"], topn=10))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The DBOW results are in line with what the paper shows in Table 2a), revealing similar singers in the U.S.\n", "\n", "Interestingly, the DM results seem to capture more \"fact about Lady Gaga\" (her albums, trivia), whereas DBOW recovered \"similar artists\".\n", "\n", "Finally, let's do some of the wilder arithmetics that vectors embeddings are famous for. What are the entries most similar to \"Lady Gaga\" - \"American\" + \"Japanese\"? Table 2b) in the paper.\n", "\n", "Note that \"American\" and \"Japanese\" are word vectors, but they live in the same space as the document vectors so we can add / subtract them at will, for some interesting results. All word vectors were already lowercased by our tokenizer above, so we look for the lowercased version here:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doc2Vec<dbow+w,d200,n5,w8,mc5,s0.001,t20>\n", "[('Ayumi Hamasaki', 0.6339365839958191),\n", " ('Katy Perry', 0.5903329849243164),\n", " ('2NE1', 0.5886631608009338),\n", " (\"Girls' Generation\", 0.5769038796424866),\n", " ('Flying Easy Loving Crazy', 0.5748921036720276),\n", " ('Love Life 2', 0.5738793611526489),\n", " ('Ariana Grande', 0.5715743899345398),\n", " ('Game (Perfume album)', 0.569789707660675),\n", " ('We Are \"Lonely Girl\"', 0.5696560740470886),\n", " ('H (Ayumi Hamasaki EP)', 0.5691372156143188)]\n", "Doc2Vec<dm/m,d200,n5,w8,mc5,s0.001,t20>\n", "[('Radwimps', 0.548571765422821),\n", " ('Chisato Moritaka', 0.5456540584564209),\n", " ('Suzuki Ami Around the World: Live House Tour 2005', 0.5375290513038635),\n", " ('Anna Suda', 0.5338292121887207),\n", " ('Beautiful, Dirty, Rich', 0.5309030413627625),\n", " ('Momoiro Clover Z', 0.5304197072982788),\n", " ('Pink Lady (duo)', 0.5268998742103577),\n", " ('Reol (singer)', 0.5237400531768799),\n", " ('Ami Suzuki', 0.5232592225074768),\n", " ('Kaela Kimura', 0.5219823122024536)]\n" ] } ], "source": [ "for model in [model_dbow, model_dm]:\n", " print(model)\n", " vec = [model.dv[\"Lady Gaga\"] - model.wv[\"american\"] + model.wv[\"japanese\"]]\n", " pprint([m for m in model.dv.most_similar(vec, topn=11) if m[0] != \"Lady Gaga\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a result, the DBOW model surfaced artists similar to Lady Gaga in Japan, such as Ayumi Hamasaki whose Wiki bio says:\n", "\n", "> Ayumi Hamasaki is a Japanese singer, songwriter, record producer, actress, model, spokesperson, and entrepreneur.\n", "\n", "So that sounds like a success. It's also the nr. 1 hit in the paper we're replicating – success!\n", "\n", "The DM model results are opaque to me, but seem art & Japan related as well. The score deltas between these DM results are marginal, so it's likely they would change if retrained on a different version of Wikipedia. Or even when simply re-run on the same version – the doc2vec training algorithm is stochastic.\n", "\n", "These results demonstrate that both training modes employed in the original paper are outstanding for calculating similarity between document vectors, word vectors, or a combination of both. The DM mode has the added advantage of being 4x faster to train." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you wanted to continue working with these trained models, you could save them to disk, to avoid having to re-train the models from scratch every time:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-04-19 07:54:48,399 : INFO : Doc2Vec lifecycle event {'fname_or_handle': 'doc2vec_dbow.model', 'separately': 'None', 'sep_limit': 10485760, 'ignore': frozenset(), 'datetime': '2022-04-19T07:54:48.399560', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'saving'}\n", "2022-04-19 07:54:48,400 : INFO : storing np array 'vectors' to doc2vec_dbow.model.dv.vectors.npy\n", "2022-04-19 07:54:49,613 : INFO : storing np array 'vectors' to doc2vec_dbow.model.wv.vectors.npy\n", "2022-04-19 07:54:49,875 : INFO : storing np array 'syn1neg' to doc2vec_dbow.model.syn1neg.npy\n", "2022-04-19 07:54:50,135 : INFO : not storing attribute cum_table\n", "2022-04-19 07:54:53,026 : INFO : saved doc2vec_dbow.model\n", "2022-04-19 07:54:53,027 : INFO : Doc2Vec lifecycle event {'fname_or_handle': 'doc2vec_dm.model', 'separately': 'None', 'sep_limit': 10485760, 'ignore': frozenset(), 'datetime': '2022-04-19T07:54:53.027661', 'gensim': '4.1.3.dev0', 'python': '3.8.10 (default, Nov 26 2021, 20:14:08) \\n[GCC 9.3.0]', 'platform': 'Linux-5.4.0-94-generic-x86_64-with-glibc2.29', 'event': 'saving'}\n", "2022-04-19 07:54:53,028 : INFO : storing np array 'vectors' to doc2vec_dm.model.dv.vectors.npy\n", "2022-04-19 07:54:54,556 : INFO : storing np array 'vectors' to doc2vec_dm.model.wv.vectors.npy\n", "2022-04-19 07:54:54,808 : INFO : storing np array 'syn1neg' to doc2vec_dm.model.syn1neg.npy\n", "2022-04-19 07:54:55,058 : INFO : not storing attribute cum_table\n", "2022-04-19 07:54:57,872 : INFO : saved doc2vec_dm.model\n" ] } ], "source": [ "model_dbow.save('doc2vec_dbow.model')\n", "model_dm.save('doc2vec_dm.model')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To continue your doc2vec explorations, refer to the official API documentation in Gensim: https://radimrehurek.com/gensim/models/doc2vec.html" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 1 }	38,876	Python	.py	654	54.191131	734	0.650564	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,931	Topic_dendrogram.ipynb	piskvorky_gensim/docs/notebooks/Topic_dendrogram.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing Topic clusters\n", "\n", "In this notebook, we will learn how to visualize topic clusters using dendrogram. Dendrogram is a tree-structured graph which can be used to visualize the result of a hierarchical clustering calculation. Hierarchical clustering puts individual data points into similarity groups, without prior knowledge of groups. We can use it to explore the topic models and see how the topics are connected to each other in a sequence of successive fusions or divisions that occur in the clustering process." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install plotly>=2.0.16 # 2.0.16 need for support 'hovertext' argument from create_dendrogram function" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script type='text/javascript'>if(!window.Plotly){define('plotly', function(require, exports, module) {/*\n", " plotly.js v1.31.0\n", "* Copyright 2012-2017, Plotly, Inc.\n", "* All rights reserved.\n", "* Licensed under the MIT license\n", "/\n", "!function(t){if(\"object\"==typeof exports&&\"undefined\"!=typeof module)module.exports=t();else if(\"function\"==typeof define&&define.amd)define([],t);else{var e;e=\"undefined\"!=typeof window?window:\"undefined\"!=typeof 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this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative\|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative\|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0\|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26\|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]\|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();var r,n;this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0\|r.words[a])+(0\|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0\|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r=this.cmp(t);if(0===r)return this.negative=0,this.length=1,this.words[0]=0,this;var n,i;r>0?(n=this,i=t):(n=t,i=this);for(var a=0,o=0;o<i.length;o++)e=(0\|n.words[o])-(0\|i.words[o])+a,a=e>>26,this.words[o]=67108863&e;for(;0!==a&&o<n.length;o++)e=(0\|n.words[o])+a,a=e>>26,this.words[o]=67108863&e;if(0===a&&o<n.length&&n!==this)for(;o<n.length;o++)this.words[o]=n.words[o];return this.length=Math.max(this.length,o),n!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var k=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0\|o[0],h=8191&c,f=c>>>13,d=0\|o[1],p=8191&d,m=d>>>13,v=0\|o[2],g=8191&v,y=v>>>13,b=0\|o[3],x=8191&b,_=b>>>13,w=0\|o[4],M=8191&w,k=w>>>13,A=0\|o[5],T=8191&A,S=A>>>13,E=0\|o[6],L=8191&E,C=E>>>13,I=0\|o[7],z=8191&I,D=I>>>13,P=0\|o[8],O=8191&P,R=P>>>13,F=0\|o[9],j=8191&F,N=F>>>13,B=0\|s[0],U=8191&B,V=B>>>13,H=0\|s[1],q=8191&H,G=H>>>13,Y=0\|s[2],W=8191&Y,X=Y>>>13,Z=0\|s[3],J=8191&Z,K=Z>>>13,Q=0\|s[4],$=8191&Q,tt=Q>>>13,et=0\|s[5],rt=8191&et,nt=et>>>13,it=0\|s[6],at=8191&it,ot=it>>>13,st=0\|s[7],lt=8191&st,ut=st>>>13,ct=0\|s[8],ht=8191&ct,ft=ct>>>13,dt=0\|s[9],pt=8191&dt,mt=dt>>>13;r.negative=t.negative^e.negative,r.length=19,n=Math.imul(h,U),i=Math.imul(h,V),i=i+Math.imul(f,U)\|0,a=Math.imul(f,V);var vt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(vt>>>26)\|0,vt&=67108863,n=Math.imul(p,U),i=Math.imul(p,V),i=i+Math.imul(m,U)\|0,a=Math.imul(m,V),n=n+Math.imul(h,q)\|0,i=i+Math.imul(h,G)\|0,i=i+Math.imul(f,q)\|0,a=a+Math.imul(f,G)\|0;var gt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(gt>>>26)\|0,gt&=67108863,n=Math.imul(g,U),i=Math.imul(g,V),i=i+Math.imul(y,U)\|0,a=Math.imul(y,V),n=n+Math.imul(p,q)\|0,i=i+Math.imul(p,G)\|0,i=i+Math.imul(m,q)\|0,a=a+Math.imul(m,G)\|0,n=n+Math.imul(h,W)\|0,i=i+Math.imul(h,X)\|0,i=i+Math.imul(f,W)\|0,a=a+Math.imul(f,X)\|0;var yt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(yt>>>26)\|0,yt&=67108863,n=Math.imul(x,U),i=Math.imul(x,V),i=i+Math.imul(_,U)\|0,a=Math.imul(_,V),n=n+Math.imul(g,q)\|0,i=i+Math.imul(g,G)\|0,i=i+Math.imul(y,q)\|0,a=a+Math.imul(y,G)\|0,n=n+Math.imul(p,W)\|0,i=i+Math.imul(p,X)\|0,i=i+Math.imul(m,W)\|0,a=a+Math.imul(m,X)\|0,n=n+Math.imul(h,J)\|0,i=i+Math.imul(h,K)\|0,i=i+Math.imul(f,J)\|0,a=a+Math.imul(f,K)\|0;var bt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(bt>>>26)\|0,bt&=67108863,n=Math.imul(M,U),i=Math.imul(M,V),i=i+Math.imul(k,U)\|0,a=Math.imul(k,V),n=n+Math.imul(x,q)\|0,i=i+Math.imul(x,G)\|0,i=i+Math.imul(_,q)\|0,a=a+Math.imul(_,G)\|0,n=n+Math.imul(g,W)\|0,i=i+Math.imul(g,X)\|0,i=i+Math.imul(y,W)\|0,a=a+Math.imul(y,X)\|0,n=n+Math.imul(p,J)\|0,i=i+Math.imul(p,K)\|0,i=i+Math.imul(m,J)\|0,a=a+Math.imul(m,K)\|0,n=n+Math.imul(h,$)\|0,i=i+Math.imul(h,tt)\|0,i=i+Math.imul(f,$)\|0,a=a+Math.imul(f,tt)\|0;var xt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(xt>>>26)\|0,xt&=67108863,n=Math.imul(T,U),i=Math.imul(T,V),i=i+Math.imul(S,U)\|0,a=Math.imul(S,V),n=n+Math.imul(M,q)\|0,i=i+Math.imul(M,G)\|0,i=i+Math.imul(k,q)\|0,a=a+Math.imul(k,G)\|0,n=n+Math.imul(x,W)\|0,i=i+Math.imul(x,X)\|0,i=i+Math.imul(_,W)\|0,a=a+Math.imul(_,X)\|0,n=n+Math.imul(g,J)\|0,i=i+Math.imul(g,K)\|0,i=i+Math.imul(y,J)\|0,a=a+Math.imul(y,K)\|0,n=n+Math.imul(p,$)\|0,i=i+Math.imul(p,tt)\|0,i=i+Math.imul(m,$)\|0,a=a+Math.imul(m,tt)\|0,n=n+Math.imul(h,rt)\|0,i=i+Math.imul(h,nt)\|0,i=i+Math.imul(f,rt)\|0,a=a+Math.imul(f,nt)\|0;var _t=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(_t>>>26)\|0,_t&=67108863,n=Math.imul(L,U),i=Math.imul(L,V),i=i+Math.imul(C,U)\|0,a=Math.imul(C,V),n=n+Math.imul(T,q)\|0,i=i+Math.imul(T,G)\|0,i=i+Math.imul(S,q)\|0,a=a+Math.imul(S,G)\|0,n=n+Math.imul(M,W)\|0,i=i+Math.imul(M,X)\|0,i=i+Math.imul(k,W)\|0,a=a+Math.imul(k,X)\|0,n=n+Math.imul(x,J)\|0,i=i+Math.imul(x,K)\|0,i=i+Math.imul(_,J)\|0,a=a+Math.imul(_,K)\|0,n=n+Math.imul(g,$)\|0,i=i+Math.imul(g,tt)\|0,i=i+Math.imul(y,$)\|0,a=a+Math.imul(y,tt)\|0,n=n+Math.imul(p,rt)\|0,i=i+Math.imul(p,nt)\|0,i=i+Math.imul(m,rt)\|0,a=a+Math.imul(m,nt)\|0,n=n+Math.imul(h,at)\|0,i=i+Math.imul(h,ot)\|0,i=i+Math.imul(f,at)\|0,a=a+Math.imul(f,ot)\|0;var wt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(wt>>>26)\|0,wt&=67108863,n=Math.imul(z,U),i=Math.imul(z,V),i=i+Math.imul(D,U)\|0,a=Math.imul(D,V),n=n+Math.imul(L,q)\|0,i=i+Math.imul(L,G)\|0,i=i+Math.imul(C,q)\|0,a=a+Math.imul(C,G)\|0,n=n+Math.imul(T,W)\|0,i=i+Math.imul(T,X)\|0,i=i+Math.imul(S,W)\|0,a=a+Math.imul(S,X)\|0,n=n+Math.imul(M,J)\|0,i=i+Math.imul(M,K)\|0,i=i+Math.imul(k,J)\|0,a=a+Math.imul(k,K)\|0,n=n+Math.imul(x,$)\|0,i=i+Math.imul(x,tt)\|0,i=i+Math.imul(_,$)\|0,a=a+Math.imul(_,tt)\|0,n=n+Math.imul(g,rt)\|0,i=i+Math.imul(g,nt)\|0,i=i+Math.imul(y,rt)\|0,a=a+Math.imul(y,nt)\|0,n=n+Math.imul(p,at)\|0,i=i+Math.imul(p,ot)\|0,i=i+Math.imul(m,at)\|0,a=a+Math.imul(m,ot)\|0,n=n+Math.imul(h,lt)\|0,i=i+Math.imul(h,ut)\|0,i=i+Math.imul(f,lt)\|0,a=a+Math.imul(f,ut)\|0;var Mt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Mt>>>26)\|0,Mt&=67108863,n=Math.imul(O,U),i=Math.imul(O,V),i=i+Math.imul(R,U)\|0,a=Math.imul(R,V),n=n+Math.imul(z,q)\|0,i=i+Math.imul(z,G)\|0,i=i+Math.imul(D,q)\|0,a=a+Math.imul(D,G)\|0,n=n+Math.imul(L,W)\|0,i=i+Math.imul(L,X)\|0,i=i+Math.imul(C,W)\|0,a=a+Math.imul(C,X)\|0,n=n+Math.imul(T,J)\|0,i=i+Math.imul(T,K)\|0,i=i+Math.imul(S,J)\|0,a=a+Math.imul(S,K)\|0,n=n+Math.imul(M,$)\|0,i=i+Math.imul(M,tt)\|0,i=i+Math.imul(k,$)\|0,a=a+Math.imul(k,tt)\|0,n=n+Math.imul(x,rt)\|0,i=i+Math.imul(x,nt)\|0,i=i+Math.imul(_,rt)\|0,a=a+Math.imul(_,nt)\|0,n=n+Math.imul(g,at)\|0,i=i+Math.imul(g,ot)\|0,i=i+Math.imul(y,at)\|0,a=a+Math.imul(y,ot)\|0,n=n+Math.imul(p,lt)\|0,i=i+Math.imul(p,ut)\|0,i=i+Math.imul(m,lt)\|0,a=a+Math.imul(m,ut)\|0,n=n+Math.imul(h,ht)\|0,i=i+Math.imul(h,ft)\|0,i=i+Math.imul(f,ht)\|0,a=a+Math.imul(f,ft)\|0;var kt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(kt>>>26)\|0,kt&=67108863,n=Math.imul(j,U),i=Math.imul(j,V),i=i+Math.imul(N,U)\|0,a=Math.imul(N,V),n=n+Math.imul(O,q)\|0,i=i+Math.imul(O,G)\|0,i=i+Math.imul(R,q)\|0,a=a+Math.imul(R,G)\|0,n=n+Math.imul(z,W)\|0,i=i+Math.imul(z,X)\|0,i=i+Math.imul(D,W)\|0,a=a+Math.imul(D,X)\|0,n=n+Math.imul(L,J)\|0,i=i+Math.imul(L,K)\|0,i=i+Math.imul(C,J)\|0,a=a+Math.imul(C,K)\|0,n=n+Math.imul(T,$)\|0,i=i+Math.imul(T,tt)\|0,i=i+Math.imul(S,$)\|0,a=a+Math.imul(S,tt)\|0,n=n+Math.imul(M,rt)\|0,i=i+Math.imul(M,nt)\|0,i=i+Math.imul(k,rt)\|0,a=a+Math.imul(k,nt)\|0,n=n+Math.imul(x,at)\|0,i=i+Math.imul(x,ot)\|0,i=i+Math.imul(_,at)\|0,a=a+Math.imul(_,ot)\|0,n=n+Math.imul(g,lt)\|0,i=i+Math.imul(g,ut)\|0,i=i+Math.imul(y,lt)\|0,a=a+Math.imul(y,ut)\|0,n=n+Math.imul(p,ht)\|0,i=i+Math.imul(p,ft)\|0,i=i+Math.imul(m,ht)\|0,a=a+Math.imul(m,ft)\|0,n=n+Math.imul(h,pt)\|0,i=i+Math.imul(h,mt)\|0,i=i+Math.imul(f,pt)\|0,a=a+Math.imul(f,mt)\|0;var At=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(At>>>26)\|0,At&=67108863,n=Math.imul(j,q),i=Math.imul(j,G),i=i+Math.imul(N,q)\|0,a=Math.imul(N,G),n=n+Math.imul(O,W)\|0,i=i+Math.imul(O,X)\|0,i=i+Math.imul(R,W)\|0,a=a+Math.imul(R,X)\|0,n=n+Math.imul(z,J)\|0,i=i+Math.imul(z,K)\|0,i=i+Math.imul(D,J)\|0,a=a+Math.imul(D,K)\|0,n=n+Math.imul(L,$)\|0,i=i+Math.imul(L,tt)\|0,i=i+Math.imul(C,$)\|0,a=a+Math.imul(C,tt)\|0,n=n+Math.imul(T,rt)\|0,i=i+Math.imul(T,nt)\|0,i=i+Math.imul(S,rt)\|0,a=a+Math.imul(S,nt)\|0,n=n+Math.imul(M,at)\|0,i=i+Math.imul(M,ot)\|0,i=i+Math.imul(k,at)\|0,a=a+Math.imul(k,ot)\|0,n=n+Math.imul(x,lt)\|0,i=i+Math.imul(x,ut)\|0,i=i+Math.imul(_,lt)\|0,a=a+Math.imul(_,ut)\|0,n=n+Math.imul(g,ht)\|0,i=i+Math.imul(g,ft)\|0,i=i+Math.imul(y,ht)\|0,a=a+Math.imul(y,ft)\|0,n=n+Math.imul(p,pt)\|0,i=i+Math.imul(p,mt)\|0,i=i+Math.imul(m,pt)\|0,a=a+Math.imul(m,mt)\|0;var Tt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Tt>>>26)\|0,Tt&=67108863,n=Math.imul(j,W),i=Math.imul(j,X),i=i+Math.imul(N,W)\|0,a=Math.imul(N,X),n=n+Math.imul(O,J)\|0,i=i+Math.imul(O,K)\|0,i=i+Math.imul(R,J)\|0,a=a+Math.imul(R,K)\|0,n=n+Math.imul(z,$)\|0,i=i+Math.imul(z,tt)\|0,i=i+Math.imul(D,$)\|0,a=a+Math.imul(D,tt)\|0,n=n+Math.imul(L,rt)\|0,i=i+Math.imul(L,nt)\|0,i=i+Math.imul(C,rt)\|0,a=a+Math.imul(C,nt)\|0,n=n+Math.imul(T,at)\|0,i=i+Math.imul(T,ot)\|0,i=i+Math.imul(S,at)\|0,a=a+Math.imul(S,ot)\|0,n=n+Math.imul(M,lt)\|0,i=i+Math.imul(M,ut)\|0,i=i+Math.imul(k,lt)\|0,a=a+Math.imul(k,ut)\|0,n=n+Math.imul(x,ht)\|0,i=i+Math.imul(x,ft)\|0,i=i+Math.imul(_,ht)\|0,a=a+Math.imul(_,ft)\|0,n=n+Math.imul(g,pt)\|0,i=i+Math.imul(g,mt)\|0,i=i+Math.imul(y,pt)\|0,a=a+Math.imul(y,mt)\|0;var St=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(St>>>26)\|0,St&=67108863,n=Math.imul(j,J),i=Math.imul(j,K),i=i+Math.imul(N,J)\|0,a=Math.imul(N,K),n=n+Math.imul(O,$)\|0,i=i+Math.imul(O,tt)\|0,i=i+Math.imul(R,$)\|0,a=a+Math.imul(R,tt)\|0,n=n+Math.imul(z,rt)\|0,i=i+Math.imul(z,nt)\|0,i=i+Math.imul(D,rt)\|0,a=a+Math.imul(D,nt)\|0,n=n+Math.imul(L,at)\|0,i=i+Math.imul(L,ot)\|0,i=i+Math.imul(C,at)\|0,a=a+Math.imul(C,ot)\|0,n=n+Math.imul(T,lt)\|0,i=i+Math.imul(T,ut)\|0,i=i+Math.imul(S,lt)\|0,a=a+Math.imul(S,ut)\|0,n=n+Math.imul(M,ht)\|0,i=i+Math.imul(M,ft)\|0,i=i+Math.imul(k,ht)\|0,a=a+Math.imul(k,ft)\|0,n=n+Math.imul(x,pt)\|0,i=i+Math.imul(x,mt)\|0,i=i+Math.imul(_,pt)\|0,a=a+Math.imul(_,mt)\|0;var Et=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Et>>>26)\|0,Et&=67108863,n=Math.imul(j,$),i=Math.imul(j,tt),i=i+Math.imul(N,$)\|0,a=Math.imul(N,tt),n=n+Math.imul(O,rt)\|0,i=i+Math.imul(O,nt)\|0,i=i+Math.imul(R,rt)\|0,a=a+Math.imul(R,nt)\|0,n=n+Math.imul(z,at)\|0,i=i+Math.imul(z,ot)\|0,i=i+Math.imul(D,at)\|0,a=a+Math.imul(D,ot)\|0,n=n+Math.imul(L,lt)\|0,i=i+Math.imul(L,ut)\|0,i=i+Math.imul(C,lt)\|0,a=a+Math.imul(C,ut)\|0,n=n+Math.imul(T,ht)\|0,i=i+Math.imul(T,ft)\|0,i=i+Math.imul(S,ht)\|0,a=a+Math.imul(S,ft)\|0,n=n+Math.imul(M,pt)\|0,i=i+Math.imul(M,mt)\|0,i=i+Math.imul(k,pt)\|0,a=a+Math.imul(k,mt)\|0;var Lt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Lt>>>26)\|0,Lt&=67108863,n=Math.imul(j,rt),i=Math.imul(j,nt),i=i+Math.imul(N,rt)\|0,a=Math.imul(N,nt),n=n+Math.imul(O,at)\|0,i=i+Math.imul(O,ot)\|0,i=i+Math.imul(R,at)\|0,a=a+Math.imul(R,ot)\|0,n=n+Math.imul(z,lt)\|0,i=i+Math.imul(z,ut)\|0,i=i+Math.imul(D,lt)\|0,a=a+Math.imul(D,ut)\|0,n=n+Math.imul(L,ht)\|0,\n", "i=i+Math.imul(L,ft)\|0,i=i+Math.imul(C,ht)\|0,a=a+Math.imul(C,ft)\|0,n=n+Math.imul(T,pt)\|0,i=i+Math.imul(T,mt)\|0,i=i+Math.imul(S,pt)\|0,a=a+Math.imul(S,mt)\|0;var Ct=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Ct>>>26)\|0,Ct&=67108863,n=Math.imul(j,at),i=Math.imul(j,ot),i=i+Math.imul(N,at)\|0,a=Math.imul(N,ot),n=n+Math.imul(O,lt)\|0,i=i+Math.imul(O,ut)\|0,i=i+Math.imul(R,lt)\|0,a=a+Math.imul(R,ut)\|0,n=n+Math.imul(z,ht)\|0,i=i+Math.imul(z,ft)\|0,i=i+Math.imul(D,ht)\|0,a=a+Math.imul(D,ft)\|0,n=n+Math.imul(L,pt)\|0,i=i+Math.imul(L,mt)\|0,i=i+Math.imul(C,pt)\|0,a=a+Math.imul(C,mt)\|0;var It=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(It>>>26)\|0,It&=67108863,n=Math.imul(j,lt),i=Math.imul(j,ut),i=i+Math.imul(N,lt)\|0,a=Math.imul(N,ut),n=n+Math.imul(O,ht)\|0,i=i+Math.imul(O,ft)\|0,i=i+Math.imul(R,ht)\|0,a=a+Math.imul(R,ft)\|0,n=n+Math.imul(z,pt)\|0,i=i+Math.imul(z,mt)\|0,i=i+Math.imul(D,pt)\|0,a=a+Math.imul(D,mt)\|0;var zt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(zt>>>26)\|0,zt&=67108863,n=Math.imul(j,ht),i=Math.imul(j,ft),i=i+Math.imul(N,ht)\|0,a=Math.imul(N,ft),n=n+Math.imul(O,pt)\|0,i=i+Math.imul(O,mt)\|0,i=i+Math.imul(R,pt)\|0,a=a+Math.imul(R,mt)\|0;var Dt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Dt>>>26)\|0,Dt&=67108863,n=Math.imul(j,pt),i=Math.imul(j,mt),i=i+Math.imul(N,pt)\|0,a=Math.imul(N,mt);var Pt=(u+n\|0)+((8191&i)<<13)\|0;return u=(a+(i>>>13)\|0)+(Pt>>>26)\|0,Pt&=67108863,l[0]=vt,l[1]=gt,l[2]=yt,l[3]=bt,l[4]=xt,l[5]=_t,l[6]=wt,l[7]=Mt,l[8]=kt,l[9]=At,l[10]=Tt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=It,l[16]=zt,l[17]=Dt,l[18]=Pt,0!==u&&(l[19]=u,r.length++),r};Math.imul\|\|(k=u),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?k(this,t,e):r<63?u(this,t,e):r<1024?c(this,t,e):h(this,t,e)},f.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},f.prototype.revBin=function(t,e,r){if(0===t\|\|t===r-1)return t;for(var n=0,i=0;i<e;i++)n\|=(1&t)<<e-i-1,t>>=1;return n},f.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},f.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2Math.PI/s),u=Math.sin(2Math.PI/s),c=0;c<i;c+=s)for(var h=l,f=u,d=0;d<o;d++){var p=r[c+d],m=n[c+d],v=r[c+d+o],g=n[c+d+o],y=hv-fg;g=hg+fv,v=y,r[c+d]=p+v,n[c+d]=m+g,r[c+d+o]=p-v,n[c+d+o]=m-g,d!==s&&(y=lh-uf,f=lf+uh,h=y)}},f.prototype.guessLen13b=function(t,e){var r=1\|Math.max(e,t),n=1&r,i=0;for(r=r/2\|0;r;r>>>=1)i++;return 1<<i+1+n},f.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},f.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192Math.round(t[2n+1]/e)+Math.round(t[2n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864\|0}return t},f.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0\|t[o],r[2o]=8191&a,a>>>=13,r[2o+1]=8191&a,a>>>=13;for(o=2e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},f.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},f.prototype.mulp=function(t,e,r){var n=2this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,h,n,i);for(var d=0;d<n;d++){var p=s[d]c[d]-l[d]h[d];l[d]=s[d]h[d]+l[d]c[d],s[d]=p}return this.conjugate(s,l,n),this.transform(s,l,f,a,n,i),this.conjugate(f,a,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),h(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0\|this.words[r])t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864\|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=l(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0\|this.words[e])-s<<r;this.words[e]=l\|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){n(\"number\"==typeof t&&t>=0);var i;i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c\|\|u>=i);u--){var h=0\|this.words[u];this.words[u]=c<<26-a\|h>>>a,c=h&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0\|this.words[0])<t?(this.words[0]=t-(0\|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a=t.length+r;this._expand(a);var o,s=0;for(i=0;i<t.length;i++){o=(0\|this.words[i+r])+s;var l=(0\|t.words[i])e;o-=67108863&l,s=(o>>26)-(l/67108864\|0),this.words[i+r]=67108863&o}for(;i<this.length-r;i++)o=(0\|this.words[i+r])+s,s=o>>26,this.words[i+r]=67108863&o;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)o=-(0\|this.words[i])+s,s=o>>26,this.words[i]=67108863&o;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=this.length-t.length,n=this.clone(),i=t,o=0\|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0\|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){s=new a(null),s.length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864(0\|n.words[i.length+h])+(0\|n.words[i.length+h-1]);for(f=Math.min(f/o\|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()\|\|(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s\|\|null,mod:n}},a.prototype.divmod=function(t,e,r){if(n(!t.isZero()),this.isZero())return{div:new a(0),mod:new a(0)};var i,o,s;return 0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length\|\|this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e)},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0\|\|1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(er+(0\|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0\|this.words[r])+67108864e;this.words[r]=i/t\|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),h=e.clone();!e.isZero();){for(var f=0,d=1;0==(e.words[0]&d)&&f<26;++f,d<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()\|\|o.isOdd())&&(i.iadd(c),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var p=0,m=1;0==(r.words[0]&m)&&p<26;++p,m<<=1);if(p>0)for(r.iushrn(p);p-- >0;)(s.isOdd()\|\|l.isOdd())&&(s.iadd(c),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var l=0,u=1;0==(e.words[0]&u)&&l<26;++l,u<<=1);if(l>0)for(e.iushrn(l);l-- >0;)i.isOdd()&&i.iadd(s),i.iushrn(1);for(var c=0,h=1;0==(r.words[0]&h)&&c<26;++c,h<<=1);if(c>0)for(r.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(s),o.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(o)):(r.isub(e),o.isub(i))}var f;return f=0===e.cmpn(1)?i:o,f.cmpn(0)<0&&f.iadd(t),f},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i\|\|0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]\|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0\|this.words[o];s+=a,a=s>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e=t<0;if(0!==this.negative&&!e)return-1;if(0===this.negative&&e)return 1;this.strip();var r;if(this.length>1)r=1;else{e&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0\|this.words[0];r=i===t?0:i<t?-1:1}return 0!==this.negative?0\|-r:r},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0\|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0\|this.words[r],i=0\|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new y(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var A={k256:null,p224:null,p192:null,p25519:null};d.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},d.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),r=this.imulK(r),r=r.iadd(this.tmp),e=r.bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},d.prototype.split=function(t,e){t.iushrn(this.n,0,e)},d.prototype.imulK=function(t){return t.imul(this.k)},i(p,d),p.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0\|t.words[n];t.words[n-10]=(4194303&a)<<4\|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},p.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0\|t.words[r];e+=977n,t.words[r]=67108863&e,e=64n+(e/67108864\|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(m,d),i(v,d),i(g,d),g.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19(0\|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(A[t])return A[t];var e;if(\"k256\"===t)e=new p;else if(\"p224\"===t)e=new m;else if(\"p192\"===t)e=new v;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new g}return A[t]=e,e},y.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},y.prototype._verify2=function(t,e){n(0==(t.negative\|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},y.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},y.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},y.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},y.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},y.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},y.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},y.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},y.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},y.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},y.prototype.isqr=function(t){return this.imul(t,t.clone())},y.prototype.sqr=function(t){return this.mul(t,t)},y.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2cc).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var h=this.pow(c,i),f=this.pow(t,i.addn(1).iushrn(1)),d=this.pow(t,i),p=o;0!==d.cmp(s);){for(var m=d,v=0;0!==m.cmp(s);v++)m=m.redSqr();n(v<p);var g=this.pow(h,new a(1).iushln(p-v-1));f=f.redMul(g),h=g.redSqr(),d=d.redMul(h),p=v}return f},y.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},y.prototype.pow=function(t,e){if(e.isZero())return new a(1);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var h=u>>c&1;i!==r[0]&&(i=this.sqr(i)),0!==h\|\|0!==o?(o<<=1,o\|=h,(4===++s\|\|0===n&&0===c)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},y.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},y.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new b(t)},i(b,y),b.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},b.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},b.prototype.imul=function(t,e){if(t.isZero()\|\|e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},b.prototype.mul=function(t,e){if(t.isZero()\|\|e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},b.prototype.invm=function(t){return 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c=t(\"./convert\"),h=t(\"./transform\"),f=t(\"./clip\"),d=t(\"./wrap\"),p=t(\"./tile\");i.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,solidChildren:!1,tolerance:3,extent:4096,buffer:64,debug:0},i.prototype.splitTile=function(t,e,r,n,i,l,c){for(var h=[t,e,r,n],d=this.options,m=d.debug,v=null;h.length;){n=h.pop(),r=h.pop(),e=h.pop(),t=h.pop();var g=1<<e,y=a(e,r,n),b=this.tiles[y],x=e===d.maxZoom?0:d.tolerance/(gd.extent);if(!b&&(m>1&&console.time(\"creation\"),b=this.tiles[y]=p(t,g,r,n,x,e===d.maxZoom),this.tileCoords.push({z:e,x:r,y:n}),m)){m>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,b.numFeatures,b.numPoints,b.numSimplified),console.timeEnd(\"creation\"));var _=\"z\"+e;this.stats[_]=(this.stats[_]\|\|0)+1,this.total++}if(b.source=t,i){if(e===d.maxZoom\|\|e===i)continue;var w=1<<i-e;if(r!==Math.floor(l/w)\|\|n!==Math.floor(c/w))continue}else 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m=a(\"labels\");a(\"labelFont\")&&(m=!0),i(\"labelEnable\"),n(\"labelSize\"),n(\"labelPad\"),o(\"labelColor\"),i(\"lineEnable\"),i(\"lineMirror\"),n(\"lineWidth\"),o(\"lineColor\"),i(\"lineTickEnable\"),i(\"lineTickMirror\"),n(\"lineTickLength\"),n(\"lineTickWidth\"),o(\"lineTickColor\"),i(\"gridEnable\"),n(\"gridWidth\"),o(\"gridColor\"),i(\"zeroEnable\"),o(\"zeroLineColor\"),n(\"zeroLineWidth\"),i(\"backgroundEnable\"),o(\"backgroundColor\"),this._text?this._text&&(m\|\|s)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=l(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&s&&(this._lines.dispose(),this._lines=null),this._lines\|\|(this._lines=u(this.gl,this.bounds,this.ticks))};var m=[new a,new a,new a],v=[0,0,0],g={model:d,view:d,projection:d};p.isOpaque=function(){return!0},p.isTransparent=function(){return!1},p.drawTransparent=function(t){};var y=[0,0,0],b=[0,0,0],x=[0,0,0];p.draw=function(t){t=t\|\|g;for(var e=this.gl,r=t.model\|\|d,i=t.view\|\|d,a=t.projection\|\|d,s=this.bounds,l=h(r,i,a,s),u=l.cubeEdges,c=l.axis,f=i[12],p=i[13],_=i[14],w=i[15],M=this.pixelRatio(a[3]f+a[7]p+a[11]_+a[15]w)/e.drawingBufferHeight,k=0;k<3;++k)this.lastCubeProps.cubeEdges[k]=u[k],this.lastCubeProps.axis[k]=c[k];for(var A=m,k=0;k<3;++k)o(m[k],k,this.bounds,u,c);for(var e=this.gl,T=v,k=0;k<3;++k)this.backgroundEnable[k]?T[k]=c[k]:T[k]=0;this._background.draw(r,i,a,s,T,this.backgroundColor),this._lines.bind(r,i,a,this);for(var k=0;k<3;++k){var S=[0,0,0];c[k]>0?S[k]=s[1][k]:S[k]=s[0][k];for(var E=0;E<2;++E){var L=(k+1+E)%3,C=(k+1+(1^E))%3;this.gridEnable[L]&&this._lines.drawGrid(L,C,this.bounds,S,this.gridColor[L],this.gridWidth[L]this.pixelRatio)}for(var E=0;E<2;++E){var L=(k+1+E)%3,C=(k+1+(1^E))%3;this.zeroEnable[C]&&s[0][C]<=0&&s[1][C]>=0&&this._lines.drawZero(L,C,this.bounds,S,this.zeroLineColor[C],this.zeroLineWidth[C]this.pixelRatio)}}for(var k=0;k<3;++k){this.lineEnable[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].primalOffset,this.lineColor[k],this.lineWidth[k]this.pixelRatio),this.lineMirror[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].mirrorOffset,this.lineColor[k],this.lineWidth[k]this.pixelRatio);for(var I=n(y,A[k].primalMinor),z=n(b,A[k].mirrorMinor),D=this.lineTickLength,E=0;E<3;++E){var P=M/r[5E];I[E]=D[E]P,z[E]=D[E]P}this.lineTickEnable[k]&&this._lines.drawAxisTicks(k,A[k].primalOffset,I,this.lineTickColor[k],this.lineTickWidth[k]this.pixelRatio),this.lineTickMirror[k]&&this._lines.drawAxisTicks(k,A[k].mirrorOffset,z,this.lineTickColor[k],this.lineTickWidth[k]this.pixelRatio)}this._text.bind(r,i,a,this.pixelRatio);for(var k=0;k<3;++k){for(var O=A[k].primalMinor,R=n(x,A[k].primalOffset),E=0;E<3;++E)this.lineTickEnable[k]&&(R[E]+=MO[E]Math.max(this.lineTickLength[E],0)/r[5E]);if(this.tickEnable[k]){for(var E=0;E<3;++E)R[E]+=MO[E]this.tickPad[E]/r[5E];this._text.drawTicks(k,this.tickSize[k],this.tickAngle[k],R,this.tickColor[k])}if(this.labelEnable[k]){for(var E=0;E<3;++E)R[E]+=MO[E]this.labelPad[E]/r[5E];R[k]+=.5(s[0][k]+s[1][k]),this._text.drawLabel(k,this.labelSize[k],this.labelAngle[k],R,this.labelColor[k])}}},p.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":149,\"./lib/cube.js\":150,\"./lib/lines.js\":151,\"./lib/text.js\":153,\"./lib/ticks.js\":154}],149:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}function i(t){for(var e=[],r=[],i=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,h=[0,0,0],f=[0,0,0],d=-1;d<=1;d+=2){r.push(i,i+2,i+1,i+1,i+2,i+3),h[l]=d,f[l]=d;for(var p=-1;p<=1;p+=2){h[u]=p;for(var m=-1;m<=1;m+=2)h[c]=m,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),i+=1}var v=u;u=c,c=v}var g=a(t,new 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e=0;e<m.length;++e)if(t=l.positive(t,m[e]),t.length<3)return 0;for(var r=t[0],n=r[0]/r[3],i=r[1]/r[3],a=0,e=1;e+1<t.length;++e){var o=t[e],s=t[e+1],u=o[0]/o[3],c=o[1]/o[3],h=s[0]/s[3],f=s[1]/s[3],d=u-n,p=c-i,v=h-n,g=f-i;a+=Math.abs(dg-pv)}return a}function a(t,e,r,a){s(c,e,t),s(c,r,c);for(var l=0,m=0;m<2;++m){d[2]=a[m][2];for(var b=0;b<2;++b){d[1]=a[b][1];for(var x=0;x<2;++x)d[0]=a[x][0],n(h[l],d,c),l+=1}}for(var _=-1,m=0;m<8;++m){for(var w=h[m][3],M=0;M<3;++M)f[m][M]=h[m][M]/w;w<0&&(_<0?_=m:f[m][2]<f[_][2]&&(_=m))}if(_<0){_=0;for(var k=0;k<3;++k){for(var A=(k+2)%3,T=(k+1)%3,S=-1,E=-1,L=0;L<2;++L){var C=L<<k,I=C+(L<<A)+(1-L<<T),z=C+(1-L<<A)+(L<<T);u(f[C],f[I],f[z],p)<0\|\|(L?S=1:E=1)}if(S<0\|\|E<0)E>S&&(_\|=1<<k);else{for(var L=0;L<2;++L){var C=L<<k,I=C+(L<<A)+(1-L<<T),z=C+(1-L<<A)+(L<<T),D=i([h[C],h[I],h[z],h[C+(1<<A)+(1<<T)]]);L?S=D:E=D}E>S&&(_\|=1<<k)}}}for(var P=7^_,O=-1,m=0;m<8;++m)m!==_&&m!==P&&(O<0?O=m:f[O][1]>f[m][1]&&(O=m));for(var R=-1,m=0;m<3;++m){var 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t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function a(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}function o(t,e,r){var n=[],i=[0,0,0],o=[0,0,0],c=[0,0,0],h=[0,0,0];n.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var d=n.length/3\|0,p=0;p<r[f].length;++p){var m=+r[f][p].x;n.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var v=n.length/3\|0;i[f]=d,o[f]=v-d;for(var d=n.length/3\|0,g=0;g<r[f].length;++g){var m=+r[f][g].x;n.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var v=n.length/3\|0;c[f]=d,h[f]=v-d}var y=s(t,new Float32Array(n)),b=l(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),x=u(t);return x.attributes.position.location=0,new a(t,y,b,x,o,i,h,c)}e.exports=o;var s=t(\"gl-buffer\"),l=t(\"gl-vao\"),u=t(\"./shaders\").line,c=[0,0,0],h=[0,0,0],f=[0,0,0],d=[0,0,0],p=[1,1],m=a.prototype;m.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,p[0]=this.gl.drawingBufferWidth,p[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=p,this.vao.bind()},m.drawAxisLine=function(t,e,r,a,o){var s=n(h);this.shader.uniforms.majorAxis=h,s[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=s;var l=i(d,r);l[t]+=e[0][t],this.shader.uniforms.offset=l,this.shader.uniforms.lineWidth=o,this.shader.uniforms.color=a;var u=n(f);u[(t+2)%3]=1,this.shader.uniforms.screenAxis=u,this.vao.draw(this.gl.TRIANGLES,6);var u=n(f);u[(t+1)%3]=1,this.shader.uniforms.screenAxis=u,this.vao.draw(this.gl.TRIANGLES,6)},m.drawAxisTicks=function(t,e,r,i,a){if(this.tickCount[t]){var 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n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":157}],159:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.plot=t,this.shader=e,this.bufferHi=r,this.bufferLo=n,this.bounds=[1/0,1/0,-1/0,-1/0],this.numPoints=0,this.color=[0,0,0,1]}function i(t,e){var r=a(t.gl,l.vertex,l.fragment),i=o(t.gl),s=o(t.gl),u=new n(t,r,i,s);return u.update(e),t.addObject(u),u}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"typedarray-pool\"),l=t(\"./lib/shaders\");e.exports=i;var 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s=t(\"gl-buffer\"),l=t(\"gl-vao\"),u=t(\"./shaders/index\"),c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],h=n.prototype;h.isOpaque=function(){return this.opacity>=1},h.isTransparent=function(){return this.opacity<1},h.drawTransparent=h.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view\|\|c,i=r.projection=t.projection\|\|c;r.model=t.model\|\|c,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],o=n[13],s=n[14],l=n[15],u=this.pixelRatio(i[3]a+i[7]o+i[11]s+i[15]l)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]),r.capSize=this.capSize[h]u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var f=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=(n+e)%3,o=[0,0,0];o[a]=i,r.push(o)}t[e]=r}return t}();h.update=function(t){t=t\|\|{},\"lineWidth\"in 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m=h.slice();m[u]+=d[1][u],o.push(h[0],h[1],h[2],p[0],p[1],p[2],p[3],0,0,0,m[0],m[1],m[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,m),l+=2+a(o,m,p,u)}}}this.lineCount[u]=l-this.lineOffset[u]}this.buffer.update(o)}},h.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":163,\"gl-buffer\":156,\"gl-vao\":271}],162:[function(t,e,r){e.exports=function(t){\"string\"==typeof t&&(t=[t]);for(var e=[].slice.call(arguments,1),r=[],n=0;n<t.length-1;n++)r.push(t[n],e[n]\|\|\"\");return r.push(t[n]),r.join(\"\")}},{}],163:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = model * vec4(position, 1.0);\\n worldPosition = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n gl_Position = projection * view * worldPosition;\\n fragColor = color;\\n fragPosition = position;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(fragPosition, clipBounds[0])) \|\| any(greaterThan(fragPosition, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":255,glslify:162}],164:[function(t,e,r){\"use strict\";function n(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function i(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function a(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);y=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;y[n]=i}}function o(t){switch(t){case p:throw new Error(\"gl-fbo: Framebuffer unsupported\");case m:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case v:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case g:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function s(t,e,r,n,i,a){if(!n)return null;var o=d(t,e,r,i,n);return o.magFilter=t.NEAREST,o.minFilter=t.NEAREST,o.mipSamples=1,o.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,a,t.TEXTURE_2D,o.handle,0),o}function l(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function u(t){var e=n(t.gl),r=t.gl,a=t.handle=r.createFramebuffer(),u=t._shape[0],c=t._shape[1],h=t.color.length,f=t._ext,d=t._useStencil,p=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,a);for(var v=0;v<h;++v)t.color[v]=s(r,u,c,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===h?(t._color_rb=l(r,u,c,r.RGBA4,r.COLOR_ATTACHMENT0),f&&f.drawBuffersWEBGL(y[0])):h>1&&f.drawBuffersWEBGL(y[h]);var g=r.getExtension(\"WEBGL_depth_texture\");g?d?t.depth=s(r,u,c,g.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p&&(t.depth=s(r,u,c,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):p&&d?t._depth_rb=l(r,u,c,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p?t._depth_rb=l(r,u,c,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=l(r,u,c,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(var v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),i(r,e),o(b)}i(r,e)}function c(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0\|e,0\|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var l=0;l<i;++l)this.color[l]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var c=this,h=[0\|e,0\|r];Object.defineProperties(h,{0:{get:function(){return c._shape[0]},set:function(t){return c.width=t}},1:{get:function(){return c._shape[1]},set:function(t){return c.height=t}}}),this._shapeVector=h,u(this)}function h(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e\|\|t._shape[1]!==r){var a=t.gl,s=a.getParameter(a.MAX_RENDERBUFFER_SIZE);if(e<0\|\|e>s\|\|r<0\|\|r>s)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var l=n(a),u=0;u<t.color.length;++u)t.color[u].shape=t._shape;t._color_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._color_rb),a.renderbufferStorage(a.RENDERBUFFER,a.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&a.renderbufferStorage(a.RENDERBUFFER,a.STENCIL_INDEX,t._shape[0],t._shape[1])),a.bindFramebuffer(a.FRAMEBUFFER,t.handle);var c=a.checkFramebufferStatus(a.FRAMEBUFFER);c!==a.FRAMEBUFFER_COMPLETE&&(t.dispose(),i(a,l),o(c)),i(a,l)}}function f(t,e,r,n){p\|\|(p=t.FRAMEBUFFER_UNSUPPORTED,m=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,v=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,g=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var i=t.getExtension(\"WEBGL_draw_buffers\");if(!y&&i&&a(t,i),Array.isArray(e)&&(n=r,r=0\|e[1],e=0\|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var o=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0\|\|e>o\|\|r<0\|\|r>o)throw new Error(\"gl-fbo: Parameters are too large for FBO\");n=n\|\|{};var s=1;if(\"color\"in n){if((s=Math.max(0\|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(s>1){if(!i)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(s>t.getParameter(i.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+s+\" draw buffers\")}}var l=t.UNSIGNED_BYTE,u=t.getExtension(\"OES_texture_float\");if(n.float&&s>0){if(!u)throw new Error(\"gl-fbo: Context does not support floating point textures\");l=t.FLOAT}else n.preferFloat&&s>0&&u&&(l=t.FLOAT);var h=!0;\"depth\"in n&&(h=!!n.depth);var f=!1;return\"stencil\"in n&&(f=!!n.stencil),new c(t,e,r,l,s,h,f,i)}var d=t(\"gl-texture2d\");e.exports=f;var p,m,v,g,y=null,b=c.prototype;Object.defineProperties(b,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)\|\|(t=[0\|t,0\|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0\|t[0],r=0\|t[1];return h(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return t\|=0,h(this,t,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t\|=0,h(this,this._shape[0],t),t},enumerable:!1}}),b.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},b.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":267}],165:[function(t,e,r){function n(t,e,r){\"use strict\";var n=o(e)\|\|\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===a.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var u=i(\"Error compiling %s shader %s:\\n\",l,n),c=i(\"%s%s\",u,t),h=t.split(\"\\n\"),f={},d=0;d<h.length;d++){var p=h[d];if(\"\"!==p){var m=parseInt(p.split(\":\")[2]);if(isNaN(m))throw new Error(i(\"Could not parse error: %s\",p));f[m]=p}}for(var v=s(e).split(\"\\n\"),d=0;d<v.length;d++)if(f[d+3]\|\|f[d+2]\|\|f[d+1]){var g=v[d];if(u+=g+\"\\n\",f[d+1]){var y=f[d+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),u+=i(\"^^^ %s\\n\\n\",y)}}return{long:u.trim(),short:c.trim()}}var i=t(\"sprintf-js\").sprintf,a=t(\"gl-constants/lookup\"),o=t(\"glsl-shader-name\"),s=t(\"add-line-numbers\");e.exports=n},{\"add-line-numbers\":40,\"gl-constants/lookup\":158,\"glsl-shader-name\":279,\"sprintf-js\":527}],166:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}function i(t,e){var r=t.gl,i=l(r,c.vertex,c.fragment),a=l(r,c.pickVertex,c.pickFragment),o=u(r),s=u(r),h=u(r),f=u(r),d=new n(t,i,a,o,s,h,f);return d.update(e),t.addObject(d),d}e.exports=i;var a=t(\"binary-search-bounds\"),o=t(\"iota-array\"),s=t(\"typedarray-pool\"),l=t(\"gl-shader\"),u=t(\"gl-buffer\"),c=t(\"./lib/shaders\"),h=n.prototype,f=[0,0,1,0,0,1,1,0,1,1,0,1];h.draw=function(){var t=[1,0,0,0,1,0,0,0,1];return function(){var e=this.plot,r=this.shader,n=this.bounds,i=this.numVertices;if(!(i<=0)){var a=e.gl,o=e.dataBox,s=n[2]-n[0],l=n[3]-n[1],u=o[2]-o[0],c=o[3]-o[1];t[0]=2s/u,t[4]=2l/c,t[6]=2(n[0]-o[0])/u-1,t[7]=2(n[1]-o[1])/c-1,r.bind();var h=r.uniforms;h.viewTransform=t,h.shape=this.shape;var f=r.attributes;this.positionBuffer.bind(),f.position.pointer(),this.weightBuffer.bind(),f.weight.pointer(a.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(a.UNSIGNED_BYTE,!0),a.drawArrays(a.TRIANGLES,0,i)}}}(),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2u/h,t[4]=2c/f,t[6]=2(a[0]-l[0])/h-1,t[7]=2(a[1]-l[1])/f-1;for(var d=0;d<4;++d)e[d]=r>>8d&255;this.pickOffset=r,i.bind();var p=i.uniforms;p.viewTransform=t,p.pickOffset=e,p.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]this.shape[1];if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]\|0]]}},h.update=function(t){t=t\|\|{};var e=t.shape\|\|[0,0],r=t.x\|\|o(e[0]),n=t.y\|\|o(e[1]),i=t.z\|\|new Float32Array(e[0]e[1]);this.xData=r,this.yData=n;var l=t.colorLevels\|\|[0],u=t.colorValues\|\|[0,0,0,1],c=l.length,h=this.bounds,d=h[0]=r[0],p=h[1]=n[0],m=h[2]=r[r.length-1],v=h[3]=n[n.length-1],g=1/(m-d),y=1/(v-p),b=e[0],x=e[1];this.shape=[b,x];var _=(b-1)(x-1)(f.length>>>1);this.numVertices=_;for(var w=s.mallocUint8(4_),M=s.mallocFloat32(2_),k=s.mallocUint8(2_),A=s.mallocUint32(_),T=0,S=0;S<x-1;++S)for(var E=y(n[S]-p),L=y(n[S+1]-p),C=0;C<b-1;++C)for(var I=g(r[C]-d),z=g(r[C+1]-d),D=0;D<f.length;D+=2){var P,O,R,F,j=f[D],N=f[D+1],B=(S+N)b+(C+j),U=i[B],V=a.le(l,U);if(V<0)P=u[0],O=u[1],R=u[2],F=u[3];else if(V===c-1)P=u[4c-4],O=u[4c-3],R=u[4c-2],F=u[4c-1];else{var H=(U-l[V])/(l[V+1]-l[V]),q=1-H,G=4V,Y=4(V+1);P=qu[G]+Hu[Y],O=qu[G+1]+Hu[Y+1],R=qu[G+2]+Hu[Y+2],F=qu[G+3]+Hu[Y+3]}w[4T]=255P,w[4T+1]=255O,w[4T+2]=255R,w[4T+3]=255F,M[2T]=.5I+.5z,M[2T+1]=.5E+.5L,k[2T]=j,k[2T+1]=N,A[T]=Sb+C,T+=1}this.positionBuffer.update(M),this.weightBuffer.update(k),this.colorBuffer.update(w),this.idBuffer.update(A),s.free(M),s.free(w),s.free(k),s.free(A)},h.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":167,\"binary-search-bounds\":168,\"gl-buffer\":156,\"gl-shader\":255,\"iota-array\":293,\"typedarray-pool\":541}],167:[function(t,e,r){\"use strict\";e.exports={fragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = vec4(fragColor.rgb fragColor.a, fragColor.a);\\n}\\n\",vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n fragColor = color;\\n gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\",pickFragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n vec2 d = step(.5, vWeight);\\n vec4 id = fragId + pickOffset;\\n id.x += d.x + d.yshape.x;\\n\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n gl_FragColor = id/255.;\\n}\\n\",pickVertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n vWeight = weight;\\n\\n fragId = pickId;\\n\\n vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"}},{}],168:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],169:[function(t,e,r){r.lineVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi, dLo;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, screenShape;\\nuniform float width;\\n\\nvarying vec2 direction;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n return (posHi + trHi) * scHi\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo;\\n}\\n\\n\\nvec2 project_2_1(vec2 scHi, vec2 scLo, vec2 posHi, vec2 posLo) {\\n return scHi * posHi\\n + scLo * posHi\\n + scHi * posLo\\n + scLo * posLo;\\n}\\n\\nvoid main() {\\n vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n vec2 dir = project_2_1(scaleHi, scaleLo, dHi, dLo);\\n vec2 n = 0.5 * width * normalize(screenShape.yx * vec2(dir.y, -dir.x)) / screenShape.xy;\\n vec2 tangent = normalize(screenShape.xy * dir);\\n if(dir.x < 0.0 \|\| (dir.x == 0.0 && dir.y < 0.0)) {\\n direction = -tangent;\\n } else {\\n direction = tangent;\\n }\\n gl_Position = vec4(p + n, 0.0, 1.0);\\n}\",r.lineFragment=\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nuniform vec2 screenShape;\\nuniform sampler2D dashPattern;\\nuniform float dashLength;\\n\\nvarying vec2 direction;\\n\\nvoid main() {\\n float t = fract(dot(direction, gl_FragCoord.xy) / dashLength);\\n vec4 pcolor = color * texture2D(dashPattern, vec2(t, 0.0)).r;\\n gl_FragColor = vec4(pcolor.rgb * pcolor.a, pcolor.a);\\n}\",r.mitreVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo;\\nuniform float radius;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n return (posHi + trHi) * scHi\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n gl_Position = vec4(p, 0.0, 1.0);\\n gl_PointSize = radius;\\n}\",r.mitreFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n if(length(gl_PointCoord.xy - 0.5) > 0.25) {\\n discard;\\n }\\n gl_FragColor = vec4(color.rgb, color.a);\\n}\",r.pickVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi;\\nattribute vec4 pick0, pick1;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, screenShape;\\nuniform float width;\\n\\nvarying vec4 pickA, pickB;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n return (posHi + trHi) * scHi\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n vec2 n = width * normalize(screenShape.yx * vec2(dHi.y, -dHi.x)) / screenShape.xy;\\n gl_Position = vec4(p + n, 0, 1);\\n pickA = pick0;\\n pickB = pick1;\\n}\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 pickOffset;\\n\\nvarying vec4 pickA, pickB;\\n\\nvoid main() {\\n vec4 fragId = vec4(pickA.xyz, 0.0);\\n if(pickB.w > pickA.w) {\\n fragId.xyz = pickB.xyz;\\n }\\n\\n fragId += pickOffset;\\n\\n fragId.y += floor(fragId.x / 256.0);\\n fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n fragId.z += floor(fragId.y / 256.0);\\n fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n fragId.w += floor(fragId.z / 256.0);\\n fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n gl_FragColor = fragId / 255.0;\\n}\",r.fillVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, projectAxis;\\nuniform float projectValue, depth;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n return (posHi + trHi) * scHi\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n if(dHi.y < 0.0 \|\| (dHi.y == 0.0 && dHi.x < 0.0)) {\\n if(dot(p, projectAxis) < projectValue) {\\n p = p * (1.0 - abs(projectAxis)) + projectAxis * projectValue;\\n }\\n }\\n gl_Position = vec4(p, depth, 1);\\n}\",r.fillFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = vec4(color.rgb * color.a, color.a);\\n}\"},{}],170:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l){this.plot=t,this.dashPattern=e,this.lineBufferHi=r,this.lineBufferLo=n,this.pickBuffer=i,this.lineShader=a,this.mitreShader=o,this.fillShader=s,this.pickShader=l,this.usingDashes=!1,this.bounds=[1/0,1/0,-1/0,-1/0],this.width=1,this.color=[0,0,1,1],this.fill=[!1,!1,!1,!1],this.fillColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.data=null,this.numPoints=0,this.vertCount=0,this.pickOffset=0}function i(t){return t.map(function(t){return t.slice()})}function a(t,e){var r=t.gl,i=s(r),a=s(r),u=s(r),c=l(r,[1,1]),f=o(r,h.lineVertex,h.lineFragment),d=o(r,h.mitreVertex,h.mitreFragment),p=o(r,h.fillVertex,h.fillFragment),m=o(r,h.pickVertex,h.pickFragment),v=new n(t,c,i,a,u,f,d,p,m);return t.addObject(v),v.update(e),v}e.exports=a;var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"gl-texture2d\"),u=t(\"ndarray\"),c=t(\"typedarray-pool\"),h=t(\"./lib/shaders\"),f=n.prototype;f.setProjectionModel=function(){var t={scaleHi:new Float32Array([0,0]),scaleLo:new Float32Array([0,0]),translateHi:new Float32Array([0,0]),translateLo:new Float32Array([0,0]),screenShape:[0,0]};return function(){var e=this.bounds,r=this.plot.viewBox,n=this.plot.dataBox,i=e[2]-e[0],a=e[3]-e[1],o=n[2]-n[0],s=n[3]-n[1],l=r[2]-r[0],u=r[3]-r[1],c=2i/o,h=2a/s,f=(e[0]-n[0]-.5o)/i,d=(e[1]-n[1]-.5s)/a;return t.scaleHi[0]=c,t.scaleHi[1]=h,t.scaleLo[0]=c-t.scaleHi[0],t.scaleLo[1]=h-t.scaleHi[1],t.translateHi[0]=f,t.translateHi[1]=d,t.translateLo[0]=f-t.translateHi[0],t.translateLo[1]=d-t.translateHi[1],t.screenShape[0]=l,t.screenShape[1]=u,t}}(),f.setProjectionUniforms=function(t,e){t.scaleHi=e.scaleHi,t.scaleLo=e.scaleLo,t.translateHi=e.translateHi,t.translateLo=e.translateLo,t.screenShape=e.screenShape},f.draw=function(){var t=[1,0],e=[-1,0],r=[0,1],n=[0,-1];return function(){var i=this.vertCount;if(i){var a=this.setProjectionModel(),o=this.plot,s=this.width,l=o.gl,u=o.pixelRatio,c=this.color,h=this.fillShader.attributes;this.lineBufferLo.bind(),h.aLo.pointer(l.FLOAT,!1,16,0),this.lineBufferHi.bind();var f=this.fill;if(f[0]\|\|f[1]\|\|f[2]\|\|f[3]){var d=this.fillShader;d.bind();var p=d.uniforms;this.setProjectionUniforms(p,a),p.depth=o.nextDepthValue(),h.aHi.pointer(l.FLOAT,!1,16,0),h.dHi.pointer(l.FLOAT,!1,16,8),l.depthMask(!0),l.enable(l.DEPTH_TEST);var m=this.fillColor;f[0]&&(p.color=m[0],p.projectAxis=e,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[1]&&(p.color=m[1],p.projectAxis=n,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[2]&&(p.color=m[2],p.projectAxis=t,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[3]&&(p.color=m[3],p.projectAxis=r,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),l.depthMask(!1),l.disable(l.DEPTH_TEST)}var v=this.lineShader;v.bind(),this.lineBufferLo.bind(),v.attributes.aLo.pointer(l.FLOAT,!1,16,0),v.attributes.dLo.pointer(l.FLOAT,!1,16,8),this.lineBufferHi.bind();var g=v.uniforms;this.setProjectionUniforms(g,a),g.color=c,g.width=su,g.dashPattern=this.dashPattern.bind(),g.dashLength=this.dashLengthu;var y=v.attributes;if(y.aHi.pointer(l.FLOAT,!1,16,0),y.dHi.pointer(l.FLOAT,!1,16,8),l.drawArrays(l.TRIANGLES,0,i),s>2&&!this.usingDashes){var b=this.mitreShader;this.lineBufferLo.bind(),b.attributes.aLo.pointer(l.FLOAT,!1,48,0),this.lineBufferHi.bind(),b.bind();var x=b.uniforms;this.setProjectionUniforms(x,a),x.color=c,x.radius=su,b.attributes.aHi.pointer(l.FLOAT,!1,48,0),l.drawArrays(l.POINTS,0,i/3\|0)}}}}(),f.drawPick=function(){var t=[0,0,0,0];return function(e){var r=this.vertCount,n=this.numPoints;if(this.pickOffset=e,!r)return e+n;var i=this.setProjectionModel(),a=this.plot,o=this.width,s=a.gl,l=a.pickPixelRatio,u=this.pickShader,c=this.pickBuffer;t[0]=255&e,t[1]=e>>>8&255,t[2]=e>>>16&255,t[3]=e>>>24,u.bind();var h=u.uniforms;this.setProjectionUniforms(h,i),h.width=ol,h.pickOffset=t;var f=u.attributes;return this.lineBufferHi.bind(),f.aHi.pointer(s.FLOAT,!1,16,0),f.dHi.pointer(s.FLOAT,!1,16,8),this.lineBufferLo.bind(),f.aLo.pointer(s.FLOAT,!1,16,0),c.bind(),f.pick0.pointer(s.UNSIGNED_BYTE,!1,8,0),f.pick1.pointer(s.UNSIGNED_BYTE,!1,8,4),s.drawArrays(s.TRIANGLES,0,r),e+n}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.data;return{object:this,pointId:a,dataCoord:[o[2a],o[2a+1]]}},f.update=function(t){t=t\|\|{};var e,r,n,a,o,s=this.plot.gl;this.color=(t.color\|\|[0,0,1,1]).slice(),this.width=+(t.width\|\|1),this.fill=(t.fill\|\|[!1,!1,!1,!1]).slice(),this.fillColor=i(t.fillColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var h=t.dashes\|\|[1],f=0;for(e=0;e<h.length;++e)f+=h[e];var d=c.mallocUint8(f);n=0;var p=255;for(e=0;e<h.length;++e){for(r=0;r<h[e];++r)d[n++]=p;p^=255}this.dashPattern.dispose(),this.usingDashes=h.length>1,this.dashPattern=l(s,u(d,[f,1,4],[1,0,0])),this.dashPattern.minFilter=s.NEAREST,this.dashPattern.magFilter=s.NEAREST,this.dashLength=f,c.free(d);var m=t.positions;this.data=m;var v=this.bounds;v[0]=v[1]=1/0,v[2]=v[3]=-1/0;var g=this.numPoints=m.length>>>1;if(0!==g){for(e=0;e<g;++e)a=m[2e],o=m[2e+1],isNaN(a)\|\|isNaN(o)\|\|(v[0]=Math.min(v[0],a),v[1]=Math.min(v[1],o),v[2]=Math.max(v[2],a),v[3]=Math.max(v[3],o));v[0]===v[2]&&(v[2]+=1),v[3]===v[1]&&(v[3]+=1);var y=c.mallocFloat64(24(g-1)),b=c.mallocFloat32(24(g-1)),x=c.mallocFloat32(24(g-1)),_=c.mallocUint32(12(g-1)),w=b.length,M=_.length;n=g;for(var k=0;n>1;){var A=--n;a=m[2n],o=m[2n+1];var T=A-1,S=m[2T],E=m[2T+1];if(!(isNaN(a)\|\|isNaN(o)\|\|isNaN(S)\|\|isNaN(E))){k+=1,a=(a-v[0])/(v[2]-v[0]),o=(o-v[1])/(v[3]-v[1]),S=(S-v[0])/(v[2]-v[0]),E=(E-v[1])/(v[3]-v[1]);var L=S-a,C=E-o,I=A\|1<<24,z=A-1,D=A,P=A-1\|1<<24;y[--w]=-C,y[--w]=-L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z,y[--w]=C,y[--w]=L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=-C,y[--w]=-L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=C,y[--w]=L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=-C,y[--w]=-L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z,y[--w]=C,y[--w]=L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z}}for(e=0;e<y.length;e++)b[e]=y[e],x[e]=y[e]-b[e];this.vertCount=6k,this.lineBufferHi.update(b.subarray(w)),this.lineBufferLo.update(x.subarray(w)),this.pickBuffer.update(_.subarray(M)),c.free(y),c.free(b),c.free(x),c.free(_)}},f.dispose=function(){this.plot.removeObject(this),this.lineBufferLo.dispose(),this.lineBufferHi.dispose(),this.pickBuffer.dispose(),this.lineShader.dispose(),this.mitreShader.dispose(),this.fillShader.dispose(),this.pickShader.dispose(),this.dashPattern.dispose()}},{\"./lib/shaders\":169,\"gl-buffer\":156,\"gl-shader\":255,\"gl-texture2d\":267,ndarray:467,\"typedarray-pool\":541}],171:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvoid main() {\\n vec4 projected = projection view * model * vec4(position, 1.0);\\n vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\\n vec2 tangent = normalize(screenShape * tangentClip.xy);\\n vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\\n\\n gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\\n\\n worldPosition = position;\\n pixelArcLength = arcLength;\\n fragColor = color;\\n}\\n\",a=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return n(t,i,\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(worldPosition, clipBounds[0])) \|\| any(greaterThan(worldPosition, clipBounds[1]))) {\\n discard;\\n }\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\",null,a)},r.createPickShader=function(t){return n(t,i,\"precision mediump float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\nlowp vec4 encode_float_1_0(highp float v) {\\n highp float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n highp vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n highp float e = floor(log2(av));\\n highp float m = av * pow(2.0, -e) - 1.0;\\n \\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n \\n //Unpack exponent\\n highp float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0; \\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\n\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(worldPosition, clipBounds[0])) \|\| any(greaterThan(worldPosition, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = vec4(pickId/255.0, encode_float_1_0(pixelArcLength).xyz);\\n}\",null,a)}},{\"gl-shader\":255}],172:[function(t,e,r){\"use strict\";function n(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=ii}return Math.sqrt(r)}function i(t){\n", "for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function a(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function o(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}function s(t){var e=t.gl\|\|t.scene&&t.scene.gl,r=m(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var n=v(e);n.attributes.position.location=0,n.attributes.nextPosition.location=1,n.attributes.arcLength.location=2,n.attributes.lineWidth.location=3,n.attributes.color.location=4;for(var i=l(e),a=u(e,[{buffer:i,size:3,offset:0,stride:48},{buffer:i,size:3,offset:12,stride:48},{buffer:i,size:1,offset:24,stride:48},{buffer:i,size:1,offset:28,stride:48},{buffer:i,size:4,offset:32,stride:48}]),s=d(new Array(1024),[256,1,4]),h=0;h<1024;++h)s.data[h]=255;var f=c(e,s);f.wrap=e.REPEAT;var p=new o(e,r,n,i,a,f);return p.update(t),p}e.exports=s;var l=t(\"gl-buffer\"),u=t(\"gl-vao\"),c=t(\"gl-texture2d\"),h=t(\"glsl-read-float\"),f=t(\"binary-search-bounds\"),d=t(\"ndarray\"),p=t(\"./lib/shaders\"),m=p.createShader,v=p.createPickShader,g=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],y=o.prototype;y.isTransparent=function(){return this.opacity<1},y.isOpaque=function(){return this.opacity>=1},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model\|\|g,view:t.view\|\|g,projection:t.projection\|\|g,clipBounds:i(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model\|\|g,view:t.view\|\|g,projection:t.projection\|\|g,pickId:this.pickId,clipBounds:i(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.update=function(t){var e,r;this.dirty=!0;var i=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),\"opacity\"in t&&(this.opacity=+t.opacity);var a=t.position\|\|t.positions;if(a){var o=t.color\|\|t.colors\|\|[0,0,0,1],s=t.lineWidth\|\|1,l=[],u=[],c=[],h=0,p=0,m=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],v=!1;t:for(e=1;e<a.length;++e){var g=a[e-1],y=a[e];for(u.push(h),c.push(g.slice()),r=0;r<3;++r){if(isNaN(g[r])\|\|isNaN(y[r])\|\|!isFinite(g[r])\|\|!isFinite(y[r])){if(!i&&l.length>0){for(var b=0;b<24;++b)l.push(l[l.length-12]);p+=2,v=!0}continue t}m[0][r]=Math.min(m[0][r],g[r],y[r]),m[1][r]=Math.max(m[1][r],g[r],y[r])}var x,_;Array.isArray(o[0])?(x=o[e-1],_=o[e]):x=_=o,3===x.length&&(x=[x[0],x[1],x[2],1]),3===_.length&&(_=[_[0],_[1],_[2],1]);var w;w=Array.isArray(s)?s[e-1]:s;var M=h;if(h+=n(g,y),v){for(r=0;r<2;++r)l.push(g[0],g[1],g[2],y[0],y[1],y[2],M,w,x[0],x[1],x[2],x[3]);p+=2,v=!1}l.push(g[0],g[1],g[2],y[0],y[1],y[2],M,w,x[0],x[1],x[2],x[3],g[0],g[1],g[2],y[0],y[1],y[2],M,-w,x[0],x[1],x[2],x[3],y[0],y[1],y[2],g[0],g[1],g[2],h,-w,_[0],_[1],_[2],_[3],y[0],y[1],y[2],g[0],g[1],g[2],h,w,_[0],_[1],_[2],_[3]),p+=4}if(this.buffer.update(l),u.push(h),c.push(a[a.length-1].slice()),this.bounds=m,this.vertexCount=p,this.points=c,this.arcLength=u,\"dashes\"in t){var k=t.dashes,A=k.slice();for(A.unshift(0),e=1;e<A.length;++e)A[e]=A[e-1]+A[e];var T=d(new 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t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t[3]=e[3]-r[3],t[4]=e[4]-r[4],t[5]=e[5]-r[5],t[6]=e[6]-r[6],t[7]=e[7]-r[7],t[8]=e[8]-r[8],t},i.sub=i.subtract,i.multiplyScalar=function(t,e,r){return t[0]=e[0]r,t[1]=e[1]r,t[2]=e[2]r,t[3]=e[3]r,t[4]=e[4]r,t[5]=e[5]r,t[6]=e[6]r,t[7]=e[7]r,t[8]=e[8]r,t},i.multiplyScalarAndAdd=function(t,e,r,n){return t[0]=e[0]+r[0]n,t[1]=e[1]+r[1]n,t[2]=e[2]+r[2]n,t[3]=e[3]+r[3]n,t[4]=e[4]+r[4]n,t[5]=e[5]+r[5]n,t[6]=e[6]+r[6]n,t[7]=e[7]+r[7]n,t[8]=e[8]+r[8]n,t},i.exactEquals=function(t,e){return t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]&&t[4]===e[4]&&t[5]===e[5]&&t[6]===e[6]&&t[7]===e[7]&&t[8]===e[8]},i.equals=function(t,e){var r=t[0],i=t[1],a=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],h=t[8],f=e[0],d=e[1],p=e[2],m=e[3],v=e[4],g=e[5],y=t[6],b=e[7],x=e[8];return Math.abs(r-f)<=n.EPSILONMath.max(1,Math.abs(r),Math.abs(f))&&Math.abs(i-d)<=n.EPSILONMath.max(1,Math.abs(i),Math.abs(d))&&Math.abs(a-p)<=n.EPSILONMath.max(1,Math.abs(a),Math.abs(p))&&Math.abs(o-m)<=n.EPSILONMath.max(1,Math.abs(o),Math.abs(m))&&Math.abs(s-v)<=n.EPSILONMath.max(1,Math.abs(s),Math.abs(v))&&Math.abs(l-g)<=n.EPSILONMath.max(1,Math.abs(l),Math.abs(g))&&Math.abs(u-y)<=n.EPSILONMath.max(1,Math.abs(u),Math.abs(y))&&Math.abs(c-b)<=n.EPSILONMath.max(1,Math.abs(c),Math.abs(b))&&Math.abs(h-x)<=n.EPSILONMath.max(1,Math.abs(h),Math.abs(x))},e.exports=i},{\"./common.js\":194}],198:[function(t,e,r){var n=t(\"./common.js\"),i={scalar:{},SIMD:{}};i.create=function(){var t=new n.ARRAY_TYPE(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.clone=function(t){var e=new n.ARRAY_TYPE(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},i.copy=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.fromValues=function(t,e,r,i,a,o,s,l,u,c,h,f,d,p,m,v){var g=new n.ARRAY_TYPE(16);return g[0]=t,g[1]=e,g[2]=r,g[3]=i,g[4]=a,g[5]=o,g[6]=s,g[7]=l,g[8]=u,g[9]=c,g[10]=h,g[11]=f,g[12]=d,g[13]=p,g[14]=m,g[15]=v,g},i.set=function(t,e,r,n,i,a,o,s,l,u,c,h,f,d,p,m,v){return t[0]=e,t[1]=r,t[2]=n,t[3]=i,t[4]=a,t[5]=o,t[6]=s,t[7]=l,t[8]=u,t[9]=c,t[10]=h,t[11]=f,t[12]=d,t[13]=p,t[14]=m,t[15]=v,t},i.identity=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.scalar.transpose=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t},i.SIMD.transpose=function(t,e){var r,n,i,a,o,s,l,u,c,h;return r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12),o=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,a,0,1,4,5),l=SIMD.Float32x4.shuffle(o,s,0,2,4,6),u=SIMD.Float32x4.shuffle(o,s,1,3,5,7),SIMD.Float32x4.store(t,0,l),SIMD.Float32x4.store(t,4,u),o=SIMD.Float32x4.shuffle(r,n,2,3,6,7),s=SIMD.Float32x4.shuffle(i,a,2,3,6,7),c=SIMD.Float32x4.shuffle(o,s,0,2,4,6),h=SIMD.Float32x4.shuffle(o,s,1,3,5,7),SIMD.Float32x4.store(t,8,c),SIMD.Float32x4.store(t,12,h),t},i.transpose=n.USE_SIMD?i.SIMD.transpose:i.scalar.transpose,i.scalar.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15],y=rs-no,b=rl-io,x=ru-ao,_=nl-is,w=nu-as,M=iu-al,k=cm-hp,A=cv-fp,T=cg-dp,S=hv-fm,E=hg-dm,L=fg-dv,C=yL-bE+xS+_T-wA+Mk;return C?(C=1/C,t[0]=(sL-lE+uS)C,t[1]=(iE-nL-aS)C,t[2]=(mM-vw+g_)C,t[3]=(fw-hM-d_)C,t[4]=(lT-oL-uA)C,t[5]=(rL-iT+aA)C,t[6]=(vx-pM-gb)C,t[7]=(cM-fx+db)C,t[8]=(oE-sT+uk)C,t[9]=(nT-rE-ak)C,t[10]=(pw-mx+gy)C,t[11]=(hx-cw-dy)C,t[12]=(sA-oS-lk)C,t[13]=(rS-nA+ik)C,t[14]=(mb-p_-vy)C,t[15]=(c_-hb+fy)C,t):null},i.SIMD.invert=function(t,e){var r,n,i,a,o,s,l,u,c,h,f=SIMD.Float32x4.load(e,0),d=SIMD.Float32x4.load(e,4),p=SIMD.Float32x4.load(e,8),m=SIMD.Float32x4.load(e,12);return o=SIMD.Float32x4.shuffle(f,d,0,1,4,5),n=SIMD.Float32x4.shuffle(p,m,0,1,4,5),r=SIMD.Float32x4.shuffle(o,n,0,2,4,6),n=SIMD.Float32x4.shuffle(n,o,1,3,5,7),o=SIMD.Float32x4.shuffle(f,d,2,3,6,7),a=SIMD.Float32x4.shuffle(p,m,2,3,6,7),i=SIMD.Float32x4.shuffle(o,a,0,2,4,6),a=SIMD.Float32x4.shuffle(a,o,1,3,5,7),o=SIMD.Float32x4.mul(i,a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),s=SIMD.Float32x4.mul(n,o),l=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(SIMD.Float32x4.mul(n,o),s),l=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),l),l=SIMD.Float32x4.swizzle(l,2,3,0,1),o=SIMD.Float32x4.mul(n,i),o=SIMD.Float32x4.swizzle(o,1,0,3,2),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),s),c=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(a,o)),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),o=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(n,2,3,0,1),a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),i=SIMD.Float32x4.swizzle(i,2,3,0,1),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,o),s),u=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(i,o)),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),u),u=SIMD.Float32x4.swizzle(u,2,3,0,1),o=SIMD.Float32x4.mul(r,n),o=SIMD.Float32x4.swizzle(o,1,0,3,2),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),u),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(i,o),c),o=SIMD.Float32x4.swizzle(o,2,3,0,1),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,o),u),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(i,o)),o=SIMD.Float32x4.mul(r,a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(i,o)),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,o),u),o=SIMD.Float32x4.swizzle(o,2,3,0,1),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,o),l),u=SIMD.Float32x4.sub(u,SIMD.Float32x4.mul(n,o)),o=SIMD.Float32x4.mul(r,i),o=SIMD.Float32x4.swizzle(o,1,0,3,2),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),l),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(n,o)),o=SIMD.Float32x4.swizzle(o,2,3,0,1),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(a,o)),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,o),c),h=SIMD.Float32x4.mul(r,s),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,2,3,0,1),h),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,1,0,3,2),h),o=SIMD.Float32x4.reciprocalApproximation(h),h=SIMD.Float32x4.sub(SIMD.Float32x4.add(o,o),SIMD.Float32x4.mul(h,SIMD.Float32x4.mul(o,o))),(h=SIMD.Float32x4.swizzle(h,0,0,0,0))?(SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(h,s)),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(h,l)),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(h,u)),SIMD.Float32x4.store(t,12,SIMD.Float32x4.mul(h,c)),t):null},i.invert=n.USE_SIMD?i.SIMD.invert:i.scalar.invert,i.scalar.adjoint=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15];return t[0]=s(fg-dv)-h(lg-uv)+m(ld-uf),t[1]=-(n(fg-dv)-h(ig-av)+m(id-af)),t[2]=n(lg-uv)-s(ig-av)+m(iu-al),t[3]=-(n(ld-uf)-s(id-af)+h(iu-al)),t[4]=-(o(fg-dv)-c(lg-uv)+p(ld-uf)),t[5]=r(fg-dv)-c(ig-av)+p(id-af),t[6]=-(r(lg-uv)-o(ig-av)+p(iu-al)),t[7]=r(ld-uf)-o(id-af)+c(iu-al),t[8]=o(hg-dm)-c(sg-um)+p(sd-uh),t[9]=-(r(hg-dm)-c(ng-am)+p(nd-ah)),t[10]=r(sg-um)-o(ng-am)+p(nu-as),t[11]=-(r(sd-uh)-o(nd-ah)+c(nu-as)),t[12]=-(o(hv-fm)-c(sv-lm)+p(sf-lh)),t[13]=r(hv-fm)-c(nv-im)+p(nf-ih),t[14]=-(r(sv-lm)-o(nv-im)+p(nl-is)),t[15]=r(sf-lh)-o(nf-ih)+c(nl-is),t},i.SIMD.adjoint=function(t,e){var r,n,i,a,o,s,l,u,c,h,f,d,p,r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12)\n", ";return c=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,a,0,1,4,5),o=SIMD.Float32x4.shuffle(c,s,0,2,4,6),s=SIMD.Float32x4.shuffle(s,c,1,3,5,7),c=SIMD.Float32x4.shuffle(r,n,2,3,6,7),u=SIMD.Float32x4.shuffle(i,a,2,3,6,7),l=SIMD.Float32x4.shuffle(c,u,0,2,4,6),u=SIMD.Float32x4.shuffle(u,c,1,3,5,7),c=SIMD.Float32x4.mul(l,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.mul(s,c),f=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(SIMD.Float32x4.mul(s,c),h),f=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),f),f=SIMD.Float32x4.swizzle(f,2,3,0,1),c=SIMD.Float32x4.mul(s,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),h),p=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(u,c)),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),p),p=SIMD.Float32x4.swizzle(p,2,3,0,1),c=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,3,0,1),u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),l=SIMD.Float32x4.swizzle(l,2,3,0,1),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),h),d=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(l,c)),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),d),d=SIMD.Float32x4.swizzle(d,2,3,0,1),c=SIMD.Float32x4.mul(o,s),c=SIMD.Float32x4.swizzle(c,1,0,3,2),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),d),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(l,c),p),c=SIMD.Float32x4.swizzle(c,2,3,0,1),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(u,c),d),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(l,c)),c=SIMD.Float32x4.mul(o,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(l,c)),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),d),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),f),d=SIMD.Float32x4.sub(d,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.mul(o,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),f),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(u,c)),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),p),SIMD.Float32x4.store(t,0,h),SIMD.Float32x4.store(t,4,f),SIMD.Float32x4.store(t,8,d),SIMD.Float32x4.store(t,12,p),t},i.adjoint=n.USE_SIMD?i.SIMD.adjoint:i.scalar.adjoint,i.determinant=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],h=t[10],f=t[11],d=t[12],p=t[13],m=t[14],v=t[15];return(eo-ra)(hv-fm)-(es-na)(cv-fp)+(el-ia)(cm-hp)+(rs-no)(uv-fd)-(rl-io)(um-hd)+(nl-is)(up-cd)},i.SIMD.multiply=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4.load(r,0),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,3,3,3,3),o))));SIMD.Float32x4.store(t,0,l);var u=SIMD.Float32x4.load(r,4),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,3,3,3,3),o))));SIMD.Float32x4.store(t,4,c);var h=SIMD.Float32x4.load(r,8),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,3,3,3,3),o))));SIMD.Float32x4.store(t,8,f);var d=SIMD.Float32x4.load(r,12),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,3,3,3,3),o))));return SIMD.Float32x4.store(t,12,p),t},i.scalar.multiply=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],m=e[12],v=e[13],g=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=bn+xs+_h+wm,t[1]=bi+xl+_f+wv,t[2]=ba+xu+_d+wg,t[3]=bo+xc+_p+wy,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=bn+xs+_h+wm,t[5]=bi+xl+_f+wv,t[6]=ba+xu+_d+wg,t[7]=bo+xc+_p+wy,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=bn+xs+_h+wm,t[9]=bi+xl+_f+wv,t[10]=ba+xu+_d+wg,t[11]=bo+xc+_p+wy,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=bn+xs+_h+wm,t[13]=bi+xl+_f+wv,t[14]=ba+xu+_d+wg,t[15]=bo+xc+_p+wy,t},i.multiply=n.USE_SIMD?i.SIMD.multiply:i.scalar.multiply,i.mul=i.multiply,i.scalar.translate=function(t,e,r){var n,i,a,o,s,l,u,c,h,f,d,p,m=r[0],v=r[1],g=r[2];return e===t?(t[12]=e[0]m+e[4]v+e[8]g+e[12],t[13]=e[1]m+e[5]v+e[9]g+e[13],t[14]=e[2]m+e[6]v+e[10]g+e[14],t[15]=e[3]m+e[7]v+e[11]g+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=h,t[9]=f,t[10]=d,t[11]=p,t[12]=nm+sv+hg+e[12],t[13]=im+lv+fg+e[13],t[14]=am+uv+dg+e[14],t[15]=om+cv+pg+e[15]),t},i.SIMD.translate=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4(r[0],r[1],r[2],0);e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11]),n=SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(s,0,0,0,0)),i=SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(s,1,1,1,1)),a=SIMD.Float32x4.mul(a,SIMD.Float32x4.swizzle(s,2,2,2,2));var l=SIMD.Float32x4.add(n,SIMD.Float32x4.add(i,SIMD.Float32x4.add(a,o)));return SIMD.Float32x4.store(t,12,l),t},i.translate=n.USE_SIMD?i.SIMD.translate:i.scalar.translate,i.scalar.scale=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]n,t[1]=e[1]n,t[2]=e[2]n,t[3]=e[3]n,t[4]=e[4]i,t[5]=e[5]i,t[6]=e[6]i,t[7]=e[7]i,t[8]=e[8]a,t[9]=e[9]a,t[10]=e[10]a,t[11]=e[11]a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.SIMD.scale=function(t,e,r){var n,i,a,o=SIMD.Float32x4(r[0],r[1],r[2],0);return n=SIMD.Float32x4.load(e,0),SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(o,0,0,0,0))),i=SIMD.Float32x4.load(e,4),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(o,1,1,1,1))),a=SIMD.Float32x4.load(e,8),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(a,SIMD.Float32x4.swizzle(o,2,2,2,2))),t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.scale=n.USE_SIMD?i.SIMD.scale:i.scalar.scale,i.rotate=function(t,e,r,i){var a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T,S,E,L=i[0],C=i[1],I=i[2],z=Math.sqrt(LL+CC+II);return Math.abs(z)<n.EPSILON?null:(z=1/z,L=z,C=z,I=z,a=Math.sin(r),o=Math.cos(r),s=1-o,l=e[0],u=e[1],c=e[2],h=e[3],f=e[4],d=e[5],p=e[6],m=e[7],v=e[8],g=e[9],y=e[10],b=e[11],x=LLs+o,_=CLs+Ia,w=ILs-Ca,M=LCs-Ia,k=CCs+o,A=ICs+La,T=LIs+Ca,S=CIs-La,E=IIs+o,t[0]=lx+f_+vw,t[1]=ux+d_+gw,t[2]=cx+p_+yw,t[3]=hx+m_+bw,t[4]=lM+fk+vA,t[5]=uM+dk+gA,t[6]=cM+pk+yA,t[7]=hM+mk+bA,t[8]=lT+fS+vE,t[9]=uT+dS+gE,t[10]=cT+pS+yE,t[11]=hT+mS+bE,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)},i.scalar.rotateX=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=ai+un,t[5]=oi+cn,t[6]=si+hn,t[7]=li+fn,t[8]=ui-an,t[9]=ci-on,t[10]=hi-sn,t[11]=fi-ln,t},i.SIMD.rotateX=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,4),o=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,4,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),t},i.rotateX=n.USE_SIMD?i.SIMD.rotateX:i.scalar.rotateX,i.scalar.rotateY=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=ai-un,t[1]=oi-cn,t[2]=si-hn,t[3]=li-fn,t[8]=an+ui,t[9]=on+ci,t[10]=sn+hi,t[11]=ln+fi,t},i.SIMD.rotateY=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,0),o=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,n),SIMD.Float32x4.mul(o,i))),t},i.rotateY=n.USE_SIMD?i.SIMD.rotateY:i.scalar.rotateY,i.scalar.rotateZ=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],h=e[6],f=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=ai+un,t[1]=oi+cn,t[2]=si+hn,t[3]=li+fn,t[4]=ui-an,t[5]=ci-on,t[6]=hi-sn,t[7]=fi-ln,t},i.SIMD.rotateZ=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,0),o=SIMD.Float32x4.load(e,4);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,4,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),t},i.rotateZ=n.USE_SIMD?i.SIMD.rotateZ:i.scalar.rotateZ,i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotation=function(t,e,r){var i,a,o,s=r[0],l=r[1],u=r[2],c=Math.sqrt(ss+ll+uu);return Math.abs(c)<n.EPSILON?null:(c=1/c,s=c,l=c,u=c,i=Math.sin(e),a=Math.cos(e),o=1-a,t[0]=sso+a,t[1]=lso+ui,t[2]=uso-li,t[3]=0,t[4]=slo-ui,t[5]=llo+a,t[6]=ulo+si,t[7]=0,t[8]=suo+li,t[9]=luo-si,t[10]=uuo+a,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t)},i.fromXRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromYRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromZRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotationTranslation=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=ns,h=nl,f=nu,d=il,p=iu,m=au,v=os,g=ol,y=ou;return t[0]=1-(d+m),t[1]=h+y,t[2]=f-g,t[3]=0,t[4]=h-y,t[5]=1-(c+m),t[6]=p+v,t[7]=0,t[8]=f+g,t[9]=p-v,t[10]=1-(c+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.getTranslation=function(t,e){return t[0]=e[12],t[1]=e[13],t[2]=e[14],t},i.getRotation=function(t,e){var r=e[0]+e[5]+e[10],n=0;return r>0?(n=2Math.sqrt(r+1),t[3]=.25n,t[0]=(e[6]-e[9])/n,t[1]=(e[8]-e[2])/n,t[2]=(e[1]-e[4])/n):e[0]>e[5]&e[0]>e[10]?(n=2Math.sqrt(1+e[0]-e[5]-e[10]),t[3]=(e[6]-e[9])/n,t[0]=.25n,t[1]=(e[1]+e[4])/n,t[2]=(e[8]+e[2])/n):e[5]>e[10]?(n=2Math.sqrt(1+e[5]-e[0]-e[10]),t[3]=(e[8]-e[2])/n,t[0]=(e[1]+e[4])/n,t[1]=.25n,t[2]=(e[6]+e[9])/n):(n=2Math.sqrt(1+e[10]-e[0]-e[5]),t[3]=(e[1]-e[4])/n,t[0]=(e[8]+e[2])/n,t[1]=(e[6]+e[9])/n,t[2]=.25n),t},i.fromRotationTranslationScale=function(t,e,r,n){var i=e[0],a=e[1],o=e[2],s=e[3],l=i+i,u=a+a,c=o+o,h=il,f=iu,d=ic,p=au,m=ac,v=oc,g=sl,y=su,b=sc,x=n[0],_=n[1],w=n[2];return t[0]=(1-(p+v))x,t[1]=(f+b)x,t[2]=(d-y)x,t[3]=0,t[4]=(f-b)_,t[5]=(1-(h+v))_,t[6]=(m+g)_,t[7]=0,t[8]=(d+y)w,t[9]=(m-g)w,t[10]=(1-(h+p))w,t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.fromRotationTranslationScaleOrigin=function(t,e,r,n,i){var a=e[0],o=e[1],s=e[2],l=e[3],u=a+a,c=o+o,h=s+s,f=au,d=ac,p=ah,m=oc,v=oh,g=sh,y=lu,b=lc,x=lh,_=n[0],w=n[1],M=n[2],k=i[0],A=i[1],T=i[2];return t[0]=(1-(m+g))_,t[1]=(d+x)_,t[2]=(p-b)_,t[3]=0,t[4]=(d-x)w,t[5]=(1-(f+g))w,t[6]=(v+y)w,t[7]=0,t[8]=(p+b)M,t[9]=(v-y)M,t[10]=(1-(f+m))M,t[11]=0,t[12]=r[0]+k-(t[0]k+t[4]A+t[8]T),t[13]=r[1]+A-(t[1]k+t[5]A+t[9]T),t[14]=r[2]+T-(t[2]k+t[6]A+t[10]T),t[15]=1,t},i.fromQuat=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=ro,c=no,h=ns,f=io,d=is,p=il,m=ao,v=as,g=al;return t[0]=1-h-p,t[1]=c+g,t[2]=f-v,t[3]=0,t[4]=c-g,t[5]=1-u-p,t[6]=d+m,t[7]=0,t[8]=f+v,t[9]=d-m,t[10]=1-u-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.frustum=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),u=1/(a-o);return 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o,s,l,u,c,h,f,d,p,m,v=e[0],g=e[1],y=e[2],b=a[0],x=a[1],_=a[2],w=r[0],M=r[1],k=r[2];return Math.abs(v-w)<n.EPSILON&&Math.abs(g-M)<n.EPSILON&&Math.abs(y-k)<n.EPSILON?i.identity(t):(f=v-w,d=g-M,p=y-k,m=1/Math.sqrt(ff+dd+pp),f=m,d=m,p=m,o=xp-_d,s=_f-bp,l=bd-xf,m=Math.sqrt(oo+ss+ll),m?(m=1/m,o=m,s=m,l=m):(o=0,s=0,l=0),u=dl-ps,c=po-fl,h=fs-do,m=Math.sqrt(uu+cc+hh),m?(m=1/m,u=m,c=m,h=m):(u=0,c=0,h=0),t[0]=o,t[1]=u,t[2]=f,t[3]=0,t[4]=s,t[5]=c,t[6]=d,t[7]=0,t[8]=l,t[9]=h,t[10]=p,t[11]=0,t[12]=-(ov+sg+ly),t[13]=-(uv+cg+hy),t[14]=-(fv+dg+py),t[15]=1,t)},i.str=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"},i.frob=function(t){return 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lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution_2_0(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(f_data, clipBounds[0])) \|\| \\n any(greaterThan(f_data, clipBounds[1]))) {\\n discard;\\n }\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n \\n if(!gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular_1_1(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\",attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(f_data, clipBounds[0])) \|\| \\n any(greaterThan(f_data, clipBounds[1]))) {\\n discard;\\n }\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\| \\n any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\",fragment:n,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\| \\n any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\",fragment:n,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor,1);\\n}\\n\",attributes:[{name:\"position\",type:\"vec3\"}]}},{}],205:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=p,this.edgeColors=v,this.edgeUVs=g,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=b,this.pointColors=_,this.pointUVs=w,this.pointSizes=M,this.pointIds=x,this.pointVAO=k,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=T,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=D,this._view=D,this._projection=D,this._resolution=[1,1]}function i(t){for(var e=w({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4n+a]=i[a];r[4n+3]=255i[3]}return _(r,[256,256,4],[4,0,1])}function a(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;for(var a=t.length,i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}function o(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}function s(t){var e=p(t,S.vertex,S.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function l(t){var e=p(t,E.vertex,E.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function u(t){var e=p(t,L.vertex,L.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function c(t){var e=p(t,C.vertex,C.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function h(t){var e=p(t,I.vertex,I.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function f(t){var e=p(t,z.vertex,z.fragment);return e.attributes.position.location=0,e}function d(t,e){1===arguments.length&&(e=t,t=e.gl);var r=s(t),i=l(t),a=u(t),o=c(t),d=h(t),p=f(t),y=g(t,_(new Uint8Array([255,255,255,255]),[1,1,4]));y.generateMipmap(),y.minFilter=t.LINEAR_MIPMAP_LINEAR,y.magFilter=t.LINEAR;var b=m(t),x=m(t),w=m(t),M=m(t),k=m(t),A=v(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:x,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2},{buffer:M,type:t.FLOAT,size:3}]),T=m(t),S=m(t),E=m(t),L=m(t),C=v(t,[{buffer:T,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:S,type:t.FLOAT,size:4},{buffer:E,type:t.FLOAT,size:2}]),I=m(t),z=m(t),D=m(t),P=m(t),O=m(t),R=v(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:O,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:D,type:t.FLOAT,size:2},{buffer:P,type:t.FLOAT,size:1}]),F=m(t),j=v(t,[{buffer:F,type:t.FLOAT,size:3}]),N=new n(t,y,r,i,a,o,d,p,b,k,x,w,M,A,T,L,S,E,C,I,O,z,D,P,R,F,j);return N.update(e),N}var p=t(\"gl-shader\"),m=t(\"gl-buffer\"),v=t(\"gl-vao\"),g=t(\"gl-texture2d\"),y=t(\"normals\"),b=t(\"gl-mat4/multiply\"),x=t(\"gl-mat4/invert\"),_=t(\"ndarray\"),w=t(\"colormap\"),M=t(\"simplicial-complex-contour\"),k=t(\"typedarray-pool\"),A=t(\"./lib/shaders\"),T=t(\"./lib/closest-point\"),S=A.meshShader,E=A.wireShader,L=A.pointShader,C=A.pickShader,I=A.pointPickShader,z=A.contourShader,D=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=n.prototype;P.isOpaque=function(){return this.opacity>=1},P.isTransparent=function(){return this.opacity<1},P.pickSlots=1,P.setPickBase=function(t){this.pickId=t},P.highlight=function(t){if(!t\|\|!this.contourEnable)return void(this.contourCount=0);for(var e=M(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=k.mallocFloat32(6a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var h=u[0];2===u.length&&(h=u[c]);for(var f=n[h][0],d=n[h][1],p=i[h],m=1-p,v=this.positions[f],g=this.positions[d],y=0;y<3;++y)o[s++]=pv[y]+mg[y]}this.contourCount=s/3\|0,this.contourPositions.update(o.subarray(0,s)),k.free(o)},P.update=function(t){t=t\|\|{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=g(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(i(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var s=[],l=[],u=[],c=[],h=[],f=[],d=[],p=[],m=[],v=[],b=[],x=[],_=[],w=[];this.cells=r,this.positions=n;var M=t.vertexNormals,k=t.cellNormals,A=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,T=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=y.faceNormals(r,n,T)),k\|\|M\|\|(M=y.vertexNormals(r,n,A));var S=t.vertexColors,E=t.cellColors,L=t.meshColor\|\|[1,1,1,1],C=t.vertexUVs,I=t.vertexIntensity,z=t.cellUVs,D=t.cellIntensity,P=1/0,O=-1/0;if(!C&&!z)if(I)if(t.vertexIntensityBounds)P=+t.vertexIntensityBounds[0],O=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];P=Math.min(P,F),O=Math.max(O,F)}else if(D)for(var R=0;R<D.length;++R){var F=D[R];P=Math.min(P,F),O=Math.max(O,F)}else for(var R=0;R<n.length;++R){var F=n[R][2];P=Math.min(P,F),O=Math.max(O,F)}this.intensity=I\|\|(D?a(r,n.length,D):o(n));var j=t.pointSizes,N=t.pointSize\|\|1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var R=0;R<n.length;++R)for(var B=n[R],U=0;U<3;++U)!isNaN(B[U])&&isFinite(B[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],B[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],B[U]));var V=0,H=0,q=0;t:for(var R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(var Y=G[0],B=n[Y],U=0;U<3;++U)if(isNaN(B[U])\|\|!isFinite(B[U]))continue t;v.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?b.push(W[0],W[1],W[2],1):b.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],x.push(X[0],X[1]),j?_.push(j[Y]):_.push(N),w.push(R),q+=1;break;case 2:for(var U=0;U<2;++U)for(var Y=G[U],B=n[Y],Z=0;Z<3;++Z)if(isNaN(B[Z])\|\|!isFinite(B[Z]))continue t;for(var U=0;U<2;++U){var Y=G[U],B=n[Y];f.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?d.push(W[0],W[1],W[2],1):d.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],p.push(X[0],X[1]),m.push(R)}H+=1;break;case 3:for(var U=0;U<3;++U)for(var Y=G[U],B=n[Y],Z=0;Z<3;++Z)if(isNaN(B[Z])\|\|!isFinite(B[Z]))continue t;for(var U=0;U<3;++U){var Y=G[U],B=n[Y];s.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?l.push(W[0],W[1],W[2],1):l.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],c.push(X[0],X[1]);var J;J=M?M[Y]:k[R],u.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(b),this.pointUVs.update(x),this.pointSizes.update(_),this.pointIds.update(new Uint32Array(w)),this.edgePositions.update(f),this.edgeColors.update(d),this.edgeUVs.update(p),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(s),this.triangleColors.update(l),this.triangleUVs.update(c),this.triangleNormals.update(u),this.triangleIds.update(new Uint32Array(h))}},P.drawTransparent=P.draw=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|D,n=t.view\|\|D,i=t.projection\|\|D,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var l=new Array(16);b(l,s.view,s.model),b(l,s.projection,l),x(l,l);for(var o=0;o<3;++o)s.eyePosition[o]=l[12+o]/l[15];for(var u=l[15],o=0;o<3;++o)u+=this.lightPosition[o]l[4o+3];for(var o=0;o<3;++o){for(var c=l[12+o],h=0;h<3;++h)c+=l[4h+o]this.lightPosition[h];s.lightPosition[o]=c/u}if(this.triangleCount>0){var f=this.triShader;f.bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var f=this.lineShader;f.bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()}if(this.pointCount>0){var f=this.pointShader;f.bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var f=this.contourShader;f.bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind()}},P.drawPick=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|D,n=t.view\|\|D,i=t.projection\|\|D,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255},l=this.pickShader;if(l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0){var l=this.pointPickShader;l.bind(),l.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}},P.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256t.value[1]+65536t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=T(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;for(var s=o[2],l=0,a=0;a<r.length;++a)l+=s[a]this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},P.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=d},{\"./lib/closest-point\":203,\"./lib/shaders\":204,colormap:99,\"gl-buffer\":156,\"gl-mat4/invert\":181,\"gl-mat4/multiply\":183,\"gl-shader\":255,\"gl-texture2d\":267,\"gl-vao\":271,ndarray:467,normals:469,\"simplicial-complex-contour\":517,\"typedarray-pool\":541}],206:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r}function i(t){var e=t.gl;return new n(t,a(e,[0,0,0,1,1,0,1,1]),o(e,s.boxVert,s.lineFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"./shaders\"),l=n.prototype;l.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},l.drawBox=function(){var t=[0,0],e=[0,0];return function(r,n,i,a,o){var s=this.plot,l=this.shader,u=s.gl;t[0]=r,t[1]=n,e[0]=i,e[1]=a,l.uniforms.lo=t,l.uniforms.hi=e,l.uniforms.color=o,u.drawArrays(u.TRIANGLE_STRIP,0,4)}}(),l.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":209,\"gl-buffer\":156,\"gl-shader\":212}],207:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function i(t,e){return t-e}function a(t){var e=t.gl;return new n(t,o(e),s(e,u.gridVert,u.gridFrag),s(e,u.tickVert,u.gridFrag))}e.exports=a;var 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s=this.plot,u=this.vbo,c=this.tickShader,h=this.ticks,f=s.gl,d=s._tickBounds,p=s.dataBox,m=s.viewBox,v=s.pixelRatio,g=s.screenBox,y=g[2]-g[0],b=g[3]-g[1],x=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var M=d[w],k=d[w+2],A=k-M,T=.5(p[w+2]+p[w]),S=p[w+2]-p[w];e[w]=2A/S,t[w]=2(M-T)/S}e[0]=x/y,t[0]=x/y,e[1]=_/b,t[1]=_/b,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var E=c.uniforms;E.dataShift=t,E.dataScale=e;var 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t=[1,1,-1,-1,1,-1],e=[1,-1,1,1,-1,-1];return function(r){for(var n=r.ticks,i=r.bounds,a=new Float32Array(18(n[0].length+n[1].length)),o=(this.plot.zeroLineEnable,0),s=[[],[]],l=0;l<2;++l)for(var u=s[l],c=n[l],h=i[l],f=i[l+2],d=0;d<c.length;++d){var p=(c[d].x-h)/(f-h);u.push(p);for(var m=0;m<6;++m)a[o++]=p,a[o++]=t[m],a[o++]=e[m]}this.ticks=s,this.vbo.update(a)}}(),c.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":209,\"binary-search-bounds\":211,\"gl-buffer\":156,\"gl-shader\":212}],208:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r}function i(t){var e=t.gl;return new n(t,a(e,[-1,-1,-1,1,1,-1,1,1]),o(e,s.lineVert,s.lineFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"./shaders\"),l=n.prototype;l.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},l.drawLine=function(){var t=[0,0],e=[0,0];return function(r,n,i,a,o,s){var l=this.plot,u=this.shader,c=l.gl;t[0]=r,t[1]=n,e[0]=i,e[1]=a,u.uniforms.start=t,u.uniforms.end=e,u.uniforms.width=ol.pixelRatio,u.uniforms.color=s,c.drawArrays(c.TRIANGLE_STRIP,0,4)}}(),l.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":209,\"gl-buffer\":156,\"gl-shader\":212}],209:[function(t,e,r){\"use strict\";var n=\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\";e.exports={lineVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n vec2 delta = normalize(perp(start - end));\\n vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n 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1);\\n}\\n\",gridFrag:n,boxVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\",tickVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n gl_Position = vec4(pos + tickScaledataCoord.yz + screenOffset, 0, 1);\\n}\\n\"}},{}],210:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}function i(t){var e=t.gl;return new n(t,a(e),o(e,u.textVert,u.textFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"text-cache\"),l=t(\"binary-search-bounds\"),u=t(\"./shaders\"),c=n.prototype;c.drawTicks=function(){var t=[0,0],e=[0,0],r=[0,0];return function(n){var i=this.plot,a=this.shader,o=this.tickX[n],s=this.tickOffset[n],u=i.gl,c=i.viewBox,h=i.dataBox,f=i.screenBox,d=i.pixelRatio,p=i.tickEnable,m=i.tickPad,v=i.tickColor,g=i.tickAngle,y=i.labelEnable,b=i.labelPad,x=i.labelColor,_=i.labelAngle,w=this.labelOffset[n],M=this.labelCount[n],k=l.lt(o,h[n]),A=l.le(o,h[n+2]);t[0]=t[1]=0,t[n]=1,e[n]=(c[2+n]+c[n])/(f[2+n]-f[n])-1;var T=2/f[2+(1^n)]-f[1^n];e[1^n]=Tc[1^n]-1,p[n]&&(e[1^n]-=Tdm[n],k<A&&s[A]>s[k]&&(a.uniforms.dataAxis=t,a.uniforms.screenOffset=e,a.uniforms.color=v[n],a.uniforms.angle=g[n],u.drawArrays(u.TRIANGLES,s[k],s[A]-s[k]))),y[n]&&M&&(e[1^n]-=Tdb[n],a.uniforms.dataAxis=r,a.uniforms.screenOffset=e,a.uniforms.color=x[n],a.uniforms.angle=_[n],u.drawArrays(u.TRIANGLES,w,M)),e[1^n]=Tc[2+(1^n)]-1,p[n+2]&&(e[1^n]+=Tdm[n+2],k<A&&s[A]>s[k]&&(a.uniforms.dataAxis=t,a.uniforms.screenOffset=e,a.uniforms.color=v[n+2],a.uniforms.angle=g[n+2],u.drawArrays(u.TRIANGLES,s[k],s[A]-s[k]))),y[n+2]&&M&&(e[1^n]+=Tdb[n+2],a.uniforms.dataAxis=r,a.uniforms.screenOffset=e,a.uniforms.color=x[n+2],a.uniforms.angle=_[n+2],u.drawArrays(u.TRIANGLES,w,M))}}(),c.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var 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t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]u[3],u[1]u[3],u[2]u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT\|t.DEPTH_BUFFER_BIT),t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]c[3],c[1]c[3],c[2]c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,d=this.zeroLineWidth;if(h[0]\|\|h[1]){o.bind();for(var p=0;p<2;++p)if(h[p]&&n[p]<=0&&n[p+2]>=0){var m=e[p]-n[p](e[p+2]-e[p])/(n[p+2]-n[p]);0===p?o.drawLine(m,e[1],m,e[3],d[p],f[p]):o.drawLine(e[0],m,e[2],m,d[p],f[p])}}for(var p=0;p<l.length;++p)l[p].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var v=this.borderLineEnable,g=this.borderLineWidth,y=this.borderLineColor;v[1]&&o.drawLine(r[0],r[1]-.5g[1]i,r[0],r[3]+.5g[3]i,g[1],y[1]),v[0]&&o.drawLine(r[0]-.5g[0]i,r[1],r[2]+.5g[2]i,r[1],g[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5g[1]i,r[2],r[3]+.5g[3]i,g[3],y[3]),v[2]&&o.drawLine(r[0]-.5g[0]i,r[3],r[2]+.5g[2]i,r[3],g[2],y[2]),s.bind();for(var p=0;p<2;++p)s.drawTicks(p);this.titleEnable&&s.drawTitle();for(var b=this.overlays,p=0;p<b.length;++p)b[p].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}}}(),f.drawPick=function(){return function(){if(!this.static){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}}}(),f.pick=function(){return function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0\|Math.round((t-i[0]/r)n),o=0\|Math.round((e-i[1]/r)n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var h=u[c].pick(a,o,l);if(h)return h}return null}}}(),f.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0\|Math.round(t[0]r),e[1]=0\|Math.round(t[1]r),e[2]=0\|Math.round(t[2]r),e[3]=0\|Math.round(t[3]r),this.setDirty()},f.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]\|\|e[1]!==t[1]\|\|e[2]!==t[2]\|\|e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},f.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0\|Math.round(t[0]e),r[1]=0\|Math.round(t[1]e),r[2]=0\|Math.round(t[2]e),r[3]=0\|Math.round(t[3]e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0\|Math.round((t[2]-t[0])n),0\|Math.round((t[3]-t[1])n)],this.setDirty()},f.update=function(t){t=t\|\|{};var e=this.gl;this.pixelRatio=t.pixelRatio\|\|1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox\|\|[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox\|\|[.125(this.screenBox[2]-this.screenBox[0])/r,.125(this.screenBox[3]-this.screenBox[1])/r,.875(this.screenBox[2]-this.screenBox[0])/r,.875(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,o=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox\|\|[-10,-10/o,10,10/o]),this.borderColor=(t.borderColor\|\|[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor\|\|[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable\|\|[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth\|\|[1,1]).slice(),this.gridLineColor=i(t.gridLineColor\|\|[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable\|\|[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth\|\|[4,4]).slice(),this.zeroLineColor=i(t.zeroLineColor\|\|[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength\|\|[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth\|\|[0,0,0,0]).slice(),this.tickMarkColor=i(t.tickMarkColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter\|\|[.5(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t&&!t.titleEnable),this.titleAngle=t.titleAngle\|\|0,this.titleColor=(t.titleColor\|\|[0,0,0,1]).slice(),this.labelPad=(t.labelPad\|\|[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle\|\|[0,Math.PI/2,0,3Math.PI/2]).slice(),this.labelEnable=(t.labelEnable\|\|[!0,!0,!0,!0]).slice(),this.labelColor=i(t.labelColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad\|\|[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle\|\|[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable\|\|[!0,!0,!0,!0]).slice(),this.tickColor=i(t.tickColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable\|\|[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth\|\|[2,2,2,2]).slice(),this.borderLineColor=i(t.borderLineColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var s=t.ticks\|\|[[],[]],l=this._tickBounds;l[0]=l[1]=1/0,l[2]=l[3]=-1/0;for(var u=0;u<2;++u){var c=s[u].slice(0);0!==c.length&&(c.sort(a),l[u]=Math.min(l[u],c[0].x),l[u+2]=Math.max(l[u+2],c[c.length-1].x))}this.grid.update({bounds:l,ticks:s}),this.text.update({bounds:l,ticks:s,labels:t.labels\|\|[\"x\",\"y\"],labelSize:t.labelSize\|\|[12,12],labelFont:t.labelFont\|\|[\"sans-serif\",\"sans-serif\"],title:t.title\|\|\"\",titleSize:t.titleSize\|\|18,titleFont:t.titleFont\|\|\"sans-serif\"}),this.static=!!t.static,this.setDirty()},f.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(var t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},f.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},f.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},f.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},f.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":206,\"./lib/grid\":207,\"./lib/line\":208,\"./lib/text\":210,\"gl-select-static\":254}],220:[function(t,e,r){var n=t(\"gl-shader\");e.exports=function(t){return n(t,\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\",\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\",null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":255}],221:[function(t,e,r){\"use strict\";function n(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function i(t,e){var r=null;try{r=t.getContext(\"webgl\",e),r\|\|(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}function a(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(tr)/r}if(e>0){var r=Math.round(Math.pow(10,e));return Math.ceil(t/r)r}return Math.ceil(t)}function o(t){return\"boolean\"!=typeof t\|\|t}function s(t){function e(){if(!w&&G.autoResize){var t=M.parentNode,e=1,r=1;t&&t!==document.body?(e=t.clientWidth,r=t.clientHeight):(e=window.innerWidth,r=window.innerHeight);var n=0\|Math.ceil(eG.pixelRatio),i=0\|Math.ceil(rG.pixelRatio);if(n!==M.width\|\|i!==M.height){M.width=n,M.height=i;var a=M.style;a.position=a.position\|\|\"absolute\",a.left=\"0px\",a.top=\"0px\",a.width=e+\"px\",a.height=r+\"px\",N=!0}}}function r(){for(var t=O.length,e=j.length,r=0;r<e;++r)F[r]=0;t:for(var r=0;r<t;++r){var n=O[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(F[a]+i<255){R[r]=a,n.setPickBase(F[a]+1),F[a]+=i;continue t}var o=f(A,q);R[r]=e,j.push(o),F.push(i),n.setPickBase(1),e+=1}else R[r]=-1}for(;e>0&&0===F[e-1];)F.pop(),j.pop().dispose()}function s(){if(G.contextLost)return!0;A.isContextLost()&&(G.contextLost=!0,G.mouseListener.enabled=!1,G.selection.object=null,G.oncontextloss&&G.oncontextloss())}function b(){if(!s()){A.colorMask(!0,!0,!0,!0),A.depthMask(!0),A.disable(A.BLEND),A.enable(A.DEPTH_TEST);for(var t=O.length,e=j.length,r=0;r<e;++r){var n=j[r];n.shape=Y,n.begin();for(var i=0;i<t;++i)if(R[i]===r){var a=O[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(H))}n.end()}}}function x(){if(!s()){e();var t=G.camera.tick();H.view=G.camera.matrix,N=N\|\|t,B=B\|\|t,z.pixelRatio=G.pixelRatio,P.pixelRatio=G.pixelRatio;var r=O.length,n=Z[0],i=Z[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<r;++o){var l=O[o];l.pixelRatio=G.pixelRatio,l.axes=G.axes,N=N\|\|!!l.dirty,B=B\|\|!!l.dirty;var u=l.bounds;if(u)for(var h=u[0],f=u[1],d=0;d<3;++d)n[d]=Math.min(n[d],h[d]),i[d]=Math.max(i[d],f[d])}var m=G.bounds;if(G.autoBounds)for(var d=0;d<3;++d){if(i[d]<n[d])n[d]=-1,i[d]=1;else{n[d]===i[d]&&(n[d]-=1,i[d]+=1);var g=.05(i[d]-n[d]);n[d]=n[d]-g,i[d]=i[d]+g}m[0][d]=n[d],m[1][d]=i[d]}for(var y=!1,d=0;d<3;++d)y=y\|\|J[0][d]!==m[0][d]\|\|J[1][d]!==m[1][d],J[0][d]=m[0][d],J[1][d]=m[1][d];if(B=B\|\|y,N=N\|\|y){if(y){for(var x=[0,0,0],o=0;o<3;++o)x[o]=a((m[1][o]-m[0][o])/10);z.autoTicks?z.update({bounds:m,tickSpacing:x}):z.update({bounds:m})}var _=A.drawingBufferWidth,w=A.drawingBufferHeight;q[0]=_,q[1]=w,Y[0]=0\|Math.max(_/G.pixelRatio,1),Y[1]=0\|Math.max(w/G.pixelRatio,1),v(U,G.fovy,_/w,G.zNear,G.zFar);for(var o=0;o<16;++o)V[o]=0;V[15]=1;for(var M=0,o=0;o<3;++o)M=Math.max(M,m[1][o]-m[0][o]);for(var o=0;o<3;++o)G.autoScale?V[5o]=G.aspect[o]/(m[1][o]-m[0][o]):V[5o]=1/M,G.autoCenter&&(V[12+o]=.5-V[5o](m[0][o]+m[1][o]));for(var o=0;o<r;++o){var l=O[o];l.axesBounds=m,G.clipToBounds&&(l.clipBounds=m)}S.object&&(G.snapToData?P.position=S.dataCoordinate:P.position=S.dataPosition,P.bounds=m),B&&(B=!1,b()),G.axesPixels=c(G.axes,H,_,w),G.onrender&&G.onrender(),A.bindFramebuffer(A.FRAMEBUFFER,null),A.viewport(0,0,_,w);var k=G.clearColor;A.clearColor(k[0],k[1],k[2],k[3]),A.clear(A.COLOR_BUFFER_BIT\|A.DEPTH_BUFFER_BIT),A.depthMask(!0),A.colorMask(!0,!0,!0,!0),A.enable(A.DEPTH_TEST),A.depthFunc(A.LEQUAL),A.disable(A.BLEND),A.disable(A.CULL_FACE);var T=!1;z.enable&&(T=T\|\|z.isTransparent(),z.draw(H)),P.axes=z,S.object&&P.draw(H),A.disable(A.CULL_FACE);for(var o=0;o<r;++o){var l=O[o];l.axes=z,l.pixelRatio=G.pixelRatio,l.isOpaque&&l.isOpaque()&&l.draw(H),l.isTransparent&&l.isTransparent()&&(T=!0)}if(T){E.shape=q,E.bind(),A.clear(A.DEPTH_BUFFER_BIT),A.colorMask(!1,!1,!1,!1),A.depthMask(!0),A.depthFunc(A.LESS),z.enable&&z.isTransparent()&&z.drawTransparent(H);for(var o=0;o<r;++o){var l=O[o];l.isOpaque&&l.isOpaque()&&l.draw(H)}A.enable(A.BLEND),A.blendEquation(A.FUNC_ADD),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.colorMask(!0,!0,!0,!0),A.depthMask(!1),A.clearColor(0,0,0,0),A.clear(A.COLOR_BUFFER_BIT),z.isTransparent()&&z.drawTransparent(H);for(var o=0;o<r;++o){var l=O[o];l.isTransparent&&l.isTransparent()&&l.drawTransparent(H)}A.bindFramebuffer(A.FRAMEBUFFER,null),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.disable(A.DEPTH_TEST),L.bind(),E.color[0].bind(0),L.uniforms.accumBuffer=0,p(A),A.disable(A.BLEND)}N=!1;for(var o=0;o<r;++o)O[o].dirty=!1}}}function _(){w\|\|G.contextLost\|\|(requestAnimationFrame(_),x())}t=t\|\|{};var w=!1,M=(t.pixelRatio\|\|parseFloat(window.devicePixelRatio),t.canvas);if(!M)if(M=document.createElement(\"canvas\"),t.container){var k=t.container;k.appendChild(M)}else document.body.appendChild(M);var A=t.gl;if(A\|\|(A=i(M,t.glOptions\|\|{premultipliedAlpha:!0,antialias:!0})),!A)throw new Error(\"webgl not supported\");var T=t.bounds\|\|[[-10,-10,-10],[10,10,10]],S=new n,E=d(A,[A.drawingBufferWidth,A.drawingBufferHeight],{preferFloat:!y}),L=g(A),C=t.camera\|\|{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:\"turntable\"},I=t.axes\|\|{},z=u(A,I);z.enable=!I.disable;var D=t.spikes\|\|{},P=h(A,D),O=[],R=[],F=[],j=[],N=!0,B=!0,U=new Array(16),V=new Array(16),H={view:null,projection:U,model:V},B=!0,q=[A.drawingBufferWidth,A.drawingBufferHeight],G={gl:A,contextLost:!1,pixelRatio:t.pixelRatio\|\|parseFloat(window.devicePixelRatio),canvas:M,selection:S,camera:l(M,C),axes:z,axesPixels:null,spikes:P,bounds:T,objects:O,shape:q,aspect:t.aspectRatio\|\|[1,1,1],pickRadius:t.pickRadius\|\|10,zNear:t.zNear\|\|.01,zFar:t.zFar\|\|1e3,fovy:t.fovy\|\|Math.PI/4,clearColor:t.clearColor\|\|[0,0,0,0],autoResize:o(t.autoResize),autoBounds:o(t.autoBounds),autoScale:!!t.autoScale,autoCenter:o(t.autoCenter),clipToBounds:o(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect\|\|null,onrender:t.onrender\|\|null,onclick:t.onclick\|\|null,cameraParams:H,oncontextloss:null,mouseListener:null},Y=[A.drawingBufferWidth/G.pixelRatio\|0,A.drawingBufferHeight/G.pixelRatio\|0];G.autoResize&&e(),window.addEventListener(\"resize\",e),G.update=function(t){w\|\|(t=t\|\|{},N=!0,B=!0)},G.add=function(t){w\|\|(t.axes=z,O.push(t),R.push(-1),N=!0,B=!0,r())},G.remove=function(t){if(!w){var e=O.indexOf(t);e<0\|\|(O.splice(e,1),R.pop(),N=!0,B=!0,r())}},G.dispose=function(){if(!w&&(w=!0,window.removeEventListener(\"resize\",e),M.removeEventListener(\"webglcontextlost\",s),G.mouseListener.enabled=!1,!G.contextLost)){z.dispose(),P.dispose();for(var t=0;t<O.length;++t)O[t].dispose();E.dispose();for(var t=0;t<j.length;++t)j[t].dispose();L.dispose(),A=null,z=null,P=null,O=[]}};var W=!1,X=0;G.mouseListener=m(M,function(t,e,r){if(!w){var n=j.length,i=O.length,a=S.object;S.distance=1/0,S.mouse[0]=e,S.mouse[1]=r,S.object=null,S.screen=null,S.dataCoordinate=S.dataPosition=null;var o=!1;if(t&&X)W=!0;else{W&&(B=!0),W=!1;for(var s=0;s<n;++s){var l=j[s].query(e,Y[1]-r-1,G.pickRadius);if(l){if(l.distance>S.distance)continue;for(var u=0;u<i;++u){var c=O[u];if(R[u]===s){var h=c.pick(l);h&&(S.buttons=t,S.screen=l.coord,S.distance=l.distance,S.object=c,S.index=h.distance,S.dataPosition=h.position,S.dataCoordinate=h.dataCoordinate,S.data=h,o=!0)}}}}}a&&a!==S.object&&(a.highlight&&a.highlight(null),N=!0),S.object&&(S.object.highlight&&S.object.highlight(S.data),N=!0),o=o\|\|S.object!==a,o&&G.onselect&&G.onselect(S),1&t&&!(1&X)&&G.onclick&&G.onclick(S),X=t}}),M.addEventListener(\"webglcontextlost\",s);var Z=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],J=[Z[0].slice(),Z[1].slice()];return _(),G.redraw=function(){w\|\|(N=!0,x())},G}e.exports=s;var l=t(\"3d-view-controls\"),u=t(\"gl-axes3d\"),c=t(\"gl-axes3d/properties\"),h=t(\"gl-spikes3d\"),f=t(\"gl-select-static\"),d=t(\"gl-fbo\"),p=t(\"a-big-triangle\"),m=t(\"mouse-change\"),v=t(\"gl-mat4/perspective\"),g=t(\"./lib/shader\"),y=t(\"is-mobile\")()},{\"./lib/shader\":220,\"3d-view-controls\":36,\"a-big-triangle\":39,\"gl-axes3d\":148,\"gl-axes3d/properties\":155,\"gl-fbo\":164,\"gl-mat4/perspective\":184,\"gl-select-static\":254,\"gl-spikes3d\":264,\"is-mobile\":296,\"mouse-change\":452}],222:[function(t,e,r){r.pointVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n highp float a = 12.9898;\\n highp float b = 78.233;\\n highp float c = 43758.5453;\\n highp float d = dot(co.xy, vec2(a, b));\\n highp float e = mod(d, 3.14);\\n return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n // if we don't jitter the point size a bit, overall point cloud\\n // saturation 'jumps' on zooming, which is disturbing and confusing\\n gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n if(pointCloud != 0.0) { // pointCloud is truthy\\n // get the same square surface as circle would be\\n gl_PointSize = 0.886;\\n }\\n}\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n float radius;\\n vec4 baseColor;\\n if(pointCloud != 0.0) { // pointCloud is truthy\\n if(centerFraction == 1.0) {\\n gl_FragColor = color;\\n } else {\\n gl_FragColor = mix(borderColor, color, centerFraction);\\n }\\n } else {\\n radius = length(2.0 gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\",r.pickVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"},{}],223:[function(t,e,r){arguments[4][212][0].apply(r,arguments)},{\"./lib/GLError\":224,\"./lib/create-attributes\":225,\"./lib/create-uniforms\":226,\"./lib/reflect\":227,\"./lib/runtime-reflect\":228,\"./lib/shader-cache\":229,dup:212}],224:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],225:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\n", "\"./GLError\":224,dup:214}],226:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":224,\"./reflect\":227,dup:215}],227:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],228:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],229:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":224,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],230:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}function i(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2r],o=t[2r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}function a(t,e){var r=t.gl,i=s(r),a=s(r),l=o(r,u.pointVertex,u.pointFragment),c=o(r,u.pickVertex,u.pickFragment),h=new n(t,i,a,l,c);return h.update(e),t.addObject(h),h}var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"typedarray-pool\"),u=t(\"./lib/shader\");e.exports=a;var c=n.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){function e(e,r){return e in t?t[e]:r}var r;t=t\|\|{},this.sizeMin=e(\"sizeMin\",.5),this.sizeMax=e(\"sizeMax\",20),this.color=e(\"color\",[1,0,0,1]).slice(),this.areaRatio=e(\"areaRatio\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.blend=e(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,a=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,o=t.positions,s=i?o:l.mallocFloat32(o.length),u=a?t.idToIndex:l.mallocInt32(n);if(i\|\|s.set(o),!a)for(s.set(o),r=0;r<n;r++)u[r]=r;this.points=o,this.offsetBuffer.update(s),this.pickBuffer.update(u),i\|\|l.free(s),a\|\|l.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=void 0!==r,a=n?this.pickShader:this.shader,o=this.plot.gl,s=this.plot.dataBox;if(0===this.pointCount)return r;var l=s[2]-s[0],u=s[3]-s[1],c=i(this.points,s),h=this.plot.pickPixelRatioMath.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(c,.33333)));t[0]=2/l,t[4]=2/u,t[6]=-2s[0]/l-1,t[7]=-2s[1]/u-1,this.offsetBuffer.bind(),a.bind(),a.attributes.position.pointer(),a.uniforms.matrix=t,a.uniforms.color=this.color,a.uniforms.borderColor=this.borderColor,a.uniforms.pointCloud=h<5,a.uniforms.pointSize=h,a.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),n&&(e[0]=255&r,e[1]=r>>8&255,e[2]=r>>16&255,e[3]=r>>24&255,this.pickBuffer.bind(),a.attributes.pickId.pointer(o.UNSIGNED_BYTE),a.uniforms.pickOffset=e,this.pickOffset=r);var f=o.getParameter(o.BLEND),d=o.getParameter(o.DITHER);return f&&!this.blend&&o.disable(o.BLEND),d&&o.disable(o.DITHER),o.drawArrays(o.POINTS,0,this.pointCount),f&&!this.blend&&o.enable(o.BLEND),d&&o.enable(o.DITHER),r+this.pointCount}}(),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2a],o[2a+1]]}}},{\"./lib/shader\":222,\"gl-buffer\":156,\"gl-shader\":223,\"typedarray-pool\":541}],231:[function(t,e,r){function n(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],h=e[2],f=e[3],d=r[0],p=r[1],m=r[2],v=r[3];return a=ud+cp+hm+fv,a<0&&(a=-a,d=-d,p=-p,m=-m,v=-v),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)i)/o,l=Math.sin(ni)/o):(s=1-n,l=n),t[0]=su+ld,t[1]=sc+lp,t[2]=sh+lm,t[3]=sf+lv,t}e.exports=n},{}],232:[function(t,e,r){\"use strict\";e.exports={vertex:\"precision highp float;\\n#define GLSLIFY 1\\n\\n\\nvec4 computePosition_1_0(vec2 posHi, vec2 posLo, vec2 scHi, vec2 scLo, vec2 trHi, vec2 trLo) {\\n return vec4((posHi + trHi) scHi\\n \\t\\t\\t//FIXME: this thingy does not give noticeable precision gain, need test\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo\\n , 0, 1);\\n}\\n\\n\\nattribute vec2 positionHi, positionLo;\\nattribute float size, border;\\nattribute vec2 char, color;\\n\\n//this is 64-bit form of scale and translate\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pixelRatio;\\nuniform vec4 viewBox;\\nuniform sampler2D palette;\\n\\nvarying vec4 charColor, borderColor;\\nvarying vec2 charId;\\nvarying vec2 pointCoord;\\nvarying float pointSize;\\nvarying float borderWidth;\\n\\n\\nvoid main() {\\n charColor = texture2D(palette, vec2(color.x / 255., 0));\\n borderColor = texture2D(palette, vec2(color.y / 255., 0));\\n\\n gl_PointSize = size * pixelRatio;\\n pointSize = size * pixelRatio;\\n\\n charId = char;\\n borderWidth = border;\\n\\n gl_Position = computePosition_1_0(\\n positionHi, positionLo,\\n scaleHi, scaleLo,\\n translateHi, translateLo);\\n\\n pointCoord = viewBox.xy + (viewBox.zw - viewBox.xy) * (gl_Position.xy * .5 + .5);\\n}\\n\",fragment:\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D chars;\\nuniform vec2 charsShape;\\nuniform float charsStep, pixelRatio, charOffset;\\n\\nvarying vec4 borderColor;\\nvarying vec4 charColor;\\nvarying vec2 charId;\\nvarying vec2 pointCoord;\\nvarying float pointSize;\\nvarying float borderWidth;\\n\\nvoid main() {\\n\\tvec2 pointUV = (pointCoord - gl_FragCoord.xy + pointSize * .5) / pointSize;\\n\\tpointUV.x = 1. - pointUV.x;\\n\\tvec2 texCoord = ((charId + pointUV) * charsStep) / charsShape;\\n\\tfloat dist = texture2D(chars, texCoord).r;\\n\\n\\t//max-distance alpha\\n\\tif (dist < 1e-2)\\n\\t\\tdiscard;\\n\\n\\tfloat gamma = .0045 * charsStep / pointSize;\\n\\n //null-border case\\n \\tif (borderWidth * borderColor.a == 0.) {\\n\\t\\tfloat charAmt = smoothstep(.748 - gamma, .748 + gamma, dist);\\n\\t\\tgl_FragColor = vec4(charColor.rgb, charAmtcharColor.a);\\n\\t\\treturn;\\n\\t}\\n\\n\\tfloat dif = 5. pixelRatio * borderWidth / pointSize;\\n\\tfloat borderLevel = .748 - dif * .5;\\n\\tfloat charLevel = .748 + dif * .5;\\n\\n\\tfloat borderAmt = smoothstep(borderLevel - gamma, borderLevel + gamma, dist);\\n\\tfloat charAmt = smoothstep(charLevel - gamma, charLevel + gamma, dist);\\n\\n\\tvec4 color = borderColor;\\n\\tcolor.a = borderAmt;\\n\\n\\tgl_FragColor = mix(color, charColor, charAmt);\\n}\\n\",pickVertex:\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 positionHi, positionLo;\\nattribute vec4 id;\\nattribute float size;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform vec4 pickOffset;\\nuniform float pixelRatio;\\n\\nvarying vec4 fragColor;\\n\\n\\nvec4 computePosition_1_0(vec2 posHi, vec2 posLo, vec2 scHi, vec2 scLo, vec2 trHi, vec2 trLo) {\\n return vec4((posHi + trHi) scHi\\n \\t\\t\\t//FIXME: this thingy does not give noticeable precision gain, need test\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo\\n , 0, 1);\\n}\\n\\n\\nvoid main() {\\n vec4 fragId = id + pickOffset;\\n\\n fragId.y += floor(fragId.x / 256.0);\\n fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n fragId.z += floor(fragId.y / 256.0);\\n fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n fragId.w += floor(fragId.z / 256.0);\\n fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n fragColor = fragId / 255.0;\\n\\n gl_PointSize = size * .25 * pixelRatio;\\n\\n gl_Position = computePosition_1_0(\\n positionHi, positionLo,\\n scaleHi, scaleLo,\\n translateHi, translateLo);\\n}\\n\",pickFragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\\n\"}},{}],233:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],234:[function(t,e,r){arguments[4][212][0].apply(r,arguments)},{\"./lib/GLError\":235,\"./lib/create-attributes\":236,\"./lib/create-uniforms\":237,\"./lib/reflect\":238,\"./lib/runtime-reflect\":239,\"./lib/shader-cache\":240,dup:212}],235:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],236:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\"./GLError\":235,dup:214}],237:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":235,\"./reflect\":238,dup:215}],238:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],239:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],240:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":235,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],241:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){a<=4f?i(0,a-1,t,e,r,n):h(0,a-1,t,e,r,n)}function i(t,e,r,n,i,a){for(var o=t+1;o<=e;++o){for(var s=r[o],l=n[2o],u=n[2o+1],c=i[o],h=a[o],f=o;f>t;){var d=r[f-1],p=n[2(f-1)];if((d-s\|\|l-p)>=0)break;r[f]=d,n[2f]=p,n[2f+1]=n[2f-1],i[f]=i[f-1],a[f]=a[f-1],f-=1}r[f]=s,n[2f]=l,n[2f+1]=u,i[f]=c,a[f]=h}}function a(t,e,r,n,i,a){var o=r[t],s=n[2t],l=n[2t+1],u=i[t],c=a[t];r[t]=r[e],n[2t]=n[2e],n[2t+1]=n[2e+1],i[t]=i[e],a[t]=a[e],r[e]=o,n[2e]=s,n[2e+1]=l,i[e]=u,a[e]=c}function o(t,e,r,n,i,a){r[t]=r[e],n[2t]=n[2e],n[2t+1]=n[2e+1],i[t]=i[e],a[t]=a[e]}function s(t,e,r,n,i,a,o){var s=n[t],l=i[2t],u=i[2t+1],c=a[t],h=o[t];n[t]=n[e],i[2t]=i[2e],i[2t+1]=i[2e+1],a[t]=a[e],o[t]=o[e],n[e]=n[r],i[2e]=i[2r],i[2e+1]=i[2r+1],a[e]=a[r],o[e]=o[r],n[r]=s,i[2r]=l,i[2r+1]=u,a[r]=c,o[r]=h}function l(t,e,r,n,i,a,o,s,l,u,c){s[t]=s[e],l[2t]=l[2e],l[2t+1]=l[2e+1],u[t]=u[e],c[t]=c[e],s[e]=r,l[2e]=n,l[2e+1]=i,u[e]=a,c[e]=o}function u(t,e,r,n,i){return(r[t]-r[e]\|\|n[2e]-n[2t]\|\|i[t]-i[e])<0}function c(t,e,r,n,i,a,o,s){return(e-a[t]\|\|o[2t]-r\|\|i-s[t])<0}function h(t,e,r,n,d,p){var m=(e-t+1)/6\|0,v=t+m,g=e-m,y=t+e>>1,b=y-m,x=y+m,_=v,w=b,M=y,k=x,A=g,T=t+1,S=e-1,E=0;u(_,w,r,n,d,p)&&(E=_,_=w,w=E),u(k,A,r,n,d,p)&&(E=k,k=A,A=E),u(_,M,r,n,d,p)&&(E=_,_=M,M=E),u(w,M,r,n,d,p)&&(E=w,w=M,M=E),u(_,k,r,n,d,p)&&(E=_,_=k,k=E),u(M,k,r,n,d,p)&&(E=M,M=k,k=E),u(w,A,r,n,d,p)&&(E=w,w=A,A=E),u(w,M,r,n,d,p)&&(E=w,w=M,M=E),u(k,A,r,n,d,p)&&(E=k,k=A,A=E);var L=r[w],C=n[2w],I=n[2w+1],z=d[w],D=p[w],P=r[k],O=n[2k],R=n[2k+1],F=d[k],j=p[k],N=_,B=M,U=A,V=v,H=y,q=g,G=r[N],Y=r[B],W=r[U];r[V]=G,r[H]=Y,r[q]=W;for(var X=0;X<2;++X){var Z=n[2N+X],J=n[2B+X],K=n[2U+X];n[2V+X]=Z,n[2H+X]=J,n[2q+X]=K}var Q=d[N],$=d[B],tt=d[U];d[V]=Q,d[H]=$,d[q]=tt;var et=p[N],rt=p[B],nt=p[U];p[V]=et,p[H]=rt,p[q]=nt,o(b,t,r,n,d,p),o(x,e,r,n,d,p);for(var it=T;it<=S;++it)if(c(it,L,C,I,z,r,n,d))it!==T&&a(it,T,r,n,d,p),++T;else if(!c(it,P,O,R,F,r,n,d))for(;;){if(c(S,P,O,R,F,r,n,d)){c(S,L,C,I,z,r,n,d)?(s(it,T,S,r,n,d,p),++T,--S):(a(it,S,r,n,d,p),--S);break}if(--S<it)break}l(t,T-1,L,C,I,z,D,r,n,d,p),l(e,S+1,P,O,R,F,j,r,n,d,p),T-2-t<=f?i(t,T-2,r,n,d,p):h(t,T-2,r,n,d,p),e-(S+2)<=f?i(S+2,e,r,n,d,p):h(S+2,e,r,n,d,p),S-T<=f?i(T,S,r,n,d,p):h(T,S,r,n,d,p)}e.exports=n;var f=32},{}],242:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){for(var l=r,u=r;u<n;++u){var c=t[2u],h=t[2u+1],f=e[u];i<=c&&c<=o&&a<=h&&h<=s&&(u===l?l+=1:(t[2u]=t[2l],t[2u+1]=t[2l+1],e[u]=e[l],t[2l]=c,t[2l+1]=h,e[l]=f,l+=1))}return l}function i(t,e,r){this.pixelSize=t,this.offset=e,this.count=r}function a(t,e,r,a){function l(i,a,o,s,u,c){var h=.5o,f=s+1,d=u-s;r[_]=d,x[_++]=c;for(var p=0;p<2;++p)for(var m=0;m<2;++m){var v=i+ph,g=a+mh,y=n(t,e,f,u,v,g,v+h,g+h);if(y!==f){if(y-f>=Math.max(.9d,32)){var b=u+s>>>1;l(v,g,h,f,b,c+1),f=b}l(v,g,h,f,y,c+1),f=y}}}var u=t.length>>>1;if(u<1)return[];for(var c=1/0,h=1/0,f=-1/0,d=-1/0,p=0;p<u;++p){var m=t[2p],v=t[2p+1];c=Math.min(c,m),f=Math.max(f,m),h=Math.min(h,v),d=Math.max(d,v),e[p]=p}c===f&&(f+=1+Math.abs(f)),h===d&&(d+=1+Math.abs(f));var g=1/(f-c),y=1/(d-h),b=Math.max(f-c,d-h);a=a\|\|[0,0,0,0],a[0]=c,a[1]=h,a[2]=f,a[3]=d;var x=o.mallocInt32(u),_=0;l(c,h,b,0,u,0),s(x,t,e,r,u);for(var w=[],M=0,k=u,_=u-1;_>=0;--_){t[2_]=(t[2_]-c)g,t[2_+1]=(t[2_+1]-h)y;var A=x[_];A!==M&&(w.push(new i(bMath.pow(.5,A),_+1,k-(_+1))),k=_+1,M=A)}return w.push(new i(bMath.pow(.5,A+1),0,k)),o.free(x),w}var o=t(\"typedarray-pool\"),s=t(\"./lib/sort\");e.exports=a},{\"./lib/sort\":241,\"typedarray-pool\":541}],243:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.sizeBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.charBuffer=s,this.pointCount=0,this.pickOffset=0,this.points=null,this.scales=[],this.xCoords=[],this.charCanvas=document.createElement(\"canvas\"),this.charTexture=m(this.plot.gl,this.charCanvas),this.charStep=400,this.charFit=.255,this.snapThreshold=1e4,this.paletteTexture=m(this.plot.gl,[256,1])}function i(){var t=this.plot,e=t.viewBox,r=t.dataBox,n=t.pixelRatio,i=r[2]-r[0],a=r[3]-r[1],u=2/i,c=2/a,h=-r[0]-.5i,f=-r[1]-.5a;_[0]=u,w[0]=u-_[0],_[1]=c,w[1]=c-_[1],M[0]=h,k[0]=h-M[0],M[1]=f,k[1]=f-M[1];var d=e[2]-e[0],p=e[3]-e[1];o=Math.min(i/d,a/p),A[0]=2n/d,A[1]=2n/p,s=r[0],l=r[2]}function a(t,e){var r=t.gl,i=u(r,f.vertex,f.fragment),a=u(r,f.pickVertex,f.pickFragment),o=c(r),s=c(r),l=c(r),h=c(r),d=c(r),p=new n(t,i,a,o,s,l,h,d);return p.update(e),t.addObject(p),p}e.exports=a;var o,s,l,u=t(\"gl-shader\"),c=t(\"gl-buffer\"),h=t(\"typedarray-pool\"),f=t(\"./lib/shaders\"),d=t(\"snap-points-2d\"),p=t(\"font-atlas-sdf\"),m=t(\"gl-texture2d\"),v=t(\"color-id\"),g=t(\"ndarray\"),y=t(\"clamp\"),b=t(\"binary-search-bounds\"),x=n.prototype,_=new Float32Array([0,0]),w=new Float32Array([0,0]),M=new Float32Array([0,0]),k=new Float32Array([0,0]),A=[0,0],T=[0,0,0,0];x.drawPick=function(t){var e=void 0!==t,r=this.plot,n=this.pointCount,a=n>this.snapThreshold;if(!n)return t;i.call(this);var u=r.gl,c=e?this.pickShader:this.shader,h=u.isEnabled(u.BLEND);if(c.bind(),e){this.pickOffset=t;for(var f=0;f<4;++f)T[f]=t>>8f&255;c.uniforms.pickOffset=T,this.idBuffer.bind(),c.attributes.id.pointer(u.UNSIGNED_BYTE,!1)}else u.blendFuncSeparate(u.SRC_ALPHA,u.ONE_MINUS_SRC_ALPHA,u.ONE,u.ONE_MINUS_SRC_ALPHA),u.blendColor(0,0,0,1),h\|\|u.enable(u.BLEND),this.colorBuffer.bind(),c.attributes.color.pointer(u.UNSIGNED_BYTE,!1),this.charBuffer.bind(),c.attributes.char.pointer(u.UNSIGNED_BYTE,!1),c.uniforms.chars=this.charTexture.bind(0),c.uniforms.charsShape=[this.charCanvas.width,this.charCanvas.height],c.uniforms.charsStep=this.charStep,c.uniforms.palette=this.paletteTexture.bind(1);this.sizeBuffer.bind(),c.attributes.size.pointer(u.FLOAT,!1,8,0),e\|\|c.attributes.border.pointer(u.FLOAT,!1,8,4),this.positionBuffer.bind(),c.attributes.positionHi.pointer(u.FLOAT,!1,16,0),c.attributes.positionLo.pointer(u.FLOAT,!1,16,8),c.uniforms.pixelRatio=r.pixelRatio,c.uniforms.scaleHi=_,c.uniforms.scaleLo=w,c.uniforms.translateHi=M,c.uniforms.translateLo=k,c.uniforms.viewBox=r.viewBox;var d=this.scales;if(a)for(var p=d.length-1;p>=0;p--){var m=d[p];if(!(m.pixelSize&&m.pixelSize<1.25o&&p>1)){var v=m.offset,g=m.count+v,y=b.ge(this.xCoords,s,v,g-1),x=b.lt(this.xCoords,l,y,g-1)+1;x>y&&u.drawArrays(u.POINTS,y,x-y)}}else u.drawArrays(u.POINTS,0,n);if(e)return t+n;h?u.blendFunc(u.ONE,u.ONE_MINUS_SRC_ALPHA):u.disable(u.BLEND)},x.draw=x.drawPick,x.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2a],o[2a+1]]}},x.update=function(t){t=t\|\|{};var e=t.positions\|\|[],r=t.colors\|\|[],n=t.glyphs\|\|[],i=t.sizes\|\|[],a=t.borderWidths\|\|[],o=t.borderColors\|\|[],s=this.plot.gl,l=this.pointCount,u=l>this.snapThreshold;if(null!=t.positions){this.points=e,l=this.points.length/2,u=l>this.snapThreshold;var c=h.mallocFloat32(2l),f=h.mallocFloat64(2l),m=h.mallocUint32(l),b=h.mallocFloat32(4l);f.set(this.points),u&&(this.i2idx&&h.free(this.i2idx),this.i2idx=h.mallocInt32(l),this.scales=d(f,this.i2idx,c)),this.pointCount=l;for(var x=0;x<l;++x){var _=u?this.i2idx[x]:x;m[x]=_;var w=e[2_],M=e[2_+1];b[4x]=w,b[4x+1]=M,b[4x+2]=w-b[4x],b[4x+3]=M-b[4x+1],this.xCoords[x]=w}this.idBuffer.update(m),this.positionBuffer.update(b),h.free(b),h.free(m),h.free(f),h.free(c)}for(var k=h.mallocFloat32(2l),A=h.mallocUint8(2l),T=h.mallocUint8(2l),S={},E=[],L=[],C=[],x=0,I=l,z=0;x<I;++x){var D=[255r[4x],255r[4x+1],255r[4x+2],255r[4x+3]],P=v(D,!1);null==S[P]&&(S[P]=z++,L.push(D[0]),L.push(D[1]),L.push(D[2]),L.push(D[3])),E.push(P),o&&o.length&&(D=[255o[4x],255o[4x+1],255o[4x+2],255o[4x+3]],P=v(D,!1),null==S[P]&&(S[P]=z++,L.push(D[0]),L.push(D[1]),L.push(D[2]),L.push(D[3])),C.push(P))}for(var O={},x=0,I=l,z=0;x<I;x++){var R=n[x];null==O[R]&&(O[R]=z++)}for(var F=0,x=0,I=i.length;x<I;++x)i[x]>F&&(F=i[x]);var j=this.charStep;this.charStep=y(Math.ceil(4F),128,768);var N=Object.keys(O),B=this.charStep,U=Math.floor(B/2),V=s.getParameter(s.MAX_TEXTURE_SIZE),H=V/B(V/B),q=Math.min(V,BN.length),G=Math.min(V,BMath.ceil(BN.length/V)),Y=Math.floor(q/B);N.length>H&&console.warn(\"gl-scatter2d-fancy: number of characters is more than maximum texture size. Try reducing it.\"),this.chars&&this.chars+\"\"==N+\"\"&&this.charStep==j\|\|(this.charCanvas=p({canvas:this.charCanvas,family:\"sans-serif\",size:U,shape:[q,G],step:[B,B],chars:N,align:!0,fit:this.charFit}),this.chars=N);for(var x=0;x<l;++x){var _=u?this.i2idx[x]:x,W=i[_],X=a[_];k[2x]=2W,k[2x+1]=X;var P=E[_],Z=S[P];A[2x]=Z;var J=C[_],K=S[J];A[2x+1]=K;var R=n[_],Q=O[R];T[2x+1]=Math.floor(Q/Y),T[2x]=Q%Y}this.sizeBuffer.update(k),this.colorBuffer.update(A),this.charBuffer.update(T),this.charTexture.shape=[this.charCanvas.width,this.charCanvas.height],this.charCanvas&&this.charCanvas.width&&this.charTexture.setPixels(this.charCanvas),this.paletteTexture.setPixels(g(L.slice(0,1024),[256,1,4])),h.free(k),h.free(A),h.free(T)},x.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.sizeBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.charBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":232,\"binary-search-bounds\":233,clamp:88,\"color-id\":92,\"font-atlas-sdf\":134,\"gl-buffer\":156,\"gl-shader\":234,\"gl-texture2d\":267,ndarray:467,\"snap-points-2d\":242,\"typedarray-pool\":541}],244:[function(t,e,r){r.pointVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 positionHi, positionLo;\\nattribute float weight;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pointSize, useWeight;\\n\\nvarying float fragWeight;\\n\\n\\nvec4 pfx_1_0(vec2 scaleHi, vec2 scaleLo, vec2 translateHi, vec2 translateLo, vec2 positionHi, vec2 positionLo) {\\n return vec4((positionHi + translateHi) * scaleHi\\n + (positionLo + translateLo) * scaleHi\\n + (positionHi + translateHi) * scaleLo\\n + (positionLo + translateLo) * scaleLo, 0.0, 1.0);\\n}\\n\\nvoid main() {\\n gl_Position = pfx_1_0(scaleHi, scaleLo, translateHi, translateLo, positionHi, positionLo);\\n gl_PointSize = pointSize;\\n fragWeight = mix(1.0, weight, useWeight);\\n}\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\n\\nvarying float fragWeight;\\n\\nfloat smoothStep(float x, float y) {\\n return 1.0 / (1.0 + exp(50.0(x - y)));\\n}\\n\\nvoid main() {\\n float radius = length(2.0gl_PointCoord.xy-1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n vec4 baseColor = mix(borderColor, color, smoothStep(radius, centerFraction));\\n float alpha = 1.0 - pow(1.0 - baseColor.a, fragWeight);\\n gl_FragColor = vec4(baseColor.rgb * alpha, alpha);\\n}\\n\",r.pickVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nvec4 pfx_1_0(vec2 scaleHi, vec2 scaleLo, vec2 translateHi, vec2 translateLo, vec2 positionHi, vec2 positionLo) {\\n return vec4((positionHi + translateHi) * scaleHi\\n + (positionLo + translateLo) * scaleHi\\n + (positionHi + translateHi) * scaleLo\\n + (positionLo + translateLo) * scaleLo, 0.0, 1.0);\\n}\\n\\nattribute vec2 positionHi, positionLo;\\nattribute vec4 pickId;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n gl_Position = pfx_1_0(scaleHi, scaleLo, translateHi, translateLo, positionHi, positionLo);\\n gl_PointSize = pointSize;\\n fragId = id;\\n}\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\"},{}],245:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],246:[function(t,e,r){arguments[4][241][0].apply(r,arguments)},{dup:241}],247:[function(t,e,r){arguments[4][242][0].apply(r,arguments)},{\"./lib/sort\":246,dup:242,\"typedarray-pool\":541}],248:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o){this.plot=t,this.positionBufferHi=e,this.positionBufferLo=r,this.pickBuffer=n,this.weightBuffer=i,this.shader=a,this.pickShader=o,this.scales=[],this.size=12,this.borderSize=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0,this.points=null,this.xCoords=null,this.snapPoints=!0}function i(t,e){var r=t.gl,i=o(r),s=o(r),l=o(r),u=o(r),h=a(r,c.pointVertex,c.pointFragment),f=a(r,c.pickVertex,c.pickFragment),d=new n(t,i,s,l,u,h,f);return d.update(e),t.addObject(d),d}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"binary-search-bounds\"),l=t(\"snap-points-2d\"),u=t(\"typedarray-pool\"),c=t(\"./lib/shader\"),h=t(\"array-normalize\"),f=t(\"array-bounds\");e.exports=i;var d=n.prototype,p=new Float32Array(2),m=new Float32Array(2),v=new Float32Array(2),g=new Float32Array(2),y=[0,0,0,0];d.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBufferHi.dispose(),this.positionBufferLo.dispose(),this.pickBuffer.dispose(),this.xCoords&&u.free(this.xCoords),this.plot.removeObject(this)},d.update=function(t){function e(e,r){return e in t?t[e]:r}if(t=t\|\|{},this.size=e(\"size\",12),this.color=e(\"color\",[1,0,0,1]).slice(),this.borderSize=e(\"borderSize\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.snapPoints=e(\"snapPoints\",!0),null!=t.positions){this.xCoords&&u.free(this.xCoords),this.points=t.positions;var r=this.points.length>>>1,n=u.mallocInt32(r),i=u.mallocFloat32(r),a=u.mallocFloat64(2r);if(a.set(this.points),this.snapPoints)this.scales=l(a,n,i,this.bounds);else{this.bounds=f(a,2),h(a,2,this.bounds);for(var o=0;o<r;o++)n[o]=o,i[o]=1}var s=u.mallocFloat64(r),c=u.mallocFloat32(2r),d=u.mallocFloat32(2r);c.set(a);for(var o=0,p=0;o<r;o++,p+=2)d[p]=a[p]-c[p],d[p+1]=a[p+1]-c[p+1],s[o]=a[p];this.positionBufferHi.update(c),this.positionBufferLo.update(d),this.pickBuffer.update(n),this.weightBuffer.update(i),u.free(c),u.free(d),u.free(i),u.free(a),u.free(n),this.xCoords=s,this.pointCount=r,this.pickOffset=0}},d.draw=function(t){var e=void 0!==t,r=this.plot,n=e?this.pickShader:this.shader,i=this.scales,a=this.positionBufferHi,o=this.positionBufferLo,s=this.pickBuffer,l=this.bounds,u=this.size,c=this.borderSize,h=r.gl,f=e?r.pickPixelRatio:r.pixelRatio,d=r.viewBox,b=r.dataBox;if(0===this.pointCount)return t;var x=l[2]-l[0],_=l[3]-l[1],w=b[2]-b[0],M=b[3]-b[1],k=(d[2]-d[0])f/r.pixelRatio,A=(d[3]-d[1])f/r.pixelRatio,T=this.pixelSize=Math.min(w/k,M/A),S=2x/w,E=2_/M;p[0]=S,p[1]=E,m[0]=S-p[0],m[1]=E-p[1];var L=(l[0]-b[0]-.5w)/x,C=(l[1]-b[1]-.5M)/_;v[0]=L,v[1]=C,g[0]=L-v[0],g[1]=C-v[1],n.bind(),n.uniforms.scaleHi=p,n.uniforms.scaleLo=m,n.uniforms.translateHi=v,n.uniforms.translateLo=g,n.uniforms.color=this.color,n.uniforms.borderColor=this.borderColor,n.uniforms.pointSize=f(u+c),n.uniforms.centerFraction=0===this.borderSize?2:u/(u+c+1.25),a.bind(),n.attributes.positionHi.pointer(),o.bind(),n.attributes.positionLo.pointer(),e?(this.pickOffset=t,y[0]=255&t,y[1]=t>>8&255,y[2]=t>>16&255,y[3]=t>>24&255,n.uniforms.pickOffset=y,s.bind(),n.attributes.pickId.pointer(h.UNSIGNED_BYTE)):(n.uniforms.useWeight=1,this.weightBuffer.bind(),n.attributes.weight.pointer());var I=!0;if(this.snapPoints)for(var z=i.length-1;z>=0;z--){var D=i[z];if(!(D.pixelSize<T&&z>1)){var P=this.getVisibleRange(D),O=P[0],R=P[1];R>O&&h.drawArrays(h.POINTS,O,R-O),!e&&I&&(I=!1,n.uniforms.useWeight=0)}}else h.drawArrays(h.POINTS,0,this.pointCount);return t+this.pointCount},d.getVisibleRange=function(t){var e=this.plot.dataBox,r=this.bounds,n=this.pixelSize,i=this.size,a=this.plot.pixelRatio,o=r[2]-r[0];r[3],r[1];if(!t)for(var t,l=this.scales.length-1;l>=0&&(t=this.scales[l],t.pixelSize<n&&l>1);l--);var u=this.xCoords,c=(e[0]-r[0]-nia)/o,h=(e[2]-r[0]+nia)/o,f=t.offset,d=t.count+f,p=s.ge(u,c,f,d-1);return[p,s.lt(u,h,p,d-1)+1]},d.drawPick=d.draw,d.pick=function(t,e,r){var n=r-this.pickOffset;return n<0\|\|n>=this.pointCount?null:{object:this,pointId:n,dataCoord:[this.points[2n],this.points[2n+1]]}}},{\"./lib/shader\":244,\"array-bounds\":44,\"array-normalize\":45,\"binary-search-bounds\":245,\"gl-buffer\":156,\"gl-shader\":255,\"snap-points-2d\":247,\"typedarray-pool\":541}],249:[function(t,e,r){\"use strict\";function n(t,e){var r=a[e];if(r\|\|(r=a[e]={}),t in r)return r[t];for(var n=i(t,{textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),o=i(t,{triangles:!0,textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<n.positions.length;++l)for(var u=n.positions[l],c=0;c<2;++c)s[0][c]=Math.min(s[0][c],u[c]),s[1][c]=Math.max(s[1][c],u[c]);return r[t]=[o,n,s]}var i=t(\"vectorize-text\");e.exports=n;var a={}},{\"vectorize-text\":554}],250:[function(t,e,r){function n(t,e){var r=i(t,e),n=r.attributes;return n.position.location=0,n.color.location=1,n.glyph.location=2,n.id.location=3,r}var i=t(\"gl-shader\"),a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\| \\n any(greaterThan(position, clipBounds[1])) ) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n \\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\| any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n \\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\|\\n any(greaterThan(position, clipBounds[1])) ) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale = highlightScale;\\n }\\n\\n vec4 clipCenter = projection view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5lscale(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\",l=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(dataCoordinate, fragClipBounds[0])) \|\|\\n any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n discard;\\n } else {\\n gl_FragColor = interpColor * opacity;\\n }\\n}\\n\",u=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(dataCoordinate, fragClipBounds[0])) \|\| \\n any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n discard;\\n } else {\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n }\\n}\",c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],h={vertex:a,fragment:l,attributes:c},f={vertex:o,fragment:l,attributes:c},d={vertex:s,fragment:l,attributes:c},p={vertex:a,fragment:u,attributes:c},m={vertex:o,fragment:u,attributes:c},v={vertex:s,fragment:u,attributes:c};r.createPerspective=function(t){return n(t,h)},r.createOrtho=function(t){return n(t,f)},r.createProject=function(t){return n(t,d)},r.createPickPerspective=function(t){return n(t,p)},r.createPickOrtho=function(t){return n(t,m)},r.createPickProject=function(t){return n(t,v)}},{\"gl-shader\":255}],251:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]r+e[4]n+e[8]i+e[12]a,t[1]=e[1]r+e[5]n+e[9]i+e[13]a,t[2]=e[2]r+e[6]n+e[10]i+e[14]a,t[3]=e[3]r+e[7]n+e[11]i+e[15]a,t}function i(t,e,r,i){return n(i,i,r),n(i,i,e),n(i,i,t)}function a(t,e){this.index=t,this.dataCoordinate=this.position=e}function o(t,e,r,n,i,o,s,l,u,c,h,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=o,this.glyphBuffer=s,this.idBuffer=l,this.vao=u,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=h,this.pickProjectShader=f,this.points=[],this._selectResult=new a(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}function s(t){return t[0]=t[1]=t[2]=0,t}function l(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function u(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function c(t){for(var e=L,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}function h(t,e,r,n,a){var o,h=e.axesProject,f=e.gl,d=t.uniforms,p=r.model\|\|x,m=r.view\|\|x,v=r.projection\|\|x,y=e.axesBounds,b=c(e.clipBounds);o=e.axes?e.axes.lastCubeProps.axis:[1,1,1],w[0]=2/f.drawingBufferWidth,w[1]=2/f.drawingBufferHeight,t.bind(),d.view=m,d.projection=v,d.screenSize=w,d.highlightId=e.highlightId,d.highlightScale=e.highlightScale,d.clipBounds=b,\n", "d.pickGroup=e.pickId/255,d.pixelRatio=e.pixelRatio;for(var _=0;_<3;++_)if(h[_]&&e.projectOpacity[_]<1===n){d.scale=e.projectScale[_],d.opacity=e.projectOpacity[_];for(var L=S,C=0;C<16;++C)L[C]=0;for(var C=0;C<4;++C)L[5C]=1;L[5_]=0,o[_]<0?L[12+_]=y[0][_]:L[12+_]=y[1][_],g(L,p,L),d.model=L;var I=(_+1)%3,z=(_+2)%3,D=s(M),P=s(k);D[I]=1,P[z]=1;var O=i(v,m,p,l(A,D)),R=i(v,m,p,l(T,P));if(Math.abs(O[1])>Math.abs(R[1])){var F=O;O=R,R=F,F=D,D=P,P=F;var j=I;I=z,z=j}O[0]<0&&(D[I]=-1),R[1]>0&&(P[z]=-1);for(var N=0,B=0,C=0;C<4;++C)N+=Math.pow(p[4I+C],2),B+=Math.pow(p[4z+C],2);D[I]/=Math.sqrt(N),P[z]/=Math.sqrt(B),d.axes[0]=D,d.axes[1]=P,d.fragClipBounds[0]=u(E,b[0],_,-1e8),d.fragClipBounds[1]=u(E,b[1],_,1e8),e.vao.draw(f.TRIANGLES,e.vertexCount),e.lineWidth>0&&(f.lineWidth(e.lineWidth),e.vao.draw(f.LINES,e.lineVertexCount,e.vertexCount))}}function f(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1\|\|a){t.bind();var s=t.uniforms;s.model=n.model\|\|x,s.view=n.view\|\|x,s.projection=n.projection\|\|x,w[0]=2/o.drawingBufferWidth,w[1]=2/o.drawingBufferHeight,s.screenSize=w,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=z,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}h(e,r,n,i,a),r.vao.unbind()}function d(t){var e=t.gl,r=y.createPerspective(e),n=y.createOrtho(e),i=y.createProject(e),a=y.createPickPerspective(e),s=y.createPickOrtho(e),l=y.createPickProject(e),u=p(e),c=p(e),h=p(e),f=p(e),d=m(e,[{buffer:u,size:3,type:e.FLOAT},{buffer:c,size:4,type:e.FLOAT},{buffer:h,size:2,type:e.FLOAT},{buffer:f,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new o(e,r,n,i,u,c,h,f,d,a,s,l);return v.update(t),v}var p=t(\"gl-buffer\"),m=t(\"gl-vao\"),v=t(\"typedarray-pool\"),g=t(\"gl-mat4/multiply\"),y=t(\"./lib/shaders\"),b=t(\"./lib/glyphs\"),x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];e.exports=d;var _=o.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},_.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var w=[0,0],M=[0,0,0],k=[0,0,0],A=[0,0,0,1],T=[0,0,0,1],S=x.slice(),E=[0,0,0],L=[[0,0,0],[0,0,0]],C=[-1e8,-1e8,-1e8],I=[1e8,1e8,1e8],z=[C,I];_.draw=function(t){f(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},_.drawTransparent=function(t){f(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},_.drawPick=function(t){f(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},_.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount\|\|e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(t=t\|\|{},\"perspective\"in t&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(\"projectOpacity\"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{var r=+t.projectOpacity;this.projectOpacity=[r,r,r]}\"opacity\"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font\|\|\"normal\",a=t.alignment\|\|[0,0],o=[1/0,1/0,1/0],s=[-1/0,-1/0,-1/0],l=t.glyph,u=t.color,c=t.size,h=t.angle,f=t.lineColor,d=0,p=0,m=0,g=n.length;t:for(var y=0;y<g;++y){for(var x=n[y],_=0;_<3;++_)if(isNaN(x[_])\|\|!isFinite(x[_]))continue t;var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var M=w[0],k=w[1],A=w[2];p+=3M.cells.length,m+=2k.edges.length}var T=p+m,S=v.mallocFloat(3T),E=v.mallocFloat(4T),L=v.mallocFloat(2T),C=v.mallocUint32(T),I=[0,a[1]],z=0,D=p,P=[0,0,0,1],O=[0,0,0,1],R=Array.isArray(u)&&Array.isArray(u[0]),F=Array.isArray(f)&&Array.isArray(f[0]);t:for(var y=0;y<g;++y){for(var x=n[y],_=0;_<3;++_){if(isNaN(x[_])\|\|!isFinite(x[_])){d+=1;continue t}s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_])}var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var M=w[0],k=w[1],A=w[2];if(Array.isArray(u)){var j;if(j=R?u[y]:u,3===j.length){for(var _=0;_<3;++_)P[_]=j[_];P[3]=1}else if(4===j.length)for(var _=0;_<4;++_)P[_]=j[_]}else P[0]=P[1]=P[2]=0,P[3]=1;if(Array.isArray(f)){var j;if(j=F?f[y]:f,3===j.length){for(var _=0;_<3;++_)O[_]=j[_];O[_]=1}else if(4===j.length)for(var _=0;_<4;++_)O[_]=j[_]}else O[0]=O[1]=O[2]=0,O[3]=1;var N=.5;Array.isArray(c)?N=+c[y]:c?N=+c:this.useOrtho&&(N=12);var B=0;Array.isArray(h)?B=+h[y]:h&&(B=+h);for(var U=Math.cos(B),V=Math.sin(B),x=n[y],_=0;_<3;++_)s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_]);a[0]<0?I[0]=a[0](1+A[1][0]):a[0]>0&&(I[0]=-a[0](1+A[0][0]));for(var H=M.cells,q=M.positions,_=0;_<H.length;++_)for(var G=H[_],Y=0;Y<3;++Y){for(var W=0;W<3;++W)S[3z+W]=x[W];for(var W=0;W<4;++W)E[4z+W]=P[W];C[z]=d;var X=q[G[Y]];L[2z]=N(UX[0]-VX[1]+I[0]),L[2z+1]=N(VX[0]+UX[1]+I[1]),z+=1}for(var H=k.edges,q=k.positions,_=0;_<H.length;++_)for(var G=H[_],Y=0;Y<2;++Y){for(var W=0;W<3;++W)S[3D+W]=x[W];for(var W=0;W<4;++W)E[4D+W]=O[W];C[D]=d;var X=q[G[Y]];L[2D]=N(UX[0]-VX[1]+I[0]),L[2D+1]=N(VX[0]+UX[1]+I[1]),D+=1}d+=1}this.vertexCount=p,this.lineVertexCount=m,this.pointBuffer.update(S),this.colorBuffer.update(E),this.glyphBuffer.update(L),this.idBuffer.update(new Uint32Array(C)),v.free(S),v.free(E),v.free(L),v.free(C),this.bounds=[o,s],this.points=n,this.pointCount=n.length}},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/glyphs\":249,\"./lib/shaders\":250,\"gl-buffer\":156,\"gl-mat4/multiply\":183,\"gl-vao\":271,\"typedarray-pool\":541}],252:[function(t,e,r){\"use strict\";r.boxVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\",r.boxFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = color;\\n}\\n\"},{}],253:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}function i(t,e){var r=t.gl,i=o(r,[0,0,0,1,1,0,1,1]),l=a(r,s.boxVertex,s.boxFragment),u=new n(t,i,l);return u.update(e),t.addOverlay(u),u}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"./lib/shaders\");e.exports=i;var l=n.prototype;l.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,h=(e[0]-l[0])(u[2]-u[0])/(l[2]-l[0])+u[0],f=(e[1]-l[1])(u[3]-u[1])/(l[3]-l[1])+u[1],d=(e[2]-l[0])(u[2]-u[0])/(l[2]-l[0])+u[0],p=(e[3]-l[1])(u[3]-u[1])/(l[3]-l[1])+u[1];if(h=Math.max(h,u[0]),f=Math.max(f,u[1]),d=Math.min(d,u[2]),p=Math.min(p,u[3]),!(d<h\|\|p<f)){o.bind();var m=s[2]-s[0],v=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,f,i),o.drawBox(0,f,h,p,i),o.drawBox(0,p,m,v,i),o.drawBox(d,f,m,p,i)),this.innerFill&&o.drawBox(h,f,d,p,n),r>0){var g=rc;o.drawBox(h-g,f-g,d+g,f+g,a),o.drawBox(h-g,p-g,d+g,p+g,a),o.drawBox(h-g,f-g,h+g,p+g,a),o.drawBox(d-g,f-g,d+g,p+g,a)}}}},l.update=function(t){t=t\|\|{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor\|\|[0,0,0,.5]).slice(),this.outerColor=(t.outerColor\|\|[0,0,0,.5]).slice(),this.borderColor=(t.borderColor\|\|[0,0,0,1]).slice(),this.borderWidth=t.borderWidth\|\|0,this.selectBox=(t.selectBox\|\|this.selectBox).slice()},l.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":252,\"gl-buffer\":156,\"gl-shader\":255}],254:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function i(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}function a(t,e){return new i(t,o(t,e),s.mallocUint8(e[0]e[1]4))}e.exports=a;var o=t(\"gl-fbo\"),s=t(\"typedarray-pool\"),l=t(\"ndarray\"),u=t(\"bit-twiddle\").nextPow2,c=t(\"cwise/lib/wrapper\")({args:[\"array\",{offset:[0,0,1],array:0},{offset:[0,0,2],array:0},{offset:[0,0,3],array:0},\"scalar\",\"scalar\",\"index\"],pre:{body:\"{this_closestD2=1e8,this_closestX=-1,this_closestY=-1}\",args:[],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[]},body:{body:\"{if(_inline_46_arg0_<255\|\|_inline_46_arg1_<255\|\|_inline_46_arg2_<255\|\|_inline_46_arg3_<255){var 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this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(re4>this.buffer.length){s.free(this.buffer);for(var n=this.buffer=s.mallocUint8(u(re4)),i=0;i<re4;++i)n[i]=255}return t}}}),h.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT\|t.DEPTH_BUFFER_BIT))},h.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout\|\|clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},h.query=function(t,e,r){if(!this.gl)return null;var i=this.fbo.shape.slice();t\|=0,e\|=0,\"number\"!=typeof r&&(r=1);var a=0\|Math.min(Math.max(t-r,0),i[0]),o=0\|Math.min(Math.max(t+r,0),i[0]),s=0\|Math.min(Math.max(e-r,0),i[1]),u=0\|Math.min(Math.max(e+r,0),i[1]);if(o<=a\|\|u<=s)return null;var h=[o-a,u-s],f=l(this.buffer,[h[0],h[1],4],[4,4i[0],1],4(a+i[0]s)),d=c(f.hi(h[0],h[1],1),r,r),p=d[0],m=d[1];return p<0\|\|Math.pow(this.radius,2)<d[2]?null:new n(p+a\|0,m+s\|0,f.get(p,m,0),[f.get(p,m,1),f.get(p,m,2),f.get(p,m,3)],Math.sqrt(d[2]))},h.dispose=function(){this.gl&&(this.fbo.dispose(),s.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":67,\"cwise/lib/wrapper\":113,\"gl-fbo\":164,ndarray:467,\"typedarray-pool\":541}],255:[function(t,e,r){\"use strict\";function n(t){this.gl=t,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}function i(t,e){return t.name<e.name?-1:1}function a(t,e,r,i,a){var o=new n(t);return o.update(e,r,i,a),o}var o=t(\"./lib/create-uniforms\"),s=t(\"./lib/create-attributes\"),l=t(\"./lib/reflect\"),u=t(\"./lib/shader-cache\"),c=t(\"./lib/runtime-reflect\"),h=t(\"./lib/GLError\"),f=n.prototype;f.bind=function(){this.program\|\|this._relink(),this.gl.useProgram(this.program)},f.dispose=function(){this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},f.update=function(t,e,r,n){function a(){d.program=u.program(p,d._vref,d._fref,x,_);for(var t=0;t<r.length;++t)E[t]=p.getUniformLocation(d.program,r[t].name)}if(!e\|\|1===arguments.length){var f=t;t=f.vertex,e=f.fragment,r=f.uniforms,n=f.attributes}var d=this,p=d.gl,m=d._vref;d._vref=u.shader(p,p.VERTEX_SHADER,t),m&&m.dispose(),d.vertShader=d._vref.shader;var v=this._fref;if(d._fref=u.shader(p,p.FRAGMENT_SHADER,e),v&&v.dispose(),d.fragShader=d._fref.shader,!r\|\|!n){var g=p.createProgram();if(p.attachShader(g,d.fragShader),p.attachShader(g,d.vertShader),p.linkProgram(g),!p.getProgramParameter(g,p.LINK_STATUS)){var y=p.getProgramInfoLog(g);throw new h(y,\"Error linking program:\"+y)}r=r\|\|c.uniforms(p,g),n=n\|\|c.attributes(p,g),p.deleteProgram(g)}n=n.slice(),n.sort(i);for(var b=[],x=[],_=[],w=0;w<n.length;++w){var M=n[w];if(M.type.indexOf(\"mat\")>=0){for(var k=0\|M.type.charAt(M.type.length-1),A=new Array(k),T=0;T<k;++T)A[T]=_.length,x.push(M.name+\"[\"+T+\"]\"),\"number\"==typeof M.location?_.push(M.location+T):Array.isArray(M.location)&&M.location.length===k&&\"number\"==typeof M.location[T]?_.push(0\|M.location[T]):_.push(-1);b.push({name:M.name,type:M.type,locations:A})}else b.push({name:M.name,type:M.type,locations:[_.length]}),x.push(M.name),\"number\"==typeof M.location?_.push(0\|M.location):_.push(-1)}for(var S=0,w=0;w<_.length;++w)if(_[w]<0){for(;_.indexOf(S)>=0;)S+=1;_[w]=S}var E=new Array(r.length);a(),d._relink=a,d.types={uniforms:l(r),attributes:l(n)},d.attributes=s(p,d,b,_),Object.defineProperty(d,\"uniforms\",o(p,d,r,E))},e.exports=a},{\"./lib/GLError\":256,\"./lib/create-attributes\":257,\"./lib/create-uniforms\":258,\"./lib/reflect\":259,\"./lib/runtime-reflect\":260,\"./lib/shader-cache\":261}],256:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],257:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\"./GLError\":256,dup:214}],258:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":256,\"./reflect\":259,dup:215}],259:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],260:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],261:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":256,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],262:[function(t,e,r){\"use strict\";function n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}function i(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r}e.exports=i;var a=n.prototype;a.update=function(t){t=t\|\|{},this.enable=(t.enable\|\|[!0,!0,!1,!1]).slice(),this.width=(t.width\|\|[1,1,1,1]).slice(),this.color=(t.color\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map(function(t){return t.slice()}),this.center=(t.center\|\|[1/0,1/0]).slice(),this.plot.setOverlayDirty()},a.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])(s[2]-s[0]),u=s[1]+(n[1]-o[1])/(o[3]-o[1])(s[3]-s[1]);t[0]&&a.drawLine(l,u,s[0],u,e[0],r[0]),t[1]&&a.drawLine(l,u,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,u,s[2],u,e[2],r[2]),t[3]&&a.drawLine(l,u,l,s[3],e[3],r[3])}},a.dispose=function(){this.plot.removeOverlay(this)}},{}],263:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\");e.exports=function(t){return n(t,\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vertexPosition = mix(coordinates[0],\\n mix(coordinates[2], coordinates[1], 0.5 (position + 1.0)), abs(position));\\n\\n vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n vec2 delta = weight * clipOffset * screenShape;\\n vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n gl_Position = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n fragColor = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\",\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\",null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":255}],264:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}function i(t,e){function r(t,e,r,n,a,o){var s=[t,e,r,0,0,0,1];s[n+3]=1,s[n]=a,i.push.apply(i,s),s[6]=-1,i.push.apply(i,s),s[n]=o,i.push.apply(i,s),i.push.apply(i,s),s[6]=1,i.push.apply(i,s),s[n]=a,i.push.apply(i,s)}var i=[];r(0,0,0,0,0,1),r(0,0,0,1,0,1),r(0,0,0,2,0,1),r(1,0,0,1,-1,1),r(1,0,0,2,-1,1),r(0,1,0,0,-1,1),r(0,1,0,2,-1,1),r(0,0,1,0,-1,1),r(0,0,1,1,-1,1);var l=a(t,i),u=o(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),c=s(t);c.attributes.position.location=0,c.attributes.color.location=1,c.attributes.weight.location=2;var h=new n(t,l,u,c);return h.update(e),h}var a=t(\"gl-buffer\"),o=t(\"gl-vao\"),s=t(\"./shaders/index\");e.exports=i;var l=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],u=n.prototype,c=[0,0,0],h=[0,0,0],f=[0,0];u.isTransparent=function(){return!1},u.drawTransparent=function(t){},u.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model\|\|l,o=t.view\|\|l,s=t.projection\|\|l;this.axes&&(i=this.axes.lastCubeProps.axis);for(var u=c,d=h,p=0;p<3;++p)i&&i[p]<0?(u[p]=this.bounds[0][p],d[p]=this.bounds[1][p]):(u[p]=this.bounds[1][p],d[p]=this.bounds[0][p]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=o,n.uniforms.projection=s,n.uniforms.coordinates=[this.position,u,d],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(var p=0;p<3;++p)n.uniforms.lineWidth=this.lineWidth[p]this.pixelRatio,this.enabled[p]&&(r.draw(e.TRIANGLES,6,6p),this.drawSides[p]&&r.draw(e.TRIANGLES,12,18+12p));r.unbind()},u.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},u.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":263,\"gl-buffer\":156,\"gl-vao\":271}],265:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n worldCoordinate = vec3(uv.zw, f.x);\\n vec4 worldPosition = model vec4(worldCoordinate, 1.0);\\n vec4 clipPosition = projection * view * worldPosition;\\n gl_Position = clipPosition;\\n kill = f.y;\\n value = f.z;\\n planeCoordinate = uv.xy;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * worldPosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n lightDirection = lightPosition - cameraCoordinate.xyz;\\n eyeDirection = eyePosition - cameraCoordinate.xyz;\\n surfaceNormal = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution_2_0(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\n\\n\\nfloat beckmannSpecular_1_1(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness) {\\n return beckmannDistribution_2_0(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\n\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n if (kill > 0.0 \|\|\\n any(lessThan(worldCoordinate, clipBounds[0])) \|\| any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n discard;\\n }\\n\\n vec3 N = normalize(surfaceNormal);\\n vec3 V = normalize(eyeDirection);\\n vec3 L = normalize(lightDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = max(beckmannSpecular_1_1(L, V, N, roughness), 0.);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n //decide how to interpolate color \\u2014 in vertex or in fragment\\n vec4 surfaceColor = step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) + step(.5, vertexColor) * vColor;\\n\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * view * worldPosition;\\n clipPosition.z = clipPosition.z + zOffset;\\n\\n gl_Position = clipPosition;\\n value = f;\\n kill = -1.0;\\n worldCoordinate = dataCoordinate;\\n planeCoordinate = uv.zw;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if(kill > 0.0 \|\|\\n any(lessThan(worldCoordinate, clipBounds[0])) \|\| any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n discard;\\n }\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\";r.createShader=function(t){var e=n(t,i,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,i,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,o,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,o,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":255}],266:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}function i(t){var e=x([y({colormap:t,nshades:R,format:\"rgba\"}).map(function(t){return[t[0],t[1],t[2],255t[3]]})]);return b.divseq(e,255),e}function a(t,e,r,i,a,o,s,l,u,c,h,f,d,p){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=i,this._pickShader=a,this._coordinateBuffer=o,this._vao=s,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=f,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new n([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=p,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[_(g.mallocFloat(1024),[0,0]),_(g.mallocFloat(1024),[0,0]),_(g.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}function o(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis\|\|j,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o\|\|e.surfaceProject[r],n=0;n<3;++n)s=s\|\|e.contourProject[r][n];for(r=0;r<3;++r){var l=N.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5n]=1;l[5r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],M(l,t.model,l);var u=N.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return N.showSurface=o,N.showContour=s,N}function s(t,e){t=t\|\|{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=B;n.model=t.model\|\|D,n.view=t.view\|\|D,n.projection=t.projection\|\|D,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]\|\|this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]\|\|this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=k(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],s=0;s<3;++s)a[s]=Math.min(Math.max(this.clipBounds[i][s],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=V,n.vertexColor=this.vertexColor;var l=U;for(M(l,n.view,n.model),M(l,n.projection,l),k(l,l),i=0;i<3;++i)n.eyePosition[i]=l[12+i]/l[15];var u=l[15];for(i=0;i<3;++i)u+=this.lightPosition[i]l[4i+3];for(i=0;i<3;++i){var c=l[12+i];for(s=0;s<3;++s)c+=l[4s+i]this.lightPosition[s];n.lightPosition[i]=c/u}var h=o(n,this);if(h.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=h.projections[i],this._shader.uniforms.clipBounds=h.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(h.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var d=this._contourVAO;for(d.bind(),i=0;i<3;++i)for(f.uniforms.permutation=O[i],r.lineWidth(this.contourWidth[i]),s=0;s<this.contourLevels[i].length;++s)this._contourCounts[i][s]&&(s===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==s&&s-1!==this.highlightLevel[i]\|\|(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),f.uniforms.height=this.contourLevels[i][s],d.draw(r.LINES,this._contourCounts[i][s],this._contourOffsets[i][s]));for(i=0;i<3;++i)for(f.uniforms.model=h.projections[i],f.uniforms.clipBounds=h.clipBounds[i],s=0;s<3;++s)if(this.contourProject[i][s]){f.uniforms.permutation=O[s],r.lineWidth(this.contourWidth[s]);for(var p=0;p<this.contourLevels[s].length;++p)p===this.highlightLevel[s]?(f.uniforms.contourColor=this.highlightColor[s],f.uniforms.contourTint=this.highlightTint[s]):0!==p&&p-1!==this.highlightLevel[s]\|\|(f.uniforms.contourColor=this.contourColor[s],f.uniforms.contourTint=this.contourTint[s]),f.uniforms.height=this.contourLevels[s][p],d.draw(r.LINES,this._contourCounts[s][p],this._contourOffsets[s][p])}for(d=this._dynamicVAO,d.bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=O[i],r.lineWidth(this.dynamicWidth[i]),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),s=0;s<3;++s)this.contourProject[s][i]&&(f.uniforms.model=h.projections[s],f.uniforms.clipBounds=h.clipBounds[s],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));d.unbind()}}function l(t,e){var r=e.shape.slice(),n=t.shape.slice();b.assign(t.lo(1,1).hi(r[0],r[1]),e),b.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),b.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),b.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),b.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function u(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function c(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function h(t){if(Array.isArray(t)){if(Array.isArray(t))return[c(t[0]),c(t[1]),c(t[2])];var e=c(t);return[e.slice(),e.slice(),e.slice()]}}function f(t){var e=t.gl,r=E(e),n=C(e),i=L(e),o=I(e),s=p(e),l=m(e,[{buffer:s,size:4,stride:z,offset:0},{buffer:s,size:3,stride:z,offset:16},{buffer:s,size:3,stride:z,offset:28}]),u=p(e),c=m(e,[{buffer:u,size:4,stride:20,offset:0},{buffer:u,size:1,stride:20,offset:16}]),h=p(e),f=m(e,[{buffer:h,size:2,type:e.FLOAT}]),d=v(e,1,R,e.RGBA,e.UNSIGNED_BYTE);d.minFilter=e.LINEAR,d.magFilter=e.LINEAR;var g=new a(e,[0,0],[[0,0,0],[0,0,0]],r,n,s,l,d,i,o,u,c,h,f),y={levels:[[],[],[]]};for(var b in t)y[b]=t[b];return y.colormap=y.colormap\|\|\"jet\",g.update(y),g}e.exports=f\n", ";var d=t(\"bit-twiddle\"),p=t(\"gl-buffer\"),m=t(\"gl-vao\"),v=t(\"gl-texture2d\"),g=t(\"typedarray-pool\"),y=t(\"colormap\"),b=t(\"ndarray-ops\"),x=t(\"ndarray-pack\"),_=t(\"ndarray\"),w=t(\"surface-nets\"),M=t(\"gl-mat4/multiply\"),k=t(\"gl-mat4/invert\"),A=t(\"binary-search-bounds\"),T=t(\"ndarray-gradient\"),S=t(\"./lib/shaders\"),E=S.createShader,L=S.createContourShader,C=S.createPickShader,I=S.createPickContourShader,z=40,D=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],O=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];!function(){for(var t=0;t<3;++t){var e=O[t],r=(t+1)%3,n=(t+2)%3;e[r+0]=1,e[n+3]=1,e[t+6]=1}}();var R=256,F=a.prototype;F.isTransparent=function(){return this.opacity<1},F.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0\|\|this._dynamicCounts[t]>0)return!0;return!1},F.pickSlots=1,F.setPickBase=function(t){this.pickId=t};var j=[0,0,0],N={showSurface:!1,showContour:!1,projections:[D.slice(),D.slice(),D.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]},B={model:D,view:D,projection:D,inverseModel:D.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},U=D.slice(),V=[1,0,0,0,1,0,0,0,1];F.draw=function(t){return s.call(this,t,!1)},F.drawTransparent=function(t){return s.call(this,t,!0)};var H={model:D,view:D,projection:D,inverseModel:D,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};F.drawPick=function(t){t=t\|\|{};var e=this.gl;e.disable(e.CULL_FACE);var r=H;r.model=t.model\|\|D,r.view=t.view\|\|D,r.projection=t.projection\|\|D,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=V;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var s=o(r,this);if(s.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=s.projections[n],this._pickShader.uniforms.clipBounds=s.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(s.showContour){var l=this._contourPickShader;l.bind(),l.uniforms=r;var u=this._contourVAO;for(u.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),l.uniforms.permutation=O[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(l.uniforms.height=this.contourLevels[a][n],u.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(l.uniforms.model=s.projections[n],l.uniforms.clipBounds=s.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){l.uniforms.permutation=O[a],e.lineWidth(this.contourWidth[a]);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(l.uniforms.height=this.contourLevels[a][c],u.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}u.unbind()}},F.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0](t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1](t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var h=c?a:1-a,f=0;f<2;++f)for(var d=f?l:1-l,p=i+c,m=s+f,v=hd,g=0;g<3;++g)u[g]+=this._field[g].get(p,m)v;for(var y=this._pickResult.level,b=0;b<3;++b)if(y[b]=A.le(this.contourLevels[b],u[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]<this.contourLevels[b].length-1){var x=this.contourLevels[b][y[b]],_=this.contourLevels[b][y[b]+1];Math.abs(x-u[b])>Math.abs(_-u[b])&&(y[b]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],g=0;g<3;++g)r.dataCoordinate[g]=this._field[g].get(r.index[0],r.index[1]);return r},F.update=function(t){t=t\|\|{},this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=u(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=u(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=u(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=h(t.contourColor)),\"contourProject\"in t&&(this.contourProject=u(t.contourProject,function(t){return u(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=h(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=u(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=u(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field\|\|t.coords&&t.coords[2]\|\|null,r=!1;if(e\|\|(e=this._field[2].shape[0]\|\|this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t\|\|\"coords\"in t){var n=(e.shape[0]+2)(e.shape[1]+2);n>this._field[2].data.length&&(g.freeFloat(this._field[2].data),this._field[2].data=g.mallocFloat(d.nextPow2(n))),this._field[2]=_(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),l(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(g.freeFloat(this._field[o].data),this._field[o].data=g.mallocFloat(this._field[2].size)),this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var s=t.coords;if(!Array.isArray(s)\|\|3!==s.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=s[o];for(y=0;y<2;++y)if(c.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");l(this._field[o],c)}}else if(t.ticks){var f=t.ticks;if(!Array.isArray(f)\|\|2!==f.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=f[o];if((Array.isArray(p)\|\|p.length)&&(p=_(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var m=_(p.data,a);m.stride[o]=p.stride[0],m.stride[1^o]=0,l(this._field[o],m)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var b=this._field,x=_(g.mallocFloat(3b[2].size2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)T(x.pick(o),b[o],\"mirror\");var M=_(g.mallocFloat(3b[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var k=x.get(0,o,y,0),A=x.get(0,o,y,1),S=x.get(1,o,y,0),E=x.get(1,o,y,1),L=x.get(2,o,y,0),C=x.get(2,o,y,1),I=SC-EL,z=LA-Ck,D=kE-AS,O=Math.sqrt(II+zz+DD);O<1e-8?(O=Math.max(Math.abs(I),Math.abs(z),Math.abs(D)),O<1e-8?(D=1,z=I=0,O=1):O=1/O):O=1/Math.sqrt(O),M.set(o,y,0,IO),M.set(o,y,1,zO),M.set(o,y,2,DO)}g.free(x.data);var R=[1/0,1/0,1/0],F=[-1/0,-1/0,-1/0],j=1/0,N=-1/0,B=(a[0]-1)(a[1]-1)6,U=g.mallocFloat(d.nextPow2(10B)),V=0,H=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var q=0;q<2;++q)for(var G=0;G<2;++G)for(var Y=0;Y<3;++Y){var W=this._field[Y].get(1+o+q,1+y+G);if(isNaN(W)\|\|!isFinite(W))continue t}for(Y=0;Y<6;++Y){var X=o+P[Y][0],Z=y+P[Y][1],J=this._field[0].get(X+1,Z+1),K=this._field[1].get(X+1,Z+1);W=this._field[2].get(X+1,Z+1);var Q=W;I=M.get(X+1,Z+1,0),z=M.get(X+1,Z+1,1),D=M.get(X+1,Z+1,2),t.intensity&&(Q=t.intensity.get(X,Z)),U[V++]=X,U[V++]=Z,U[V++]=J,U[V++]=K,U[V++]=W,U[V++]=0,U[V++]=Q,U[V++]=I,U[V++]=z,U[V++]=D,R[0]=Math.min(R[0],J),R[1]=Math.min(R[1],K),R[2]=Math.min(R[2],W),j=Math.min(j,Q),F[0]=Math.max(F[0],J),F[1]=Math.max(F[1],K),F[2]=Math.max(F[2],W),N=Math.max(N,Q),H+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],N=+t.intensityBounds[1]),o=6;o<V;o+=10)U[o]=(U[o]-j)/(N-j);this._vertexCount=H,this._coordinateBuffer.update(U.subarray(0,V)),g.freeFloat(U),g.free(M.data),this.bounds=[R,F],this.intensity=t.intensity\|\|this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===N\|\|(r=!0),this.intensityBounds=[j,N]}if(\"levels\"in t){var $=t.levels;for($=Array.isArray($[0])?$.slice():[[],[],$],o=0;o<3;++o)$[o]=$[o].slice(),$.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if($[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<$[o].length;++y)if($[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=$}if(r){b=this._field,a=this.shape;for(var tt=[],et=0;et<3;++et){$=this.contourLevels[et];var rt=[],nt=[],it=[0,0,0];for(o=0;o<$.length;++o){var at=w(this._field[et],$[o]);rt.push(tt.length/5\|0),H=0;t:for(y=0;y<at.cells.length;++y){var ot=at.cells[y];for(Y=0;Y<2;++Y){var st=at.positions[ot[Y]],lt=st[0],ut=0\|Math.floor(lt),ct=lt-ut,ht=st[1],ft=0\|Math.floor(ht),dt=ht-ft,pt=!1;e:for(var mt=0;mt<3;++mt){it[mt]=0;var vt=(et+mt+1)%3;for(q=0;q<2;++q){var gt=q?ct:1-ct;for(X=0\|Math.min(Math.max(ut+q,0),a[0]),G=0;G<2;++G){var yt=G?dt:1-dt;if(Z=0\|Math.min(Math.max(ft+G,0),a[1]),W=mt<2?this._field[vt].get(X,Z):(this.intensity.get(X,Z)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(W)\|\|isNaN(W)){pt=!0;break e}var bt=gtyt;it[mt]+=btW}}}if(pt){if(Y>0){for(var xt=0;xt<5;++xt)tt.pop();H-=1}continue t}tt.push(it[0],it[1],st[0],st[1],it[2]),H+=1}}nt.push(H)}this._contourOffsets[et]=rt,this._contourCounts[et]=nt}var _t=g.mallocFloat(tt.length);for(o=0;o<tt.length;++o)_t[o]=tt[o];this._contourBuffer.update(_t),g.freeFloat(_t)}t.colormap&&this._colorMap.setPixels(i(t.colormap))},F.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)g.freeFloat(this._field[t].data)},F.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]\|\|this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]\|\|this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=g.mallocFloat(12i[0]i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var s=(o+1)%3,l=(o+2)%3,u=this._field[o],c=this._field[s],h=this._field[l],f=(this.intensity,w(u,r[o])),d=f.cells,p=f.positions;for(this._dynamicOffsets[o]=n,e=0;e<d.length;++e)for(var m=d[e],v=0;v<2;++v){var y=p[m[v]],b=+y[0],x=0\|b,_=0\|Math.min(x+1,i[0]),M=b-x,k=1-M,A=+y[1],T=0\|A,S=0\|Math.min(T+1,i[1]),E=A-T,L=1-E,C=kL,I=kE,z=ML,D=ME,P=Cc.get(x,T)+Ic.get(x,S)+zc.get(_,T)+Dc.get(_,S),O=Ch.get(x,T)+Ih.get(x,S)+zh.get(_,T)+Dh.get(_,S);if(isNaN(P)\|\|isNaN(O)){v&&(n-=1);break}a[2n+0]=P,a[2n+1]=O,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2n)),g.freeFloat(a)}}},{\"./lib/shaders\":265,\"binary-search-bounds\":66,\"bit-twiddle\":67,colormap:99,\"gl-buffer\":156,\"gl-mat4/invert\":181,\"gl-mat4/multiply\":183,\"gl-texture2d\":267,\"gl-vao\":271,ndarray:467,\"ndarray-gradient\":458,\"ndarray-ops\":461,\"ndarray-pack\":462,\"surface-nets\":531,\"typedarray-pool\":541}],267:[function(t,e,r){\"use strict\";function n(t){g=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],y=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],b=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function i(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement\|\|\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement\|\|\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement\|\|\"undefined\"!=typeof ImageData&&t 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opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\n#pragma mapbox: define lowp vec4 color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp vec4 color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},circle:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n#pragma mapbox: define lowp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_extrude;\\nvarying lowp float v_antialiasblur;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n float t = smoothstep(1.0 - max(blur, v_antialiasblur), 1.0, length(v_extrude));\\n gl_FragColor = color * (1.0 - t) * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform bool u_scale_with_map;\\nuniform vec2 u_extrude_scale;\\nuniform float u_devicepixelratio;\\n\\nattribute vec2 a_pos;\\n\\n#pragma mapbox: define lowp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_extrude;\\nvarying lowp float v_antialiasblur;\\n\\nvoid main(void) {\\n #pragma mapbox: initialize lowp vec4 color\\n #pragma mapbox: initialize mediump float radius\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n // unencode the extrusion vector that we snuck into the a_pos vector\\n v_extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\\n\\n vec2 extrude = v_extrude * radius * u_extrude_scale;\\n // multiply a_pos by 0.5, since we had it * 2 in order to sneak\\n // in extrusion data\\n gl_Position = u_matrix * vec4(floor(a_pos * 0.5), 0, 1);\\n\\n if (u_scale_with_map) {\\n gl_Position.xy += extrude;\\n } else {\\n gl_Position.xy += extrude * gl_Position.w;\\n }\\n\\n // This is a minimum blur distance that serves as a faux-antialiasing for\\n // the circle. since blur is a ratio of the circle's size and the intent is\\n // to keep the blur at roughly 1px, the two are inversely related.\\n v_antialiasblur = 1.0 / u_devicepixelratio / radius;\\n}\\n\"},line:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\nuniform float u_blur;\\n\\nvarying vec2 v_linewidth;\\nvarying vec2 v_normal;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_linewidth.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_linewidth.t) or when fading out\\n // (v_linewidth.s)\\n float blur = u_blur * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform mediump float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\nuniform mediump float u_blur;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n\\n // We store the texture normals in the most insignificant bit\\n // transform y so that 0 => -1 and 1 => 1\\n // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = mod(a_pos, 2.0);\\n normal.y = sign(normal.y - 0.5);\\n v_normal = normal;\\n\\n float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n // Remove the texture normal bit of the position before scaling it with the\\n // model/view matrix.\\n gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n\\n // position of y on the screen\\n float y = gl_Position.y / gl_Position.w;\\n\\n // how much features are squished in the y direction by the tilt\\n float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n // how much features are squished in all directions by the perspectiveness\\n float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n v_linewidth = vec2(outset, inset);\\n v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},linepattern:{\n", "fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_blur;\\n\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_fade;\\nuniform float u_opacity;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_linewidth.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_linewidth.t) or when fading out\\n // (v_linewidth.s)\\n float blur = u_blur * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n float x_a = mod(v_linesofar / u_pattern_size_a.x, 1.0);\\n float x_b = mod(v_linesofar / u_pattern_size_b.x, 1.0);\\n float y_a = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_a.y);\\n float y_b = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_b.y);\\n vec2 pos_a = mix(u_pattern_tl_a, u_pattern_br_a, vec2(x_a, y_a));\\n vec2 pos_b = mix(u_pattern_tl_b, u_pattern_br_b, vec2(x_b, y_b));\\n\\n vec4 color = mix(texture2D(u_image, pos_a), texture2D(u_image, pos_b), u_fade);\\n\\n alpha = u_opacity;\\n\\n gl_FragColor = color alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform mediump float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n // We store the texture normals in the most insignificant bit\\n // transform y so that 0 => -1 and 1 => 1\\n // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = mod(a_pos, 2.0);\\n normal.y = sign(normal.y - 0.5);\\n v_normal = normal;\\n\\n float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n // Remove the texture normal bit of the position before scaling it with the\\n // model/view matrix.\\n gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n v_linesofar = a_linesofar;\\n\\n // position of y on the screen\\n float y = gl_Position.y / gl_Position.w;\\n\\n // how much features are squished in the y direction by the tilt\\n float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n // how much features are squished in all directions by the perspectiveness\\n float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n v_linewidth = vec2(outset, inset);\\n v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},linesdfpattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\n\\nuniform float u_blur;\\nuniform sampler2D u_image;\\nuniform float u_sdfgamma;\\nuniform float u_mix;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_linewidth.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_linewidth.t) or when fading out\\n // (v_linewidth.s)\\n float blur = u_blur * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n float sdfdist_a = texture2D(u_image, v_tex_a).a;\\n float sdfdist_b = texture2D(u_image, v_tex_b).a;\\n float sdfdist = mix(sdfdist_a, sdfdist_b, u_mix);\\n alpha = smoothstep(0.5 - u_sdfgamma, 0.5 + u_sdfgamma, sdfdist);\\n\\n gl_FragColor = u_color (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform vec2 u_patternscale_a;\\nuniform float u_tex_y_a;\\nuniform vec2 u_patternscale_b;\\nuniform float u_tex_y_b;\\nuniform float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n // We store the texture normals in the most insignificant bit\\n // transform y so that 0 => -1 and 1 => 1\\n // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = mod(a_pos, 2.0);\\n normal.y = sign(normal.y - 0.5);\\n v_normal = normal;\\n\\n float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n // Remove the texture normal bit of the position before scaling it with the\\n // model/view matrix.\\n gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n\\n v_tex_a = vec2(a_linesofar * u_patternscale_a.x, normal.y * u_patternscale_a.y + u_tex_y_a);\\n v_tex_b = vec2(a_linesofar * u_patternscale_b.x, normal.y * u_patternscale_b.y + u_tex_y_b);\\n\\n // position of y on the screen\\n float y = gl_Position.y / gl_Position.w;\\n\\n // how much features are squished in the y direction by the tilt\\n float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n // how much features are squished in all directions by the perspectiveness\\n float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n v_linewidth = vec2(outset, inset);\\n v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},outline:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n#pragma mapbox: define lowp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp vec4 outline_color\\n #pragma mapbox: initialize lowp float opacity\\n\\n float dist = length(v_pos - gl_FragCoord.xy);\\n float alpha = smoothstep(1.0, 0.0, dist);\\n gl_FragColor = outline_color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define lowp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp vec4 outline_color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},outlinepattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n // find distance to outline for alpha interpolation\\n\\n float dist = length(v_pos - gl_FragCoord.xy);\\n float alpha = smoothstep(1.0, 0.0, dist);\\n \\n\\n gl_FragColor = mix(color1, color2, u_mix) * alpha * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\\n vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\\n\\n // the correct offset needs to be calculated.\\n //\\n // The offset depends on how many pixels are between the world origin and\\n // the edge of the tile:\\n // vec2 offset = mod(pixel_coord, size)\\n //\\n // At high zoom levels there are a ton of pixels between the world origin\\n // and the edge of the tile. The glsl spec only guarantees 16 bits of\\n // precision for highp floats. We need more than that.\\n //\\n // The pixel_coord is passed in as two 16 bit values:\\n // pixel_coord_upper = floor(pixel_coord / 2^16)\\n // pixel_coord_lower = mod(pixel_coord, 2^16)\\n //\\n // The offset is calculated in a series of steps that should preserve this precision:\\n vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\\n vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\\n\\n v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\\n v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\\n\\n v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},pattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n gl_FragColor = mix(color1, color2, u_mix) * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\\n vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\\n\\n // the correct offset needs to be calculated.\\n //\\n // The offset depends on how many pixels are between the world origin and\\n // the edge of the tile:\\n // vec2 offset = mod(pixel_coord, size)\\n //\\n // At high zoom levels there are a ton of pixels between the world origin\\n // and the edge of the tile. The glsl spec only guarantees 16 bits of\\n // precision for highp floats. We need more than that.\\n //\\n // The pixel_coord is passed in as two 16 bit values:\\n // pixel_coord_upper = floor(pixel_coord / 2^16)\\n // pixel_coord_lower = mod(pixel_coord, 2^16)\\n //\\n // The offset is calculated in a series of steps that should preserve this precision:\\n vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\\n vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\\n\\n v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\\n v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\\n}\\n\"},raster:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity0;\\nuniform float u_opacity1;\\nuniform sampler2D u_image0;\\nuniform sampler2D u_image1;\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nuniform float u_brightness_low;\\nuniform float u_brightness_high;\\n\\nuniform float u_saturation_factor;\\nuniform float u_contrast_factor;\\nuniform vec3 u_spin_weights;\\n\\nvoid main() {\\n\\n // read and cross-fade colors from the main and parent tiles\\n vec4 color0 = texture2D(u_image0, v_pos0);\\n vec4 color1 = texture2D(u_image1, v_pos1);\\n vec4 color = color0 * u_opacity0 + color1 * u_opacity1;\\n vec3 rgb = color.rgb;\\n\\n // spin\\n rgb = vec3(\\n dot(rgb, u_spin_weights.xyz),\\n dot(rgb, u_spin_weights.zxy),\\n dot(rgb, u_spin_weights.yzx));\\n\\n // saturation\\n float average = (color.r + color.g + color.b) / 3.0;\\n rgb += (average - rgb) * u_saturation_factor;\\n\\n // contrast\\n rgb = (rgb - 0.5) * u_contrast_factor + 0.5;\\n\\n // brightness\\n vec3 u_high_vec = vec3(u_brightness_low, u_brightness_low, u_brightness_low);\\n vec3 u_low_vec = vec3(u_brightness_high, u_brightness_high, u_brightness_high);\\n\\n gl_FragColor = vec4(mix(u_high_vec, u_low_vec, rgb), color.a);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_tl_parent;\\nuniform float u_scale_parent;\\nuniform float u_buffer_scale;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos0 = (((a_texture_pos / 32767.0) - 0.5) / u_buffer_scale ) + 0.5;\\n v_pos1 = (v_pos0 * u_scale_parent) + u_tl_parent;\\n}\\n\"},icon:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform sampler2D u_texture;\\nuniform sampler2D u_fadetexture;\\nuniform lowp float u_opacity;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\n\\nvoid main() {\\n lowp float alpha = texture2D(u_fadetexture, v_fade_tex).a * u_opacity;\\n gl_FragColor = texture2D(u_texture, v_tex) * alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_offset;\\nattribute vec2 a_texture_pos;\\nattribute vec4 a_data;\\n\\n\\n// matrix is for the vertex position.\\nuniform mat4 u_matrix;\\n\\nuniform mediump float u_zoom;\\nuniform bool u_rotate_with_map;\\nuniform vec2 u_extrude_scale;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\n\\nvoid main() {\\n vec2 a_tex = a_texture_pos.xy;\\n mediump float a_labelminzoom = a_data[0];\\n mediump vec2 a_zoom = a_data.pq;\\n mediump float a_minzoom = a_zoom[0];\\n mediump float a_maxzoom = a_zoom[1];\\n\\n // u_zoom is the current zoom level adjusted for the change in font size\\n mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\\n\\n vec2 extrude = u_extrude_scale * (a_offset / 64.0);\\n if (u_rotate_with_map) {\\n gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\\n gl_Position.z += z * gl_Position.w;\\n } else {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n }\\n\\n v_tex = a_tex / u_texsize;\\n v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\\n}\\n\"},sdf:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform sampler2D u_texture;\\nuniform sampler2D u_fadetexture;\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\nuniform lowp float u_buffer;\\nuniform lowp float u_gamma;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n lowp float dist = texture2D(u_texture, v_tex).a;\\n lowp float fade_alpha = texture2D(u_fadetexture, v_fade_tex).a;\\n lowp float gamma = u_gamma * v_gamma_scale;\\n lowp float alpha = smoothstep(u_buffer - gamma, u_buffer + gamma, dist) * fade_alpha;\\n\\n gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nconst float PI = 3.141592653589793;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_offset;\\nattribute vec2 a_texture_pos;\\nattribute vec4 a_data;\\n\\n\\n// matrix is for the vertex position.\\nuniform mat4 u_matrix;\\n\\nuniform mediump float u_zoom;\\nuniform bool u_rotate_with_map;\\nuniform bool u_pitch_with_map;\\nuniform mediump float u_pitch;\\nuniform mediump float u_bearing;\\nuniform mediump float u_aspect_ratio;\\nuniform vec2 u_extrude_scale;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n vec2 a_tex = a_texture_pos.xy;\\n mediump float a_labelminzoom = a_data[0];\\n mediump vec2 a_zoom = a_data.pq;\\n mediump float a_minzoom = a_zoom[0];\\n mediump float a_maxzoom = a_zoom[1];\\n\\n // u_zoom is the current zoom level adjusted for the change in font size\\n mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\\n\\n // pitch-alignment: map\\n // rotation-alignment: map \| viewport\\n if (u_pitch_with_map) {\\n lowp float angle = u_rotate_with_map ? (a_data[1] / 256.0 * 2.0 * PI) : u_bearing;\\n lowp float asin = sin(angle);\\n lowp float acos = cos(angle);\\n mat2 RotationMatrix = mat2(acos, asin, -1.0 * asin, acos);\\n vec2 offset = RotationMatrix * a_offset;\\n vec2 extrude = u_extrude_scale * (offset / 64.0);\\n gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\\n gl_Position.z += z * gl_Position.w;\\n // pitch-alignment: viewport\\n // rotation-alignment: map\\n } else if (u_rotate_with_map) {\\n // foreshortening factor to apply on pitched maps\\n // as a label goes from horizontal <=> vertical in angle\\n // it goes from 0% foreshortening to up to around 70% foreshortening\\n lowp float pitchfactor = 1.0 - cos(u_pitch * sin(u_pitch * 0.75));\\n\\n lowp float lineangle = a_data[1] / 256.0 * 2.0 * PI;\\n\\n // use the lineangle to position points a,b along the line\\n // project the points and calculate the label angle in projected space\\n // this calculation allows labels to be rendered unskewed on pitched maps\\n vec4 a = u_matrix * vec4(a_pos, 0, 1);\\n vec4 b = u_matrix * vec4(a_pos + vec2(cos(lineangle),sin(lineangle)), 0, 1);\\n lowp float angle = atan((b[1]/b[3] - a[1]/a[3])/u_aspect_ratio, b[0]/b[3] - a[0]/a[3]);\\n lowp float asin = sin(angle);\\n lowp float acos = cos(angle);\\n mat2 RotationMatrix = mat2(acos, -1.0 * asin, asin, acos);\\n\\n vec2 offset = RotationMatrix * (vec2((1.0-pitchfactor)+(pitchfactorcos(angle2.0)), 1.0) * a_offset);\\n vec2 extrude = u_extrude_scale * (offset / 64.0);\\n gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n gl_Position.z += z * gl_Position.w;\\n // pitch-alignment: viewport\\n // rotation-alignment: viewport\\n } else {\\n vec2 extrude = u_extrude_scale * (a_offset / 64.0);\\n gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n }\\n\\n v_gamma_scale = (gl_Position.w - 0.5);\\n\\n v_tex = a_tex / u_texsize;\\n v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\\n}\\n\"},collisionbox:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_zoom;\\nuniform float u_maxzoom;\\n\\nvarying float v_max_zoom;\\nvarying float v_placement_zoom;\\n\\nvoid main() {\\n\\n float alpha = 0.5;\\n\\n gl_FragColor = vec4(0.0, 1.0, 0.0, 1.0) * alpha;\\n\\n if (v_placement_zoom > u_zoom) {\\n gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\\n }\\n\\n if (u_zoom >= v_max_zoom) {\\n gl_FragColor = vec4(0.0, 0.0, 0.0, 1.0) * alpha * 0.25;\\n }\\n\\n if (v_placement_zoom >= u_maxzoom) {\\n gl_FragColor = vec4(0.0, 0.0, 1.0, 1.0) * alpha * 0.2;\\n }\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_extrude;\\nattribute vec2 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform float u_scale;\\n\\nvarying float v_max_zoom;\\nvarying float v_placement_zoom;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos + a_extrude / u_scale, 0.0, 1.0);\\n\\n v_max_zoom = a_data.x;\\n v_placement_zoom = a_data.y;\\n}\\n\"}},e.exports.util=\"float evaluate_zoom_function_1(const vec4 values, const float t) {\\n if (t < 1.0) {\\n return mix(values[0], values[1], t);\\n } else if (t < 2.0) {\\n return mix(values[1], values[2], t - 1.0);\\n } else {\\n return mix(values[2], values[3], t - 2.0);\\n }\\n}\\nvec4 evaluate_zoom_function_4(const vec4 value0, const vec4 value1, const vec4 value2, const vec4 value3, const float t) {\\n if (t < 1.0) {\\n return mix(value0, value1, t);\\n } else if (t < 2.0) {\\n return mix(value1, value2, t - 1.0);\\n } else {\\n return mix(value2, value3, t - 2.0);\\n }\\n}\\n\"},{path:476}],304:[function(t,e,r){\"use strict\";function n(t,e){this.message=(t?t+\": \":\"\")+i.apply(i,Array.prototype.slice.call(arguments,2)),null!==e&&void 0!==e&&e.__line__&&(this.line=e.__line__)}var i=t(\"util\").format;e.exports=n},{util:549}],305:[function(t,e,r){\"use 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i=t(\"../util/util\");e.exports=n,n.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,n.prototype.hasCapacityFor=function(t){return this.layoutVertexArray.length+t<=n.MAX_VERTEX_ARRAY_LENGTH},n.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},n.prototype.trim=function(){this.layoutVertexArray.trim(),this.elementArray&&this.elementArray.trim(),this.elementArray2&&this.elementArray2.trim();for(var t in this.paintVertexArrays)this.paintVertexArrays[t].trim()},n.prototype.serialize=function(){return{layoutVertexArray:this.layoutVertexArray.serialize(),elementArray:this.elementArray&&this.elementArray.serialize(),elementArray2:this.elementArray2&&this.elementArray2.serialize(),paintVertexArrays:i.mapObject(this.paintVertexArrays,function(t){return t.serialize()})}},n.prototype.getTransferables=function(t){t.push(this.layoutVertexArray.arrayBuffer),this.elementArray&&t.push(this.elementArray.arrayBuffer),this.elementArray2&&t.push(this.elementArray2.arrayBuffer);for(var e in this.paintVertexArrays)t.push(this.paintVertexArrays[e].arrayBuffer)}},{\"../util/util\":442}],329:[function(t,e,r){\"use strict\";function n(t){if(this.zoom=t.zoom,this.overscaling=t.overscaling,this.layer=t.layer,this.childLayers=t.childLayers,this.type=this.layer.type,this.features=[],this.id=this.layer.id,this.index=t.index,this.sourceLayer=this.layer.sourceLayer,this.sourceLayerIndex=t.sourceLayerIndex,this.minZoom=this.layer.minzoom,this.maxZoom=this.layer.maxzoom,this.paintAttributes=i(this),t.arrays){var e=this.programInterfaces;this.bufferGroups=c.mapObject(t.arrays,function(r,n){var i=e[n],a=t.paintVertexArrayTypes[n];return r.map(function(t){return new u(t,{layoutVertexArrayType:i.layoutVertexArrayType.serialize(),elementArrayType:i.elementArrayType&&i.elementArrayType.serialize(),elementArrayType2:i.elementArrayType2&&i.elementArrayType2.serialize(),paintVertexArrayTypes:a})})})}}function i(t){var e={};for(var r in t.programInterfaces){for(var n=e[r]={},i=0;i<t.childLayers.length;i++){n[t.childLayers[i].id]={attributes:[],uniforms:[],defines:[],vertexPragmas:{define:{},initialize:{}},fragmentPragmas:{define:{},initialize:{}}}}var s=t.programInterfaces[r];if(s.paintAttributes)for(var l=0;l<s.paintAttributes.length;l++){var u=s.paintAttributes[l];u.multiplier=u.multiplier\|\|1;for(var h=0;h<t.childLayers.length;h++){var d=t.childLayers[h],p=n[d.id],m=u.name;f(\"a_\"===u.name.slice(0,2));var v,g=u.name.slice(2);if(p.fragmentPragmas.initialize[g]=\"\",d.isPaintValueFeatureConstant(u.paintProperty))p.uniforms.push(u),p.fragmentPragmas.define[g]=p.vertexPragmas.define[g]=[\"uniform\",\"{precision}\",\"{type}\",m].join(\" \")+\";\",p.fragmentPragmas.initialize[g]=p.vertexPragmas.initialize[g]=[\"{precision}\",\"{type}\",g,\"=\",m].join(\" \")+\";\\n\";else if(d.isPaintValueZoomConstant(u.paintProperty)){p.attributes.push(c.extend({},u,{name:m})),v=[\"varying\",\"{precision}\",\"{type}\",g].join(\" \")+\";\\n\"\n", ";var y=[p.fragmentPragmas.define[g],\"attribute\",\"{precision}\",\"{type}\",m].join(\" \")+\";\\n\";p.fragmentPragmas.define[g]=v,p.vertexPragmas.define[g]=v+y,p.vertexPragmas.initialize[g]=[g,\"=\",m,\"/\",u.multiplier.toFixed(1)].join(\" \")+\";\\n\"}else{for(var b=\"u_\"+m.slice(2)+\"_t\",x=d.getPaintValueStopZoomLevels(u.paintProperty),_=0;_<x.length&&x[_]<t.zoom;)_++;for(var w=Math.max(0,Math.min(x.length-4,_-2)),M=[],k=0;k<4;k++)M.push(x[Math.min(w+k,x.length-1)]);v=[\"varying\",\"{precision}\",\"{type}\",g].join(\" \")+\";\\n\",p.vertexPragmas.define[g]=v+[\"uniform\",\"lowp\",\"float\",b].join(\" \")+\";\\n\",p.fragmentPragmas.define[g]=v,p.uniforms.push(c.extend({},u,{name:b,getValue:o(u,w),components:1}));var A=u.components;if(1===A)p.attributes.push(c.extend({},u,{getValue:a(u,M),isFunction:!0,components:4A})),p.vertexPragmas.define[g]+=[\"attribute\",\"{precision}\",\"vec4\",m].join(\" \")+\";\\n\",p.vertexPragmas.initialize[g]=[g,\"=\",\"evaluate_zoom_function_1(\"+m+\", \"+b+\")\",\"/\",u.multiplier.toFixed(1)].join(\" \")+\";\\n\";else{for(var T=[],S=0;S<4;S++)T.push(m+S),p.attributes.push(c.extend({},u,{getValue:a(u,[M[S]]),isFunction:!0,name:m+S})),p.vertexPragmas.define[g]+=[\"attribute\",\"{precision}\",\"{type}\",m+S].join(\" \")+\";\\n\";p.vertexPragmas.initialize[g]=[g,\" = \",\"evaluate_zoom_function_4(\"+T.join(\", \")+\", \"+b+\")\",\"/\",u.multiplier.toFixed(1)].join(\" \")+\";\\n\"}}}}}return e}function a(t,e){return function(r,n,i){if(1===e.length)return t.getValue(r,c.extend({},n,{zoom:e[0]}),i);for(var a=[],o=0;o<e.length;o++){var s=e[o];a.push(t.getValue(r,c.extend({},n,{zoom:s}),i)[0])}return a}}function o(t,e){return function(r,n){var i=r.getPaintInterpolationT(t.paintProperty,n.zoom);return[Math.max(0,Math.min(4,i-e))]}}var s=t(\"feature-filter\"),l=t(\"./array_group\"),u=t(\"./buffer_group\"),c=t(\"../util/util\"),h=t(\"../util/struct_array\"),f=t(\"assert\");e.exports=n,n.create=function(e){return new({fill:t(\"./bucket/fill_bucket\"),line:t(\"./bucket/line_bucket\"),circle:t(\"./bucket/circle_bucket\"),symbol:t(\"./bucket/symbol_bucket\")}[e.layer.type])(e)},n.EXTENT=8192,n.prototype.populateArrays=function(){this.createArrays(),this.recalculateStyleLayers();for(var t=0;t<this.features.length;t++)this.addFeature(this.features[t]);this.trimArrays()},n.prototype.prepareArrayGroup=function(t,e){var r=this.arrayGroups[t],n=r.length&&r[r.length-1];return n&&n.hasCapacityFor(e)\|\|(n=new l({layoutVertexArrayType:this.programInterfaces[t].layoutVertexArrayType,elementArrayType:this.programInterfaces[t].elementArrayType,elementArrayType2:this.programInterfaces[t].elementArrayType2,paintVertexArrayTypes:this.paintVertexArrayTypes[t]}),n.index=r.length,r.push(n)),n},n.prototype.createArrays=function(){this.arrayGroups={},this.paintVertexArrayTypes={};for(var t in this.programInterfaces){this.arrayGroups[t]=[];var e=this.paintVertexArrayTypes[t]={},r=this.paintAttributes[t];for(var i in r)e[i]=new n.VertexArrayType(r[i].attributes)}},n.prototype.destroy=function(t){for(var e in this.bufferGroups)for(var r=this.bufferGroups[e],n=0;n<r.length;n++)r[n].destroy(t)},n.prototype.trimArrays=function(){for(var t in this.arrayGroups)for(var e=this.arrayGroups[t],r=0;r<e.length;r++)e[r].trim()},n.prototype.isEmpty=function(){for(var t in this.arrayGroups)for(var e=this.arrayGroups[t],r=0;r<e.length;r++)if(!e[r].isEmpty())return!1;return!0},n.prototype.getTransferables=function(t){for(var e in this.arrayGroups)for(var r=this.arrayGroups[e],n=0;n<r.length;n++)r[n].getTransferables(t)},n.prototype.setUniforms=function(t,e,r,n,i){for(var a=this.paintAttributes[e][n.id].uniforms,o=0;o<a.length;o++){var s=a[o],l=r[s.name];t[\"uniform\"+s.components+\"fv\"](l,s.getValue(n,i))}},n.prototype.serialize=function(){return{layerId:this.layer.id,zoom:this.zoom,arrays:c.mapObject(this.arrayGroups,function(t){return t.map(function(t){return t.serialize()})}),paintVertexArrayTypes:c.mapObject(this.paintVertexArrayTypes,function(t){return c.mapObject(t,function(t){return t.serialize()})}),childLayerIds:this.childLayers.map(function(t){return t.id})}},n.prototype.createFilter=function(){this.filter\|\|(this.filter=s(this.layer.filter))};var d={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};n.prototype.recalculateStyleLayers=function(){for(var t=0;t<this.childLayers.length;t++)this.childLayers[t].recalculate(this.zoom,d)},n.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a=0;a<this.childLayers.length;a++)for(var o=this.childLayers[a],s=this.arrayGroups[t],l=n.index;l<s.length;l++){var u=s[l],c=u.layoutVertexArray.length,h=u.paintVertexArrays[o.id];h.resize(c);for(var f=this.paintAttributes[t][o.id].attributes,d=0;d<f.length;d++)for(var p=f[d],m=p.getValue(o,e,r),v=p.multiplier\|\|1,g=p.components\|\|1,y=l===n.index?i:0,b=y;b<c;b++)for(var x=h.get(b),_=0;_<g;_++){var w=g>1?p.name+_:p.name;x[w]=m[_]v}}},n.VertexArrayType=function(t){return new h({members:t,alignment:4})},n.ElementArrayType=function(t){return new h({members:[{type:\"Uint16\",name:\"vertices\",components:t\|\|3}]})}},{\"../util/struct_array\":440,\"../util/util\":442,\"./array_group\":328,\"./bucket/circle_bucket\":330,\"./bucket/fill_bucket\":331,\"./bucket/line_bucket\":332,\"./bucket/symbol_bucket\":333,\"./buffer_group\":335,assert:47,\"feature-filter\":132}],330:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=i.EXTENT;e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.addCircleVertex=function(t,e,r,n,i){return t.emplaceBack(2e+(n+1)/2,2r+(i+1)/2)},n.prototype.programInterfaces={circle:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"}]),elementArrayType:new i.ElementArrayType,paintAttributes:[{name:\"a_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"circle-color\",e,r)},multiplier:255,paintProperty:\"circle-color\"},{name:\"a_radius\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-radius\",e,r)]},multiplier:10,paintProperty:\"circle-radius\"},{name:\"a_blur\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-blur\",e,r)]},multiplier:10,paintProperty:\"circle-blur\"},{name:\"a_opacity\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-opacity\",e,r)]},multiplier:255,paintProperty:\"circle-opacity\"}]}},n.prototype.addFeature=function(t){for(var e={zoom:this.zoom},r=o(t),n=this.prepareArrayGroup(\"circle\",0),i=n.layoutVertexArray.length,a=0;a<r.length;a++)for(var l=0;l<r[a].length;l++){var u=r[a][l].x,c=r[a][l].y;if(!(u<0\|\|u>=s\|\|c<0\|\|c>=s)){var h=this.prepareArrayGroup(\"circle\",4),f=h.layoutVertexArray,d=this.addCircleVertex(f,u,c,-1,-1);this.addCircleVertex(f,u,c,1,-1),this.addCircleVertex(f,u,c,1,1),this.addCircleVertex(f,u,c,-1,1),h.elementArray.emplaceBack(d,d+1,d+2),h.elementArray.emplaceBack(d,d+3,d+2)}}this.populatePaintArrays(\"circle\",e,t.properties,n,i)}},{\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337}],331:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=t(\"earcut\"),l=t(\"../../util/classify_rings\");e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.programInterfaces={fill:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"}]),elementArrayType:new i.ElementArrayType(1),elementArrayType2:new i.ElementArrayType(2),paintAttributes:[{name:\"a_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"fill-color\",e,r)},multiplier:255,paintProperty:\"fill-color\"},{name:\"a_outline_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"fill-outline-color\",e,r)},multiplier:255,paintProperty:\"fill-outline-color\"},{name:\"a_opacity\",components:1,type:\"Uint8\",getValue:function(t,e,r){return[t.getPaintValue(\"fill-opacity\",e,r)]},multiplier:255,paintProperty:\"fill-opacity\"}]}},n.prototype.addFeature=function(t){for(var e=o(t),r=l(e,500),n=this.prepareArrayGroup(\"fill\",0),i=n.layoutVertexArray.length,a=0;a<r.length;a++)this.addPolygon(r[a]);this.populatePaintArrays(\"fill\",{zoom:this.zoom},t.properties,n,i)},n.prototype.addPolygon=function(t){for(var e=0,r=0;r<t.length;r++)e+=t[r].length;for(var n=this.prepareArrayGroup(\"fill\",e),i=[],a=[],o=n.layoutVertexArray.length,l=0;l<t.length;l++){var u=t[l];l>0&&a.push(i.length/2);for(var c=0;c<u.length;c++){var h=u[c],f=n.layoutVertexArray.emplaceBack(h.x,h.y);c>=1&&n.elementArray2.emplaceBack(f-1,f),i.push(h.x),i.push(h.y)}}for(var d=s(i,a),p=0;p<d.length;p++)n.elementArray.emplaceBack(d[p]+o)}},{\"../../util/classify_rings\":430,\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337,earcut:126}],332:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=i.EXTENT,l=Math.cos(Math.PI/18037.5),u=Math.pow(2,14)/.5;e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.addLineVertex=function(t,e,r,n,i,a,o){return t.emplaceBack(e.x<<1\|n,e.y<<1\|i,Math.round(63r.x)+128,Math.round(63r.y)+128,1+(0===a?0:a<0?-1:1)\|(.5o&63)<<2,.5o>>6)},n.prototype.programInterfaces={line:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}]),elementArrayType:new i.ElementArrayType}},n.prototype.addFeature=function(t){for(var e=o(t,15),r=0;r<e.length;r++)this.addLine(e[r],this.layer.layout[\"line-join\"],this.layer.layout[\"line-cap\"],this.layer.layout[\"line-miter-limit\"],this.layer.layout[\"line-round-limit\"])},n.prototype.addLine=function(t,e,r,n,i){for(var a=t.length;a>2&&t[a-1].equals(t[a-2]);)a--;if(!(t.length<2)){\"bevel\"===e&&(n=1.05);var o=s/(512this.overscaling)15,u=t[0],c=t[a-1],h=u.equals(c);if(this.prepareArrayGroup(\"line\",10a),2!==a\|\|!h){this.distance=0;var f,d,p,m,v,g,y,b=r,x=h?\"butt\":r,_=!0;this.e1=this.e2=this.e3=-1,h&&(f=t[a-2],v=u.sub(f)._unit()._perp());for(var w=0;w<a;w++)if(!(p=h&&w===a-1?t[1]:t[w+1])\|\|!t[w].equals(p)){v&&(m=v),f&&(d=f),f=t[w],v=p?p.sub(f)._unit()._perp():m,m=m\|\|v;var M=m.add(v)._unit(),k=M.xv.x+M.yv.y,A=1/k,T=k<l&&d&&p;if(T&&w>0){var S=f.dist(d);if(S>2o){var E=f.sub(f.sub(d)._mult(o/S)._round());this.distance+=E.dist(d),this.addCurrentVertex(E,this.distance,m.mult(1),0,0,!1),d=E}}var L=d&&p,C=L?e:p?b:x;if(L&&\"round\"===C&&(A<i?C=\"miter\":A<=2&&(C=\"fakeround\")),\"miter\"===C&&A>n&&(C=\"bevel\"),\"bevel\"===C&&(A>2&&(C=\"flipbevel\"),A<n&&(C=\"miter\")),d&&(this.distance+=f.dist(d)),\"miter\"===C)M._mult(A),this.addCurrentVertex(f,this.distance,M,0,0,!1);else if(\"flipbevel\"===C){if(A>100)M=v.clone();else{var I=m.xv.y-m.yv.x>0?-1:1,z=Am.add(v).mag()/m.sub(v).mag();M._perp()._mult(zI)}this.addCurrentVertex(f,this.distance,M,0,0,!1),this.addCurrentVertex(f,this.distance,M.mult(-1),0,0,!1)}else if(\"bevel\"===C\|\|\"fakeround\"===C){var D=m.xv.y-m.yv.x>0,P=-Math.sqrt(AA-1);if(D?(y=0,g=P):(g=0,y=P),_\|\|this.addCurrentVertex(f,this.distance,m,g,y,!1),\"fakeround\"===C){for(var O,R=Math.floor(8(.5-(k-.5))),F=0;F<R;F++)O=v.mult((F+1)/(R+1))._add(m)._unit(),this.addPieSliceVertex(f,this.distance,O,D);this.addPieSliceVertex(f,this.distance,M,D);for(var j=R-1;j>=0;j--)O=m.mult((j+1)/(R+1))._add(v)._unit(),this.addPieSliceVertex(f,this.distance,O,D)}p&&this.addCurrentVertex(f,this.distance,v,-g,-y,!1)}else\"butt\"===C?(_\|\|this.addCurrentVertex(f,this.distance,m,0,0,!1),p&&this.addCurrentVertex(f,this.distance,v,0,0,!1)):\"square\"===C?(_\|\|(this.addCurrentVertex(f,this.distance,m,1,1,!1),this.e1=this.e2=-1),p&&this.addCurrentVertex(f,this.distance,v,-1,-1,!1)):\"round\"===C&&(_\|\|(this.addCurrentVertex(f,this.distance,m,0,0,!1),this.addCurrentVertex(f,this.distance,m,1,1,!0),this.e1=this.e2=-1),p&&(this.addCurrentVertex(f,this.distance,v,-1,-1,!0),this.addCurrentVertex(f,this.distance,v,0,0,!1)));if(T&&w<a-1){var N=f.dist(p);if(N>2o){var B=f.add(p.sub(f)._mult(o/N)._round());this.distance+=B.dist(f),this.addCurrentVertex(B,this.distance,v.mult(1),0,0,!1),f=B}}_=!1}}}},n.prototype.addCurrentVertex=function(t,e,r,n,i,a){var o,s=a?1:0,l=this.arrayGroups.line[this.arrayGroups.line.length-1],c=l.layoutVertexArray,h=l.elementArray;o=r.clone(),n&&o._sub(r.perp()._mult(n)),this.e3=this.addLineVertex(c,t,o,s,0,n,e),this.e1>=0&&this.e2>=0&&h.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,o=r.mult(-1),i&&o._sub(r.perp()._mult(i)),this.e3=this.addLineVertex(c,t,o,s,1,-i,e),this.e1>=0&&this.e2>=0&&h.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,e>u/2&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,a))},n.prototype.addPieSliceVertex=function(t,e,r,n){var i=n?1:0;r=r.mult(n?-1:1);var a=this.arrayGroups.line[this.arrayGroups.line.length-1],o=a.layoutVertexArray,s=a.elementArray;this.e3=this.addLineVertex(o,t,r,0,i,0,e),this.e1>=0&&this.e2>=0&&s.emplaceBack(this.e1,this.e2,this.e3),n?this.e2=this.e3:this.e1=this.e3}},{\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337}],333:[function(t,e,r){\"use strict\";function n(t){o.apply(this,arguments),this.showCollisionBoxes=t.showCollisionBoxes,this.overscaling=t.overscaling,this.collisionBoxArray=t.collisionBoxArray,this.symbolQuadsArray=t.symbolQuadsArray,this.symbolInstancesArray=t.symbolInstancesArray,this.sdfIcons=t.sdfIcons,this.iconsNeedLinear=t.iconsNeedLinear,this.adjustedTextSize=t.adjustedTextSize,this.adjustedIconSize=t.adjustedIconSize,this.fontstack=t.fontstack}function i(t,e,r,n,i,a,o,s,l,u,c){return t.emplaceBack(e,r,Math.round(64n),Math.round(64i),a/4,o/4,10(u\|\|0),c,10(s\|\|0),10Math.min(l\|\|25,25))}var a=t(\"point-geometry\"),o=t(\"../bucket\"),s=t(\"../../symbol/anchor\"),l=t(\"../../symbol/get_anchors\"),u=t(\"../../util/token\"),c=t(\"../../symbol/quads\"),h=t(\"../../symbol/shaping\"),f=t(\"../../symbol/resolve_text\"),d=t(\"../../symbol/mergelines\"),p=t(\"../../symbol/clip_line\"),m=t(\"../../util/util\"),v=t(\"../load_geometry\"),g=t(\"../../symbol/collision_feature\"),y=h.shapeText,b=h.shapeIcon,x=c.getGlyphQuads,_=c.getIconQuads,w=o.EXTENT;e.exports=n,n.MAX_QUADS=65535,n.prototype=m.inherit(o,{}),n.prototype.serialize=function(){var t=o.prototype.serialize.apply(this);return t.sdfIcons=this.sdfIcons,t.iconsNeedLinear=this.iconsNeedLinear,t.adjustedTextSize=this.adjustedTextSize,t.adjustedIconSize=this.adjustedIconSize,t.fontstack=this.fontstack,t};var M=new o.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_offset\",components:2,type:\"Int16\"},{name:\"a_texture_pos\",components:2,type:\"Uint16\"},{name:\"a_data\",components:4,type:\"Uint8\"}]),k=new o.ElementArrayType;n.prototype.addCollisionBoxVertex=function(t,e,r,n,i){return t.emplaceBack(e.x,e.y,Math.round(r.x),Math.round(r.y),10n,10i)},n.prototype.programInterfaces={glyph:{layoutVertexArrayType:M,elementArrayType:k},icon:{layoutVertexArrayType:M,elementArrayType:k},collisionBox:{layoutVertexArrayType:new o.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"},{name:\"a_data\",components:2,type:\"Uint8\"}])}},n.prototype.populateArrays=function(t,e,r){var n={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};this.adjustedTextMaxSize=this.layer.getLayoutValue(\"text-size\",{zoom:18,zoomHistory:n}),this.adjustedTextSize=this.layer.getLayoutValue(\"text-size\",{zoom:this.zoom+1,zoomHistory:n}),this.adjustedIconMaxSize=this.layer.getLayoutValue(\"icon-size\",{zoom:18,zoomHistory:n}),this.adjustedIconSize=this.layer.getLayoutValue(\"icon-size\",{zoom:this.zoom+1,zoomHistory:n});var i=512this.overscaling;this.tilePixelRatio=w/i,this.compareText={},this.iconsNeedLinear=!1,this.symbolInstancesStartIndex=this.symbolInstancesArray.length;var a=this.layer.layout,o=this.features,s=this.textFeatures,l=.5,c=.5;switch(a[\"text-anchor\"]){case\"right\":case\"top-right\":case\"bottom-right\":l=1;break;case\"left\":case\"top-left\":case\"bottom-left\":l=0}switch(a[\"text-anchor\"]){case\"bottom\":case\"bottom-right\":case\"bottom-left\":c=1;break;case\"top\":case\"top-right\":case\"top-left\":c=0}for(var h=\"right\"===a[\"text-justify\"]?1:\"left\"===a[\"text-justify\"]?0:.5,f=24a[\"text-line-height\"],p=\"line\"!==a[\"symbol-placement\"]?24a[\"text-max-width\"]:0,g=24a[\"text-letter-spacing\"],x=[24a[\"text-offset\"][0],24a[\"text-offset\"][1]],_=this.fontstack=a[\"text-font\"].join(\",\"),M=[],k=0;k<o.length;k++)M.push(v(o[k]));if(\"line\"===a[\"symbol-placement\"]){var A=d(o,s,M);M=A.geometries,o=A.features,s=A.textFeatures}for(var T,S,E=0;E<o.length;E++)if(M[E]){if(T=s[E]?y(s[E],e[_],p,f,l,c,h,g,x):null,a[\"icon-image\"]){var L=u(o[E].properties,a[\"icon-image\"]),C=r[L];S=b(C,a),C&&(void 0===this.sdfIcons?this.sdfIcons=C.sdf:this.sdfIcons!==C.sdf&&m.warnOnce(\"Style sheet warning: Cannot mix SDF and non-SDF icons in one buffer\"),1!==C.pixelRatio?this.iconsNeedLinear=!0:0===a[\"icon-rotate\"]&&this.layer.isLayoutValueFeatureConstant(\"icon-rotate\")\|\|(this.iconsNeedLinear=!0))}else S=null;(T\|\|S)&&this.addFeature(M[E],T,S,o[E])}this.symbolInstancesEndIndex=this.symbolInstancesArray.length,this.placeFeatures(t,this.showCollisionBoxes),this.trimArrays()},n.prototype.addFeature=function(t,e,r,n){var i=this.layer.layout,a=this.adjustedTextSize/24,o=void 0!==this.adjustedTextMaxSize?this.adjustedTextMaxSize:this.adjustedTextSize,u=this.tilePixelRatioa,c=this.tilePixelRatioo/24,h=this.tilePixelRatiothis.adjustedIconSize,f=this.tilePixelRatioi[\"symbol-spacing\"],d=i[\"symbol-avoid-edges\"],m=i[\"text-padding\"]this.tilePixelRatio,v=i[\"icon-padding\"]this.tilePixelRatio,g=i[\"text-max-angle\"]/180Math.PI,y=\"map\"===i[\"text-rotation-alignment\"]&&\"line\"===i[\"symbol-placement\"],b=\"map\"===i[\"icon-rotation-alignment\"]&&\"line\"===i[\"symbol-placement\"],x=i[\"text-allow-overlap\"]\|\|i[\"icon-allow-overlap\"]\|\|i[\"text-ignore-placement\"]\|\|i[\"icon-ignore-placement\"],_=\"line\"===i[\"symbol-placement\"],M=f/2;_&&(t=p(t,0,0,w,w));for(var k=0;k<t.length;k++){var A,T=t[k];A=_?l(T,f,g,e,r,24,c,this.overscaling,w):[new s(T[0].x,T[0].y,0)];for(var S=0,E=A.length;S<E;S++){var L=A[S];if(!(e&&_&&this.anchorIsTooClose(e.text,M,L))){var C=!(L.x<0\|\|L.x>w\|\|L.y<0\|\|L.y>w);if(!d\|\|C){var I=C\|\|x;this.addSymbolInstance(L,T,e,r,this.layer,I,this.symbolInstancesArray.length,this.collisionBoxArray,n.index,this.sourceLayerIndex,this.index,u,m,y,h,v,b,{zoom:this.zoom},n.properties)}}}}},n.prototype.anchorIsTooClose=function(t,e,r){var n=this.compareText;if(t in n){for(var i=n[t],a=i.length-1;a>=0;a--)if(r.dist(i[a])<e)return!0}else n[t]=[];return n[t].push(r),!1},n.prototype.placeFeatures=function(t,e){this.recalculateStyleLayers(),this.createArrays();var r=this.layer.layout,n=t.maxScale,i=\"map\"===r[\"text-rotation-alignment\"]&&\"line\"===r[\"symbol-placement\"],a=\"map\"===r[\"icon-rotation-alignment\"]&&\"line\"===r[\"symbol-placement\"];if(r[\"text-allow-overlap\"]\|\|r[\"icon-allow-overlap\"]\|\|r[\"text-ignore-placement\"]\|\|r[\"icon-ignore-placement\"]){var o=this.symbolInstancesArray.toArray(this.symbolInstancesStartIndex,this.symbolInstancesEndIndex),s=t.angle,l=Math.sin(s),u=Math.cos(s);this.sortedSymbolInstances=o.sort(function(t,e){return(lt.anchorPointX+ut.anchorPointY\|0)-(le.anchorPointX+ue.anchorPointY\|0)\|\|e.index-t.index})}for(var c=this.symbolInstancesStartIndex;c<this.symbolInstancesEndIndex;c++){var h=this.sortedSymbolInstances?this.sortedSymbolInstances[c-this.symbolInstancesStartIndex]:this.symbolInstancesArray.get(c),f={boxStartIndex:h.textBoxStartIndex,boxEndIndex:h.textBoxEndIndex},d={boxStartIndex:h.iconBoxStartIndex,boxEndIndex:h.iconBoxEndIndex},p=!(h.textBoxStartIndex===h.textBoxEndIndex),m=!(h.iconBoxStartIndex===h.iconBoxEndIndex),v=r[\"text-optional\"]\|\|!p,g=r[\"icon-optional\"]\|\|!m,y=p?t.placeCollisionFeature(f,r[\"text-allow-overlap\"],r[\"symbol-avoid-edges\"]):t.minScale,b=m?t.placeCollisionFeature(d,r[\"icon-allow-overlap\"],r[\"symbol-avoid-edges\"]):t.minScale;v\|\|g?!g&&y?y=Math.max(b,y):!v&&b&&(b=Math.max(b,y)):b=y=Math.max(b,y),p&&(t.insertCollisionFeature(f,y,r[\"text-ignore-placement\"]),y<=n&&this.addSymbols(\"glyph\",h.glyphQuadStartIndex,h.glyphQuadEndIndex,y,r[\"text-keep-upright\"],i,t.angle)),m&&(t.insertCollisionFeature(d,b,r[\"icon-ignore-placement\"]),b<=n&&this.addSymbols(\"icon\",h.iconQuadStartIndex,h.iconQuadEndIndex,b,r[\"icon-keep-upright\"],a,t.angle))}e&&this.addToDebugBuffers(t)},n.prototype.addSymbols=function(t,e,r,n,a,o,s){for(var l=this.prepareArrayGroup(t,4(r-e)),u=l.elementArray,c=l.layoutVertexArray,h=this.zoom,f=Math.max(Math.log(n)/Math.LN2+h,0),d=e;d<r;d++){var p=this.symbolQuadsArray.get(d).SymbolQuad,m=(p.anchorAngle+s+Math.PI)%(2Math.PI);if(!(a&&o&&(m<=Math.PI/2\|\|m>3Math.PI/2))){var v=p.tl,g=p.tr,y=p.bl,b=p.br,x=p.tex,_=p.anchorPoint,w=Math.max(h+Math.log(p.minScale)/Math.LN2,f),M=Math.min(h+Math.log(p.maxScale)/Math.LN2,25);if(!(M<=w)){w===f&&(w=0);var k=Math.round(p.glyphAngle/(2Math.PI)256),A=i(c,_.x,_.y,v.x,v.y,x.x,x.y,w,M,f,k);i(c,_.x,_.y,g.x,g.y,x.x+x.w,x.y,w,M,f,k),i(c,_.x,_.y,y.x,y.y,x.x,x.y+x.h,w,M,f,k),i(c,_.x,_.y,b.x,b.y,x.x+x.w,x.y+x.h,w,M,f,k),u.emplaceBack(A,A+1,A+2),u.emplaceBack(A+1,A+2,A+3)}}}},n.prototype.updateIcons=function(t){this.recalculateStyleLayers();var e=this.layer.layout[\"icon-image\"];if(e)for(var r=0;r<this.features.length;r++){var n=u(this.features[r].properties,e);n&&(t[n]=!0)}},n.prototype.updateFont=function(t){this.recalculateStyleLayers();var e=this.layer.layout[\"text-font\"],r=t[e]=t[e]\|\|{};this.textFeatures=f(this.features,this.layer.layout,r)},n.prototype.addToDebugBuffers=function(t){for(var e=this.prepareArrayGroup(\"collisionBox\",0),r=e.layoutVertexArray,n=-t.angle,i=t.yStretch,o=this.symbolInstancesStartIndex;o<this.symbolInstancesEndIndex;o++){var s=this.symbolInstancesArray.get(o);s.textCollisionFeature={boxStartIndex:s.textBoxStartIndex,boxEndIndex:s.textBoxEndIndex},s.iconCollisionFeature={boxStartIndex:s.iconBoxStartIndex,boxEndIndex:s.iconBoxEndIndex};for(var l=0;l<2;l++){var u=s[0===l?\"textCollisionFeature\":\"iconCollisionFeature\"];if(u)for(var c=u.boxStartIndex;c<u.boxEndIndex;c++){var h=this.collisionBoxArray.get(c),f=h.anchorPoint,d=new a(h.x1,h.y1i)._rotate(n),p=new a(h.x2,h.y1i)._rotate(n),m=new a(h.x1,h.y2i)._rotate(n),v=new a(h.x2,h.y2i)._rotate(n),g=Math.max(0,Math.min(25,this.zoom+Math.log(h.maxScale)/Math.LN2)),y=Math.max(0,Math.min(25,this.zoom+Math.log(h.placementScale)/Math.LN2));this.addCollisionBoxVertex(r,f,d,g,y),this.addCollisionBoxVertex(r,f,p,g,y),this.addCollisionBoxVertex(r,f,p,g,y),this.addCollisionBoxVertex(r,f,v,g,y),this.addCollisionBoxVertex(r,f,v,g,y),this.addCollisionBoxVertex(r,f,m,g,y),this.addCollisionBoxVertex(r,f,m,g,y),this.addCollisionBoxVertex(r,f,d,g,y)}}}},n.prototype.addSymbolInstance=function(t,e,r,i,a,o,s,l,u,c,h,f,d,p,v,y,b,w,M){var k,A,T,S,E,L,C,I;if(r&&(C=o?x(t,r,f,e,a,p):[],E=new g(l,e,t,u,c,h,r,f,d,p,!1)),k=this.symbolQuadsArray.length,C&&C.length)for(var z=0;z<C.length;z++)this.addSymbolQuad(C[z]);A=this.symbolQuadsArray.length;var D=E?E.boxStartIndex:this.collisionBoxArray.length,P=E?E.boxEndIndex:this.collisionBoxArray.length;i&&(I=o?_(t,i,v,e,a,b,r,w,M):[],L=new g(l,e,t,u,c,h,i,v,y,b,!0)),T=this.symbolQuadsArray.length,I&&1===I.length&&this.addSymbolQuad(I[0]),S=this.symbolQuadsArray.length;var O=L?L.boxStartIndex:this.collisionBoxArray.length,R=L?L.boxEndIndex:this.collisionBoxArray.length;return S>n.MAX_QUADS&&m.warnOnce(\"Too many symbols being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),A>n.MAX_QUADS&&m.warnOnce(\"Too many glyphs being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),this.symbolInstancesArray.emplaceBack(D,P,O,R,k,A,T,S,t.x,t.y,s)},n.prototype.addSymbolQuad=function(t){return this.symbolQuadsArray.emplaceBack(t.anchorPoint.x,t.anchorPoint.y,t.tl.x,t.tl.y,t.tr.x,t.tr.y,t.bl.x,t.bl.y,t.br.x,t.br.y,t.tex.h,t.tex.w,t.tex.x,t.tex.y,t.anchorAngle,t.glyphAngle,t.maxScale,t.minScale)}},{\"../../symbol/anchor\":391,\"../../symbol/clip_line\":393,\"../../symbol/collision_feature\":395,\"../../symbol/get_anchors\":397,\"../../symbol/mergelines\":400,\"../../symbol/quads\":401,\"../../symbol/resolve_text\":402,\"../../symbol/shaping\":403,\"../../util/token\":441,\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337,\"point-geometry\":484}],334:[function(t,e,r){\"use strict\";function n(t,e,r){this.arrayBuffer=t.arrayBuffer,this.length=t.length,this.attributes=e.members,this.itemSize=e.bytesPerElement,this.type=r,this.arrayType=e}e.exports=n,n.prototype.bind=function(t){var e=t[this.type];this.buffer?t.bindBuffer(e,this.buffer):(this.buffer=t.createBuffer(),t.bindBuffer(e,this.buffer),t.bufferData(e,this.arrayBuffer,t.STATIC_DRAW),this.arrayBuffer=null)};var i={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\"};n.prototype.setVertexAttribPointers=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],a=e[n.name];void 0!==a&&t.vertexAttribPointer(a,n.components,t[i[n.type]],!1,this.arrayType.bytesPerElement,n.offset)}},n.prototype.destroy=function(t){this.buffer&&t.deleteBuffer(this.buffer)},n.BufferType={VERTEX:\"ARRAY_BUFFER\",ELEMENT:\"ELEMENT_ARRAY_BUFFER\"}},{}],335:[function(t,e,r){\"use strict\";function n(t,e){this.layoutVertexBuffer=new a(t.layoutVertexArray,e.layoutVertexArrayType,a.BufferType.VERTEX),t.elementArray&&(this.elementBuffer=new a(t.elementArray,e.elementArrayType,a.BufferType.ELEMENT));var r,n=this.vaos={};t.elementArray2&&(this.elementBuffer2=new a(t.elementArray2,e.elementArrayType2,a.BufferType.ELEMENT),r=this.secondVaos={}),this.paintVertexBuffers=i.mapObject(t.paintVertexArrays,function(i,s){return n[s]=new o,t.elementArray2&&(r[s]=new o),new a(i,e.paintVertexArrayTypes[s],a.BufferType.VERTEX)})}var i=t(\"../util/util\"),a=t(\"./buffer\"),o=t(\"../render/vertex_array_object\");e.exports=n,n.prototype.destroy=function(t){this.layoutVertexBuffer.destroy(t),this.elementBuffer&&this.elementBuffer.destroy(t),this.elementBuffer2&&this.elementBuffer2.destroy(t);for(var e in this.paintVertexBuffers)this.paintVertexBuffers[e].destroy(t);for(var r in this.vaos)this.vaos[r].destroy(t);for(var n in this.secondVaos)this.secondVaos[n].destroy(t)}},{\"../render/vertex_array_object\":357,\"../util/util\":442,\"./buffer\":334}],336:[function(t,e,r){\"use strict\";function n(t,e,r){if(t.grid){var n=t,i=e;t=n.coord,e=n.overscaling,this.grid=new p(n.grid),this.featureIndexArray=new k(n.featureIndexArray),this.rawTileData=i,this.bucketLayerIDs=n.bucketLayerIDs}else this.grid=new p(h,16,0),this.featureIndexArray=new k;this.coord=t,this.overscaling=e,this.x=t.x,this.y=t.y,this.z=t.z-Math.log(e)/Math.LN2,this.setCollisionTile(r)}function i(t){return Math.sqrt(t[0]t[0]+t[1]t[1])}function a(t,e){return e-t}function o(t){return t[\"line-gap-width\"]>0?t[\"line-gap-width\"]+2t[\"line-width\"]:t[\"line-width\"]}function s(t,e,r,n,i){if(!e[0]&&!e[1])return t;e=u.convert(e),\"viewport\"===r&&e._rotate(-n);for(var a=[],o=0;o<t.length;o++){for(var s=t[o],l=[],c=0;c<s.length;c++)l.push(s[c].sub(e._mult(i)));a.push(l)}return a}function l(t,e){for(var r=[],n=new u(0,0),i=0;i<t.length;i++){for(var a=t[i],o=[],s=0;s<a.length;s++){var l=a[s-1],c=a[s],h=a[s+1],f=0===s?n:c.sub(l)._unit()._perp(),d=s===a.length-1?n:h.sub(c)._unit()._perp(),p=f._add(d)._unit(),m=p.xd.x+p.yd.y;p._mult(1/m),o.push(p._mult(e)._add(c))}r.push(o)}return r}var u=t(\"point-geometry\"),c=t(\"./load_geometry\"),h=t(\"./bucket\").EXTENT,f=t(\"feature-filter\"),d=t(\"../util/struct_array\"),p=t(\"grid-index\"),m=t(\"../util/dictionary_coder\"),v=t(\"vector-tile\"),g=t(\"pbf\"),y=t(\"../util/vectortile_to_geojson\"),b=t(\"../util/util\").arraysIntersect,x=t(\"../util/intersection_tests\"),_=x.multiPolygonIntersectsBufferedMultiPoint,w=x.multiPolygonIntersectsMultiPolygon,M=x.multiPolygonIntersectsBufferedMultiLine,k=new d({members:[{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]});e.exports=n,n.prototype.insert=function(t,e,r,n){var i=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(e,r,n);for(var a=c(t),o=0;o<a.length;o++){for(var s=a[o],l=[1/0,1/0,-1/0,-1/0],u=0;u<s.length;u++){var h=s[u];l[0]=Math.min(l[0],h.x),l[1]=Math.min(l[1],h.y),l[2]=Math.max(l[2],h.x),l[3]=Math.max(l[3],h.y)}this.grid.insert(i,l[0],l[1],l[2],l[3])}},n.prototype.setCollisionTile=function(t){this.collisionTile=t},n.prototype.serialize=function(){var t={coord:this.coord,overscaling:this.overscaling,grid:this.grid.toArrayBuffer(),featureIndexArray:this.featureIndexArray.serialize(),bucketLayerIDs:this.bucketLayerIDs};return{data:t,transferables:[t.grid,t.featureIndexArray.arrayBuffer]}},n.prototype.query=function(t,e){this.vtLayers\|\|(this.vtLayers=new v.VectorTile(new g(new Uint8Array(this.rawTileData))).layers,this.sourceLayerCoder=new m(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"]));var r={},n=t.params\|\|{},s=h/t.tileSize/t.scale,l=f(n.filter),c=0;for(var d in e){var p=e[d],y=p.paint,b=0;\"line\"===p.type?b=o(y)/2+Math.abs(y[\"line-offset\"])+i(y[\"line-translate\"]):\"fill\"===p.type?b=i(y[\"fill-translate\"]):\"circle\"===p.type&&(b=y[\"circle-radius\"]+i(y[\"circle-translate\"])),c=Math.max(c,bs)}for(var x=t.queryGeometry.map(function(t){return t.map(function(t){return new u(t.x,t.y)})}),_=1/0,w=1/0,M=-1/0,k=-1/0,A=0;A<x.length;A++)for(var T=x[A],S=0;S<T.length;S++){var E=T[S];_=Math.min(_,E.x),w=Math.min(w,E.y),M=Math.max(M,E.x),k=Math.max(k,E.y)}var L=this.grid.query(_-c,w-c,M+c,k+c);L.sort(a),this.filterMatching(r,L,this.featureIndexArray,x,l,n.layers,e,t.bearing,s);var C=this.collisionTile.queryRenderedSymbols(_,w,M,k,t.scale);return C.sort(),this.filterMatching(r,C,this.collisionTile.collisionBoxArray,x,l,n.layers,e,t.bearing,s),r},n.prototype.filterMatching=function(t,e,r,n,i,a,u,h,f){for(var d,p=0;p<e.length;p++){var m=e[p];if(m!==d){d=m;var v=r.get(m),g=this.bucketLayerIDs[v.bucketIndex];if(!a\|\|b(a,g)){var x=this.sourceLayerCoder.decode(v.sourceLayerIndex),k=this.vtLayers[x],A=k.feature(v.featureIndex);if(i(A))for(var T=null,S=0;S<g.length;S++){var E=g[S];if(!(a&&a.indexOf(E)<0)){var L=u[E];if(L){var C;if(\"symbol\"!==L.type){T\|\|(T=c(A));var I=L.paint;if(\"line\"===L.type){C=s(n,I[\"line-translate\"],I[\"line-translate-anchor\"],h,f);var z=o(I)/2f;if(I[\"line-offset\"]&&(T=l(T,I[\"line-offset\"]f)),!M(C,T,z))continue}else if(\"fill\"===L.type){if(C=s(n,I[\"fill-translate\"],I[\"fill-translate-anchor\"],h,f),!w(C,T))continue}else if(\"circle\"===L.type){C=s(n,I[\"circle-translate\"],I[\"circle-translate-anchor\"],h,f);var D=I[\"circle-radius\"]f;if(!_(C,T,D))continue}}var P=new y(A,this.z,this.x,this.y);P.layer=L.serialize({includeRefProperties:!0});var O=t[E];void 0===O&&(O=t[E]=[]),O.push(P)}}}}}}}},{\"../util/dictionary_coder\":432,\"../util/intersection_tests\":437,\"../util/struct_array\":440,\"../util/util\":442,\"../util/vectortile_to_geojson\":443,\"./bucket\":329,\"./load_geometry\":337,\"feature-filter\":132,\"grid-index\":287,pbf:478,\"point-geometry\":484,\"vector-tile\":550}],337:[function(t,e,r){\"use strict\";function n(t){return{min:-1Math.pow(2,t-1),max:Math.pow(2,t-1)-1}}var i=t(\"../util/util\"),a=t(\"./bucket\").EXTENT,o=t(\"assert\"),s={15:n(15),16:n(16)};e.exports=function(t,e){var r=s[e\|\|16];o(r);for(var n=a/t.extent,l=t.loadGeometry(),u=0;u<l.length;u++)for(var c=l[u],h=0;h<c.length;h++){var f=c[h];f.x=Math.round(f.xn),f.y=Math.round(f.yn),(f.x<r.min\|\|f.x>r.max\|\|f.y<r.min\|\|f.y>r.max)&&i.warnOnce(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\")}return l}},{\"../util/util\":442,\"./bucket\":329,assert:47}],338:[function(t,e,r){\"use strict\";function n(t,e,r){this.column=t,this.row=e,this.zoom=r}e.exports=n,n.prototype={clone:function(){return new n(this.column,this.row,this.zoom)},zoomTo:function(t){return this.clone()._zoomTo(t)},sub:function(t){return this.clone()._sub(t)},_zoomTo:function(t){var e=Math.pow(2,t-this.zoom);return this.column=e,this.row=e,this.zoom=t,this},_sub:function(t){return t=t.zoomTo(this.zoom),this.column-=t.column,this.row-=t.row,this}}},{}],339:[function(t,e,r){\"use strict\";function n(t,e){if(isNaN(t)\|\|isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90\|\|this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}e.exports=n\n", ";var i=t(\"../util/util\").wrap;n.prototype.wrap=function(){return new n(i(this.lng,-180,180),this.lat)},n.prototype.toArray=function(){return[this.lng,this.lat]},n.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},n.convert=function(t){return t instanceof n?t:Array.isArray(t)?new n(t[0],t[1]):t}},{\"../util/util\":442}],340:[function(t,e,r){\"use strict\";function n(t,e){t&&(e?this.extend(t).extend(e):4===t.length?this.extend([t[0],t[1]]).extend([t[2],t[3]]):this.extend(t[0]).extend(t[1]))}e.exports=n;var i=t(\"./lng_lat\");n.prototype={extend:function(t){var e,r,a=this._sw,o=this._ne;if(t instanceof i)e=t,r=t;else{if(!(t instanceof n))return t?this.extend(i.convert(t)\|\|n.convert(t)):this;if(e=t._sw,r=t._ne,!e\|\|!r)return this}return a\|\|o?(a.lng=Math.min(e.lng,a.lng),a.lat=Math.min(e.lat,a.lat),o.lng=Math.max(r.lng,o.lng),o.lat=Math.max(r.lat,o.lat)):(this._sw=new i(e.lng,e.lat),this._ne=new i(r.lng,r.lat)),this},getCenter:function(){return new i((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},getSouthWest:function(){return this._sw},getNorthEast:function(){return this._ne},getNorthWest:function(){return new i(this.getWest(),this.getNorth())},getSouthEast:function(){return new i(this.getEast(),this.getSouth())},getWest:function(){return this._sw.lng},getSouth:function(){return this._sw.lat},getEast:function(){return this._ne.lng},getNorth:function(){return this._ne.lat},toArray:function(){return[this._sw.toArray(),this._ne.toArray()]},toString:function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"}},n.convert=function(t){return!t\|\|t instanceof n?t:new n(t)}},{\"./lng_lat\":339}],341:[function(t,e,r){\"use strict\";function n(t,e){this.tileSize=512,this._minZoom=t\|\|0,this._maxZoom=e\|\|22,this.latRange=[-85.05113,85.05113],this.width=0,this.height=0,this._center=new i(0,0),this.zoom=0,this.angle=0,this._altitude=1.5,this._pitch=0,this._unmodified=!0}var i=t(\"./lng_lat\"),a=t(\"point-geometry\"),o=t(\"./coordinate\"),s=t(\"../util/util\").wrap,l=t(\"../util/interpolate\"),u=t(\"../source/tile_coord\"),c=t(\"../data/bucket\").EXTENT,h=t(\"gl-matrix\"),f=h.vec4,d=h.mat4,p=h.mat2;e.exports=n,n.prototype={get minZoom(){return this._minZoom},set minZoom(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},get maxZoom(){return this._maxZoom},set maxZoom(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},get worldSize(){return this.tileSizethis.scale},get centerPoint(){return this.size._div(2)},get size(){return new a(this.width,this.height)},get bearing(){return-this.angle/Math.PI180},set bearing(t){var e=-s(t,-180,180)Math.PI/180;this.angle!==e&&(this._unmodified=!1,this.angle=e,this._calcMatrices(),this.rotationMatrix=p.create(),p.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},get pitch(){return this._pitch/Math.PI180},set pitch(t){var e=Math.min(60,t)/180Math.PI;this._pitch!==e&&(this._unmodified=!1,this._pitch=e,this._calcMatrices())},get altitude(){return this._altitude},set altitude(t){var e=Math.max(.75,t);this._altitude!==e&&(this._unmodified=!1,this._altitude=e,this._calcMatrices())},get zoom(){return this._zoom},set zoom(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._calcMatrices(),this._constrain())},get center(){return this._center},set center(t){t.lat===this._center.lat&&t.lng===this._center.lng\|\|(this._unmodified=!1,this._center=t,this._calcMatrices(),this._constrain())},coveringZoomLevel:function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},coveringTiles:function(t){var e=this.coveringZoomLevel(t),r=e;if(e<t.minzoom)return[];e>t.maxzoom&&(e=t.maxzoom);var n=this,i=n.locationCoordinate(n.center)._zoomTo(e),o=new a(i.column-.5,i.row-.5);return u.cover(e,[n.pointCoordinate(new a(0,0))._zoomTo(e),n.pointCoordinate(new a(n.width,0))._zoomTo(e),n.pointCoordinate(new a(n.width,n.height))._zoomTo(e),n.pointCoordinate(new a(0,n.height))._zoomTo(e)],t.reparseOverscaled?r:e).sort(function(t,e){return o.dist(t)-o.dist(e)})},resize:function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._calcMatrices(),this._constrain()},get unmodified(){return this._unmodified},zoomScale:function(t){return Math.pow(2,t)},scaleZoom:function(t){return Math.log(t)/Math.LN2},project:function(t,e){return new a(this.lngX(t.lng,e),this.latY(t.lat,e))},unproject:function(t,e){return new i(this.xLng(t.x,e),this.yLat(t.y,e))},get x(){return this.lngX(this.center.lng)},get y(){return this.latY(this.center.lat)},get point(){return new a(this.x,this.y)},lngX:function(t,e){return(180+t)(e\|\|this.worldSize)/360},latY:function(t,e){return(180-180/Math.PIMath.log(Math.tan(Math.PI/4+tMath.PI/360)))(e\|\|this.worldSize)/360},xLng:function(t,e){return 360t/(e\|\|this.worldSize)-180},yLat:function(t,e){var r=180-360t/(e\|\|this.worldSize);return 360/Math.PIMath.atan(Math.exp(rMath.PI/180))-90},panBy:function(t){var e=this.centerPoint._add(t);this.center=this.pointLocation(e)},setLocationAtPoint:function(t,e){var r=this.locationCoordinate(t),n=this.pointCoordinate(e),i=this.pointCoordinate(this.centerPoint),a=n._sub(r);this._unmodified=!1,this.center=this.coordinateLocation(i._sub(a))},locationPoint:function(t){return this.coordinatePoint(this.locationCoordinate(t))},pointLocation:function(t){return this.coordinateLocation(this.pointCoordinate(t))},locationCoordinate:function(t){var e=this.zoomScale(this.tileZoom)/this.worldSize,r=i.convert(t);return new o(this.lngX(r.lng)e,this.latY(r.lat)e,this.tileZoom)},coordinateLocation:function(t){var e=this.zoomScale(t.zoom);return new i(this.xLng(t.column,e),this.yLat(t.row,e))},pointCoordinate:function(t){var e=[t.x,t.y,0,1],r=[t.x,t.y,1,1];f.transformMat4(e,e,this.pixelMatrixInverse),f.transformMat4(r,r,this.pixelMatrixInverse);var n=e[3],i=r[3],a=e[0]/n,s=r[0]/i,u=e[1]/n,c=r[1]/i,h=e[2]/n,d=r[2]/i,p=h===d?0:(0-h)/(d-h),m=this.worldSize/this.zoomScale(this.tileZoom);return new o(l(a,s,p)/m,l(u,c,p)/m,this.tileZoom)},coordinatePoint:function(t){var e=this.worldSize/this.zoomScale(t.zoom),r=[t.columne,t.rowe,0,1];return f.transformMat4(r,r,this.pixelMatrix),new a(r[0]/r[3],r[1]/r[3])},calculatePosMatrix:function(t,e){void 0===e&&(e=1/0),t instanceof u&&(t=t.toCoordinate(e));var r=Math.min(t.zoom,e),n=this.worldSize/Math.pow(2,r),i=new Float64Array(16);return d.identity(i),d.translate(i,i,[t.columnn,t.rown,0]),d.scale(i,i,[n/c,n/c,1]),d.multiply(i,this.projMatrix,i),new Float32Array(i)},_constrain:function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var t,e,r,n,i,o,s,l,u=this.size,c=this._unmodified;this.latRange&&(t=this.latY(this.latRange[1]),e=this.latY(this.latRange[0]),i=e-t<u.y?u.y/(e-t):0),this.lngRange&&(r=this.lngX(this.lngRange[0]),n=this.lngX(this.lngRange[1]),o=n-r<u.x?u.x/(n-r):0);var h=Math.max(o\|\|0,i\|\|0);if(h)return this.center=this.unproject(new a(o?(n+r)/2:this.x,i?(e+t)/2:this.y)),this.zoom+=this.scaleZoom(h),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var f=this.y,d=u.y/2;f-d<t&&(l=t+d),f+d>e&&(l=e-d)}if(this.lngRange){var p=this.x,m=u.x/2;p-m<r&&(s=r+m),p+m>n&&(s=n-m)}void 0===s&&void 0===l\|\|(this.center=this.unproject(new a(void 0!==s?s:this.x,void 0!==l?l:this.y))),this._unmodified=c,this._constraining=!1}},_calcMatrices:function(){if(this.height){var t=Math.atan(.5/this.altitude),e=Math.sin(t)this.altitude/Math.sin(Math.PI/2-this._pitch-t),r=Math.cos(Math.PI/2-this._pitch)e+this.altitude,n=new Float64Array(16);if(d.perspective(n,2Math.atan(this.height/2/this.altitude),this.width/this.height,.1,r),d.translate(n,n,[0,0,-this.altitude]),d.scale(n,n,[1,-1,1/this.height]),d.rotateX(n,n,this._pitch),d.rotateZ(n,n,this.angle),d.translate(n,n,[-this.x,-this.y,0]),this.projMatrix=n,n=d.create(),d.scale(n,n,[this.width/2,-this.height/2,1]),d.translate(n,n,[1,-1,0]),this.pixelMatrix=d.multiply(new Float64Array(16),n,this.projMatrix),!(n=d.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=n}}}},{\"../data/bucket\":329,\"../source/tile_coord\":369,\"../util/interpolate\":436,\"../util/util\":442,\"./coordinate\":338,\"./lng_lat\":339,\"gl-matrix\":193,\"point-geometry\":484}],342:[function(t,e,r){\"use strict\";var n={\" \":[16,[]],\"!\":[10,[5,21,5,7,-1,-1,5,2,4,1,5,0,6,1,5,2]],'\"':[16,[4,21,4,14,-1,-1,12,21,12,14]],\"#\":[21,[11,25,4,-7,-1,-1,17,25,10,-7,-1,-1,4,12,18,12,-1,-1,3,6,17,6]],$:[20,[8,25,8,-4,-1,-1,12,25,12,-4,-1,-1,17,18,15,20,12,21,8,21,5,20,3,18,3,16,4,14,5,13,7,12,13,10,15,9,16,8,17,6,17,3,15,1,12,0,8,0,5,1,3,3]],\"%\":[24,[21,21,3,0,-1,-1,8,21,10,19,10,17,9,15,7,14,5,14,3,16,3,18,4,20,6,21,8,21,10,20,13,19,16,19,19,20,21,21,-1,-1,17,7,15,6,14,4,14,2,16,0,18,0,20,1,21,3,21,5,19,7,17,7]],\"&\":[26,[23,12,23,13,22,14,21,14,20,13,19,11,17,6,15,3,13,1,11,0,7,0,5,1,4,2,3,4,3,6,4,8,5,9,12,13,13,14,14,16,14,18,13,20,11,21,9,20,8,18,8,16,9,13,11,10,16,3,18,1,20,0,22,0,23,1,23,2]],\"'\":[10,[5,19,4,20,5,21,6,20,6,18,5,16,4,15]],\"(\":[14,[11,25,9,23,7,20,5,16,4,11,4,7,5,2,7,-2,9,-5,11,-7]],\")\":[14,[3,25,5,23,7,20,9,16,10,11,10,7,9,2,7,-2,5,-5,3,-7]],\"\":[16,[8,21,8,9,-1,-1,3,18,13,12,-1,-1,13,18,3,12]],\"+\":[26,[13,18,13,0,-1,-1,4,9,22,9]],\",\":[10,[6,1,5,0,4,1,5,2,6,1,6,-1,5,-3,4,-4]],\"-\":[26,[4,9,22,9]],\".\":[10,[5,2,4,1,5,0,6,1,5,2]],\"/\":[22,[20,25,2,-7]],0:[20,[9,21,6,20,4,17,3,12,3,9,4,4,6,1,9,0,11,0,14,1,16,4,17,9,17,12,16,17,14,20,11,21,9,21]],1:[20,[6,17,8,18,11,21,11,0]],2:[20,[4,16,4,17,5,19,6,20,8,21,12,21,14,20,15,19,16,17,16,15,15,13,13,10,3,0,17,0]],3:[20,[5,21,16,21,10,13,13,13,15,12,16,11,17,8,17,6,16,3,14,1,11,0,8,0,5,1,4,2,3,4]],4:[20,[13,21,3,7,18,7,-1,-1,13,21,13,0]],5:[20,[15,21,5,21,4,12,5,13,8,14,11,14,14,13,16,11,17,8,17,6,16,3,14,1,11,0,8,0,5,1,4,2,3,4]],6:[20,[16,18,15,20,12,21,10,21,7,20,5,17,4,12,4,7,5,3,7,1,10,0,11,0,14,1,16,3,17,6,17,7,16,10,14,12,11,13,10,13,7,12,5,10,4,7]],7:[20,[17,21,7,0,-1,-1,3,21,17,21]],8:[20,[8,21,5,20,4,18,4,16,5,14,7,13,11,12,14,11,16,9,17,7,17,4,16,2,15,1,12,0,8,0,5,1,4,2,3,4,3,7,4,9,6,11,9,12,13,13,15,14,16,16,16,18,15,20,12,21,8,21]],9:[20,[16,14,15,11,13,9,10,8,9,8,6,9,4,11,3,14,3,15,4,18,6,20,9,21,10,21,13,20,15,18,16,14,16,9,15,4,13,1,10,0,8,0,5,1,4,3]],\":\":[10,[5,14,4,13,5,12,6,13,5,14,-1,-1,5,2,4,1,5,0,6,1,5,2]],\";\":[10,[5,14,4,13,5,12,6,13,5,14,-1,-1,6,1,5,0,4,1,5,2,6,1,6,-1,5,-3,4,-4]],\"<\":[24,[20,18,4,9,20,0]],\"=\":[26,[4,12,22,12,-1,-1,4,6,22,6]],\">\":[24,[4,18,20,9,4,0]],\"?\":[18,[3,16,3,17,4,19,5,20,7,21,11,21,13,20,14,19,15,17,15,15,14,13,13,12,9,10,9,7,-1,-1,9,2,8,1,9,0,10,1,9,2]],\"@\":[27,[18,13,17,15,15,16,12,16,10,15,9,14,8,11,8,8,9,6,11,5,14,5,16,6,17,8,-1,-1,12,16,10,14,9,11,9,8,10,6,11,5,-1,-1,18,16,17,8,17,6,19,5,21,5,23,7,24,10,24,12,23,15,22,17,20,19,18,20,15,21,12,21,9,20,7,19,5,17,4,15,3,12,3,9,4,6,5,4,7,2,9,1,12,0,15,0,18,1,20,2,21,3,-1,-1,19,16,18,8,18,6,19,5]],A:[18,[9,21,1,0,-1,-1,9,21,17,0,-1,-1,4,7,14,7]],B:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,15,17,13,16,12,13,11,-1,-1,4,11,13,11,16,10,17,9,18,7,18,4,17,2,16,1,13,0,4,0]],C:[21,[18,16,17,18,15,20,13,21,9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5]],D:[21,[4,21,4,0,-1,-1,4,21,11,21,14,20,16,18,17,16,18,13,18,8,17,5,16,3,14,1,11,0,4,0]],E:[19,[4,21,4,0,-1,-1,4,21,17,21,-1,-1,4,11,12,11,-1,-1,4,0,17,0]],F:[18,[4,21,4,0,-1,-1,4,21,17,21,-1,-1,4,11,12,11]],G:[21,[18,16,17,18,15,20,13,21,9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,18,8,-1,-1,13,8,18,8]],H:[22,[4,21,4,0,-1,-1,18,21,18,0,-1,-1,4,11,18,11]],I:[8,[4,21,4,0]],J:[16,[12,21,12,5,11,2,10,1,8,0,6,0,4,1,3,2,2,5,2,7]],K:[21,[4,21,4,0,-1,-1,18,21,4,7,-1,-1,9,12,18,0]],L:[17,[4,21,4,0,-1,-1,4,0,16,0]],M:[24,[4,21,4,0,-1,-1,4,21,12,0,-1,-1,20,21,12,0,-1,-1,20,21,20,0]],N:[22,[4,21,4,0,-1,-1,4,21,18,0,-1,-1,18,21,18,0]],O:[22,[9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,19,8,19,13,18,16,17,18,15,20,13,21,9,21]],P:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,14,17,12,16,11,13,10,4,10]],Q:[22,[9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,19,8,19,13,18,16,17,18,15,20,13,21,9,21,-1,-1,12,4,18,-2]],R:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,15,17,13,16,12,13,11,4,11,-1,-1,11,11,18,0]],S:[20,[17,18,15,20,12,21,8,21,5,20,3,18,3,16,4,14,5,13,7,12,13,10,15,9,16,8,17,6,17,3,15,1,12,0,8,0,5,1,3,3]],T:[16,[8,21,8,0,-1,-1,1,21,15,21]],U:[22,[4,21,4,6,5,3,7,1,10,0,12,0,15,1,17,3,18,6,18,21]],V:[18,[1,21,9,0,-1,-1,17,21,9,0]],W:[24,[2,21,7,0,-1,-1,12,21,7,0,-1,-1,12,21,17,0,-1,-1,22,21,17,0]],X:[20,[3,21,17,0,-1,-1,17,21,3,0]],Y:[18,[1,21,9,11,9,0,-1,-1,17,21,9,11]],Z:[20,[17,21,3,0,-1,-1,3,21,17,21,-1,-1,3,0,17,0]],\"[\":[14,[4,25,4,-7,-1,-1,5,25,5,-7,-1,-1,4,25,11,25,-1,-1,4,-7,11,-7]],\"\\\\\":[14,[0,21,14,-3]],\"]\":[14,[9,25,9,-7,-1,-1,10,25,10,-7,-1,-1,3,25,10,25,-1,-1,3,-7,10,-7]],\"^\":[16,[6,15,8,18,10,15,-1,-1,3,12,8,17,13,12,-1,-1,8,17,8,0]],_:[16,[0,-2,16,-2]],\"`\":[10,[6,21,5,20,4,18,4,16,5,15,6,16,5,17]],a:[19,[15,14,15,0,-1,-1,15,11,13,13,11,14,8,14,6,13,4,11,3,8,3,6,4,3,6,1,8,0,11,0,13,1,15,3]],b:[19,[4,21,4,0,-1,-1,4,11,6,13,8,14,11,14,13,13,15,11,16,8,16,6,15,3,13,1,11,0,8,0,6,1,4,3]],c:[18,[15,11,13,13,11,14,8,14,6,13,4,11,3,8,3,6,4,3,6,1,8,0,11,0,13,1,15,3]],d:[19,[15,21,15,0,-1,-1,15,11,13,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a,o,s,l,u,c,h,f,d=[];for(a=0,o=t.length;a<o;a++)if(u=n[t[a]]){for(f=null,s=0,l=u[1].length;s<l;s+=2)-1===u[1][s]&&-1===u[1][s+1]?f=null:(c=e+u[1][s]i,h=r-u[1][s+1]i,f&&d.push(f.x,f.y,c,h),f={x:c,y:h});e+=u[0]i}return d}},{}],343:[function(t,e,r){\"use strict\";var n=e.exports={};n.version=t(\"../package.json\").version,n.Map=t(\"./ui/map\"),n.Control=t(\"./ui/control/control\"),n.Navigation=t(\"./ui/control/navigation\"),n.Geolocate=t(\"./ui/control/geolocate\"),n.Attribution=t(\"./ui/control/attribution\"),n.Popup=t(\"./ui/popup\"),n.Marker=t(\"./ui/marker\"),n.Style=t(\"./style/style\"),n.LngLat=t(\"./geo/lng_lat\"),n.LngLatBounds=t(\"./geo/lng_lat_bounds\"),n.Point=t(\"point-geometry\"),n.Evented=t(\"./util/evented\"),n.util=t(\"./util/util\"),n.supported=t(\"./util/browser\").supported;var i=t(\"./util/ajax\");n.util.getJSON=i.getJSON,n.util.getArrayBuffer=i.getArrayBuffer;var a=t(\"./util/config\");n.config=a,Object.defineProperty(n,\"accessToken\",{get:function(){return a.ACCESS_TOKEN},set:function(t){a.ACCESS_TOKEN=t}})},{\"../package.json\":444,\"./geo/lng_lat\":339,\"./geo/lng_lat_bounds\":340,\"./style/style\":378,\"./ui/control/attribution\":409,\"./ui/control/control\":410,\"./ui/control/geolocate\":411,\"./ui/control/navigation\":412,\"./ui/map\":421,\"./ui/marker\":422,\"./ui/popup\":423,\"./util/ajax\":425,\"./util/browser\":426,\"./util/config\":431,\"./util/evented\":434,\"./util/util\":442,\"point-geometry\":484}],344:[function(t,e,r){\"use strict\";var n=t(\"assert\");e.exports=function(t){for(var e={define:{},initialize:{}},r=0;r<t.length;r++){var i=t[r];n(\"u_\"===i.name.slice(0,2));var a=\"{precision} \"+(1===i.components?\"float\":\"vec\"+i.components);e.define[i.name.slice(2)]=\"uniform \"+a+\" \"+i.name+\";\\n\",e.initialize[i.name.slice(2)]=a+\" \"+i.name.slice(2)+\" = \"+i.name+\";\\n\"}return e}},{assert:47}],345:[function(t,e,r){\"use strict\";function n(t,e,r){var n,s=t.gl,l=t.transform,u=r.paint[\"background-color\"],c=r.paint[\"background-pattern\"],h=r.paint[\"background-opacity\"],f=c?t.spriteAtlas.getPosition(c.from,!0):null,d=c?t.spriteAtlas.getPosition(c.to,!0):null;if(t.setDepthSublayer(0),f&&d){if(t.isOpaquePass)return;n=t.useProgram(\"pattern\"),s.uniform1i(n.u_image,0),s.uniform2fv(n.u_pattern_tl_a,f.tl),s.uniform2fv(n.u_pattern_br_a,f.br),s.uniform2fv(n.u_pattern_tl_b,d.tl),s.uniform2fv(n.u_pattern_br_b,d.br),s.uniform1f(n.u_opacity,h),s.uniform1f(n.u_mix,c.t),s.uniform2fv(n.u_pattern_size_a,f.size),s.uniform2fv(n.u_pattern_size_b,d.size),s.uniform1f(n.u_scale_a,c.fromScale),s.uniform1f(n.u_scale_b,c.toScale),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),t.tileExtentPatternVAO.bind(s,n,t.tileExtentBuffer)}else{if(t.isOpaquePass!==(1===u[3]))return;var p=a([{name:\"u_color\",components:4},{name:\"u_opacity\",components:1}]);n=t.useProgram(\"fill\",[],p,p),s.uniform4fv(n.u_color,u),s.uniform1f(n.u_opacity,h),t.tileExtentVAO.bind(s,n,t.tileExtentBuffer)}s.disable(s.STENCIL_TEST);for(var m=l.coveringTiles({tileSize:o}),v=0;v<m.length;v++){var g=m[v];if(f&&d){var y={coord:g,tileSize:o};s.uniform1f(n.u_tile_units_to_pixels,1/i(y,1,t.transform.tileZoom));var b=y.tileSizeMath.pow(2,t.transform.tileZoom-y.coord.z),x=b(y.coord.x+g.wMath.pow(2,y.coord.z)),_=by.coord.y;s.uniform2f(n.u_pixel_coord_upper,x>>16,_>>16),s.uniform2f(n.u_pixel_coord_lower,65535&x,65535&_)}s.uniformMatrix4fv(n.u_matrix,!1,t.transform.calculatePosMatrix(g)),s.drawArrays(s.TRIANGLE_STRIP,0,t.tileExtentBuffer.length)}s.stencilMask(0),s.stencilFunc(s.EQUAL,128,128)}var i=t(\"../source/pixels_to_tile_units\"),a=t(\"./create_uniform_pragmas\"),o=512;e.exports=n},{\"../source/pixels_to_tile_units\":363,\"./create_uniform_pragmas\":344}],346:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=t.gl;t.setDepthSublayer(0),t.depthMask(!1),a.disable(a.STENCIL_TEST);for(var o=0;o<n.length;o++){var s=n[o],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.circle;if(c){var h=u.paintAttributes.circle[r.id],f=t.useProgram(\"circle\",h.defines,h.vertexPragmas,h.fragmentPragmas);\"map\"===r.paint[\"circle-pitch-scale\"]?(a.uniform1i(f.u_scale_with_map,!0),a.uniform2f(f.u_extrude_scale,t.transform.pixelsToGLUnits[0]t.transform.altitude,t.transform.pixelsToGLUnits[1]t.transform.altitude)):(a.uniform1i(f.u_scale_with_map,!1),a.uniform2fv(f.u_extrude_scale,t.transform.pixelsToGLUnits)),a.uniform1f(f.u_devicepixelratio,i.devicePixelRatio),a.uniformMatrix4fv(f.u_matrix,!1,t.translatePosMatrix(s.posMatrix,l,r.paint[\"circle-translate\"],r.paint[\"circle-translate-anchor\"])),u.setUniforms(a,\"circle\",f,r,{zoom:t.transform.zoom});for(var d=0;d<c.length;d++){var p=c[d];p.vaos[r.id].bind(a,f,p.layoutVertexBuffer,p.elementBuffer,p.paintVertexBuffers[r.id]),a.drawElements(a.TRIANGLES,3p.elementBuffer.length,a.UNSIGNED_SHORT,0)}}}}}}var i=t(\"../util/browser\");e.exports=n},{\"../util/browser\":426}],347:[function(t,e,r){\"use strict\";function n(t,e,r,n){var i=t.gl;i.enable(i.STENCIL_TEST);for(var a=t.useProgram(\"collisionbox\"),o=0;o<n.length;o++){var s=n[o],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.collisionBox;if(c&&c.length){var h=c[0];0!==h.layoutVertexBuffer.length&&(i.uniformMatrix4fv(a.u_matrix,!1,s.posMatrix),t.enableTileClippingMask(s),t.lineWidth(1),i.uniform1f(a.u_scale,Math.pow(2,t.transform.zoom-l.coord.z)),i.uniform1f(a.u_zoom,10t.transform.zoom),i.uniform1f(a.u_maxzoom,10(l.coord.z+1)),h.vaos[r.id].bind(i,a,h.layoutVertexBuffer),i.drawArrays(i.LINES,0,h.layoutVertexBuffer.length))}}}}e.exports=n},{}],348:[function(t,e,r){\"use strict\";function n(t,e,r){if(!t.isOpaquePass&&t.options.debug)for(var n=0;n<r.length;n++)i(t,e,r[n])}function i(t,e,r){var n=t.gl;n.disable(n.STENCIL_TEST),t.lineWidth(1o.devicePixelRatio);var i=r.posMatrix,h=t.useProgram(\"debug\");n.uniformMatrix4fv(h.u_matrix,!1,i),n.uniform4f(h.u_color,1,0,0,1),t.debugVAO.bind(n,h,t.debugBuffer),n.drawArrays(n.LINE_STRIP,0,t.debugBuffer.length);for(var f=a(r.toString(),50,200,5),d=new t.PosArray,p=0;p<f.length;p+=2)d.emplaceBack(f[p],f[p+1]);var m=new u(d.serialize(),t.PosArray.serialize(),u.BufferType.VERTEX);(new c).bind(n,h,m),n.uniform4f(h.u_color,1,1,1,1);for(var v=e.getTile(r).tileSize,g=l/(Math.pow(2,t.transform.zoom-r.z)v),y=[[-1,-1],[-1,1],[1,-1],[1,1]],b=0;b<y.length;b++){var x=y[b];n.uniformMatrix4fv(h.u_matrix,!1,s.translate([],i,[gx[0],gx[1],0])),n.drawArrays(n.LINES,0,m.length)}n.uniform4f(h.u_color,0,0,0,1),n.uniformMatrix4fv(h.u_matrix,!1,i),n.drawArrays(n.LINES,0,m.length)}var a=t(\"../lib/debugtext\"),o=t(\"../util/browser\"),s=t(\"gl-matrix\").mat4,l=t(\"../data/bucket\").EXTENT,u=t(\"../data/buffer\"),c=t(\"./vertex_array_object\");e.exports=n},{\"../data/bucket\":329,\"../data/buffer\":334,\"../lib/debugtext\":342,\"../util/browser\":426,\"./vertex_array_object\":357,\"gl-matrix\":193}],349:[function(t,e,r){\"use strict\";function n(t,e,r,n){var o=t.gl;o.enable(o.STENCIL_TEST);var s;if(s=!r.paint[\"fill-pattern\"]&&(r.isPaintValueFeatureConstant(\"fill-color\")&&r.isPaintValueFeatureConstant(\"fill-opacity\")&&1===r.paint[\"fill-color\"][3]&&1===r.paint[\"fill-opacity\"]),t.isOpaquePass===s){t.setDepthSublayer(1);for(var l=0;l<n.length;l++)i(t,e,r,n[l])}if(!t.isOpaquePass&&r.paint[\"fill-antialias\"]){t.lineWidth(2),t.depthMask(!1);var u=r.getPaintProperty(\"fill-outline-color\");(u\|\|!r.paint[\"fill-pattern\"])&&u?t.setDepthSublayer(2):t.setDepthSublayer(0);for(var c=0;c<n.length;c++)a(t,e,r,n[c])}}function i(t,e,r,n){var i=e.getTile(n),a=i.getBucket(r);if(a){var s=a.bufferGroups.fill;if(s){var l,u=t.gl,c=r.paint[\"fill-pattern\"];if(c)l=t.useProgram(\"pattern\"),o(c,r.paint[\"fill-opacity\"],i,n,t,l),u.activeTexture(u.TEXTURE0),t.spriteAtlas.bind(u,!0);else{var h=a.paintAttributes.fill[r.id];l=t.useProgram(\"fill\",h.defines,h.vertexPragmas,h.fragmentPragmas),a.setUniforms(u,\"fill\",l,r,{zoom:t.transform.zoom})}u.uniformMatrix4fv(l.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint[\"fill-translate\"],r.paint[\"fill-translate-anchor\"])),t.enableTileClippingMask(n);for(var f=0;f<s.length;f++){var d=s[f];d.vaos[r.id].bind(u,l,d.layoutVertexBuffer,d.elementBuffer,d.paintVertexBuffers[r.id]),u.drawElements(u.TRIANGLES,d.elementBuffer.length,u.UNSIGNED_SHORT,0)}}}}function a(t,e,r,n){var i=e.getTile(n),a=i.getBucket(r);if(a){var s,l=t.gl,u=a.bufferGroups.fill,c=r.paint[\"fill-pattern\"],h=r.paint[\"fill-opacity\"],f=r.getPaintProperty(\"fill-outline-color\");if(c&&!f)s=t.useProgram(\"outlinepattern\"),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight);else{var d=a.paintAttributes.fill[r.id];s=t.useProgram(\"outline\",d.defines,d.vertexPragmas,d.fragmentPragmas),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight),l.uniform1f(s.u_opacity,h),a.setUniforms(l,\"fill\",s,r,{zoom:t.transform.zoom})}l.uniformMatrix4fv(s.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint[\"fill-translate\"],r.paint[\"fill-translate-anchor\"])),c&&o(c,h,i,n,t,s),t.enableTileClippingMask(n);for(var p=0;p<u.length;p++){var m=u[p];m.secondVaos[r.id].bind(l,s,m.layoutVertexBuffer,m.elementBuffer2,m.paintVertexBuffers[r.id]),l.drawElements(l.LINES,2m.elementBuffer2.length,l.UNSIGNED_SHORT,0)}}}function o(t,e,r,n,i,a){var o=i.gl,l=i.spriteAtlas.getPosition(t.from,!0),u=i.spriteAtlas.getPosition(t.to,!0);if(l&&u){o.uniform1i(a.u_image,0),o.uniform2fv(a.u_pattern_tl_a,l.tl),o.uniform2fv(a.u_pattern_br_a,l.br),o.uniform2fv(a.u_pattern_tl_b,u.tl),o.uniform2fv(a.u_pattern_br_b,u.br),o.uniform1f(a.u_opacity,e),o.uniform1f(a.u_mix,t.t),o.uniform1f(a.u_tile_units_to_pixels,1/s(r,1,i.transform.tileZoom)),o.uniform2fv(a.u_pattern_size_a,l.size),o.uniform2fv(a.u_pattern_size_b,u.size),o.uniform1f(a.u_scale_a,t.fromScale),o.uniform1f(a.u_scale_b,t.toScale);var c=r.tileSizeMath.pow(2,i.transform.tileZoom-r.coord.z),h=c(r.coord.x+n.wMath.pow(2,r.coord.z)),f=cr.coord.y;o.uniform2f(a.u_pixel_coord_upper,h>>16,f>>16),o.uniform2f(a.u_pixel_coord_lower,65535&h,65535&f),o.activeTexture(o.TEXTURE0),i.spriteAtlas.bind(o,!0)}}var s=t(\"../source/pixels_to_tile_units\");e.exports=n},{\"../source/pixels_to_tile_units\":363}],350:[function(t,e,r){\"use strict\";var n=t(\"../util/browser\"),i=t(\"gl-matrix\").mat2,a=t(\"../source/pixels_to_tile_units\");e.exports=function(t,e,r,o){if(!t.isOpaquePass){t.setDepthSublayer(0),t.depthMask(!1);var s=t.gl;if(s.enable(s.STENCIL_TEST),!(r.paint[\"line-width\"]<=0)){var l=1/n.devicePixelRatio,u=r.paint[\"line-blur\"]+l,c=r.paint[\"line-color\"],h=t.transform,f=i.create();i.scale(f,f,[1,Math.cos(h._pitch)]),i.rotate(f,f,t.transform.angle);var d,p,m,v,g,y=Math.sqrt(h.heighth.height/4(1+h.altitudeh.altitude)),b=h.height/2Math.tan(h._pitch),x=(y+b)/y-1,_=r.paint[\"line-dasharray\"],w=r.paint[\"line-pattern\"];if(_)d=t.useProgram(\"linesdfpattern\"),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform4fv(d.u_color,c),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]),p=t.lineAtlas.getDash(_.from,\"round\"===r.layout[\"line-cap\"]),m=t.lineAtlas.getDash(_.to,\"round\"===r.layout[\"line-cap\"]),s.uniform1i(d.u_image,0),s.activeTexture(s.TEXTURE0),t.lineAtlas.bind(s),s.uniform1f(d.u_tex_y_a,p.y),s.uniform1f(d.u_tex_y_b,m.y),s.uniform1f(d.u_mix,_.t),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f);else if(w){if(v=t.spriteAtlas.getPosition(w.from,!0),g=t.spriteAtlas.getPosition(w.to,!0),!v\|\|!g)return;d=t.useProgram(\"linepattern\"),s.uniform1i(d.u_image,0),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform2fv(d.u_pattern_tl_a,v.tl),s.uniform2fv(d.u_pattern_br_a,v.br),s.uniform2fv(d.u_pattern_tl_b,g.tl),s.uniform2fv(d.u_pattern_br_b,g.br),s.uniform1f(d.u_fade,w.t),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f)}else d=t.useProgram(\"line\"),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f),s.uniform4fv(d.u_color,c),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]);for(var M=0;M<o.length;M++){var k=o[M],A=e.getTile(k),T=A.getBucket(r);if(T){var S=T.bufferGroups.line;if(S){t.enableTileClippingMask(k);var E=t.translatePosMatrix(k.posMatrix,A,r.paint[\"line-translate\"],r.paint[\"line-translate-anchor\"]);s.uniformMatrix4fv(d.u_matrix,!1,E);var L=1/a(A,1,t.transform.zoom);if(_){var C=p.width_.fromScale,I=m.width_.toScale,z=[1/a(A,C,t.transform.tileZoom),-p.height/2],D=[1/a(A,I,t.transform.tileZoom),-m.height/2],P=t.lineAtlas.width/(256Math.min(C,I)n.devicePixelRatio)/2;s.uniform1f(d.u_ratio,L),s.uniform2fv(d.u_patternscale_a,z),s.uniform2fv(d.u_patternscale_b,D),s.uniform1f(d.u_sdfgamma,P)}else w?(s.uniform1f(d.u_ratio,L),s.uniform2fv(d.u_pattern_size_a,[a(A,v.size[0]w.fromScale,t.transform.tileZoom),g.size[1]]),s.uniform2fv(d.u_pattern_size_b,[a(A,g.size[0]w.toScale,t.transform.tileZoom),g.size[1]])):s.uniform1f(d.u_ratio,L);for(var O=0;O<S.length;O++){var R=S[O];R.vaos[r.id].bind(s,d,R.layoutVertexBuffer,R.elementBuffer),s.drawElements(s.TRIANGLES,3R.elementBuffer.length,s.UNSIGNED_SHORT,0)}}}}}}}},{\"../source/pixels_to_tile_units\":363,\"../util/browser\":426,\"gl-matrix\":193}],351:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=t.gl;a.enable(a.DEPTH_TEST),t.depthMask(!0),a.depthFunc(a.LESS);for(var o=n.length&&n[0].z,s=0;s<n.length;s++){var l=n[s];t.setDepthSublayer(l.z-o),i(t,e,r,l)}a.depthFunc(a.LEQUAL)}}function i(t,e,r,n){var i=t.gl;i.disable(i.STENCIL_TEST);var u=e.getTile(n),c=t.transform.calculatePosMatrix(n,e.maxzoom),h=t.useProgram(\"raster\");i.uniformMatrix4fv(h.u_matrix,!1,c),i.uniform1f(h.u_brightness_low,r.paint[\"raster-brightness-min\"]),i.uniform1f(h.u_brightness_high,r.paint[\"raster-brightness-max\"]),i.uniform1f(h.u_saturation_factor,s(r.paint[\"raster-saturation\"])),i.uniform1f(h.u_contrast_factor,o(r.paint[\"raster-contrast\"])),i.uniform3fv(h.u_spin_weights,a(r.paint[\"raster-hue-rotate\"]));var f,d,p=u.source&&u.source.findLoadedParent(n,0,{}),m=l(u,p,r,t.transform);i.activeTexture(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,u.texture),i.activeTexture(i.TEXTURE1),p?(i.bindTexture(i.TEXTURE_2D,p.texture),f=Math.pow(2,p.coord.z-u.coord.z),d=[u.coord.xf%1,u.coord.yf%1]):(i.bindTexture(i.TEXTURE_2D,u.texture),m[1]=0),i.uniform2fv(h.u_tl_parent,d\|\|[0,0]),i.uniform1f(h.u_scale_parent,f\|\|1),i.uniform1f(h.u_buffer_scale,1),i.uniform1f(h.u_opacity0,m[0]),i.uniform1f(h.u_opacity1,m[1]),i.uniform1i(h.u_image0,0),i.uniform1i(h.u_image1,1);var v=u.boundsBuffer\|\|t.rasterBoundsBuffer;(u.boundsVAO\|\|t.rasterBoundsVAO).bind(i,h,v),i.drawArrays(i.TRIANGLE_STRIP,0,v.length)}function a(t){t=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2r+1)/3,(-Math.sqrt(3)e-r+1)/3,(Math.sqrt(3)e-r+1)/3]}function o(t){return t>0?1/(1-t):1+t}function s(t){return t>0?1-1/(1.001-t):-t}function l(t,e,r,n){var i=[1,0],a=r.paint[\"raster-fade-duration\"];if(t.source&&a>0){var o=(new Date).getTime(),s=(o-t.timeAdded)/a,l=e?(o-e.timeAdded)/a:-1,c=n.coveringZoomLevel(t.source),h=!!e&&Math.abs(e.coord.z-c)>Math.abs(t.coord.z-c);!e\|\|h?(i[0]=u.clamp(s,0,1),i[1]=1-i[0]):(i[0]=u.clamp(1-l,0,1),i[1]=1-i[0])}var f=r.paint[\"raster-opacity\"];return i[0]=f,i[1]=f,i}var u=t(\"../util/util\"),c=t(\"../util/struct_array\");e.exports=n,n.RasterBoundsArray=new c({members:[{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]})},{\"../util/struct_array\":440,\"../util/util\":442}],352:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=!(r.layout[\"text-allow-overlap\"]\|\|r.layout[\"icon-allow-overlap\"]\|\|r.layout[\"text-ignore-placement\"]\|\|r.layout[\"icon-ignore-placement\"]),o=t.gl;a?o.disable(o.STENCIL_TEST):o.enable(o.STENCIL_TEST),t.setDepthSublayer(0),t.depthMask(!1),o.disable(o.DEPTH_TEST),i(t,e,r,n,!1,r.paint[\"icon-translate\"],r.paint[\"icon-translate-anchor\"],r.layout[\"icon-rotation-alignment\"],r.layout[\"icon-rotation-alignment\"],r.layout[\"icon-size\"],r.paint[\"icon-halo-width\"],r.paint[\"icon-halo-color\"],r.paint[\"icon-halo-blur\"],r.paint[\"icon-opacity\"],r.paint[\"icon-color\"]),\n", "i(t,e,r,n,!0,r.paint[\"text-translate\"],r.paint[\"text-translate-anchor\"],r.layout[\"text-rotation-alignment\"],r.layout[\"text-pitch-alignment\"],r.layout[\"text-size\"],r.paint[\"text-halo-width\"],r.paint[\"text-halo-color\"],r.paint[\"text-halo-blur\"],r.paint[\"text-opacity\"],r.paint[\"text-color\"]),o.enable(o.DEPTH_TEST),e.map.showCollisionBoxes&&s(t,e,r,n)}}function i(t,e,r,n,i,o,s,l,u,c,h,f,d,p,m){for(var v=0;v<n.length;v++){var g=e.getTile(n[v]),y=g.getBucket(r);if(y){var b=y.bufferGroups,x=i?b.glyph:b.icon;x.length&&(t.enableTileClippingMask(n[v]),a(t,r,n[v].posMatrix,g,y,x,i,i\|\|y.sdfIcons,!i&&y.iconsNeedLinear,i?y.adjustedTextSize:y.adjustedIconSize,y.fontstack,o,s,l,u,c,h,f,d,p,m))}}}function a(t,e,r,n,i,a,s,u,c,h,f,d,p,m,v,g,y,b,x,_,w){var M,k,A,T=t.gl,S=t.transform,E=\"map\"===m,L=\"map\"===v,C=s?24:1,I=g/C;if(L?(k=l(n,1,t.transform.zoom)I,A=1/Math.cos(S._pitch),M=[k,k]):(k=t.transform.altitudeI,A=1,M=[S.pixelsToGLUnits[0]k,S.pixelsToGLUnits[1]k]),s\|\|t.style.sprite.loaded()){var z=t.useProgram(u?\"sdf\":\"icon\");if(T.uniformMatrix4fv(z.u_matrix,!1,t.translatePosMatrix(r,n,d,p)),T.uniform1i(z.u_rotate_with_map,E),T.uniform1i(z.u_pitch_with_map,L),T.uniform2fv(z.u_extrude_scale,M),T.activeTexture(T.TEXTURE0),T.uniform1i(z.u_texture,0),s){var D=f&&t.glyphSource.getGlyphAtlas(f);if(!D)return;D.updateTexture(T),T.uniform2f(z.u_texsize,D.width/4,D.height/4)}else{var P=t.options.rotating\|\|t.options.zooming,O=1!==I\|\|o.devicePixelRatio!==t.spriteAtlas.pixelRatio\|\|c,R=L\|\|t.transform.pitch;t.spriteAtlas.bind(T,u\|\|P\|\|O\|\|R),T.uniform2f(z.u_texsize,t.spriteAtlas.width/4,t.spriteAtlas.height/4)}var F=Math.log(g/h)/Math.LN2\|\|0;T.uniform1f(z.u_zoom,10(t.transform.zoom-F)),T.activeTexture(T.TEXTURE1),t.frameHistory.bind(T),T.uniform1i(z.u_fadetexture,1);var j;if(u){var N=.105C/g/o.devicePixelRatio;if(y){T.uniform1f(z.u_gamma,(1.19x/I/8+N)A),T.uniform4fv(z.u_color,b),T.uniform1f(z.u_opacity,_),T.uniform1f(z.u_buffer,(6-y/I)/8);for(var B=0;B<a.length;B++)j=a[B],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3j.elementBuffer.length,T.UNSIGNED_SHORT,0)}T.uniform1f(z.u_gamma,NA),T.uniform4fv(z.u_color,w),T.uniform1f(z.u_opacity,_),T.uniform1f(z.u_buffer,.75),T.uniform1f(z.u_pitch,S.pitch/3602Math.PI),T.uniform1f(z.u_bearing,S.bearing/3602Math.PI),T.uniform1f(z.u_aspect_ratio,S.width/S.height);for(var U=0;U<a.length;U++)j=a[U],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3j.elementBuffer.length,T.UNSIGNED_SHORT,0)}else{T.uniform1f(z.u_opacity,_);for(var V=0;V<a.length;V++)j=a[V],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3j.elementBuffer.length,T.UNSIGNED_SHORT,0)}}}var o=t(\"../util/browser\"),s=t(\"./draw_collision_debug\"),l=t(\"../source/pixels_to_tile_units\");e.exports=n},{\"../source/pixels_to_tile_units\":363,\"../util/browser\":426,\"./draw_collision_debug\":347}],353:[function(t,e,r){\"use strict\";function n(){this.changeTimes=new Float64Array(256),this.changeOpacities=new Uint8Array(256),this.opacities=new Uint8ClampedArray(256),this.array=new Uint8Array(this.opacities.buffer),this.fadeDuration=300,this.previousZoom=0,this.firstFrame=!0}e.exports=n,n.prototype.record=function(t){var e=Date.now();this.firstFrame&&(e=0,this.firstFrame=!1),t=Math.floor(10t);var r;if(t<this.previousZoom)for(r=t+1;r<=this.previousZoom;r++)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];else for(r=t;r>this.previousZoom;r--)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];for(r=0;r<256;r++){var n=e-this.changeTimes[r],i=n/this.fadeDuration255;this.opacities[r]=r<=t?this.changeOpacities[r]+i:this.changeOpacities[r]-i}this.changed=!0,this.previousZoom=t},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.changed&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,256,1,t.ALPHA,t.UNSIGNED_BYTE,this.array),this.changed=!1)):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,256,1,0,t.ALPHA,t.UNSIGNED_BYTE,this.array))}},{}],354:[function(t,e,r){\"use strict\";function n(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.widththis.heightthis.bytes),this.positions={}}var i=t(\"../util/util\");e.exports=n,n.prototype.setSprite=function(t){this.sprite=t},n.prototype.getDash=function(t,e){var r=t.join(\",\")+e;return this.positions[r]\|\|(this.positions[r]=this.addDash(t,e)),this.positions[r]},n.prototype.addDash=function(t,e){var r=e?7:0,n=2r+1;if(this.nextRow+n>this.height)return i.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<t.length;o++)a+=t[o];for(var s=this.width/a,l=s/2,u=t.length%2==1,c=-r;c<=r;c++)for(var h=this.nextRow+r+c,f=this.widthh,d=u?-t[t.length-1]:0,p=t[0],m=1,v=0;v<this.width;v++){for(;p<v/s;)d=p,p+=t[m],u&&m===t.length-1&&(p+=t[0]),m++;var g,y=Math.abs(v-ds),b=Math.abs(v-ps),x=Math.min(y,b),_=m%2==1;if(e){var w=r?c/r(l+1):0;if(_){var M=l-Math.abs(w);g=Math.sqrt(xx+MM)}else g=l-Math.sqrt(xx+ww)}else g=(_?1:-1)x;this.data[3+4(f+v)]=Math.max(0,Math.min(255,g+128))}var k={y:(this.nextRow+r+.5)/this.height,height:2r/this.height,width:a};return this.nextRow+=n,this.dirty=!0,k},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.RGBA,t.UNSIGNED_BYTE,this.data))):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width,this.height,0,t.RGBA,t.UNSIGNED_BYTE,this.data))}},{\"../util/util\":442}],355:[function(t,e,r){\"use strict\";function n(t,e){this.gl=t,this.transform=e,this.reusableTextures={},this.preFbos={},this.frameHistory=new o,this.setup(),this.numSublayers=s.maxUnderzooming+s.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.lineWidthRange=t.getParameter(t.ALIASED_LINE_WIDTH_RANGE)}var i=t(\"../util/browser\"),a=t(\"gl-matrix\").mat4,o=t(\"./frame_history\"),s=t(\"../source/source_cache\"),l=t(\"../data/bucket\").EXTENT,u=t(\"../source/pixels_to_tile_units\"),c=t(\"../util/util\"),h=t(\"../util/struct_array\"),f=t(\"../data/buffer\"),d=t(\"./vertex_array_object\"),p=t(\"./draw_raster\").RasterBoundsArray,m=t(\"./create_uniform_pragmas\");e.exports=n,c.extend(n.prototype,t(\"./painter/use_program\")),n.prototype.resize=function(t,e){var r=this.gl;this.width=ti.devicePixelRatio,this.height=ei.devicePixelRatio,r.viewport(0,0,this.width,this.height)},n.prototype.setup=function(){var t=this.gl;t.verbose=!0,t.enable(t.BLEND),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.enable(t.STENCIL_TEST),t.enable(t.DEPTH_TEST),t.depthFunc(t.LEQUAL),this._depthMask=!1,t.depthMask(!1);var e=this.PosArray=new h({members:[{name:\"a_pos\",type:\"Int16\",components:2}]}),r=new e;r.emplaceBack(0,0),r.emplaceBack(l,0),r.emplaceBack(0,l),r.emplaceBack(l,l),this.tileExtentBuffer=new f(r.serialize(),e.serialize(),f.BufferType.VERTEX),this.tileExtentVAO=new d,this.tileExtentPatternVAO=new d;var n=new e;n.emplaceBack(0,0),n.emplaceBack(l,0),n.emplaceBack(l,l),n.emplaceBack(0,l),n.emplaceBack(0,0),this.debugBuffer=new f(n.serialize(),e.serialize(),f.BufferType.VERTEX),this.debugVAO=new d;var i=new p;i.emplaceBack(0,0,0,0),i.emplaceBack(l,0,32767,0),i.emplaceBack(0,l,0,32767),i.emplaceBack(l,l,32767,32767),this.rasterBoundsBuffer=new f(i.serialize(),p.serialize(),f.BufferType.VERTEX),this.rasterBoundsVAO=new d},n.prototype.clearColor=function(){var t=this.gl;t.clearColor(0,0,0,0),t.clear(t.COLOR_BUFFER_BIT)},n.prototype.clearStencil=function(){var t=this.gl;t.clearStencil(0),t.stencilMask(255),t.clear(t.STENCIL_BUFFER_BIT)},n.prototype.clearDepth=function(){var t=this.gl;t.clearDepth(1),this.depthMask(!0),t.clear(t.DEPTH_BUFFER_BIT)},n.prototype._renderTileClippingMasks=function(t){var e=this.gl;e.colorMask(!1,!1,!1,!1),this.depthMask(!1),e.disable(e.DEPTH_TEST),e.enable(e.STENCIL_TEST),e.stencilMask(248),e.stencilOp(e.KEEP,e.KEEP,e.REPLACE);var r=1;this._tileClippingMaskIDs={};for(var n=0;n<t.length;n++){var i=t[n],a=this._tileClippingMaskIDs[i.id]=r++<<3;e.stencilFunc(e.ALWAYS,a,248);var o=m([{name:\"u_color\",components:4},{name:\"u_opacity\",components:1}]),s=this.useProgram(\"fill\",[],o,o);e.uniformMatrix4fv(s.u_matrix,!1,i.posMatrix),this.tileExtentVAO.bind(e,s,this.tileExtentBuffer),e.drawArrays(e.TRIANGLE_STRIP,0,this.tileExtentBuffer.length)}e.stencilMask(0),e.colorMask(!0,!0,!0,!0),this.depthMask(!0),e.enable(e.DEPTH_TEST)},n.prototype.enableTileClippingMask=function(t){var e=this.gl;e.stencilFunc(e.EQUAL,this._tileClippingMaskIDs[t.id],248)},n.prototype.prepareBuffers=function(){},n.prototype.bindDefaultFramebuffer=function(){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,null)};var v={symbol:t(\"./draw_symbol\"),circle:t(\"./draw_circle\"),line:t(\"./draw_line\"),fill:t(\"./draw_fill\"),raster:t(\"./draw_raster\"),background:t(\"./draw_background\"),debug:t(\"./draw_debug\")};n.prototype.render=function(t,e){this.style=t,this.options=e,this.lineAtlas=t.lineAtlas,this.spriteAtlas=t.spriteAtlas,this.spriteAtlas.setSprite(t.sprite),this.glyphSource=t.glyphSource,this.frameHistory.record(this.transform.zoom),this.prepareBuffers(),this.clearColor(),this.clearDepth(),this.showOverdrawInspector(e.showOverdrawInspector),this.depthRange=(t._order.length+2)this.numSublayersthis.depthEpsilon,this.renderPass({isOpaquePass:!0}),this.renderPass({isOpaquePass:!1})},n.prototype.renderPass=function(t){var e=this.style._groups,r=t.isOpaquePass;this.currentLayer=r?this.style._order.length:-1;for(var n=0;n<e.length;n++){var i,a=e[r?e.length-1-n:n],o=this.style.sources[a.source],s=[];if(o){for(s=o.getVisibleCoordinates(),i=0;i<s.length;i++)s[i].posMatrix=this.transform.calculatePosMatrix(s[i],o.maxzoom);this.clearStencil(),o.prepare&&o.prepare(),o.isTileClipped&&this._renderTileClippingMasks(s)}for(r?(this._showOverdrawInspector\|\|this.gl.disable(this.gl.BLEND),this.isOpaquePass=!0):(this.gl.enable(this.gl.BLEND),this.isOpaquePass=!1,s.reverse()),i=0;i<a.length;i++){var l=a[r?a.length-1-i:i];this.currentLayer+=r?-1:1,this.renderLayer(this,o,l,s)}o&&v.debug(this,o,s)}},n.prototype.depthMask=function(t){t!==this._depthMask&&(this._depthMask=t,this.gl.depthMask(t))},n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)\|\|(\"background\"===r.type\|\|n.length)&&(this.id=r.id,v[r.type](t,e,r,n))},n.prototype.setDepthSublayer=function(t){var e=1-((1+this.currentLayer)this.numSublayers+t)this.depthEpsilon,r=e-1+this.depthRange;this.gl.depthRange(r,e)},n.prototype.translatePosMatrix=function(t,e,r,n){if(!r[0]&&!r[1])return t;if(\"viewport\"===n){var i=Math.sin(-this.transform.angle),o=Math.cos(-this.transform.angle);r=[r[0]o-r[1]i,r[0]i+r[1]o]}var s=[u(e,r[0],this.transform.zoom),u(e,r[1],this.transform.zoom),0],l=new Float32Array(16);return a.translate(l,t,s),l},n.prototype.saveTexture=function(t){var e=this.reusableTextures[t.size];e?e.push(t):this.reusableTextures[t.size]=[t]},n.prototype.getTexture=function(t){var e=this.reusableTextures[t];return e&&e.length>0?e.pop():null},n.prototype.lineWidth=function(t){this.gl.lineWidth(c.clamp(t,this.lineWidthRange[0],this.lineWidthRange[1]))},n.prototype.showOverdrawInspector=function(t){if(t\|\|this._showOverdrawInspector){this._showOverdrawInspector=t;var e=this.gl;if(t){e.blendFunc(e.CONSTANT_COLOR,e.ONE);e.blendColor(1/8,1/8,1/8,0),e.clearColor(0,0,0,1),e.clear(e.COLOR_BUFFER_BIT)}else e.blendFunc(e.ONE,e.ONE_MINUS_SRC_ALPHA)}}},{\"../data/bucket\":329,\"../data/buffer\":334,\"../source/pixels_to_tile_units\":363,\"../source/source_cache\":367,\"../util/browser\":426,\"../util/struct_array\":440,\"../util/util\":442,\"./create_uniform_pragmas\":344,\"./draw_background\":345,\"./draw_circle\":346,\"./draw_debug\":348,\"./draw_fill\":349,\"./draw_line\":350,\"./draw_raster\":351,\"./draw_symbol\":352,\"./frame_history\":353,\"./painter/use_program\":356,\"./vertex_array_object\":357,\"gl-matrix\":193}],356:[function(t,e,r){\"use strict\";function n(t,e){return t.replace(/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,function(t,r,n,i,a){return e[r][a].replace(/{type}/g,i).replace(/{precision}/g,n)})}var i=t(\"assert\"),a=t(\"../../util/util\"),o=t(\"mapbox-gl-shaders\"),s=o.util;e.exports._createProgram=function(t,e,r,l){for(var u=this.gl,c=u.createProgram(),h=o[t],f=\"#define MAPBOX_GL_JS;\\n\",d=0;d<e.length;d++)f+=\"#define \"+e[d]+\";\\n\";var p=u.createShader(u.FRAGMENT_SHADER);u.shaderSource(p,n(f+h.fragmentSource,l)),u.compileShader(p),i(u.getShaderParameter(p,u.COMPILE_STATUS),u.getShaderInfoLog(p)),u.attachShader(c,p);var m=u.createShader(u.VERTEX_SHADER);u.shaderSource(m,n(f+s+h.vertexSource,r)),u.compileShader(m),i(u.getShaderParameter(m,u.COMPILE_STATUS),u.getShaderInfoLog(m)),u.attachShader(c,m),u.linkProgram(c),i(u.getProgramParameter(c,u.LINK_STATUS),u.getProgramInfoLog(c));for(var v={},g=u.getProgramParameter(c,u.ACTIVE_ATTRIBUTES),y=0;y<g;y++){var b=u.getActiveAttrib(c,y);v[b.name]=u.getAttribLocation(c,b.name)}for(var x={},_=u.getProgramParameter(c,u.ACTIVE_UNIFORMS),w=0;w<_;w++){var M=u.getActiveUniform(c,w);x[M.name]=u.getUniformLocation(c,M.name)}return a.extend({program:c,definition:h,attributes:v,numAttributes:g},v,x)},e.exports._createProgramCached=function(t,e,r,n){this.cache=this.cache\|\|{};var i=JSON.stringify({name:t,defines:e,vertexPragmas:r,fragmentPragmas:n});return this.cache[i]\|\|(this.cache[i]=this._createProgram(t,e,r,n)),this.cache[i]},e.exports.useProgram=function(t,e,r,n){var i=this.gl;e=e\|\|[],this._showOverdrawInspector&&(e=e.concat(\"OVERDRAW_INSPECTOR\"));var a=this._createProgramCached(t,e,r,n);return this.currentProgram!==a&&(i.useProgram(a.program),this.currentProgram=a),a}},{\"../../util/util\":442,assert:47,\"mapbox-gl-shaders\":303}],357:[function(t,e,r){\"use strict\";function n(){this.boundProgram=null,this.boundVertexBuffer=null,this.boundVertexBuffer2=null,this.boundElementBuffer=null,this.vao=null}var i=t(\"assert\");e.exports=n,n.prototype.bind=function(t,e,r,n,i){void 0===t.extVertexArrayObject&&(t.extVertexArrayObject=t.getExtension(\"OES_vertex_array_object\"));var a=!this.vao\|\|this.boundProgram!==e\|\|this.boundVertexBuffer!==r\|\|this.boundVertexBuffer2!==i\|\|this.boundElementBuffer!==n;!t.extVertexArrayObject\|\|a?this.freshBind(t,e,r,n,i):t.extVertexArrayObject.bindVertexArrayOES(this.vao)},n.prototype.freshBind=function(t,e,r,n,a){var o,s=e.numAttributes;if(t.extVertexArrayObject)this.vao&&this.destroy(t),this.vao=t.extVertexArrayObject.createVertexArrayOES(),t.extVertexArrayObject.bindVertexArrayOES(this.vao),o=0,this.boundProgram=e,this.boundVertexBuffer=r,this.boundVertexBuffer2=a,this.boundElementBuffer=n;else{o=t.currentNumAttributes\|\|0;for(var l=s;l<o;l++)i(0!==l),t.disableVertexAttribArray(l)}for(var u=o;u<s;u++)t.enableVertexAttribArray(u);r.bind(t),r.setVertexAttribPointers(t,e),a&&(a.bind(t),a.setVertexAttribPointers(t,e)),n&&n.bind(t),t.currentNumAttributes=s},n.prototype.unbind=function(t){var e=t.extVertexArrayObject;e&&e.bindVertexArrayOES(null)},n.prototype.destroy=function(t){var e=t.extVertexArrayObject;e&&this.vao&&(e.deleteVertexArrayOES(this.vao),this.vao=null)}},{assert:47}],358:[function(t,e,r){\"use strict\";function n(t,e,r){e=e\|\|{},this.id=t,this.dispatcher=r,this._data=e.data,void 0!==e.maxzoom&&(this.maxzoom=e.maxzoom),e.type&&(this.type=e.type);var n=s/this.tileSize;this.workerOptions=a.extend({source:this.id,cluster:e.cluster\|\|!1,geojsonVtOptions:{buffer:(void 0!==e.buffer?e.buffer:128)n,tolerance:(void 0!==e.tolerance?e.tolerance:.375)n,extent:s,maxZoom:this.maxzoom},superclusterOptions:{maxZoom:Math.min(e.clusterMaxZoom,this.maxzoom-1)\|\|this.maxzoom-1,extent:s,radius:(e.clusterRadius\|\|50)n,log:!1}},e.workerOptions),this._updateWorkerData(function(t){if(t)return void this.fire(\"error\",{error:t});this.fire(\"load\")}.bind(this))}var i=t(\"../util/evented\"),a=t(\"../util/util\"),o=t(\"resolve-url\"),s=t(\"../data/bucket\").EXTENT;e.exports=n,n.prototype=a.inherit(i,{type:\"geojson\",minzoom:0,maxzoom:18,tileSize:512,isTileClipped:!0,reparseOverscaled:!0,onAdd:function(t){this.map=t},setData:function(t){return this._data=t,this._updateWorkerData(function(t){if(t)return this.fire(\"error\",{error:t});this.fire(\"change\")}.bind(this)),this},_updateWorkerData:function(t){var e=a.extend({},this.workerOptions),r=this._data;\"string\"==typeof r?e.url=\"undefined\"!=typeof window?o(window.location.href,r):r:e.data=JSON.stringify(r),this.workerID=this.dispatcher.send(this.type+\".loadData\",e,function(e){this._loaded=!0,t(e)}.bind(this))},loadTile:function(t,e){var r=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,n={type:this.type,uid:t.uid,coord:t.coord,zoom:t.coord.z,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,overscaling:r,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID=this.dispatcher.send(\"load tile\",n,function(r,n){if(t.unloadVectorData(this.map.painter),!t.aborted)return r?e(r):(t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null))}.bind(this),this.workerID)},abortTile:function(t){t.aborted=!0},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send(\"remove tile\",{uid:t.uid,source:this.id},function(){},t.workerID)},serialize:function(){return{type:this.type,data:this._data}}})},{\"../data/bucket\":329,\"../util/evented\":434,\"../util/util\":442,\"resolve-url\":501}],359:[function(t,e,r){\"use strict\";function n(t,e,r){r&&(this.loadGeoJSON=r),h.call(this,t,e)}var i=t(\"../util/util\"),a=t(\"../util/ajax\"),o=t(\"geojson-rewind\"),s=t(\"./geojson_wrapper\"),l=t(\"vt-pbf\"),u=t(\"supercluster\"),c=t(\"geojson-vt\"),h=t(\"./vector_tile_worker_source\");e.exports=n,n.prototype=i.inherit(h,{_geoJSONIndexes:{},loadVectorData:function(t,e){var r=t.source,n=t.coord;if(!this._geoJSONIndexes[r])return e(null,null);var i=this._geoJSONIndexes[r].getTile(Math.min(n.z,t.maxZoom),n.x,n.y);if(!i)return e(null,null);var a=new s(i.features);a.name=\"_geojsonTileLayer\";var o=l({layers:{_geojsonTileLayer:a}});0===o.byteOffset&&o.byteLength===o.buffer.byteLength\|\|(o=new Uint8Array(o)),e(null,{tile:a,rawTileData:o.buffer})},loadData:function(t,e){var r=function(r,n){return r?e(r):\"object\"!=typeof n?e(new Error(\"Input data is not a valid GeoJSON object.\")):(o(n,!0),void this._indexData(n,t,function(r,n){if(r)return e(r);this._geoJSONIndexes[t.source]=n,e(null)}.bind(this)))}.bind(this);this.loadGeoJSON(t,r)},loadGeoJSON:function(t,e){if(t.url)a.getJSON(t.url,e);else{if(\"string\"!=typeof t.data)return e(new Error(\"Input data is not a valid GeoJSON object.\"));try{return e(null,JSON.parse(t.data))}catch(t){return e(new Error(\"Input data is not a valid GeoJSON object.\"))}}},_indexData:function(t,e,r){try{e.cluster?r(null,u(e.superclusterOptions).load(t.features)):r(null,c(t,e.geojsonVtOptions))}catch(t){return r(t)}}})},{\"../util/ajax\":425,\"../util/util\":442,\"./geojson_wrapper\":360,\"./vector_tile_worker_source\":371,\"geojson-rewind\":138,\"geojson-vt\":142,supercluster:529,\"vt-pbf\":556}],360:[function(t,e,r){\"use strict\";function n(t){this.features=t,this.length=t.length,this.extent=s}function i(t){if(this.type=t.type,1===t.type){this.rawGeometry=[];for(var e=0;e<t.geometry.length;e++)this.rawGeometry.push([t.geometry[e]])}else this.rawGeometry=t.geometry;this.properties=t.tags,this.extent=s}var a=t(\"point-geometry\"),o=t(\"vector-tile\").VectorTileFeature,s=t(\"../data/bucket\").EXTENT;e.exports=n,n.prototype.feature=function(t){return new i(this.features[t])},i.prototype.loadGeometry=function(){var t=this.rawGeometry;this.geometry=[];for(var e=0;e<t.length;e++){for(var r=t[e],n=[],i=0;i<r.length;i++)n.push(new a(r[i][0],r[i][1]));this.geometry.push(n)}return this.geometry},i.prototype.bbox=function(){this.geometry\|\|this.loadGeometry();for(var t=this.geometry,e=1/0,r=-1/0,n=1/0,i=-1/0,a=0;a<t.length;a++)for(var o=t[a],s=0;s<o.length;s++){var l=o[s];e=Math.min(e,l.x),r=Math.max(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.y)}return[e,n,r,i]},i.prototype.toGeoJSON=o.prototype.toGeoJSON},{\"../data/bucket\":329,\"point-geometry\":484,\"vector-tile\":550}],361:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r,this.url=e.url,this.coordinates=e.coordinates,u.getImage(e.url,function(t,r){if(t)return this.fire(\"error\",{error:t});this.image=r,this.image.addEventListener(\"load\",function(){this.map._rerender()}.bind(this)),this._loaded=!0,this.fire(\"load\"),this.map&&this.setCoordinates(e.coordinates)}.bind(this))}var i=t(\"../util/util\"),a=t(\"./tile_coord\"),o=t(\"../geo/lng_lat\"),s=t(\"point-geometry\"),l=t(\"../util/evented\"),u=t(\"../util/ajax\"),c=t(\"../data/bucket\").EXTENT,h=t(\"../render/draw_raster\").RasterBoundsArray,f=t(\"../data/buffer\"),d=t(\"../render/vertex_array_object\");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,onAdd:function(t){this.map=t,this.image&&this.setCoordinates(this.coordinates)},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(o.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new a(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)c),Math.round((e.row-n.row)c))}),this.fire(\"change\"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=new h;e.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),e.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,32767,0),e.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,32767),e.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,32767,32767),this.tile.buckets={},this.tile.boundsBuffer=new f(e.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new d,this.tile.state=\"loaded\"},prepare:function(){if(this._loaded&&this.image&&this.image.complete&&this.tile){var t=this.map.painter,e=t.gl;this._prepared?(e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,this.image)):(this.tile.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,this.image))}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state=\"errored\",e(null))},serialize:function(){return{type:\"image\",urls:this.url,coordinates:this.coordinates}}})},{\"../data/bucket\":329,\"../data/buffer\":334,\"../geo/lng_lat\":339,\"../render/draw_raster\":351,\"../render/vertex_array_object\":357,\"../util/ajax\":425,\"../util/evented\":434,\"../util/util\":442,\"./tile_coord\":369,\"point-geometry\":484}],362:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i=t(\"../util/ajax\"),a=t(\"../util/browser\"),o=t(\"../util/mapbox\").normalizeSourceURL;e.exports=function(t,e){var r=function(t,r){if(t)return e(t);var i=n.pick(r,[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\"]);r.vector_layers&&(i.vectorLayers=r.vector_layers,i.vectorLayerIds=i.vectorLayers.map(function(t){return t.id})),e(null,i)};t.url?i.getJSON(o(t.url),r):a.frame(r.bind(null,null,t))}},{\"../util/ajax\":425,\"../util/browser\":426,\"../util/mapbox\":439,\"../util/util\":442}],363:[function(t,e,r){\"use strict\";var n=t(\"../data/bucket\");e.exports=function(t,e,r){return e(n.EXTENT/(t.tileSizeMath.pow(2,r-t.coord.z)))}},{\"../data/bucket\":329}],364:[function(t,e,r){\"use strict\";function n(t,e){var r=t.coord,n=e.coord;return r.z-n.z\|\|r.y-n.y\|\|r.w-n.w\|\|r.x-n.x}function i(t){for(var e=t[0]\|\|{},r=1;r<t.length;r++){var n=t[r];for(var i in n){var a=n[i],o=e[i];if(void 0===o)o=e[i]=a;else for(var s=0;s<a.length;s++)o.push(a[s])}}return e}var a=t(\"./tile_coord\");r.rendered=function(t,e,r,a,o,s){var l=t.tilesIn(r);l.sort(n);for(var u=[],c=0;c<l.length;c++){var h=l[c];h.tile.featureIndex&&u.push(h.tile.featureIndex.query({queryGeometry:h.queryGeometry,scale:h.scale,tileSize:h.tile.tileSize,bearing:s,params:a},e))}return i(u)},r.source=function(t,e){for(var r=t.getRenderableIds().map(function(e){return t.getTileByID(e)}),n=[],i={},o=0;o<r.length;o++){var s=r[o],l=new a(Math.min(s.sourceMaxZoom,s.coord.z),s.coord.x,s.coord.y,0).id;i[l]\|\|(i[l]=!0,s.querySourceFeatures(n,e))}return n}},{\"./tile_coord\":369}],365:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r,i.extend(this,i.pick(e,[\"url\",\"scheme\",\"tileSize\"])),s(e,function(t,e){if(t)return this.fire(\"error\",t);i.extend(this,e),this.fire(\"load\")}.bind(this))}var i=t(\"../util/util\"),a=t(\"../util/ajax\"),o=t(\"../util/evented\"),s=t(\"./load_tilejson\"),l=t(\"../util/mapbox\").normalizeTileURL;e.exports=n,n.prototype=i.inherit(o,{minzoom:0,maxzoom:22,roundZoom:!0,scheme:\"xyz\",tileSize:512,_loaded:!1,onAdd:function(t){this.map=t},serialize:function(){return{type:\"raster\",url:this.url,tileSize:this.tileSize}},loadTile:function(t,e){function r(r,n){if(delete t.request,!t.aborted){if(r)return e(r);var i=this.map.painter.gl;t.texture=this.map.painter.getTexture(n.width),t.texture?(i.bindTexture(i.TEXTURE_2D,t.texture),i.texSubImage2D(i.TEXTURE_2D,0,0,0,i.RGBA,i.UNSIGNED_BYTE,n)):(t.texture=i.createTexture(),i.bindTexture(i.TEXTURE_2D,t.texture),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR_MIPMAP_NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,n),t.texture.size=n.width),i.generateMipmap(i.TEXTURE_2D),this.map.animationLoop.set(this.map.style.rasterFadeDuration),t.state=\"loaded\",e(null)}}var n=l(t.coord.url(this.tiles,null,this.scheme),this.url,this.tileSize);t.request=a.getImage(n,r.bind(this))},abortTile:function(t){t.request&&(t.request.abort(),delete t.request)},unloadTile:function(t){t.texture&&this.map.painter.saveTexture(t.texture)}})},{\"../util/ajax\":425,\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./load_tilejson\":362}],366:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i={vector:t(\"../source/vector_tile_source\"),raster:t(\"../source/raster_tile_source\"),geojson:t(\"../source/geojson_source\"),video:t(\"../source/video_source\"),image:t(\"../source/image_source\")};r.create=function(t,e,r){if(e=new i[e.type](t,e,r),e.id!==t)throw new Error(\"Expected Source id to be \"+t+\" instead of \"+e.id);return n.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],e),e},r.getType=function(t){return i[t]},r.setType=function(t,e){i[t]=e}},{\"../source/geojson_source\":358,\"../source/image_source\":361,\"../source/raster_tile_source\":365,\"../source/vector_tile_source\":370,\"../source/video_source\":372,\"../util/util\":442}],367:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r;var n=this._source=o.create(t,e,r).on(\"load\",function(){this.map&&this._source.onAdd&&this._source.onAdd(this.map),this._sourceLoaded=!0,this.tileSize=n.tileSize,this.minzoom=n.minzoom,this.maxzoom=n.maxzoom,this.roundZoom=n.roundZoom,this.reparseOverscaled=n.reparseOverscaled,this.isTileClipped=n.isTileClipped,this.attribution=n.attribution,this.vectorLayerIds=n.vectorLayerIds,this.fire(\"load\")}.bind(this)).on(\"error\",function(t){this._sourceErrored=!0,this.fire(\"error\",t)}.bind(this)).on(\"change\",function(){this.reload(),this.transform&&this.update(this.transform,this.map&&this.map.style.rasterFadeDuration),this.fire(\"change\")}.bind(this));this._tiles={},this._cache=new c(0,this.unloadTile.bind(this)),this._isIdRenderable=this._isIdRenderable.bind(this)}function i(t,e,r){var n=r.zoomTo(Math.min(t.z,e));return{x:(n.column-(t.x+t.wMath.pow(2,t.z)))d,y:(n.row-t.y)d}}function a(t,e){return t%32-e%32}var o=t(\"./source\"),s=t(\"./tile\"),l=t(\"../util/evented\"),u=t(\"./tile_coord\"),c=t(\"../util/lru_cache\"),h=t(\"../geo/coordinate\"),f=t(\"../util/util\"),d=t(\"../data/bucket\").EXTENT;e.exports=n,n.maxOverzooming=10,n.maxUnderzooming=3,n.prototype=f.inherit(l,{onAdd:function(t){this.map=t,this._source&&this._source.onAdd&&this._source.onAdd(t)},loaded:function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},getSource:function(){return this._source},loadTile:function(t,e){return this._source.loadTile(t,e)},unloadTile:function(t){if(this._source.unloadTile)return this._source.unloadTile(t)},abortTile:function(t){if(this._source.abortTile)return this._source.abortTile(t)},serialize:function(){return this._source.serialize()},prepare:function(){if(this._sourceLoaded&&this._source.prepare)return this._source.prepare()},getIds:function(){return Object.keys(this._tiles).map(Number).sort(a)},getRenderableIds:function(){return this.getIds().filter(this._isIdRenderable)},_isIdRenderable:function(t){return this._tiles[t].isRenderable()&&!this._coveredTiles[t]},reload:function(){this._cache.reset();for(var t in this._tiles){var e=this._tiles[t];\"loading\"!==e.state&&(e.state=\"reloading\"),this.loadTile(this._tiles[t],this._tileLoaded.bind(this,this._tiles[t]))}},_tileLoaded:function(t,e){if(e)return t.state=\"errored\",this.fire(\"tile.error\",{tile:t,error:e}),void this._source.fire(\"tile.error\",{tile:t,error:e});t.source=this,t.timeAdded=(new Date).getTime(),this.fire(\"tile.load\",{tile:t}),this._source.fire(\"tile.load\",{tile:t})},getTile:function(t){return this.getTileByID(t.id)},getTileByID:function(t){return this._tiles[t]},getZoom:function(t){return t.zoom+t.scaleZoom(t.tileSize/this.tileSize)},findLoadedChildren:function(t,e,r){var n=!1;for(var i in this._tiles){var a=this._tiles[i];if(!(r[i]\|\|!a.isRenderable()\|\|a.coord.z<=t.z\|\|a.coord.z>e)){var o=Math.pow(2,Math.min(a.coord.z,this.maxzoom)-Math.min(t.z,this.maxzoom));if(Math.floor(a.coord.x/o)===t.x&&Math.floor(a.coord.y/o)===t.y)for(r[i]=!0,n=!0;a&&a.coord.z-1>t.z;){var s=a.coord.parent(this.maxzoom).id;a=this._tiles[s],a&&a.isRenderable()&&(delete r[i],r[s]=!0)}}}return n},findLoadedParent:function(t,e,r){for(var n=t.z-1;n>=e;n--){t=t.parent(this.maxzoom);var i=this._tiles[t.id];if(i&&i.isRenderable())return r[t.id]=!0,i;if(this._cache.has(t.id))return this.addTile(t),r[t.id]=!0,this._tiles[t.id]}},updateCacheSize:function(t){var e=Math.ceil(t.width/t.tileSize)+1,r=Math.ceil(t.height/t.tileSize)+1,n=er;this._cache.setMaxSize(Math.floor(5n))},update:function(t,e){if(this._sourceLoaded){var r,i,a;this.updateCacheSize(t);var o=(this.roundZoom?Math.round:Math.floor)(this.getZoom(t)),s=Math.max(o-n.maxOverzooming,this.minzoom),l=Math.max(o+n.maxUnderzooming,this.minzoom),c={},h=(new Date).getTime();this._coveredTiles={};var d=this.used?t.coveringTiles(this._source):[];for(r=0;r<d.length;r++)i=d[r],a=this.addTile(i),c[i.id]=!0,a.isRenderable()\|\|this.findLoadedChildren(i,l,c)\|\|this.findLoadedParent(i,s,c);for(var p={},m=Object.keys(c),v=0;v<m.length;v++){var g=m[v];i=u.fromID(g),a=this._tiles[g],a&&a.timeAdded>h-(e\|\|0)&&(this.findLoadedChildren(i,l,c)&&(c[g]=!0),this.findLoadedParent(i,s,p))}var y;for(y in p)c[y]\|\|(this._coveredTiles[y]=!0);for(y in p)c[y]=!0;var b=f.keysDifference(this._tiles,c);for(r=0;r<b.length;r++)this.removeTile(+b[r]);this.transform=t}},addTile:function(t){var e=this._tiles[t.id];if(e)return e;var r=t.wrapped()\n", ";if(e=this._tiles[r.id],e\|\|(e=this._cache.get(r.id))&&this._redoPlacement&&this._redoPlacement(e),!e){var n=t.z,i=n>this.maxzoom?Math.pow(2,n-this.maxzoom):1;e=new s(r,this.tileSizei,this.maxzoom),this.loadTile(e,this._tileLoaded.bind(this,e))}return e.uses++,this._tiles[t.id]=e,this.fire(\"tile.add\",{tile:e}),this._source.fire(\"tile.add\",{tile:e}),e},removeTile:function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this.fire(\"tile.remove\",{tile:e}),this._source.fire(\"tile.remove\",{tile:e}),e.uses>0\|\|(e.isRenderable()?this._cache.add(e.coord.wrapped().id,e):(e.aborted=!0,this.abortTile(e),this.unloadTile(e))))},clearTiles:function(){for(var t in this._tiles)this.removeTile(t);this._cache.reset()},tilesIn:function(t){for(var e={},r=this.getIds(),n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0].zoom,c=0;c<t.length;c++){var f=t[c];n=Math.min(n,f.column),a=Math.min(a,f.row),o=Math.max(o,f.column),s=Math.max(s,f.row)}for(var p=0;p<r.length;p++){var m=this._tiles[r[p]],v=u.fromID(r[p]),g=[i(v,m.sourceMaxZoom,new h(n,a,l)),i(v,m.sourceMaxZoom,new h(o,s,l))];if(g[0].x<d&&g[0].y<d&&g[1].x>=0&&g[1].y>=0){for(var y=[],b=0;b<t.length;b++)y.push(i(v,m.sourceMaxZoom,t[b]));var x=e[m.coord.id];void 0===x&&(x=e[m.coord.id]={tile:m,coord:v,queryGeometry:[],scale:Math.pow(2,this.transform.zoom-m.coord.z)}),x.queryGeometry.push(y)}}var _=[];for(var w in e)_.push(e[w]);return _},redoPlacement:function(){for(var t=this.getIds(),e=0;e<t.length;e++){this.getTileByID(t[e]).redoPlacement(this)}},getVisibleCoordinates:function(){return this.getRenderableIds().map(u.fromID)}})},{\"../data/bucket\":329,\"../geo/coordinate\":338,\"../util/evented\":434,\"../util/lru_cache\":438,\"../util/util\":442,\"./source\":366,\"./tile\":368,\"./tile_coord\":369}],368:[function(t,e,r){\"use strict\";function n(t,e,r){this.coord=t,this.uid=a.uniqueId(),this.uses=0,this.tileSize=e,this.sourceMaxZoom=r,this.buckets={},this.state=\"loading\"}function i(t,e){if(e){for(var r={},n=0;n<t.length;n++){var i=e.getLayer(t[n].layerId);if(i){var s=o.create(a.extend({layer:i,childLayers:t[n].childLayerIds.map(e.getLayer.bind(e)).filter(function(t){return t})},t[n]));r[s.id]=s}}return r}}var a=t(\"../util/util\"),o=t(\"../data/bucket\"),s=t(\"../data/feature_index\"),l=t(\"vector-tile\"),u=t(\"pbf\"),c=t(\"../util/vectortile_to_geojson\"),h=t(\"feature-filter\"),f=t(\"../symbol/collision_tile\"),d=t(\"../symbol/collision_box\"),p=t(\"../symbol/symbol_instances\"),m=t(\"../symbol/symbol_quads\");e.exports=n,n.prototype={loadVectorData:function(t,e){this.state=\"loaded\",t&&(this.collisionBoxArray=new d(t.collisionBoxArray),this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.symbolInstancesArray=new p(t.symbolInstancesArray),this.symbolQuadsArray=new m(t.symbolQuadsArray),this.featureIndex=new s(t.featureIndex,t.rawTileData,this.collisionTile),this.rawTileData=t.rawTileData,this.buckets=i(t.buckets,e))},reloadSymbolData:function(t,e,r){if(\"unloaded\"!==this.state){this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.featureIndex.setCollisionTile(this.collisionTile);for(var n in this.buckets){var o=this.buckets[n];\"symbol\"===o.type&&(o.destroy(e.gl),delete this.buckets[n])}a.extend(this.buckets,i(t.buckets,r))}},unloadVectorData:function(t){for(var e in this.buckets){this.buckets[e].destroy(t.gl)}this.collisionBoxArray=null,this.symbolQuadsArray=null,this.symbolInstancesArray=null,this.collisionTile=null,this.featureIndex=null,this.rawTileData=null,this.buckets=null,this.state=\"unloaded\"},redoPlacement:function(t){function e(e,r){this.reloadSymbolData(r,t.map.painter,t.map.style),t.fire(\"tile.load\",{tile:this}),this.state=\"loaded\",this.redoWhenDone&&(this.redoPlacement(t),this.redoWhenDone=!1)}if(\"loaded\"!==this.state\|\|\"reloading\"===this.state)return void(this.redoWhenDone=!0);this.state=\"reloading\",t.dispatcher.send(\"redo placement\",{uid:this.uid,source:t.id,angle:t.map.transform.angle,pitch:t.map.transform.pitch,showCollisionBoxes:t.map.showCollisionBoxes},e.bind(this),this.workerID)},getBucket:function(t){return this.buckets&&this.buckets[t.ref\|\|t.id]},querySourceFeatures:function(t,e){if(this.rawTileData){this.vtLayers\|\|(this.vtLayers=new l.VectorTile(new u(new Uint8Array(this.rawTileData))).layers);var r=this.vtLayers._geojsonTileLayer\|\|this.vtLayers[e.sourceLayer];if(r)for(var n=h(e.filter),i={z:this.coord.z,x:this.coord.x,y:this.coord.y},a=0;a<r.length;a++){var o=r.feature(a);if(n(o)){var s=new c(o,this.coord.z,this.coord.x,this.coord.y);s.tile=i,t.push(s)}}}},isRenderable:function(){return\"loaded\"===this.state\|\|\"reloading\"===this.state}}},{\"../data/bucket\":329,\"../data/feature_index\":336,\"../symbol/collision_box\":394,\"../symbol/collision_tile\":396,\"../symbol/symbol_instances\":405,\"../symbol/symbol_quads\":406,\"../util/util\":442,\"../util/vectortile_to_geojson\":443,\"feature-filter\":132,pbf:478,\"vector-tile\":550}],369:[function(t,e,r){\"use strict\";function n(t,e,r,n){l(!isNaN(t)&&t>=0&&t%1==0),l(!isNaN(e)&&e>=0&&e%1==0),l(!isNaN(r)&&r>=0&&r%1==0),isNaN(n)&&(n=0),this.z=+t,this.x=+e,this.y=+r,this.w=+n,(n=2)<0&&(n=-1n-1);var i=1<<this.z;this.id=32(iin+ithis.y+this.x)+this.z,this.posMatrix=null}function i(t,e,r){for(var n,i=\"\",a=t;a>0;a--)n=1<<a-1,i+=(e&n?1:0)+(r&n?2:0);return i}function a(t,e){if(t.row>e.row){var r=t;t=e,e=r}return{x0:t.column,y0:t.row,x1:e.column,y1:e.row,dx:e.column-t.column,dy:e.row-t.row}}function o(t,e,r,n,i){var a=Math.max(r,Math.floor(e.y0)),o=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dyt.dx<e.x1:t.x1-e.dy/t.dyt.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,u=e.dx/e.dy,c=t.dx>0,h=e.dx<0,f=a;f<o;f++){var d=lMath.max(0,Math.min(t.dy,f+c-t.y0))+t.x0,p=uMath.max(0,Math.min(e.dy,f+h-e.y0))+e.x0;i(Math.floor(p),Math.ceil(d),f)}}function s(t,e,r,n,i,s){var l,u=a(t,e),c=a(e,r),h=a(r,t);u.dy>c.dy&&(l=u,u=c,c=l),u.dy>h.dy&&(l=u,u=h,h=l),c.dy>h.dy&&(l=c,c=h,h=l),u.dy&&o(h,u,n,i,s),c.dy&&o(h,c,n,i,s)}var l=t(\"assert\"),u=t(\"whoots-js\"),c=t(\"../geo/coordinate\");e.exports=n,n.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y},n.prototype.toCoordinate=function(t){var e=Math.min(this.z,t),r=Math.pow(2,e),n=this.y,i=this.x+rthis.w;return new c(i,n,e)},n.fromID=function(t){var e=t%32,r=1<<e,i=(t-e)/32,a=i%r,o=(i-a)/r%r,s=Math.floor(i/(rr));return s%2!=0&&(s=-1s-1),s/=2,new n(e,a,o,s)},n.prototype.url=function(t,e,r){var n=u.getTileBBox(this.x,this.y,this.z),a=i(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",Math.min(this.z,e\|\|this.z)).replace(\"{x}\",this.x).replace(\"{y}\",\"tms\"===r?Math.pow(2,this.z)-this.y-1:this.y).replace(\"{quadkey}\",a).replace(\"{bbox-epsg-3857}\",n)},n.prototype.parent=function(t){return 0===this.z?null:this.z>t?new n(this.z-1,this.x,this.y,this.w):new n(this.z-1,Math.floor(this.x/2),Math.floor(this.y/2),this.w)},n.prototype.wrapped=function(){return new n(this.z,this.x,this.y,0)},n.prototype.children=function(t){if(this.z>=t)return[new n(this.z+1,this.x,this.y,this.w)];var e=this.z+1,r=2this.x,i=2this.y;return[new n(e,r,i,this.w),new n(e,r+1,i,this.w),new n(e,r,i+1,this.w),new n(e,r+1,i+1,this.w)]},n.cover=function(t,e,r){function i(t,e,i){var s,l,u;if(i>=0&&i<=a)for(s=t;s<e;s++)l=(s%a+a)%a,u=new n(r,l,i,Math.floor(s/a)),o[u.id]=u}var a=1<<t,o={};return s(e[0],e[1],e[2],0,a,i),s(e[2],e[3],e[0],0,a,i),Object.keys(o).map(function(t){return o[t]})}},{\"../geo/coordinate\":338,assert:47,\"whoots-js\":566}],370:[function(t,e,r){\"use strict\";function n(t,e,r){if(this.id=t,this.dispatcher=r,a.extend(this,a.pick(e,[\"url\",\"scheme\",\"tileSize\"])),this._options=a.extend({type:\"vector\"},e),512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");o(e,function(t,e){if(t)return void this.fire(\"error\",t);a.extend(this,e),this.fire(\"load\")}.bind(this))}var i=t(\"../util/evented\"),a=t(\"../util/util\"),o=t(\"./load_tilejson\"),s=t(\"../util/mapbox\").normalizeTileURL;e.exports=n,n.prototype=a.inherit(i,{minzoom:0,maxzoom:22,scheme:\"xyz\",tileSize:512,reparseOverscaled:!0,isTileClipped:!0,onAdd:function(t){this.map=t},serialize:function(){return a.extend({},this._options)},loadTile:function(t,e){function r(r,n){if(!t.aborted){if(r)return e(r);t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null),t.reloadCallback&&(this.loadTile(t,t.reloadCallback),t.reloadCallback=null)}}var n=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,i={url:s(t.coord.url(this.tiles,this.maxzoom,this.scheme),this.url),uid:t.uid,coord:t.coord,zoom:t.coord.z,tileSize:this.tileSizen,source:this.id,overscaling:n,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID?\"loading\"===t.state?t.reloadCallback=e:(i.rawTileData=t.rawTileData,this.dispatcher.send(\"reload tile\",i,r.bind(this),t.workerID)):t.workerID=this.dispatcher.send(\"load tile\",i,r.bind(this))},abortTile:function(t){this.dispatcher.send(\"abort tile\",{uid:t.uid,source:this.id},null,t.workerID)},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send(\"remove tile\",{uid:t.uid,source:this.id},null,t.workerID)}})},{\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./load_tilejson\":362}],371:[function(t,e,r){\"use strict\";function n(t,e,r){this.actor=t,this.styleLayers=e,r&&(this.loadVectorData=r),this.loading={},this.loaded={}}var i=t(\"../util/ajax\"),a=t(\"vector-tile\"),o=t(\"pbf\"),s=t(\"./worker_tile\");e.exports=n,n.prototype={loadTile:function(t,e){function r(t,r){return delete this.loading[n][i],t?e(t):r?(a.data=r.tile,a.parse(a.data,this.styleLayers.getLayerFamilies(),this.actor,r.rawTileData,e),this.loaded[n]=this.loaded[n]\|\|{},void(this.loaded[n][i]=a)):e(null,null)}var n=t.source,i=t.uid;this.loading[n]\|\|(this.loading[n]={});var a=this.loading[n][i]=new s(t);a.abort=this.loadVectorData(t,r.bind(this))},reloadTile:function(t,e){var r=this.loaded[t.source],n=t.uid;if(r&&r[n]){var i=r[n];i.parse(i.data,this.styleLayers.getLayerFamilies(),this.actor,t.rawTileData,e)}},abortTile:function(t){var e=this.loading[t.source],r=t.uid;e&&e[r]&&e[r].abort&&(e[r].abort(),delete e[r])},removeTile:function(t){var e=this.loaded[t.source],r=t.uid;e&&e[r]&&delete e[r]},loadVectorData:function(t,e){function r(t,r){if(t)return e(t);var n=new a.VectorTile(new o(new Uint8Array(r)));e(t,{tile:n,rawTileData:r})}var n=i.getArrayBuffer(t.url,r.bind(this));return function(){n.abort()}},redoPlacement:function(t,e){var r=this.loaded[t.source],n=this.loading[t.source],i=t.uid;if(r&&r[i]){var a=r[i],o=a.redoPlacement(t.angle,t.pitch,t.showCollisionBoxes);o.result&&e(null,o.result,o.transferables)}else n&&n[i]&&(n[i].angle=t.angle)}}},{\"../util/ajax\":425,\"./worker_tile\":374,pbf:478,\"vector-tile\":550}],372:[function(t,e,r){\"use strict\";function n(t,e){this.id=t,this.urls=e.urls,this.coordinates=e.coordinates,u.getVideo(e.urls,function(t,r){if(t)return this.fire(\"error\",{error:t});this.video=r,this.video.loop=!0;var n;this.video.addEventListener(\"playing\",function(){n=this.map.style.animationLoop.set(1/0),this.map._rerender()}.bind(this)),this.video.addEventListener(\"pause\",function(){this.map.style.animationLoop.cancel(n)}.bind(this)),this.map&&(this.video.play(),this.setCoordinates(e.coordinates)),this.fire(\"load\")}.bind(this))}var i=t(\"../util/util\"),a=t(\"./tile_coord\"),o=t(\"../geo/lng_lat\"),s=t(\"point-geometry\"),l=t(\"../util/evented\"),u=t(\"../util/ajax\"),c=t(\"../data/bucket\").EXTENT,h=t(\"../render/draw_raster\").RasterBoundsArray,f=t(\"../data/buffer\"),d=t(\"../render/vertex_array_object\");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,roundZoom:!0,getVideo:function(){return this.video},onAdd:function(t){this.map\|\|(this.map=t,this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(o.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new a(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)c),Math.round((e.row-n.row)c))}),this.fire(\"change\"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=new h;e.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),e.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,32767,0),e.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,32767),e.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,32767,32767),this.tile.buckets={},this.tile.boundsBuffer=new f(e.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new d,this.tile.state=\"loaded\"},prepare:function(){if(!(this.video.readyState<2)&&this.tile){var t=this.map.painter.gl;this._prepared?(t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texSubImage2D(t.TEXTURE_2D,0,0,0,t.RGBA,t.UNSIGNED_BYTE,this.video)):(this._prepared=!0,this.tile.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,this.video)),this._currentTime=this.video.currentTime}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state=\"errored\",e(null))},serialize:function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}})},{\"../data/bucket\":329,\"../data/buffer\":334,\"../geo/lng_lat\":339,\"../render/draw_raster\":351,\"../render/vertex_array_object\":357,\"../util/ajax\":425,\"../util/evented\":434,\"../util/util\":442,\"./tile_coord\":369,\"point-geometry\":484}],373:[function(t,e,r){\"use strict\";function n(t){this.self=t,this.actor=new a(t,this);var e={getLayers:function(){return this.layers}.bind(this),getLayerFamilies:function(){return this.layerFamilies}.bind(this)};this.workerSources={vector:new l(this.actor,e),geojson:new u(this.actor,e)},this.self.registerWorkerSource=function(t,r){if(this.workerSources[t])throw new Error('Worker source with name \"'+t+'\" already registered.');this.workerSources[t]=new r(this.actor,e)}.bind(this)}function i(t){var e={};for(var r in t){var n=t[r],i=n.ref\|\|n.id,a=t[i];a.layout&&\"none\"===a.layout.visibility\|\|(e[i]=e[i]\|\|[],r===i?e[i].unshift(n):e[i].push(n))}return e}var a=t(\"../util/actor\"),o=t(\"../style/style_layer\"),s=t(\"../util/util\"),l=t(\"./vector_tile_worker_source\"),u=t(\"./geojson_worker_source\");e.exports=function(t){return new n(t)},s.extend(n.prototype,{\"set layers\":function(t){function e(t){var e=o.create(t,t.ref&&r.layers[t.ref]);e.updatePaintTransitions({},{transition:!1}),r.layers[e.id]=e}this.layers={};for(var r=this,n=[],a=0;a<t.length;a++){var s=t[a];\"fill\"!==s.type&&\"line\"!==s.type&&\"circle\"!==s.type&&\"symbol\"!==s.type\|\|(s.ref?n.push(a):e(s))}for(var l=0;l<n.length;l++)e(t[n[l]]);this.layerFamilies=i(this.layers)},\"update layers\":function(t){function e(t){var e=a.layers[t.ref];a.layers[t.id]?a.layers[t.id].set(t,e):a.layers[t.id]=o.create(t,e),a.layers[t.id].updatePaintTransitions({},{transition:!1})}var r,n,a=this;for(r in t)n=t[r],n.ref&&e(n);for(r in t)n=t[r],n.ref\|\|e(n);this.layerFamilies=i(this.layers)},\"load tile\":function(t,e){var r=t.type\|\|\"vector\";this.workerSources[r].loadTile(t,e)},\"reload tile\":function(t,e){var r=t.type\|\|\"vector\";this.workerSources[r].reloadTile(t,e)},\"abort tile\":function(t){var e=t.type\|\|\"vector\";this.workerSources[e].abortTile(t)},\"remove tile\":function(t){var e=t.type\|\|\"vector\";this.workerSources[e].removeTile(t)},\"redo placement\":function(t,e){var r=t.type\|\|\"vector\";this.workerSources[r].redoPlacement(t,e)},\"load worker source\":function(t,e){try{this.self.importScripts(t.url),e()}catch(t){e(t)}}})},{\"../style/style_layer\":381,\"../util/actor\":424,\"../util/util\":442,\"./geojson_worker_source\":359,\"./vector_tile_worker_source\":371}],374:[function(t,e,r){\"use strict\";function n(t){this.coord=t.coord,this.uid=t.uid,this.zoom=t.zoom,this.tileSize=t.tileSize,this.source=t.source,this.overscaling=t.overscaling,this.angle=t.angle,this.pitch=t.pitch,this.showCollisionBoxes=t.showCollisionBoxes}function i(t){return!t.isEmpty()}function a(t){return t.serialize()}function o(t){var e=[];for(var r in t)t[r].getTransferables(e);return e}function s(t){return t.id}var l=t(\"../data/feature_index\"),u=t(\"../symbol/collision_tile\"),c=t(\"../data/bucket\"),h=t(\"../symbol/collision_box\"),f=t(\"../util/dictionary_coder\"),d=t(\"../util/util\"),p=t(\"../symbol/symbol_instances\"),m=t(\"../symbol/symbol_quads\");e.exports=n,n.prototype.parse=function(t,e,r,n,v){function g(t,e){for(var r=0;r<t.length;r++){var n=t.feature(r);n.index=r;for(var i in e)e[i].filter(n)&&e[i].features.push(n)}}function y(t){if(t)return v(t);if(2===++N){for(var e=P.length-1;e>=0;e--)b(E,P[e]);x()}}function b(t,e){if(e.populateArrays(A,j,F),\"symbol\"!==e.type)for(var r=0;r<e.features.length;r++){var n=e.features[r];T.insert(n,n.index,e.sourceLayerIndex,e.index)}e.features=null}function x(){E.status=\"done\",E.redoPlacementAfterDone&&(E.redoPlacement(E.angle,E.pitch,null),E.redoPlacementAfterDone=!1);var t=T.serialize(),e=A.serialize(),r=E.collisionBoxArray.serialize(),s=E.symbolInstancesArray.serialize(),l=E.symbolQuadsArray.serialize(),u=[n].concat(t.transferables).concat(e.transferables),c=D.filter(i);v(null,{buckets:c.map(a),featureIndex:t.data,collisionTile:e.data,collisionBoxArray:r,symbolInstancesArray:s,symbolQuadsArray:l,rawTileData:n},o(c).concat(u))}this.status=\"parsing\",this.data=t,this.collisionBoxArray=new h,this.symbolInstancesArray=new p,this.symbolQuadsArray=new m;var _,w,M,k,A=new u(this.angle,this.pitch,this.collisionBoxArray),T=new l(this.coord,this.overscaling,A,t.layers),S=new f(t.layers?Object.keys(t.layers).sort():[\"_geojsonTileLayer\"]),E=this,L={},C={},I=0;for(var z in e)w=e[z][0],w.source===this.source&&(w.ref\|\|w.minzoom&&this.zoom<w.minzoom\|\|w.maxzoom&&this.zoom>=w.maxzoom\|\|w.layout&&\"none\"===w.layout.visibility\|\|t.layers&&!t.layers[w.sourceLayer]\|\|(k=c.create({layer:w,index:I++,childLayers:e[z],zoom:this.zoom,overscaling:this.overscaling,showCollisionBoxes:this.showCollisionBoxes,collisionBoxArray:this.collisionBoxArray,symbolQuadsArray:this.symbolQuadsArray,symbolInstancesArray:this.symbolInstancesArray,sourceLayerIndex:S.encode(w.sourceLayer\|\|\"_geojsonTileLayer\")}),k.createFilter(),L[w.id]=k,t.layers&&(M=w.sourceLayer,C[M]=C[M]\|\|{},C[M][w.id]=k)));if(t.layers)for(M in C)1===w.version&&d.warnOnce('Vector tile source \"'+this.source+'\" layer \"'+M+'\" does not use vector tile spec v2 and therefore may have some rendering errors.'),(w=t.layers[M])&&g(w,C[M]);else g(t,L);var D=[],P=this.symbolBuckets=[],O=[];T.bucketLayerIDs={};for(var R in L)k=L[R],0!==k.features.length&&(T.bucketLayerIDs[k.index]=k.childLayers.map(s),D.push(k),\"symbol\"===k.type?P.push(k):O.push(k));var F={},j={},N=0;if(P.length>0){for(_=P.length-1;_>=0;_--)P[_].updateIcons(F),P[_].updateFont(j);for(var B in j)j[B]=Object.keys(j[B]).map(Number);F=Object.keys(F),r.send(\"get glyphs\",{uid:this.uid,stacks:j},function(t,e){j=e,y(t)}),F.length?r.send(\"get icons\",{icons:F},function(t,e){F=e,y(t)}):y()}for(_=O.length-1;_>=0;_--)b(this,O[_]);if(0===P.length)return x()},n.prototype.redoPlacement=function(t,e,r){if(\"done\"!==this.status)return this.redoPlacementAfterDone=!0,this.angle=t,{};for(var n=new u(t,e,this.collisionBoxArray),s=this.symbolBuckets,l=s.length-1;l>=0;l--)s[l].placeFeatures(n,r);var c=n.serialize(),h=s.filter(i);return{result:{buckets:h.map(a),collisionTile:c.data},transferables:o(h).concat(c.transferables)}}},{\"../data/bucket\":329,\"../data/feature_index\":336,\"../symbol/collision_box\":394,\"../symbol/collision_tile\":396,\"../symbol/symbol_instances\":405,\"../symbol/symbol_quads\":406,\"../util/dictionary_coder\":432,\"../util/util\":442}],375:[function(t,e,r){\"use strict\";function n(){this.n=0,this.times=[]}e.exports=n,n.prototype.stopped=function(){return this.times=this.times.filter(function(t){return t.time>=(new Date).getTime()}),!this.times.length},n.prototype.set=function(t){return this.times.push({id:this.n,time:t+(new Date).getTime()}),this.n++},n.prototype.cancel=function(t){this.times=this.times.filter(function(e){return e.id!==t})}},{}],376:[function(t,e,r){\"use strict\";function n(t){this.base=t,this.retina=s.devicePixelRatio>1;var e=this.retina?\"@2x\":\"\";o.getJSON(l(t,e,\".json\"),function(t,e){if(t)return void this.fire(\"error\",{error:t});this.data=e,this.img&&this.fire(\"load\")}.bind(this)),o.getImage(l(t,e,\".png\"),function(t,e){if(t)return void this.fire(\"error\",{error:t});for(var r=e.getData(),n=e.data=new Uint8Array(r.length),i=0;i<r.length;i+=4){var a=r[i+3]/255;n[i+0]=r[i+0]a,n[i+1]=r[i+1]a,n[i+2]=r[i+2]a,n[i+3]=r[i+3]}this.img=e,this.data&&this.fire(\"load\")}.bind(this))}function i(){}var a=t(\"../util/evented\"),o=t(\"../util/ajax\"),s=t(\"../util/browser\"),l=t(\"../util/mapbox\").normalizeSpriteURL;e.exports=n,n.prototype=Object.create(a),n.prototype.toJSON=function(){return this.base},n.prototype.loaded=function(){return!(!this.data\|\|!this.img)},n.prototype.resize=function(){if(s.devicePixelRatio>1!==this.retina){var t=new n(this.base);t.on(\"load\",function(){this.img=t.img,this.data=t.data,this.retina=t.retina}.bind(this))}},i.prototype={x:0,y:0,width:0,height:0,pixelRatio:1,sdf:!1},n.prototype.getSpritePosition=function(t){if(!this.loaded())return new i;var e=this.data&&this.data[t];return e&&this.img?e:new i}},{\"../util/ajax\":425,\"../util/browser\":426,\"../util/evented\":434,\"../util/mapbox\":439}],377:[function(t,e,r){\"use strict\";var n=t(\"csscolorparser\").parseCSSColor,i=t(\"../util/util\"),a=t(\"./style_function\"),o={};e.exports=function t(e){if(a.isFunctionDefinition(e))return i.extend({},e,{stops:e.stops.map(function(e){return[e[0],t(e[1])]})});if(\"string\"==typeof e){if(!o[e]){var r=n(e);if(!r)throw new Error(\"Invalid color \"+e);o[e]=[r[0]/255r[3],r[1]/255r[3],r[2]/255r[3],r[3]]}return o[e]}throw new Error(\"Invalid color \"+e)}},{\"../util/util\":442,\"./style_function\":380,csscolorparser:108}],378:[function(t,e,r){\"use strict\";function n(t,e,r){this.animationLoop=e\|\|new m,this.dispatcher=new p(r\|\|1,this),this.spriteAtlas=new l(1024,1024),this.lineAtlas=new u(256,512),this._layers={},this._order=[],this._groups=[],this.sources={},this.zoomHistory={},c.bindAll([\"_forwardSourceEvent\",\"_forwardTileEvent\",\"_forwardLayerEvent\",\"_redoPlacement\"],this),this._resetUpdates();var n=function(t,e){if(t)return void this.fire(\"error\",{error:t});if(!v.emitErrors(this,v(e))){this._loaded=!0,this.stylesheet=e,this.updateClasses();var r=e.sources;for(var n in r)this.addSource(n,r[n]);e.sprite&&(this.sprite=new o(e.sprite),this.sprite.on(\"load\",this.fire.bind(this,\"change\"))),this.glyphSource=new s(e.glyphs),this._resolve(),this.fire(\"load\")}}.bind(this);\"string\"==typeof t?h.getJSON(f(t),n):d.frame(n.bind(this,null,t)),this.on(\"source.load\",function(t){var e=t.source;if(e&&e.vectorLayerIds)for(var r in this._layers){var n=this._layers[r];n.source===e.id&&this._validateLayer(n)}})}var i=t(\"../util/evented\"),a=t(\"./style_layer\"),o=t(\"./image_sprite\"),s=t(\"../symbol/glyph_source\"),l=t(\"../symbol/sprite_atlas\"),u=t(\"../render/line_atlas\"),c=t(\"../util/util\"),h=t(\"../util/ajax\"),f=t(\"../util/mapbox\").normalizeStyleURL,d=t(\"../util/browser\"),p=t(\"../util/dispatcher\"),m=t(\"./animation_loop\"),v=t(\"./validate_style\"),g=t(\"../source/source\"),y=t(\"../source/query_features\"),b=t(\"../source/source_cache\"),x=t(\"./style_spec\"),_=t(\"./style_function\");e.exports=n,n.prototype=c.inherit(i,{_loaded:!1,_validateLayer:function(t){var e=this.sources[t.source];t.sourceLayer&&e&&e.vectorLayerIds&&-1===e.vectorLayerIds.indexOf(t.sourceLayer)&&this.fire(\"error\",{error:new Error('Source layer \"'+t.sourceLayer+'\" does not exist on source \"'+e.id+'\" as specified by style layer \"'+t.id+'\"')})},loaded:function(){if(!this._loaded)return!1;if(Object.keys(this._updates.sources).length)return!1;for(var t in this.sources)if(!this.sources[t].loaded())return!1;return!(this.sprite&&!this.sprite.loaded())},_resolve:function(){var t,e;this._layers={},this._order=this.stylesheet.layers.map(function(t){return t.id});for(var r=0;r<this.stylesheet.layers.length;r++)e=this.stylesheet.layers[r],e.ref\|\|(t=a.create(e),this._layers[t.id]=t,t.on(\"error\",this._forwardLayerEvent));for(var n=0;n<this.stylesheet.layers.length;n++)if(e=this.stylesheet.layers[n],e.ref){var i=this.getLayer(e.ref);t=a.create(e,i),this._layers[t.id]=t,t.on(\"error\",this._forwardLayerEvent)}this._groupLayers(),this._updateWorkerLayers()},_groupLayers:function(){var t;this._groups=[];for(var e=0;e<this._order.length;++e){var r=this._layers[this._order[e]];t&&r.source===t.source\|\|(t=[],t.source=r.source,this._groups.push(t)),t.push(r)}},_updateWorkerLayers:function(t){this.dispatcher.broadcast(t?\"update layers\":\"set layers\",this._serializeLayers(t))},_serializeLayers:function(t){t=t\|\|this._order;for(var e=[],r={includeRefProperties:!0},n=0;n<t.length;n++)e.push(this._layers[t[n]].serialize(r));return e},_applyClasses:function(t,e){if(this._loaded){t=t\|\|[],e=e\|\|{transition:!0};var r=this.stylesheet.transition\|\|{},n=this._updates.allPaintProps?this._layers:this._updates.paintProps;for(var i in n){var a=this._layers[i],o=this._updates.paintProps[i];if(this._updates.allPaintProps\|\|o.all)a.updatePaintTransitions(t,e,r,this.animationLoop);else for(var s in o)this._layers[i].updatePaintTransition(s,t,e,r,this.animationLoop)}}},_recalculate:function(t){for(var e in this.sources)this.sources[e].used=!1;this._updateZoomHistory(t),this.rasterFadeDuration=300;for(var r in this._layers){var n=this._layers[r];n.recalculate(t,this.zoomHistory),!n.isHidden(t)&&n.source&&(this.sources[n.source].used=!0)}Math.floor(this.z)!==Math.floor(t)&&this.animationLoop.set(300),this.z=t,this.fire(\"zoom\")},_updateZoomHistory:function(t){var e=this.zoomHistory;void 0===e.lastIntegerZoom&&(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=0,e.lastZoom=t),Math.floor(e.lastZoom)<Math.floor(t)?(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=Date.now()):Math.floor(e.lastZoom)>Math.floor(t)&&(e.lastIntegerZoom=Math.floor(t+1),e.lastIntegerZoomTime=Date.now()),e.lastZoom=t},_checkLoaded:function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},update:function(t,e){if(!this._updates.changed)return this;if(this._updates.allLayers)this._groupLayers(),this._updateWorkerLayers();else{var r=Object.keys(this._updates.layers);r.length&&this._updateWorkerLayers(r)}var n,i=Object.keys(this._updates.sources);for(n=0;n<i.length;n++)this._reloadSource(i[n]);for(n=0;n<this._updates.events.length;n++){var a=this._updates.events[n];this.fire(a[0],a[1])}return this._applyClasses(t,e),this._updates.changed&&this.fire(\"change\"),this._resetUpdates(),this},_resetUpdates:function(){this._updates={events:[],layers:{},sources:{},paintProps:{}}},addSource:function(t,e){if(this._checkLoaded(),void 0!==this.sources[t])throw new Error(\"There is already a source with this ID\");if(!e.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(e)+\".\");return[\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(e.type)>=0&&this._handleErrors(v.source,\"sources.\"+t,e)?this:(e=new b(t,e,this.dispatcher),this.sources[t]=e,e.style=this,e.on(\"load\",this._forwardSourceEvent).on(\"error\",this._forwardSourceEvent).on(\"change\",this._forwardSourceEvent).on(\"tile.add\",this._forwardTileEvent).on(\"tile.load\",this._forwardTileEvent).on(\"tile.error\",this._forwardTileEvent).on(\"tile.remove\",this._forwardTileEvent).on(\"tile.stats\",this._forwardTileEvent),this._updates.events.push([\"source.add\",{source:e}]),this._updates.changed=!0,this)},removeSource:function(t){if(this._checkLoaded(),void 0===this.sources[t])throw new Error(\"There is no source with this ID\");var e=this.sources[t];return delete this.sources[t],delete this._updates.sources[t],e.off(\"load\",this._forwardSourceEvent).off(\"error\",this._forwardSourceEvent).off(\"change\",this._forwardSourceEvent).off(\"tile.add\",this._forwardTileEvent).off(\"tile.load\",this._forwardTileEvent).off(\"tile.error\",this._forwardTileEvent).off(\"tile.remove\",this._forwardTileEvent).off(\"tile.stats\",this._forwardTileEvent),this._updates.events.push([\"source.remove\",{source:e}]),this._updates.changed=!0,this},getSource:function(t){return this.sources[t]&&this.sources[t].getSource()},addLayer:function(t,e){if(this._checkLoaded(),!(t instanceof a)){if(this._handleErrors(v.layer,\"layers.\"+t.id,t,!1,{arrayIndex:-1}))return this;var r=t.ref&&this.getLayer(t.ref);t=a.create(t,r)}return this._validateLayer(t),t.on(\"error\",this._forwardLayerEvent),this._layers[t.id]=t,this._order.splice(e?this._order.indexOf(e):1/0,0,t.id),this._updates.allLayers=!0,t.source&&(this._updates.sources[t.source]=!0),this._updates.events.push([\"layer.add\",{layer:t}]),this.updateClasses(t.id)},removeLayer:function(t){this._checkLoaded();var e=this._layers[t];if(void 0===e)throw new Error(\"There is no layer with this ID\");for(var r in this._layers)this._layers[r].ref===t&&this.removeLayer(r);return e.off(\"error\",this._forwardLayerEvent),delete this._layers[t],delete this._updates.layers[t],delete this._updates.paintProps[t],this._order.splice(this._order.indexOf(t),1),this._updates.allLayers=!0,this._updates.events.push([\"layer.remove\",{layer:e}]),this._updates.changed=!0,this},getLayer:function(t){return this._layers[t]},getReferentLayer:function(t){var e=this.getLayer(t);return e.ref&&(e=this.getLayer(e.ref)),e},setLayerZoomRange:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return n.minzoom===e&&n.maxzoom===r?this:(null!=e&&(n.minzoom=e),null!=r&&(n.maxzoom=r),this._updateLayer(n))},setFilter:function(t,e){this._checkLoaded();var r=this.getReferentLayer(t);return null!==e&&this._handleErrors(v.filter,\"layers.\"+r.id+\".filter\",e)?this:c.deepEqual(r.filter,e)?this:(r.filter=c.clone(e),this._updateLayer(r))},getFilter:function(t){return this.getReferentLayer(t).filter},setLayoutProperty:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return c.deepEqual(n.getLayoutProperty(e),r)?this:(n.setLayoutProperty(e,r),this._updateLayer(n))},getLayoutProperty:function(t,e){return this.getReferentLayer(t).getLayoutProperty(e)},setPaintProperty:function(t,e,r,n){this._checkLoaded();var i=this.getLayer(t);if(c.deepEqual(i.getPaintProperty(e,n),r))return this;var a=i.isPaintValueFeatureConstant(e);return i.setPaintProperty(e,r,n),!(r&&_.isFunctionDefinition(r)&&\"$zoom\"!==r.property&&void 0!==r.property)&&a\|\|(this._updates.layers[t]=!0,i.source&&(this._updates.sources[i.source]=!0)),this.updateClasses(t,e)},getPaintProperty:function(t,e,r){return this.getLayer(t).getPaintProperty(e,r)},updateClasses:function(t,e){if(this._updates.changed=!0,t){var r=this._updates.paintProps;r[t]\|\|(r[t]={}),r[t][e\|\|\"all\"]=!0}else this._updates.allPaintProps=!0;return this},serialize:function(){return c.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:c.mapObject(this.sources,function(t){return t.serialize()}),layers:this._order.map(function(t){return this._layers[t].serialize()},this)},function(t){return void 0!==t})},_updateLayer:function(t){return this._updates.layers[t.id]=!0,t.source&&(this._updates.sources[t.source]=!0),this._updates.changed=!0,this},_flattenRenderedFeatures:function(t){for(var e=[],r=this._order.length-1;r>=0;r--)for(var n=this._order[r],i=0;i<t.length;i++){var a=t[i][n];if(a)for(var o=0;o<a.length;o++)e.push(a[o])}return e},queryRenderedFeatures:function(t,e,r,n){\n", "e&&e.filter&&this._handleErrors(v.filter,\"queryRenderedFeatures.filter\",e.filter,!0);var i={};if(e&&e.layers)for(var a=0;a<e.layers.length;a++){var o=e.layers[a];i[this._layers[o].source]=!0}var s=[];for(var l in this.sources)if(!e.layers\|\|i[l]){var u=this.sources[l],c=y.rendered(u,this._layers,t,e,r,n);s.push(c)}return this._flattenRenderedFeatures(s)},querySourceFeatures:function(t,e){e&&e.filter&&this._handleErrors(v.filter,\"querySourceFeatures.filter\",e.filter,!0);var r=this.sources[t];return r?y.source(r,e):[]},addSourceType:function(t,e,r){return g.getType(t)?r(new Error('A source type called \"'+t+'\" already exists.')):(g.setType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"load worker source\",{name:t,url:e.workerSourceURL},r):r(null,null))},_handleErrors:function(t,e,r,n,i){var a=n?v.throwErrors:v.emitErrors,o=t.call(v,c.extend({key:e,style:this.serialize(),value:r,styleSpec:x},i));return a.call(v,this,o)},_remove:function(){this.dispatcher.remove()},_reloadSource:function(t){this.sources[t].reload()},_updateSources:function(t){for(var e in this.sources)this.sources[e].update(t)},_redoPlacement:function(){for(var t in this.sources)this.sources[t].redoPlacement&&this.sources[t].redoPlacement()},_forwardSourceEvent:function(t){this.fire(\"source.\"+t.type,c.extend({source:t.target.getSource()},t))},_forwardTileEvent:function(t){this.fire(t.type,c.extend({source:t.target},t))},_forwardLayerEvent:function(t){this.fire(\"layer.\"+t.type,c.extend({layer:{id:t.target.id}},t))},\"get sprite json\":function(t,e){var r=this.sprite;r.loaded()?e(null,{sprite:r.data,retina:r.retina}):r.on(\"load\",function(){e(null,{sprite:r.data,retina:r.retina})})},\"get icons\":function(t,e){var r=this.sprite,n=this.spriteAtlas;r.loaded()?(n.setSprite(r),n.addIcons(t.icons,e)):r.on(\"load\",function(){n.setSprite(r),n.addIcons(t.icons,e)})},\"get glyphs\":function(t,e){function r(t,r,n){t&&console.error(t),a[n]=r,0===--i&&e(null,a)}var n=t.stacks,i=Object.keys(n).length,a={};for(var o in n)this.glyphSource.getSimpleGlyphs(o,n[o],t.uid,r)}})},{\"../render/line_atlas\":354,\"../source/query_features\":364,\"../source/source\":366,\"../source/source_cache\":367,\"../symbol/glyph_source\":399,\"../symbol/sprite_atlas\":404,\"../util/ajax\":425,\"../util/browser\":426,\"../util/dispatcher\":433,\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./animation_loop\":375,\"./image_sprite\":376,\"./style_function\":380,\"./style_layer\":381,\"./style_spec\":388,\"./validate_style\":390}],379:[function(t,e,r){\"use strict\";function n(t,e){this.value=s.clone(e),this.isFunction=a.isFunctionDefinition(e),this.json=JSON.stringify(this.value);var r=\"color\"===t.type&&this.value?o(this.value):e;if(this.calculate=a[t.function\|\|\"piecewise-constant\"](r),this.isFeatureConstant=this.calculate.isFeatureConstant,this.isZoomConstant=this.calculate.isZoomConstant,\"piecewise-constant\"===t.function&&t.transition&&(this.calculate=i(this.calculate)),!this.isFeatureConstant&&!this.isZoomConstant){this.stopZoomLevels=[];for(var n=[],l=this.value.stops,u=0;u<this.value.stops.length;u++){var c=l[u][0].zoom;this.stopZoomLevels.indexOf(c)<0&&(this.stopZoomLevels.push(c),n.push([c,n.length]))}this.calculateInterpolationT=a.interpolated({stops:n,base:e.base})}}function i(t){return function(e,r){var n,i,a,o=e.zoom,s=e.zoomHistory,l=e.duration,u=o%1,c=Math.min((Date.now()-s.lastIntegerZoomTime)/l,1),h=1;return o>s.lastIntegerZoom?(n=u+(1-u)c,h=2,i=t({zoom:o-1},r),a=t({zoom:o},r)):(n=1-(1-c)u,a=t({zoom:o},r),i=t({zoom:o+1},r),h/=2),void 0===i\|\|void 0===a?void 0:{from:i,fromScale:h,to:a,toScale:1,t:n}}}var a=t(\"./style_function\"),o=t(\"./parse_color\"),s=t(\"../util/util\");e.exports=n},{\"../util/util\":442,\"./parse_color\":377,\"./style_function\":380}],380:[function(t,e,r){\"use strict\";var n=t(\"mapbox-gl-function\");r.interpolated=function(t){var e=n.interpolated(t),r=function(t,r){return e(t&&t.zoom,r\|\|{})};return r.isFeatureConstant=e.isFeatureConstant,r.isZoomConstant=e.isZoomConstant,r},r[\"piecewise-constant\"]=function(t){var e=n[\"piecewise-constant\"](t),r=function(t,r){return e(t&&t.zoom,r\|\|{})};return r.isFeatureConstant=e.isFeatureConstant,r.isZoomConstant=e.isZoomConstant,r},r.isFunctionDefinition=n.isFunctionDefinition},{\"mapbox-gl-function\":302}],381:[function(t,e,r){\"use strict\";function n(t,e){this.set(t,e)}function i(t){return t.value}var a=t(\"../util/util\"),o=t(\"./style_transition\"),s=t(\"./style_declaration\"),l=t(\"./style_spec\"),u=t(\"./validate_style\"),c=t(\"./parse_color\"),h=t(\"../util/evented\");e.exports=n;n.create=function(e,r){return new({background:t(\"./style_layer/background_style_layer\"),circle:t(\"./style_layer/circle_style_layer\"),fill:t(\"./style_layer/fill_style_layer\"),line:t(\"./style_layer/line_style_layer\"),raster:t(\"./style_layer/raster_style_layer\"),symbol:t(\"./style_layer/symbol_style_layer\")}[(r\|\|e).type])(e,r)},n.prototype=a.inherit(h,{set:function(t,e){this.id=t.id,this.ref=t.ref,this.metadata=t.metadata,this.type=(e\|\|t).type,this.source=(e\|\|t).source,this.sourceLayer=(e\|\|t)[\"source-layer\"],this.minzoom=(e\|\|t).minzoom,this.maxzoom=(e\|\|t).maxzoom,this.filter=(e\|\|t).filter,this.paint={},this.layout={},this._paintSpecifications=l[\"paint_\"+this.type],this._layoutSpecifications=l[\"layout_\"+this.type],this._paintTransitions={},this._paintTransitionOptions={},this._paintDeclarations={},this._layoutDeclarations={},this._layoutFunctions={};var r,n;for(var i in t){var a=i.match(/^paint(?:\\.(.))?$/);if(a){var o=a[1]\|\|\"\";for(r in t[i])this.setPaintProperty(r,t[i][r],o)}}if(this.ref)this._layoutDeclarations=e._layoutDeclarations;else for(n in t.layout)this.setLayoutProperty(n,t.layout[n]);for(r in this._paintSpecifications)this.paint[r]=this.getPaintValue(r);for(n in this._layoutSpecifications)this._updateLayoutValue(n)},setLayoutProperty:function(t,e){if(null==e)delete this._layoutDeclarations[t];else{var r=\"layers.\"+this.id+\".layout.\"+t;if(this._handleErrors(u.layoutProperty,r,t,e))return;this._layoutDeclarations[t]=new s(this._layoutSpecifications[t],e)}this._updateLayoutValue(t)},getLayoutProperty:function(t){return this._layoutDeclarations[t]&&this._layoutDeclarations[t].value},getLayoutValue:function(t,e,r){var n=this._layoutSpecifications[t],i=this._layoutDeclarations[t];return i?i.calculate(e,r):n.default},setPaintProperty:function(t,e,r){var n=\"layers.\"+this.id+(r?'[\"paint.'+r+'\"].':\".paint.\")+t;if(a.endsWith(t,\"-transition\"))if(this._paintTransitionOptions[r\|\|\"\"]\|\|(this._paintTransitionOptions[r\|\|\"\"]={}),null===e\|\|void 0===e)delete this._paintTransitionOptions[r\|\|\"\"][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintTransitionOptions[r\|\|\"\"][t]=e}else if(this._paintDeclarations[r\|\|\"\"]\|\|(this._paintDeclarations[r\|\|\"\"]={}),null===e\|\|void 0===e)delete this._paintDeclarations[r\|\|\"\"][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintDeclarations[r\|\|\"\"][t]=new s(this._paintSpecifications[t],e)}},getPaintProperty:function(t,e){return e=e\|\|\"\",a.endsWith(t,\"-transition\")?this._paintTransitionOptions[e]&&this._paintTransitionOptions[e][t]:this._paintDeclarations[e]&&this._paintDeclarations[e][t]&&this._paintDeclarations[e][t].value},getPaintValue:function(t,e,r){var n=this._paintSpecifications[t],i=this._paintTransitions[t];return 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this._paintDeclarations){e[\"\"===r?\"paint\":\"paint.\"+r]=a.mapObject(this._paintDeclarations[r],i)}return(!this.ref\|\|t&&t.includeRefProperties)&&a.extend(e,{type:this.type,source:this.source,\"source-layer\":this.sourceLayer,filter:this.filter,layout:a.mapObject(this._layoutDeclarations,i)}),a.filterObject(e,function(t,e){return void 0!==t&&!(\"layout\"===e&&!Object.keys(t).length)})},_applyPaintDeclaration:function(t,e,r,n,i){var l=r.transition?this._paintTransitions[t]:void 0,u=this._paintSpecifications[t];if(null!==e&&void 0!==e\|\|(e=new s(u,u.default)),!l\|\|l.declaration.json!==e.json){var c=a.extend({duration:300,delay:0},n,this.getPaintProperty(t+\"-transition\")),h=this._paintTransitions[t]=new o(u,e,l,c);h.instant()\|\|(h.loopID=i.set(h.endTime-Date.now())),l&&i.cancel(l.loopID)}},_updateLayoutValue:function(t){var e=this._layoutDeclarations[t];e&&e.isFunction?this._layoutFunctions[t]=!0:(delete this._layoutFunctions[t],this.layout[t]=this.getLayoutValue(t))},_handleErrors:function(t,e,r,n){return u.emitErrors(this,t.call(u,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:l,style:{glyphs:!0,sprite:!0}}))}})},{\"../util/evented\":434,\"../util/util\":442,\"./parse_color\":377,\"./style_declaration\":379,\"./style_layer/background_style_layer\":382,\"./style_layer/circle_style_layer\":383,\"./style_layer/fill_style_layer\":384,\"./style_layer/line_style_layer\":385,\"./style_layer/raster_style_layer\":386,\"./style_layer/symbol_style_layer\":387,\"./style_spec\":388,\"./style_transition\":389,\"./validate_style\":390}],382:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],383:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],384:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");n.prototype=i.inherit(a,{getPaintValue:function(t,e,r){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintValue.call(this,\"fill-color\",e,r):a.prototype.getPaintValue.call(this,t,e,r)},getPaintValueStopZoomLevels:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintValueStopZoomLevels.call(this,\"fill-color\"):a.prototype.getPaintValueStopZoomLevels.call(this,arguments)},getPaintInterpolationT:function(t,e){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintInterpolationT.call(this,\"fill-color\",e):a.prototype.getPaintInterpolationT.call(this,t,e)},isPaintValueFeatureConstant:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.isPaintValueFeatureConstant.call(this,\"fill-color\"):a.prototype.isPaintValueFeatureConstant.call(this,t)},isPaintValueZoomConstant:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.isPaintValueZoomConstant.call(this,\"fill-color\"):a.prototype.isPaintValueZoomConstant.call(this,t)}}),e.exports=n},{\"../../util/util\":442,\"../style_layer\":381}],385:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{getPaintValue:function(t,e,r){var n=a.prototype.getPaintValue.apply(this,arguments);if(n&&\"line-dasharray\"===t){var i=Math.floor(e.zoom);this._flooredZoom!==i&&(this._flooredZoom=i,this._flooredLineWidth=this.getPaintValue(\"line-width\",e,r)),n.fromScale=this._flooredLineWidth,n.toScale=this._flooredLineWidth}return n}})},{\"../../util/util\":442,\"../style_layer\":381}],386:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],387:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{isHidden:function(){if(a.prototype.isHidden.apply(this,arguments))return!0;var t=0===this.paint[\"text-opacity\"]\|\|!this.layout[\"text-field\"],e=0===this.paint[\"icon-opacity\"]\|\|!this.layout[\"icon-image\"];return!(!t\|\|!e)},getLayoutValue:function(t,e,r){return(\"text-rotation-alignment\"!==t\|\|\"line\"!==this.getLayoutValue(\"symbol-placement\",e,r)\|\|this.getLayoutProperty(\"text-rotation-alignment\"))&&(\"icon-rotation-alignment\"!==t\|\|\"line\"!==this.getLayoutValue(\"symbol-placement\",e,r)\|\|this.getLayoutProperty(\"icon-rotation-alignment\"))?\"text-pitch-alignment\"!==t\|\|this.getLayoutProperty(\"text-pitch-alignment\")?a.prototype.getLayoutValue.apply(this,arguments):this.getLayoutValue(\"text-rotation-alignment\"):\"map\"}})},{\"../../util/util\":442,\"../style_layer\":381}],388:[function(t,e,r){\"use strict\";e.exports=t(\"mapbox-gl-style-spec/reference/latest.min\")},{\"mapbox-gl-style-spec/reference/latest.min\":325}],389:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.declaration=e,this.startTime=this.endTime=(new Date).getTime(),\"piecewise-constant\"===t.function&&t.transition?this.interp=i:this.interp=o[t.type],this.oldTransition=r,this.duration=n.duration\|\|0,this.delay=n.delay\|\|0,this.instant()\|\|(this.endTime=this.startTime+this.duration+this.delay,this.ease=a.easeCubicInOut),r&&r.endTime<=this.startTime&&delete r.oldTransition}function i(t,e,r){return void 0===(t&&t.to)\|\|void 0===(e&&e.to)?void 0:{from:t.to,fromScale:t.toScale,to:e.to,toScale:e.toScale,t:r}}var a=t(\"../util/util\"),o=t(\"../util/interpolate\");e.exports=n,n.prototype.instant=function(){return!this.oldTransition\|\|!this.interp\|\|0===this.duration&&0===this.delay},n.prototype.calculate=function(t,e){var r=this.declaration.calculate(a.extend({},t,{duration:this.duration}),e);if(this.instant())return r;var n=t.time\|\|Date.now();if(n<this.endTime){var i=this.oldTransition.calculate(a.extend({},t,{time:this.startTime}),e),o=this.ease((n-this.startTime-this.delay)/this.duration);r=this.interp(i,r,o)}return r}},{\"../util/interpolate\":436,\"../util/util\":442}],390:[function(t,e,r){\"use strict\";e.exports=t(\"mapbox-gl-style-spec/lib/validate_style.min\"),e.exports.emitErrors=function(t,e){if(e&&e.length){for(var r=0;r<e.length;r++)t.fire(\"error\",{error:new Error(e[r].message)});return!0}return!1},e.exports.throwErrors=function(t,e){if(e)for(var r=0;r<e.length;r++)throw new Error(e[r].message)}},{\"mapbox-gl-style-spec/lib/validate_style.min\":324}],391:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.x=t,this.y=e,this.angle=r,void 0!==n&&(this.segment=n)}var i=t(\"point-geometry\");e.exports=n,n.prototype=Object.create(i.prototype),n.prototype.clone=function(){return new n(this.x,this.y,this.angle,this.segment)}},{\"point-geometry\":484}],392:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],u=0;s<r/2;){var c=t[o-1],h=t[o],f=t[o+1];if(!f)return!1;var d=c.angleTo(h)-h.angleTo(f);for(d=Math.abs((d+3Math.PI)%(2Math.PI)-Math.PI),l.push({distance:s,angleDelta:d}),u+=d;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;o++,s+=h.dist(f)}return!0}e.exports=n},{}],393:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){for(var o=[],s=0;s<t.length;s++)for(var l,u=t[s],c=0;c<u.length-1;c++){var h=u[c],f=u[c+1];h.x<e&&f.x<e\|\|(h.x<e?h=new i(e,h.y+(f.y-h.y)((e-h.x)/(f.x-h.x)))._round():f.x<e&&(f=new i(e,h.y+(f.y-h.y)((e-h.x)/(f.x-h.x)))._round()),h.y<r&&f.y<r\|\|(h.y<r?h=new i(h.x+(f.x-h.x)((r-h.y)/(f.y-h.y)),r)._round():f.y<r&&(f=new i(h.x+(f.x-h.x)((r-h.y)/(f.y-h.y)),r)._round()),h.x>=n&&f.x>=n\|\|(h.x>=n?h=new i(n,h.y+(f.y-h.y)((n-h.x)/(f.x-h.x)))._round():f.x>=n&&(f=new i(n,h.y+(f.y-h.y)((n-h.x)/(f.x-h.x)))._round()),h.y>=a&&f.y>=a\|\|(h.y>=a?h=new i(h.x+(f.x-h.x)((a-h.y)/(f.y-h.y)),a)._round():f.y>=a&&(f=new i(h.x+(f.x-h.x)((a-h.y)/(f.y-h.y)),a)._round()),l&&h.equals(l[l.length-1])\|\|(l=[h],o.push(l)),l.push(f)))))}return o}var i=t(\"point-geometry\");e.exports=n},{\"point-geometry\":484}],394:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=e.exports=new n({members:[{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Float32\",name:\"maxScale\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"},{type:\"Int16\",name:\"bbox0\"},{type:\"Int16\",name:\"bbox1\"},{type:\"Int16\",name:\"bbox2\"},{type:\"Int16\",name:\"bbox3\"},{type:\"Float32\",name:\"placementScale\"}]});i.extendAll(o.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"point-geometry\":484}],395:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u,c){var h=o.tops-l,f=o.bottoms+l,d=o.lefts-l,p=o.rights+l;if(this.boxStartIndex=t.length,u){var m=f-h,v=p-d;if(m>0)if(m=Math.max(10s,m),c){var g=e[r.segment+1].sub(e[r.segment])._unit()._mult(v),y=[r.sub(g),r.add(g)];this._addLineCollisionBoxes(t,y,r,0,v,m,n,i,a)}else this._addLineCollisionBoxes(t,e,r,r.segment,v,m,n,i,a)}else t.emplaceBack(r.x,r.y,d,h,p,f,1/0,n,i,a,0,0,0,0,0);this.boxEndIndex=t.length}e.exports=n,n.prototype._addLineCollisionBoxes=function(t,e,r,n,i,a,o,s,l){var u=a/2,c=Math.floor(i/u),h=-a/2,f=this.boxes,d=r,p=n+1,m=h;do{if(--p<0)return f;m-=e[p].dist(d),d=e[p]}while(m>-i/2);for(var v=e[p].dist(e[p+1]),g=0;g<c;g++){for(var y=-i/2+gu;m+v<y;){if(m+=v,++p+1>=e.length)return f;v=e[p].dist(e[p+1])}var b=y-m,x=e[p],_=e[p+1],w=_.sub(x)._unit()._mult(b)._add(x)._round(),M=Math.max(Math.abs(y-h)-u/2,0),k=i/2/M;t.emplaceBack(w.x,w.y,-a/2,-a/2,a/2,a/2,k,o,s,l,0,0,0,0,0)}return f}},{}],396:[function(t,e,r){\"use strict\";function n(t,e,r){if(\"object\"==typeof t){var n=t;r=e,t=n.angle,e=n.pitch,this.grid=new o(n.grid),this.ignoredGrid=new o(n.ignoredGrid)}else this.grid=new o(a,12,6),this.ignoredGrid=new o(a,12,0);this.angle=t,this.pitch=e;var i=Math.sin(t),s=Math.cos(t);if(this.rotationMatrix=[s,-i,i,s],this.reverseRotationMatrix=[s,i,-i,s],this.yStretch=1/Math.cos(e/180Math.PI),this.yStretch=Math.pow(this.yStretch,1.3),this.collisionBoxArray=r,0===r.length){r.emplaceBack();r.emplaceBack(0,0,0,-32767,0,32767,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(a,0,0,-32767,0,32767,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,0,-32767,0,32767,0,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,a,-32767,0,32767,0,32767,0,0,0,0,0,0,0,0,0)}this.tempCollisionBox=r.get(0),this.edges=[r.get(1),r.get(2),r.get(3),r.get(4)]}var i=t(\"point-geometry\"),a=t(\"../data/bucket\").EXTENT,o=t(\"grid-index\");e.exports=n,n.prototype.serialize=function(){var t={angle:this.angle,pitch:this.pitch,grid:this.grid.toArrayBuffer(),ignoredGrid:this.ignoredGrid.toArrayBuffer()};return{data:t,transferables:[t.grid,t.ignoredGrid]}},n.prototype.minScale=.25,n.prototype.maxScale=2,n.prototype.placeCollisionFeature=function(t,e,r){for(var n=this.collisionBoxArray,a=this.minScale,o=this.rotationMatrix,s=this.yStretch,l=t.boxStartIndex;l<t.boxEndIndex;l++){var u=n.get(l),c=u.anchorPoint._matMult(o),h=c.x,f=c.y,d=h+u.x1,p=f+u.y1s,m=h+u.x2,v=f+u.y2s;if(u.bbox0=d,u.bbox1=p,u.bbox2=m,u.bbox3=v,!e)for(var g=this.grid.query(d,p,m,v),y=0;y<g.length;y++){var b=n.get(g[y]),x=b.anchorPoint._matMult(o);if((a=this.getPlacementScale(a,c,u,x,b))>=this.maxScale)return a}if(r){var _;if(this.angle){var w=this.reverseRotationMatrix,M=new i(u.x1,u.y1).matMult(w),k=new i(u.x2,u.y1).matMult(w),A=new i(u.x1,u.y2).matMult(w),T=new i(u.x2,u.y2).matMult(w);_=this.tempCollisionBox,_.anchorPointX=u.anchorPoint.x,_.anchorPointY=u.anchorPoint.y,_.x1=Math.min(M.x,k.x,A.x,T.x),_.y1=Math.min(M.y,k.x,A.x,T.x),_.x2=Math.max(M.x,k.x,A.x,T.x),_.y2=Math.max(M.y,k.x,A.x,T.x),_.maxScale=u.maxScale}else _=u;for(var S=0;S<this.edges.length;S++){var E=this.edges[S];if((a=this.getPlacementScale(a,u.anchorPoint,_,E.anchorPoint,E))>=this.maxScale)return a}}}return a},n.prototype.queryRenderedSymbols=function(t,e,r,n,a){var o={},s=[],l=this.collisionBoxArray,u=this.rotationMatrix,c=new i(t,e)._matMult(u),h=this.tempCollisionBox;h.anchorX=c.x,h.anchorY=c.y,h.x1=0,h.y1=0,h.x2=r-t,h.y2=n-e,h.maxScale=a,a=h.maxScale;for(var f=[c.x+h.x1/a,c.y+h.y1/athis.yStretch,c.x+h.x2/a,c.y+h.y2/athis.yStretch],d=this.grid.query(f[0],f[1],f[2],f[3]),p=this.ignoredGrid.query(f[0],f[1],f[2],f[3]),m=0;m<p.length;m++)d.push(p[m]);for(var v=0;v<d.length;v++){var g=l.get(d[v]),y=g.sourceLayerIndex,b=g.featureIndex;if(void 0===o[y]&&(o[y]={}),!o[y][b]){var x=g.anchorPoint.matMult(u);this.getPlacementScale(this.minScale,c,h,x,g)>=a&&(o[y][b]=!0,s.push(d[v]))}}return s},n.prototype.getPlacementScale=function(t,e,r,n,i){var a=e.x-n.x,o=e.y-n.y,s=(i.x1-r.x2)/a,l=(i.x2-r.x1)/a,u=(i.y1-r.y2)this.yStretch/o,c=(i.y2-r.y1)this.yStretch/o;(isNaN(s)\|\|isNaN(l))&&(s=l=1),(isNaN(u)\|\|isNaN(c))&&(u=c=1);var h=Math.min(Math.max(s,l),Math.max(u,c)),f=i.maxScale,d=r.maxScale;return h>f&&(h=f),h>d&&(h=d),h>t&&h>=i.placementScale&&(t=h),t},n.prototype.insertCollisionFeature=function(t,e,r){for(var n=r?this.ignoredGrid:this.grid,i=this.collisionBoxArray,a=t.boxStartIndex;a<t.boxEndIndex;a++){var o=i.get(a);o.placementScale=e,e<this.maxScale&&n.insert(a,o.bbox0,o.bbox1,o.bbox2,o.bbox3)}}},{\"../data/bucket\":329,\"grid-index\":287,\"point-geometry\":484}],397:[function(t,e,r){\"use strict\";function n(t,e,r,n,a,o,s,l,u){var c=n?.6os:0,h=Math.max(n?n.right-n.left:0,a?a.right-a.left:0),f=0===t[0].x\|\|t[0].x===u\|\|0===t[0].y\|\|t[0].y===u;e-hs<e/4&&(e=hs+e/4);var d=2o;return i(t,f?e/2l%e:(h/2+d)sl%e,e,c,r,hs,f,!1,u)}function i(t,e,r,n,l,u,c,h,f){for(var d=u/2,p=0,m=0;m<t.length-1;m++)p+=t[m].dist(t[m+1]);for(var v=0,g=e-r,y=[],b=0;b<t.length-1;b++){for(var x=t[b],_=t[b+1],w=x.dist(_),M=_.angleTo(x);g+r<v+w;){g+=r;var k=(g-v)/w,A=a(x.x,_.x,k),T=a(x.y,_.y,k);if(A>=0&&A<f&&T>=0&&T<f&&g-d>=0&&g+d<=p){var S=new o(A,T,M,b)._round();n&&!s(t,S,u,n,l)\|\|y.push(S)}}v+=w}return h\|\|y.length\|\|c\|\|(y=i(t,v/2,r,n,l,u,c,!0,f)),y}var a=t(\"../util/interpolate\"),o=t(\"../symbol/anchor\"),s=t(\"./check_max_angle\");e.exports=n},{\"../symbol/anchor\":391,\"../util/interpolate\":436,\"./check_max_angle\":392}],398:[function(t,e,r){\"use strict\";function n(){this.width=o,this.height=o,this.bin=new i(this.width,this.height),this.index={},this.ids={},this.data=new Uint8Array(this.widththis.height)}var i=t(\"shelf-pack\"),a=t(\"../util/util\"),o=128;e.exports=n,n.prototype.getGlyphs=function(){var t,e,r,n={};for(var i in this.ids)t=i.split(\"#\"),e=t[0],r=t[1],n[e]\|\|(n[e]=[]),n[e].push(r);return n},n.prototype.getRects=function(){var t,e,r,n={};for(var i in this.ids)t=i.split(\"#\"),e=t[0],r=t[1],n[e]\|\|(n[e]={}),n[e][r]=this.index[i];return n},n.prototype.addGlyph=function(t,e,r,n){if(!r)return null;var i=e+\"#\"+r.id;if(this.index[i])return this.ids[i].indexOf(t)<0&&this.ids[i].push(t),this.index[i];if(!r.bitmap)return null;var o=r.width+2n,s=r.height+2n,l=o+2,u=s+2;l+=4-l%4,u+=4-u%4;var c=this.bin.packOne(l,u);if(c\|\|(this.resize(),c=this.bin.packOne(l,u)),!c)return a.warnOnce(\"glyph bitmap overflow\"),null;this.index[i]=c,this.ids[i]=[t];for(var h=this.data,f=r.bitmap,d=0;d<s;d++)for(var p=this.width(c.y+d+1)+c.x+1,m=od,v=0;v<o;v++)h[p+v]=f[m+v];return this.dirty=!0,c},n.prototype.resize=function(){var t=this.width,e=this.height;if(!(t>=2048\|\|e>=2048)){this.texture&&(this.gl&&this.gl.deleteTexture(this.texture),this.texture=null),this.width=4,this.height=4,this.bin.resize(this.width,this.height);for(var r=new ArrayBuffer(this.widththis.height),n=0;n<e;n++){var i=new Uint8Array(this.data.buffer,en,t);new Uint8Array(r,en4,t).set(i)}this.data=new Uint8Array(r)}},n.prototype.bind=function(t){this.gl=t,this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,this.width,this.height,0,t.ALPHA,t.UNSIGNED_BYTE,null))},n.prototype.updateTexture=function(t){this.bind(t),this.dirty&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.ALPHA,t.UNSIGNED_BYTE,this.data),this.dirty=!1)}},{\"../util/util\":442,\"shelf-pack\":514}],399:[function(t,e,r){\"use strict\";function n(t){this.url=t&&o(t),this.atlases={},this.stacks={},this.loading={}}function i(t,e,r){this.advance=t.advance,this.left=t.left-r-1,this.top=t.top+r+1,this.rect=e}function a(t,e,r,n){return n=n\|\|\"abc\",r.replace(\"{s}\",n[t.length%n.length]).replace(\"{fontstack}\",t).replace(\"{range}\",e)}var o=t(\"../util/mapbox\").normalizeGlyphsURL,s=t(\"../util/ajax\").getArrayBuffer,l=t(\"../util/glyphs\"),u=t(\"../symbol/glyph_atlas\"),c=t(\"pbf\");e.exports=n,n.prototype.getSimpleGlyphs=function(t,e,r,n){void 0===this.stacks[t]&&(this.stacks[t]={}),void 0===this.atlases[t]&&(this.atlases[t]=new u);for(var a,o={},s=this.stacks[t],l=this.atlases[t],c={},h=0,f=0;f<e.length;f++){var d=e[f];if(a=Math.floor(d/256),s[a]){var p=s[a].glyphs[d],m=l.addGlyph(r,t,p,3);p&&(o[d]=new i(p,m,3))}else void 0===c[a]&&(c[a]=[],h++),c[a].push(d)}h\|\|n(void 0,o,t);var v=function(e,a,s){if(!e)for(var u=this.stacks[t][a]=s.stacks[0],f=0;f<c[a].length;f++){var d=c[a][f],p=u.glyphs[d],m=l.addGlyph(r,t,p,3);p&&(o[d]=new i(p,m,3))}--h\|\|n(void 0,o,t)}.bind(this);for(var g in c)this.loadRange(t,g,v)},n.prototype.loadRange=function(t,e,r){if(256e>65535)return r(\"glyphs > 65535 not supported\");void 0===this.loading[t]&&(this.loading[t]={});var n=this.loading[t];if(n[e])n[e].push(r);else{n[e]=[r];var i=256e+\"-\"+(256e+255),o=a(t,i,this.url);s(o,function(t,r){for(var i=!t&&new l(new c(new Uint8Array(r))),a=0;a<n[e].length;a++)n[e][a](t,e,i);delete n[e]})}},n.prototype.getGlyphAtlas=function(t){return this.atlases[t]}},{\"../symbol/glyph_atlas\":398,\"../util/ajax\":425,\"../util/glyphs\":435,\"../util/mapbox\":439,pbf:478}],400:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){function n(n){c.push(t[n]),h.push(r[n]),f.push(e[n]),d++}function i(t,e,r){var n=u[t];return delete u[t],u[e]=n,h[n][0].pop(),h[n][0]=h[n][0].concat(r[0]),n}function a(t,e,r){var n=l[e];return delete l[e],l[t]=n,h[n][0].shift(),h[n][0]=r[0].concat(h[n][0]),n}function o(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}var s,l={},u={},c=[],h=[],f=[],d=0;for(s=0;s<t.length;s++){var p=r[s],m=e[s];if(m){var v=o(m,p),g=o(m,p,!0);if(v in u&&g in l&&u[v]!==l[g]){var y=a(v,g,p),b=i(v,g,h[y]);delete l[v],delete u[g],u[o(m,h[b],!0)]=b,h[y]=null}else v in u?i(v,g,p):g in l?a(v,g,p):(n(s),l[v]=d-1,u[g]=d-1)}else n(s)}return{features:c,textFeatures:f,geometries:h}}},{}],401:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u){this.anchorPoint=t,this.tl=e,this.tr=r,this.bl=n,this.br=i,this.tex=a,this.anchorAngle=o,this.glyphAngle=s,this.minScale=l,this.maxScale=u}function i(t,e,r,i,a,o,u,c,h){var f,d,p,m,v=e.image.rect,g=a.layout,y=e.left-1,b=y+v.w/e.image.pixelRatio,x=e.top-1,_=x+v.h/e.image.pixelRatio;if(\"none\"!==g[\"icon-text-fit\"]&&u){var w=b-y,M=_-x,k=g[\"text-size\"]/24,A=u.leftk,T=u.rightk,S=u.topk,E=u.bottomk,L=T-A,C=E-S,I=g[\"icon-text-fit-padding\"][0],z=g[\"icon-text-fit-padding\"][1],D=g[\"icon-text-fit-padding\"][2],P=g[\"icon-text-fit-padding\"][3],O=\"width\"===g[\"icon-text-fit\"]?.5(C-M):0,R=\"height\"===g[\"icon-text-fit\"]?.5(L-w):0,F=\"width\"===g[\"icon-text-fit\"]\|\|\"both\"===g[\"icon-text-fit\"]?L:w,j=\"height\"===g[\"icon-text-fit\"]\|\|\"both\"===g[\"icon-text-fit\"]?C:M;f=new s(A+R-P,S+O-I),d=new s(A+R+z+F,S+O-I),p=new s(A+R+z+F,S+O+D+j),m=new s(A+R-P,S+O+D+j)}else f=new s(y,x),d=new s(b,x),p=new s(b,_),m=new s(y,_);var N=a.getLayoutValue(\"icon-rotate\",c,h)Math.PI/180;if(o){var B=i[t.segment];if(t.y===B.y&&t.x===B.x&&t.segment+1<i.length){var U=i[t.segment+1];N+=Math.atan2(t.y-U.y,t.x-U.x)+Math.PI}else N+=Math.atan2(t.y-B.y,t.x-B.x)}if(N){var V=Math.sin(N),H=Math.cos(N),q=[H,-V,V,H];f=f.matMult(q),d=d.matMult(q),m=m.matMult(q),p=p.matMult(q)}return[new n(new s(t.x,t.y),f,d,m,p,e.image.rect,0,0,l,1/0)]}function a(t,e,r,i,a,u){for(var c=a.layout[\"text-rotate\"]Math.PI/180,h=a.layout[\"text-keep-upright\"],f=e.positionedGlyphs,d=[],p=0;p<f.length;p++){var m=f[p],v=m.glyph,g=v.rect;if(g){var y,b=(m.x+v.advance/2)r,x=l;u?(y=[],x=o(y,t,b,i,t.segment,!0),h&&(x=Math.min(x,o(y,t,b,i,t.segment,!1)))):y=[{anchorPoint:new s(t.x,t.y),offset:0,angle:0,maxScale:1/0,minScale:l}];for(var _=m.x+v.left,w=m.y-v.top,M=_+g.w,k=w+g.h,A=new s(_,w),T=new s(M,w),S=new s(_,k),E=new s(M,k),L=0;L<y.length;L++){var C=y[L],I=A,z=T,D=S,P=E;if(c){var O=Math.sin(c),R=Math.cos(c),F=[R,-O,O,R];I=I.matMult(F),z=z.matMult(F),D=D.matMult(F),P=P.matMult(F)}var j=Math.max(C.minScale,x),N=(t.angle+C.offset+2Math.PI)%(2Math.PI),B=(C.angle+C.offset+2Math.PI)%(2Math.PI);d.push(new n(C.anchorPoint,I,z,D,P,g,N,B,j,C.maxScale))}}}return d}function o(t,e,r,n,i,a){var o=!a;r<0&&(a=!a),a&&i++;var u=new s(e.x,e.y),c=n[i],h=1/0;r=Math.abs(r);for(var f=l;;){var d=u.dist(c),p=r/d,m=Math.atan2(c.y-u.y,c.x-u.x);if(a\|\|(m+=Math.PI),t.push({anchorPoint:u,offset:o?Math.PI:0,minScale:p,maxScale:h,angle:(m+2Math.PI)%(2Math.PI)}),p<=f)break;for(u=c;u.equals(c);)if(i+=a?1:-1,!(c=n[i]))return p;var v=c.sub(u)._unit();u=u.sub(v._mult(d)),h=p}return f}var s=t(\"point-geometry\");e.exports={getIconQuads:i,getGlyphQuads:a,SymbolQuad:n};var l=.5},{\"point-geometry\":484}],402:[function(t,e,r){\"use strict\";function n(t,e,r){for(var n=[],a=0,o=t.length;a<o;a++){var s=i(t[a].properties,e[\"text-field\"]);if(s){s=s.toString();var l=e[\"text-transform\"];\"uppercase\"===l?s=s.toLocaleUpperCase():\"lowercase\"===l&&(s=s.toLocaleLowerCase());for(var u=0;u<s.length;u++)r[s.charCodeAt(u)]=!0;n[a]=s}else n[a]=null}return n}var i=t(\"../util/token\");e.exports=n},{\"../util/token\":441}],403:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.codePoint=t,this.x=e,this.y=r,this.glyph=n}function i(t,e,r,n,i,a){this.positionedGlyphs=t,this.text=e,this.top=r,this.bottom=n,this.left=i,this.right=a}function a(t,e,r,a,s,l,u,c,h){for(var f=[],d=new i(f,t,h[1],h[1],h[0],h[0]),p=0,m=0;m<t.length;m++){var v=t.charCodeAt(m),g=e[v];g&&(f.push(new n(v,p,-17,g)),p+=g.advance+c)}return!!f.length&&(o(d,e,a,r,s,l,u,h),d)}function o(t,e,r,n,i,a,o,u){var c=null,d=0,p=0,m=0,v=0,g=t.positionedGlyphs;if(n)for(var y=0;y<g.length;y++){var b=g[y];if(b.x-=d,b.y+=rm,b.x>n&&null!==c){var x=g[c+1].x;v=Math.max(x,v);for(var _=c+1;_<=y;_++)g[_].y+=r,g[_].x-=x;if(o){var w=c;h[g[c].codePoint]&&w--,s(g,e,p,w,o)}p=c+1,c=null,d+=x,m++}f[b.codePoint]&&(c=y)}var M=g[g.length-1],k=M.x+e[M.codePoint].advance;v=Math.max(v,k);var A=(m+1)r;s(g,e,p,g.length-1,o),l(g,o,i,a,v,r,m,u),t.top+=-aA,t.bottom=t.top+A,t.left+=-iv,t.right=t.left+v}function s(t,e,r,n,i){for(var a=e[t[n].codePoint].advance,o=(t[n].x+a)i,s=r;s<=n;s++)t[s].x-=o}\n", "function l(t,e,r,n,i,a,o,s){for(var l=(e-r)i+s[0],u=(-n(o+1)+.5)a+s[1],c=0;c<t.length;c++)t[c].x+=l,t[c].y+=u}function u(t,e){if(!t\|\|!t.rect)return null;var r=e[\"icon-offset\"][0],n=e[\"icon-offset\"][1],i=r-t.width/2,a=i+t.width,o=n-t.height/2;return new c(t,o,o+t.height,i,a)}function c(t,e,r,n,i){this.image=t,this.top=e,this.bottom=r,this.left=n,this.right=i}e.exports={shapeText:a,shapeIcon:u};var h={32:!0,8203:!0},f={32:!0,38:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0}},{}],404:[function(t,e,r){\"use strict\";function n(t,e){this.width=t,this.height=e,this.bin=new o(t,e),this.images={},this.data=!1,this.texture=0,this.filter=0,this.pixelRatio=1,this.dirty=!0}function i(t,e,r,n,i,a,o,s,l,u,c){var h,f,d=ne+r,p=sa+o;if(c)for(p-=a,f=-1;f<=u;f++,d=((f+u)%u+n)e+r,p+=a)for(h=-1;h<=l;h++)i[p+h]=t[d+(h+l)%l];else for(f=0;f<u;f++,d+=e,p+=a)for(h=0;h<l;h++)i[p+h]=t[d+h]}function a(t,e,r,n,i){this.rect=t,this.width=e,this.height=r,this.sdf=n,this.pixelRatio=i}var o=t(\"shelf-pack\"),s=t(\"../util/browser\"),l=t(\"../util/util\");e.exports=n,n.prototype.allocateImage=function(t,e){t/=this.pixelRatio,e/=this.pixelRatio;var r=t+2+(4-(t+2)%4),n=e+2+(4-(e+2)%4),i=this.bin.packOne(r,n);return i\|\|(l.warnOnce(\"SpriteAtlas out of space.\"),null)},n.prototype.getImage=function(t,e){if(this.images[t])return this.images[t];if(!this.sprite)return null;var r=this.sprite.getSpritePosition(t);if(!r.width\|\|!r.height)return null;var n=this.allocateImage(r.width,r.height);if(!n)return null;var i=new a(n,r.width/r.pixelRatio,r.height/r.pixelRatio,r.sdf,r.pixelRatio/this.pixelRatio);return this.images[t]=i,this.copy(n,r,e),i},n.prototype.getPosition=function(t,e){var r=this.getImage(t,e),n=r&&r.rect;if(!n)return null;var i=r.widthr.pixelRatio,a=r.heightr.pixelRatio;return{size:[r.width,r.height],tl:[(n.x+1)/this.width,(n.y+1)/this.height],br:[(n.x+1+i)/this.width,(n.y+1+a)/this.height]}},n.prototype.allocate=function(){if(!this.data){var t=Math.floor(this.widththis.pixelRatio),e=Math.floor(this.heightthis.pixelRatio);this.data=new Uint32Array(te);for(var r=0;r<this.data.length;r++)this.data[r]=0}},n.prototype.copy=function(t,e,r){if(this.sprite.img.data){var n=new Uint32Array(this.sprite.img.data.buffer);this.allocate();var a=this.data;i(n,this.sprite.img.width,e.x,e.y,a,this.widththis.pixelRatio,(t.x+1)this.pixelRatio,(t.y+1)this.pixelRatio,e.width,e.height,r),this.dirty=!0}},n.prototype.setSprite=function(t){t&&(this.pixelRatio=s.devicePixelRatio>1?2:1,this.canvas&&(this.canvas.width=this.widththis.pixelRatio,this.canvas.height=this.heightthis.pixelRatio)),this.sprite=t},n.prototype.addIcons=function(t,e){for(var r=0;r<t.length;r++)this.getImage(t[r]);e(null,this.images)},n.prototype.bind=function(t,e){var r=!1;this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),r=!0);var n=e?t.LINEAR:t.NEAREST;n!==this.filter&&(t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,n),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,n),this.filter=n),this.dirty&&(this.allocate(),r?t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.widththis.pixelRatio,this.heightthis.pixelRatio,0,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)):t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.widththis.pixelRatio,this.heightthis.pixelRatio,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)),this.dirty=!1)}},{\"../util/browser\":426,\"../util/util\":442,\"shelf-pack\":514}],405:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=e.exports=new n({members:[{type:\"Uint16\",name:\"textBoxStartIndex\"},{type:\"Uint16\",name:\"textBoxEndIndex\"},{type:\"Uint16\",name:\"iconBoxStartIndex\"},{type:\"Uint16\",name:\"iconBoxEndIndex\"},{type:\"Uint16\",name:\"glyphQuadStartIndex\"},{type:\"Uint16\",name:\"glyphQuadEndIndex\"},{type:\"Uint16\",name:\"iconQuadStartIndex\"},{type:\"Uint16\",name:\"iconQuadEndIndex\"},{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int8\",name:\"index\"}]});i.extendAll(o.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"point-geometry\":484}],406:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=t(\"./quads\").SymbolQuad,s=e.exports=new n({members:[{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Float32\",name:\"tlX\"},{type:\"Float32\",name:\"tlY\"},{type:\"Float32\",name:\"trX\"},{type:\"Float32\",name:\"trY\"},{type:\"Float32\",name:\"blX\"},{type:\"Float32\",name:\"blY\"},{type:\"Float32\",name:\"brX\"},{type:\"Float32\",name:\"brY\"},{type:\"Int16\",name:\"texH\"},{type:\"Int16\",name:\"texW\"},{type:\"Int16\",name:\"texX\"},{type:\"Int16\",name:\"texY\"},{type:\"Float32\",name:\"anchorAngle\"},{type:\"Float32\",name:\"glyphAngle\"},{type:\"Float32\",name:\"maxScale\"},{type:\"Float32\",name:\"minScale\"}]});i.extendAll(s.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)},get SymbolQuad(){return new o(this.anchorPoint,new a(this.tlX,this.tlY),new a(this.trX,this.trY),new a(this.blX,this.blY),new a(this.brX,this.brY),{x:this.texX,y:this.texY,h:this.texH,w:this.texW,height:this.texH,width:this.texW},this.anchorAngle,this.glyphAngle,this.minScale,this.maxScale)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"./quads\":401,\"point-geometry\":484}],407:[function(t,e,r){\"use strict\";var n=t(\"../util/dom\"),i=t(\"point-geometry\"),a={scrollZoom:t(\"./handler/scroll_zoom\"),boxZoom:t(\"./handler/box_zoom\"),dragRotate:t(\"./handler/drag_rotate\"),dragPan:t(\"./handler/drag_pan\"),keyboard:t(\"./handler/keyboard\"),doubleClickZoom:t(\"./handler/dblclick_zoom\"),touchZoomRotate:t(\"./handler/touch_zoom_rotate\")};e.exports=function(t,e){function r(t){g(\"mouseout\",t)}function o(e){t.stop(),_=n.mousePos(b,e),g(\"mousedown\",e)}function s(e){var r=t.dragRotate&&t.dragRotate.isActive();x&&!r&&g(\"contextmenu\",x),x=null,g(\"mouseup\",e)}function l(e){if(!(t.dragPan&&t.dragPan.isActive()\|\|t.dragRotate&&t.dragRotate.isActive())){for(var r=e.toElement\|\|e.target;r&&r!==b;)r=r.parentNode;r===b&&g(\"mousemove\",e)}}function u(e){t.stop(),y(\"touchstart\",e),!e.touches\|\|e.touches.length>1\|\|(w?(clearTimeout(w),w=null,g(\"dblclick\",e)):w=setTimeout(d,300))}function c(t){y(\"touchmove\",t)}function h(t){y(\"touchend\",t)}function f(t){y(\"touchcancel\",t)}function d(){w=null}function p(t){n.mousePos(b,t).equals(_)&&g(\"click\",t)}function m(t){g(\"dblclick\",t),t.preventDefault()}function v(t){x=t,t.preventDefault()}function g(e,r){var i=n.mousePos(b,r);return t.fire(e,{lngLat:t.unproject(i),point:i,originalEvent:r})}function y(e,r){var a=n.touchPos(b,r),o=a.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new i(0,0));return t.fire(e,{lngLat:t.unproject(o),point:o,lngLats:a.map(function(e){return t.unproject(e)},this),points:a,originalEvent:r})}var b=t.getCanvasContainer(),x=null,_=null,w=null;for(var M in a)t[M]=new a[M](t,e),e.interactive&&e[M]&&t[M].enable();b.addEventListener(\"mouseout\",r,!1),b.addEventListener(\"mousedown\",o,!1),b.addEventListener(\"mouseup\",s,!1),b.addEventListener(\"mousemove\",l,!1),b.addEventListener(\"touchstart\",u,!1),b.addEventListener(\"touchend\",h,!1),b.addEventListener(\"touchmove\",c,!1),b.addEventListener(\"touchcancel\",f,!1),b.addEventListener(\"click\",p,!1),b.addEventListener(\"dblclick\",m,!1),b.addEventListener(\"contextmenu\",v,!1)}},{\"../util/dom\":428,\"./handler/box_zoom\":413,\"./handler/dblclick_zoom\":414,\"./handler/drag_pan\":415,\"./handler/drag_rotate\":416,\"./handler/keyboard\":417,\"./handler/scroll_zoom\":418,\"./handler/touch_zoom_rotate\":419,\"point-geometry\":484}],408:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i=t(\"../util/interpolate\"),a=t(\"../util/browser\"),o=t(\"../geo/lng_lat\"),s=t(\"../geo/lng_lat_bounds\"),l=t(\"point-geometry\"),u=e.exports=function(){};n.extend(u.prototype,{getCenter:function(){return this.transform.center},setCenter:function(t,e){return this.jumpTo({center:t},e),this},panBy:function(t,e,r){return this.panTo(this.transform.center,n.extend({offset:l.convert(t).mult(-1)},e),r),this},panTo:function(t,e,r){return this.easeTo(n.extend({center:t},e),r)},getZoom:function(){return this.transform.zoom},setZoom:function(t,e){return this.jumpTo({zoom:t},e),this},zoomTo:function(t,e,r){return this.easeTo(n.extend({zoom:t},e),r)},zoomIn:function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},zoomOut:function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},getBearing:function(){return this.transform.bearing},setBearing:function(t,e){return this.jumpTo({bearing:t},e),this},rotateTo:function(t,e,r){return this.easeTo(n.extend({bearing:t},e),r)},resetNorth:function(t,e){return this.rotateTo(0,n.extend({duration:1e3},t),e),this},snapToNorth:function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},getPitch:function(){return this.transform.pitch},setPitch:function(t,e){return this.jumpTo({pitch:t},e),this},fitBounds:function(t,e,r){e=n.extend({padding:0,offset:[0,0],maxZoom:1/0},e),t=s.convert(t);var i=l.convert(e.offset),a=this.transform,o=a.project(t.getNorthWest()),u=a.project(t.getSouthEast()),c=u.sub(o),h=(a.width-2e.padding-2Math.abs(i.x))/c.x,f=(a.height-2e.padding-2Math.abs(i.y))/c.y;return e.center=a.unproject(o.add(u).div(2)),e.zoom=Math.min(a.scaleZoom(a.scaleMath.min(h,f)),e.maxZoom),e.bearing=0,e.linear?this.easeTo(e,r):this.flyTo(e,r)},jumpTo:function(t,e){this.stop();var r=this.transform,n=!1,i=!1,a=!1;return\"zoom\"in t&&r.zoom!==+t.zoom&&(n=!0,r.zoom=+t.zoom),\"center\"in t&&(r.center=o.convert(t.center)),\"bearing\"in t&&r.bearing!==+t.bearing&&(i=!0,r.bearing=+t.bearing),\"pitch\"in t&&r.pitch!==+t.pitch&&(a=!0,r.pitch=+t.pitch),this.fire(\"movestart\",e).fire(\"move\",e),n&&this.fire(\"zoomstart\",e).fire(\"zoom\",e).fire(\"zoomend\",e),i&&this.fire(\"rotate\",e),a&&this.fire(\"pitch\",e),this.fire(\"moveend\",e)},easeTo:function(t,e){this.stop(),t=n.extend({offset:[0,0],duration:500,easing:n.ease},t);var r,a,s=this.transform,u=l.convert(t.offset),c=this.getZoom(),h=this.getBearing(),f=this.getPitch(),d=\"zoom\"in t?+t.zoom:c,p=\"bearing\"in t?this._normalizeBearing(t.bearing,h):h,m=\"pitch\"in t?+t.pitch:f;\"center\"in t?(r=o.convert(t.center),a=s.centerPoint.add(u)):\"around\"in t?(r=o.convert(t.around),a=s.locationPoint(r)):(a=s.centerPoint.add(u),r=s.pointLocation(a));var v=s.locationPoint(r);return!1===t.animate&&(t.duration=0),this.zooming=d!==c,this.rotating=h!==p,this.pitching=m!==f,t.noMoveStart\|\|this.fire(\"movestart\",e),this.zooming&&this.fire(\"zoomstart\",e),clearTimeout(this._onEaseEnd),this._ease(function(t){this.zooming&&(s.zoom=i(c,d,t)),this.rotating&&(s.bearing=i(h,p,t)),this.pitching&&(s.pitch=i(f,m,t)),s.setLocationAtPoint(r,v.add(a.sub(v)._mult(t))),this.fire(\"move\",e),this.zooming&&this.fire(\"zoom\",e),this.rotating&&this.fire(\"rotate\",e),this.pitching&&this.fire(\"pitch\",e)},function(){t.delayEndEvents?this._onEaseEnd=setTimeout(this._easeToEnd.bind(this,e),t.delayEndEvents):this._easeToEnd(e)}.bind(this),t),this},_easeToEnd:function(t){var e=this.zooming;this.zooming=!1,this.rotating=!1,this.pitching=!1,e&&this.fire(\"zoomend\",t),this.fire(\"moveend\",t)},flyTo:function(t,e){function r(t){var e=(AA-kk+(t?-1:1)LLTT)/(2(t?A:k)LT);return Math.log(Math.sqrt(ee+1)-e)}function a(t){return(Math.exp(t)-Math.exp(-t))/2}function s(t){return(Math.exp(t)+Math.exp(-t))/2}function u(t){return a(t)/s(t)}this.stop(),t=n.extend({offset:[0,0],speed:1.2,curve:1.42,easing:n.ease},t);var c=this.transform,h=l.convert(t.offset),f=this.getZoom(),d=this.getBearing(),p=this.getPitch(),m=\"center\"in t?o.convert(t.center):this.getCenter(),v=\"zoom\"in t?+t.zoom:f,g=\"bearing\"in t?this._normalizeBearing(t.bearing,d):d,y=\"pitch\"in t?+t.pitch:p;Math.abs(c.center.lng)+Math.abs(m.lng)>180&&(c.center.lng>0&&m.lng<0?m.lng+=360:c.center.lng<0&&m.lng>0&&(m.lng-=360));var b=c.zoomScale(v-f),x=c.point,_=\"center\"in t?c.project(m).sub(h.div(b)):x,w=c.worldSize,M=t.curve,k=Math.max(c.width,c.height),A=k/b,T=_.sub(x).mag();if(\"minZoom\"in t){var S=n.clamp(Math.min(t.minZoom,f,v),c.minZoom,c.maxZoom),E=k/c.zoomScale(S-f);M=Math.sqrt(E/T2)}var L=MM,C=r(0),I=function(t){return s(C)/s(C+Mt)},z=function(t){return k((s(C)u(C+Mt)-a(C))/L)/T},D=(r(1)-C)/M;if(Math.abs(T)<1e-6){if(Math.abs(k-A)<1e-6)return this.easeTo(t);var P=A<k?-1:1;D=Math.abs(Math.log(A/k))/M,z=function(){return 0},I=function(t){return Math.exp(PMt)}}if(\"duration\"in t)t.duration=+t.duration;else{var O=\"screenSpeed\"in t?+t.screenSpeed/M:+t.speed;t.duration=1e3D/O}return this.zooming=!0,d!==g&&(this.rotating=!0),p!==y&&(this.pitching=!0),this.fire(\"movestart\",e),this.fire(\"zoomstart\",e),this._ease(function(t){var r=tD,n=z(r);c.zoom=f+c.scaleZoom(1/I(r)),c.center=c.unproject(x.add(_.sub(x).mult(n)),w),this.rotating&&(c.bearing=i(d,g,t)),this.pitching&&(c.pitch=i(p,y,t)),this.fire(\"move\",e),this.fire(\"zoom\",e),this.rotating&&this.fire(\"rotate\",e),this.pitching&&this.fire(\"pitch\",e)},function(){this.zooming=!1,this.rotating=!1,this.pitching=!1,this.fire(\"zoomend\",e),this.fire(\"moveend\",e)},t),this},isEasing:function(){return!!this._abortFn},stop:function(){return this._abortFn&&(this._abortFn(),this._finishEase()),this},_ease:function(t,e,r){this._finishFn=e,this._abortFn=a.timed(function(e){t.call(this,r.easing(e)),1===e&&this._finishEase()},!1===r.animate?0:r.duration,this)},_finishEase:function(){delete this._abortFn;var t=this._finishFn;delete this._finishFn,t.call(this)},_normalizeBearing:function(t,e){t=n.wrap(t,-180,180);var r=Math.abs(t-e);return Math.abs(t-360-e)<r&&(t-=360),Math.abs(t+360-e)<r&&(t+=360),t},_updateEasing:function(t,e,r){var i;if(this.ease){var a=this.ease,o=(Date.now()-a.start)/a.duration,s=a.easing(o+.01)-a.easing(o),l=.27/Math.sqrt(ss+1e-4).01,u=Math.sqrt(.0729-ll);i=n.bezier(l,u,.25,1)}else i=r?n.bezier.apply(n,r):n.ease;return this.ease={start:(new Date).getTime(),to:Math.pow(2,e),duration:t,easing:i},i}})},{\"../geo/lng_lat\":339,\"../geo/lng_lat_bounds\":340,\"../util/browser\":426,\"../util/interpolate\":436,\"../util/util\":442,\"point-geometry\":484}],409:[function(t,e,r){\"use strict\";function n(t){o.setOptions(this,t)}var i=t(\"./control\"),a=t(\"../../util/dom\"),o=t(\"../../util/util\");e.exports=n,n.createAttributionString=function(t){var e=[];for(var r in t){var n=t[r];n.attribution&&e.indexOf(n.attribution)<0&&e.push(n.attribution)}return e.sort(function(t,e){return t.length-e.length}),e=e.filter(function(t,r){for(var n=r+1;n<e.length;n++)if(e[n].indexOf(t)>=0)return!1;return!0}),e.join(\" \| \")},n.prototype=o.inherit(i,{options:{position:\"bottom-right\"},onAdd:function(t){var e=this._container=a.create(\"div\",\"mapboxgl-ctrl-attrib\",t.getContainer());return this._update(),t.on(\"source.load\",this._update.bind(this)),t.on(\"source.change\",this._update.bind(this)),t.on(\"source.remove\",this._update.bind(this)),t.on(\"moveend\",this._updateEditLink.bind(this)),e},_update:function(){this._map.style&&(this._container.innerHTML=n.createAttributionString(this._map.style.sources)),this._editLink=this._container.getElementsByClassName(\"mapbox-improve-map\")[0],this._updateEditLink()},_updateEditLink:function(){if(this._editLink){var t=this._map.getCenter();this._editLink.href=\"https://www.mapbox.com/map-feedback/#/\"+t.lng+\"/\"+t.lat+\"/\"+Math.round(this._map.getZoom()+1)}}})},{\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],410:[function(t,e,r){\"use strict\";function n(){}var i=t(\"../../util/util\"),a=t(\"../../util/evented\");e.exports=n,n.prototype={addTo:function(t){this._map=t;var e=this._container=this.onAdd(t);if(this.options&&this.options.position){var r=this.options.position,n=t._controlCorners[r];e.className+=\" mapboxgl-ctrl\",-1!==r.indexOf(\"bottom\")?n.insertBefore(e,n.firstChild):n.appendChild(e)}return this},remove:function(){return this._container.parentNode.removeChild(this._container),this.onRemove&&this.onRemove(this._map),this._map=null,this}},i.extend(n.prototype,a)},{\"../../util/evented\":434,\"../../util/util\":442}],411:[function(t,e,r){\"use strict\";function n(t){s.setOptions(this,t)}var i=t(\"./control\"),a=t(\"../../util/browser\"),o=t(\"../../util/dom\"),s=t(\"../../util/util\");e.exports=n;var l={enableHighAccuracy:!1,timeout:6e3};n.prototype=s.inherit(i,{options:{position:\"top-right\"},onAdd:function(t){var e=this._container=o.create(\"div\",\"mapboxgl-ctrl-group\",t.getContainer());return a.supportsGeolocation?(this._container.addEventListener(\"contextmenu\",this._onContextMenu.bind(this)),this._geolocateButton=o.create(\"button\",\"mapboxgl-ctrl-icon mapboxgl-ctrl-geolocate\",this._container),this._geolocateButton.type=\"button\",this._geolocateButton.addEventListener(\"click\",this._onClickGeolocate.bind(this)),e):e},_onContextMenu:function(t){t.preventDefault()},_onClickGeolocate:function(){navigator.geolocation.getCurrentPosition(this._success.bind(this),this._error.bind(this),l),this._timeoutId=setTimeout(this._finish.bind(this),1e4)},_success:function(t){this._map.jumpTo({center:[t.coords.longitude,t.coords.latitude],zoom:17,bearing:0,pitch:0}),this.fire(\"geolocate\",t),this._finish()},_error:function(t){this.fire(\"error\",t),this._finish()},_finish:function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0}})},{\"../../util/browser\":426,\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],412:[function(t,e,r){\"use strict\";function n(t){s.setOptions(this,t)}function i(t){return new MouseEvent(t.type,{button:2,buttons:2,bubbles:!0,cancelable:!0,detail:t.detail,view:t.view,screenX:t.screenX,screenY:t.screenY,clientX:t.clientX,clientY:t.clientY,movementX:t.movementX,movementY:t.movementY,ctrlKey:t.ctrlKey,shiftKey:t.shiftKey,altKey:t.altKey,metaKey:t.metaKey})}var a=t(\"./control\"),o=t(\"../../util/dom\"),s=t(\"../../util/util\");e.exports=n,n.prototype=s.inherit(a,{options:{position:\"top-right\"},onAdd:function(t){var e=\"mapboxgl-ctrl\",r=this._container=o.create(\"div\",e+\"-group\",t.getContainer());return this._container.addEventListener(\"contextmenu\",this._onContextMenu.bind(this)),this._zoomInButton=this._createButton(e+\"-icon \"+e+\"-zoom-in\",t.zoomIn.bind(t)),this._zoomOutButton=this._createButton(e+\"-icon \"+e+\"-zoom-out\",t.zoomOut.bind(t)),this._compass=this._createButton(e+\"-icon \"+e+\"-compass\",t.resetNorth.bind(t)),this._compassArrow=o.create(\"div\",\"arrow\",this._compass),this._compass.addEventListener(\"mousedown\",this._onCompassDown.bind(this)),this._onCompassMove=this._onCompassMove.bind(this),this._onCompassUp=this._onCompassUp.bind(this),t.on(\"rotate\",this._rotateCompassArrow.bind(this)),this._rotateCompassArrow(),this._el=t.getCanvasContainer(),r},_onContextMenu:function(t){t.preventDefault()},_onCompassDown:function(t){0===t.button&&(o.disableDrag(),document.addEventListener(\"mousemove\",this._onCompassMove),document.addEventListener(\"mouseup\",this._onCompassUp),this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassMove:function(t){0===t.button&&(this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassUp:function(t){0===t.button&&(document.removeEventListener(\"mousemove\",this._onCompassMove),document.removeEventListener(\"mouseup\",this._onCompassUp),o.enableDrag(),this._el.dispatchEvent(i(t)),t.stopPropagation())},_createButton:function(t,e){var r=o.create(\"button\",t,this._container);return r.type=\"button\",r.addEventListener(\"click\",function(){e()}),r},_rotateCompassArrow:function(){var t=\"rotate(\"+this._map.transform.angle(180/Math.PI)+\"deg)\";this._compassArrow.style.transform=t}})},{\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],413:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../geo/lng_lat_bounds\"),o=t(\"../../util/util\");e.exports=n,n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"mousedown\",this._onMouseDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onMouseDown),this._enabled=!1)},_onMouseDown:function(t){t.shiftKey&&0===t.button&&(document.addEventListener(\"mousemove\",this._onMouseMove,!1),document.addEventListener(\"keydown\",this._onKeyDown,!1),document.addEventListener(\"mouseup\",this._onMouseUp,!1),i.disableDrag(),this._startPos=i.mousePos(this._el,t),this._active=!0)},_onMouseMove:function(t){var e=this._startPos,r=i.mousePos(this._el,t);this._box\|\|(this._box=i.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var n=Math.min(e.x,r.x),a=Math.max(e.x,r.x),o=Math.min(e.y,r.y),s=Math.max(e.y,r.y);i.setTransform(this._box,\"translate(\"+n+\"px,\"+o+\"px)\"),this._box.style.width=a-n+\"px\",this._box.style.height=s-o+\"px\"},_onMouseUp:function(t){if(0===t.button){var e=this._startPos,r=i.mousePos(this._el,t),n=new a(this._map.unproject(e),this._map.unproject(r));this._finish(),e.x===r.x&&e.y===r.y?this._fireEvent(\"boxzoomcancel\",t):this._map.fitBounds(n,{linear:!0}).fire(\"boxzoomend\",{originalEvent:t,boxZoomBounds:n})}},_onKeyDown:function(t){27===t.keyCode&&(this._finish(),this._fireEvent(\"boxzoomcancel\",t))},_finish:function(){this._active=!1,document.removeEventListener(\"mousemove\",this._onMouseMove,!1),document.removeEventListener(\"keydown\",this._onKeyDown,!1),document.removeEventListener(\"mouseup\",this._onMouseUp,!1),this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(this._box.parentNode.removeChild(this._box),this._box=null),i.enableDrag()},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})}}},{\"../../geo/lng_lat_bounds\":340,\"../../util/dom\":428,\"../../util/util\":442}],414:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._onDblClick=this._onDblClick.bind(this)}e.exports=n,n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()\|\|(this._map.on(\"dblclick\",this._onDblClick),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._map.off(\"dblclick\",this._onDblClick),this._enabled=!1)},_onDblClick:function(t){this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)}}},{}],415:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/util\");e.exports=n;var o=a.bezier(0,0,.3,1);n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"mousedown\",this._onDown),this._el.addEventListener(\"touchstart\",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onDown),this._el.removeEventListener(\"touchstart\",this._onDown),this._enabled=!1)},_onDown:function(t){this._ignoreEvent(t)\|\|this.isActive()\|\|(t.touches?(document.addEventListener(\"touchmove\",this._onMove),document.addEventListener(\"touchend\",this._onTouchEnd)):(document.addEventListener(\"mousemove\",this._onMove),document.addEventListener(\"mouseup\",this._onMouseUp)),this._active=!1,this._startPos=this._pos=i.mousePos(this._el,t),this._inertia=[[Date.now(),this._pos]])},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()\|\|(this._active=!0,this._fireEvent(\"dragstart\",t),this._fireEvent(\"movestart\",t));var e=i.mousePos(this._el,t),r=this._map;r.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),e]),r.transform.setLocationAtPoint(r.transform.pointLocation(this._pos),e),this._fireEvent(\"drag\",t),this._fireEvent(\"move\",t),this._pos=e,t.preventDefault()}},_onUp:function(t){if(this.isActive()){this._active=!1,this._fireEvent(\"dragend\",t),this._drainInertiaBuffer();var e=function(){this._fireEvent(\"moveend\",t)}.bind(this),r=this._inertia;if(r.length<2)return void e();var n=r[r.length-1],i=r[0],a=n[1].sub(i[1]),s=(n[0]-i[0])/1e3;if(0===s\|\|n[1].equals(i[1]))return void e();var l=a.mult(.3/s),u=l.mag();u>1400&&(u=1400,l._unit()._mult(u));var c=u/750,h=l.mult(-c/2);this._map.panBy(h,{duration:1e3c,easing:o,noMoveStart:!0},{originalEvent:t})}},_onMouseUp:function(t){this._ignoreEvent(t)\|\|(this._onUp(t),document.removeEventListener(\"mousemove\",this._onMove),document.removeEventListener(\"mouseup\",this._onMouseUp))},_onTouchEnd:function(t){this._ignoreEvent(t)\|\|(this._onUp(t),document.removeEventListener(\"touchmove\",this._onMove),document.removeEventListener(\"touchend\",this._onTouchEnd))},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragRotate&&e.dragRotate.isActive())return!0;if(t.touches)return t.touches.length>1;if(t.ctrlKey)return!0;return\"mousemove\"===t.type?!1&t.buttons:0!==t.button},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>0&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442}],416:[function(t,e,r){\"use strict\";function n(t,e){this._map=t,this._el=t.getCanvasContainer(),this._bearingSnap=e.bearingSnap,o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"point-geometry\"),o=t(\"../../util/util\");e.exports=n;var s=o.bezier(0,0,.25,1);n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"mousedown\",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onDown),this._enabled=!1)},_onDown:function(t){if(!this._ignoreEvent(t)&&!this.isActive()){document.addEventListener(\"mousemove\",this._onMove),document.addEventListener(\"mouseup\",this._onUp),this._active=!1,this._inertia=[[Date.now(),this._map.getBearing()]],this._startPos=this._pos=i.mousePos(this._el,t),this._center=this._map.transform.centerPoint;var e=this._startPos.sub(this._center);e.mag()<200&&(this._center=this._startPos.add(new a(-200,0)._rotate(e.angle()))),t.preventDefault()}},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()\|\|(this._active=!0,this._fireEvent(\"rotatestart\",t),this._fireEvent(\"movestart\",t));var e=this._map;e.stop();var r=this._pos,n=i.mousePos(this._el,t),a=this._center,o=r.sub(a).angleWith(n.sub(a))/Math.PI180,s=e.getBearing()-o,l=this._inertia,u=l[l.length-1];this._drainInertiaBuffer(),l.push([Date.now(),e._normalizeBearing(s,u[1])]),e.transform.bearing=s,this._fireEvent(\"rotate\",t),this._fireEvent(\"move\",t),this._pos=n}},_onUp:function(t){if(!this._ignoreEvent(t)&&(document.removeEventListener(\"mousemove\",this._onMove),document.removeEventListener(\"mouseup\",this._onUp),this.isActive())){this._active=!1,this._fireEvent(\"rotateend\",t),this._drainInertiaBuffer();var e=this._map,r=e.getBearing(),n=this._inertia,i=function(){Math.abs(r)<this._bearingSnap?e.resetNorth({noMoveStart:!0},{originalEvent:t}):this._fireEvent(\"moveend\",t)}.bind(this);if(n.length<2)return void i();var a=n[0],o=n[n.length-1],l=n[n.length-2],u=e._normalizeBearing(r,l[1]),c=o[1]-a[1],h=c<0?-1:1,f=(o[0]-a[0])/1e3;if(0===c\|\|0===f)return void i();var d=Math.abs(c(.25/f));d>180&&(d=180);var p=d/180;u+=hd(p/2),Math.abs(e._normalizeBearing(u,0))<this._bearingSnap&&(u=e._normalizeBearing(0,u)),e.rotateTo(u,{duration:1e3p,easing:s,noMoveStart:!0},{originalEvent:t})}},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragPan&&e.dragPan.isActive())return!0;if(t.touches)return t.touches.length>1;var r=t.ctrlKey?1:2,n=t.ctrlKey?0:2;return\"mousemove\"===t.type?t.buttons&0===r:t.button!==n},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>0&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442,\"point-geometry\":484}],417:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._onKeyDown=this._onKeyDown.bind(this)}e.exports=n;n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"keydown\",this._onKeyDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"keydown\",this._onKeyDown),this._enabled=!1)},_onKeyDown:function(t){if(!(t.altKey\|\|t.ctrlKey\|\|t.metaKey)){var e=this._map,r={originalEvent:t};if(!e.isEasing())switch(t.keyCode){case 61:case 107:case 171:case 187:e.zoomTo(Math.round(e.getZoom())+(t.shiftKey?2:1),r);break;case 189:case 109:case 173:e.zoomTo(Math.round(e.getZoom())-(t.shiftKey?2:1),r);break;case 37:t.shiftKey?e.easeTo({bearing:e.getBearing()-2},r):(t.preventDefault(),e.panBy([-80,0],r));break;case 39:t.shiftKey?e.easeTo({bearing:e.getBearing()+2},r):(t.preventDefault(),e.panBy([80,0],r));break;case 38:t.shiftKey?e.easeTo({pitch:e.getPitch()+5},r):(t.preventDefault(),e.panBy([0,-80],r));break;case 40:t.shiftKey?e.easeTo({pitch:Math.max(e.getPitch()-5,0)},r):(t.preventDefault(),e.panBy([0,80],r))}}}}},{}],418:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/browser\"),o=t(\"../../util/util\");e.exports=n;var s=\"undefined\"!=typeof navigator?navigator.userAgent.toLowerCase():\"\",l=-1!==s.indexOf(\"firefox\"),u=-1!==s.indexOf(\"safari\")&&-1===s.indexOf(\"chrom\");n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"wheel\",this._onWheel,!1),this._el.addEventListener(\"mousewheel\",this._onWheel,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"wheel\",this._onWheel),this._el.removeEventListener(\"mousewheel\",this._onWheel),this._enabled=!1)},_onWheel:function(t){var e;\"wheel\"===t.type?(e=t.deltaY,l&&t.deltaMode===window.WheelEvent.DOM_DELTA_PIXEL&&(e/=a.devicePixelRatio),t.deltaMode===window.WheelEvent.DOM_DELTA_LINE&&(e=40)):\"mousewheel\"===t.type&&(e=-t.wheelDeltaY,u&&(e/=3));var r=a.now(),n=r-(this._time\|\|0);this._pos=i.mousePos(this._el,t),this._time=r,0!==e&&e%4.000244140625==0?(this._type=\"wheel\",e=Math.floor(e/4)):0!==e&&Math.abs(e)<4?this._type=\"trackpad\":n>400?(this._type=null,this._lastValue=e,this._timeout=setTimeout(this._onTimeout,40)):this._type\|\|(this._type=Math.abs(ne)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,e+=this._lastValue)),t.shiftKey&&e&&(e/=4),this._type&&this._zoom(-e,t),t.preventDefault()},_onTimeout:function(){this._type=\"wheel\",this._zoom(-this._lastValue)},_zoom:function(t,e){if(0!==t){var r=this._map,n=2/(1+Math.exp(-Math.abs(t/100)));t<0&&0!==n&&(n=1/n);var i=r.ease?r.ease.to:r.transform.scale,a=r.transform.scaleZoom(in);r.zoomTo(a,{duration:0,around:r.unproject(this._pos),delayEndEvents:200},{originalEvent:e})}}}},{\"../../util/browser\":426,\"../../util/dom\":428,\"../../util/util\":442}],419:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/util\");e.exports=n;var o=a.bezier(0,0,.15,1);n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"touchstart\",this._onStart,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"touchstart\",this._onStart),this._enabled=!1)},disableRotation:function(){this._rotationDisabled=!0},enableRotation:function(){this._rotationDisabled=!1},_onStart:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]);this._startVec=e.sub(r),this._startScale=this._map.transform.scale,this._startBearing=this._map.transform.bearing,this._gestureIntent=void 0,this._inertia=[],\n", "document.addEventListener(\"touchmove\",this._onMove,!1),document.addEventListener(\"touchend\",this._onEnd,!1)}},_onMove:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]),n=e.add(r).div(2),a=e.sub(r),o=a.mag()/this._startVec.mag(),s=this._rotationDisabled?0:180a.angleWith(this._startVec)/Math.PI,l=this._map;if(this._gestureIntent){var u={duration:0,around:l.unproject(n)};\"rotate\"===this._gestureIntent&&(u.bearing=this._startBearing+s),\"zoom\"!==this._gestureIntent&&\"rotate\"!==this._gestureIntent\|\|(u.zoom=l.transform.scaleZoom(this._startScaleo)),l.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),o,n]),l.easeTo(u,{originalEvent:t})}else{var c=Math.abs(1-o)>.15;Math.abs(s)>4?this._gestureIntent=\"rotate\":c&&(this._gestureIntent=\"zoom\"),this._gestureIntent&&(this._startVec=a,this._startScale=l.transform.scale,this._startBearing=l.transform.bearing)}t.preventDefault()}},_onEnd:function(t){document.removeEventListener(\"touchmove\",this._onMove),document.removeEventListener(\"touchend\",this._onEnd),this._drainInertiaBuffer();var e=this._inertia,r=this._map;if(e.length<2)return void r.snapToNorth({},{originalEvent:t});var n=e[e.length-1],i=e[0],a=r.transform.scaleZoom(this._startScalen[1]),s=r.transform.scaleZoom(this._startScalei[1]),l=a-s,u=(n[0]-i[0])/1e3,c=n[2];if(0===u\|\|a===s)return void r.snapToNorth({},{originalEvent:t});var h=.15l/u;Math.abs(h)>2.5&&(h=h>0?2.5:-2.5);var f=1e3Math.abs(h/(12.15)),d=a+hf/2e3;d<0&&(d=0),r.easeTo({zoom:d,duration:f,easing:o,around:r.unproject(c)},{originalEvent:t})},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>2&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442}],420:[function(t,e,r){\"use strict\";function n(){i.bindAll([\"_onHashChange\",\"_updateHash\"],this)}e.exports=n;var i=t(\"../util/util\");n.prototype={addTo:function(t){return this._map=t,window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},remove:function(){return window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),delete this._map,this},_onHashChange:function(){var t=location.hash.replace(\"#\",\"\").split(\"/\");return t.length>=3&&(this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:+(t[3]\|\|0)}),!0)},_updateHash:function(){var t=this._map.getCenter(),e=this._map.getZoom(),r=this._map.getBearing(),n=Math.max(0,Math.ceil(Math.log(e)/Math.LN2)),i=\"#\"+Math.round(100e)/100+\"/\"+t.lat.toFixed(n)+\"/\"+t.lng.toFixed(n)+(r?\"/\"+Math.round(10r)/10:\"\");window.history.replaceState(\"\",\"\",i)}}},{\"../util/util\":442}],421:[function(t,e,r){\"use strict\";function n(t){t.parentNode&&t.parentNode.removeChild(t)}var i=t(\"../util/canvas\"),a=t(\"../util/util\"),o=t(\"../util/browser\"),s=t(\"../util/browser\").window,l=t(\"../util/evented\"),u=t(\"../util/dom\"),c=t(\"../style/style\"),h=t(\"../style/animation_loop\"),f=t(\"../render/painter\"),d=t(\"../geo/transform\"),p=t(\"./hash\"),m=t(\"./bind_handlers\"),v=t(\"./camera\"),g=t(\"../geo/lng_lat\"),y=t(\"../geo/lng_lat_bounds\"),b=t(\"point-geometry\"),x=t(\"./control/attribution\"),_={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:0,maxZoom:20,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,workerCount:Math.max(o.hardwareConcurrency-1,1)},w=e.exports=function(t){if(t=a.extend({},_,t),t.workerCount<1)throw new Error(\"workerCount must an integer greater than or equal to 1.\");this._interactive=t.interactive,this._failIfMajorPerformanceCaveat=t.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=t.preserveDrawingBuffer,this._trackResize=t.trackResize,this._workerCount=t.workerCount,this._bearingSnap=t.bearingSnap,\"string\"==typeof t.container?this._container=document.getElementById(t.container):this._container=t.container,this.animationLoop=new h,this.transform=new d(t.minZoom,t.maxZoom),t.maxBounds&&this.setMaxBounds(t.maxBounds),a.bindAll([\"_forwardStyleEvent\",\"_forwardSourceEvent\",\"_forwardLayerEvent\",\"_forwardTileEvent\",\"_onStyleLoad\",\"_onStyleChange\",\"_onSourceAdd\",\"_onSourceRemove\",\"_onSourceUpdate\",\"_onWindowOnline\",\"_onWindowResize\",\"_update\",\"_render\"],this),this._setupContainer(),this._setupPainter(),this.on(\"move\",this._update.bind(this,!1)),this.on(\"zoom\",this._update.bind(this,!0)),this.on(\"moveend\",function(){this.animationLoop.set(300),this._rerender()}.bind(this)),void 0!==s&&(s.addEventListener(\"online\",this._onWindowOnline,!1),s.addEventListener(\"resize\",this._onWindowResize,!1)),m(this,t),this._hash=t.hash&&(new p).addTo(this),this._hash&&this._hash._onHashChange()\|\|this.jumpTo({center:t.center,zoom:t.zoom,bearing:t.bearing,pitch:t.pitch}),this.stacks={},this._classes=[],this.resize(),t.classes&&this.setClasses(t.classes),t.style&&this.setStyle(t.style),t.attributionControl&&this.addControl(new x(t.attributionControl));var e=this.fire.bind(this,\"error\");this.on(\"style.error\",e),this.on(\"source.error\",e),this.on(\"tile.error\",e),this.on(\"layer.error\",e)};a.extend(w.prototype,l),a.extend(w.prototype,v.prototype),a.extend(w.prototype,{addControl:function(t){return t.addTo(this),this},addClass:function(t,e){return this._classes.indexOf(t)>=0\|\|\"\"===t?this:(this._classes.push(t),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},removeClass:function(t,e){var r=this._classes.indexOf(t);return r<0\|\|\"\"===t?this:(this._classes.splice(r,1),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},setClasses:function(t,e){for(var r={},n=0;n<t.length;n++)\"\"!==t[n]&&(r[t[n]]=!0);return this._classes=Object.keys(r),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0)},hasClass:function(t){return this._classes.indexOf(t)>=0},getClasses:function(){return this._classes},resize:function(){var t=0,e=0;return this._container&&(t=this._container.offsetWidth\|\|400,e=this._container.offsetHeight\|\|300),this._canvas.resize(t,e),this.transform.resize(t,e),this.painter.resize(t,e),this.fire(\"movestart\").fire(\"move\").fire(\"resize\").fire(\"moveend\")},getBounds:function(){var t=new y(this.transform.pointLocation(new b(0,0)),this.transform.pointLocation(this.transform.size));return(this.transform.angle\|\|this.transform.pitch)&&(t.extend(this.transform.pointLocation(new b(this.transform.size.x,0))),t.extend(this.transform.pointLocation(new b(0,this.transform.size.y)))),t},setMaxBounds:function(t){if(t){var e=y.convert(t);this.transform.lngRange=[e.getWest(),e.getEast()],this.transform.latRange=[e.getSouth(),e.getNorth()],this.transform._constrain(),this._update()}else null!==t&&void 0!==t\|\|(this.transform.lngRange=[],this.transform.latRange=[],this._update());return this},setMinZoom:function(t){if((t=null===t\|\|void 0===t?0:t)>=0&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between 0 and the current maxZoom, inclusive\")},setMaxZoom:function(t){if((t=null===t\|\|void 0===t?20:t)>=this.transform.minZoom&&t<=20)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be between the current minZoom and 20, inclusive\")},project:function(t){return this.transform.locationPoint(g.convert(t))},unproject:function(t){return this.transform.pointLocation(b.convert(t))},queryRenderedFeatures:function(){var t,e={};return 2===arguments.length?(t=arguments[0],e=arguments[1]):1===arguments.length&&function(t){return t instanceof b\|\|Array.isArray(t)}(arguments[0])?t=arguments[0]:1===arguments.length&&(e=arguments[0]),this.style.queryRenderedFeatures(this._makeQueryGeometry(t),e,this.transform.zoom,this.transform.angle)},_makeQueryGeometry:function(t){void 0===t&&(t=[b.convert([0,0]),b.convert([this.transform.width,this.transform.height])]);var e;if(t instanceof b\|\|\"number\"==typeof t[0])e=[b.convert(t)];else{var r=[b.convert(t[0]),b.convert(t[1])];e=[r[0],new b(r[1].x,r[0].y),r[1],new b(r[0].x,r[1].y),r[0]]}return e=e.map(function(t){return this.transform.pointCoordinate(t)}.bind(this))},querySourceFeatures:function(t,e){return this.style.querySourceFeatures(t,e)},setStyle:function(t){return this.style&&(this.style.off(\"load\",this._onStyleLoad).off(\"error\",this._forwardStyleEvent).off(\"change\",this._onStyleChange).off(\"source.add\",this._onSourceAdd).off(\"source.remove\",this._onSourceRemove).off(\"source.load\",this._onSourceUpdate).off(\"source.error\",this._forwardSourceEvent).off(\"source.change\",this._onSourceUpdate).off(\"layer.add\",this._forwardLayerEvent).off(\"layer.remove\",this._forwardLayerEvent).off(\"layer.error\",this._forwardLayerEvent).off(\"tile.add\",this._forwardTileEvent).off(\"tile.remove\",this._forwardTileEvent).off(\"tile.load\",this._update).off(\"tile.error\",this._forwardTileEvent).off(\"tile.stats\",this._forwardTileEvent)._remove(),this.off(\"rotate\",this.style._redoPlacement),this.off(\"pitch\",this.style._redoPlacement)),t?(this.style=t instanceof c?t:new 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this.style.addSourceType(t,e,r)},removeSource:function(t){return this.style.removeSource(t),this._update(!0),this},getSource:function(t){return this.style.getSource(t)},addLayer:function(t,e){return this.style.addLayer(t,e),this._update(!0),this},removeLayer:function(t){return this.style.removeLayer(t),this._update(!0),this},getLayer:function(t){return this.style.getLayer(t)},setFilter:function(t,e){return this.style.setFilter(t,e),this._update(!0),this},setLayerZoomRange:function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0),this},getFilter:function(t){return this.style.getFilter(t)},setPaintProperty:function(t,e,r,n){return this.style.setPaintProperty(t,e,r,n),this._update(!0),this},getPaintProperty:function(t,e,r){return this.style.getPaintProperty(t,e,r)},setLayoutProperty:function(t,e,r){return this.style.setLayoutProperty(t,e,r),this._update(!0),this},getLayoutProperty:function(t,e){return this.style.getLayoutProperty(t,e)},getContainer:function(){return this._container},getCanvasContainer:function(){return this._canvasContainer},getCanvas:function(){return this._canvas.getElement()},_setupContainer:function(){var t=this._container;t.classList.add(\"mapboxgl-map\");var e=this._canvasContainer=u.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=new i(this,e);var r=this._controlContainer=u.create(\"div\",\"mapboxgl-control-container\",t),n=this._controlCorners={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(t){n[t]=u.create(\"div\",\"mapboxgl-ctrl-\"+t,r)})},_setupPainter:function(){var t=this._canvas.getWebGLContext({failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer});if(!t)return void this.fire(\"error\",{error:new Error(\"Failed to initialize WebGL\")});this.painter=new f(t,this.transform)},_contextLost:function(t){t.preventDefault(),this._frameId&&o.cancelFrame(this._frameId),this.fire(\"webglcontextlost\",{originalEvent:t})},_contextRestored:function(t){this._setupPainter(),this.resize(),this._update(),this.fire(\"webglcontextrestored\",{originalEvent:t})},loaded:function(){return!this._styleDirty&&!this._sourcesDirty&&!(!this.style\|\|!this.style.loaded())},_update:function(t){return this.style?(this._styleDirty=this._styleDirty\|\|t,this._sourcesDirty=!0,this._rerender(),this):this},_render:function(){try{this.style&&this._styleDirty&&(this._styleDirty=!1,this.style.update(this._classes,this._classOptions),this._classOptions=null,this.style._recalculate(this.transform.zoom)),this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.painter.render(this.style,{debug:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,vertices:this.vertices,rotating:this.rotating,zooming:this.zooming}),this.fire(\"render\"),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(\"load\")),this._frameId=null,this.animationLoop.stopped()\|\|(this._styleDirty=!0),(this._sourcesDirty\|\|this._repaint\|\|this._styleDirty)&&this._rerender()}catch(t){this.fire(\"error\",{error:t})}return this},remove:function(){this._hash&&this._hash.remove(),o.cancelFrame(this._frameId),this.setStyle(null),void 0!==s&&s.removeEventListener(\"resize\",this._onWindowResize,!1);var t=this.painter.gl.getExtension(\"WEBGL_lose_context\");t&&t.loseContext(),n(this._canvasContainer),n(this._controlContainer),this._container.classList.remove(\"mapboxgl-map\")},_rerender:function(){this.style&&!this._frameId&&(this._frameId=o.frame(this._render))},_forwardStyleEvent:function(t){this.fire(\"style.\"+t.type,a.extend({style:t.target},t))},_forwardSourceEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_forwardLayerEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_forwardTileEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_onStyleLoad:function(t){this.transform.unmodified&&this.jumpTo(this.style.stylesheet),this.style.update(this._classes,{transition:!1}),this._forwardStyleEvent(t)},_onStyleChange:function(t){this._update(!0),this._forwardStyleEvent(t)},_onSourceAdd:function(t){var e=t.source;e.onAdd&&e.onAdd(this),this._forwardSourceEvent(t)},_onSourceRemove:function(t){var e=t.source;e.onRemove&&e.onRemove(this),this._forwardSourceEvent(t)},_onSourceUpdate:function(t){this._update(),this._forwardSourceEvent(t)},_onWindowOnline:function(){this._update()},_onWindowResize:function(){this._trackResize&&this.stop().resize()._update()}}),a.extendAll(w.prototype,{_showTileBoundaries:!1,get showTileBoundaries(){return this._showTileBoundaries},set showTileBoundaries(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},_showCollisionBoxes:!1,get showCollisionBoxes(){return this._showCollisionBoxes},set showCollisionBoxes(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,this.style._redoPlacement())},_showOverdrawInspector:!1,get showOverdrawInspector(){return this._showOverdrawInspector},set showOverdrawInspector(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},_repaint:!1,get repaint(){return this._repaint},set repaint(t){this._repaint=t,this._update()},_vertices:!1,get vertices(){return this._vertices},set vertices(t){this._vertices=t,this._update()}})},{\"../geo/lng_lat\":339,\"../geo/lng_lat_bounds\":340,\"../geo/transform\":341,\"../render/painter\":355,\"../style/animation_loop\":375,\"../style/style\":378,\"../util/browser\":426,\"../util/canvas\":427,\"../util/dom\":428,\"../util/evented\":434,\"../util/util\":442,\"./bind_handlers\":407,\"./camera\":408,\"./control/attribution\":409,\"./hash\":420,\"point-geometry\":484}],422:[function(t,e,r){\"use strict\";function n(t,e){t\|\|(t=i.create(\"div\")),t.classList.add(\"mapboxgl-marker\"),this._el=t,this._offset=o.convert(e&&e.offset\|\|[0,0]),this._update=this._update.bind(this)}e.exports=n;var i=t(\"../util/dom\"),a=t(\"../geo/lng_lat\"),o=t(\"point-geometry\");n.prototype={addTo:function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._el),t.on(\"move\",this._update),this._update(),this},remove:function(){this._map&&(this._map.off(\"move\",this._update),this._map=null);var t=this._el.parentNode;return t&&t.removeChild(this._el),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=a.convert(t),this._update(),this},getElement:function(){return this._el},_update:function(){if(this._map){var t=this._map.project(this._lngLat)._add(this._offset);i.setTransform(this._el,\"translate(\"+t.x+\"px,\"+t.y+\"px)\")}}}},{\"../geo/lng_lat\":339,\"../util/dom\":428,\"point-geometry\":484}],423:[function(t,e,r){\"use strict\";function n(t){i.setOptions(this,t),i.bindAll([\"_update\",\"_onClickClose\"],this)}e.exports=n;var i=t(\"../util/util\"),a=t(\"../util/evented\"),o=t(\"../util/dom\"),s=t(\"../geo/lng_lat\");n.prototype=i.inherit(a,{options:{closeButton:!0,closeOnClick:!0},addTo:function(t){return this._map=t,this._map.on(\"move\",this._update),this.options.closeOnClick&&this._map.on(\"click\",this._onClickClose),this._update(),this},remove:function(){return this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._container&&(this._container.parentNode.removeChild(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"click\",this._onClickClose),delete this._map),this.fire(\"close\"),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=s.convert(t),this._update(),this},setText:function(t){return this.setDOMContent(document.createTextNode(t))},setHTML:function(t){var e,r=document.createDocumentFragment(),n=document.createElement(\"body\");for(n.innerHTML=t;;){if(!(e=n.firstChild))break;r.appendChild(e)}return this.setDOMContent(r)},setDOMContent:function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},_createContent:function(){this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._content=o.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=o.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClickClose))},_update:function(){if(this._map&&this._lngLat&&this._content){this._container\|\|(this._container=o.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=o.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content));var t=this._map.project(this._lngLat).round(),e=this.options.anchor;if(!e){var r=this._container.offsetWidth,n=this._container.offsetHeight;e=t.y<n?[\"top\"]:t.y>this._map.transform.height-n?[\"bottom\"]:[],t.x<r/2?e.push(\"left\"):t.x>this._map.transform.width-r/2&&e.push(\"right\"),e=0===e.length?\"bottom\":e.join(\"-\")}var i={top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"},a=this._container.classList;for(var s in i)a.remove(\"mapboxgl-popup-anchor-\"+s);a.add(\"mapboxgl-popup-anchor-\"+e),o.setTransform(this._container,i[e]+\" translate(\"+t.x+\"px,\"+t.y+\"px)\")}},_onClickClose:function(){this.remove()}})},{\"../geo/lng_lat\":339,\"../util/dom\":428,\"../util/evented\":434,\"../util/util\":442}],424:[function(t,e,r){\"use strict\";function n(t,e){this.target=t,this.parent=e,this.callbacks={},this.callbackID=0,this.receive=this.receive.bind(this),this.target.addEventListener(\"message\",this.receive,!1)}e.exports=n,n.prototype.receive=function(t){function e(t,e,r){this.postMessage({type:\"<response>\",id:String(i),error:t?String(t):null,data:e},r)}var r,n=t.data,i=n.id;if(\"<response>\"===n.type)r=this.callbacks[n.id],delete this.callbacks[n.id],r&&r(n.error\|\|null,n.data);else if(void 0!==n.id&&this.parent[n.type])this.parent[n.type](n.data,e.bind(this));else if(void 0!==n.id&&this.parent.workerSources){var a=n.type.split(\".\");this.parent.workerSources[a[0]][a[1]](n.data,e.bind(this))}else this.parent[n.type](n.data)},n.prototype.send=function(t,e,r,n){var i=null;r&&(this.callbacks[i=this.callbackID++]=r),this.postMessage({type:t,id:String(i),data:e},n)},n.prototype.postMessage=function(t,e){this.target.postMessage(t,e)}},{}],425:[function(t,e,r){\"use strict\";function n(t){var e=document.createElement(\"a\");return e.href=t,e.protocol===document.location.protocol&&e.host===document.location.host}r.getJSON=function(t,e){var r=new XMLHttpRequest;return r.open(\"GET\",t,!0),r.setRequestHeader(\"Accept\",\"application/json\"),r.onerror=function(t){e(t)},r.onload=function(){if(r.status>=200&&r.status<300&&r.response){var t;try{t=JSON.parse(r.response)}catch(t){return e(t)}e(null,t)}else e(new Error(r.statusText))},r.send(),r},r.getArrayBuffer=function(t,e){var r=new XMLHttpRequest;return r.open(\"GET\",t,!0),r.responseType=\"arraybuffer\",r.onerror=function(t){e(t)},r.onload=function(){r.status>=200&&r.status<300&&r.response?e(null,r.response):e(new Error(r.statusText))},r.send(),r},r.getImage=function(t,e){return r.getArrayBuffer(t,function(t,r){if(t)return e(t);var n=new Image;n.onload=function(){e(null,n),(window.URL\|\|window.webkitURL).revokeObjectURL(n.src)};var i=new Blob([new Uint8Array(r)],{type:\"image/png\"});return n.src=(window.URL\|\|window.webkitURL).createObjectURL(i),n.getData=function(){var t=document.createElement(\"canvas\"),e=t.getContext(\"2d\");return t.width=n.width,t.height=n.height,e.drawImage(n,0,0),e.getImageData(0,0,n.width,n.height).data},n})},r.getVideo=function(t,e){var r=document.createElement(\"video\");r.onloadstart=function(){e(null,r)};for(var i=0;i<t.length;i++){var a=document.createElement(\"source\");n(t[i])\|\|(r.crossOrigin=\"Anonymous\"),a.src=t[i],r.appendChild(a)}return r.getData=function(){return r},r}},{}],426:[function(t,e,r){\"use strict\";r.window=window,e.exports.now=function(){return window.performance&&window.performance.now?window.performance.now.bind(window.performance):Date.now.bind(Date)}();var n=window.requestAnimationFrame\|\|window.mozRequestAnimationFrame\|\|window.webkitRequestAnimationFrame\|\|window.msRequestAnimationFrame;r.frame=function(t){return n(t)};var i=window.cancelAnimationFrame\|\|window.mozCancelAnimationFrame\|\|window.webkitCancelAnimationFrame\|\|window.msCancelAnimationFrame;r.cancelFrame=function(t){i(t)},r.timed=function(t,n,i){function a(l){o\|\|(l=e.exports.now(),l>=s+n?t.call(i,1):(t.call(i,(l-s)/n),r.frame(a)))}if(!n)return t.call(i,1),null;var o=!1,s=e.exports.now();return r.frame(a),function(){o=!0}},r.supported=t(\"mapbox-gl-supported\"),r.hardwareConcurrency=navigator.hardwareConcurrency\|\|4,Object.defineProperty(r,\"devicePixelRatio\",{get:function(){return window.devicePixelRatio}}),r.supportsWebp=!1;var a=document.createElement(\"img\");a.onload=function(){r.supportsWebp=!0},a.src=\"data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=\",r.supportsGeolocation=!!navigator.geolocation},{\"mapbox-gl-supported\":327}],427:[function(t,e,r){\"use strict\";function n(t,e){this.canvas=document.createElement(\"canvas\"),t&&e&&(this.canvas.style.position=\"absolute\",this.canvas.classList.add(\"mapboxgl-canvas\"),this.canvas.addEventListener(\"webglcontextlost\",t._contextLost.bind(t),!1),this.canvas.addEventListener(\"webglcontextrestored\",t._contextRestored.bind(t),!1),this.canvas.setAttribute(\"tabindex\",0),e.appendChild(this.canvas))}var i=t(\"../util\"),a=t(\"mapbox-gl-supported\");e.exports=n,n.prototype.resize=function(t,e){var r=window.devicePixelRatio\|\|1;this.canvas.width=rt,this.canvas.height=re,this.canvas.style.width=t+\"px\",this.canvas.style.height=e+\"px\"},n.prototype.getWebGLContext=function(t){return t=i.extend({},t,a.webGLContextAttributes),this.canvas.getContext(\"webgl\",t)\|\|this.canvas.getContext(\"experimental-webgl\",t)},n.prototype.getElement=function(){return this.canvas}},{\"../util\":442,\"mapbox-gl-supported\":327}],428:[function(t,e,r){\"use strict\";function n(t){for(var e=0;e<t.length;e++)if(t[e]in s)return 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o=this.computedRotation;f(o,i[0],i[1],i[2],i[4],i[5],i[6],i[8],i[9],i[10]),a(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var s=0,l=0;l<3;++l)s+=Math.pow(r[l]-e[l],2);this.radius.set(t,.5Math.log(Math.max(s,1e-6))),this.center.set(t,r[0],r[1],r[2])},d.translate=function(t,e,r,n){this.center.move(t,e\|\|0,r\|\|0,n\|\|0)},d.setMatrix=function(t,e){var r=this.computedRotation;f(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),a(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;h(n,e);var i=n[15];if(Math.abs(i)>1e-6){var o=n[12]/i,s=n[13]/i,l=n[14]/i;this.recalcMatrix(t);var u=Math.exp(this.computedRadius[0]);this.center.set(t,o-n[2]u,s-n[6]u,l-n[10]u),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},d.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},d.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},d.getDistanceLimits=function(t){var e=this.radius.bounds;return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},d.toJSON=function(){return this.recalcMatrix(this.lastT()),{center:this.computedCenter.slice(),rotation:this.computedRotation.slice(),distance:Math.log(this.computedRadius[0]),zoomMin:this.radius.bounds[0][0],zoomMax:this.radius.bounds[1][0]}},d.fromJSON=function(t){var e=this.lastT(),r=t.center;r&&this.center.set(e,r[0],r[1],r[2]);var n=t.rotation;n&&this.rotation.set(e,n[0],n[1],n[2],n[3]);var i=t.distance;i&&i>0&&this.radius.set(e,Math.log(i)),this.setDistanceLimits(t.zoomMin,t.zoomMax)}},{\"./lib/quatFromFrame\":472,\"filtered-vector\":133,\"gl-mat4/fromQuat\":178,\"gl-mat4/invert\":181,\"gl-mat4/lookAt\":182}],474:[function(t,e,r){\"use strict\";var n=t(\"repeat-string\");e.exports=function(t,e,r){return r=void 0!==r?r+\"\":\" \",n(r,e)+t}},{\"repeat-string\":500}],475:[function(t,e,r){e.exports=function(t,e){e\|\|(e=[0,\"\"]),t=String(t);var 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r=\"prepare \"+t.length+\" points\";e&&console.time(r),this.points=t;var n=t.map(o);e&&console.timeEnd(r);for(var i=this.options.maxZoom;i>=this.options.minZoom;i--){var a=+Date.now();this.trees[i+1]=v(n,p,m,this.options.nodeSize,Float32Array),n=this._cluster(n,i),e&&console.log(\"z%d: %d clusters in %dms\",i,n.length,+Date.now()-a)}return this.trees[this.options.minZoom]=v(n,p,m,this.options.nodeSize,Float32Array),e&&console.timeEnd(\"total time\"),this},getClusters:function(t,e){for(var r=this.trees[this._limitZoom(e)],n=r.range(u(t[0]),c(t[3]),u(t[2]),c(t[1])),i=[],a=0;a<n.length;a++){var o=r.points[n[a]];i.push(o.numPoints?s(o):this.points[o.id])}return i},getChildren:function(t,e){for(var r=this.trees[e+1].points[t],n=this.options.radius/(this.options.extentMath.pow(2,e)),i=this.trees[e+1].within(r.x,r.y,n),a=[],o=0;o<i.length;o++){var l=this.trees[e+1].points[i[o]];l.parentId===t&&a.push(l.numPoints?s(l):this.points[l.id])}return 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s=e[t[o]];a.features.push({type:1,geometry:[[Math.round(this.options.extent(s.xi-r)),Math.round(this.options.extent(s.yi-n))]],tags:s.numPoints?l(s):this.points[s.id].properties})}},_limitZoom:function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},_cluster:function(t,e){for(var r=[],n=this.options.radius/(this.options.extentMath.pow(2,e)),i=0;i<t.length;i++){var o=t[i];if(!(o.zoom<=e)){o.zoom=e;var s=this.trees[e+1],l=s.within(o.x,o.y,n),u=o.numPoints\|\|1,c=o.xu,h=o.yu,f=null;this.options.reduce&&(f=this.options.initial(),this._accumulate(f,o));for(var d=0;d<l.length;d++){var p=s.points[l[d]];if(e<p.zoom){var m=p.numPoints\|\|1;p.zoom=e,c+=p.xm,h+=p.ym,u+=m,p.parentId=i,this.options.reduce&&this._accumulate(f,p)}}1===u?r.push(o):(o.parentId=i,r.push(a(c/u,h/u,u,i,f)))}}return r},_accumulate:function(t,e){var r=e.numPoints?e.properties:this.options.map(this.points[e.id].properties);this.options.reduce(t,r)}}},{kdbush:298}],530:[function(t,e,r){\"use 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a=[],l=[],u=0;u<r;++u)a.push(\"d\"+u),l.push(\"d\"+u);for(var u=0;u<1<<r;++u)a.push(\"v\"+u),l.push(\"v\"+u);for(var u=0;u<1<<r;++u)a.push(\"p\"+u),l.push(\"p\"+u);a.push(\"a\",\"b\",\"c\"),l.push(\"a\",\"c\"),n.push(\"vertex:function vertexFunc(\",a.join(),\"){\");for(var c=[],u=0;u<1<<r;++u)c.push(\"(p\"+u+\"<<\"+u+\")\");n.push(\"var m=(\",c.join(\"+\"),\")\|0;if(m===0\|\|m===\",(1<<(1<<r))-1,\"){return}\");var h=[],f=[];1<<(1<<r)<=128?(n.push(\"switch(m){\"),f=n):n.push(\"switch(m>>>7){\");for(var u=0;u<1<<(1<<r);++u){if(1<<(1<<r)>128&&u%128==0){h.length>0&&f.push(\"}}\");var d=\"vExtra\"+h.length;n.push(\"case \",u>>>7,\":\",d,\"(m&0x7f,\",l.join(),\");break;\"),f=[\"function \",d,\"(m,\",l.join(),\"){switch(m){\"],h.push(f)}f.push(\"case \",127&u,\":\");for(var p=new Array(r),m=new Array(r),v=new Array(r),g=new Array(r),y=0,b=0;b<r;++b)p[b]=[],m[b]=[],v[b]=0,g[b]=0;for(var b=0;b<1<<r;++b)for(var x=0;x<r;++x){var _=b^1<<x;if(!(_>b)&&!(u&1<<_)!=!(u&1<<b)){var w=1;u&1<<_?m[x].push(\"v\"+_+\"-v\"+b):(m[x].push(\"v\"+b+\"-v\"+_),w=-w),w<0?(p[x].push(\"-v\"+b+\"-v\"+_),v[x]+=2):(p[x].push(\"v\"+b+\"+v\"+_),v[x]-=2),y+=1;for(var M=0;M<r;++M)M!==x&&(_&1<<M?g[M]+=1:g[M]-=1)}}for(var k=[],x=0;x<r;++x)if(0===p[x].length)k.push(\"d\"+x+\"-0.5\");else{var A=\"\";v[x]<0?A=v[x]+\"c\":v[x]>0&&(A=\"+\"+v[x]+\"c\");var T=p[x].length/y.5,S=.5+g[x]/y.5;k.push(\"d\"+x+\"-\"+S+\"-\"+T+\"(\"+p[x].join(\"+\")+A+\")/(\"+m[x].join(\"+\")+\")\")}f.push(\"a.push([\",k.join(),\"]);\",\"break;\")}n.push(\"}},\"),h.length>0&&f.push(\"}}\");for(var E=[],u=0;u<1<<r-1;++u)E.push(\"v\"+u);E.push(\"c0\",\"c1\",\"p0\",\"p1\",\"a\",\"b\",\"c\"),n.push(\"cell:function cellFunc(\",E.join(),\"){\");var L=s(r-1);n.push(\"if(p0){b.push(\",L.map(function(t){return\"[\"+t.map(function(t){return\"v\"+t})+\"]\"}).join(),\")}else{b.push(\",L.map(function(t){var e=t.slice();return e.reverse(),\"[\"+e.map(function(t){return\"v\"+t})+\"]\"}).join(),\")}}});function \",i,\"(array,level){var verts=[],cells=[];contour(array,verts,cells,level);return {positions:verts,cells:cells};} return \",i,\";\");for(var u=0;u<h.length;++u)n.push(h[u].join(\"\"));return new Function(\"genContour\",n.join(\"\"))(o)}function i(t,e){for(var r=l(t,e),n=r.length,i=new Array(n),a=new Array(n),o=0;o<n;++o)i[o]=[r[o]],a[o]=[o];return{positions:i,cells:a}}function a(t,e){if(t.dimension<=0)return{positions:[],cells:[]};if(1===t.dimension)return i(t,e);var r=t.order.join()+\"-\"+t.dtype,a=u[r],e=+e\|\|0;return a\|\|(a=u[r]=n(t.order,t.dtype)),a(t,e)}e.exports=a;var o=t(\"ndarray-extract-contour\"),s=t(\"triangulate-hypercube\"),l=t(\"zero-crossings\"),u={}},{\"ndarray-extract-contour\":456,\"triangulate-hypercube\":537,\"zero-crossings\":584}],532:[function(t,e,r){(function(r){\"use strict\";function n(t){for(var e=t.cells,r=t.positions,n=new Float32Array(6e.length),i=0,a=0,o=0;o<e.length;++o)for(var s=e[o],l=0;l<3;++l){var u=r[s[l]];n[i++]=u[0],n[i++]=u[1]+1.4,a=Math.max(u[0],a)}return{data:n,shape:a}}function i(t,e,r){var r=r\|\|{},o=s[t];o\|\|(o=s[t]={\" \":{data:new Float32Array(0),shape:.2}});var l=o[e];if(!l)if(e.length<=1\|\|!/\\d/.test(e))l=o[e]=n(a(e,{triangles:!0,font:t,textAlign:r.textAlign\|\|\"left\",textBaseline:\"alphabetic\"}));else{for(var u=e.split(/(\\d\|\\s)/),c=new Array(u.length),h=0,f=0,d=0;d<u.length;++d)c[d]=i(t,u[d]),h+=c[d].data.length,f+=c[d].shape,d>0&&(f+=.02);for(var p=new Float32Array(h),m=0,v=-.5f,d=0;d<c.length;++d){for(var g=c[d].data,y=0;y<g.length;y+=2)p[m++]=g[y]+v,p[m++]=g[y+1];v+=c[d].shape+.02}l=o[e]={data:p,shape:f}}return l}e.exports=i;var a=t(\"vectorize-text\"),o=window\|\|r.global\|\|{},s=o.__TEXT_CACHE\|\|{};o.__TEXT_CACHE={}}).call(this,t(\"_process\"))},{_process:487,\"vectorize-text\":554}],533:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.fontSize=t\|\|24,this.buffer=void 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l=(t[a]+aa-(t[r[s]]+r[s]r[s]))/(2a-2r[s]);l<=n[s];)s--,l=(t[a]+aa-(t[r[s]]+r[s]r[s]))/(2a-2r[s]);s++,r[s]=a,n[s]=l,n[s+1]=+o}for(a=0,s=0;a<i;a++){\n", "for(;n[s+1]<a;)s++;e[a]=(a-r[s])(a-r[s])+t[r[s]]}}e.exports=n;var o=1e20;n.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=e.data,n=0;n<this.sizethis.size;n++){var a=r[4n+3]/255;this.gridOuter[n]=1===a?0:0===a?o:Math.pow(Math.max(0,.5-a),2),this.gridInner[n]=1===a?o:0===a?0:Math.pow(Math.max(0,a-.5),2)}for(i(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),i(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.sizethis.size;n++){var s=this.gridOuter[n]-this.gridInner[n],l=Math.max(0,Math.min(255,Math.round(255-255(s/this.radius+this.cutoff))));r[4n+0]=l,r[4n+1]=l,r[4n+2]=l,r[4n+3]=255}return e}},{}],534:[function(e,r,n){!function(e){function n(t,e){if(t=t\|\|\"\",e=e\|\|{},t 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n.add(4-(n.dayOfWeek()\|\|7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate);return t=n.year(),e=n.month(),r=n.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25(t+4716))+Math.floor(30.6001(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5),r=e+1524,n=Math.floor((r-122.1)/365.25),i=Math.floor(365.25n),a=Math.floor((r-i)/30.6001),o=a-Math.floor(a<14?1:13),s=n-Math.floor(o>2?4716:4715),l=r-i-Math.floor(30.6001a);return s<=0&&s--,this.newDate(s,o,l)}}),i.calendars.julian=n},{\"../main\":581,\"object-assign\":470}],574:[function(t,e,r){function n(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}function i(t,e){return t-eMath.floor(t/e)}function a(t,e){return 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this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,o.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if(t=t.split(\".\"),t.length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19\|\|r>0&&n<0)throw\"Invalid Mayan year\";e=20e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,o.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate),!0},extraInfo:function(t,e,r){var n=this._validate(t,e,r,o.local.invalidDate),i=n.toJD(),a=this._toHaab(i),s=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[s[0]-1],tzolkinDay:s[0],tzolkinTrecena:s[1]}},_toHaab:function(t){t-=this.jdEpoch;var e=i(t+8+340,365);return[Math.floor(e/20)+1,i(e,20)]},_toTzolkin:function(t){return t-=this.jdEpoch,[a(t+20,20),a(t+4,13)]},toJD:function(t,e,r){var n=this._validate(t,e,r,o.local.invalidDate);return n.day()+20n.month()+360n.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),o.calendars.mayan=n},{\"../main\":581,\"object-assign\":470}],575:[function(t,e,r){function n(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\");n.prototype=new i.baseCalendar;var o=i.instance(\"gregorian\");a(n.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear\|\|i.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()\|\|7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidMonth),t=n.year();t<0&&t++;for(var a=n.day(),s=1;s<n.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return 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i.baseCalendar,a(n.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,i.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var a=i.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9\|\|9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,\n", "s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=i.instance(),r=e.fromJD(t),n=r.year(),a=r.dayOfYear(),o=n+56;this._createMissingCalendarData(o);for(var s=9,l=this.NEPALI_CALENDAR_DATA[o][0],u=this.NEPALI_CALENDAR_DATA[o][s]-l+1;a>u;)s++,s>12&&(s=1,o++),u+=this.NEPALI_CALENDAR_DATA[o][s];var c=this.NEPALI_CALENDAR_DATA[o][s]-(u-a);return this.newDate(o,s,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29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e=this._validate(t,this.minMonth,this.minDay,a.local.invalidYear);return 682((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,a.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var n=this._validate(t,e,r,a.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var o=t-(t>=0?474:473),s=474+i(o,2820);return r+(e<=7?31(e-1):30(e-1)+6)+Math.floor((682s-110)/2816)+365(s-1)+1029983Math.floor(o/2820)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=t-this.toJD(475,1,1),r=Math.floor(e/1029983),n=i(e,1029983),a=2820;if(1029982!==n){var o=Math.floor(n/366),s=i(n,366);a=Math.floor((2134o+2816s+2815)/1028522)+o+1}var l=a+2820r+474;l=l<=0?l-1:l;var 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n=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(n.year());return o.weekOfYear(t,n.month(),n.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate),t=this._t2gYear(n.year());return o.toJD(t,n.month(),n.day())},fromJD:function(t){var e=o.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),i.calendars.taiwan=n},{\"../main\":581,\"object-assign\":470}],579:[function(t,e,r){function n(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\"),o=i.instance();n.prototype=new i.baseCalendar,a(n.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(e.year());return o.leapYear(t)},weekOfYear:function(t,e,r){var n=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(n.year());return o.weekOfYear(t,n.month(),n.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate),t=this._t2gYear(n.year());return o.toJD(t,n.month(),n.day())},fromJD:function(t){var e=o.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),i.calendars.thai=n},{\"../main\":581,\"object-assign\":470}],580:[function(t,e,r){function n(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}var 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n(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function i(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function a(t,e){return t=\"\"+t,\"000000\".substring(0,e-t.length)+t}function o(){this.shortYearCutoff=\"+10\"}function s(t){this.local=this.regionalOptions[t]\|\|this.regionalOptions[\"\"]}var l=t(\"object-assign\");l(n.prototype,{instance:function(t,e){t=(t\|\|\"gregorian\").toLowerCase(),e=e\|\|\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar\|\|this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)\|\|this.instance(),n.newDate(t,e,r)},substituteDigits:function(t){\n", "return function(e){return(e+\"\").replace(/[0-9]/g,function(e){return t[e]})}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n\|\|t[0]}}}),l(i.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars\|\|u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+a(Math.abs(this.year()),4)+\"-\"+a(this.month(),2)+\"-\"+a(this.day(),2)}}),l(o.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new i(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear\|\|u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear\|\|u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+a(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear\|\|u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth\|\|u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth\|\|u.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear\|\|u.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r\|\|\"w\"===r){var n=t.toJD()+e(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0),i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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a=r.dayNamesShort\|\|this.local.dayNamesShort,o=r.dayNames\|\|this.local.dayNames,s=r.parseMonth\|\|this.local.parseMonth,l=r.monthNumbers\|\|this.local.monthNumbers,u=r.monthNamesShort\|\|this.local.monthNamesShort,c=r.monthNames\|\|this.local.monthNames,h=-1,f=-1,d=-1,p=-1,m=-1,v=!1,g=!1,y=function(e,r){for(var n=1;k+n<t.length&&t.charAt(k+n)===e;)n++;return k+=n-1,Math.floor(n/(r\|\|1))>1},b=function(t,r){var n=y(t,r),a=[2,3,n?4:2,n?4:2,10,11,20][\"oyYJ@!\".indexOf(t)+1],o=new RegExp(\"^-?\\\\d{1,\"+a+\"}\"),s=e.substring(M).match(o);if(!s)throw(i.local.missingNumberAt\|\|i.regionalOptions[\"\"].missingNumberAt).replace(/\\{0\\}/,M);return M+=s[0].length,parseInt(s[0],10)},x=this,_=function(t,r,n,a){for(var o=y(t,a)?n:r,s=0;s<o.length;s++)if(e.substr(M,o[s].length).toLowerCase()===o[s].toLowerCase())return 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o=i[e];o.anchor&&\"free\"!==o.anchor&&(o.anchor=f.cleanId(o.anchor)),o.overlaying&&(o.overlaying=f.cleanId(o.overlaying)),o.type\|\|(o.isdate?o.type=\"date\":o.islog?o.type=\"log\":!1===o.isdate&&!1===o.islog&&(o.type=\"linear\")),\"withzero\"!==o.autorange&&\"tozero\"!==o.autorange\|\|(o.autorange=!0,o.rangemode=\"tozero\"),delete o.islog,delete o.isdate,delete o.categories,a(o,\"domain\")&&delete o.domain,void 0!==o.autotick&&(void 0===o.tickmode&&(o.tickmode=o.autotick?\"auto\":\"linear\"),delete o.autotick)}var s=Array.isArray(t.annotations)?t.annotations.length:0;for(e=0;e<s;e++){var u=t.annotations[e];c.isPlainObject(u)&&(u.ref&&(\"paper\"===u.ref?(u.xref=\"paper\",u.yref=\"paper\"):\"data\"===u.ref&&(u.xref=\"x\",u.yref=\"y\"),delete u.ref),n(u,\"xref\"),n(u,\"yref\"))}var p=Array.isArray(t.shapes)?t.shapes.length:0;for(e=0;e<p;e++){var m=t.shapes[e];c.isPlainObject(m)&&(n(m,\"xref\"),n(m,\"yref\"))}var v=t.legend;v&&(v.x>3?(v.x=1.02,v.xanchor=\"left\"):v.x<-2&&(v.x=-.02,v.xanchor=\"right\"),v.y>3?(v.y=1.02,v.yanchor=\"bottom\"):v.y<-2&&(v.y=-.02,v.yanchor=\"top\")),\"rotate\"===t.dragmode&&(t.dragmode=\"orbit\"),t.scene1&&(t.scene\|\|(t.scene=t.scene1),delete t.scene1);var g=h.getSubplotIds(t,\"gl3d\");for(e=0;e<g.length;e++){var y=t[g[e]],b=y.cameraposition;if(Array.isArray(b)&&4===b[0].length){var x=b[0],_=b[1],w=b[2],M=l([],x),k=[];for(r=0;r<3;++r)k[r]=_[e]+wM[2+4r];y.camera={eye:{x:k[0],y:k[1],z:k[2]},center:{x:_[0],y:_[1],z:_[2]},up:{x:M[1],y:M[5],z:M[9]}},delete y.cameraposition}}return d.clean(t),t},r.cleanData=function(t,e){for(var n=[],o=(t.concat(Array.isArray(e)?e:[]).filter(function(t){return\"uid\"in t}).map(function(t){return t.uid})),s=0;s<t.length;s++){var l,p=t[s];if(!(\"uid\"in p)\|\|-1!==n.indexOf(p.uid)){var m;for(l=0;l<100&&(m=c.randstr(o),-1!==n.indexOf(m));l++);p.uid=c.randstr(o),o.push(p.uid)}if(n.push(p.uid),\"histogramy\"===p.type&&\"xbins\"in p&&!(\"ybins\"in p)&&(p.ybins=p.xbins,delete p.xbins),p.error_y&&\"opacity\"in p.error_y){var v=d.defaults,g=p.error_y.color\|\|(u.traceIs(p,\"bar\")?d.defaultLine:v[s%v.length]);p.error_y.color=d.addOpacity(d.rgb(g),d.opacity(g)p.error_y.opacity),delete p.error_y.opacity}if(\"bardir\"in p&&(\"h\"!==p.bardir\|\|!u.traceIs(p,\"bar\")&&\"histogram\"!==p.type.substr(0,9)\|\|(p.orientation=\"h\",r.swapXYData(p)),delete p.bardir),\"histogramy\"===p.type&&r.swapXYData(p),\"histogramx\"!==p.type&&\"histogramy\"!==p.type\|\|(p.type=\"histogram\"),\"scl\"in p&&(p.colorscale=p.scl,delete p.scl),\"reversescl\"in p&&(p.reversescale=p.reversescl,delete p.reversescl),p.xaxis&&(p.xaxis=f.cleanId(p.xaxis,\"x\")),p.yaxis&&(p.yaxis=f.cleanId(p.yaxis,\"y\")),u.traceIs(p,\"gl3d\")&&p.scene&&(p.scene=h.subplotsRegistry.gl3d.cleanId(p.scene)),u.traceIs(p,\"pie\")\|\|u.traceIs(p,\"bar\")\|\|(Array.isArray(p.textposition)?p.textposition=p.textposition.map(i):p.textposition&&(p.textposition=i(p.textposition))),u.traceIs(p,\"2dMap\")&&(\"YIGnBu\"===p.colorscale&&(p.colorscale=\"YlGnBu\"),\"YIOrRd\"===p.colorscale&&(p.colorscale=\"YlOrRd\")),u.traceIs(p,\"markerColorscale\")&&p.marker){var y=p.marker;\"YIGnBu\"===y.colorscale&&(y.colorscale=\"YlGnBu\"),\"YIOrRd\"===y.colorscale&&(y.colorscale=\"YlOrRd\")}if(\"surface\"===p.type&&c.isPlainObject(p.contours)){var b=[\"x\",\"y\",\"z\"];for(l=0;l<b.length;l++){var x=p.contours[b[l]];c.isPlainObject(x)&&(x.highlightColor&&(x.highlightcolor=x.highlightColor,delete x.highlightColor),x.highlightWidth&&(x.highlightwidth=x.highlightWidth,delete x.highlightWidth))}}if(Array.isArray(p.transforms)){var _=p.transforms;for(l=0;l<_.length;l++){var w=_[l];if(c.isPlainObject(w))switch(w.type){case\"filter\":w.filtersrc&&(w.target=w.filtersrc,delete w.filtersrc),w.calendar&&(w.valuecalendar\|\|(w.valuecalendar=w.calendar),delete w.calendar);break;case\"groupby\":if(w.styles=w.styles\|\|w.style,w.styles&&!Array.isArray(w.styles)){var M=w.styles,k=Object.keys(M);w.styles=[];for(var A=0;A<k.length;A++)w.styles.push({target:k[A],value:M[k[A]]})}}}}a(p,\"line\")&&delete p.line,\"marker\"in p&&(a(p.marker,\"line\")&&delete p.marker.line,a(p,\"marker\")&&delete p.marker),d.clean(p)}},r.swapXYData=function(t){var e;if(c.swapAttrs(t,[\"?\",\"?0\",\"d?\",\"?bins\",\"nbins?\",\"autobin?\",\"?src\",\"error_?\"]),Array.isArray(t.z)&&Array.isArray(t.z[0])&&(t.transpose?delete t.transpose:t.transpose=!0),t.error_x&&t.error_y){var r=t.error_y,n=\"copy_ystyle\"in r?r.copy_ystyle:!(r.color\|\|r.thickness\|\|r.width);c.swapAttrs(t,[\"error_?.copy_ystyle\"]),n&&c.swapAttrs(t,[\"error_?.color\",\"error_?.thickness\",\"error_?.width\"])}if(\"string\"==typeof t.hoverinfo){var i=t.hoverinfo.split(\"+\");for(e=0;e<i.length;e++)\"x\"===i[e]?i[e]=\"y\":\"y\"===i[e]&&(i[e]=\"x\");t.hoverinfo=i.join(\"+\")}},r.coerceTraceIndices=function(t,e){return s(e)?[e]:Array.isArray(e)&&e.length?e:t.data.map(function(t,e){return e})},r.manageArrayContainers=function(t,e,r){var n=t.obj,i=t.parts,a=i.length,o=i[a-1],l=s(o);if(l&&null===e){var u=i.slice(0,a-1).join(\".\");c.nestedProperty(n,u).get().splice(o,1)}else l&&void 0===t.get()?(void 0===t.get()&&(r[t.astr]=null),t.set(e)):t.set(e)};var p=/(\\.[^\\[\\]\\.]+\|\\[[^\\[\\]\\.]+\\])$/;r.hasParent=function(t,e){for(var r=o(e);r;){if(r in t)return!0;r=o(r)}return!1};var m=[\"x\",\"y\",\"z\"];r.clearAxisTypes=function(t,e,r){for(var n=0;n<e.length;n++)for(var i=t._fullData[n],a=0;a<3;a++){var o=f.getFromTrace(t,i,m[a]);if(o&&\"log\"!==o.type){var s=o._name,l=o._id.substr(1);if(\"scene\"===l.substr(0,5)){if(void 0!==r[l])continue;s=l+\".\"+s}var u=s+\".type\";void 0===r[s]&&void 0===r[u]&&c.nestedProperty(t.layout,u).set(null)}}}},{\"../components/color\":604,\"../lib\":728,\"../plots/cartesian/axes\":772,\"../plots/plots\":831,\"../registry\":846,\"fast-isnumeric\":131,\"gl-mat4/fromQuat\":178}],758:[function(t,e,r){\"use strict\";var n=t(\"../lib/nested_property\"),i=t(\"../lib/is_plain_object\"),a=t(\"../lib/noop\"),o=t(\"../lib/loggers\"),s=t(\"../lib/search\").sorterAsc,l=t(\"../registry\");r.containerArrayMatch=t(\"./container_array_match\");var u=r.isAddVal=function(t){return\"add\"===t\|\|i(t)},c=r.isRemoveVal=function(t){return null===t\|\|\"remove\"===t};r.applyContainerArrayChanges=function(t,e,r,i){var h=e.astr,f=l.getComponentMethod(h,\"supplyLayoutDefaults\"),d=l.getComponentMethod(h,\"draw\"),p=l.getComponentMethod(h,\"drawOne\"),m=i.replot\|\|i.recalc\|\|f===a\|\|d===a,v=t.layout,g=t._fullLayout;if(r[\"\"]){Object.keys(r).length>1&&o.warn(\"Full array edits are incompatible with other edits\",h);var y=r[\"\"][\"\"];if(c(y))e.set(null);else{if(!Array.isArray(y))return o.warn(\"Unrecognized full array edit value\",h,y),!0;e.set(y)}return!m&&(f(v,g),d(t),!0)}var b,x,_,w,M,k,A,T=Object.keys(r).map(Number).sort(s),S=e.get(),E=S\|\|[],L=n(g,h).get(),C=[],I=-1,z=E.length;for(b=0;b<T.length;b++)if(_=T[b],w=r[_],M=Object.keys(w),k=w[\"\"],A=u(k),_<0\|\|_>E.length-(A?0:1))o.warn(\"index out of range\",h,_);else if(void 0!==k)M.length>1&&o.warn(\"Insertion & removal are incompatible with edits to the same index.\",h,_),c(k)?C.push(_):A?(\"add\"===k&&(k={}),E.splice(_,0,k),L&&L.splice(_,0,{})):o.warn(\"Unrecognized full object edit value\",h,_,k),-1===I&&(I=_);else for(x=0;x<M.length;x++)n(E[_],M[x]).set(w[M[x]]);for(b=C.length-1;b>=0;b--)E.splice(C[b],1),L&&L.splice(C[b],1);if(E.length?S\|\|e.set(E):e.set(null),m)return!1;if(f(v,g),p!==a){var D;if(-1===I)D=T;else{for(z=Math.max(E.length,z),D=[],b=0;b<T.length&&!((_=T[b])>=I);b++)D.push(_);for(b=I;b<z;b++)D.push(b)}for(b=0;b<D.length;b++)p(t,D[b])}else d(t);return!0}},{\"../lib/is_plain_object\":730,\"../lib/loggers\":732,\"../lib/nested_property\":735,\"../lib/noop\":736,\"../lib/search\":745,\"../registry\":846,\"./container_array_match\":755}],759:[function(t,e,r){\"use strict\";function n(t,e){try{t._fullLayout._paper.style(\"background\",e)}catch(t){w.error(t)}}function i(t,e){n(t,I.combine(e,\"white\"))}function a(t,e){t._context\|\|(t._context=w.extendDeep({},_.defaultConfig));var r,a,o,s=t._context;if(e){for(a=Object.keys(e),r=0;r<a.length;r++)\"editable\"!==(o=a[r])&&\"edits\"!==o&&o in s&&(\"setBackground\"===o&&\"opaque\"===e[o]?s[o]=i:s[o]=e[o]);e.plot3dPixelRatio&&!s.plotGlPixelRatio&&(s.plotGlPixelRatio=s.plot3dPixelRatio);var l=e.editable;if(void 0!==l)for(s.editable=l,a=Object.keys(s.edits),r=0;r<a.length;r++)s.edits[a[r]]=l;if(e.edits)for(a=Object.keys(e.edits),r=0;r<a.length;r++)(o=a[r])in s.edits&&(s.edits[o]=e.edits[o])}s.staticPlot&&(s.editable=!1,s.edits={},s.autosizable=!1,s.scrollZoom=!1,s.doubleClick=!1,s.showTips=!1,s.showLink=!1,s.displayModeBar=!1),\"hover\"!==s.displayModeBar\|\|x\|\|(s.displayModeBar=!0),\"transparent\"!==s.setBackground&&\"function\"==typeof s.setBackground\|\|(s.setBackground=n)}function o(t,e,r){var n=y.select(t).selectAll(\".plot-container\").data([0]);n.enter().insert(\"div\",\":first-child\").classed(\"plot-container plotly\",!0);var i=n.selectAll(\".svg-container\").data([0]);i.enter().append(\"div\").classed(\"svg-container\",!0).style(\"position\",\"relative\"),i.html(\"\"),e&&(t.data=e),r&&(t.layout=r),E.manager.fillLayout(t),i.style({width:t._fullLayout.width+\"px\",height:t._fullLayout.height+\"px\"}),t.framework=E.manager.framework(t),t.framework({data:t.data,layout:t.layout},i.node()),t.framework.setUndoPoint();var a=t.framework.svg(),o=1,s=t._fullLayout.title;\"\"!==s&&s\|\|(o=0);var l=function(){this.call(P.convertToTspans,t)},u=a.select(\".title-group text\").call(l);if(t._context.edits.titleText){s&&\"Click to enter title\"!==s\|\|(o=.2,u.attr({\"data-unformatted\":\"Click to enter title\"}).text(\"Click to enter title\").style({opacity:o}).on(\"mouseover.opacity\",function(){y.select(this).transition().duration(100).style(\"opacity\",1)}).on(\"mouseout.opacity\",function(){y.select(this).transition().duration(1e3).style(\"opacity\",0)}));var c=function(){this.call(P.makeEditable,{gd:t}).on(\"edit\",function(e){t.framework({layout:{title:e}}),this.text(e).call(l),this.call(c)}).on(\"cancel\",function(){var t=this.attr(\"data-unformatted\");this.text(t).call(l)})};u.call(c)}return t._context.setBackground(t,t._fullLayout.paper_bgcolor),S.addLinks(t),Promise.resolve()}function s(t,e){var r,n,i=e+1,a=[];for(r=0;r<t.length;r++)n=t[r],n<0?a.push(i+n):a.push(n);return a}function l(t,e,r){var n,i;for(n=0;n<e.length;n++){if((i=e[n])!==parseInt(i,10))throw new Error(\"all values in \"+r+\" must be integers\");if(i>=t.data.length\|\|i<-t.data.length)throw new Error(r+\" must be valid indices for gd.data.\");if(e.indexOf(i,n+1)>-1\|\|i>=0&&e.indexOf(-t.data.length+i)>-1\|\|i<0&&e.indexOf(t.data.length+i)>-1)throw new Error(\"each index in \"+r+\" must be unique.\")}}function u(t,e,r){if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array.\");if(void 0===e)throw new Error(\"currentIndices is a required argument.\");if(Array.isArray(e)\|\|(e=[e]),l(t,e,\"currentIndices\"),void 0===r\|\|Array.isArray(r)\|\|(r=[r]),void 0!==r&&l(t,r,\"newIndices\"),void 0!==r&&e.length!==r.length)throw new Error(\"current and new indices must be of equal length.\")}function c(t,e,r){var n,i;if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array.\");if(void 0===e)throw new Error(\"traces must be defined.\");for(Array.isArray(e)\|\|(e=[e]),n=0;n<e.length;n++)if(\"object\"!=typeof(i=e[n])\|\|Array.isArray(i)\|\|null===i)throw new Error(\"all values in traces array must be non-array objects\");if(void 0===r\|\|Array.isArray(r)\|\|(r=[r]),void 0!==r&&r.length!==e.length)throw new Error(\"if indices is specified, traces.length must equal indices.length\")}function h(t,e,r,n){var i=w.isPlainObject(n);if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array\");if(!w.isPlainObject(e))throw new Error(\"update must be a key:value object\");if(void 0===r)throw new Error(\"indices must be an integer or array of integers\");l(t,r,\"indices\");for(var a in e){if(!Array.isArray(e[a])\|\|e[a].length!==r.length)throw new Error(\"attribute \"+a+\" must be an array of length equal to indices array length\");if(i&&(!(a in n)\|\|!Array.isArray(n[a])\|\|n[a].length!==e[a].length))throw new Error(\"when maxPoints is set as a key:value object it must contain a 1:1 corrispondence with the keys and number of traces in the update object\")}}function f(t,e,r,n){var i,a,o,l,u,c=w.isPlainObject(n),h=[];Array.isArray(r)\|\|(r=[r]),r=s(r,t.data.length-1);for(var f in e)for(var d=0;d<r.length;d++){if(i=t.data[r[d]],o=w.nestedProperty(i,f),a=o.get(),l=e[f][d],!Array.isArray(l))throw new Error(\"attribute: \"+f+\" index: \"+d+\" must be an array\");if(!Array.isArray(a))throw new Error(\"cannot extend missing or non-array attribute: \"+f);u=c?n[f][d]:n,b(u)\|\|(u=-1),h.push({prop:o,target:a,insert:l,maxp:Math.floor(u)})}return h}function d(t,e,r,n,i,a){h(t,e,r,n);for(var o,s,l,u=f(t,e,r,n),c=[],d={},p={},m=0;m<u.length;m++)s=u[m].prop,l=u[m].maxp,o=i(u[m].target,u[m].insert),l>=0&&l<o.length&&(c=a(o,l)),l=u[m].target.length,s.set(o),Array.isArray(d[s.astr])\|\|(d[s.astr]=[]),Array.isArray(p[s.astr])\|\|(p[s.astr]=[]),d[s.astr].push(c),p[s.astr].push(l);return{update:d,maxPoints:p}}function p(t){return void 0===t?null:t}function m(t,e,r){function n(){return r.map(function(){})}function i(t){var e=_.Axes.id2name(t);-1===u.indexOf(e)&&u.push(e)}function a(t){return\"LAYOUT\"+t+\".autorange\"}function o(t){return\"LAYOUT\"+t+\".range\"}function s(i,a,o){if(Array.isArray(i))return void i.forEach(function(t){s(t,a,o)});if(!(i in e\|\|R.hasParent(e,i))){var l;l=\"LAYOUT\"===i.substr(0,6)?w.nestedProperty(t.layout,i.replace(\"LAYOUT\",\"\")):w.nestedProperty(f[r[o]],i),i in v\|\|(v[i]=n()),void 0===v[i][o]&&(v[i][o]=p(l.get())),void 0!==a&&l.set(a)}}var l,u,c=t._fullLayout,h=t._fullData,f=t.data,d=j.traceFlags(),m={},v={},g={};for(var y in 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n,i,a,o,s=V.getFromId(t,e),l=w.select(t).selectAll(\"g.\"+e+\"tick\"),u={selection:l,side:s.side},h=e.charAt(0),f=t._fullLayout._size,d=s.titlefont.size;if(l.size()){var p=L.getTranslate(l.node().parentNode);u.offsetLeft=p.x,u.offsetTop=p.y}var 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h.boxEnabled?(s=h.boxStart[0]!==h.boxEnd[0],u=h.boxStart[1]!==h.boxEnd[1],s\|\|u?(s&&(o(0,h.boxStart[0],h.boxEnd[0]),t.xaxis.autorange=!1),u&&(o(1,h.boxStart[1],h.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),h.boxEnabled=!1,h.boxInited=!1):h.boxInited&&(h.boxInited=!1);break;case\"pan\":h.boxEnabled=!1,h.boxInited=!1,n?(h.panning\|\|(h.dragStart[0]=i,h.dragStart[1]=a),Math.abs(h.dragStart[0]-i)<v&&(i=h.dragStart[0]),Math.abs(h.dragStart[1]-a)<v&&(a=h.dragStart[1]),s=(p-i)(f[2]-f[0])/(c.viewBox[2]-c.viewBox[0]),u=(m-a)(f[3]-f[1])/(c.viewBox[3]-c.viewBox[1]),f[0]+=s,f[2]+=s,f[1]+=u,f[3]+=u,t.setRanges(f),h.panning=!0,h.lastInputTime=Date.now(),e(),t.cameraChanged(),t.handleAnnotations()):h.panning&&(h.panning=!1,t.relayoutCallback())}h.lastPos[0]=i,h.lastPos[1]=a}var u=t.mouseContainer,c=t.glplot,h=new n(u,c);return h.mouseListener=a(u,i),u.addEventListener(\"touchstart\",function(t){var e=s(t.changedTouches[0],u);i(0,e[0],e[1]),i(1,e[0],e[1])}),u.addEventListener(\"touchmove\",function(t){t.preventDefault();var e=s(t.changedTouches[0],u);i(1,e[0],e[1])}),u.addEventListener(\"touchend\",function(){i(0,h.lastPos[0],h.lastPos[1])}),h.wheelListener=o(u,function(r,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=c.viewBox,o=h.lastPos[0],s=h.lastPos[1],l=Math.exp(5n/(a[3]-a[1])),u=o/(a[2]-a[0])(i[2]-i[0])+i[0],f=s/(a[3]-a[1])(i[3]-i[1])+i[1];return i[0]=(i[0]-u)l+u,i[2]=(i[2]-u)l+u,i[1]=(i[1]-f)l+f,i[3]=(i[3]-f)l+f,t.setRanges(i),h.lastInputTime=Date.now(),e(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),h}var a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"../cartesian/constants\");e.exports=i},{\"../cartesian/constants\":777,\"mouse-change\":452,\"mouse-event-offset\":453,\"mouse-wheel\":455}],807:[function(t,e,r){\"use strict\";function n(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}function i(t){return new n(t)}var a=t(\"../plots\"),o=t(\"../cartesian/axes\"),s=t(\"../../lib/html2unicode\"),l=t(\"../../lib/str2rgbarray\"),u=n.prototype,c=[\"xaxis\",\"yaxis\"];u.merge=function(t){this.titleEnable=!1,this.backgroundColor=l(t.plot_bgcolor);var e,r,n,i,a,o,u,h,f,d,p;for(d=0;d<2;++d){for(e=c[d],r=t[this.scene[e]._name],n=/Click to enter .+ title/.test(r.title)?\"\":r.title,p=0;p<=2;p+=2)this.labelEnable[d+p]=!1,this.labels[d+p]=s(n),this.labelColor[d+p]=l(r.titlefont.color),this.labelFont[d+p]=r.titlefont.family,this.labelSize[d+p]=r.titlefont.size,this.labelPad[d+p]=this.getLabelPad(e,r),this.tickEnable[d+p]=!1,this.tickColor[d+p]=l((r.tickfont\|\|{}).color),this.tickAngle[d+p]=\"auto\"===r.tickangle?0:Math.PI-r.tickangle/180,this.tickPad[d+p]=this.getTickPad(r),this.tickMarkLength[d+p]=0,this.tickMarkWidth[d+p]=r.tickwidth\|\|0,this.tickMarkColor[d+p]=l(r.tickcolor),this.borderLineEnable[d+p]=!1,this.borderLineColor[d+p]=l(r.linecolor),this.borderLineWidth[d+p]=r.linewidth\|\|0;u=this.hasSharedAxis(r),a=this.hasAxisInDfltPos(e,r)&&!u,o=this.hasAxisInAltrPos(e,r)&&!u,i=r.mirror\|\|!1,h=u?-1!==String(i).indexOf(\"all\"):!!i,f=u?\"allticks\"===i:-1!==String(i).indexOf(\"ticks\"),a?this.labelEnable[d]=!0:o&&(this.labelEnable[d+2]=!0),a?this.tickEnable[d]=r.showticklabels:o&&(this.tickEnable[d+2]=r.showticklabels),(a\|\|h)&&(this.borderLineEnable[d]=r.showline),(o\|\|h)&&(this.borderLineEnable[d+2]=r.showline),(a\|\|f)&&(this.tickMarkLength[d]=this.getTickMarkLength(r)),(o\|\|f)&&(this.tickMarkLength[d+2]=this.getTickMarkLength(r)),this.gridLineEnable[d]=r.showgrid,this.gridLineColor[d]=l(r.gridcolor),this.gridLineWidth[d]=r.gridwidth,this.zeroLineEnable[d]=r.zeroline,this.zeroLineColor[d]=l(r.zerolinecolor),this.zeroLineWidth[d]=r.zerolinewidth}},u.hasSharedAxis=function(t){var e=this.scene,r=a.getSubplotIds(e.fullLayout,\"gl2d\");return 0!==o.findSubplotsWithAxis(r,t).indexOf(e.id)},u.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},u.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},u.getLabelPad=function(t,e){var r=e.titlefont.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r(1.5+(n?1:0))-10:r(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r(1.5+(n?1:.5)):10+r(1.5+(n?.5:0)):void 0},u.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},u.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=i},{\n", "\"../../lib/html2unicode\":726,\"../../lib/str2rgbarray\":749,\"../cartesian/axes\":772,\"../plots\":831}],808:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../plots\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),u=t(\"../../components/fx/layout_attributes\");r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.layoutAttributes.plot_bgcolor,hoverlabel:u.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=a.getSubplotIds(e,\"gl2d\"),o=0;o<n.length;o++){var s=n[o],l=e._plots[s],u=a.getSubplotData(r,\"gl2d\",s),c=l._scene2d;void 0===c&&(c=new i({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),l._scene2d=c),c.plot(u,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=a.getSubplotIds(n,\"gl2d\"),o=0;o<i.length;o++){var s=i[o],u=n._plots[s];if(u._scene2d){0===a.getSubplotData(t,\"gl2d\",s).length&&(u._scene2d.destroy(),delete n._plots[s])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot\|\|l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=a.getSubplotIds(e,\"gl2d\"),n=0;n<r.length;n++){var i=e._plots[r[n]],s=i._scene2d,l=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":l,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),s.destroy()}},r.updateFx=function(t){for(var e=a.getSubplotIds(t,\"gl2d\"),r=0;r<e.length;r++){t._plots[e[r]]._scene2d.updateFx(t.dragmode)}}},{\"../../components/fx/layout_attributes\":646,\"../../constants/xmlns_namespaces\":709,\"../../plot_api/edit_types\":756,\"../cartesian\":782,\"../cartesian/attributes\":771,\"../cartesian/constants\":777,\"../plots\":831,\"./scene2d\":809}],809:[function(t,e,r){\"use strict\";function n(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio\|\|window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context.scrollZoom,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.glplotOptions=p(this),this.glplotOptions.merge(e),this.glplot=c(this.glplotOptions),this.camera=m(this),this.traces={},this.spikes=h(this.glplot),this.selectBox=f(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.bounds=[1/0,1/0,-1/0,-1/0],this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw()}function i(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1}var a,o,s=t(\"../../registry\"),l=t(\"../../plots/cartesian/axes\"),u=t(\"../../components/fx\"),c=t(\"gl-plot2d\"),h=t(\"gl-spikes2d\"),f=t(\"gl-select-box\"),d=t(\"webgl-context\"),p=t(\"./convert\"),m=t(\"./camera\"),v=t(\"../../lib/html2unicode\"),g=t(\"../../lib/show_no_webgl_msg\"),y=t(\"../../plots/cartesian/constraints\"),b=y.enforce,x=y.clean,_=[\"xaxis\",\"yaxis\"];e.exports=n;var w=n.prototype;w.makeFramework=function(){if(this.staticPlot){if(!(o\|\|(a=document.createElement(\"canvas\"),o=d({canvas:a,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=a,this.gl=o}else{var t=document.createElement(\"canvas\"),e=d({canvas:t,premultipliedAlpha:!0});e\|\|g(this),this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r),r.className+=\"user-select-none\";var n=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");n.style.position=\"absolute\",n.style.top=n.style.left=\"0px\",n.style.width=n.style.height=\"100%\",n.style[\"z-index\"]=20,n.style[\"pointer-events\"]=\"none\";var i=this.mouseContainer=document.createElement(\"div\");i.style.position=\"absolute\",i.style[\"pointer-events\"]=\"auto\";var s=this.container;s.appendChild(r),s.appendChild(n),s.appendChild(i);var l=this;i.addEventListener(\"mouseout\",function(){l.isMouseOver=!1,l.unhover()}),i.addEventListener(\"mouseover\",function(){l.isMouseOver=!0})},w.toImage=function(t){t\|\|(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(a),this.updateSize(this.canvas),this.glplot.setDirty(),this.glplot.draw();var e=this.glplot.gl,r=e.drawingBufferWidth,n=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var i=new Uint8Array(rn4);e.readPixels(0,0,r,n,e.RGBA,e.UNSIGNED_BYTE,i);for(var o=0,s=n-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=i[4(ro+l)+u];i[4(ro+l)+u]=i[4(rs+l)+u],i[4(rs+l)+u]=c}var h=document.createElement(\"canvas\");h.width=r,h.height=n;var f=h.getContext(\"2d\"),d=f.createImageData(r,n);d.data.set(i),f.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=h.toDataURL(\"image/jpeg\");break;case\"webp\":p=h.toDataURL(\"image/webp\");break;default:p=h.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(a),p},w.updateSize=function(t){t\|\|(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0\|Math.ceil(en),o=0\|Math.ceil(ei);return t.width===a&&t.height===o\|\|(t.width=a,t.height=o),this.redraw&&this.redraw(),t},w.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[l.calcTicks(this.xaxis),l.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=v(t[e][r].text+\"\");return t},w.updateRefs=function(t){this.fullLayout=t;var e=l.subplotMatch,r=\"xaxis\"+this.id.match(e)[1],n=\"yaxis\"+this.id.match(e)[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},w.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout;n.xaxis.autorange=e.autorange,n.xaxis.range=e.range.slice(0),n.yaxis.autorange=r.autorange,n.yaxis.range=r.range.slice(0);var i={lastInputTime:this.camera.lastInputTime};i[e._name]=e.range.slice(0),i[r._name]=r.range.slice(0),t.emit(\"plotly_relayout\",i)},w.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();i(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},w.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&s.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},w.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map(function(e){t[e].dispose(),delete t[e]}),this.glplot.dispose(),this.staticPlot\|\|this.container.removeChild(this.canvas),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},w.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};x(s,this.xaxis),x(s,this.yaxis);var u=r._size,c=this.xaxis.domain,h=this.yaxis.domain;o.viewBox=[u.l+c[0]u.w,u.b+h[0]u.h,i-u.r-(1-c[1])u.w,a-u.t-(1-h[1])u.h],this.mouseContainer.style.width=u.w(c[1]-c[0])+\"px\",this.mouseContainer.style.height=u.h(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h(h[1]-h[0]),this.mouseContainer.style.left=u.l+c[0]u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])u.h+\"px\";var f=this.bounds;f[0]=f[1]=1/0,f[2]=f[3]=-1/0;var d,p,m=Object.keys(this.traces);for(p=0;p<m.length;++p)for(var v=this.traces[m[p]],g=0;g<2;++g)f[g]=Math.min(f[g],v.bounds[g]),f[g+2]=Math.max(f[g+2],v.bounds[g+2]);for(p=0;p<2;++p)f[p]>f[p+2]&&(f[p]=-1,f[p+2]=1),d=this[_[p]],d._length=o.viewBox[p+2]-o.viewBox[p],l.doAutoRange(d),d.setScale();b(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},w.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},w.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},w.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if(i=t[n],i.uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort(function(t,e){return t._trace.index-e._trace.index})},w.updateFx=function(t){this.mouseContainer.style[\"pointer-events\"]=\"lasso\"===t\|\|\"select\"===t?\"none\":\"auto\",this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},w.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};u.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},w.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.xt.pixelRatio,s=this.canvas.height-t.pixelRatior.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var l=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(l[c]=t.dataBox[c],l[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var h=i._size,f=this.xaxis.domain,d=this.yaxis.domain;a=t.pick(o/t.pixelRatio+h.l+f[0]h.w,s/t.pixelRatio-(h.t+(1-d[1])h.h));var p=a&&a.object._trace.handlePick(a);if(p&&n&&this.emitPointAction(p,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&p&&(!this.lastPickResult\|\|this.lastPickResult.traceUid!==p.trace.uid\|\|this.lastPickResult.dataCoord[0]!==p.dataCoord[0]\|\|this.lastPickResult.dataCoord[1]!==p.dataCoord[1])){var m=p;this.lastPickResult={traceUid:p.trace?p.trace.uid:null,dataCoord:p.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(p,\"plotly_hover\");var v=this.fullData[m.trace.index]\|\|{},g=m.pointIndex,y=u.castHoverinfo(v,i,g);if(y&&\"all\"!==y){var b=y.split(\"+\");-1===b.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===b.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===b.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===b.indexOf(\"text\")&&(m.textLabel=void 0),-1===b.indexOf(\"name\")&&(m.name=void 0)}u.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:u.castHoverOption(v,g,\"bgcolor\")\|\|m.color,borderColor:u.castHoverOption(v,g,\"bordercolor\"),fontFamily:u.castHoverOption(v,g,\"font.family\"),fontSize:u.castHoverOption(v,g,\"font.size\"),fontColor:u.castHoverOption(v,g,\"font.color\")},{container:this.svgContainer,gd:this.graphDiv})}}a\|\|this.unhover(),t.draw()}},w.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),u.loneUnhover(this.svgContainer))},w.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return l.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/fx\":645,\"../../lib/html2unicode\":726,\"../../lib/show_no_webgl_msg\":747,\"../../plots/cartesian/axes\":772,\"../../plots/cartesian/constraints\":779,\"../../registry\":846,\"./camera\":806,\"./convert\":807,\"gl-plot2d\":219,\"gl-select-box\":253,\"gl-spikes2d\":262,\"webgl-context\":563}],810:[function(t,e,r){\"use strict\";function n(t,e){function r(e,r,n,a){var o=p.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,c=\"zoom\"===o,f=!!a.control,d=!!a.alt,y=!!a.shift,b=!!(1&e),x=!!(2&e),_=!!(4&e),w=1/t.clientHeight,M=w(r-m),k=w(n-v),A=p.flipX?1:-1,T=p.flipY?1:-1,S=i(),E=Math.PIp.rotateSpeed;if((s&&b&&!f&&!d&&!y\|\|b&&!f&&!d&&y)&&u.rotate(S,AEM,-TEk,0),(l&&b&&!f&&!d&&!y\|\|x\|\|b&&f&&!d&&!y)&&u.pan(S,-p.translateSpeedMh,p.translateSpeedkh,0),c&&b&&!f&&!d&&!y\|\|_\|\|b&&!f&&d&&!y){var L=-p.zoomSpeedk/window.innerHeight(S-u.lastT())100;u.pan(S,0,0,h(Math.exp(L)-1))}return m=r,v=n,g=a,!0}}t=t\|\|document.body,e=e\|\|{};var n=[.01,1/0];\"distanceLimits\"in e&&(n[0]=e.distanceLimits[0],n[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(n[0]=e.zoomMin),\"zoomMax\"in e&&(n[1]=e.zoomMax);var u=a({center:e.center\|\|[0,0,0],up:e.up\|\|[0,1,0],eye:e.eye\|\|[0,0,10],mode:e.mode\|\|\"orbit\",distanceLimits:n}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],h=0,f=t.clientWidth,d=t.clientHeight,p={keyBindingMode:\"rotate\",view:u,element:t,delay:e.delay\|\|16,rotateSpeed:e.rotateSpeed\|\|1,zoomSpeed:e.zoomSpeed\|\|1,translateSpeed:e.translateSpeed\|\|1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,tick:function(){var e=i(),r=this.delay,n=e-2r;u.idle(e-r),u.recalcMatrix(n),u.flush(e-(100+2r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===f&&t.clientHeight===d;return f=t.clientWidth,d=t.clientHeight,a?!l:(h=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};Object.defineProperties(p,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),n=u.computedCenter.slice();if(u.setMode(t),\"turntable\"===t){var a=i();u._active.lookAt(a,r,n,e),u._active.lookAt(a+500,r,n,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return h},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(n)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",function(t){return t.preventDefault(),!1});var m=0,v=0,g={shift:!1,control:!1,alt:!1,meta:!1};return p.mouseListener=o(t,r),t.addEventListener(\"touchstart\",function(e){var n=l(e.changedTouches[0],t);r(0,n[0],n[1],g),r(1,n[0],n[1],g)}),t.addEventListener(\"touchmove\",function(e){var n=l(e.changedTouches[0],t);r(1,n[0],n[1],g)}),t.addEventListener(\"touchend\",function(){r(0,m,v,g)}),p.wheelListener=s(t,function(t,e){if(!1!==p.keyBindingMode){var r=p.flipX?1:-1,n=p.flipY?1:-1,a=i();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-trMath.PIp.rotateSpeed/window.innerWidth);else{var o=-p.zoomSpeedne/window.innerHeight(a-u.lastT())/20;u.pan(a,0,0,h(Math.exp(o)-1))}}},!0),p}e.exports=n;var i=t(\"right-now\"),a=t(\"3d-view\"),o=t(\"mouse-change\"),s=t(\"mouse-wheel\"),l=t(\"mouse-event-offset\")},{\"3d-view\":37,\"mouse-change\":452,\"mouse-event-offset\":453,\"mouse-wheel\":455,\"right-now\":502}],811:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../plots\"),s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=o.getSubplotIds(e,\"gl3d\"),i=0;i<n.length;i++){var l=n[i],u=o.getSubplotData(r,\"gl3d\",l),c=e[l],h=c._scene;h\|\|(h=new a({id:l,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),c._scene=h),h.cameraInitial\|\|(h.cameraInitial=s.extendDeep({},c.camera)),h.plot(u,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=o.getSubplotIds(n,\"gl3d\"),a=0;a<i.length;a++){var s=i[a];!e[s]&&n[s]._scene&&(n[s]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+s).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=o.getSubplotIds(e,\"gl3d\"),n=e._size,i=0;i<r.length;i++){var a=e[r[i]],s=a.domain,u=a._scene,c=u.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.ws.x[0],y:n.t+n.h(1-s.y[1]),width:n.w(s.x[1]-s.x[0]),height:n.h(s.y[1]-s.y[0]),preserveAspectRatio:\"none\"}),u.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=o.getSubplotIds(t,\"gl3d\"),r=0;r<e.length;r++){t[e[r]]._scene.updateFx(t.dragmode,t.hovermode)}}},{\"../../components/fx/layout_attributes\":646,\"../../constants/xmlns_namespaces\":709,\"../../lib\":728,\"../../plot_api/edit_types\":756,\"../plots\":831,\"./layout/attributes\":812,\"./layout/defaults\":816,\"./layout/layout_attributes\":817,\"./scene\":821}],812:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],813:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:i.title,titlefont:i.titlefont,type:i.type,autorange:i.autorange,rangemode:i.rangemode,range:i.range,tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,separatethousands:i.separatethousands,tickformat:i.tickformat,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth},\"plot\",\"from-root\")},{\"../../../components/color\":604,\"../../../lib/extend\":717,\"../../../plot_api/edit_types\":756,\"../../cartesian/layout_attributes\":783}],814:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"./axis_attributes\"),o=t(\"../../cartesian/type_defaults\"),s=t(\"../../cartesian/axis_defaults\"),l=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){function u(t,e){return i.coerce(c,h,a,t,e)}for(var c,h,f=0;f<l.length;f++){var d=l[f];c=t[d]\|\|{},h=e[d]={_id:d[0]+r.scene,_name:d},o(c,h,u,r.data),s(c,h,u,{font:r.font,letter:d[0],data:r.data,showGrid:!0,bgColor:r.bgColor,calendar:r.calendar}),u(\"gridcolor\",n(h.color,r.bgColor,13600/187).toRgbString()),u(\"title\",d[0]),h.setScale=i.noop,u(\"showspikes\")&&(u(\"spikesides\"),u(\"spikethickness\"),u(\"spikecolor\",h.color)),u(\"showaxeslabels\"),u(\"showbackground\")&&u(\"backgroundcolor\")}}},{\"../../../lib\":728,\"../../cartesian/axis_defaults\":774,\"../../cartesian/type_defaults\":794,\"./axis_attributes\":813,tinycolor2:534}],815:[function(t,e,r){\"use strict\";function n(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}function i(t){var e=new n;return e.merge(t),e}var a=t(\"../../../lib/html2unicode\"),o=t(\"../../../lib/str2rgbarray\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"];n.prototype.merge=function(t){for(var e=this,r=0;r<3;++r){var n=t[s[r]];n.visible?(e.labels[r]=a(n.title),\"titlefont\"in n&&(n.titlefont.color&&(e.labelColor[r]=o(n.titlefont.color)),n.titlefont.family&&(e.labelFont[r]=n.titlefont.family),n.titlefont.size&&(e.labelSize[r]=n.titlefont.size)),\"showline\"in n&&(e.lineEnable[r]=n.showline),\"linecolor\"in n&&(e.lineColor[r]=o(n.linecolor)),\"linewidth\"in n&&(e.lineWidth[r]=n.linewidth),\"showgrid\"in n&&(e.gridEnable[r]=n.showgrid),\"gridcolor\"in n&&(e.gridColor[r]=o(n.gridcolor)),\"gridwidth\"in n&&(e.gridWidth[r]=n.gridwidth),\"log\"===n.type?e.zeroEnable[r]=!1:\"zeroline\"in n&&(e.zeroEnable[r]=n.zeroline),\"zerolinecolor\"in n&&(e.zeroLineColor[r]=o(n.zerolinecolor)),\"zerolinewidth\"in n&&(e.zeroLineWidth[r]=n.zerolinewidth),\"ticks\"in n&&n.ticks?e.lineTickEnable[r]=!0:e.lineTickEnable[r]=!1,\"ticklen\"in n&&(e.lineTickLength[r]=e._defaultLineTickLength[r]=n.ticklen),\"tickcolor\"in n&&(e.lineTickColor[r]=o(n.tickcolor)),\"tickwidth\"in n&&(e.lineTickWidth[r]=n.tickwidth),\"tickangle\"in n&&(e.tickAngle[r]=\"auto\"===n.tickangle?0:Math.PI-n.tickangle/180),\"showticklabels\"in n&&(e.tickEnable[r]=n.showticklabels),\"tickfont\"in n&&(n.tickfont.color&&(e.tickColor[r]=o(n.tickfont.color)),n.tickfont.family&&(e.tickFont[r]=n.tickfont.family),n.tickfont.size&&(e.tickSize[r]=n.tickfont.size)),\"mirror\"in n?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(n.mirror)?(e.lineTickMirror[r]=!0,e.lineMirror[r]=!0):!0===n.mirror?(e.lineTickMirror[r]=!1,e.lineMirror[r]=!0):(e.lineTickMirror[r]=!1,e.lineMirror[r]=!1):e.lineMirror[r]=!1,\"showbackground\"in n&&!1!==n.showbackground?(e.backgroundEnable[r]=!0,e.backgroundColor[r]=o(n.backgroundcolor)):e.backgroundEnable[r]=!1):(e.tickEnable[r]=!1,e.labelEnable[r]=!1,e.lineEnable[r]=!1,e.lineTickEnable[r]=!1,e.gridEnable[r]=!1,e.zeroEnable[r]=!1,e.backgroundEnable[r]=!1)}},e.exports=i},{\"../../../lib/html2unicode\":726,\"../../../lib/str2rgbarray\":749}],816:[function(t,e,r){\"use strict\";function n(t,e,r,n){for(var i=r(\"bgcolor\"),s=a.combine(i,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],c=0;c<u.length;c++)r(\"camera.\"+u[c]+\".x\"),r(\"camera.\"+u[c]+\".y\"),r(\"camera.\"+u[c]+\".z\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),f=h?\"manual\":\"auto\",d=r(\"aspectmode\",f);h\|\|(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode),l(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:s,calendar:n.calendar}),o.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n),r(\"dragmode\",n.getDfltFromLayout(\"dragmode\")),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}var i=t(\"../../../lib\"),a=t(\"../../../components/color\"),o=t(\"../../../registry\"),s=t(\"../../subplot_defaults\"),l=t(\"./axis_defaults\"),u=t(\"./layout_attributes\");e.exports=function(t,e,r){function a(e){if(!o){return i.validate(t[e],u[e])?t[e]:void 0}}var o=e._basePlotModules.length>1;s(t,e,r,{type:\"gl3d\",attributes:u,handleDefaults:n,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:a,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":604,\"../../../lib\":728,\"../../../registry\":846,\"../../subplot_defaults\":838,\"./axis_defaults\":814,\"./layout_attributes\":817}],817:[function(t,e,r){\"use strict\";function n(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}var i=t(\"./axis_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(n(0,0,1),{}),center:a(n(0,0,0),{}),eye:a(n(1.25,1.25,1.25),{}),editType:\"camera\"},domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1,editType:\"plot\"},{valType:\"number\",min:0,max:1,editType:\"plot\"}],dflt:[0,1],editType:\"plot\"},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1,editType:\"plot\"},{valType:\"number\",min:0,max:1,editType:\"plot\"}],dflt:[0,1],editType:\"plot\"},editType:\"plot\"},aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:i,yaxis:i,zaxis:i,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],dflt:\"turntable\",editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":728,\"../../../lib/extend\":717,\"./axis_attributes\":813}],818:[function(t,e,r){\"use strict\";function n(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}function i(t){var e=new n;return e.merge(t),e}var a=t(\"../../../lib/str2rgbarray\"),o=[\"xaxis\",\"yaxis\",\"zaxis\"];n.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[o[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=a(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=i},{\"../../../lib/str2rgbarray\":749}],819:[function(t,e,r){\"use strict\";function n(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}function i(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,i=t.fullSceneLayout,c=[[],[],[]],h=0;h<3;++h){var f=i[l[h]];if(f._length=(r[h].hi-r[h].lo)r[h].pixelsPerDataUnit/t.dataScale[h],Math.abs(f._length)===1/0)c[h]=[];else{f.range[0]=r[h].lo/t.dataScale[h],f.range[1]=r[h].hi/t.dataScale[h],f._m=1/(t.dataScale[h]r[h].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var d=f.tickmode;if(\"auto\"===f.tickmode){f.tickmode=\"linear\";var p=f.nticks\|\|o.constrain(f._length/40,4,9);a.autoTicks(f,Math.abs(f.range[1]-f.range[0])/p)}for(var m=a.calcTicks(f),v=0;v<m.length;++v)m[v].x=m[v].xt.dataScale[h],m[v].text=s(m[v].text);c[h]=m,f.tickmode=d}}e.ticks=c;for(var h=0;h<3;++h){u[h]=.5(t.glplot.bounds[0][h]+t.glplot.bounds[1][h]);for(var v=0;v<2;++v)e.bounds[v][h]=t.glplot.bounds[v][h]}t.contourLevels=n(c)}e.exports=i;var a=t(\"../../cartesian/axes\"),o=t(\"../../../lib\"),s=t(\"../../../lib/html2unicode\"),l=[\"xaxis\",\"yaxis\",\"zaxis\"],u=[0,0,0]},{\"../../../lib\":728,\"../../../lib/html2unicode\":726,\"../../cartesian/axes\":772}],820:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4r+n]e[r];return i}function i(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}e.exports=i},{}],821:[function(t,e,r){\"use strict\";function n(t){function e(e,r){var n=t.fullSceneLayout[e];return v.tickText(n,n.d2l(r),\"hover\").text}var r,n=t.svgContainer,i=t.container.getBoundingClientRect(),a=i.width,o=i.height;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+a+\" \"+o),n.setAttributeNS(null,\"width\",a),n.setAttributeNS(null,\"height\",o),k(t),t.glplot.axes.update(t.axesOptions);for(var s=Object.keys(t.traces),l=null,u=t.glplot.selection,c=0;c<s.length;++c)r=t.traces[s[c]],\"skip\"!==r.data.hoverinfo&&r.handlePick(u)&&(l=r),r.setContourLevels&&r.setContourLevels();var h;if(null!==l){var f=_(t.glplot.cameraParams,u.dataCoordinate);r=l.data;var d=u.index,p=g.castHoverinfo(r,t.fullLayout,d),m=e(\"xaxis\",u.traceCoordinate[0]),y=e(\"yaxis\",u.traceCoordinate[1]),b=e(\"zaxis\",u.traceCoordinate[2]);if(\"all\"!==p){var x=p.split(\"+\");-1===x.indexOf(\"x\")&&(m=void 0),-1===x.indexOf(\"y\")&&(y=void 0),-1===x.indexOf(\"z\")&&(b=void 0),-1===x.indexOf(\"text\")&&(u.textLabel=void 0),-1===x.indexOf(\"name\")&&(l.name=void 0)}t.fullSceneLayout.hovermode&&g.loneHover({x:(.5+.5f[0]/f[3])a,y:(.5-.5f[1]/f[3])o,xLabel:m,yLabel:y,zLabel:b,text:u.textLabel,name:l.name,color:g.castHoverOption(r,d,\"bgcolor\")\|\|l.color,borderColor:g.castHoverOption(r,d,\"bordercolor\"),fontFamily:g.castHoverOption(r,d,\"font.family\"),fontSize:g.castHoverOption(r,d,\"font.size\"),fontColor:g.castHoverOption(r,d,\"font.color\")},{container:n,gd:t.graphDiv});var w={x:u.traceCoordinate[0],y:u.traceCoordinate[1],z:u.traceCoordinate[2],data:r._input,fullData:r,curveNumber:r.index,pointNumber:d};g.appendArrayPointValue(w,r,d);var M={points:[w]};u.buttons&&u.distance<5?t.graphDiv.emit(\"plotly_click\",M):t.graphDiv.emit(\"plotly_hover\",M),h=M}else g.loneUnhover(n),t.graphDiv.emit(\"plotly_unhover\",h);t.drawAnnotations(t)}function i(t,e,r,i){var a={canvas:r,gl:i,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!(h\|\|(c=document.createElement(\"canvas\"),h=d({canvas:c,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");a.pixelRatio=t.pixelRatio,a.gl=h,a.canvas=c}try{t.glplot=f(a)}catch(e){b(t)}var o=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+\".camera\"]=u(t.camera),t.saveCamera(t.graphDiv.layout),t.graphDiv.emit(\"plotly_relayout\",e)}};if(t.glplot.canvas.addEventListener(\"mouseup\",o.bind(null,t)),t.glplot.canvas.addEventListener(\"wheel\",o.bind(null,t)),t.staticMode\|\|t.glplot.canvas.addEventListener(\"webglcontextlost\",function(t){m.warn(\"Lost WebGL context.\"),t.preventDefault()}),!t.camera){var s=t.fullSceneLayout.camera;t.camera=x(t.container,{center:[s.center.x,s.center.y,s.center.z],\n", "eye:[s.eye.x,s.eye.y,s.eye.z],up:[s.up.x,s.up.y,s.up.z],zoomMin:.1,zoomMax:100,mode:\"orbit\"})}return t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=n.bind(null,t),t.traces={},!0}function a(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\",r.appendChild(a),this.svgContainer=a,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id\|\|\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=w(e[this.id]),this.spikeOptions=M(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio\|\|2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=p.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=p.getComponentMethod(\"annotations3d\",\"draw\"),i(this,e)}function o(t,e,r,n,i){for(var a,o=0;o<e.length;++o)if(Array.isArray(e[o]))for(var s=0;s<e[o].length;++s)a=t.d2l(e[o][s],0,i),!isNaN(a)&&isFinite(a)&&(n[0][r]=Math.min(n[0][r],a),n[1][r]=Math.max(n[1][r],a));else a=t.d2l(e[o],0,i),!isNaN(a)&&isFinite(a)&&(n[0][r]=Math.min(n[0][r],a),n[1][r]=Math.max(n[1][r],a))}function s(t,e,r){var n=t.fullSceneLayout;o(n.xaxis,e.x,0,r,e.xcalendar),o(n.yaxis,e.y,1,r,e.ycalendar),o(n.zaxis,e.z,2,r,e.zcalendar)}function l(t){return[[t.eye.x,t.eye.y,t.eye.z],[t.center.x,t.center.y,t.center.z],[t.up.x,t.up.y,t.up.z]]}function u(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}var c,h,f=t(\"gl-plot3d\"),d=t(\"webgl-context\"),p=t(\"../../registry\"),m=t(\"../../lib\"),v=t(\"../../plots/cartesian/axes\"),g=t(\"../../components/fx\"),y=t(\"../../lib/str2rgbarray\"),b=t(\"../../lib/show_no_webgl_msg\"),x=t(\"./camera\"),_=t(\"./project\"),w=t(\"./layout/convert\"),M=t(\"./layout/spikes\"),k=t(\"./layout/tick_marks\"),A=a.prototype;A.recoverContext=function(){function t(){return r.isContextLost()?void requestAnimationFrame(t):i(e,e.fullLayout,n,r)?void e.plot.apply(e,e.plotArgs):void m.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")}var e=this,r=this.glplot.gl,n=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(t)};var T=[\"xaxis\",\"yaxis\",\"zaxis\"];A.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,l,u,c=e[this.id],h=r[this.id];c.bgcolor?this.glplot.clearColor=y(c.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=c,this.glplotLayout=c,this.axesOptions.merge(c),this.spikeOptions.merge(c),this.setCamera(c.camera),this.updateFx(c.dragmode,c.hovermode),this.glplot.update({}),this.setConvert(l),t?Array.isArray(t)\|\|(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)n=t[a],!0===n.visible&&s(this,n,f);var d=[1,1,1];for(o=0;o<3;++o)f[0][o]>f[1][o]?d[o]=1:f[1][o]===f[0][o]?d[o]=1:d[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=d,this.convertAnnotations(this),a=0;a<t.length;++a)n=t[a],!0===n.visible&&(i=this.traces[n.uid],i?i.update(n):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible)continue t;i=this.traces[p[a]],i.dispose(),delete this.traces[p[a]]}this.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var m=[[0,0,0],[0,0,0]],v=[],g={};for(a=0;a<3;++a){if(l=c[T[a]],u=l.type,u in g?(g[u].acc=d[a],g[u].count+=1):g[u]={acc:d[a],count:1},l.autorange){m[0][a]=1/0,m[1][a]=-1/0;var b=this.glplot.objects,x=this.fullSceneLayout.annotations\|\|[],_=l._name.charAt(0);for(o=0;o<b.length;o++){var w=b[o].bounds;m[0][a]=Math.min(m[0][a],w[0][a]/d[a]),m[1][a]=Math.max(m[1][a],w[1][a]/d[a])}for(o=0;o<x.length;o++){var M=x[o];if(M.visible){var k=l.r2l(M[_]);m[0][a]=Math.min(m[0][a],k),m[1][a]=Math.max(m[1][a],k)}}if(\"rangemode\"in l&&\"tozero\"===l.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var A=m[1][a]-m[0][a];m[0][a]-=A/32,m[1][a]+=A/32}}else{var S=l.range;m[0][a]=l.r2l(S[0]),m[1][a]=l.r2l(S[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.bounds[0][a]=m[0][a]d[a],this.glplot.bounds[1][a]=m[1][a]d[a]}var E=[1,1,1];for(a=0;a<3;++a){l=c[T[a]],u=l.type;var L=g[u];E[a]=Math.pow(L.acc,1/L.count)/d[a]}var C;if(\"auto\"===c.aspectmode)C=Math.max.apply(null,E)/Math.min.apply(null,E)<=4?E:[1,1,1];else if(\"cube\"===c.aspectmode)C=[1,1,1];else if(\"data\"===c.aspectmode)C=E;else{if(\"manual\"!==c.aspectmode)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var I=c.aspectratio;C=[I.x,I.y,I.z]}c.aspectratio.x=h.aspectratio.x=C[0],c.aspectratio.y=h.aspectratio.y=C[1],c.aspectratio.z=h.aspectratio.z=C[2],this.glplot.aspect=C;var z=c.domain\|\|null,D=e._size\|\|null;if(z&&D){var P=this.container.style;P.position=\"absolute\",P.left=D.l+z.x[0]D.w+\"px\",P.top=D.t+(1-z.y[1])D.h+\"px\",P.width=D.w(z.x[1]-z.x[0])+\"px\",P.height=D.h(z.y[1]-z.y[0])+\"px\"}this.glplot.redraw()}},A.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=this.glplot.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},A.getCamera=function(){return this.glplot.camera.view.recalcMatrix(this.camera.view.lastT()),u(this.glplot.camera)},A.setCamera=function(t){this.glplot.camera.lookAt.apply(this,l(t))},A.saveCamera=function(t){var e=this.getCamera(),r=m.nestedProperty(t,this.id+\".camera\"),n=r.get(),i=!1;if(void 0===n)i=!0;else for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!function(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}(e,n,a,o)){i=!0;break}return i&&r.set(e),i},A.updateFx=function(t,e){var r=this.camera;r&&(\"orbit\"===t?(r.mode=\"orbit\",r.keyBindingMode=\"rotate\"):\"turntable\"===t?(r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\"):r.keyBindingMode=t),this.fullSceneLayout.hovermode=e},A.toImage=function(t){t\|\|(t=\"png\"),this.staticMode&&this.container.appendChild(c),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,n=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var i=new Uint8Array(rn4);e.readPixels(0,0,r,n,e.RGBA,e.UNSIGNED_BYTE,i);for(var a=0,o=n-1;a<o;++a,--o)for(var s=0;s<r;++s)for(var l=0;l<4;++l){var u=i[4(ra+s)+l];i[4(ra+s)+l]=i[4(ro+s)+l],i[4(ro+s)+l]=u}var h=document.createElement(\"canvas\");h.width=r,h.height=n;var f=h.getContext(\"2d\"),d=f.createImageData(r,n);d.data.set(i),f.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=h.toDataURL(\"image/jpeg\");break;case\"webp\":p=h.toDataURL(\"image/webp\");break;default:p=h.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(c),p},A.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[T[t]];v.setConvert(e,this.fullLayout),e.setScale=m.noop}},e.exports=a},{\"../../components/fx\":645,\"../../lib\":728,\"../../lib/show_no_webgl_msg\":747,\"../../lib/str2rgbarray\":749,\"../../plots/cartesian/axes\":772,\"../../registry\":846,\"./camera\":810,\"./layout/convert\":815,\"./layout/spikes\":818,\"./layout/tick_marks\":819,\"./project\":820,\"gl-plot3d\":221,\"webgl-context\":563}],822:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"../components/color/attributes\"),a=n({editType:\"calc\"});a.family.dflt='\"Open Sans\", verdana, arial, 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e={},r={};switch(t.type){case\"circle\":s.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":s.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity});break;case\"fill\":s.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var n=t.symbol,i=l(n.textposition,n.iconsize);s.extendFlat(e,{\"icon-image\":n.icon+\"-15\",\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":i.anchor,\"text-offset\":i.offset}),s.extendFlat(r,{\"icon-color\":t.color,\"text-color\":n.textfont.color,\"text-opacity\":t.opacity})}return{layout:e,paint:r}}function o(t){var e,r=t.sourcetype,n=t.source,i={type:r},a=\"string\"==typeof n;return\"geojson\"===r?e=\"data\":\"vector\"===r&&(e=a?\"url\":\"tiles\"),i[e]=n,i}var 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r={visibility:\"visible\"};this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",r)}},u.updateStyle=function(t){var e=a(t);i(t)&&(this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.mapbox.setOptions(this.idLayer,\"setPaintProperty\",e.paint))},u.dispose=function(){var t=this.map;t.removeLayer(this.idLayer),t.removeSource(this.idSource)},e.exports=function(t,e,r){var i=new n(t,e);return i.update(r),i}},{\"../../lib\":728,\"./convert_text_opts\":824}],827:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../font_attributes\"),o=t(\"../../traces/scatter/attributes\").textposition,s=t(\"../../plot_api/edit_types\").overrideAll,l=a({});l.family.dflt=\"Open Sans Regular, Arial Unicode MS 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a=t(\"../../lib\"),o=t(\"../subplot_defaults\"),s=t(\"./layout_attributes\");e.exports=function(t,e,r){o(t,e,r,{type:\"mapbox\",attributes:s,handleDefaults:n,partition:\"y\"})}},{\"../../lib\":728,\"../subplot_defaults\":838,\"./layout_attributes\":827}],829:[function(t,e,r){\"use strict\";function n(t){this.id=t.id,this.gd=t.gd,this.container=t.container,this.isStatic=t.staticPlot;var e=t.fullLayout;this.uid=e._uid+\"-\"+this.id,this.opts=e[this.id],this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(e),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[]}function i(t){var e=d.style.values,r=d.style.dflt,n={};return u.isPlainObject(t)?(n.id=t.id,n.style=t):\"string\"==typeof t?(n.id=t,n.style=-1!==e.indexOf(t)?a(t):t):(n.id=r,n.style=a(r)),n}function a(t){return f.styleUrlPrefix+t+\"-\"+f.styleUrlSuffix}function o(t){return[t.lon,t.lat]}var s=t(\"mapbox-gl\"),l=t(\"../../components/fx\"),u=t(\"../../lib\"),c=t(\"../../components/dragelement\"),h=t(\"../cartesian/select\"),f=t(\"./constants\"),d=t(\"./layout_attributes\"),p=t(\"./layers\"),m=n.prototype;e.exports=function(t){return new n(t)},m.plot=function(t,e,r){var n=this,i=n.opts=e[this.id];n.map&&i.accesstoken!==n.accessToken&&(n.map.remove(),n.map=null,n.styleObj=null,n.traceHash=[],n.layerList={});var a;a=n.map?new Promise(function(r,i){n.updateMap(t,e,r,i)}):new Promise(function(r,i){n.createMap(t,e,r,i)}),r.push(a)},m.createMap=function(t,e,r,n){function a(){l.loneUnhover(e._toppaper)}var c=this,h=c.gd,d=c.opts,p=c.styleObj=i(d.style);c.accessToken=d.accesstoken;var m=c.map=new 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e=t[this.id].domain,r=t._size,n=this.div.style;n.width=r.w(e.x[1]-e.x[0])+\"px\",n.height=r.h(e.y[1]-e.y[0])+\"px\",n.left=r.l+e.x[0]r.w+\"px\",n.top=r.t+(1-e.y[1])r.h+\"px\",this.xaxis._offset=r.l+e.x[0]r.w,this.xaxis._length=r.w(e.x[1]-e.x[0]),this.yaxis._offset=r.t+(1-e.y[1])r.h,this.yaxis._length=r.h(e.y[1]-e.y[0])},m.updateLayers=function(){var t,e=this.opts,r=e.layers,n=this.layerList;if(r.length!==n.length){for(t=0;t<n.length;t++)n[t].dispose();for(n=this.layerList=[],t=0;t<r.length;t++)n.push(p(this,t,r[t]))}else for(t=0;t<r.length;t++)n[t].update(r[t])},m.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))},m.toImage=function(){return this.map.getCanvas().toDataURL()},m.initSource=function(t){var e={type:\"geojson\",data:{type:\"Feature\",geometry:{type:\"Point\",coordinates:[]}}};return this.map.addSource(t,e)},m.setSourceData=function(t,e){this.map.getSource(t).setData(e)},m.setOptions=function(t,e,r){for(var 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s.PolyChart()},s.DotPlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"dot\",dotType:\"circle\"}}},s.LinePlot=function(){return s.PolyChart()},s.LinePlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"line\"}}},s.Legend=function(){function t(){var r=e.legendConfig,i=e.data.map(function(t,e){return[].concat(t).map(function(t,n){var i=a({},r.elements[e]);return i.name=t,i.color=[].concat(r.elements[e].color)[n],i})}),o=n.merge(i);o=o.filter(function(t,e){return r.elements[e]&&(r.elements[e].visibleInLegend\|\|void 0===r.elements[e].visibleInLegend)}),r.reverseOrder&&(o=o.reverse());var s=r.container;(\"string\"==typeof s\|\|s.nodeName)&&(s=n.select(s));var l=o.map(function(t,e){return t.color}),u=r.fontSize,c=null==r.isContinuous?\"number\"==typeof 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n=a({},t);return[[n,[\"marker\",\"color\"],[\"color\"]],[n,[\"marker\",\"opacity\"],[\"opacity\"]],[n,[\"marker\",\"line\",\"color\"],[\"strokeColor\"]],[n,[\"marker\",\"line\",\"dash\"],[\"strokeDash\"]],[n,[\"marker\",\"line\",\"width\"],[\"strokeSize\"]],[n,[\"marker\",\"symbol\"],[\"dotType\"]],[n,[\"marker\",\"size\"],[\"dotSize\"]],[n,[\"marker\",\"barWidth\"],[\"barWidth\"]],[n,[\"line\",\"interpolation\"],[\"lineInterpolation\"]],[n,[\"showlegend\"],[\"visibleInLegend\"]]].forEach(function(t,r){s.util.translator.apply(null,t.concat(e))}),e\|\|delete n.marker,e&&delete n.groupId,e?(\"LinePlot\"===n.geometry?(n.type=\"scatter\",!0===n.dotVisible?(delete n.dotVisible,n.mode=\"lines+markers\"):n.mode=\"lines\"):\"DotPlot\"===n.geometry?(n.type=\"scatter\",n.mode=\"markers\"):\"AreaChart\"===n.geometry?n.type=\"area\":\"BarChart\"===n.geometry&&(n.type=\"bar\"),delete n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n}),!e&&t.layout&&\"stack\"===t.layout.barmode)){var i=s.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var n=i.indexOf(t.geometry);-1!=n&&(r.data[e].groupId=n)})}if(t.layout){var o=a({},t.layout);if([[o,[\"plot_bgcolor\"],[\"backgroundColor\"]],[o,[\"showlegend\"],[\"showLegend\"]],[o,[\"radialaxis\"],[\"radialAxis\"]],[o,[\"angularaxis\"],[\"angularAxis\"]],[o.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[o.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[o.angularaxis,[\"nticks\"],[\"ticksCount\"]],[o.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[o.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[o.angularaxis,[\"range\"],[\"domain\"]],[o.angularaxis,[\"endpadding\"],[\"endPadding\"]],[o.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[o.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[o.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[o.radialaxis,[\"range\"],[\"domain\"]],[o.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[o.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[o.angularAxis,[\"nticks\"],[\"ticksCount\"]],[o.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[o.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[o.angularAxis,[\"range\"],[\"domain\"]],[o.angularAxis,[\"endpadding\"],[\"endPadding\"]],[o.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[o.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[o.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[o.radialAxis,[\"range\"],[\"domain\"]],[o.font,[\"outlinecolor\"],[\"outlineColor\"]],[o.legend,[\"traceorder\"],[\"reverseOrder\"]],[o,[\"labeloffset\"],[\"labelOffset\"]],[o,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach(function(t,r){s.util.translator.apply(null,t.concat(e))}),e?(void 0!==o.tickLength&&(o.angularaxis.ticklen=o.tickLength,delete o.tickLength),o.tickColor&&(o.angularaxis.tickcolor=o.tickColor,delete o.tickColor)):(o.angularAxis&&void 0!==o.angularAxis.ticklen&&(o.tickLength=o.angularAxis.ticklen),o.angularAxis&&void 0!==o.angularAxis.tickcolor&&(o.tickColor=o.angularAxis.tickcolor)),o.legend&&\"boolean\"!=typeof o.legend.reverseOrder&&(o.legend.reverseOrder=\"normal\"!=o.legend.reverseOrder),o.legend&&\"boolean\"==typeof o.legend.traceorder&&(o.legend.traceorder=o.legend.traceorder?\"reversed\":\"normal\",delete o.legend.reverseOrder),o.margin&&void 0!==o.margin.t){var l=[\"t\",\"r\",\"b\",\"l\",\"pad\"],u=[\"top\",\"right\",\"bottom\",\"left\",\"pad\"],c={};n.entries(o.margin).forEach(function(t,e){c[u[l.indexOf(t.key)]]=t.value}),o.margin=c}e&&(delete o.needsEndSpacing,delete o.minorTickColor,delete o.minorTicks,delete o.angularaxis.ticksCount,delete o.angularaxis.ticksCount,delete o.angularaxis.ticksStep,delete o.angularaxis.rewriteTicks,delete o.angularaxis.nticks,delete o.radialaxis.ticksCount,delete o.radialaxis.ticksCount,delete o.radialaxis.ticksStep,delete o.radialaxis.rewriteTicks,delete o.radialaxis.nticks),r.layout=o}return r},t}},{\"../../constants/alignment\":701,\"../../lib\":728,d3:122}],836:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../components/color\"),o=t(\"./micropolar\"),s=t(\"./undo_manager\"),l=i.extendDeepAll,u=e.exports={};u.framework=function(t){function e(e,i){return i&&(h=i),n.select(n.select(h).node().parentNode).selectAll(\".svg-container>:not(.chart-root)\").remove(),r=r?l(r,e):e,a\|\|(a=o.Axis()),c=o.adapter.plotly().convert(r),a.config(c).render(h),t.data=r.data,t.layout=r.layout,u.fillLayout(t),r}var r,i,a,c,h,f=new s;return e.isPolar=!0,e.svg=function(){return a.svg()},e.getConfig=function(){return r},e.getLiveConfig=function(){return o.adapter.plotly().convert(a.getLiveConfig(),!0)},e.getLiveScales=function(){return{t:a.angularScale(),r:a.radialScale()}},e.setUndoPoint=function(){var t=this,e=o.util.cloneJson(r);!function(e,r){f.add({undo:function(){r&&t(r)},redo:function(){t(e)}})}(e,i),i=o.util.cloneJson(e)},e.undo=function(){f.undo()},e.redo=function(){f.redo()},e},u.fillLayout=function(t){var e=n.select(t).selectAll(\".plot-container\"),r=e.selectAll(\".svg-container\"),i=t.framework&&t.framework.svg&&t.framework.svg(),o={width:800,height:600,paper_bgcolor:a.background,_container:e,_paperdiv:r,_paper:i};t._fullLayout=l(o,t.layout)}},{\"../../components/color\":604,\"../../lib\":728,\"./micropolar\":835,\"./undo_manager\":837,d3:122}],837:[function(t,e,r){\"use strict\";e.exports=function(){function t(t,e){return t?(i=!0,t[e](),i=!1,this):this}var e,r=[],n=-1,i=!1;return{add:function(t){return i?this:(r.splice(n+1,r.length-n),r.push(t),n=r.length-1,this)},setCallback:function(t){e=t},undo:function(){var i=r[n];return i?(t(i,\"undo\"),n-=1,e&&e(i.undo),this):this},redo:function(){var i=r[n+1];return i?(t(i,\"redo\"),n+=1,e&&e(i.redo),this):this},clear:function(){r=[],n=-1},hasUndo:function(){return-1!==n},hasRedo:function(){return n<r.length-1},getCommands:function(){return r},getPreviousCommand:function(){return r[n-1]},getIndex:function(){return n}}}},{}],838:[function(t,e,r){\"use strict\";var n=t(\"../lib\"),i=t(\"./plots\");e.exports=function(t,e,r,a){function o(t,e){return n.coerce(s,l,c,t,e)}for(var s,l,u=a.type,c=a.attributes,h=a.handleDefaults,f=a.partition\|\|\"x\",d=i.findSubplotIds(r,u),p=d.length,m=0;m<p;m++){var v=d[m];s=t[v]?t[v]:t[v]={},e[v]=l={},o(\"domain.\"+f,[m/p,(m+1)/p]),o(\"domain.\"+{x:\"y\",y:\"x\"}[f]),a.id=v,h(s,l,o,a)}}},{\"../lib\":728,\"./plots\":831}],839:[function(t,e,r){\"use strict\";var n=t(\"./ternary\"),i=t(\"../../plots/plots\"),a=t(\"../../lib\").counterRegex;r.name=\"ternary\",r.attr=\"subplot\",r.idRoot=\"ternary\",r.idRegex=r.attrRegex=a(\"ternary\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t.calcdata,a=i.getSubplotIds(e,\"ternary\"),o=0;o<a.length;o++){var s=a[o],l=i.getSubplotCalcData(r,\"ternary\",s),u=e[s]._subplot;u\|\|(u=new n({id:s,graphDiv:t,container:e._ternarylayer.node()},e),e[s]._subplot=u),u.plot(l,e,t._promises)}},r.clean=function(t,e,r,n){for(var a=i.getSubplotIds(n,\"ternary\"),o=0;o<a.length;o++){var s=a[o],l=n[s]._subplot;!e[s]&&l&&(l.plotContainer.remove(),l.clipDef.remove(),l.clipDefRelative.remove())}}},{\"../../lib\":728,\"../../plots/plots\":831,\"./layout/attributes\":840,\"./layout/defaults\":843,\"./layout/layout_attributes\":844,\"./ternary\":845}],840:[function(t,e,r){\"use strict\";e.exports={subplot:{valType:\"subplotid\",dflt:\"ternary\",editType:\"calc\"}}},{}],841:[function(t,e,r){\"use strict\";var n=t(\"../../cartesian/layout_attributes\"),i=t(\"../../../lib/extend\").extendFlat;e.exports={title:n.title,titlefont:n.titlefont,color:n.color,tickmode:n.tickmode,nticks:i({},n.nticks,{dflt:6,min:1}),tick0:n.tick0,dtick:n.dtick,tickvals:n.tickvals,ticktext:n.ticktext,ticks:n.ticks,ticklen:n.ticklen,tickwidth:n.tickwidth,tickcolor:n.tickcolor,showticklabels:n.showticklabels,showtickprefix:n.showtickprefix,tickprefix:n.tickprefix,showticksuffix:n.showticksuffix,ticksuffix:n.ticksuffix,showexponent:n.showexponent,exponentformat:n.exponentformat,separatethousands:n.separatethousands,tickfont:n.tickfont,tickangle:n.tickangle,tickformat:n.tickformat,hoverformat:n.hoverformat,showline:i({},n.showline,{dflt:!0}),linecolor:n.linecolor,linewidth:n.linewidth,showgrid:i({},n.showgrid,{dflt:!0}),gridcolor:n.gridcolor,gridwidth:n.gridwidth,layer:n.layer,min:{valType:\"number\",dflt:0,min:0}}},{\"../../../lib/extend\":717,\"../../cartesian/layout_attributes\":783}],842:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"./axis_attributes\"),o=t(\"../../cartesian/tick_label_defaults\"),s=t(\"../../cartesian/tick_mark_defaults\"),l=t(\"../../cartesian/tick_value_defaults\");e.exports=function(t,e,r){function u(r,n){return i.coerce(t,e,a,r,n)}e.type=\"linear\";var c=u(\"color\"),h=c===t.color?c:r.font.color,f=e._name,d=f.charAt(0).toUpperCase(),p=\"Component \"+d,m=u(\"title\",p);e._hovertitle=m===p?m:d,i.coerceFont(u,\"titlefont\",{family:r.font.family,size:Math.round(1.2r.font.size),color:h}),u(\"min\"),l(t,e,u,\"linear\"),o(t,e,u,\"linear\",{noHover:!1}),s(t,e,u,{outerTicks:!0}),u(\"showticklabels\")&&(i.coerceFont(u,\"tickfont\",{family:r.font.family,size:r.font.size,color:h}),u(\"tickangle\"),u(\"tickformat\")),u(\"hoverformat\"),u(\"showline\")&&(u(\"linecolor\",c),u(\"linewidth\")),u(\"showgrid\")&&(u(\"gridcolor\",n(c,r.bgColor,60).toRgbString()),u(\"gridwidth\")),u(\"layer\")}},{\"../../../lib\":728,\"../../cartesian/tick_label_defaults\":790,\"../../cartesian/tick_mark_defaults\":791,\"../../cartesian/tick_value_defaults\":792,\"./axis_attributes\":841,tinycolor2:534}],843:[function(t,e,r){\"use strict\";function n(t,e,r,n){var a=r(\"bgcolor\"),o=r(\"sum\");n.bgColor=i.combine(a,n.paper_bgcolor);for(var u,c,h,f=0;f<l.length;f++)u=l[f],c=t[u]\|\|{},h=e[u]={_name:u,type:\"linear\"},s(c,h,n);var d=e.aaxis,p=e.baxis,m=e.caxis;d.min+p.min+m.min>=o&&(d.min=0,p.min=0,m.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}var i=t(\"../../../components/color\"),a=t(\"../../subplot_defaults\"),o=t(\"./layout_attributes\"),s=t(\"./axis_defaults\"),l=[\"aaxis\",\"baxis\",\"caxis\"];e.exports=function(t,e,r){a(t,e,r,{type:\"ternary\",attributes:o,handleDefaults:n,font:e.font,paper_bgcolor:e.paper_bgcolor})}},{\"../../../components/color\":604,\"../../subplot_defaults\":838,\"./axis_defaults\":842,\"./layout_attributes\":844}],844:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color/attributes\"),i=t(\"./axis_attributes\"),a=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=a({domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:i,baxis:i,caxis:i},\"plot\",\"from-root\")},{\"../../../components/color/attributes\":603,\"../../../plot_api/edit_types\":756,\"./axis_attributes\":841}],845:[function(t,e,r){\"use strict\";function n(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e)}function i(t){a.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}var a=t(\"d3\"),o=t(\"tinycolor2\"),s=t(\"../../plotly\"),l=t(\"../../lib\"),u=t(\"../../components/color\"),c=t(\"../../components/drawing\"),h=t(\"../cartesian/set_convert\"),f=t(\"../../lib/extend\").extendFlat,d=t(\"../plots\"),p=t(\"../cartesian/axes\"),m=t(\"../../components/dragelement\"),v=t(\"../../components/fx\"),g=t(\"../../components/titles\"),y=t(\"../cartesian/select\"),b=t(\"../cartesian/constants\");e.exports=n;var x=n.prototype;x.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},x.plot=function(t,e){var r=this,n=e[r.id],i=e._size;r._hasClipOnAxisFalse=!1;for(var a=0;a<t.length;a++){if(!1===t[a][0].trace.cliponaxis){r._hasClipOnAxisFalse=!0;break}}r.updateLayers(n),r.adjustLayout(n,i),d.generalUpdatePerTraceModule(r,t,n),r.layers.plotbg.select(\"path\").call(u.fill,n.bgcolor)},x.makeFramework=function(t){var e=this,r=t[e.id],n=e.clipId=\"clip\"+e.layoutId+e.id;e.clipDef=t._clips.selectAll(\"#\"+n).data([0]),e.clipDef.enter().append(\"clipPath\").attr(\"id\",n).append(\"path\").attr(\"d\",\"M0,0Z\");var i=e.clipIdRelative=\"clip-relative\"+e.layoutId+e.id;e.clipDefRelative=t._clips.selectAll(\"#\"+i).data([0]),e.clipDefRelative.enter().append(\"clipPath\").attr(\"id\",i).append(\"path\").attr(\"d\",\"M0,0Z\"),e.plotContainer=e.container.selectAll(\"g.\"+e.id).data([0]),e.plotContainer.enter().append(\"g\").classed(e.id,!0),e.updateLayers(r),c.setClipUrl(e.layers.backplot,n),c.setClipUrl(e.layers.grids,n)},x.updateLayers=function(t){var e=this,r=e.layers,n=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below traces\"===t.aaxis.layer&&n.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&n.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&n.push(\"caxis\",\"cline\"),n.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&n.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&n.push(\"baxis\",\"bline\"),\"above traces\"===t.caxis.layer&&n.push(\"caxis\",\"cline\");var i=e.plotContainer.selectAll(\"g.toplevel\").data(n,String),o=[\"agrid\",\"bgrid\",\"cgrid\"];i.enter().append(\"g\").attr(\"class\",function(t){return\"toplevel \"+t}).each(function(t){var e=a.select(this);r[t]=e,\"frontplot\"===t?e.append(\"g\").classed(\"scatterlayer\",!0):\"backplot\"===t?e.append(\"g\").classed(\"maplayer\",!0):\"plotbg\"===t?e.append(\"path\").attr(\"d\",\"M0,0Z\"):\"aline\"===t\|\|\"bline\"===t\|\|\"cline\"===t?e.append(\"path\"):\"grids\"===t&&o.forEach(function(t){r[t]=e.append(\"g\").classed(\"grid \"+t,!0)})}),i.order()};var _=Math.sqrt(4/3);x.adjustLayout=function(t,e){var r,n,i,a,o,s,l=this,d=t.domain,p=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],g=d.y[1]-d.y[0],y=ve.w,b=ge.h,x=t.sum,w=t.aaxis.min,M=t.baxis.min,k=t.caxis.min;y>_b?(a=b,i=a_):(i=y,a=i/_),o=vi/y,s=ga/b,r=e.l+e.wp-i/2,n=e.t+e.h(1-m)-a/2,l.x0=r,l.y0=n,l.w=i,l.h=a,l.sum=x,l.xaxis={type:\"linear\",range:[w+2k-x,x-w-2M],domain:[p-o/2,p+o/2],_id:\"x\"},h(l.xaxis,l.graphDiv._fullLayout),l.xaxis.setScale(),l.xaxis.isPtWithinRange=function(t){return t.a>=l.aaxis.range[0]&&t.a<=l.aaxis.range[1]&&t.b>=l.baxis.range[1]&&t.b<=l.baxis.range[0]&&t.c>=l.caxis.range[1]&&t.c<=l.caxis.range[0]},l.yaxis={type:\"linear\",range:[w,x-M-k],domain:[m-s/2,m+s/2],_id:\"y\"},h(l.yaxis,l.graphDiv._fullLayout),l.yaxis.setScale(),l.yaxis.isPtWithinRange=function(){return!0};var A=l.yaxis.domain[0],T=l.aaxis=f({},t.aaxis,{visible:!0,range:[w,x-M-k],side:\"left\",_counterangle:30,tickangle:(+t.aaxis.tickangle\|\|0)-30,domain:[A,A+s_],_axislayer:l.layers.aaxis,_gridlayer:l.layers.agrid,_pos:0,_id:\"y\",_length:i,_gridpath:\"M0,0l\"+a+\",-\"+i/2});h(T,l.graphDiv._fullLayout),T.setScale();var S=l.baxis=f({},t.baxis,{visible:!0,range:[x-w-k,M],side:\"bottom\",_counterangle:30,domain:l.xaxis.domain,_axislayer:l.layers.baxis,_gridlayer:l.layers.bgrid,_counteraxis:l.aaxis,_pos:0,_id:\"x\",_length:i,_gridpath:\"M0,0l-\"+i/2+\",-\"+a});h(S,l.graphDiv._fullLayout),S.setScale(),T._counteraxis=S;var E=l.caxis=f({},t.caxis,{visible:!0,range:[x-w-M,k],side:\"right\",_counterangle:30,tickangle:(+t.caxis.tickangle\|\|0)+30,domain:[A,A+s_],_axislayer:l.layers.caxis,_gridlayer:l.layers.cgrid,_counteraxis:l.baxis,_pos:0,_id:\"y\",_length:i,_gridpath:\"M0,0l-\"+a+\",\"+i/2});h(E,l.graphDiv._fullLayout),E.setScale();var L=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";l.clipDef.select(\"path\").attr(\"d\",L),l.layers.plotbg.select(\"path\").attr(\"d\",L);var C=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";l.clipDefRelative.select(\"path\").attr(\"d\",C);var I=\"translate(\"+r+\",\"+n+\")\";l.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",I),l.clipDefRelative.select(\"path\").attr(\"transform\",null);var z=\"translate(\"+r+\",\"+(n+a)+\")\";l.layers.baxis.attr(\"transform\",z),l.layers.bgrid.attr(\"transform\",z);var D=\"translate(\"+(r+i/2)+\",\"+n+\")rotate(30)\";l.layers.aaxis.attr(\"transform\",D),l.layers.agrid.attr(\"transform\",D);var 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../contour/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"../contour/style\"),n.moduleType=\"trace\",n.name=\"contourcarpet\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"cartesian\",\"carpet\",\"contour\",\"symbols\",\"showLegend\",\"hasLines\",\"carpetDependent\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":782,\"../contour/colorbar\":922,\"../contour/style\":933,\"./attributes\":935,\"./calc\":936,\"./defaults\":942,\"./plot\":947}],945:[function(t,e,r){\"use strict\";var n=t(\"../../components/drawing\"),i=t(\"../carpet/axis_aligned_line\"),a=t(\"../../lib\");e.exports=function(t,e,r,o,s,l,u,c){function h(t){return Math.abs(t[1]-r[0][1])<S}function f(t){return Math.abs(t[1]-r[2][1])<S}function d(t){return Math.abs(t[0]-r[0][0])<T}function p(t){return Math.abs(t[0]-r[2][0])<T}function m(t,e){var 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lo[1], hi[1]),\\n clamp(m[2], lo[2], hi[2]),\\n clamp(m[3], lo[3], hi[3]));\\n}\\n\\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\\n return mclamp(p, lo, hi) == p;\\n}\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) * unit, unit);\\n}\\n\\nvoid main() {\\n\\n float x = 0.5 * sign(pf[3]) + 0.5;\\n float prominence = abs(pf[3]);\\n float depth = 1.0 - prominence;\\n\\n mat4 pA = mat4(p0, p1, p2, p3);\\n mat4 pB = mat4(p4, p5, p6, p7);\\n mat4 pC = mat4(p8, p9, pa, pb);\\n mat4 pD = mat4(pc, pd, pe, abs(pf));\\n\\n float show = float(mshow(pA, loA, hiA) &&\\n mshow(pB, loB, hiB) &&\\n mshow(pC, loC, hiC) &&\\n mshow(pD, loD, hiD));\\n\\n vec2 yy = show * vec2(val(pA, dim2A) + val(pB, dim2B) + val(pC, dim2C) + val(pD, dim2D),\\n val(pA, dim1A) + val(pB, dim1B) + val(pC, dim1C) + val(pD, dim1D));\\n\\n vec2 dimensionToggle = vec2(x, 1.0 - x);\\n\\n vec2 scatterToggle = vec2(scatter, 1.0 - scatter);\\n\\n float y = dot(yy, dimensionToggle);\\n mat2 xy = mat2(viewBoxSize * yy + dimensionToggle, viewBoxSize * vec2(x, y));\\n\\n vec2 viewBoxXY = viewBoxPosition + xy * scatterToggle;\\n\\n float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n gl_Position = vec4(\\n xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n depthOrHide,\\n 1.0\\n );\\n\\n // visible coloring\\n float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\\n fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\\n}\\n\",frag:\"precision lowp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n gl_FragColor = 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l},{\"../../components/color\":604,\"./helpers\":1011,\"fast-isnumeric\":131,tinycolor2:534}],1010:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r,a){function o(r,a){return n.coerce(t,e,i,r,a)}var s=n.coerceFont,l=o(\"values\");if(!Array.isArray(l)\|\|!l.length)return void(e.visible=!1);var u=o(\"labels\");Array.isArray(u)\|\|(o(\"label0\"),o(\"dlabel\")),o(\"marker.line.width\")&&o(\"marker.line.color\");var c=o(\"marker.colors\");Array.isArray(c)\|\|(e.marker.colors=[]),o(\"scalegroup\");var h=o(\"text\"),f=o(\"textinfo\",Array.isArray(h)?\"text+percent\":\"percent\");if(o(\"hovertext\"),f&&\"none\"!==f){var d=o(\"textposition\"),p=Array.isArray(d)\|\|\"auto\"===d,m=p\|\|\"inside\"===d,v=p\|\|\"outside\"===d;if(m\|\|v){var g=s(o,\"textfont\",a.font);m&&s(o,\"insidetextfont\",g),v&&s(o,\"outsidetextfont\",g)}}o(\"domain.x\"),o(\"domain.y\"),o(\"hole\"),o(\"sort\"),o(\"direction\"),o(\"rotation\"),o(\"pull\")}},{\"../../lib\":728,\"./attributes\":1007}],1011:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");r.formatPiePercent=function(t,e){var r=(100t).toPrecision(3);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)}},{\"../../lib\":728}],1012:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":1007,\"./base_plot\":1008,\"./calc\":1009,\"./defaults\":1010,\"./layout_attributes\":1013,\"./layout_defaults\":1014,\"./plot\":1015,\"./style\":1016,\"./style_one\":1017}],1013:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"}}},{}],1014:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,a){n.coerce(t,e,i,r,a)}(\"hiddenlabels\")}},{\"../../lib\":728,\"./layout_attributes\":1013}],1015:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.widtht.width+t.heightt.height),a=t.width/t.height,o=Math.PIMath.min(e.v/r.vTotal,.5),s=1-r.trace.hole,l=i(e,r),u={scale:lr.r2/n,rCenter:1-l,rotate:0};if(u.scale>=1)return u;var c=a+1/(2Math.tan(o)),h=r.rMath.min(1/(Math.sqrt(cc+.5)+c),s/(Math.sqrt(aa+s/2)+a)),f={scale:2h/t.height,rCenter:Math.cos(h/r.r)-ha/r.r,rotate:(180/Math.PIe.midangle+720)%180-90},d=1/a,p=d+1/(2Math.tan(o)),m=r.rMath.min(1/(Math.sqrt(pp+.5)+p),s/(Math.sqrt(dd+s/2)+d)),v={scale:2m/t.width,rCenter:Math.cos(m/r.r)-m/a/r.r,rotate:(180/Math.PIe.midangle+810)%180-90},g=v.scale>f.scale?v:f;return u.scale<1&&g.scale>u.scale?g:u}function i(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PIMath.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function a(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i=-1),n<0&&(a=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)(i>0?1:-1)/2,y:a/(1+rr/(nn)),outside:!0}}function o(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var i,a,o,s,l,u,c,h,f,d,p,m,v;for(a=0;a<2;a++)for(o=a?r:n,l=a?Math.max:Math.min,c=a?1:-1,i=0;i<2;i++){for(s=i?Math.max:Math.min,u=i?1:-1,h=t[a][i],h.sort(o),f=t[1-a][i],d=f.concat(h),m=[],p=0;p<h.length;p++)void 0!==h[p].yLabelMid&&m.push(h[p]);for(v=!1,p=0;a&&p<f.length;p++)if(void 0!==f[p].yLabelMid){v=f[p];break}for(p=0;p<m.length;p++){var g=p&&m[p-1];v&&!p&&(g=v),function(t,r){r\|\|(r={});var n,i,o,h,f,p,m=r.labelExtraY+(a?r.yLabelMax:r.yLabelMin),v=a?t.yLabelMin:t.yLabelMax,g=a?t.yLabelMax:t.yLabelMin,y=t.cyFinal+l(t.px0[1],t.px1[1]),b=m-v;if(bc>0&&(t.labelExtraY=b),Array.isArray(e.pull))for(i=0;i<d.length;i++)(o=d[i])===t\|\|(e.pull[t.i]\|\|0)>=e.pull[o.i]\|\|((t.pxmid[1]-o.pxmid[1])c>0?(h=o.cyFinal+l(o.px0[1],o.px1[1]),(b=h-v-t.labelExtraY)c>0&&(t.labelExtraY+=b)):(g+t.labelExtraY-y)c>0&&(n=3uMath.abs(i-d.indexOf(t)),f=o.cxFinal+s(o.px0[0],o.px1[0]),(p=f+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)u>0&&(t.labelExtraX+=p)))}(m[p],g)}}}function s(t,e){var r,n,i,a,o,s,l,c,h,f,d=[];for(i=0;i<t.length;i++){if(o=t[i][0],s=o.trace,r=e.w(s.domain.x[1]-s.domain.x[0]),n=e.h(s.domain.y[1]-s.domain.y[0]),l=s.tiltaxisMath.PI/180,c=s.pull,Array.isArray(c))for(c=0,a=0;a<s.pull.length;a++)s.pull[a]>c&&(c=s.pull[a]);o.r=Math.min(r/u(s.tilt,Math.sin(l),s.depth),n/u(s.tilt,Math.cos(l),s.depth))/(2+2c),o.cx=e.l+e.w(s.domain.x[1]+s.domain.x[0])/2,o.cy=e.t+e.h(2-s.domain.y[1]-s.domain.y[0])/2,s.scalegroup&&-1===d.indexOf(s.scalegroup)&&d.push(s.scalegroup)}for(a=0;a<d.length;a++){for(f=1/0,h=d[a],i=0;i<t.length;i++)o=t[i][0],o.trace.scalegroup===h&&(f=Math.min(f,o.ro.r/o.vTotal));for(i=0;i<t.length;i++)o=t[i][0],o.trace.scalegroup===h&&(o.r=Math.sqrt(fo.vTotal))}}function l(t){function e(t){var e=h.rMath.sin(t),r=-h.rMath.cos(t);return d?[e(1-snn)+ros,eos+r(1-sii),Math.sin(a)(ri-en)]:[e,r]}var r,n,i,a,o,s,l,u,c,h=t[0],f=h.trace,d=f.tilt,p=f.rotationMath.PI/180,m=2Math.PI/h.vTotal,v=\"px0\",g=\"px1\";if(\"counterclockwise\"===f.direction){for(l=0;l<t.length&&t[l].hidden;l++);if(l===t.length)return;p+=mt[l].v,m=-1,v=\"px1\",g=\"px0\"}for(d&&(a=dMath.PI/180,r=f.tiltaxisMath.PI/180,o=Math.sin(r)Math.cos(r),s=1-Math.cos(a),n=Math.sin(r),i=Math.cos(r)),c=e(p),l=0;l<t.length;l++)u=t[l],u.hidden\|\|(u[v]=c,p+=mu.v/2,u.pxmid=e(p),u.midangle=p,p+=mu.v/2,c=e(p),u[g]=c,u.largeArc=u.v>h.vTotal/2?1:0)}function u(t,e,r){if(!t)return 1;var n=Math.sin(tMath.PI/180);return Math.max(.01,rnMath.abs(e)+2Math.sqrt(1-nnee))}var c=t(\"d3\"),h=t(\"../../components/fx\"),f=t(\"../../components/color\"),d=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),m=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;s(e,r._size);var u=r._pielayer.selectAll(\"g.trace\").data(e);u.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),u.exit().remove(),u.order(),u.each(function(e){var s=c.select(this),u=e[0],v=u.trace,g=(v.depth\|\|0)u.rMath.sin(0)/2,y=v.tiltaxis\|\|0,b=yMath.PI/180,x=[gMath.sin(b),gMath.cos(b)],_=u.rMath.cos(0),w=s.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),l(e),s.selectAll(\".top\").each(function(){var s=c.select(this).selectAll(\"g.slice\").data(e);s.enter().append(\"g\").classed(\"slice\",!0),s.exit().remove();var l=[[[],[]],[[],[]]],g=!1;s.each(function(e){function o(n){n.originalEvent=c.event;var a=t._fullLayout,o=t._fullData[v.index],s=h.castHoverinfo(o,a,e.i);if(\"all\"===s&&(s=\"label+text+value+percent+name\"),t._dragging\|\|!1===a.hovermode\|\|\"none\"===s\|\|\"skip\"===s\|\|!s)return void h.hover(t,n,\"pie\");var l=i(e,u),f=w+e.pxmid[0](1-l),d=M+e.pxmid[1](1-l),p=r.separators,g=[];-1!==s.indexOf(\"label\")&&g.push(e.label),-1!==s.indexOf(\"text\")&&(o.hovertext?g.push(Array.isArray(o.hovertext)?o.hovertext[e.i]:o.hovertext):o.text&&o.text[e.i]&&g.push(o.text[e.i])),-1!==s.indexOf(\"value\")&&g.push(m.formatPieValue(e.v,p)),-1!==s.indexOf(\"percent\")&&g.push(m.formatPiePercent(e.v/u.vTotal,p)),h.loneHover({x0:f-lu.r,x1:f+lu.r,y:d,text:g.join(\"<br>\"),name:-1!==s.indexOf(\"name\")?o.name:void 0,idealAlign:e.pxmid[0]<0?\"left\":\"right\",color:h.castHoverOption(v,e.i,\"bgcolor\")\|\|e.color,borderColor:h.castHoverOption(v,e.i,\"bordercolor\"),fontFamily:h.castHoverOption(v,e.i,\"font.family\"),fontSize:h.castHoverOption(v,e.i,\"font.size\"),fontColor:h.castHoverOption(v,e.i,\"font.color\")},{container:a._hoverlayer.node(),outerContainer:a._paper.node(),gd:t}),h.hover(t,n,\"pie\"),T=!0}function s(e){e.originalEvent=c.event,t.emit(\"plotly_unhover\",{event:c.event,points:[e]}),T&&(h.loneUnhover(r._hoverlayer.node()),T=!1)}function f(){t._hoverdata=[e],t._hoverdata.trace=u.trace,h.click(t,c.event)}function b(t,r,n,i){return\"a\"+iu.r+\",\"+i_+\" \"+y+\" \"+e.largeArc+(n?\" 1 \":\" 0 \")+i(r[0]-t[0])+\",\"+i(r[1]-t[1])}if(e.hidden)return void c.select(this).selectAll(\"path,g\").remove();e.pointNumber=e.i,e.curveNumber=v.index,l[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var w=u.cx+x[0],M=u.cy+x[1],k=c.select(this),A=k.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),k.select(\"path.textline\").remove(),k.on(\"mouseover\",o).on(\"mouseout\",s).on(\"click\",f),v.pull){var S=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)\|\|0;S>0&&(w+=Se.pxmid[0],M+=Se.pxmid[1])}e.cxFinal=w,e.cyFinal=M;var E=v.hole;if(e.v===u.vTotal){var L=\"M\"+(w+e.px0[0])+\",\"+(M+e.px0[1])+b(e.px0,e.pxmid,!0,1)+b(e.pxmid,e.px0,!0,1)+\"Z\";E?A.attr(\"d\",\"M\"+(w+Ee.px0[0])+\",\"+(M+Ee.px0[1])+b(e.px0,e.pxmid,!1,E)+b(e.pxmid,e.px0,!1,E)+\"Z\"+L):A.attr(\"d\",L)}else{var C=b(e.px0,e.px1,!0,1);if(E){var I=1-E;A.attr(\"d\",\"M\"+(w+Ee.px1[0])+\",\"+(M+Ee.px1[1])+b(e.px1,e.px0,!1,E)+\"l\"+Ie.px0[0]+\",\"+Ie.px0[1]+C+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+M+\"l\"+e.px0[0]+\",\"+e.px0[1]+C+\"Z\")}var z=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,D=k.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==z?[0]:[]);D.enter().append(\"g\").classed(\"slicetext\",!0),D.exit().remove(),D.each(function(){var r=c.select(this).selectAll(\"text\").data([0]);r.enter().append(\"text\").attr(\"data-notex\",1),r.exit().remove(),r.text(e.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(d.font,\"outside\"===z?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans,t);var i,o=d.bBox(r.node());\"outside\"===z?i=a(o,e):(i=n(o,e,u),\"auto\"===z&&i.scale<1&&(r.call(d.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size\|\|(o=d.bBox(r.node())),i=a(o,e)));var s=w+e.pxmid[0]i.rCenter+(i.x\|\|0),l=M+e.pxmid[1]i.rCenter+(i.y\|\|0);i.outside&&(e.yLabelMin=l-o.height/2,e.yLabelMid=l,e.yLabelMax=l+o.height/2,e.labelExtraX=0,e.labelExtraY=0,g=!0),r.attr(\"transform\",\"translate(\"+s+\",\"+l+\")\"+(i.scale<1?\"scale(\"+i.scale+\")\":\"\")+(i.rotate?\"rotate(\"+i.rotate+\")\":\"\")+\"translate(\"+-(o.left+o.right)/2+\",\"+-(o.top+o.bottom)/2+\")\")})}),g&&o(l,v),s.each(function(t){if(t.labelExtraX\|\|t.labelExtraY){var e=c.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],i=t.cyFinal+t.pxmid[1],a=\"M\"+n+\",\"+i,o=(t.yLabelMax-t.yLabelMin)(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var s=t.labelExtraXt.pxmid[1]/t.pxmid[0],l=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(s)>Math.abs(l)?a+=\"l\"+lt.pxmid[0]/t.pxmid[1]+\",\"+l+\"H\"+(n+t.labelExtraX+o):a+=\"l\"+t.labelExtraX+\",\"+s+\"v\"+(l-s)+\"h\"+o}else a+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+o;e.append(\"path\").classed(\"textline\",!0).call(f.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:a,fill:\"none\"})}})})}),setTimeout(function(){u.selectAll(\"tspan\").each(function(){var t=c.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":604,\"../../components/drawing\":628,\"../../components/fx\":645,\"../../lib/svg_text_utils\":750,\"./helpers\":1011,d3:122}],1016:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,a=n.select(this);a.style({opacity:r.opacity}),a.selectAll(\".top path.surface\").each(function(t){n.select(this).call(i,t,r)})})}},{\"./style_one\":1017,d3:122}],1017:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var i=r.marker.line.color;Array.isArray(i)&&(i=i[e.i]\|\|n.defaultLine);var a=r.marker.line.width\|\|0;Array.isArray(a)&&(a=a[e.i]\|\|0),t.style({\"stroke-width\":a}).call(n.fill,e.color).call(n.stroke,i)}},{\"../../components/color\":604}],1018:[function(t,e,r){\"use strict\";var n=t(\"../scattergl/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"}}},{\"../scattergl/attributes\":1077}],1019:[function(t,e,r){\"use strict\";function n(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=a(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}function i(t,e){var r=new n(t,e.uid);return r.update(e),r}var a=t(\"gl-pointcloud2d\"),o=t(\"../../lib/str2rgbarray\"),s=t(\"../scatter/get_trace_color\"),l=[\"xaxis\",\"yaxis\"],u=n.prototype;u.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2e],this.pickXYData[2e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},u.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=s(t,{})},u.updateFast=function(t){var e,r,n,i,a,s,l=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,c=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,f=t.indices,d=this.bounds;if(c){if(n=c,e=c.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(s=0;s<e;s++)i=n[2s],a=n[2s+1],i<d[0]&&(d[0]=i),i>d[2]&&(d[2]=i),a<d[1]&&(d[1]=a),a>d[3]&&(d[3]=a);if(f)r=f;else for(r=new Int32Array(e),s=0;s<e;s++)r[s]=s}else for(e=l.length,n=new Float32Array(2e),r=new 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r,n,i,a=e\|\|.5,o=0;o<2;o++)r=this.scene[l[o]],n=r._min,n\|\|(n=[]),n.push({val:t[o],pad:a}),i=r._max,i\|\|(i=[]),i.push({val:t[o+2],pad:a})},u.dispose=function(){this.pointcloud.dispose()},e.exports=i},{\"../../lib/str2rgbarray\":749,\"../scatter/get_trace_color\":1040,\"gl-pointcloud2d\":230}],1020:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\")}},{\"../../lib\":728,\"./attributes\":1018}],1021:[function(t,e,r){\"use strict\";var 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strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../plots/plots\"),a=t(\"./plot\"),o=t(\"../../components/fx/layout_attributes\");r.name=\"sankey\",r.attr=\"type\",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){var e=i.getSubplotCalcData(t.calcdata,\"sankey\",\"sankey\");e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"sankey\"),a=e._has&&e._has(\"sankey\");i&&!a&&n._paperdiv.selectAll(\".sankey\").remove()}},{\"../../components/fx/layout_attributes\":646,\"../../plot_api/edit_types\":756,\"../../plots/plots\":831,\"./plot\":1028}],1024:[function(t,e,r){\"use strict\";function n(t,e,r){for(var n=t.map(function(){return[]}),a=0;a<Math.min(e.length,r.length);a++){if(e[a]===r[a])return!0;n[e[a]].push(r[a])}return i(n).components.some(function(t){return t.length>1})}var i=t(\"strongly-connected-components\"),a=t(\"../../lib\");e.exports=function(t,e){return n(e.node.label,e.link.source,e.link.target)&&(a.error(\"Circularity is present in the Sankey data. Removing all nodes and links.\"),e.link.label=[],e.link.source=[],e.link.target=[],e.link.value=[],e.link.color=[],e.node.label=[],e.node.color=[]),[{link:e.link,node:e.node}]}},{\"../../lib\":728,\"strongly-connected-components\":528}],1025:[function(t,e,r){\"use strict\";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:\"cubic-in-out\"}},{}],1026:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../components/color/attributes\").defaults,o=t(\"../../components/color\"),s=t(\"tinycolor2\");e.exports=function(t,e,r,l){function u(r,a){return n.coerce(t,e,i,r,a)}u(\"node.label\"),u(\"node.pad\"),u(\"node.thickness\"),u(\"node.line.color\"),u(\"node.line.width\");var c=function(t){return a[t%a.length]};u(\"node.color\",e.node.label.map(function(t,e){return o.addOpacity(c(e),.8)})),u(\"link.label\"),u(\"link.source\"),u(\"link.target\"),u(\"link.value\"),u(\"link.line.color\"),u(\"link.line.width\"),u(\"link.color\",e.link.value.map(function(){return s(l.paper_bgcolor).getLuminance()<.333?\"rgba(255, 255, 255, 0.6)\":\"rgba(0, 0, 0, 0.2)\"})),u(\"domain.x\"),u(\"domain.y\"),u(\"orientation\"),u(\"valueformat\"),u(\"valuesuffix\"),u(\"arrangement\"),n.coerceFont(u,\"textfont\",n.extendFlat({},l.font));var h=function(t,r){return-1===e.link.source.indexOf(r)&&-1===e.link.target.indexOf(r)};e.node.label.some(h)&&n.warn(\"Some of the nodes are neither sources nor targets, they will not be displayed.\")}},{\"../../components/color\":604,\"../../components/color/attributes\":603,\"../../lib\":728,\"./attributes\":1022,tinycolor2:534}],1027:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.moduleType=\"trace\",n.name=\"sankey\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"noOpacity\"],n.meta={},e.exports=n},{\"./attributes\":1022,\"./base_plot\":1023,\"./calc\":1024,\"./defaults\":1026,\"./plot\":1028}],1028:[function(t,e,r){\"use strict\";function n(t){return\"\"!==t}function i(t,e){return t.filter(function(t){return t.key===e.traceId})}function a(t,e){p.select(t).select(\"path\").style(\"fill-opacity\",e),p.select(t).select(\"rect\").style(\"fill-opacity\",e)}function o(t){p.select(t).select(\"text.name\").style(\"fill\",\"black\")}function s(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)\|\|-1!==t.node.targetLinks.indexOf(e.link)}}function l(t){return function(e){return-1!==e.node.sourceLinks.indexOf(t.link)\|\|-1!==e.node.targetLinks.indexOf(t.link)}}function u(t,e,r){\n", "e&&r&&i(r,e).selectAll(\".sankeyLink\").filter(s(e)).call(h.bind(0,e,r,!1))}function c(t,e,r){e&&r&&i(r,e).selectAll(\".sankeyLink\").filter(s(e)).call(f.bind(0,e,r,!1))}function h(t,e,r,n){var a=n.datum().link.label;n.style(\"fill-opacity\",.4),a&&i(e,t).selectAll(\".sankeyLink\").filter(function(t){return t.link.label===a}).style(\"fill-opacity\",.4),r&&i(e,t).selectAll(\".sankeyNode\").filter(l(t)).call(u)}function f(t,e,r,n){var a=n.datum().link.label;n.style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),a&&i(e,t).selectAll(\".sankeyLink\").filter(function(t){return t.link.label===a}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),r&&i(e,t).selectAll(\".sankeyNode\").filter(l(t)).call(c)}function d(t,e){var r=t.hoverlabel\|\|{},n=y.nestedProperty(r,e).get();return!Array.isArray(n)&&n}var p=t(\"d3\"),m=t(\"./render\"),v=t(\"../../components/fx\"),g=t(\"../../components/color\"),y=t(\"../../lib\");e.exports=function(t,e){var r=t._fullLayout,i=r._paper,s=r._size,l=function(e,r){var n=r.link;n.originalEvent=p.event,t._hoverdata=[n],v.click(t,{target:!0})},y=function(e,r,n){var i=r.link;i.originalEvent=p.event,p.select(e).call(h.bind(0,r,n,!0)),v.hover(t,i,\"sankey\")},b=function(e,i){var s=i.link.trace,l=t._fullLayout._paperdiv.node().getBoundingClientRect(),u=e.getBoundingClientRect(),c=u.left+u.width/2,h=u.top+u.height/2,f=v.loneHover({x:c-l.left,y:h-l.top,name:p.format(i.valueFormat)(i.link.value)+i.valueSuffix,text:[i.link.label\|\|\"\",[\"Source:\",i.link.source.label].join(\" \"),[\"Target:\",i.link.target.label].join(\" 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s=i.node.trace,l=p.select(e).select(\".nodeRect\"),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),c=l.node().getBoundingClientRect(),h=c.left-2-u.left,f=c.right+2-u.left,m=c.top+c.height/4-u.top,g=v.loneHover({x0:h,x1:f,y:m,name:p.format(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,[\"Incoming flow count:\",i.node.targetLinks.length].join(\" \"),[\"Outgoing flow count:\",i.node.sourceLinks.length].join(\" 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n,i=a(e).trace,o=i.domain,s=i.node,l=i.link,c=i.arrangement,h=\"h\"===i.orientation,f=i.node.pad,d=i.node.thickness,p=i.node.line.color,m=i.node.line.width,v=i.link.line.color,g=i.link.line.width,y=i.valueformat,b=i.valuesuffix,x=i.textfont,_=t.width(o.x[1]-o.x[0]),w=t.height(o.y[1]-o.y[0]),M=s.label.map(function(t,e){return{pointNumber:e,label:t,color:N.isArray(s.color)?s.color[e]:s.color}}),k=l.value.map(function(t,e){return{pointNumber:e,label:l.label[e],color:N.isArray(l.color)?l.color[e]:l.color,source:l.source[e],target:l.target[e],value:t}}),A=F().size(h?[_,w]:[w,_]).nodeWidth(d).nodePadding(f).nodes(M).links(k).layout(z.sankeyIterations),T=A.nodes(),S=0;S<T.length;S++)n=T[S],n.width=_,n.height=w;return 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y=[];(o.hasMarkers(e)\|\|o.hasText(e))&&(m(\"marker.maxdisplayed\"),y.push(\"points\")),m(\"fill\"),\"none\"!==e.fill&&(f(t,e,r,m),o.hasLines(e)\|\|c(t,e,m)),\"tonext\"!==e.fill&&\"toself\"!==e.fill\|\|y.push(\"fills\"),m(\"hoveron\",y.join(\"+\")\|\|\"points\"),d(t,e,r,{axis:\"y\"}),d(t,e,r,{axis:\"x\",inherit:\"y\"}),m(\"cliponaxis\")}},{\"../../components/errorbars/defaults\":633,\"../../lib\":728,\"./attributes\":1031,\"./constants\":1036,\"./fillcolor_defaults\":1039,\"./line_defaults\":1043,\"./line_shape_defaults\":1045,\"./marker_defaults\":1048,\"./subtypes\":1052,\"./text_defaults\":1053,\"./xy_defaults\":1054}],1038:[function(t,e,r){\"use strict\";function n(t){return t\|\|0===t}var i=t(\"../../lib\");e.exports=function(t,e,r){var a=Array.isArray(r)?function(t){r.push(t)}:function(t){r.text=t},o=i.extractOption(t,e,\"htx\",\"hovertext\");if(n(o))return a(o);var s=i.extractOption(t,e,\"tx\",\"text\");return n(s)?a(s):void 0}},{\"../../lib\":728}],1039:[function(t,e,r){\"use 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n=t(\"../../registry\");e.exports=function(t,e,r,i){var a,o=i(\"x\"),s=i(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),o)s?(a=Math.min(o.length,s.length),a<o.length&&(e.x=o.slice(0,a)),a<s.length&&(e.y=s.slice(0,a))):(a=o.length,i(\"y0\"),i(\"dy\"));else{if(!s)return 0;a=e.y.length,i(\"x0\"),i(\"dx\")}return a}},{\"../../registry\":846}],1055:[function(t,e,r){\"use strict\";function n(t){return{show:{valType:\"boolean\",dflt:!1},opacity:{valType:\"number\",min:0,max:1,dflt:1},scale:{valType:\"number\",min:0,max:10,dflt:2/3}}}var i=t(\"../scatter/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/errorbars/attributes\"),s=t(\"../../constants/gl3d_dashes\"),l=t(\"../../constants/gl3d_markers\"),u=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,h=i.line,f=i.marker,d=f.line,p=e.exports=c({x:i.x,y:i.y,z:{valType:\"data_array\"},text:u({},i.text,{}),hovertext:u({},i.hovertext,{}),mode:u({},i.mode,{dflt:\"lines+markers\"}),surfaceaxis:{valType:\"enumerated\",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:\"color\"},projection:{x:n(\"x\"),y:n(\"y\"),z:n(\"z\")},connectgaps:i.connectgaps,line:u({width:h.width,dash:{valType:\"enumerated\",values:Object.keys(s),dflt:\"solid\"},showscale:{valType:\"boolean\",dflt:!1}},a(\"line\")),marker:u({symbol:{valType:\"enumerated\",values:Object.keys(l),dflt:\"circle\",arrayOk:!0},size:u({},f.size,{dflt:8}),sizeref:f.sizeref,sizemin:f.sizemin,sizemode:f.sizemode,opacity:u({},f.opacity,{arrayOk:!1}),showscale:f.showscale,colorbar:f.colorbar,line:u({width:u({},d.width,{arrayOk:!1})},a(\"marker.line\"))},a(\"marker\")),textposition:u({},i.textposition,{dflt:\"top 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e=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[0,0,0],i=0;i<3;i++){var a=t[i];a&&!1!==a.copy_zstyle&&(a=t[2]),a&&(e[i]=a.width/2,r[i]=b(a.color),n=a.thickness)}return{capSize:e,color:r,lineWidth:n}}function o(t){var e=[0,0];return Array.isArray(t)?[0,-1]:(t.indexOf(\"bottom\")>=0&&(e[1]+=1),t.indexOf(\"top\")>=0&&(e[1]-=1),t.indexOf(\"left\")>=0&&(e[0]-=1),t.indexOf(\"right\")>=0&&(e[0]+=1),e)}function s(t,e){return e(4t)}function l(t){return M[t]}function u(t,e,r,n,i){var a=null;if(Array.isArray(t)){a=[];for(var o=0;o<e;o++)void 0===t[o]?a[o]=n:a[o]=r(t[o],i)}else a=r(t,y.identity);return a}function c(t,e){var r,n,i,c,h,f,d=[],p=t.fullSceneLayout,m=t.dataScale,v=p.xaxis,g=p.yaxis,w=p.zaxis,M=e.marker,A=e.line,T=e.x\|\|[],S=e.y\|\|[],E=e.z\|\|[],L=T.length,C=e.xcalendar,I=e.ycalendar,z=e.zcalendar;for(n=0;n<L;n++)i=v.d2l(T[n],0,C)m[0],c=g.d2l(S[n],0,I)m[1],h=w.d2l(E[n],0,z)m[2],d[n]=[i,c,h];if(Array.isArray(e.text))f=e.text;else if(void 0!==e.text)for(f=new 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g=\"projection.\"+m[v];f(g+\".show\")&&(f(g+\".opacity\"),f(g+\".scale\"))}c(t,e,r,{axis:\"z\"}),c(t,e,r,{axis:\"y\",inherit:\"z\"}),c(t,e,r,{axis:\"x\",inherit:\"z\"})}},{\"../../components/errorbars/defaults\":633,\"../../lib\":728,\"../../registry\":846,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1055}],1060:[function(t,e,r){\"use strict\";var n={};n.plot=t(\"./convert\"),n.attributes=t(\"./attributes\"),n.markerSymbols=t(\"../../constants/gl3d_markers\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.moduleType=\"trace\",n.name=\"scatter3d\",n.basePlotModule=t(\"../../plots/gl3d\"),n.categories=[\"gl3d\",\"symbols\",\"markerColorscale\",\"showLegend\"],n.meta={},e.exports=n},{\"../../constants/gl3d_markers\":705,\"../../plots/gl3d\":811,\"../scatter/colorbar\":1034,\"./attributes\":1055,\"./calc\":1056,\"./convert\":1058,\"./defaults\":1059}],1061:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=n.marker,u=n.line,c=l.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:s({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:s({symbol:l.symbol,opacity:l.opacity,maxdisplayed:l.maxdisplayed,size:l.size,sizeref:l.sizeref,sizemin:l.sizemin,sizemode:l.sizemode,line:s({width:c.width,editType:\"calc\"},a(\"marker\".line)),gradient:l.gradient,editType:\"calc\"},a(\"marker\"),{showscale:l.showscale,colorbar:o}),textfont:n.textfont,textposition:n.textposition,hoverinfo:s({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":605,\"../../components/colorscale/color_attributes\":611,\"../../lib/extend\":717,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1062:[function(t,e,r){\"use 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o(e),s(d,e),d}}},{\"../../plots/cartesian/axes\":772,\"../carpet/lookup_carpetid\":903,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035,\"../scatter/subtypes\":1052,\"fast-isnumeric\":131}],1063:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),u=t(\"../scatter/text_defaults\"),c=t(\"../scatter/fillcolor_defaults\"),h=t(\"./attributes\");e.exports=function(t,e,r,f){function d(r,i){return n.coerce(t,e,h,r,i)}d(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var p,m=d(\"a\"),v=d(\"b\");if(!(p=Math.min(m.length,v.length)))return void(e.visible=!1);m&&p<m.length&&(e.a=m.slice(0,p)),v&&p<v.length&&(e.b=v.slice(0,p)),d(\"text\"),d(\"mode\",p<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,f,d),l(t,e,d),d(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,f,d,{gradient:!0}),a.hasText(e)&&u(t,e,f,d);var g=[];(a.hasMarkers(e)\|\|a.hasText(e))&&(d(\"marker.maxdisplayed\"),g.push(\"points\")),d(\"fill\"),\"none\"!==e.fill&&(c(t,e,r,d),a.hasLines(e)\|\|l(t,e,d)),\"tonext\"!==e.fill&&\"toself\"!==e.fill\|\|g.push(\"fills\"),d(\"hoveron\",g.join(\"+\")\|\|\"points\")}},{\"../../lib\":728,\"../scatter/constants\":1036,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/line_shape_defaults\":1045,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1061}],1064:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\");e.exports=function(t,e,r,i){function a(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}var o=n(t,e,r,i);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,u=t.xa._length,c=ul/2,h=u-c;return s.x0=Math.max(Math.min(s.x0,h),c),s.x1=Math.max(Math.min(s.x1,h),c),o}var f=s.cd[s.index];s.a=f.a,s.b=f.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var d=s.trace,p=d._carpet,m=f.hi\|\|d.hoverinfo,v=m.split(\"+\"),g=[];-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\"]),-1!==v.indexOf(\"a\")&&a(p.aaxis,f.a),-1!==v.indexOf(\"b\")&&a(p.baxis,f.b);var y=p.ab2ij([f.a,f.b]),b=Math.floor(y[0]),x=y[0]-b,_=Math.floor(y[1]),w=y[1]-_,M=p.evalxy([],b,_,x,w);return g.push(\"y: \"+M[1].toFixed(3)),s.extraText=g.join(\"<br>\"),o}}},{\"../scatter/hover\":1041}],1065:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattercarpet\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"carpet\",\"symbols\",\"markerColorscale\",\"showLegend\",\"carpetDependent\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":782,\"../scatter/colorbar\":1034,\"./attributes\":1061,\"./calc\":1062,\"./defaults\":1063,\"./hover\":1064,\"./plot\":1066,\"./select\":1067,\"./style\":1068}],1066:[function(t,e,r){\"use strict\";var n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r){var o,s,l,u=r[0][0].carpet,c={xaxis:i.getFromId(t,u.xaxis\|\|\"x\"),yaxis:i.getFromId(t,u.yaxis\|\|\"y\"),plot:e.plot};for(n(t,c,r),o=0;o<r.length;o++)s=r[o][0].trace,l=c.plot.selectAll(\"g.trace\"+s.uid+\" .js-line\"),a.setClipUrl(l,u._clipPathId)}},{\"../../components/drawing\":628,\"../../plots/cartesian/axes\":772,\"../scatter/plot\":1049}],1067:[function(t,e,r){\"use strict\";var n=t(\"../scatter/select\");e.exports=function(t,e){var r=n(t,e);if(r){var i,a,o,s=t.cd;for(o=0;o<r.length;o++)i=r[o],a=s[i.pointNumber],i.a=a.a,i.b=a.b,i.c=a.c,delete i.x,delete i.y;return r}}},{\"../scatter/select\":1050}],1068:[function(t,e,r){\"use strict\";var n=t(\"../scatter/style\");e.exports=function(t){for(var e=t._fullLayout._modules,r=0;r<e.length;r++)if(\"scatter\"===e[r].name)return;n(t)}},{\"../scatter/style\":1051}],1069:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,u=n.marker,c=n.line,h=u.line;e.exports=l({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country names\"],dflt:\"ISO-3\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),hovertext:s({},n.hovertext,{}),textfont:n.textfont,textposition:n.textposition,line:{color:c.color,width:c.width,dash:o},connectgaps:n.connectgaps,marker:s({symbol:u.symbol,opacity:u.opacity,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,showscale:u.showscale,colorbar:u.colorbar,line:s({width:h.width},a(\"marker.line\")),gradient:u.gradient},a(\"marker\")),fill:{valType:\"enumerated\",values:[\"none\",\"toself\"],dflt:\"none\"},fillcolor:n.fillcolor,hoverinfo:s({},i.hoverinfo,{flags:[\"lon\",\"lat\",\"location\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorscale/color_attributes\":611,\"../../components/drawing/attributes\":627,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1070:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/colorscale_calc\"),o=t(\"../scatter/arrays_to_calcdata\");e.exports=function(t,e){for(var r=Array.isArray(e.locations),s=r?e.locations.length:e.lon.length,l=new Array(s),u=0;u<s;u++){var c=l[u]={};if(r){var h=e.locations[u];c.loc=\"string\"==typeof h?h:null}else{var f=e.lon[u],d=e.lat[u];n(f)&&n(d)?c.lonlat=[+f,+d]:c.lonlat=[i,i]}}return o(l,e),a(e),l}},{\"../../constants/numerical\":707,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035,\"fast-isnumeric\":131}],1071:[function(t,e,r){\"use strict\";function n(t,e,r){var n,i,a=0,o=r(\"locations\");return o?(r(\"locationmode\"),a=o.length):(n=r(\"lon\")\|\|[],i=r(\"lat\")\|\|[],a=Math.min(n.length,i.length),a<n.length&&(e.lon=n.slice(0,a)),a<i.length&&(e.lat=i.slice(0,a)),a)}var i=t(\"../../lib\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,h){function f(r,n){return i.coerce(t,e,c,r,n)}if(!n(t,e,f))return void(e.visible=!1);f(\"text\"),f(\"hovertext\"),f(\"mode\"),a.hasLines(e)&&(s(t,e,r,h,f),f(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,f,{gradient:!0}),a.hasText(e)&&l(t,e,h,f),f(\"fill\"),\"none\"!==e.fill&&u(t,e,r,f)}},{\"../../lib\":728,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1069}],1072:[function(t,e,r){\"use strict\";e.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null,t}},{}],1073:[function(t,e,r){\"use strict\";function n(t,e,r){function n(t){return a.tickText(r,r.c2l(t),\"hover\").text+\"\\xb0\"}var i=e.hi\|\|t.hoverinfo,o=\"all\"===i?u.hoverinfo.flags:i.split(\"+\"),s=-1!==o.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==o.indexOf(\"lon\"),h=-1!==o.indexOf(\"lat\"),f=-1!==o.indexOf(\"text\"),d=[];return s?d.push(e.loc):c&&h?d.push(\"(\"+n(e.lonlat[0])+\", \"+n(e.lonlat[1])+\")\"):c?d.push(\"lon: \"+n(e.lonlat[0])):h&&d.push(\"lat: \"+n(e.lonlat[1])),f&&l(e,t,d),d.join(\"<br>\")}var i=t(\"../../components/fx\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,s=t(\"../scatter/get_trace_color\"),l=t(\"../scatter/fill_hover_text\"),u=t(\"./attributes\");e.exports=function(t,e,r){function a(t){var n=t.lonlat;if(n[0]===o)return 1/0;if(d(n))return 1/0;var i=p(n),a=p([e,r]),s=Math.abs(i[0]-a[0]),l=Math.abs(i[1]-a[1]),u=Math.max(3,t.mrc\|\|0);return Math.max(Math.sqrt(ss+ll)-u,1-3/u)}var l=t.cd,u=l[0].trace,c=t.xa,h=t.ya,f=t.subplot,d=f.projection.isLonLatOverEdges,p=f.project;if(i.getClosest(l,a,t),!1!==t.index){var m=l[t.index],v=m.lonlat,g=[c.c2p(v),h.c2p(v)],y=m.mrc\|\|1;return t.x0=g[0]-y,t.x1=g[0]+y,t.y0=g[1]-y,t.y1=g[1]+y,t.loc=m.loc,t.lon=v[0],t.lat=v[1],t.color=s(u,m),t.extraText=n(u,m,f.mockAxis),[t]}}},{\"../../components/fx\":645,\"../../constants/numerical\":707,\"../../plots/cartesian/axes\":772,\"../scatter/fill_hover_text\":1038,\"../scatter/get_trace_color\":1040,\"./attributes\":1069}],1074:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.hoverPoints=t(\"./hover\"),n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergeo\",n.basePlotModule=t(\"../../plots/geo\"),n.categories=[\"geo\",\"symbols\",\"markerColorscale\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/geo\":800,\"../scatter/colorbar\":1034,\"./attributes\":1069,\"./calc\":1070,\"./defaults\":1071,\"./event_data\":1072,\"./hover\":1073,\"./plot\":1075,\"./select\":1076}],1075:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0].trace;if(Array.isArray(r.locations))for(var n=c(r,e),i=r.locationmode,a=0;a<t.length;a++){var o=t[a],s=h(i,o.loc,n);o.lonlat=s?s.properties.ct:[u,u]}}function i(t){var e=t.layers.frontplot.selectAll(\".trace.scattergeo\");e.style(\"opacity\",function(t){return t[0].trace.opacity}),e.each(function(e){var r=e[0].trace,n=a.select(this);n.selectAll(\"path.point\").call(o.pointStyle,r,t.graphDiv),n.selectAll(\"text\").call(o.textPointStyle,r,t.graphDiv)}),e.selectAll(\"path.js-line\").style(\"fill\",\"none\").each(function(t){var e=a.select(this),r=t.trace,n=r.line\|\|{};e.call(s.stroke,n.color).call(o.dashLine,n.dash\|\|\"\",n.width\|\|0),\"none\"!==r.fill&&e.call(s.fill,r.fillcolor)})}var a=t(\"d3\"),o=t(\"../../components/drawing\"),s=t(\"../../components/color\"),l=t(\"../../lib\"),u=t(\"../../constants/numerical\").BADNUM,c=t(\"../../lib/topojson_utils\").getTopojsonFeatures,h=t(\"../../lib/geo_location_utils\").locationToFeature,f=t(\"../../lib/geojson_utils\"),d=t(\"../scatter/subtypes\")\n", ";e.exports=function(t,e){function r(t){return t[0].trace.uid}function o(t,e){t.lonlat[0]===u&&a.select(e).remove()}for(var s=0;s<e.length;s++)n(e[s],t.topojson);var c=t.layers.frontplot.select(\".scatterlayer\").selectAll(\"g.trace.scattergeo\").data(e,r);c.enter().append(\"g\").attr(\"class\",\"trace scattergeo\"),c.exit().remove(),c.selectAll(\"\").remove(),c.each(function(t){var e=t[0].node3=a.select(this),r=t[0].trace;if(d.hasLines(r)\|\|\"none\"!==r.fill){var n=f.calcTraceToLineCoords(t),i=\"none\"!==r.fill?f.makePolygon(n):f.makeLine(n);e.selectAll(\"path.js-line\").data([{geojson:i,trace:r}]).enter().append(\"path\").classed(\"js-line\",!0).style(\"stroke-miterlimit\",2)}d.hasMarkers(r)&&e.selectAll(\"path.point\").data(l.identity).enter().append(\"path\").classed(\"point\",!0).each(function(t){o(t,this)}),d.hasText(r)&&e.selectAll(\"g\").data(l.identity).enter().append(\"g\").append(\"text\").each(function(t){o(t,this)})}),i(t)}},{\"../../components/color\":604,\"../../components/drawing\":628,\"../../constants/numerical\":707,\"../../lib\":728,\"../../lib/geo_location_utils\":720,\"../../lib/geojson_utils\":721,\"../../lib/topojson_utils\":753,\"../scatter/subtypes\":1052,d3:122}],1076:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"../../constants/interactions\").DESELECTDIM;e.exports=function(t,e){var r,a,o,s,l,u=t.cd,c=t.xaxis,h=t.yaxis,f=[],d=u[0].trace,p=u[0].node3,m=!n.hasMarkers(d)&&!n.hasText(d);if(!0===d.visible&&!m){var v=d.marker,g=Array.isArray(v.opacity)?1:v.opacity;if(!1===e)for(l=0;l<u.length;l++)u[l].dim=0;else for(l=0;l<u.length;l++)r=u[l],a=r.lonlat,o=c.c2p(a),s=h.c2p(a),e.contains([o,s])?(f.push({pointNumber:l,lon:a[0],lat:a[1]}),r.dim=0):r.dim=1;return p.selectAll(\"path.point\").style(\"opacity\",function(t){return((t.mo+1\|\|g+1)-1)(t.dim?i:1)}),p.selectAll(\"text\").style(\"opacity\",function(t){return t.dim?i:1}),f}}},{\"../../constants/interactions\":706,\"../scatter/subtypes\":1052}],1077:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../constants/gl2d_dashes\"),o=t(\"../../constants/gl2d_markers\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,u=n.line,c=n.marker,h=c.line,f=e.exports=l({x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,text:s({},n.text,{}),mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\"],extras:[\"none\"]},line:{color:u.color,width:u.width,dash:{valType:\"enumerated\",values:Object.keys(a),dflt:\"solid\"}},marker:s({},i(\"marker\"),{symbol:{valType:\"enumerated\",values:Object.keys(o),dflt:\"circle\",arrayOk:!0},size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,opacity:c.opacity,showscale:c.showscale,colorbar:c.colorbar,line:s({},i(\"marker.line\"),{width:h.width})}),connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"tozeroy\",\"tozerox\"]}),fillcolor:n.fillcolor,error_y:n.error_y,error_x:n.error_x},\"calc\",\"nested\");f.x.editType=f.y.editType=f.x0.editType=f.y0.editType=\"calc+clearAxisTypes\"},{\"../../components/colorscale/color_attributes\":611,\"../../constants/gl2d_dashes\":702,\"../../constants/gl2d_markers\":703,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../scatter/attributes\":1031}],1078:[function(t,e,r){\"use 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r,n,i,a=e\|\|10,o=0;o<2;o++)r=this.scene[I[o]],n=r._min,n\|\|(n=[]),n.push({val:t[o],pad:a}),i=r._max,i\|\|(i=[]),i.push({val:t[o+2],pad:a})},D.expandAxesFancy=function(t,e,r){var n=this.scene,i={padded:!0,ppad:r};b.expand(n.xaxis,t,i),b.expand(n.yaxis,e,i)},D.dispose=function(){this.line.dispose(),this.errorX.dispose(),this.errorY.dispose(),this.scatter.dispose(),this.fancyScatter.dispose()},e.exports=f},{\"../../components/errorbars\":634,\"../../constants/gl2d_dashes\":702,\"../../constants/gl2d_markers\":703,\"../../constants/interactions\":706,\"../../lib\":728,\"../../lib/gl_format_color\":724,\"../../lib/str2rgbarray\":749,\"../../lib/typed_array_truncate\":754,\"../../plots/cartesian/axes\":772,\"../../plots/cartesian/axis_autotype\":773,\"../scatter/get_trace_color\":1040,\"../scatter/make_bubble_size_func\":1047,\"../scatter/subtypes\":1052,\"fast-isnumeric\":131,\"gl-error2d\":159,\"gl-line2d\":170,\"gl-scatter2d\":248,\"gl-scatter2d-sdf\":243}],1080:[function(t,e,r){\"use 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.hoverPoints=t(\"../scatter/hover\"),n.calc=t(\"./calc\"),n.plot=t(\"./convert\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergl\",n.basePlotModule=t(\"../../plots/gl2d\"),n.categories=[\"gl2d\",\"symbols\",\"errorBarsOK\",\"markerColorscale\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/gl2d\":808,\"../scatter/colorbar\":1034,\"../scatter/hover\":1041,\"./attributes\":1077,\"./calc\":1078,\"./convert\":1079,\"./defaults\":1080,\"./select\":1082}],1082:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\");e.exports=function(t,e){var r,i,a,o,s=t.cd,l=t.xaxis,u=t.yaxis,c=[],h=s[0].trace,f=s[0]._glTrace,d=f.scene,p=!n.hasMarkers(h)&&!n.hasText(h);if(!0===h.visible&&!p){if(!1===e)for(r=0;r<s.length;r++)s[r].dim=0;else 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n=t(\"../scattergeo/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/mapbox/layout_attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorbar/attributes\"),l=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,c=n.line,h=n.marker;e.exports=u({lon:n.lon,lat:n.lat,mode:l({},i.mode,{dflt:\"markers\"}),text:l({},i.text,{}),hovertext:l({},i.hovertext,{}),line:{color:c.color,width:c.width},connectgaps:i.connectgaps,marker:{symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},opacity:h.opacity,size:h.size,sizeref:h.sizeref,sizemin:h.sizemin,sizemode:h.sizemode,color:h.color,colorscale:h.colorscale,cauto:h.cauto,cmax:h.cmax,cmin:h.cmin,autocolorscale:h.autocolorscale,reversescale:h.reversescale,showscale:h.showscale,colorbar:s},fill:n.fill,fillcolor:i.fillcolor,textfont:a.layers.symbol.textfont,textposition:a.layers.symbol.textposition,hoverinfo:l({},o.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorbar/attributes\":605,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../../plots/mapbox/layout_attributes\":827,\"../scatter/attributes\":1031,\"../scattergeo/attributes\":1069}],1084:[function(t,e,r){\"use 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t=this.map;t.removeLayer(this.idLayerFill),t.removeLayer(this.idLayerLine),t.removeLayer(this.idLayerCircle),t.removeLayer(this.idLayerSymbol),t.removeSource(this.idSourceFill),t.removeSource(this.idSourceLine),t.removeSource(this.idSourceCircle),t.removeSource(this.idSourceSymbol)},e.exports=function(t,e){var r=e[0].trace,i=new n(t,r.uid);return i.update(e),i}},{\"./convert\":1084}],1090:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\");e.exports=function(t,e){var r,i,a,o,s,l=t.cd,u=t.xaxis,c=t.yaxis,h=[],f=l[0].trace;if(f._hasDimmedPts=!1,!0===f.visible&&n.hasMarkers(f)){if(!1===e)for(s=0;s<l.length;s++)l[s].dim=0;else for(s=0;s<l.length;s++)r=l[s],i=r.lonlat,a=u.c2p(i),o=c.c2p(i),e.contains([a,o])?(f._hasDimmedPts=!0,h.push({pointNumber:s,lon:i[0],lat:i[1]}),r.dim=0):r.dim=1;return f._glTrace.update(l),h}}},{\"../scatter/subtypes\":1052}],1091:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../components/drawing/attributes\").dash,l=t(\"../../lib/extend\").extendFlat,u=n.marker,c=n.line,h=u.line;e.exports={a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},c:{valType:\"data_array\",editType:\"calc\"},sum:{valType:\"number\",dflt:0,min:0,editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),hovertext:l({},n.hovertext,{}),line:{color:c.color,width:c.width,dash:s,shape:l({},c.shape,{values:[\"linear\",\"spline\"]}),smoothing:c.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,cliponaxis:n.cliponaxis,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:l({symbol:u.symbol,opacity:u.opacity,maxdisplayed:u.maxdisplayed,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,line:l({width:h.width,editType:\"calc\"},a(\"marker.line\")),gradient:u.gradient,editType:\"calc\"},a(\"marker\"),{showscale:u.showscale,colorbar:o}),textfont:n.textfont,textposition:n.textposition,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"c\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":605,\"../../components/colorscale/color_attributes\":611,\"../../components/drawing/attributes\":627,\"../../lib/extend\":717,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1092:[function(t,e,r){\"use 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n=t(\"../scatter/hover\"),i=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r,a){function o(t,e){y.push(t._hovertitle+\": \"+i.tickText(t,e,\"hover\").text)}var s=n(t,e,r,a);if(s&&!1!==s[0].index){var l=s[0];if(void 0===l.index){var u=1-l.y0/t.ya._length,c=t.xa._length,h=cu/2,f=c-h;return l.x0=Math.max(Math.min(l.x0,f),h),l.x1=Math.max(Math.min(l.x1,f),h),s}var d=l.cd[l.index];l.a=d.a,l.b=d.b,l.c=d.c,l.xLabelVal=void 0,l.yLabelVal=void 0;var p=l.trace,m=p._ternary,v=d.hi\|\|p.hoverinfo,g=v.split(\"+\"),y=[];return-1!==g.indexOf(\"all\")&&(g=[\"a\",\"b\",\"c\"]),-1!==g.indexOf(\"a\")&&o(m.aaxis,d.a),-1!==g.indexOf(\"b\")&&o(m.baxis,d.b),-1!==g.indexOf(\"c\")&&o(m.caxis,d.c),l.extraText=y.join(\"<br>\"),s}}},{\"../../plots/cartesian/axes\":772,\"../scatter/hover\":1041}],1095:[function(t,e,r){\"use strict\";var 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n=t(\"../../components/annotations/attributes\"),i=t(\"../../lib/extend\").extendFlat,a=t(\"../../plot_api/edit_types\").overrideAll;e.exports=a({domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},columnwidth:{valType:\"number\",arrayOk:!0,dflt:null},columnorder:{valType:\"data_array\"},header:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[]},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:{family:{valType:\"string\",arrayOk:!0,noBlank:!0,strict:!0},size:{valType:\"number\",arrayOk:!0},color:{valType:\"color\",arrayOk:!0}}},cells:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[]},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:20},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:{family:{valType:\"string\",arrayOk:!0,noBlank:!0,strict:!0},size:{valType:\"number\",arrayOk:!0},color:{valType:\"color\",arrayOk:!0}}}},\"calc\",\"from-root\")},{\"../../components/annotations/attributes\":587,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756}],1106:[function(t,e,r){\"use 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this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative\|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative\|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0\|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26\|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]\|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();var r,n;this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0\|r.words[a])+(0\|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0\|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r=this.cmp(t);if(0===r)return this.negative=0,this.length=1,this.words[0]=0,this;var n,i;r>0?(n=this,i=t):(n=t,i=this);for(var a=0,o=0;o<i.length;o++)e=(0\|n.words[o])-(0\|i.words[o])+a,a=e>>26,this.words[o]=67108863&e;for(;0!==a&&o<n.length;o++)e=(0\|n.words[o])+a,a=e>>26,this.words[o]=67108863&e;if(0===a&&o<n.length&&n!==this)for(;o<n.length;o++)this.words[o]=n.words[o];return this.length=Math.max(this.length,o),n!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var k=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0\|o[0],h=8191&c,f=c>>>13,d=0\|o[1],p=8191&d,m=d>>>13,v=0\|o[2],g=8191&v,y=v>>>13,b=0\|o[3],x=8191&b,_=b>>>13,w=0\|o[4],M=8191&w,k=w>>>13,A=0\|o[5],T=8191&A,S=A>>>13,E=0\|o[6],L=8191&E,C=E>>>13,I=0\|o[7],z=8191&I,D=I>>>13,P=0\|o[8],O=8191&P,R=P>>>13,F=0\|o[9],j=8191&F,N=F>>>13,B=0\|s[0],U=8191&B,V=B>>>13,H=0\|s[1],q=8191&H,G=H>>>13,Y=0\|s[2],W=8191&Y,X=Y>>>13,Z=0\|s[3],J=8191&Z,K=Z>>>13,Q=0\|s[4],$=8191&Q,tt=Q>>>13,et=0\|s[5],rt=8191&et,nt=et>>>13,it=0\|s[6],at=8191&it,ot=it>>>13,st=0\|s[7],lt=8191&st,ut=st>>>13,ct=0\|s[8],ht=8191&ct,ft=ct>>>13,dt=0\|s[9],pt=8191&dt,mt=dt>>>13;r.negative=t.negative^e.negative,r.length=19,n=Math.imul(h,U),i=Math.imul(h,V),i=i+Math.imul(f,U)\|0,a=Math.imul(f,V);var vt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(vt>>>26)\|0,vt&=67108863,n=Math.imul(p,U),i=Math.imul(p,V),i=i+Math.imul(m,U)\|0,a=Math.imul(m,V),n=n+Math.imul(h,q)\|0,i=i+Math.imul(h,G)\|0,i=i+Math.imul(f,q)\|0,a=a+Math.imul(f,G)\|0;var gt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(gt>>>26)\|0,gt&=67108863,n=Math.imul(g,U),i=Math.imul(g,V),i=i+Math.imul(y,U)\|0,a=Math.imul(y,V),n=n+Math.imul(p,q)\|0,i=i+Math.imul(p,G)\|0,i=i+Math.imul(m,q)\|0,a=a+Math.imul(m,G)\|0,n=n+Math.imul(h,W)\|0,i=i+Math.imul(h,X)\|0,i=i+Math.imul(f,W)\|0,a=a+Math.imul(f,X)\|0;var yt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(yt>>>26)\|0,yt&=67108863,n=Math.imul(x,U),i=Math.imul(x,V),i=i+Math.imul(_,U)\|0,a=Math.imul(_,V),n=n+Math.imul(g,q)\|0,i=i+Math.imul(g,G)\|0,i=i+Math.imul(y,q)\|0,a=a+Math.imul(y,G)\|0,n=n+Math.imul(p,W)\|0,i=i+Math.imul(p,X)\|0,i=i+Math.imul(m,W)\|0,a=a+Math.imul(m,X)\|0,n=n+Math.imul(h,J)\|0,i=i+Math.imul(h,K)\|0,i=i+Math.imul(f,J)\|0,a=a+Math.imul(f,K)\|0;var bt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(bt>>>26)\|0,bt&=67108863,n=Math.imul(M,U),i=Math.imul(M,V),i=i+Math.imul(k,U)\|0,a=Math.imul(k,V),n=n+Math.imul(x,q)\|0,i=i+Math.imul(x,G)\|0,i=i+Math.imul(_,q)\|0,a=a+Math.imul(_,G)\|0,n=n+Math.imul(g,W)\|0,i=i+Math.imul(g,X)\|0,i=i+Math.imul(y,W)\|0,a=a+Math.imul(y,X)\|0,n=n+Math.imul(p,J)\|0,i=i+Math.imul(p,K)\|0,i=i+Math.imul(m,J)\|0,a=a+Math.imul(m,K)\|0,n=n+Math.imul(h,$)\|0,i=i+Math.imul(h,tt)\|0,i=i+Math.imul(f,$)\|0,a=a+Math.imul(f,tt)\|0;var xt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(xt>>>26)\|0,xt&=67108863,n=Math.imul(T,U),i=Math.imul(T,V),i=i+Math.imul(S,U)\|0,a=Math.imul(S,V),n=n+Math.imul(M,q)\|0,i=i+Math.imul(M,G)\|0,i=i+Math.imul(k,q)\|0,a=a+Math.imul(k,G)\|0,n=n+Math.imul(x,W)\|0,i=i+Math.imul(x,X)\|0,i=i+Math.imul(_,W)\|0,a=a+Math.imul(_,X)\|0,n=n+Math.imul(g,J)\|0,i=i+Math.imul(g,K)\|0,i=i+Math.imul(y,J)\|0,a=a+Math.imul(y,K)\|0,n=n+Math.imul(p,$)\|0,i=i+Math.imul(p,tt)\|0,i=i+Math.imul(m,$)\|0,a=a+Math.imul(m,tt)\|0,n=n+Math.imul(h,rt)\|0,i=i+Math.imul(h,nt)\|0,i=i+Math.imul(f,rt)\|0,a=a+Math.imul(f,nt)\|0;var _t=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(_t>>>26)\|0,_t&=67108863,n=Math.imul(L,U),i=Math.imul(L,V),i=i+Math.imul(C,U)\|0,a=Math.imul(C,V),n=n+Math.imul(T,q)\|0,i=i+Math.imul(T,G)\|0,i=i+Math.imul(S,q)\|0,a=a+Math.imul(S,G)\|0,n=n+Math.imul(M,W)\|0,i=i+Math.imul(M,X)\|0,i=i+Math.imul(k,W)\|0,a=a+Math.imul(k,X)\|0,n=n+Math.imul(x,J)\|0,i=i+Math.imul(x,K)\|0,i=i+Math.imul(_,J)\|0,a=a+Math.imul(_,K)\|0,n=n+Math.imul(g,$)\|0,i=i+Math.imul(g,tt)\|0,i=i+Math.imul(y,$)\|0,a=a+Math.imul(y,tt)\|0,n=n+Math.imul(p,rt)\|0,i=i+Math.imul(p,nt)\|0,i=i+Math.imul(m,rt)\|0,a=a+Math.imul(m,nt)\|0,n=n+Math.imul(h,at)\|0,i=i+Math.imul(h,ot)\|0,i=i+Math.imul(f,at)\|0,a=a+Math.imul(f,ot)\|0;var wt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(wt>>>26)\|0,wt&=67108863,n=Math.imul(z,U),i=Math.imul(z,V),i=i+Math.imul(D,U)\|0,a=Math.imul(D,V),n=n+Math.imul(L,q)\|0,i=i+Math.imul(L,G)\|0,i=i+Math.imul(C,q)\|0,a=a+Math.imul(C,G)\|0,n=n+Math.imul(T,W)\|0,i=i+Math.imul(T,X)\|0,i=i+Math.imul(S,W)\|0,a=a+Math.imul(S,X)\|0,n=n+Math.imul(M,J)\|0,i=i+Math.imul(M,K)\|0,i=i+Math.imul(k,J)\|0,a=a+Math.imul(k,K)\|0,n=n+Math.imul(x,$)\|0,i=i+Math.imul(x,tt)\|0,i=i+Math.imul(_,$)\|0,a=a+Math.imul(_,tt)\|0,n=n+Math.imul(g,rt)\|0,i=i+Math.imul(g,nt)\|0,i=i+Math.imul(y,rt)\|0,a=a+Math.imul(y,nt)\|0,n=n+Math.imul(p,at)\|0,i=i+Math.imul(p,ot)\|0,i=i+Math.imul(m,at)\|0,a=a+Math.imul(m,ot)\|0,n=n+Math.imul(h,lt)\|0,i=i+Math.imul(h,ut)\|0,i=i+Math.imul(f,lt)\|0,a=a+Math.imul(f,ut)\|0;var Mt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Mt>>>26)\|0,Mt&=67108863,n=Math.imul(O,U),i=Math.imul(O,V),i=i+Math.imul(R,U)\|0,a=Math.imul(R,V),n=n+Math.imul(z,q)\|0,i=i+Math.imul(z,G)\|0,i=i+Math.imul(D,q)\|0,a=a+Math.imul(D,G)\|0,n=n+Math.imul(L,W)\|0,i=i+Math.imul(L,X)\|0,i=i+Math.imul(C,W)\|0,a=a+Math.imul(C,X)\|0,n=n+Math.imul(T,J)\|0,i=i+Math.imul(T,K)\|0,i=i+Math.imul(S,J)\|0,a=a+Math.imul(S,K)\|0,n=n+Math.imul(M,$)\|0,i=i+Math.imul(M,tt)\|0,i=i+Math.imul(k,$)\|0,a=a+Math.imul(k,tt)\|0,n=n+Math.imul(x,rt)\|0,i=i+Math.imul(x,nt)\|0,i=i+Math.imul(_,rt)\|0,a=a+Math.imul(_,nt)\|0,n=n+Math.imul(g,at)\|0,i=i+Math.imul(g,ot)\|0,i=i+Math.imul(y,at)\|0,a=a+Math.imul(y,ot)\|0,n=n+Math.imul(p,lt)\|0,i=i+Math.imul(p,ut)\|0,i=i+Math.imul(m,lt)\|0,a=a+Math.imul(m,ut)\|0,n=n+Math.imul(h,ht)\|0,i=i+Math.imul(h,ft)\|0,i=i+Math.imul(f,ht)\|0,a=a+Math.imul(f,ft)\|0;var kt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(kt>>>26)\|0,kt&=67108863,n=Math.imul(j,U),i=Math.imul(j,V),i=i+Math.imul(N,U)\|0,a=Math.imul(N,V),n=n+Math.imul(O,q)\|0,i=i+Math.imul(O,G)\|0,i=i+Math.imul(R,q)\|0,a=a+Math.imul(R,G)\|0,n=n+Math.imul(z,W)\|0,i=i+Math.imul(z,X)\|0,i=i+Math.imul(D,W)\|0,a=a+Math.imul(D,X)\|0,n=n+Math.imul(L,J)\|0,i=i+Math.imul(L,K)\|0,i=i+Math.imul(C,J)\|0,a=a+Math.imul(C,K)\|0,n=n+Math.imul(T,$)\|0,i=i+Math.imul(T,tt)\|0,i=i+Math.imul(S,$)\|0,a=a+Math.imul(S,tt)\|0,n=n+Math.imul(M,rt)\|0,i=i+Math.imul(M,nt)\|0,i=i+Math.imul(k,rt)\|0,a=a+Math.imul(k,nt)\|0,n=n+Math.imul(x,at)\|0,i=i+Math.imul(x,ot)\|0,i=i+Math.imul(_,at)\|0,a=a+Math.imul(_,ot)\|0,n=n+Math.imul(g,lt)\|0,i=i+Math.imul(g,ut)\|0,i=i+Math.imul(y,lt)\|0,a=a+Math.imul(y,ut)\|0,n=n+Math.imul(p,ht)\|0,i=i+Math.imul(p,ft)\|0,i=i+Math.imul(m,ht)\|0,a=a+Math.imul(m,ft)\|0,n=n+Math.imul(h,pt)\|0,i=i+Math.imul(h,mt)\|0,i=i+Math.imul(f,pt)\|0,a=a+Math.imul(f,mt)\|0;var At=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(At>>>26)\|0,At&=67108863,n=Math.imul(j,q),i=Math.imul(j,G),i=i+Math.imul(N,q)\|0,a=Math.imul(N,G),n=n+Math.imul(O,W)\|0,i=i+Math.imul(O,X)\|0,i=i+Math.imul(R,W)\|0,a=a+Math.imul(R,X)\|0,n=n+Math.imul(z,J)\|0,i=i+Math.imul(z,K)\|0,i=i+Math.imul(D,J)\|0,a=a+Math.imul(D,K)\|0,n=n+Math.imul(L,$)\|0,i=i+Math.imul(L,tt)\|0,i=i+Math.imul(C,$)\|0,a=a+Math.imul(C,tt)\|0,n=n+Math.imul(T,rt)\|0,i=i+Math.imul(T,nt)\|0,i=i+Math.imul(S,rt)\|0,a=a+Math.imul(S,nt)\|0,n=n+Math.imul(M,at)\|0,i=i+Math.imul(M,ot)\|0,i=i+Math.imul(k,at)\|0,a=a+Math.imul(k,ot)\|0,n=n+Math.imul(x,lt)\|0,i=i+Math.imul(x,ut)\|0,i=i+Math.imul(_,lt)\|0,a=a+Math.imul(_,ut)\|0,n=n+Math.imul(g,ht)\|0,i=i+Math.imul(g,ft)\|0,i=i+Math.imul(y,ht)\|0,a=a+Math.imul(y,ft)\|0,n=n+Math.imul(p,pt)\|0,i=i+Math.imul(p,mt)\|0,i=i+Math.imul(m,pt)\|0,a=a+Math.imul(m,mt)\|0;var Tt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Tt>>>26)\|0,Tt&=67108863,n=Math.imul(j,W),i=Math.imul(j,X),i=i+Math.imul(N,W)\|0,a=Math.imul(N,X),n=n+Math.imul(O,J)\|0,i=i+Math.imul(O,K)\|0,i=i+Math.imul(R,J)\|0,a=a+Math.imul(R,K)\|0,n=n+Math.imul(z,$)\|0,i=i+Math.imul(z,tt)\|0,i=i+Math.imul(D,$)\|0,a=a+Math.imul(D,tt)\|0,n=n+Math.imul(L,rt)\|0,i=i+Math.imul(L,nt)\|0,i=i+Math.imul(C,rt)\|0,a=a+Math.imul(C,nt)\|0,n=n+Math.imul(T,at)\|0,i=i+Math.imul(T,ot)\|0,i=i+Math.imul(S,at)\|0,a=a+Math.imul(S,ot)\|0,n=n+Math.imul(M,lt)\|0,i=i+Math.imul(M,ut)\|0,i=i+Math.imul(k,lt)\|0,a=a+Math.imul(k,ut)\|0,n=n+Math.imul(x,ht)\|0,i=i+Math.imul(x,ft)\|0,i=i+Math.imul(_,ht)\|0,a=a+Math.imul(_,ft)\|0,n=n+Math.imul(g,pt)\|0,i=i+Math.imul(g,mt)\|0,i=i+Math.imul(y,pt)\|0,a=a+Math.imul(y,mt)\|0;var St=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(St>>>26)\|0,St&=67108863,n=Math.imul(j,J),i=Math.imul(j,K),i=i+Math.imul(N,J)\|0,a=Math.imul(N,K),n=n+Math.imul(O,$)\|0,i=i+Math.imul(O,tt)\|0,i=i+Math.imul(R,$)\|0,a=a+Math.imul(R,tt)\|0,n=n+Math.imul(z,rt)\|0,i=i+Math.imul(z,nt)\|0,i=i+Math.imul(D,rt)\|0,a=a+Math.imul(D,nt)\|0,n=n+Math.imul(L,at)\|0,i=i+Math.imul(L,ot)\|0,i=i+Math.imul(C,at)\|0,a=a+Math.imul(C,ot)\|0,n=n+Math.imul(T,lt)\|0,i=i+Math.imul(T,ut)\|0,i=i+Math.imul(S,lt)\|0,a=a+Math.imul(S,ut)\|0,n=n+Math.imul(M,ht)\|0,i=i+Math.imul(M,ft)\|0,i=i+Math.imul(k,ht)\|0,a=a+Math.imul(k,ft)\|0,n=n+Math.imul(x,pt)\|0,i=i+Math.imul(x,mt)\|0,i=i+Math.imul(_,pt)\|0,a=a+Math.imul(_,mt)\|0;var Et=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Et>>>26)\|0,Et&=67108863,n=Math.imul(j,$),i=Math.imul(j,tt),i=i+Math.imul(N,$)\|0,a=Math.imul(N,tt),n=n+Math.imul(O,rt)\|0,i=i+Math.imul(O,nt)\|0,i=i+Math.imul(R,rt)\|0,a=a+Math.imul(R,nt)\|0,n=n+Math.imul(z,at)\|0,i=i+Math.imul(z,ot)\|0,i=i+Math.imul(D,at)\|0,a=a+Math.imul(D,ot)\|0,n=n+Math.imul(L,lt)\|0,i=i+Math.imul(L,ut)\|0,i=i+Math.imul(C,lt)\|0,a=a+Math.imul(C,ut)\|0,n=n+Math.imul(T,ht)\|0,i=i+Math.imul(T,ft)\|0,i=i+Math.imul(S,ht)\|0,a=a+Math.imul(S,ft)\|0,n=n+Math.imul(M,pt)\|0,i=i+Math.imul(M,mt)\|0,i=i+Math.imul(k,pt)\|0,a=a+Math.imul(k,mt)\|0;var Lt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Lt>>>26)\|0,Lt&=67108863,n=Math.imul(j,rt),i=Math.imul(j,nt),i=i+Math.imul(N,rt)\|0,a=Math.imul(N,nt),n=n+Math.imul(O,at)\|0,i=i+Math.imul(O,ot)\|0,i=i+Math.imul(R,at)\|0,a=a+Math.imul(R,ot)\|0,n=n+Math.imul(z,lt)\|0,i=i+Math.imul(z,ut)\|0,i=i+Math.imul(D,lt)\|0,a=a+Math.imul(D,ut)\|0,n=n+Math.imul(L,ht)\|0,\n", "i=i+Math.imul(L,ft)\|0,i=i+Math.imul(C,ht)\|0,a=a+Math.imul(C,ft)\|0,n=n+Math.imul(T,pt)\|0,i=i+Math.imul(T,mt)\|0,i=i+Math.imul(S,pt)\|0,a=a+Math.imul(S,mt)\|0;var Ct=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Ct>>>26)\|0,Ct&=67108863,n=Math.imul(j,at),i=Math.imul(j,ot),i=i+Math.imul(N,at)\|0,a=Math.imul(N,ot),n=n+Math.imul(O,lt)\|0,i=i+Math.imul(O,ut)\|0,i=i+Math.imul(R,lt)\|0,a=a+Math.imul(R,ut)\|0,n=n+Math.imul(z,ht)\|0,i=i+Math.imul(z,ft)\|0,i=i+Math.imul(D,ht)\|0,a=a+Math.imul(D,ft)\|0,n=n+Math.imul(L,pt)\|0,i=i+Math.imul(L,mt)\|0,i=i+Math.imul(C,pt)\|0,a=a+Math.imul(C,mt)\|0;var It=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(It>>>26)\|0,It&=67108863,n=Math.imul(j,lt),i=Math.imul(j,ut),i=i+Math.imul(N,lt)\|0,a=Math.imul(N,ut),n=n+Math.imul(O,ht)\|0,i=i+Math.imul(O,ft)\|0,i=i+Math.imul(R,ht)\|0,a=a+Math.imul(R,ft)\|0,n=n+Math.imul(z,pt)\|0,i=i+Math.imul(z,mt)\|0,i=i+Math.imul(D,pt)\|0,a=a+Math.imul(D,mt)\|0;var zt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(zt>>>26)\|0,zt&=67108863,n=Math.imul(j,ht),i=Math.imul(j,ft),i=i+Math.imul(N,ht)\|0,a=Math.imul(N,ft),n=n+Math.imul(O,pt)\|0,i=i+Math.imul(O,mt)\|0,i=i+Math.imul(R,pt)\|0,a=a+Math.imul(R,mt)\|0;var Dt=(u+n\|0)+((8191&i)<<13)\|0;u=(a+(i>>>13)\|0)+(Dt>>>26)\|0,Dt&=67108863,n=Math.imul(j,pt),i=Math.imul(j,mt),i=i+Math.imul(N,pt)\|0,a=Math.imul(N,mt);var Pt=(u+n\|0)+((8191&i)<<13)\|0;return u=(a+(i>>>13)\|0)+(Pt>>>26)\|0,Pt&=67108863,l[0]=vt,l[1]=gt,l[2]=yt,l[3]=bt,l[4]=xt,l[5]=_t,l[6]=wt,l[7]=Mt,l[8]=kt,l[9]=At,l[10]=Tt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=It,l[16]=zt,l[17]=Dt,l[18]=Pt,0!==u&&(l[19]=u,r.length++),r};Math.imul\|\|(k=u),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?k(this,t,e):r<63?u(this,t,e):r<1024?c(this,t,e):h(this,t,e)},f.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},f.prototype.revBin=function(t,e,r){if(0===t\|\|t===r-1)return t;for(var n=0,i=0;i<e;i++)n\|=(1&t)<<e-i-1,t>>=1;return n},f.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},f.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2Math.PI/s),u=Math.sin(2Math.PI/s),c=0;c<i;c+=s)for(var h=l,f=u,d=0;d<o;d++){var p=r[c+d],m=n[c+d],v=r[c+d+o],g=n[c+d+o],y=hv-fg;g=hg+fv,v=y,r[c+d]=p+v,n[c+d]=m+g,r[c+d+o]=p-v,n[c+d+o]=m-g,d!==s&&(y=lh-uf,f=lf+uh,h=y)}},f.prototype.guessLen13b=function(t,e){var r=1\|Math.max(e,t),n=1&r,i=0;for(r=r/2\|0;r;r>>>=1)i++;return 1<<i+1+n},f.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},f.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192Math.round(t[2n+1]/e)+Math.round(t[2n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864\|0}return t},f.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0\|t[o],r[2o]=8191&a,a>>>=13,r[2o+1]=8191&a,a>>>=13;for(o=2e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},f.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},f.prototype.mulp=function(t,e,r){var n=2this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,h,n,i);for(var d=0;d<n;d++){var p=s[d]c[d]-l[d]h[d];l[d]=s[d]h[d]+l[d]c[d],s[d]=p}return this.conjugate(s,l,n),this.transform(s,l,f,a,n,i),this.conjugate(f,a,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),h(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0\|this.words[r])t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864\|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=l(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0\|this.words[e])-s<<r;this.words[e]=l\|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){n(\"number\"==typeof t&&t>=0);var i;i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c\|\|u>=i);u--){var h=0\|this.words[u];this.words[u]=c<<26-a\|h>>>a,c=h&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0\|this.words[0])<t?(this.words[0]=t-(0\|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a=t.length+r;this._expand(a);var o,s=0;for(i=0;i<t.length;i++){o=(0\|this.words[i+r])+s;var l=(0\|t.words[i])e;o-=67108863&l,s=(o>>26)-(l/67108864\|0),this.words[i+r]=67108863&o}for(;i<this.length-r;i++)o=(0\|this.words[i+r])+s,s=o>>26,this.words[i+r]=67108863&o;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)o=-(0\|this.words[i])+s,s=o>>26,this.words[i]=67108863&o;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=this.length-t.length,n=this.clone(),i=t,o=0\|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0\|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){s=new a(null),s.length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864(0\|n.words[i.length+h])+(0\|n.words[i.length+h-1]);for(f=Math.min(f/o\|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()\|\|(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s\|\|null,mod:n}},a.prototype.divmod=function(t,e,r){if(n(!t.isZero()),this.isZero())return{div:new a(0),mod:new a(0)};var i,o,s;return 0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length\|\|this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e)},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0\|\|1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(er+(0\|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0\|this.words[r])+67108864e;this.words[r]=i/t\|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),h=e.clone();!e.isZero();){for(var f=0,d=1;0==(e.words[0]&d)&&f<26;++f,d<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()\|\|o.isOdd())&&(i.iadd(c),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var p=0,m=1;0==(r.words[0]&m)&&p<26;++p,m<<=1);if(p>0)for(r.iushrn(p);p-- >0;)(s.isOdd()\|\|l.isOdd())&&(s.iadd(c),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var l=0,u=1;0==(e.words[0]&u)&&l<26;++l,u<<=1);if(l>0)for(e.iushrn(l);l-- >0;)i.isOdd()&&i.iadd(s),i.iushrn(1);for(var c=0,h=1;0==(r.words[0]&h)&&c<26;++c,h<<=1);if(c>0)for(r.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(s),o.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(o)):(r.isub(e),o.isub(i))}var f;return f=0===e.cmpn(1)?i:o,f.cmpn(0)<0&&f.iadd(t),f},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i\|\|0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]\|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0\|this.words[o];s+=a,a=s>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e=t<0;if(0!==this.negative&&!e)return-1;if(0===this.negative&&e)return 1;this.strip();var r;if(this.length>1)r=1;else{e&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0\|this.words[0];r=i===t?0:i<t?-1:1}return 0!==this.negative?0\|-r:r},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0\|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0\|this.words[r],i=0\|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new y(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var A={k256:null,p224:null,p192:null,p25519:null};d.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},d.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),r=this.imulK(r),r=r.iadd(this.tmp),e=r.bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},d.prototype.split=function(t,e){t.iushrn(this.n,0,e)},d.prototype.imulK=function(t){return t.imul(this.k)},i(p,d),p.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0\|t.words[n];t.words[n-10]=(4194303&a)<<4\|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},p.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0\|t.words[r];e+=977n,t.words[r]=67108863&e,e=64n+(e/67108864\|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(m,d),i(v,d),i(g,d),g.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19(0\|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(A[t])return A[t];var e;if(\"k256\"===t)e=new p;else if(\"p224\"===t)e=new m;else if(\"p192\"===t)e=new v;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new g}return A[t]=e,e},y.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},y.prototype._verify2=function(t,e){n(0==(t.negative\|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},y.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},y.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},y.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},y.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},y.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},y.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},y.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},y.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},y.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},y.prototype.isqr=function(t){return this.imul(t,t.clone())},y.prototype.sqr=function(t){return this.mul(t,t)},y.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2cc).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var h=this.pow(c,i),f=this.pow(t,i.addn(1).iushrn(1)),d=this.pow(t,i),p=o;0!==d.cmp(s);){for(var m=d,v=0;0!==m.cmp(s);v++)m=m.redSqr();n(v<p);var g=this.pow(h,new a(1).iushln(p-v-1));f=f.redMul(g),h=g.redSqr(),d=d.redMul(h),p=v}return f},y.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},y.prototype.pow=function(t,e){if(e.isZero())return new a(1);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var h=u>>c&1;i!==r[0]&&(i=this.sqr(i)),0!==h\|\|0!==o?(o<<=1,o\|=h,(4===++s\|\|0===n&&0===c)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},y.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},y.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new b(t)},i(b,y),b.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},b.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},b.prototype.imul=function(t,e){if(t.isZero()\|\|e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},b.prototype.mul=function(t,e){if(t.isZero()\|\|e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},b.prototype.invm=function(t){return 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c=t(\"./convert\"),h=t(\"./transform\"),f=t(\"./clip\"),d=t(\"./wrap\"),p=t(\"./tile\");i.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,solidChildren:!1,tolerance:3,extent:4096,buffer:64,debug:0},i.prototype.splitTile=function(t,e,r,n,i,l,c){for(var h=[t,e,r,n],d=this.options,m=d.debug,v=null;h.length;){n=h.pop(),r=h.pop(),e=h.pop(),t=h.pop();var g=1<<e,y=a(e,r,n),b=this.tiles[y],x=e===d.maxZoom?0:d.tolerance/(gd.extent);if(!b&&(m>1&&console.time(\"creation\"),b=this.tiles[y]=p(t,g,r,n,x,e===d.maxZoom),this.tileCoords.push({z:e,x:r,y:n}),m)){m>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,b.numFeatures,b.numPoints,b.numSimplified),console.timeEnd(\"creation\"));var _=\"z\"+e;this.stats[_]=(this.stats[_]\|\|0)+1,this.total++}if(b.source=t,i){if(e===d.maxZoom\|\|e===i)continue;var w=1<<i-e;if(r!==Math.floor(l/w)\|\|n!==Math.floor(c/w))continue}else 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m=a(\"labels\");a(\"labelFont\")&&(m=!0),i(\"labelEnable\"),n(\"labelSize\"),n(\"labelPad\"),o(\"labelColor\"),i(\"lineEnable\"),i(\"lineMirror\"),n(\"lineWidth\"),o(\"lineColor\"),i(\"lineTickEnable\"),i(\"lineTickMirror\"),n(\"lineTickLength\"),n(\"lineTickWidth\"),o(\"lineTickColor\"),i(\"gridEnable\"),n(\"gridWidth\"),o(\"gridColor\"),i(\"zeroEnable\"),o(\"zeroLineColor\"),n(\"zeroLineWidth\"),i(\"backgroundEnable\"),o(\"backgroundColor\"),this._text?this._text&&(m\|\|s)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=l(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&s&&(this._lines.dispose(),this._lines=null),this._lines\|\|(this._lines=u(this.gl,this.bounds,this.ticks))};var m=[new a,new a,new a],v=[0,0,0],g={model:d,view:d,projection:d};p.isOpaque=function(){return!0},p.isTransparent=function(){return!1},p.drawTransparent=function(t){};var y=[0,0,0],b=[0,0,0],x=[0,0,0];p.draw=function(t){t=t\|\|g;for(var e=this.gl,r=t.model\|\|d,i=t.view\|\|d,a=t.projection\|\|d,s=this.bounds,l=h(r,i,a,s),u=l.cubeEdges,c=l.axis,f=i[12],p=i[13],_=i[14],w=i[15],M=this.pixelRatio(a[3]f+a[7]p+a[11]_+a[15]w)/e.drawingBufferHeight,k=0;k<3;++k)this.lastCubeProps.cubeEdges[k]=u[k],this.lastCubeProps.axis[k]=c[k];for(var A=m,k=0;k<3;++k)o(m[k],k,this.bounds,u,c);for(var e=this.gl,T=v,k=0;k<3;++k)this.backgroundEnable[k]?T[k]=c[k]:T[k]=0;this._background.draw(r,i,a,s,T,this.backgroundColor),this._lines.bind(r,i,a,this);for(var k=0;k<3;++k){var S=[0,0,0];c[k]>0?S[k]=s[1][k]:S[k]=s[0][k];for(var E=0;E<2;++E){var L=(k+1+E)%3,C=(k+1+(1^E))%3;this.gridEnable[L]&&this._lines.drawGrid(L,C,this.bounds,S,this.gridColor[L],this.gridWidth[L]this.pixelRatio)}for(var E=0;E<2;++E){var L=(k+1+E)%3,C=(k+1+(1^E))%3;this.zeroEnable[C]&&s[0][C]<=0&&s[1][C]>=0&&this._lines.drawZero(L,C,this.bounds,S,this.zeroLineColor[C],this.zeroLineWidth[C]this.pixelRatio)}}for(var k=0;k<3;++k){this.lineEnable[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].primalOffset,this.lineColor[k],this.lineWidth[k]this.pixelRatio),this.lineMirror[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].mirrorOffset,this.lineColor[k],this.lineWidth[k]this.pixelRatio);for(var I=n(y,A[k].primalMinor),z=n(b,A[k].mirrorMinor),D=this.lineTickLength,E=0;E<3;++E){var P=M/r[5E];I[E]=D[E]P,z[E]=D[E]P}this.lineTickEnable[k]&&this._lines.drawAxisTicks(k,A[k].primalOffset,I,this.lineTickColor[k],this.lineTickWidth[k]this.pixelRatio),this.lineTickMirror[k]&&this._lines.drawAxisTicks(k,A[k].mirrorOffset,z,this.lineTickColor[k],this.lineTickWidth[k]this.pixelRatio)}this._text.bind(r,i,a,this.pixelRatio);for(var k=0;k<3;++k){for(var O=A[k].primalMinor,R=n(x,A[k].primalOffset),E=0;E<3;++E)this.lineTickEnable[k]&&(R[E]+=MO[E]Math.max(this.lineTickLength[E],0)/r[5E]);if(this.tickEnable[k]){for(var E=0;E<3;++E)R[E]+=MO[E]this.tickPad[E]/r[5E];this._text.drawTicks(k,this.tickSize[k],this.tickAngle[k],R,this.tickColor[k])}if(this.labelEnable[k]){for(var E=0;E<3;++E)R[E]+=MO[E]this.labelPad[E]/r[5E];R[k]+=.5(s[0][k]+s[1][k]),this._text.drawLabel(k,this.labelSize[k],this.labelAngle[k],R,this.labelColor[k])}}},p.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":149,\"./lib/cube.js\":150,\"./lib/lines.js\":151,\"./lib/text.js\":153,\"./lib/ticks.js\":154}],149:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}function i(t){for(var e=[],r=[],i=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,h=[0,0,0],f=[0,0,0],d=-1;d<=1;d+=2){r.push(i,i+2,i+1,i+1,i+2,i+3),h[l]=d,f[l]=d;for(var p=-1;p<=1;p+=2){h[u]=p;for(var m=-1;m<=1;m+=2)h[c]=m,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),i+=1}var v=u;u=c,c=v}var g=a(t,new 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e=0;e<m.length;++e)if(t=l.positive(t,m[e]),t.length<3)return 0;for(var r=t[0],n=r[0]/r[3],i=r[1]/r[3],a=0,e=1;e+1<t.length;++e){var o=t[e],s=t[e+1],u=o[0]/o[3],c=o[1]/o[3],h=s[0]/s[3],f=s[1]/s[3],d=u-n,p=c-i,v=h-n,g=f-i;a+=Math.abs(dg-pv)}return a}function a(t,e,r,a){s(c,e,t),s(c,r,c);for(var l=0,m=0;m<2;++m){d[2]=a[m][2];for(var b=0;b<2;++b){d[1]=a[b][1];for(var x=0;x<2;++x)d[0]=a[x][0],n(h[l],d,c),l+=1}}for(var _=-1,m=0;m<8;++m){for(var w=h[m][3],M=0;M<3;++M)f[m][M]=h[m][M]/w;w<0&&(_<0?_=m:f[m][2]<f[_][2]&&(_=m))}if(_<0){_=0;for(var k=0;k<3;++k){for(var A=(k+2)%3,T=(k+1)%3,S=-1,E=-1,L=0;L<2;++L){var C=L<<k,I=C+(L<<A)+(1-L<<T),z=C+(1-L<<A)+(L<<T);u(f[C],f[I],f[z],p)<0\|\|(L?S=1:E=1)}if(S<0\|\|E<0)E>S&&(_\|=1<<k);else{for(var L=0;L<2;++L){var C=L<<k,I=C+(L<<A)+(1-L<<T),z=C+(1-L<<A)+(L<<T),D=i([h[C],h[I],h[z],h[C+(1<<A)+(1<<T)]]);L?S=D:E=D}E>S&&(_\|=1<<k)}}}for(var P=7^_,O=-1,m=0;m<8;++m)m!==_&&m!==P&&(O<0?O=m:f[O][1]>f[m][1]&&(O=m));for(var R=-1,m=0;m<3;++m){var 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t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function a(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}function o(t,e,r){var n=[],i=[0,0,0],o=[0,0,0],c=[0,0,0],h=[0,0,0];n.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var d=n.length/3\|0,p=0;p<r[f].length;++p){var m=+r[f][p].x;n.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var v=n.length/3\|0;i[f]=d,o[f]=v-d;for(var d=n.length/3\|0,g=0;g<r[f].length;++g){var m=+r[f][g].x;n.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var v=n.length/3\|0;c[f]=d,h[f]=v-d}var y=s(t,new Float32Array(n)),b=l(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),x=u(t);return x.attributes.position.location=0,new a(t,y,b,x,o,i,h,c)}e.exports=o;var s=t(\"gl-buffer\"),l=t(\"gl-vao\"),u=t(\"./shaders\").line,c=[0,0,0],h=[0,0,0],f=[0,0,0],d=[0,0,0],p=[1,1],m=a.prototype;m.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,p[0]=this.gl.drawingBufferWidth,p[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=p,this.vao.bind()},m.drawAxisLine=function(t,e,r,a,o){var s=n(h);this.shader.uniforms.majorAxis=h,s[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=s;var l=i(d,r);l[t]+=e[0][t],this.shader.uniforms.offset=l,this.shader.uniforms.lineWidth=o,this.shader.uniforms.color=a;var u=n(f);u[(t+2)%3]=1,this.shader.uniforms.screenAxis=u,this.vao.draw(this.gl.TRIANGLES,6);var u=n(f);u[(t+1)%3]=1,this.shader.uniforms.screenAxis=u,this.vao.draw(this.gl.TRIANGLES,6)},m.drawAxisTicks=function(t,e,r,i,a){if(this.tickCount[t]){var 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n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":157}],159:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.plot=t,this.shader=e,this.bufferHi=r,this.bufferLo=n,this.bounds=[1/0,1/0,-1/0,-1/0],this.numPoints=0,this.color=[0,0,0,1]}function i(t,e){var r=a(t.gl,l.vertex,l.fragment),i=o(t.gl),s=o(t.gl),u=new n(t,r,i,s);return u.update(e),t.addObject(u),u}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"typedarray-pool\"),l=t(\"./lib/shaders\");e.exports=i;var 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s=t(\"gl-buffer\"),l=t(\"gl-vao\"),u=t(\"./shaders/index\"),c=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],h=n.prototype;h.isOpaque=function(){return this.opacity>=1},h.isTransparent=function(){return this.opacity<1},h.drawTransparent=h.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view\|\|c,i=r.projection=t.projection\|\|c;r.model=t.model\|\|c,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],o=n[13],s=n[14],l=n[15],u=this.pixelRatio(i[3]a+i[7]o+i[11]s+i[15]l)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]),r.capSize=this.capSize[h]u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var f=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=(n+e)%3,o=[0,0,0];o[a]=i,r.push(o)}t[e]=r}return t}();h.update=function(t){t=t\|\|{},\"lineWidth\"in 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m=h.slice();m[u]+=d[1][u],o.push(h[0],h[1],h[2],p[0],p[1],p[2],p[3],0,0,0,m[0],m[1],m[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,m),l+=2+a(o,m,p,u)}}}this.lineCount[u]=l-this.lineOffset[u]}this.buffer.update(o)}},h.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":163,\"gl-buffer\":156,\"gl-vao\":271}],162:[function(t,e,r){e.exports=function(t){\"string\"==typeof t&&(t=[t]);for(var e=[].slice.call(arguments,1),r=[],n=0;n<t.length-1;n++)r.push(t[n],e[n]\|\|\"\");return r.push(t[n]),r.join(\"\")}},{}],163:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = model * vec4(position, 1.0);\\n worldPosition = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n gl_Position = projection * view * worldPosition;\\n fragColor = color;\\n fragPosition = position;\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(fragPosition, clipBounds[0])) \|\| any(greaterThan(fragPosition, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":255,glslify:162}],164:[function(t,e,r){\"use strict\";function n(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function i(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function a(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);y=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;a<r;++a)i[a]=t.NONE;y[n]=i}}function o(t){switch(t){case p:throw new Error(\"gl-fbo: Framebuffer unsupported\");case m:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case v:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case g:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function s(t,e,r,n,i,a){if(!n)return null;var o=d(t,e,r,i,n);return o.magFilter=t.NEAREST,o.minFilter=t.NEAREST,o.mipSamples=1,o.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,a,t.TEXTURE_2D,o.handle,0),o}function l(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function u(t){var e=n(t.gl),r=t.gl,a=t.handle=r.createFramebuffer(),u=t._shape[0],c=t._shape[1],h=t.color.length,f=t._ext,d=t._useStencil,p=t._useDepth,m=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,a);for(var v=0;v<h;++v)t.color[v]=s(r,u,c,m,r.RGBA,r.COLOR_ATTACHMENT0+v);0===h?(t._color_rb=l(r,u,c,r.RGBA4,r.COLOR_ATTACHMENT0),f&&f.drawBuffersWEBGL(y[0])):h>1&&f.drawBuffersWEBGL(y[h]);var g=r.getExtension(\"WEBGL_depth_texture\");g?d?t.depth=s(r,u,c,g.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p&&(t.depth=s(r,u,c,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):p&&d?t._depth_rb=l(r,u,c,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p?t._depth_rb=l(r,u,c,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=l(r,u,c,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(var v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),i(r,e),o(b)}i(r,e)}function c(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0\|e,0\|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var l=0;l<i;++l)this.color[l]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var c=this,h=[0\|e,0\|r];Object.defineProperties(h,{0:{get:function(){return c._shape[0]},set:function(t){return c.width=t}},1:{get:function(){return c._shape[1]},set:function(t){return c.height=t}}}),this._shapeVector=h,u(this)}function h(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e\|\|t._shape[1]!==r){var a=t.gl,s=a.getParameter(a.MAX_RENDERBUFFER_SIZE);if(e<0\|\|e>s\|\|r<0\|\|r>s)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var l=n(a),u=0;u<t.color.length;++u)t.color[u].shape=t._shape;t._color_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._color_rb),a.renderbufferStorage(a.RENDERBUFFER,a.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&a.renderbufferStorage(a.RENDERBUFFER,a.STENCIL_INDEX,t._shape[0],t._shape[1])),a.bindFramebuffer(a.FRAMEBUFFER,t.handle);var c=a.checkFramebufferStatus(a.FRAMEBUFFER);c!==a.FRAMEBUFFER_COMPLETE&&(t.dispose(),i(a,l),o(c)),i(a,l)}}function f(t,e,r,n){p\|\|(p=t.FRAMEBUFFER_UNSUPPORTED,m=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,v=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,g=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var i=t.getExtension(\"WEBGL_draw_buffers\");if(!y&&i&&a(t,i),Array.isArray(e)&&(n=r,r=0\|e[1],e=0\|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var o=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0\|\|e>o\|\|r<0\|\|r>o)throw new Error(\"gl-fbo: Parameters are too large for FBO\");n=n\|\|{};var s=1;if(\"color\"in n){if((s=Math.max(0\|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(s>1){if(!i)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(s>t.getParameter(i.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+s+\" draw buffers\")}}var l=t.UNSIGNED_BYTE,u=t.getExtension(\"OES_texture_float\");if(n.float&&s>0){if(!u)throw new Error(\"gl-fbo: Context does not support floating point textures\");l=t.FLOAT}else n.preferFloat&&s>0&&u&&(l=t.FLOAT);var h=!0;\"depth\"in n&&(h=!!n.depth);var f=!1;return\"stencil\"in n&&(f=!!n.stencil),new c(t,e,r,l,s,h,f,i)}var d=t(\"gl-texture2d\");e.exports=f;var p,m,v,g,y=null,b=c.prototype;Object.defineProperties(b,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)\|\|(t=[0\|t,0\|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0\|t[0],r=0\|t[1];return h(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return t\|=0,h(this,t,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t\|=0,h(this,this._shape[0],t),t},enumerable:!1}}),b.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},b.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":267}],165:[function(t,e,r){function n(t,e,r){\"use strict\";var n=o(e)\|\|\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===a.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var u=i(\"Error compiling %s shader %s:\\n\",l,n),c=i(\"%s%s\",u,t),h=t.split(\"\\n\"),f={},d=0;d<h.length;d++){var p=h[d];if(\"\"!==p){var m=parseInt(p.split(\":\")[2]);if(isNaN(m))throw new Error(i(\"Could not parse error: %s\",p));f[m]=p}}for(var v=s(e).split(\"\\n\"),d=0;d<v.length;d++)if(f[d+3]\|\|f[d+2]\|\|f[d+1]){var g=v[d];if(u+=g+\"\\n\",f[d+1]){var y=f[d+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),u+=i(\"^^^ %s\\n\\n\",y)}}return{long:u.trim(),short:c.trim()}}var i=t(\"sprintf-js\").sprintf,a=t(\"gl-constants/lookup\"),o=t(\"glsl-shader-name\"),s=t(\"add-line-numbers\");e.exports=n},{\"add-line-numbers\":40,\"gl-constants/lookup\":158,\"glsl-shader-name\":279,\"sprintf-js\":527}],166:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}function i(t,e){var r=t.gl,i=l(r,c.vertex,c.fragment),a=l(r,c.pickVertex,c.pickFragment),o=u(r),s=u(r),h=u(r),f=u(r),d=new n(t,i,a,o,s,h,f);return d.update(e),t.addObject(d),d}e.exports=i;var a=t(\"binary-search-bounds\"),o=t(\"iota-array\"),s=t(\"typedarray-pool\"),l=t(\"gl-shader\"),u=t(\"gl-buffer\"),c=t(\"./lib/shaders\"),h=n.prototype,f=[0,0,1,0,0,1,1,0,1,1,0,1];h.draw=function(){var t=[1,0,0,0,1,0,0,0,1];return function(){var e=this.plot,r=this.shader,n=this.bounds,i=this.numVertices;if(!(i<=0)){var a=e.gl,o=e.dataBox,s=n[2]-n[0],l=n[3]-n[1],u=o[2]-o[0],c=o[3]-o[1];t[0]=2s/u,t[4]=2l/c,t[6]=2(n[0]-o[0])/u-1,t[7]=2(n[1]-o[1])/c-1,r.bind();var h=r.uniforms;h.viewTransform=t,h.shape=this.shape;var f=r.attributes;this.positionBuffer.bind(),f.position.pointer(),this.weightBuffer.bind(),f.weight.pointer(a.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(a.UNSIGNED_BYTE,!0),a.drawArrays(a.TRIANGLES,0,i)}}}(),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2u/h,t[4]=2c/f,t[6]=2(a[0]-l[0])/h-1,t[7]=2(a[1]-l[1])/f-1;for(var d=0;d<4;++d)e[d]=r>>8d&255;this.pickOffset=r,i.bind();var p=i.uniforms;p.viewTransform=t,p.pickOffset=e,p.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]this.shape[1];if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]\|0]]}},h.update=function(t){t=t\|\|{};var e=t.shape\|\|[0,0],r=t.x\|\|o(e[0]),n=t.y\|\|o(e[1]),i=t.z\|\|new Float32Array(e[0]e[1]);this.xData=r,this.yData=n;var l=t.colorLevels\|\|[0],u=t.colorValues\|\|[0,0,0,1],c=l.length,h=this.bounds,d=h[0]=r[0],p=h[1]=n[0],m=h[2]=r[r.length-1],v=h[3]=n[n.length-1],g=1/(m-d),y=1/(v-p),b=e[0],x=e[1];this.shape=[b,x];var _=(b-1)(x-1)(f.length>>>1);this.numVertices=_;for(var w=s.mallocUint8(4_),M=s.mallocFloat32(2_),k=s.mallocUint8(2_),A=s.mallocUint32(_),T=0,S=0;S<x-1;++S)for(var E=y(n[S]-p),L=y(n[S+1]-p),C=0;C<b-1;++C)for(var I=g(r[C]-d),z=g(r[C+1]-d),D=0;D<f.length;D+=2){var P,O,R,F,j=f[D],N=f[D+1],B=(S+N)b+(C+j),U=i[B],V=a.le(l,U);if(V<0)P=u[0],O=u[1],R=u[2],F=u[3];else if(V===c-1)P=u[4c-4],O=u[4c-3],R=u[4c-2],F=u[4c-1];else{var H=(U-l[V])/(l[V+1]-l[V]),q=1-H,G=4V,Y=4(V+1);P=qu[G]+Hu[Y],O=qu[G+1]+Hu[Y+1],R=qu[G+2]+Hu[Y+2],F=qu[G+3]+Hu[Y+3]}w[4T]=255P,w[4T+1]=255O,w[4T+2]=255R,w[4T+3]=255F,M[2T]=.5I+.5z,M[2T+1]=.5E+.5L,k[2T]=j,k[2T+1]=N,A[T]=Sb+C,T+=1}this.positionBuffer.update(M),this.weightBuffer.update(k),this.colorBuffer.update(w),this.idBuffer.update(A),s.free(M),s.free(w),s.free(k),s.free(A)},h.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":167,\"binary-search-bounds\":168,\"gl-buffer\":156,\"gl-shader\":255,\"iota-array\":293,\"typedarray-pool\":541}],167:[function(t,e,r){\"use strict\";e.exports={fragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = vec4(fragColor.rgb fragColor.a, fragColor.a);\\n}\\n\",vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n fragColor = color;\\n gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\",pickFragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n vec2 d = step(.5, vWeight);\\n vec4 id = fragId + pickOffset;\\n id.x += d.x + d.yshape.x;\\n\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n gl_FragColor = id/255.;\\n}\\n\",pickVertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n vWeight = weight;\\n\\n fragId = pickId;\\n\\n vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"}},{}],168:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],169:[function(t,e,r){r.lineVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi, dLo;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, screenShape;\\nuniform float width;\\n\\nvarying vec2 direction;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n return (posHi + trHi) * scHi\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo;\\n}\\n\\n\\nvec2 project_2_1(vec2 scHi, vec2 scLo, vec2 posHi, vec2 posLo) {\\n return scHi * posHi\\n + scLo * posHi\\n + scHi * posLo\\n + scLo * posLo;\\n}\\n\\nvoid main() {\\n vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n vec2 dir = project_2_1(scaleHi, scaleLo, dHi, dLo);\\n vec2 n = 0.5 * width * normalize(screenShape.yx * vec2(dir.y, -dir.x)) / screenShape.xy;\\n vec2 tangent = normalize(screenShape.xy * dir);\\n if(dir.x < 0.0 \|\| (dir.x == 0.0 && dir.y < 0.0)) {\\n direction = -tangent;\\n } else {\\n direction = tangent;\\n }\\n gl_Position = vec4(p + n, 0.0, 1.0);\\n}\",r.lineFragment=\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nuniform vec2 screenShape;\\nuniform sampler2D dashPattern;\\nuniform float dashLength;\\n\\nvarying vec2 direction;\\n\\nvoid main() {\\n float t = fract(dot(direction, gl_FragCoord.xy) / dashLength);\\n vec4 pcolor = color * texture2D(dashPattern, vec2(t, 0.0)).r;\\n gl_FragColor = vec4(pcolor.rgb * pcolor.a, pcolor.a);\\n}\",r.mitreVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo;\\nuniform float radius;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n return (posHi + trHi) * scHi\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n gl_Position = vec4(p, 0.0, 1.0);\\n gl_PointSize = radius;\\n}\",r.mitreFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n if(length(gl_PointCoord.xy - 0.5) > 0.25) {\\n discard;\\n }\\n gl_FragColor = vec4(color.rgb, color.a);\\n}\",r.pickVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi;\\nattribute vec4 pick0, pick1;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, screenShape;\\nuniform float width;\\n\\nvarying vec4 pickA, pickB;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n return (posHi + trHi) * scHi\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n vec2 n = width * normalize(screenShape.yx * vec2(dHi.y, -dHi.x)) / screenShape.xy;\\n gl_Position = vec4(p + n, 0, 1);\\n pickA = pick0;\\n pickB = pick1;\\n}\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 pickOffset;\\n\\nvarying vec4 pickA, pickB;\\n\\nvoid main() {\\n vec4 fragId = vec4(pickA.xyz, 0.0);\\n if(pickB.w > pickA.w) {\\n fragId.xyz = pickB.xyz;\\n }\\n\\n fragId += pickOffset;\\n\\n fragId.y += floor(fragId.x / 256.0);\\n fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n fragId.z += floor(fragId.y / 256.0);\\n fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n fragId.w += floor(fragId.z / 256.0);\\n fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n gl_FragColor = fragId / 255.0;\\n}\",r.fillVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aHi, aLo, dHi;\\n\\nuniform vec2 scaleHi, translateHi, scaleLo, translateLo, projectAxis;\\nuniform float projectValue, depth;\\n\\n\\nvec2 project_1_0(vec2 scHi, vec2 trHi, vec2 scLo, vec2 trLo, vec2 posHi, vec2 posLo) {\\n return (posHi + trHi) * scHi\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo;\\n}\\n\\n\\nvoid main() {\\n vec2 p = project_1_0(scaleHi, translateHi, scaleLo, translateLo, aHi, aLo);\\n if(dHi.y < 0.0 \|\| (dHi.y == 0.0 && dHi.x < 0.0)) {\\n if(dot(p, projectAxis) < projectValue) {\\n p = p * (1.0 - abs(projectAxis)) + projectAxis * projectValue;\\n }\\n }\\n gl_Position = vec4(p, depth, 1);\\n}\",r.fillFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = vec4(color.rgb * color.a, color.a);\\n}\"},{}],170:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l){this.plot=t,this.dashPattern=e,this.lineBufferHi=r,this.lineBufferLo=n,this.pickBuffer=i,this.lineShader=a,this.mitreShader=o,this.fillShader=s,this.pickShader=l,this.usingDashes=!1,this.bounds=[1/0,1/0,-1/0,-1/0],this.width=1,this.color=[0,0,1,1],this.fill=[!1,!1,!1,!1],this.fillColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.data=null,this.numPoints=0,this.vertCount=0,this.pickOffset=0}function i(t){return t.map(function(t){return t.slice()})}function a(t,e){var r=t.gl,i=s(r),a=s(r),u=s(r),c=l(r,[1,1]),f=o(r,h.lineVertex,h.lineFragment),d=o(r,h.mitreVertex,h.mitreFragment),p=o(r,h.fillVertex,h.fillFragment),m=o(r,h.pickVertex,h.pickFragment),v=new n(t,c,i,a,u,f,d,p,m);return t.addObject(v),v.update(e),v}e.exports=a;var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"gl-texture2d\"),u=t(\"ndarray\"),c=t(\"typedarray-pool\"),h=t(\"./lib/shaders\"),f=n.prototype;f.setProjectionModel=function(){var t={scaleHi:new Float32Array([0,0]),scaleLo:new Float32Array([0,0]),translateHi:new Float32Array([0,0]),translateLo:new Float32Array([0,0]),screenShape:[0,0]};return function(){var e=this.bounds,r=this.plot.viewBox,n=this.plot.dataBox,i=e[2]-e[0],a=e[3]-e[1],o=n[2]-n[0],s=n[3]-n[1],l=r[2]-r[0],u=r[3]-r[1],c=2i/o,h=2a/s,f=(e[0]-n[0]-.5o)/i,d=(e[1]-n[1]-.5s)/a;return t.scaleHi[0]=c,t.scaleHi[1]=h,t.scaleLo[0]=c-t.scaleHi[0],t.scaleLo[1]=h-t.scaleHi[1],t.translateHi[0]=f,t.translateHi[1]=d,t.translateLo[0]=f-t.translateHi[0],t.translateLo[1]=d-t.translateHi[1],t.screenShape[0]=l,t.screenShape[1]=u,t}}(),f.setProjectionUniforms=function(t,e){t.scaleHi=e.scaleHi,t.scaleLo=e.scaleLo,t.translateHi=e.translateHi,t.translateLo=e.translateLo,t.screenShape=e.screenShape},f.draw=function(){var t=[1,0],e=[-1,0],r=[0,1],n=[0,-1];return function(){var i=this.vertCount;if(i){var a=this.setProjectionModel(),o=this.plot,s=this.width,l=o.gl,u=o.pixelRatio,c=this.color,h=this.fillShader.attributes;this.lineBufferLo.bind(),h.aLo.pointer(l.FLOAT,!1,16,0),this.lineBufferHi.bind();var f=this.fill;if(f[0]\|\|f[1]\|\|f[2]\|\|f[3]){var d=this.fillShader;d.bind();var p=d.uniforms;this.setProjectionUniforms(p,a),p.depth=o.nextDepthValue(),h.aHi.pointer(l.FLOAT,!1,16,0),h.dHi.pointer(l.FLOAT,!1,16,8),l.depthMask(!0),l.enable(l.DEPTH_TEST);var m=this.fillColor;f[0]&&(p.color=m[0],p.projectAxis=e,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[1]&&(p.color=m[1],p.projectAxis=n,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[2]&&(p.color=m[2],p.projectAxis=t,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),f[3]&&(p.color=m[3],p.projectAxis=r,p.projectValue=1,l.drawArrays(l.TRIANGLES,0,i)),l.depthMask(!1),l.disable(l.DEPTH_TEST)}var v=this.lineShader;v.bind(),this.lineBufferLo.bind(),v.attributes.aLo.pointer(l.FLOAT,!1,16,0),v.attributes.dLo.pointer(l.FLOAT,!1,16,8),this.lineBufferHi.bind();var g=v.uniforms;this.setProjectionUniforms(g,a),g.color=c,g.width=su,g.dashPattern=this.dashPattern.bind(),g.dashLength=this.dashLengthu;var y=v.attributes;if(y.aHi.pointer(l.FLOAT,!1,16,0),y.dHi.pointer(l.FLOAT,!1,16,8),l.drawArrays(l.TRIANGLES,0,i),s>2&&!this.usingDashes){var b=this.mitreShader;this.lineBufferLo.bind(),b.attributes.aLo.pointer(l.FLOAT,!1,48,0),this.lineBufferHi.bind(),b.bind();var x=b.uniforms;this.setProjectionUniforms(x,a),x.color=c,x.radius=su,b.attributes.aHi.pointer(l.FLOAT,!1,48,0),l.drawArrays(l.POINTS,0,i/3\|0)}}}}(),f.drawPick=function(){var t=[0,0,0,0];return function(e){var r=this.vertCount,n=this.numPoints;if(this.pickOffset=e,!r)return e+n;var i=this.setProjectionModel(),a=this.plot,o=this.width,s=a.gl,l=a.pickPixelRatio,u=this.pickShader,c=this.pickBuffer;t[0]=255&e,t[1]=e>>>8&255,t[2]=e>>>16&255,t[3]=e>>>24,u.bind();var h=u.uniforms;this.setProjectionUniforms(h,i),h.width=ol,h.pickOffset=t;var f=u.attributes;return this.lineBufferHi.bind(),f.aHi.pointer(s.FLOAT,!1,16,0),f.dHi.pointer(s.FLOAT,!1,16,8),this.lineBufferLo.bind(),f.aLo.pointer(s.FLOAT,!1,16,0),c.bind(),f.pick0.pointer(s.UNSIGNED_BYTE,!1,8,0),f.pick1.pointer(s.UNSIGNED_BYTE,!1,8,4),s.drawArrays(s.TRIANGLES,0,r),e+n}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.data;return{object:this,pointId:a,dataCoord:[o[2a],o[2a+1]]}},f.update=function(t){t=t\|\|{};var e,r,n,a,o,s=this.plot.gl;this.color=(t.color\|\|[0,0,1,1]).slice(),this.width=+(t.width\|\|1),this.fill=(t.fill\|\|[!1,!1,!1,!1]).slice(),this.fillColor=i(t.fillColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var h=t.dashes\|\|[1],f=0;for(e=0;e<h.length;++e)f+=h[e];var d=c.mallocUint8(f);n=0;var p=255;for(e=0;e<h.length;++e){for(r=0;r<h[e];++r)d[n++]=p;p^=255}this.dashPattern.dispose(),this.usingDashes=h.length>1,this.dashPattern=l(s,u(d,[f,1,4],[1,0,0])),this.dashPattern.minFilter=s.NEAREST,this.dashPattern.magFilter=s.NEAREST,this.dashLength=f,c.free(d);var m=t.positions;this.data=m;var v=this.bounds;v[0]=v[1]=1/0,v[2]=v[3]=-1/0;var g=this.numPoints=m.length>>>1;if(0!==g){for(e=0;e<g;++e)a=m[2e],o=m[2e+1],isNaN(a)\|\|isNaN(o)\|\|(v[0]=Math.min(v[0],a),v[1]=Math.min(v[1],o),v[2]=Math.max(v[2],a),v[3]=Math.max(v[3],o));v[0]===v[2]&&(v[2]+=1),v[3]===v[1]&&(v[3]+=1);var y=c.mallocFloat64(24(g-1)),b=c.mallocFloat32(24(g-1)),x=c.mallocFloat32(24(g-1)),_=c.mallocUint32(12(g-1)),w=b.length,M=_.length;n=g;for(var k=0;n>1;){var A=--n;a=m[2n],o=m[2n+1];var T=A-1,S=m[2T],E=m[2T+1];if(!(isNaN(a)\|\|isNaN(o)\|\|isNaN(S)\|\|isNaN(E))){k+=1,a=(a-v[0])/(v[2]-v[0]),o=(o-v[1])/(v[3]-v[1]),S=(S-v[0])/(v[2]-v[0]),E=(E-v[1])/(v[3]-v[1]);var L=S-a,C=E-o,I=A\|1<<24,z=A-1,D=A,P=A-1\|1<<24;y[--w]=-C,y[--w]=-L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z,y[--w]=C,y[--w]=L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=-C,y[--w]=-L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=C,y[--w]=L,y[--w]=E,y[--w]=S,_[--M]=D,_[--M]=P,y[--w]=-C,y[--w]=-L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z,y[--w]=C,y[--w]=L,y[--w]=o,y[--w]=a,_[--M]=I,_[--M]=z}}for(e=0;e<y.length;e++)b[e]=y[e],x[e]=y[e]-b[e];this.vertCount=6k,this.lineBufferHi.update(b.subarray(w)),this.lineBufferLo.update(x.subarray(w)),this.pickBuffer.update(_.subarray(M)),c.free(y),c.free(b),c.free(x),c.free(_)}},f.dispose=function(){this.plot.removeObject(this),this.lineBufferLo.dispose(),this.lineBufferHi.dispose(),this.pickBuffer.dispose(),this.lineShader.dispose(),this.mitreShader.dispose(),this.fillShader.dispose(),this.pickShader.dispose(),this.dashPattern.dispose()}},{\"./lib/shaders\":169,\"gl-buffer\":156,\"gl-shader\":255,\"gl-texture2d\":267,ndarray:467,\"typedarray-pool\":541}],171:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvoid main() {\\n vec4 projected = projection view * model * vec4(position, 1.0);\\n vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\\n vec2 tangent = normalize(screenShape * tangentClip.xy);\\n vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\\n\\n gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\\n\\n worldPosition = position;\\n pixelArcLength = arcLength;\\n fragColor = color;\\n}\\n\",a=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return n(t,i,\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(worldPosition, clipBounds[0])) \|\| any(greaterThan(worldPosition, clipBounds[1]))) {\\n discard;\\n }\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\",null,a)},r.createPickShader=function(t){return n(t,i,\"precision mediump float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\nlowp vec4 encode_float_1_0(highp float v) {\\n highp float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n highp vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n highp float e = floor(log2(av));\\n highp float m = av * pow(2.0, -e) - 1.0;\\n \\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n \\n //Unpack exponent\\n highp float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0; \\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\n\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(worldPosition, clipBounds[0])) \|\| any(greaterThan(worldPosition, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = vec4(pickId/255.0, encode_float_1_0(pixelArcLength).xyz);\\n}\",null,a)}},{\"gl-shader\":255}],172:[function(t,e,r){\"use strict\";function n(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=ii}return Math.sqrt(r)}function i(t){\n", "for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function a(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function o(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}function s(t){var e=t.gl\|\|t.scene&&t.scene.gl,r=m(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var n=v(e);n.attributes.position.location=0,n.attributes.nextPosition.location=1,n.attributes.arcLength.location=2,n.attributes.lineWidth.location=3,n.attributes.color.location=4;for(var i=l(e),a=u(e,[{buffer:i,size:3,offset:0,stride:48},{buffer:i,size:3,offset:12,stride:48},{buffer:i,size:1,offset:24,stride:48},{buffer:i,size:1,offset:28,stride:48},{buffer:i,size:4,offset:32,stride:48}]),s=d(new Array(1024),[256,1,4]),h=0;h<1024;++h)s.data[h]=255;var f=c(e,s);f.wrap=e.REPEAT;var p=new o(e,r,n,i,a,f);return p.update(t),p}e.exports=s;var l=t(\"gl-buffer\"),u=t(\"gl-vao\"),c=t(\"gl-texture2d\"),h=t(\"glsl-read-float\"),f=t(\"binary-search-bounds\"),d=t(\"ndarray\"),p=t(\"./lib/shaders\"),m=p.createShader,v=p.createPickShader,g=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],y=o.prototype;y.isTransparent=function(){return this.opacity<1},y.isOpaque=function(){return this.opacity>=1},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model\|\|g,view:t.view\|\|g,projection:t.projection\|\|g,clipBounds:i(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model\|\|g,view:t.view\|\|g,projection:t.projection\|\|g,pickId:this.pickId,clipBounds:i(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.update=function(t){var e,r;this.dirty=!0;var i=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),\"opacity\"in t&&(this.opacity=+t.opacity);var a=t.position\|\|t.positions;if(a){var o=t.color\|\|t.colors\|\|[0,0,0,1],s=t.lineWidth\|\|1,l=[],u=[],c=[],h=0,p=0,m=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],v=!1;t:for(e=1;e<a.length;++e){var g=a[e-1],y=a[e];for(u.push(h),c.push(g.slice()),r=0;r<3;++r){if(isNaN(g[r])\|\|isNaN(y[r])\|\|!isFinite(g[r])\|\|!isFinite(y[r])){if(!i&&l.length>0){for(var b=0;b<24;++b)l.push(l[l.length-12]);p+=2,v=!0}continue t}m[0][r]=Math.min(m[0][r],g[r],y[r]),m[1][r]=Math.max(m[1][r],g[r],y[r])}var x,_;Array.isArray(o[0])?(x=o[e-1],_=o[e]):x=_=o,3===x.length&&(x=[x[0],x[1],x[2],1]),3===_.length&&(_=[_[0],_[1],_[2],1]);var w;w=Array.isArray(s)?s[e-1]:s;var M=h;if(h+=n(g,y),v){for(r=0;r<2;++r)l.push(g[0],g[1],g[2],y[0],y[1],y[2],M,w,x[0],x[1],x[2],x[3]);p+=2,v=!1}l.push(g[0],g[1],g[2],y[0],y[1],y[2],M,w,x[0],x[1],x[2],x[3],g[0],g[1],g[2],y[0],y[1],y[2],M,-w,x[0],x[1],x[2],x[3],y[0],y[1],y[2],g[0],g[1],g[2],h,-w,_[0],_[1],_[2],_[3],y[0],y[1],y[2],g[0],g[1],g[2],h,w,_[0],_[1],_[2],_[3]),p+=4}if(this.buffer.update(l),u.push(h),c.push(a[a.length-1].slice()),this.bounds=m,this.vertexCount=p,this.points=c,this.arcLength=u,\"dashes\"in t){var k=t.dashes,A=k.slice();for(A.unshift(0),e=1;e<A.length;++e)A[e]=A[e-1]+A[e];var T=d(new 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t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t[3]=e[3]-r[3],t[4]=e[4]-r[4],t[5]=e[5]-r[5],t[6]=e[6]-r[6],t[7]=e[7]-r[7],t[8]=e[8]-r[8],t},i.sub=i.subtract,i.multiplyScalar=function(t,e,r){return t[0]=e[0]r,t[1]=e[1]r,t[2]=e[2]r,t[3]=e[3]r,t[4]=e[4]r,t[5]=e[5]r,t[6]=e[6]r,t[7]=e[7]r,t[8]=e[8]r,t},i.multiplyScalarAndAdd=function(t,e,r,n){return t[0]=e[0]+r[0]n,t[1]=e[1]+r[1]n,t[2]=e[2]+r[2]n,t[3]=e[3]+r[3]n,t[4]=e[4]+r[4]n,t[5]=e[5]+r[5]n,t[6]=e[6]+r[6]n,t[7]=e[7]+r[7]n,t[8]=e[8]+r[8]n,t},i.exactEquals=function(t,e){return t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]&&t[4]===e[4]&&t[5]===e[5]&&t[6]===e[6]&&t[7]===e[7]&&t[8]===e[8]},i.equals=function(t,e){var r=t[0],i=t[1],a=t[2],o=t[3],s=t[4],l=t[5],u=t[6],c=t[7],h=t[8],f=e[0],d=e[1],p=e[2],m=e[3],v=e[4],g=e[5],y=t[6],b=e[7],x=e[8];return Math.abs(r-f)<=n.EPSILONMath.max(1,Math.abs(r),Math.abs(f))&&Math.abs(i-d)<=n.EPSILONMath.max(1,Math.abs(i),Math.abs(d))&&Math.abs(a-p)<=n.EPSILONMath.max(1,Math.abs(a),Math.abs(p))&&Math.abs(o-m)<=n.EPSILONMath.max(1,Math.abs(o),Math.abs(m))&&Math.abs(s-v)<=n.EPSILONMath.max(1,Math.abs(s),Math.abs(v))&&Math.abs(l-g)<=n.EPSILONMath.max(1,Math.abs(l),Math.abs(g))&&Math.abs(u-y)<=n.EPSILONMath.max(1,Math.abs(u),Math.abs(y))&&Math.abs(c-b)<=n.EPSILONMath.max(1,Math.abs(c),Math.abs(b))&&Math.abs(h-x)<=n.EPSILONMath.max(1,Math.abs(h),Math.abs(x))},e.exports=i},{\"./common.js\":194}],198:[function(t,e,r){var n=t(\"./common.js\"),i={scalar:{},SIMD:{}};i.create=function(){var t=new n.ARRAY_TYPE(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.clone=function(t){var e=new n.ARRAY_TYPE(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},i.copy=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.fromValues=function(t,e,r,i,a,o,s,l,u,c,h,f,d,p,m,v){var g=new n.ARRAY_TYPE(16);return g[0]=t,g[1]=e,g[2]=r,g[3]=i,g[4]=a,g[5]=o,g[6]=s,g[7]=l,g[8]=u,g[9]=c,g[10]=h,g[11]=f,g[12]=d,g[13]=p,g[14]=m,g[15]=v,g},i.set=function(t,e,r,n,i,a,o,s,l,u,c,h,f,d,p,m,v){return t[0]=e,t[1]=r,t[2]=n,t[3]=i,t[4]=a,t[5]=o,t[6]=s,t[7]=l,t[8]=u,t[9]=c,t[10]=h,t[11]=f,t[12]=d,t[13]=p,t[14]=m,t[15]=v,t},i.identity=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.scalar.transpose=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t},i.SIMD.transpose=function(t,e){var r,n,i,a,o,s,l,u,c,h;return r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12),o=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,a,0,1,4,5),l=SIMD.Float32x4.shuffle(o,s,0,2,4,6),u=SIMD.Float32x4.shuffle(o,s,1,3,5,7),SIMD.Float32x4.store(t,0,l),SIMD.Float32x4.store(t,4,u),o=SIMD.Float32x4.shuffle(r,n,2,3,6,7),s=SIMD.Float32x4.shuffle(i,a,2,3,6,7),c=SIMD.Float32x4.shuffle(o,s,0,2,4,6),h=SIMD.Float32x4.shuffle(o,s,1,3,5,7),SIMD.Float32x4.store(t,8,c),SIMD.Float32x4.store(t,12,h),t},i.transpose=n.USE_SIMD?i.SIMD.transpose:i.scalar.transpose,i.scalar.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15],y=rs-no,b=rl-io,x=ru-ao,_=nl-is,w=nu-as,M=iu-al,k=cm-hp,A=cv-fp,T=cg-dp,S=hv-fm,E=hg-dm,L=fg-dv,C=yL-bE+xS+_T-wA+Mk;return C?(C=1/C,t[0]=(sL-lE+uS)C,t[1]=(iE-nL-aS)C,t[2]=(mM-vw+g_)C,t[3]=(fw-hM-d_)C,t[4]=(lT-oL-uA)C,t[5]=(rL-iT+aA)C,t[6]=(vx-pM-gb)C,t[7]=(cM-fx+db)C,t[8]=(oE-sT+uk)C,t[9]=(nT-rE-ak)C,t[10]=(pw-mx+gy)C,t[11]=(hx-cw-dy)C,t[12]=(sA-oS-lk)C,t[13]=(rS-nA+ik)C,t[14]=(mb-p_-vy)C,t[15]=(c_-hb+fy)C,t):null},i.SIMD.invert=function(t,e){var r,n,i,a,o,s,l,u,c,h,f=SIMD.Float32x4.load(e,0),d=SIMD.Float32x4.load(e,4),p=SIMD.Float32x4.load(e,8),m=SIMD.Float32x4.load(e,12);return o=SIMD.Float32x4.shuffle(f,d,0,1,4,5),n=SIMD.Float32x4.shuffle(p,m,0,1,4,5),r=SIMD.Float32x4.shuffle(o,n,0,2,4,6),n=SIMD.Float32x4.shuffle(n,o,1,3,5,7),o=SIMD.Float32x4.shuffle(f,d,2,3,6,7),a=SIMD.Float32x4.shuffle(p,m,2,3,6,7),i=SIMD.Float32x4.shuffle(o,a,0,2,4,6),a=SIMD.Float32x4.shuffle(a,o,1,3,5,7),o=SIMD.Float32x4.mul(i,a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),s=SIMD.Float32x4.mul(n,o),l=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(SIMD.Float32x4.mul(n,o),s),l=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),l),l=SIMD.Float32x4.swizzle(l,2,3,0,1),o=SIMD.Float32x4.mul(n,i),o=SIMD.Float32x4.swizzle(o,1,0,3,2),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),s),c=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(a,o)),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),o=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(n,2,3,0,1),a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),i=SIMD.Float32x4.swizzle(i,2,3,0,1),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,o),s),u=SIMD.Float32x4.mul(r,o),o=SIMD.Float32x4.swizzle(o,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(i,o)),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,o),u),u=SIMD.Float32x4.swizzle(u,2,3,0,1),o=SIMD.Float32x4.mul(r,n),o=SIMD.Float32x4.swizzle(o,1,0,3,2),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),u),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(i,o),c),o=SIMD.Float32x4.swizzle(o,2,3,0,1),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,o),u),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(i,o)),o=SIMD.Float32x4.mul(r,a),o=SIMD.Float32x4.swizzle(o,1,0,3,2),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(i,o)),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,o),u),o=SIMD.Float32x4.swizzle(o,2,3,0,1),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,o),l),u=SIMD.Float32x4.sub(u,SIMD.Float32x4.mul(n,o)),o=SIMD.Float32x4.mul(r,i),o=SIMD.Float32x4.swizzle(o,1,0,3,2),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(a,o),l),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(n,o)),o=SIMD.Float32x4.swizzle(o,2,3,0,1),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(a,o)),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,o),c),h=SIMD.Float32x4.mul(r,s),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,2,3,0,1),h),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,1,0,3,2),h),o=SIMD.Float32x4.reciprocalApproximation(h),h=SIMD.Float32x4.sub(SIMD.Float32x4.add(o,o),SIMD.Float32x4.mul(h,SIMD.Float32x4.mul(o,o))),(h=SIMD.Float32x4.swizzle(h,0,0,0,0))?(SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(h,s)),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(h,l)),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(h,u)),SIMD.Float32x4.store(t,12,SIMD.Float32x4.mul(h,c)),t):null},i.invert=n.USE_SIMD?i.SIMD.invert:i.scalar.invert,i.scalar.adjoint=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],d=e[11],p=e[12],m=e[13],v=e[14],g=e[15];return t[0]=s(fg-dv)-h(lg-uv)+m(ld-uf),t[1]=-(n(fg-dv)-h(ig-av)+m(id-af)),t[2]=n(lg-uv)-s(ig-av)+m(iu-al),t[3]=-(n(ld-uf)-s(id-af)+h(iu-al)),t[4]=-(o(fg-dv)-c(lg-uv)+p(ld-uf)),t[5]=r(fg-dv)-c(ig-av)+p(id-af),t[6]=-(r(lg-uv)-o(ig-av)+p(iu-al)),t[7]=r(ld-uf)-o(id-af)+c(iu-al),t[8]=o(hg-dm)-c(sg-um)+p(sd-uh),t[9]=-(r(hg-dm)-c(ng-am)+p(nd-ah)),t[10]=r(sg-um)-o(ng-am)+p(nu-as),t[11]=-(r(sd-uh)-o(nd-ah)+c(nu-as)),t[12]=-(o(hv-fm)-c(sv-lm)+p(sf-lh)),t[13]=r(hv-fm)-c(nv-im)+p(nf-ih),t[14]=-(r(sv-lm)-o(nv-im)+p(nl-is)),t[15]=r(sf-lh)-o(nf-ih)+c(nl-is),t},i.SIMD.adjoint=function(t,e){var r,n,i,a,o,s,l,u,c,h,f,d,p,r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12)\n", ";return c=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,a,0,1,4,5),o=SIMD.Float32x4.shuffle(c,s,0,2,4,6),s=SIMD.Float32x4.shuffle(s,c,1,3,5,7),c=SIMD.Float32x4.shuffle(r,n,2,3,6,7),u=SIMD.Float32x4.shuffle(i,a,2,3,6,7),l=SIMD.Float32x4.shuffle(c,u,0,2,4,6),u=SIMD.Float32x4.shuffle(u,c,1,3,5,7),c=SIMD.Float32x4.mul(l,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.mul(s,c),f=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(SIMD.Float32x4.mul(s,c),h),f=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),f),f=SIMD.Float32x4.swizzle(f,2,3,0,1),c=SIMD.Float32x4.mul(s,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),h),p=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(u,c)),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),p),p=SIMD.Float32x4.swizzle(p,2,3,0,1),c=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,3,0,1),u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),l=SIMD.Float32x4.swizzle(l,2,3,0,1),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),h),d=SIMD.Float32x4.mul(o,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(l,c)),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,c),d),d=SIMD.Float32x4.swizzle(d,2,3,0,1),c=SIMD.Float32x4.mul(o,s),c=SIMD.Float32x4.swizzle(c,1,0,3,2),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),d),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(l,c),p),c=SIMD.Float32x4.swizzle(c,2,3,0,1),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(u,c),d),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(l,c)),c=SIMD.Float32x4.mul(o,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(l,c)),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),d),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),f),d=SIMD.Float32x4.sub(d,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.mul(o,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),f),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(u,c)),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),p),SIMD.Float32x4.store(t,0,h),SIMD.Float32x4.store(t,4,f),SIMD.Float32x4.store(t,8,d),SIMD.Float32x4.store(t,12,p),t},i.adjoint=n.USE_SIMD?i.SIMD.adjoint:i.scalar.adjoint,i.determinant=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],h=t[10],f=t[11],d=t[12],p=t[13],m=t[14],v=t[15];return(eo-ra)(hv-fm)-(es-na)(cv-fp)+(el-ia)(cm-hp)+(rs-no)(uv-fd)-(rl-io)(um-hd)+(nl-is)(up-cd)},i.SIMD.multiply=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4.load(r,0),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,3,3,3,3),o))));SIMD.Float32x4.store(t,0,l);var u=SIMD.Float32x4.load(r,4),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,3,3,3,3),o))));SIMD.Float32x4.store(t,4,c);var h=SIMD.Float32x4.load(r,8),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,3,3,3,3),o))));SIMD.Float32x4.store(t,8,f);var d=SIMD.Float32x4.load(r,12),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,2,2,2,2),a),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(d,3,3,3,3),o))));return SIMD.Float32x4.store(t,12,p),t},i.scalar.multiply=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],m=e[12],v=e[13],g=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=bn+xs+_h+wm,t[1]=bi+xl+_f+wv,t[2]=ba+xu+_d+wg,t[3]=bo+xc+_p+wy,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=bn+xs+_h+wm,t[5]=bi+xl+_f+wv,t[6]=ba+xu+_d+wg,t[7]=bo+xc+_p+wy,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=bn+xs+_h+wm,t[9]=bi+xl+_f+wv,t[10]=ba+xu+_d+wg,t[11]=bo+xc+_p+wy,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=bn+xs+_h+wm,t[13]=bi+xl+_f+wv,t[14]=ba+xu+_d+wg,t[15]=bo+xc+_p+wy,t},i.multiply=n.USE_SIMD?i.SIMD.multiply:i.scalar.multiply,i.mul=i.multiply,i.scalar.translate=function(t,e,r){var n,i,a,o,s,l,u,c,h,f,d,p,m=r[0],v=r[1],g=r[2];return e===t?(t[12]=e[0]m+e[4]v+e[8]g+e[12],t[13]=e[1]m+e[5]v+e[9]g+e[13],t[14]=e[2]m+e[6]v+e[10]g+e[14],t[15]=e[3]m+e[7]v+e[11]g+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=h,t[9]=f,t[10]=d,t[11]=p,t[12]=nm+sv+hg+e[12],t[13]=im+lv+fg+e[13],t[14]=am+uv+dg+e[14],t[15]=om+cv+pg+e[15]),t},i.SIMD.translate=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4(r[0],r[1],r[2],0);e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11]),n=SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(s,0,0,0,0)),i=SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(s,1,1,1,1)),a=SIMD.Float32x4.mul(a,SIMD.Float32x4.swizzle(s,2,2,2,2));var l=SIMD.Float32x4.add(n,SIMD.Float32x4.add(i,SIMD.Float32x4.add(a,o)));return SIMD.Float32x4.store(t,12,l),t},i.translate=n.USE_SIMD?i.SIMD.translate:i.scalar.translate,i.scalar.scale=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]n,t[1]=e[1]n,t[2]=e[2]n,t[3]=e[3]n,t[4]=e[4]i,t[5]=e[5]i,t[6]=e[6]i,t[7]=e[7]i,t[8]=e[8]a,t[9]=e[9]a,t[10]=e[10]a,t[11]=e[11]a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.SIMD.scale=function(t,e,r){var n,i,a,o=SIMD.Float32x4(r[0],r[1],r[2],0);return n=SIMD.Float32x4.load(e,0),SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(o,0,0,0,0))),i=SIMD.Float32x4.load(e,4),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(o,1,1,1,1))),a=SIMD.Float32x4.load(e,8),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(a,SIMD.Float32x4.swizzle(o,2,2,2,2))),t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.scale=n.USE_SIMD?i.SIMD.scale:i.scalar.scale,i.rotate=function(t,e,r,i){var a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T,S,E,L=i[0],C=i[1],I=i[2],z=Math.sqrt(LL+CC+II);return Math.abs(z)<n.EPSILON?null:(z=1/z,L=z,C=z,I=z,a=Math.sin(r),o=Math.cos(r),s=1-o,l=e[0],u=e[1],c=e[2],h=e[3],f=e[4],d=e[5],p=e[6],m=e[7],v=e[8],g=e[9],y=e[10],b=e[11],x=LLs+o,_=CLs+Ia,w=ILs-Ca,M=LCs-Ia,k=CCs+o,A=ICs+La,T=LIs+Ca,S=CIs-La,E=IIs+o,t[0]=lx+f_+vw,t[1]=ux+d_+gw,t[2]=cx+p_+yw,t[3]=hx+m_+bw,t[4]=lM+fk+vA,t[5]=uM+dk+gA,t[6]=cM+pk+yA,t[7]=hM+mk+bA,t[8]=lT+fS+vE,t[9]=uT+dS+gE,t[10]=cT+pS+yE,t[11]=hT+mS+bE,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)},i.scalar.rotateX=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=ai+un,t[5]=oi+cn,t[6]=si+hn,t[7]=li+fn,t[8]=ui-an,t[9]=ci-on,t[10]=hi-sn,t[11]=fi-ln,t},i.SIMD.rotateX=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,4),o=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,4,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),t},i.rotateX=n.USE_SIMD?i.SIMD.rotateX:i.scalar.rotateX,i.scalar.rotateY=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=ai-un,t[1]=oi-cn,t[2]=si-hn,t[3]=li-fn,t[8]=an+ui,t[9]=on+ci,t[10]=sn+hi,t[11]=ln+fi,t},i.SIMD.rotateY=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,0),o=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,n),SIMD.Float32x4.mul(o,i))),t},i.rotateY=n.USE_SIMD?i.SIMD.rotateY:i.scalar.rotateY,i.scalar.rotateZ=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],u=e[4],c=e[5],h=e[6],f=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=ai+un,t[1]=oi+cn,t[2]=si+hn,t[3]=li+fn,t[4]=ui-an,t[5]=ci-on,t[6]=hi-sn,t[7]=fi-ln,t},i.SIMD.rotateZ=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var a=SIMD.Float32x4.load(e,0),o=SIMD.Float32x4.load(e,4);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.add(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),SIMD.Float32x4.store(t,4,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),t},i.rotateZ=n.USE_SIMD?i.SIMD.rotateZ:i.scalar.rotateZ,i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotation=function(t,e,r){var i,a,o,s=r[0],l=r[1],u=r[2],c=Math.sqrt(ss+ll+uu);return Math.abs(c)<n.EPSILON?null:(c=1/c,s=c,l=c,u=c,i=Math.sin(e),a=Math.cos(e),o=1-a,t[0]=sso+a,t[1]=lso+ui,t[2]=uso-li,t[3]=0,t[4]=slo-ui,t[5]=llo+a,t[6]=ulo+si,t[7]=0,t[8]=suo+li,t[9]=luo-si,t[10]=uuo+a,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t)},i.fromXRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromYRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromZRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotationTranslation=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=ns,h=nl,f=nu,d=il,p=iu,m=au,v=os,g=ol,y=ou;return t[0]=1-(d+m),t[1]=h+y,t[2]=f-g,t[3]=0,t[4]=h-y,t[5]=1-(c+m),t[6]=p+v,t[7]=0,t[8]=f+g,t[9]=p-v,t[10]=1-(c+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.getTranslation=function(t,e){return t[0]=e[12],t[1]=e[13],t[2]=e[14],t},i.getRotation=function(t,e){var r=e[0]+e[5]+e[10],n=0;return r>0?(n=2Math.sqrt(r+1),t[3]=.25n,t[0]=(e[6]-e[9])/n,t[1]=(e[8]-e[2])/n,t[2]=(e[1]-e[4])/n):e[0]>e[5]&e[0]>e[10]?(n=2Math.sqrt(1+e[0]-e[5]-e[10]),t[3]=(e[6]-e[9])/n,t[0]=.25n,t[1]=(e[1]+e[4])/n,t[2]=(e[8]+e[2])/n):e[5]>e[10]?(n=2Math.sqrt(1+e[5]-e[0]-e[10]),t[3]=(e[8]-e[2])/n,t[0]=(e[1]+e[4])/n,t[1]=.25n,t[2]=(e[6]+e[9])/n):(n=2Math.sqrt(1+e[10]-e[0]-e[5]),t[3]=(e[1]-e[4])/n,t[0]=(e[8]+e[2])/n,t[1]=(e[6]+e[9])/n,t[2]=.25n),t},i.fromRotationTranslationScale=function(t,e,r,n){var i=e[0],a=e[1],o=e[2],s=e[3],l=i+i,u=a+a,c=o+o,h=il,f=iu,d=ic,p=au,m=ac,v=oc,g=sl,y=su,b=sc,x=n[0],_=n[1],w=n[2];return t[0]=(1-(p+v))x,t[1]=(f+b)x,t[2]=(d-y)x,t[3]=0,t[4]=(f-b)_,t[5]=(1-(h+v))_,t[6]=(m+g)_,t[7]=0,t[8]=(d+y)w,t[9]=(m-g)w,t[10]=(1-(h+p))w,t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.fromRotationTranslationScaleOrigin=function(t,e,r,n,i){var a=e[0],o=e[1],s=e[2],l=e[3],u=a+a,c=o+o,h=s+s,f=au,d=ac,p=ah,m=oc,v=oh,g=sh,y=lu,b=lc,x=lh,_=n[0],w=n[1],M=n[2],k=i[0],A=i[1],T=i[2];return t[0]=(1-(m+g))_,t[1]=(d+x)_,t[2]=(p-b)_,t[3]=0,t[4]=(d-x)w,t[5]=(1-(f+g))w,t[6]=(v+y)w,t[7]=0,t[8]=(p+b)M,t[9]=(v-y)M,t[10]=(1-(f+m))M,t[11]=0,t[12]=r[0]+k-(t[0]k+t[4]A+t[8]T),t[13]=r[1]+A-(t[1]k+t[5]A+t[9]T),t[14]=r[2]+T-(t[2]k+t[6]A+t[10]T),t[15]=1,t},i.fromQuat=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=ro,c=no,h=ns,f=io,d=is,p=il,m=ao,v=as,g=al;return t[0]=1-h-p,t[1]=c+g,t[2]=f-v,t[3]=0,t[4]=c-g,t[5]=1-u-p,t[6]=d+m,t[7]=0,t[8]=f+v,t[9]=d-m,t[10]=1-u-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.frustum=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),u=1/(a-o);return 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o,s,l,u,c,h,f,d,p,m,v=e[0],g=e[1],y=e[2],b=a[0],x=a[1],_=a[2],w=r[0],M=r[1],k=r[2];return Math.abs(v-w)<n.EPSILON&&Math.abs(g-M)<n.EPSILON&&Math.abs(y-k)<n.EPSILON?i.identity(t):(f=v-w,d=g-M,p=y-k,m=1/Math.sqrt(ff+dd+pp),f=m,d=m,p=m,o=xp-_d,s=_f-bp,l=bd-xf,m=Math.sqrt(oo+ss+ll),m?(m=1/m,o=m,s=m,l=m):(o=0,s=0,l=0),u=dl-ps,c=po-fl,h=fs-do,m=Math.sqrt(uu+cc+hh),m?(m=1/m,u=m,c=m,h=m):(u=0,c=0,h=0),t[0]=o,t[1]=u,t[2]=f,t[3]=0,t[4]=s,t[5]=c,t[6]=d,t[7]=0,t[8]=l,t[9]=h,t[10]=p,t[11]=0,t[12]=-(ov+sg+ly),t[13]=-(uv+cg+hy),t[14]=-(fv+dg+py),t[15]=1,t)},i.str=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"},i.frob=function(t){return 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lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution_2_0(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(f_data, clipBounds[0])) \|\| \\n any(greaterThan(f_data, clipBounds[1]))) {\\n discard;\\n }\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n \\n if(!gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular_1_1(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\",attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(f_data, clipBounds[0])) \|\| \\n any(greaterThan(f_data, clipBounds[1]))) {\\n discard;\\n }\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\| \\n any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\",fragment:n,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\| \\n any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\",fragment:n,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor,1);\\n}\\n\",attributes:[{name:\"position\",type:\"vec3\"}]}},{}],205:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u,c,h,f,d,p,m,v,g,y,b,x,_,w,M,k,A,T){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=u,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=p,this.edgeColors=v,this.edgeUVs=g,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=b,this.pointColors=_,this.pointUVs=w,this.pointSizes=M,this.pointIds=x,this.pointVAO=k,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=T,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=D,this._view=D,this._projection=D,this._resolution=[1,1]}function i(t){for(var e=w({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4n+a]=i[a];r[4n+3]=255i[3]}return _(r,[256,256,4],[4,0,1])}function a(t,e,r){for(var n=new Array(e),i=0;i<e;++i)n[i]=0;for(var a=t.length,i=0;i<a;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}function o(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}function s(t){var e=p(t,S.vertex,S.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function l(t){var e=p(t,E.vertex,E.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function u(t){var e=p(t,L.vertex,L.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function c(t){var e=p(t,C.vertex,C.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function h(t){var e=p(t,I.vertex,I.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function f(t){var e=p(t,z.vertex,z.fragment);return e.attributes.position.location=0,e}function d(t,e){1===arguments.length&&(e=t,t=e.gl);var r=s(t),i=l(t),a=u(t),o=c(t),d=h(t),p=f(t),y=g(t,_(new Uint8Array([255,255,255,255]),[1,1,4]));y.generateMipmap(),y.minFilter=t.LINEAR_MIPMAP_LINEAR,y.magFilter=t.LINEAR;var b=m(t),x=m(t),w=m(t),M=m(t),k=m(t),A=v(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:x,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2},{buffer:M,type:t.FLOAT,size:3}]),T=m(t),S=m(t),E=m(t),L=m(t),C=v(t,[{buffer:T,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:S,type:t.FLOAT,size:4},{buffer:E,type:t.FLOAT,size:2}]),I=m(t),z=m(t),D=m(t),P=m(t),O=m(t),R=v(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:O,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:D,type:t.FLOAT,size:2},{buffer:P,type:t.FLOAT,size:1}]),F=m(t),j=v(t,[{buffer:F,type:t.FLOAT,size:3}]),N=new n(t,y,r,i,a,o,d,p,b,k,x,w,M,A,T,L,S,E,C,I,O,z,D,P,R,F,j);return N.update(e),N}var p=t(\"gl-shader\"),m=t(\"gl-buffer\"),v=t(\"gl-vao\"),g=t(\"gl-texture2d\"),y=t(\"normals\"),b=t(\"gl-mat4/multiply\"),x=t(\"gl-mat4/invert\"),_=t(\"ndarray\"),w=t(\"colormap\"),M=t(\"simplicial-complex-contour\"),k=t(\"typedarray-pool\"),A=t(\"./lib/shaders\"),T=t(\"./lib/closest-point\"),S=A.meshShader,E=A.wireShader,L=A.pointShader,C=A.pickShader,I=A.pointPickShader,z=A.contourShader,D=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=n.prototype;P.isOpaque=function(){return this.opacity>=1},P.isTransparent=function(){return this.opacity<1},P.pickSlots=1,P.setPickBase=function(t){this.pickId=t},P.highlight=function(t){if(!t\|\|!this.contourEnable)return void(this.contourCount=0);for(var e=M(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=k.mallocFloat32(6a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var h=u[0];2===u.length&&(h=u[c]);for(var f=n[h][0],d=n[h][1],p=i[h],m=1-p,v=this.positions[f],g=this.positions[d],y=0;y<3;++y)o[s++]=pv[y]+mg[y]}this.contourCount=s/3\|0,this.contourPositions.update(o.subarray(0,s)),k.free(o)},P.update=function(t){t=t\|\|{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=g(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(i(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var s=[],l=[],u=[],c=[],h=[],f=[],d=[],p=[],m=[],v=[],b=[],x=[],_=[],w=[];this.cells=r,this.positions=n;var M=t.vertexNormals,k=t.cellNormals,A=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,T=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!k&&(k=y.faceNormals(r,n,T)),k\|\|M\|\|(M=y.vertexNormals(r,n,A));var S=t.vertexColors,E=t.cellColors,L=t.meshColor\|\|[1,1,1,1],C=t.vertexUVs,I=t.vertexIntensity,z=t.cellUVs,D=t.cellIntensity,P=1/0,O=-1/0;if(!C&&!z)if(I)if(t.vertexIntensityBounds)P=+t.vertexIntensityBounds[0],O=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];P=Math.min(P,F),O=Math.max(O,F)}else if(D)for(var R=0;R<D.length;++R){var F=D[R];P=Math.min(P,F),O=Math.max(O,F)}else for(var R=0;R<n.length;++R){var F=n[R][2];P=Math.min(P,F),O=Math.max(O,F)}this.intensity=I\|\|(D?a(r,n.length,D):o(n));var j=t.pointSizes,N=t.pointSize\|\|1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(var R=0;R<n.length;++R)for(var B=n[R],U=0;U<3;++U)!isNaN(B[U])&&isFinite(B[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],B[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],B[U]));var V=0,H=0,q=0;t:for(var R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(var Y=G[0],B=n[Y],U=0;U<3;++U)if(isNaN(B[U])\|\|!isFinite(B[U]))continue t;v.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?b.push(W[0],W[1],W[2],1):b.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],x.push(X[0],X[1]),j?_.push(j[Y]):_.push(N),w.push(R),q+=1;break;case 2:for(var U=0;U<2;++U)for(var Y=G[U],B=n[Y],Z=0;Z<3;++Z)if(isNaN(B[Z])\|\|!isFinite(B[Z]))continue t;for(var U=0;U<2;++U){var Y=G[U],B=n[Y];f.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?d.push(W[0],W[1],W[2],1):d.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],p.push(X[0],X[1]),m.push(R)}H+=1;break;case 3:for(var U=0;U<3;++U)for(var Y=G[U],B=n[Y],Z=0;Z<3;++Z)if(isNaN(B[Z])\|\|!isFinite(B[Z]))continue t;for(var U=0;U<3;++U){var Y=G[U],B=n[Y];s.push(B[0],B[1],B[2]);var W;W=S?S[Y]:E?E[R]:L,3===W.length?l.push(W[0],W[1],W[2],1):l.push(W[0],W[1],W[2],W[3]);var X;X=C?C[Y]:I?[(I[Y]-P)/(O-P),0]:z?z[R]:D?[(D[R]-P)/(O-P),0]:[(B[2]-P)/(O-P),0],c.push(X[0],X[1]);var J;J=M?M[Y]:k[R],u.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(b),this.pointUVs.update(x),this.pointSizes.update(_),this.pointIds.update(new Uint32Array(w)),this.edgePositions.update(f),this.edgeColors.update(d),this.edgeUVs.update(p),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(s),this.triangleColors.update(l),this.triangleUVs.update(c),this.triangleNormals.update(u),this.triangleIds.update(new Uint32Array(h))}},P.drawTransparent=P.draw=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|D,n=t.view\|\|D,i=t.projection\|\|D,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var l=new Array(16);b(l,s.view,s.model),b(l,s.projection,l),x(l,l);for(var o=0;o<3;++o)s.eyePosition[o]=l[12+o]/l[15];for(var u=l[15],o=0;o<3;++o)u+=this.lightPosition[o]l[4o+3];for(var o=0;o<3;++o){for(var c=l[12+o],h=0;h<3;++h)c+=l[4h+o]this.lightPosition[h];s.lightPosition[o]=c/u}if(this.triangleCount>0){var f=this.triShader;f.bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var f=this.lineShader;f.bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()}if(this.pointCount>0){var f=this.pointShader;f.bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var f=this.contourShader;f.bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind()}},P.drawPick=function(t){t=t\|\|{};for(var e=this.gl,r=t.model\|\|D,n=t.view\|\|D,i=t.projection\|\|D,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255},l=this.pickShader;if(l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0){var l=this.pointPickShader;l.bind(),l.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}},P.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256t.value[1]+65536t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=T(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;for(var s=o[2],l=0,a=0;a<r.length;++a)l+=s[a]this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},P.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=d},{\"./lib/closest-point\":203,\"./lib/shaders\":204,colormap:99,\"gl-buffer\":156,\"gl-mat4/invert\":181,\"gl-mat4/multiply\":183,\"gl-shader\":255,\"gl-texture2d\":267,\"gl-vao\":271,ndarray:467,normals:469,\"simplicial-complex-contour\":517,\"typedarray-pool\":541}],206:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r}function i(t){var e=t.gl;return new n(t,a(e,[0,0,0,1,1,0,1,1]),o(e,s.boxVert,s.lineFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"./shaders\"),l=n.prototype;l.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},l.drawBox=function(){var t=[0,0],e=[0,0];return function(r,n,i,a,o){var s=this.plot,l=this.shader,u=s.gl;t[0]=r,t[1]=n,e[0]=i,e[1]=a,l.uniforms.lo=t,l.uniforms.hi=e,l.uniforms.color=o,u.drawArrays(u.TRIANGLE_STRIP,0,4)}}(),l.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":209,\"gl-buffer\":156,\"gl-shader\":212}],207:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function i(t,e){return t-e}function a(t){var e=t.gl;return new n(t,o(e),s(e,u.gridVert,u.gridFrag),s(e,u.tickVert,u.gridFrag))}e.exports=a;var 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s=this.plot,u=this.vbo,c=this.tickShader,h=this.ticks,f=s.gl,d=s._tickBounds,p=s.dataBox,m=s.viewBox,v=s.pixelRatio,g=s.screenBox,y=g[2]-g[0],b=g[3]-g[1],x=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var M=d[w],k=d[w+2],A=k-M,T=.5(p[w+2]+p[w]),S=p[w+2]-p[w];e[w]=2A/S,t[w]=2(M-T)/S}e[0]=x/y,t[0]=x/y,e[1]=_/b,t[1]=_/b,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var E=c.uniforms;E.dataShift=t,E.dataScale=e;var 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t=[1,1,-1,-1,1,-1],e=[1,-1,1,1,-1,-1];return function(r){for(var n=r.ticks,i=r.bounds,a=new Float32Array(18(n[0].length+n[1].length)),o=(this.plot.zeroLineEnable,0),s=[[],[]],l=0;l<2;++l)for(var u=s[l],c=n[l],h=i[l],f=i[l+2],d=0;d<c.length;++d){var p=(c[d].x-h)/(f-h);u.push(p);for(var m=0;m<6;++m)a[o++]=p,a[o++]=t[m],a[o++]=e[m]}this.ticks=s,this.vbo.update(a)}}(),c.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":209,\"binary-search-bounds\":211,\"gl-buffer\":156,\"gl-shader\":212}],208:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r}function i(t){var e=t.gl;return new n(t,a(e,[-1,-1,-1,1,1,-1,1,1]),o(e,s.lineVert,s.lineFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"./shaders\"),l=n.prototype;l.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},l.drawLine=function(){var t=[0,0],e=[0,0];return function(r,n,i,a,o,s){var l=this.plot,u=this.shader,c=l.gl;t[0]=r,t[1]=n,e[0]=i,e[1]=a,u.uniforms.start=t,u.uniforms.end=e,u.uniforms.width=ol.pixelRatio,u.uniforms.color=s,c.drawArrays(c.TRIANGLE_STRIP,0,4)}}(),l.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":209,\"gl-buffer\":156,\"gl-shader\":212}],209:[function(t,e,r){\"use strict\";var n=\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\";e.exports={lineVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n vec2 delta = normalize(perp(start - end));\\n vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n 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1);\\n}\\n\",gridFrag:n,boxVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\",tickVert:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n gl_Position = vec4(pos + tickScaledataCoord.yz + screenOffset, 0, 1);\\n}\\n\"}},{}],210:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}function i(t){var e=t.gl;return new n(t,a(e),o(e,u.textVert,u.textFrag))}e.exports=i;var a=t(\"gl-buffer\"),o=t(\"gl-shader\"),s=t(\"text-cache\"),l=t(\"binary-search-bounds\"),u=t(\"./shaders\"),c=n.prototype;c.drawTicks=function(){var t=[0,0],e=[0,0],r=[0,0];return function(n){var i=this.plot,a=this.shader,o=this.tickX[n],s=this.tickOffset[n],u=i.gl,c=i.viewBox,h=i.dataBox,f=i.screenBox,d=i.pixelRatio,p=i.tickEnable,m=i.tickPad,v=i.tickColor,g=i.tickAngle,y=i.labelEnable,b=i.labelPad,x=i.labelColor,_=i.labelAngle,w=this.labelOffset[n],M=this.labelCount[n],k=l.lt(o,h[n]),A=l.le(o,h[n+2]);t[0]=t[1]=0,t[n]=1,e[n]=(c[2+n]+c[n])/(f[2+n]-f[n])-1;var T=2/f[2+(1^n)]-f[1^n];e[1^n]=Tc[1^n]-1,p[n]&&(e[1^n]-=Tdm[n],k<A&&s[A]>s[k]&&(a.uniforms.dataAxis=t,a.uniforms.screenOffset=e,a.uniforms.color=v[n],a.uniforms.angle=g[n],u.drawArrays(u.TRIANGLES,s[k],s[A]-s[k]))),y[n]&&M&&(e[1^n]-=Tdb[n],a.uniforms.dataAxis=r,a.uniforms.screenOffset=e,a.uniforms.color=x[n],a.uniforms.angle=_[n],u.drawArrays(u.TRIANGLES,w,M)),e[1^n]=Tc[2+(1^n)]-1,p[n+2]&&(e[1^n]+=Tdm[n+2],k<A&&s[A]>s[k]&&(a.uniforms.dataAxis=t,a.uniforms.screenOffset=e,a.uniforms.color=v[n+2],a.uniforms.angle=g[n+2],u.drawArrays(u.TRIANGLES,s[k],s[A]-s[k]))),y[n+2]&&M&&(e[1^n]+=Tdb[n+2],a.uniforms.dataAxis=r,a.uniforms.screenOffset=e,a.uniforms.color=x[n+2],a.uniforms.angle=_[n+2],u.drawArrays(u.TRIANGLES,w,M))}}(),c.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var 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t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]u[3],u[1]u[3],u[2]u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT\|t.DEPTH_BUFFER_BIT),t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]c[3],c[1]c[3],c[2]c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,d=this.zeroLineWidth;if(h[0]\|\|h[1]){o.bind();for(var p=0;p<2;++p)if(h[p]&&n[p]<=0&&n[p+2]>=0){var m=e[p]-n[p](e[p+2]-e[p])/(n[p+2]-n[p]);0===p?o.drawLine(m,e[1],m,e[3],d[p],f[p]):o.drawLine(e[0],m,e[2],m,d[p],f[p])}}for(var p=0;p<l.length;++p)l[p].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var v=this.borderLineEnable,g=this.borderLineWidth,y=this.borderLineColor;v[1]&&o.drawLine(r[0],r[1]-.5g[1]i,r[0],r[3]+.5g[3]i,g[1],y[1]),v[0]&&o.drawLine(r[0]-.5g[0]i,r[1],r[2]+.5g[2]i,r[1],g[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5g[1]i,r[2],r[3]+.5g[3]i,g[3],y[3]),v[2]&&o.drawLine(r[0]-.5g[0]i,r[3],r[2]+.5g[2]i,r[3],g[2],y[2]),s.bind();for(var p=0;p<2;++p)s.drawTicks(p);this.titleEnable&&s.drawTitle();for(var b=this.overlays,p=0;p<b.length;++p)b[p].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}}}(),f.drawPick=function(){return function(){if(!this.static){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}}}(),f.pick=function(){return function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0\|Math.round((t-i[0]/r)n),o=0\|Math.round((e-i[1]/r)n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var h=u[c].pick(a,o,l);if(h)return h}return null}}}(),f.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0\|Math.round(t[0]r),e[1]=0\|Math.round(t[1]r),e[2]=0\|Math.round(t[2]r),e[3]=0\|Math.round(t[3]r),this.setDirty()},f.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]\|\|e[1]!==t[1]\|\|e[2]!==t[2]\|\|e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},f.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0\|Math.round(t[0]e),r[1]=0\|Math.round(t[1]e),r[2]=0\|Math.round(t[2]e),r[3]=0\|Math.round(t[3]e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0\|Math.round((t[2]-t[0])n),0\|Math.round((t[3]-t[1])n)],this.setDirty()},f.update=function(t){t=t\|\|{};var e=this.gl;this.pixelRatio=t.pixelRatio\|\|1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox\|\|[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox\|\|[.125(this.screenBox[2]-this.screenBox[0])/r,.125(this.screenBox[3]-this.screenBox[1])/r,.875(this.screenBox[2]-this.screenBox[0])/r,.875(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,o=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox\|\|[-10,-10/o,10,10/o]),this.borderColor=(t.borderColor\|\|[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor\|\|[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable\|\|[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth\|\|[1,1]).slice(),this.gridLineColor=i(t.gridLineColor\|\|[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable\|\|[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth\|\|[4,4]).slice(),this.zeroLineColor=i(t.zeroLineColor\|\|[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength\|\|[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth\|\|[0,0,0,0]).slice(),this.tickMarkColor=i(t.tickMarkColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter\|\|[.5(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t&&!t.titleEnable),this.titleAngle=t.titleAngle\|\|0,this.titleColor=(t.titleColor\|\|[0,0,0,1]).slice(),this.labelPad=(t.labelPad\|\|[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle\|\|[0,Math.PI/2,0,3Math.PI/2]).slice(),this.labelEnable=(t.labelEnable\|\|[!0,!0,!0,!0]).slice(),this.labelColor=i(t.labelColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad\|\|[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle\|\|[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable\|\|[!0,!0,!0,!0]).slice(),this.tickColor=i(t.tickColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable\|\|[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth\|\|[2,2,2,2]).slice(),this.borderLineColor=i(t.borderLineColor\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var s=t.ticks\|\|[[],[]],l=this._tickBounds;l[0]=l[1]=1/0,l[2]=l[3]=-1/0;for(var u=0;u<2;++u){var c=s[u].slice(0);0!==c.length&&(c.sort(a),l[u]=Math.min(l[u],c[0].x),l[u+2]=Math.max(l[u+2],c[c.length-1].x))}this.grid.update({bounds:l,ticks:s}),this.text.update({bounds:l,ticks:s,labels:t.labels\|\|[\"x\",\"y\"],labelSize:t.labelSize\|\|[12,12],labelFont:t.labelFont\|\|[\"sans-serif\",\"sans-serif\"],title:t.title\|\|\"\",titleSize:t.titleSize\|\|18,titleFont:t.titleFont\|\|\"sans-serif\"}),this.static=!!t.static,this.setDirty()},f.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(var t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},f.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},f.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},f.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},f.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":206,\"./lib/grid\":207,\"./lib/line\":208,\"./lib/text\":210,\"gl-select-static\":254}],220:[function(t,e,r){var n=t(\"gl-shader\");e.exports=function(t){return n(t,\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\",\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\",null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":255}],221:[function(t,e,r){\"use strict\";function n(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function i(t,e){var r=null;try{r=t.getContext(\"webgl\",e),r\|\|(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}function a(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(tr)/r}if(e>0){var r=Math.round(Math.pow(10,e));return Math.ceil(t/r)r}return Math.ceil(t)}function o(t){return\"boolean\"!=typeof t\|\|t}function s(t){function e(){if(!w&&G.autoResize){var t=M.parentNode,e=1,r=1;t&&t!==document.body?(e=t.clientWidth,r=t.clientHeight):(e=window.innerWidth,r=window.innerHeight);var n=0\|Math.ceil(eG.pixelRatio),i=0\|Math.ceil(rG.pixelRatio);if(n!==M.width\|\|i!==M.height){M.width=n,M.height=i;var a=M.style;a.position=a.position\|\|\"absolute\",a.left=\"0px\",a.top=\"0px\",a.width=e+\"px\",a.height=r+\"px\",N=!0}}}function r(){for(var t=O.length,e=j.length,r=0;r<e;++r)F[r]=0;t:for(var r=0;r<t;++r){var n=O[r],i=n.pickSlots;if(i){for(var a=0;a<e;++a)if(F[a]+i<255){R[r]=a,n.setPickBase(F[a]+1),F[a]+=i;continue t}var o=f(A,q);R[r]=e,j.push(o),F.push(i),n.setPickBase(1),e+=1}else R[r]=-1}for(;e>0&&0===F[e-1];)F.pop(),j.pop().dispose()}function s(){if(G.contextLost)return!0;A.isContextLost()&&(G.contextLost=!0,G.mouseListener.enabled=!1,G.selection.object=null,G.oncontextloss&&G.oncontextloss())}function b(){if(!s()){A.colorMask(!0,!0,!0,!0),A.depthMask(!0),A.disable(A.BLEND),A.enable(A.DEPTH_TEST);for(var t=O.length,e=j.length,r=0;r<e;++r){var n=j[r];n.shape=Y,n.begin();for(var i=0;i<t;++i)if(R[i]===r){var a=O[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(H))}n.end()}}}function x(){if(!s()){e();var t=G.camera.tick();H.view=G.camera.matrix,N=N\|\|t,B=B\|\|t,z.pixelRatio=G.pixelRatio,P.pixelRatio=G.pixelRatio;var r=O.length,n=Z[0],i=Z[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<r;++o){var l=O[o];l.pixelRatio=G.pixelRatio,l.axes=G.axes,N=N\|\|!!l.dirty,B=B\|\|!!l.dirty;var u=l.bounds;if(u)for(var h=u[0],f=u[1],d=0;d<3;++d)n[d]=Math.min(n[d],h[d]),i[d]=Math.max(i[d],f[d])}var m=G.bounds;if(G.autoBounds)for(var d=0;d<3;++d){if(i[d]<n[d])n[d]=-1,i[d]=1;else{n[d]===i[d]&&(n[d]-=1,i[d]+=1);var g=.05(i[d]-n[d]);n[d]=n[d]-g,i[d]=i[d]+g}m[0][d]=n[d],m[1][d]=i[d]}for(var y=!1,d=0;d<3;++d)y=y\|\|J[0][d]!==m[0][d]\|\|J[1][d]!==m[1][d],J[0][d]=m[0][d],J[1][d]=m[1][d];if(B=B\|\|y,N=N\|\|y){if(y){for(var x=[0,0,0],o=0;o<3;++o)x[o]=a((m[1][o]-m[0][o])/10);z.autoTicks?z.update({bounds:m,tickSpacing:x}):z.update({bounds:m})}var _=A.drawingBufferWidth,w=A.drawingBufferHeight;q[0]=_,q[1]=w,Y[0]=0\|Math.max(_/G.pixelRatio,1),Y[1]=0\|Math.max(w/G.pixelRatio,1),v(U,G.fovy,_/w,G.zNear,G.zFar);for(var o=0;o<16;++o)V[o]=0;V[15]=1;for(var M=0,o=0;o<3;++o)M=Math.max(M,m[1][o]-m[0][o]);for(var o=0;o<3;++o)G.autoScale?V[5o]=G.aspect[o]/(m[1][o]-m[0][o]):V[5o]=1/M,G.autoCenter&&(V[12+o]=.5-V[5o](m[0][o]+m[1][o]));for(var o=0;o<r;++o){var l=O[o];l.axesBounds=m,G.clipToBounds&&(l.clipBounds=m)}S.object&&(G.snapToData?P.position=S.dataCoordinate:P.position=S.dataPosition,P.bounds=m),B&&(B=!1,b()),G.axesPixels=c(G.axes,H,_,w),G.onrender&&G.onrender(),A.bindFramebuffer(A.FRAMEBUFFER,null),A.viewport(0,0,_,w);var k=G.clearColor;A.clearColor(k[0],k[1],k[2],k[3]),A.clear(A.COLOR_BUFFER_BIT\|A.DEPTH_BUFFER_BIT),A.depthMask(!0),A.colorMask(!0,!0,!0,!0),A.enable(A.DEPTH_TEST),A.depthFunc(A.LEQUAL),A.disable(A.BLEND),A.disable(A.CULL_FACE);var T=!1;z.enable&&(T=T\|\|z.isTransparent(),z.draw(H)),P.axes=z,S.object&&P.draw(H),A.disable(A.CULL_FACE);for(var o=0;o<r;++o){var l=O[o];l.axes=z,l.pixelRatio=G.pixelRatio,l.isOpaque&&l.isOpaque()&&l.draw(H),l.isTransparent&&l.isTransparent()&&(T=!0)}if(T){E.shape=q,E.bind(),A.clear(A.DEPTH_BUFFER_BIT),A.colorMask(!1,!1,!1,!1),A.depthMask(!0),A.depthFunc(A.LESS),z.enable&&z.isTransparent()&&z.drawTransparent(H);for(var o=0;o<r;++o){var l=O[o];l.isOpaque&&l.isOpaque()&&l.draw(H)}A.enable(A.BLEND),A.blendEquation(A.FUNC_ADD),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.colorMask(!0,!0,!0,!0),A.depthMask(!1),A.clearColor(0,0,0,0),A.clear(A.COLOR_BUFFER_BIT),z.isTransparent()&&z.drawTransparent(H);for(var o=0;o<r;++o){var l=O[o];l.isTransparent&&l.isTransparent()&&l.drawTransparent(H)}A.bindFramebuffer(A.FRAMEBUFFER,null),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.disable(A.DEPTH_TEST),L.bind(),E.color[0].bind(0),L.uniforms.accumBuffer=0,p(A),A.disable(A.BLEND)}N=!1;for(var o=0;o<r;++o)O[o].dirty=!1}}}function _(){w\|\|G.contextLost\|\|(requestAnimationFrame(_),x())}t=t\|\|{};var w=!1,M=(t.pixelRatio\|\|parseFloat(window.devicePixelRatio),t.canvas);if(!M)if(M=document.createElement(\"canvas\"),t.container){var k=t.container;k.appendChild(M)}else document.body.appendChild(M);var A=t.gl;if(A\|\|(A=i(M,t.glOptions\|\|{premultipliedAlpha:!0,antialias:!0})),!A)throw new Error(\"webgl not supported\");var T=t.bounds\|\|[[-10,-10,-10],[10,10,10]],S=new n,E=d(A,[A.drawingBufferWidth,A.drawingBufferHeight],{preferFloat:!y}),L=g(A),C=t.camera\|\|{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:\"turntable\"},I=t.axes\|\|{},z=u(A,I);z.enable=!I.disable;var D=t.spikes\|\|{},P=h(A,D),O=[],R=[],F=[],j=[],N=!0,B=!0,U=new Array(16),V=new Array(16),H={view:null,projection:U,model:V},B=!0,q=[A.drawingBufferWidth,A.drawingBufferHeight],G={gl:A,contextLost:!1,pixelRatio:t.pixelRatio\|\|parseFloat(window.devicePixelRatio),canvas:M,selection:S,camera:l(M,C),axes:z,axesPixels:null,spikes:P,bounds:T,objects:O,shape:q,aspect:t.aspectRatio\|\|[1,1,1],pickRadius:t.pickRadius\|\|10,zNear:t.zNear\|\|.01,zFar:t.zFar\|\|1e3,fovy:t.fovy\|\|Math.PI/4,clearColor:t.clearColor\|\|[0,0,0,0],autoResize:o(t.autoResize),autoBounds:o(t.autoBounds),autoScale:!!t.autoScale,autoCenter:o(t.autoCenter),clipToBounds:o(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect\|\|null,onrender:t.onrender\|\|null,onclick:t.onclick\|\|null,cameraParams:H,oncontextloss:null,mouseListener:null},Y=[A.drawingBufferWidth/G.pixelRatio\|0,A.drawingBufferHeight/G.pixelRatio\|0];G.autoResize&&e(),window.addEventListener(\"resize\",e),G.update=function(t){w\|\|(t=t\|\|{},N=!0,B=!0)},G.add=function(t){w\|\|(t.axes=z,O.push(t),R.push(-1),N=!0,B=!0,r())},G.remove=function(t){if(!w){var e=O.indexOf(t);e<0\|\|(O.splice(e,1),R.pop(),N=!0,B=!0,r())}},G.dispose=function(){if(!w&&(w=!0,window.removeEventListener(\"resize\",e),M.removeEventListener(\"webglcontextlost\",s),G.mouseListener.enabled=!1,!G.contextLost)){z.dispose(),P.dispose();for(var t=0;t<O.length;++t)O[t].dispose();E.dispose();for(var t=0;t<j.length;++t)j[t].dispose();L.dispose(),A=null,z=null,P=null,O=[]}};var W=!1,X=0;G.mouseListener=m(M,function(t,e,r){if(!w){var n=j.length,i=O.length,a=S.object;S.distance=1/0,S.mouse[0]=e,S.mouse[1]=r,S.object=null,S.screen=null,S.dataCoordinate=S.dataPosition=null;var o=!1;if(t&&X)W=!0;else{W&&(B=!0),W=!1;for(var s=0;s<n;++s){var l=j[s].query(e,Y[1]-r-1,G.pickRadius);if(l){if(l.distance>S.distance)continue;for(var u=0;u<i;++u){var c=O[u];if(R[u]===s){var h=c.pick(l);h&&(S.buttons=t,S.screen=l.coord,S.distance=l.distance,S.object=c,S.index=h.distance,S.dataPosition=h.position,S.dataCoordinate=h.dataCoordinate,S.data=h,o=!0)}}}}}a&&a!==S.object&&(a.highlight&&a.highlight(null),N=!0),S.object&&(S.object.highlight&&S.object.highlight(S.data),N=!0),o=o\|\|S.object!==a,o&&G.onselect&&G.onselect(S),1&t&&!(1&X)&&G.onclick&&G.onclick(S),X=t}}),M.addEventListener(\"webglcontextlost\",s);var Z=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],J=[Z[0].slice(),Z[1].slice()];return _(),G.redraw=function(){w\|\|(N=!0,x())},G}e.exports=s;var l=t(\"3d-view-controls\"),u=t(\"gl-axes3d\"),c=t(\"gl-axes3d/properties\"),h=t(\"gl-spikes3d\"),f=t(\"gl-select-static\"),d=t(\"gl-fbo\"),p=t(\"a-big-triangle\"),m=t(\"mouse-change\"),v=t(\"gl-mat4/perspective\"),g=t(\"./lib/shader\"),y=t(\"is-mobile\")()},{\"./lib/shader\":220,\"3d-view-controls\":36,\"a-big-triangle\":39,\"gl-axes3d\":148,\"gl-axes3d/properties\":155,\"gl-fbo\":164,\"gl-mat4/perspective\":184,\"gl-select-static\":254,\"gl-spikes3d\":264,\"is-mobile\":296,\"mouse-change\":452}],222:[function(t,e,r){r.pointVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n highp float a = 12.9898;\\n highp float b = 78.233;\\n highp float c = 43758.5453;\\n highp float d = dot(co.xy, vec2(a, b));\\n highp float e = mod(d, 3.14);\\n return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n // if we don't jitter the point size a bit, overall point cloud\\n // saturation 'jumps' on zooming, which is disturbing and confusing\\n gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n if(pointCloud != 0.0) { // pointCloud is truthy\\n // get the same square surface as circle would be\\n gl_PointSize = 0.886;\\n }\\n}\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n float radius;\\n vec4 baseColor;\\n if(pointCloud != 0.0) { // pointCloud is truthy\\n if(centerFraction == 1.0) {\\n gl_FragColor = color;\\n } else {\\n gl_FragColor = mix(borderColor, color, centerFraction);\\n }\\n } else {\\n radius = length(2.0 gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\",r.pickVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"},{}],223:[function(t,e,r){arguments[4][212][0].apply(r,arguments)},{\"./lib/GLError\":224,\"./lib/create-attributes\":225,\"./lib/create-uniforms\":226,\"./lib/reflect\":227,\"./lib/runtime-reflect\":228,\"./lib/shader-cache\":229,dup:212}],224:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],225:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\n", "\"./GLError\":224,dup:214}],226:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":224,\"./reflect\":227,dup:215}],227:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],228:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],229:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":224,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],230:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}function i(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2r],o=t[2r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}function a(t,e){var r=t.gl,i=s(r),a=s(r),l=o(r,u.pointVertex,u.pointFragment),c=o(r,u.pickVertex,u.pickFragment),h=new n(t,i,a,l,c);return h.update(e),t.addObject(h),h}var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"typedarray-pool\"),u=t(\"./lib/shader\");e.exports=a;var c=n.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){function e(e,r){return e in t?t[e]:r}var r;t=t\|\|{},this.sizeMin=e(\"sizeMin\",.5),this.sizeMax=e(\"sizeMax\",20),this.color=e(\"color\",[1,0,0,1]).slice(),this.areaRatio=e(\"areaRatio\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.blend=e(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,a=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,o=t.positions,s=i?o:l.mallocFloat32(o.length),u=a?t.idToIndex:l.mallocInt32(n);if(i\|\|s.set(o),!a)for(s.set(o),r=0;r<n;r++)u[r]=r;this.points=o,this.offsetBuffer.update(s),this.pickBuffer.update(u),i\|\|l.free(s),a\|\|l.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=void 0!==r,a=n?this.pickShader:this.shader,o=this.plot.gl,s=this.plot.dataBox;if(0===this.pointCount)return r;var l=s[2]-s[0],u=s[3]-s[1],c=i(this.points,s),h=this.plot.pickPixelRatioMath.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(c,.33333)));t[0]=2/l,t[4]=2/u,t[6]=-2s[0]/l-1,t[7]=-2s[1]/u-1,this.offsetBuffer.bind(),a.bind(),a.attributes.position.pointer(),a.uniforms.matrix=t,a.uniforms.color=this.color,a.uniforms.borderColor=this.borderColor,a.uniforms.pointCloud=h<5,a.uniforms.pointSize=h,a.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),n&&(e[0]=255&r,e[1]=r>>8&255,e[2]=r>>16&255,e[3]=r>>24&255,this.pickBuffer.bind(),a.attributes.pickId.pointer(o.UNSIGNED_BYTE),a.uniforms.pickOffset=e,this.pickOffset=r);var f=o.getParameter(o.BLEND),d=o.getParameter(o.DITHER);return f&&!this.blend&&o.disable(o.BLEND),d&&o.disable(o.DITHER),o.drawArrays(o.POINTS,0,this.pointCount),f&&!this.blend&&o.enable(o.BLEND),d&&o.enable(o.DITHER),r+this.pointCount}}(),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2a],o[2a+1]]}}},{\"./lib/shader\":222,\"gl-buffer\":156,\"gl-shader\":223,\"typedarray-pool\":541}],231:[function(t,e,r){function n(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],h=e[2],f=e[3],d=r[0],p=r[1],m=r[2],v=r[3];return a=ud+cp+hm+fv,a<0&&(a=-a,d=-d,p=-p,m=-m,v=-v),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)i)/o,l=Math.sin(ni)/o):(s=1-n,l=n),t[0]=su+ld,t[1]=sc+lp,t[2]=sh+lm,t[3]=sf+lv,t}e.exports=n},{}],232:[function(t,e,r){\"use strict\";e.exports={vertex:\"precision highp float;\\n#define GLSLIFY 1\\n\\n\\nvec4 computePosition_1_0(vec2 posHi, vec2 posLo, vec2 scHi, vec2 scLo, vec2 trHi, vec2 trLo) {\\n return vec4((posHi + trHi) scHi\\n \\t\\t\\t//FIXME: this thingy does not give noticeable precision gain, need test\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo\\n , 0, 1);\\n}\\n\\n\\nattribute vec2 positionHi, positionLo;\\nattribute float size, border;\\nattribute vec2 char, color;\\n\\n//this is 64-bit form of scale and translate\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pixelRatio;\\nuniform vec4 viewBox;\\nuniform sampler2D palette;\\n\\nvarying vec4 charColor, borderColor;\\nvarying vec2 charId;\\nvarying vec2 pointCoord;\\nvarying float pointSize;\\nvarying float borderWidth;\\n\\n\\nvoid main() {\\n charColor = texture2D(palette, vec2(color.x / 255., 0));\\n borderColor = texture2D(palette, vec2(color.y / 255., 0));\\n\\n gl_PointSize = size * pixelRatio;\\n pointSize = size * pixelRatio;\\n\\n charId = char;\\n borderWidth = border;\\n\\n gl_Position = computePosition_1_0(\\n positionHi, positionLo,\\n scaleHi, scaleLo,\\n translateHi, translateLo);\\n\\n pointCoord = viewBox.xy + (viewBox.zw - viewBox.xy) * (gl_Position.xy * .5 + .5);\\n}\\n\",fragment:\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D chars;\\nuniform vec2 charsShape;\\nuniform float charsStep, pixelRatio, charOffset;\\n\\nvarying vec4 borderColor;\\nvarying vec4 charColor;\\nvarying vec2 charId;\\nvarying vec2 pointCoord;\\nvarying float pointSize;\\nvarying float borderWidth;\\n\\nvoid main() {\\n\\tvec2 pointUV = (pointCoord - gl_FragCoord.xy + pointSize * .5) / pointSize;\\n\\tpointUV.x = 1. - pointUV.x;\\n\\tvec2 texCoord = ((charId + pointUV) * charsStep) / charsShape;\\n\\tfloat dist = texture2D(chars, texCoord).r;\\n\\n\\t//max-distance alpha\\n\\tif (dist < 1e-2)\\n\\t\\tdiscard;\\n\\n\\tfloat gamma = .0045 * charsStep / pointSize;\\n\\n //null-border case\\n \\tif (borderWidth * borderColor.a == 0.) {\\n\\t\\tfloat charAmt = smoothstep(.748 - gamma, .748 + gamma, dist);\\n\\t\\tgl_FragColor = vec4(charColor.rgb, charAmtcharColor.a);\\n\\t\\treturn;\\n\\t}\\n\\n\\tfloat dif = 5. pixelRatio * borderWidth / pointSize;\\n\\tfloat borderLevel = .748 - dif * .5;\\n\\tfloat charLevel = .748 + dif * .5;\\n\\n\\tfloat borderAmt = smoothstep(borderLevel - gamma, borderLevel + gamma, dist);\\n\\tfloat charAmt = smoothstep(charLevel - gamma, charLevel + gamma, dist);\\n\\n\\tvec4 color = borderColor;\\n\\tcolor.a = borderAmt;\\n\\n\\tgl_FragColor = mix(color, charColor, charAmt);\\n}\\n\",pickVertex:\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 positionHi, positionLo;\\nattribute vec4 id;\\nattribute float size;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform vec4 pickOffset;\\nuniform float pixelRatio;\\n\\nvarying vec4 fragColor;\\n\\n\\nvec4 computePosition_1_0(vec2 posHi, vec2 posLo, vec2 scHi, vec2 scLo, vec2 trHi, vec2 trLo) {\\n return vec4((posHi + trHi) scHi\\n \\t\\t\\t//FIXME: this thingy does not give noticeable precision gain, need test\\n + (posLo + trLo) * scHi\\n + (posHi + trHi) * scLo\\n + (posLo + trLo) * scLo\\n , 0, 1);\\n}\\n\\n\\nvoid main() {\\n vec4 fragId = id + pickOffset;\\n\\n fragId.y += floor(fragId.x / 256.0);\\n fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n fragId.z += floor(fragId.y / 256.0);\\n fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n fragId.w += floor(fragId.z / 256.0);\\n fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n fragColor = fragId / 255.0;\\n\\n gl_PointSize = size * .25 * pixelRatio;\\n\\n gl_Position = computePosition_1_0(\\n positionHi, positionLo,\\n scaleHi, scaleLo,\\n translateHi, translateLo);\\n}\\n\",pickFragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\\n\"}},{}],233:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],234:[function(t,e,r){arguments[4][212][0].apply(r,arguments)},{\"./lib/GLError\":235,\"./lib/create-attributes\":236,\"./lib/create-uniforms\":237,\"./lib/reflect\":238,\"./lib/runtime-reflect\":239,\"./lib/shader-cache\":240,dup:212}],235:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],236:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\"./GLError\":235,dup:214}],237:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":235,\"./reflect\":238,dup:215}],238:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],239:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],240:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":235,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],241:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){a<=4f?i(0,a-1,t,e,r,n):h(0,a-1,t,e,r,n)}function i(t,e,r,n,i,a){for(var o=t+1;o<=e;++o){for(var s=r[o],l=n[2o],u=n[2o+1],c=i[o],h=a[o],f=o;f>t;){var d=r[f-1],p=n[2(f-1)];if((d-s\|\|l-p)>=0)break;r[f]=d,n[2f]=p,n[2f+1]=n[2f-1],i[f]=i[f-1],a[f]=a[f-1],f-=1}r[f]=s,n[2f]=l,n[2f+1]=u,i[f]=c,a[f]=h}}function a(t,e,r,n,i,a){var o=r[t],s=n[2t],l=n[2t+1],u=i[t],c=a[t];r[t]=r[e],n[2t]=n[2e],n[2t+1]=n[2e+1],i[t]=i[e],a[t]=a[e],r[e]=o,n[2e]=s,n[2e+1]=l,i[e]=u,a[e]=c}function o(t,e,r,n,i,a){r[t]=r[e],n[2t]=n[2e],n[2t+1]=n[2e+1],i[t]=i[e],a[t]=a[e]}function s(t,e,r,n,i,a,o){var s=n[t],l=i[2t],u=i[2t+1],c=a[t],h=o[t];n[t]=n[e],i[2t]=i[2e],i[2t+1]=i[2e+1],a[t]=a[e],o[t]=o[e],n[e]=n[r],i[2e]=i[2r],i[2e+1]=i[2r+1],a[e]=a[r],o[e]=o[r],n[r]=s,i[2r]=l,i[2r+1]=u,a[r]=c,o[r]=h}function l(t,e,r,n,i,a,o,s,l,u,c){s[t]=s[e],l[2t]=l[2e],l[2t+1]=l[2e+1],u[t]=u[e],c[t]=c[e],s[e]=r,l[2e]=n,l[2e+1]=i,u[e]=a,c[e]=o}function u(t,e,r,n,i){return(r[t]-r[e]\|\|n[2e]-n[2t]\|\|i[t]-i[e])<0}function c(t,e,r,n,i,a,o,s){return(e-a[t]\|\|o[2t]-r\|\|i-s[t])<0}function h(t,e,r,n,d,p){var m=(e-t+1)/6\|0,v=t+m,g=e-m,y=t+e>>1,b=y-m,x=y+m,_=v,w=b,M=y,k=x,A=g,T=t+1,S=e-1,E=0;u(_,w,r,n,d,p)&&(E=_,_=w,w=E),u(k,A,r,n,d,p)&&(E=k,k=A,A=E),u(_,M,r,n,d,p)&&(E=_,_=M,M=E),u(w,M,r,n,d,p)&&(E=w,w=M,M=E),u(_,k,r,n,d,p)&&(E=_,_=k,k=E),u(M,k,r,n,d,p)&&(E=M,M=k,k=E),u(w,A,r,n,d,p)&&(E=w,w=A,A=E),u(w,M,r,n,d,p)&&(E=w,w=M,M=E),u(k,A,r,n,d,p)&&(E=k,k=A,A=E);var L=r[w],C=n[2w],I=n[2w+1],z=d[w],D=p[w],P=r[k],O=n[2k],R=n[2k+1],F=d[k],j=p[k],N=_,B=M,U=A,V=v,H=y,q=g,G=r[N],Y=r[B],W=r[U];r[V]=G,r[H]=Y,r[q]=W;for(var X=0;X<2;++X){var Z=n[2N+X],J=n[2B+X],K=n[2U+X];n[2V+X]=Z,n[2H+X]=J,n[2q+X]=K}var Q=d[N],$=d[B],tt=d[U];d[V]=Q,d[H]=$,d[q]=tt;var et=p[N],rt=p[B],nt=p[U];p[V]=et,p[H]=rt,p[q]=nt,o(b,t,r,n,d,p),o(x,e,r,n,d,p);for(var it=T;it<=S;++it)if(c(it,L,C,I,z,r,n,d))it!==T&&a(it,T,r,n,d,p),++T;else if(!c(it,P,O,R,F,r,n,d))for(;;){if(c(S,P,O,R,F,r,n,d)){c(S,L,C,I,z,r,n,d)?(s(it,T,S,r,n,d,p),++T,--S):(a(it,S,r,n,d,p),--S);break}if(--S<it)break}l(t,T-1,L,C,I,z,D,r,n,d,p),l(e,S+1,P,O,R,F,j,r,n,d,p),T-2-t<=f?i(t,T-2,r,n,d,p):h(t,T-2,r,n,d,p),e-(S+2)<=f?i(S+2,e,r,n,d,p):h(S+2,e,r,n,d,p),S-T<=f?i(T,S,r,n,d,p):h(T,S,r,n,d,p)}e.exports=n;var f=32},{}],242:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){for(var l=r,u=r;u<n;++u){var c=t[2u],h=t[2u+1],f=e[u];i<=c&&c<=o&&a<=h&&h<=s&&(u===l?l+=1:(t[2u]=t[2l],t[2u+1]=t[2l+1],e[u]=e[l],t[2l]=c,t[2l+1]=h,e[l]=f,l+=1))}return l}function i(t,e,r){this.pixelSize=t,this.offset=e,this.count=r}function a(t,e,r,a){function l(i,a,o,s,u,c){var h=.5o,f=s+1,d=u-s;r[_]=d,x[_++]=c;for(var p=0;p<2;++p)for(var m=0;m<2;++m){var v=i+ph,g=a+mh,y=n(t,e,f,u,v,g,v+h,g+h);if(y!==f){if(y-f>=Math.max(.9d,32)){var b=u+s>>>1;l(v,g,h,f,b,c+1),f=b}l(v,g,h,f,y,c+1),f=y}}}var u=t.length>>>1;if(u<1)return[];for(var c=1/0,h=1/0,f=-1/0,d=-1/0,p=0;p<u;++p){var m=t[2p],v=t[2p+1];c=Math.min(c,m),f=Math.max(f,m),h=Math.min(h,v),d=Math.max(d,v),e[p]=p}c===f&&(f+=1+Math.abs(f)),h===d&&(d+=1+Math.abs(f));var g=1/(f-c),y=1/(d-h),b=Math.max(f-c,d-h);a=a\|\|[0,0,0,0],a[0]=c,a[1]=h,a[2]=f,a[3]=d;var x=o.mallocInt32(u),_=0;l(c,h,b,0,u,0),s(x,t,e,r,u);for(var w=[],M=0,k=u,_=u-1;_>=0;--_){t[2_]=(t[2_]-c)g,t[2_+1]=(t[2_+1]-h)y;var A=x[_];A!==M&&(w.push(new i(bMath.pow(.5,A),_+1,k-(_+1))),k=_+1,M=A)}return w.push(new i(bMath.pow(.5,A+1),0,k)),o.free(x),w}var o=t(\"typedarray-pool\"),s=t(\"./lib/sort\");e.exports=a},{\"./lib/sort\":241,\"typedarray-pool\":541}],243:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.sizeBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.charBuffer=s,this.pointCount=0,this.pickOffset=0,this.points=null,this.scales=[],this.xCoords=[],this.charCanvas=document.createElement(\"canvas\"),this.charTexture=m(this.plot.gl,this.charCanvas),this.charStep=400,this.charFit=.255,this.snapThreshold=1e4,this.paletteTexture=m(this.plot.gl,[256,1])}function i(){var t=this.plot,e=t.viewBox,r=t.dataBox,n=t.pixelRatio,i=r[2]-r[0],a=r[3]-r[1],u=2/i,c=2/a,h=-r[0]-.5i,f=-r[1]-.5a;_[0]=u,w[0]=u-_[0],_[1]=c,w[1]=c-_[1],M[0]=h,k[0]=h-M[0],M[1]=f,k[1]=f-M[1];var d=e[2]-e[0],p=e[3]-e[1];o=Math.min(i/d,a/p),A[0]=2n/d,A[1]=2n/p,s=r[0],l=r[2]}function a(t,e){var r=t.gl,i=u(r,f.vertex,f.fragment),a=u(r,f.pickVertex,f.pickFragment),o=c(r),s=c(r),l=c(r),h=c(r),d=c(r),p=new n(t,i,a,o,s,l,h,d);return p.update(e),t.addObject(p),p}e.exports=a;var o,s,l,u=t(\"gl-shader\"),c=t(\"gl-buffer\"),h=t(\"typedarray-pool\"),f=t(\"./lib/shaders\"),d=t(\"snap-points-2d\"),p=t(\"font-atlas-sdf\"),m=t(\"gl-texture2d\"),v=t(\"color-id\"),g=t(\"ndarray\"),y=t(\"clamp\"),b=t(\"binary-search-bounds\"),x=n.prototype,_=new Float32Array([0,0]),w=new Float32Array([0,0]),M=new Float32Array([0,0]),k=new Float32Array([0,0]),A=[0,0],T=[0,0,0,0];x.drawPick=function(t){var e=void 0!==t,r=this.plot,n=this.pointCount,a=n>this.snapThreshold;if(!n)return t;i.call(this);var u=r.gl,c=e?this.pickShader:this.shader,h=u.isEnabled(u.BLEND);if(c.bind(),e){this.pickOffset=t;for(var f=0;f<4;++f)T[f]=t>>8f&255;c.uniforms.pickOffset=T,this.idBuffer.bind(),c.attributes.id.pointer(u.UNSIGNED_BYTE,!1)}else u.blendFuncSeparate(u.SRC_ALPHA,u.ONE_MINUS_SRC_ALPHA,u.ONE,u.ONE_MINUS_SRC_ALPHA),u.blendColor(0,0,0,1),h\|\|u.enable(u.BLEND),this.colorBuffer.bind(),c.attributes.color.pointer(u.UNSIGNED_BYTE,!1),this.charBuffer.bind(),c.attributes.char.pointer(u.UNSIGNED_BYTE,!1),c.uniforms.chars=this.charTexture.bind(0),c.uniforms.charsShape=[this.charCanvas.width,this.charCanvas.height],c.uniforms.charsStep=this.charStep,c.uniforms.palette=this.paletteTexture.bind(1);this.sizeBuffer.bind(),c.attributes.size.pointer(u.FLOAT,!1,8,0),e\|\|c.attributes.border.pointer(u.FLOAT,!1,8,4),this.positionBuffer.bind(),c.attributes.positionHi.pointer(u.FLOAT,!1,16,0),c.attributes.positionLo.pointer(u.FLOAT,!1,16,8),c.uniforms.pixelRatio=r.pixelRatio,c.uniforms.scaleHi=_,c.uniforms.scaleLo=w,c.uniforms.translateHi=M,c.uniforms.translateLo=k,c.uniforms.viewBox=r.viewBox;var d=this.scales;if(a)for(var p=d.length-1;p>=0;p--){var m=d[p];if(!(m.pixelSize&&m.pixelSize<1.25o&&p>1)){var v=m.offset,g=m.count+v,y=b.ge(this.xCoords,s,v,g-1),x=b.lt(this.xCoords,l,y,g-1)+1;x>y&&u.drawArrays(u.POINTS,y,x-y)}}else u.drawArrays(u.POINTS,0,n);if(e)return t+n;h?u.blendFunc(u.ONE,u.ONE_MINUS_SRC_ALPHA):u.disable(u.BLEND)},x.draw=x.drawPick,x.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n\|\|r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2a],o[2a+1]]}},x.update=function(t){t=t\|\|{};var e=t.positions\|\|[],r=t.colors\|\|[],n=t.glyphs\|\|[],i=t.sizes\|\|[],a=t.borderWidths\|\|[],o=t.borderColors\|\|[],s=this.plot.gl,l=this.pointCount,u=l>this.snapThreshold;if(null!=t.positions){this.points=e,l=this.points.length/2,u=l>this.snapThreshold;var c=h.mallocFloat32(2l),f=h.mallocFloat64(2l),m=h.mallocUint32(l),b=h.mallocFloat32(4l);f.set(this.points),u&&(this.i2idx&&h.free(this.i2idx),this.i2idx=h.mallocInt32(l),this.scales=d(f,this.i2idx,c)),this.pointCount=l;for(var x=0;x<l;++x){var _=u?this.i2idx[x]:x;m[x]=_;var w=e[2_],M=e[2_+1];b[4x]=w,b[4x+1]=M,b[4x+2]=w-b[4x],b[4x+3]=M-b[4x+1],this.xCoords[x]=w}this.idBuffer.update(m),this.positionBuffer.update(b),h.free(b),h.free(m),h.free(f),h.free(c)}for(var k=h.mallocFloat32(2l),A=h.mallocUint8(2l),T=h.mallocUint8(2l),S={},E=[],L=[],C=[],x=0,I=l,z=0;x<I;++x){var D=[255r[4x],255r[4x+1],255r[4x+2],255r[4x+3]],P=v(D,!1);null==S[P]&&(S[P]=z++,L.push(D[0]),L.push(D[1]),L.push(D[2]),L.push(D[3])),E.push(P),o&&o.length&&(D=[255o[4x],255o[4x+1],255o[4x+2],255o[4x+3]],P=v(D,!1),null==S[P]&&(S[P]=z++,L.push(D[0]),L.push(D[1]),L.push(D[2]),L.push(D[3])),C.push(P))}for(var O={},x=0,I=l,z=0;x<I;x++){var R=n[x];null==O[R]&&(O[R]=z++)}for(var F=0,x=0,I=i.length;x<I;++x)i[x]>F&&(F=i[x]);var j=this.charStep;this.charStep=y(Math.ceil(4F),128,768);var N=Object.keys(O),B=this.charStep,U=Math.floor(B/2),V=s.getParameter(s.MAX_TEXTURE_SIZE),H=V/B(V/B),q=Math.min(V,BN.length),G=Math.min(V,BMath.ceil(BN.length/V)),Y=Math.floor(q/B);N.length>H&&console.warn(\"gl-scatter2d-fancy: number of characters is more than maximum texture size. Try reducing it.\"),this.chars&&this.chars+\"\"==N+\"\"&&this.charStep==j\|\|(this.charCanvas=p({canvas:this.charCanvas,family:\"sans-serif\",size:U,shape:[q,G],step:[B,B],chars:N,align:!0,fit:this.charFit}),this.chars=N);for(var x=0;x<l;++x){var _=u?this.i2idx[x]:x,W=i[_],X=a[_];k[2x]=2W,k[2x+1]=X;var P=E[_],Z=S[P];A[2x]=Z;var J=C[_],K=S[J];A[2x+1]=K;var R=n[_],Q=O[R];T[2x+1]=Math.floor(Q/Y),T[2x]=Q%Y}this.sizeBuffer.update(k),this.colorBuffer.update(A),this.charBuffer.update(T),this.charTexture.shape=[this.charCanvas.width,this.charCanvas.height],this.charCanvas&&this.charCanvas.width&&this.charTexture.setPixels(this.charCanvas),this.paletteTexture.setPixels(g(L.slice(0,1024),[256,1,4])),h.free(k),h.free(A),h.free(T)},x.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.sizeBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.charBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":232,\"binary-search-bounds\":233,clamp:88,\"color-id\":92,\"font-atlas-sdf\":134,\"gl-buffer\":156,\"gl-shader\":234,\"gl-texture2d\":267,ndarray:467,\"snap-points-2d\":242,\"typedarray-pool\":541}],244:[function(t,e,r){r.pointVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 positionHi, positionLo;\\nattribute float weight;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pointSize, useWeight;\\n\\nvarying float fragWeight;\\n\\n\\nvec4 pfx_1_0(vec2 scaleHi, vec2 scaleLo, vec2 translateHi, vec2 translateLo, vec2 positionHi, vec2 positionLo) {\\n return vec4((positionHi + translateHi) * scaleHi\\n + (positionLo + translateLo) * scaleHi\\n + (positionHi + translateHi) * scaleLo\\n + (positionLo + translateLo) * scaleLo, 0.0, 1.0);\\n}\\n\\nvoid main() {\\n gl_Position = pfx_1_0(scaleHi, scaleLo, translateHi, translateLo, positionHi, positionLo);\\n gl_PointSize = pointSize;\\n fragWeight = mix(1.0, weight, useWeight);\\n}\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\n\\nvarying float fragWeight;\\n\\nfloat smoothStep(float x, float y) {\\n return 1.0 / (1.0 + exp(50.0(x - y)));\\n}\\n\\nvoid main() {\\n float radius = length(2.0gl_PointCoord.xy-1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n vec4 baseColor = mix(borderColor, color, smoothStep(radius, centerFraction));\\n float alpha = 1.0 - pow(1.0 - baseColor.a, fragWeight);\\n gl_FragColor = vec4(baseColor.rgb * alpha, alpha);\\n}\\n\",r.pickVertex=\"precision highp float;\\n#define GLSLIFY 1\\n\\nvec4 pfx_1_0(vec2 scaleHi, vec2 scaleLo, vec2 translateHi, vec2 translateLo, vec2 positionHi, vec2 positionLo) {\\n return vec4((positionHi + translateHi) * scaleHi\\n + (positionLo + translateLo) * scaleHi\\n + (positionHi + translateHi) * scaleLo\\n + (positionLo + translateLo) * scaleLo, 0.0, 1.0);\\n}\\n\\nattribute vec2 positionHi, positionLo;\\nattribute vec4 pickId;\\n\\nuniform vec2 scaleHi, scaleLo, translateHi, translateLo;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n gl_Position = pfx_1_0(scaleHi, scaleLo, translateHi, translateLo, positionHi, positionLo);\\n gl_PointSize = pointSize;\\n fragId = id;\\n}\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\"},{}],245:[function(t,e,r){arguments[4][84][0].apply(r,arguments)},{dup:84}],246:[function(t,e,r){arguments[4][241][0].apply(r,arguments)},{dup:241}],247:[function(t,e,r){arguments[4][242][0].apply(r,arguments)},{\"./lib/sort\":246,dup:242,\"typedarray-pool\":541}],248:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o){this.plot=t,this.positionBufferHi=e,this.positionBufferLo=r,this.pickBuffer=n,this.weightBuffer=i,this.shader=a,this.pickShader=o,this.scales=[],this.size=12,this.borderSize=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0,this.points=null,this.xCoords=null,this.snapPoints=!0}function i(t,e){var r=t.gl,i=o(r),s=o(r),l=o(r),u=o(r),h=a(r,c.pointVertex,c.pointFragment),f=a(r,c.pickVertex,c.pickFragment),d=new n(t,i,s,l,u,h,f);return d.update(e),t.addObject(d),d}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"binary-search-bounds\"),l=t(\"snap-points-2d\"),u=t(\"typedarray-pool\"),c=t(\"./lib/shader\"),h=t(\"array-normalize\"),f=t(\"array-bounds\");e.exports=i;var d=n.prototype,p=new Float32Array(2),m=new Float32Array(2),v=new Float32Array(2),g=new Float32Array(2),y=[0,0,0,0];d.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBufferHi.dispose(),this.positionBufferLo.dispose(),this.pickBuffer.dispose(),this.xCoords&&u.free(this.xCoords),this.plot.removeObject(this)},d.update=function(t){function e(e,r){return e in t?t[e]:r}if(t=t\|\|{},this.size=e(\"size\",12),this.color=e(\"color\",[1,0,0,1]).slice(),this.borderSize=e(\"borderSize\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.snapPoints=e(\"snapPoints\",!0),null!=t.positions){this.xCoords&&u.free(this.xCoords),this.points=t.positions;var r=this.points.length>>>1,n=u.mallocInt32(r),i=u.mallocFloat32(r),a=u.mallocFloat64(2r);if(a.set(this.points),this.snapPoints)this.scales=l(a,n,i,this.bounds);else{this.bounds=f(a,2),h(a,2,this.bounds);for(var o=0;o<r;o++)n[o]=o,i[o]=1}var s=u.mallocFloat64(r),c=u.mallocFloat32(2r),d=u.mallocFloat32(2r);c.set(a);for(var o=0,p=0;o<r;o++,p+=2)d[p]=a[p]-c[p],d[p+1]=a[p+1]-c[p+1],s[o]=a[p];this.positionBufferHi.update(c),this.positionBufferLo.update(d),this.pickBuffer.update(n),this.weightBuffer.update(i),u.free(c),u.free(d),u.free(i),u.free(a),u.free(n),this.xCoords=s,this.pointCount=r,this.pickOffset=0}},d.draw=function(t){var e=void 0!==t,r=this.plot,n=e?this.pickShader:this.shader,i=this.scales,a=this.positionBufferHi,o=this.positionBufferLo,s=this.pickBuffer,l=this.bounds,u=this.size,c=this.borderSize,h=r.gl,f=e?r.pickPixelRatio:r.pixelRatio,d=r.viewBox,b=r.dataBox;if(0===this.pointCount)return t;var x=l[2]-l[0],_=l[3]-l[1],w=b[2]-b[0],M=b[3]-b[1],k=(d[2]-d[0])f/r.pixelRatio,A=(d[3]-d[1])f/r.pixelRatio,T=this.pixelSize=Math.min(w/k,M/A),S=2x/w,E=2_/M;p[0]=S,p[1]=E,m[0]=S-p[0],m[1]=E-p[1];var L=(l[0]-b[0]-.5w)/x,C=(l[1]-b[1]-.5M)/_;v[0]=L,v[1]=C,g[0]=L-v[0],g[1]=C-v[1],n.bind(),n.uniforms.scaleHi=p,n.uniforms.scaleLo=m,n.uniforms.translateHi=v,n.uniforms.translateLo=g,n.uniforms.color=this.color,n.uniforms.borderColor=this.borderColor,n.uniforms.pointSize=f(u+c),n.uniforms.centerFraction=0===this.borderSize?2:u/(u+c+1.25),a.bind(),n.attributes.positionHi.pointer(),o.bind(),n.attributes.positionLo.pointer(),e?(this.pickOffset=t,y[0]=255&t,y[1]=t>>8&255,y[2]=t>>16&255,y[3]=t>>24&255,n.uniforms.pickOffset=y,s.bind(),n.attributes.pickId.pointer(h.UNSIGNED_BYTE)):(n.uniforms.useWeight=1,this.weightBuffer.bind(),n.attributes.weight.pointer());var I=!0;if(this.snapPoints)for(var z=i.length-1;z>=0;z--){var D=i[z];if(!(D.pixelSize<T&&z>1)){var P=this.getVisibleRange(D),O=P[0],R=P[1];R>O&&h.drawArrays(h.POINTS,O,R-O),!e&&I&&(I=!1,n.uniforms.useWeight=0)}}else h.drawArrays(h.POINTS,0,this.pointCount);return t+this.pointCount},d.getVisibleRange=function(t){var e=this.plot.dataBox,r=this.bounds,n=this.pixelSize,i=this.size,a=this.plot.pixelRatio,o=r[2]-r[0];r[3],r[1];if(!t)for(var t,l=this.scales.length-1;l>=0&&(t=this.scales[l],t.pixelSize<n&&l>1);l--);var u=this.xCoords,c=(e[0]-r[0]-nia)/o,h=(e[2]-r[0]+nia)/o,f=t.offset,d=t.count+f,p=s.ge(u,c,f,d-1);return[p,s.lt(u,h,p,d-1)+1]},d.drawPick=d.draw,d.pick=function(t,e,r){var n=r-this.pickOffset;return n<0\|\|n>=this.pointCount?null:{object:this,pointId:n,dataCoord:[this.points[2n],this.points[2n+1]]}}},{\"./lib/shader\":244,\"array-bounds\":44,\"array-normalize\":45,\"binary-search-bounds\":245,\"gl-buffer\":156,\"gl-shader\":255,\"snap-points-2d\":247,\"typedarray-pool\":541}],249:[function(t,e,r){\"use strict\";function n(t,e){var r=a[e];if(r\|\|(r=a[e]={}),t in r)return r[t];for(var n=i(t,{textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),o=i(t,{triangles:!0,textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),s=[[1/0,1/0],[-1/0,-1/0]],l=0;l<n.positions.length;++l)for(var u=n.positions[l],c=0;c<2;++c)s[0][c]=Math.min(s[0][c],u[c]),s[1][c]=Math.max(s[1][c],u[c]);return r[t]=[o,n,s]}var i=t(\"vectorize-text\");e.exports=n;var a={}},{\"vectorize-text\":554}],250:[function(t,e,r){function n(t,e){var r=i(t,e),n=r.attributes;return n.position.location=0,n.color.location=1,n.glyph.location=2,n.id.location=3,r}var i=t(\"gl-shader\"),a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\| \\n any(greaterThan(position, clipBounds[1])) ) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n \\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\| any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n \\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) \|\|\\n any(greaterThan(position, clipBounds[1])) ) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale = highlightScale;\\n }\\n\\n vec4 clipCenter = projection view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5lscale(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\",l=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(dataCoordinate, fragClipBounds[0])) \|\|\\n any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n discard;\\n } else {\\n gl_FragColor = interpColor * opacity;\\n }\\n}\\n\",u=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(dataCoordinate, fragClipBounds[0])) \|\| \\n any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n discard;\\n } else {\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n }\\n}\",c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],h={vertex:a,fragment:l,attributes:c},f={vertex:o,fragment:l,attributes:c},d={vertex:s,fragment:l,attributes:c},p={vertex:a,fragment:u,attributes:c},m={vertex:o,fragment:u,attributes:c},v={vertex:s,fragment:u,attributes:c};r.createPerspective=function(t){return n(t,h)},r.createOrtho=function(t){return n(t,f)},r.createProject=function(t){return n(t,d)},r.createPickPerspective=function(t){return n(t,p)},r.createPickOrtho=function(t){return n(t,m)},r.createPickProject=function(t){return n(t,v)}},{\"gl-shader\":255}],251:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]r+e[4]n+e[8]i+e[12]a,t[1]=e[1]r+e[5]n+e[9]i+e[13]a,t[2]=e[2]r+e[6]n+e[10]i+e[14]a,t[3]=e[3]r+e[7]n+e[11]i+e[15]a,t}function i(t,e,r,i){return n(i,i,r),n(i,i,e),n(i,i,t)}function a(t,e){this.index=t,this.dataCoordinate=this.position=e}function o(t,e,r,n,i,o,s,l,u,c,h,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=o,this.glyphBuffer=s,this.idBuffer=l,this.vao=u,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=h,this.pickProjectShader=f,this.points=[],this._selectResult=new a(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}function s(t){return t[0]=t[1]=t[2]=0,t}function l(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function u(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function c(t){for(var e=L,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}function h(t,e,r,n,a){var o,h=e.axesProject,f=e.gl,d=t.uniforms,p=r.model\|\|x,m=r.view\|\|x,v=r.projection\|\|x,y=e.axesBounds,b=c(e.clipBounds);o=e.axes?e.axes.lastCubeProps.axis:[1,1,1],w[0]=2/f.drawingBufferWidth,w[1]=2/f.drawingBufferHeight,t.bind(),d.view=m,d.projection=v,d.screenSize=w,d.highlightId=e.highlightId,d.highlightScale=e.highlightScale,d.clipBounds=b,\n", "d.pickGroup=e.pickId/255,d.pixelRatio=e.pixelRatio;for(var _=0;_<3;++_)if(h[_]&&e.projectOpacity[_]<1===n){d.scale=e.projectScale[_],d.opacity=e.projectOpacity[_];for(var L=S,C=0;C<16;++C)L[C]=0;for(var C=0;C<4;++C)L[5C]=1;L[5_]=0,o[_]<0?L[12+_]=y[0][_]:L[12+_]=y[1][_],g(L,p,L),d.model=L;var I=(_+1)%3,z=(_+2)%3,D=s(M),P=s(k);D[I]=1,P[z]=1;var O=i(v,m,p,l(A,D)),R=i(v,m,p,l(T,P));if(Math.abs(O[1])>Math.abs(R[1])){var F=O;O=R,R=F,F=D,D=P,P=F;var j=I;I=z,z=j}O[0]<0&&(D[I]=-1),R[1]>0&&(P[z]=-1);for(var N=0,B=0,C=0;C<4;++C)N+=Math.pow(p[4I+C],2),B+=Math.pow(p[4z+C],2);D[I]/=Math.sqrt(N),P[z]/=Math.sqrt(B),d.axes[0]=D,d.axes[1]=P,d.fragClipBounds[0]=u(E,b[0],_,-1e8),d.fragClipBounds[1]=u(E,b[1],_,1e8),e.vao.draw(f.TRIANGLES,e.vertexCount),e.lineWidth>0&&(f.lineWidth(e.lineWidth),e.vao.draw(f.LINES,e.lineVertexCount,e.vertexCount))}}function f(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1\|\|a){t.bind();var s=t.uniforms;s.model=n.model\|\|x,s.view=n.view\|\|x,s.projection=n.projection\|\|x,w[0]=2/o.drawingBufferWidth,w[1]=2/o.drawingBufferHeight,s.screenSize=w,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=z,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}h(e,r,n,i,a),r.vao.unbind()}function d(t){var e=t.gl,r=y.createPerspective(e),n=y.createOrtho(e),i=y.createProject(e),a=y.createPickPerspective(e),s=y.createPickOrtho(e),l=y.createPickProject(e),u=p(e),c=p(e),h=p(e),f=p(e),d=m(e,[{buffer:u,size:3,type:e.FLOAT},{buffer:c,size:4,type:e.FLOAT},{buffer:h,size:2,type:e.FLOAT},{buffer:f,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new o(e,r,n,i,u,c,h,f,d,a,s,l);return v.update(t),v}var p=t(\"gl-buffer\"),m=t(\"gl-vao\"),v=t(\"typedarray-pool\"),g=t(\"gl-mat4/multiply\"),y=t(\"./lib/shaders\"),b=t(\"./lib/glyphs\"),x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];e.exports=d;var _=o.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},_.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var w=[0,0],M=[0,0,0],k=[0,0,0],A=[0,0,0,1],T=[0,0,0,1],S=x.slice(),E=[0,0,0],L=[[0,0,0],[0,0,0]],C=[-1e8,-1e8,-1e8],I=[1e8,1e8,1e8],z=[C,I];_.draw=function(t){f(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!1,!1)},_.drawTransparent=function(t){f(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,!0,!1)},_.drawPick=function(t){f(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,!1,!0)},_.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount\|\|e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(t=t\|\|{},\"perspective\"in t&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(\"projectOpacity\"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{var r=+t.projectOpacity;this.projectOpacity=[r,r,r]}\"opacity\"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font\|\|\"normal\",a=t.alignment\|\|[0,0],o=[1/0,1/0,1/0],s=[-1/0,-1/0,-1/0],l=t.glyph,u=t.color,c=t.size,h=t.angle,f=t.lineColor,d=0,p=0,m=0,g=n.length;t:for(var y=0;y<g;++y){for(var x=n[y],_=0;_<3;++_)if(isNaN(x[_])\|\|!isFinite(x[_]))continue t;var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var M=w[0],k=w[1],A=w[2];p+=3M.cells.length,m+=2k.edges.length}var T=p+m,S=v.mallocFloat(3T),E=v.mallocFloat(4T),L=v.mallocFloat(2T),C=v.mallocUint32(T),I=[0,a[1]],z=0,D=p,P=[0,0,0,1],O=[0,0,0,1],R=Array.isArray(u)&&Array.isArray(u[0]),F=Array.isArray(f)&&Array.isArray(f[0]);t:for(var y=0;y<g;++y){for(var x=n[y],_=0;_<3;++_){if(isNaN(x[_])\|\|!isFinite(x[_])){d+=1;continue t}s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_])}var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var M=w[0],k=w[1],A=w[2];if(Array.isArray(u)){var j;if(j=R?u[y]:u,3===j.length){for(var _=0;_<3;++_)P[_]=j[_];P[3]=1}else if(4===j.length)for(var _=0;_<4;++_)P[_]=j[_]}else P[0]=P[1]=P[2]=0,P[3]=1;if(Array.isArray(f)){var j;if(j=F?f[y]:f,3===j.length){for(var _=0;_<3;++_)O[_]=j[_];O[_]=1}else if(4===j.length)for(var _=0;_<4;++_)O[_]=j[_]}else O[0]=O[1]=O[2]=0,O[3]=1;var N=.5;Array.isArray(c)?N=+c[y]:c?N=+c:this.useOrtho&&(N=12);var B=0;Array.isArray(h)?B=+h[y]:h&&(B=+h);for(var U=Math.cos(B),V=Math.sin(B),x=n[y],_=0;_<3;++_)s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_]);a[0]<0?I[0]=a[0](1+A[1][0]):a[0]>0&&(I[0]=-a[0](1+A[0][0]));for(var H=M.cells,q=M.positions,_=0;_<H.length;++_)for(var G=H[_],Y=0;Y<3;++Y){for(var W=0;W<3;++W)S[3z+W]=x[W];for(var W=0;W<4;++W)E[4z+W]=P[W];C[z]=d;var X=q[G[Y]];L[2z]=N(UX[0]-VX[1]+I[0]),L[2z+1]=N(VX[0]+UX[1]+I[1]),z+=1}for(var H=k.edges,q=k.positions,_=0;_<H.length;++_)for(var G=H[_],Y=0;Y<2;++Y){for(var W=0;W<3;++W)S[3D+W]=x[W];for(var W=0;W<4;++W)E[4D+W]=O[W];C[D]=d;var X=q[G[Y]];L[2D]=N(UX[0]-VX[1]+I[0]),L[2D+1]=N(VX[0]+UX[1]+I[1]),D+=1}d+=1}this.vertexCount=p,this.lineVertexCount=m,this.pointBuffer.update(S),this.colorBuffer.update(E),this.glyphBuffer.update(L),this.idBuffer.update(new Uint32Array(C)),v.free(S),v.free(E),v.free(L),v.free(C),this.bounds=[o,s],this.points=n,this.pointCount=n.length}},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/glyphs\":249,\"./lib/shaders\":250,\"gl-buffer\":156,\"gl-mat4/multiply\":183,\"gl-vao\":271,\"typedarray-pool\":541}],252:[function(t,e,r){\"use strict\";r.boxVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\",r.boxFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = color;\\n}\\n\"},{}],253:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}function i(t,e){var r=t.gl,i=o(r,[0,0,0,1,1,0,1,1]),l=a(r,s.boxVertex,s.boxFragment),u=new n(t,i,l);return u.update(e),t.addOverlay(u),u}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"./lib/shaders\");e.exports=i;var l=n.prototype;l.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,h=(e[0]-l[0])(u[2]-u[0])/(l[2]-l[0])+u[0],f=(e[1]-l[1])(u[3]-u[1])/(l[3]-l[1])+u[1],d=(e[2]-l[0])(u[2]-u[0])/(l[2]-l[0])+u[0],p=(e[3]-l[1])(u[3]-u[1])/(l[3]-l[1])+u[1];if(h=Math.max(h,u[0]),f=Math.max(f,u[1]),d=Math.min(d,u[2]),p=Math.min(p,u[3]),!(d<h\|\|p<f)){o.bind();var m=s[2]-s[0],v=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,f,i),o.drawBox(0,f,h,p,i),o.drawBox(0,p,m,v,i),o.drawBox(d,f,m,p,i)),this.innerFill&&o.drawBox(h,f,d,p,n),r>0){var g=rc;o.drawBox(h-g,f-g,d+g,f+g,a),o.drawBox(h-g,p-g,d+g,p+g,a),o.drawBox(h-g,f-g,h+g,p+g,a),o.drawBox(d-g,f-g,d+g,p+g,a)}}}},l.update=function(t){t=t\|\|{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor\|\|[0,0,0,.5]).slice(),this.outerColor=(t.outerColor\|\|[0,0,0,.5]).slice(),this.borderColor=(t.borderColor\|\|[0,0,0,1]).slice(),this.borderWidth=t.borderWidth\|\|0,this.selectBox=(t.selectBox\|\|this.selectBox).slice()},l.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":252,\"gl-buffer\":156,\"gl-shader\":255}],254:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function i(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}function a(t,e){return new i(t,o(t,e),s.mallocUint8(e[0]e[1]4))}e.exports=a;var o=t(\"gl-fbo\"),s=t(\"typedarray-pool\"),l=t(\"ndarray\"),u=t(\"bit-twiddle\").nextPow2,c=t(\"cwise/lib/wrapper\")({args:[\"array\",{offset:[0,0,1],array:0},{offset:[0,0,2],array:0},{offset:[0,0,3],array:0},\"scalar\",\"scalar\",\"index\"],pre:{body:\"{this_closestD2=1e8,this_closestX=-1,this_closestY=-1}\",args:[],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[]},body:{body:\"{if(_inline_46_arg0_<255\|\|_inline_46_arg1_<255\|\|_inline_46_arg2_<255\|\|_inline_46_arg3_<255){var 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this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(re4>this.buffer.length){s.free(this.buffer);for(var n=this.buffer=s.mallocUint8(u(re4)),i=0;i<re4;++i)n[i]=255}return t}}}),h.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT\|t.DEPTH_BUFFER_BIT))},h.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout\|\|clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},h.query=function(t,e,r){if(!this.gl)return null;var i=this.fbo.shape.slice();t\|=0,e\|=0,\"number\"!=typeof r&&(r=1);var a=0\|Math.min(Math.max(t-r,0),i[0]),o=0\|Math.min(Math.max(t+r,0),i[0]),s=0\|Math.min(Math.max(e-r,0),i[1]),u=0\|Math.min(Math.max(e+r,0),i[1]);if(o<=a\|\|u<=s)return null;var h=[o-a,u-s],f=l(this.buffer,[h[0],h[1],4],[4,4i[0],1],4(a+i[0]s)),d=c(f.hi(h[0],h[1],1),r,r),p=d[0],m=d[1];return p<0\|\|Math.pow(this.radius,2)<d[2]?null:new n(p+a\|0,m+s\|0,f.get(p,m,0),[f.get(p,m,1),f.get(p,m,2),f.get(p,m,3)],Math.sqrt(d[2]))},h.dispose=function(){this.gl&&(this.fbo.dispose(),s.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":67,\"cwise/lib/wrapper\":113,\"gl-fbo\":164,ndarray:467,\"typedarray-pool\":541}],255:[function(t,e,r){\"use strict\";function n(t){this.gl=t,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}function i(t,e){return t.name<e.name?-1:1}function a(t,e,r,i,a){var o=new n(t);return o.update(e,r,i,a),o}var o=t(\"./lib/create-uniforms\"),s=t(\"./lib/create-attributes\"),l=t(\"./lib/reflect\"),u=t(\"./lib/shader-cache\"),c=t(\"./lib/runtime-reflect\"),h=t(\"./lib/GLError\"),f=n.prototype;f.bind=function(){this.program\|\|this._relink(),this.gl.useProgram(this.program)},f.dispose=function(){this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},f.update=function(t,e,r,n){function a(){d.program=u.program(p,d._vref,d._fref,x,_);for(var t=0;t<r.length;++t)E[t]=p.getUniformLocation(d.program,r[t].name)}if(!e\|\|1===arguments.length){var f=t;t=f.vertex,e=f.fragment,r=f.uniforms,n=f.attributes}var d=this,p=d.gl,m=d._vref;d._vref=u.shader(p,p.VERTEX_SHADER,t),m&&m.dispose(),d.vertShader=d._vref.shader;var v=this._fref;if(d._fref=u.shader(p,p.FRAGMENT_SHADER,e),v&&v.dispose(),d.fragShader=d._fref.shader,!r\|\|!n){var g=p.createProgram();if(p.attachShader(g,d.fragShader),p.attachShader(g,d.vertShader),p.linkProgram(g),!p.getProgramParameter(g,p.LINK_STATUS)){var y=p.getProgramInfoLog(g);throw new h(y,\"Error linking program:\"+y)}r=r\|\|c.uniforms(p,g),n=n\|\|c.attributes(p,g),p.deleteProgram(g)}n=n.slice(),n.sort(i);for(var b=[],x=[],_=[],w=0;w<n.length;++w){var M=n[w];if(M.type.indexOf(\"mat\")>=0){for(var k=0\|M.type.charAt(M.type.length-1),A=new Array(k),T=0;T<k;++T)A[T]=_.length,x.push(M.name+\"[\"+T+\"]\"),\"number\"==typeof M.location?_.push(M.location+T):Array.isArray(M.location)&&M.location.length===k&&\"number\"==typeof M.location[T]?_.push(0\|M.location[T]):_.push(-1);b.push({name:M.name,type:M.type,locations:A})}else b.push({name:M.name,type:M.type,locations:[_.length]}),x.push(M.name),\"number\"==typeof M.location?_.push(0\|M.location):_.push(-1)}for(var S=0,w=0;w<_.length;++w)if(_[w]<0){for(;_.indexOf(S)>=0;)S+=1;_[w]=S}var E=new Array(r.length);a(),d._relink=a,d.types={uniforms:l(r),attributes:l(n)},d.attributes=s(p,d,b,_),Object.defineProperty(d,\"uniforms\",o(p,d,r,E))},e.exports=a},{\"./lib/GLError\":256,\"./lib/create-attributes\":257,\"./lib/create-uniforms\":258,\"./lib/reflect\":259,\"./lib/runtime-reflect\":260,\"./lib/shader-cache\":261}],256:[function(t,e,r){arguments[4][213][0].apply(r,arguments)},{dup:213}],257:[function(t,e,r){arguments[4][214][0].apply(r,arguments)},{\"./GLError\":256,dup:214}],258:[function(t,e,r){arguments[4][215][0].apply(r,arguments)},{\"./GLError\":256,\"./reflect\":259,dup:215}],259:[function(t,e,r){arguments[4][216][0].apply(r,arguments)},{dup:216}],260:[function(t,e,r){arguments[4][217][0].apply(r,arguments)},{dup:217}],261:[function(t,e,r){arguments[4][218][0].apply(r,arguments)},{\"./GLError\":256,dup:218,\"gl-format-compiler-error\":165,\"weakmap-shim\":562}],262:[function(t,e,r){\"use strict\";function n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}function i(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r}e.exports=i;var a=n.prototype;a.update=function(t){t=t\|\|{},this.enable=(t.enable\|\|[!0,!0,!1,!1]).slice(),this.width=(t.width\|\|[1,1,1,1]).slice(),this.color=(t.color\|\|[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map(function(t){return t.slice()}),this.center=(t.center\|\|[1/0,1/0]).slice(),this.plot.setOverlayDirty()},a.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])(s[2]-s[0]),u=s[1]+(n[1]-o[1])/(o[3]-o[1])(s[3]-s[1]);t[0]&&a.drawLine(l,u,s[0],u,e[0],r[0]),t[1]&&a.drawLine(l,u,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,u,s[2],u,e[2],r[2]),t[3]&&a.drawLine(l,u,l,s[3],e[3],r[3])}},a.dispose=function(){this.plot.removeOverlay(this)}},{}],263:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\");e.exports=function(t){return n(t,\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vertexPosition = mix(coordinates[0],\\n mix(coordinates[2], coordinates[1], 0.5 (position + 1.0)), abs(position));\\n\\n vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n vec2 delta = weight * clipOffset * screenShape;\\n vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n gl_Position = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n fragColor = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\",\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\",null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":255}],264:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}function i(t,e){function r(t,e,r,n,a,o){var s=[t,e,r,0,0,0,1];s[n+3]=1,s[n]=a,i.push.apply(i,s),s[6]=-1,i.push.apply(i,s),s[n]=o,i.push.apply(i,s),i.push.apply(i,s),s[6]=1,i.push.apply(i,s),s[n]=a,i.push.apply(i,s)}var i=[];r(0,0,0,0,0,1),r(0,0,0,1,0,1),r(0,0,0,2,0,1),r(1,0,0,1,-1,1),r(1,0,0,2,-1,1),r(0,1,0,0,-1,1),r(0,1,0,2,-1,1),r(0,0,1,0,-1,1),r(0,0,1,1,-1,1);var l=a(t,i),u=o(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),c=s(t);c.attributes.position.location=0,c.attributes.color.location=1,c.attributes.weight.location=2;var h=new n(t,l,u,c);return h.update(e),h}var a=t(\"gl-buffer\"),o=t(\"gl-vao\"),s=t(\"./shaders/index\");e.exports=i;var l=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],u=n.prototype,c=[0,0,0],h=[0,0,0],f=[0,0];u.isTransparent=function(){return!1},u.drawTransparent=function(t){},u.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model\|\|l,o=t.view\|\|l,s=t.projection\|\|l;this.axes&&(i=this.axes.lastCubeProps.axis);for(var u=c,d=h,p=0;p<3;++p)i&&i[p]<0?(u[p]=this.bounds[0][p],d[p]=this.bounds[1][p]):(u[p]=this.bounds[1][p],d[p]=this.bounds[0][p]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=o,n.uniforms.projection=s,n.uniforms.coordinates=[this.position,u,d],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(var p=0;p<3;++p)n.uniforms.lineWidth=this.lineWidth[p]this.pixelRatio,this.enabled[p]&&(r.draw(e.TRIANGLES,6,6p),this.drawSides[p]&&r.draw(e.TRIANGLES,12,18+12p));r.unbind()},u.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},u.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":263,\"gl-buffer\":156,\"gl-vao\":271}],265:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n worldCoordinate = vec3(uv.zw, f.x);\\n vec4 worldPosition = model vec4(worldCoordinate, 1.0);\\n vec4 clipPosition = projection * view * worldPosition;\\n gl_Position = clipPosition;\\n kill = f.y;\\n value = f.z;\\n planeCoordinate = uv.xy;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * worldPosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n lightDirection = lightPosition - cameraCoordinate.xyz;\\n eyeDirection = eyePosition - cameraCoordinate.xyz;\\n surfaceNormal = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution_2_0(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\n\\n\\nfloat beckmannSpecular_1_1(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness) {\\n return beckmannDistribution_2_0(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\n\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n if (kill > 0.0 \|\|\\n any(lessThan(worldCoordinate, clipBounds[0])) \|\| any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n discard;\\n }\\n\\n vec3 N = normalize(surfaceNormal);\\n vec3 V = normalize(eyeDirection);\\n vec3 L = normalize(lightDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = max(beckmannSpecular_1_1(L, V, N, roughness), 0.);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n //decide how to interpolate color \\u2014 in vertex or in fragment\\n vec4 surfaceColor = step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) + step(.5, vertexColor) * vColor;\\n\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * view * worldPosition;\\n clipPosition.z = clipPosition.z + zOffset;\\n\\n gl_Position = clipPosition;\\n value = f;\\n kill = -1.0;\\n worldCoordinate = dataCoordinate;\\n planeCoordinate = uv.zw;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if(kill > 0.0 \|\|\\n any(lessThan(worldCoordinate, clipBounds[0])) \|\| any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n discard;\\n }\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\";r.createShader=function(t){var e=n(t,i,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,i,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,o,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,o,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":255}],266:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}function i(t){var e=x([y({colormap:t,nshades:R,format:\"rgba\"}).map(function(t){return[t[0],t[1],t[2],255t[3]]})]);return b.divseq(e,255),e}function a(t,e,r,i,a,o,s,l,u,c,h,f,d,p){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=i,this._pickShader=a,this._coordinateBuffer=o,this._vao=s,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=f,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new n([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=p,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[_(g.mallocFloat(1024),[0,0]),_(g.mallocFloat(1024),[0,0]),_(g.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}function o(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis\|\|j,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o\|\|e.surfaceProject[r],n=0;n<3;++n)s=s\|\|e.contourProject[r][n];for(r=0;r<3;++r){var l=N.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5n]=1;l[5r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],M(l,t.model,l);var u=N.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return N.showSurface=o,N.showContour=s,N}function s(t,e){t=t\|\|{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=B;n.model=t.model\|\|D,n.view=t.view\|\|D,n.projection=t.projection\|\|D,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]\|\|this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]\|\|this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=k(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],s=0;s<3;++s)a[s]=Math.min(Math.max(this.clipBounds[i][s],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=V,n.vertexColor=this.vertexColor;var l=U;for(M(l,n.view,n.model),M(l,n.projection,l),k(l,l),i=0;i<3;++i)n.eyePosition[i]=l[12+i]/l[15];var u=l[15];for(i=0;i<3;++i)u+=this.lightPosition[i]l[4i+3];for(i=0;i<3;++i){var c=l[12+i];for(s=0;s<3;++s)c+=l[4s+i]this.lightPosition[s];n.lightPosition[i]=c/u}var h=o(n,this);if(h.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=h.projections[i],this._shader.uniforms.clipBounds=h.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(h.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var d=this._contourVAO;for(d.bind(),i=0;i<3;++i)for(f.uniforms.permutation=O[i],r.lineWidth(this.contourWidth[i]),s=0;s<this.contourLevels[i].length;++s)this._contourCounts[i][s]&&(s===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==s&&s-1!==this.highlightLevel[i]\|\|(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),f.uniforms.height=this.contourLevels[i][s],d.draw(r.LINES,this._contourCounts[i][s],this._contourOffsets[i][s]));for(i=0;i<3;++i)for(f.uniforms.model=h.projections[i],f.uniforms.clipBounds=h.clipBounds[i],s=0;s<3;++s)if(this.contourProject[i][s]){f.uniforms.permutation=O[s],r.lineWidth(this.contourWidth[s]);for(var p=0;p<this.contourLevels[s].length;++p)p===this.highlightLevel[s]?(f.uniforms.contourColor=this.highlightColor[s],f.uniforms.contourTint=this.highlightTint[s]):0!==p&&p-1!==this.highlightLevel[s]\|\|(f.uniforms.contourColor=this.contourColor[s],f.uniforms.contourTint=this.contourTint[s]),f.uniforms.height=this.contourLevels[s][p],d.draw(r.LINES,this._contourCounts[s][p],this._contourOffsets[s][p])}for(d=this._dynamicVAO,d.bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=O[i],r.lineWidth(this.dynamicWidth[i]),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),s=0;s<3;++s)this.contourProject[s][i]&&(f.uniforms.model=h.projections[s],f.uniforms.clipBounds=h.clipBounds[s],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));d.unbind()}}function l(t,e){var r=e.shape.slice(),n=t.shape.slice();b.assign(t.lo(1,1).hi(r[0],r[1]),e),b.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),b.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),b.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),b.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function u(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function c(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function h(t){if(Array.isArray(t)){if(Array.isArray(t))return[c(t[0]),c(t[1]),c(t[2])];var e=c(t);return[e.slice(),e.slice(),e.slice()]}}function f(t){var e=t.gl,r=E(e),n=C(e),i=L(e),o=I(e),s=p(e),l=m(e,[{buffer:s,size:4,stride:z,offset:0},{buffer:s,size:3,stride:z,offset:16},{buffer:s,size:3,stride:z,offset:28}]),u=p(e),c=m(e,[{buffer:u,size:4,stride:20,offset:0},{buffer:u,size:1,stride:20,offset:16}]),h=p(e),f=m(e,[{buffer:h,size:2,type:e.FLOAT}]),d=v(e,1,R,e.RGBA,e.UNSIGNED_BYTE);d.minFilter=e.LINEAR,d.magFilter=e.LINEAR;var g=new a(e,[0,0],[[0,0,0],[0,0,0]],r,n,s,l,d,i,o,u,c,h,f),y={levels:[[],[],[]]};for(var b in t)y[b]=t[b];return y.colormap=y.colormap\|\|\"jet\",g.update(y),g}e.exports=f\n", ";var d=t(\"bit-twiddle\"),p=t(\"gl-buffer\"),m=t(\"gl-vao\"),v=t(\"gl-texture2d\"),g=t(\"typedarray-pool\"),y=t(\"colormap\"),b=t(\"ndarray-ops\"),x=t(\"ndarray-pack\"),_=t(\"ndarray\"),w=t(\"surface-nets\"),M=t(\"gl-mat4/multiply\"),k=t(\"gl-mat4/invert\"),A=t(\"binary-search-bounds\"),T=t(\"ndarray-gradient\"),S=t(\"./lib/shaders\"),E=S.createShader,L=S.createContourShader,C=S.createPickShader,I=S.createPickContourShader,z=40,D=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],P=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],O=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];!function(){for(var t=0;t<3;++t){var e=O[t],r=(t+1)%3,n=(t+2)%3;e[r+0]=1,e[n+3]=1,e[t+6]=1}}();var R=256,F=a.prototype;F.isTransparent=function(){return this.opacity<1},F.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;t<3;++t)if(this._contourCounts[t].length>0\|\|this._dynamicCounts[t]>0)return!0;return!1},F.pickSlots=1,F.setPickBase=function(t){this.pickId=t};var j=[0,0,0],N={showSurface:!1,showContour:!1,projections:[D.slice(),D.slice(),D.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]},B={model:D,view:D,projection:D,inverseModel:D.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},U=D.slice(),V=[1,0,0,0,1,0,0,0,1];F.draw=function(t){return s.call(this,t,!1)},F.drawTransparent=function(t){return s.call(this,t,!0)};var H={model:D,view:D,projection:D,inverseModel:D,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};F.drawPick=function(t){t=t\|\|{};var e=this.gl;e.disable(e.CULL_FACE);var r=H;r.model=t.model\|\|D,r.view=t.view\|\|D,r.projection=t.projection\|\|D,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=V;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var s=o(r,this);if(s.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=s.projections[n],this._pickShader.uniforms.clipBounds=s.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(s.showContour){var l=this._contourPickShader;l.bind(),l.uniforms=r;var u=this._contourVAO;for(u.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]),l.uniforms.permutation=O[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(l.uniforms.height=this.contourLevels[a][n],u.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(l.uniforms.model=s.projections[n],l.uniforms.clipBounds=s.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){l.uniforms.permutation=O[a],e.lineWidth(this.contourWidth[a]);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(l.uniforms.height=this.contourLevels[a][c],u.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}u.unbind()}},F.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0](t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1](t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var h=c?a:1-a,f=0;f<2;++f)for(var d=f?l:1-l,p=i+c,m=s+f,v=hd,g=0;g<3;++g)u[g]+=this._field[g].get(p,m)v;for(var y=this._pickResult.level,b=0;b<3;++b)if(y[b]=A.le(this.contourLevels[b],u[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]<this.contourLevels[b].length-1){var x=this.contourLevels[b][y[b]],_=this.contourLevels[b][y[b]+1];Math.abs(x-u[b])>Math.abs(_-u[b])&&(y[b]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],g=0;g<3;++g)r.dataCoordinate[g]=this._field[g].get(r.index[0],r.index[1]);return r},F.update=function(t){t=t\|\|{},this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=u(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=u(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=u(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=h(t.contourColor)),\"contourProject\"in t&&(this.contourProject=u(t.contourProject,function(t){return u(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=h(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=u(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=u(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0);var e=t.field\|\|t.coords&&t.coords[2]\|\|null,r=!1;if(e\|\|(e=this._field[2].shape[0]\|\|this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t\|\|\"coords\"in t){var n=(e.shape[0]+2)(e.shape[1]+2);n>this._field[2].data.length&&(g.freeFloat(this._field[2].data),this._field[2].data=g.mallocFloat(d.nextPow2(n))),this._field[2]=_(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),l(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(g.freeFloat(this._field[o].data),this._field[o].data=g.mallocFloat(this._field[2].size)),this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var s=t.coords;if(!Array.isArray(s)\|\|3!==s.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=s[o];for(y=0;y<2;++y)if(c.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");l(this._field[o],c)}}else if(t.ticks){var f=t.ticks;if(!Array.isArray(f)\|\|2!==f.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=f[o];if((Array.isArray(p)\|\|p.length)&&(p=_(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var m=_(p.data,a);m.stride[o]=p.stride[0],m.stride[1^o]=0,l(this._field[o],m)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var b=this._field,x=_(g.mallocFloat(3b[2].size2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)T(x.pick(o),b[o],\"mirror\");var M=_(g.mallocFloat(3b[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var k=x.get(0,o,y,0),A=x.get(0,o,y,1),S=x.get(1,o,y,0),E=x.get(1,o,y,1),L=x.get(2,o,y,0),C=x.get(2,o,y,1),I=SC-EL,z=LA-Ck,D=kE-AS,O=Math.sqrt(II+zz+DD);O<1e-8?(O=Math.max(Math.abs(I),Math.abs(z),Math.abs(D)),O<1e-8?(D=1,z=I=0,O=1):O=1/O):O=1/Math.sqrt(O),M.set(o,y,0,IO),M.set(o,y,1,zO),M.set(o,y,2,DO)}g.free(x.data);var R=[1/0,1/0,1/0],F=[-1/0,-1/0,-1/0],j=1/0,N=-1/0,B=(a[0]-1)(a[1]-1)6,U=g.mallocFloat(d.nextPow2(10B)),V=0,H=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var q=0;q<2;++q)for(var G=0;G<2;++G)for(var Y=0;Y<3;++Y){var W=this._field[Y].get(1+o+q,1+y+G);if(isNaN(W)\|\|!isFinite(W))continue t}for(Y=0;Y<6;++Y){var X=o+P[Y][0],Z=y+P[Y][1],J=this._field[0].get(X+1,Z+1),K=this._field[1].get(X+1,Z+1);W=this._field[2].get(X+1,Z+1);var Q=W;I=M.get(X+1,Z+1,0),z=M.get(X+1,Z+1,1),D=M.get(X+1,Z+1,2),t.intensity&&(Q=t.intensity.get(X,Z)),U[V++]=X,U[V++]=Z,U[V++]=J,U[V++]=K,U[V++]=W,U[V++]=0,U[V++]=Q,U[V++]=I,U[V++]=z,U[V++]=D,R[0]=Math.min(R[0],J),R[1]=Math.min(R[1],K),R[2]=Math.min(R[2],W),j=Math.min(j,Q),F[0]=Math.max(F[0],J),F[1]=Math.max(F[1],K),F[2]=Math.max(F[2],W),N=Math.max(N,Q),H+=1}}for(t.intensityBounds&&(j=+t.intensityBounds[0],N=+t.intensityBounds[1]),o=6;o<V;o+=10)U[o]=(U[o]-j)/(N-j);this._vertexCount=H,this._coordinateBuffer.update(U.subarray(0,V)),g.freeFloat(U),g.free(M.data),this.bounds=[R,F],this.intensity=t.intensity\|\|this._field[2],this.intensityBounds[0]===j&&this.intensityBounds[1]===N\|\|(r=!0),this.intensityBounds=[j,N]}if(\"levels\"in t){var $=t.levels;for($=Array.isArray($[0])?$.slice():[[],[],$],o=0;o<3;++o)$[o]=$[o].slice(),$.sort(function(t,e){return t-e});t:for(o=0;o<3;++o){if($[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<$[o].length;++y)if($[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=$}if(r){b=this._field,a=this.shape;for(var tt=[],et=0;et<3;++et){$=this.contourLevels[et];var rt=[],nt=[],it=[0,0,0];for(o=0;o<$.length;++o){var at=w(this._field[et],$[o]);rt.push(tt.length/5\|0),H=0;t:for(y=0;y<at.cells.length;++y){var ot=at.cells[y];for(Y=0;Y<2;++Y){var st=at.positions[ot[Y]],lt=st[0],ut=0\|Math.floor(lt),ct=lt-ut,ht=st[1],ft=0\|Math.floor(ht),dt=ht-ft,pt=!1;e:for(var mt=0;mt<3;++mt){it[mt]=0;var vt=(et+mt+1)%3;for(q=0;q<2;++q){var gt=q?ct:1-ct;for(X=0\|Math.min(Math.max(ut+q,0),a[0]),G=0;G<2;++G){var yt=G?dt:1-dt;if(Z=0\|Math.min(Math.max(ft+G,0),a[1]),W=mt<2?this._field[vt].get(X,Z):(this.intensity.get(X,Z)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(W)\|\|isNaN(W)){pt=!0;break e}var bt=gtyt;it[mt]+=btW}}}if(pt){if(Y>0){for(var xt=0;xt<5;++xt)tt.pop();H-=1}continue t}tt.push(it[0],it[1],st[0],st[1],it[2]),H+=1}}nt.push(H)}this._contourOffsets[et]=rt,this._contourCounts[et]=nt}var _t=g.mallocFloat(tt.length);for(o=0;o<tt.length;++o)_t[o]=tt[o];this._contourBuffer.update(_t),g.freeFloat(_t)}t.colormap&&this._colorMap.setPixels(i(t.colormap))},F.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)g.freeFloat(this._field[t].data)},F.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]\|\|this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]\|\|this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=g.mallocFloat(12i[0]i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var s=(o+1)%3,l=(o+2)%3,u=this._field[o],c=this._field[s],h=this._field[l],f=(this.intensity,w(u,r[o])),d=f.cells,p=f.positions;for(this._dynamicOffsets[o]=n,e=0;e<d.length;++e)for(var m=d[e],v=0;v<2;++v){var y=p[m[v]],b=+y[0],x=0\|b,_=0\|Math.min(x+1,i[0]),M=b-x,k=1-M,A=+y[1],T=0\|A,S=0\|Math.min(T+1,i[1]),E=A-T,L=1-E,C=kL,I=kE,z=ML,D=ME,P=Cc.get(x,T)+Ic.get(x,S)+zc.get(_,T)+Dc.get(_,S),O=Ch.get(x,T)+Ih.get(x,S)+zh.get(_,T)+Dh.get(_,S);if(isNaN(P)\|\|isNaN(O)){v&&(n-=1);break}a[2n+0]=P,a[2n+1]=O,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2n)),g.freeFloat(a)}}},{\"./lib/shaders\":265,\"binary-search-bounds\":66,\"bit-twiddle\":67,colormap:99,\"gl-buffer\":156,\"gl-mat4/invert\":181,\"gl-mat4/multiply\":183,\"gl-texture2d\":267,\"gl-vao\":271,ndarray:467,\"ndarray-gradient\":458,\"ndarray-ops\":461,\"ndarray-pack\":462,\"surface-nets\":531,\"typedarray-pool\":541}],267:[function(t,e,r){\"use strict\";function n(t){g=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],y=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],b=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function i(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement\|\|\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement\|\|\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement\|\|\"undefined\"!=typeof ImageData&&t 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opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\n\\n#pragma mapbox: define lowp vec4 color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp vec4 color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n}\\n\"},circle:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n#pragma mapbox: define lowp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_extrude;\\nvarying lowp float v_antialiasblur;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp vec4 color\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n float t = smoothstep(1.0 - max(blur, v_antialiasblur), 1.0, length(v_extrude));\\n gl_FragColor = color * (1.0 - t) * opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform bool u_scale_with_map;\\nuniform vec2 u_extrude_scale;\\nuniform float u_devicepixelratio;\\n\\nattribute vec2 a_pos;\\n\\n#pragma mapbox: define lowp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_extrude;\\nvarying lowp float v_antialiasblur;\\n\\nvoid main(void) {\\n #pragma mapbox: initialize lowp vec4 color\\n #pragma mapbox: initialize mediump float radius\\n #pragma mapbox: initialize lowp float blur\\n #pragma mapbox: initialize lowp float opacity\\n\\n // unencode the extrusion vector that we snuck into the a_pos vector\\n v_extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\\n\\n vec2 extrude = v_extrude * radius * u_extrude_scale;\\n // multiply a_pos by 0.5, since we had it * 2 in order to sneak\\n // in extrusion data\\n gl_Position = u_matrix * vec4(floor(a_pos * 0.5), 0, 1);\\n\\n if (u_scale_with_map) {\\n gl_Position.xy += extrude;\\n } else {\\n gl_Position.xy += extrude * gl_Position.w;\\n }\\n\\n // This is a minimum blur distance that serves as a faux-antialiasing for\\n // the circle. since blur is a ratio of the circle's size and the intent is\\n // to keep the blur at roughly 1px, the two are inversely related.\\n v_antialiasblur = 1.0 / u_devicepixelratio / radius;\\n}\\n\"},line:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\nuniform float u_blur;\\n\\nvarying vec2 v_linewidth;\\nvarying vec2 v_normal;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_linewidth.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_linewidth.t) or when fading out\\n // (v_linewidth.s)\\n float blur = u_blur * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform mediump float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\nuniform mediump float u_blur;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n\\n // We store the texture normals in the most insignificant bit\\n // transform y so that 0 => -1 and 1 => 1\\n // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = mod(a_pos, 2.0);\\n normal.y = sign(normal.y - 0.5);\\n v_normal = normal;\\n\\n float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n // Remove the texture normal bit of the position before scaling it with the\\n // model/view matrix.\\n gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n\\n // position of y on the screen\\n float y = gl_Position.y / gl_Position.w;\\n\\n // how much features are squished in the y direction by the tilt\\n float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n // how much features are squished in all directions by the perspectiveness\\n float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n v_linewidth = vec2(outset, inset);\\n v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},linepattern:{\n", "fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_blur;\\n\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_fade;\\nuniform float u_opacity;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_linewidth.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_linewidth.t) or when fading out\\n // (v_linewidth.s)\\n float blur = u_blur * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n float x_a = mod(v_linesofar / u_pattern_size_a.x, 1.0);\\n float x_b = mod(v_linesofar / u_pattern_size_b.x, 1.0);\\n float y_a = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_a.y);\\n float y_b = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_b.y);\\n vec2 pos_a = mix(u_pattern_tl_a, u_pattern_br_a, vec2(x_a, y_a));\\n vec2 pos_b = mix(u_pattern_tl_b, u_pattern_br_b, vec2(x_b, y_b));\\n\\n vec4 color = mix(texture2D(u_image, pos_a), texture2D(u_image, pos_b), u_fade);\\n\\n alpha = u_opacity;\\n\\n gl_FragColor = color alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform mediump float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying float v_linesofar;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n // We store the texture normals in the most insignificant bit\\n // transform y so that 0 => -1 and 1 => 1\\n // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = mod(a_pos, 2.0);\\n normal.y = sign(normal.y - 0.5);\\n v_normal = normal;\\n\\n float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n // Remove the texture normal bit of the position before scaling it with the\\n // model/view matrix.\\n gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n v_linesofar = a_linesofar;\\n\\n // position of y on the screen\\n float y = gl_Position.y / gl_Position.w;\\n\\n // how much features are squished in the y direction by the tilt\\n float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n // how much features are squished in all directions by the perspectiveness\\n float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n v_linewidth = vec2(outset, inset);\\n v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},linesdfpattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\n\\nuniform float u_blur;\\nuniform sampler2D u_image;\\nuniform float u_sdfgamma;\\nuniform float u_mix;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n // Calculate the distance of the pixel from the line in pixels.\\n float dist = length(v_normal) * v_linewidth.s;\\n\\n // Calculate the antialiasing fade factor. This is either when fading in\\n // the line in case of an offset line (v_linewidth.t) or when fading out\\n // (v_linewidth.s)\\n float blur = u_blur * v_gamma_scale;\\n float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\\n\\n float sdfdist_a = texture2D(u_image, v_tex_a).a;\\n float sdfdist_b = texture2D(u_image, v_tex_b).a;\\n float sdfdist = mix(sdfdist_a, sdfdist_b, u_mix);\\n alpha = smoothstep(0.5 - u_sdfgamma, 0.5 + u_sdfgamma, sdfdist);\\n\\n gl_FragColor = u_color (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n// floor(127 / 2) == 63.0\\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\\n// there are also \\\"special\\\" normals that have a bigger length (of up to 126 in\\n// this case).\\n// #define scale 63.0\\n#define scale 0.015873016\\n\\n// We scale the distance before adding it to the buffers so that we can store\\n// long distances for long segments. Use this value to unscale the distance.\\n#define LINE_DISTANCE_SCALE 2.0\\n\\nattribute vec2 a_pos;\\nattribute vec4 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform mediump float u_ratio;\\nuniform mediump float u_linewidth;\\nuniform mediump float u_gapwidth;\\nuniform mediump float u_antialiasing;\\nuniform vec2 u_patternscale_a;\\nuniform float u_tex_y_a;\\nuniform vec2 u_patternscale_b;\\nuniform float u_tex_y_b;\\nuniform float u_extra;\\nuniform mat2 u_antialiasingmatrix;\\nuniform mediump float u_offset;\\n\\nvarying vec2 v_normal;\\nvarying vec2 v_linewidth;\\nvarying vec2 v_tex_a;\\nvarying vec2 v_tex_b;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n vec2 a_extrude = a_data.xy - 128.0;\\n float a_direction = mod(a_data.z, 4.0) - 1.0;\\n float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\\n\\n // We store the texture normals in the most insignificant bit\\n // transform y so that 0 => -1 and 1 => 1\\n // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\\n // y is 1 if the normal points up, and -1 if it points down\\n mediump vec2 normal = mod(a_pos, 2.0);\\n normal.y = sign(normal.y - 0.5);\\n v_normal = normal;\\n\\n float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\\n float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\\n\\n // Scale the extrusion vector down to a normal and then up by the line width\\n // of this vertex.\\n mediump vec2 dist = outset * a_extrude * scale;\\n\\n // Calculate the offset when drawing a line that is to the side of the actual line.\\n // We do this by creating a vector that points towards the extrude, but rotate\\n // it when we're drawing round end points (a_direction = -1 or 1) since their\\n // extrude vector points in another direction.\\n mediump float u = 0.5 * a_direction;\\n mediump float t = 1.0 - abs(u);\\n mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\\n\\n // Remove the texture normal bit of the position before scaling it with the\\n // model/view matrix.\\n gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\\n\\n v_tex_a = vec2(a_linesofar * u_patternscale_a.x, normal.y * u_patternscale_a.y + u_tex_y_a);\\n v_tex_b = vec2(a_linesofar * u_patternscale_b.x, normal.y * u_patternscale_b.y + u_tex_y_b);\\n\\n // position of y on the screen\\n float y = gl_Position.y / gl_Position.w;\\n\\n // how much features are squished in the y direction by the tilt\\n float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\\n\\n // how much features are squished in all directions by the perspectiveness\\n float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\\n\\n v_linewidth = vec2(outset, inset);\\n v_gamma_scale = perspective_scale * squish_scale;\\n}\\n\"},outline:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\n#pragma mapbox: define lowp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp vec4 outline_color\\n #pragma mapbox: initialize lowp float opacity\\n\\n float dist = length(v_pos - gl_FragCoord.xy);\\n float alpha = smoothstep(1.0, 0.0, dist);\\n gl_FragColor = outline_color * (alpha * opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos;\\n\\n#pragma mapbox: define lowp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\n\\nvoid main() {\\n #pragma mapbox: initialize lowp vec4 outline_color\\n #pragma mapbox: initialize lowp float opacity\\n\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},outlinepattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n // find distance to outline for alpha interpolation\\n\\n float dist = length(v_pos - gl_FragCoord.xy);\\n float alpha = smoothstep(1.0, 0.0, dist);\\n \\n\\n gl_FragColor = mix(color1, color2, u_mix) * alpha * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_world;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\nvarying vec2 v_pos;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\\n vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\\n\\n // the correct offset needs to be calculated.\\n //\\n // The offset depends on how many pixels are between the world origin and\\n // the edge of the tile:\\n // vec2 offset = mod(pixel_coord, size)\\n //\\n // At high zoom levels there are a ton of pixels between the world origin\\n // and the edge of the tile. The glsl spec only guarantees 16 bits of\\n // precision for highp floats. We need more than that.\\n //\\n // The pixel_coord is passed in as two 16 bit values:\\n // pixel_coord_upper = floor(pixel_coord / 2^16)\\n // pixel_coord_lower = mod(pixel_coord, 2^16)\\n //\\n // The offset is calculated in a series of steps that should preserve this precision:\\n vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\\n vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\\n\\n v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\\n v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\\n\\n v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\\n}\\n\"},pattern:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity;\\nuniform vec2 u_pattern_tl_a;\\nuniform vec2 u_pattern_br_a;\\nuniform vec2 u_pattern_tl_b;\\nuniform vec2 u_pattern_br_b;\\nuniform float u_mix;\\n\\nuniform sampler2D u_image;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n\\n vec2 imagecoord = mod(v_pos_a, 1.0);\\n vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\\n vec4 color1 = texture2D(u_image, pos);\\n\\n vec2 imagecoord_b = mod(v_pos_b, 1.0);\\n vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\\n vec4 color2 = texture2D(u_image, pos2);\\n\\n gl_FragColor = mix(color1, color2, u_mix) * u_opacity;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_pattern_size_a;\\nuniform vec2 u_pattern_size_b;\\nuniform vec2 u_pixel_coord_upper;\\nuniform vec2 u_pixel_coord_lower;\\nuniform float u_scale_a;\\nuniform float u_scale_b;\\nuniform float u_tile_units_to_pixels;\\n\\nattribute vec2 a_pos;\\n\\nvarying vec2 v_pos_a;\\nvarying vec2 v_pos_b;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\\n vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\\n\\n // the correct offset needs to be calculated.\\n //\\n // The offset depends on how many pixels are between the world origin and\\n // the edge of the tile:\\n // vec2 offset = mod(pixel_coord, size)\\n //\\n // At high zoom levels there are a ton of pixels between the world origin\\n // and the edge of the tile. The glsl spec only guarantees 16 bits of\\n // precision for highp floats. We need more than that.\\n //\\n // The pixel_coord is passed in as two 16 bit values:\\n // pixel_coord_upper = floor(pixel_coord / 2^16)\\n // pixel_coord_lower = mod(pixel_coord, 2^16)\\n //\\n // The offset is calculated in a series of steps that should preserve this precision:\\n vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\\n vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\\n\\n v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\\n v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\\n}\\n\"},raster:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_opacity0;\\nuniform float u_opacity1;\\nuniform sampler2D u_image0;\\nuniform sampler2D u_image1;\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nuniform float u_brightness_low;\\nuniform float u_brightness_high;\\n\\nuniform float u_saturation_factor;\\nuniform float u_contrast_factor;\\nuniform vec3 u_spin_weights;\\n\\nvoid main() {\\n\\n // read and cross-fade colors from the main and parent tiles\\n vec4 color0 = texture2D(u_image0, v_pos0);\\n vec4 color1 = texture2D(u_image1, v_pos1);\\n vec4 color = color0 * u_opacity0 + color1 * u_opacity1;\\n vec3 rgb = color.rgb;\\n\\n // spin\\n rgb = vec3(\\n dot(rgb, u_spin_weights.xyz),\\n dot(rgb, u_spin_weights.zxy),\\n dot(rgb, u_spin_weights.yzx));\\n\\n // saturation\\n float average = (color.r + color.g + color.b) / 3.0;\\n rgb += (average - rgb) * u_saturation_factor;\\n\\n // contrast\\n rgb = (rgb - 0.5) * u_contrast_factor + 0.5;\\n\\n // brightness\\n vec3 u_high_vec = vec3(u_brightness_low, u_brightness_low, u_brightness_low);\\n vec3 u_low_vec = vec3(u_brightness_high, u_brightness_high, u_brightness_high);\\n\\n gl_FragColor = vec4(mix(u_high_vec, u_low_vec, rgb), color.a);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform mat4 u_matrix;\\nuniform vec2 u_tl_parent;\\nuniform float u_scale_parent;\\nuniform float u_buffer_scale;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_texture_pos;\\n\\nvarying vec2 v_pos0;\\nvarying vec2 v_pos1;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1);\\n v_pos0 = (((a_texture_pos / 32767.0) - 0.5) / u_buffer_scale ) + 0.5;\\n v_pos1 = (v_pos0 * u_scale_parent) + u_tl_parent;\\n}\\n\"},icon:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform sampler2D u_texture;\\nuniform sampler2D u_fadetexture;\\nuniform lowp float u_opacity;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\n\\nvoid main() {\\n lowp float alpha = texture2D(u_fadetexture, v_fade_tex).a * u_opacity;\\n gl_FragColor = texture2D(u_texture, v_tex) * alpha;\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_offset;\\nattribute vec2 a_texture_pos;\\nattribute vec4 a_data;\\n\\n\\n// matrix is for the vertex position.\\nuniform mat4 u_matrix;\\n\\nuniform mediump float u_zoom;\\nuniform bool u_rotate_with_map;\\nuniform vec2 u_extrude_scale;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\n\\nvoid main() {\\n vec2 a_tex = a_texture_pos.xy;\\n mediump float a_labelminzoom = a_data[0];\\n mediump vec2 a_zoom = a_data.pq;\\n mediump float a_minzoom = a_zoom[0];\\n mediump float a_maxzoom = a_zoom[1];\\n\\n // u_zoom is the current zoom level adjusted for the change in font size\\n mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\\n\\n vec2 extrude = u_extrude_scale * (a_offset / 64.0);\\n if (u_rotate_with_map) {\\n gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\\n gl_Position.z += z * gl_Position.w;\\n } else {\\n gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n }\\n\\n v_tex = a_tex / u_texsize;\\n v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\\n}\\n\"},sdf:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform sampler2D u_texture;\\nuniform sampler2D u_fadetexture;\\nuniform lowp vec4 u_color;\\nuniform lowp float u_opacity;\\nuniform lowp float u_buffer;\\nuniform lowp float u_gamma;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n lowp float dist = texture2D(u_texture, v_tex).a;\\n lowp float fade_alpha = texture2D(u_fadetexture, v_fade_tex).a;\\n lowp float gamma = u_gamma * v_gamma_scale;\\n lowp float alpha = smoothstep(u_buffer - gamma, u_buffer + gamma, dist) * fade_alpha;\\n\\n gl_FragColor = u_color * (alpha * u_opacity);\\n\\n#ifdef OVERDRAW_INSPECTOR\\n gl_FragColor = vec4(1.0);\\n#endif\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nconst float PI = 3.141592653589793;\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_offset;\\nattribute vec2 a_texture_pos;\\nattribute vec4 a_data;\\n\\n\\n// matrix is for the vertex position.\\nuniform mat4 u_matrix;\\n\\nuniform mediump float u_zoom;\\nuniform bool u_rotate_with_map;\\nuniform bool u_pitch_with_map;\\nuniform mediump float u_pitch;\\nuniform mediump float u_bearing;\\nuniform mediump float u_aspect_ratio;\\nuniform vec2 u_extrude_scale;\\n\\nuniform vec2 u_texsize;\\n\\nvarying vec2 v_tex;\\nvarying vec2 v_fade_tex;\\nvarying float v_gamma_scale;\\n\\nvoid main() {\\n vec2 a_tex = a_texture_pos.xy;\\n mediump float a_labelminzoom = a_data[0];\\n mediump vec2 a_zoom = a_data.pq;\\n mediump float a_minzoom = a_zoom[0];\\n mediump float a_maxzoom = a_zoom[1];\\n\\n // u_zoom is the current zoom level adjusted for the change in font size\\n mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\\n\\n // pitch-alignment: map\\n // rotation-alignment: map \| viewport\\n if (u_pitch_with_map) {\\n lowp float angle = u_rotate_with_map ? (a_data[1] / 256.0 * 2.0 * PI) : u_bearing;\\n lowp float asin = sin(angle);\\n lowp float acos = cos(angle);\\n mat2 RotationMatrix = mat2(acos, asin, -1.0 * asin, acos);\\n vec2 offset = RotationMatrix * a_offset;\\n vec2 extrude = u_extrude_scale * (offset / 64.0);\\n gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\\n gl_Position.z += z * gl_Position.w;\\n // pitch-alignment: viewport\\n // rotation-alignment: map\\n } else if (u_rotate_with_map) {\\n // foreshortening factor to apply on pitched maps\\n // as a label goes from horizontal <=> vertical in angle\\n // it goes from 0% foreshortening to up to around 70% foreshortening\\n lowp float pitchfactor = 1.0 - cos(u_pitch * sin(u_pitch * 0.75));\\n\\n lowp float lineangle = a_data[1] / 256.0 * 2.0 * PI;\\n\\n // use the lineangle to position points a,b along the line\\n // project the points and calculate the label angle in projected space\\n // this calculation allows labels to be rendered unskewed on pitched maps\\n vec4 a = u_matrix * vec4(a_pos, 0, 1);\\n vec4 b = u_matrix * vec4(a_pos + vec2(cos(lineangle),sin(lineangle)), 0, 1);\\n lowp float angle = atan((b[1]/b[3] - a[1]/a[3])/u_aspect_ratio, b[0]/b[3] - a[0]/a[3]);\\n lowp float asin = sin(angle);\\n lowp float acos = cos(angle);\\n mat2 RotationMatrix = mat2(acos, -1.0 * asin, asin, acos);\\n\\n vec2 offset = RotationMatrix * (vec2((1.0-pitchfactor)+(pitchfactorcos(angle2.0)), 1.0) * a_offset);\\n vec2 extrude = u_extrude_scale * (offset / 64.0);\\n gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n gl_Position.z += z * gl_Position.w;\\n // pitch-alignment: viewport\\n // rotation-alignment: viewport\\n } else {\\n vec2 extrude = u_extrude_scale * (a_offset / 64.0);\\n gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\\n }\\n\\n v_gamma_scale = (gl_Position.w - 0.5);\\n\\n v_tex = a_tex / u_texsize;\\n v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\\n}\\n\"},collisionbox:{fragmentSource:\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nuniform float u_zoom;\\nuniform float u_maxzoom;\\n\\nvarying float v_max_zoom;\\nvarying float v_placement_zoom;\\n\\nvoid main() {\\n\\n float alpha = 0.5;\\n\\n gl_FragColor = vec4(0.0, 1.0, 0.0, 1.0) * alpha;\\n\\n if (v_placement_zoom > u_zoom) {\\n gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\\n }\\n\\n if (u_zoom >= v_max_zoom) {\\n gl_FragColor = vec4(0.0, 0.0, 0.0, 1.0) * alpha * 0.25;\\n }\\n\\n if (v_placement_zoom >= u_maxzoom) {\\n gl_FragColor = vec4(0.0, 0.0, 1.0, 1.0) * alpha * 0.2;\\n }\\n}\\n\",vertexSource:\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#define lowp\\n#define mediump\\n#define highp\\n#endif\\n\\nattribute vec2 a_pos;\\nattribute vec2 a_extrude;\\nattribute vec2 a_data;\\n\\nuniform mat4 u_matrix;\\nuniform float u_scale;\\n\\nvarying float v_max_zoom;\\nvarying float v_placement_zoom;\\n\\nvoid main() {\\n gl_Position = u_matrix * vec4(a_pos + a_extrude / u_scale, 0.0, 1.0);\\n\\n v_max_zoom = a_data.x;\\n v_placement_zoom = a_data.y;\\n}\\n\"}},e.exports.util=\"float evaluate_zoom_function_1(const vec4 values, const float t) {\\n if (t < 1.0) {\\n return mix(values[0], values[1], t);\\n } else if (t < 2.0) {\\n return mix(values[1], values[2], t - 1.0);\\n } else {\\n return mix(values[2], values[3], t - 2.0);\\n }\\n}\\nvec4 evaluate_zoom_function_4(const vec4 value0, const vec4 value1, const vec4 value2, const vec4 value3, const float t) {\\n if (t < 1.0) {\\n return mix(value0, value1, t);\\n } else if (t < 2.0) {\\n return mix(value1, value2, t - 1.0);\\n } else {\\n return mix(value2, value3, t - 2.0);\\n }\\n}\\n\"},{path:476}],304:[function(t,e,r){\"use strict\";function n(t,e){this.message=(t?t+\": \":\"\")+i.apply(i,Array.prototype.slice.call(arguments,2)),null!==e&&void 0!==e&&e.__line__&&(this.line=e.__line__)}var i=t(\"util\").format;e.exports=n},{util:549}],305:[function(t,e,r){\"use 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i=t(\"../util/util\");e.exports=n,n.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,n.prototype.hasCapacityFor=function(t){return this.layoutVertexArray.length+t<=n.MAX_VERTEX_ARRAY_LENGTH},n.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},n.prototype.trim=function(){this.layoutVertexArray.trim(),this.elementArray&&this.elementArray.trim(),this.elementArray2&&this.elementArray2.trim();for(var t in this.paintVertexArrays)this.paintVertexArrays[t].trim()},n.prototype.serialize=function(){return{layoutVertexArray:this.layoutVertexArray.serialize(),elementArray:this.elementArray&&this.elementArray.serialize(),elementArray2:this.elementArray2&&this.elementArray2.serialize(),paintVertexArrays:i.mapObject(this.paintVertexArrays,function(t){return t.serialize()})}},n.prototype.getTransferables=function(t){t.push(this.layoutVertexArray.arrayBuffer),this.elementArray&&t.push(this.elementArray.arrayBuffer),this.elementArray2&&t.push(this.elementArray2.arrayBuffer);for(var e in this.paintVertexArrays)t.push(this.paintVertexArrays[e].arrayBuffer)}},{\"../util/util\":442}],329:[function(t,e,r){\"use strict\";function n(t){if(this.zoom=t.zoom,this.overscaling=t.overscaling,this.layer=t.layer,this.childLayers=t.childLayers,this.type=this.layer.type,this.features=[],this.id=this.layer.id,this.index=t.index,this.sourceLayer=this.layer.sourceLayer,this.sourceLayerIndex=t.sourceLayerIndex,this.minZoom=this.layer.minzoom,this.maxZoom=this.layer.maxzoom,this.paintAttributes=i(this),t.arrays){var e=this.programInterfaces;this.bufferGroups=c.mapObject(t.arrays,function(r,n){var i=e[n],a=t.paintVertexArrayTypes[n];return r.map(function(t){return new u(t,{layoutVertexArrayType:i.layoutVertexArrayType.serialize(),elementArrayType:i.elementArrayType&&i.elementArrayType.serialize(),elementArrayType2:i.elementArrayType2&&i.elementArrayType2.serialize(),paintVertexArrayTypes:a})})})}}function i(t){var e={};for(var r in t.programInterfaces){for(var n=e[r]={},i=0;i<t.childLayers.length;i++){n[t.childLayers[i].id]={attributes:[],uniforms:[],defines:[],vertexPragmas:{define:{},initialize:{}},fragmentPragmas:{define:{},initialize:{}}}}var s=t.programInterfaces[r];if(s.paintAttributes)for(var l=0;l<s.paintAttributes.length;l++){var u=s.paintAttributes[l];u.multiplier=u.multiplier\|\|1;for(var h=0;h<t.childLayers.length;h++){var d=t.childLayers[h],p=n[d.id],m=u.name;f(\"a_\"===u.name.slice(0,2));var v,g=u.name.slice(2);if(p.fragmentPragmas.initialize[g]=\"\",d.isPaintValueFeatureConstant(u.paintProperty))p.uniforms.push(u),p.fragmentPragmas.define[g]=p.vertexPragmas.define[g]=[\"uniform\",\"{precision}\",\"{type}\",m].join(\" \")+\";\",p.fragmentPragmas.initialize[g]=p.vertexPragmas.initialize[g]=[\"{precision}\",\"{type}\",g,\"=\",m].join(\" \")+\";\\n\";else if(d.isPaintValueZoomConstant(u.paintProperty)){p.attributes.push(c.extend({},u,{name:m})),v=[\"varying\",\"{precision}\",\"{type}\",g].join(\" \")+\";\\n\"\n", ";var y=[p.fragmentPragmas.define[g],\"attribute\",\"{precision}\",\"{type}\",m].join(\" \")+\";\\n\";p.fragmentPragmas.define[g]=v,p.vertexPragmas.define[g]=v+y,p.vertexPragmas.initialize[g]=[g,\"=\",m,\"/\",u.multiplier.toFixed(1)].join(\" \")+\";\\n\"}else{for(var b=\"u_\"+m.slice(2)+\"_t\",x=d.getPaintValueStopZoomLevels(u.paintProperty),_=0;_<x.length&&x[_]<t.zoom;)_++;for(var w=Math.max(0,Math.min(x.length-4,_-2)),M=[],k=0;k<4;k++)M.push(x[Math.min(w+k,x.length-1)]);v=[\"varying\",\"{precision}\",\"{type}\",g].join(\" \")+\";\\n\",p.vertexPragmas.define[g]=v+[\"uniform\",\"lowp\",\"float\",b].join(\" \")+\";\\n\",p.fragmentPragmas.define[g]=v,p.uniforms.push(c.extend({},u,{name:b,getValue:o(u,w),components:1}));var A=u.components;if(1===A)p.attributes.push(c.extend({},u,{getValue:a(u,M),isFunction:!0,components:4A})),p.vertexPragmas.define[g]+=[\"attribute\",\"{precision}\",\"vec4\",m].join(\" \")+\";\\n\",p.vertexPragmas.initialize[g]=[g,\"=\",\"evaluate_zoom_function_1(\"+m+\", \"+b+\")\",\"/\",u.multiplier.toFixed(1)].join(\" \")+\";\\n\";else{for(var T=[],S=0;S<4;S++)T.push(m+S),p.attributes.push(c.extend({},u,{getValue:a(u,[M[S]]),isFunction:!0,name:m+S})),p.vertexPragmas.define[g]+=[\"attribute\",\"{precision}\",\"{type}\",m+S].join(\" \")+\";\\n\";p.vertexPragmas.initialize[g]=[g,\" = \",\"evaluate_zoom_function_4(\"+T.join(\", \")+\", \"+b+\")\",\"/\",u.multiplier.toFixed(1)].join(\" \")+\";\\n\"}}}}}return e}function a(t,e){return function(r,n,i){if(1===e.length)return t.getValue(r,c.extend({},n,{zoom:e[0]}),i);for(var a=[],o=0;o<e.length;o++){var s=e[o];a.push(t.getValue(r,c.extend({},n,{zoom:s}),i)[0])}return a}}function o(t,e){return function(r,n){var i=r.getPaintInterpolationT(t.paintProperty,n.zoom);return[Math.max(0,Math.min(4,i-e))]}}var s=t(\"feature-filter\"),l=t(\"./array_group\"),u=t(\"./buffer_group\"),c=t(\"../util/util\"),h=t(\"../util/struct_array\"),f=t(\"assert\");e.exports=n,n.create=function(e){return new({fill:t(\"./bucket/fill_bucket\"),line:t(\"./bucket/line_bucket\"),circle:t(\"./bucket/circle_bucket\"),symbol:t(\"./bucket/symbol_bucket\")}[e.layer.type])(e)},n.EXTENT=8192,n.prototype.populateArrays=function(){this.createArrays(),this.recalculateStyleLayers();for(var t=0;t<this.features.length;t++)this.addFeature(this.features[t]);this.trimArrays()},n.prototype.prepareArrayGroup=function(t,e){var r=this.arrayGroups[t],n=r.length&&r[r.length-1];return n&&n.hasCapacityFor(e)\|\|(n=new l({layoutVertexArrayType:this.programInterfaces[t].layoutVertexArrayType,elementArrayType:this.programInterfaces[t].elementArrayType,elementArrayType2:this.programInterfaces[t].elementArrayType2,paintVertexArrayTypes:this.paintVertexArrayTypes[t]}),n.index=r.length,r.push(n)),n},n.prototype.createArrays=function(){this.arrayGroups={},this.paintVertexArrayTypes={};for(var t in this.programInterfaces){this.arrayGroups[t]=[];var e=this.paintVertexArrayTypes[t]={},r=this.paintAttributes[t];for(var i in r)e[i]=new n.VertexArrayType(r[i].attributes)}},n.prototype.destroy=function(t){for(var e in this.bufferGroups)for(var r=this.bufferGroups[e],n=0;n<r.length;n++)r[n].destroy(t)},n.prototype.trimArrays=function(){for(var t in this.arrayGroups)for(var e=this.arrayGroups[t],r=0;r<e.length;r++)e[r].trim()},n.prototype.isEmpty=function(){for(var t in this.arrayGroups)for(var e=this.arrayGroups[t],r=0;r<e.length;r++)if(!e[r].isEmpty())return!1;return!0},n.prototype.getTransferables=function(t){for(var e in this.arrayGroups)for(var r=this.arrayGroups[e],n=0;n<r.length;n++)r[n].getTransferables(t)},n.prototype.setUniforms=function(t,e,r,n,i){for(var a=this.paintAttributes[e][n.id].uniforms,o=0;o<a.length;o++){var s=a[o],l=r[s.name];t[\"uniform\"+s.components+\"fv\"](l,s.getValue(n,i))}},n.prototype.serialize=function(){return{layerId:this.layer.id,zoom:this.zoom,arrays:c.mapObject(this.arrayGroups,function(t){return t.map(function(t){return t.serialize()})}),paintVertexArrayTypes:c.mapObject(this.paintVertexArrayTypes,function(t){return c.mapObject(t,function(t){return t.serialize()})}),childLayerIds:this.childLayers.map(function(t){return t.id})}},n.prototype.createFilter=function(){this.filter\|\|(this.filter=s(this.layer.filter))};var d={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};n.prototype.recalculateStyleLayers=function(){for(var t=0;t<this.childLayers.length;t++)this.childLayers[t].recalculate(this.zoom,d)},n.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a=0;a<this.childLayers.length;a++)for(var o=this.childLayers[a],s=this.arrayGroups[t],l=n.index;l<s.length;l++){var u=s[l],c=u.layoutVertexArray.length,h=u.paintVertexArrays[o.id];h.resize(c);for(var f=this.paintAttributes[t][o.id].attributes,d=0;d<f.length;d++)for(var p=f[d],m=p.getValue(o,e,r),v=p.multiplier\|\|1,g=p.components\|\|1,y=l===n.index?i:0,b=y;b<c;b++)for(var x=h.get(b),_=0;_<g;_++){var w=g>1?p.name+_:p.name;x[w]=m[_]v}}},n.VertexArrayType=function(t){return new h({members:t,alignment:4})},n.ElementArrayType=function(t){return new h({members:[{type:\"Uint16\",name:\"vertices\",components:t\|\|3}]})}},{\"../util/struct_array\":440,\"../util/util\":442,\"./array_group\":328,\"./bucket/circle_bucket\":330,\"./bucket/fill_bucket\":331,\"./bucket/line_bucket\":332,\"./bucket/symbol_bucket\":333,\"./buffer_group\":335,assert:47,\"feature-filter\":132}],330:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=i.EXTENT;e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.addCircleVertex=function(t,e,r,n,i){return t.emplaceBack(2e+(n+1)/2,2r+(i+1)/2)},n.prototype.programInterfaces={circle:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"}]),elementArrayType:new i.ElementArrayType,paintAttributes:[{name:\"a_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"circle-color\",e,r)},multiplier:255,paintProperty:\"circle-color\"},{name:\"a_radius\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-radius\",e,r)]},multiplier:10,paintProperty:\"circle-radius\"},{name:\"a_blur\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-blur\",e,r)]},multiplier:10,paintProperty:\"circle-blur\"},{name:\"a_opacity\",components:1,type:\"Uint16\",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue(\"circle-opacity\",e,r)]},multiplier:255,paintProperty:\"circle-opacity\"}]}},n.prototype.addFeature=function(t){for(var e={zoom:this.zoom},r=o(t),n=this.prepareArrayGroup(\"circle\",0),i=n.layoutVertexArray.length,a=0;a<r.length;a++)for(var l=0;l<r[a].length;l++){var u=r[a][l].x,c=r[a][l].y;if(!(u<0\|\|u>=s\|\|c<0\|\|c>=s)){var h=this.prepareArrayGroup(\"circle\",4),f=h.layoutVertexArray,d=this.addCircleVertex(f,u,c,-1,-1);this.addCircleVertex(f,u,c,1,-1),this.addCircleVertex(f,u,c,1,1),this.addCircleVertex(f,u,c,-1,1),h.elementArray.emplaceBack(d,d+1,d+2),h.elementArray.emplaceBack(d,d+3,d+2)}}this.populatePaintArrays(\"circle\",e,t.properties,n,i)}},{\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337}],331:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=t(\"earcut\"),l=t(\"../../util/classify_rings\");e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.programInterfaces={fill:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"}]),elementArrayType:new i.ElementArrayType(1),elementArrayType2:new i.ElementArrayType(2),paintAttributes:[{name:\"a_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"fill-color\",e,r)},multiplier:255,paintProperty:\"fill-color\"},{name:\"a_outline_color\",components:4,type:\"Uint8\",getValue:function(t,e,r){return t.getPaintValue(\"fill-outline-color\",e,r)},multiplier:255,paintProperty:\"fill-outline-color\"},{name:\"a_opacity\",components:1,type:\"Uint8\",getValue:function(t,e,r){return[t.getPaintValue(\"fill-opacity\",e,r)]},multiplier:255,paintProperty:\"fill-opacity\"}]}},n.prototype.addFeature=function(t){for(var e=o(t),r=l(e,500),n=this.prepareArrayGroup(\"fill\",0),i=n.layoutVertexArray.length,a=0;a<r.length;a++)this.addPolygon(r[a]);this.populatePaintArrays(\"fill\",{zoom:this.zoom},t.properties,n,i)},n.prototype.addPolygon=function(t){for(var e=0,r=0;r<t.length;r++)e+=t[r].length;for(var n=this.prepareArrayGroup(\"fill\",e),i=[],a=[],o=n.layoutVertexArray.length,l=0;l<t.length;l++){var u=t[l];l>0&&a.push(i.length/2);for(var c=0;c<u.length;c++){var h=u[c],f=n.layoutVertexArray.emplaceBack(h.x,h.y);c>=1&&n.elementArray2.emplaceBack(f-1,f),i.push(h.x),i.push(h.y)}}for(var d=s(i,a),p=0;p<d.length;p++)n.elementArray.emplaceBack(d[p]+o)}},{\"../../util/classify_rings\":430,\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337,earcut:126}],332:[function(t,e,r){\"use strict\";function n(){i.apply(this,arguments)}var i=t(\"../bucket\"),a=t(\"../../util/util\"),o=t(\"../load_geometry\"),s=i.EXTENT,l=Math.cos(Math.PI/18037.5),u=Math.pow(2,14)/.5;e.exports=n,n.prototype=a.inherit(i,{}),n.prototype.addLineVertex=function(t,e,r,n,i,a,o){return t.emplaceBack(e.x<<1\|n,e.y<<1\|i,Math.round(63r.x)+128,Math.round(63r.y)+128,1+(0===a?0:a<0?-1:1)\|(.5o&63)<<2,.5o>>6)},n.prototype.programInterfaces={line:{layoutVertexArrayType:new i.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}]),elementArrayType:new i.ElementArrayType}},n.prototype.addFeature=function(t){for(var e=o(t,15),r=0;r<e.length;r++)this.addLine(e[r],this.layer.layout[\"line-join\"],this.layer.layout[\"line-cap\"],this.layer.layout[\"line-miter-limit\"],this.layer.layout[\"line-round-limit\"])},n.prototype.addLine=function(t,e,r,n,i){for(var a=t.length;a>2&&t[a-1].equals(t[a-2]);)a--;if(!(t.length<2)){\"bevel\"===e&&(n=1.05);var o=s/(512this.overscaling)15,u=t[0],c=t[a-1],h=u.equals(c);if(this.prepareArrayGroup(\"line\",10a),2!==a\|\|!h){this.distance=0;var f,d,p,m,v,g,y,b=r,x=h?\"butt\":r,_=!0;this.e1=this.e2=this.e3=-1,h&&(f=t[a-2],v=u.sub(f)._unit()._perp());for(var w=0;w<a;w++)if(!(p=h&&w===a-1?t[1]:t[w+1])\|\|!t[w].equals(p)){v&&(m=v),f&&(d=f),f=t[w],v=p?p.sub(f)._unit()._perp():m,m=m\|\|v;var M=m.add(v)._unit(),k=M.xv.x+M.yv.y,A=1/k,T=k<l&&d&&p;if(T&&w>0){var S=f.dist(d);if(S>2o){var E=f.sub(f.sub(d)._mult(o/S)._round());this.distance+=E.dist(d),this.addCurrentVertex(E,this.distance,m.mult(1),0,0,!1),d=E}}var L=d&&p,C=L?e:p?b:x;if(L&&\"round\"===C&&(A<i?C=\"miter\":A<=2&&(C=\"fakeround\")),\"miter\"===C&&A>n&&(C=\"bevel\"),\"bevel\"===C&&(A>2&&(C=\"flipbevel\"),A<n&&(C=\"miter\")),d&&(this.distance+=f.dist(d)),\"miter\"===C)M._mult(A),this.addCurrentVertex(f,this.distance,M,0,0,!1);else if(\"flipbevel\"===C){if(A>100)M=v.clone();else{var I=m.xv.y-m.yv.x>0?-1:1,z=Am.add(v).mag()/m.sub(v).mag();M._perp()._mult(zI)}this.addCurrentVertex(f,this.distance,M,0,0,!1),this.addCurrentVertex(f,this.distance,M.mult(-1),0,0,!1)}else if(\"bevel\"===C\|\|\"fakeround\"===C){var D=m.xv.y-m.yv.x>0,P=-Math.sqrt(AA-1);if(D?(y=0,g=P):(g=0,y=P),_\|\|this.addCurrentVertex(f,this.distance,m,g,y,!1),\"fakeround\"===C){for(var O,R=Math.floor(8(.5-(k-.5))),F=0;F<R;F++)O=v.mult((F+1)/(R+1))._add(m)._unit(),this.addPieSliceVertex(f,this.distance,O,D);this.addPieSliceVertex(f,this.distance,M,D);for(var j=R-1;j>=0;j--)O=m.mult((j+1)/(R+1))._add(v)._unit(),this.addPieSliceVertex(f,this.distance,O,D)}p&&this.addCurrentVertex(f,this.distance,v,-g,-y,!1)}else\"butt\"===C?(_\|\|this.addCurrentVertex(f,this.distance,m,0,0,!1),p&&this.addCurrentVertex(f,this.distance,v,0,0,!1)):\"square\"===C?(_\|\|(this.addCurrentVertex(f,this.distance,m,1,1,!1),this.e1=this.e2=-1),p&&this.addCurrentVertex(f,this.distance,v,-1,-1,!1)):\"round\"===C&&(_\|\|(this.addCurrentVertex(f,this.distance,m,0,0,!1),this.addCurrentVertex(f,this.distance,m,1,1,!0),this.e1=this.e2=-1),p&&(this.addCurrentVertex(f,this.distance,v,-1,-1,!0),this.addCurrentVertex(f,this.distance,v,0,0,!1)));if(T&&w<a-1){var N=f.dist(p);if(N>2o){var B=f.add(p.sub(f)._mult(o/N)._round());this.distance+=B.dist(f),this.addCurrentVertex(B,this.distance,v.mult(1),0,0,!1),f=B}}_=!1}}}},n.prototype.addCurrentVertex=function(t,e,r,n,i,a){var o,s=a?1:0,l=this.arrayGroups.line[this.arrayGroups.line.length-1],c=l.layoutVertexArray,h=l.elementArray;o=r.clone(),n&&o._sub(r.perp()._mult(n)),this.e3=this.addLineVertex(c,t,o,s,0,n,e),this.e1>=0&&this.e2>=0&&h.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,o=r.mult(-1),i&&o._sub(r.perp()._mult(i)),this.e3=this.addLineVertex(c,t,o,s,1,-i,e),this.e1>=0&&this.e2>=0&&h.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,e>u/2&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,a))},n.prototype.addPieSliceVertex=function(t,e,r,n){var i=n?1:0;r=r.mult(n?-1:1);var a=this.arrayGroups.line[this.arrayGroups.line.length-1],o=a.layoutVertexArray,s=a.elementArray;this.e3=this.addLineVertex(o,t,r,0,i,0,e),this.e1>=0&&this.e2>=0&&s.emplaceBack(this.e1,this.e2,this.e3),n?this.e2=this.e3:this.e1=this.e3}},{\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337}],333:[function(t,e,r){\"use strict\";function n(t){o.apply(this,arguments),this.showCollisionBoxes=t.showCollisionBoxes,this.overscaling=t.overscaling,this.collisionBoxArray=t.collisionBoxArray,this.symbolQuadsArray=t.symbolQuadsArray,this.symbolInstancesArray=t.symbolInstancesArray,this.sdfIcons=t.sdfIcons,this.iconsNeedLinear=t.iconsNeedLinear,this.adjustedTextSize=t.adjustedTextSize,this.adjustedIconSize=t.adjustedIconSize,this.fontstack=t.fontstack}function i(t,e,r,n,i,a,o,s,l,u,c){return t.emplaceBack(e,r,Math.round(64n),Math.round(64i),a/4,o/4,10(u\|\|0),c,10(s\|\|0),10Math.min(l\|\|25,25))}var a=t(\"point-geometry\"),o=t(\"../bucket\"),s=t(\"../../symbol/anchor\"),l=t(\"../../symbol/get_anchors\"),u=t(\"../../util/token\"),c=t(\"../../symbol/quads\"),h=t(\"../../symbol/shaping\"),f=t(\"../../symbol/resolve_text\"),d=t(\"../../symbol/mergelines\"),p=t(\"../../symbol/clip_line\"),m=t(\"../../util/util\"),v=t(\"../load_geometry\"),g=t(\"../../symbol/collision_feature\"),y=h.shapeText,b=h.shapeIcon,x=c.getGlyphQuads,_=c.getIconQuads,w=o.EXTENT;e.exports=n,n.MAX_QUADS=65535,n.prototype=m.inherit(o,{}),n.prototype.serialize=function(){var t=o.prototype.serialize.apply(this);return t.sdfIcons=this.sdfIcons,t.iconsNeedLinear=this.iconsNeedLinear,t.adjustedTextSize=this.adjustedTextSize,t.adjustedIconSize=this.adjustedIconSize,t.fontstack=this.fontstack,t};var M=new o.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_offset\",components:2,type:\"Int16\"},{name:\"a_texture_pos\",components:2,type:\"Uint16\"},{name:\"a_data\",components:4,type:\"Uint8\"}]),k=new o.ElementArrayType;n.prototype.addCollisionBoxVertex=function(t,e,r,n,i){return t.emplaceBack(e.x,e.y,Math.round(r.x),Math.round(r.y),10n,10i)},n.prototype.programInterfaces={glyph:{layoutVertexArrayType:M,elementArrayType:k},icon:{layoutVertexArrayType:M,elementArrayType:k},collisionBox:{layoutVertexArrayType:new o.VertexArrayType([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"},{name:\"a_data\",components:2,type:\"Uint8\"}])}},n.prototype.populateArrays=function(t,e,r){var n={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};this.adjustedTextMaxSize=this.layer.getLayoutValue(\"text-size\",{zoom:18,zoomHistory:n}),this.adjustedTextSize=this.layer.getLayoutValue(\"text-size\",{zoom:this.zoom+1,zoomHistory:n}),this.adjustedIconMaxSize=this.layer.getLayoutValue(\"icon-size\",{zoom:18,zoomHistory:n}),this.adjustedIconSize=this.layer.getLayoutValue(\"icon-size\",{zoom:this.zoom+1,zoomHistory:n});var i=512this.overscaling;this.tilePixelRatio=w/i,this.compareText={},this.iconsNeedLinear=!1,this.symbolInstancesStartIndex=this.symbolInstancesArray.length;var a=this.layer.layout,o=this.features,s=this.textFeatures,l=.5,c=.5;switch(a[\"text-anchor\"]){case\"right\":case\"top-right\":case\"bottom-right\":l=1;break;case\"left\":case\"top-left\":case\"bottom-left\":l=0}switch(a[\"text-anchor\"]){case\"bottom\":case\"bottom-right\":case\"bottom-left\":c=1;break;case\"top\":case\"top-right\":case\"top-left\":c=0}for(var h=\"right\"===a[\"text-justify\"]?1:\"left\"===a[\"text-justify\"]?0:.5,f=24a[\"text-line-height\"],p=\"line\"!==a[\"symbol-placement\"]?24a[\"text-max-width\"]:0,g=24a[\"text-letter-spacing\"],x=[24a[\"text-offset\"][0],24a[\"text-offset\"][1]],_=this.fontstack=a[\"text-font\"].join(\",\"),M=[],k=0;k<o.length;k++)M.push(v(o[k]));if(\"line\"===a[\"symbol-placement\"]){var A=d(o,s,M);M=A.geometries,o=A.features,s=A.textFeatures}for(var T,S,E=0;E<o.length;E++)if(M[E]){if(T=s[E]?y(s[E],e[_],p,f,l,c,h,g,x):null,a[\"icon-image\"]){var L=u(o[E].properties,a[\"icon-image\"]),C=r[L];S=b(C,a),C&&(void 0===this.sdfIcons?this.sdfIcons=C.sdf:this.sdfIcons!==C.sdf&&m.warnOnce(\"Style sheet warning: Cannot mix SDF and non-SDF icons in one buffer\"),1!==C.pixelRatio?this.iconsNeedLinear=!0:0===a[\"icon-rotate\"]&&this.layer.isLayoutValueFeatureConstant(\"icon-rotate\")\|\|(this.iconsNeedLinear=!0))}else S=null;(T\|\|S)&&this.addFeature(M[E],T,S,o[E])}this.symbolInstancesEndIndex=this.symbolInstancesArray.length,this.placeFeatures(t,this.showCollisionBoxes),this.trimArrays()},n.prototype.addFeature=function(t,e,r,n){var i=this.layer.layout,a=this.adjustedTextSize/24,o=void 0!==this.adjustedTextMaxSize?this.adjustedTextMaxSize:this.adjustedTextSize,u=this.tilePixelRatioa,c=this.tilePixelRatioo/24,h=this.tilePixelRatiothis.adjustedIconSize,f=this.tilePixelRatioi[\"symbol-spacing\"],d=i[\"symbol-avoid-edges\"],m=i[\"text-padding\"]this.tilePixelRatio,v=i[\"icon-padding\"]this.tilePixelRatio,g=i[\"text-max-angle\"]/180Math.PI,y=\"map\"===i[\"text-rotation-alignment\"]&&\"line\"===i[\"symbol-placement\"],b=\"map\"===i[\"icon-rotation-alignment\"]&&\"line\"===i[\"symbol-placement\"],x=i[\"text-allow-overlap\"]\|\|i[\"icon-allow-overlap\"]\|\|i[\"text-ignore-placement\"]\|\|i[\"icon-ignore-placement\"],_=\"line\"===i[\"symbol-placement\"],M=f/2;_&&(t=p(t,0,0,w,w));for(var k=0;k<t.length;k++){var A,T=t[k];A=_?l(T,f,g,e,r,24,c,this.overscaling,w):[new s(T[0].x,T[0].y,0)];for(var S=0,E=A.length;S<E;S++){var L=A[S];if(!(e&&_&&this.anchorIsTooClose(e.text,M,L))){var C=!(L.x<0\|\|L.x>w\|\|L.y<0\|\|L.y>w);if(!d\|\|C){var I=C\|\|x;this.addSymbolInstance(L,T,e,r,this.layer,I,this.symbolInstancesArray.length,this.collisionBoxArray,n.index,this.sourceLayerIndex,this.index,u,m,y,h,v,b,{zoom:this.zoom},n.properties)}}}}},n.prototype.anchorIsTooClose=function(t,e,r){var n=this.compareText;if(t in n){for(var i=n[t],a=i.length-1;a>=0;a--)if(r.dist(i[a])<e)return!0}else n[t]=[];return n[t].push(r),!1},n.prototype.placeFeatures=function(t,e){this.recalculateStyleLayers(),this.createArrays();var r=this.layer.layout,n=t.maxScale,i=\"map\"===r[\"text-rotation-alignment\"]&&\"line\"===r[\"symbol-placement\"],a=\"map\"===r[\"icon-rotation-alignment\"]&&\"line\"===r[\"symbol-placement\"];if(r[\"text-allow-overlap\"]\|\|r[\"icon-allow-overlap\"]\|\|r[\"text-ignore-placement\"]\|\|r[\"icon-ignore-placement\"]){var o=this.symbolInstancesArray.toArray(this.symbolInstancesStartIndex,this.symbolInstancesEndIndex),s=t.angle,l=Math.sin(s),u=Math.cos(s);this.sortedSymbolInstances=o.sort(function(t,e){return(lt.anchorPointX+ut.anchorPointY\|0)-(le.anchorPointX+ue.anchorPointY\|0)\|\|e.index-t.index})}for(var c=this.symbolInstancesStartIndex;c<this.symbolInstancesEndIndex;c++){var h=this.sortedSymbolInstances?this.sortedSymbolInstances[c-this.symbolInstancesStartIndex]:this.symbolInstancesArray.get(c),f={boxStartIndex:h.textBoxStartIndex,boxEndIndex:h.textBoxEndIndex},d={boxStartIndex:h.iconBoxStartIndex,boxEndIndex:h.iconBoxEndIndex},p=!(h.textBoxStartIndex===h.textBoxEndIndex),m=!(h.iconBoxStartIndex===h.iconBoxEndIndex),v=r[\"text-optional\"]\|\|!p,g=r[\"icon-optional\"]\|\|!m,y=p?t.placeCollisionFeature(f,r[\"text-allow-overlap\"],r[\"symbol-avoid-edges\"]):t.minScale,b=m?t.placeCollisionFeature(d,r[\"icon-allow-overlap\"],r[\"symbol-avoid-edges\"]):t.minScale;v\|\|g?!g&&y?y=Math.max(b,y):!v&&b&&(b=Math.max(b,y)):b=y=Math.max(b,y),p&&(t.insertCollisionFeature(f,y,r[\"text-ignore-placement\"]),y<=n&&this.addSymbols(\"glyph\",h.glyphQuadStartIndex,h.glyphQuadEndIndex,y,r[\"text-keep-upright\"],i,t.angle)),m&&(t.insertCollisionFeature(d,b,r[\"icon-ignore-placement\"]),b<=n&&this.addSymbols(\"icon\",h.iconQuadStartIndex,h.iconQuadEndIndex,b,r[\"icon-keep-upright\"],a,t.angle))}e&&this.addToDebugBuffers(t)},n.prototype.addSymbols=function(t,e,r,n,a,o,s){for(var l=this.prepareArrayGroup(t,4(r-e)),u=l.elementArray,c=l.layoutVertexArray,h=this.zoom,f=Math.max(Math.log(n)/Math.LN2+h,0),d=e;d<r;d++){var p=this.symbolQuadsArray.get(d).SymbolQuad,m=(p.anchorAngle+s+Math.PI)%(2Math.PI);if(!(a&&o&&(m<=Math.PI/2\|\|m>3Math.PI/2))){var v=p.tl,g=p.tr,y=p.bl,b=p.br,x=p.tex,_=p.anchorPoint,w=Math.max(h+Math.log(p.minScale)/Math.LN2,f),M=Math.min(h+Math.log(p.maxScale)/Math.LN2,25);if(!(M<=w)){w===f&&(w=0);var k=Math.round(p.glyphAngle/(2Math.PI)256),A=i(c,_.x,_.y,v.x,v.y,x.x,x.y,w,M,f,k);i(c,_.x,_.y,g.x,g.y,x.x+x.w,x.y,w,M,f,k),i(c,_.x,_.y,y.x,y.y,x.x,x.y+x.h,w,M,f,k),i(c,_.x,_.y,b.x,b.y,x.x+x.w,x.y+x.h,w,M,f,k),u.emplaceBack(A,A+1,A+2),u.emplaceBack(A+1,A+2,A+3)}}}},n.prototype.updateIcons=function(t){this.recalculateStyleLayers();var e=this.layer.layout[\"icon-image\"];if(e)for(var r=0;r<this.features.length;r++){var n=u(this.features[r].properties,e);n&&(t[n]=!0)}},n.prototype.updateFont=function(t){this.recalculateStyleLayers();var e=this.layer.layout[\"text-font\"],r=t[e]=t[e]\|\|{};this.textFeatures=f(this.features,this.layer.layout,r)},n.prototype.addToDebugBuffers=function(t){for(var e=this.prepareArrayGroup(\"collisionBox\",0),r=e.layoutVertexArray,n=-t.angle,i=t.yStretch,o=this.symbolInstancesStartIndex;o<this.symbolInstancesEndIndex;o++){var s=this.symbolInstancesArray.get(o);s.textCollisionFeature={boxStartIndex:s.textBoxStartIndex,boxEndIndex:s.textBoxEndIndex},s.iconCollisionFeature={boxStartIndex:s.iconBoxStartIndex,boxEndIndex:s.iconBoxEndIndex};for(var l=0;l<2;l++){var u=s[0===l?\"textCollisionFeature\":\"iconCollisionFeature\"];if(u)for(var c=u.boxStartIndex;c<u.boxEndIndex;c++){var h=this.collisionBoxArray.get(c),f=h.anchorPoint,d=new a(h.x1,h.y1i)._rotate(n),p=new a(h.x2,h.y1i)._rotate(n),m=new a(h.x1,h.y2i)._rotate(n),v=new a(h.x2,h.y2i)._rotate(n),g=Math.max(0,Math.min(25,this.zoom+Math.log(h.maxScale)/Math.LN2)),y=Math.max(0,Math.min(25,this.zoom+Math.log(h.placementScale)/Math.LN2));this.addCollisionBoxVertex(r,f,d,g,y),this.addCollisionBoxVertex(r,f,p,g,y),this.addCollisionBoxVertex(r,f,p,g,y),this.addCollisionBoxVertex(r,f,v,g,y),this.addCollisionBoxVertex(r,f,v,g,y),this.addCollisionBoxVertex(r,f,m,g,y),this.addCollisionBoxVertex(r,f,m,g,y),this.addCollisionBoxVertex(r,f,d,g,y)}}}},n.prototype.addSymbolInstance=function(t,e,r,i,a,o,s,l,u,c,h,f,d,p,v,y,b,w,M){var k,A,T,S,E,L,C,I;if(r&&(C=o?x(t,r,f,e,a,p):[],E=new g(l,e,t,u,c,h,r,f,d,p,!1)),k=this.symbolQuadsArray.length,C&&C.length)for(var z=0;z<C.length;z++)this.addSymbolQuad(C[z]);A=this.symbolQuadsArray.length;var D=E?E.boxStartIndex:this.collisionBoxArray.length,P=E?E.boxEndIndex:this.collisionBoxArray.length;i&&(I=o?_(t,i,v,e,a,b,r,w,M):[],L=new g(l,e,t,u,c,h,i,v,y,b,!0)),T=this.symbolQuadsArray.length,I&&1===I.length&&this.addSymbolQuad(I[0]),S=this.symbolQuadsArray.length;var O=L?L.boxStartIndex:this.collisionBoxArray.length,R=L?L.boxEndIndex:this.collisionBoxArray.length;return S>n.MAX_QUADS&&m.warnOnce(\"Too many symbols being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),A>n.MAX_QUADS&&m.warnOnce(\"Too many glyphs being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),this.symbolInstancesArray.emplaceBack(D,P,O,R,k,A,T,S,t.x,t.y,s)},n.prototype.addSymbolQuad=function(t){return this.symbolQuadsArray.emplaceBack(t.anchorPoint.x,t.anchorPoint.y,t.tl.x,t.tl.y,t.tr.x,t.tr.y,t.bl.x,t.bl.y,t.br.x,t.br.y,t.tex.h,t.tex.w,t.tex.x,t.tex.y,t.anchorAngle,t.glyphAngle,t.maxScale,t.minScale)}},{\"../../symbol/anchor\":391,\"../../symbol/clip_line\":393,\"../../symbol/collision_feature\":395,\"../../symbol/get_anchors\":397,\"../../symbol/mergelines\":400,\"../../symbol/quads\":401,\"../../symbol/resolve_text\":402,\"../../symbol/shaping\":403,\"../../util/token\":441,\"../../util/util\":442,\"../bucket\":329,\"../load_geometry\":337,\"point-geometry\":484}],334:[function(t,e,r){\"use strict\";function n(t,e,r){this.arrayBuffer=t.arrayBuffer,this.length=t.length,this.attributes=e.members,this.itemSize=e.bytesPerElement,this.type=r,this.arrayType=e}e.exports=n,n.prototype.bind=function(t){var e=t[this.type];this.buffer?t.bindBuffer(e,this.buffer):(this.buffer=t.createBuffer(),t.bindBuffer(e,this.buffer),t.bufferData(e,this.arrayBuffer,t.STATIC_DRAW),this.arrayBuffer=null)};var i={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\"};n.prototype.setVertexAttribPointers=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],a=e[n.name];void 0!==a&&t.vertexAttribPointer(a,n.components,t[i[n.type]],!1,this.arrayType.bytesPerElement,n.offset)}},n.prototype.destroy=function(t){this.buffer&&t.deleteBuffer(this.buffer)},n.BufferType={VERTEX:\"ARRAY_BUFFER\",ELEMENT:\"ELEMENT_ARRAY_BUFFER\"}},{}],335:[function(t,e,r){\"use strict\";function n(t,e){this.layoutVertexBuffer=new a(t.layoutVertexArray,e.layoutVertexArrayType,a.BufferType.VERTEX),t.elementArray&&(this.elementBuffer=new a(t.elementArray,e.elementArrayType,a.BufferType.ELEMENT));var r,n=this.vaos={};t.elementArray2&&(this.elementBuffer2=new a(t.elementArray2,e.elementArrayType2,a.BufferType.ELEMENT),r=this.secondVaos={}),this.paintVertexBuffers=i.mapObject(t.paintVertexArrays,function(i,s){return n[s]=new o,t.elementArray2&&(r[s]=new o),new a(i,e.paintVertexArrayTypes[s],a.BufferType.VERTEX)})}var i=t(\"../util/util\"),a=t(\"./buffer\"),o=t(\"../render/vertex_array_object\");e.exports=n,n.prototype.destroy=function(t){this.layoutVertexBuffer.destroy(t),this.elementBuffer&&this.elementBuffer.destroy(t),this.elementBuffer2&&this.elementBuffer2.destroy(t);for(var e in this.paintVertexBuffers)this.paintVertexBuffers[e].destroy(t);for(var r in this.vaos)this.vaos[r].destroy(t);for(var n in this.secondVaos)this.secondVaos[n].destroy(t)}},{\"../render/vertex_array_object\":357,\"../util/util\":442,\"./buffer\":334}],336:[function(t,e,r){\"use strict\";function n(t,e,r){if(t.grid){var n=t,i=e;t=n.coord,e=n.overscaling,this.grid=new p(n.grid),this.featureIndexArray=new k(n.featureIndexArray),this.rawTileData=i,this.bucketLayerIDs=n.bucketLayerIDs}else this.grid=new p(h,16,0),this.featureIndexArray=new k;this.coord=t,this.overscaling=e,this.x=t.x,this.y=t.y,this.z=t.z-Math.log(e)/Math.LN2,this.setCollisionTile(r)}function i(t){return Math.sqrt(t[0]t[0]+t[1]t[1])}function a(t,e){return e-t}function o(t){return t[\"line-gap-width\"]>0?t[\"line-gap-width\"]+2t[\"line-width\"]:t[\"line-width\"]}function s(t,e,r,n,i){if(!e[0]&&!e[1])return t;e=u.convert(e),\"viewport\"===r&&e._rotate(-n);for(var a=[],o=0;o<t.length;o++){for(var s=t[o],l=[],c=0;c<s.length;c++)l.push(s[c].sub(e._mult(i)));a.push(l)}return a}function l(t,e){for(var r=[],n=new u(0,0),i=0;i<t.length;i++){for(var a=t[i],o=[],s=0;s<a.length;s++){var l=a[s-1],c=a[s],h=a[s+1],f=0===s?n:c.sub(l)._unit()._perp(),d=s===a.length-1?n:h.sub(c)._unit()._perp(),p=f._add(d)._unit(),m=p.xd.x+p.yd.y;p._mult(1/m),o.push(p._mult(e)._add(c))}r.push(o)}return r}var u=t(\"point-geometry\"),c=t(\"./load_geometry\"),h=t(\"./bucket\").EXTENT,f=t(\"feature-filter\"),d=t(\"../util/struct_array\"),p=t(\"grid-index\"),m=t(\"../util/dictionary_coder\"),v=t(\"vector-tile\"),g=t(\"pbf\"),y=t(\"../util/vectortile_to_geojson\"),b=t(\"../util/util\").arraysIntersect,x=t(\"../util/intersection_tests\"),_=x.multiPolygonIntersectsBufferedMultiPoint,w=x.multiPolygonIntersectsMultiPolygon,M=x.multiPolygonIntersectsBufferedMultiLine,k=new d({members:[{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]});e.exports=n,n.prototype.insert=function(t,e,r,n){var i=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(e,r,n);for(var a=c(t),o=0;o<a.length;o++){for(var s=a[o],l=[1/0,1/0,-1/0,-1/0],u=0;u<s.length;u++){var h=s[u];l[0]=Math.min(l[0],h.x),l[1]=Math.min(l[1],h.y),l[2]=Math.max(l[2],h.x),l[3]=Math.max(l[3],h.y)}this.grid.insert(i,l[0],l[1],l[2],l[3])}},n.prototype.setCollisionTile=function(t){this.collisionTile=t},n.prototype.serialize=function(){var t={coord:this.coord,overscaling:this.overscaling,grid:this.grid.toArrayBuffer(),featureIndexArray:this.featureIndexArray.serialize(),bucketLayerIDs:this.bucketLayerIDs};return{data:t,transferables:[t.grid,t.featureIndexArray.arrayBuffer]}},n.prototype.query=function(t,e){this.vtLayers\|\|(this.vtLayers=new v.VectorTile(new g(new Uint8Array(this.rawTileData))).layers,this.sourceLayerCoder=new m(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"]));var r={},n=t.params\|\|{},s=h/t.tileSize/t.scale,l=f(n.filter),c=0;for(var d in e){var p=e[d],y=p.paint,b=0;\"line\"===p.type?b=o(y)/2+Math.abs(y[\"line-offset\"])+i(y[\"line-translate\"]):\"fill\"===p.type?b=i(y[\"fill-translate\"]):\"circle\"===p.type&&(b=y[\"circle-radius\"]+i(y[\"circle-translate\"])),c=Math.max(c,bs)}for(var x=t.queryGeometry.map(function(t){return t.map(function(t){return new u(t.x,t.y)})}),_=1/0,w=1/0,M=-1/0,k=-1/0,A=0;A<x.length;A++)for(var T=x[A],S=0;S<T.length;S++){var E=T[S];_=Math.min(_,E.x),w=Math.min(w,E.y),M=Math.max(M,E.x),k=Math.max(k,E.y)}var L=this.grid.query(_-c,w-c,M+c,k+c);L.sort(a),this.filterMatching(r,L,this.featureIndexArray,x,l,n.layers,e,t.bearing,s);var C=this.collisionTile.queryRenderedSymbols(_,w,M,k,t.scale);return C.sort(),this.filterMatching(r,C,this.collisionTile.collisionBoxArray,x,l,n.layers,e,t.bearing,s),r},n.prototype.filterMatching=function(t,e,r,n,i,a,u,h,f){for(var d,p=0;p<e.length;p++){var m=e[p];if(m!==d){d=m;var v=r.get(m),g=this.bucketLayerIDs[v.bucketIndex];if(!a\|\|b(a,g)){var x=this.sourceLayerCoder.decode(v.sourceLayerIndex),k=this.vtLayers[x],A=k.feature(v.featureIndex);if(i(A))for(var T=null,S=0;S<g.length;S++){var E=g[S];if(!(a&&a.indexOf(E)<0)){var L=u[E];if(L){var C;if(\"symbol\"!==L.type){T\|\|(T=c(A));var I=L.paint;if(\"line\"===L.type){C=s(n,I[\"line-translate\"],I[\"line-translate-anchor\"],h,f);var z=o(I)/2f;if(I[\"line-offset\"]&&(T=l(T,I[\"line-offset\"]f)),!M(C,T,z))continue}else if(\"fill\"===L.type){if(C=s(n,I[\"fill-translate\"],I[\"fill-translate-anchor\"],h,f),!w(C,T))continue}else if(\"circle\"===L.type){C=s(n,I[\"circle-translate\"],I[\"circle-translate-anchor\"],h,f);var D=I[\"circle-radius\"]f;if(!_(C,T,D))continue}}var P=new y(A,this.z,this.x,this.y);P.layer=L.serialize({includeRefProperties:!0});var O=t[E];void 0===O&&(O=t[E]=[]),O.push(P)}}}}}}}},{\"../util/dictionary_coder\":432,\"../util/intersection_tests\":437,\"../util/struct_array\":440,\"../util/util\":442,\"../util/vectortile_to_geojson\":443,\"./bucket\":329,\"./load_geometry\":337,\"feature-filter\":132,\"grid-index\":287,pbf:478,\"point-geometry\":484,\"vector-tile\":550}],337:[function(t,e,r){\"use strict\";function n(t){return{min:-1Math.pow(2,t-1),max:Math.pow(2,t-1)-1}}var i=t(\"../util/util\"),a=t(\"./bucket\").EXTENT,o=t(\"assert\"),s={15:n(15),16:n(16)};e.exports=function(t,e){var r=s[e\|\|16];o(r);for(var n=a/t.extent,l=t.loadGeometry(),u=0;u<l.length;u++)for(var c=l[u],h=0;h<c.length;h++){var f=c[h];f.x=Math.round(f.xn),f.y=Math.round(f.yn),(f.x<r.min\|\|f.x>r.max\|\|f.y<r.min\|\|f.y>r.max)&&i.warnOnce(\"Geometry exceeds allowed extent, reduce your vector tile buffer size\")}return l}},{\"../util/util\":442,\"./bucket\":329,assert:47}],338:[function(t,e,r){\"use strict\";function n(t,e,r){this.column=t,this.row=e,this.zoom=r}e.exports=n,n.prototype={clone:function(){return new n(this.column,this.row,this.zoom)},zoomTo:function(t){return this.clone()._zoomTo(t)},sub:function(t){return this.clone()._sub(t)},_zoomTo:function(t){var e=Math.pow(2,t-this.zoom);return this.column=e,this.row=e,this.zoom=t,this},_sub:function(t){return t=t.zoomTo(this.zoom),this.column-=t.column,this.row-=t.row,this}}},{}],339:[function(t,e,r){\"use strict\";function n(t,e){if(isNaN(t)\|\|isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90\|\|this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}e.exports=n\n", ";var i=t(\"../util/util\").wrap;n.prototype.wrap=function(){return new n(i(this.lng,-180,180),this.lat)},n.prototype.toArray=function(){return[this.lng,this.lat]},n.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},n.convert=function(t){return t instanceof n?t:Array.isArray(t)?new n(t[0],t[1]):t}},{\"../util/util\":442}],340:[function(t,e,r){\"use strict\";function n(t,e){t&&(e?this.extend(t).extend(e):4===t.length?this.extend([t[0],t[1]]).extend([t[2],t[3]]):this.extend(t[0]).extend(t[1]))}e.exports=n;var i=t(\"./lng_lat\");n.prototype={extend:function(t){var e,r,a=this._sw,o=this._ne;if(t instanceof i)e=t,r=t;else{if(!(t instanceof n))return t?this.extend(i.convert(t)\|\|n.convert(t)):this;if(e=t._sw,r=t._ne,!e\|\|!r)return this}return a\|\|o?(a.lng=Math.min(e.lng,a.lng),a.lat=Math.min(e.lat,a.lat),o.lng=Math.max(r.lng,o.lng),o.lat=Math.max(r.lat,o.lat)):(this._sw=new i(e.lng,e.lat),this._ne=new i(r.lng,r.lat)),this},getCenter:function(){return new i((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},getSouthWest:function(){return this._sw},getNorthEast:function(){return this._ne},getNorthWest:function(){return new i(this.getWest(),this.getNorth())},getSouthEast:function(){return new i(this.getEast(),this.getSouth())},getWest:function(){return this._sw.lng},getSouth:function(){return this._sw.lat},getEast:function(){return this._ne.lng},getNorth:function(){return this._ne.lat},toArray:function(){return[this._sw.toArray(),this._ne.toArray()]},toString:function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"}},n.convert=function(t){return!t\|\|t instanceof n?t:new n(t)}},{\"./lng_lat\":339}],341:[function(t,e,r){\"use strict\";function n(t,e){this.tileSize=512,this._minZoom=t\|\|0,this._maxZoom=e\|\|22,this.latRange=[-85.05113,85.05113],this.width=0,this.height=0,this._center=new i(0,0),this.zoom=0,this.angle=0,this._altitude=1.5,this._pitch=0,this._unmodified=!0}var i=t(\"./lng_lat\"),a=t(\"point-geometry\"),o=t(\"./coordinate\"),s=t(\"../util/util\").wrap,l=t(\"../util/interpolate\"),u=t(\"../source/tile_coord\"),c=t(\"../data/bucket\").EXTENT,h=t(\"gl-matrix\"),f=h.vec4,d=h.mat4,p=h.mat2;e.exports=n,n.prototype={get minZoom(){return this._minZoom},set minZoom(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},get maxZoom(){return this._maxZoom},set maxZoom(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},get worldSize(){return this.tileSizethis.scale},get centerPoint(){return this.size._div(2)},get size(){return new a(this.width,this.height)},get bearing(){return-this.angle/Math.PI180},set bearing(t){var e=-s(t,-180,180)Math.PI/180;this.angle!==e&&(this._unmodified=!1,this.angle=e,this._calcMatrices(),this.rotationMatrix=p.create(),p.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},get pitch(){return this._pitch/Math.PI180},set pitch(t){var e=Math.min(60,t)/180Math.PI;this._pitch!==e&&(this._unmodified=!1,this._pitch=e,this._calcMatrices())},get altitude(){return this._altitude},set altitude(t){var e=Math.max(.75,t);this._altitude!==e&&(this._unmodified=!1,this._altitude=e,this._calcMatrices())},get zoom(){return this._zoom},set zoom(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._calcMatrices(),this._constrain())},get center(){return this._center},set center(t){t.lat===this._center.lat&&t.lng===this._center.lng\|\|(this._unmodified=!1,this._center=t,this._calcMatrices(),this._constrain())},coveringZoomLevel:function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},coveringTiles:function(t){var e=this.coveringZoomLevel(t),r=e;if(e<t.minzoom)return[];e>t.maxzoom&&(e=t.maxzoom);var n=this,i=n.locationCoordinate(n.center)._zoomTo(e),o=new a(i.column-.5,i.row-.5);return u.cover(e,[n.pointCoordinate(new a(0,0))._zoomTo(e),n.pointCoordinate(new a(n.width,0))._zoomTo(e),n.pointCoordinate(new a(n.width,n.height))._zoomTo(e),n.pointCoordinate(new a(0,n.height))._zoomTo(e)],t.reparseOverscaled?r:e).sort(function(t,e){return o.dist(t)-o.dist(e)})},resize:function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._calcMatrices(),this._constrain()},get unmodified(){return this._unmodified},zoomScale:function(t){return Math.pow(2,t)},scaleZoom:function(t){return Math.log(t)/Math.LN2},project:function(t,e){return new a(this.lngX(t.lng,e),this.latY(t.lat,e))},unproject:function(t,e){return new i(this.xLng(t.x,e),this.yLat(t.y,e))},get x(){return this.lngX(this.center.lng)},get y(){return this.latY(this.center.lat)},get point(){return new a(this.x,this.y)},lngX:function(t,e){return(180+t)(e\|\|this.worldSize)/360},latY:function(t,e){return(180-180/Math.PIMath.log(Math.tan(Math.PI/4+tMath.PI/360)))(e\|\|this.worldSize)/360},xLng:function(t,e){return 360t/(e\|\|this.worldSize)-180},yLat:function(t,e){var r=180-360t/(e\|\|this.worldSize);return 360/Math.PIMath.atan(Math.exp(rMath.PI/180))-90},panBy:function(t){var e=this.centerPoint._add(t);this.center=this.pointLocation(e)},setLocationAtPoint:function(t,e){var r=this.locationCoordinate(t),n=this.pointCoordinate(e),i=this.pointCoordinate(this.centerPoint),a=n._sub(r);this._unmodified=!1,this.center=this.coordinateLocation(i._sub(a))},locationPoint:function(t){return this.coordinatePoint(this.locationCoordinate(t))},pointLocation:function(t){return this.coordinateLocation(this.pointCoordinate(t))},locationCoordinate:function(t){var e=this.zoomScale(this.tileZoom)/this.worldSize,r=i.convert(t);return new o(this.lngX(r.lng)e,this.latY(r.lat)e,this.tileZoom)},coordinateLocation:function(t){var e=this.zoomScale(t.zoom);return new i(this.xLng(t.column,e),this.yLat(t.row,e))},pointCoordinate:function(t){var e=[t.x,t.y,0,1],r=[t.x,t.y,1,1];f.transformMat4(e,e,this.pixelMatrixInverse),f.transformMat4(r,r,this.pixelMatrixInverse);var n=e[3],i=r[3],a=e[0]/n,s=r[0]/i,u=e[1]/n,c=r[1]/i,h=e[2]/n,d=r[2]/i,p=h===d?0:(0-h)/(d-h),m=this.worldSize/this.zoomScale(this.tileZoom);return new o(l(a,s,p)/m,l(u,c,p)/m,this.tileZoom)},coordinatePoint:function(t){var e=this.worldSize/this.zoomScale(t.zoom),r=[t.columne,t.rowe,0,1];return f.transformMat4(r,r,this.pixelMatrix),new a(r[0]/r[3],r[1]/r[3])},calculatePosMatrix:function(t,e){void 0===e&&(e=1/0),t instanceof u&&(t=t.toCoordinate(e));var r=Math.min(t.zoom,e),n=this.worldSize/Math.pow(2,r),i=new Float64Array(16);return d.identity(i),d.translate(i,i,[t.columnn,t.rown,0]),d.scale(i,i,[n/c,n/c,1]),d.multiply(i,this.projMatrix,i),new Float32Array(i)},_constrain:function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var t,e,r,n,i,o,s,l,u=this.size,c=this._unmodified;this.latRange&&(t=this.latY(this.latRange[1]),e=this.latY(this.latRange[0]),i=e-t<u.y?u.y/(e-t):0),this.lngRange&&(r=this.lngX(this.lngRange[0]),n=this.lngX(this.lngRange[1]),o=n-r<u.x?u.x/(n-r):0);var h=Math.max(o\|\|0,i\|\|0);if(h)return this.center=this.unproject(new a(o?(n+r)/2:this.x,i?(e+t)/2:this.y)),this.zoom+=this.scaleZoom(h),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var f=this.y,d=u.y/2;f-d<t&&(l=t+d),f+d>e&&(l=e-d)}if(this.lngRange){var p=this.x,m=u.x/2;p-m<r&&(s=r+m),p+m>n&&(s=n-m)}void 0===s&&void 0===l\|\|(this.center=this.unproject(new a(void 0!==s?s:this.x,void 0!==l?l:this.y))),this._unmodified=c,this._constraining=!1}},_calcMatrices:function(){if(this.height){var t=Math.atan(.5/this.altitude),e=Math.sin(t)this.altitude/Math.sin(Math.PI/2-this._pitch-t),r=Math.cos(Math.PI/2-this._pitch)e+this.altitude,n=new Float64Array(16);if(d.perspective(n,2Math.atan(this.height/2/this.altitude),this.width/this.height,.1,r),d.translate(n,n,[0,0,-this.altitude]),d.scale(n,n,[1,-1,1/this.height]),d.rotateX(n,n,this._pitch),d.rotateZ(n,n,this.angle),d.translate(n,n,[-this.x,-this.y,0]),this.projMatrix=n,n=d.create(),d.scale(n,n,[this.width/2,-this.height/2,1]),d.translate(n,n,[1,-1,0]),this.pixelMatrix=d.multiply(new Float64Array(16),n,this.projMatrix),!(n=d.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=n}}}},{\"../data/bucket\":329,\"../source/tile_coord\":369,\"../util/interpolate\":436,\"../util/util\":442,\"./coordinate\":338,\"./lng_lat\":339,\"gl-matrix\":193,\"point-geometry\":484}],342:[function(t,e,r){\"use strict\";var n={\" \":[16,[]],\"!\":[10,[5,21,5,7,-1,-1,5,2,4,1,5,0,6,1,5,2]],'\"':[16,[4,21,4,14,-1,-1,12,21,12,14]],\"#\":[21,[11,25,4,-7,-1,-1,17,25,10,-7,-1,-1,4,12,18,12,-1,-1,3,6,17,6]],$:[20,[8,25,8,-4,-1,-1,12,25,12,-4,-1,-1,17,18,15,20,12,21,8,21,5,20,3,18,3,16,4,14,5,13,7,12,13,10,15,9,16,8,17,6,17,3,15,1,12,0,8,0,5,1,3,3]],\"%\":[24,[21,21,3,0,-1,-1,8,21,10,19,10,17,9,15,7,14,5,14,3,16,3,18,4,20,6,21,8,21,10,20,13,19,16,19,19,20,21,21,-1,-1,17,7,15,6,14,4,14,2,16,0,18,0,20,1,21,3,21,5,19,7,17,7]],\"&\":[26,[23,12,23,13,22,14,21,14,20,13,19,11,17,6,15,3,13,1,11,0,7,0,5,1,4,2,3,4,3,6,4,8,5,9,12,13,13,14,14,16,14,18,13,20,11,21,9,20,8,18,8,16,9,13,11,10,16,3,18,1,20,0,22,0,23,1,23,2]],\"'\":[10,[5,19,4,20,5,21,6,20,6,18,5,16,4,15]],\"(\":[14,[11,25,9,23,7,20,5,16,4,11,4,7,5,2,7,-2,9,-5,11,-7]],\")\":[14,[3,25,5,23,7,20,9,16,10,11,10,7,9,2,7,-2,5,-5,3,-7]],\"\":[16,[8,21,8,9,-1,-1,3,18,13,12,-1,-1,13,18,3,12]],\"+\":[26,[13,18,13,0,-1,-1,4,9,22,9]],\",\":[10,[6,1,5,0,4,1,5,2,6,1,6,-1,5,-3,4,-4]],\"-\":[26,[4,9,22,9]],\".\":[10,[5,2,4,1,5,0,6,1,5,2]],\"/\":[22,[20,25,2,-7]],0:[20,[9,21,6,20,4,17,3,12,3,9,4,4,6,1,9,0,11,0,14,1,16,4,17,9,17,12,16,17,14,20,11,21,9,21]],1:[20,[6,17,8,18,11,21,11,0]],2:[20,[4,16,4,17,5,19,6,20,8,21,12,21,14,20,15,19,16,17,16,15,15,13,13,10,3,0,17,0]],3:[20,[5,21,16,21,10,13,13,13,15,12,16,11,17,8,17,6,16,3,14,1,11,0,8,0,5,1,4,2,3,4]],4:[20,[13,21,3,7,18,7,-1,-1,13,21,13,0]],5:[20,[15,21,5,21,4,12,5,13,8,14,11,14,14,13,16,11,17,8,17,6,16,3,14,1,11,0,8,0,5,1,4,2,3,4]],6:[20,[16,18,15,20,12,21,10,21,7,20,5,17,4,12,4,7,5,3,7,1,10,0,11,0,14,1,16,3,17,6,17,7,16,10,14,12,11,13,10,13,7,12,5,10,4,7]],7:[20,[17,21,7,0,-1,-1,3,21,17,21]],8:[20,[8,21,5,20,4,18,4,16,5,14,7,13,11,12,14,11,16,9,17,7,17,4,16,2,15,1,12,0,8,0,5,1,4,2,3,4,3,7,4,9,6,11,9,12,13,13,15,14,16,16,16,18,15,20,12,21,8,21]],9:[20,[16,14,15,11,13,9,10,8,9,8,6,9,4,11,3,14,3,15,4,18,6,20,9,21,10,21,13,20,15,18,16,14,16,9,15,4,13,1,10,0,8,0,5,1,4,3]],\":\":[10,[5,14,4,13,5,12,6,13,5,14,-1,-1,5,2,4,1,5,0,6,1,5,2]],\";\":[10,[5,14,4,13,5,12,6,13,5,14,-1,-1,6,1,5,0,4,1,5,2,6,1,6,-1,5,-3,4,-4]],\"<\":[24,[20,18,4,9,20,0]],\"=\":[26,[4,12,22,12,-1,-1,4,6,22,6]],\">\":[24,[4,18,20,9,4,0]],\"?\":[18,[3,16,3,17,4,19,5,20,7,21,11,21,13,20,14,19,15,17,15,15,14,13,13,12,9,10,9,7,-1,-1,9,2,8,1,9,0,10,1,9,2]],\"@\":[27,[18,13,17,15,15,16,12,16,10,15,9,14,8,11,8,8,9,6,11,5,14,5,16,6,17,8,-1,-1,12,16,10,14,9,11,9,8,10,6,11,5,-1,-1,18,16,17,8,17,6,19,5,21,5,23,7,24,10,24,12,23,15,22,17,20,19,18,20,15,21,12,21,9,20,7,19,5,17,4,15,3,12,3,9,4,6,5,4,7,2,9,1,12,0,15,0,18,1,20,2,21,3,-1,-1,19,16,18,8,18,6,19,5]],A:[18,[9,21,1,0,-1,-1,9,21,17,0,-1,-1,4,7,14,7]],B:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,15,17,13,16,12,13,11,-1,-1,4,11,13,11,16,10,17,9,18,7,18,4,17,2,16,1,13,0,4,0]],C:[21,[18,16,17,18,15,20,13,21,9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5]],D:[21,[4,21,4,0,-1,-1,4,21,11,21,14,20,16,18,17,16,18,13,18,8,17,5,16,3,14,1,11,0,4,0]],E:[19,[4,21,4,0,-1,-1,4,21,17,21,-1,-1,4,11,12,11,-1,-1,4,0,17,0]],F:[18,[4,21,4,0,-1,-1,4,21,17,21,-1,-1,4,11,12,11]],G:[21,[18,16,17,18,15,20,13,21,9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,18,8,-1,-1,13,8,18,8]],H:[22,[4,21,4,0,-1,-1,18,21,18,0,-1,-1,4,11,18,11]],I:[8,[4,21,4,0]],J:[16,[12,21,12,5,11,2,10,1,8,0,6,0,4,1,3,2,2,5,2,7]],K:[21,[4,21,4,0,-1,-1,18,21,4,7,-1,-1,9,12,18,0]],L:[17,[4,21,4,0,-1,-1,4,0,16,0]],M:[24,[4,21,4,0,-1,-1,4,21,12,0,-1,-1,20,21,12,0,-1,-1,20,21,20,0]],N:[22,[4,21,4,0,-1,-1,4,21,18,0,-1,-1,18,21,18,0]],O:[22,[9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,19,8,19,13,18,16,17,18,15,20,13,21,9,21]],P:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,14,17,12,16,11,13,10,4,10]],Q:[22,[9,21,7,20,5,18,4,16,3,13,3,8,4,5,5,3,7,1,9,0,13,0,15,1,17,3,18,5,19,8,19,13,18,16,17,18,15,20,13,21,9,21,-1,-1,12,4,18,-2]],R:[21,[4,21,4,0,-1,-1,4,21,13,21,16,20,17,19,18,17,18,15,17,13,16,12,13,11,4,11,-1,-1,11,11,18,0]],S:[20,[17,18,15,20,12,21,8,21,5,20,3,18,3,16,4,14,5,13,7,12,13,10,15,9,16,8,17,6,17,3,15,1,12,0,8,0,5,1,3,3]],T:[16,[8,21,8,0,-1,-1,1,21,15,21]],U:[22,[4,21,4,6,5,3,7,1,10,0,12,0,15,1,17,3,18,6,18,21]],V:[18,[1,21,9,0,-1,-1,17,21,9,0]],W:[24,[2,21,7,0,-1,-1,12,21,7,0,-1,-1,12,21,17,0,-1,-1,22,21,17,0]],X:[20,[3,21,17,0,-1,-1,17,21,3,0]],Y:[18,[1,21,9,11,9,0,-1,-1,17,21,9,11]],Z:[20,[17,21,3,0,-1,-1,3,21,17,21,-1,-1,3,0,17,0]],\"[\":[14,[4,25,4,-7,-1,-1,5,25,5,-7,-1,-1,4,25,11,25,-1,-1,4,-7,11,-7]],\"\\\\\":[14,[0,21,14,-3]],\"]\":[14,[9,25,9,-7,-1,-1,10,25,10,-7,-1,-1,3,25,10,25,-1,-1,3,-7,10,-7]],\"^\":[16,[6,15,8,18,10,15,-1,-1,3,12,8,17,13,12,-1,-1,8,17,8,0]],_:[16,[0,-2,16,-2]],\"`\":[10,[6,21,5,20,4,18,4,16,5,15,6,16,5,17]],a:[19,[15,14,15,0,-1,-1,15,11,13,13,11,14,8,14,6,13,4,11,3,8,3,6,4,3,6,1,8,0,11,0,13,1,15,3]],b:[19,[4,21,4,0,-1,-1,4,11,6,13,8,14,11,14,13,13,15,11,16,8,16,6,15,3,13,1,11,0,8,0,6,1,4,3]],c:[18,[15,11,13,13,11,14,8,14,6,13,4,11,3,8,3,6,4,3,6,1,8,0,11,0,13,1,15,3]],d:[19,[15,21,15,0,-1,-1,15,11,13,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a,o,s,l,u,c,h,f,d=[];for(a=0,o=t.length;a<o;a++)if(u=n[t[a]]){for(f=null,s=0,l=u[1].length;s<l;s+=2)-1===u[1][s]&&-1===u[1][s+1]?f=null:(c=e+u[1][s]i,h=r-u[1][s+1]i,f&&d.push(f.x,f.y,c,h),f={x:c,y:h});e+=u[0]i}return d}},{}],343:[function(t,e,r){\"use strict\";var n=e.exports={};n.version=t(\"../package.json\").version,n.Map=t(\"./ui/map\"),n.Control=t(\"./ui/control/control\"),n.Navigation=t(\"./ui/control/navigation\"),n.Geolocate=t(\"./ui/control/geolocate\"),n.Attribution=t(\"./ui/control/attribution\"),n.Popup=t(\"./ui/popup\"),n.Marker=t(\"./ui/marker\"),n.Style=t(\"./style/style\"),n.LngLat=t(\"./geo/lng_lat\"),n.LngLatBounds=t(\"./geo/lng_lat_bounds\"),n.Point=t(\"point-geometry\"),n.Evented=t(\"./util/evented\"),n.util=t(\"./util/util\"),n.supported=t(\"./util/browser\").supported;var i=t(\"./util/ajax\");n.util.getJSON=i.getJSON,n.util.getArrayBuffer=i.getArrayBuffer;var a=t(\"./util/config\");n.config=a,Object.defineProperty(n,\"accessToken\",{get:function(){return a.ACCESS_TOKEN},set:function(t){a.ACCESS_TOKEN=t}})},{\"../package.json\":444,\"./geo/lng_lat\":339,\"./geo/lng_lat_bounds\":340,\"./style/style\":378,\"./ui/control/attribution\":409,\"./ui/control/control\":410,\"./ui/control/geolocate\":411,\"./ui/control/navigation\":412,\"./ui/map\":421,\"./ui/marker\":422,\"./ui/popup\":423,\"./util/ajax\":425,\"./util/browser\":426,\"./util/config\":431,\"./util/evented\":434,\"./util/util\":442,\"point-geometry\":484}],344:[function(t,e,r){\"use strict\";var n=t(\"assert\");e.exports=function(t){for(var e={define:{},initialize:{}},r=0;r<t.length;r++){var i=t[r];n(\"u_\"===i.name.slice(0,2));var a=\"{precision} \"+(1===i.components?\"float\":\"vec\"+i.components);e.define[i.name.slice(2)]=\"uniform \"+a+\" \"+i.name+\";\\n\",e.initialize[i.name.slice(2)]=a+\" \"+i.name.slice(2)+\" = \"+i.name+\";\\n\"}return e}},{assert:47}],345:[function(t,e,r){\"use strict\";function n(t,e,r){var n,s=t.gl,l=t.transform,u=r.paint[\"background-color\"],c=r.paint[\"background-pattern\"],h=r.paint[\"background-opacity\"],f=c?t.spriteAtlas.getPosition(c.from,!0):null,d=c?t.spriteAtlas.getPosition(c.to,!0):null;if(t.setDepthSublayer(0),f&&d){if(t.isOpaquePass)return;n=t.useProgram(\"pattern\"),s.uniform1i(n.u_image,0),s.uniform2fv(n.u_pattern_tl_a,f.tl),s.uniform2fv(n.u_pattern_br_a,f.br),s.uniform2fv(n.u_pattern_tl_b,d.tl),s.uniform2fv(n.u_pattern_br_b,d.br),s.uniform1f(n.u_opacity,h),s.uniform1f(n.u_mix,c.t),s.uniform2fv(n.u_pattern_size_a,f.size),s.uniform2fv(n.u_pattern_size_b,d.size),s.uniform1f(n.u_scale_a,c.fromScale),s.uniform1f(n.u_scale_b,c.toScale),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),t.tileExtentPatternVAO.bind(s,n,t.tileExtentBuffer)}else{if(t.isOpaquePass!==(1===u[3]))return;var p=a([{name:\"u_color\",components:4},{name:\"u_opacity\",components:1}]);n=t.useProgram(\"fill\",[],p,p),s.uniform4fv(n.u_color,u),s.uniform1f(n.u_opacity,h),t.tileExtentVAO.bind(s,n,t.tileExtentBuffer)}s.disable(s.STENCIL_TEST);for(var m=l.coveringTiles({tileSize:o}),v=0;v<m.length;v++){var g=m[v];if(f&&d){var y={coord:g,tileSize:o};s.uniform1f(n.u_tile_units_to_pixels,1/i(y,1,t.transform.tileZoom));var b=y.tileSizeMath.pow(2,t.transform.tileZoom-y.coord.z),x=b(y.coord.x+g.wMath.pow(2,y.coord.z)),_=by.coord.y;s.uniform2f(n.u_pixel_coord_upper,x>>16,_>>16),s.uniform2f(n.u_pixel_coord_lower,65535&x,65535&_)}s.uniformMatrix4fv(n.u_matrix,!1,t.transform.calculatePosMatrix(g)),s.drawArrays(s.TRIANGLE_STRIP,0,t.tileExtentBuffer.length)}s.stencilMask(0),s.stencilFunc(s.EQUAL,128,128)}var i=t(\"../source/pixels_to_tile_units\"),a=t(\"./create_uniform_pragmas\"),o=512;e.exports=n},{\"../source/pixels_to_tile_units\":363,\"./create_uniform_pragmas\":344}],346:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=t.gl;t.setDepthSublayer(0),t.depthMask(!1),a.disable(a.STENCIL_TEST);for(var o=0;o<n.length;o++){var s=n[o],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.circle;if(c){var h=u.paintAttributes.circle[r.id],f=t.useProgram(\"circle\",h.defines,h.vertexPragmas,h.fragmentPragmas);\"map\"===r.paint[\"circle-pitch-scale\"]?(a.uniform1i(f.u_scale_with_map,!0),a.uniform2f(f.u_extrude_scale,t.transform.pixelsToGLUnits[0]t.transform.altitude,t.transform.pixelsToGLUnits[1]t.transform.altitude)):(a.uniform1i(f.u_scale_with_map,!1),a.uniform2fv(f.u_extrude_scale,t.transform.pixelsToGLUnits)),a.uniform1f(f.u_devicepixelratio,i.devicePixelRatio),a.uniformMatrix4fv(f.u_matrix,!1,t.translatePosMatrix(s.posMatrix,l,r.paint[\"circle-translate\"],r.paint[\"circle-translate-anchor\"])),u.setUniforms(a,\"circle\",f,r,{zoom:t.transform.zoom});for(var d=0;d<c.length;d++){var p=c[d];p.vaos[r.id].bind(a,f,p.layoutVertexBuffer,p.elementBuffer,p.paintVertexBuffers[r.id]),a.drawElements(a.TRIANGLES,3p.elementBuffer.length,a.UNSIGNED_SHORT,0)}}}}}}var i=t(\"../util/browser\");e.exports=n},{\"../util/browser\":426}],347:[function(t,e,r){\"use strict\";function n(t,e,r,n){var i=t.gl;i.enable(i.STENCIL_TEST);for(var a=t.useProgram(\"collisionbox\"),o=0;o<n.length;o++){var s=n[o],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.collisionBox;if(c&&c.length){var h=c[0];0!==h.layoutVertexBuffer.length&&(i.uniformMatrix4fv(a.u_matrix,!1,s.posMatrix),t.enableTileClippingMask(s),t.lineWidth(1),i.uniform1f(a.u_scale,Math.pow(2,t.transform.zoom-l.coord.z)),i.uniform1f(a.u_zoom,10t.transform.zoom),i.uniform1f(a.u_maxzoom,10(l.coord.z+1)),h.vaos[r.id].bind(i,a,h.layoutVertexBuffer),i.drawArrays(i.LINES,0,h.layoutVertexBuffer.length))}}}}e.exports=n},{}],348:[function(t,e,r){\"use strict\";function n(t,e,r){if(!t.isOpaquePass&&t.options.debug)for(var n=0;n<r.length;n++)i(t,e,r[n])}function i(t,e,r){var n=t.gl;n.disable(n.STENCIL_TEST),t.lineWidth(1o.devicePixelRatio);var i=r.posMatrix,h=t.useProgram(\"debug\");n.uniformMatrix4fv(h.u_matrix,!1,i),n.uniform4f(h.u_color,1,0,0,1),t.debugVAO.bind(n,h,t.debugBuffer),n.drawArrays(n.LINE_STRIP,0,t.debugBuffer.length);for(var f=a(r.toString(),50,200,5),d=new t.PosArray,p=0;p<f.length;p+=2)d.emplaceBack(f[p],f[p+1]);var m=new u(d.serialize(),t.PosArray.serialize(),u.BufferType.VERTEX);(new c).bind(n,h,m),n.uniform4f(h.u_color,1,1,1,1);for(var v=e.getTile(r).tileSize,g=l/(Math.pow(2,t.transform.zoom-r.z)v),y=[[-1,-1],[-1,1],[1,-1],[1,1]],b=0;b<y.length;b++){var x=y[b];n.uniformMatrix4fv(h.u_matrix,!1,s.translate([],i,[gx[0],gx[1],0])),n.drawArrays(n.LINES,0,m.length)}n.uniform4f(h.u_color,0,0,0,1),n.uniformMatrix4fv(h.u_matrix,!1,i),n.drawArrays(n.LINES,0,m.length)}var a=t(\"../lib/debugtext\"),o=t(\"../util/browser\"),s=t(\"gl-matrix\").mat4,l=t(\"../data/bucket\").EXTENT,u=t(\"../data/buffer\"),c=t(\"./vertex_array_object\");e.exports=n},{\"../data/bucket\":329,\"../data/buffer\":334,\"../lib/debugtext\":342,\"../util/browser\":426,\"./vertex_array_object\":357,\"gl-matrix\":193}],349:[function(t,e,r){\"use strict\";function n(t,e,r,n){var o=t.gl;o.enable(o.STENCIL_TEST);var s;if(s=!r.paint[\"fill-pattern\"]&&(r.isPaintValueFeatureConstant(\"fill-color\")&&r.isPaintValueFeatureConstant(\"fill-opacity\")&&1===r.paint[\"fill-color\"][3]&&1===r.paint[\"fill-opacity\"]),t.isOpaquePass===s){t.setDepthSublayer(1);for(var l=0;l<n.length;l++)i(t,e,r,n[l])}if(!t.isOpaquePass&&r.paint[\"fill-antialias\"]){t.lineWidth(2),t.depthMask(!1);var u=r.getPaintProperty(\"fill-outline-color\");(u\|\|!r.paint[\"fill-pattern\"])&&u?t.setDepthSublayer(2):t.setDepthSublayer(0);for(var c=0;c<n.length;c++)a(t,e,r,n[c])}}function i(t,e,r,n){var i=e.getTile(n),a=i.getBucket(r);if(a){var s=a.bufferGroups.fill;if(s){var l,u=t.gl,c=r.paint[\"fill-pattern\"];if(c)l=t.useProgram(\"pattern\"),o(c,r.paint[\"fill-opacity\"],i,n,t,l),u.activeTexture(u.TEXTURE0),t.spriteAtlas.bind(u,!0);else{var h=a.paintAttributes.fill[r.id];l=t.useProgram(\"fill\",h.defines,h.vertexPragmas,h.fragmentPragmas),a.setUniforms(u,\"fill\",l,r,{zoom:t.transform.zoom})}u.uniformMatrix4fv(l.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint[\"fill-translate\"],r.paint[\"fill-translate-anchor\"])),t.enableTileClippingMask(n);for(var f=0;f<s.length;f++){var d=s[f];d.vaos[r.id].bind(u,l,d.layoutVertexBuffer,d.elementBuffer,d.paintVertexBuffers[r.id]),u.drawElements(u.TRIANGLES,d.elementBuffer.length,u.UNSIGNED_SHORT,0)}}}}function a(t,e,r,n){var i=e.getTile(n),a=i.getBucket(r);if(a){var s,l=t.gl,u=a.bufferGroups.fill,c=r.paint[\"fill-pattern\"],h=r.paint[\"fill-opacity\"],f=r.getPaintProperty(\"fill-outline-color\");if(c&&!f)s=t.useProgram(\"outlinepattern\"),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight);else{var d=a.paintAttributes.fill[r.id];s=t.useProgram(\"outline\",d.defines,d.vertexPragmas,d.fragmentPragmas),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight),l.uniform1f(s.u_opacity,h),a.setUniforms(l,\"fill\",s,r,{zoom:t.transform.zoom})}l.uniformMatrix4fv(s.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint[\"fill-translate\"],r.paint[\"fill-translate-anchor\"])),c&&o(c,h,i,n,t,s),t.enableTileClippingMask(n);for(var p=0;p<u.length;p++){var m=u[p];m.secondVaos[r.id].bind(l,s,m.layoutVertexBuffer,m.elementBuffer2,m.paintVertexBuffers[r.id]),l.drawElements(l.LINES,2m.elementBuffer2.length,l.UNSIGNED_SHORT,0)}}}function o(t,e,r,n,i,a){var o=i.gl,l=i.spriteAtlas.getPosition(t.from,!0),u=i.spriteAtlas.getPosition(t.to,!0);if(l&&u){o.uniform1i(a.u_image,0),o.uniform2fv(a.u_pattern_tl_a,l.tl),o.uniform2fv(a.u_pattern_br_a,l.br),o.uniform2fv(a.u_pattern_tl_b,u.tl),o.uniform2fv(a.u_pattern_br_b,u.br),o.uniform1f(a.u_opacity,e),o.uniform1f(a.u_mix,t.t),o.uniform1f(a.u_tile_units_to_pixels,1/s(r,1,i.transform.tileZoom)),o.uniform2fv(a.u_pattern_size_a,l.size),o.uniform2fv(a.u_pattern_size_b,u.size),o.uniform1f(a.u_scale_a,t.fromScale),o.uniform1f(a.u_scale_b,t.toScale);var c=r.tileSizeMath.pow(2,i.transform.tileZoom-r.coord.z),h=c(r.coord.x+n.wMath.pow(2,r.coord.z)),f=cr.coord.y;o.uniform2f(a.u_pixel_coord_upper,h>>16,f>>16),o.uniform2f(a.u_pixel_coord_lower,65535&h,65535&f),o.activeTexture(o.TEXTURE0),i.spriteAtlas.bind(o,!0)}}var s=t(\"../source/pixels_to_tile_units\");e.exports=n},{\"../source/pixels_to_tile_units\":363}],350:[function(t,e,r){\"use strict\";var n=t(\"../util/browser\"),i=t(\"gl-matrix\").mat2,a=t(\"../source/pixels_to_tile_units\");e.exports=function(t,e,r,o){if(!t.isOpaquePass){t.setDepthSublayer(0),t.depthMask(!1);var s=t.gl;if(s.enable(s.STENCIL_TEST),!(r.paint[\"line-width\"]<=0)){var l=1/n.devicePixelRatio,u=r.paint[\"line-blur\"]+l,c=r.paint[\"line-color\"],h=t.transform,f=i.create();i.scale(f,f,[1,Math.cos(h._pitch)]),i.rotate(f,f,t.transform.angle);var d,p,m,v,g,y=Math.sqrt(h.heighth.height/4(1+h.altitudeh.altitude)),b=h.height/2Math.tan(h._pitch),x=(y+b)/y-1,_=r.paint[\"line-dasharray\"],w=r.paint[\"line-pattern\"];if(_)d=t.useProgram(\"linesdfpattern\"),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform4fv(d.u_color,c),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]),p=t.lineAtlas.getDash(_.from,\"round\"===r.layout[\"line-cap\"]),m=t.lineAtlas.getDash(_.to,\"round\"===r.layout[\"line-cap\"]),s.uniform1i(d.u_image,0),s.activeTexture(s.TEXTURE0),t.lineAtlas.bind(s),s.uniform1f(d.u_tex_y_a,p.y),s.uniform1f(d.u_tex_y_b,m.y),s.uniform1f(d.u_mix,_.t),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f);else if(w){if(v=t.spriteAtlas.getPosition(w.from,!0),g=t.spriteAtlas.getPosition(w.to,!0),!v\|\|!g)return;d=t.useProgram(\"linepattern\"),s.uniform1i(d.u_image,0),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform2fv(d.u_pattern_tl_a,v.tl),s.uniform2fv(d.u_pattern_br_a,v.br),s.uniform2fv(d.u_pattern_tl_b,g.tl),s.uniform2fv(d.u_pattern_br_b,g.br),s.uniform1f(d.u_fade,w.t),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f)}else d=t.useProgram(\"line\"),s.uniform1f(d.u_linewidth,r.paint[\"line-width\"]/2),s.uniform1f(d.u_gapwidth,r.paint[\"line-gap-width\"]/2),s.uniform1f(d.u_antialiasing,l/2),s.uniform1f(d.u_blur,u),s.uniform1f(d.u_extra,x),s.uniform1f(d.u_offset,-r.paint[\"line-offset\"]),s.uniformMatrix2fv(d.u_antialiasingmatrix,!1,f),s.uniform4fv(d.u_color,c),s.uniform1f(d.u_opacity,r.paint[\"line-opacity\"]);for(var M=0;M<o.length;M++){var k=o[M],A=e.getTile(k),T=A.getBucket(r);if(T){var S=T.bufferGroups.line;if(S){t.enableTileClippingMask(k);var E=t.translatePosMatrix(k.posMatrix,A,r.paint[\"line-translate\"],r.paint[\"line-translate-anchor\"]);s.uniformMatrix4fv(d.u_matrix,!1,E);var L=1/a(A,1,t.transform.zoom);if(_){var C=p.width_.fromScale,I=m.width_.toScale,z=[1/a(A,C,t.transform.tileZoom),-p.height/2],D=[1/a(A,I,t.transform.tileZoom),-m.height/2],P=t.lineAtlas.width/(256Math.min(C,I)n.devicePixelRatio)/2;s.uniform1f(d.u_ratio,L),s.uniform2fv(d.u_patternscale_a,z),s.uniform2fv(d.u_patternscale_b,D),s.uniform1f(d.u_sdfgamma,P)}else w?(s.uniform1f(d.u_ratio,L),s.uniform2fv(d.u_pattern_size_a,[a(A,v.size[0]w.fromScale,t.transform.tileZoom),g.size[1]]),s.uniform2fv(d.u_pattern_size_b,[a(A,g.size[0]w.toScale,t.transform.tileZoom),g.size[1]])):s.uniform1f(d.u_ratio,L);for(var O=0;O<S.length;O++){var R=S[O];R.vaos[r.id].bind(s,d,R.layoutVertexBuffer,R.elementBuffer),s.drawElements(s.TRIANGLES,3R.elementBuffer.length,s.UNSIGNED_SHORT,0)}}}}}}}},{\"../source/pixels_to_tile_units\":363,\"../util/browser\":426,\"gl-matrix\":193}],351:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=t.gl;a.enable(a.DEPTH_TEST),t.depthMask(!0),a.depthFunc(a.LESS);for(var o=n.length&&n[0].z,s=0;s<n.length;s++){var l=n[s];t.setDepthSublayer(l.z-o),i(t,e,r,l)}a.depthFunc(a.LEQUAL)}}function i(t,e,r,n){var i=t.gl;i.disable(i.STENCIL_TEST);var u=e.getTile(n),c=t.transform.calculatePosMatrix(n,e.maxzoom),h=t.useProgram(\"raster\");i.uniformMatrix4fv(h.u_matrix,!1,c),i.uniform1f(h.u_brightness_low,r.paint[\"raster-brightness-min\"]),i.uniform1f(h.u_brightness_high,r.paint[\"raster-brightness-max\"]),i.uniform1f(h.u_saturation_factor,s(r.paint[\"raster-saturation\"])),i.uniform1f(h.u_contrast_factor,o(r.paint[\"raster-contrast\"])),i.uniform3fv(h.u_spin_weights,a(r.paint[\"raster-hue-rotate\"]));var f,d,p=u.source&&u.source.findLoadedParent(n,0,{}),m=l(u,p,r,t.transform);i.activeTexture(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,u.texture),i.activeTexture(i.TEXTURE1),p?(i.bindTexture(i.TEXTURE_2D,p.texture),f=Math.pow(2,p.coord.z-u.coord.z),d=[u.coord.xf%1,u.coord.yf%1]):(i.bindTexture(i.TEXTURE_2D,u.texture),m[1]=0),i.uniform2fv(h.u_tl_parent,d\|\|[0,0]),i.uniform1f(h.u_scale_parent,f\|\|1),i.uniform1f(h.u_buffer_scale,1),i.uniform1f(h.u_opacity0,m[0]),i.uniform1f(h.u_opacity1,m[1]),i.uniform1i(h.u_image0,0),i.uniform1i(h.u_image1,1);var v=u.boundsBuffer\|\|t.rasterBoundsBuffer;(u.boundsVAO\|\|t.rasterBoundsVAO).bind(i,h,v),i.drawArrays(i.TRIANGLE_STRIP,0,v.length)}function a(t){t=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2r+1)/3,(-Math.sqrt(3)e-r+1)/3,(Math.sqrt(3)e-r+1)/3]}function o(t){return t>0?1/(1-t):1+t}function s(t){return t>0?1-1/(1.001-t):-t}function l(t,e,r,n){var i=[1,0],a=r.paint[\"raster-fade-duration\"];if(t.source&&a>0){var o=(new Date).getTime(),s=(o-t.timeAdded)/a,l=e?(o-e.timeAdded)/a:-1,c=n.coveringZoomLevel(t.source),h=!!e&&Math.abs(e.coord.z-c)>Math.abs(t.coord.z-c);!e\|\|h?(i[0]=u.clamp(s,0,1),i[1]=1-i[0]):(i[0]=u.clamp(1-l,0,1),i[1]=1-i[0])}var f=r.paint[\"raster-opacity\"];return i[0]=f,i[1]=f,i}var u=t(\"../util/util\"),c=t(\"../util/struct_array\");e.exports=n,n.RasterBoundsArray=new c({members:[{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]})},{\"../util/struct_array\":440,\"../util/util\":442}],352:[function(t,e,r){\"use strict\";function n(t,e,r,n){if(!t.isOpaquePass){var a=!(r.layout[\"text-allow-overlap\"]\|\|r.layout[\"icon-allow-overlap\"]\|\|r.layout[\"text-ignore-placement\"]\|\|r.layout[\"icon-ignore-placement\"]),o=t.gl;a?o.disable(o.STENCIL_TEST):o.enable(o.STENCIL_TEST),t.setDepthSublayer(0),t.depthMask(!1),o.disable(o.DEPTH_TEST),i(t,e,r,n,!1,r.paint[\"icon-translate\"],r.paint[\"icon-translate-anchor\"],r.layout[\"icon-rotation-alignment\"],r.layout[\"icon-rotation-alignment\"],r.layout[\"icon-size\"],r.paint[\"icon-halo-width\"],r.paint[\"icon-halo-color\"],r.paint[\"icon-halo-blur\"],r.paint[\"icon-opacity\"],r.paint[\"icon-color\"]),\n", "i(t,e,r,n,!0,r.paint[\"text-translate\"],r.paint[\"text-translate-anchor\"],r.layout[\"text-rotation-alignment\"],r.layout[\"text-pitch-alignment\"],r.layout[\"text-size\"],r.paint[\"text-halo-width\"],r.paint[\"text-halo-color\"],r.paint[\"text-halo-blur\"],r.paint[\"text-opacity\"],r.paint[\"text-color\"]),o.enable(o.DEPTH_TEST),e.map.showCollisionBoxes&&s(t,e,r,n)}}function i(t,e,r,n,i,o,s,l,u,c,h,f,d,p,m){for(var v=0;v<n.length;v++){var g=e.getTile(n[v]),y=g.getBucket(r);if(y){var b=y.bufferGroups,x=i?b.glyph:b.icon;x.length&&(t.enableTileClippingMask(n[v]),a(t,r,n[v].posMatrix,g,y,x,i,i\|\|y.sdfIcons,!i&&y.iconsNeedLinear,i?y.adjustedTextSize:y.adjustedIconSize,y.fontstack,o,s,l,u,c,h,f,d,p,m))}}}function a(t,e,r,n,i,a,s,u,c,h,f,d,p,m,v,g,y,b,x,_,w){var M,k,A,T=t.gl,S=t.transform,E=\"map\"===m,L=\"map\"===v,C=s?24:1,I=g/C;if(L?(k=l(n,1,t.transform.zoom)I,A=1/Math.cos(S._pitch),M=[k,k]):(k=t.transform.altitudeI,A=1,M=[S.pixelsToGLUnits[0]k,S.pixelsToGLUnits[1]k]),s\|\|t.style.sprite.loaded()){var z=t.useProgram(u?\"sdf\":\"icon\");if(T.uniformMatrix4fv(z.u_matrix,!1,t.translatePosMatrix(r,n,d,p)),T.uniform1i(z.u_rotate_with_map,E),T.uniform1i(z.u_pitch_with_map,L),T.uniform2fv(z.u_extrude_scale,M),T.activeTexture(T.TEXTURE0),T.uniform1i(z.u_texture,0),s){var D=f&&t.glyphSource.getGlyphAtlas(f);if(!D)return;D.updateTexture(T),T.uniform2f(z.u_texsize,D.width/4,D.height/4)}else{var P=t.options.rotating\|\|t.options.zooming,O=1!==I\|\|o.devicePixelRatio!==t.spriteAtlas.pixelRatio\|\|c,R=L\|\|t.transform.pitch;t.spriteAtlas.bind(T,u\|\|P\|\|O\|\|R),T.uniform2f(z.u_texsize,t.spriteAtlas.width/4,t.spriteAtlas.height/4)}var F=Math.log(g/h)/Math.LN2\|\|0;T.uniform1f(z.u_zoom,10(t.transform.zoom-F)),T.activeTexture(T.TEXTURE1),t.frameHistory.bind(T),T.uniform1i(z.u_fadetexture,1);var j;if(u){var N=.105C/g/o.devicePixelRatio;if(y){T.uniform1f(z.u_gamma,(1.19x/I/8+N)A),T.uniform4fv(z.u_color,b),T.uniform1f(z.u_opacity,_),T.uniform1f(z.u_buffer,(6-y/I)/8);for(var B=0;B<a.length;B++)j=a[B],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3j.elementBuffer.length,T.UNSIGNED_SHORT,0)}T.uniform1f(z.u_gamma,NA),T.uniform4fv(z.u_color,w),T.uniform1f(z.u_opacity,_),T.uniform1f(z.u_buffer,.75),T.uniform1f(z.u_pitch,S.pitch/3602Math.PI),T.uniform1f(z.u_bearing,S.bearing/3602Math.PI),T.uniform1f(z.u_aspect_ratio,S.width/S.height);for(var U=0;U<a.length;U++)j=a[U],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3j.elementBuffer.length,T.UNSIGNED_SHORT,0)}else{T.uniform1f(z.u_opacity,_);for(var V=0;V<a.length;V++)j=a[V],j.vaos[e.id].bind(T,z,j.layoutVertexBuffer,j.elementBuffer),T.drawElements(T.TRIANGLES,3j.elementBuffer.length,T.UNSIGNED_SHORT,0)}}}var o=t(\"../util/browser\"),s=t(\"./draw_collision_debug\"),l=t(\"../source/pixels_to_tile_units\");e.exports=n},{\"../source/pixels_to_tile_units\":363,\"../util/browser\":426,\"./draw_collision_debug\":347}],353:[function(t,e,r){\"use strict\";function n(){this.changeTimes=new Float64Array(256),this.changeOpacities=new Uint8Array(256),this.opacities=new Uint8ClampedArray(256),this.array=new Uint8Array(this.opacities.buffer),this.fadeDuration=300,this.previousZoom=0,this.firstFrame=!0}e.exports=n,n.prototype.record=function(t){var e=Date.now();this.firstFrame&&(e=0,this.firstFrame=!1),t=Math.floor(10t);var r;if(t<this.previousZoom)for(r=t+1;r<=this.previousZoom;r++)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];else for(r=t;r>this.previousZoom;r--)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];for(r=0;r<256;r++){var n=e-this.changeTimes[r],i=n/this.fadeDuration255;this.opacities[r]=r<=t?this.changeOpacities[r]+i:this.changeOpacities[r]-i}this.changed=!0,this.previousZoom=t},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.changed&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,256,1,t.ALPHA,t.UNSIGNED_BYTE,this.array),this.changed=!1)):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,256,1,0,t.ALPHA,t.UNSIGNED_BYTE,this.array))}},{}],354:[function(t,e,r){\"use strict\";function n(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.widththis.heightthis.bytes),this.positions={}}var i=t(\"../util/util\");e.exports=n,n.prototype.setSprite=function(t){this.sprite=t},n.prototype.getDash=function(t,e){var r=t.join(\",\")+e;return this.positions[r]\|\|(this.positions[r]=this.addDash(t,e)),this.positions[r]},n.prototype.addDash=function(t,e){var r=e?7:0,n=2r+1;if(this.nextRow+n>this.height)return i.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<t.length;o++)a+=t[o];for(var s=this.width/a,l=s/2,u=t.length%2==1,c=-r;c<=r;c++)for(var h=this.nextRow+r+c,f=this.widthh,d=u?-t[t.length-1]:0,p=t[0],m=1,v=0;v<this.width;v++){for(;p<v/s;)d=p,p+=t[m],u&&m===t.length-1&&(p+=t[0]),m++;var g,y=Math.abs(v-ds),b=Math.abs(v-ps),x=Math.min(y,b),_=m%2==1;if(e){var w=r?c/r(l+1):0;if(_){var M=l-Math.abs(w);g=Math.sqrt(xx+MM)}else g=l-Math.sqrt(xx+ww)}else g=(_?1:-1)x;this.data[3+4(f+v)]=Math.max(0,Math.min(255,g+128))}var k={y:(this.nextRow+r+.5)/this.height,height:2r/this.height,width:a};return this.nextRow+=n,this.dirty=!0,k},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.RGBA,t.UNSIGNED_BYTE,this.data))):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width,this.height,0,t.RGBA,t.UNSIGNED_BYTE,this.data))}},{\"../util/util\":442}],355:[function(t,e,r){\"use strict\";function n(t,e){this.gl=t,this.transform=e,this.reusableTextures={},this.preFbos={},this.frameHistory=new o,this.setup(),this.numSublayers=s.maxUnderzooming+s.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.lineWidthRange=t.getParameter(t.ALIASED_LINE_WIDTH_RANGE)}var i=t(\"../util/browser\"),a=t(\"gl-matrix\").mat4,o=t(\"./frame_history\"),s=t(\"../source/source_cache\"),l=t(\"../data/bucket\").EXTENT,u=t(\"../source/pixels_to_tile_units\"),c=t(\"../util/util\"),h=t(\"../util/struct_array\"),f=t(\"../data/buffer\"),d=t(\"./vertex_array_object\"),p=t(\"./draw_raster\").RasterBoundsArray,m=t(\"./create_uniform_pragmas\");e.exports=n,c.extend(n.prototype,t(\"./painter/use_program\")),n.prototype.resize=function(t,e){var r=this.gl;this.width=ti.devicePixelRatio,this.height=ei.devicePixelRatio,r.viewport(0,0,this.width,this.height)},n.prototype.setup=function(){var t=this.gl;t.verbose=!0,t.enable(t.BLEND),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.enable(t.STENCIL_TEST),t.enable(t.DEPTH_TEST),t.depthFunc(t.LEQUAL),this._depthMask=!1,t.depthMask(!1);var e=this.PosArray=new h({members:[{name:\"a_pos\",type:\"Int16\",components:2}]}),r=new e;r.emplaceBack(0,0),r.emplaceBack(l,0),r.emplaceBack(0,l),r.emplaceBack(l,l),this.tileExtentBuffer=new f(r.serialize(),e.serialize(),f.BufferType.VERTEX),this.tileExtentVAO=new d,this.tileExtentPatternVAO=new d;var n=new e;n.emplaceBack(0,0),n.emplaceBack(l,0),n.emplaceBack(l,l),n.emplaceBack(0,l),n.emplaceBack(0,0),this.debugBuffer=new f(n.serialize(),e.serialize(),f.BufferType.VERTEX),this.debugVAO=new d;var i=new p;i.emplaceBack(0,0,0,0),i.emplaceBack(l,0,32767,0),i.emplaceBack(0,l,0,32767),i.emplaceBack(l,l,32767,32767),this.rasterBoundsBuffer=new f(i.serialize(),p.serialize(),f.BufferType.VERTEX),this.rasterBoundsVAO=new d},n.prototype.clearColor=function(){var t=this.gl;t.clearColor(0,0,0,0),t.clear(t.COLOR_BUFFER_BIT)},n.prototype.clearStencil=function(){var t=this.gl;t.clearStencil(0),t.stencilMask(255),t.clear(t.STENCIL_BUFFER_BIT)},n.prototype.clearDepth=function(){var t=this.gl;t.clearDepth(1),this.depthMask(!0),t.clear(t.DEPTH_BUFFER_BIT)},n.prototype._renderTileClippingMasks=function(t){var e=this.gl;e.colorMask(!1,!1,!1,!1),this.depthMask(!1),e.disable(e.DEPTH_TEST),e.enable(e.STENCIL_TEST),e.stencilMask(248),e.stencilOp(e.KEEP,e.KEEP,e.REPLACE);var r=1;this._tileClippingMaskIDs={};for(var n=0;n<t.length;n++){var i=t[n],a=this._tileClippingMaskIDs[i.id]=r++<<3;e.stencilFunc(e.ALWAYS,a,248);var o=m([{name:\"u_color\",components:4},{name:\"u_opacity\",components:1}]),s=this.useProgram(\"fill\",[],o,o);e.uniformMatrix4fv(s.u_matrix,!1,i.posMatrix),this.tileExtentVAO.bind(e,s,this.tileExtentBuffer),e.drawArrays(e.TRIANGLE_STRIP,0,this.tileExtentBuffer.length)}e.stencilMask(0),e.colorMask(!0,!0,!0,!0),this.depthMask(!0),e.enable(e.DEPTH_TEST)},n.prototype.enableTileClippingMask=function(t){var e=this.gl;e.stencilFunc(e.EQUAL,this._tileClippingMaskIDs[t.id],248)},n.prototype.prepareBuffers=function(){},n.prototype.bindDefaultFramebuffer=function(){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,null)};var v={symbol:t(\"./draw_symbol\"),circle:t(\"./draw_circle\"),line:t(\"./draw_line\"),fill:t(\"./draw_fill\"),raster:t(\"./draw_raster\"),background:t(\"./draw_background\"),debug:t(\"./draw_debug\")};n.prototype.render=function(t,e){this.style=t,this.options=e,this.lineAtlas=t.lineAtlas,this.spriteAtlas=t.spriteAtlas,this.spriteAtlas.setSprite(t.sprite),this.glyphSource=t.glyphSource,this.frameHistory.record(this.transform.zoom),this.prepareBuffers(),this.clearColor(),this.clearDepth(),this.showOverdrawInspector(e.showOverdrawInspector),this.depthRange=(t._order.length+2)this.numSublayersthis.depthEpsilon,this.renderPass({isOpaquePass:!0}),this.renderPass({isOpaquePass:!1})},n.prototype.renderPass=function(t){var e=this.style._groups,r=t.isOpaquePass;this.currentLayer=r?this.style._order.length:-1;for(var n=0;n<e.length;n++){var i,a=e[r?e.length-1-n:n],o=this.style.sources[a.source],s=[];if(o){for(s=o.getVisibleCoordinates(),i=0;i<s.length;i++)s[i].posMatrix=this.transform.calculatePosMatrix(s[i],o.maxzoom);this.clearStencil(),o.prepare&&o.prepare(),o.isTileClipped&&this._renderTileClippingMasks(s)}for(r?(this._showOverdrawInspector\|\|this.gl.disable(this.gl.BLEND),this.isOpaquePass=!0):(this.gl.enable(this.gl.BLEND),this.isOpaquePass=!1,s.reverse()),i=0;i<a.length;i++){var l=a[r?a.length-1-i:i];this.currentLayer+=r?-1:1,this.renderLayer(this,o,l,s)}o&&v.debug(this,o,s)}},n.prototype.depthMask=function(t){t!==this._depthMask&&(this._depthMask=t,this.gl.depthMask(t))},n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)\|\|(\"background\"===r.type\|\|n.length)&&(this.id=r.id,v[r.type](t,e,r,n))},n.prototype.setDepthSublayer=function(t){var e=1-((1+this.currentLayer)this.numSublayers+t)this.depthEpsilon,r=e-1+this.depthRange;this.gl.depthRange(r,e)},n.prototype.translatePosMatrix=function(t,e,r,n){if(!r[0]&&!r[1])return t;if(\"viewport\"===n){var i=Math.sin(-this.transform.angle),o=Math.cos(-this.transform.angle);r=[r[0]o-r[1]i,r[0]i+r[1]o]}var s=[u(e,r[0],this.transform.zoom),u(e,r[1],this.transform.zoom),0],l=new Float32Array(16);return a.translate(l,t,s),l},n.prototype.saveTexture=function(t){var e=this.reusableTextures[t.size];e?e.push(t):this.reusableTextures[t.size]=[t]},n.prototype.getTexture=function(t){var e=this.reusableTextures[t];return e&&e.length>0?e.pop():null},n.prototype.lineWidth=function(t){this.gl.lineWidth(c.clamp(t,this.lineWidthRange[0],this.lineWidthRange[1]))},n.prototype.showOverdrawInspector=function(t){if(t\|\|this._showOverdrawInspector){this._showOverdrawInspector=t;var e=this.gl;if(t){e.blendFunc(e.CONSTANT_COLOR,e.ONE);e.blendColor(1/8,1/8,1/8,0),e.clearColor(0,0,0,1),e.clear(e.COLOR_BUFFER_BIT)}else e.blendFunc(e.ONE,e.ONE_MINUS_SRC_ALPHA)}}},{\"../data/bucket\":329,\"../data/buffer\":334,\"../source/pixels_to_tile_units\":363,\"../source/source_cache\":367,\"../util/browser\":426,\"../util/struct_array\":440,\"../util/util\":442,\"./create_uniform_pragmas\":344,\"./draw_background\":345,\"./draw_circle\":346,\"./draw_debug\":348,\"./draw_fill\":349,\"./draw_line\":350,\"./draw_raster\":351,\"./draw_symbol\":352,\"./frame_history\":353,\"./painter/use_program\":356,\"./vertex_array_object\":357,\"gl-matrix\":193}],356:[function(t,e,r){\"use strict\";function n(t,e){return t.replace(/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,function(t,r,n,i,a){return e[r][a].replace(/{type}/g,i).replace(/{precision}/g,n)})}var i=t(\"assert\"),a=t(\"../../util/util\"),o=t(\"mapbox-gl-shaders\"),s=o.util;e.exports._createProgram=function(t,e,r,l){for(var u=this.gl,c=u.createProgram(),h=o[t],f=\"#define MAPBOX_GL_JS;\\n\",d=0;d<e.length;d++)f+=\"#define \"+e[d]+\";\\n\";var p=u.createShader(u.FRAGMENT_SHADER);u.shaderSource(p,n(f+h.fragmentSource,l)),u.compileShader(p),i(u.getShaderParameter(p,u.COMPILE_STATUS),u.getShaderInfoLog(p)),u.attachShader(c,p);var m=u.createShader(u.VERTEX_SHADER);u.shaderSource(m,n(f+s+h.vertexSource,r)),u.compileShader(m),i(u.getShaderParameter(m,u.COMPILE_STATUS),u.getShaderInfoLog(m)),u.attachShader(c,m),u.linkProgram(c),i(u.getProgramParameter(c,u.LINK_STATUS),u.getProgramInfoLog(c));for(var v={},g=u.getProgramParameter(c,u.ACTIVE_ATTRIBUTES),y=0;y<g;y++){var b=u.getActiveAttrib(c,y);v[b.name]=u.getAttribLocation(c,b.name)}for(var x={},_=u.getProgramParameter(c,u.ACTIVE_UNIFORMS),w=0;w<_;w++){var M=u.getActiveUniform(c,w);x[M.name]=u.getUniformLocation(c,M.name)}return a.extend({program:c,definition:h,attributes:v,numAttributes:g},v,x)},e.exports._createProgramCached=function(t,e,r,n){this.cache=this.cache\|\|{};var i=JSON.stringify({name:t,defines:e,vertexPragmas:r,fragmentPragmas:n});return this.cache[i]\|\|(this.cache[i]=this._createProgram(t,e,r,n)),this.cache[i]},e.exports.useProgram=function(t,e,r,n){var i=this.gl;e=e\|\|[],this._showOverdrawInspector&&(e=e.concat(\"OVERDRAW_INSPECTOR\"));var a=this._createProgramCached(t,e,r,n);return this.currentProgram!==a&&(i.useProgram(a.program),this.currentProgram=a),a}},{\"../../util/util\":442,assert:47,\"mapbox-gl-shaders\":303}],357:[function(t,e,r){\"use strict\";function n(){this.boundProgram=null,this.boundVertexBuffer=null,this.boundVertexBuffer2=null,this.boundElementBuffer=null,this.vao=null}var i=t(\"assert\");e.exports=n,n.prototype.bind=function(t,e,r,n,i){void 0===t.extVertexArrayObject&&(t.extVertexArrayObject=t.getExtension(\"OES_vertex_array_object\"));var a=!this.vao\|\|this.boundProgram!==e\|\|this.boundVertexBuffer!==r\|\|this.boundVertexBuffer2!==i\|\|this.boundElementBuffer!==n;!t.extVertexArrayObject\|\|a?this.freshBind(t,e,r,n,i):t.extVertexArrayObject.bindVertexArrayOES(this.vao)},n.prototype.freshBind=function(t,e,r,n,a){var o,s=e.numAttributes;if(t.extVertexArrayObject)this.vao&&this.destroy(t),this.vao=t.extVertexArrayObject.createVertexArrayOES(),t.extVertexArrayObject.bindVertexArrayOES(this.vao),o=0,this.boundProgram=e,this.boundVertexBuffer=r,this.boundVertexBuffer2=a,this.boundElementBuffer=n;else{o=t.currentNumAttributes\|\|0;for(var l=s;l<o;l++)i(0!==l),t.disableVertexAttribArray(l)}for(var u=o;u<s;u++)t.enableVertexAttribArray(u);r.bind(t),r.setVertexAttribPointers(t,e),a&&(a.bind(t),a.setVertexAttribPointers(t,e)),n&&n.bind(t),t.currentNumAttributes=s},n.prototype.unbind=function(t){var e=t.extVertexArrayObject;e&&e.bindVertexArrayOES(null)},n.prototype.destroy=function(t){var e=t.extVertexArrayObject;e&&this.vao&&(e.deleteVertexArrayOES(this.vao),this.vao=null)}},{assert:47}],358:[function(t,e,r){\"use strict\";function n(t,e,r){e=e\|\|{},this.id=t,this.dispatcher=r,this._data=e.data,void 0!==e.maxzoom&&(this.maxzoom=e.maxzoom),e.type&&(this.type=e.type);var n=s/this.tileSize;this.workerOptions=a.extend({source:this.id,cluster:e.cluster\|\|!1,geojsonVtOptions:{buffer:(void 0!==e.buffer?e.buffer:128)n,tolerance:(void 0!==e.tolerance?e.tolerance:.375)n,extent:s,maxZoom:this.maxzoom},superclusterOptions:{maxZoom:Math.min(e.clusterMaxZoom,this.maxzoom-1)\|\|this.maxzoom-1,extent:s,radius:(e.clusterRadius\|\|50)n,log:!1}},e.workerOptions),this._updateWorkerData(function(t){if(t)return void this.fire(\"error\",{error:t});this.fire(\"load\")}.bind(this))}var i=t(\"../util/evented\"),a=t(\"../util/util\"),o=t(\"resolve-url\"),s=t(\"../data/bucket\").EXTENT;e.exports=n,n.prototype=a.inherit(i,{type:\"geojson\",minzoom:0,maxzoom:18,tileSize:512,isTileClipped:!0,reparseOverscaled:!0,onAdd:function(t){this.map=t},setData:function(t){return this._data=t,this._updateWorkerData(function(t){if(t)return this.fire(\"error\",{error:t});this.fire(\"change\")}.bind(this)),this},_updateWorkerData:function(t){var e=a.extend({},this.workerOptions),r=this._data;\"string\"==typeof r?e.url=\"undefined\"!=typeof window?o(window.location.href,r):r:e.data=JSON.stringify(r),this.workerID=this.dispatcher.send(this.type+\".loadData\",e,function(e){this._loaded=!0,t(e)}.bind(this))},loadTile:function(t,e){var r=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,n={type:this.type,uid:t.uid,coord:t.coord,zoom:t.coord.z,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,overscaling:r,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID=this.dispatcher.send(\"load tile\",n,function(r,n){if(t.unloadVectorData(this.map.painter),!t.aborted)return r?e(r):(t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null))}.bind(this),this.workerID)},abortTile:function(t){t.aborted=!0},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send(\"remove tile\",{uid:t.uid,source:this.id},function(){},t.workerID)},serialize:function(){return{type:this.type,data:this._data}}})},{\"../data/bucket\":329,\"../util/evented\":434,\"../util/util\":442,\"resolve-url\":501}],359:[function(t,e,r){\"use strict\";function n(t,e,r){r&&(this.loadGeoJSON=r),h.call(this,t,e)}var i=t(\"../util/util\"),a=t(\"../util/ajax\"),o=t(\"geojson-rewind\"),s=t(\"./geojson_wrapper\"),l=t(\"vt-pbf\"),u=t(\"supercluster\"),c=t(\"geojson-vt\"),h=t(\"./vector_tile_worker_source\");e.exports=n,n.prototype=i.inherit(h,{_geoJSONIndexes:{},loadVectorData:function(t,e){var r=t.source,n=t.coord;if(!this._geoJSONIndexes[r])return e(null,null);var i=this._geoJSONIndexes[r].getTile(Math.min(n.z,t.maxZoom),n.x,n.y);if(!i)return e(null,null);var a=new s(i.features);a.name=\"_geojsonTileLayer\";var o=l({layers:{_geojsonTileLayer:a}});0===o.byteOffset&&o.byteLength===o.buffer.byteLength\|\|(o=new Uint8Array(o)),e(null,{tile:a,rawTileData:o.buffer})},loadData:function(t,e){var r=function(r,n){return r?e(r):\"object\"!=typeof n?e(new Error(\"Input data is not a valid GeoJSON object.\")):(o(n,!0),void this._indexData(n,t,function(r,n){if(r)return e(r);this._geoJSONIndexes[t.source]=n,e(null)}.bind(this)))}.bind(this);this.loadGeoJSON(t,r)},loadGeoJSON:function(t,e){if(t.url)a.getJSON(t.url,e);else{if(\"string\"!=typeof t.data)return e(new Error(\"Input data is not a valid GeoJSON object.\"));try{return e(null,JSON.parse(t.data))}catch(t){return e(new Error(\"Input data is not a valid GeoJSON object.\"))}}},_indexData:function(t,e,r){try{e.cluster?r(null,u(e.superclusterOptions).load(t.features)):r(null,c(t,e.geojsonVtOptions))}catch(t){return r(t)}}})},{\"../util/ajax\":425,\"../util/util\":442,\"./geojson_wrapper\":360,\"./vector_tile_worker_source\":371,\"geojson-rewind\":138,\"geojson-vt\":142,supercluster:529,\"vt-pbf\":556}],360:[function(t,e,r){\"use strict\";function n(t){this.features=t,this.length=t.length,this.extent=s}function i(t){if(this.type=t.type,1===t.type){this.rawGeometry=[];for(var e=0;e<t.geometry.length;e++)this.rawGeometry.push([t.geometry[e]])}else this.rawGeometry=t.geometry;this.properties=t.tags,this.extent=s}var a=t(\"point-geometry\"),o=t(\"vector-tile\").VectorTileFeature,s=t(\"../data/bucket\").EXTENT;e.exports=n,n.prototype.feature=function(t){return new i(this.features[t])},i.prototype.loadGeometry=function(){var t=this.rawGeometry;this.geometry=[];for(var e=0;e<t.length;e++){for(var r=t[e],n=[],i=0;i<r.length;i++)n.push(new a(r[i][0],r[i][1]));this.geometry.push(n)}return this.geometry},i.prototype.bbox=function(){this.geometry\|\|this.loadGeometry();for(var t=this.geometry,e=1/0,r=-1/0,n=1/0,i=-1/0,a=0;a<t.length;a++)for(var o=t[a],s=0;s<o.length;s++){var l=o[s];e=Math.min(e,l.x),r=Math.max(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.y)}return[e,n,r,i]},i.prototype.toGeoJSON=o.prototype.toGeoJSON},{\"../data/bucket\":329,\"point-geometry\":484,\"vector-tile\":550}],361:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r,this.url=e.url,this.coordinates=e.coordinates,u.getImage(e.url,function(t,r){if(t)return this.fire(\"error\",{error:t});this.image=r,this.image.addEventListener(\"load\",function(){this.map._rerender()}.bind(this)),this._loaded=!0,this.fire(\"load\"),this.map&&this.setCoordinates(e.coordinates)}.bind(this))}var i=t(\"../util/util\"),a=t(\"./tile_coord\"),o=t(\"../geo/lng_lat\"),s=t(\"point-geometry\"),l=t(\"../util/evented\"),u=t(\"../util/ajax\"),c=t(\"../data/bucket\").EXTENT,h=t(\"../render/draw_raster\").RasterBoundsArray,f=t(\"../data/buffer\"),d=t(\"../render/vertex_array_object\");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,onAdd:function(t){this.map=t,this.image&&this.setCoordinates(this.coordinates)},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(o.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new a(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)c),Math.round((e.row-n.row)c))}),this.fire(\"change\"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=new h;e.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),e.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,32767,0),e.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,32767),e.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,32767,32767),this.tile.buckets={},this.tile.boundsBuffer=new f(e.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new d,this.tile.state=\"loaded\"},prepare:function(){if(this._loaded&&this.image&&this.image.complete&&this.tile){var t=this.map.painter,e=t.gl;this._prepared?(e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,this.image)):(this.tile.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,this.image))}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state=\"errored\",e(null))},serialize:function(){return{type:\"image\",urls:this.url,coordinates:this.coordinates}}})},{\"../data/bucket\":329,\"../data/buffer\":334,\"../geo/lng_lat\":339,\"../render/draw_raster\":351,\"../render/vertex_array_object\":357,\"../util/ajax\":425,\"../util/evented\":434,\"../util/util\":442,\"./tile_coord\":369,\"point-geometry\":484}],362:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i=t(\"../util/ajax\"),a=t(\"../util/browser\"),o=t(\"../util/mapbox\").normalizeSourceURL;e.exports=function(t,e){var r=function(t,r){if(t)return e(t);var i=n.pick(r,[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\"]);r.vector_layers&&(i.vectorLayers=r.vector_layers,i.vectorLayerIds=i.vectorLayers.map(function(t){return t.id})),e(null,i)};t.url?i.getJSON(o(t.url),r):a.frame(r.bind(null,null,t))}},{\"../util/ajax\":425,\"../util/browser\":426,\"../util/mapbox\":439,\"../util/util\":442}],363:[function(t,e,r){\"use strict\";var n=t(\"../data/bucket\");e.exports=function(t,e,r){return e(n.EXTENT/(t.tileSizeMath.pow(2,r-t.coord.z)))}},{\"../data/bucket\":329}],364:[function(t,e,r){\"use strict\";function n(t,e){var r=t.coord,n=e.coord;return r.z-n.z\|\|r.y-n.y\|\|r.w-n.w\|\|r.x-n.x}function i(t){for(var e=t[0]\|\|{},r=1;r<t.length;r++){var n=t[r];for(var i in n){var a=n[i],o=e[i];if(void 0===o)o=e[i]=a;else for(var s=0;s<a.length;s++)o.push(a[s])}}return e}var a=t(\"./tile_coord\");r.rendered=function(t,e,r,a,o,s){var l=t.tilesIn(r);l.sort(n);for(var u=[],c=0;c<l.length;c++){var h=l[c];h.tile.featureIndex&&u.push(h.tile.featureIndex.query({queryGeometry:h.queryGeometry,scale:h.scale,tileSize:h.tile.tileSize,bearing:s,params:a},e))}return i(u)},r.source=function(t,e){for(var r=t.getRenderableIds().map(function(e){return t.getTileByID(e)}),n=[],i={},o=0;o<r.length;o++){var s=r[o],l=new a(Math.min(s.sourceMaxZoom,s.coord.z),s.coord.x,s.coord.y,0).id;i[l]\|\|(i[l]=!0,s.querySourceFeatures(n,e))}return n}},{\"./tile_coord\":369}],365:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r,i.extend(this,i.pick(e,[\"url\",\"scheme\",\"tileSize\"])),s(e,function(t,e){if(t)return this.fire(\"error\",t);i.extend(this,e),this.fire(\"load\")}.bind(this))}var i=t(\"../util/util\"),a=t(\"../util/ajax\"),o=t(\"../util/evented\"),s=t(\"./load_tilejson\"),l=t(\"../util/mapbox\").normalizeTileURL;e.exports=n,n.prototype=i.inherit(o,{minzoom:0,maxzoom:22,roundZoom:!0,scheme:\"xyz\",tileSize:512,_loaded:!1,onAdd:function(t){this.map=t},serialize:function(){return{type:\"raster\",url:this.url,tileSize:this.tileSize}},loadTile:function(t,e){function r(r,n){if(delete t.request,!t.aborted){if(r)return e(r);var i=this.map.painter.gl;t.texture=this.map.painter.getTexture(n.width),t.texture?(i.bindTexture(i.TEXTURE_2D,t.texture),i.texSubImage2D(i.TEXTURE_2D,0,0,0,i.RGBA,i.UNSIGNED_BYTE,n)):(t.texture=i.createTexture(),i.bindTexture(i.TEXTURE_2D,t.texture),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR_MIPMAP_NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,n),t.texture.size=n.width),i.generateMipmap(i.TEXTURE_2D),this.map.animationLoop.set(this.map.style.rasterFadeDuration),t.state=\"loaded\",e(null)}}var n=l(t.coord.url(this.tiles,null,this.scheme),this.url,this.tileSize);t.request=a.getImage(n,r.bind(this))},abortTile:function(t){t.request&&(t.request.abort(),delete t.request)},unloadTile:function(t){t.texture&&this.map.painter.saveTexture(t.texture)}})},{\"../util/ajax\":425,\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./load_tilejson\":362}],366:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i={vector:t(\"../source/vector_tile_source\"),raster:t(\"../source/raster_tile_source\"),geojson:t(\"../source/geojson_source\"),video:t(\"../source/video_source\"),image:t(\"../source/image_source\")};r.create=function(t,e,r){if(e=new i[e.type](t,e,r),e.id!==t)throw new Error(\"Expected Source id to be \"+t+\" instead of \"+e.id);return n.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],e),e},r.getType=function(t){return i[t]},r.setType=function(t,e){i[t]=e}},{\"../source/geojson_source\":358,\"../source/image_source\":361,\"../source/raster_tile_source\":365,\"../source/vector_tile_source\":370,\"../source/video_source\":372,\"../util/util\":442}],367:[function(t,e,r){\"use strict\";function n(t,e,r){this.id=t,this.dispatcher=r;var n=this._source=o.create(t,e,r).on(\"load\",function(){this.map&&this._source.onAdd&&this._source.onAdd(this.map),this._sourceLoaded=!0,this.tileSize=n.tileSize,this.minzoom=n.minzoom,this.maxzoom=n.maxzoom,this.roundZoom=n.roundZoom,this.reparseOverscaled=n.reparseOverscaled,this.isTileClipped=n.isTileClipped,this.attribution=n.attribution,this.vectorLayerIds=n.vectorLayerIds,this.fire(\"load\")}.bind(this)).on(\"error\",function(t){this._sourceErrored=!0,this.fire(\"error\",t)}.bind(this)).on(\"change\",function(){this.reload(),this.transform&&this.update(this.transform,this.map&&this.map.style.rasterFadeDuration),this.fire(\"change\")}.bind(this));this._tiles={},this._cache=new c(0,this.unloadTile.bind(this)),this._isIdRenderable=this._isIdRenderable.bind(this)}function i(t,e,r){var n=r.zoomTo(Math.min(t.z,e));return{x:(n.column-(t.x+t.wMath.pow(2,t.z)))d,y:(n.row-t.y)d}}function a(t,e){return t%32-e%32}var o=t(\"./source\"),s=t(\"./tile\"),l=t(\"../util/evented\"),u=t(\"./tile_coord\"),c=t(\"../util/lru_cache\"),h=t(\"../geo/coordinate\"),f=t(\"../util/util\"),d=t(\"../data/bucket\").EXTENT;e.exports=n,n.maxOverzooming=10,n.maxUnderzooming=3,n.prototype=f.inherit(l,{onAdd:function(t){this.map=t,this._source&&this._source.onAdd&&this._source.onAdd(t)},loaded:function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},getSource:function(){return this._source},loadTile:function(t,e){return this._source.loadTile(t,e)},unloadTile:function(t){if(this._source.unloadTile)return this._source.unloadTile(t)},abortTile:function(t){if(this._source.abortTile)return this._source.abortTile(t)},serialize:function(){return this._source.serialize()},prepare:function(){if(this._sourceLoaded&&this._source.prepare)return this._source.prepare()},getIds:function(){return Object.keys(this._tiles).map(Number).sort(a)},getRenderableIds:function(){return this.getIds().filter(this._isIdRenderable)},_isIdRenderable:function(t){return this._tiles[t].isRenderable()&&!this._coveredTiles[t]},reload:function(){this._cache.reset();for(var t in this._tiles){var e=this._tiles[t];\"loading\"!==e.state&&(e.state=\"reloading\"),this.loadTile(this._tiles[t],this._tileLoaded.bind(this,this._tiles[t]))}},_tileLoaded:function(t,e){if(e)return t.state=\"errored\",this.fire(\"tile.error\",{tile:t,error:e}),void this._source.fire(\"tile.error\",{tile:t,error:e});t.source=this,t.timeAdded=(new Date).getTime(),this.fire(\"tile.load\",{tile:t}),this._source.fire(\"tile.load\",{tile:t})},getTile:function(t){return this.getTileByID(t.id)},getTileByID:function(t){return this._tiles[t]},getZoom:function(t){return t.zoom+t.scaleZoom(t.tileSize/this.tileSize)},findLoadedChildren:function(t,e,r){var n=!1;for(var i in this._tiles){var a=this._tiles[i];if(!(r[i]\|\|!a.isRenderable()\|\|a.coord.z<=t.z\|\|a.coord.z>e)){var o=Math.pow(2,Math.min(a.coord.z,this.maxzoom)-Math.min(t.z,this.maxzoom));if(Math.floor(a.coord.x/o)===t.x&&Math.floor(a.coord.y/o)===t.y)for(r[i]=!0,n=!0;a&&a.coord.z-1>t.z;){var s=a.coord.parent(this.maxzoom).id;a=this._tiles[s],a&&a.isRenderable()&&(delete r[i],r[s]=!0)}}}return n},findLoadedParent:function(t,e,r){for(var n=t.z-1;n>=e;n--){t=t.parent(this.maxzoom);var i=this._tiles[t.id];if(i&&i.isRenderable())return r[t.id]=!0,i;if(this._cache.has(t.id))return this.addTile(t),r[t.id]=!0,this._tiles[t.id]}},updateCacheSize:function(t){var e=Math.ceil(t.width/t.tileSize)+1,r=Math.ceil(t.height/t.tileSize)+1,n=er;this._cache.setMaxSize(Math.floor(5n))},update:function(t,e){if(this._sourceLoaded){var r,i,a;this.updateCacheSize(t);var o=(this.roundZoom?Math.round:Math.floor)(this.getZoom(t)),s=Math.max(o-n.maxOverzooming,this.minzoom),l=Math.max(o+n.maxUnderzooming,this.minzoom),c={},h=(new Date).getTime();this._coveredTiles={};var d=this.used?t.coveringTiles(this._source):[];for(r=0;r<d.length;r++)i=d[r],a=this.addTile(i),c[i.id]=!0,a.isRenderable()\|\|this.findLoadedChildren(i,l,c)\|\|this.findLoadedParent(i,s,c);for(var p={},m=Object.keys(c),v=0;v<m.length;v++){var g=m[v];i=u.fromID(g),a=this._tiles[g],a&&a.timeAdded>h-(e\|\|0)&&(this.findLoadedChildren(i,l,c)&&(c[g]=!0),this.findLoadedParent(i,s,p))}var y;for(y in p)c[y]\|\|(this._coveredTiles[y]=!0);for(y in p)c[y]=!0;var b=f.keysDifference(this._tiles,c);for(r=0;r<b.length;r++)this.removeTile(+b[r]);this.transform=t}},addTile:function(t){var e=this._tiles[t.id];if(e)return e;var r=t.wrapped()\n", ";if(e=this._tiles[r.id],e\|\|(e=this._cache.get(r.id))&&this._redoPlacement&&this._redoPlacement(e),!e){var n=t.z,i=n>this.maxzoom?Math.pow(2,n-this.maxzoom):1;e=new s(r,this.tileSizei,this.maxzoom),this.loadTile(e,this._tileLoaded.bind(this,e))}return e.uses++,this._tiles[t.id]=e,this.fire(\"tile.add\",{tile:e}),this._source.fire(\"tile.add\",{tile:e}),e},removeTile:function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this.fire(\"tile.remove\",{tile:e}),this._source.fire(\"tile.remove\",{tile:e}),e.uses>0\|\|(e.isRenderable()?this._cache.add(e.coord.wrapped().id,e):(e.aborted=!0,this.abortTile(e),this.unloadTile(e))))},clearTiles:function(){for(var t in this._tiles)this.removeTile(t);this._cache.reset()},tilesIn:function(t){for(var e={},r=this.getIds(),n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0].zoom,c=0;c<t.length;c++){var f=t[c];n=Math.min(n,f.column),a=Math.min(a,f.row),o=Math.max(o,f.column),s=Math.max(s,f.row)}for(var p=0;p<r.length;p++){var m=this._tiles[r[p]],v=u.fromID(r[p]),g=[i(v,m.sourceMaxZoom,new h(n,a,l)),i(v,m.sourceMaxZoom,new h(o,s,l))];if(g[0].x<d&&g[0].y<d&&g[1].x>=0&&g[1].y>=0){for(var y=[],b=0;b<t.length;b++)y.push(i(v,m.sourceMaxZoom,t[b]));var x=e[m.coord.id];void 0===x&&(x=e[m.coord.id]={tile:m,coord:v,queryGeometry:[],scale:Math.pow(2,this.transform.zoom-m.coord.z)}),x.queryGeometry.push(y)}}var _=[];for(var w in e)_.push(e[w]);return _},redoPlacement:function(){for(var t=this.getIds(),e=0;e<t.length;e++){this.getTileByID(t[e]).redoPlacement(this)}},getVisibleCoordinates:function(){return this.getRenderableIds().map(u.fromID)}})},{\"../data/bucket\":329,\"../geo/coordinate\":338,\"../util/evented\":434,\"../util/lru_cache\":438,\"../util/util\":442,\"./source\":366,\"./tile\":368,\"./tile_coord\":369}],368:[function(t,e,r){\"use strict\";function n(t,e,r){this.coord=t,this.uid=a.uniqueId(),this.uses=0,this.tileSize=e,this.sourceMaxZoom=r,this.buckets={},this.state=\"loading\"}function i(t,e){if(e){for(var r={},n=0;n<t.length;n++){var i=e.getLayer(t[n].layerId);if(i){var s=o.create(a.extend({layer:i,childLayers:t[n].childLayerIds.map(e.getLayer.bind(e)).filter(function(t){return t})},t[n]));r[s.id]=s}}return r}}var a=t(\"../util/util\"),o=t(\"../data/bucket\"),s=t(\"../data/feature_index\"),l=t(\"vector-tile\"),u=t(\"pbf\"),c=t(\"../util/vectortile_to_geojson\"),h=t(\"feature-filter\"),f=t(\"../symbol/collision_tile\"),d=t(\"../symbol/collision_box\"),p=t(\"../symbol/symbol_instances\"),m=t(\"../symbol/symbol_quads\");e.exports=n,n.prototype={loadVectorData:function(t,e){this.state=\"loaded\",t&&(this.collisionBoxArray=new d(t.collisionBoxArray),this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.symbolInstancesArray=new p(t.symbolInstancesArray),this.symbolQuadsArray=new m(t.symbolQuadsArray),this.featureIndex=new s(t.featureIndex,t.rawTileData,this.collisionTile),this.rawTileData=t.rawTileData,this.buckets=i(t.buckets,e))},reloadSymbolData:function(t,e,r){if(\"unloaded\"!==this.state){this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.featureIndex.setCollisionTile(this.collisionTile);for(var n in this.buckets){var o=this.buckets[n];\"symbol\"===o.type&&(o.destroy(e.gl),delete this.buckets[n])}a.extend(this.buckets,i(t.buckets,r))}},unloadVectorData:function(t){for(var e in this.buckets){this.buckets[e].destroy(t.gl)}this.collisionBoxArray=null,this.symbolQuadsArray=null,this.symbolInstancesArray=null,this.collisionTile=null,this.featureIndex=null,this.rawTileData=null,this.buckets=null,this.state=\"unloaded\"},redoPlacement:function(t){function e(e,r){this.reloadSymbolData(r,t.map.painter,t.map.style),t.fire(\"tile.load\",{tile:this}),this.state=\"loaded\",this.redoWhenDone&&(this.redoPlacement(t),this.redoWhenDone=!1)}if(\"loaded\"!==this.state\|\|\"reloading\"===this.state)return void(this.redoWhenDone=!0);this.state=\"reloading\",t.dispatcher.send(\"redo placement\",{uid:this.uid,source:t.id,angle:t.map.transform.angle,pitch:t.map.transform.pitch,showCollisionBoxes:t.map.showCollisionBoxes},e.bind(this),this.workerID)},getBucket:function(t){return this.buckets&&this.buckets[t.ref\|\|t.id]},querySourceFeatures:function(t,e){if(this.rawTileData){this.vtLayers\|\|(this.vtLayers=new l.VectorTile(new u(new Uint8Array(this.rawTileData))).layers);var r=this.vtLayers._geojsonTileLayer\|\|this.vtLayers[e.sourceLayer];if(r)for(var n=h(e.filter),i={z:this.coord.z,x:this.coord.x,y:this.coord.y},a=0;a<r.length;a++){var o=r.feature(a);if(n(o)){var s=new c(o,this.coord.z,this.coord.x,this.coord.y);s.tile=i,t.push(s)}}}},isRenderable:function(){return\"loaded\"===this.state\|\|\"reloading\"===this.state}}},{\"../data/bucket\":329,\"../data/feature_index\":336,\"../symbol/collision_box\":394,\"../symbol/collision_tile\":396,\"../symbol/symbol_instances\":405,\"../symbol/symbol_quads\":406,\"../util/util\":442,\"../util/vectortile_to_geojson\":443,\"feature-filter\":132,pbf:478,\"vector-tile\":550}],369:[function(t,e,r){\"use strict\";function n(t,e,r,n){l(!isNaN(t)&&t>=0&&t%1==0),l(!isNaN(e)&&e>=0&&e%1==0),l(!isNaN(r)&&r>=0&&r%1==0),isNaN(n)&&(n=0),this.z=+t,this.x=+e,this.y=+r,this.w=+n,(n=2)<0&&(n=-1n-1);var i=1<<this.z;this.id=32(iin+ithis.y+this.x)+this.z,this.posMatrix=null}function i(t,e,r){for(var n,i=\"\",a=t;a>0;a--)n=1<<a-1,i+=(e&n?1:0)+(r&n?2:0);return i}function a(t,e){if(t.row>e.row){var r=t;t=e,e=r}return{x0:t.column,y0:t.row,x1:e.column,y1:e.row,dx:e.column-t.column,dy:e.row-t.row}}function o(t,e,r,n,i){var a=Math.max(r,Math.floor(e.y0)),o=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dyt.dx<e.x1:t.x1-e.dy/t.dyt.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,u=e.dx/e.dy,c=t.dx>0,h=e.dx<0,f=a;f<o;f++){var d=lMath.max(0,Math.min(t.dy,f+c-t.y0))+t.x0,p=uMath.max(0,Math.min(e.dy,f+h-e.y0))+e.x0;i(Math.floor(p),Math.ceil(d),f)}}function s(t,e,r,n,i,s){var l,u=a(t,e),c=a(e,r),h=a(r,t);u.dy>c.dy&&(l=u,u=c,c=l),u.dy>h.dy&&(l=u,u=h,h=l),c.dy>h.dy&&(l=c,c=h,h=l),u.dy&&o(h,u,n,i,s),c.dy&&o(h,c,n,i,s)}var l=t(\"assert\"),u=t(\"whoots-js\"),c=t(\"../geo/coordinate\");e.exports=n,n.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y},n.prototype.toCoordinate=function(t){var e=Math.min(this.z,t),r=Math.pow(2,e),n=this.y,i=this.x+rthis.w;return new c(i,n,e)},n.fromID=function(t){var e=t%32,r=1<<e,i=(t-e)/32,a=i%r,o=(i-a)/r%r,s=Math.floor(i/(rr));return s%2!=0&&(s=-1s-1),s/=2,new n(e,a,o,s)},n.prototype.url=function(t,e,r){var n=u.getTileBBox(this.x,this.y,this.z),a=i(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",Math.min(this.z,e\|\|this.z)).replace(\"{x}\",this.x).replace(\"{y}\",\"tms\"===r?Math.pow(2,this.z)-this.y-1:this.y).replace(\"{quadkey}\",a).replace(\"{bbox-epsg-3857}\",n)},n.prototype.parent=function(t){return 0===this.z?null:this.z>t?new n(this.z-1,this.x,this.y,this.w):new n(this.z-1,Math.floor(this.x/2),Math.floor(this.y/2),this.w)},n.prototype.wrapped=function(){return new n(this.z,this.x,this.y,0)},n.prototype.children=function(t){if(this.z>=t)return[new n(this.z+1,this.x,this.y,this.w)];var e=this.z+1,r=2this.x,i=2this.y;return[new n(e,r,i,this.w),new n(e,r+1,i,this.w),new n(e,r,i+1,this.w),new n(e,r+1,i+1,this.w)]},n.cover=function(t,e,r){function i(t,e,i){var s,l,u;if(i>=0&&i<=a)for(s=t;s<e;s++)l=(s%a+a)%a,u=new n(r,l,i,Math.floor(s/a)),o[u.id]=u}var a=1<<t,o={};return s(e[0],e[1],e[2],0,a,i),s(e[2],e[3],e[0],0,a,i),Object.keys(o).map(function(t){return o[t]})}},{\"../geo/coordinate\":338,assert:47,\"whoots-js\":566}],370:[function(t,e,r){\"use strict\";function n(t,e,r){if(this.id=t,this.dispatcher=r,a.extend(this,a.pick(e,[\"url\",\"scheme\",\"tileSize\"])),this._options=a.extend({type:\"vector\"},e),512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");o(e,function(t,e){if(t)return void this.fire(\"error\",t);a.extend(this,e),this.fire(\"load\")}.bind(this))}var i=t(\"../util/evented\"),a=t(\"../util/util\"),o=t(\"./load_tilejson\"),s=t(\"../util/mapbox\").normalizeTileURL;e.exports=n,n.prototype=a.inherit(i,{minzoom:0,maxzoom:22,scheme:\"xyz\",tileSize:512,reparseOverscaled:!0,isTileClipped:!0,onAdd:function(t){this.map=t},serialize:function(){return a.extend({},this._options)},loadTile:function(t,e){function r(r,n){if(!t.aborted){if(r)return e(r);t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null),t.reloadCallback&&(this.loadTile(t,t.reloadCallback),t.reloadCallback=null)}}var n=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,i={url:s(t.coord.url(this.tiles,this.maxzoom,this.scheme),this.url),uid:t.uid,coord:t.coord,zoom:t.coord.z,tileSize:this.tileSizen,source:this.id,overscaling:n,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID?\"loading\"===t.state?t.reloadCallback=e:(i.rawTileData=t.rawTileData,this.dispatcher.send(\"reload tile\",i,r.bind(this),t.workerID)):t.workerID=this.dispatcher.send(\"load tile\",i,r.bind(this))},abortTile:function(t){this.dispatcher.send(\"abort tile\",{uid:t.uid,source:this.id},null,t.workerID)},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send(\"remove tile\",{uid:t.uid,source:this.id},null,t.workerID)}})},{\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./load_tilejson\":362}],371:[function(t,e,r){\"use strict\";function n(t,e,r){this.actor=t,this.styleLayers=e,r&&(this.loadVectorData=r),this.loading={},this.loaded={}}var i=t(\"../util/ajax\"),a=t(\"vector-tile\"),o=t(\"pbf\"),s=t(\"./worker_tile\");e.exports=n,n.prototype={loadTile:function(t,e){function r(t,r){return delete this.loading[n][i],t?e(t):r?(a.data=r.tile,a.parse(a.data,this.styleLayers.getLayerFamilies(),this.actor,r.rawTileData,e),this.loaded[n]=this.loaded[n]\|\|{},void(this.loaded[n][i]=a)):e(null,null)}var n=t.source,i=t.uid;this.loading[n]\|\|(this.loading[n]={});var a=this.loading[n][i]=new s(t);a.abort=this.loadVectorData(t,r.bind(this))},reloadTile:function(t,e){var r=this.loaded[t.source],n=t.uid;if(r&&r[n]){var i=r[n];i.parse(i.data,this.styleLayers.getLayerFamilies(),this.actor,t.rawTileData,e)}},abortTile:function(t){var e=this.loading[t.source],r=t.uid;e&&e[r]&&e[r].abort&&(e[r].abort(),delete e[r])},removeTile:function(t){var e=this.loaded[t.source],r=t.uid;e&&e[r]&&delete e[r]},loadVectorData:function(t,e){function r(t,r){if(t)return e(t);var n=new a.VectorTile(new o(new Uint8Array(r)));e(t,{tile:n,rawTileData:r})}var n=i.getArrayBuffer(t.url,r.bind(this));return function(){n.abort()}},redoPlacement:function(t,e){var r=this.loaded[t.source],n=this.loading[t.source],i=t.uid;if(r&&r[i]){var a=r[i],o=a.redoPlacement(t.angle,t.pitch,t.showCollisionBoxes);o.result&&e(null,o.result,o.transferables)}else n&&n[i]&&(n[i].angle=t.angle)}}},{\"../util/ajax\":425,\"./worker_tile\":374,pbf:478,\"vector-tile\":550}],372:[function(t,e,r){\"use strict\";function n(t,e){this.id=t,this.urls=e.urls,this.coordinates=e.coordinates,u.getVideo(e.urls,function(t,r){if(t)return this.fire(\"error\",{error:t});this.video=r,this.video.loop=!0;var n;this.video.addEventListener(\"playing\",function(){n=this.map.style.animationLoop.set(1/0),this.map._rerender()}.bind(this)),this.video.addEventListener(\"pause\",function(){this.map.style.animationLoop.cancel(n)}.bind(this)),this.map&&(this.video.play(),this.setCoordinates(e.coordinates)),this.fire(\"load\")}.bind(this))}var i=t(\"../util/util\"),a=t(\"./tile_coord\"),o=t(\"../geo/lng_lat\"),s=t(\"point-geometry\"),l=t(\"../util/evented\"),u=t(\"../util/ajax\"),c=t(\"../data/bucket\").EXTENT,h=t(\"../render/draw_raster\").RasterBoundsArray,f=t(\"../data/buffer\"),d=t(\"../render/vertex_array_object\");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,roundZoom:!0,getVideo:function(){return this.video},onAdd:function(t){this.map\|\|(this.map=t,this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(o.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new a(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)c),Math.round((e.row-n.row)c))}),this.fire(\"change\"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=new h;e.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),e.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,32767,0),e.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,32767),e.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,32767,32767),this.tile.buckets={},this.tile.boundsBuffer=new f(e.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new d,this.tile.state=\"loaded\"},prepare:function(){if(!(this.video.readyState<2)&&this.tile){var t=this.map.painter.gl;this._prepared?(t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texSubImage2D(t.TEXTURE_2D,0,0,0,t.RGBA,t.UNSIGNED_BYTE,this.video)):(this._prepared=!0,this.tile.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,this.video)),this._currentTime=this.video.currentTime}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state=\"errored\",e(null))},serialize:function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}})},{\"../data/bucket\":329,\"../data/buffer\":334,\"../geo/lng_lat\":339,\"../render/draw_raster\":351,\"../render/vertex_array_object\":357,\"../util/ajax\":425,\"../util/evented\":434,\"../util/util\":442,\"./tile_coord\":369,\"point-geometry\":484}],373:[function(t,e,r){\"use strict\";function n(t){this.self=t,this.actor=new a(t,this);var e={getLayers:function(){return this.layers}.bind(this),getLayerFamilies:function(){return this.layerFamilies}.bind(this)};this.workerSources={vector:new l(this.actor,e),geojson:new u(this.actor,e)},this.self.registerWorkerSource=function(t,r){if(this.workerSources[t])throw new Error('Worker source with name \"'+t+'\" already registered.');this.workerSources[t]=new r(this.actor,e)}.bind(this)}function i(t){var e={};for(var r in t){var n=t[r],i=n.ref\|\|n.id,a=t[i];a.layout&&\"none\"===a.layout.visibility\|\|(e[i]=e[i]\|\|[],r===i?e[i].unshift(n):e[i].push(n))}return e}var a=t(\"../util/actor\"),o=t(\"../style/style_layer\"),s=t(\"../util/util\"),l=t(\"./vector_tile_worker_source\"),u=t(\"./geojson_worker_source\");e.exports=function(t){return new n(t)},s.extend(n.prototype,{\"set layers\":function(t){function e(t){var e=o.create(t,t.ref&&r.layers[t.ref]);e.updatePaintTransitions({},{transition:!1}),r.layers[e.id]=e}this.layers={};for(var r=this,n=[],a=0;a<t.length;a++){var s=t[a];\"fill\"!==s.type&&\"line\"!==s.type&&\"circle\"!==s.type&&\"symbol\"!==s.type\|\|(s.ref?n.push(a):e(s))}for(var l=0;l<n.length;l++)e(t[n[l]]);this.layerFamilies=i(this.layers)},\"update layers\":function(t){function e(t){var e=a.layers[t.ref];a.layers[t.id]?a.layers[t.id].set(t,e):a.layers[t.id]=o.create(t,e),a.layers[t.id].updatePaintTransitions({},{transition:!1})}var r,n,a=this;for(r in t)n=t[r],n.ref&&e(n);for(r in t)n=t[r],n.ref\|\|e(n);this.layerFamilies=i(this.layers)},\"load tile\":function(t,e){var r=t.type\|\|\"vector\";this.workerSources[r].loadTile(t,e)},\"reload tile\":function(t,e){var r=t.type\|\|\"vector\";this.workerSources[r].reloadTile(t,e)},\"abort tile\":function(t){var e=t.type\|\|\"vector\";this.workerSources[e].abortTile(t)},\"remove tile\":function(t){var e=t.type\|\|\"vector\";this.workerSources[e].removeTile(t)},\"redo placement\":function(t,e){var r=t.type\|\|\"vector\";this.workerSources[r].redoPlacement(t,e)},\"load worker source\":function(t,e){try{this.self.importScripts(t.url),e()}catch(t){e(t)}}})},{\"../style/style_layer\":381,\"../util/actor\":424,\"../util/util\":442,\"./geojson_worker_source\":359,\"./vector_tile_worker_source\":371}],374:[function(t,e,r){\"use strict\";function n(t){this.coord=t.coord,this.uid=t.uid,this.zoom=t.zoom,this.tileSize=t.tileSize,this.source=t.source,this.overscaling=t.overscaling,this.angle=t.angle,this.pitch=t.pitch,this.showCollisionBoxes=t.showCollisionBoxes}function i(t){return!t.isEmpty()}function a(t){return t.serialize()}function o(t){var e=[];for(var r in t)t[r].getTransferables(e);return e}function s(t){return t.id}var l=t(\"../data/feature_index\"),u=t(\"../symbol/collision_tile\"),c=t(\"../data/bucket\"),h=t(\"../symbol/collision_box\"),f=t(\"../util/dictionary_coder\"),d=t(\"../util/util\"),p=t(\"../symbol/symbol_instances\"),m=t(\"../symbol/symbol_quads\");e.exports=n,n.prototype.parse=function(t,e,r,n,v){function g(t,e){for(var r=0;r<t.length;r++){var n=t.feature(r);n.index=r;for(var i in e)e[i].filter(n)&&e[i].features.push(n)}}function y(t){if(t)return v(t);if(2===++N){for(var e=P.length-1;e>=0;e--)b(E,P[e]);x()}}function b(t,e){if(e.populateArrays(A,j,F),\"symbol\"!==e.type)for(var r=0;r<e.features.length;r++){var n=e.features[r];T.insert(n,n.index,e.sourceLayerIndex,e.index)}e.features=null}function x(){E.status=\"done\",E.redoPlacementAfterDone&&(E.redoPlacement(E.angle,E.pitch,null),E.redoPlacementAfterDone=!1);var t=T.serialize(),e=A.serialize(),r=E.collisionBoxArray.serialize(),s=E.symbolInstancesArray.serialize(),l=E.symbolQuadsArray.serialize(),u=[n].concat(t.transferables).concat(e.transferables),c=D.filter(i);v(null,{buckets:c.map(a),featureIndex:t.data,collisionTile:e.data,collisionBoxArray:r,symbolInstancesArray:s,symbolQuadsArray:l,rawTileData:n},o(c).concat(u))}this.status=\"parsing\",this.data=t,this.collisionBoxArray=new h,this.symbolInstancesArray=new p,this.symbolQuadsArray=new m;var _,w,M,k,A=new u(this.angle,this.pitch,this.collisionBoxArray),T=new l(this.coord,this.overscaling,A,t.layers),S=new f(t.layers?Object.keys(t.layers).sort():[\"_geojsonTileLayer\"]),E=this,L={},C={},I=0;for(var z in e)w=e[z][0],w.source===this.source&&(w.ref\|\|w.minzoom&&this.zoom<w.minzoom\|\|w.maxzoom&&this.zoom>=w.maxzoom\|\|w.layout&&\"none\"===w.layout.visibility\|\|t.layers&&!t.layers[w.sourceLayer]\|\|(k=c.create({layer:w,index:I++,childLayers:e[z],zoom:this.zoom,overscaling:this.overscaling,showCollisionBoxes:this.showCollisionBoxes,collisionBoxArray:this.collisionBoxArray,symbolQuadsArray:this.symbolQuadsArray,symbolInstancesArray:this.symbolInstancesArray,sourceLayerIndex:S.encode(w.sourceLayer\|\|\"_geojsonTileLayer\")}),k.createFilter(),L[w.id]=k,t.layers&&(M=w.sourceLayer,C[M]=C[M]\|\|{},C[M][w.id]=k)));if(t.layers)for(M in C)1===w.version&&d.warnOnce('Vector tile source \"'+this.source+'\" layer \"'+M+'\" does not use vector tile spec v2 and therefore may have some rendering errors.'),(w=t.layers[M])&&g(w,C[M]);else g(t,L);var D=[],P=this.symbolBuckets=[],O=[];T.bucketLayerIDs={};for(var R in L)k=L[R],0!==k.features.length&&(T.bucketLayerIDs[k.index]=k.childLayers.map(s),D.push(k),\"symbol\"===k.type?P.push(k):O.push(k));var F={},j={},N=0;if(P.length>0){for(_=P.length-1;_>=0;_--)P[_].updateIcons(F),P[_].updateFont(j);for(var B in j)j[B]=Object.keys(j[B]).map(Number);F=Object.keys(F),r.send(\"get glyphs\",{uid:this.uid,stacks:j},function(t,e){j=e,y(t)}),F.length?r.send(\"get icons\",{icons:F},function(t,e){F=e,y(t)}):y()}for(_=O.length-1;_>=0;_--)b(this,O[_]);if(0===P.length)return x()},n.prototype.redoPlacement=function(t,e,r){if(\"done\"!==this.status)return this.redoPlacementAfterDone=!0,this.angle=t,{};for(var n=new u(t,e,this.collisionBoxArray),s=this.symbolBuckets,l=s.length-1;l>=0;l--)s[l].placeFeatures(n,r);var c=n.serialize(),h=s.filter(i);return{result:{buckets:h.map(a),collisionTile:c.data},transferables:o(h).concat(c.transferables)}}},{\"../data/bucket\":329,\"../data/feature_index\":336,\"../symbol/collision_box\":394,\"../symbol/collision_tile\":396,\"../symbol/symbol_instances\":405,\"../symbol/symbol_quads\":406,\"../util/dictionary_coder\":432,\"../util/util\":442}],375:[function(t,e,r){\"use strict\";function n(){this.n=0,this.times=[]}e.exports=n,n.prototype.stopped=function(){return this.times=this.times.filter(function(t){return t.time>=(new Date).getTime()}),!this.times.length},n.prototype.set=function(t){return this.times.push({id:this.n,time:t+(new Date).getTime()}),this.n++},n.prototype.cancel=function(t){this.times=this.times.filter(function(e){return e.id!==t})}},{}],376:[function(t,e,r){\"use strict\";function n(t){this.base=t,this.retina=s.devicePixelRatio>1;var e=this.retina?\"@2x\":\"\";o.getJSON(l(t,e,\".json\"),function(t,e){if(t)return void this.fire(\"error\",{error:t});this.data=e,this.img&&this.fire(\"load\")}.bind(this)),o.getImage(l(t,e,\".png\"),function(t,e){if(t)return void this.fire(\"error\",{error:t});for(var r=e.getData(),n=e.data=new Uint8Array(r.length),i=0;i<r.length;i+=4){var a=r[i+3]/255;n[i+0]=r[i+0]a,n[i+1]=r[i+1]a,n[i+2]=r[i+2]a,n[i+3]=r[i+3]}this.img=e,this.data&&this.fire(\"load\")}.bind(this))}function i(){}var a=t(\"../util/evented\"),o=t(\"../util/ajax\"),s=t(\"../util/browser\"),l=t(\"../util/mapbox\").normalizeSpriteURL;e.exports=n,n.prototype=Object.create(a),n.prototype.toJSON=function(){return this.base},n.prototype.loaded=function(){return!(!this.data\|\|!this.img)},n.prototype.resize=function(){if(s.devicePixelRatio>1!==this.retina){var t=new n(this.base);t.on(\"load\",function(){this.img=t.img,this.data=t.data,this.retina=t.retina}.bind(this))}},i.prototype={x:0,y:0,width:0,height:0,pixelRatio:1,sdf:!1},n.prototype.getSpritePosition=function(t){if(!this.loaded())return new i;var e=this.data&&this.data[t];return e&&this.img?e:new i}},{\"../util/ajax\":425,\"../util/browser\":426,\"../util/evented\":434,\"../util/mapbox\":439}],377:[function(t,e,r){\"use strict\";var n=t(\"csscolorparser\").parseCSSColor,i=t(\"../util/util\"),a=t(\"./style_function\"),o={};e.exports=function t(e){if(a.isFunctionDefinition(e))return i.extend({},e,{stops:e.stops.map(function(e){return[e[0],t(e[1])]})});if(\"string\"==typeof e){if(!o[e]){var r=n(e);if(!r)throw new Error(\"Invalid color \"+e);o[e]=[r[0]/255r[3],r[1]/255r[3],r[2]/255r[3],r[3]]}return o[e]}throw new Error(\"Invalid color \"+e)}},{\"../util/util\":442,\"./style_function\":380,csscolorparser:108}],378:[function(t,e,r){\"use strict\";function n(t,e,r){this.animationLoop=e\|\|new m,this.dispatcher=new p(r\|\|1,this),this.spriteAtlas=new l(1024,1024),this.lineAtlas=new u(256,512),this._layers={},this._order=[],this._groups=[],this.sources={},this.zoomHistory={},c.bindAll([\"_forwardSourceEvent\",\"_forwardTileEvent\",\"_forwardLayerEvent\",\"_redoPlacement\"],this),this._resetUpdates();var n=function(t,e){if(t)return void this.fire(\"error\",{error:t});if(!v.emitErrors(this,v(e))){this._loaded=!0,this.stylesheet=e,this.updateClasses();var r=e.sources;for(var n in r)this.addSource(n,r[n]);e.sprite&&(this.sprite=new o(e.sprite),this.sprite.on(\"load\",this.fire.bind(this,\"change\"))),this.glyphSource=new s(e.glyphs),this._resolve(),this.fire(\"load\")}}.bind(this);\"string\"==typeof t?h.getJSON(f(t),n):d.frame(n.bind(this,null,t)),this.on(\"source.load\",function(t){var e=t.source;if(e&&e.vectorLayerIds)for(var r in this._layers){var n=this._layers[r];n.source===e.id&&this._validateLayer(n)}})}var i=t(\"../util/evented\"),a=t(\"./style_layer\"),o=t(\"./image_sprite\"),s=t(\"../symbol/glyph_source\"),l=t(\"../symbol/sprite_atlas\"),u=t(\"../render/line_atlas\"),c=t(\"../util/util\"),h=t(\"../util/ajax\"),f=t(\"../util/mapbox\").normalizeStyleURL,d=t(\"../util/browser\"),p=t(\"../util/dispatcher\"),m=t(\"./animation_loop\"),v=t(\"./validate_style\"),g=t(\"../source/source\"),y=t(\"../source/query_features\"),b=t(\"../source/source_cache\"),x=t(\"./style_spec\"),_=t(\"./style_function\");e.exports=n,n.prototype=c.inherit(i,{_loaded:!1,_validateLayer:function(t){var e=this.sources[t.source];t.sourceLayer&&e&&e.vectorLayerIds&&-1===e.vectorLayerIds.indexOf(t.sourceLayer)&&this.fire(\"error\",{error:new Error('Source layer \"'+t.sourceLayer+'\" does not exist on source \"'+e.id+'\" as specified by style layer \"'+t.id+'\"')})},loaded:function(){if(!this._loaded)return!1;if(Object.keys(this._updates.sources).length)return!1;for(var t in this.sources)if(!this.sources[t].loaded())return!1;return!(this.sprite&&!this.sprite.loaded())},_resolve:function(){var t,e;this._layers={},this._order=this.stylesheet.layers.map(function(t){return t.id});for(var r=0;r<this.stylesheet.layers.length;r++)e=this.stylesheet.layers[r],e.ref\|\|(t=a.create(e),this._layers[t.id]=t,t.on(\"error\",this._forwardLayerEvent));for(var n=0;n<this.stylesheet.layers.length;n++)if(e=this.stylesheet.layers[n],e.ref){var i=this.getLayer(e.ref);t=a.create(e,i),this._layers[t.id]=t,t.on(\"error\",this._forwardLayerEvent)}this._groupLayers(),this._updateWorkerLayers()},_groupLayers:function(){var t;this._groups=[];for(var e=0;e<this._order.length;++e){var r=this._layers[this._order[e]];t&&r.source===t.source\|\|(t=[],t.source=r.source,this._groups.push(t)),t.push(r)}},_updateWorkerLayers:function(t){this.dispatcher.broadcast(t?\"update layers\":\"set layers\",this._serializeLayers(t))},_serializeLayers:function(t){t=t\|\|this._order;for(var e=[],r={includeRefProperties:!0},n=0;n<t.length;n++)e.push(this._layers[t[n]].serialize(r));return e},_applyClasses:function(t,e){if(this._loaded){t=t\|\|[],e=e\|\|{transition:!0};var r=this.stylesheet.transition\|\|{},n=this._updates.allPaintProps?this._layers:this._updates.paintProps;for(var i in n){var a=this._layers[i],o=this._updates.paintProps[i];if(this._updates.allPaintProps\|\|o.all)a.updatePaintTransitions(t,e,r,this.animationLoop);else for(var s in o)this._layers[i].updatePaintTransition(s,t,e,r,this.animationLoop)}}},_recalculate:function(t){for(var e in this.sources)this.sources[e].used=!1;this._updateZoomHistory(t),this.rasterFadeDuration=300;for(var r in this._layers){var n=this._layers[r];n.recalculate(t,this.zoomHistory),!n.isHidden(t)&&n.source&&(this.sources[n.source].used=!0)}Math.floor(this.z)!==Math.floor(t)&&this.animationLoop.set(300),this.z=t,this.fire(\"zoom\")},_updateZoomHistory:function(t){var e=this.zoomHistory;void 0===e.lastIntegerZoom&&(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=0,e.lastZoom=t),Math.floor(e.lastZoom)<Math.floor(t)?(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=Date.now()):Math.floor(e.lastZoom)>Math.floor(t)&&(e.lastIntegerZoom=Math.floor(t+1),e.lastIntegerZoomTime=Date.now()),e.lastZoom=t},_checkLoaded:function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},update:function(t,e){if(!this._updates.changed)return this;if(this._updates.allLayers)this._groupLayers(),this._updateWorkerLayers();else{var r=Object.keys(this._updates.layers);r.length&&this._updateWorkerLayers(r)}var n,i=Object.keys(this._updates.sources);for(n=0;n<i.length;n++)this._reloadSource(i[n]);for(n=0;n<this._updates.events.length;n++){var a=this._updates.events[n];this.fire(a[0],a[1])}return this._applyClasses(t,e),this._updates.changed&&this.fire(\"change\"),this._resetUpdates(),this},_resetUpdates:function(){this._updates={events:[],layers:{},sources:{},paintProps:{}}},addSource:function(t,e){if(this._checkLoaded(),void 0!==this.sources[t])throw new Error(\"There is already a source with this ID\");if(!e.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(e)+\".\");return[\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(e.type)>=0&&this._handleErrors(v.source,\"sources.\"+t,e)?this:(e=new b(t,e,this.dispatcher),this.sources[t]=e,e.style=this,e.on(\"load\",this._forwardSourceEvent).on(\"error\",this._forwardSourceEvent).on(\"change\",this._forwardSourceEvent).on(\"tile.add\",this._forwardTileEvent).on(\"tile.load\",this._forwardTileEvent).on(\"tile.error\",this._forwardTileEvent).on(\"tile.remove\",this._forwardTileEvent).on(\"tile.stats\",this._forwardTileEvent),this._updates.events.push([\"source.add\",{source:e}]),this._updates.changed=!0,this)},removeSource:function(t){if(this._checkLoaded(),void 0===this.sources[t])throw new Error(\"There is no source with this ID\");var e=this.sources[t];return delete this.sources[t],delete this._updates.sources[t],e.off(\"load\",this._forwardSourceEvent).off(\"error\",this._forwardSourceEvent).off(\"change\",this._forwardSourceEvent).off(\"tile.add\",this._forwardTileEvent).off(\"tile.load\",this._forwardTileEvent).off(\"tile.error\",this._forwardTileEvent).off(\"tile.remove\",this._forwardTileEvent).off(\"tile.stats\",this._forwardTileEvent),this._updates.events.push([\"source.remove\",{source:e}]),this._updates.changed=!0,this},getSource:function(t){return this.sources[t]&&this.sources[t].getSource()},addLayer:function(t,e){if(this._checkLoaded(),!(t instanceof a)){if(this._handleErrors(v.layer,\"layers.\"+t.id,t,!1,{arrayIndex:-1}))return this;var r=t.ref&&this.getLayer(t.ref);t=a.create(t,r)}return this._validateLayer(t),t.on(\"error\",this._forwardLayerEvent),this._layers[t.id]=t,this._order.splice(e?this._order.indexOf(e):1/0,0,t.id),this._updates.allLayers=!0,t.source&&(this._updates.sources[t.source]=!0),this._updates.events.push([\"layer.add\",{layer:t}]),this.updateClasses(t.id)},removeLayer:function(t){this._checkLoaded();var e=this._layers[t];if(void 0===e)throw new Error(\"There is no layer with this ID\");for(var r in this._layers)this._layers[r].ref===t&&this.removeLayer(r);return e.off(\"error\",this._forwardLayerEvent),delete this._layers[t],delete this._updates.layers[t],delete this._updates.paintProps[t],this._order.splice(this._order.indexOf(t),1),this._updates.allLayers=!0,this._updates.events.push([\"layer.remove\",{layer:e}]),this._updates.changed=!0,this},getLayer:function(t){return this._layers[t]},getReferentLayer:function(t){var e=this.getLayer(t);return e.ref&&(e=this.getLayer(e.ref)),e},setLayerZoomRange:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return n.minzoom===e&&n.maxzoom===r?this:(null!=e&&(n.minzoom=e),null!=r&&(n.maxzoom=r),this._updateLayer(n))},setFilter:function(t,e){this._checkLoaded();var r=this.getReferentLayer(t);return null!==e&&this._handleErrors(v.filter,\"layers.\"+r.id+\".filter\",e)?this:c.deepEqual(r.filter,e)?this:(r.filter=c.clone(e),this._updateLayer(r))},getFilter:function(t){return this.getReferentLayer(t).filter},setLayoutProperty:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return c.deepEqual(n.getLayoutProperty(e),r)?this:(n.setLayoutProperty(e,r),this._updateLayer(n))},getLayoutProperty:function(t,e){return this.getReferentLayer(t).getLayoutProperty(e)},setPaintProperty:function(t,e,r,n){this._checkLoaded();var i=this.getLayer(t);if(c.deepEqual(i.getPaintProperty(e,n),r))return this;var a=i.isPaintValueFeatureConstant(e);return i.setPaintProperty(e,r,n),!(r&&_.isFunctionDefinition(r)&&\"$zoom\"!==r.property&&void 0!==r.property)&&a\|\|(this._updates.layers[t]=!0,i.source&&(this._updates.sources[i.source]=!0)),this.updateClasses(t,e)},getPaintProperty:function(t,e,r){return this.getLayer(t).getPaintProperty(e,r)},updateClasses:function(t,e){if(this._updates.changed=!0,t){var r=this._updates.paintProps;r[t]\|\|(r[t]={}),r[t][e\|\|\"all\"]=!0}else this._updates.allPaintProps=!0;return this},serialize:function(){return c.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:c.mapObject(this.sources,function(t){return t.serialize()}),layers:this._order.map(function(t){return this._layers[t].serialize()},this)},function(t){return void 0!==t})},_updateLayer:function(t){return this._updates.layers[t.id]=!0,t.source&&(this._updates.sources[t.source]=!0),this._updates.changed=!0,this},_flattenRenderedFeatures:function(t){for(var e=[],r=this._order.length-1;r>=0;r--)for(var n=this._order[r],i=0;i<t.length;i++){var a=t[i][n];if(a)for(var o=0;o<a.length;o++)e.push(a[o])}return e},queryRenderedFeatures:function(t,e,r,n){\n", "e&&e.filter&&this._handleErrors(v.filter,\"queryRenderedFeatures.filter\",e.filter,!0);var i={};if(e&&e.layers)for(var a=0;a<e.layers.length;a++){var o=e.layers[a];i[this._layers[o].source]=!0}var s=[];for(var l in this.sources)if(!e.layers\|\|i[l]){var u=this.sources[l],c=y.rendered(u,this._layers,t,e,r,n);s.push(c)}return this._flattenRenderedFeatures(s)},querySourceFeatures:function(t,e){e&&e.filter&&this._handleErrors(v.filter,\"querySourceFeatures.filter\",e.filter,!0);var r=this.sources[t];return r?y.source(r,e):[]},addSourceType:function(t,e,r){return g.getType(t)?r(new Error('A source type called \"'+t+'\" already exists.')):(g.setType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"load worker source\",{name:t,url:e.workerSourceURL},r):r(null,null))},_handleErrors:function(t,e,r,n,i){var a=n?v.throwErrors:v.emitErrors,o=t.call(v,c.extend({key:e,style:this.serialize(),value:r,styleSpec:x},i));return a.call(v,this,o)},_remove:function(){this.dispatcher.remove()},_reloadSource:function(t){this.sources[t].reload()},_updateSources:function(t){for(var e in this.sources)this.sources[e].update(t)},_redoPlacement:function(){for(var t in this.sources)this.sources[t].redoPlacement&&this.sources[t].redoPlacement()},_forwardSourceEvent:function(t){this.fire(\"source.\"+t.type,c.extend({source:t.target.getSource()},t))},_forwardTileEvent:function(t){this.fire(t.type,c.extend({source:t.target},t))},_forwardLayerEvent:function(t){this.fire(\"layer.\"+t.type,c.extend({layer:{id:t.target.id}},t))},\"get sprite json\":function(t,e){var r=this.sprite;r.loaded()?e(null,{sprite:r.data,retina:r.retina}):r.on(\"load\",function(){e(null,{sprite:r.data,retina:r.retina})})},\"get icons\":function(t,e){var r=this.sprite,n=this.spriteAtlas;r.loaded()?(n.setSprite(r),n.addIcons(t.icons,e)):r.on(\"load\",function(){n.setSprite(r),n.addIcons(t.icons,e)})},\"get glyphs\":function(t,e){function r(t,r,n){t&&console.error(t),a[n]=r,0===--i&&e(null,a)}var n=t.stacks,i=Object.keys(n).length,a={};for(var o in n)this.glyphSource.getSimpleGlyphs(o,n[o],t.uid,r)}})},{\"../render/line_atlas\":354,\"../source/query_features\":364,\"../source/source\":366,\"../source/source_cache\":367,\"../symbol/glyph_source\":399,\"../symbol/sprite_atlas\":404,\"../util/ajax\":425,\"../util/browser\":426,\"../util/dispatcher\":433,\"../util/evented\":434,\"../util/mapbox\":439,\"../util/util\":442,\"./animation_loop\":375,\"./image_sprite\":376,\"./style_function\":380,\"./style_layer\":381,\"./style_spec\":388,\"./validate_style\":390}],379:[function(t,e,r){\"use strict\";function n(t,e){this.value=s.clone(e),this.isFunction=a.isFunctionDefinition(e),this.json=JSON.stringify(this.value);var r=\"color\"===t.type&&this.value?o(this.value):e;if(this.calculate=a[t.function\|\|\"piecewise-constant\"](r),this.isFeatureConstant=this.calculate.isFeatureConstant,this.isZoomConstant=this.calculate.isZoomConstant,\"piecewise-constant\"===t.function&&t.transition&&(this.calculate=i(this.calculate)),!this.isFeatureConstant&&!this.isZoomConstant){this.stopZoomLevels=[];for(var n=[],l=this.value.stops,u=0;u<this.value.stops.length;u++){var c=l[u][0].zoom;this.stopZoomLevels.indexOf(c)<0&&(this.stopZoomLevels.push(c),n.push([c,n.length]))}this.calculateInterpolationT=a.interpolated({stops:n,base:e.base})}}function i(t){return function(e,r){var n,i,a,o=e.zoom,s=e.zoomHistory,l=e.duration,u=o%1,c=Math.min((Date.now()-s.lastIntegerZoomTime)/l,1),h=1;return o>s.lastIntegerZoom?(n=u+(1-u)c,h=2,i=t({zoom:o-1},r),a=t({zoom:o},r)):(n=1-(1-c)u,a=t({zoom:o},r),i=t({zoom:o+1},r),h/=2),void 0===i\|\|void 0===a?void 0:{from:i,fromScale:h,to:a,toScale:1,t:n}}}var a=t(\"./style_function\"),o=t(\"./parse_color\"),s=t(\"../util/util\");e.exports=n},{\"../util/util\":442,\"./parse_color\":377,\"./style_function\":380}],380:[function(t,e,r){\"use strict\";var n=t(\"mapbox-gl-function\");r.interpolated=function(t){var e=n.interpolated(t),r=function(t,r){return e(t&&t.zoom,r\|\|{})};return r.isFeatureConstant=e.isFeatureConstant,r.isZoomConstant=e.isZoomConstant,r},r[\"piecewise-constant\"]=function(t){var e=n[\"piecewise-constant\"](t),r=function(t,r){return e(t&&t.zoom,r\|\|{})};return r.isFeatureConstant=e.isFeatureConstant,r.isZoomConstant=e.isZoomConstant,r},r.isFunctionDefinition=n.isFunctionDefinition},{\"mapbox-gl-function\":302}],381:[function(t,e,r){\"use strict\";function n(t,e){this.set(t,e)}function i(t){return t.value}var a=t(\"../util/util\"),o=t(\"./style_transition\"),s=t(\"./style_declaration\"),l=t(\"./style_spec\"),u=t(\"./validate_style\"),c=t(\"./parse_color\"),h=t(\"../util/evented\");e.exports=n;n.create=function(e,r){return new({background:t(\"./style_layer/background_style_layer\"),circle:t(\"./style_layer/circle_style_layer\"),fill:t(\"./style_layer/fill_style_layer\"),line:t(\"./style_layer/line_style_layer\"),raster:t(\"./style_layer/raster_style_layer\"),symbol:t(\"./style_layer/symbol_style_layer\")}[(r\|\|e).type])(e,r)},n.prototype=a.inherit(h,{set:function(t,e){this.id=t.id,this.ref=t.ref,this.metadata=t.metadata,this.type=(e\|\|t).type,this.source=(e\|\|t).source,this.sourceLayer=(e\|\|t)[\"source-layer\"],this.minzoom=(e\|\|t).minzoom,this.maxzoom=(e\|\|t).maxzoom,this.filter=(e\|\|t).filter,this.paint={},this.layout={},this._paintSpecifications=l[\"paint_\"+this.type],this._layoutSpecifications=l[\"layout_\"+this.type],this._paintTransitions={},this._paintTransitionOptions={},this._paintDeclarations={},this._layoutDeclarations={},this._layoutFunctions={};var r,n;for(var i in t){var a=i.match(/^paint(?:\\.(.))?$/);if(a){var o=a[1]\|\|\"\";for(r in t[i])this.setPaintProperty(r,t[i][r],o)}}if(this.ref)this._layoutDeclarations=e._layoutDeclarations;else for(n in t.layout)this.setLayoutProperty(n,t.layout[n]);for(r in this._paintSpecifications)this.paint[r]=this.getPaintValue(r);for(n in this._layoutSpecifications)this._updateLayoutValue(n)},setLayoutProperty:function(t,e){if(null==e)delete this._layoutDeclarations[t];else{var r=\"layers.\"+this.id+\".layout.\"+t;if(this._handleErrors(u.layoutProperty,r,t,e))return;this._layoutDeclarations[t]=new s(this._layoutSpecifications[t],e)}this._updateLayoutValue(t)},getLayoutProperty:function(t){return this._layoutDeclarations[t]&&this._layoutDeclarations[t].value},getLayoutValue:function(t,e,r){var n=this._layoutSpecifications[t],i=this._layoutDeclarations[t];return i?i.calculate(e,r):n.default},setPaintProperty:function(t,e,r){var n=\"layers.\"+this.id+(r?'[\"paint.'+r+'\"].':\".paint.\")+t;if(a.endsWith(t,\"-transition\"))if(this._paintTransitionOptions[r\|\|\"\"]\|\|(this._paintTransitionOptions[r\|\|\"\"]={}),null===e\|\|void 0===e)delete this._paintTransitionOptions[r\|\|\"\"][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintTransitionOptions[r\|\|\"\"][t]=e}else if(this._paintDeclarations[r\|\|\"\"]\|\|(this._paintDeclarations[r\|\|\"\"]={}),null===e\|\|void 0===e)delete this._paintDeclarations[r\|\|\"\"][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintDeclarations[r\|\|\"\"][t]=new s(this._paintSpecifications[t],e)}},getPaintProperty:function(t,e){return e=e\|\|\"\",a.endsWith(t,\"-transition\")?this._paintTransitionOptions[e]&&this._paintTransitionOptions[e][t]:this._paintDeclarations[e]&&this._paintDeclarations[e][t]&&this._paintDeclarations[e][t].value},getPaintValue:function(t,e,r){var n=this._paintSpecifications[t],i=this._paintTransitions[t];return 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this._paintDeclarations){e[\"\"===r?\"paint\":\"paint.\"+r]=a.mapObject(this._paintDeclarations[r],i)}return(!this.ref\|\|t&&t.includeRefProperties)&&a.extend(e,{type:this.type,source:this.source,\"source-layer\":this.sourceLayer,filter:this.filter,layout:a.mapObject(this._layoutDeclarations,i)}),a.filterObject(e,function(t,e){return void 0!==t&&!(\"layout\"===e&&!Object.keys(t).length)})},_applyPaintDeclaration:function(t,e,r,n,i){var l=r.transition?this._paintTransitions[t]:void 0,u=this._paintSpecifications[t];if(null!==e&&void 0!==e\|\|(e=new s(u,u.default)),!l\|\|l.declaration.json!==e.json){var c=a.extend({duration:300,delay:0},n,this.getPaintProperty(t+\"-transition\")),h=this._paintTransitions[t]=new o(u,e,l,c);h.instant()\|\|(h.loopID=i.set(h.endTime-Date.now())),l&&i.cancel(l.loopID)}},_updateLayoutValue:function(t){var e=this._layoutDeclarations[t];e&&e.isFunction?this._layoutFunctions[t]=!0:(delete this._layoutFunctions[t],this.layout[t]=this.getLayoutValue(t))},_handleErrors:function(t,e,r,n){return u.emitErrors(this,t.call(u,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:l,style:{glyphs:!0,sprite:!0}}))}})},{\"../util/evented\":434,\"../util/util\":442,\"./parse_color\":377,\"./style_declaration\":379,\"./style_layer/background_style_layer\":382,\"./style_layer/circle_style_layer\":383,\"./style_layer/fill_style_layer\":384,\"./style_layer/line_style_layer\":385,\"./style_layer/raster_style_layer\":386,\"./style_layer/symbol_style_layer\":387,\"./style_spec\":388,\"./style_transition\":389,\"./validate_style\":390}],382:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],383:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],384:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");n.prototype=i.inherit(a,{getPaintValue:function(t,e,r){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintValue.call(this,\"fill-color\",e,r):a.prototype.getPaintValue.call(this,t,e,r)},getPaintValueStopZoomLevels:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintValueStopZoomLevels.call(this,\"fill-color\"):a.prototype.getPaintValueStopZoomLevels.call(this,arguments)},getPaintInterpolationT:function(t,e){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.getPaintInterpolationT.call(this,\"fill-color\",e):a.prototype.getPaintInterpolationT.call(this,t,e)},isPaintValueFeatureConstant:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.isPaintValueFeatureConstant.call(this,\"fill-color\"):a.prototype.isPaintValueFeatureConstant.call(this,t)},isPaintValueZoomConstant:function(t){return\"fill-outline-color\"===t&&void 0===this.getPaintProperty(\"fill-outline-color\")?a.prototype.isPaintValueZoomConstant.call(this,\"fill-color\"):a.prototype.isPaintValueZoomConstant.call(this,t)}}),e.exports=n},{\"../../util/util\":442,\"../style_layer\":381}],385:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{getPaintValue:function(t,e,r){var n=a.prototype.getPaintValue.apply(this,arguments);if(n&&\"line-dasharray\"===t){var i=Math.floor(e.zoom);this._flooredZoom!==i&&(this._flooredZoom=i,this._flooredLineWidth=this.getPaintValue(\"line-width\",e,r)),n.fromScale=this._flooredLineWidth,n.toScale=this._flooredLineWidth}return n}})},{\"../../util/util\":442,\"../style_layer\":381}],386:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{})},{\"../../util/util\":442,\"../style_layer\":381}],387:[function(t,e,r){\"use strict\";function n(){a.apply(this,arguments)}var i=t(\"../../util/util\"),a=t(\"../style_layer\");e.exports=n,n.prototype=i.inherit(a,{isHidden:function(){if(a.prototype.isHidden.apply(this,arguments))return!0;var t=0===this.paint[\"text-opacity\"]\|\|!this.layout[\"text-field\"],e=0===this.paint[\"icon-opacity\"]\|\|!this.layout[\"icon-image\"];return!(!t\|\|!e)},getLayoutValue:function(t,e,r){return(\"text-rotation-alignment\"!==t\|\|\"line\"!==this.getLayoutValue(\"symbol-placement\",e,r)\|\|this.getLayoutProperty(\"text-rotation-alignment\"))&&(\"icon-rotation-alignment\"!==t\|\|\"line\"!==this.getLayoutValue(\"symbol-placement\",e,r)\|\|this.getLayoutProperty(\"icon-rotation-alignment\"))?\"text-pitch-alignment\"!==t\|\|this.getLayoutProperty(\"text-pitch-alignment\")?a.prototype.getLayoutValue.apply(this,arguments):this.getLayoutValue(\"text-rotation-alignment\"):\"map\"}})},{\"../../util/util\":442,\"../style_layer\":381}],388:[function(t,e,r){\"use strict\";e.exports=t(\"mapbox-gl-style-spec/reference/latest.min\")},{\"mapbox-gl-style-spec/reference/latest.min\":325}],389:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.declaration=e,this.startTime=this.endTime=(new Date).getTime(),\"piecewise-constant\"===t.function&&t.transition?this.interp=i:this.interp=o[t.type],this.oldTransition=r,this.duration=n.duration\|\|0,this.delay=n.delay\|\|0,this.instant()\|\|(this.endTime=this.startTime+this.duration+this.delay,this.ease=a.easeCubicInOut),r&&r.endTime<=this.startTime&&delete r.oldTransition}function i(t,e,r){return void 0===(t&&t.to)\|\|void 0===(e&&e.to)?void 0:{from:t.to,fromScale:t.toScale,to:e.to,toScale:e.toScale,t:r}}var a=t(\"../util/util\"),o=t(\"../util/interpolate\");e.exports=n,n.prototype.instant=function(){return!this.oldTransition\|\|!this.interp\|\|0===this.duration&&0===this.delay},n.prototype.calculate=function(t,e){var r=this.declaration.calculate(a.extend({},t,{duration:this.duration}),e);if(this.instant())return r;var n=t.time\|\|Date.now();if(n<this.endTime){var i=this.oldTransition.calculate(a.extend({},t,{time:this.startTime}),e),o=this.ease((n-this.startTime-this.delay)/this.duration);r=this.interp(i,r,o)}return r}},{\"../util/interpolate\":436,\"../util/util\":442}],390:[function(t,e,r){\"use strict\";e.exports=t(\"mapbox-gl-style-spec/lib/validate_style.min\"),e.exports.emitErrors=function(t,e){if(e&&e.length){for(var r=0;r<e.length;r++)t.fire(\"error\",{error:new Error(e[r].message)});return!0}return!1},e.exports.throwErrors=function(t,e){if(e)for(var r=0;r<e.length;r++)throw new Error(e[r].message)}},{\"mapbox-gl-style-spec/lib/validate_style.min\":324}],391:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.x=t,this.y=e,this.angle=r,void 0!==n&&(this.segment=n)}var i=t(\"point-geometry\");e.exports=n,n.prototype=Object.create(i.prototype),n.prototype.clone=function(){return new n(this.x,this.y,this.angle,this.segment)}},{\"point-geometry\":484}],392:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],u=0;s<r/2;){var c=t[o-1],h=t[o],f=t[o+1];if(!f)return!1;var d=c.angleTo(h)-h.angleTo(f);for(d=Math.abs((d+3Math.PI)%(2Math.PI)-Math.PI),l.push({distance:s,angleDelta:d}),u+=d;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;o++,s+=h.dist(f)}return!0}e.exports=n},{}],393:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){for(var o=[],s=0;s<t.length;s++)for(var l,u=t[s],c=0;c<u.length-1;c++){var h=u[c],f=u[c+1];h.x<e&&f.x<e\|\|(h.x<e?h=new i(e,h.y+(f.y-h.y)((e-h.x)/(f.x-h.x)))._round():f.x<e&&(f=new i(e,h.y+(f.y-h.y)((e-h.x)/(f.x-h.x)))._round()),h.y<r&&f.y<r\|\|(h.y<r?h=new i(h.x+(f.x-h.x)((r-h.y)/(f.y-h.y)),r)._round():f.y<r&&(f=new i(h.x+(f.x-h.x)((r-h.y)/(f.y-h.y)),r)._round()),h.x>=n&&f.x>=n\|\|(h.x>=n?h=new i(n,h.y+(f.y-h.y)((n-h.x)/(f.x-h.x)))._round():f.x>=n&&(f=new i(n,h.y+(f.y-h.y)((n-h.x)/(f.x-h.x)))._round()),h.y>=a&&f.y>=a\|\|(h.y>=a?h=new i(h.x+(f.x-h.x)((a-h.y)/(f.y-h.y)),a)._round():f.y>=a&&(f=new i(h.x+(f.x-h.x)((a-h.y)/(f.y-h.y)),a)._round()),l&&h.equals(l[l.length-1])\|\|(l=[h],o.push(l)),l.push(f)))))}return o}var i=t(\"point-geometry\");e.exports=n},{\"point-geometry\":484}],394:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=e.exports=new n({members:[{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Float32\",name:\"maxScale\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"},{type:\"Int16\",name:\"bbox0\"},{type:\"Int16\",name:\"bbox1\"},{type:\"Int16\",name:\"bbox2\"},{type:\"Int16\",name:\"bbox3\"},{type:\"Float32\",name:\"placementScale\"}]});i.extendAll(o.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"point-geometry\":484}],395:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u,c){var h=o.tops-l,f=o.bottoms+l,d=o.lefts-l,p=o.rights+l;if(this.boxStartIndex=t.length,u){var m=f-h,v=p-d;if(m>0)if(m=Math.max(10s,m),c){var g=e[r.segment+1].sub(e[r.segment])._unit()._mult(v),y=[r.sub(g),r.add(g)];this._addLineCollisionBoxes(t,y,r,0,v,m,n,i,a)}else this._addLineCollisionBoxes(t,e,r,r.segment,v,m,n,i,a)}else t.emplaceBack(r.x,r.y,d,h,p,f,1/0,n,i,a,0,0,0,0,0);this.boxEndIndex=t.length}e.exports=n,n.prototype._addLineCollisionBoxes=function(t,e,r,n,i,a,o,s,l){var u=a/2,c=Math.floor(i/u),h=-a/2,f=this.boxes,d=r,p=n+1,m=h;do{if(--p<0)return f;m-=e[p].dist(d),d=e[p]}while(m>-i/2);for(var v=e[p].dist(e[p+1]),g=0;g<c;g++){for(var y=-i/2+gu;m+v<y;){if(m+=v,++p+1>=e.length)return f;v=e[p].dist(e[p+1])}var b=y-m,x=e[p],_=e[p+1],w=_.sub(x)._unit()._mult(b)._add(x)._round(),M=Math.max(Math.abs(y-h)-u/2,0),k=i/2/M;t.emplaceBack(w.x,w.y,-a/2,-a/2,a/2,a/2,k,o,s,l,0,0,0,0,0)}return f}},{}],396:[function(t,e,r){\"use strict\";function n(t,e,r){if(\"object\"==typeof t){var n=t;r=e,t=n.angle,e=n.pitch,this.grid=new o(n.grid),this.ignoredGrid=new o(n.ignoredGrid)}else this.grid=new o(a,12,6),this.ignoredGrid=new o(a,12,0);this.angle=t,this.pitch=e;var i=Math.sin(t),s=Math.cos(t);if(this.rotationMatrix=[s,-i,i,s],this.reverseRotationMatrix=[s,i,-i,s],this.yStretch=1/Math.cos(e/180Math.PI),this.yStretch=Math.pow(this.yStretch,1.3),this.collisionBoxArray=r,0===r.length){r.emplaceBack();r.emplaceBack(0,0,0,-32767,0,32767,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(a,0,0,-32767,0,32767,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,0,-32767,0,32767,0,32767,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,a,-32767,0,32767,0,32767,0,0,0,0,0,0,0,0,0)}this.tempCollisionBox=r.get(0),this.edges=[r.get(1),r.get(2),r.get(3),r.get(4)]}var i=t(\"point-geometry\"),a=t(\"../data/bucket\").EXTENT,o=t(\"grid-index\");e.exports=n,n.prototype.serialize=function(){var t={angle:this.angle,pitch:this.pitch,grid:this.grid.toArrayBuffer(),ignoredGrid:this.ignoredGrid.toArrayBuffer()};return{data:t,transferables:[t.grid,t.ignoredGrid]}},n.prototype.minScale=.25,n.prototype.maxScale=2,n.prototype.placeCollisionFeature=function(t,e,r){for(var n=this.collisionBoxArray,a=this.minScale,o=this.rotationMatrix,s=this.yStretch,l=t.boxStartIndex;l<t.boxEndIndex;l++){var u=n.get(l),c=u.anchorPoint._matMult(o),h=c.x,f=c.y,d=h+u.x1,p=f+u.y1s,m=h+u.x2,v=f+u.y2s;if(u.bbox0=d,u.bbox1=p,u.bbox2=m,u.bbox3=v,!e)for(var g=this.grid.query(d,p,m,v),y=0;y<g.length;y++){var b=n.get(g[y]),x=b.anchorPoint._matMult(o);if((a=this.getPlacementScale(a,c,u,x,b))>=this.maxScale)return a}if(r){var _;if(this.angle){var w=this.reverseRotationMatrix,M=new i(u.x1,u.y1).matMult(w),k=new i(u.x2,u.y1).matMult(w),A=new i(u.x1,u.y2).matMult(w),T=new i(u.x2,u.y2).matMult(w);_=this.tempCollisionBox,_.anchorPointX=u.anchorPoint.x,_.anchorPointY=u.anchorPoint.y,_.x1=Math.min(M.x,k.x,A.x,T.x),_.y1=Math.min(M.y,k.x,A.x,T.x),_.x2=Math.max(M.x,k.x,A.x,T.x),_.y2=Math.max(M.y,k.x,A.x,T.x),_.maxScale=u.maxScale}else _=u;for(var S=0;S<this.edges.length;S++){var E=this.edges[S];if((a=this.getPlacementScale(a,u.anchorPoint,_,E.anchorPoint,E))>=this.maxScale)return a}}}return a},n.prototype.queryRenderedSymbols=function(t,e,r,n,a){var o={},s=[],l=this.collisionBoxArray,u=this.rotationMatrix,c=new i(t,e)._matMult(u),h=this.tempCollisionBox;h.anchorX=c.x,h.anchorY=c.y,h.x1=0,h.y1=0,h.x2=r-t,h.y2=n-e,h.maxScale=a,a=h.maxScale;for(var f=[c.x+h.x1/a,c.y+h.y1/athis.yStretch,c.x+h.x2/a,c.y+h.y2/athis.yStretch],d=this.grid.query(f[0],f[1],f[2],f[3]),p=this.ignoredGrid.query(f[0],f[1],f[2],f[3]),m=0;m<p.length;m++)d.push(p[m]);for(var v=0;v<d.length;v++){var g=l.get(d[v]),y=g.sourceLayerIndex,b=g.featureIndex;if(void 0===o[y]&&(o[y]={}),!o[y][b]){var x=g.anchorPoint.matMult(u);this.getPlacementScale(this.minScale,c,h,x,g)>=a&&(o[y][b]=!0,s.push(d[v]))}}return s},n.prototype.getPlacementScale=function(t,e,r,n,i){var a=e.x-n.x,o=e.y-n.y,s=(i.x1-r.x2)/a,l=(i.x2-r.x1)/a,u=(i.y1-r.y2)this.yStretch/o,c=(i.y2-r.y1)this.yStretch/o;(isNaN(s)\|\|isNaN(l))&&(s=l=1),(isNaN(u)\|\|isNaN(c))&&(u=c=1);var h=Math.min(Math.max(s,l),Math.max(u,c)),f=i.maxScale,d=r.maxScale;return h>f&&(h=f),h>d&&(h=d),h>t&&h>=i.placementScale&&(t=h),t},n.prototype.insertCollisionFeature=function(t,e,r){for(var n=r?this.ignoredGrid:this.grid,i=this.collisionBoxArray,a=t.boxStartIndex;a<t.boxEndIndex;a++){var o=i.get(a);o.placementScale=e,e<this.maxScale&&n.insert(a,o.bbox0,o.bbox1,o.bbox2,o.bbox3)}}},{\"../data/bucket\":329,\"grid-index\":287,\"point-geometry\":484}],397:[function(t,e,r){\"use strict\";function n(t,e,r,n,a,o,s,l,u){var c=n?.6os:0,h=Math.max(n?n.right-n.left:0,a?a.right-a.left:0),f=0===t[0].x\|\|t[0].x===u\|\|0===t[0].y\|\|t[0].y===u;e-hs<e/4&&(e=hs+e/4);var d=2o;return i(t,f?e/2l%e:(h/2+d)sl%e,e,c,r,hs,f,!1,u)}function i(t,e,r,n,l,u,c,h,f){for(var d=u/2,p=0,m=0;m<t.length-1;m++)p+=t[m].dist(t[m+1]);for(var v=0,g=e-r,y=[],b=0;b<t.length-1;b++){for(var x=t[b],_=t[b+1],w=x.dist(_),M=_.angleTo(x);g+r<v+w;){g+=r;var k=(g-v)/w,A=a(x.x,_.x,k),T=a(x.y,_.y,k);if(A>=0&&A<f&&T>=0&&T<f&&g-d>=0&&g+d<=p){var S=new o(A,T,M,b)._round();n&&!s(t,S,u,n,l)\|\|y.push(S)}}v+=w}return h\|\|y.length\|\|c\|\|(y=i(t,v/2,r,n,l,u,c,!0,f)),y}var a=t(\"../util/interpolate\"),o=t(\"../symbol/anchor\"),s=t(\"./check_max_angle\");e.exports=n},{\"../symbol/anchor\":391,\"../util/interpolate\":436,\"./check_max_angle\":392}],398:[function(t,e,r){\"use strict\";function n(){this.width=o,this.height=o,this.bin=new i(this.width,this.height),this.index={},this.ids={},this.data=new Uint8Array(this.widththis.height)}var i=t(\"shelf-pack\"),a=t(\"../util/util\"),o=128;e.exports=n,n.prototype.getGlyphs=function(){var t,e,r,n={};for(var i in this.ids)t=i.split(\"#\"),e=t[0],r=t[1],n[e]\|\|(n[e]=[]),n[e].push(r);return n},n.prototype.getRects=function(){var t,e,r,n={};for(var i in this.ids)t=i.split(\"#\"),e=t[0],r=t[1],n[e]\|\|(n[e]={}),n[e][r]=this.index[i];return n},n.prototype.addGlyph=function(t,e,r,n){if(!r)return null;var i=e+\"#\"+r.id;if(this.index[i])return this.ids[i].indexOf(t)<0&&this.ids[i].push(t),this.index[i];if(!r.bitmap)return null;var o=r.width+2n,s=r.height+2n,l=o+2,u=s+2;l+=4-l%4,u+=4-u%4;var c=this.bin.packOne(l,u);if(c\|\|(this.resize(),c=this.bin.packOne(l,u)),!c)return a.warnOnce(\"glyph bitmap overflow\"),null;this.index[i]=c,this.ids[i]=[t];for(var h=this.data,f=r.bitmap,d=0;d<s;d++)for(var p=this.width(c.y+d+1)+c.x+1,m=od,v=0;v<o;v++)h[p+v]=f[m+v];return this.dirty=!0,c},n.prototype.resize=function(){var t=this.width,e=this.height;if(!(t>=2048\|\|e>=2048)){this.texture&&(this.gl&&this.gl.deleteTexture(this.texture),this.texture=null),this.width=4,this.height=4,this.bin.resize(this.width,this.height);for(var r=new ArrayBuffer(this.widththis.height),n=0;n<e;n++){var i=new Uint8Array(this.data.buffer,en,t);new Uint8Array(r,en4,t).set(i)}this.data=new Uint8Array(r)}},n.prototype.bind=function(t){this.gl=t,this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,this.width,this.height,0,t.ALPHA,t.UNSIGNED_BYTE,null))},n.prototype.updateTexture=function(t){this.bind(t),this.dirty&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.ALPHA,t.UNSIGNED_BYTE,this.data),this.dirty=!1)}},{\"../util/util\":442,\"shelf-pack\":514}],399:[function(t,e,r){\"use strict\";function n(t){this.url=t&&o(t),this.atlases={},this.stacks={},this.loading={}}function i(t,e,r){this.advance=t.advance,this.left=t.left-r-1,this.top=t.top+r+1,this.rect=e}function a(t,e,r,n){return n=n\|\|\"abc\",r.replace(\"{s}\",n[t.length%n.length]).replace(\"{fontstack}\",t).replace(\"{range}\",e)}var o=t(\"../util/mapbox\").normalizeGlyphsURL,s=t(\"../util/ajax\").getArrayBuffer,l=t(\"../util/glyphs\"),u=t(\"../symbol/glyph_atlas\"),c=t(\"pbf\");e.exports=n,n.prototype.getSimpleGlyphs=function(t,e,r,n){void 0===this.stacks[t]&&(this.stacks[t]={}),void 0===this.atlases[t]&&(this.atlases[t]=new u);for(var a,o={},s=this.stacks[t],l=this.atlases[t],c={},h=0,f=0;f<e.length;f++){var d=e[f];if(a=Math.floor(d/256),s[a]){var p=s[a].glyphs[d],m=l.addGlyph(r,t,p,3);p&&(o[d]=new i(p,m,3))}else void 0===c[a]&&(c[a]=[],h++),c[a].push(d)}h\|\|n(void 0,o,t);var v=function(e,a,s){if(!e)for(var u=this.stacks[t][a]=s.stacks[0],f=0;f<c[a].length;f++){var d=c[a][f],p=u.glyphs[d],m=l.addGlyph(r,t,p,3);p&&(o[d]=new i(p,m,3))}--h\|\|n(void 0,o,t)}.bind(this);for(var g in c)this.loadRange(t,g,v)},n.prototype.loadRange=function(t,e,r){if(256e>65535)return r(\"glyphs > 65535 not supported\");void 0===this.loading[t]&&(this.loading[t]={});var n=this.loading[t];if(n[e])n[e].push(r);else{n[e]=[r];var i=256e+\"-\"+(256e+255),o=a(t,i,this.url);s(o,function(t,r){for(var i=!t&&new l(new c(new Uint8Array(r))),a=0;a<n[e].length;a++)n[e][a](t,e,i);delete n[e]})}},n.prototype.getGlyphAtlas=function(t){return this.atlases[t]}},{\"../symbol/glyph_atlas\":398,\"../util/ajax\":425,\"../util/glyphs\":435,\"../util/mapbox\":439,pbf:478}],400:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){function n(n){c.push(t[n]),h.push(r[n]),f.push(e[n]),d++}function i(t,e,r){var n=u[t];return delete u[t],u[e]=n,h[n][0].pop(),h[n][0]=h[n][0].concat(r[0]),n}function a(t,e,r){var n=l[e];return delete l[e],l[t]=n,h[n][0].shift(),h[n][0]=r[0].concat(h[n][0]),n}function o(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}var s,l={},u={},c=[],h=[],f=[],d=0;for(s=0;s<t.length;s++){var p=r[s],m=e[s];if(m){var v=o(m,p),g=o(m,p,!0);if(v in u&&g in l&&u[v]!==l[g]){var y=a(v,g,p),b=i(v,g,h[y]);delete l[v],delete u[g],u[o(m,h[b],!0)]=b,h[y]=null}else v in u?i(v,g,p):g in l?a(v,g,p):(n(s),l[v]=d-1,u[g]=d-1)}else n(s)}return{features:c,textFeatures:f,geometries:h}}},{}],401:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,u){this.anchorPoint=t,this.tl=e,this.tr=r,this.bl=n,this.br=i,this.tex=a,this.anchorAngle=o,this.glyphAngle=s,this.minScale=l,this.maxScale=u}function i(t,e,r,i,a,o,u,c,h){var f,d,p,m,v=e.image.rect,g=a.layout,y=e.left-1,b=y+v.w/e.image.pixelRatio,x=e.top-1,_=x+v.h/e.image.pixelRatio;if(\"none\"!==g[\"icon-text-fit\"]&&u){var w=b-y,M=_-x,k=g[\"text-size\"]/24,A=u.leftk,T=u.rightk,S=u.topk,E=u.bottomk,L=T-A,C=E-S,I=g[\"icon-text-fit-padding\"][0],z=g[\"icon-text-fit-padding\"][1],D=g[\"icon-text-fit-padding\"][2],P=g[\"icon-text-fit-padding\"][3],O=\"width\"===g[\"icon-text-fit\"]?.5(C-M):0,R=\"height\"===g[\"icon-text-fit\"]?.5(L-w):0,F=\"width\"===g[\"icon-text-fit\"]\|\|\"both\"===g[\"icon-text-fit\"]?L:w,j=\"height\"===g[\"icon-text-fit\"]\|\|\"both\"===g[\"icon-text-fit\"]?C:M;f=new s(A+R-P,S+O-I),d=new s(A+R+z+F,S+O-I),p=new s(A+R+z+F,S+O+D+j),m=new s(A+R-P,S+O+D+j)}else f=new s(y,x),d=new s(b,x),p=new s(b,_),m=new s(y,_);var N=a.getLayoutValue(\"icon-rotate\",c,h)Math.PI/180;if(o){var B=i[t.segment];if(t.y===B.y&&t.x===B.x&&t.segment+1<i.length){var U=i[t.segment+1];N+=Math.atan2(t.y-U.y,t.x-U.x)+Math.PI}else N+=Math.atan2(t.y-B.y,t.x-B.x)}if(N){var V=Math.sin(N),H=Math.cos(N),q=[H,-V,V,H];f=f.matMult(q),d=d.matMult(q),m=m.matMult(q),p=p.matMult(q)}return[new n(new s(t.x,t.y),f,d,m,p,e.image.rect,0,0,l,1/0)]}function a(t,e,r,i,a,u){for(var c=a.layout[\"text-rotate\"]Math.PI/180,h=a.layout[\"text-keep-upright\"],f=e.positionedGlyphs,d=[],p=0;p<f.length;p++){var m=f[p],v=m.glyph,g=v.rect;if(g){var y,b=(m.x+v.advance/2)r,x=l;u?(y=[],x=o(y,t,b,i,t.segment,!0),h&&(x=Math.min(x,o(y,t,b,i,t.segment,!1)))):y=[{anchorPoint:new s(t.x,t.y),offset:0,angle:0,maxScale:1/0,minScale:l}];for(var _=m.x+v.left,w=m.y-v.top,M=_+g.w,k=w+g.h,A=new s(_,w),T=new s(M,w),S=new s(_,k),E=new s(M,k),L=0;L<y.length;L++){var C=y[L],I=A,z=T,D=S,P=E;if(c){var O=Math.sin(c),R=Math.cos(c),F=[R,-O,O,R];I=I.matMult(F),z=z.matMult(F),D=D.matMult(F),P=P.matMult(F)}var j=Math.max(C.minScale,x),N=(t.angle+C.offset+2Math.PI)%(2Math.PI),B=(C.angle+C.offset+2Math.PI)%(2Math.PI);d.push(new n(C.anchorPoint,I,z,D,P,g,N,B,j,C.maxScale))}}}return d}function o(t,e,r,n,i,a){var o=!a;r<0&&(a=!a),a&&i++;var u=new s(e.x,e.y),c=n[i],h=1/0;r=Math.abs(r);for(var f=l;;){var d=u.dist(c),p=r/d,m=Math.atan2(c.y-u.y,c.x-u.x);if(a\|\|(m+=Math.PI),t.push({anchorPoint:u,offset:o?Math.PI:0,minScale:p,maxScale:h,angle:(m+2Math.PI)%(2Math.PI)}),p<=f)break;for(u=c;u.equals(c);)if(i+=a?1:-1,!(c=n[i]))return p;var v=c.sub(u)._unit();u=u.sub(v._mult(d)),h=p}return f}var s=t(\"point-geometry\");e.exports={getIconQuads:i,getGlyphQuads:a,SymbolQuad:n};var l=.5},{\"point-geometry\":484}],402:[function(t,e,r){\"use strict\";function n(t,e,r){for(var n=[],a=0,o=t.length;a<o;a++){var s=i(t[a].properties,e[\"text-field\"]);if(s){s=s.toString();var l=e[\"text-transform\"];\"uppercase\"===l?s=s.toLocaleUpperCase():\"lowercase\"===l&&(s=s.toLocaleLowerCase());for(var u=0;u<s.length;u++)r[s.charCodeAt(u)]=!0;n[a]=s}else n[a]=null}return n}var i=t(\"../util/token\");e.exports=n},{\"../util/token\":441}],403:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.codePoint=t,this.x=e,this.y=r,this.glyph=n}function i(t,e,r,n,i,a){this.positionedGlyphs=t,this.text=e,this.top=r,this.bottom=n,this.left=i,this.right=a}function a(t,e,r,a,s,l,u,c,h){for(var f=[],d=new i(f,t,h[1],h[1],h[0],h[0]),p=0,m=0;m<t.length;m++){var v=t.charCodeAt(m),g=e[v];g&&(f.push(new n(v,p,-17,g)),p+=g.advance+c)}return!!f.length&&(o(d,e,a,r,s,l,u,h),d)}function o(t,e,r,n,i,a,o,u){var c=null,d=0,p=0,m=0,v=0,g=t.positionedGlyphs;if(n)for(var y=0;y<g.length;y++){var b=g[y];if(b.x-=d,b.y+=rm,b.x>n&&null!==c){var x=g[c+1].x;v=Math.max(x,v);for(var _=c+1;_<=y;_++)g[_].y+=r,g[_].x-=x;if(o){var w=c;h[g[c].codePoint]&&w--,s(g,e,p,w,o)}p=c+1,c=null,d+=x,m++}f[b.codePoint]&&(c=y)}var M=g[g.length-1],k=M.x+e[M.codePoint].advance;v=Math.max(v,k);var A=(m+1)r;s(g,e,p,g.length-1,o),l(g,o,i,a,v,r,m,u),t.top+=-aA,t.bottom=t.top+A,t.left+=-iv,t.right=t.left+v}function s(t,e,r,n,i){for(var a=e[t[n].codePoint].advance,o=(t[n].x+a)i,s=r;s<=n;s++)t[s].x-=o}\n", "function l(t,e,r,n,i,a,o,s){for(var l=(e-r)i+s[0],u=(-n(o+1)+.5)a+s[1],c=0;c<t.length;c++)t[c].x+=l,t[c].y+=u}function u(t,e){if(!t\|\|!t.rect)return null;var r=e[\"icon-offset\"][0],n=e[\"icon-offset\"][1],i=r-t.width/2,a=i+t.width,o=n-t.height/2;return new c(t,o,o+t.height,i,a)}function c(t,e,r,n,i){this.image=t,this.top=e,this.bottom=r,this.left=n,this.right=i}e.exports={shapeText:a,shapeIcon:u};var h={32:!0,8203:!0},f={32:!0,38:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0}},{}],404:[function(t,e,r){\"use strict\";function n(t,e){this.width=t,this.height=e,this.bin=new o(t,e),this.images={},this.data=!1,this.texture=0,this.filter=0,this.pixelRatio=1,this.dirty=!0}function i(t,e,r,n,i,a,o,s,l,u,c){var h,f,d=ne+r,p=sa+o;if(c)for(p-=a,f=-1;f<=u;f++,d=((f+u)%u+n)e+r,p+=a)for(h=-1;h<=l;h++)i[p+h]=t[d+(h+l)%l];else for(f=0;f<u;f++,d+=e,p+=a)for(h=0;h<l;h++)i[p+h]=t[d+h]}function a(t,e,r,n,i){this.rect=t,this.width=e,this.height=r,this.sdf=n,this.pixelRatio=i}var o=t(\"shelf-pack\"),s=t(\"../util/browser\"),l=t(\"../util/util\");e.exports=n,n.prototype.allocateImage=function(t,e){t/=this.pixelRatio,e/=this.pixelRatio;var r=t+2+(4-(t+2)%4),n=e+2+(4-(e+2)%4),i=this.bin.packOne(r,n);return i\|\|(l.warnOnce(\"SpriteAtlas out of space.\"),null)},n.prototype.getImage=function(t,e){if(this.images[t])return this.images[t];if(!this.sprite)return null;var r=this.sprite.getSpritePosition(t);if(!r.width\|\|!r.height)return null;var n=this.allocateImage(r.width,r.height);if(!n)return null;var i=new a(n,r.width/r.pixelRatio,r.height/r.pixelRatio,r.sdf,r.pixelRatio/this.pixelRatio);return this.images[t]=i,this.copy(n,r,e),i},n.prototype.getPosition=function(t,e){var r=this.getImage(t,e),n=r&&r.rect;if(!n)return null;var i=r.widthr.pixelRatio,a=r.heightr.pixelRatio;return{size:[r.width,r.height],tl:[(n.x+1)/this.width,(n.y+1)/this.height],br:[(n.x+1+i)/this.width,(n.y+1+a)/this.height]}},n.prototype.allocate=function(){if(!this.data){var t=Math.floor(this.widththis.pixelRatio),e=Math.floor(this.heightthis.pixelRatio);this.data=new Uint32Array(te);for(var r=0;r<this.data.length;r++)this.data[r]=0}},n.prototype.copy=function(t,e,r){if(this.sprite.img.data){var n=new Uint32Array(this.sprite.img.data.buffer);this.allocate();var a=this.data;i(n,this.sprite.img.width,e.x,e.y,a,this.widththis.pixelRatio,(t.x+1)this.pixelRatio,(t.y+1)this.pixelRatio,e.width,e.height,r),this.dirty=!0}},n.prototype.setSprite=function(t){t&&(this.pixelRatio=s.devicePixelRatio>1?2:1,this.canvas&&(this.canvas.width=this.widththis.pixelRatio,this.canvas.height=this.heightthis.pixelRatio)),this.sprite=t},n.prototype.addIcons=function(t,e){for(var r=0;r<t.length;r++)this.getImage(t[r]);e(null,this.images)},n.prototype.bind=function(t,e){var r=!1;this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),r=!0);var n=e?t.LINEAR:t.NEAREST;n!==this.filter&&(t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,n),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,n),this.filter=n),this.dirty&&(this.allocate(),r?t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.widththis.pixelRatio,this.heightthis.pixelRatio,0,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)):t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.widththis.pixelRatio,this.heightthis.pixelRatio,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)),this.dirty=!1)}},{\"../util/browser\":426,\"../util/util\":442,\"shelf-pack\":514}],405:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=e.exports=new n({members:[{type:\"Uint16\",name:\"textBoxStartIndex\"},{type:\"Uint16\",name:\"textBoxEndIndex\"},{type:\"Uint16\",name:\"iconBoxStartIndex\"},{type:\"Uint16\",name:\"iconBoxEndIndex\"},{type:\"Uint16\",name:\"glyphQuadStartIndex\"},{type:\"Uint16\",name:\"glyphQuadEndIndex\"},{type:\"Uint16\",name:\"iconQuadStartIndex\"},{type:\"Uint16\",name:\"iconQuadEndIndex\"},{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int8\",name:\"index\"}]});i.extendAll(o.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"point-geometry\":484}],406:[function(t,e,r){\"use strict\";var n=t(\"../util/struct_array\"),i=t(\"../util/util\"),a=t(\"point-geometry\"),o=t(\"./quads\").SymbolQuad,s=e.exports=new n({members:[{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Float32\",name:\"tlX\"},{type:\"Float32\",name:\"tlY\"},{type:\"Float32\",name:\"trX\"},{type:\"Float32\",name:\"trY\"},{type:\"Float32\",name:\"blX\"},{type:\"Float32\",name:\"blY\"},{type:\"Float32\",name:\"brX\"},{type:\"Float32\",name:\"brY\"},{type:\"Int16\",name:\"texH\"},{type:\"Int16\",name:\"texW\"},{type:\"Int16\",name:\"texX\"},{type:\"Int16\",name:\"texY\"},{type:\"Float32\",name:\"anchorAngle\"},{type:\"Float32\",name:\"glyphAngle\"},{type:\"Float32\",name:\"maxScale\"},{type:\"Float32\",name:\"minScale\"}]});i.extendAll(s.prototype.StructType.prototype,{get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)},get SymbolQuad(){return new o(this.anchorPoint,new a(this.tlX,this.tlY),new a(this.trX,this.trY),new a(this.blX,this.blY),new a(this.brX,this.brY),{x:this.texX,y:this.texY,h:this.texH,w:this.texW,height:this.texH,width:this.texW},this.anchorAngle,this.glyphAngle,this.minScale,this.maxScale)}})},{\"../util/struct_array\":440,\"../util/util\":442,\"./quads\":401,\"point-geometry\":484}],407:[function(t,e,r){\"use strict\";var n=t(\"../util/dom\"),i=t(\"point-geometry\"),a={scrollZoom:t(\"./handler/scroll_zoom\"),boxZoom:t(\"./handler/box_zoom\"),dragRotate:t(\"./handler/drag_rotate\"),dragPan:t(\"./handler/drag_pan\"),keyboard:t(\"./handler/keyboard\"),doubleClickZoom:t(\"./handler/dblclick_zoom\"),touchZoomRotate:t(\"./handler/touch_zoom_rotate\")};e.exports=function(t,e){function r(t){g(\"mouseout\",t)}function o(e){t.stop(),_=n.mousePos(b,e),g(\"mousedown\",e)}function s(e){var r=t.dragRotate&&t.dragRotate.isActive();x&&!r&&g(\"contextmenu\",x),x=null,g(\"mouseup\",e)}function l(e){if(!(t.dragPan&&t.dragPan.isActive()\|\|t.dragRotate&&t.dragRotate.isActive())){for(var r=e.toElement\|\|e.target;r&&r!==b;)r=r.parentNode;r===b&&g(\"mousemove\",e)}}function u(e){t.stop(),y(\"touchstart\",e),!e.touches\|\|e.touches.length>1\|\|(w?(clearTimeout(w),w=null,g(\"dblclick\",e)):w=setTimeout(d,300))}function c(t){y(\"touchmove\",t)}function h(t){y(\"touchend\",t)}function f(t){y(\"touchcancel\",t)}function d(){w=null}function p(t){n.mousePos(b,t).equals(_)&&g(\"click\",t)}function m(t){g(\"dblclick\",t),t.preventDefault()}function v(t){x=t,t.preventDefault()}function g(e,r){var i=n.mousePos(b,r);return t.fire(e,{lngLat:t.unproject(i),point:i,originalEvent:r})}function y(e,r){var a=n.touchPos(b,r),o=a.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new i(0,0));return t.fire(e,{lngLat:t.unproject(o),point:o,lngLats:a.map(function(e){return t.unproject(e)},this),points:a,originalEvent:r})}var b=t.getCanvasContainer(),x=null,_=null,w=null;for(var M in a)t[M]=new a[M](t,e),e.interactive&&e[M]&&t[M].enable();b.addEventListener(\"mouseout\",r,!1),b.addEventListener(\"mousedown\",o,!1),b.addEventListener(\"mouseup\",s,!1),b.addEventListener(\"mousemove\",l,!1),b.addEventListener(\"touchstart\",u,!1),b.addEventListener(\"touchend\",h,!1),b.addEventListener(\"touchmove\",c,!1),b.addEventListener(\"touchcancel\",f,!1),b.addEventListener(\"click\",p,!1),b.addEventListener(\"dblclick\",m,!1),b.addEventListener(\"contextmenu\",v,!1)}},{\"../util/dom\":428,\"./handler/box_zoom\":413,\"./handler/dblclick_zoom\":414,\"./handler/drag_pan\":415,\"./handler/drag_rotate\":416,\"./handler/keyboard\":417,\"./handler/scroll_zoom\":418,\"./handler/touch_zoom_rotate\":419,\"point-geometry\":484}],408:[function(t,e,r){\"use strict\";var n=t(\"../util/util\"),i=t(\"../util/interpolate\"),a=t(\"../util/browser\"),o=t(\"../geo/lng_lat\"),s=t(\"../geo/lng_lat_bounds\"),l=t(\"point-geometry\"),u=e.exports=function(){};n.extend(u.prototype,{getCenter:function(){return this.transform.center},setCenter:function(t,e){return this.jumpTo({center:t},e),this},panBy:function(t,e,r){return this.panTo(this.transform.center,n.extend({offset:l.convert(t).mult(-1)},e),r),this},panTo:function(t,e,r){return this.easeTo(n.extend({center:t},e),r)},getZoom:function(){return this.transform.zoom},setZoom:function(t,e){return this.jumpTo({zoom:t},e),this},zoomTo:function(t,e,r){return this.easeTo(n.extend({zoom:t},e),r)},zoomIn:function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},zoomOut:function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},getBearing:function(){return this.transform.bearing},setBearing:function(t,e){return this.jumpTo({bearing:t},e),this},rotateTo:function(t,e,r){return this.easeTo(n.extend({bearing:t},e),r)},resetNorth:function(t,e){return this.rotateTo(0,n.extend({duration:1e3},t),e),this},snapToNorth:function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},getPitch:function(){return this.transform.pitch},setPitch:function(t,e){return this.jumpTo({pitch:t},e),this},fitBounds:function(t,e,r){e=n.extend({padding:0,offset:[0,0],maxZoom:1/0},e),t=s.convert(t);var i=l.convert(e.offset),a=this.transform,o=a.project(t.getNorthWest()),u=a.project(t.getSouthEast()),c=u.sub(o),h=(a.width-2e.padding-2Math.abs(i.x))/c.x,f=(a.height-2e.padding-2Math.abs(i.y))/c.y;return e.center=a.unproject(o.add(u).div(2)),e.zoom=Math.min(a.scaleZoom(a.scaleMath.min(h,f)),e.maxZoom),e.bearing=0,e.linear?this.easeTo(e,r):this.flyTo(e,r)},jumpTo:function(t,e){this.stop();var r=this.transform,n=!1,i=!1,a=!1;return\"zoom\"in t&&r.zoom!==+t.zoom&&(n=!0,r.zoom=+t.zoom),\"center\"in t&&(r.center=o.convert(t.center)),\"bearing\"in t&&r.bearing!==+t.bearing&&(i=!0,r.bearing=+t.bearing),\"pitch\"in t&&r.pitch!==+t.pitch&&(a=!0,r.pitch=+t.pitch),this.fire(\"movestart\",e).fire(\"move\",e),n&&this.fire(\"zoomstart\",e).fire(\"zoom\",e).fire(\"zoomend\",e),i&&this.fire(\"rotate\",e),a&&this.fire(\"pitch\",e),this.fire(\"moveend\",e)},easeTo:function(t,e){this.stop(),t=n.extend({offset:[0,0],duration:500,easing:n.ease},t);var r,a,s=this.transform,u=l.convert(t.offset),c=this.getZoom(),h=this.getBearing(),f=this.getPitch(),d=\"zoom\"in t?+t.zoom:c,p=\"bearing\"in t?this._normalizeBearing(t.bearing,h):h,m=\"pitch\"in t?+t.pitch:f;\"center\"in t?(r=o.convert(t.center),a=s.centerPoint.add(u)):\"around\"in t?(r=o.convert(t.around),a=s.locationPoint(r)):(a=s.centerPoint.add(u),r=s.pointLocation(a));var v=s.locationPoint(r);return!1===t.animate&&(t.duration=0),this.zooming=d!==c,this.rotating=h!==p,this.pitching=m!==f,t.noMoveStart\|\|this.fire(\"movestart\",e),this.zooming&&this.fire(\"zoomstart\",e),clearTimeout(this._onEaseEnd),this._ease(function(t){this.zooming&&(s.zoom=i(c,d,t)),this.rotating&&(s.bearing=i(h,p,t)),this.pitching&&(s.pitch=i(f,m,t)),s.setLocationAtPoint(r,v.add(a.sub(v)._mult(t))),this.fire(\"move\",e),this.zooming&&this.fire(\"zoom\",e),this.rotating&&this.fire(\"rotate\",e),this.pitching&&this.fire(\"pitch\",e)},function(){t.delayEndEvents?this._onEaseEnd=setTimeout(this._easeToEnd.bind(this,e),t.delayEndEvents):this._easeToEnd(e)}.bind(this),t),this},_easeToEnd:function(t){var e=this.zooming;this.zooming=!1,this.rotating=!1,this.pitching=!1,e&&this.fire(\"zoomend\",t),this.fire(\"moveend\",t)},flyTo:function(t,e){function r(t){var e=(AA-kk+(t?-1:1)LLTT)/(2(t?A:k)LT);return Math.log(Math.sqrt(ee+1)-e)}function a(t){return(Math.exp(t)-Math.exp(-t))/2}function s(t){return(Math.exp(t)+Math.exp(-t))/2}function u(t){return a(t)/s(t)}this.stop(),t=n.extend({offset:[0,0],speed:1.2,curve:1.42,easing:n.ease},t);var c=this.transform,h=l.convert(t.offset),f=this.getZoom(),d=this.getBearing(),p=this.getPitch(),m=\"center\"in t?o.convert(t.center):this.getCenter(),v=\"zoom\"in t?+t.zoom:f,g=\"bearing\"in t?this._normalizeBearing(t.bearing,d):d,y=\"pitch\"in t?+t.pitch:p;Math.abs(c.center.lng)+Math.abs(m.lng)>180&&(c.center.lng>0&&m.lng<0?m.lng+=360:c.center.lng<0&&m.lng>0&&(m.lng-=360));var b=c.zoomScale(v-f),x=c.point,_=\"center\"in t?c.project(m).sub(h.div(b)):x,w=c.worldSize,M=t.curve,k=Math.max(c.width,c.height),A=k/b,T=_.sub(x).mag();if(\"minZoom\"in t){var S=n.clamp(Math.min(t.minZoom,f,v),c.minZoom,c.maxZoom),E=k/c.zoomScale(S-f);M=Math.sqrt(E/T2)}var L=MM,C=r(0),I=function(t){return s(C)/s(C+Mt)},z=function(t){return k((s(C)u(C+Mt)-a(C))/L)/T},D=(r(1)-C)/M;if(Math.abs(T)<1e-6){if(Math.abs(k-A)<1e-6)return this.easeTo(t);var P=A<k?-1:1;D=Math.abs(Math.log(A/k))/M,z=function(){return 0},I=function(t){return Math.exp(PMt)}}if(\"duration\"in t)t.duration=+t.duration;else{var O=\"screenSpeed\"in t?+t.screenSpeed/M:+t.speed;t.duration=1e3D/O}return this.zooming=!0,d!==g&&(this.rotating=!0),p!==y&&(this.pitching=!0),this.fire(\"movestart\",e),this.fire(\"zoomstart\",e),this._ease(function(t){var r=tD,n=z(r);c.zoom=f+c.scaleZoom(1/I(r)),c.center=c.unproject(x.add(_.sub(x).mult(n)),w),this.rotating&&(c.bearing=i(d,g,t)),this.pitching&&(c.pitch=i(p,y,t)),this.fire(\"move\",e),this.fire(\"zoom\",e),this.rotating&&this.fire(\"rotate\",e),this.pitching&&this.fire(\"pitch\",e)},function(){this.zooming=!1,this.rotating=!1,this.pitching=!1,this.fire(\"zoomend\",e),this.fire(\"moveend\",e)},t),this},isEasing:function(){return!!this._abortFn},stop:function(){return this._abortFn&&(this._abortFn(),this._finishEase()),this},_ease:function(t,e,r){this._finishFn=e,this._abortFn=a.timed(function(e){t.call(this,r.easing(e)),1===e&&this._finishEase()},!1===r.animate?0:r.duration,this)},_finishEase:function(){delete this._abortFn;var t=this._finishFn;delete this._finishFn,t.call(this)},_normalizeBearing:function(t,e){t=n.wrap(t,-180,180);var r=Math.abs(t-e);return Math.abs(t-360-e)<r&&(t-=360),Math.abs(t+360-e)<r&&(t+=360),t},_updateEasing:function(t,e,r){var i;if(this.ease){var a=this.ease,o=(Date.now()-a.start)/a.duration,s=a.easing(o+.01)-a.easing(o),l=.27/Math.sqrt(ss+1e-4).01,u=Math.sqrt(.0729-ll);i=n.bezier(l,u,.25,1)}else i=r?n.bezier.apply(n,r):n.ease;return this.ease={start:(new Date).getTime(),to:Math.pow(2,e),duration:t,easing:i},i}})},{\"../geo/lng_lat\":339,\"../geo/lng_lat_bounds\":340,\"../util/browser\":426,\"../util/interpolate\":436,\"../util/util\":442,\"point-geometry\":484}],409:[function(t,e,r){\"use strict\";function n(t){o.setOptions(this,t)}var i=t(\"./control\"),a=t(\"../../util/dom\"),o=t(\"../../util/util\");e.exports=n,n.createAttributionString=function(t){var e=[];for(var r in t){var n=t[r];n.attribution&&e.indexOf(n.attribution)<0&&e.push(n.attribution)}return e.sort(function(t,e){return t.length-e.length}),e=e.filter(function(t,r){for(var n=r+1;n<e.length;n++)if(e[n].indexOf(t)>=0)return!1;return!0}),e.join(\" \| \")},n.prototype=o.inherit(i,{options:{position:\"bottom-right\"},onAdd:function(t){var e=this._container=a.create(\"div\",\"mapboxgl-ctrl-attrib\",t.getContainer());return this._update(),t.on(\"source.load\",this._update.bind(this)),t.on(\"source.change\",this._update.bind(this)),t.on(\"source.remove\",this._update.bind(this)),t.on(\"moveend\",this._updateEditLink.bind(this)),e},_update:function(){this._map.style&&(this._container.innerHTML=n.createAttributionString(this._map.style.sources)),this._editLink=this._container.getElementsByClassName(\"mapbox-improve-map\")[0],this._updateEditLink()},_updateEditLink:function(){if(this._editLink){var t=this._map.getCenter();this._editLink.href=\"https://www.mapbox.com/map-feedback/#/\"+t.lng+\"/\"+t.lat+\"/\"+Math.round(this._map.getZoom()+1)}}})},{\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],410:[function(t,e,r){\"use strict\";function n(){}var i=t(\"../../util/util\"),a=t(\"../../util/evented\");e.exports=n,n.prototype={addTo:function(t){this._map=t;var e=this._container=this.onAdd(t);if(this.options&&this.options.position){var r=this.options.position,n=t._controlCorners[r];e.className+=\" mapboxgl-ctrl\",-1!==r.indexOf(\"bottom\")?n.insertBefore(e,n.firstChild):n.appendChild(e)}return this},remove:function(){return this._container.parentNode.removeChild(this._container),this.onRemove&&this.onRemove(this._map),this._map=null,this}},i.extend(n.prototype,a)},{\"../../util/evented\":434,\"../../util/util\":442}],411:[function(t,e,r){\"use strict\";function n(t){s.setOptions(this,t)}var i=t(\"./control\"),a=t(\"../../util/browser\"),o=t(\"../../util/dom\"),s=t(\"../../util/util\");e.exports=n;var l={enableHighAccuracy:!1,timeout:6e3};n.prototype=s.inherit(i,{options:{position:\"top-right\"},onAdd:function(t){var e=this._container=o.create(\"div\",\"mapboxgl-ctrl-group\",t.getContainer());return a.supportsGeolocation?(this._container.addEventListener(\"contextmenu\",this._onContextMenu.bind(this)),this._geolocateButton=o.create(\"button\",\"mapboxgl-ctrl-icon mapboxgl-ctrl-geolocate\",this._container),this._geolocateButton.type=\"button\",this._geolocateButton.addEventListener(\"click\",this._onClickGeolocate.bind(this)),e):e},_onContextMenu:function(t){t.preventDefault()},_onClickGeolocate:function(){navigator.geolocation.getCurrentPosition(this._success.bind(this),this._error.bind(this),l),this._timeoutId=setTimeout(this._finish.bind(this),1e4)},_success:function(t){this._map.jumpTo({center:[t.coords.longitude,t.coords.latitude],zoom:17,bearing:0,pitch:0}),this.fire(\"geolocate\",t),this._finish()},_error:function(t){this.fire(\"error\",t),this._finish()},_finish:function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0}})},{\"../../util/browser\":426,\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],412:[function(t,e,r){\"use strict\";function n(t){s.setOptions(this,t)}function i(t){return new MouseEvent(t.type,{button:2,buttons:2,bubbles:!0,cancelable:!0,detail:t.detail,view:t.view,screenX:t.screenX,screenY:t.screenY,clientX:t.clientX,clientY:t.clientY,movementX:t.movementX,movementY:t.movementY,ctrlKey:t.ctrlKey,shiftKey:t.shiftKey,altKey:t.altKey,metaKey:t.metaKey})}var a=t(\"./control\"),o=t(\"../../util/dom\"),s=t(\"../../util/util\");e.exports=n,n.prototype=s.inherit(a,{options:{position:\"top-right\"},onAdd:function(t){var e=\"mapboxgl-ctrl\",r=this._container=o.create(\"div\",e+\"-group\",t.getContainer());return this._container.addEventListener(\"contextmenu\",this._onContextMenu.bind(this)),this._zoomInButton=this._createButton(e+\"-icon \"+e+\"-zoom-in\",t.zoomIn.bind(t)),this._zoomOutButton=this._createButton(e+\"-icon \"+e+\"-zoom-out\",t.zoomOut.bind(t)),this._compass=this._createButton(e+\"-icon \"+e+\"-compass\",t.resetNorth.bind(t)),this._compassArrow=o.create(\"div\",\"arrow\",this._compass),this._compass.addEventListener(\"mousedown\",this._onCompassDown.bind(this)),this._onCompassMove=this._onCompassMove.bind(this),this._onCompassUp=this._onCompassUp.bind(this),t.on(\"rotate\",this._rotateCompassArrow.bind(this)),this._rotateCompassArrow(),this._el=t.getCanvasContainer(),r},_onContextMenu:function(t){t.preventDefault()},_onCompassDown:function(t){0===t.button&&(o.disableDrag(),document.addEventListener(\"mousemove\",this._onCompassMove),document.addEventListener(\"mouseup\",this._onCompassUp),this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassMove:function(t){0===t.button&&(this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassUp:function(t){0===t.button&&(document.removeEventListener(\"mousemove\",this._onCompassMove),document.removeEventListener(\"mouseup\",this._onCompassUp),o.enableDrag(),this._el.dispatchEvent(i(t)),t.stopPropagation())},_createButton:function(t,e){var r=o.create(\"button\",t,this._container);return r.type=\"button\",r.addEventListener(\"click\",function(){e()}),r},_rotateCompassArrow:function(){var t=\"rotate(\"+this._map.transform.angle(180/Math.PI)+\"deg)\";this._compassArrow.style.transform=t}})},{\"../../util/dom\":428,\"../../util/util\":442,\"./control\":410}],413:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../geo/lng_lat_bounds\"),o=t(\"../../util/util\");e.exports=n,n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"mousedown\",this._onMouseDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onMouseDown),this._enabled=!1)},_onMouseDown:function(t){t.shiftKey&&0===t.button&&(document.addEventListener(\"mousemove\",this._onMouseMove,!1),document.addEventListener(\"keydown\",this._onKeyDown,!1),document.addEventListener(\"mouseup\",this._onMouseUp,!1),i.disableDrag(),this._startPos=i.mousePos(this._el,t),this._active=!0)},_onMouseMove:function(t){var e=this._startPos,r=i.mousePos(this._el,t);this._box\|\|(this._box=i.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var n=Math.min(e.x,r.x),a=Math.max(e.x,r.x),o=Math.min(e.y,r.y),s=Math.max(e.y,r.y);i.setTransform(this._box,\"translate(\"+n+\"px,\"+o+\"px)\"),this._box.style.width=a-n+\"px\",this._box.style.height=s-o+\"px\"},_onMouseUp:function(t){if(0===t.button){var e=this._startPos,r=i.mousePos(this._el,t),n=new a(this._map.unproject(e),this._map.unproject(r));this._finish(),e.x===r.x&&e.y===r.y?this._fireEvent(\"boxzoomcancel\",t):this._map.fitBounds(n,{linear:!0}).fire(\"boxzoomend\",{originalEvent:t,boxZoomBounds:n})}},_onKeyDown:function(t){27===t.keyCode&&(this._finish(),this._fireEvent(\"boxzoomcancel\",t))},_finish:function(){this._active=!1,document.removeEventListener(\"mousemove\",this._onMouseMove,!1),document.removeEventListener(\"keydown\",this._onKeyDown,!1),document.removeEventListener(\"mouseup\",this._onMouseUp,!1),this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(this._box.parentNode.removeChild(this._box),this._box=null),i.enableDrag()},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})}}},{\"../../geo/lng_lat_bounds\":340,\"../../util/dom\":428,\"../../util/util\":442}],414:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._onDblClick=this._onDblClick.bind(this)}e.exports=n,n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()\|\|(this._map.on(\"dblclick\",this._onDblClick),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._map.off(\"dblclick\",this._onDblClick),this._enabled=!1)},_onDblClick:function(t){this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)}}},{}],415:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/util\");e.exports=n;var o=a.bezier(0,0,.3,1);n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"mousedown\",this._onDown),this._el.addEventListener(\"touchstart\",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onDown),this._el.removeEventListener(\"touchstart\",this._onDown),this._enabled=!1)},_onDown:function(t){this._ignoreEvent(t)\|\|this.isActive()\|\|(t.touches?(document.addEventListener(\"touchmove\",this._onMove),document.addEventListener(\"touchend\",this._onTouchEnd)):(document.addEventListener(\"mousemove\",this._onMove),document.addEventListener(\"mouseup\",this._onMouseUp)),this._active=!1,this._startPos=this._pos=i.mousePos(this._el,t),this._inertia=[[Date.now(),this._pos]])},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()\|\|(this._active=!0,this._fireEvent(\"dragstart\",t),this._fireEvent(\"movestart\",t));var e=i.mousePos(this._el,t),r=this._map;r.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),e]),r.transform.setLocationAtPoint(r.transform.pointLocation(this._pos),e),this._fireEvent(\"drag\",t),this._fireEvent(\"move\",t),this._pos=e,t.preventDefault()}},_onUp:function(t){if(this.isActive()){this._active=!1,this._fireEvent(\"dragend\",t),this._drainInertiaBuffer();var e=function(){this._fireEvent(\"moveend\",t)}.bind(this),r=this._inertia;if(r.length<2)return void e();var n=r[r.length-1],i=r[0],a=n[1].sub(i[1]),s=(n[0]-i[0])/1e3;if(0===s\|\|n[1].equals(i[1]))return void e();var l=a.mult(.3/s),u=l.mag();u>1400&&(u=1400,l._unit()._mult(u));var c=u/750,h=l.mult(-c/2);this._map.panBy(h,{duration:1e3c,easing:o,noMoveStart:!0},{originalEvent:t})}},_onMouseUp:function(t){this._ignoreEvent(t)\|\|(this._onUp(t),document.removeEventListener(\"mousemove\",this._onMove),document.removeEventListener(\"mouseup\",this._onMouseUp))},_onTouchEnd:function(t){this._ignoreEvent(t)\|\|(this._onUp(t),document.removeEventListener(\"touchmove\",this._onMove),document.removeEventListener(\"touchend\",this._onTouchEnd))},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragRotate&&e.dragRotate.isActive())return!0;if(t.touches)return t.touches.length>1;if(t.ctrlKey)return!0;return\"mousemove\"===t.type?!1&t.buttons:0!==t.button},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>0&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442}],416:[function(t,e,r){\"use strict\";function n(t,e){this._map=t,this._el=t.getCanvasContainer(),this._bearingSnap=e.bearingSnap,o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"point-geometry\"),o=t(\"../../util/util\");e.exports=n;var s=o.bezier(0,0,.25,1);n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"mousedown\",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"mousedown\",this._onDown),this._enabled=!1)},_onDown:function(t){if(!this._ignoreEvent(t)&&!this.isActive()){document.addEventListener(\"mousemove\",this._onMove),document.addEventListener(\"mouseup\",this._onUp),this._active=!1,this._inertia=[[Date.now(),this._map.getBearing()]],this._startPos=this._pos=i.mousePos(this._el,t),this._center=this._map.transform.centerPoint;var e=this._startPos.sub(this._center);e.mag()<200&&(this._center=this._startPos.add(new a(-200,0)._rotate(e.angle()))),t.preventDefault()}},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()\|\|(this._active=!0,this._fireEvent(\"rotatestart\",t),this._fireEvent(\"movestart\",t));var e=this._map;e.stop();var r=this._pos,n=i.mousePos(this._el,t),a=this._center,o=r.sub(a).angleWith(n.sub(a))/Math.PI180,s=e.getBearing()-o,l=this._inertia,u=l[l.length-1];this._drainInertiaBuffer(),l.push([Date.now(),e._normalizeBearing(s,u[1])]),e.transform.bearing=s,this._fireEvent(\"rotate\",t),this._fireEvent(\"move\",t),this._pos=n}},_onUp:function(t){if(!this._ignoreEvent(t)&&(document.removeEventListener(\"mousemove\",this._onMove),document.removeEventListener(\"mouseup\",this._onUp),this.isActive())){this._active=!1,this._fireEvent(\"rotateend\",t),this._drainInertiaBuffer();var e=this._map,r=e.getBearing(),n=this._inertia,i=function(){Math.abs(r)<this._bearingSnap?e.resetNorth({noMoveStart:!0},{originalEvent:t}):this._fireEvent(\"moveend\",t)}.bind(this);if(n.length<2)return void i();var a=n[0],o=n[n.length-1],l=n[n.length-2],u=e._normalizeBearing(r,l[1]),c=o[1]-a[1],h=c<0?-1:1,f=(o[0]-a[0])/1e3;if(0===c\|\|0===f)return void i();var d=Math.abs(c(.25/f));d>180&&(d=180);var p=d/180;u+=hd(p/2),Math.abs(e._normalizeBearing(u,0))<this._bearingSnap&&(u=e._normalizeBearing(0,u)),e.rotateTo(u,{duration:1e3p,easing:s,noMoveStart:!0},{originalEvent:t})}},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragPan&&e.dragPan.isActive())return!0;if(t.touches)return t.touches.length>1;var r=t.ctrlKey?1:2,n=t.ctrlKey?0:2;return\"mousemove\"===t.type?t.buttons&0===r:t.button!==n},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>0&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442,\"point-geometry\":484}],417:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._onKeyDown=this._onKeyDown.bind(this)}e.exports=n;n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"keydown\",this._onKeyDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"keydown\",this._onKeyDown),this._enabled=!1)},_onKeyDown:function(t){if(!(t.altKey\|\|t.ctrlKey\|\|t.metaKey)){var e=this._map,r={originalEvent:t};if(!e.isEasing())switch(t.keyCode){case 61:case 107:case 171:case 187:e.zoomTo(Math.round(e.getZoom())+(t.shiftKey?2:1),r);break;case 189:case 109:case 173:e.zoomTo(Math.round(e.getZoom())-(t.shiftKey?2:1),r);break;case 37:t.shiftKey?e.easeTo({bearing:e.getBearing()-2},r):(t.preventDefault(),e.panBy([-80,0],r));break;case 39:t.shiftKey?e.easeTo({bearing:e.getBearing()+2},r):(t.preventDefault(),e.panBy([80,0],r));break;case 38:t.shiftKey?e.easeTo({pitch:e.getPitch()+5},r):(t.preventDefault(),e.panBy([0,-80],r));break;case 40:t.shiftKey?e.easeTo({pitch:Math.max(e.getPitch()-5,0)},r):(t.preventDefault(),e.panBy([0,80],r))}}}}},{}],418:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),o.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/browser\"),o=t(\"../../util/util\");e.exports=n;var s=\"undefined\"!=typeof navigator?navigator.userAgent.toLowerCase():\"\",l=-1!==s.indexOf(\"firefox\"),u=-1!==s.indexOf(\"safari\")&&-1===s.indexOf(\"chrom\");n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"wheel\",this._onWheel,!1),this._el.addEventListener(\"mousewheel\",this._onWheel,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"wheel\",this._onWheel),this._el.removeEventListener(\"mousewheel\",this._onWheel),this._enabled=!1)},_onWheel:function(t){var e;\"wheel\"===t.type?(e=t.deltaY,l&&t.deltaMode===window.WheelEvent.DOM_DELTA_PIXEL&&(e/=a.devicePixelRatio),t.deltaMode===window.WheelEvent.DOM_DELTA_LINE&&(e=40)):\"mousewheel\"===t.type&&(e=-t.wheelDeltaY,u&&(e/=3));var r=a.now(),n=r-(this._time\|\|0);this._pos=i.mousePos(this._el,t),this._time=r,0!==e&&e%4.000244140625==0?(this._type=\"wheel\",e=Math.floor(e/4)):0!==e&&Math.abs(e)<4?this._type=\"trackpad\":n>400?(this._type=null,this._lastValue=e,this._timeout=setTimeout(this._onTimeout,40)):this._type\|\|(this._type=Math.abs(ne)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,e+=this._lastValue)),t.shiftKey&&e&&(e/=4),this._type&&this._zoom(-e,t),t.preventDefault()},_onTimeout:function(){this._type=\"wheel\",this._zoom(-this._lastValue)},_zoom:function(t,e){if(0!==t){var r=this._map,n=2/(1+Math.exp(-Math.abs(t/100)));t<0&&0!==n&&(n=1/n);var i=r.ease?r.ease.to:r.transform.scale,a=r.transform.scaleZoom(in);r.zoomTo(a,{duration:0,around:r.unproject(this._pos),delayEndEvents:200},{originalEvent:e})}}}},{\"../../util/browser\":426,\"../../util/dom\":428,\"../../util/util\":442}],419:[function(t,e,r){\"use strict\";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t(\"../../util/dom\"),a=t(\"../../util/util\");e.exports=n;var o=a.bezier(0,0,.15,1);n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()\|\|(this._el.addEventListener(\"touchstart\",this._onStart,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener(\"touchstart\",this._onStart),this._enabled=!1)},disableRotation:function(){this._rotationDisabled=!0},enableRotation:function(){this._rotationDisabled=!1},_onStart:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]);this._startVec=e.sub(r),this._startScale=this._map.transform.scale,this._startBearing=this._map.transform.bearing,this._gestureIntent=void 0,this._inertia=[],\n", "document.addEventListener(\"touchmove\",this._onMove,!1),document.addEventListener(\"touchend\",this._onEnd,!1)}},_onMove:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]),n=e.add(r).div(2),a=e.sub(r),o=a.mag()/this._startVec.mag(),s=this._rotationDisabled?0:180a.angleWith(this._startVec)/Math.PI,l=this._map;if(this._gestureIntent){var u={duration:0,around:l.unproject(n)};\"rotate\"===this._gestureIntent&&(u.bearing=this._startBearing+s),\"zoom\"!==this._gestureIntent&&\"rotate\"!==this._gestureIntent\|\|(u.zoom=l.transform.scaleZoom(this._startScaleo)),l.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),o,n]),l.easeTo(u,{originalEvent:t})}else{var c=Math.abs(1-o)>.15;Math.abs(s)>4?this._gestureIntent=\"rotate\":c&&(this._gestureIntent=\"zoom\"),this._gestureIntent&&(this._startVec=a,this._startScale=l.transform.scale,this._startBearing=l.transform.bearing)}t.preventDefault()}},_onEnd:function(t){document.removeEventListener(\"touchmove\",this._onMove),document.removeEventListener(\"touchend\",this._onEnd),this._drainInertiaBuffer();var e=this._inertia,r=this._map;if(e.length<2)return void r.snapToNorth({},{originalEvent:t});var n=e[e.length-1],i=e[0],a=r.transform.scaleZoom(this._startScalen[1]),s=r.transform.scaleZoom(this._startScalei[1]),l=a-s,u=(n[0]-i[0])/1e3,c=n[2];if(0===u\|\|a===s)return void r.snapToNorth({},{originalEvent:t});var h=.15l/u;Math.abs(h)>2.5&&(h=h>0?2.5:-2.5);var f=1e3Math.abs(h/(12.15)),d=a+hf/2e3;d<0&&(d=0),r.easeTo({zoom:d,duration:f,easing:o,around:r.unproject(c)},{originalEvent:t})},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now();t.length>2&&e-t[0][0]>160;)t.shift()}}},{\"../../util/dom\":428,\"../../util/util\":442}],420:[function(t,e,r){\"use strict\";function n(){i.bindAll([\"_onHashChange\",\"_updateHash\"],this)}e.exports=n;var i=t(\"../util/util\");n.prototype={addTo:function(t){return this._map=t,window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},remove:function(){return window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),delete this._map,this},_onHashChange:function(){var t=location.hash.replace(\"#\",\"\").split(\"/\");return t.length>=3&&(this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:+(t[3]\|\|0)}),!0)},_updateHash:function(){var t=this._map.getCenter(),e=this._map.getZoom(),r=this._map.getBearing(),n=Math.max(0,Math.ceil(Math.log(e)/Math.LN2)),i=\"#\"+Math.round(100e)/100+\"/\"+t.lat.toFixed(n)+\"/\"+t.lng.toFixed(n)+(r?\"/\"+Math.round(10r)/10:\"\");window.history.replaceState(\"\",\"\",i)}}},{\"../util/util\":442}],421:[function(t,e,r){\"use strict\";function n(t){t.parentNode&&t.parentNode.removeChild(t)}var i=t(\"../util/canvas\"),a=t(\"../util/util\"),o=t(\"../util/browser\"),s=t(\"../util/browser\").window,l=t(\"../util/evented\"),u=t(\"../util/dom\"),c=t(\"../style/style\"),h=t(\"../style/animation_loop\"),f=t(\"../render/painter\"),d=t(\"../geo/transform\"),p=t(\"./hash\"),m=t(\"./bind_handlers\"),v=t(\"./camera\"),g=t(\"../geo/lng_lat\"),y=t(\"../geo/lng_lat_bounds\"),b=t(\"point-geometry\"),x=t(\"./control/attribution\"),_={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:0,maxZoom:20,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,workerCount:Math.max(o.hardwareConcurrency-1,1)},w=e.exports=function(t){if(t=a.extend({},_,t),t.workerCount<1)throw new Error(\"workerCount must an integer greater than or equal to 1.\");this._interactive=t.interactive,this._failIfMajorPerformanceCaveat=t.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=t.preserveDrawingBuffer,this._trackResize=t.trackResize,this._workerCount=t.workerCount,this._bearingSnap=t.bearingSnap,\"string\"==typeof t.container?this._container=document.getElementById(t.container):this._container=t.container,this.animationLoop=new h,this.transform=new d(t.minZoom,t.maxZoom),t.maxBounds&&this.setMaxBounds(t.maxBounds),a.bindAll([\"_forwardStyleEvent\",\"_forwardSourceEvent\",\"_forwardLayerEvent\",\"_forwardTileEvent\",\"_onStyleLoad\",\"_onStyleChange\",\"_onSourceAdd\",\"_onSourceRemove\",\"_onSourceUpdate\",\"_onWindowOnline\",\"_onWindowResize\",\"_update\",\"_render\"],this),this._setupContainer(),this._setupPainter(),this.on(\"move\",this._update.bind(this,!1)),this.on(\"zoom\",this._update.bind(this,!0)),this.on(\"moveend\",function(){this.animationLoop.set(300),this._rerender()}.bind(this)),void 0!==s&&(s.addEventListener(\"online\",this._onWindowOnline,!1),s.addEventListener(\"resize\",this._onWindowResize,!1)),m(this,t),this._hash=t.hash&&(new p).addTo(this),this._hash&&this._hash._onHashChange()\|\|this.jumpTo({center:t.center,zoom:t.zoom,bearing:t.bearing,pitch:t.pitch}),this.stacks={},this._classes=[],this.resize(),t.classes&&this.setClasses(t.classes),t.style&&this.setStyle(t.style),t.attributionControl&&this.addControl(new x(t.attributionControl));var e=this.fire.bind(this,\"error\");this.on(\"style.error\",e),this.on(\"source.error\",e),this.on(\"tile.error\",e),this.on(\"layer.error\",e)};a.extend(w.prototype,l),a.extend(w.prototype,v.prototype),a.extend(w.prototype,{addControl:function(t){return t.addTo(this),this},addClass:function(t,e){return this._classes.indexOf(t)>=0\|\|\"\"===t?this:(this._classes.push(t),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},removeClass:function(t,e){var r=this._classes.indexOf(t);return r<0\|\|\"\"===t?this:(this._classes.splice(r,1),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},setClasses:function(t,e){for(var r={},n=0;n<t.length;n++)\"\"!==t[n]&&(r[t[n]]=!0);return this._classes=Object.keys(r),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0)},hasClass:function(t){return this._classes.indexOf(t)>=0},getClasses:function(){return this._classes},resize:function(){var t=0,e=0;return this._container&&(t=this._container.offsetWidth\|\|400,e=this._container.offsetHeight\|\|300),this._canvas.resize(t,e),this.transform.resize(t,e),this.painter.resize(t,e),this.fire(\"movestart\").fire(\"move\").fire(\"resize\").fire(\"moveend\")},getBounds:function(){var t=new y(this.transform.pointLocation(new b(0,0)),this.transform.pointLocation(this.transform.size));return(this.transform.angle\|\|this.transform.pitch)&&(t.extend(this.transform.pointLocation(new b(this.transform.size.x,0))),t.extend(this.transform.pointLocation(new b(0,this.transform.size.y)))),t},setMaxBounds:function(t){if(t){var e=y.convert(t);this.transform.lngRange=[e.getWest(),e.getEast()],this.transform.latRange=[e.getSouth(),e.getNorth()],this.transform._constrain(),this._update()}else null!==t&&void 0!==t\|\|(this.transform.lngRange=[],this.transform.latRange=[],this._update());return this},setMinZoom:function(t){if((t=null===t\|\|void 0===t?0:t)>=0&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between 0 and the current maxZoom, inclusive\")},setMaxZoom:function(t){if((t=null===t\|\|void 0===t?20:t)>=this.transform.minZoom&&t<=20)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be between the current minZoom and 20, inclusive\")},project:function(t){return this.transform.locationPoint(g.convert(t))},unproject:function(t){return this.transform.pointLocation(b.convert(t))},queryRenderedFeatures:function(){var t,e={};return 2===arguments.length?(t=arguments[0],e=arguments[1]):1===arguments.length&&function(t){return t instanceof b\|\|Array.isArray(t)}(arguments[0])?t=arguments[0]:1===arguments.length&&(e=arguments[0]),this.style.queryRenderedFeatures(this._makeQueryGeometry(t),e,this.transform.zoom,this.transform.angle)},_makeQueryGeometry:function(t){void 0===t&&(t=[b.convert([0,0]),b.convert([this.transform.width,this.transform.height])]);var e;if(t instanceof b\|\|\"number\"==typeof t[0])e=[b.convert(t)];else{var r=[b.convert(t[0]),b.convert(t[1])];e=[r[0],new b(r[1].x,r[0].y),r[1],new b(r[0].x,r[1].y),r[0]]}return e=e.map(function(t){return this.transform.pointCoordinate(t)}.bind(this))},querySourceFeatures:function(t,e){return this.style.querySourceFeatures(t,e)},setStyle:function(t){return this.style&&(this.style.off(\"load\",this._onStyleLoad).off(\"error\",this._forwardStyleEvent).off(\"change\",this._onStyleChange).off(\"source.add\",this._onSourceAdd).off(\"source.remove\",this._onSourceRemove).off(\"source.load\",this._onSourceUpdate).off(\"source.error\",this._forwardSourceEvent).off(\"source.change\",this._onSourceUpdate).off(\"layer.add\",this._forwardLayerEvent).off(\"layer.remove\",this._forwardLayerEvent).off(\"layer.error\",this._forwardLayerEvent).off(\"tile.add\",this._forwardTileEvent).off(\"tile.remove\",this._forwardTileEvent).off(\"tile.load\",this._update).off(\"tile.error\",this._forwardTileEvent).off(\"tile.stats\",this._forwardTileEvent)._remove(),this.off(\"rotate\",this.style._redoPlacement),this.off(\"pitch\",this.style._redoPlacement)),t?(this.style=t instanceof c?t:new 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this.style.addSourceType(t,e,r)},removeSource:function(t){return this.style.removeSource(t),this._update(!0),this},getSource:function(t){return this.style.getSource(t)},addLayer:function(t,e){return this.style.addLayer(t,e),this._update(!0),this},removeLayer:function(t){return this.style.removeLayer(t),this._update(!0),this},getLayer:function(t){return this.style.getLayer(t)},setFilter:function(t,e){return this.style.setFilter(t,e),this._update(!0),this},setLayerZoomRange:function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0),this},getFilter:function(t){return this.style.getFilter(t)},setPaintProperty:function(t,e,r,n){return this.style.setPaintProperty(t,e,r,n),this._update(!0),this},getPaintProperty:function(t,e,r){return this.style.getPaintProperty(t,e,r)},setLayoutProperty:function(t,e,r){return this.style.setLayoutProperty(t,e,r),this._update(!0),this},getLayoutProperty:function(t,e){return this.style.getLayoutProperty(t,e)},getContainer:function(){return this._container},getCanvasContainer:function(){return this._canvasContainer},getCanvas:function(){return this._canvas.getElement()},_setupContainer:function(){var t=this._container;t.classList.add(\"mapboxgl-map\");var e=this._canvasContainer=u.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=new i(this,e);var r=this._controlContainer=u.create(\"div\",\"mapboxgl-control-container\",t),n=this._controlCorners={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(function(t){n[t]=u.create(\"div\",\"mapboxgl-ctrl-\"+t,r)})},_setupPainter:function(){var t=this._canvas.getWebGLContext({failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer});if(!t)return void this.fire(\"error\",{error:new Error(\"Failed to initialize WebGL\")});this.painter=new f(t,this.transform)},_contextLost:function(t){t.preventDefault(),this._frameId&&o.cancelFrame(this._frameId),this.fire(\"webglcontextlost\",{originalEvent:t})},_contextRestored:function(t){this._setupPainter(),this.resize(),this._update(),this.fire(\"webglcontextrestored\",{originalEvent:t})},loaded:function(){return!this._styleDirty&&!this._sourcesDirty&&!(!this.style\|\|!this.style.loaded())},_update:function(t){return this.style?(this._styleDirty=this._styleDirty\|\|t,this._sourcesDirty=!0,this._rerender(),this):this},_render:function(){try{this.style&&this._styleDirty&&(this._styleDirty=!1,this.style.update(this._classes,this._classOptions),this._classOptions=null,this.style._recalculate(this.transform.zoom)),this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.painter.render(this.style,{debug:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,vertices:this.vertices,rotating:this.rotating,zooming:this.zooming}),this.fire(\"render\"),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(\"load\")),this._frameId=null,this.animationLoop.stopped()\|\|(this._styleDirty=!0),(this._sourcesDirty\|\|this._repaint\|\|this._styleDirty)&&this._rerender()}catch(t){this.fire(\"error\",{error:t})}return this},remove:function(){this._hash&&this._hash.remove(),o.cancelFrame(this._frameId),this.setStyle(null),void 0!==s&&s.removeEventListener(\"resize\",this._onWindowResize,!1);var t=this.painter.gl.getExtension(\"WEBGL_lose_context\");t&&t.loseContext(),n(this._canvasContainer),n(this._controlContainer),this._container.classList.remove(\"mapboxgl-map\")},_rerender:function(){this.style&&!this._frameId&&(this._frameId=o.frame(this._render))},_forwardStyleEvent:function(t){this.fire(\"style.\"+t.type,a.extend({style:t.target},t))},_forwardSourceEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_forwardLayerEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_forwardTileEvent:function(t){this.fire(t.type,a.extend({style:t.target},t))},_onStyleLoad:function(t){this.transform.unmodified&&this.jumpTo(this.style.stylesheet),this.style.update(this._classes,{transition:!1}),this._forwardStyleEvent(t)},_onStyleChange:function(t){this._update(!0),this._forwardStyleEvent(t)},_onSourceAdd:function(t){var e=t.source;e.onAdd&&e.onAdd(this),this._forwardSourceEvent(t)},_onSourceRemove:function(t){var e=t.source;e.onRemove&&e.onRemove(this),this._forwardSourceEvent(t)},_onSourceUpdate:function(t){this._update(),this._forwardSourceEvent(t)},_onWindowOnline:function(){this._update()},_onWindowResize:function(){this._trackResize&&this.stop().resize()._update()}}),a.extendAll(w.prototype,{_showTileBoundaries:!1,get showTileBoundaries(){return this._showTileBoundaries},set showTileBoundaries(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},_showCollisionBoxes:!1,get showCollisionBoxes(){return this._showCollisionBoxes},set showCollisionBoxes(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,this.style._redoPlacement())},_showOverdrawInspector:!1,get showOverdrawInspector(){return this._showOverdrawInspector},set showOverdrawInspector(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},_repaint:!1,get repaint(){return this._repaint},set repaint(t){this._repaint=t,this._update()},_vertices:!1,get vertices(){return this._vertices},set vertices(t){this._vertices=t,this._update()}})},{\"../geo/lng_lat\":339,\"../geo/lng_lat_bounds\":340,\"../geo/transform\":341,\"../render/painter\":355,\"../style/animation_loop\":375,\"../style/style\":378,\"../util/browser\":426,\"../util/canvas\":427,\"../util/dom\":428,\"../util/evented\":434,\"../util/util\":442,\"./bind_handlers\":407,\"./camera\":408,\"./control/attribution\":409,\"./hash\":420,\"point-geometry\":484}],422:[function(t,e,r){\"use strict\";function n(t,e){t\|\|(t=i.create(\"div\")),t.classList.add(\"mapboxgl-marker\"),this._el=t,this._offset=o.convert(e&&e.offset\|\|[0,0]),this._update=this._update.bind(this)}e.exports=n;var i=t(\"../util/dom\"),a=t(\"../geo/lng_lat\"),o=t(\"point-geometry\");n.prototype={addTo:function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._el),t.on(\"move\",this._update),this._update(),this},remove:function(){this._map&&(this._map.off(\"move\",this._update),this._map=null);var t=this._el.parentNode;return t&&t.removeChild(this._el),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=a.convert(t),this._update(),this},getElement:function(){return this._el},_update:function(){if(this._map){var t=this._map.project(this._lngLat)._add(this._offset);i.setTransform(this._el,\"translate(\"+t.x+\"px,\"+t.y+\"px)\")}}}},{\"../geo/lng_lat\":339,\"../util/dom\":428,\"point-geometry\":484}],423:[function(t,e,r){\"use strict\";function n(t){i.setOptions(this,t),i.bindAll([\"_update\",\"_onClickClose\"],this)}e.exports=n;var i=t(\"../util/util\"),a=t(\"../util/evented\"),o=t(\"../util/dom\"),s=t(\"../geo/lng_lat\");n.prototype=i.inherit(a,{options:{closeButton:!0,closeOnClick:!0},addTo:function(t){return this._map=t,this._map.on(\"move\",this._update),this.options.closeOnClick&&this._map.on(\"click\",this._onClickClose),this._update(),this},remove:function(){return this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._container&&(this._container.parentNode.removeChild(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"click\",this._onClickClose),delete this._map),this.fire(\"close\"),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=s.convert(t),this._update(),this},setText:function(t){return this.setDOMContent(document.createTextNode(t))},setHTML:function(t){var e,r=document.createDocumentFragment(),n=document.createElement(\"body\");for(n.innerHTML=t;;){if(!(e=n.firstChild))break;r.appendChild(e)}return this.setDOMContent(r)},setDOMContent:function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},_createContent:function(){this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._content=o.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=o.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClickClose))},_update:function(){if(this._map&&this._lngLat&&this._content){this._container\|\|(this._container=o.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=o.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content));var t=this._map.project(this._lngLat).round(),e=this.options.anchor;if(!e){var r=this._container.offsetWidth,n=this._container.offsetHeight;e=t.y<n?[\"top\"]:t.y>this._map.transform.height-n?[\"bottom\"]:[],t.x<r/2?e.push(\"left\"):t.x>this._map.transform.width-r/2&&e.push(\"right\"),e=0===e.length?\"bottom\":e.join(\"-\")}var i={top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"},a=this._container.classList;for(var s in i)a.remove(\"mapboxgl-popup-anchor-\"+s);a.add(\"mapboxgl-popup-anchor-\"+e),o.setTransform(this._container,i[e]+\" translate(\"+t.x+\"px,\"+t.y+\"px)\")}},_onClickClose:function(){this.remove()}})},{\"../geo/lng_lat\":339,\"../util/dom\":428,\"../util/evented\":434,\"../util/util\":442}],424:[function(t,e,r){\"use strict\";function n(t,e){this.target=t,this.parent=e,this.callbacks={},this.callbackID=0,this.receive=this.receive.bind(this),this.target.addEventListener(\"message\",this.receive,!1)}e.exports=n,n.prototype.receive=function(t){function e(t,e,r){this.postMessage({type:\"<response>\",id:String(i),error:t?String(t):null,data:e},r)}var r,n=t.data,i=n.id;if(\"<response>\"===n.type)r=this.callbacks[n.id],delete this.callbacks[n.id],r&&r(n.error\|\|null,n.data);else if(void 0!==n.id&&this.parent[n.type])this.parent[n.type](n.data,e.bind(this));else if(void 0!==n.id&&this.parent.workerSources){var a=n.type.split(\".\");this.parent.workerSources[a[0]][a[1]](n.data,e.bind(this))}else this.parent[n.type](n.data)},n.prototype.send=function(t,e,r,n){var i=null;r&&(this.callbacks[i=this.callbackID++]=r),this.postMessage({type:t,id:String(i),data:e},n)},n.prototype.postMessage=function(t,e){this.target.postMessage(t,e)}},{}],425:[function(t,e,r){\"use strict\";function n(t){var e=document.createElement(\"a\");return e.href=t,e.protocol===document.location.protocol&&e.host===document.location.host}r.getJSON=function(t,e){var r=new XMLHttpRequest;return r.open(\"GET\",t,!0),r.setRequestHeader(\"Accept\",\"application/json\"),r.onerror=function(t){e(t)},r.onload=function(){if(r.status>=200&&r.status<300&&r.response){var t;try{t=JSON.parse(r.response)}catch(t){return e(t)}e(null,t)}else e(new Error(r.statusText))},r.send(),r},r.getArrayBuffer=function(t,e){var r=new XMLHttpRequest;return r.open(\"GET\",t,!0),r.responseType=\"arraybuffer\",r.onerror=function(t){e(t)},r.onload=function(){r.status>=200&&r.status<300&&r.response?e(null,r.response):e(new Error(r.statusText))},r.send(),r},r.getImage=function(t,e){return r.getArrayBuffer(t,function(t,r){if(t)return e(t);var n=new Image;n.onload=function(){e(null,n),(window.URL\|\|window.webkitURL).revokeObjectURL(n.src)};var i=new Blob([new Uint8Array(r)],{type:\"image/png\"});return n.src=(window.URL\|\|window.webkitURL).createObjectURL(i),n.getData=function(){var t=document.createElement(\"canvas\"),e=t.getContext(\"2d\");return t.width=n.width,t.height=n.height,e.drawImage(n,0,0),e.getImageData(0,0,n.width,n.height).data},n})},r.getVideo=function(t,e){var r=document.createElement(\"video\");r.onloadstart=function(){e(null,r)};for(var i=0;i<t.length;i++){var a=document.createElement(\"source\");n(t[i])\|\|(r.crossOrigin=\"Anonymous\"),a.src=t[i],r.appendChild(a)}return r.getData=function(){return r},r}},{}],426:[function(t,e,r){\"use strict\";r.window=window,e.exports.now=function(){return window.performance&&window.performance.now?window.performance.now.bind(window.performance):Date.now.bind(Date)}();var n=window.requestAnimationFrame\|\|window.mozRequestAnimationFrame\|\|window.webkitRequestAnimationFrame\|\|window.msRequestAnimationFrame;r.frame=function(t){return n(t)};var i=window.cancelAnimationFrame\|\|window.mozCancelAnimationFrame\|\|window.webkitCancelAnimationFrame\|\|window.msCancelAnimationFrame;r.cancelFrame=function(t){i(t)},r.timed=function(t,n,i){function a(l){o\|\|(l=e.exports.now(),l>=s+n?t.call(i,1):(t.call(i,(l-s)/n),r.frame(a)))}if(!n)return t.call(i,1),null;var o=!1,s=e.exports.now();return r.frame(a),function(){o=!0}},r.supported=t(\"mapbox-gl-supported\"),r.hardwareConcurrency=navigator.hardwareConcurrency\|\|4,Object.defineProperty(r,\"devicePixelRatio\",{get:function(){return window.devicePixelRatio}}),r.supportsWebp=!1;var a=document.createElement(\"img\");a.onload=function(){r.supportsWebp=!0},a.src=\"data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=\",r.supportsGeolocation=!!navigator.geolocation},{\"mapbox-gl-supported\":327}],427:[function(t,e,r){\"use strict\";function n(t,e){this.canvas=document.createElement(\"canvas\"),t&&e&&(this.canvas.style.position=\"absolute\",this.canvas.classList.add(\"mapboxgl-canvas\"),this.canvas.addEventListener(\"webglcontextlost\",t._contextLost.bind(t),!1),this.canvas.addEventListener(\"webglcontextrestored\",t._contextRestored.bind(t),!1),this.canvas.setAttribute(\"tabindex\",0),e.appendChild(this.canvas))}var i=t(\"../util\"),a=t(\"mapbox-gl-supported\");e.exports=n,n.prototype.resize=function(t,e){var r=window.devicePixelRatio\|\|1;this.canvas.width=rt,this.canvas.height=re,this.canvas.style.width=t+\"px\",this.canvas.style.height=e+\"px\"},n.prototype.getWebGLContext=function(t){return t=i.extend({},t,a.webGLContextAttributes),this.canvas.getContext(\"webgl\",t)\|\|this.canvas.getContext(\"experimental-webgl\",t)},n.prototype.getElement=function(){return this.canvas}},{\"../util\":442,\"mapbox-gl-supported\":327}],428:[function(t,e,r){\"use strict\";function n(t){for(var e=0;e<t.length;e++)if(t[e]in s)return 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o=this.computedRotation;f(o,i[0],i[1],i[2],i[4],i[5],i[6],i[8],i[9],i[10]),a(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var s=0,l=0;l<3;++l)s+=Math.pow(r[l]-e[l],2);this.radius.set(t,.5Math.log(Math.max(s,1e-6))),this.center.set(t,r[0],r[1],r[2])},d.translate=function(t,e,r,n){this.center.move(t,e\|\|0,r\|\|0,n\|\|0)},d.setMatrix=function(t,e){var r=this.computedRotation;f(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),a(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;h(n,e);var i=n[15];if(Math.abs(i)>1e-6){var o=n[12]/i,s=n[13]/i,l=n[14]/i;this.recalcMatrix(t);var u=Math.exp(this.computedRadius[0]);this.center.set(t,o-n[2]u,s-n[6]u,l-n[10]u),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},d.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},d.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},d.getDistanceLimits=function(t){var e=this.radius.bounds;return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},d.toJSON=function(){return this.recalcMatrix(this.lastT()),{center:this.computedCenter.slice(),rotation:this.computedRotation.slice(),distance:Math.log(this.computedRadius[0]),zoomMin:this.radius.bounds[0][0],zoomMax:this.radius.bounds[1][0]}},d.fromJSON=function(t){var e=this.lastT(),r=t.center;r&&this.center.set(e,r[0],r[1],r[2]);var n=t.rotation;n&&this.rotation.set(e,n[0],n[1],n[2],n[3]);var i=t.distance;i&&i>0&&this.radius.set(e,Math.log(i)),this.setDistanceLimits(t.zoomMin,t.zoomMax)}},{\"./lib/quatFromFrame\":472,\"filtered-vector\":133,\"gl-mat4/fromQuat\":178,\"gl-mat4/invert\":181,\"gl-mat4/lookAt\":182}],474:[function(t,e,r){\"use strict\";var n=t(\"repeat-string\");e.exports=function(t,e,r){return r=void 0!==r?r+\"\":\" \",n(r,e)+t}},{\"repeat-string\":500}],475:[function(t,e,r){e.exports=function(t,e){e\|\|(e=[0,\"\"]),t=String(t);var 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r=\"prepare \"+t.length+\" points\";e&&console.time(r),this.points=t;var n=t.map(o);e&&console.timeEnd(r);for(var i=this.options.maxZoom;i>=this.options.minZoom;i--){var a=+Date.now();this.trees[i+1]=v(n,p,m,this.options.nodeSize,Float32Array),n=this._cluster(n,i),e&&console.log(\"z%d: %d clusters in %dms\",i,n.length,+Date.now()-a)}return this.trees[this.options.minZoom]=v(n,p,m,this.options.nodeSize,Float32Array),e&&console.timeEnd(\"total time\"),this},getClusters:function(t,e){for(var r=this.trees[this._limitZoom(e)],n=r.range(u(t[0]),c(t[3]),u(t[2]),c(t[1])),i=[],a=0;a<n.length;a++){var o=r.points[n[a]];i.push(o.numPoints?s(o):this.points[o.id])}return i},getChildren:function(t,e){for(var r=this.trees[e+1].points[t],n=this.options.radius/(this.options.extentMath.pow(2,e)),i=this.trees[e+1].within(r.x,r.y,n),a=[],o=0;o<i.length;o++){var l=this.trees[e+1].points[i[o]];l.parentId===t&&a.push(l.numPoints?s(l):this.points[l.id])}return 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s=e[t[o]];a.features.push({type:1,geometry:[[Math.round(this.options.extent(s.xi-r)),Math.round(this.options.extent(s.yi-n))]],tags:s.numPoints?l(s):this.points[s.id].properties})}},_limitZoom:function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},_cluster:function(t,e){for(var r=[],n=this.options.radius/(this.options.extentMath.pow(2,e)),i=0;i<t.length;i++){var o=t[i];if(!(o.zoom<=e)){o.zoom=e;var s=this.trees[e+1],l=s.within(o.x,o.y,n),u=o.numPoints\|\|1,c=o.xu,h=o.yu,f=null;this.options.reduce&&(f=this.options.initial(),this._accumulate(f,o));for(var d=0;d<l.length;d++){var p=s.points[l[d]];if(e<p.zoom){var m=p.numPoints\|\|1;p.zoom=e,c+=p.xm,h+=p.ym,u+=m,p.parentId=i,this.options.reduce&&this._accumulate(f,p)}}1===u?r.push(o):(o.parentId=i,r.push(a(c/u,h/u,u,i,f)))}}return r},_accumulate:function(t,e){var r=e.numPoints?e.properties:this.options.map(this.points[e.id].properties);this.options.reduce(t,r)}}},{kdbush:298}],530:[function(t,e,r){\"use 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a=[],l=[],u=0;u<r;++u)a.push(\"d\"+u),l.push(\"d\"+u);for(var u=0;u<1<<r;++u)a.push(\"v\"+u),l.push(\"v\"+u);for(var u=0;u<1<<r;++u)a.push(\"p\"+u),l.push(\"p\"+u);a.push(\"a\",\"b\",\"c\"),l.push(\"a\",\"c\"),n.push(\"vertex:function vertexFunc(\",a.join(),\"){\");for(var c=[],u=0;u<1<<r;++u)c.push(\"(p\"+u+\"<<\"+u+\")\");n.push(\"var m=(\",c.join(\"+\"),\")\|0;if(m===0\|\|m===\",(1<<(1<<r))-1,\"){return}\");var h=[],f=[];1<<(1<<r)<=128?(n.push(\"switch(m){\"),f=n):n.push(\"switch(m>>>7){\");for(var u=0;u<1<<(1<<r);++u){if(1<<(1<<r)>128&&u%128==0){h.length>0&&f.push(\"}}\");var d=\"vExtra\"+h.length;n.push(\"case \",u>>>7,\":\",d,\"(m&0x7f,\",l.join(),\");break;\"),f=[\"function \",d,\"(m,\",l.join(),\"){switch(m){\"],h.push(f)}f.push(\"case \",127&u,\":\");for(var p=new Array(r),m=new Array(r),v=new Array(r),g=new Array(r),y=0,b=0;b<r;++b)p[b]=[],m[b]=[],v[b]=0,g[b]=0;for(var b=0;b<1<<r;++b)for(var x=0;x<r;++x){var _=b^1<<x;if(!(_>b)&&!(u&1<<_)!=!(u&1<<b)){var w=1;u&1<<_?m[x].push(\"v\"+_+\"-v\"+b):(m[x].push(\"v\"+b+\"-v\"+_),w=-w),w<0?(p[x].push(\"-v\"+b+\"-v\"+_),v[x]+=2):(p[x].push(\"v\"+b+\"+v\"+_),v[x]-=2),y+=1;for(var M=0;M<r;++M)M!==x&&(_&1<<M?g[M]+=1:g[M]-=1)}}for(var k=[],x=0;x<r;++x)if(0===p[x].length)k.push(\"d\"+x+\"-0.5\");else{var A=\"\";v[x]<0?A=v[x]+\"c\":v[x]>0&&(A=\"+\"+v[x]+\"c\");var T=p[x].length/y.5,S=.5+g[x]/y.5;k.push(\"d\"+x+\"-\"+S+\"-\"+T+\"(\"+p[x].join(\"+\")+A+\")/(\"+m[x].join(\"+\")+\")\")}f.push(\"a.push([\",k.join(),\"]);\",\"break;\")}n.push(\"}},\"),h.length>0&&f.push(\"}}\");for(var E=[],u=0;u<1<<r-1;++u)E.push(\"v\"+u);E.push(\"c0\",\"c1\",\"p0\",\"p1\",\"a\",\"b\",\"c\"),n.push(\"cell:function cellFunc(\",E.join(),\"){\");var L=s(r-1);n.push(\"if(p0){b.push(\",L.map(function(t){return\"[\"+t.map(function(t){return\"v\"+t})+\"]\"}).join(),\")}else{b.push(\",L.map(function(t){var e=t.slice();return e.reverse(),\"[\"+e.map(function(t){return\"v\"+t})+\"]\"}).join(),\")}}});function \",i,\"(array,level){var verts=[],cells=[];contour(array,verts,cells,level);return {positions:verts,cells:cells};} return \",i,\";\");for(var u=0;u<h.length;++u)n.push(h[u].join(\"\"));return new Function(\"genContour\",n.join(\"\"))(o)}function i(t,e){for(var r=l(t,e),n=r.length,i=new Array(n),a=new Array(n),o=0;o<n;++o)i[o]=[r[o]],a[o]=[o];return{positions:i,cells:a}}function a(t,e){if(t.dimension<=0)return{positions:[],cells:[]};if(1===t.dimension)return i(t,e);var r=t.order.join()+\"-\"+t.dtype,a=u[r],e=+e\|\|0;return a\|\|(a=u[r]=n(t.order,t.dtype)),a(t,e)}e.exports=a;var o=t(\"ndarray-extract-contour\"),s=t(\"triangulate-hypercube\"),l=t(\"zero-crossings\"),u={}},{\"ndarray-extract-contour\":456,\"triangulate-hypercube\":537,\"zero-crossings\":584}],532:[function(t,e,r){(function(r){\"use strict\";function n(t){for(var e=t.cells,r=t.positions,n=new Float32Array(6e.length),i=0,a=0,o=0;o<e.length;++o)for(var s=e[o],l=0;l<3;++l){var u=r[s[l]];n[i++]=u[0],n[i++]=u[1]+1.4,a=Math.max(u[0],a)}return{data:n,shape:a}}function i(t,e,r){var r=r\|\|{},o=s[t];o\|\|(o=s[t]={\" \":{data:new Float32Array(0),shape:.2}});var l=o[e];if(!l)if(e.length<=1\|\|!/\\d/.test(e))l=o[e]=n(a(e,{triangles:!0,font:t,textAlign:r.textAlign\|\|\"left\",textBaseline:\"alphabetic\"}));else{for(var u=e.split(/(\\d\|\\s)/),c=new Array(u.length),h=0,f=0,d=0;d<u.length;++d)c[d]=i(t,u[d]),h+=c[d].data.length,f+=c[d].shape,d>0&&(f+=.02);for(var p=new Float32Array(h),m=0,v=-.5f,d=0;d<c.length;++d){for(var g=c[d].data,y=0;y<g.length;y+=2)p[m++]=g[y]+v,p[m++]=g[y+1];v+=c[d].shape+.02}l=o[e]={data:p,shape:f}}return l}e.exports=i;var a=t(\"vectorize-text\"),o=window\|\|r.global\|\|{},s=o.__TEXT_CACHE\|\|{};o.__TEXT_CACHE={}}).call(this,t(\"_process\"))},{_process:487,\"vectorize-text\":554}],533:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.fontSize=t\|\|24,this.buffer=void 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l=(t[a]+aa-(t[r[s]]+r[s]r[s]))/(2a-2r[s]);l<=n[s];)s--,l=(t[a]+aa-(t[r[s]]+r[s]r[s]))/(2a-2r[s]);s++,r[s]=a,n[s]=l,n[s+1]=+o}for(a=0,s=0;a<i;a++){\n", "for(;n[s+1]<a;)s++;e[a]=(a-r[s])(a-r[s])+t[r[s]]}}e.exports=n;var o=1e20;n.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=e.data,n=0;n<this.sizethis.size;n++){var a=r[4n+3]/255;this.gridOuter[n]=1===a?0:0===a?o:Math.pow(Math.max(0,.5-a),2),this.gridInner[n]=1===a?o:0===a?0:Math.pow(Math.max(0,a-.5),2)}for(i(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),i(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.sizethis.size;n++){var s=this.gridOuter[n]-this.gridInner[n],l=Math.max(0,Math.min(255,Math.round(255-255(s/this.radius+this.cutoff))));r[4n+0]=l,r[4n+1]=l,r[4n+2]=l,r[4n+3]=255}return e}},{}],534:[function(e,r,n){!function(e){function n(t,e){if(t=t\|\|\"\",e=e\|\|{},t 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n.add(4-(n.dayOfWeek()\|\|7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate);return t=n.year(),e=n.month(),r=n.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25(t+4716))+Math.floor(30.6001(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5),r=e+1524,n=Math.floor((r-122.1)/365.25),i=Math.floor(365.25n),a=Math.floor((r-i)/30.6001),o=a-Math.floor(a<14?1:13),s=n-Math.floor(o>2?4716:4715),l=r-i-Math.floor(30.6001a);return s<=0&&s--,this.newDate(s,o,l)}}),i.calendars.julian=n},{\"../main\":581,\"object-assign\":470}],574:[function(t,e,r){function n(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}function i(t,e){return t-eMath.floor(t/e)}function a(t,e){return 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this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,o.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if(t=t.split(\".\"),t.length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19\|\|r>0&&n<0)throw\"Invalid Mayan year\";e=20e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,o.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,o.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,o.local.invalidDate),!0},extraInfo:function(t,e,r){var n=this._validate(t,e,r,o.local.invalidDate),i=n.toJD(),a=this._toHaab(i),s=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[s[0]-1],tzolkinDay:s[0],tzolkinTrecena:s[1]}},_toHaab:function(t){t-=this.jdEpoch;var e=i(t+8+340,365);return[Math.floor(e/20)+1,i(e,20)]},_toTzolkin:function(t){return t-=this.jdEpoch,[a(t+20,20),a(t+4,13)]},toJD:function(t,e,r){var n=this._validate(t,e,r,o.local.invalidDate);return n.day()+20n.month()+360n.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),o.calendars.mayan=n},{\"../main\":581,\"object-assign\":470}],575:[function(t,e,r){function n(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\");n.prototype=new i.baseCalendar;var o=i.instance(\"gregorian\");a(n.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear\|\|i.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()\|\|7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidMonth),t=n.year();t<0&&t++;for(var a=n.day(),s=1;s<n.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return 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i.baseCalendar,a(n.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,i.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var a=i.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9\|\|9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,\n", "s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=i.instance(),r=e.fromJD(t),n=r.year(),a=r.dayOfYear(),o=n+56;this._createMissingCalendarData(o);for(var s=9,l=this.NEPALI_CALENDAR_DATA[o][0],u=this.NEPALI_CALENDAR_DATA[o][s]-l+1;a>u;)s++,s>12&&(s=1,o++),u+=this.NEPALI_CALENDAR_DATA[o][s];var c=this.NEPALI_CALENDAR_DATA[o][s]-(u-a);return this.newDate(o,s,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29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e=this._validate(t,this.minMonth,this.minDay,a.local.invalidYear);return 682((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,a.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var n=this._validate(t,e,r,a.local.invalidDate);t=n.year(),e=n.month(),r=n.day();var o=t-(t>=0?474:473),s=474+i(o,2820);return r+(e<=7?31(e-1):30(e-1)+6)+Math.floor((682s-110)/2816)+365(s-1)+1029983Math.floor(o/2820)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=t-this.toJD(475,1,1),r=Math.floor(e/1029983),n=i(e,1029983),a=2820;if(1029982!==n){var o=Math.floor(n/366),s=i(n,366);a=Math.floor((2134o+2816s+2815)/1028522)+o+1}var l=a+2820r+474;l=l<=0?l-1:l;var 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n=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(n.year());return o.weekOfYear(t,n.month(),n.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate),t=this._t2gYear(n.year());return o.toJD(t,n.month(),n.day())},fromJD:function(t){var e=o.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),i.calendars.taiwan=n},{\"../main\":581,\"object-assign\":470}],579:[function(t,e,r){function n(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}var i=t(\"../main\"),a=t(\"object-assign\"),o=i.instance();n.prototype=new i.baseCalendar,a(n.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(e.year());return o.leapYear(t)},weekOfYear:function(t,e,r){var n=this._validate(t,this.minMonth,this.minDay,i.local.invalidYear),t=this._t2gYear(n.year());return o.weekOfYear(t,n.month(),n.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,i.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)\|\|7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,i.local.invalidDate),t=this._t2gYear(n.year());return o.toJD(t,n.month(),n.day())},fromJD:function(t){var e=o.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),i.calendars.thai=n},{\"../main\":581,\"object-assign\":470}],580:[function(t,e,r){function n(t){this.local=this.regionalOptions[t\|\|\"\"]\|\|this.regionalOptions[\"\"]}var 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n(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function i(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function a(t,e){return t=\"\"+t,\"000000\".substring(0,e-t.length)+t}function o(){this.shortYearCutoff=\"+10\"}function s(t){this.local=this.regionalOptions[t]\|\|this.regionalOptions[\"\"]}var l=t(\"object-assign\");l(n.prototype,{instance:function(t,e){t=(t\|\|\"gregorian\").toLowerCase(),e=e\|\|\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar\|\|this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)\|\|this.instance(),n.newDate(t,e,r)},substituteDigits:function(t){\n", "return function(e){return(e+\"\").replace(/[0-9]/g,function(e){return t[e]})}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n\|\|t[0]}}}),l(i.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars\|\|u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+a(Math.abs(this.year()),4)+\"-\"+a(this.month(),2)+\"-\"+a(this.day(),2)}}),l(o.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new i(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear\|\|u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear\|\|u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+a(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear\|\|u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth\|\|u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth\|\|u.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear\|\|u.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,u.local.invalidDate\|\|u.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r\|\|\"w\"===r){var n=t.toJD()+e(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0),i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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a=r.dayNamesShort\|\|this.local.dayNamesShort,o=r.dayNames\|\|this.local.dayNames,s=r.parseMonth\|\|this.local.parseMonth,l=r.monthNumbers\|\|this.local.monthNumbers,u=r.monthNamesShort\|\|this.local.monthNamesShort,c=r.monthNames\|\|this.local.monthNames,h=-1,f=-1,d=-1,p=-1,m=-1,v=!1,g=!1,y=function(e,r){for(var n=1;k+n<t.length&&t.charAt(k+n)===e;)n++;return k+=n-1,Math.floor(n/(r\|\|1))>1},b=function(t,r){var n=y(t,r),a=[2,3,n?4:2,n?4:2,10,11,20][\"oyYJ@!\".indexOf(t)+1],o=new RegExp(\"^-?\\\\d{1,\"+a+\"}\"),s=e.substring(M).match(o);if(!s)throw(i.local.missingNumberAt\|\|i.regionalOptions[\"\"].missingNumberAt).replace(/\\{0\\}/,M);return M+=s[0].length,parseInt(s[0],10)},x=this,_=function(t,r,n,a){for(var o=y(t,a)?n:r,s=0;s<o.length;s++)if(e.substr(M,o[s].length).toLowerCase()===o[s].toLowerCase())return 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o=i[e];o.anchor&&\"free\"!==o.anchor&&(o.anchor=f.cleanId(o.anchor)),o.overlaying&&(o.overlaying=f.cleanId(o.overlaying)),o.type\|\|(o.isdate?o.type=\"date\":o.islog?o.type=\"log\":!1===o.isdate&&!1===o.islog&&(o.type=\"linear\")),\"withzero\"!==o.autorange&&\"tozero\"!==o.autorange\|\|(o.autorange=!0,o.rangemode=\"tozero\"),delete o.islog,delete o.isdate,delete o.categories,a(o,\"domain\")&&delete o.domain,void 0!==o.autotick&&(void 0===o.tickmode&&(o.tickmode=o.autotick?\"auto\":\"linear\"),delete o.autotick)}var s=Array.isArray(t.annotations)?t.annotations.length:0;for(e=0;e<s;e++){var u=t.annotations[e];c.isPlainObject(u)&&(u.ref&&(\"paper\"===u.ref?(u.xref=\"paper\",u.yref=\"paper\"):\"data\"===u.ref&&(u.xref=\"x\",u.yref=\"y\"),delete u.ref),n(u,\"xref\"),n(u,\"yref\"))}var p=Array.isArray(t.shapes)?t.shapes.length:0;for(e=0;e<p;e++){var m=t.shapes[e];c.isPlainObject(m)&&(n(m,\"xref\"),n(m,\"yref\"))}var v=t.legend;v&&(v.x>3?(v.x=1.02,v.xanchor=\"left\"):v.x<-2&&(v.x=-.02,v.xanchor=\"right\"),v.y>3?(v.y=1.02,v.yanchor=\"bottom\"):v.y<-2&&(v.y=-.02,v.yanchor=\"top\")),\"rotate\"===t.dragmode&&(t.dragmode=\"orbit\"),t.scene1&&(t.scene\|\|(t.scene=t.scene1),delete t.scene1);var g=h.getSubplotIds(t,\"gl3d\");for(e=0;e<g.length;e++){var y=t[g[e]],b=y.cameraposition;if(Array.isArray(b)&&4===b[0].length){var x=b[0],_=b[1],w=b[2],M=l([],x),k=[];for(r=0;r<3;++r)k[r]=_[e]+wM[2+4r];y.camera={eye:{x:k[0],y:k[1],z:k[2]},center:{x:_[0],y:_[1],z:_[2]},up:{x:M[1],y:M[5],z:M[9]}},delete y.cameraposition}}return d.clean(t),t},r.cleanData=function(t,e){for(var n=[],o=(t.concat(Array.isArray(e)?e:[]).filter(function(t){return\"uid\"in t}).map(function(t){return t.uid})),s=0;s<t.length;s++){var l,p=t[s];if(!(\"uid\"in p)\|\|-1!==n.indexOf(p.uid)){var m;for(l=0;l<100&&(m=c.randstr(o),-1!==n.indexOf(m));l++);p.uid=c.randstr(o),o.push(p.uid)}if(n.push(p.uid),\"histogramy\"===p.type&&\"xbins\"in p&&!(\"ybins\"in p)&&(p.ybins=p.xbins,delete p.xbins),p.error_y&&\"opacity\"in p.error_y){var v=d.defaults,g=p.error_y.color\|\|(u.traceIs(p,\"bar\")?d.defaultLine:v[s%v.length]);p.error_y.color=d.addOpacity(d.rgb(g),d.opacity(g)p.error_y.opacity),delete p.error_y.opacity}if(\"bardir\"in p&&(\"h\"!==p.bardir\|\|!u.traceIs(p,\"bar\")&&\"histogram\"!==p.type.substr(0,9)\|\|(p.orientation=\"h\",r.swapXYData(p)),delete p.bardir),\"histogramy\"===p.type&&r.swapXYData(p),\"histogramx\"!==p.type&&\"histogramy\"!==p.type\|\|(p.type=\"histogram\"),\"scl\"in p&&(p.colorscale=p.scl,delete p.scl),\"reversescl\"in p&&(p.reversescale=p.reversescl,delete p.reversescl),p.xaxis&&(p.xaxis=f.cleanId(p.xaxis,\"x\")),p.yaxis&&(p.yaxis=f.cleanId(p.yaxis,\"y\")),u.traceIs(p,\"gl3d\")&&p.scene&&(p.scene=h.subplotsRegistry.gl3d.cleanId(p.scene)),u.traceIs(p,\"pie\")\|\|u.traceIs(p,\"bar\")\|\|(Array.isArray(p.textposition)?p.textposition=p.textposition.map(i):p.textposition&&(p.textposition=i(p.textposition))),u.traceIs(p,\"2dMap\")&&(\"YIGnBu\"===p.colorscale&&(p.colorscale=\"YlGnBu\"),\"YIOrRd\"===p.colorscale&&(p.colorscale=\"YlOrRd\")),u.traceIs(p,\"markerColorscale\")&&p.marker){var y=p.marker;\"YIGnBu\"===y.colorscale&&(y.colorscale=\"YlGnBu\"),\"YIOrRd\"===y.colorscale&&(y.colorscale=\"YlOrRd\")}if(\"surface\"===p.type&&c.isPlainObject(p.contours)){var b=[\"x\",\"y\",\"z\"];for(l=0;l<b.length;l++){var x=p.contours[b[l]];c.isPlainObject(x)&&(x.highlightColor&&(x.highlightcolor=x.highlightColor,delete x.highlightColor),x.highlightWidth&&(x.highlightwidth=x.highlightWidth,delete x.highlightWidth))}}if(Array.isArray(p.transforms)){var _=p.transforms;for(l=0;l<_.length;l++){var w=_[l];if(c.isPlainObject(w))switch(w.type){case\"filter\":w.filtersrc&&(w.target=w.filtersrc,delete w.filtersrc),w.calendar&&(w.valuecalendar\|\|(w.valuecalendar=w.calendar),delete w.calendar);break;case\"groupby\":if(w.styles=w.styles\|\|w.style,w.styles&&!Array.isArray(w.styles)){var M=w.styles,k=Object.keys(M);w.styles=[];for(var A=0;A<k.length;A++)w.styles.push({target:k[A],value:M[k[A]]})}}}}a(p,\"line\")&&delete p.line,\"marker\"in p&&(a(p.marker,\"line\")&&delete p.marker.line,a(p,\"marker\")&&delete p.marker),d.clean(p)}},r.swapXYData=function(t){var e;if(c.swapAttrs(t,[\"?\",\"?0\",\"d?\",\"?bins\",\"nbins?\",\"autobin?\",\"?src\",\"error_?\"]),Array.isArray(t.z)&&Array.isArray(t.z[0])&&(t.transpose?delete t.transpose:t.transpose=!0),t.error_x&&t.error_y){var r=t.error_y,n=\"copy_ystyle\"in r?r.copy_ystyle:!(r.color\|\|r.thickness\|\|r.width);c.swapAttrs(t,[\"error_?.copy_ystyle\"]),n&&c.swapAttrs(t,[\"error_?.color\",\"error_?.thickness\",\"error_?.width\"])}if(\"string\"==typeof t.hoverinfo){var i=t.hoverinfo.split(\"+\");for(e=0;e<i.length;e++)\"x\"===i[e]?i[e]=\"y\":\"y\"===i[e]&&(i[e]=\"x\");t.hoverinfo=i.join(\"+\")}},r.coerceTraceIndices=function(t,e){return s(e)?[e]:Array.isArray(e)&&e.length?e:t.data.map(function(t,e){return e})},r.manageArrayContainers=function(t,e,r){var n=t.obj,i=t.parts,a=i.length,o=i[a-1],l=s(o);if(l&&null===e){var u=i.slice(0,a-1).join(\".\");c.nestedProperty(n,u).get().splice(o,1)}else l&&void 0===t.get()?(void 0===t.get()&&(r[t.astr]=null),t.set(e)):t.set(e)};var p=/(\\.[^\\[\\]\\.]+\|\\[[^\\[\\]\\.]+\\])$/;r.hasParent=function(t,e){for(var r=o(e);r;){if(r in t)return!0;r=o(r)}return!1};var m=[\"x\",\"y\",\"z\"];r.clearAxisTypes=function(t,e,r){for(var n=0;n<e.length;n++)for(var i=t._fullData[n],a=0;a<3;a++){var o=f.getFromTrace(t,i,m[a]);if(o&&\"log\"!==o.type){var s=o._name,l=o._id.substr(1);if(\"scene\"===l.substr(0,5)){if(void 0!==r[l])continue;s=l+\".\"+s}var u=s+\".type\";void 0===r[s]&&void 0===r[u]&&c.nestedProperty(t.layout,u).set(null)}}}},{\"../components/color\":604,\"../lib\":728,\"../plots/cartesian/axes\":772,\"../plots/plots\":831,\"../registry\":846,\"fast-isnumeric\":131,\"gl-mat4/fromQuat\":178}],758:[function(t,e,r){\"use strict\";var n=t(\"../lib/nested_property\"),i=t(\"../lib/is_plain_object\"),a=t(\"../lib/noop\"),o=t(\"../lib/loggers\"),s=t(\"../lib/search\").sorterAsc,l=t(\"../registry\");r.containerArrayMatch=t(\"./container_array_match\");var u=r.isAddVal=function(t){return\"add\"===t\|\|i(t)},c=r.isRemoveVal=function(t){return null===t\|\|\"remove\"===t};r.applyContainerArrayChanges=function(t,e,r,i){var h=e.astr,f=l.getComponentMethod(h,\"supplyLayoutDefaults\"),d=l.getComponentMethod(h,\"draw\"),p=l.getComponentMethod(h,\"drawOne\"),m=i.replot\|\|i.recalc\|\|f===a\|\|d===a,v=t.layout,g=t._fullLayout;if(r[\"\"]){Object.keys(r).length>1&&o.warn(\"Full array edits are incompatible with other edits\",h);var y=r[\"\"][\"\"];if(c(y))e.set(null);else{if(!Array.isArray(y))return o.warn(\"Unrecognized full array edit value\",h,y),!0;e.set(y)}return!m&&(f(v,g),d(t),!0)}var b,x,_,w,M,k,A,T=Object.keys(r).map(Number).sort(s),S=e.get(),E=S\|\|[],L=n(g,h).get(),C=[],I=-1,z=E.length;for(b=0;b<T.length;b++)if(_=T[b],w=r[_],M=Object.keys(w),k=w[\"\"],A=u(k),_<0\|\|_>E.length-(A?0:1))o.warn(\"index out of range\",h,_);else if(void 0!==k)M.length>1&&o.warn(\"Insertion & removal are incompatible with edits to the same index.\",h,_),c(k)?C.push(_):A?(\"add\"===k&&(k={}),E.splice(_,0,k),L&&L.splice(_,0,{})):o.warn(\"Unrecognized full object edit value\",h,_,k),-1===I&&(I=_);else for(x=0;x<M.length;x++)n(E[_],M[x]).set(w[M[x]]);for(b=C.length-1;b>=0;b--)E.splice(C[b],1),L&&L.splice(C[b],1);if(E.length?S\|\|e.set(E):e.set(null),m)return!1;if(f(v,g),p!==a){var D;if(-1===I)D=T;else{for(z=Math.max(E.length,z),D=[],b=0;b<T.length&&!((_=T[b])>=I);b++)D.push(_);for(b=I;b<z;b++)D.push(b)}for(b=0;b<D.length;b++)p(t,D[b])}else d(t);return!0}},{\"../lib/is_plain_object\":730,\"../lib/loggers\":732,\"../lib/nested_property\":735,\"../lib/noop\":736,\"../lib/search\":745,\"../registry\":846,\"./container_array_match\":755}],759:[function(t,e,r){\"use strict\";function n(t,e){try{t._fullLayout._paper.style(\"background\",e)}catch(t){w.error(t)}}function i(t,e){n(t,I.combine(e,\"white\"))}function a(t,e){t._context\|\|(t._context=w.extendDeep({},_.defaultConfig));var r,a,o,s=t._context;if(e){for(a=Object.keys(e),r=0;r<a.length;r++)\"editable\"!==(o=a[r])&&\"edits\"!==o&&o in s&&(\"setBackground\"===o&&\"opaque\"===e[o]?s[o]=i:s[o]=e[o]);e.plot3dPixelRatio&&!s.plotGlPixelRatio&&(s.plotGlPixelRatio=s.plot3dPixelRatio);var l=e.editable;if(void 0!==l)for(s.editable=l,a=Object.keys(s.edits),r=0;r<a.length;r++)s.edits[a[r]]=l;if(e.edits)for(a=Object.keys(e.edits),r=0;r<a.length;r++)(o=a[r])in s.edits&&(s.edits[o]=e.edits[o])}s.staticPlot&&(s.editable=!1,s.edits={},s.autosizable=!1,s.scrollZoom=!1,s.doubleClick=!1,s.showTips=!1,s.showLink=!1,s.displayModeBar=!1),\"hover\"!==s.displayModeBar\|\|x\|\|(s.displayModeBar=!0),\"transparent\"!==s.setBackground&&\"function\"==typeof s.setBackground\|\|(s.setBackground=n)}function o(t,e,r){var n=y.select(t).selectAll(\".plot-container\").data([0]);n.enter().insert(\"div\",\":first-child\").classed(\"plot-container plotly\",!0);var i=n.selectAll(\".svg-container\").data([0]);i.enter().append(\"div\").classed(\"svg-container\",!0).style(\"position\",\"relative\"),i.html(\"\"),e&&(t.data=e),r&&(t.layout=r),E.manager.fillLayout(t),i.style({width:t._fullLayout.width+\"px\",height:t._fullLayout.height+\"px\"}),t.framework=E.manager.framework(t),t.framework({data:t.data,layout:t.layout},i.node()),t.framework.setUndoPoint();var a=t.framework.svg(),o=1,s=t._fullLayout.title;\"\"!==s&&s\|\|(o=0);var l=function(){this.call(P.convertToTspans,t)},u=a.select(\".title-group text\").call(l);if(t._context.edits.titleText){s&&\"Click to enter title\"!==s\|\|(o=.2,u.attr({\"data-unformatted\":\"Click to enter title\"}).text(\"Click to enter title\").style({opacity:o}).on(\"mouseover.opacity\",function(){y.select(this).transition().duration(100).style(\"opacity\",1)}).on(\"mouseout.opacity\",function(){y.select(this).transition().duration(1e3).style(\"opacity\",0)}));var c=function(){this.call(P.makeEditable,{gd:t}).on(\"edit\",function(e){t.framework({layout:{title:e}}),this.text(e).call(l),this.call(c)}).on(\"cancel\",function(){var t=this.attr(\"data-unformatted\");this.text(t).call(l)})};u.call(c)}return t._context.setBackground(t,t._fullLayout.paper_bgcolor),S.addLinks(t),Promise.resolve()}function s(t,e){var r,n,i=e+1,a=[];for(r=0;r<t.length;r++)n=t[r],n<0?a.push(i+n):a.push(n);return a}function l(t,e,r){var n,i;for(n=0;n<e.length;n++){if((i=e[n])!==parseInt(i,10))throw new Error(\"all values in \"+r+\" must be integers\");if(i>=t.data.length\|\|i<-t.data.length)throw new Error(r+\" must be valid indices for gd.data.\");if(e.indexOf(i,n+1)>-1\|\|i>=0&&e.indexOf(-t.data.length+i)>-1\|\|i<0&&e.indexOf(t.data.length+i)>-1)throw new Error(\"each index in \"+r+\" must be unique.\")}}function u(t,e,r){if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array.\");if(void 0===e)throw new Error(\"currentIndices is a required argument.\");if(Array.isArray(e)\|\|(e=[e]),l(t,e,\"currentIndices\"),void 0===r\|\|Array.isArray(r)\|\|(r=[r]),void 0!==r&&l(t,r,\"newIndices\"),void 0!==r&&e.length!==r.length)throw new Error(\"current and new indices must be of equal length.\")}function c(t,e,r){var n,i;if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array.\");if(void 0===e)throw new Error(\"traces must be defined.\");for(Array.isArray(e)\|\|(e=[e]),n=0;n<e.length;n++)if(\"object\"!=typeof(i=e[n])\|\|Array.isArray(i)\|\|null===i)throw new Error(\"all values in traces array must be non-array objects\");if(void 0===r\|\|Array.isArray(r)\|\|(r=[r]),void 0!==r&&r.length!==e.length)throw new Error(\"if indices is specified, traces.length must equal indices.length\")}function h(t,e,r,n){var i=w.isPlainObject(n);if(!Array.isArray(t.data))throw new Error(\"gd.data must be an array\");if(!w.isPlainObject(e))throw new Error(\"update must be a key:value object\");if(void 0===r)throw new Error(\"indices must be an integer or array of integers\");l(t,r,\"indices\");for(var a in e){if(!Array.isArray(e[a])\|\|e[a].length!==r.length)throw new Error(\"attribute \"+a+\" must be an array of length equal to indices array length\");if(i&&(!(a in n)\|\|!Array.isArray(n[a])\|\|n[a].length!==e[a].length))throw new Error(\"when maxPoints is set as a key:value object it must contain a 1:1 corrispondence with the keys and number of traces in the update object\")}}function f(t,e,r,n){var i,a,o,l,u,c=w.isPlainObject(n),h=[];Array.isArray(r)\|\|(r=[r]),r=s(r,t.data.length-1);for(var f in e)for(var d=0;d<r.length;d++){if(i=t.data[r[d]],o=w.nestedProperty(i,f),a=o.get(),l=e[f][d],!Array.isArray(l))throw new Error(\"attribute: \"+f+\" index: \"+d+\" must be an array\");if(!Array.isArray(a))throw new Error(\"cannot extend missing or non-array attribute: \"+f);u=c?n[f][d]:n,b(u)\|\|(u=-1),h.push({prop:o,target:a,insert:l,maxp:Math.floor(u)})}return h}function d(t,e,r,n,i,a){h(t,e,r,n);for(var o,s,l,u=f(t,e,r,n),c=[],d={},p={},m=0;m<u.length;m++)s=u[m].prop,l=u[m].maxp,o=i(u[m].target,u[m].insert),l>=0&&l<o.length&&(c=a(o,l)),l=u[m].target.length,s.set(o),Array.isArray(d[s.astr])\|\|(d[s.astr]=[]),Array.isArray(p[s.astr])\|\|(p[s.astr]=[]),d[s.astr].push(c),p[s.astr].push(l);return{update:d,maxPoints:p}}function p(t){return void 0===t?null:t}function m(t,e,r){function n(){return r.map(function(){})}function i(t){var e=_.Axes.id2name(t);-1===u.indexOf(e)&&u.push(e)}function a(t){return\"LAYOUT\"+t+\".autorange\"}function o(t){return\"LAYOUT\"+t+\".range\"}function s(i,a,o){if(Array.isArray(i))return void i.forEach(function(t){s(t,a,o)});if(!(i in e\|\|R.hasParent(e,i))){var l;l=\"LAYOUT\"===i.substr(0,6)?w.nestedProperty(t.layout,i.replace(\"LAYOUT\",\"\")):w.nestedProperty(f[r[o]],i),i in v\|\|(v[i]=n()),void 0===v[i][o]&&(v[i][o]=p(l.get())),void 0!==a&&l.set(a)}}var l,u,c=t._fullLayout,h=t._fullData,f=t.data,d=j.traceFlags(),m={},v={},g={};for(var y in 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n,i,a,o,s=V.getFromId(t,e),l=w.select(t).selectAll(\"g.\"+e+\"tick\"),u={selection:l,side:s.side},h=e.charAt(0),f=t._fullLayout._size,d=s.titlefont.size;if(l.size()){var p=L.getTranslate(l.node().parentNode);u.offsetLeft=p.x,u.offsetTop=p.y}var 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h.boxEnabled?(s=h.boxStart[0]!==h.boxEnd[0],u=h.boxStart[1]!==h.boxEnd[1],s\|\|u?(s&&(o(0,h.boxStart[0],h.boxEnd[0]),t.xaxis.autorange=!1),u&&(o(1,h.boxStart[1],h.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),h.boxEnabled=!1,h.boxInited=!1):h.boxInited&&(h.boxInited=!1);break;case\"pan\":h.boxEnabled=!1,h.boxInited=!1,n?(h.panning\|\|(h.dragStart[0]=i,h.dragStart[1]=a),Math.abs(h.dragStart[0]-i)<v&&(i=h.dragStart[0]),Math.abs(h.dragStart[1]-a)<v&&(a=h.dragStart[1]),s=(p-i)(f[2]-f[0])/(c.viewBox[2]-c.viewBox[0]),u=(m-a)(f[3]-f[1])/(c.viewBox[3]-c.viewBox[1]),f[0]+=s,f[2]+=s,f[1]+=u,f[3]+=u,t.setRanges(f),h.panning=!0,h.lastInputTime=Date.now(),e(),t.cameraChanged(),t.handleAnnotations()):h.panning&&(h.panning=!1,t.relayoutCallback())}h.lastPos[0]=i,h.lastPos[1]=a}var u=t.mouseContainer,c=t.glplot,h=new n(u,c);return h.mouseListener=a(u,i),u.addEventListener(\"touchstart\",function(t){var e=s(t.changedTouches[0],u);i(0,e[0],e[1]),i(1,e[0],e[1])}),u.addEventListener(\"touchmove\",function(t){t.preventDefault();var e=s(t.changedTouches[0],u);i(1,e[0],e[1])}),u.addEventListener(\"touchend\",function(){i(0,h.lastPos[0],h.lastPos[1])}),h.wheelListener=o(u,function(r,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=c.viewBox,o=h.lastPos[0],s=h.lastPos[1],l=Math.exp(5n/(a[3]-a[1])),u=o/(a[2]-a[0])(i[2]-i[0])+i[0],f=s/(a[3]-a[1])(i[3]-i[1])+i[1];return i[0]=(i[0]-u)l+u,i[2]=(i[2]-u)l+u,i[1]=(i[1]-f)l+f,i[3]=(i[3]-f)l+f,t.setRanges(i),h.lastInputTime=Date.now(),e(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),h}var a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"../cartesian/constants\");e.exports=i},{\"../cartesian/constants\":777,\"mouse-change\":452,\"mouse-event-offset\":453,\"mouse-wheel\":455}],807:[function(t,e,r){\"use strict\";function n(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}function i(t){return new n(t)}var a=t(\"../plots\"),o=t(\"../cartesian/axes\"),s=t(\"../../lib/html2unicode\"),l=t(\"../../lib/str2rgbarray\"),u=n.prototype,c=[\"xaxis\",\"yaxis\"];u.merge=function(t){this.titleEnable=!1,this.backgroundColor=l(t.plot_bgcolor);var e,r,n,i,a,o,u,h,f,d,p;for(d=0;d<2;++d){for(e=c[d],r=t[this.scene[e]._name],n=/Click to enter .+ title/.test(r.title)?\"\":r.title,p=0;p<=2;p+=2)this.labelEnable[d+p]=!1,this.labels[d+p]=s(n),this.labelColor[d+p]=l(r.titlefont.color),this.labelFont[d+p]=r.titlefont.family,this.labelSize[d+p]=r.titlefont.size,this.labelPad[d+p]=this.getLabelPad(e,r),this.tickEnable[d+p]=!1,this.tickColor[d+p]=l((r.tickfont\|\|{}).color),this.tickAngle[d+p]=\"auto\"===r.tickangle?0:Math.PI-r.tickangle/180,this.tickPad[d+p]=this.getTickPad(r),this.tickMarkLength[d+p]=0,this.tickMarkWidth[d+p]=r.tickwidth\|\|0,this.tickMarkColor[d+p]=l(r.tickcolor),this.borderLineEnable[d+p]=!1,this.borderLineColor[d+p]=l(r.linecolor),this.borderLineWidth[d+p]=r.linewidth\|\|0;u=this.hasSharedAxis(r),a=this.hasAxisInDfltPos(e,r)&&!u,o=this.hasAxisInAltrPos(e,r)&&!u,i=r.mirror\|\|!1,h=u?-1!==String(i).indexOf(\"all\"):!!i,f=u?\"allticks\"===i:-1!==String(i).indexOf(\"ticks\"),a?this.labelEnable[d]=!0:o&&(this.labelEnable[d+2]=!0),a?this.tickEnable[d]=r.showticklabels:o&&(this.tickEnable[d+2]=r.showticklabels),(a\|\|h)&&(this.borderLineEnable[d]=r.showline),(o\|\|h)&&(this.borderLineEnable[d+2]=r.showline),(a\|\|f)&&(this.tickMarkLength[d]=this.getTickMarkLength(r)),(o\|\|f)&&(this.tickMarkLength[d+2]=this.getTickMarkLength(r)),this.gridLineEnable[d]=r.showgrid,this.gridLineColor[d]=l(r.gridcolor),this.gridLineWidth[d]=r.gridwidth,this.zeroLineEnable[d]=r.zeroline,this.zeroLineColor[d]=l(r.zerolinecolor),this.zeroLineWidth[d]=r.zerolinewidth}},u.hasSharedAxis=function(t){var e=this.scene,r=a.getSubplotIds(e.fullLayout,\"gl2d\");return 0!==o.findSubplotsWithAxis(r,t).indexOf(e.id)},u.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},u.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},u.getLabelPad=function(t,e){var r=e.titlefont.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r(1.5+(n?1:0))-10:r(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r(1.5+(n?1:.5)):10+r(1.5+(n?.5:0)):void 0},u.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},u.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=i},{\n", "\"../../lib/html2unicode\":726,\"../../lib/str2rgbarray\":749,\"../cartesian/axes\":772,\"../plots\":831}],808:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../plots\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),u=t(\"../../components/fx/layout_attributes\");r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.layoutAttributes.plot_bgcolor,hoverlabel:u.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=a.getSubplotIds(e,\"gl2d\"),o=0;o<n.length;o++){var s=n[o],l=e._plots[s],u=a.getSubplotData(r,\"gl2d\",s),c=l._scene2d;void 0===c&&(c=new i({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),l._scene2d=c),c.plot(u,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=a.getSubplotIds(n,\"gl2d\"),o=0;o<i.length;o++){var s=i[o],u=n._plots[s];if(u._scene2d){0===a.getSubplotData(t,\"gl2d\",s).length&&(u._scene2d.destroy(),delete n._plots[s])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot\|\|l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=a.getSubplotIds(e,\"gl2d\"),n=0;n<r.length;n++){var i=e._plots[r[n]],s=i._scene2d,l=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":l,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),s.destroy()}},r.updateFx=function(t){for(var e=a.getSubplotIds(t,\"gl2d\"),r=0;r<e.length;r++){t._plots[e[r]]._scene2d.updateFx(t.dragmode)}}},{\"../../components/fx/layout_attributes\":646,\"../../constants/xmlns_namespaces\":709,\"../../plot_api/edit_types\":756,\"../cartesian\":782,\"../cartesian/attributes\":771,\"../cartesian/constants\":777,\"../plots\":831,\"./scene2d\":809}],809:[function(t,e,r){\"use strict\";function n(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio\|\|window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context.scrollZoom,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.glplotOptions=p(this),this.glplotOptions.merge(e),this.glplot=c(this.glplotOptions),this.camera=m(this),this.traces={},this.spikes=h(this.glplot),this.selectBox=f(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.bounds=[1/0,1/0,-1/0,-1/0],this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw()}function i(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1}var a,o,s=t(\"../../registry\"),l=t(\"../../plots/cartesian/axes\"),u=t(\"../../components/fx\"),c=t(\"gl-plot2d\"),h=t(\"gl-spikes2d\"),f=t(\"gl-select-box\"),d=t(\"webgl-context\"),p=t(\"./convert\"),m=t(\"./camera\"),v=t(\"../../lib/html2unicode\"),g=t(\"../../lib/show_no_webgl_msg\"),y=t(\"../../plots/cartesian/constraints\"),b=y.enforce,x=y.clean,_=[\"xaxis\",\"yaxis\"];e.exports=n;var w=n.prototype;w.makeFramework=function(){if(this.staticPlot){if(!(o\|\|(a=document.createElement(\"canvas\"),o=d({canvas:a,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=a,this.gl=o}else{var t=document.createElement(\"canvas\"),e=d({canvas:t,premultipliedAlpha:!0});e\|\|g(this),this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r),r.className+=\"user-select-none\";var n=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");n.style.position=\"absolute\",n.style.top=n.style.left=\"0px\",n.style.width=n.style.height=\"100%\",n.style[\"z-index\"]=20,n.style[\"pointer-events\"]=\"none\";var i=this.mouseContainer=document.createElement(\"div\");i.style.position=\"absolute\",i.style[\"pointer-events\"]=\"auto\";var s=this.container;s.appendChild(r),s.appendChild(n),s.appendChild(i);var l=this;i.addEventListener(\"mouseout\",function(){l.isMouseOver=!1,l.unhover()}),i.addEventListener(\"mouseover\",function(){l.isMouseOver=!0})},w.toImage=function(t){t\|\|(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(a),this.updateSize(this.canvas),this.glplot.setDirty(),this.glplot.draw();var e=this.glplot.gl,r=e.drawingBufferWidth,n=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var i=new Uint8Array(rn4);e.readPixels(0,0,r,n,e.RGBA,e.UNSIGNED_BYTE,i);for(var o=0,s=n-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=i[4(ro+l)+u];i[4(ro+l)+u]=i[4(rs+l)+u],i[4(rs+l)+u]=c}var h=document.createElement(\"canvas\");h.width=r,h.height=n;var f=h.getContext(\"2d\"),d=f.createImageData(r,n);d.data.set(i),f.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=h.toDataURL(\"image/jpeg\");break;case\"webp\":p=h.toDataURL(\"image/webp\");break;default:p=h.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(a),p},w.updateSize=function(t){t\|\|(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0\|Math.ceil(en),o=0\|Math.ceil(ei);return t.width===a&&t.height===o\|\|(t.width=a,t.height=o),this.redraw&&this.redraw(),t},w.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[l.calcTicks(this.xaxis),l.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=v(t[e][r].text+\"\");return t},w.updateRefs=function(t){this.fullLayout=t;var e=l.subplotMatch,r=\"xaxis\"+this.id.match(e)[1],n=\"yaxis\"+this.id.match(e)[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},w.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout;n.xaxis.autorange=e.autorange,n.xaxis.range=e.range.slice(0),n.yaxis.autorange=r.autorange,n.yaxis.range=r.range.slice(0);var i={lastInputTime:this.camera.lastInputTime};i[e._name]=e.range.slice(0),i[r._name]=r.range.slice(0),t.emit(\"plotly_relayout\",i)},w.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();i(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},w.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&s.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},w.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map(function(e){t[e].dispose(),delete t[e]}),this.glplot.dispose(),this.staticPlot\|\|this.container.removeChild(this.canvas),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},w.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:this.graphDiv._fullLayout._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis}};x(s,this.xaxis),x(s,this.yaxis);var u=r._size,c=this.xaxis.domain,h=this.yaxis.domain;o.viewBox=[u.l+c[0]u.w,u.b+h[0]u.h,i-u.r-(1-c[1])u.w,a-u.t-(1-h[1])u.h],this.mouseContainer.style.width=u.w(c[1]-c[0])+\"px\",this.mouseContainer.style.height=u.h(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h(h[1]-h[0]),this.mouseContainer.style.left=u.l+c[0]u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])u.h+\"px\";var f=this.bounds;f[0]=f[1]=1/0,f[2]=f[3]=-1/0;var d,p,m=Object.keys(this.traces);for(p=0;p<m.length;++p)for(var v=this.traces[m[p]],g=0;g<2;++g)f[g]=Math.min(f[g],v.bounds[g]),f[g+2]=Math.max(f[g+2],v.bounds[g+2]);for(p=0;p<2;++p)f[p]>f[p+2]&&(f[p]=-1,f[p+2]=1),d=this[_[p]],d._length=o.viewBox[p+2]-o.viewBox[p],l.doAutoRange(d),d.setScale();b(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},w.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},w.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},w.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if(i=t[n],i.uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort(function(t,e){return t._trace.index-e._trace.index})},w.updateFx=function(t){this.mouseContainer.style[\"pointer-events\"]=\"lasso\"===t\|\|\"select\"===t?\"none\":\"auto\",this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},w.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};u.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},w.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.xt.pixelRatio,s=this.canvas.height-t.pixelRatior.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var l=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(l[c]=t.dataBox[c],l[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var h=i._size,f=this.xaxis.domain,d=this.yaxis.domain;a=t.pick(o/t.pixelRatio+h.l+f[0]h.w,s/t.pixelRatio-(h.t+(1-d[1])h.h));var p=a&&a.object._trace.handlePick(a);if(p&&n&&this.emitPointAction(p,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&p&&(!this.lastPickResult\|\|this.lastPickResult.traceUid!==p.trace.uid\|\|this.lastPickResult.dataCoord[0]!==p.dataCoord[0]\|\|this.lastPickResult.dataCoord[1]!==p.dataCoord[1])){var m=p;this.lastPickResult={traceUid:p.trace?p.trace.uid:null,dataCoord:p.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(p,\"plotly_hover\");var v=this.fullData[m.trace.index]\|\|{},g=m.pointIndex,y=u.castHoverinfo(v,i,g);if(y&&\"all\"!==y){var b=y.split(\"+\");-1===b.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===b.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===b.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===b.indexOf(\"text\")&&(m.textLabel=void 0),-1===b.indexOf(\"name\")&&(m.name=void 0)}u.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:u.castHoverOption(v,g,\"bgcolor\")\|\|m.color,borderColor:u.castHoverOption(v,g,\"bordercolor\"),fontFamily:u.castHoverOption(v,g,\"font.family\"),fontSize:u.castHoverOption(v,g,\"font.size\"),fontColor:u.castHoverOption(v,g,\"font.color\")},{container:this.svgContainer,gd:this.graphDiv})}}a\|\|this.unhover(),t.draw()}},w.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),u.loneUnhover(this.svgContainer))},w.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return l.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/fx\":645,\"../../lib/html2unicode\":726,\"../../lib/show_no_webgl_msg\":747,\"../../plots/cartesian/axes\":772,\"../../plots/cartesian/constraints\":779,\"../../registry\":846,\"./camera\":806,\"./convert\":807,\"gl-plot2d\":219,\"gl-select-box\":253,\"gl-spikes2d\":262,\"webgl-context\":563}],810:[function(t,e,r){\"use strict\";function n(t,e){function r(e,r,n,a){var o=p.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,c=\"zoom\"===o,f=!!a.control,d=!!a.alt,y=!!a.shift,b=!!(1&e),x=!!(2&e),_=!!(4&e),w=1/t.clientHeight,M=w(r-m),k=w(n-v),A=p.flipX?1:-1,T=p.flipY?1:-1,S=i(),E=Math.PIp.rotateSpeed;if((s&&b&&!f&&!d&&!y\|\|b&&!f&&!d&&y)&&u.rotate(S,AEM,-TEk,0),(l&&b&&!f&&!d&&!y\|\|x\|\|b&&f&&!d&&!y)&&u.pan(S,-p.translateSpeedMh,p.translateSpeedkh,0),c&&b&&!f&&!d&&!y\|\|_\|\|b&&!f&&d&&!y){var L=-p.zoomSpeedk/window.innerHeight(S-u.lastT())100;u.pan(S,0,0,h(Math.exp(L)-1))}return m=r,v=n,g=a,!0}}t=t\|\|document.body,e=e\|\|{};var n=[.01,1/0];\"distanceLimits\"in e&&(n[0]=e.distanceLimits[0],n[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(n[0]=e.zoomMin),\"zoomMax\"in e&&(n[1]=e.zoomMax);var u=a({center:e.center\|\|[0,0,0],up:e.up\|\|[0,1,0],eye:e.eye\|\|[0,0,10],mode:e.mode\|\|\"orbit\",distanceLimits:n}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],h=0,f=t.clientWidth,d=t.clientHeight,p={keyBindingMode:\"rotate\",view:u,element:t,delay:e.delay\|\|16,rotateSpeed:e.rotateSpeed\|\|1,zoomSpeed:e.zoomSpeed\|\|1,translateSpeed:e.translateSpeed\|\|1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,tick:function(){var e=i(),r=this.delay,n=e-2r;u.idle(e-r),u.recalcMatrix(n),u.flush(e-(100+2r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===f&&t.clientHeight===d;return f=t.clientWidth,d=t.clientHeight,a?!l:(h=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};Object.defineProperties(p,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),n=u.computedCenter.slice();if(u.setMode(t),\"turntable\"===t){var a=i();u._active.lookAt(a,r,n,e),u._active.lookAt(a+500,r,n,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return h},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(n)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",function(t){return t.preventDefault(),!1});var m=0,v=0,g={shift:!1,control:!1,alt:!1,meta:!1};return p.mouseListener=o(t,r),t.addEventListener(\"touchstart\",function(e){var n=l(e.changedTouches[0],t);r(0,n[0],n[1],g),r(1,n[0],n[1],g)}),t.addEventListener(\"touchmove\",function(e){var n=l(e.changedTouches[0],t);r(1,n[0],n[1],g)}),t.addEventListener(\"touchend\",function(){r(0,m,v,g)}),p.wheelListener=s(t,function(t,e){if(!1!==p.keyBindingMode){var r=p.flipX?1:-1,n=p.flipY?1:-1,a=i();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-trMath.PIp.rotateSpeed/window.innerWidth);else{var o=-p.zoomSpeedne/window.innerHeight(a-u.lastT())/20;u.pan(a,0,0,h(Math.exp(o)-1))}}},!0),p}e.exports=n;var i=t(\"right-now\"),a=t(\"3d-view\"),o=t(\"mouse-change\"),s=t(\"mouse-wheel\"),l=t(\"mouse-event-offset\")},{\"3d-view\":37,\"mouse-change\":452,\"mouse-event-offset\":453,\"mouse-wheel\":455,\"right-now\":502}],811:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../plots\"),s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=o.getSubplotIds(e,\"gl3d\"),i=0;i<n.length;i++){var l=n[i],u=o.getSubplotData(r,\"gl3d\",l),c=e[l],h=c._scene;h\|\|(h=new a({id:l,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),c._scene=h),h.cameraInitial\|\|(h.cameraInitial=s.extendDeep({},c.camera)),h.plot(u,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=o.getSubplotIds(n,\"gl3d\"),a=0;a<i.length;a++){var s=i[a];!e[s]&&n[s]._scene&&(n[s]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+s).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=o.getSubplotIds(e,\"gl3d\"),n=e._size,i=0;i<r.length;i++){var a=e[r[i]],s=a.domain,u=a._scene,c=u.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.ws.x[0],y:n.t+n.h(1-s.y[1]),width:n.w(s.x[1]-s.x[0]),height:n.h(s.y[1]-s.y[0]),preserveAspectRatio:\"none\"}),u.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=o.getSubplotIds(t,\"gl3d\"),r=0;r<e.length;r++){t[e[r]]._scene.updateFx(t.dragmode,t.hovermode)}}},{\"../../components/fx/layout_attributes\":646,\"../../constants/xmlns_namespaces\":709,\"../../lib\":728,\"../../plot_api/edit_types\":756,\"../plots\":831,\"./layout/attributes\":812,\"./layout/defaults\":816,\"./layout/layout_attributes\":817,\"./scene\":821}],812:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],813:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:i.title,titlefont:i.titlefont,type:i.type,autorange:i.autorange,rangemode:i.rangemode,range:i.range,tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,separatethousands:i.separatethousands,tickformat:i.tickformat,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth},\"plot\",\"from-root\")},{\"../../../components/color\":604,\"../../../lib/extend\":717,\"../../../plot_api/edit_types\":756,\"../../cartesian/layout_attributes\":783}],814:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"./axis_attributes\"),o=t(\"../../cartesian/type_defaults\"),s=t(\"../../cartesian/axis_defaults\"),l=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){function u(t,e){return i.coerce(c,h,a,t,e)}for(var c,h,f=0;f<l.length;f++){var d=l[f];c=t[d]\|\|{},h=e[d]={_id:d[0]+r.scene,_name:d},o(c,h,u,r.data),s(c,h,u,{font:r.font,letter:d[0],data:r.data,showGrid:!0,bgColor:r.bgColor,calendar:r.calendar}),u(\"gridcolor\",n(h.color,r.bgColor,13600/187).toRgbString()),u(\"title\",d[0]),h.setScale=i.noop,u(\"showspikes\")&&(u(\"spikesides\"),u(\"spikethickness\"),u(\"spikecolor\",h.color)),u(\"showaxeslabels\"),u(\"showbackground\")&&u(\"backgroundcolor\")}}},{\"../../../lib\":728,\"../../cartesian/axis_defaults\":774,\"../../cartesian/type_defaults\":794,\"./axis_attributes\":813,tinycolor2:534}],815:[function(t,e,r){\"use strict\";function n(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}function i(t){var e=new n;return e.merge(t),e}var a=t(\"../../../lib/html2unicode\"),o=t(\"../../../lib/str2rgbarray\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"];n.prototype.merge=function(t){for(var e=this,r=0;r<3;++r){var n=t[s[r]];n.visible?(e.labels[r]=a(n.title),\"titlefont\"in n&&(n.titlefont.color&&(e.labelColor[r]=o(n.titlefont.color)),n.titlefont.family&&(e.labelFont[r]=n.titlefont.family),n.titlefont.size&&(e.labelSize[r]=n.titlefont.size)),\"showline\"in n&&(e.lineEnable[r]=n.showline),\"linecolor\"in n&&(e.lineColor[r]=o(n.linecolor)),\"linewidth\"in n&&(e.lineWidth[r]=n.linewidth),\"showgrid\"in n&&(e.gridEnable[r]=n.showgrid),\"gridcolor\"in n&&(e.gridColor[r]=o(n.gridcolor)),\"gridwidth\"in n&&(e.gridWidth[r]=n.gridwidth),\"log\"===n.type?e.zeroEnable[r]=!1:\"zeroline\"in n&&(e.zeroEnable[r]=n.zeroline),\"zerolinecolor\"in n&&(e.zeroLineColor[r]=o(n.zerolinecolor)),\"zerolinewidth\"in n&&(e.zeroLineWidth[r]=n.zerolinewidth),\"ticks\"in n&&n.ticks?e.lineTickEnable[r]=!0:e.lineTickEnable[r]=!1,\"ticklen\"in n&&(e.lineTickLength[r]=e._defaultLineTickLength[r]=n.ticklen),\"tickcolor\"in n&&(e.lineTickColor[r]=o(n.tickcolor)),\"tickwidth\"in n&&(e.lineTickWidth[r]=n.tickwidth),\"tickangle\"in n&&(e.tickAngle[r]=\"auto\"===n.tickangle?0:Math.PI-n.tickangle/180),\"showticklabels\"in n&&(e.tickEnable[r]=n.showticklabels),\"tickfont\"in n&&(n.tickfont.color&&(e.tickColor[r]=o(n.tickfont.color)),n.tickfont.family&&(e.tickFont[r]=n.tickfont.family),n.tickfont.size&&(e.tickSize[r]=n.tickfont.size)),\"mirror\"in n?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(n.mirror)?(e.lineTickMirror[r]=!0,e.lineMirror[r]=!0):!0===n.mirror?(e.lineTickMirror[r]=!1,e.lineMirror[r]=!0):(e.lineTickMirror[r]=!1,e.lineMirror[r]=!1):e.lineMirror[r]=!1,\"showbackground\"in n&&!1!==n.showbackground?(e.backgroundEnable[r]=!0,e.backgroundColor[r]=o(n.backgroundcolor)):e.backgroundEnable[r]=!1):(e.tickEnable[r]=!1,e.labelEnable[r]=!1,e.lineEnable[r]=!1,e.lineTickEnable[r]=!1,e.gridEnable[r]=!1,e.zeroEnable[r]=!1,e.backgroundEnable[r]=!1)}},e.exports=i},{\"../../../lib/html2unicode\":726,\"../../../lib/str2rgbarray\":749}],816:[function(t,e,r){\"use strict\";function n(t,e,r,n){for(var i=r(\"bgcolor\"),s=a.combine(i,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],c=0;c<u.length;c++)r(\"camera.\"+u[c]+\".x\"),r(\"camera.\"+u[c]+\".y\"),r(\"camera.\"+u[c]+\".z\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),f=h?\"manual\":\"auto\",d=r(\"aspectmode\",f);h\|\|(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode),l(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:s,calendar:n.calendar}),o.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n),r(\"dragmode\",n.getDfltFromLayout(\"dragmode\")),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}var i=t(\"../../../lib\"),a=t(\"../../../components/color\"),o=t(\"../../../registry\"),s=t(\"../../subplot_defaults\"),l=t(\"./axis_defaults\"),u=t(\"./layout_attributes\");e.exports=function(t,e,r){function a(e){if(!o){return i.validate(t[e],u[e])?t[e]:void 0}}var o=e._basePlotModules.length>1;s(t,e,r,{type:\"gl3d\",attributes:u,handleDefaults:n,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:a,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":604,\"../../../lib\":728,\"../../../registry\":846,\"../../subplot_defaults\":838,\"./axis_defaults\":814,\"./layout_attributes\":817}],817:[function(t,e,r){\"use strict\";function n(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}var i=t(\"./axis_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(n(0,0,1),{}),center:a(n(0,0,0),{}),eye:a(n(1.25,1.25,1.25),{}),editType:\"camera\"},domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1,editType:\"plot\"},{valType:\"number\",min:0,max:1,editType:\"plot\"}],dflt:[0,1],editType:\"plot\"},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1,editType:\"plot\"},{valType:\"number\",min:0,max:1,editType:\"plot\"}],dflt:[0,1],editType:\"plot\"},editType:\"plot\"},aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:i,yaxis:i,zaxis:i,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],dflt:\"turntable\",editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":728,\"../../../lib/extend\":717,\"./axis_attributes\":813}],818:[function(t,e,r){\"use strict\";function n(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}function i(t){var e=new n;return e.merge(t),e}var a=t(\"../../../lib/str2rgbarray\"),o=[\"xaxis\",\"yaxis\",\"zaxis\"];n.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[o[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=a(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=i},{\"../../../lib/str2rgbarray\":749}],819:[function(t,e,r){\"use strict\";function n(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}function i(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,i=t.fullSceneLayout,c=[[],[],[]],h=0;h<3;++h){var f=i[l[h]];if(f._length=(r[h].hi-r[h].lo)r[h].pixelsPerDataUnit/t.dataScale[h],Math.abs(f._length)===1/0)c[h]=[];else{f.range[0]=r[h].lo/t.dataScale[h],f.range[1]=r[h].hi/t.dataScale[h],f._m=1/(t.dataScale[h]r[h].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var d=f.tickmode;if(\"auto\"===f.tickmode){f.tickmode=\"linear\";var p=f.nticks\|\|o.constrain(f._length/40,4,9);a.autoTicks(f,Math.abs(f.range[1]-f.range[0])/p)}for(var m=a.calcTicks(f),v=0;v<m.length;++v)m[v].x=m[v].xt.dataScale[h],m[v].text=s(m[v].text);c[h]=m,f.tickmode=d}}e.ticks=c;for(var h=0;h<3;++h){u[h]=.5(t.glplot.bounds[0][h]+t.glplot.bounds[1][h]);for(var v=0;v<2;++v)e.bounds[v][h]=t.glplot.bounds[v][h]}t.contourLevels=n(c)}e.exports=i;var a=t(\"../../cartesian/axes\"),o=t(\"../../../lib\"),s=t(\"../../../lib/html2unicode\"),l=[\"xaxis\",\"yaxis\",\"zaxis\"],u=[0,0,0]},{\"../../../lib\":728,\"../../../lib/html2unicode\":726,\"../../cartesian/axes\":772}],820:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4r+n]e[r];return i}function i(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}e.exports=i},{}],821:[function(t,e,r){\"use strict\";function n(t){function e(e,r){var n=t.fullSceneLayout[e];return v.tickText(n,n.d2l(r),\"hover\").text}var r,n=t.svgContainer,i=t.container.getBoundingClientRect(),a=i.width,o=i.height;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+a+\" \"+o),n.setAttributeNS(null,\"width\",a),n.setAttributeNS(null,\"height\",o),k(t),t.glplot.axes.update(t.axesOptions);for(var s=Object.keys(t.traces),l=null,u=t.glplot.selection,c=0;c<s.length;++c)r=t.traces[s[c]],\"skip\"!==r.data.hoverinfo&&r.handlePick(u)&&(l=r),r.setContourLevels&&r.setContourLevels();var h;if(null!==l){var f=_(t.glplot.cameraParams,u.dataCoordinate);r=l.data;var d=u.index,p=g.castHoverinfo(r,t.fullLayout,d),m=e(\"xaxis\",u.traceCoordinate[0]),y=e(\"yaxis\",u.traceCoordinate[1]),b=e(\"zaxis\",u.traceCoordinate[2]);if(\"all\"!==p){var x=p.split(\"+\");-1===x.indexOf(\"x\")&&(m=void 0),-1===x.indexOf(\"y\")&&(y=void 0),-1===x.indexOf(\"z\")&&(b=void 0),-1===x.indexOf(\"text\")&&(u.textLabel=void 0),-1===x.indexOf(\"name\")&&(l.name=void 0)}t.fullSceneLayout.hovermode&&g.loneHover({x:(.5+.5f[0]/f[3])a,y:(.5-.5f[1]/f[3])o,xLabel:m,yLabel:y,zLabel:b,text:u.textLabel,name:l.name,color:g.castHoverOption(r,d,\"bgcolor\")\|\|l.color,borderColor:g.castHoverOption(r,d,\"bordercolor\"),fontFamily:g.castHoverOption(r,d,\"font.family\"),fontSize:g.castHoverOption(r,d,\"font.size\"),fontColor:g.castHoverOption(r,d,\"font.color\")},{container:n,gd:t.graphDiv});var w={x:u.traceCoordinate[0],y:u.traceCoordinate[1],z:u.traceCoordinate[2],data:r._input,fullData:r,curveNumber:r.index,pointNumber:d};g.appendArrayPointValue(w,r,d);var M={points:[w]};u.buttons&&u.distance<5?t.graphDiv.emit(\"plotly_click\",M):t.graphDiv.emit(\"plotly_hover\",M),h=M}else g.loneUnhover(n),t.graphDiv.emit(\"plotly_unhover\",h);t.drawAnnotations(t)}function i(t,e,r,i){var a={canvas:r,gl:i,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!(h\|\|(c=document.createElement(\"canvas\"),h=d({canvas:c,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");a.pixelRatio=t.pixelRatio,a.gl=h,a.canvas=c}try{t.glplot=f(a)}catch(e){b(t)}var o=function(t){if(!1!==t.fullSceneLayout.dragmode){var e={};e[t.id+\".camera\"]=u(t.camera),t.saveCamera(t.graphDiv.layout),t.graphDiv.emit(\"plotly_relayout\",e)}};if(t.glplot.canvas.addEventListener(\"mouseup\",o.bind(null,t)),t.glplot.canvas.addEventListener(\"wheel\",o.bind(null,t)),t.staticMode\|\|t.glplot.canvas.addEventListener(\"webglcontextlost\",function(t){m.warn(\"Lost WebGL context.\"),t.preventDefault()}),!t.camera){var s=t.fullSceneLayout.camera;t.camera=x(t.container,{center:[s.center.x,s.center.y,s.center.z],\n", "eye:[s.eye.x,s.eye.y,s.eye.z],up:[s.up.x,s.up.y,s.up.z],zoomMin:.1,zoomMax:100,mode:\"orbit\"})}return t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=n.bind(null,t),t.traces={},!0}function a(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\",r.appendChild(a),this.svgContainer=a,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id\|\|\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=w(e[this.id]),this.spikeOptions=M(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio\|\|2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=p.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=p.getComponentMethod(\"annotations3d\",\"draw\"),i(this,e)}function o(t,e,r,n,i){for(var a,o=0;o<e.length;++o)if(Array.isArray(e[o]))for(var s=0;s<e[o].length;++s)a=t.d2l(e[o][s],0,i),!isNaN(a)&&isFinite(a)&&(n[0][r]=Math.min(n[0][r],a),n[1][r]=Math.max(n[1][r],a));else a=t.d2l(e[o],0,i),!isNaN(a)&&isFinite(a)&&(n[0][r]=Math.min(n[0][r],a),n[1][r]=Math.max(n[1][r],a))}function s(t,e,r){var n=t.fullSceneLayout;o(n.xaxis,e.x,0,r,e.xcalendar),o(n.yaxis,e.y,1,r,e.ycalendar),o(n.zaxis,e.z,2,r,e.zcalendar)}function l(t){return[[t.eye.x,t.eye.y,t.eye.z],[t.center.x,t.center.y,t.center.z],[t.up.x,t.up.y,t.up.z]]}function u(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}var c,h,f=t(\"gl-plot3d\"),d=t(\"webgl-context\"),p=t(\"../../registry\"),m=t(\"../../lib\"),v=t(\"../../plots/cartesian/axes\"),g=t(\"../../components/fx\"),y=t(\"../../lib/str2rgbarray\"),b=t(\"../../lib/show_no_webgl_msg\"),x=t(\"./camera\"),_=t(\"./project\"),w=t(\"./layout/convert\"),M=t(\"./layout/spikes\"),k=t(\"./layout/tick_marks\"),A=a.prototype;A.recoverContext=function(){function t(){return r.isContextLost()?void requestAnimationFrame(t):i(e,e.fullLayout,n,r)?void e.plot.apply(e,e.plotArgs):void m.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")}var e=this,r=this.glplot.gl,n=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(t)};var T=[\"xaxis\",\"yaxis\",\"zaxis\"];A.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,l,u,c=e[this.id],h=r[this.id];c.bgcolor?this.glplot.clearColor=y(c.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullLayout=e,this.fullSceneLayout=c,this.glplotLayout=c,this.axesOptions.merge(c),this.spikeOptions.merge(c),this.setCamera(c.camera),this.updateFx(c.dragmode,c.hovermode),this.glplot.update({}),this.setConvert(l),t?Array.isArray(t)\|\|(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)n=t[a],!0===n.visible&&s(this,n,f);var d=[1,1,1];for(o=0;o<3;++o)f[0][o]>f[1][o]?d[o]=1:f[1][o]===f[0][o]?d[o]=1:d[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=d,this.convertAnnotations(this),a=0;a<t.length;++a)n=t[a],!0===n.visible&&(i=this.traces[n.uid],i?i.update(n):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible)continue t;i=this.traces[p[a]],i.dispose(),delete this.traces[p[a]]}this.glplot.objects.sort(function(t,e){return t._trace.data.index-e._trace.data.index});var m=[[0,0,0],[0,0,0]],v=[],g={};for(a=0;a<3;++a){if(l=c[T[a]],u=l.type,u in g?(g[u].acc=d[a],g[u].count+=1):g[u]={acc:d[a],count:1},l.autorange){m[0][a]=1/0,m[1][a]=-1/0;var b=this.glplot.objects,x=this.fullSceneLayout.annotations\|\|[],_=l._name.charAt(0);for(o=0;o<b.length;o++){var w=b[o].bounds;m[0][a]=Math.min(m[0][a],w[0][a]/d[a]),m[1][a]=Math.max(m[1][a],w[1][a]/d[a])}for(o=0;o<x.length;o++){var M=x[o];if(M.visible){var k=l.r2l(M[_]);m[0][a]=Math.min(m[0][a],k),m[1][a]=Math.max(m[1][a],k)}}if(\"rangemode\"in l&&\"tozero\"===l.rangemode&&(m[0][a]=Math.min(m[0][a],0),m[1][a]=Math.max(m[1][a],0)),m[0][a]>m[1][a])m[0][a]=-1,m[1][a]=1;else{var A=m[1][a]-m[0][a];m[0][a]-=A/32,m[1][a]+=A/32}}else{var S=l.range;m[0][a]=l.r2l(S[0]),m[1][a]=l.r2l(S[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.bounds[0][a]=m[0][a]d[a],this.glplot.bounds[1][a]=m[1][a]d[a]}var E=[1,1,1];for(a=0;a<3;++a){l=c[T[a]],u=l.type;var L=g[u];E[a]=Math.pow(L.acc,1/L.count)/d[a]}var C;if(\"auto\"===c.aspectmode)C=Math.max.apply(null,E)/Math.min.apply(null,E)<=4?E:[1,1,1];else if(\"cube\"===c.aspectmode)C=[1,1,1];else if(\"data\"===c.aspectmode)C=E;else{if(\"manual\"!==c.aspectmode)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var I=c.aspectratio;C=[I.x,I.y,I.z]}c.aspectratio.x=h.aspectratio.x=C[0],c.aspectratio.y=h.aspectratio.y=C[1],c.aspectratio.z=h.aspectratio.z=C[2],this.glplot.aspect=C;var z=c.domain\|\|null,D=e._size\|\|null;if(z&&D){var P=this.container.style;P.position=\"absolute\",P.left=D.l+z.x[0]D.w+\"px\",P.top=D.t+(1-z.y[1])D.h+\"px\",P.width=D.w(z.x[1]-z.x[0])+\"px\",P.height=D.h(z.y[1]-z.y[0])+\"px\"}this.glplot.redraw()}},A.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=this.glplot.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},A.getCamera=function(){return this.glplot.camera.view.recalcMatrix(this.camera.view.lastT()),u(this.glplot.camera)},A.setCamera=function(t){this.glplot.camera.lookAt.apply(this,l(t))},A.saveCamera=function(t){var e=this.getCamera(),r=m.nestedProperty(t,this.id+\".camera\"),n=r.get(),i=!1;if(void 0===n)i=!0;else for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!function(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}(e,n,a,o)){i=!0;break}return i&&r.set(e),i},A.updateFx=function(t,e){var r=this.camera;r&&(\"orbit\"===t?(r.mode=\"orbit\",r.keyBindingMode=\"rotate\"):\"turntable\"===t?(r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\"):r.keyBindingMode=t),this.fullSceneLayout.hovermode=e},A.toImage=function(t){t\|\|(t=\"png\"),this.staticMode&&this.container.appendChild(c),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,n=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var i=new Uint8Array(rn4);e.readPixels(0,0,r,n,e.RGBA,e.UNSIGNED_BYTE,i);for(var a=0,o=n-1;a<o;++a,--o)for(var s=0;s<r;++s)for(var l=0;l<4;++l){var u=i[4(ra+s)+l];i[4(ra+s)+l]=i[4(ro+s)+l],i[4(ro+s)+l]=u}var h=document.createElement(\"canvas\");h.width=r,h.height=n;var f=h.getContext(\"2d\"),d=f.createImageData(r,n);d.data.set(i),f.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=h.toDataURL(\"image/jpeg\");break;case\"webp\":p=h.toDataURL(\"image/webp\");break;default:p=h.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(c),p},A.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[T[t]];v.setConvert(e,this.fullLayout),e.setScale=m.noop}},e.exports=a},{\"../../components/fx\":645,\"../../lib\":728,\"../../lib/show_no_webgl_msg\":747,\"../../lib/str2rgbarray\":749,\"../../plots/cartesian/axes\":772,\"../../registry\":846,\"./camera\":810,\"./layout/convert\":815,\"./layout/spikes\":818,\"./layout/tick_marks\":819,\"./project\":820,\"gl-plot3d\":221,\"webgl-context\":563}],822:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"../components/color/attributes\"),a=n({editType:\"calc\"});a.family.dflt='\"Open Sans\", verdana, arial, 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e={},r={};switch(t.type){case\"circle\":s.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":s.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity});break;case\"fill\":s.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var n=t.symbol,i=l(n.textposition,n.iconsize);s.extendFlat(e,{\"icon-image\":n.icon+\"-15\",\"icon-size\":n.iconsize/10,\"text-field\":n.text,\"text-size\":n.textfont.size,\"text-anchor\":i.anchor,\"text-offset\":i.offset}),s.extendFlat(r,{\"icon-color\":t.color,\"text-color\":n.textfont.color,\"text-opacity\":t.opacity})}return{layout:e,paint:r}}function o(t){var e,r=t.sourcetype,n=t.source,i={type:r},a=\"string\"==typeof n;return\"geojson\"===r?e=\"data\":\"vector\"===r&&(e=a?\"url\":\"tiles\"),i[e]=n,i}var 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r={visibility:\"visible\"};this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",r)}},u.updateStyle=function(t){var e=a(t);i(t)&&(this.mapbox.setOptions(this.idLayer,\"setLayoutProperty\",e.layout),this.mapbox.setOptions(this.idLayer,\"setPaintProperty\",e.paint))},u.dispose=function(){var t=this.map;t.removeLayer(this.idLayer),t.removeSource(this.idSource)},e.exports=function(t,e,r){var i=new n(t,e);return i.update(r),i}},{\"../../lib\":728,\"./convert_text_opts\":824}],827:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\").defaultLine,a=t(\"../font_attributes\"),o=t(\"../../traces/scatter/attributes\").textposition,s=t(\"../../plot_api/edit_types\").overrideAll,l=a({});l.family.dflt=\"Open Sans Regular, Arial Unicode MS 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a=t(\"../../lib\"),o=t(\"../subplot_defaults\"),s=t(\"./layout_attributes\");e.exports=function(t,e,r){o(t,e,r,{type:\"mapbox\",attributes:s,handleDefaults:n,partition:\"y\"})}},{\"../../lib\":728,\"../subplot_defaults\":838,\"./layout_attributes\":827}],829:[function(t,e,r){\"use strict\";function n(t){this.id=t.id,this.gd=t.gd,this.container=t.container,this.isStatic=t.staticPlot;var e=t.fullLayout;this.uid=e._uid+\"-\"+this.id,this.opts=e[this.id],this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(e),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[]}function i(t){var e=d.style.values,r=d.style.dflt,n={};return u.isPlainObject(t)?(n.id=t.id,n.style=t):\"string\"==typeof t?(n.id=t,n.style=-1!==e.indexOf(t)?a(t):t):(n.id=r,n.style=a(r)),n}function a(t){return f.styleUrlPrefix+t+\"-\"+f.styleUrlSuffix}function o(t){return[t.lon,t.lat]}var s=t(\"mapbox-gl\"),l=t(\"../../components/fx\"),u=t(\"../../lib\"),c=t(\"../../components/dragelement\"),h=t(\"../cartesian/select\"),f=t(\"./constants\"),d=t(\"./layout_attributes\"),p=t(\"./layers\"),m=n.prototype;e.exports=function(t){return new n(t)},m.plot=function(t,e,r){var n=this,i=n.opts=e[this.id];n.map&&i.accesstoken!==n.accessToken&&(n.map.remove(),n.map=null,n.styleObj=null,n.traceHash=[],n.layerList={});var a;a=n.map?new Promise(function(r,i){n.updateMap(t,e,r,i)}):new Promise(function(r,i){n.createMap(t,e,r,i)}),r.push(a)},m.createMap=function(t,e,r,n){function a(){l.loneUnhover(e._toppaper)}var c=this,h=c.gd,d=c.opts,p=c.styleObj=i(d.style);c.accessToken=d.accesstoken;var m=c.map=new 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e=t[this.id].domain,r=t._size,n=this.div.style;n.width=r.w(e.x[1]-e.x[0])+\"px\",n.height=r.h(e.y[1]-e.y[0])+\"px\",n.left=r.l+e.x[0]r.w+\"px\",n.top=r.t+(1-e.y[1])r.h+\"px\",this.xaxis._offset=r.l+e.x[0]r.w,this.xaxis._length=r.w(e.x[1]-e.x[0]),this.yaxis._offset=r.t+(1-e.y[1])r.h,this.yaxis._length=r.h(e.y[1]-e.y[0])},m.updateLayers=function(){var t,e=this.opts,r=e.layers,n=this.layerList;if(r.length!==n.length){for(t=0;t<n.length;t++)n[t].dispose();for(n=this.layerList=[],t=0;t<r.length;t++)n.push(p(this,t,r[t]))}else for(t=0;t<r.length;t++)n[t].update(r[t])},m.destroy=function(){this.map&&(this.map.remove(),this.map=null,this.container.removeChild(this.div))},m.toImage=function(){return this.map.getCanvas().toDataURL()},m.initSource=function(t){var e={type:\"geojson\",data:{type:\"Feature\",geometry:{type:\"Point\",coordinates:[]}}};return this.map.addSource(t,e)},m.setSourceData=function(t,e){this.map.getSource(t).setData(e)},m.setOptions=function(t,e,r){for(var 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s.PolyChart()},s.DotPlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"dot\",dotType:\"circle\"}}},s.LinePlot=function(){return s.PolyChart()},s.LinePlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"line\"}}},s.Legend=function(){function t(){var r=e.legendConfig,i=e.data.map(function(t,e){return[].concat(t).map(function(t,n){var i=a({},r.elements[e]);return i.name=t,i.color=[].concat(r.elements[e].color)[n],i})}),o=n.merge(i);o=o.filter(function(t,e){return r.elements[e]&&(r.elements[e].visibleInLegend\|\|void 0===r.elements[e].visibleInLegend)}),r.reverseOrder&&(o=o.reverse());var s=r.container;(\"string\"==typeof s\|\|s.nodeName)&&(s=n.select(s));var l=o.map(function(t,e){return t.color}),u=r.fontSize,c=null==r.isContinuous?\"number\"==typeof 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n=a({},t);return[[n,[\"marker\",\"color\"],[\"color\"]],[n,[\"marker\",\"opacity\"],[\"opacity\"]],[n,[\"marker\",\"line\",\"color\"],[\"strokeColor\"]],[n,[\"marker\",\"line\",\"dash\"],[\"strokeDash\"]],[n,[\"marker\",\"line\",\"width\"],[\"strokeSize\"]],[n,[\"marker\",\"symbol\"],[\"dotType\"]],[n,[\"marker\",\"size\"],[\"dotSize\"]],[n,[\"marker\",\"barWidth\"],[\"barWidth\"]],[n,[\"line\",\"interpolation\"],[\"lineInterpolation\"]],[n,[\"showlegend\"],[\"visibleInLegend\"]]].forEach(function(t,r){s.util.translator.apply(null,t.concat(e))}),e\|\|delete n.marker,e&&delete n.groupId,e?(\"LinePlot\"===n.geometry?(n.type=\"scatter\",!0===n.dotVisible?(delete n.dotVisible,n.mode=\"lines+markers\"):n.mode=\"lines\"):\"DotPlot\"===n.geometry?(n.type=\"scatter\",n.mode=\"markers\"):\"AreaChart\"===n.geometry?n.type=\"area\":\"BarChart\"===n.geometry&&(n.type=\"bar\"),delete n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n}),!e&&t.layout&&\"stack\"===t.layout.barmode)){var i=s.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var n=i.indexOf(t.geometry);-1!=n&&(r.data[e].groupId=n)})}if(t.layout){var o=a({},t.layout);if([[o,[\"plot_bgcolor\"],[\"backgroundColor\"]],[o,[\"showlegend\"],[\"showLegend\"]],[o,[\"radialaxis\"],[\"radialAxis\"]],[o,[\"angularaxis\"],[\"angularAxis\"]],[o.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[o.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[o.angularaxis,[\"nticks\"],[\"ticksCount\"]],[o.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[o.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[o.angularaxis,[\"range\"],[\"domain\"]],[o.angularaxis,[\"endpadding\"],[\"endPadding\"]],[o.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[o.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[o.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[o.radialaxis,[\"range\"],[\"domain\"]],[o.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[o.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[o.angularAxis,[\"nticks\"],[\"ticksCount\"]],[o.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[o.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[o.angularAxis,[\"range\"],[\"domain\"]],[o.angularAxis,[\"endpadding\"],[\"endPadding\"]],[o.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[o.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[o.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[o.radialAxis,[\"range\"],[\"domain\"]],[o.font,[\"outlinecolor\"],[\"outlineColor\"]],[o.legend,[\"traceorder\"],[\"reverseOrder\"]],[o,[\"labeloffset\"],[\"labelOffset\"]],[o,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach(function(t,r){s.util.translator.apply(null,t.concat(e))}),e?(void 0!==o.tickLength&&(o.angularaxis.ticklen=o.tickLength,delete o.tickLength),o.tickColor&&(o.angularaxis.tickcolor=o.tickColor,delete o.tickColor)):(o.angularAxis&&void 0!==o.angularAxis.ticklen&&(o.tickLength=o.angularAxis.ticklen),o.angularAxis&&void 0!==o.angularAxis.tickcolor&&(o.tickColor=o.angularAxis.tickcolor)),o.legend&&\"boolean\"!=typeof o.legend.reverseOrder&&(o.legend.reverseOrder=\"normal\"!=o.legend.reverseOrder),o.legend&&\"boolean\"==typeof o.legend.traceorder&&(o.legend.traceorder=o.legend.traceorder?\"reversed\":\"normal\",delete o.legend.reverseOrder),o.margin&&void 0!==o.margin.t){var l=[\"t\",\"r\",\"b\",\"l\",\"pad\"],u=[\"top\",\"right\",\"bottom\",\"left\",\"pad\"],c={};n.entries(o.margin).forEach(function(t,e){c[u[l.indexOf(t.key)]]=t.value}),o.margin=c}e&&(delete o.needsEndSpacing,delete o.minorTickColor,delete o.minorTicks,delete o.angularaxis.ticksCount,delete o.angularaxis.ticksCount,delete o.angularaxis.ticksStep,delete o.angularaxis.rewriteTicks,delete o.angularaxis.nticks,delete o.radialaxis.ticksCount,delete o.radialaxis.ticksCount,delete o.radialaxis.ticksStep,delete o.radialaxis.rewriteTicks,delete o.radialaxis.nticks),r.layout=o}return r},t}},{\"../../constants/alignment\":701,\"../../lib\":728,d3:122}],836:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../components/color\"),o=t(\"./micropolar\"),s=t(\"./undo_manager\"),l=i.extendDeepAll,u=e.exports={};u.framework=function(t){function e(e,i){return i&&(h=i),n.select(n.select(h).node().parentNode).selectAll(\".svg-container>:not(.chart-root)\").remove(),r=r?l(r,e):e,a\|\|(a=o.Axis()),c=o.adapter.plotly().convert(r),a.config(c).render(h),t.data=r.data,t.layout=r.layout,u.fillLayout(t),r}var r,i,a,c,h,f=new s;return e.isPolar=!0,e.svg=function(){return a.svg()},e.getConfig=function(){return r},e.getLiveConfig=function(){return o.adapter.plotly().convert(a.getLiveConfig(),!0)},e.getLiveScales=function(){return{t:a.angularScale(),r:a.radialScale()}},e.setUndoPoint=function(){var t=this,e=o.util.cloneJson(r);!function(e,r){f.add({undo:function(){r&&t(r)},redo:function(){t(e)}})}(e,i),i=o.util.cloneJson(e)},e.undo=function(){f.undo()},e.redo=function(){f.redo()},e},u.fillLayout=function(t){var e=n.select(t).selectAll(\".plot-container\"),r=e.selectAll(\".svg-container\"),i=t.framework&&t.framework.svg&&t.framework.svg(),o={width:800,height:600,paper_bgcolor:a.background,_container:e,_paperdiv:r,_paper:i};t._fullLayout=l(o,t.layout)}},{\"../../components/color\":604,\"../../lib\":728,\"./micropolar\":835,\"./undo_manager\":837,d3:122}],837:[function(t,e,r){\"use strict\";e.exports=function(){function t(t,e){return t?(i=!0,t[e](),i=!1,this):this}var e,r=[],n=-1,i=!1;return{add:function(t){return i?this:(r.splice(n+1,r.length-n),r.push(t),n=r.length-1,this)},setCallback:function(t){e=t},undo:function(){var i=r[n];return i?(t(i,\"undo\"),n-=1,e&&e(i.undo),this):this},redo:function(){var i=r[n+1];return i?(t(i,\"redo\"),n+=1,e&&e(i.redo),this):this},clear:function(){r=[],n=-1},hasUndo:function(){return-1!==n},hasRedo:function(){return n<r.length-1},getCommands:function(){return r},getPreviousCommand:function(){return r[n-1]},getIndex:function(){return n}}}},{}],838:[function(t,e,r){\"use strict\";var n=t(\"../lib\"),i=t(\"./plots\");e.exports=function(t,e,r,a){function o(t,e){return n.coerce(s,l,c,t,e)}for(var s,l,u=a.type,c=a.attributes,h=a.handleDefaults,f=a.partition\|\|\"x\",d=i.findSubplotIds(r,u),p=d.length,m=0;m<p;m++){var v=d[m];s=t[v]?t[v]:t[v]={},e[v]=l={},o(\"domain.\"+f,[m/p,(m+1)/p]),o(\"domain.\"+{x:\"y\",y:\"x\"}[f]),a.id=v,h(s,l,o,a)}}},{\"../lib\":728,\"./plots\":831}],839:[function(t,e,r){\"use strict\";var n=t(\"./ternary\"),i=t(\"../../plots/plots\"),a=t(\"../../lib\").counterRegex;r.name=\"ternary\",r.attr=\"subplot\",r.idRoot=\"ternary\",r.idRegex=r.attrRegex=a(\"ternary\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t.calcdata,a=i.getSubplotIds(e,\"ternary\"),o=0;o<a.length;o++){var s=a[o],l=i.getSubplotCalcData(r,\"ternary\",s),u=e[s]._subplot;u\|\|(u=new n({id:s,graphDiv:t,container:e._ternarylayer.node()},e),e[s]._subplot=u),u.plot(l,e,t._promises)}},r.clean=function(t,e,r,n){for(var a=i.getSubplotIds(n,\"ternary\"),o=0;o<a.length;o++){var s=a[o],l=n[s]._subplot;!e[s]&&l&&(l.plotContainer.remove(),l.clipDef.remove(),l.clipDefRelative.remove())}}},{\"../../lib\":728,\"../../plots/plots\":831,\"./layout/attributes\":840,\"./layout/defaults\":843,\"./layout/layout_attributes\":844,\"./ternary\":845}],840:[function(t,e,r){\"use strict\";e.exports={subplot:{valType:\"subplotid\",dflt:\"ternary\",editType:\"calc\"}}},{}],841:[function(t,e,r){\"use strict\";var n=t(\"../../cartesian/layout_attributes\"),i=t(\"../../../lib/extend\").extendFlat;e.exports={title:n.title,titlefont:n.titlefont,color:n.color,tickmode:n.tickmode,nticks:i({},n.nticks,{dflt:6,min:1}),tick0:n.tick0,dtick:n.dtick,tickvals:n.tickvals,ticktext:n.ticktext,ticks:n.ticks,ticklen:n.ticklen,tickwidth:n.tickwidth,tickcolor:n.tickcolor,showticklabels:n.showticklabels,showtickprefix:n.showtickprefix,tickprefix:n.tickprefix,showticksuffix:n.showticksuffix,ticksuffix:n.ticksuffix,showexponent:n.showexponent,exponentformat:n.exponentformat,separatethousands:n.separatethousands,tickfont:n.tickfont,tickangle:n.tickangle,tickformat:n.tickformat,hoverformat:n.hoverformat,showline:i({},n.showline,{dflt:!0}),linecolor:n.linecolor,linewidth:n.linewidth,showgrid:i({},n.showgrid,{dflt:!0}),gridcolor:n.gridcolor,gridwidth:n.gridwidth,layer:n.layer,min:{valType:\"number\",dflt:0,min:0}}},{\"../../../lib/extend\":717,\"../../cartesian/layout_attributes\":783}],842:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"./axis_attributes\"),o=t(\"../../cartesian/tick_label_defaults\"),s=t(\"../../cartesian/tick_mark_defaults\"),l=t(\"../../cartesian/tick_value_defaults\");e.exports=function(t,e,r){function u(r,n){return i.coerce(t,e,a,r,n)}e.type=\"linear\";var c=u(\"color\"),h=c===t.color?c:r.font.color,f=e._name,d=f.charAt(0).toUpperCase(),p=\"Component \"+d,m=u(\"title\",p);e._hovertitle=m===p?m:d,i.coerceFont(u,\"titlefont\",{family:r.font.family,size:Math.round(1.2r.font.size),color:h}),u(\"min\"),l(t,e,u,\"linear\"),o(t,e,u,\"linear\",{noHover:!1}),s(t,e,u,{outerTicks:!0}),u(\"showticklabels\")&&(i.coerceFont(u,\"tickfont\",{family:r.font.family,size:r.font.size,color:h}),u(\"tickangle\"),u(\"tickformat\")),u(\"hoverformat\"),u(\"showline\")&&(u(\"linecolor\",c),u(\"linewidth\")),u(\"showgrid\")&&(u(\"gridcolor\",n(c,r.bgColor,60).toRgbString()),u(\"gridwidth\")),u(\"layer\")}},{\"../../../lib\":728,\"../../cartesian/tick_label_defaults\":790,\"../../cartesian/tick_mark_defaults\":791,\"../../cartesian/tick_value_defaults\":792,\"./axis_attributes\":841,tinycolor2:534}],843:[function(t,e,r){\"use strict\";function n(t,e,r,n){var a=r(\"bgcolor\"),o=r(\"sum\");n.bgColor=i.combine(a,n.paper_bgcolor);for(var u,c,h,f=0;f<l.length;f++)u=l[f],c=t[u]\|\|{},h=e[u]={_name:u,type:\"linear\"},s(c,h,n);var d=e.aaxis,p=e.baxis,m=e.caxis;d.min+p.min+m.min>=o&&(d.min=0,p.min=0,m.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}var i=t(\"../../../components/color\"),a=t(\"../../subplot_defaults\"),o=t(\"./layout_attributes\"),s=t(\"./axis_defaults\"),l=[\"aaxis\",\"baxis\",\"caxis\"];e.exports=function(t,e,r){a(t,e,r,{type:\"ternary\",attributes:o,handleDefaults:n,font:e.font,paper_bgcolor:e.paper_bgcolor})}},{\"../../../components/color\":604,\"../../subplot_defaults\":838,\"./axis_defaults\":842,\"./layout_attributes\":844}],844:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color/attributes\"),i=t(\"./axis_attributes\"),a=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=a({domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:i,baxis:i,caxis:i},\"plot\",\"from-root\")},{\"../../../components/color/attributes\":603,\"../../../plot_api/edit_types\":756,\"./axis_attributes\":841}],845:[function(t,e,r){\"use strict\";function n(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e)}function i(t){a.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}var a=t(\"d3\"),o=t(\"tinycolor2\"),s=t(\"../../plotly\"),l=t(\"../../lib\"),u=t(\"../../components/color\"),c=t(\"../../components/drawing\"),h=t(\"../cartesian/set_convert\"),f=t(\"../../lib/extend\").extendFlat,d=t(\"../plots\"),p=t(\"../cartesian/axes\"),m=t(\"../../components/dragelement\"),v=t(\"../../components/fx\"),g=t(\"../../components/titles\"),y=t(\"../cartesian/select\"),b=t(\"../cartesian/constants\");e.exports=n;var x=n.prototype;x.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},x.plot=function(t,e){var r=this,n=e[r.id],i=e._size;r._hasClipOnAxisFalse=!1;for(var a=0;a<t.length;a++){if(!1===t[a][0].trace.cliponaxis){r._hasClipOnAxisFalse=!0;break}}r.updateLayers(n),r.adjustLayout(n,i),d.generalUpdatePerTraceModule(r,t,n),r.layers.plotbg.select(\"path\").call(u.fill,n.bgcolor)},x.makeFramework=function(t){var e=this,r=t[e.id],n=e.clipId=\"clip\"+e.layoutId+e.id;e.clipDef=t._clips.selectAll(\"#\"+n).data([0]),e.clipDef.enter().append(\"clipPath\").attr(\"id\",n).append(\"path\").attr(\"d\",\"M0,0Z\");var i=e.clipIdRelative=\"clip-relative\"+e.layoutId+e.id;e.clipDefRelative=t._clips.selectAll(\"#\"+i).data([0]),e.clipDefRelative.enter().append(\"clipPath\").attr(\"id\",i).append(\"path\").attr(\"d\",\"M0,0Z\"),e.plotContainer=e.container.selectAll(\"g.\"+e.id).data([0]),e.plotContainer.enter().append(\"g\").classed(e.id,!0),e.updateLayers(r),c.setClipUrl(e.layers.backplot,n),c.setClipUrl(e.layers.grids,n)},x.updateLayers=function(t){var e=this,r=e.layers,n=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below traces\"===t.aaxis.layer&&n.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&n.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&n.push(\"caxis\",\"cline\"),n.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&n.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&n.push(\"baxis\",\"bline\"),\"above traces\"===t.caxis.layer&&n.push(\"caxis\",\"cline\");var i=e.plotContainer.selectAll(\"g.toplevel\").data(n,String),o=[\"agrid\",\"bgrid\",\"cgrid\"];i.enter().append(\"g\").attr(\"class\",function(t){return\"toplevel \"+t}).each(function(t){var e=a.select(this);r[t]=e,\"frontplot\"===t?e.append(\"g\").classed(\"scatterlayer\",!0):\"backplot\"===t?e.append(\"g\").classed(\"maplayer\",!0):\"plotbg\"===t?e.append(\"path\").attr(\"d\",\"M0,0Z\"):\"aline\"===t\|\|\"bline\"===t\|\|\"cline\"===t?e.append(\"path\"):\"grids\"===t&&o.forEach(function(t){r[t]=e.append(\"g\").classed(\"grid \"+t,!0)})}),i.order()};var _=Math.sqrt(4/3);x.adjustLayout=function(t,e){var r,n,i,a,o,s,l=this,d=t.domain,p=(d.x[0]+d.x[1])/2,m=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],g=d.y[1]-d.y[0],y=ve.w,b=ge.h,x=t.sum,w=t.aaxis.min,M=t.baxis.min,k=t.caxis.min;y>_b?(a=b,i=a_):(i=y,a=i/_),o=vi/y,s=ga/b,r=e.l+e.wp-i/2,n=e.t+e.h(1-m)-a/2,l.x0=r,l.y0=n,l.w=i,l.h=a,l.sum=x,l.xaxis={type:\"linear\",range:[w+2k-x,x-w-2M],domain:[p-o/2,p+o/2],_id:\"x\"},h(l.xaxis,l.graphDiv._fullLayout),l.xaxis.setScale(),l.xaxis.isPtWithinRange=function(t){return t.a>=l.aaxis.range[0]&&t.a<=l.aaxis.range[1]&&t.b>=l.baxis.range[1]&&t.b<=l.baxis.range[0]&&t.c>=l.caxis.range[1]&&t.c<=l.caxis.range[0]},l.yaxis={type:\"linear\",range:[w,x-M-k],domain:[m-s/2,m+s/2],_id:\"y\"},h(l.yaxis,l.graphDiv._fullLayout),l.yaxis.setScale(),l.yaxis.isPtWithinRange=function(){return!0};var A=l.yaxis.domain[0],T=l.aaxis=f({},t.aaxis,{visible:!0,range:[w,x-M-k],side:\"left\",_counterangle:30,tickangle:(+t.aaxis.tickangle\|\|0)-30,domain:[A,A+s_],_axislayer:l.layers.aaxis,_gridlayer:l.layers.agrid,_pos:0,_id:\"y\",_length:i,_gridpath:\"M0,0l\"+a+\",-\"+i/2});h(T,l.graphDiv._fullLayout),T.setScale();var S=l.baxis=f({},t.baxis,{visible:!0,range:[x-w-k,M],side:\"bottom\",_counterangle:30,domain:l.xaxis.domain,_axislayer:l.layers.baxis,_gridlayer:l.layers.bgrid,_counteraxis:l.aaxis,_pos:0,_id:\"x\",_length:i,_gridpath:\"M0,0l-\"+i/2+\",-\"+a});h(S,l.graphDiv._fullLayout),S.setScale(),T._counteraxis=S;var E=l.caxis=f({},t.caxis,{visible:!0,range:[x-w-M,k],side:\"right\",_counterangle:30,tickangle:(+t.caxis.tickangle\|\|0)+30,domain:[A,A+s_],_axislayer:l.layers.caxis,_gridlayer:l.layers.cgrid,_counteraxis:l.baxis,_pos:0,_id:\"y\",_length:i,_gridpath:\"M0,0l-\"+a+\",\"+i/2});h(E,l.graphDiv._fullLayout),E.setScale();var L=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";l.clipDef.select(\"path\").attr(\"d\",L),l.layers.plotbg.select(\"path\").attr(\"d\",L);var C=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";l.clipDefRelative.select(\"path\").attr(\"d\",C);var I=\"translate(\"+r+\",\"+n+\")\";l.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",I),l.clipDefRelative.select(\"path\").attr(\"transform\",null);var z=\"translate(\"+r+\",\"+(n+a)+\")\";l.layers.baxis.attr(\"transform\",z),l.layers.bgrid.attr(\"transform\",z);var D=\"translate(\"+(r+i/2)+\",\"+n+\")rotate(30)\";l.layers.aaxis.attr(\"transform\",D),l.layers.agrid.attr(\"transform\",D);var 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../contour/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"../contour/style\"),n.moduleType=\"trace\",n.name=\"contourcarpet\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"cartesian\",\"carpet\",\"contour\",\"symbols\",\"showLegend\",\"hasLines\",\"carpetDependent\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":782,\"../contour/colorbar\":922,\"../contour/style\":933,\"./attributes\":935,\"./calc\":936,\"./defaults\":942,\"./plot\":947}],945:[function(t,e,r){\"use strict\";var n=t(\"../../components/drawing\"),i=t(\"../carpet/axis_aligned_line\"),a=t(\"../../lib\");e.exports=function(t,e,r,o,s,l,u,c){function h(t){return Math.abs(t[1]-r[0][1])<S}function f(t){return Math.abs(t[1]-r[2][1])<S}function d(t){return Math.abs(t[0]-r[0][0])<T}function p(t){return Math.abs(t[0]-r[2][0])<T}function m(t,e){var 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lo[1], hi[1]),\\n clamp(m[2], lo[2], hi[2]),\\n clamp(m[3], lo[3], hi[3]));\\n}\\n\\nbool mshow(mat4 p, mat4 lo, mat4 hi) {\\n return mclamp(p, lo, hi) == p;\\n}\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) * unit, unit);\\n}\\n\\nvoid main() {\\n\\n float x = 0.5 * sign(pf[3]) + 0.5;\\n float prominence = abs(pf[3]);\\n float depth = 1.0 - prominence;\\n\\n mat4 pA = mat4(p0, p1, p2, p3);\\n mat4 pB = mat4(p4, p5, p6, p7);\\n mat4 pC = mat4(p8, p9, pa, pb);\\n mat4 pD = mat4(pc, pd, pe, abs(pf));\\n\\n float show = float(mshow(pA, loA, hiA) &&\\n mshow(pB, loB, hiB) &&\\n mshow(pC, loC, hiC) &&\\n mshow(pD, loD, hiD));\\n\\n vec2 yy = show * vec2(val(pA, dim2A) + val(pB, dim2B) + val(pC, dim2C) + val(pD, dim2D),\\n val(pA, dim1A) + val(pB, dim1B) + val(pC, dim1C) + val(pD, dim1D));\\n\\n vec2 dimensionToggle = vec2(x, 1.0 - x);\\n\\n vec2 scatterToggle = vec2(scatter, 1.0 - scatter);\\n\\n float y = dot(yy, dimensionToggle);\\n mat2 xy = mat2(viewBoxSize * yy + dimensionToggle, viewBoxSize * vec2(x, y));\\n\\n vec2 viewBoxXY = viewBoxPosition + xy * scatterToggle;\\n\\n float depthOrHide = depth + 2.0 * (1.0 - show);\\n\\n gl_Position = vec4(\\n xyProjection * (2.0 * viewBoxXY / resolution - 1.0),\\n depthOrHide,\\n 1.0\\n );\\n\\n // visible coloring\\n float clampedColorIndex = clamp((prominence - colorClamp[0]) / (colorClamp[1] - colorClamp[0]), 0.0, 1.0);\\n fragColor = texture2D(palette, vec2((clampedColorIndex * 255.0 + 0.5) / 256.0, 0.5));\\n}\\n\",frag:\"precision lowp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n gl_FragColor = 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l},{\"../../components/color\":604,\"./helpers\":1011,\"fast-isnumeric\":131,tinycolor2:534}],1010:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r,a){function o(r,a){return n.coerce(t,e,i,r,a)}var s=n.coerceFont,l=o(\"values\");if(!Array.isArray(l)\|\|!l.length)return void(e.visible=!1);var u=o(\"labels\");Array.isArray(u)\|\|(o(\"label0\"),o(\"dlabel\")),o(\"marker.line.width\")&&o(\"marker.line.color\");var c=o(\"marker.colors\");Array.isArray(c)\|\|(e.marker.colors=[]),o(\"scalegroup\");var h=o(\"text\"),f=o(\"textinfo\",Array.isArray(h)?\"text+percent\":\"percent\");if(o(\"hovertext\"),f&&\"none\"!==f){var d=o(\"textposition\"),p=Array.isArray(d)\|\|\"auto\"===d,m=p\|\|\"inside\"===d,v=p\|\|\"outside\"===d;if(m\|\|v){var g=s(o,\"textfont\",a.font);m&&s(o,\"insidetextfont\",g),v&&s(o,\"outsidetextfont\",g)}}o(\"domain.x\"),o(\"domain.y\"),o(\"hole\"),o(\"sort\"),o(\"direction\"),o(\"rotation\"),o(\"pull\")}},{\"../../lib\":728,\"./attributes\":1007}],1011:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");r.formatPiePercent=function(t,e){var r=(100t).toPrecision(3);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return-1!==r.lastIndexOf(\".\")&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)}},{\"../../lib\":728}],1012:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":1007,\"./base_plot\":1008,\"./calc\":1009,\"./defaults\":1010,\"./layout_attributes\":1013,\"./layout_defaults\":1014,\"./plot\":1015,\"./style\":1016,\"./style_one\":1017}],1013:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"}}},{}],1014:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,a){n.coerce(t,e,i,r,a)}(\"hiddenlabels\")}},{\"../../lib\":728,\"./layout_attributes\":1013}],1015:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.widtht.width+t.heightt.height),a=t.width/t.height,o=Math.PIMath.min(e.v/r.vTotal,.5),s=1-r.trace.hole,l=i(e,r),u={scale:lr.r2/n,rCenter:1-l,rotate:0};if(u.scale>=1)return u;var c=a+1/(2Math.tan(o)),h=r.rMath.min(1/(Math.sqrt(cc+.5)+c),s/(Math.sqrt(aa+s/2)+a)),f={scale:2h/t.height,rCenter:Math.cos(h/r.r)-ha/r.r,rotate:(180/Math.PIe.midangle+720)%180-90},d=1/a,p=d+1/(2Math.tan(o)),m=r.rMath.min(1/(Math.sqrt(pp+.5)+p),s/(Math.sqrt(dd+s/2)+d)),v={scale:2m/t.width,rCenter:Math.cos(m/r.r)-m/a/r.r,rotate:(180/Math.PIe.midangle+810)%180-90},g=v.scale>f.scale?v:f;return u.scale<1&&g.scale>u.scale?g:u}function i(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PIMath.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function a(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i=-1),n<0&&(a=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)(i>0?1:-1)/2,y:a/(1+rr/(nn)),outside:!0}}function o(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var i,a,o,s,l,u,c,h,f,d,p,m,v;for(a=0;a<2;a++)for(o=a?r:n,l=a?Math.max:Math.min,c=a?1:-1,i=0;i<2;i++){for(s=i?Math.max:Math.min,u=i?1:-1,h=t[a][i],h.sort(o),f=t[1-a][i],d=f.concat(h),m=[],p=0;p<h.length;p++)void 0!==h[p].yLabelMid&&m.push(h[p]);for(v=!1,p=0;a&&p<f.length;p++)if(void 0!==f[p].yLabelMid){v=f[p];break}for(p=0;p<m.length;p++){var g=p&&m[p-1];v&&!p&&(g=v),function(t,r){r\|\|(r={});var n,i,o,h,f,p,m=r.labelExtraY+(a?r.yLabelMax:r.yLabelMin),v=a?t.yLabelMin:t.yLabelMax,g=a?t.yLabelMax:t.yLabelMin,y=t.cyFinal+l(t.px0[1],t.px1[1]),b=m-v;if(bc>0&&(t.labelExtraY=b),Array.isArray(e.pull))for(i=0;i<d.length;i++)(o=d[i])===t\|\|(e.pull[t.i]\|\|0)>=e.pull[o.i]\|\|((t.pxmid[1]-o.pxmid[1])c>0?(h=o.cyFinal+l(o.px0[1],o.px1[1]),(b=h-v-t.labelExtraY)c>0&&(t.labelExtraY+=b)):(g+t.labelExtraY-y)c>0&&(n=3uMath.abs(i-d.indexOf(t)),f=o.cxFinal+s(o.px0[0],o.px1[0]),(p=f+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)u>0&&(t.labelExtraX+=p)))}(m[p],g)}}}function s(t,e){var r,n,i,a,o,s,l,c,h,f,d=[];for(i=0;i<t.length;i++){if(o=t[i][0],s=o.trace,r=e.w(s.domain.x[1]-s.domain.x[0]),n=e.h(s.domain.y[1]-s.domain.y[0]),l=s.tiltaxisMath.PI/180,c=s.pull,Array.isArray(c))for(c=0,a=0;a<s.pull.length;a++)s.pull[a]>c&&(c=s.pull[a]);o.r=Math.min(r/u(s.tilt,Math.sin(l),s.depth),n/u(s.tilt,Math.cos(l),s.depth))/(2+2c),o.cx=e.l+e.w(s.domain.x[1]+s.domain.x[0])/2,o.cy=e.t+e.h(2-s.domain.y[1]-s.domain.y[0])/2,s.scalegroup&&-1===d.indexOf(s.scalegroup)&&d.push(s.scalegroup)}for(a=0;a<d.length;a++){for(f=1/0,h=d[a],i=0;i<t.length;i++)o=t[i][0],o.trace.scalegroup===h&&(f=Math.min(f,o.ro.r/o.vTotal));for(i=0;i<t.length;i++)o=t[i][0],o.trace.scalegroup===h&&(o.r=Math.sqrt(fo.vTotal))}}function l(t){function e(t){var e=h.rMath.sin(t),r=-h.rMath.cos(t);return d?[e(1-snn)+ros,eos+r(1-sii),Math.sin(a)(ri-en)]:[e,r]}var r,n,i,a,o,s,l,u,c,h=t[0],f=h.trace,d=f.tilt,p=f.rotationMath.PI/180,m=2Math.PI/h.vTotal,v=\"px0\",g=\"px1\";if(\"counterclockwise\"===f.direction){for(l=0;l<t.length&&t[l].hidden;l++);if(l===t.length)return;p+=mt[l].v,m=-1,v=\"px1\",g=\"px0\"}for(d&&(a=dMath.PI/180,r=f.tiltaxisMath.PI/180,o=Math.sin(r)Math.cos(r),s=1-Math.cos(a),n=Math.sin(r),i=Math.cos(r)),c=e(p),l=0;l<t.length;l++)u=t[l],u.hidden\|\|(u[v]=c,p+=mu.v/2,u.pxmid=e(p),u.midangle=p,p+=mu.v/2,c=e(p),u[g]=c,u.largeArc=u.v>h.vTotal/2?1:0)}function u(t,e,r){if(!t)return 1;var n=Math.sin(tMath.PI/180);return Math.max(.01,rnMath.abs(e)+2Math.sqrt(1-nnee))}var c=t(\"d3\"),h=t(\"../../components/fx\"),f=t(\"../../components/color\"),d=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),m=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;s(e,r._size);var u=r._pielayer.selectAll(\"g.trace\").data(e);u.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),u.exit().remove(),u.order(),u.each(function(e){var s=c.select(this),u=e[0],v=u.trace,g=(v.depth\|\|0)u.rMath.sin(0)/2,y=v.tiltaxis\|\|0,b=yMath.PI/180,x=[gMath.sin(b),gMath.cos(b)],_=u.rMath.cos(0),w=s.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),l(e),s.selectAll(\".top\").each(function(){var s=c.select(this).selectAll(\"g.slice\").data(e);s.enter().append(\"g\").classed(\"slice\",!0),s.exit().remove();var l=[[[],[]],[[],[]]],g=!1;s.each(function(e){function o(n){n.originalEvent=c.event;var a=t._fullLayout,o=t._fullData[v.index],s=h.castHoverinfo(o,a,e.i);if(\"all\"===s&&(s=\"label+text+value+percent+name\"),t._dragging\|\|!1===a.hovermode\|\|\"none\"===s\|\|\"skip\"===s\|\|!s)return void h.hover(t,n,\"pie\");var l=i(e,u),f=w+e.pxmid[0](1-l),d=M+e.pxmid[1](1-l),p=r.separators,g=[];-1!==s.indexOf(\"label\")&&g.push(e.label),-1!==s.indexOf(\"text\")&&(o.hovertext?g.push(Array.isArray(o.hovertext)?o.hovertext[e.i]:o.hovertext):o.text&&o.text[e.i]&&g.push(o.text[e.i])),-1!==s.indexOf(\"value\")&&g.push(m.formatPieValue(e.v,p)),-1!==s.indexOf(\"percent\")&&g.push(m.formatPiePercent(e.v/u.vTotal,p)),h.loneHover({x0:f-lu.r,x1:f+lu.r,y:d,text:g.join(\"<br>\"),name:-1!==s.indexOf(\"name\")?o.name:void 0,idealAlign:e.pxmid[0]<0?\"left\":\"right\",color:h.castHoverOption(v,e.i,\"bgcolor\")\|\|e.color,borderColor:h.castHoverOption(v,e.i,\"bordercolor\"),fontFamily:h.castHoverOption(v,e.i,\"font.family\"),fontSize:h.castHoverOption(v,e.i,\"font.size\"),fontColor:h.castHoverOption(v,e.i,\"font.color\")},{container:a._hoverlayer.node(),outerContainer:a._paper.node(),gd:t}),h.hover(t,n,\"pie\"),T=!0}function s(e){e.originalEvent=c.event,t.emit(\"plotly_unhover\",{event:c.event,points:[e]}),T&&(h.loneUnhover(r._hoverlayer.node()),T=!1)}function f(){t._hoverdata=[e],t._hoverdata.trace=u.trace,h.click(t,c.event)}function b(t,r,n,i){return\"a\"+iu.r+\",\"+i_+\" \"+y+\" \"+e.largeArc+(n?\" 1 \":\" 0 \")+i(r[0]-t[0])+\",\"+i(r[1]-t[1])}if(e.hidden)return void c.select(this).selectAll(\"path,g\").remove();e.pointNumber=e.i,e.curveNumber=v.index,l[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var w=u.cx+x[0],M=u.cy+x[1],k=c.select(this),A=k.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),k.select(\"path.textline\").remove(),k.on(\"mouseover\",o).on(\"mouseout\",s).on(\"click\",f),v.pull){var S=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)\|\|0;S>0&&(w+=Se.pxmid[0],M+=Se.pxmid[1])}e.cxFinal=w,e.cyFinal=M;var E=v.hole;if(e.v===u.vTotal){var L=\"M\"+(w+e.px0[0])+\",\"+(M+e.px0[1])+b(e.px0,e.pxmid,!0,1)+b(e.pxmid,e.px0,!0,1)+\"Z\";E?A.attr(\"d\",\"M\"+(w+Ee.px0[0])+\",\"+(M+Ee.px0[1])+b(e.px0,e.pxmid,!1,E)+b(e.pxmid,e.px0,!1,E)+\"Z\"+L):A.attr(\"d\",L)}else{var C=b(e.px0,e.px1,!0,1);if(E){var I=1-E;A.attr(\"d\",\"M\"+(w+Ee.px1[0])+\",\"+(M+Ee.px1[1])+b(e.px1,e.px0,!1,E)+\"l\"+Ie.px0[0]+\",\"+Ie.px0[1]+C+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+M+\"l\"+e.px0[0]+\",\"+e.px0[1]+C+\"Z\")}var z=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,D=k.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==z?[0]:[]);D.enter().append(\"g\").classed(\"slicetext\",!0),D.exit().remove(),D.each(function(){var r=c.select(this).selectAll(\"text\").data([0]);r.enter().append(\"text\").attr(\"data-notex\",1),r.exit().remove(),r.text(e.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(d.font,\"outside\"===z?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans,t);var i,o=d.bBox(r.node());\"outside\"===z?i=a(o,e):(i=n(o,e,u),\"auto\"===z&&i.scale<1&&(r.call(d.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size\|\|(o=d.bBox(r.node())),i=a(o,e)));var s=w+e.pxmid[0]i.rCenter+(i.x\|\|0),l=M+e.pxmid[1]i.rCenter+(i.y\|\|0);i.outside&&(e.yLabelMin=l-o.height/2,e.yLabelMid=l,e.yLabelMax=l+o.height/2,e.labelExtraX=0,e.labelExtraY=0,g=!0),r.attr(\"transform\",\"translate(\"+s+\",\"+l+\")\"+(i.scale<1?\"scale(\"+i.scale+\")\":\"\")+(i.rotate?\"rotate(\"+i.rotate+\")\":\"\")+\"translate(\"+-(o.left+o.right)/2+\",\"+-(o.top+o.bottom)/2+\")\")})}),g&&o(l,v),s.each(function(t){if(t.labelExtraX\|\|t.labelExtraY){var e=c.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],i=t.cyFinal+t.pxmid[1],a=\"M\"+n+\",\"+i,o=(t.yLabelMax-t.yLabelMin)(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var s=t.labelExtraXt.pxmid[1]/t.pxmid[0],l=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(s)>Math.abs(l)?a+=\"l\"+lt.pxmid[0]/t.pxmid[1]+\",\"+l+\"H\"+(n+t.labelExtraX+o):a+=\"l\"+t.labelExtraX+\",\"+s+\"v\"+(l-s)+\"h\"+o}else a+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+o;e.append(\"path\").classed(\"textline\",!0).call(f.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:a,fill:\"none\"})}})})}),setTimeout(function(){u.selectAll(\"tspan\").each(function(){var t=c.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":604,\"../../components/drawing\":628,\"../../components/fx\":645,\"../../lib/svg_text_utils\":750,\"./helpers\":1011,d3:122}],1016:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,a=n.select(this);a.style({opacity:r.opacity}),a.selectAll(\".top path.surface\").each(function(t){n.select(this).call(i,t,r)})})}},{\"./style_one\":1017,d3:122}],1017:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var i=r.marker.line.color;Array.isArray(i)&&(i=i[e.i]\|\|n.defaultLine);var a=r.marker.line.width\|\|0;Array.isArray(a)&&(a=a[e.i]\|\|0),t.style({\"stroke-width\":a}).call(n.fill,e.color).call(n.stroke,i)}},{\"../../components/color\":604}],1018:[function(t,e,r){\"use strict\";var n=t(\"../scattergl/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"}}},{\"../scattergl/attributes\":1077}],1019:[function(t,e,r){\"use strict\";function n(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=a(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}function i(t,e){var r=new n(t,e.uid);return r.update(e),r}var a=t(\"gl-pointcloud2d\"),o=t(\"../../lib/str2rgbarray\"),s=t(\"../scatter/get_trace_color\"),l=[\"xaxis\",\"yaxis\"],u=n.prototype;u.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2e],this.pickXYData[2e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},u.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=s(t,{})},u.updateFast=function(t){var e,r,n,i,a,s,l=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,c=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,f=t.indices,d=this.bounds;if(c){if(n=c,e=c.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(s=0;s<e;s++)i=n[2s],a=n[2s+1],i<d[0]&&(d[0]=i),i>d[2]&&(d[2]=i),a<d[1]&&(d[1]=a),a>d[3]&&(d[3]=a);if(f)r=f;else for(r=new Int32Array(e),s=0;s<e;s++)r[s]=s}else for(e=l.length,n=new Float32Array(2e),r=new 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r,n,i,a=e\|\|.5,o=0;o<2;o++)r=this.scene[l[o]],n=r._min,n\|\|(n=[]),n.push({val:t[o],pad:a}),i=r._max,i\|\|(i=[]),i.push({val:t[o+2],pad:a})},u.dispose=function(){this.pointcloud.dispose()},e.exports=i},{\"../../lib/str2rgbarray\":749,\"../scatter/get_trace_color\":1040,\"gl-pointcloud2d\":230}],1020:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\")}},{\"../../lib\":728,\"./attributes\":1018}],1021:[function(t,e,r){\"use strict\";var 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strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../plots/plots\"),a=t(\"./plot\"),o=t(\"../../components/fx/layout_attributes\");r.name=\"sankey\",r.attr=\"type\",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){var e=i.getSubplotCalcData(t.calcdata,\"sankey\",\"sankey\");e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"sankey\"),a=e._has&&e._has(\"sankey\");i&&!a&&n._paperdiv.selectAll(\".sankey\").remove()}},{\"../../components/fx/layout_attributes\":646,\"../../plot_api/edit_types\":756,\"../../plots/plots\":831,\"./plot\":1028}],1024:[function(t,e,r){\"use strict\";function n(t,e,r){for(var n=t.map(function(){return[]}),a=0;a<Math.min(e.length,r.length);a++){if(e[a]===r[a])return!0;n[e[a]].push(r[a])}return i(n).components.some(function(t){return t.length>1})}var i=t(\"strongly-connected-components\"),a=t(\"../../lib\");e.exports=function(t,e){return n(e.node.label,e.link.source,e.link.target)&&(a.error(\"Circularity is present in the Sankey data. Removing all nodes and links.\"),e.link.label=[],e.link.source=[],e.link.target=[],e.link.value=[],e.link.color=[],e.node.label=[],e.node.color=[]),[{link:e.link,node:e.node}]}},{\"../../lib\":728,\"strongly-connected-components\":528}],1025:[function(t,e,r){\"use strict\";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:\"cubic-in-out\"}},{}],1026:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../components/color/attributes\").defaults,o=t(\"../../components/color\"),s=t(\"tinycolor2\");e.exports=function(t,e,r,l){function u(r,a){return n.coerce(t,e,i,r,a)}u(\"node.label\"),u(\"node.pad\"),u(\"node.thickness\"),u(\"node.line.color\"),u(\"node.line.width\");var c=function(t){return a[t%a.length]};u(\"node.color\",e.node.label.map(function(t,e){return o.addOpacity(c(e),.8)})),u(\"link.label\"),u(\"link.source\"),u(\"link.target\"),u(\"link.value\"),u(\"link.line.color\"),u(\"link.line.width\"),u(\"link.color\",e.link.value.map(function(){return s(l.paper_bgcolor).getLuminance()<.333?\"rgba(255, 255, 255, 0.6)\":\"rgba(0, 0, 0, 0.2)\"})),u(\"domain.x\"),u(\"domain.y\"),u(\"orientation\"),u(\"valueformat\"),u(\"valuesuffix\"),u(\"arrangement\"),n.coerceFont(u,\"textfont\",n.extendFlat({},l.font));var h=function(t,r){return-1===e.link.source.indexOf(r)&&-1===e.link.target.indexOf(r)};e.node.label.some(h)&&n.warn(\"Some of the nodes are neither sources nor targets, they will not be displayed.\")}},{\"../../components/color\":604,\"../../components/color/attributes\":603,\"../../lib\":728,\"./attributes\":1022,tinycolor2:534}],1027:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.moduleType=\"trace\",n.name=\"sankey\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"noOpacity\"],n.meta={},e.exports=n},{\"./attributes\":1022,\"./base_plot\":1023,\"./calc\":1024,\"./defaults\":1026,\"./plot\":1028}],1028:[function(t,e,r){\"use strict\";function n(t){return\"\"!==t}function i(t,e){return t.filter(function(t){return t.key===e.traceId})}function a(t,e){p.select(t).select(\"path\").style(\"fill-opacity\",e),p.select(t).select(\"rect\").style(\"fill-opacity\",e)}function o(t){p.select(t).select(\"text.name\").style(\"fill\",\"black\")}function s(t){return function(e){return-1!==t.node.sourceLinks.indexOf(e.link)\|\|-1!==t.node.targetLinks.indexOf(e.link)}}function l(t){return function(e){return-1!==e.node.sourceLinks.indexOf(t.link)\|\|-1!==e.node.targetLinks.indexOf(t.link)}}function u(t,e,r){\n", "e&&r&&i(r,e).selectAll(\".sankeyLink\").filter(s(e)).call(h.bind(0,e,r,!1))}function c(t,e,r){e&&r&&i(r,e).selectAll(\".sankeyLink\").filter(s(e)).call(f.bind(0,e,r,!1))}function h(t,e,r,n){var a=n.datum().link.label;n.style(\"fill-opacity\",.4),a&&i(e,t).selectAll(\".sankeyLink\").filter(function(t){return t.link.label===a}).style(\"fill-opacity\",.4),r&&i(e,t).selectAll(\".sankeyNode\").filter(l(t)).call(u)}function f(t,e,r,n){var a=n.datum().link.label;n.style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),a&&i(e,t).selectAll(\".sankeyLink\").filter(function(t){return t.link.label===a}).style(\"fill-opacity\",function(t){return t.tinyColorAlpha}),r&&i(e,t).selectAll(\".sankeyNode\").filter(l(t)).call(c)}function d(t,e){var r=t.hoverlabel\|\|{},n=y.nestedProperty(r,e).get();return!Array.isArray(n)&&n}var p=t(\"d3\"),m=t(\"./render\"),v=t(\"../../components/fx\"),g=t(\"../../components/color\"),y=t(\"../../lib\");e.exports=function(t,e){var r=t._fullLayout,i=r._paper,s=r._size,l=function(e,r){var n=r.link;n.originalEvent=p.event,t._hoverdata=[n],v.click(t,{target:!0})},y=function(e,r,n){var i=r.link;i.originalEvent=p.event,p.select(e).call(h.bind(0,r,n,!0)),v.hover(t,i,\"sankey\")},b=function(e,i){var s=i.link.trace,l=t._fullLayout._paperdiv.node().getBoundingClientRect(),u=e.getBoundingClientRect(),c=u.left+u.width/2,h=u.top+u.height/2,f=v.loneHover({x:c-l.left,y:h-l.top,name:p.format(i.valueFormat)(i.link.value)+i.valueSuffix,text:[i.link.label\|\|\"\",[\"Source:\",i.link.source.label].join(\" \"),[\"Target:\",i.link.target.label].join(\" 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s=i.node.trace,l=p.select(e).select(\".nodeRect\"),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),c=l.node().getBoundingClientRect(),h=c.left-2-u.left,f=c.right+2-u.left,m=c.top+c.height/4-u.top,g=v.loneHover({x0:h,x1:f,y:m,name:p.format(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,[\"Incoming flow count:\",i.node.targetLinks.length].join(\" \"),[\"Outgoing flow count:\",i.node.sourceLinks.length].join(\" 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n,i=a(e).trace,o=i.domain,s=i.node,l=i.link,c=i.arrangement,h=\"h\"===i.orientation,f=i.node.pad,d=i.node.thickness,p=i.node.line.color,m=i.node.line.width,v=i.link.line.color,g=i.link.line.width,y=i.valueformat,b=i.valuesuffix,x=i.textfont,_=t.width(o.x[1]-o.x[0]),w=t.height(o.y[1]-o.y[0]),M=s.label.map(function(t,e){return{pointNumber:e,label:t,color:N.isArray(s.color)?s.color[e]:s.color}}),k=l.value.map(function(t,e){return{pointNumber:e,label:l.label[e],color:N.isArray(l.color)?l.color[e]:l.color,source:l.source[e],target:l.target[e],value:t}}),A=F().size(h?[_,w]:[w,_]).nodeWidth(d).nodePadding(f).nodes(M).links(k).layout(z.sankeyIterations),T=A.nodes(),S=0;S<T.length;S++)n=T[S],n.width=_,n.height=w;return 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y=[];(o.hasMarkers(e)\|\|o.hasText(e))&&(m(\"marker.maxdisplayed\"),y.push(\"points\")),m(\"fill\"),\"none\"!==e.fill&&(f(t,e,r,m),o.hasLines(e)\|\|c(t,e,m)),\"tonext\"!==e.fill&&\"toself\"!==e.fill\|\|y.push(\"fills\"),m(\"hoveron\",y.join(\"+\")\|\|\"points\"),d(t,e,r,{axis:\"y\"}),d(t,e,r,{axis:\"x\",inherit:\"y\"}),m(\"cliponaxis\")}},{\"../../components/errorbars/defaults\":633,\"../../lib\":728,\"./attributes\":1031,\"./constants\":1036,\"./fillcolor_defaults\":1039,\"./line_defaults\":1043,\"./line_shape_defaults\":1045,\"./marker_defaults\":1048,\"./subtypes\":1052,\"./text_defaults\":1053,\"./xy_defaults\":1054}],1038:[function(t,e,r){\"use strict\";function n(t){return t\|\|0===t}var i=t(\"../../lib\");e.exports=function(t,e,r){var a=Array.isArray(r)?function(t){r.push(t)}:function(t){r.text=t},o=i.extractOption(t,e,\"htx\",\"hovertext\");if(n(o))return a(o);var s=i.extractOption(t,e,\"tx\",\"text\");return n(s)?a(s):void 0}},{\"../../lib\":728}],1039:[function(t,e,r){\"use 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n=t(\"../../registry\");e.exports=function(t,e,r,i){var a,o=i(\"x\"),s=i(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),o)s?(a=Math.min(o.length,s.length),a<o.length&&(e.x=o.slice(0,a)),a<s.length&&(e.y=s.slice(0,a))):(a=o.length,i(\"y0\"),i(\"dy\"));else{if(!s)return 0;a=e.y.length,i(\"x0\"),i(\"dx\")}return a}},{\"../../registry\":846}],1055:[function(t,e,r){\"use strict\";function n(t){return{show:{valType:\"boolean\",dflt:!1},opacity:{valType:\"number\",min:0,max:1,dflt:1},scale:{valType:\"number\",min:0,max:10,dflt:2/3}}}var i=t(\"../scatter/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/errorbars/attributes\"),s=t(\"../../constants/gl3d_dashes\"),l=t(\"../../constants/gl3d_markers\"),u=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,h=i.line,f=i.marker,d=f.line,p=e.exports=c({x:i.x,y:i.y,z:{valType:\"data_array\"},text:u({},i.text,{}),hovertext:u({},i.hovertext,{}),mode:u({},i.mode,{dflt:\"lines+markers\"}),surfaceaxis:{valType:\"enumerated\",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:\"color\"},projection:{x:n(\"x\"),y:n(\"y\"),z:n(\"z\")},connectgaps:i.connectgaps,line:u({width:h.width,dash:{valType:\"enumerated\",values:Object.keys(s),dflt:\"solid\"},showscale:{valType:\"boolean\",dflt:!1}},a(\"line\")),marker:u({symbol:{valType:\"enumerated\",values:Object.keys(l),dflt:\"circle\",arrayOk:!0},size:u({},f.size,{dflt:8}),sizeref:f.sizeref,sizemin:f.sizemin,sizemode:f.sizemode,opacity:u({},f.opacity,{arrayOk:!1}),showscale:f.showscale,colorbar:f.colorbar,line:u({width:u({},d.width,{arrayOk:!1})},a(\"marker.line\"))},a(\"marker\")),textposition:u({},i.textposition,{dflt:\"top 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e=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[0,0,0],i=0;i<3;i++){var a=t[i];a&&!1!==a.copy_zstyle&&(a=t[2]),a&&(e[i]=a.width/2,r[i]=b(a.color),n=a.thickness)}return{capSize:e,color:r,lineWidth:n}}function o(t){var e=[0,0];return Array.isArray(t)?[0,-1]:(t.indexOf(\"bottom\")>=0&&(e[1]+=1),t.indexOf(\"top\")>=0&&(e[1]-=1),t.indexOf(\"left\")>=0&&(e[0]-=1),t.indexOf(\"right\")>=0&&(e[0]+=1),e)}function s(t,e){return e(4t)}function l(t){return M[t]}function u(t,e,r,n,i){var a=null;if(Array.isArray(t)){a=[];for(var o=0;o<e;o++)void 0===t[o]?a[o]=n:a[o]=r(t[o],i)}else a=r(t,y.identity);return a}function c(t,e){var r,n,i,c,h,f,d=[],p=t.fullSceneLayout,m=t.dataScale,v=p.xaxis,g=p.yaxis,w=p.zaxis,M=e.marker,A=e.line,T=e.x\|\|[],S=e.y\|\|[],E=e.z\|\|[],L=T.length,C=e.xcalendar,I=e.ycalendar,z=e.zcalendar;for(n=0;n<L;n++)i=v.d2l(T[n],0,C)m[0],c=g.d2l(S[n],0,I)m[1],h=w.d2l(E[n],0,z)m[2],d[n]=[i,c,h];if(Array.isArray(e.text))f=e.text;else if(void 0!==e.text)for(f=new 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g=\"projection.\"+m[v];f(g+\".show\")&&(f(g+\".opacity\"),f(g+\".scale\"))}c(t,e,r,{axis:\"z\"}),c(t,e,r,{axis:\"y\",inherit:\"z\"}),c(t,e,r,{axis:\"x\",inherit:\"z\"})}},{\"../../components/errorbars/defaults\":633,\"../../lib\":728,\"../../registry\":846,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1055}],1060:[function(t,e,r){\"use strict\";var n={};n.plot=t(\"./convert\"),n.attributes=t(\"./attributes\"),n.markerSymbols=t(\"../../constants/gl3d_markers\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.moduleType=\"trace\",n.name=\"scatter3d\",n.basePlotModule=t(\"../../plots/gl3d\"),n.categories=[\"gl3d\",\"symbols\",\"markerColorscale\",\"showLegend\"],n.meta={},e.exports=n},{\"../../constants/gl3d_markers\":705,\"../../plots/gl3d\":811,\"../scatter/colorbar\":1034,\"./attributes\":1055,\"./calc\":1056,\"./convert\":1058,\"./defaults\":1059}],1061:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=n.marker,u=n.line,c=l.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:s({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:s({symbol:l.symbol,opacity:l.opacity,maxdisplayed:l.maxdisplayed,size:l.size,sizeref:l.sizeref,sizemin:l.sizemin,sizemode:l.sizemode,line:s({width:c.width,editType:\"calc\"},a(\"marker\".line)),gradient:l.gradient,editType:\"calc\"},a(\"marker\"),{showscale:l.showscale,colorbar:o}),textfont:n.textfont,textposition:n.textposition,hoverinfo:s({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":605,\"../../components/colorscale/color_attributes\":611,\"../../lib/extend\":717,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1062:[function(t,e,r){\"use 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o(e),s(d,e),d}}},{\"../../plots/cartesian/axes\":772,\"../carpet/lookup_carpetid\":903,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035,\"../scatter/subtypes\":1052,\"fast-isnumeric\":131}],1063:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),u=t(\"../scatter/text_defaults\"),c=t(\"../scatter/fillcolor_defaults\"),h=t(\"./attributes\");e.exports=function(t,e,r,f){function d(r,i){return n.coerce(t,e,h,r,i)}d(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var p,m=d(\"a\"),v=d(\"b\");if(!(p=Math.min(m.length,v.length)))return void(e.visible=!1);m&&p<m.length&&(e.a=m.slice(0,p)),v&&p<v.length&&(e.b=v.slice(0,p)),d(\"text\"),d(\"mode\",p<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,f,d),l(t,e,d),d(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,f,d,{gradient:!0}),a.hasText(e)&&u(t,e,f,d);var g=[];(a.hasMarkers(e)\|\|a.hasText(e))&&(d(\"marker.maxdisplayed\"),g.push(\"points\")),d(\"fill\"),\"none\"!==e.fill&&(c(t,e,r,d),a.hasLines(e)\|\|l(t,e,d)),\"tonext\"!==e.fill&&\"toself\"!==e.fill\|\|g.push(\"fills\"),d(\"hoveron\",g.join(\"+\")\|\|\"points\")}},{\"../../lib\":728,\"../scatter/constants\":1036,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/line_shape_defaults\":1045,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1061}],1064:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\");e.exports=function(t,e,r,i){function a(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}var o=n(t,e,r,i);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,u=t.xa._length,c=ul/2,h=u-c;return s.x0=Math.max(Math.min(s.x0,h),c),s.x1=Math.max(Math.min(s.x1,h),c),o}var f=s.cd[s.index];s.a=f.a,s.b=f.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var d=s.trace,p=d._carpet,m=f.hi\|\|d.hoverinfo,v=m.split(\"+\"),g=[];-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\"]),-1!==v.indexOf(\"a\")&&a(p.aaxis,f.a),-1!==v.indexOf(\"b\")&&a(p.baxis,f.b);var y=p.ab2ij([f.a,f.b]),b=Math.floor(y[0]),x=y[0]-b,_=Math.floor(y[1]),w=y[1]-_,M=p.evalxy([],b,_,x,w);return g.push(\"y: \"+M[1].toFixed(3)),s.extraText=g.join(\"<br>\"),o}}},{\"../scatter/hover\":1041}],1065:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattercarpet\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"carpet\",\"symbols\",\"markerColorscale\",\"showLegend\",\"carpetDependent\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":782,\"../scatter/colorbar\":1034,\"./attributes\":1061,\"./calc\":1062,\"./defaults\":1063,\"./hover\":1064,\"./plot\":1066,\"./select\":1067,\"./style\":1068}],1066:[function(t,e,r){\"use strict\";var n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r){var o,s,l,u=r[0][0].carpet,c={xaxis:i.getFromId(t,u.xaxis\|\|\"x\"),yaxis:i.getFromId(t,u.yaxis\|\|\"y\"),plot:e.plot};for(n(t,c,r),o=0;o<r.length;o++)s=r[o][0].trace,l=c.plot.selectAll(\"g.trace\"+s.uid+\" .js-line\"),a.setClipUrl(l,u._clipPathId)}},{\"../../components/drawing\":628,\"../../plots/cartesian/axes\":772,\"../scatter/plot\":1049}],1067:[function(t,e,r){\"use strict\";var n=t(\"../scatter/select\");e.exports=function(t,e){var r=n(t,e);if(r){var i,a,o,s=t.cd;for(o=0;o<r.length;o++)i=r[o],a=s[i.pointNumber],i.a=a.a,i.b=a.b,i.c=a.c,delete i.x,delete i.y;return r}}},{\"../scatter/select\":1050}],1068:[function(t,e,r){\"use strict\";var n=t(\"../scatter/style\");e.exports=function(t){for(var e=t._fullLayout._modules,r=0;r<e.length;r++)if(\"scatter\"===e[r].name)return;n(t)}},{\"../scatter/style\":1051}],1069:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,u=n.marker,c=n.line,h=u.line;e.exports=l({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country names\"],dflt:\"ISO-3\"},mode:s({},n.mode,{dflt:\"markers\"}),text:s({},n.text,{}),hovertext:s({},n.hovertext,{}),textfont:n.textfont,textposition:n.textposition,line:{color:c.color,width:c.width,dash:o},connectgaps:n.connectgaps,marker:s({symbol:u.symbol,opacity:u.opacity,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,showscale:u.showscale,colorbar:u.colorbar,line:s({width:h.width},a(\"marker.line\")),gradient:u.gradient},a(\"marker\")),fill:{valType:\"enumerated\",values:[\"none\",\"toself\"],dflt:\"none\"},fillcolor:n.fillcolor,hoverinfo:s({},i.hoverinfo,{flags:[\"lon\",\"lat\",\"location\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorscale/color_attributes\":611,\"../../components/drawing/attributes\":627,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1070:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/colorscale_calc\"),o=t(\"../scatter/arrays_to_calcdata\");e.exports=function(t,e){for(var r=Array.isArray(e.locations),s=r?e.locations.length:e.lon.length,l=new Array(s),u=0;u<s;u++){var c=l[u]={};if(r){var h=e.locations[u];c.loc=\"string\"==typeof h?h:null}else{var f=e.lon[u],d=e.lat[u];n(f)&&n(d)?c.lonlat=[+f,+d]:c.lonlat=[i,i]}}return o(l,e),a(e),l}},{\"../../constants/numerical\":707,\"../scatter/arrays_to_calcdata\":1030,\"../scatter/colorscale_calc\":1035,\"fast-isnumeric\":131}],1071:[function(t,e,r){\"use strict\";function n(t,e,r){var n,i,a=0,o=r(\"locations\");return o?(r(\"locationmode\"),a=o.length):(n=r(\"lon\")\|\|[],i=r(\"lat\")\|\|[],a=Math.min(n.length,i.length),a<n.length&&(e.lon=n.slice(0,a)),a<i.length&&(e.lat=i.slice(0,a)),a)}var i=t(\"../../lib\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,h){function f(r,n){return i.coerce(t,e,c,r,n)}if(!n(t,e,f))return void(e.visible=!1);f(\"text\"),f(\"hovertext\"),f(\"mode\"),a.hasLines(e)&&(s(t,e,r,h,f),f(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,f,{gradient:!0}),a.hasText(e)&&l(t,e,h,f),f(\"fill\"),\"none\"!==e.fill&&u(t,e,r,f)}},{\"../../lib\":728,\"../scatter/fillcolor_defaults\":1039,\"../scatter/line_defaults\":1043,\"../scatter/marker_defaults\":1048,\"../scatter/subtypes\":1052,\"../scatter/text_defaults\":1053,\"./attributes\":1069}],1072:[function(t,e,r){\"use strict\";e.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null,t}},{}],1073:[function(t,e,r){\"use strict\";function n(t,e,r){function n(t){return a.tickText(r,r.c2l(t),\"hover\").text+\"\\xb0\"}var i=e.hi\|\|t.hoverinfo,o=\"all\"===i?u.hoverinfo.flags:i.split(\"+\"),s=-1!==o.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==o.indexOf(\"lon\"),h=-1!==o.indexOf(\"lat\"),f=-1!==o.indexOf(\"text\"),d=[];return s?d.push(e.loc):c&&h?d.push(\"(\"+n(e.lonlat[0])+\", \"+n(e.lonlat[1])+\")\"):c?d.push(\"lon: \"+n(e.lonlat[0])):h&&d.push(\"lat: \"+n(e.lonlat[1])),f&&l(e,t,d),d.join(\"<br>\")}var i=t(\"../../components/fx\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,s=t(\"../scatter/get_trace_color\"),l=t(\"../scatter/fill_hover_text\"),u=t(\"./attributes\");e.exports=function(t,e,r){function a(t){var n=t.lonlat;if(n[0]===o)return 1/0;if(d(n))return 1/0;var i=p(n),a=p([e,r]),s=Math.abs(i[0]-a[0]),l=Math.abs(i[1]-a[1]),u=Math.max(3,t.mrc\|\|0);return Math.max(Math.sqrt(ss+ll)-u,1-3/u)}var l=t.cd,u=l[0].trace,c=t.xa,h=t.ya,f=t.subplot,d=f.projection.isLonLatOverEdges,p=f.project;if(i.getClosest(l,a,t),!1!==t.index){var m=l[t.index],v=m.lonlat,g=[c.c2p(v),h.c2p(v)],y=m.mrc\|\|1;return t.x0=g[0]-y,t.x1=g[0]+y,t.y0=g[1]-y,t.y1=g[1]+y,t.loc=m.loc,t.lon=v[0],t.lat=v[1],t.color=s(u,m),t.extraText=n(u,m,f.mockAxis),[t]}}},{\"../../components/fx\":645,\"../../constants/numerical\":707,\"../../plots/cartesian/axes\":772,\"../scatter/fill_hover_text\":1038,\"../scatter/get_trace_color\":1040,\"./attributes\":1069}],1074:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.hoverPoints=t(\"./hover\"),n.eventData=t(\"./event_data\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergeo\",n.basePlotModule=t(\"../../plots/geo\"),n.categories=[\"geo\",\"symbols\",\"markerColorscale\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/geo\":800,\"../scatter/colorbar\":1034,\"./attributes\":1069,\"./calc\":1070,\"./defaults\":1071,\"./event_data\":1072,\"./hover\":1073,\"./plot\":1075,\"./select\":1076}],1075:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0].trace;if(Array.isArray(r.locations))for(var n=c(r,e),i=r.locationmode,a=0;a<t.length;a++){var o=t[a],s=h(i,o.loc,n);o.lonlat=s?s.properties.ct:[u,u]}}function i(t){var e=t.layers.frontplot.selectAll(\".trace.scattergeo\");e.style(\"opacity\",function(t){return t[0].trace.opacity}),e.each(function(e){var r=e[0].trace,n=a.select(this);n.selectAll(\"path.point\").call(o.pointStyle,r,t.graphDiv),n.selectAll(\"text\").call(o.textPointStyle,r,t.graphDiv)}),e.selectAll(\"path.js-line\").style(\"fill\",\"none\").each(function(t){var e=a.select(this),r=t.trace,n=r.line\|\|{};e.call(s.stroke,n.color).call(o.dashLine,n.dash\|\|\"\",n.width\|\|0),\"none\"!==r.fill&&e.call(s.fill,r.fillcolor)})}var a=t(\"d3\"),o=t(\"../../components/drawing\"),s=t(\"../../components/color\"),l=t(\"../../lib\"),u=t(\"../../constants/numerical\").BADNUM,c=t(\"../../lib/topojson_utils\").getTopojsonFeatures,h=t(\"../../lib/geo_location_utils\").locationToFeature,f=t(\"../../lib/geojson_utils\"),d=t(\"../scatter/subtypes\")\n", ";e.exports=function(t,e){function r(t){return t[0].trace.uid}function o(t,e){t.lonlat[0]===u&&a.select(e).remove()}for(var s=0;s<e.length;s++)n(e[s],t.topojson);var c=t.layers.frontplot.select(\".scatterlayer\").selectAll(\"g.trace.scattergeo\").data(e,r);c.enter().append(\"g\").attr(\"class\",\"trace scattergeo\"),c.exit().remove(),c.selectAll(\"\").remove(),c.each(function(t){var e=t[0].node3=a.select(this),r=t[0].trace;if(d.hasLines(r)\|\|\"none\"!==r.fill){var n=f.calcTraceToLineCoords(t),i=\"none\"!==r.fill?f.makePolygon(n):f.makeLine(n);e.selectAll(\"path.js-line\").data([{geojson:i,trace:r}]).enter().append(\"path\").classed(\"js-line\",!0).style(\"stroke-miterlimit\",2)}d.hasMarkers(r)&&e.selectAll(\"path.point\").data(l.identity).enter().append(\"path\").classed(\"point\",!0).each(function(t){o(t,this)}),d.hasText(r)&&e.selectAll(\"g\").data(l.identity).enter().append(\"g\").append(\"text\").each(function(t){o(t,this)})}),i(t)}},{\"../../components/color\":604,\"../../components/drawing\":628,\"../../constants/numerical\":707,\"../../lib\":728,\"../../lib/geo_location_utils\":720,\"../../lib/geojson_utils\":721,\"../../lib/topojson_utils\":753,\"../scatter/subtypes\":1052,d3:122}],1076:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"../../constants/interactions\").DESELECTDIM;e.exports=function(t,e){var r,a,o,s,l,u=t.cd,c=t.xaxis,h=t.yaxis,f=[],d=u[0].trace,p=u[0].node3,m=!n.hasMarkers(d)&&!n.hasText(d);if(!0===d.visible&&!m){var v=d.marker,g=Array.isArray(v.opacity)?1:v.opacity;if(!1===e)for(l=0;l<u.length;l++)u[l].dim=0;else for(l=0;l<u.length;l++)r=u[l],a=r.lonlat,o=c.c2p(a),s=h.c2p(a),e.contains([o,s])?(f.push({pointNumber:l,lon:a[0],lat:a[1]}),r.dim=0):r.dim=1;return p.selectAll(\"path.point\").style(\"opacity\",function(t){return((t.mo+1\|\|g+1)-1)(t.dim?i:1)}),p.selectAll(\"text\").style(\"opacity\",function(t){return t.dim?i:1}),f}}},{\"../../constants/interactions\":706,\"../scatter/subtypes\":1052}],1077:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../constants/gl2d_dashes\"),o=t(\"../../constants/gl2d_markers\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll,u=n.line,c=n.marker,h=c.line,f=e.exports=l({x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,text:s({},n.text,{}),mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\"],extras:[\"none\"]},line:{color:u.color,width:u.width,dash:{valType:\"enumerated\",values:Object.keys(a),dflt:\"solid\"}},marker:s({},i(\"marker\"),{symbol:{valType:\"enumerated\",values:Object.keys(o),dflt:\"circle\",arrayOk:!0},size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,opacity:c.opacity,showscale:c.showscale,colorbar:c.colorbar,line:s({},i(\"marker.line\"),{width:h.width})}),connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"tozeroy\",\"tozerox\"]}),fillcolor:n.fillcolor,error_y:n.error_y,error_x:n.error_x},\"calc\",\"nested\");f.x.editType=f.y.editType=f.x0.editType=f.y0.editType=\"calc+clearAxisTypes\"},{\"../../components/colorscale/color_attributes\":611,\"../../constants/gl2d_dashes\":702,\"../../constants/gl2d_markers\":703,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../scatter/attributes\":1031}],1078:[function(t,e,r){\"use 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r,n,i,a=e\|\|10,o=0;o<2;o++)r=this.scene[I[o]],n=r._min,n\|\|(n=[]),n.push({val:t[o],pad:a}),i=r._max,i\|\|(i=[]),i.push({val:t[o+2],pad:a})},D.expandAxesFancy=function(t,e,r){var n=this.scene,i={padded:!0,ppad:r};b.expand(n.xaxis,t,i),b.expand(n.yaxis,e,i)},D.dispose=function(){this.line.dispose(),this.errorX.dispose(),this.errorY.dispose(),this.scatter.dispose(),this.fancyScatter.dispose()},e.exports=f},{\"../../components/errorbars\":634,\"../../constants/gl2d_dashes\":702,\"../../constants/gl2d_markers\":703,\"../../constants/interactions\":706,\"../../lib\":728,\"../../lib/gl_format_color\":724,\"../../lib/str2rgbarray\":749,\"../../lib/typed_array_truncate\":754,\"../../plots/cartesian/axes\":772,\"../../plots/cartesian/axis_autotype\":773,\"../scatter/get_trace_color\":1040,\"../scatter/make_bubble_size_func\":1047,\"../scatter/subtypes\":1052,\"fast-isnumeric\":131,\"gl-error2d\":159,\"gl-line2d\":170,\"gl-scatter2d\":248,\"gl-scatter2d-sdf\":243}],1080:[function(t,e,r){\"use 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.hoverPoints=t(\"../scatter/hover\"),n.calc=t(\"./calc\"),n.plot=t(\"./convert\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scattergl\",n.basePlotModule=t(\"../../plots/gl2d\"),n.categories=[\"gl2d\",\"symbols\",\"errorBarsOK\",\"markerColorscale\",\"showLegend\",\"scatter-like\"],n.meta={},e.exports=n},{\"../../plots/gl2d\":808,\"../scatter/colorbar\":1034,\"../scatter/hover\":1041,\"./attributes\":1077,\"./calc\":1078,\"./convert\":1079,\"./defaults\":1080,\"./select\":1082}],1082:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\");e.exports=function(t,e){var r,i,a,o,s=t.cd,l=t.xaxis,u=t.yaxis,c=[],h=s[0].trace,f=s[0]._glTrace,d=f.scene,p=!n.hasMarkers(h)&&!n.hasText(h);if(!0===h.visible&&!p){if(!1===e)for(r=0;r<s.length;r++)s[r].dim=0;else 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n=t(\"../scattergeo/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/mapbox/layout_attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorbar/attributes\"),l=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,c=n.line,h=n.marker;e.exports=u({lon:n.lon,lat:n.lat,mode:l({},i.mode,{dflt:\"markers\"}),text:l({},i.text,{}),hovertext:l({},i.hovertext,{}),line:{color:c.color,width:c.width},connectgaps:i.connectgaps,marker:{symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},opacity:h.opacity,size:h.size,sizeref:h.sizeref,sizemin:h.sizemin,sizemode:h.sizemode,color:h.color,colorscale:h.colorscale,cauto:h.cauto,cmax:h.cmax,cmin:h.cmin,autocolorscale:h.autocolorscale,reversescale:h.reversescale,showscale:h.showscale,colorbar:s},fill:n.fill,fillcolor:i.fillcolor,textfont:a.layers.symbol.textfont,textposition:a.layers.symbol.textposition,hoverinfo:l({},o.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]})},\"calc\",\"nested\")},{\"../../components/colorbar/attributes\":605,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756,\"../../plots/attributes\":770,\"../../plots/mapbox/layout_attributes\":827,\"../scatter/attributes\":1031,\"../scattergeo/attributes\":1069}],1084:[function(t,e,r){\"use 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t=this.map;t.removeLayer(this.idLayerFill),t.removeLayer(this.idLayerLine),t.removeLayer(this.idLayerCircle),t.removeLayer(this.idLayerSymbol),t.removeSource(this.idSourceFill),t.removeSource(this.idSourceLine),t.removeSource(this.idSourceCircle),t.removeSource(this.idSourceSymbol)},e.exports=function(t,e){var r=e[0].trace,i=new n(t,r.uid);return i.update(e),i}},{\"./convert\":1084}],1090:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\");e.exports=function(t,e){var r,i,a,o,s,l=t.cd,u=t.xaxis,c=t.yaxis,h=[],f=l[0].trace;if(f._hasDimmedPts=!1,!0===f.visible&&n.hasMarkers(f)){if(!1===e)for(s=0;s<l.length;s++)l[s].dim=0;else for(s=0;s<l.length;s++)r=l[s],i=r.lonlat,a=u.c2p(i),o=c.c2p(i),e.contains([a,o])?(f._hasDimmedPts=!0,h.push({pointNumber:s,lon:i[0],lat:i[1]}),r.dim=0):r.dim=1;return f._glTrace.update(l),h}}},{\"../scatter/subtypes\":1052}],1091:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../components/colorbar/attributes\"),s=t(\"../../components/drawing/attributes\").dash,l=t(\"../../lib/extend\").extendFlat,u=n.marker,c=n.line,h=u.line;e.exports={a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},c:{valType:\"data_array\",editType:\"calc\"},sum:{valType:\"number\",dflt:0,min:0,editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),hovertext:l({},n.hovertext,{}),line:{color:c.color,width:c.width,dash:s,shape:l({},c.shape,{values:[\"linear\",\"spline\"]}),smoothing:c.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,cliponaxis:n.cliponaxis,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:l({symbol:u.symbol,opacity:u.opacity,maxdisplayed:u.maxdisplayed,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,line:l({width:h.width,editType:\"calc\"},a(\"marker.line\")),gradient:u.gradient,editType:\"calc\"},a(\"marker\"),{showscale:u.showscale,colorbar:o}),textfont:n.textfont,textposition:n.textposition,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"c\",\"text\",\"name\"]}),hoveron:n.hoveron}},{\"../../components/colorbar/attributes\":605,\"../../components/colorscale/color_attributes\":611,\"../../components/drawing/attributes\":627,\"../../lib/extend\":717,\"../../plots/attributes\":770,\"../scatter/attributes\":1031}],1092:[function(t,e,r){\"use 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n=t(\"../scatter/hover\"),i=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r,a){function o(t,e){y.push(t._hovertitle+\": \"+i.tickText(t,e,\"hover\").text)}var s=n(t,e,r,a);if(s&&!1!==s[0].index){var l=s[0];if(void 0===l.index){var u=1-l.y0/t.ya._length,c=t.xa._length,h=cu/2,f=c-h;return l.x0=Math.max(Math.min(l.x0,f),h),l.x1=Math.max(Math.min(l.x1,f),h),s}var d=l.cd[l.index];l.a=d.a,l.b=d.b,l.c=d.c,l.xLabelVal=void 0,l.yLabelVal=void 0;var p=l.trace,m=p._ternary,v=d.hi\|\|p.hoverinfo,g=v.split(\"+\"),y=[];return-1!==g.indexOf(\"all\")&&(g=[\"a\",\"b\",\"c\"]),-1!==g.indexOf(\"a\")&&o(m.aaxis,d.a),-1!==g.indexOf(\"b\")&&o(m.baxis,d.b),-1!==g.indexOf(\"c\")&&o(m.caxis,d.c),l.extraText=y.join(\"<br>\"),s}}},{\"../../plots/cartesian/axes\":772,\"../scatter/hover\":1041}],1095:[function(t,e,r){\"use strict\";var 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n=t(\"../../components/annotations/attributes\"),i=t(\"../../lib/extend\").extendFlat,a=t(\"../../plot_api/edit_types\").overrideAll;e.exports=a({domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},columnwidth:{valType:\"number\",arrayOk:!0,dflt:null},columnorder:{valType:\"data_array\"},header:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[]},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:{family:{valType:\"string\",arrayOk:!0,noBlank:!0,strict:!0},size:{valType:\"number\",arrayOk:!0},color:{valType:\"color\",arrayOk:!0}}},cells:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[]},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:20},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:{family:{valType:\"string\",arrayOk:!0,noBlank:!0,strict:!0},size:{valType:\"number\",arrayOk:!0},color:{valType:\"color\",arrayOk:!0}}}},\"calc\",\"from-root\")},{\"../../components/annotations/attributes\":587,\"../../lib/extend\":717,\"../../plot_api/edit_types\":756}],1106:[function(t,e,r){\"use 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e(\"enabled\")&&(e(\"target\"),e(\"order\")),n},r.calcTransform=function(t,e,r){if(r.enabled){var i=a.getTargetArray(e,r);if(i)for(var s=r.target,l=i.length,u=e._arrayAttrs,c=o.getDataToCoordFunc(t,e,s,i),h=n(r,i,c),f=0;f<u.length;f++){for(var d=a.nestedProperty(e,u[f]),p=d.get(),m=new Array(l),v=0;v<l;v++)m[v]=p[h[v]];d.set(m)}}}},{\"../lib\":728,\"../plots/cartesian/axes\":772}]},{},[20])(20)});\n", "});require(['plotly'], function(Plotly) {window.Plotly = Plotly;});}</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from gensim.models.ldamodel import LdaModel\n", "from gensim.corpora import Dictionary\n", "from gensim.parsing.preprocessing import remove_stopwords, strip_punctuation\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import re\n", "\n", "import plotly.offline as py\n", "import plotly.graph_objs as go\n", "import plotly.figure_factory as ff\n", "\n", "py.init_notebook_mode()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train Model\n", "\n", "We'll use the [fake news dataset](https://www.kaggle.com/mrisdal/fake-news) from kaggle for this notebook. First step is to preprocess the data and train our topic model using LDA. You can refer to this [notebook](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/lda_training_tips.ipynb) also for tips and suggestions of pre-processing the text data, and how to train LDA model for getting good results." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df_fake = pd.read_csv('fake.csv')\n", "df_fake[['title', 'text', 'language']].head()\n", "df_fake = df_fake.loc[(pd.notnull(df_fake.text)) & (df_fake.language == 'english')]\n", "\n", "# remove stopwords and punctuations\n", "def preprocess(row):\n", " return strip_punctuation(remove_stopwords(row.lower()))\n", " \n", "df_fake['text'] = df_fake['text'].apply(preprocess)\n", "\n", "# Convert data to required input format by LDA\n", "texts = []\n", "for line in df_fake.text:\n", " lowered = line.lower()\n", " words = re.findall(r'\\w+', lowered, flags=re.UNICODE\|re.LOCALE)\n", " texts.append(words)\n", "# Create a dictionary representation of the documents.\n", "dictionary = Dictionary(texts)\n", "\n", "# Filter out words that occur less than 2 documents, or more than 30% of the documents.\n", "dictionary.filter_extremes(no_below=2, no_above=0.4)\n", "# Bag-of-words representation of the documents.\n", "corpus_fake = [dictionary.doc2bow(text) for text in texts]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "lda_fake = LdaModel(corpus=corpus_fake, id2word=dictionary, num_topics=35, passes=30, chunksize=1500, iterations=200, alpha='auto')\n", "lda_fake.save('lda_35')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "lda_fake = LdaModel.load('lda_35')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Dendrogram\n", "\n", "Firstly, a distance matrix is calculated to store distance between every topic pair. These distances are then used ascendingly to cluster the topics together whose process is depicted by the dendrogram." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [], "source": [ "from gensim.matutils import jensen_shannon\n", "from scipy import spatial as scs\n", "from scipy.cluster import hierarchy as sch\n", "from scipy.spatial.distance import pdist, squareform\n", "\n", "\n", "# get topic distributions\n", "topic_dist = lda_fake.state.get_lambda()\n", "\n", "# get topic terms\n", "num_words = 300\n", "topic_terms = [{w for (w, _) in lda_fake.show_topic(topic, topn=num_words)} for topic in range(topic_dist.shape[0])]\n", "\n", "# no. of terms to display in annotation\n", "n_ann_terms = 10\n", "\n", "# use Jensen-Shannon distance metric in dendrogram\n", "def js_dist(X):\n", " return pdist(X, lambda u, v: jensen_shannon(u, v))\n", "\n", "# define method for distance calculation in clusters\n", "linkagefun=lambda x: sch.linkage(x, 'single')\n", "\n", "# calculate text annotations\n", "def text_annotation(topic_dist, topic_terms, n_ann_terms, linkagefun):\n", " # get dendrogram hierarchy data\n", " linkagefun = lambda x: sch.linkage(x, 'single')\n", " d = js_dist(topic_dist)\n", " Z = linkagefun(d)\n", " P = sch.dendrogram(Z, orientation=\"bottom\", no_plot=True)\n", "\n", " # store topic no.(leaves) corresponding to the x-ticks in dendrogram\n", " x_ticks = np.arange(5, len(P['leaves']) * 10 + 5, 10)\n", " x_topic = dict(zip(P['leaves'], x_ticks))\n", "\n", " # store {topic no.:topic terms}\n", " topic_vals = dict()\n", " for key, val in x_topic.items():\n", " topic_vals[val] = (topic_terms[key], topic_terms[key])\n", "\n", " text_annotations = []\n", " # loop through every trace (scatter plot) in dendrogram\n", " for trace in P['icoord']:\n", " fst_topic = topic_vals[trace[0]]\n", " scnd_topic = topic_vals[trace[2]]\n", " \n", " # annotation for two ends of current trace\n", " pos_tokens_t1 = list(fst_topic[0])[:min(len(fst_topic[0]), n_ann_terms)]\n", " neg_tokens_t1 = list(fst_topic[1])[:min(len(fst_topic[1]), n_ann_terms)]\n", "\n", " pos_tokens_t4 = list(scnd_topic[0])[:min(len(scnd_topic[0]), n_ann_terms)]\n", " neg_tokens_t4 = list(scnd_topic[1])[:min(len(scnd_topic[1]), n_ann_terms)]\n", "\n", " t1 = \"<br>\".join((\": \".join((\"+++\", str(pos_tokens_t1))), \": \".join((\"---\", str(neg_tokens_t1)))))\n", " t2 = t3 = ()\n", " t4 = \"<br>\".join((\": \".join((\"+++\", str(pos_tokens_t4))), \": \".join((\"---\", str(neg_tokens_t4)))))\n", "\n", " # show topic terms in leaves\n", " if trace[0] in x_ticks:\n", " t1 = str(list(topic_vals[trace[0]][0])[:n_ann_terms])\n", " if trace[2] in x_ticks:\n", " t4 = str(list(topic_vals[trace[2]][0])[:n_ann_terms])\n", "\n", " text_annotations.append([t1, t2, t3, t4])\n", "\n", " # calculate intersecting/diff for upper level\n", " intersecting = fst_topic[0] & scnd_topic[0]\n", " different = fst_topic[0].symmetric_difference(scnd_topic[0])\n", "\n", " center = (trace[0] + trace[2]) / 2\n", " topic_vals[center] = (intersecting, different)\n", "\n", " # remove trace value after it is annotated\n", " topic_vals.pop(trace[0], None)\n", " topic_vals.pop(trace[2], None) \n", " \n", " return text_annotations" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "text", "marker": { "color": "rgb(0,116,217)" }, "mode": "lines", "text": [ "[u'coup', u'peace', u'shot', u'citizenry', u'maduro', u'teams', u'actions', u'cross', u'seen', u'unrest']", [], [], "[u'monte', u'sprayed', u'shot', u'they', u'corps', u'september', u'sound', u'jurisdiction', u'resistance', u'sites']" ], "type": "scatter", "x": [ 85, 85, 95, 95 ], "xaxis": "x", "y": [ 0, 0.5239915229525761, 0.5239915229525761, 0 ], "yaxis": "y" }, { "hoverinfo": "text", "marker": { "color": "rgb(0,116,217)" }, 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the leaves of hierarchy represent the topics of our LDA model, y-axis is a measure of closeness of either individual topics or their cluster. Essentially, the y-axis level at which the branches merge (relative to the \"root\" of the tree) is related to their similarity. For ex., topic 4 and 30 are more similar to each other than to topic 32. In addition, topic 18 and 24 are more similar to 35 than topic 4 and 30 are to topic 32 as the height on which they merge is lower than the merge height of 4/30 to 32.\n", "\n", "Text annotations visible on hovering over the cluster nodes show the intersecting/different terms of it's two child nodes. Cluster node on first hierarchy level uses the topics on leaves directly to calculate intersecting/different terms, and the upper nodes assume the intersection(+++) as the topic terms of it's child node.\n", "\n", "This type of tree graph could help us see the high level cluster theme that might exist in our data as we can see the common/different terms of combined topics in a cluster head annotation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dendrogram with a Heatmap\n", "\n", "Now lets append the distance matrix of the topics below the dendrogram in form of heatmap so that we can see the exact distances between all pair of topics." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# get text annotations\n", "annotation = text_annotation(topic_dist, topic_terms, n_ann_terms, linkagefun)\n", "\n", "# Initialize figure by creating upper dendrogram\n", "figure = ff.create_dendrogram(topic_dist, distfun=js_dist, labels=range(1, 36), linkagefun=linkagefun, hovertext=annotation)\n", "for i in range(len(figure['data'])):\n", " figure['data'][i]['yaxis'] = 'y2'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# get distance matrix and it's topic annotations\n", "mdiff, annotation = lda_fake.diff(lda_fake, distance=\"jensen_shannon\", normed=False)\n", "\n", "# get reordered topic list\n", "dendro_leaves = figure['layout']['xaxis']['ticktext']\n", "dendro_leaves = [x - 1 for x in dendro_leaves]\n", "\n", "# reorder distance matrix\n", "heat_data = mdiff[dendro_leaves, :]\n", "heat_data = heat_data[:, dendro_leaves]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "text", "marker": { "color": "rgb(0,116,217)" }, "mode": "lines", "text": [ "[u'coup', u'peace', u'shot', u'citizenry', u'maduro', u'teams', u'actions', u'cross', u'seen', u'unrest']", [], 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electricity, environment, going", "+++ again, great, right, says, i, things, this, country, m, long<br>--- saying, all, tweeted, supporter, certainly, believe, 8, hope, do, stop", "+++ and, long<br>--- saying, all, wakingtimes, nevada, occupation, trying, edward, bundy, do, torture", "+++ didn, good, years<br>--- saying, all, stephens, sexist, talks, alt, hate, trying, bush, going", "+++ what, sure, want, i, better, start, live, way, in, need<br>--- saying, all, text, results, children, formating, trying, send, going, meant", "+++ going<br>--- saying, all, violation, issued, believe, sheriff, justice, crime, local, activities", "+++ great, day, best, d<br>--- saying, all, cdc, results, skin, children, trying, sugar, going, helps", "+++ real, think, right, says, d, i, country, years, re, going<br>--- saying, all, switched, global, results, votes, voter, tape, voted, 8", "+++ real, life, good, point, feel, things, work, long, one, better<br>--- saying, all, consider, focus, issues, 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rtd, saying, battle, soldiers, debate, tv, thursday, veterans, hit, mainstream", "+++ information, account, released, campaign, reported, sources, york, news, the, public<br>--- saying, probe, discovered, debate, staff, justice, tweet, huma, sent, mainstream", "+++ published<br>--- saying, atmosphere, caused, magnetic, earth, debate, electricity, tweet, environment, risk", "+++ article, share, anti, day, campaign<br>--- saying, tweeted, supporter, certainly, tv, tweet, going, 8, he, hope", "+++ conspiracy, published<br>--- saying, wakingtimes, nevada, debate, occupation, tv, tweet, edward, bundy, mainstream", "+++ story, media, times, stories, york, truth, news<br>--- saying, stephens, sexist, talks, alt, hate, debate, tv, tweet, bush", "+++ comment, post<br>--- saying, text, results, children, formating, tv, tweet, send, meant, mainstream", "+++ information, sources, reported, public, told<br>--- saying, violation, issued, debate, sheriff, justice, daily, tweet, crime, going", "+++ 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\"+++ group, government, country, rights, state, 000, groups<br>--- particularly, protest, gulf, riots, black, thousands, protestors, organizations, cannon, stop\", \"+++ city, the, come, crisis<br>--- ron, lack, protest, jay, obamacare, riots, black, thousands, protestors, brown\", \"+++ state, crisis, government<br>--- sector, gold, global, dollar, protest, chinese, riots, black, treasury, thousands\", \"+++ the, left, black, according, team<br>--- planetary, queen, discovered, protest, earth, explain, riots, thousands, protestors, cannon\", \"+++ revolution, government, country, national, rights, peace, nation, state, black, groups<br>--- global, protest, trying, justice, riots, local, protestors, mainstream, far, cannon\", \"+++ city, police, lives, stop, according, officers, state, black, home, the<br>--- shot, allegations, protest, gang, children, suicide, crime, riots, thousands, protestors\", \"+++ city, group, groups, government, according, state, 000, opposition, security, 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\"+++ news, the, public, propaganda<br>--- saying, chinese, german, hungary, debate, tv, tweet, communist, population, mainstream\", \"+++ states, news, national, says<br>--- corps, german, mile, hungary, ground, partners, chinese, sheriff, paris, state\", \"+++ chinese, german, london, hungary, death, paris, communist, east, happened, them<br>--- \", \"+++ says, far, second, news, world, the, today, population<br>--- chinese, german, global, month, hungary, paris, state, 0, 8, communist\", \"+++ and, government, far, public, the, called, fact<br>--- all, chinese, german, hungary, staff, paris, writes, wmw, to, communist\", \"+++ national, italy, minister<br>--- rtd, chinese, german, battle, soldiers, paris, tweet, thursday, communist, population\", \"+++ news, the, public<br>--- chinese, german, probe, discovered, hungary, staff, justice, communist, huma, sent\", \"+++ known, similar<br>--- atmosphere, chinese, german, caused, magnetic, earth, electricity, environment, communist, 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0.6161852903563267, 0.6011191937433236, 0.5749384426875435, 0.5944513903788413, 0.592623781294704, 0.5808742088503307, 0.5817460691707508, 0.558689895182277, 0.5562304608733372, 0.5862176582456311, 0.6044441297491218, 0.6133969347058739, 0.586345343852107, 0.5594039754519595, 0.5653892059345309, 0.6071040524632135, 0.550757272447808, 0.5728594745376, 0.5554399385855842, 0.5679891481525867, 0.5133567057162977, 0.565335421607568, 0.5236068728622354, 0.5276814582288774, 0.5817393051734978, 0.5759623956074625, 0.5743680052676738, 0.5809043412314943, 0.5580927972583049, 0.5399389353213216, 0.513614182506119, 0.0]], \"type\": \"heatmap\"}], {\"autosize\": false, \"yaxis\": {\"domain\": [0, 0.75], \"showticklabels\": true, \"tickmode\": \"array\", \"ticks\": \"\", \"showgrid\": false, \"mirror\": false, \"zeroline\": false, \"showline\": false, \"ticktext\": [5, 12, 18, 35, 28, 25, 16, 30, 6, 8, 17, 27, 9, 2, 14, 20, 34, 10, 29, 3, 19, 32, 24, 13, 15, 26, 11, 33, 1, 23, 22, 31, 21, 4, 7], \"rangemode\": \"tozero\", \"type\": \"linear\", \"tickvals\": [5.0, 15.0, 25.0, 35.0, 45.0, 55.0, 65.0, 75.0, 85.0, 95.0, 105.0, 115.0, 125.0, 135.0, 145.0, 155.0, 165.0, 175.0, 185.0, 195.0, 205.0, 215.0, 225.0, 235.0, 245.0, 255.0, 265.0, 275.0, 285.0, 295.0, 305.0, 315.0, 325.0, 335.0, 345.0]}, \"showlegend\": false, \"height\": 800, \"width\": 800, \"yaxis2\": {\"domain\": [0.75, 1], \"showticklabels\": false, \"ticks\": \"\", \"showgrid\": false, \"mirror\": false, \"zeroline\": false, \"showline\": false}, \"xaxis\": {\"domain\": [0.25, 1], \"showticklabels\": true, \"tickmode\": \"array\", \"ticks\": \"\", \"showgrid\": false, \"mirror\": false, \"zeroline\": false, \"showline\": false, \"ticktext\": [5, 12, 18, 35, 28, 25, 16, 30, 6, 8, 17, 27, 9, 2, 14, 20, 34, 10, 29, 3, 19, 32, 24, 13, 15, 26, 11, 33, 1, 23, 22, 31, 21, 4, 7], \"rangemode\": \"tozero\", \"type\": \"linear\", \"tickvals\": [5.0, 15.0, 25.0, 35.0, 45.0, 55.0, 65.0, 75.0, 85.0, 95.0, 105.0, 115.0, 125.0, 135.0, 145.0, 155.0, 165.0, 175.0, 185.0, 195.0, 205.0, 215.0, 225.0, 235.0, 245.0, 255.0, 265.0, 275.0, 285.0, 295.0, 305.0, 315.0, 325.0, 335.0, 345.0]}, \"hovermode\": \"closest\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# heatmap annotation\n", "annotation_html = [[\"+++ {}<br>--- {}\".format(\", \".join(int_tokens), \", \".join(diff_tokens))\n", " for (int_tokens, diff_tokens) in row] for row in annotation]\n", "\n", "# plot heatmap of distance matrix\n", "heatmap = go.Data([\n", " go.Heatmap(\n", " z=heat_data,\n", " colorscale='YIGnBu',\n", " text=annotation_html,\n", " hoverinfo='x+y+z+text'\n", " )\n", "])\n", "\n", "heatmap[0]['x'] = figure['layout']['xaxis']['tickvals']\n", "heatmap[0]['y'] = figure['layout']['xaxis']['tickvals']\n", "\n", "# Add Heatmap Data to Figure\n", "figure['data'].extend(heatmap)\n", "\n", "dendro_leaves = [x + 1 for x in dendro_leaves]\n", "\n", "# Edit Layout\n", "figure['layout'].update({'width': 800, 'height': 800,\n", " 'showlegend':False, 'hovermode': 'closest',\n", " })\n", "\n", "# Edit xaxis\n", "figure['layout']['xaxis'].update({'domain': [.25, 1],\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " \"showticklabels\": True, \n", " \"tickmode\": \"array\",\n", " \"ticktext\": dendro_leaves,\n", " \"tickvals\": figure['layout']['xaxis']['tickvals'],\n", " 'zeroline': False,\n", " 'ticks': \"\"})\n", "# Edit yaxis\n", "figure['layout']['yaxis'].update({'domain': [0, 0.75],\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " \"showticklabels\": True, \n", " \"tickmode\": \"array\",\n", " \"ticktext\": dendro_leaves,\n", " \"tickvals\": figure['layout']['xaxis']['tickvals'],\n", " 'zeroline': False,\n", " 'ticks': \"\"})\n", "# Edit yaxis2\n", "figure['layout'].update({'yaxis2':{'domain': [0.75, 1],\n", " 'mirror': False,\n", " 'showgrid': False,\n", " 'showline': False,\n", " 'zeroline': False,\n", " 'showticklabels': False,\n", " 'ticks': \"\"}})\n", "\n", "py.iplot(figure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The heatmap lets us see the exact distance measure between any two topics in the z-value of their corresponding cell and also their intersecting or different terms in the +++/--- annotation. This could help see the distance between those topics also which are not directly connected in the dendrogram." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	5,581,352	Python	.py	5,448	1,013.827643	212,401	0.638205	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,932	annoytutorial.ipynb	piskvorky_gensim/docs/notebooks/annoytutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,933	pivoted_document_length_normalisation.ipynb	piskvorky_gensim/docs/notebooks/pivoted_document_length_normalisation.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pivoted Document Length Normalization\n", "\n", "## Background\n", "\n", "In many cases, normalizing the tfidf weights for each term favors weight of terms of the documents with shorter length. The _pivoted document length normalization_ scheme counters the effect of this bias for short documents by making tfidf independent of the document length.\n", "\n", "This is achieved by tilting the normalization curve along the pivot point defined by user with some slope.\n", "Roughly following the equation:\n", "\n", "`pivoted_norm = (1 - slope) * pivot + slope * old_norm`\n", "\n", "This scheme is proposed in the paper [Pivoted Document Length Normalization](http://singhal.info/pivoted-dln.pdf) by Singhal, Buckley and Mitra.\n", "\n", "Overall this approach can in many cases help increase the accuracy of the model where the document lengths are hugely varying in the entire corpus.\n", "\n", "## Introduction\n", "\n", "This guide demonstrates how to perform pivoted document length normalization.\n", "We will train a logistic regression to distinguish between text from two different newsgroups.\n", "Our results will show that using pivoted document length normalization yields a better model (higher classification accuracy)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#\n", "# Download our dataset\n", "#\n", "import gensim.downloader as api\n", "nws = api.load(\"20-newsgroups\")\n", "\n", "#\n", "# Pick texts from relevant newsgroups, split into training and test set.\n", "#\n", "cat1, cat2 = ('sci.electronics', 'sci.space')\n", "\n", "#\n", "# X_* contain the actual texts as strings.\n", "# Y_* contain labels, 0 for cat1 (sci.electronics) and 1 for cat2 (sci.space)\n", "#\n", "X_train = []\n", "X_test = []\n", "y_train = []\n", "y_test = []\n", "\n", "for i in nws:\n", " if i[\"set\"] == \"train\" and i[\"topic\"] == cat1:\n", " X_train.append(i[\"data\"])\n", " y_train.append(0)\n", " elif i[\"set\"] == \"train\" and i[\"topic\"] == cat2:\n", " X_train.append(i[\"data\"])\n", " y_train.append(1)\n", " elif i[\"set\"] == \"test\" and i[\"topic\"] == cat1:\n", " X_test.append(i[\"data\"])\n", " y_test.append(0)\n", " elif i[\"set\"] == \"test\" and i[\"topic\"] == cat2:\n", " X_test.append(i[\"data\"])\n", " y_test.append(1)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from gensim.parsing.preprocessing import preprocess_string\n", "from gensim.corpora import Dictionary\n", "\n", "id2word = Dictionary([preprocess_string(doc) for doc in X_train])\n", "train_corpus = [id2word.doc2bow(preprocess_string(doc)) for doc in X_train]\n", "test_corpus = [id2word.doc2bow(preprocess_string(doc)) for doc in X_test]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1184 787\n" ] } ], "source": [ "print(len(X_train), len(X_test))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# We perform our analysis on top k documents which is almost top 10% most scored documents\n", "k = len(X_test) // 10" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api.tfidf import TfIdfTransformer\n", "from sklearn.linear_model import LogisticRegression\n", "from gensim.matutils import corpus2csc\n", "\n", "# This function returns the model accuracy and indivitual document prob values using\n", "# gensim's TfIdfTransformer and sklearn's LogisticRegression\n", "def get_tfidf_scores(kwargs):\n", " tfidf_transformer = TfIdfTransformer(**kwargs).fit(train_corpus)\n", "\n", " X_train_tfidf = corpus2csc(tfidf_transformer.transform(train_corpus), num_terms=len(id2word)).T\n", " X_test_tfidf = corpus2csc(tfidf_transformer.transform(test_corpus), num_terms=len(id2word)).T\n", "\n", " clf = LogisticRegression().fit(X_train_tfidf, y_train)\n", "\n", " model_accuracy = clf.score(X_test_tfidf, y_test)\n", " doc_scores = clf.decision_function(X_test_tfidf)\n", "\n", " return model_accuracy, doc_scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get TFIDF scores for corpus without pivoted document length normalisation" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9682337992376112\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] } ], "source": [ "params = {}\n", "model_accuracy, doc_scores = get_tfidf_scores(params)\n", "print(model_accuracy)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normal cosine normalisation favors short documents as our top 78 docs have a smaller mean doc length of 1668.179 compared to the corpus mean doc length of 1577.799\n" ] } ], "source": [ "import numpy as np\n", "\n", "# Sort the document scores by their scores and return a sorted list\n", "# of document score and corresponding document lengths.\n", "def sort_length_by_score(doc_scores, X_test):\n", " doc_scores = sorted(enumerate(doc_scores), key=lambda x: x[1])\n", " doc_leng = np.empty(len(doc_scores))\n", "\n", " ds = np.empty(len(doc_scores))\n", "\n", " for i, _ in enumerate(doc_scores):\n", " doc_leng[i] = len(X_test[_[0]])\n", " ds[i] = _[1]\n", "\n", " return ds, doc_leng\n", "\n", "\n", "print(\n", " \"Normal cosine normalisation favors short documents as our top {} \"\n", " \"docs have a smaller mean doc length of {:.3f} compared to the corpus mean doc length of {:.3f}\"\n", " .format(\n", " k, sort_length_by_score(doc_scores, X_test)[1][:k].mean(), \n", " sort_length_by_score(doc_scores, X_test)[1].mean()\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get TFIDF scores for corpus with pivoted document length normalisation testing on various values of alpha." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score for slope 0.0 is 0.9720457433290979\n", "Score for slope 0.1 is 0.9758576874205845\n", "Score for slope 0.2 is 0.97712833545108\n", "Score for slope 0.30000000000000004 is 0.9783989834815756\n", "Score for slope 0.4 is 0.97712833545108\n", "Score for slope 0.5 is 0.9758576874205845\n", "Score for slope 0.6000000000000001 is 0.9733163913595934\n", "Score for slope 0.7000000000000001 is 0.9733163913595934\n", "Score for slope 0.8 is 0.9733163913595934\n", "Score for slope 0.9 is 0.9733163913595934\n", "Score for slope 1.0 is 0.9682337992376112\n", "We get best score of 0.9783989834815756 at slope 0.30000000000000004\n" ] } ], "source": [ "best_model_accuracy = 0\n", "optimum_slope = 0\n", "for slope in np.arange(0, 1.1, 0.1):\n", " params = {\"pivot\": 10, \"slope\": slope}\n", "\n", " model_accuracy, doc_scores = get_tfidf_scores(params)\n", "\n", " if model_accuracy > best_model_accuracy:\n", " best_model_accuracy = model_accuracy\n", " optimum_slope = slope\n", "\n", " print(\"Score for slope {} is {}\".format(slope, model_accuracy))\n", "\n", "print(\"We get best score of {} at slope {}\".format(best_model_accuracy, optimum_slope))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9783989834815756\n" ] } ], "source": [ "params = {\"pivot\": 10, \"slope\": optimum_slope}\n", "model_accuracy, doc_scores = get_tfidf_scores(params)\n", "print(model_accuracy)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "With pivoted normalisation top 78 docs have mean length of 2077.346 which is much closer to the corpus mean doc length of 1577.799\n" ] } ], "source": [ "print(\n", " \"With pivoted normalisation top {} docs have mean length of {:.3f} \"\n", " \"which is much closer to the corpus mean doc length of {:.3f}\"\n", " .format(\n", " k, sort_length_by_score(doc_scores, X_test)[1][:k].mean(), \n", " sort_length_by_score(doc_scores, X_test)[1].mean()\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the pivoted normalization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since cosine normalization favors retrieval of short documents from the plot we can see that when slope was 1 (when pivoted normalisation was not applied) short documents with length of around 500 had very good score hence the bias for short documents can be seen. As we varied the value of slope from 1 to 0 we introdcued a new bias for long documents to counter the bias caused by cosine normalisation. Therefore at a certain point we got an optimum value of slope which is 0.5 where the overall accuracy of the model is increased.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as py\n", "\n", "best_model_accuracy = 0\n", "optimum_slope = 0\n", "\n", "w = 2\n", "h = 2\n", "f, axarr = py.subplots(h, w, figsize=(15, 7))\n", "\n", "it = 0\n", "for slope in [1, 0.2]:\n", " params = {\"pivot\": 10, \"slope\": slope}\n", "\n", " model_accuracy, doc_scores = get_tfidf_scores(params)\n", "\n", " if model_accuracy > best_model_accuracy:\n", " best_model_accuracy = model_accuracy\n", " optimum_slope = slope\n", "\n", " doc_scores, doc_leng = sort_length_by_score(doc_scores, X_test)\n", "\n", " y = abs(doc_scores[:k, np.newaxis])\n", " x = doc_leng[:k, np.newaxis]\n", "\n", " py.subplot(1, 2, it+1).bar(x, y, width=20, linewidth=0)\n", " py.title(\"slope = \" + str(slope) + \" Model accuracy = \" + str(model_accuracy))\n", " py.ylim([0, 4.5])\n", " py.xlim([0, 3200])\n", " py.xlabel(\"document length\")\n", " py.ylabel(\"confidence score\")\n", " \n", " it += 1\n", "\n", "py.tight_layout()\n", "py.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above histogram plot helps us visualize the effect of `slope`. For top k documents we have document length on the x axis and their respective scores of belonging to a specific class on y axis. \n", "As we decrease the slope the density of bins is shifted from low document length (around ~250-500) to over ~500 document length. This suggests that the positive biasness which was seen at `slope=1` (or when regular tfidf was used) for short documents is now reduced. We get the optimum slope or the max model accuracy when slope is 0.2.\n", "\n", "# Conclusion\n", "\n", "Using pivoted document normalization improved the classification accuracy significantly:\n", "\n", "- Before (slope=1, identical to default cosine normalization): 0.9682\n", "- After (slope=0.2): 0.9771" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	35,923	Python	.py	419	81.02148	22,256	0.825259	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,934	ensemble_lda_with_opinosis.ipynb	piskvorky_gensim/docs/notebooks/ensemble_lda_with_opinosis.ipynb	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import logging\n", "from gensim.models import EnsembleLda, LdaMulticore\n", "from gensim.models.ensemblelda import rank_masking\n", "from gensim.corpora import OpinosisCorpus\n", "import os" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "enable the ensemble logger to show what it is doing currently" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "elda_logger = logging.getLogger(EnsembleLda.__module__)\n", "elda_logger.setLevel(logging.INFO)\n", "elda_logger.addHandler(logging.StreamHandler())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def pretty_print_topics():\n", " # note that the words are stemmed so they appear chopped off\n", " for t in elda.print_topics(num_words=7):\n", " print('-', t[1].replace('',' ').replace('\"','').replace(' +',','), '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Opinosis Dataset\n", "\n", "Opinosis [1] is a small (but redundant) corpus that contains 289 product reviews for 51 products. Since it's so small, the results are rather unstable.\n", "\n", "[1] Kavita Ganesan, ChengXiang Zhai, and Jiawei Han, _Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions [online],_ Proceedings of the 23rd International Conference on Computational Linguistics, Association for Computational Linguistics, 2010, pp. 340â€“348. Available from: https://kavita-ganesan.com/opinosis/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing the corpus\n", "\n", "First, download the opinosis dataset. On linux it can be done like this for example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!mkdir ~/opinosis\n", "!wget -P ~/opinosis https://github.com/kavgan/opinosis/raw/master/OpinosisDataset1.0_0.zip\n", "!unzip ~/opinosis/OpinosisDataset1.0_0.zip -d ~/opinosis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path = os.path.expanduser('~/opinosis/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Corpus and id2word mapping can be created using the load_opinosis_data function provided in the package.\n", "It preprocesses the data using the PorterStemmer and stopwords from the nltk package.\n", "\n", "The parameter of the function is the relative path to the folder, into which the zip file was extracted before. That folder contains a 'summaries-gold' subfolder." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opinosis = OpinosisCorpus(path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "parameters\n", "\n", "topic_model_kind* ldamulticore is highly recommended for EnsembleLda. ensemble_workers and distance_workers are used to improve the time needed to train the models, as well as the masking_method 'rank'. ldamulticore is not able to fully utilize all cores on this small corpus, so ensemble_workers can be set to 3 to get 95 - 100% cpu usage on my i5 3470.\n", "\n", "Since the corpus is so small, a high number of num_models is needed to extract stable topics. The Opinosis corpus contains 51 categories, however, some of them are quite similar. For example there are 3 categories about the batteries of portable products. There are also multiple categories about cars. So I chose 20 for num_topics, which is smaller than the number of categories." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "elda = EnsembleLda(\n", " corpus=opinosis.corpus, id2word=opinosis.id2word, num_models=128, num_topics=20,\n", " passes=20, iterations=100, ensemble_workers=3, distance_workers=4,\n", " topic_model_class='ldamulticore', masking_method=rank_masking,\n", ")\n", "pretty_print_topics()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default for min_samples would be 64, half of the number of models and eps would be 0.1. You basically play around with them until you find a sweetspot that fits for your needs." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "elda.recluster(min_samples=55, eps=0.14)\n", "pretty_print_topics()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 2 }	5,446	Python	.py	178	26.617978	390	0.63003	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,935	doc2vec-IMDB.ipynb	piskvorky_gensim/docs/notebooks/doc2vec-IMDB.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,936	topic_network.ipynb	piskvorky_gensim/docs/notebooks/topic_network.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Topic Networks\n", "\n", "In this notebook, we will learn how to visualize topic model using network graphs. Networks can be a great way to explore topic models. We can use it to navigate that how topics belonging to one context may relate to some topics in other context and discover common factors between them. We can use them to find communities of similar topics and pinpoint the most influential topic that has large no. of connections or perform any number of other workflows designed for network analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mYou are using pip version 19.0.1, however version 19.1 is available.\r\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\r\n" ] } ], "source": [ "!pip install plotly>=2.0.16 # 2.0.16 need for support 'hovertext' argument from create_dendrogram function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from gensim.models.ldamodel import LdaModel\n", "from gensim.corpora import Dictionary\n", "import pandas as pd\n", "import re\n", "from gensim.parsing.preprocessing import remove_stopwords, strip_punctuation\n", "\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train Model\n", "\n", "We'll use the [fake news dataset](https://www.kaggle.com/mrisdal/fake-news) from kaggle for this notebook. First step is to preprocess the data and train our topic model using LDA. You can refer to this [notebook](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/lda_training_tips.ipynb) also for tips and suggestions of pre-processing the text data, and how to train LDA model for getting good results." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2019-05-12 18:54:33-- https://www.kaggle.com/mrisdal/fake-news/downloads/fake-news.zip/1\n", "Resolving www.kaggle.com (www.kaggle.com)... 35.244.233.98\n", "Connecting to www.kaggle.com (www.kaggle.com)\|35.244.233.98\|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: /account/login?returnUrl=%2Fmrisdal%2Ffake-news%2Fversion%2F1 [following]\n", "--2019-05-12 18:54:35-- https://www.kaggle.com/account/login?returnUrl=%2Fmrisdal%2Ffake-news%2Fversion%2F1\n", "Reusing existing connection to www.kaggle.com:443.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: unspecified [text/html]\n", "Saving to: ‘fake.news.zip’\n", "\n", "fake.news.zip [ <=> ] 8.46K --.-KB/s in 0.01s \n", "\n", "2019-05-12 18:54:36 (640 KB/s) - ‘fake.news.zip’ saved [8668]\n", "\n" ] } ], "source": [ "!wget https://www.kaggle.com/mrisdal/fake-news/downloads/fake-news.zip/1 -O fake.news.zip" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Archive: fake.news.zip\r\n", " End-of-central-directory signature not found. Either this file is not\r\n", " a zipfile, or it constitutes one disk of a multi-part archive. In the\r\n", " latter case the central directory and zipfile comment will be found on\r\n", " the last disk(s) of this archive.\r\n", "unzip: cannot find zipfile directory in one of fake.news.zip or\r\n", " fake.news.zip.zip, and cannot find fake.news.zip.ZIP, period.\r\n" ] } ], "source": [ "!unzip fake.news.zip" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] File b'fake.csv' does not exist: b'fake.csv'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-e7a6ec7d0ac2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_fake\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fake.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf_fake\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'title'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'text'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'language'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf_fake\u001b[0m \u001b[0;34m=\u001b[0m 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"\n", "# Convert data to required input format by LDA\n", "texts = []\n", "for line in df_fake.text:\n", " lowered = line.lower()\n", " words = re.findall(r'\\w+', lowered, flags=re.UNICODE\|re.LOCALE)\n", " texts.append(words)\n", "# Create a dictionary representation of the documents.\n", "dictionary = Dictionary(texts)\n", "\n", "# Filter out words that occur less than 2 documents, or more than 30% of the documents.\n", "dictionary.filter_extremes(no_below=2, no_above=0.4)\n", "# Bag-of-words representation of the documents.\n", "corpus_fake = [dictionary.doc2bow(text) for text in texts]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lda_fake = LdaModel(corpus=corpus_fake, id2word=dictionary, num_topics=35, chunksize=1500, iterations=200, alpha='auto')\n", "lda_fake.save('lda_35')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lda_fake = LdaModel.load('lda_35')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize topic network\n", "\n", "Firstly, a distance matrix is calculated to store distance between every topic pair. The nodes of the network graph will represent topics and the edges between them will be created based on the distance between two connecting nodes/topics." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# get topic distributions\n", "topic_dist = lda_fake.state.get_lambda()\n", "\n", "# get topic terms\n", "num_words = 50\n", "topic_terms = [{w for (w, _) in lda_fake.show_topic(topic, topn=num_words)} for topic in range(topic_dist.shape[0])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To draw the edges, we can use different types of distance metrics available in gensim for calculating the distance between every topic pair. Next, we'd have to define a threshold of distance value such that the topic-pairs with distance above that does not get connected. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.spatial.distance import pdist, squareform\n", "from gensim.matutils import jensen_shannon\n", "import networkx as nx\n", "import itertools as itt\n", "\n", "# calculate distance matrix using the input distance metric\n", "def distance(X, dist_metric):\n", " return squareform(pdist(X, lambda u, v: dist_metric(u, v)))\n", "\n", "topic_distance = distance(topic_dist, jensen_shannon)\n", "\n", "# store edges b/w every topic pair along with their distance\n", "edges = [(i, j, {'weight': topic_distance[i, j]})\n", " for i, j in itt.combinations(range(topic_dist.shape[0]), 2)]\n", "\n", "# keep edges with distance below the threshold value\n", "k = np.percentile(np.array([e[2]['weight'] for e in edges]), 20)\n", "edges = [e for e in edges if e[2]['weight'] < k]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our edges, let's plot the annotated network graph. On hovering over the nodes, we'll see the topic_id along with it's top words and on hovering over the edges, we'll see the intersecting/different words of the two topics that it connects. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import plotly.offline as py\n", "from plotly.graph_objs import \n", "import plotly.figure_factory as ff\n", "\n", "py.init_notebook_mode()\n", "\n", "# add nodes and edges to graph layout\n", "G = nx.Graph()\n", "G.add_nodes_from(range(topic_dist.shape[0]))\n", "G.add_edges_from(edges)\n", "\n", "graph_pos = nx.spring_layout(G)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# initialize traces for drawing nodes and edges \n", "node_trace = Scatter(\n", " x=[],\n", " y=[],\n", " text=[],\n", " mode='markers',\n", " hoverinfo='text',\n", " marker=Marker(\n", " showscale=True,\n", " colorscale='YIGnBu',\n", " reversescale=True,\n", " color=[],\n", " size=10,\n", " colorbar=dict(\n", " thickness=15,\n", " xanchor='left'\n", " ),\n", " line=dict(width=2)))\n", "\n", "edge_trace = Scatter(\n", " x=[],\n", " y=[],\n", " text=[],\n", " line=Line(width=0.5, color='#888'),\n", " hoverinfo='text',\n", " mode='lines')\n", "\n", "\n", "# no. of terms to display in annotation\n", "n_ann_terms = 10\n", "\n", "# add edge trace with annotations\n", "for edge in G.edges():\n", " x0, y0 = graph_pos[edge[0]]\n", " x1, y1 = graph_pos[edge[1]]\n", " \n", " pos_tokens = topic_terms[edge[0]] & topic_terms[edge[1]]\n", " neg_tokens = topic_terms[edge[0]].symmetric_difference(topic_terms[edge[1]])\n", " pos_tokens = list(pos_tokens)[:min(len(pos_tokens), n_ann_terms)]\n", " neg_tokens = list(neg_tokens)[:min(len(neg_tokens), n_ann_terms)]\n", " annotation = \"<br>\".join((\": \".join((\"+++\", str(pos_tokens))), \": \".join((\"---\", str(neg_tokens)))))\n", " \n", " x_trace = list(np.linspace(x0, x1, 10))\n", " y_trace = list(np.linspace(y0, y1, 10))\n", " text_annotation = [annotation] * 10\n", " x_trace.append(None)\n", " y_trace.append(None)\n", " text_annotation.append(None)\n", " \n", " edge_trace['x'] += x_trace\n", " edge_trace['y'] += y_trace\n", " edge_trace['text'] += text_annotation\n", "\n", "# add node trace with annotations\n", "for node in G.nodes():\n", " x, y = graph_pos[node]\n", " node_trace['x'].append(x)\n", " node_trace['y'].append(y)\n", " node_info = ''.join((str(node+1), ': ', str(list(topic_terms[node])[:n_ann_terms])))\n", " node_trace['text'].append(node_info)\n", " \n", "# color node according to no. of connections\n", "for node, adjacencies in enumerate(G.adjacency()):\n", " node_trace['marker']['color'].append(len(adjacencies))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = Figure(data=Data([edge_trace, node_trace]),\n", " layout=Layout(showlegend=False,\n", " hovermode='closest',\n", " xaxis=XAxis(showgrid=True, zeroline=False, showticklabels=True),\n", " yaxis=YAxis(showgrid=True, zeroline=False, showticklabels=True)))\n", "\n", "py.iplot(fig)" ] }, { "cell_type": "markdown", "metadata": { "scrolled": false }, "source": [ "For the above graph, we just used the 20th percentile of all the distance values. But we can experiment with few different values also such that the graph doesn’t become too crowded or too sparse and we could get an optimum amount of information about similar topics or any interesting relations b/w different topics.\n", "\n", "Or we can also get an idea of threshold from the dendrogram (with ‘single’ linkage function). You can refer to [this notebook](http://nbviewer.jupyter.org/github/parulsethi/gensim/blob/b9e7ab54dde98438b0e4f766ee764b81af704367/docs/notebooks/Topic_dendrogram.ipynb) for more details on topic dendrogram visualization. The y-values in the dendrogram represent the metric distances and if we choose a certain y-value then only those topics which are clustered below it would be connected. So let's plot the dendrogram now to see the sequential clustering process with increasing distance values." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from gensim.matutils import jensen_shannon\n", "import scipy as scp\n", "from scipy.cluster import hierarchy as sch\n", "from scipy import spatial as scs\n", "\n", "# get topic distributions\n", "topic_dist = lda_fake.state.get_lambda()\n", "\n", "# get topic terms\n", "num_words = 300\n", "topic_terms = [{w for (w, _) in lda_fake.show_topic(topic, topn=num_words)} for topic in range(topic_dist.shape[0])]\n", "\n", "# no. of terms to display in annotation\n", "n_ann_terms = 10\n", "\n", "# use Jenson-Shannon distance metric in dendrogram\n", "def js_dist(X):\n", " return pdist(X, lambda u, v: jensen_shannon(u, v))\n", "\n", "# define method for distance calculation in clusters\n", "linkagefun=lambda x: sch.linkage(x, 'single')\n", "\n", "# calculate text annotations\n", "def text_annotation(topic_dist, topic_terms, n_ann_terms, linkagefun):\n", " # get dendrogram hierarchy data\n", " d = js_dist(topic_dist)\n", " Z = linkagefun(d)\n", " P = sch.dendrogram(Z, orientation=\"bottom\", no_plot=True)\n", "\n", " # store topic no.(leaves) corresponding to the x-ticks in dendrogram\n", " x_ticks = np.arange(5, len(P['leaves']) * 10 + 5, 10)\n", " x_topic = dict(zip(P['leaves'], x_ticks))\n", "\n", " # store {topic no.:topic terms}\n", " topic_vals = dict()\n", " for key, val in x_topic.items():\n", " topic_vals[val] = (topic_terms[key], topic_terms[key])\n", "\n", " text_annotations = []\n", " # loop through every trace (scatter plot) in dendrogram\n", " for trace in P['icoord']:\n", " fst_topic = topic_vals[trace[0]]\n", " scnd_topic = topic_vals[trace[2]]\n", " \n", " # annotation for two ends of current trace\n", " pos_tokens_t1 = list(fst_topic[0])[:min(len(fst_topic[0]), n_ann_terms)]\n", " neg_tokens_t1 = list(fst_topic[1])[:min(len(fst_topic[1]), n_ann_terms)]\n", "\n", " pos_tokens_t4 = list(scnd_topic[0])[:min(len(scnd_topic[0]), n_ann_terms)]\n", " neg_tokens_t4 = list(scnd_topic[1])[:min(len(scnd_topic[1]), n_ann_terms)]\n", "\n", " t1 = \"<br>\".join((\": \".join((\"+++\", str(pos_tokens_t1))), \": \".join((\"---\", str(neg_tokens_t1)))))\n", " t2 = t3 = ()\n", " t4 = \"<br>\".join((\": \".join((\"+++\", str(pos_tokens_t4))), \": \".join((\"---\", str(neg_tokens_t4)))))\n", "\n", " # show topic terms in leaves\n", " if trace[0] in x_ticks:\n", " t1 = str(list(topic_vals[trace[0]][0])[:n_ann_terms])\n", " if trace[2] in x_ticks:\n", " t4 = str(list(topic_vals[trace[2]][0])[:n_ann_terms])\n", "\n", " text_annotations.append([t1, t2, t3, t4])\n", "\n", " # calculate intersecting/diff for upper level\n", " intersecting = fst_topic[0] & scnd_topic[0]\n", " different = fst_topic[0].symmetric_difference(scnd_topic[0])\n", "\n", " center = (trace[0] + trace[2]) / 2\n", " topic_vals[center] = (intersecting, different)\n", "\n", " # remove trace value after it is annotated\n", " topic_vals.pop(trace[0], None)\n", " topic_vals.pop(trace[2], None) \n", " \n", " return text_annotations\n", "\n", "# get text annotations\n", "annotation = text_annotation(topic_dist, topic_terms, n_ann_terms, linkagefun)\n", "\n", "# Plot dendrogram\n", "dendro = ff.create_dendrogram(topic_dist, distfun=js_dist, labels=range(1, 36), linkagefun=linkagefun, hovertext=annotation)\n", "dendro['layout'].update({'width': 1000, 'height': 600})\n", "py.iplot(dendro)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From observing this dendrogram, we can try the threshold values between 0.3 to 0.35 for network graph, as the topics are clustered in distinct groups below them and this could plot separate clusters of related topics in the network graph.\n", "\n", "But then why do we need to use network graph if the dendrogram already shows the topic clusters with a clear sequence of how topics joined one after the other. The problem is that we can't see the direct relation of any topic with another topic except if they are directly paired at the first hierarchy level. The network graph let's us explore the inter-topic distances and at the same time observe clusters of closely related topics." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	26,720	Python	.py	482	50.717842	1,689	0.636097	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,937	sklearn_api.ipynb	piskvorky_gensim/docs/notebooks/sklearn_api.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Using wrappers for Scikit learn API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial is about using gensim models as a part of your scikit learn workflow with the help of wrappers found at ```gensim.sklearn_integration```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The wrappers available (as of now) are :\n", "* LdaModel (```gensim.sklearn_api.ldamodel.LdaTransformer```), which implements gensim's ```LDA Model``` in a scikit-learn interface\n", "\n", "* LsiModel (```gensim.sklearn_api.lsimodel.LsiTransformer```), which implements gensim's ```LSI Model``` in a scikit-learn interface\n", "\n", "* RpModel (```gensim.sklearn_api.rpmodel.RpTransformer```), which implements gensim's ```Random Projections Model``` in a scikit-learn interface\n", "\n", "* LDASeq Model (```gensim.sklearn_api.ldaseqmodel.LdaSeqTransformer```), which implements gensim's ```LdaSeqModel``` in a scikit-learn interface\n", "\n", "* Word2Vec Model (```gensim.sklearn_api.w2vmodel.W2VTransformer```), which implements gensim's ```Word2Vec``` in a scikit-learn interface\n", "\n", "* AuthorTopicModel Model (```gensim.sklearn_api.atmodel.AuthorTopicTransformer```), which implements gensim's ```AuthorTopicModel``` in a scikit-learn interface\n", "\n", "* Doc2Vec Model (```gensim.sklearn_api.d2vmodel.D2VTransformer```), which implements gensim's ```Doc2Vec``` in a scikit-learn interface\n", "\n", "* Text2Bow Model (```gensim.sklearn_api.text2bow.Text2BowTransformer```), which implements gensim's ```Dictionary``` in a scikit-learn interface\n", "\n", "* TfidfModel Model (```gensim.sklearn_api.tfidf.TfIdfTransformer```), which implements gensim's ```TfidfModel``` in a scikit-learn interface\n", "\n", "* HdpModel Model (```gensim.sklearn_api.hdp.HdpTransformer```), which implements gensim's ```HdpModel``` in a scikit-learn interface" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LDA Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use LdaModel begin with importing LdaModel wrapper" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import LdaTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will create a dummy set of texts and convert it into a corpus" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from gensim.corpora import Dictionary\n", "texts = [\n", " ['complier', 'system', 'computer'],\n", " ['eulerian', 'node', 'cycle', 'graph', 'tree', 'path'],\n", " ['graph', 'flow', 'network', 'graph'],\n", " ['loading', 'computer', 'system'],\n", " ['user', 'server', 'system'],\n", " ['tree', 'hamiltonian'],\n", " ['graph', 'trees'],\n", " ['computer', 'kernel', 'malfunction', 'computer'],\n", " ['server', 'system', 'computer']\n", "]\n", "dictionary = Dictionary(texts)\n", "corpus = [dictionary.doc2bow(text) for text in texts]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then to run the LdaModel on it" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.84165996, 0.15834005],\n", " [0.716593 , 0.28340697],\n", " [0.11434125, 0.88565874],\n", " [0.80545014, 0.19454984],\n", " [0.39609504, 0.603905 ],\n", " [0.80124027, 0.19875973],\n", " [0.19269218, 0.80730784],\n", " [0.8466452 , 0.15335481],\n", " [0.67057097, 0.32942903]], dtype=float32)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = LdaTransformer(num_topics=2, id2word=dictionary, iterations=20, random_state=1)\n", "model.fit(corpus)\n", "model.transform(corpus)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Integration with Sklearn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To provide a better example of how it can be used with Sklearn, Let's use CountVectorizer method of sklearn. For this example we will use [20 Newsgroups data set](http://qwone.com/~jason/20Newsgroups/). We will only use the categories rec.sport.baseball and sci.crypt and use it to generate topics." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from gensim import matutils\n", "from gensim.models.ldamodel import LdaModel\n", "from sklearn.datasets import fetch_20newsgroups\n", "from gensim.sklearn_api.ldamodel import LdaTransformer" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "rand = np.random.mtrand.RandomState(1) # set seed for getting same result\n", "cats = ['rec.sport.baseball', 'sci.crypt']\n", "data = fetch_20newsgroups(subset='train', categories=cats, shuffle=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we use use the loaded data to create our dictionary and corpus." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data_texts = [_.split() for _ in data.data]\n", "id2word = Dictionary(data_texts)\n", "corpus = [id2word.doc2bow(i.split()) for i in data.data]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we just need to fit corpus and id2word to our Lda wrapper." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "obj = LdaTransformer(id2word=id2word, num_topics=5, iterations=20)\n", "lda = obj.fit(corpus)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Example for Using Grid Search" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The inbuilt `score` function of Lda wrapper class provides two modes : `perplexity` and `u_mass` for computing the scores of the candidate models. The preferred mode for the scoring function is specified using `scorer` parameter of the wrapper as follows : " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'iterations': 20, 'num_topics': 3}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "obj = LdaTransformer(id2word=id2word, num_topics=2, iterations=5, scorer='u_mass') # here 'scorer' can be 'perplexity' or 'u_mass'\n", "parameters = {'num_topics': (2, 3, 5, 10), 'iterations': (1, 20, 50)}\n", "\n", "# set `scoring` as `None` to use the inbuilt score function of `SklLdaModel` class\n", "model = GridSearchCV(obj, parameters, cv=3, scoring=None)\n", "model.fit(corpus)\n", "\n", "model.best_params_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also supply a custom scoring function of your choice using the `scoring` parameter of `GridSearchCV` function. The example shown below uses `c_v` mode of `CoherenceModel` class for computing the scores of the candidate models." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'iterations': 50, 'num_topics': 2}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from gensim.models.coherencemodel import CoherenceModel\n", "\n", "# supplying a custom scoring function\n", "def scoring_function(estimator, X, y=None):\n", " goodcm = CoherenceModel(model=estimator.gensim_model, texts=data_texts, dictionary=estimator.gensim_model.id2word, coherence='c_v')\n", " return goodcm.get_coherence()\n", "\n", "obj = LdaTransformer(id2word=id2word, num_topics=5, iterations=5)\n", "parameters = {'num_topics': (2, 3, 5, 10), 'iterations': (1, 20, 50)}\n", "\n", "# set `scoring` as your custom scoring function\n", "model = GridSearchCV(obj, parameters, cv=2, scoring=scoring_function)\n", "model.fit(corpus)\n", "\n", "model.best_params_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn import linear_model\n", "\n", "def print_features_pipe(clf, vocab, n=10):\n", " ''' Better printing for sorted list '''\n", " # FIXME: this function is broken\n", " coef = clf.named_steps['classifier'].coef_[0]\n", " print(coef)\n", " print('Positive features: %s' % (' '.join(['%s:%.2f' % (vocab[j], coef[j]) for j in np.argsort(coef)[::-1][:n] if coef[j] > 0])))\n", " print('Negative features: %s' % (' '.join(['%s:%.2f' % (vocab[j], coef[j]) for j in np.argsort(coef)[:n] if coef[j] < 0])))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "id2word = Dictionary([_.split() for _ in data.data])\n", "corpus = [id2word.doc2bow(i.split()) for i in data.data]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.6459731543624161\n" ] } ], "source": [ "model = LdaTransformer(num_topics=15, id2word=id2word, iterations=10, random_state=37)\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1) # l2 penalty used\n", "pipe = Pipeline([('features', model,), ('classifier', clf)])\n", "pipe.fit(corpus, data.target)\n", "# print_features_pipe(pipe, id2word.values())\n", "\n", "print(pipe.score(corpus, data.target))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LSI Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use LsiModel begin with importing LsiModel wrapper" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import LsiTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.8657718120805369\n" ] } ], "source": [ "model = LsiTransformer(num_topics=15, id2word=id2word)\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1) # l2 penalty used\n", "pipe = Pipeline([('features', model,), ('classifier', clf)])\n", "pipe.fit(corpus, data.target)\n", "# print_features_pipe(pipe, id2word.values())\n", "\n", "print(pipe.score(corpus, data.target))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Projections Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use RpModel begin with importing RpModel wrapper" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import RpTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.5461409395973155\n" ] } ], "source": [ "model = RpTransformer(num_topics=2)\n", "np.random.mtrand.RandomState(1) # set seed for getting same result\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1) # l2 penalty used\n", "pipe = Pipeline([('features', model,), ('classifier', clf)])\n", "pipe.fit(corpus, data.target)\n", "# print_features_pipe(pipe, id2word.values())\n", "\n", "print(pipe.score(corpus, data.target))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LDASeq Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use LdaSeqModel begin with importing LdaSeqModel wrapper" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import LdaSeqTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/git/gensim/gensim/models/ldaseqmodel.py:293: RuntimeWarning: divide by zero encountered in double_scalars\n", " convergence = np.fabs((bound - old_bound) / old_bound)\n", "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "test_data = data.data[0:2]\n", "test_target = data.target[0:2]\n", "id2word_ldaseq = Dictionary(map(lambda x: x.split(), test_data))\n", "corpus_ldaseq = [id2word_ldaseq.doc2bow(i.split()) for i in test_data]\n", "\n", "model = LdaSeqTransformer(id2word=id2word_ldaseq, num_topics=2, time_slice=[1, 1, 1], initialize='gensim')\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1) # l2 penalty used\n", "pipe = Pipeline([('features', model,), ('classifier', clf)])\n", "pipe.fit(corpus_ldaseq, test_target)\n", "# print_features_pipe(pipe, id2word_ldaseq.values())\n", "\n", "print(pipe.score(corpus_ldaseq, test_target))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Word2Vec Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use Word2Vec model begin with importing Word2Vec wrapper" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import W2VTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] } ], "source": [ "w2v_texts = [\n", " ['calculus', 'is', 'the', 'mathematical', 'study', 'of', 'continuous', 'change'],\n", " ['geometry', 'is', 'the', 'study', 'of', 'shape'],\n", " ['algebra', 'is', 'the', 'study', 'of', 'generalizations', 'of', 'arithmetic', 'operations'],\n", " ['differential', 'calculus', 'is', 'related', 'to', 'rates', 'of', 'change', 'and', 'slopes', 'of', 'curves'],\n", " ['integral', 'calculus', 'is', 'realted', 'to', 'accumulation', 'of', 'quantities', 'and', 'the', 'areas', 'under', 'and', 'between', 'curves'],\n", " ['physics', 'is', 'the', 'natural', 'science', 'that', 'involves', 'the', 'study', 'of', 'matter', 'and', 'its', 'motion', 'and', 'behavior', 'through', 'space', 'and', 'time'],\n", " ['the', 'main', 'goal', 'of', 'physics', 'is', 'to', 'understand', 'how', 'the', 'universe', 'behaves'],\n", " ['physics', 'also', 'makes', 'significant', 'contributions', 'through', 'advances', 'in', 'new', 'technologies', 'that', 'arise', 'from', 'theoretical', 'breakthroughs'],\n", " ['advances', 'in', 'the', 'understanding', 'of', 'electromagnetism', 'or', 'nuclear', 'physics', 'led', 'directly', 'to', 'the', 'development', 'of', 'new', 'products', 'that', 'have', 'dramatically', 'transformed', 'modern', 'day', 'society']\n", "]\n", "\n", "model = W2VTransformer(size=10, min_count=1)\n", "model.fit(w2v_texts)\n", "\n", "class_dict = {'mathematics': 1, 'physics': 0}\n", "train_data = [\n", " ('calculus', 'mathematics'), ('mathematical', 'mathematics'), ('geometry', 'mathematics'), ('operations', 'mathematics'), ('curves', 'mathematics'),\n", " ('natural', 'physics'), ('nuclear', 'physics'), ('science', 'physics'), ('electromagnetism', 'physics'), ('natural', 'physics')\n", "]\n", "\n", "train_input = list(map(lambda x: x[0], train_data))\n", "train_target = list(map(lambda x: class_dict[x[1]], train_data))\n", "\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1)\n", "clf.fit(model.transform(train_input), train_target)\n", "text_w2v = Pipeline([('features', model,), ('classifier', clf)])\n", "score = text_w2v.score(train_input, train_target)\n", "\n", "print(score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AuthorTopic Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use AuthorTopic model begin with importing AuthorTopic wrapper" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import AuthorTopicTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 1 0]\n" ] } ], "source": [ "from sklearn import cluster\n", "\n", "atm_texts = [\n", " ['complier', 'system', 'computer'],\n", " ['eulerian', 'node', 'cycle', 'graph', 'tree', 'path'],\n", " ['graph', 'flow', 'network', 'graph'],\n", " ['loading', 'computer', 'system'],\n", " ['user', 'server', 'system'],\n", " ['tree', 'hamiltonian'],\n", " ['graph', 'trees'],\n", " ['computer', 'kernel', 'malfunction', 'computer'],\n", " ['server', 'system', 'computer'],\n", "]\n", "atm_dictionary = Dictionary(atm_texts)\n", "atm_corpus = [atm_dictionary.doc2bow(text) for text in atm_texts]\n", "author2doc = {'john': [0, 1, 2, 3, 4, 5, 6], 'jane': [2, 3, 4, 5, 6, 7, 8], 'jack': [0, 2, 4, 6, 8], 'jill': [1, 3, 5, 7]}\n", "\n", "model = AuthorTopicTransformer(id2word=atm_dictionary, author2doc=author2doc, num_topics=10, passes=100)\n", "model.fit(atm_corpus)\n", "\n", "# create and train clustering model\n", "clstr = cluster.MiniBatchKMeans(n_clusters=2)\n", "authors_full = ['john', 'jane', 'jack', 'jill']\n", "clstr.fit(model.transform(authors_full))\n", "\n", "# stack together the two models in a pipeline\n", "text_atm = Pipeline([('features', model,), ('cluster', clstr)])\n", "author_list = ['jane', 'jack', 'jill']\n", "ret_val = text_atm.predict(author_list)\n", "\n", "print(ret_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Doc2Vec Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use Doc2Vec model begin with importing Doc2Vec wrapper" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import D2VTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] } ], "source": [ "from gensim.models import doc2vec\n", "d2v_sentences = [doc2vec.TaggedDocument(words, [i]) for i, words in enumerate(w2v_texts)]\n", "\n", "model = D2VTransformer(min_count=1)\n", "model.fit(d2v_sentences)\n", "\n", "class_dict = {'mathematics': 1, 'physics': 0}\n", "train_data = [\n", " (['calculus', 'mathematical'], 'mathematics'), (['geometry', 'operations', 'curves'], 'mathematics'),\n", " (['natural', 'nuclear'], 'physics'), (['science', 'electromagnetism', 'natural'], 'physics')\n", "]\n", "train_input = list(map(lambda x: x[0], train_data))\n", "train_target = list(map(lambda x: class_dict[x[1]], train_data))\n", "\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1)\n", "clf.fit(model.transform(train_input), train_target)\n", "text_d2v = Pipeline([('features', model,), ('classifier', clf)])\n", "score = text_d2v.score(train_input, train_target)\n", "\n", "print(score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Text2Bow Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use Text2Bow model begin with importing Text2Bow wrapper" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import Text2BowTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.9723154362416108\n" ] } ], "source": [ "text2bow_model = Text2BowTransformer()\n", "lda_model = LdaTransformer(num_topics=2, passes=10, minimum_probability=0, random_state=np.random.seed(0))\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1)\n", "text_t2b = Pipeline([('bow_model', text2bow_model), ('ldamodel', lda_model), ('classifier', clf)])\n", "text_t2b.fit(data.data, data.target)\n", "score = text_t2b.score(data.data, data.target)\n", "\n", "print(score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TfIdf Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use TfIdf model begin with importing TfIdf wrapper" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import TfIdfTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.735738255033557\n" ] } ], "source": [ "tfidf_model = TfIdfTransformer()\n", "tfidf_model.fit(corpus)\n", "lda_model = LdaTransformer(num_topics=2, passes=10, minimum_probability=0, random_state=np.random.seed(0))\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1)\n", "text_tfidf = Pipeline((('tfidf_model', tfidf_model), ('ldamodel', lda_model), ('classifier', clf)))\n", "text_tfidf.fit(corpus, data.target)\n", "score = text_tfidf.score(corpus, data.target)\n", "\n", "print(score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HDP Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use HDP model begin with importing HDP wrapper" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "from gensim.sklearn_api import HdpTransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example of Using Pipeline" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/envs/gensim/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.8271812080536913\n" ] } ], "source": [ "model = HdpTransformer(id2word=id2word)\n", "clf = linear_model.LogisticRegression(penalty='l2', C=0.1)\n", "text_hdp = Pipeline([('features', model,), ('classifier', clf)])\n", "text_hdp.fit(corpus, data.target)\n", "score = text_hdp.score(corpus, data.target)\n", "\n", "print(score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	28,710	Python	.py	1,009	23.969277	304	0.574131	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,938	Poincare Tutorial.ipynb	piskvorky_gensim/docs/notebooks/Poincare Tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial on Poincaré Embeddings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook discusses the basic ideas and use-cases for Poincaré embeddings and demonstrates what kind of operations can be done with them. For more comprehensive technical details and results, this [blog post](https://rare-technologies.com/implementing-poincare-embeddings) may be a more appropriate resource." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 Concept and use-case\n", "\n", "Poincaré embeddings are a method to learn vector representations of nodes in a graph. The input data is of the form of a list of relations (edges) between nodes, and the model tries to learn representations such that the vectors for the nodes accurately represent the distances between them.\n", "\n", "The learnt embeddings capture notions of both _hierarchy_ and _similarity_ - similarity by placing connected nodes close to each other and unconnected nodes far from each other; hierarchy by placing nodes lower in the hierarchy farther from the origin, i.e. with higher norms.\n", "\n", "The paper uses this model to learn embeddings of nodes in the WordNet noun hierarchy, and evaluates these on 3 tasks - reconstruction, link prediction and lexical entailment, which are described in the section on evaluation. We have compared the results of our Poincaré model implementation on these tasks to other open-source implementations and the results mentioned in the paper.\n", "\n", "The paper also describes a variant of the Poincaré model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n", "\n", "This variant has not been implemented yet, and is therefore not a part of our tutorial and experiments.\n", "\n", "\n", "### 1.2 Motivation\n", "\n", "The main innovation here is that these embeddings are learnt in hyperbolic space, as opposed to the commonly used Euclidean space. The reason behind this is that hyperbolic space is more suitable for capturing any hierarchical information inherently present in the graph. Embedding nodes into a Euclidean space while preserving the distance between the nodes usually requires a very high number of dimensions. A simple illustration of this can be seen below - \n", " \n", " ![Example tree](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/example_tree.png)\n", "\n", "Here, the positions of nodes represent the positions of their vectors in 2-D euclidean space. Ideally, the distances between the vectors for nodes `(A, D)` should be the same as that between `(D, H)` and as that between `H` and its child nodes. Similarly, all the child nodes of `H` must be equally far away from node `A`. It becomes progressively hard to accurately preserve these distances in Euclidean space as the degree and depth of the tree grows larger. Hierarchical structures may also have cross-connections (effectively a directed graph), making this harder.\n", "\n", "There is no representation of this simple tree in 2-dimensional Euclidean space which can reflect these distances correctly. This can be solved by adding more dimensions, but this becomes computationally infeasible as the number of required dimensions grows exponentially. \n", "Hyperbolic space is a metric space in which distances aren't straight lines - they are curves, and this allows such tree-like hierarchical structures to have a representation that captures the distances more accurately even in low dimensions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Training the embedding" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/misha/git/gensim\n" ] } ], "source": [ "%cd ../.." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# %load_ext autoreload \n", "# %autoreload 2\n", "\n", "import os\n", "import logging\n", "import numpy as np\n", "\n", "from gensim.models.poincare import PoincareModel, PoincareKeyedVectors, PoincareRelations\n", "\n", "logging.basicConfig(level=logging.INFO)\n", "\n", "poincare_directory = os.path.join(os.getcwd(), 'docs', 'notebooks', 'poincare')\n", "data_directory = os.path.join(poincare_directory, 'data')\n", "wordnet_mammal_file = os.path.join(data_directory, 'wordnet_mammal_hypernyms.tsv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model can be initialized using an iterable of relations, where a relation is simply a pair of nodes - " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.models.poincare:loading relations from train data..\n", "INFO:gensim.models.poincare:loaded 2 relations from train data, 3 nodes\n" ] } ], "source": [ "model = PoincareModel(train_data=[('node.1', 'node.2'), ('node.2', 'node.3')])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model can also be initialized from a csv-like file containing one relation per line. The module provides a convenience class `PoincareRelations` to do so." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.models.poincare:loading relations from train data..\n", "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n", "INFO:gensim.models.poincare:loaded 7724 relations from train data, 1182 nodes\n" ] } ], "source": [ "relations = PoincareRelations(file_path=wordnet_mammal_file, delimiter='\\t')\n", "model = PoincareModel(train_data=relations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the above only initializes the model and does not begin training. To train the model - " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.models.poincare:loading relations from train data..\n", "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n", "INFO:gensim.models.poincare:loaded 7724 relations from train data, 1182 nodes\n", "INFO:gensim.models.poincare:training model of size 2 with 1 workers on 7724 relations for 1 epochs and 0 burn-in epochs, using lr=0.10000 burn-in lr=0.01000 negative=10\n", "INFO:gensim.models.poincare:starting training (1 epochs)----------------------------------------\n", "INFO:gensim.models.poincare:training on epoch 1, examples #4990-#5000, loss: 23.57\n", "INFO:gensim.models.poincare:time taken for 5000 examples: 0.69 s, 7268.98 examples / s\n", "INFO:gensim.models.poincare:training finished\n" ] } ], "source": [ "model = PoincareModel(train_data=relations, size=2, burn_in=0)\n", "model.train(epochs=1, print_every=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same model can be trained further on more epochs in case the user decides that the model hasn't converged yet." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.models.poincare:training model of size 2 with 1 workers on 7724 relations for 1 epochs and 0 burn-in epochs, using lr=0.10000 burn-in lr=0.01000 negative=10\n", "INFO:gensim.models.poincare:starting training (1 epochs)----------------------------------------\n", "INFO:gensim.models.poincare:training on epoch 1, examples #4990-#5000, loss: 22.37\n", "INFO:gensim.models.poincare:time taken for 5000 examples: 0.67 s, 7412.15 examples / s\n", "INFO:gensim.models.poincare:training finished\n" ] } ], "source": [ "model.train(epochs=1, print_every=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model can be saved and loaded using two different methods - " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.utils:saving PoincareModel object under /tmp/test_model, separately None\n", "INFO:gensim.utils:not storing attribute _node_probabilities\n", "INFO:gensim.utils:not storing attribute _node_counts_cumsum\n", "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n", "INFO:gensim.utils:saved /tmp/test_model\n", "INFO:gensim.utils:loading PoincareModel object from /tmp/test_model\n", "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n", "INFO:gensim.utils:loading kv recursively from /tmp/test_model.kv.* with mmap=None\n", "INFO:gensim.utils:setting ignored attribute _node_probabilities to None\n", "INFO:gensim.utils:setting ignored attribute _node_counts_cumsum to None\n", "INFO:gensim.utils:loaded /tmp/test_model\n" ] }, { "data": { "text/plain": [ "<gensim.models.poincare.PoincareModel at 0x7f354560b860>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Saves the entire PoincareModel instance, the loaded model can be trained further\n", "model.save('/tmp/test_model')\n", "PoincareModel.load('/tmp/test_model')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.models.utils_any2vec:storing 1182x2 projection weights into /tmp/test_vectors\n", "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n", "INFO:gensim.models.utils_any2vec:loading projection weights from /tmp/test_vectors\n", "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n", "INFO:gensim.models.utils_any2vec:loaded (1182, 2) matrix from /tmp/test_vectors\n" ] }, { "data": { "text/plain": [ "<gensim.models.poincare.PoincareKeyedVectors at 0x7f3545623b38>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Saves only the vectors from the PoincareModel instance, in the commonly used word2vec format\n", "model.kv.save_word2vec_format('/tmp/test_vectors')\n", "PoincareKeyedVectors.load_word2vec_format('/tmp/test_vectors')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. What the embedding can be used for" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.utils:loading PoincareModel object from /home/misha/git/gensim/docs/notebooks/poincare/models/gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20_dim_50\n", "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n" ] }, { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '/home/misha/git/gensim/docs/notebooks/poincare/models/gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20_dim_50'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-9-6ead1966aa14>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m 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os.path.join(poincare_directory, 'models')\n", "test_model_path = os.path.join(models_directory, 'gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20_dim_50')\n", "model = PoincareModel.load(test_model_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The learnt representations can be used to perform various kinds of useful operations. This section is split into two - some simple operations that are directly mentioned in the paper, as well as some experimental operations that are hinted at, and might require more work to refine.\n", "\n", "The models that are used in this section have been trained on the transitive closure of the WordNet hypernym graph. The transitive closure is the list of all the direct and indirect hypernyms in the WordNet graph. An example of a direct hypernym is `(seat.n.03, furniture.n.01)` while an example of an indirect hypernym is `(seat.n.03, physical_entity.n.01)`.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Simple operations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the following operations are based simply on the notion of distance between two nodes in hyperbolic space." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Distance between any two nodes\n", "model.kv.distance('plant.n.02', 'tree.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.kv.distance('plant.n.02', 'animal.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Nodes most similar to a given input node\n", "model.kv.most_similar('electricity.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.kv.most_similar('man.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Nodes closer to node 1 than node 2 is from node 1\n", "model.kv.nodes_closer_than('dog.n.01', 'carnivore.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Rank of distance of node 2 from node 1 in relation to distances of all nodes from node 1\n", "model.kv.rank('dog.n.01', 'carnivore.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Finding Poincare distance between input vectors\n", "vector_1 = np.random.uniform(size=(100,))\n", "vector_2 = np.random.uniform(size=(100,))\n", "vectors_multiple = np.random.uniform(size=(5, 100))\n", "\n", "# Distance between vector_1 and vector_2\n", "print(PoincareKeyedVectors.vector_distance(vector_1, vector_2))\n", "# Distance between vector_1 and each vector in vectors_multiple\n", "print(PoincareKeyedVectors.vector_distance_batch(vector_1, vectors_multiple))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Experimental operations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These operations are based on the notion that the norm of a vector represents its hierarchical position. Leaf nodes typically tend to have the highest norms, and as we move up the hierarchy, the norm decreases, with the root node being close to the center (or origin)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Closest child node\n", "model.kv.closest_child('person.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Closest parent node\n", "model.kv.closest_parent('person.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Position in hierarchy - lower values represent that the node is higher in the hierarchy\n", "print(model.kv.norm('person.n.01'))\n", "print(model.kv.norm('teacher.n.01'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Difference in hierarchy between the first node and the second node\n", "# Positive values indicate the first node is higher in the hierarchy\n", "print(model.kv.difference_in_hierarchy('person.n.01', 'teacher.n.01'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# One possible descendant chain\n", "model.kv.descendants('mammal.n.01')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# One possible ancestor chain\n", "model.kv.ancestors('dog.n.01')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the chains are not symmetric - while descending to the closest child recursively, starting with `mammal`, the closest child of `carnivore` is `dog`, however, while ascending from `dog` to the closest parent, the closest parent to `dog` is `canine`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is despite the fact that Poincaré distance is symmetric (like any distance in a metric space). The asymmetry stems from the fact that even if node `Y` is the closest node to node `X` amongst all nodes with a higher norm (lower in the hierarchy) than `X`, node `X` may not be the closest node to node `Y` amongst all the nodes with a lower norm (higher in the hierarchy) than `Y`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Useful Links" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. [Original paper by Facebook AI Research](https://arxiv.org/pdf/1705.08039)\n", "2. [Blog post describing technical challenges in implementation](https://rare-technologies.com/implementing-poincare-embeddings)\n", "3. [Detailed evaluation notebook to reproduce results](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Poincare%20Evaluation.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	28,599	Python	.py	562	46.371886	1,631	0.67511	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,939	wikinews-bigram-en.ipynb	piskvorky_gensim/docs/notebooks/wikinews-bigram-en.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Illustrating common terms usage using Wikinews in english" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## getting data\n", "\n", "We get the cirrussearch dump of wikinews (a dump meant for elastic-search indexation)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "LANG=\"english\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "--2019-05-12 19:33:27-- https://dumps.wikimedia.org/other/cirrussearch/20170327/enwikinews-20170327-cirrussearch-content.json.gz\n", "Resolving dumps.wikimedia.org (dumps.wikimedia.org)... 2620:0:861:4:208:80:155:106, 208.80.155.106\n", "Connecting to dumps.wikimedia.org (dumps.wikimedia.org)\|2620:0:861:4:208:80:155:106\|:443... connected.\n", "HTTP request sent, awaiting response... 404 Not Found\n", "2019-05-12 19:33:28 ERROR 404: Not Found.\n", "\n" ] }, { "ename": "CalledProcessError", "evalue": "Command 'b'\\nfdate=20170327\\nfname=enwikinews-$fdate-cirrussearch-content.json.gz\\nif [ ! -e $fname ]\\nthen\\n wget \"https://dumps.wikimedia.org/other/cirrussearch/$fdate/$fname\"\\nfi\\n'' returned non-zero exit status 8.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-2-fd54bac10b20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bash'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m 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state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m 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\u001b[0mCalledProcessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_run_script\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_close\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mCalledProcessError\u001b[0m: Command 'b'\\nfdate=20170327\\nfname=enwikinews-$fdate-cirrussearch-content.json.gz\\nif [ ! -e $fname ]\\nthen\\n wget \"https://dumps.wikimedia.org/other/cirrussearch/$fdate/$fname\"\\nfi\\n'' returned non-zero exit status 8." ] } ], "source": [ "%%bash\n", "\n", "fdate=20170327\n", "fname=enwikinews-$fdate-cirrussearch-content.json.gz\n", "if [ ! -e $fname ]\n", "then\n", " wget \"https://dumps.wikimedia.org/other/cirrussearch/$fdate/$fname\"\n", "fi\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# iterator\n", "import gzip\n", "import json\n", "\n", "FDATE = 20170327\n", "FNAME = \"enwikinews-%s-cirrussearch-content.json.gz\" % FDATE\n", "\n", "def iter_texts(fpath=FNAME):\n", " with gzip.open(fpath, \"rt\") as f:\n", " for l in f:\n", " data = json.loads(l)\n", " if \"title\" in data:\n", " yield data[\"title\"]\n", " yield data[\"text\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# also prepare nltk\n", "import nltk\n", "nltk.download(\"punkt\")\n", "nltk.download(\"stopwords\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing data\n", "\n", "we arrange the corpus as required by gensim" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# make a custom tokenizer\n", "import re\n", "from nltk.tokenize import sent_tokenize\n", "from nltk.tokenize import RegexpTokenizer\n", "tokenizer = RegexpTokenizer('\\w[\\w-]\|\\d[\\d,]')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# prepare a text\n", "def prepare(txt):\n", " # lower case\n", " txt = txt.lower()\n", " return [tokenizer.tokenize(sent) \n", " for sent in sent_tokenize(txt, language=LANG)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# we put all data in ram, it's not so much\n", "corpus = []\n", "for txt in iter_texts():\n", " corpus.extend(prepare(txt))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# how many sentences and words ?\n", "words_count = sum(len(s) for s in corpus)\n", "print(\"Corpus has %d words in %d sentences\" % (words_count, len(corpus)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing bigram with and without common terms\n", "\n", "The `Phrases` model gives us the possiblity of handling common terms, that is words that appears much time in a text and are there only to link objects between them.\n", "While you could remove them, you may information, for \"the president is in america\"* is not the same as \"the president of america\"\n", "\n", "The common_terms parameter Phrases can help you deal with them in a smarter way, keeping them around but avoiding them to crush frequency statistics." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from gensim.models.phrases import Phrases" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# which are the stop words we will use\n", "from nltk.corpus import stopwords\n", "\" \".join(stopwords.words(LANG))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a version of corups without stop words\n", "stop_words = frozenset(stopwords.words(LANG))\n", "def stopwords_filter(txt):\n", " return [w for w in txt if w not in stop_words]\n", "st_corpus = [stopwords_filter(txt) for txt in corpus]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# bigram std\n", "%time bigram = Phrases(st_corpus)\n", "# bigram with common terms\n", "%time bigram_ct = Phrases(corpus, common_terms=stopwords.words(LANG))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### bigram with common terms inside\n", "\n", "What are (some of) the bigram founds thanks to common terms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# grams that have more than 2 terms, are those with common terms\n", "ct_ngrams = set((g[1], g[0].decode(\"utf-8\"))\n", " for g in bigram_ct.export_phrases(corpus) \n", " if len(g[0].split()) > 2)\n", "ct_ngrams = sorted(list(ct_ngrams))\n", "print(len(ct_ngrams), \"grams with common terms found\")\n", "# highest scores\n", "ct_ngrams[-20:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# did we found any bigram with same words but different stopwords\n", "import collections\n", "by_terms = collections.defaultdict(set)\n", "for ngram, score in bigram_ct.export_phrases(corpus):\n", " grams = ngram.split()\n", " by_terms[(grams[0], grams[-1])].add(ngram)\n", "for k, v in by_terms.items():\n", " if len(v) > 1:\n", " print(b\"-\".join(k).decode(\"utf-8\"),\" : \", [w.decode(\"utf-8\") for w in v])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	14,227	Python	.py	296	43.72973	1,696	0.631685	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,940	Word2Vec_FastText_Comparison.ipynb	piskvorky_gensim/docs/notebooks/Word2Vec_FastText_Comparison.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of FastText and Word2Vec " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facebook Research open sourced a great project recently - [fastText](https://github.com/facebookresearch/fastText), a fast (no surprise) and effective method to learn word representations and perform text classification. I was curious about comparing these embeddings to other commonly used embeddings, so word2vec seemed like the obvious choice, especially considering fastText embeddings are an extension of word2vec. \n", "\n", "I've used gensim to train the word2vec models, and the analogical reasoning task (described in Section 4.1 of [[2]](https://arxiv.org/pdf/1301.3781v3.pdf)) for comparing the word2vec and fastText models. I've compared embeddings trained using the skipgram architecture." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Download data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package brown to /home/misha/nltk_data...\n", "[nltk_data] Package brown is already up-to-date!\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--2019-05-12 19:40:14-- https://mattmahoney.net/dc/enwik9.zip\n", "Resolving mattmahoney.net (mattmahoney.net)... 67.195.197.75\n", "Connecting to mattmahoney.net (mattmahoney.net)\|67.195.197.75\|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 322592222 (308M) [application/zip]\n", "Saving to: ‘enwik9.zip’\n", "\n", "enwik9.zip 47%[========> ] 145.49M 218KB/s in 10m 2s \n", "\n", "2019-05-12 19:50:17 (247 KB/s) - Connection closed at byte 152553031. Retrying.\n", "\n", "--2019-05-12 19:50:18-- (try: 2) https://mattmahoney.net/dc/enwik9.zip\n", "Connecting to mattmahoney.net (mattmahoney.net)\|67.195.197.75\|:80... connected.\n", "HTTP request sent, awaiting response... 206 Partial Content\n", "Length: 322592222 (308M), 170039191 (162M) remaining [application/zip]\n", "Saving to: ‘enwik9.zip’\n", "\n", "enwik9.zip 100%[+++++++++==========>] 307.65M 344KB/s in 8m 38s \n", "\n", "2019-05-12 19:58:57 (320 KB/s) - ‘enwik9.zip’ saved [322592222/322592222]\n", "\n", "Archive: enwik9.zip\n", " inflating: enwik9 \n", "Can't open perl script \"fastText/wikifil.pl\": No such file or directory\n" ] } ], "source": [ "import nltk\n", "from smart_open import smart_open\n", "nltk.download('brown') \n", "# Only the brown corpus is needed in case you don't have it.\n", "\n", "# Generate brown corpus text file\n", "with smart_open('brown_corp.txt', 'w+') as f:\n", " for word in nltk.corpus.brown.words():\n", " f.write('{word} '.format(word=word))\n", "\n", "# Make sure you set FT_HOME to your fastText directory root\n", "FT_HOME = 'fastText/'\n", "# download the text8 corpus (a 100 MB sample of cleaned wikipedia text)\n", "import os.path\n", "if not os.path.isfile('text8'):\n", " !wget -c https://mattmahoney.net/dc/text8.zip\n", " !unzip text8.zip\n", "# download and preprocess the text9 corpus\n", "if not os.path.isfile('text9'):\n", " !wget -c https://mattmahoney.net/dc/enwik9.zip\n", " !unzip enwik9.zip\n", " !perl {FT_HOME}wikifil.pl enwik9 > text9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For training the models yourself, you'll need to have both [Gensim](https://github.com/RaRe-Technologies/gensim) and [FastText](https://github.com/facebookresearch/fastText) set up on your machine." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training fasttext on brown_corp.txt corpus..\n", "/bin/sh: 1: fastText/fasttext: not found\n", "CPU times: user 6.02 ms, sys: 314 µs, total: 6.33 ms\n", "Wall time: 109 ms\n", "\n", "Training fasttext on brown_corp.txt corpus (without char n-grams)..\n", "/bin/sh: 1: fastText/fasttext: not found\n", "CPU times: user 2.12 ms, sys: 12.9 ms, total: 15 ms\n", "Wall time: 124 ms\n", "\n", "Training word2vec on brown_corp.txt corpus..\n", "CPU times: user 19.2 s, sys: 0 ns, total: 19.2 s\n", "Wall time: 6.71 s\n", "\n", "Saved gensim model as brown_gs.vec\n" ] } ], "source": [ "MODELS_DIR = 'models/'\n", "!mkdir -p {MODELS_DIR}\n", "\n", "lr = 0.05\n", "dim = 100\n", "ws = 5\n", "epoch = 5\n", "minCount = 5\n", "neg = 5\n", "loss = 'ns'\n", "t = 1e-4\n", "\n", "from gensim.models import Word2Vec, KeyedVectors\n", "from gensim.models.word2vec import Text8Corpus\n", "\n", "# Same values as used for fastText training above\n", "params = {\n", " 'alpha': lr,\n", " 'size': dim,\n", " 'window': ws,\n", " 'iter': epoch,\n", " 'min_count': minCount,\n", " 'sample': t,\n", " 'sg': 1,\n", " 'hs': 0,\n", " 'negative': neg\n", "}\n", "\n", "def train_models(corpus_file, output_name):\n", " output_file = '{:s}_ft'.format(output_name)\n", " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", " print('Training fasttext on {:s} corpus..'.format(corpus_file))\n", " %time !{FT_HOME}fasttext skipgram -input {corpus_file} -output {MODELS_DIR+output_file} -lr {lr} -dim {dim} -ws {ws} -epoch {epoch} -minCount {minCount} -neg {neg} -loss {loss} -t {t}\n", " else:\n", " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", " \n", " output_file = '{:s}_ft_no_ng'.format(output_name)\n", " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", " print('\\nTraining fasttext on {:s} corpus (without char n-grams)..'.format(corpus_file))\n", " %time !{FT_HOME}fasttext skipgram -input {corpus_file} -output {MODELS_DIR+output_file} -lr {lr} -dim {dim} -ws {ws} -epoch {epoch} -minCount {minCount} -neg {neg} -loss {loss} -t {t} -maxn 0\n", " else:\n", " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", " \n", " output_file = '{:s}_gs'.format(output_name)\n", " if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n", " print('\\nTraining word2vec on {:s} corpus..'.format(corpus_file))\n", " \n", " # Text8Corpus class for reading space-separated words file\n", " %time gs_model = Word2Vec(Text8Corpus(corpus_file), params); gs_model\n", " # Direct local variable lookup doesn't work properly with magic statements (%time)\n", " locals()['gs_model'].wv.save_word2vec_format(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file)))\n", " print('\\nSaved gensim model as {:s}.vec'.format(output_file))\n", " else:\n", " print('\\nUsing existing model file {:s}.vec'.format(output_file))\n", "\n", "evaluation_data = {}\n", "train_models('brown_corp.txt', 'brown')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training fasttext on text8 corpus..\n", "/bin/sh: 1: fastText/fasttext: not found\n", "CPU times: user 7.37 ms, sys: 0 ns, total: 7.37 ms\n", "Wall time: 109 ms\n", "\n", "Training fasttext on text8 corpus (without char n-grams)..\n", "/bin/sh: 1: fastText/fasttext: not found\n", "CPU times: user 12.3 ms, sys: 0 ns, total: 12.3 ms\n", "Wall time: 115 ms\n", "\n", "Training word2vec on text8 corpus..\n", "CPU times: user 7min 12s, sys: 0 ns, total: 7min 12s\n", "Wall time: 2min 26s\n", "\n", "Saved gensim model as text8_gs.vec\n" ] } ], "source": [ "train_models(corpus_file='text8', output_name='text8')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training fasttext on text9 corpus..\n", "/bin/sh: 1: fastText/fasttext: not found\n", "CPU times: user 8.81 ms, sys: 0 ns, total: 8.81 ms\n", "Wall time: 111 ms\n", "\n", "Training fasttext on text9 corpus (without char n-grams)..\n", "/bin/sh: 1: fastText/fasttext: not found\n", "CPU times: user 10.7 ms, sys: 0 ns, total: 10.7 ms\n", "Wall time: 115 ms\n", "\n", "Training word2vec on text9 corpus..\n" ] }, { "ename": "RuntimeError", "evalue": "you must first build vocabulary before training the model", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n", "\u001b[0;32m~/git/gensim/gensim/models/word2vec.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, sentences, corpus_file, size, alpha, window, min_count, max_vocab_size, sample, seed, workers, min_alpha, sg, hs, negative, ns_exponent, cbow_mean, hashfxn, iter, null_word, trim_rule, sorted_vocab, batch_words, compute_loss, callbacks, max_final_vocab)\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrim_rule\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrim_rule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 782\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhs\u001b[0m\u001b[0;34m,\u001b[0m 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fast_version, kwargs)\u001b[0m\n\u001b[1;32m 761\u001b[0m \u001b[0msentences\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msentences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorpus_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcorpus_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_examples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorpus_count\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 762\u001b[0m \u001b[0mtotal_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorpus_total_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_alpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 763\u001b[0;31m end_alpha=self.min_alpha, compute_loss=compute_loss)\n\u001b[0m\u001b[1;32m 764\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrim_rule\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/git/gensim/gensim/models/word2vec.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, sentences, corpus_file, total_examples, total_words, epochs, start_alpha, end_alpha, word_count, queue_factor, report_delay, compute_loss, callbacks)\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0msentences\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msentences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorpus_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcorpus_file\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;31mRuntimeError\u001b[0m: you must first build vocabulary before training the model" ] }, { "ename": "KeyError", "evalue": "'gs_model'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-ff23d154d505>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorpus_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'text9'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'text9'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-2-5d45cca4dc08>\u001b[0m in \u001b[0;36mtrain_models\u001b[0;34m(corpus_file, output_name)\u001b[0m\n\u001b[1;32m 49\u001b[0m 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{:s}.vec'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'gs_model'" ] } ], "source": [ "train_models(corpus_file='text9', output_name='text9')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparisons" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# download the file questions-words.txt to be used for comparing word embeddings\n", "!wget https://raw.githubusercontent.com/tmikolov/word2vec/master/questions-words.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have downloaded or trained the models and downloaded `questions-words.txt`, you're ready to run the comparison." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import logging\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", "\n", "# Training times in seconds\n", "evaluation_data['brown'] = [(18, 54.3, 32.5)]\n", "evaluation_data['text8'] = [(402, 942, 496)]\n", "evaluation_data['text9'] = [(3218, 6589, 3550)]\n", "\n", "def print_accuracy(model, questions_file):\n", " print('Evaluating...\\n')\n", " acc = model.accuracy(questions_file)\n", "\n", " sem_correct = sum((len(acc[i]['correct']) for i in range(5)))\n", " sem_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5))\n", " sem_acc = 100float(sem_correct)/sem_total\n", " print('\\nSemantic: {:d}/{:d}, Accuracy: {:.2f}%'.format(sem_correct, sem_total, sem_acc))\n", " \n", " syn_correct = sum((len(acc[i]['correct']) for i in range(5, len(acc)-1)))\n", " syn_total = sum((len(acc[i]['correct']) + len(acc[i]['incorrect'])) for i in range(5,len(acc)-1))\n", " syn_acc = 100float(syn_correct)/syn_total\n", " print('Syntactic: {:d}/{:d}, Accuracy: {:.2f}%\\n'.format(syn_correct, syn_total, syn_acc))\n", " return (sem_acc, syn_acc)\n", "\n", "word_analogies_file = 'questions-words.txt'\n", "accuracies = []\n", "print('\\nLoading Gensim embeddings')\n", "brown_gs = KeyedVectors.load_word2vec_format(MODELS_DIR + 'brown_gs.vec')\n", "print('Accuracy for Word2Vec:')\n", "accuracies.append(print_accuracy(brown_gs, word_analogies_file))\n", "\n", "print('\\nLoading FastText embeddings')\n", "brown_ft = KeyedVectors.load_word2vec_format(MODELS_DIR + 'brown_ft.vec')\n", "print('Accuracy for FastText (with n-grams):')\n", "accuracies.append(print_accuracy(brown_ft, word_analogies_file))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `accuracy` takes an optional parameter `restrict_vocab`, which limits the vocabulary of model considered for fast approximate evaluation (default is 30000).\n", "\n", "Word2Vec embeddings seem to be slightly better than fastText embeddings at the semantic tasks, while the fastText embeddings do significantly better on the syntactic analogies. Makes sense, since fastText embeddings are trained for understanding morphological nuances, and most of the syntactic analogies are morphology based. \n", "\n", "Let me explain that better.\n", "\n", "According to the paper [[1]](https://arxiv.org/abs/1607.04606), embeddings for words are represented by the sum of their n-gram embeddings. This is meant to be useful for morphologically rich languages - so theoretically, the embedding for `apparently` would include information from both character n-grams `apparent` and `ly` (as well as other n-grams), and the n-grams would combine in a simple, linear manner. This is very similar to what most of our syntactic tasks look like.\n", "\n", "Example analogy:\n", "\n", "`amazing amazingly calm calmly`\n", "\n", "This analogy is marked correct if: \n", "\n", "`embedding(amazing)` - `embedding(amazingly)` = `embedding(calm)` - `embedding(calmly)`\n", "\n", "Both these subtractions would result in a very similar set of remaining ngrams.\n", "No surprise the fastText embeddings do extremely well on this.\n", "\n", "Let's do a small test to validate this hypothesis - fastText differs from word2vec only in that it uses char n-gram embeddings as well as the actual word embedding in the scoring function to calculate scores and then likelihoods for each word, given a context word. In case char n-gram embeddings are not present, this reduces (at least theoretically) to the original word2vec model. This can be implemented by setting 0 for the max length of char n-grams for fastText.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('Loading FastText embeddings')\n", "brown_ft_no_ng = KeyedVectors.load_word2vec_format(MODELS_DIR + 'brown_ft_no_ng.vec')\n", "print('Accuracy for FastText (without n-grams):')\n", "accuracies.append(print_accuracy(brown_ft_no_ng, word_analogies_file))\n", "evaluation_data['brown'] += [[acc[0] for acc in accuracies], [acc[1] for acc in accuracies]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A-ha! The results for FastText with no n-grams and Word2Vec look a lot more similar (as they should) - the differences could easily result from differences in implementation between fastText and Gensim, and randomization. Especially telling is that the semantic accuracy for FastText has improved slightly after removing n-grams, while the syntactic accuracy has taken a giant dive. Our hypothesis that the char n-grams result in better performance on syntactic analogies seems fair. It also seems possible that char n-grams hurt semantic accuracy a little. However, the brown corpus is too small to be able to draw any definite conclusions - the accuracies seem to vary significantly over different runs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try with a larger corpus now - text8 (collection of wiki articles). I'm also curious about the impact on semantic accuracy - for models trained on the brown corpus, the difference in the semantic accuracy and the accuracy values themselves are too small to be conclusive. Hopefully a larger corpus helps, and the text8 corpus likely has a lot more information about capitals, currencies, cities etc, which should be relevant to the semantic tasks." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "accuracies = []\n", "print('Loading Gensim embeddings')\n", "text8_gs = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text8_gs.vec')\n", "print('Accuracy for word2vec:')\n", "accuracies.append(print_accuracy(text8_gs, word_analogies_file))\n", "\n", "print('Loading FastText embeddings (with n-grams)')\n", "text8_ft = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text8_ft.vec')\n", "print('Accuracy for FastText (with n-grams):')\n", "accuracies.append(print_accuracy(text8_ft, word_analogies_file))\n", "\n", "print('Loading FastText embeddings')\n", "text8_ft_no_ng = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text8_ft_no_ng.vec')\n", "print('Accuracy for FastText (without n-grams):')\n", "accuracies.append(print_accuracy(text8_ft_no_ng, word_analogies_file))\n", "\n", "evaluation_data['text8'] += [[acc[0] for acc in accuracies], [acc[1] for acc in accuracies]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the text8 corpus, we observe a similar pattern. Semantic accuracy falls by a small but significant amount when n-grams are included in FastText, while FastText with n-grams performs far better on the syntactic analogies. FastText without n-grams are largely similar to Word2Vec.\n", "\n", "My hypothesis for semantic accuracy being lower for the FastText-with-ngrams model is that most of the words in the semantic analogies are standalone words and are unrelated to their morphemes (eg: father, mother, France, Paris), hence inclusion of the char n-grams into the scoring function actually makes the embeddings worse.\n", "\n", "This trend is observed in the original paper too where the performance of embeddings with n-grams is worse on semantic tasks than both word2vec cbow and skipgram models.\n", "\n", "Let's do a quick comparison on an even larger corpus - text9 " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "accuracies = []\n", "print('Loading Gensim embeddings')\n", "text9_gs = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text9_gs.vec')\n", "print('Accuracy for word2vec:')\n", "accuracies.append(print_accuracy(text9_gs, word_analogies_file))\n", "\n", "print('Loading FastText embeddings (with n-grams)')\n", "text9_ft = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text9_ft.vec')\n", "print('Accuracy for FastText (with n-grams):')\n", "accuracies.append(print_accuracy(text9_ft, word_analogies_file))\n", "\n", "print('Loading FastText embeddings')\n", "text9_ft_no_ng = KeyedVectors.load_word2vec_format(MODELS_DIR + 'text9_ft_no_ng.vec')\n", "print('Accuracy for FastText (without n-grams):')\n", "accuracies.append(print_accuracy(text9_ft_no_ng, word_analogies_file))\n", "\n", "evaluation_data['text9'] += [[acc[0] for acc in accuracies], [acc[1] for acc in accuracies]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "def plot(ax, data, corpus_name='brown'):\n", " width = 0.25\n", " pos = [(i, i + width, i + 2width) for i in range(len(data))]\n", " colors = ['#EE3224', '#F78F1E', '#FFC222']\n", " acc_ax = ax.twinx()\n", " # Training time\n", " ax.bar(pos[0],\n", " data[0],\n", " width,\n", " alpha=0.5,\n", " color=colors\n", " )\n", " # Semantic accuracy\n", " acc_ax.bar(pos[1],\n", " data[1],\n", " width,\n", " alpha=0.5,\n", " color=colors\n", " )\n", "\n", " # Syntactic accuracy\n", " acc_ax.bar(pos[2],\n", " data[2],\n", " width,\n", " alpha=0.5,\n", " color=colors\n", " )\n", "\n", " ax.set_ylabel('Training time (s)')\n", " acc_ax.set_ylabel('Accuracy (%)')\n", " ax.set_title(corpus_name)\n", "\n", " acc_ax.set_xticks([p[0] + 1.5 width for p in pos])\n", " acc_ax.set_xticklabels(['Training Time', 'Semantic Accuracy', 'Syntactic Accuracy'])\n", "\n", " # Proxy plots for adding legend correctly\n", " proxies = [ax.bar([0], [0], width=0, color=c, alpha=0.5)[0] for c in colors]\n", " models = ('Gensim', 'FastText', 'FastText (no-ngrams)')\n", " ax.legend((proxies), models, loc='upper left')\n", " \n", " ax.set_xlim(pos[0][0]-width, pos[-1][0]+width4)\n", " ax.set_ylim([0, max(data[0])1.1] )\n", " acc_ax.set_ylim([0, max(data[1] + data[2])*1.1] )\n", "\n", " plt.grid()\n", "\n", "# Plotting the bars\n", "fig = plt.figure(figsize=(10,15))\n", "for corpus, subplot in zip(sorted(evaluation_data.keys()), [311, 312, 313]):\n", " ax = fig.add_subplot(subplot)\n", " plot(ax, evaluation_data[corpus], corpus)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results from text9 seem to confirm our hypotheses so far. Briefly summarising the main points -\n", "\n", "1. FastText models with n-grams do significantly better on syntactic tasks, because of the syntactic questions being related to morphology of the words\n", "2. Both Gensim word2vec and the fastText model with no n-grams do slightly better on the semantic tasks, presumably because words from the semantic questions are standalone words and unrelated to their char n-grams\n", "3. In general, the performance of the models seems to get closer with the increasing corpus size. However, this might possibly be due to the size of the model staying constant at 100, and a larger model size for large corpora might result in higher performance gains.\n", "4. The semantic accuracy for all models increases significantly with the increase in corpus size.\n", "5. However, the increase in syntactic accuracy from the increase in corpus size for the n-gram FastText model is lower (in both relative and absolute terms). This could possibly indicate that advantages gained by incorporating morphological information could be less significant in case of larger corpus sizes (the corpuses used in the original paper seem to indicate this too)\n", "6. Training times for gensim are slightly lower than the fastText no-ngram model, and significantly lower than the n-gram variant. This is quite impressive considering fastText is implemented in C++ and Gensim in Python (with calls to low-level BLAS routines for much of the heavy lifting). You could read [this post](https://rare-technologies.com/word2vec-in-python-part-two-optimizing/) for more details regarding word2vec optimisation in Gensim. Note that these times include importing any dependencies and serializing the models to disk, and not just the training times." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These preliminary results seem to indicate fastText embeddings are significantly better than word2vec at encoding syntactic information. This is expected, since most syntactic analogies are morphology based, and the char n-gram approach of fastText takes such information into account. The original word2vec model seems to perform better on semantic tasks, since words in semantic analogies are unrelated to their char n-grams, and the added information from irrelevant char n-grams worsens the embeddings. It'd be interesting to see how transferable these embeddings are for different kinds of tasks by comparing their performance in a downstream supervised task." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[1] [Enriching Word Vectors with Subword Information](https://arxiv.org/pdf/1607.04606v1.pdf)\n", "\n", "[2] [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781v3.pdf)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	38,456	Python	.py	595	59.74958	2,210	0.658608	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,941	ldaseqmodel.ipynb	piskvorky_gensim/docs/notebooks/ldaseqmodel.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Dynamic Topic Models Tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### What is this tutorial about?\n", "This tutorial will exaplin what Dynamic Topic Models are, and how to use them using the LdaSeqModel class of gensim. I will start with some theory and already documented examples, before moving on to an example run of Dynamic Topic Model on a sample dataset. I will also do an comparison between the python code and the already present gensim wrapper, and illustrate how to easily visualise them and do topic coherence with DTM. Any suggestions to improve documentation or code through PRs/Issues is always appreciated!\n", "\n", "\n", "### Why Dynamic Topic Models?\n", "Imagine you have a gigantic corpus which spans over a couple of years. You want to find semantically similar documents; one from the very beginning of your time-line, and one in the very end. How would you?\n", "This is where Dynamic Topic Models comes in. By having a time-based element to topics, context is preserved while key-words may change.\n", "\n", "David Blei does a good job explaining the theory behind this in this [Google talk](https://www.youtube.com/watch?v=7BMsuyBPx90). If you prefer to directly read the [paper on DTM by Blei and Lafferty](http://repository.cmu.edu/cgi/viewcontent.cgi?article=2036&context=compsci), that should get you upto speed too.\n", "\n", "In his talk, Blei gives a very interesting example of the motivation to use DTM. After running DTM on a dataset of the Science Journal from 1880 onwards, he picks up [this](http://science.sciencemag.org/content/os-1/28/326) paper - The Brain of the Orang (1880). It's topics are concentrated on a topic which must be to do with Monkeys, and with Neuroscience or brains. \n", "<img src=\"Monkey Brains.png\" width=\"500\">\n", "\n", "\n", "He goes ahead to pick up another paper with likely very less common words, but in the same context - analysing monkey brains. In fact, this one is called - \"[Representation of the visual field on the medial wall of occipital-parietal cortex in the owl monkey](http://allmanlab.caltech.edu/PDFs/AllmanKaas1976.pdf)\". Quite the title, eh? Like mentioned before, you wouldn't imagine too many common words in these two papers, about a 100 years apart.\n", "<img src=\"Monkey Brains New.png\" witdth=\"400\">\n", "\n", "\n", "\n", "But a Hellinger Distance based Document-Topic distribution gives a very high similarity value! The same topics evolved smoothly over time and the context remains. A document similarity match using other traditional techniques might not work very well on this!\n", "Blei defines this technique as - \"Time corrected Document Similarity\".\n", "\n", "Another obviously useful analysis is to see how words in a topic change over time. The same broad classified topic starts looking more 'mature' as time goes on. This image illustrates an example from the same paper linked to above.\n", "\n", "<img src=\"Dynamic Topic Model.png\" width=\"800\">\n", "\n", "#### So, briefly put : \n", "\n", "Dynamic Topic Models are used to model the evolution of topics in a corpus, over time. The Dynamic Topic Model is part of a class of probabilistic topic models, like the LDA. \n", "\n", "While most traditional topic mining algorithms do not expect time-tagged data or take into account any prior ordering, Dynamic Topic Models (DTM) leverages the knowledge of different documents belonging to a different time-slice in an attempt to map how the words in a topic change over time.\n", "\n", "[This](https://rare-technologies.com/understanding-and-coding-dynamic-topic-models/) blog post is also useful in breaking down the ideas and theory behind DTM.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Motivation to code this!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But - why even undertake this, especially when Gensim itself have a wrapper?\n", "The main motivation was the lack of documentation in the original code - and the fact that doing an only python version makes it easier to use gensim building blocks. For example, for setting up the Sufficient Statistics to initialize the DTM, you can just pass a pre-trained gensim LDA model!\n", "\n", "There is some clarity on how they built their code now - Variational Inference using Kalman Filters, as described in section 3 of the paper. The mathematical basis for the code is well described in the appendix of the paper. If the documentation is lacking or not clear, comments via Issues or PRs via the gensim repo would be useful in improving the quality.\n", "\n", "This project was part of the Google Summer of Code 2016 program: I have been regularly blogging about my progress with implementing this, which you can find [here](https://rare-technologies.com/author/bhargav/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using LdaSeqModel for DTM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gensim already has a wrapper for original C++ DTM code, but the `LdaSeqModel` class is an effort to have a pure python implementation of the same.\n", "Using it is very similar to using any other gensim topic-modelling algorithm, with all you need to start is an iterable gensim corpus, id2word and a list with the number of documents in each of your time-slices." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# setting up our imports\n", "\n", "from gensim.models import ldaseqmodel\n", "from gensim.corpora import Dictionary, bleicorpus\n", "import numpy\n", "from gensim.matutils import hellinger" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will be loading the corpus and dictionary from disk. Here our corpus in the Blei corpus format, but it can be any iterable corpus.\n", "The data set here consists of news reports over 3 months (March, April, May) downloaded from [here](https://github.com/derekgreene/dynamic-nmf/tree/master/data) and cleaned.\n", "\n", "The files can also be found after processing in the `datasets` folder, but it would be a good exercise to download the files in the link and have a shot at cleaning and processing yourself. :)\n", "\n", "Note: the dataset is not cleaned and requires pre-processing. This means that some care must be taken to group the relevant docs together (the months are mentioned as per the folder name), after which it needs to be broken into tokens, and stop words must be removed. This is a link to some basic pre-processing for gensim - [link](https://radimrehurek.com/gensim/tut1.html#from-strings-to-vectors).\n", "\n", "### What is a time-slice?\n", "A very important input for DTM to work is the `time_slice` input. It should be a list which contains the number of documents in each time-slice. In our case, the first month had 438 articles, the second 430 and the last month had 456 articles. This means we'd need an input which looks like this: `time_slice = [438, 430, 456]`. \n", "Technically, a time-slice can be a month, year, or any way you wish to split up the number of documents in your corpus, time-based.\n", "\n", "Once you have your corpus, id2word and time_slice ready, we're good to go!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# loading our corpus and dictionary\n", "try:\n", " dictionary = Dictionary.load('datasets/news_dictionary')\n", "except FileNotFoundError as e:\n", " raise ValueError(\"SKIP: Please download the Corpus/news_dictionary dataset.\")\n", "corpus = bleicorpus.BleiCorpus('datasets/news_corpus')\n", "# it's very important that your corpus is saved in order of your time-slices!\n", "\n", "time_slice = [438, 430, 456]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For DTM to work it first needs the Sufficient Statistics from a trained LDA model on the _same_ dataset. \n", "By default LdaSeqModel trains it's own model and passes those values on, but can also accept a pre-trained gensim LDA model, or a numpy matrix which contains the Suff Stats.\n", "\n", "We will be training our model in default mode, so gensim LDA will be first trained on the dataset.\n", "\n", "NOTE: You have to set logging as true to see your progress!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/git/gensim/gensim/models/ldaseqmodel.py:293: RuntimeWarning: divide by zero encountered in double_scalars\n", " convergence = np.fabs((bound - old_bound) / old_bound)\n" ] } ], "source": [ "ldaseq = ldaseqmodel.LdaSeqModel(corpus=corpus, id2word=dictionary, time_slice=time_slice, num_topics=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that our model is trained, let's see what our results look like.\n", "\n", "### Results\n", "Much like LDA, the points of interest would be in what the topics are and how the documents are made up of these topics.\n", "In DTM we have the added interest of seeing how these topics evolve over time.\n", "\n", "Let's go through some of the functions to print Topics and analyse documents.\n", "\n", "### Printing Topics\n", "\n", "To print all topics from a particular time-period, simply use `print_topics`. \n", "The input parameter to `print_topics` is a time-slice option. By passing `0` we are seeing the topics in the 1st time-slice. \n", "\n", "The result would be a list of lists, where each individual list contains a tuple of the most probable words in the topic. i.e `(word, word_probability)`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[('film', 0.005917469495987717),\n", " ('best', 0.00486801522781858),\n", " ('last', 0.003433887442830304),\n", " ('tv', 0.0034066181560892983),\n", " ('first', 0.003231913726883219),\n", " ('show', 0.003224849338213615),\n", " ('three', 0.0023048170201803537),\n", " ('bbc', 0.0022750794162480974),\n", " ('home', 0.0020196147021130373),\n", " ('second', 0.001957191475970597),\n", " ('director', 0.001955785339407403),\n", " ('star', 0.0018697442019571776),\n", " ('united', 0.0018486275961695346),\n", " ('actor', 0.0017773645590414533),\n", " ('world', 0.0017728348478406259),\n", " ('time', 0.0017660315811460685),\n", " ('took', 0.0017534951800840248),\n", " ('award', 0.0016593687293076347),\n", " ('four', 0.001646883029873571),\n", " ('rights', 0.001494912649347212)],\n", " [('blair', 0.003656853413619428),\n", " ('labour', 0.003571831088597708),\n", " ('think', 0.0033329732624369085),\n", " ('chelsea', 0.0029922287405279684),\n", " ('game', 0.0028011339801883163),\n", " ('league', 0.002610476262682161),\n", " ('players', 0.002584137822138894),\n", " ('time', 0.002562398970649943),\n", " ('minister', 0.0025281531553959192),\n", " ('club', 0.002527139415819049),\n", " ('good', 0.0024787460166518565),\n", " ('last', 0.0024499225552730096),\n", " (\"don't\", 0.002415439141733538),\n", " ('arsenal', 0.0023802238324075278),\n", " ('party', 0.002379839625412044),\n", " ('bbc', 0.0023698950465503867),\n", " ('like', 0.0023651537261334767),\n", " ('election', 0.0023497931652231656),\n", " ('want', 0.00231801726260594),\n", " ('going', 0.002301691972872612)],\n", " [('music', 0.004535324394354765),\n", " ('number', 0.0036113710103155496),\n", " ('best', 0.003320026020611955),\n", " ('like', 0.003311533622142927),\n", " ('make', 0.002863027781655863),\n", " ('games', 0.0028196051742470636),\n", " ('government', 0.002788805852512145),\n", " ('first', 0.002547823171177183),\n", " ('band', 0.0024952788511720227),\n", " ('top', 0.0024859910933818347),\n", " ('uk', 0.0024576067300663123),\n", " ('last', 0.002142118981734694),\n", " ('world', 0.0020189228825508864),\n", " ('album', 0.001992838242293178),\n", " ('technology', 0.001969315542996064),\n", " ('video', 0.00191108778009925),\n", " ('next', 0.0018981806100445784),\n", " ('song', 0.0018852686282286684),\n", " ('british', 0.001820807928747836),\n", " ('years', 0.0018139501663332197)],\n", " [('mobile', 0.007197643998061543),\n", " ('users', 0.006658644027884087),\n", " ('net', 0.0064791532730191104),\n", " ('use', 0.00576421127183059),\n", " ('phone', 0.004636221654879627),\n", " ('used', 0.004191649534638687),\n", " ('using', 0.004121003944338685),\n", " ('internet', 0.00406147222068167),\n", " ('information', 0.003952892153876258),\n", " ('data', 0.003913138986834236),\n", " ('broadband', 0.003831513514609501),\n", " ('security', 0.00374214444231794),\n", " ('software', 0.0036371490042105517),\n", " ('service', 0.003607383455833399),\n", " ('online', 0.0034560031044337833),\n", " ('computer', 0.00339076896283381),\n", " ('phones', 0.0032184885220204905),\n", " ('million', 0.0031314352626954645),\n", " ('technology', 0.003096129557983641),\n", " ('site', 0.0030781219059679782)],\n", " [('last', 0.004329814208995159),\n", " ('year', 0.0037804149324644964),\n", " ('market', 0.003676885359238659),\n", " ('sales', 0.0034766894610293083),\n", " ('economic', 0.003139945186068337),\n", " ('government', 0.002927826150837857),\n", " ('growth', 0.0028388426302171014),\n", " ('oil', 0.0026113628080416252),\n", " ('economy', 0.002605167250854227),\n", " ('bank', 0.0026044767470200696),\n", " ('company', 0.002513269599201577),\n", " ('since', 0.002392370123819913),\n", " ('firm', 0.002259595203029759),\n", " ('uk', 0.002203922126442529),\n", " ('however,', 0.002175535174978607),\n", " ('prices', 0.0021384803520053666),\n", " ('rise', 0.002095321371791152),\n", " ('year.', 0.0020281054332198746),\n", " ('companies', 0.001991460828586195),\n", " ('tax', 0.001986423523484035)]]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ldaseq.print_topics(time=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see 5 different fairly well defined topics:\n", "1. Economy\n", "2. Entertainment\n", "3. Football\n", "4. Technology and Entertainment\n", "5. Government" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Looking for Topic Evolution\n", "\n", "To fix a topic and see it evolve, use `print_topic_times`.\n", "The input parameter is the `topic_id`\n", "In this case, we are looking at the evolution of the technology topic." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you look at the lower frequencies; the word \"may\" is creeping itself into prominence. This makes sense, as the 3rd time-slice are news reports about the economy in the month of may. This isn't present at all in the first time-slice because it's still March!\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[('film', 0.005917469495987717),\n", " ('best', 0.00486801522781858),\n", " ('last', 0.003433887442830304),\n", " ('tv', 0.0034066181560892983),\n", " ('first', 0.003231913726883219),\n", " ('show', 0.003224849338213615),\n", " ('three', 0.0023048170201803537),\n", " ('bbc', 0.0022750794162480974),\n", " ('home', 0.0020196147021130373),\n", " ('second', 0.001957191475970597),\n", " ('director', 0.001955785339407403),\n", " ('star', 0.0018697442019571776),\n", " ('united', 0.0018486275961695346),\n", " ('actor', 0.0017773645590414533),\n", " ('world', 0.0017728348478406259),\n", " ('time', 0.0017660315811460685),\n", " ('took', 0.0017534951800840248),\n", " ('award', 0.0016593687293076347),\n", " ('four', 0.001646883029873571),\n", " ('rights', 0.001494912649347212)],\n", " [('film', 0.00571305545611347),\n", " ('best', 0.004837727655153439),\n", " ('show', 0.003481314386186913),\n", " ('tv', 0.0034722615227741997),\n", " ('last', 0.003386168752366683),\n", " ('first', 0.0032365624129367236),\n", " ('three', 0.0023234319442561988),\n", " ('bbc', 0.0023081792345694347),\n", " ('home', 0.001998773395520472),\n", " ('second', 0.0019645262577901016),\n", " ('united', 0.0018667489008505022),\n", " ('star', 0.0018651888307475372),\n", " ('director', 0.001864169176393472),\n", " ('time', 0.0017736738670308424),\n", " ('world', 0.0017731872594117693),\n", " ('took', 0.0017633644840274812),\n", " ('actor', 0.0016991013070837629),\n", " ('four', 0.0016705538768877733),\n", " ('award', 0.0016528333936788875),\n", " ('police', 0.0016136170157803962)],\n", " [('film', 0.005540858483231528),\n", " ('best', 0.0048262559888687185),\n", " ('show', 0.003716906775365683),\n", " ('tv', 0.0035395904398093526),\n", " ('last', 0.0034777589481244098),\n", " ('first', 0.003243689110874043),\n", " ('bbc', 0.0023370004552749914),\n", " ('three', 0.002329091349260664),\n", " ('home', 0.0020582079444666068),\n", " ('second', 0.001973888122637219),\n", " ('united', 0.001916879327887862),\n", " ('star', 0.0018340698498021293),\n", " ('world', 0.0017979775587484525),\n", " ('director', 0.0017916941003114167),\n", " ('time', 0.0017818044310012845),\n", " ('took', 0.0017774896897978856),\n", " ('four', 0.0016868970107681114),\n", " ('award', 0.001650192716396564),\n", " ('actor', 0.0016306709680404894),\n", " ('rights', 0.0015213265124893606)]]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ldaseq.print_topic_times(topic=0) # evolution of 1st topic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Document - Topic Proportions\n", "the function `doc_topics` checks the topic proportions on documents already trained on. It accepts the document number in the corpus as an input.\n", "\n", "Let's pick up document number 558 arbitrarily and have a look." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['set', 'time,\"', 'chairman', 'decision', 'news', 'director', 'former', 'vowed', '\"it', 'results', 'club', 'third', 'home', 'paul', 'saturday.', 'south', 'conference', 'leading', '\"some', 'survival', 'needed', 'coach', \"don't\", 'every', 'trouble', 'desperate', 'eight', 'first', 'win', 'going', 'park', 'near', 'chance', 'manager', 'league', 'milan', 'games', 'go', 'game', 'foot', 'say', 'upset', \"i'm\", 'poor', 'season.', 'executive', 'road', '24', 'debut', 'portsmouth.', 'give', 'claiming', 'steve', 'break', 'rivals', 'boss', 'kevin', 'premiership', 'little', 'left', 'table.', 'life', 'join', 'years.', 'bring', 'season,', 'director.', 'became', 'st', 'according', 'official', 'hope', 'shocked', 'though', 'phone', 'charge', '14', 'website.', 'time,', 'claimed', 'kept', 'bond', 'appointment', 'unveil', 'november', 'picked', 'confirmed,', 'believed', 'deep', 'position', 'surprised', 'negotiations', 'talks', 'gmt', 'middlesbrough', 'replaced', 'appear', 'football,', '\"i\\'m', 'charge.', 'saints', 'southampton', 'sturrock', 'wednesday.', 'harry', 'poised', 'ninth', 'quit', 'relieved', 'chance.\"', 'decision.\"', 'hero', 'redknapp,', 'redknapp', \"saints'\", 'first-team', \"wouldn't\", \"mary's.\", 'portsmouth', \"redknapp's\", 'pompey', 'academy', \"harry's\", 'cult', 'rupert', 'time\".', 'coast', '57,', 'succeed', 'duties', \"'i\", 'bitter,', \"mandaric's\", \"portsmouth's\", 'wigley,', 'wigley', \"southampton',\", '1500', 'mandaric', \"'absolutely\", 'lowe', '\"disappointed\"', 'velimir', 'not\\',\"', 'disgusted', 'disappointed,', 'mandaric,', 'fratton', 'replaces', 'masterminding', 'angry,', 'vowed:', 'informed.\"', 'zajec']\n" ] } ], "source": [ "# to check Document - Topic proportions, use `doc-topics`\n", "words = [dictionary[word_id] for word_id, count in corpus[558]]\n", "print (words)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's pretty clear that it's a news article about football. What topics will it likely be comprised of?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2.82842860e-01 4.26855326e-01 5.46298825e-05 5.46298825e-05\n", " 2.90192554e-01]\n" ] } ], "source": [ "doc = ldaseq.doc_topics(558) # check the 558th document in the corpuses topic distribution\n", "print (doc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at our topics as desribed by us, again:\n", "\n", "1. Economy\n", "2. Entertainment\n", "3. Football\n", "4. Technology and Entertainment\n", "5. Government\n", "\n", "Our topic distribution for the above document is largely in topics 3 and 4. Considering it is a document about a news article on a football match, the distribution makes perfect sense!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wish to analyse a document not in our training set, we can use simply pass the doc to the model similar to the `__getitem__` funciton for `LdaModel`.\n", "\n", "Let's let our document be a hypothetical news article about the effects of Ryan Giggs buying mobiles affecting the British economy." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.00110497 0.33842633 0.00110497 0.25458869 0.40477504]\n" ] } ], "source": [ "doc_football_1 = ['economy', 'bank', 'mobile', 'phone', 'markets', 'buy', 'football', 'united', 'giggs']\n", "doc_football_1 = dictionary.doc2bow(doc_football_1)\n", "doc_football_1 = ldaseq[doc_football_1]\n", "print (doc_football_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty neat! Topic 1 is about the Economy, and this document also has traces of football and technology, so topics 1, 3, and 4 got correctly activated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distances between documents\n", "\n", "One of the more handy uses of DTMs topic modelling is that we can compare documents across different time-frames and see how similar they are topic-wise. When words may not necessarily overlap over these time-periods, this is very useful.\n", "\n", "The current dataset doesn't provide us the diversity for this to be an effective example; but we will nevertheless illustrate how to do the same.\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "doc_football_2 = ['arsenal', 'fourth', 'wenger', 'oil', 'middle', 'east', 'sanction', 'fluctuation']\n", "doc_football_2 = dictionary.doc2bow(doc_football_2)\n", "doc_football_2 = ldaseq[doc_football_2]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.38644713413294984" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hellinger(doc_football_1, doc_football_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The topic distributions are quite related - matches well in football and economy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's try with documents that shouldn't be similar." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6105070034631694" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "doc_governemt_1 = ['tony', 'government', 'house', 'party', 'vote', 'european', 'official', 'house']\n", "doc_governemt_1 = dictionary.doc2bow(doc_governemt_1)\n", "doc_governemt_1 = ldaseq[doc_governemt_1]\n", "\n", "hellinger(doc_football_1, doc_governemt_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the value is very high, meaning the topic distributions are far apart.\n", "\n", "For more information on how to use the gensim distance metrics, check out [this notebook](distance_metrics.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance\n", "\n", "The code currently runs between 5 to 7 times slower than the original C++ DTM code. The bottleneck is in the scipy `optimize.fmin_cg` method for updating obs. Speeding this up would fix things up!\n", "\n", "Since it uses iterable gensim corpuses, the memory stamp is also cleaner. \n", "\n", "TODO: check memory, check BLAS, see how performance can be improved memory and speed wise. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The advantages of the python port are that unlike the C++ code we needn't treat it like a black-box; PRs to help make the code better are welcomed, as well as help to make the documentation clearer and improve performance. It is also in pure python and doesn't need any dependancy outside of what gensim already needs. The added functionality of being able to analyse new documents is also a plus!\n", "\n", "## Choosing your best Dynamic Topic Model.\n", "\n", "Like we've been going on and on before, the advantage in having a python port is the transparency with which you can train your DTM.\n", "We'll go over two key ideas: changing variance, and changing suff stats.\n", "\n", "\n", "### Chain Variance\n", "One of the key aspects of topic evolution is how fast/slow these topics evolve. And this is where the factor of `variance` comes in.\n", "By setting the `chain_variance` input to the DTM model higher, we can tweak our topic evolution.\n", "The default value is 0.005. (this is the value suggested by Blei in his tech talk and is the default value in the C++ code)\n", "\n", "Let us see a small example illustrating the same.\n", "Let's first see the evolution of values for the first time-slice." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[('blair', 0.003656853413619428),\n", " ('labour', 0.003571831088597708),\n", " ('think', 0.0033329732624369085),\n", " ('chelsea', 0.0029922287405279684),\n", " ('game', 0.0028011339801883163),\n", " ('league', 0.002610476262682161),\n", " ('players', 0.002584137822138894),\n", " ('time', 0.002562398970649943),\n", " ('minister', 0.0025281531553959192),\n", " ('club', 0.002527139415819049),\n", " ('good', 0.0024787460166518565),\n", " ('last', 0.0024499225552730096),\n", " (\"don't\", 0.002415439141733538),\n", " ('arsenal', 0.0023802238324075278),\n", " ('party', 0.002379839625412044),\n", " ('bbc', 0.0023698950465503867),\n", " ('like', 0.0023651537261334767),\n", " ('election', 0.0023497931652231656),\n", " ('want', 0.00231801726260594),\n", " ('going', 0.002301691972872612)],\n", " [('blair', 0.003713648430818856),\n", " ('labour', 0.0036449572943794075),\n", " ('think', 0.003353002849447595),\n", " ('chelsea', 0.002991152664241214),\n", " ('game', 0.002840371715052553),\n", " ('good', 0.002614374642365314),\n", " ('league', 0.0026123473809775686),\n", " ('players', 0.0026066919212031805),\n", " ('minister', 0.0025735327622053933),\n", " ('time', 0.002566662482461167),\n", " ('club', 0.002522072121908676),\n", " ('arsenal', 0.0025037396662147774),\n", " ('last', 0.0024429429322480882),\n", " ('party', 0.002438765770456181),\n", " (\"don't\", 0.0024340625164823004),\n", " ('like', 0.002409449283893575),\n", " ('election', 0.002387601302866562),\n", " ('bbc', 0.0023755665907874224),\n", " ('want', 0.0023708316249832647),\n", " ('going', 0.0023174286362697234)],\n", " [('blair', 0.003789052020451831),\n", " ('labour', 0.003756904854182253),\n", " ('think', 0.0033769676071775255),\n", " ('chelsea', 0.002988386430522845),\n", " ('minister', 0.0026322228590789872),\n", " ('players', 0.002630819930110458),\n", " ('league', 0.0026201218499371185),\n", " ('time', 0.0025720811242427775),\n", " ('game', 0.002548580400470664),\n", " ('party', 0.0025405169968800087),\n", " ('club', 0.0025145293254008045),\n", " ('good', 0.0024869026158916875),\n", " (\"don't\", 0.0024553708499001846),\n", " ('last', 0.0024434629262835893),\n", " ('election', 0.002439979749044198),\n", " ('arsenal', 0.002383892972725239),\n", " ('bbc', 0.0023820557285104097),\n", " ('prime', 0.0023747029188644644),\n", " ('next', 0.002362162655076952),\n", " ('like', 0.002349811209855933)]]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ldaseq.print_topic_times(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us do the same, but after increasing the `chain_variance` value." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/misha/git/gensim/gensim/models/ldaseqmodel.py:293: RuntimeWarning: divide by zero encountered in double_scalars\n", " convergence = np.fabs((bound - old_bound) / old_bound)\n" ] } ], "source": [ "ldaseq_chain = ldaseqmodel.LdaSeqModel(corpus=corpus, id2word=dictionary, time_slice=time_slice, num_topics=5, chain_variance=0.05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's noticable that the values are moving more freely after increasing the chain_variance. Film went from highest probability to 5th to 8th!" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[('film', 0.01375823795635665),\n", " ('best', 0.011276818151806972),\n", " ('director', 0.0037205351022692626),\n", " ('award', 0.003633238905079213),\n", " ('actor', 0.0033617813327913725),\n", " ('million', 0.003171307691973619),\n", " ('last', 0.003001466360153857),\n", " ('star', 0.0028582559564371015),\n", " ('top', 0.002712140388471245),\n", " ('number', 0.0027089038839408905),\n", " ('awards', 0.0026868686691167926),\n", " ('including', 0.002439768747498039),\n", " ('first', 0.002344462049607271),\n", " ('former', 0.0022905809196871805),\n", " ('three', 0.002194680615038633),\n", " (\"year's\", 0.0021776664285641303),\n", " ('films', 0.002083143276187699),\n", " ('actress', 0.0020164206323942174),\n", " ('show', 0.00197821093747264),\n", " ('year', 0.0019242389412551)],\n", " [('best', 0.006251718153454164),\n", " ('music', 0.006238051188736231),\n", " ('number', 0.004698151339240129),\n", " ('show', 0.004373075014477944),\n", " ('last', 0.004059693074135519),\n", " ('top', 0.00405154039904161),\n", " ('band', 0.0038494003568372613),\n", " ('film', 0.0033736878692985192),\n", " ('album', 0.0031989412560276334),\n", " ('first', 0.0031124929302574207),\n", " ('year', 0.002834362048826918),\n", " ('star', 0.002550351654850285),\n", " ('uk', 0.002519053818321902),\n", " ('former', 0.002500363208100788),\n", " ('three', 0.0024999255264368087),\n", " ('song', 0.002474514670099769),\n", " ('award', 0.002462145721864402),\n", " ('awards', 0.002365331088911364),\n", " ('including', 0.0022353211770823654),\n", " ('singer', 0.002218424423530627)],\n", " [('music', 0.010806445443709194),\n", " ('best', 0.00981469175905119),\n", " ('show', 0.005951862553416929),\n", " ('last', 0.004887788345653499),\n", " ('number', 0.004868033645733588),\n", " ('band', 0.004531100533940954),\n", " ('song', 0.004107695027634018),\n", " ('top', 0.0033553984393853925),\n", " ('album', 0.00331033540878989),\n", " ('first', 0.0031215613317256067),\n", " ('rock', 0.002974436692834055),\n", " ('three', 0.0029380521471727944),\n", " ('singer', 0.0029097633299058753),\n", " ('award', 0.00280974819746582),\n", " ('uk', 0.0027834816968850167),\n", " ('film', 0.0025622295396685907),\n", " ('hit', 0.0025414452086195195),\n", " ('record', 0.0025200223644026203),\n", " ('spam', 0.0024324731646563516),\n", " ('bbc', 0.0024080622812893727)]]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ldaseq_chain.print_topic_times(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LDA Model and DTM\n", "\n", "For the first slice of DTM to get setup, we need to provide it sufficient stats from an LDA model. As discussed before, this is done by fitting gensim LDA on the dataset first. \n", "\n", "We also, however, have the option of passing our own model or suff stats values. Our final DTM results are heavily influenced by what we pass over here. We already know what a \"Good\" or \"Bad\" LDA model is (if not, read about it [here](http://nbviewer.jupyter.org/github/dsquareindia/gensim/blob/280375fe14adea67ce6384ba7eabf362b05e6029/docs/notebooks/topic_coherence_tutorial.ipynb)). \n", "\n", "It's quite obvious, then, that by passing a \"bad\" LDA model we will get not so satisfactory results; and by passing a better fitted or better trained LDA we will get better results. The same logic goes if we wish to directly pass the `suff_stats` numpy matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualising Dynamic Topic Models.\n", "\n", "Let us use pyLDAvis to visualise both the DTM wrapper and DTM python port.\n", "With the new `DTMvis` methods it is now very straightforward to visualise DTM for a particular time-slice." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: 'dtm_news'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-15-353cbcbeba51>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# if we've saved before simply load the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mdtm_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDtmModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dtm_news'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/git/gensim/gensim/utils.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(cls, fname, mmap)\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0mcompress\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSaveLoad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_adapt_by_suffix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m 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\"\"\"\n\u001b[0;32m-> 1381\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0msmart_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1382\u001b[0m \u001b[0;31m# Because of loading from S3 load can't be used (missing readline in smart_open)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1383\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/envs/gensim/lib/python3.7/site-packages/smart_open/smart_open_lib.py\u001b[0m in 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\"/Users/bhargavvader/Downloads/dtm_release/dtm/main\"\n", "# dtm_model = DtmModel(dtm_path, corpus, time_slice, num_topics=5, id2word=dictionary, initialize_lda=True)\n", "# dtm_model.save('dtm_news')\n", "\n", "# if we've saved before simply load the model\n", "dtm_model = DtmModel.load('dtm_news')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you take some time to look at the topics, you will notice large semantic similarities with the wrapper and the python DTM. \n", "Functionally, the python DTM replicates the wrapper quite well." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "doc_topic, topic_term, doc_lengths, term_frequency, vocab = dtm_model.dtm_vis(time=0, corpus=corpus)\n", "vis_wrapper = pyLDAvis.prepare(topic_term_dists=topic_term, doc_topic_dists=doc_topic, doc_lengths=doc_lengths, vocab=vocab, term_frequency=term_frequency)\n", "pyLDAvis.display(vis_wrapper)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us do the same for the python DTM." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In particular, look at topic 2 of the wrapper and topic 3 of the python port. Notice how they are both about football." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "doc_topic, topic_term, doc_lengths, term_frequency, vocab = ldaseq.dtm_vis(time=0, corpus=corpus)\n", "vis_dtm = pyLDAvis.prepare(topic_term_dists=topic_term, doc_topic_dists=doc_topic, doc_lengths=doc_lengths, vocab=vocab, term_frequency=term_frequency)\n", "pyLDAvis.display(vis_dtm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualising topics is a handy way to compare topic models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Topic Coherence for DTM\n", "\n", "Similar to visualising DTM time-slices, finding coherence values for both python DTM and the wrapper is very easy.\n", "We just have to specify the time-slice we want to find coherence for.\n", "The following examples will illustrate this." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from gensim.models.coherencemodel import CoherenceModel\n", "import pickle\n", "\n", "# we just have to specify the time-slice we want to find coherence for.\n", "topics_wrapper = dtm_model.dtm_coherence(time=0)\n", "topics_dtm = ldaseq.dtm_coherence(time=2)\n", "\n", "# running u_mass coherence on our models\n", "cm_wrapper = CoherenceModel(topics=topics_wrapper, corpus=corpus, dictionary=dictionary, coherence='u_mass')\n", "cm_DTM = CoherenceModel(topics=topics_dtm, corpus=corpus, dictionary=dictionary, coherence='u_mass')\n", "\n", "print (\"U_mass topic coherence\")\n", "print (\"Wrapper coherence is \", cm_wrapper.get_coherence())\n", "print (\"DTM Python coherence is\", cm_DTM.get_coherence())\n", "\n", "# to use 'c_v' we need texts, which we have saved to disk.\n", "texts = pickle.load(open('Corpus/texts', 'rb'))\n", "cm_wrapper = CoherenceModel(topics=topics_wrapper, texts=texts, dictionary=dictionary, coherence='c_v')\n", "cm_DTM = CoherenceModel(topics=topics_dtm, texts=texts, dictionary=dictionary, coherence='c_v')\n", "\n", "print (\"C_v topic coherence\")\n", "print (\"Wrapper coherence is \", cm_wrapper.get_coherence())\n", "print (\"DTM Python coherence is\", cm_DTM.get_coherence())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Our values are a little behind the wrapper - but not by much. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "So while there is already a python wrapper of DTM, a pure python implementation will be useful to better understand what goes on undcer the hood and tune our model. When it comes to performance, the C++ is undoubtedly faster, but we can continue to work on ours to make it as fast.\n", "As for evaluating the results, our topics are on par if not better than the wrapper!\n", "\n", "On a more personal note, implementing Dynamic Topic Models with the Google Summer of Code 2016 program was a great learning experience. Gensim and RaRe Technologies have been a joy to work with, and Lev and Radim have been great mentors throughout, especially when things became slow or difficult. \n", "I look forward to continuing my contribution to gensim for a long time!" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	49,953	Python	.py	1,031	43.018429	1,666	0.631413	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,942	soft_cosine_tutorial.ipynb	piskvorky_gensim/docs/notebooks/soft_cosine_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Finding similar documents with Word2Vec and Soft Cosine Measure \n", "\n", "Soft Cosine Measure (SCM) [1, 3] is a promising new tool in machine learning that allows us to submit a query and return the most relevant documents. In part 1, we will show how you can compute SCM between two documents using the `inner_product` method. In part 2, we will use `SoftCosineSimilarity` to retrieve documents most similar to a query and compare the performance against other similarity measures.\n", "\n", "First, however, we go through the basics of what Soft Cosine Measure is.\n", "\n", "## Soft Cosine Measure basics\n", "\n", "Soft Cosine Measure (SCM) is a method that allows us to assess the similarity between two documents in a meaningful way, even when they have no words in common. It uses a measure of similarity between words, which can be derived [2] using [word2vec][] [4] vector embeddings of words. It has been shown to outperform many of the state-of-the-art methods in the semantic text similarity task in the context of community question answering [2].\n", "\n", "[word2vec]: https://radimrehurek.com/gensim/models/word2vec.html\n", "\n", "SCM is illustrated below for two very similar sentences. The sentences have no words in common, but by modeling synonymy, SCM is able to accurately measure the similarity between the two sentences. The method also uses the bag-of-words vector representation of the documents (simply put, the word's frequencies in the documents). The intution behind the method is that we compute standard cosine similarity assuming that the document vectors are expressed in a non-orthogonal basis, where the angle between two basis vectors is derived from the angle between the word2vec embeddings of the corresponding words.\n", "\n", "![Soft Cosine Measure](soft_cosine_tutorial.png)\n", "\n", "This method was perhaps first introduced in the article “Soft Measure and Soft Cosine Measure: Measure of Features in Vector Space Model” by Grigori Sidorov, Alexander Gelbukh, Helena Gomez-Adorno, and David Pinto ([link to PDF](http://www.scielo.org.mx/pdf/cys/v18n3/v18n3a7.pdf)).\n", "\n", "In this tutorial, we will learn how to use Gensim's SCM functionality, which consists of the `inner_product` method for one-off computation, and the `SoftCosineSimilarity` class for corpus-based similarity queries.\n", "\n", "> Note:\n", ">\n", "> If you use this software, please consider citing [1], [2], and [3].\n", ">\n", "\n", "## Running this notebook\n", "You can download this [Jupyter notebook](http://jupyter.org/), and run it on your own computer, provided you have installed the `gensim`, `jupyter`, `sklearn`, `POT`, and `wmd` Python packages.\n", "\n", "The notebook was run on an Ubuntu machine with an Intel core i7-6700HQ CPU 3.10GHz (4 cores) and 16 GB memory. Assuming all resources required by the notebook have already been downloaded, running the entire notebook on this machine takes about 30 minutes." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Initialize logging.\n", "import logging\n", "\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: Computing the Soft Cosine Measure\n", "\n", "To use SCM, we need some word embeddings first of all. You could train a [word2vec][] (see tutorial [here](https://rare-technologies.com/word2vec-tutorial/)) model on some corpus, but we will use pre-trained word2vec embeddings.\n", "\n", "[word2vec]: https://radimrehurek.com/gensim/models/word2vec.html\n", "\n", "Let's create some sentences to compare." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()\n", "sentence_president = 'The president greets the press in Chicago'.lower().split()\n", "sentence_orange = 'Having a tough time finding an orange juice press machine?'.lower().split()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first two sentences have very similar content, and as such the SCM should be large. Before we compute the SCM, we want to remove stopwords (\"the\", \"to\", etc.), as these do not contribute a lot to the information in the sentences." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "!pip install nltk" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Import and download stopwords from NLTK.\n", "from nltk.corpus import stopwords\n", "from nltk import download\n", "\n", "download('stopwords') # Download stopwords list.\n", "\n", "# Remove stopwords.\n", "stop_words = stopwords.words('english')\n", "sentence_obama = [w for w in sentence_obama if w not in stop_words]\n", "sentence_president = [w for w in sentence_president if w not in stop_words]\n", "sentence_orange = [w for w in sentence_orange if w not in stop_words]\n", "\n", "# Prepare a dictionary and a corpus.\n", "from gensim import corpora\n", "documents = [sentence_obama, sentence_president, sentence_orange]\n", "dictionary = corpora.Dictionary(documents)\n", "\n", "# Convert the sentences into bag-of-words vectors.\n", "sentence_obama = dictionary.doc2bow(sentence_obama)\n", "sentence_president = dictionary.doc2bow(sentence_president)\n", "sentence_orange = dictionary.doc2bow(sentence_orange)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, as we mentioned earlier, we will be using some downloaded pre-trained embeddings. Note that the embeddings we have chosen here require a lot of memory. We will use the embeddings to construct a term similarity matrix that will be used by the `inner_product` method." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 21.8 s, sys: 461 ms, total: 22.2 s\n", "Wall time: 33.6 s\n" ] } ], "source": [ "%%time\n", "import gensim.downloader as api\n", "\n", "from gensim.similarities import SparseTermSimilarityMatrix\n", "from gensim.similarities import WordEmbeddingSimilarityIndex\n", "\n", "w2v_model = api.load(\"glove-wiki-gigaword-50\")\n", "similarity_index = WordEmbeddingSimilarityIndex(w2v_model)\n", "similarity_matrix = SparseTermSimilarityMatrix(similarity_index, dictionary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compute SCM using the `inner_product` method." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "similarity = 0.3790\n" ] } ], "source": [ "similarity = similarity_matrix.inner_product(sentence_obama, sentence_president, normalized=True)\n", "print('similarity = %.4f' % similarity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try the same thing with two completely unrelated sentences. Notice that the similarity is smaller." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "similarity = 0.1108\n" ] } ], "source": [ "similarity = similarity_matrix.inner_product(sentence_obama, sentence_orange, normalized=True)\n", "print('similarity = %.4f' % similarity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Similarity queries using `SoftCosineSimilarity`\n", "You can use SCM to get the most similar documents to a query, using the `SoftCosineSimilarity` class. Its interface is similar to what is described in the [Similarity Queries](https://radimrehurek.com/gensim/tut3.html) Gensim tutorial.\n", "\n", "### Qatar Living unannotated dataset\n", "Contestants solving the community question answering task in the [SemEval 2016][semeval16] and [2017][semeval17] competitions had an unannotated dataset of 189,941 questions and 1,894,456 comments from the [Qatar Living][ql] discussion forums. As our first step, we will use the same dataset to build a corpus.\n", "\n", "[semeval16]: http://alt.qcri.org/semeval2016/task3/\n", "[semeval17]: http://alt.qcri.org/semeval2017/task3/\n", "[ql]: http://www.qatarliving.com/forum" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of documents: 2274338\n", "CPU times: user 2min 1s, sys: 1.9 s, total: 2min 3s\n", "Wall time: 2min 56s\n" ] } ], "source": [ "%%time\n", "from itertools import chain\n", "import json\n", "from re import sub\n", "from os.path import isfile\n", "\n", "import gensim.downloader as api\n", "from gensim.utils import simple_preprocess\n", "from nltk.corpus import stopwords\n", "from nltk import download\n", "\n", "download(\"stopwords\") # Download stopwords list.\n", "stopwords = set(stopwords.words(\"english\"))\n", "\n", "def preprocess(doc):\n", " doc = sub(r'<img[^<>]+(>\|$)', \" image_token \", doc)\n", " doc = sub(r'<[^<>]+(>\|$)', \" \", doc)\n", " doc = sub(r'\\[img_assist[^]]?\\]', \" \", doc)\n", " doc = sub(r'http[s]?://(?:[a-zA-Z]\|[0-9]\|[$-_@.&+]\|[!\$\$,]\|(?:%[0-9a-fA-F][0-9a-fA-F]))+', \" url_token \", doc)\n", " return [token for token in simple_preprocess(doc, min_len=0, max_len=float(\"inf\")) if token not in stopwords]\n", "\n", "corpus = list(chain([\n", " chain(\n", " [preprocess(thread[\"RelQuestion\"][\"RelQSubject\"]), preprocess(thread[\"RelQuestion\"][\"RelQBody\"])],\n", " [preprocess(relcomment[\"RelCText\"]) for relcomment in thread[\"RelComments\"]])\n", " for thread in api.load(\"semeval-2016-2017-task3-subtaskA-unannotated\")]))\n", "\n", "print(\"Number of documents: %d\" % len(corpus))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the corpus we have just build, we will now construct a [dictionary][], a [TF-IDF model][tfidf], a [word2vec model][word2vec], and a term similarity matrix.\n", "\n", "[dictionary]: https://radimrehurek.com/gensim/corpora/dictionary.html\n", "[tfidf]: https://radimrehurek.com/gensim/models/tfidfmodel.html\n", "[word2vec]: https://radimrehurek.com/gensim/models/word2vec.html" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 46min 38s, sys: 13min 31s, total: 1h 9s\n", "Wall time: 35min 28s\n" ] } ], "source": [ "%%time\n", "from multiprocessing import cpu_count\n", "\n", "from gensim.corpora import Dictionary\n", "from gensim.models import TfidfModel\n", "from gensim.models import Word2Vec\n", "from gensim.similarities import WordEmbeddingSimilarityIndex\n", "from gensim.similarities import SparseTermSimilarityMatrix\n", "\n", "dictionary = Dictionary(corpus)\n", "tfidf = TfidfModel(dictionary=dictionary)\n", "w2v_model = Word2Vec(corpus, workers=cpu_count(), min_count=5, size=300, seed=12345)\n", "similarity_index = WordEmbeddingSimilarityIndex(w2v_model.wv)\n", "similarity_matrix = SparseTermSimilarityMatrix(similarity_index, dictionary, tfidf, nonzero_limit=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation\n", "Next, we will load the validation and test datasets that were used by the SemEval 2016 and 2017 contestants. The datasets contain 208 original questions posted by the forum members. For each question, there is a list of 10 threads with a human annotation denoting whether or not the thread is relevant to the original question. Our task will be to order the threads so that relevant threads rank above irrelevant threads." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "datasets = api.load(\"semeval-2016-2017-task3-subtaskBC\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we will perform an evaluation to compare three unsupervised similarity measures – the Soft Cosine Measure, two different implementations of the [Word Mover's Distance][wmd], and standard cosine similarity. We will use the [Mean Average Precision (MAP)][map] as an evaluation measure and 10-fold cross-validation to get an estimate of the variance of MAP for each similarity measure.\n", "\n", "[wmd]: http://vene.ro/blog/word-movers-distance-in-python.html\n", "[map]: https://medium.com/@pds.bangalore/mean-average-precision-abd77d0b9a7e" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "!pip install wmd" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "!pip install sklearn" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "!pip install POT" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from math import isnan\n", "from time import time\n", "\n", "from gensim.similarities import MatrixSimilarity, WmdSimilarity, SoftCosineSimilarity\n", "import numpy as np\n", "from sklearn.model_selection import KFold\n", "from wmd import WMD\n", "\n", "def produce_test_data(dataset):\n", " for orgquestion in datasets[dataset]:\n", " query = preprocess(orgquestion[\"OrgQSubject\"]) + preprocess(orgquestion[\"OrgQBody\"])\n", " documents = [\n", " preprocess(thread[\"RelQuestion\"][\"RelQSubject\"]) + preprocess(thread[\"RelQuestion\"][\"RelQBody\"])\n", " for thread in orgquestion[\"Threads\"]]\n", " relevance = [\n", " thread[\"RelQuestion\"][\"RELQ_RELEVANCE2ORGQ\"] in (\"PerfectMatch\", \"Relevant\")\n", " for thread in orgquestion[\"Threads\"]]\n", " yield query, documents, relevance\n", "\n", "def cossim(query, documents):\n", " # Compute cosine similarity between the query and the documents.\n", " query = tfidf[dictionary.doc2bow(query)]\n", " index = MatrixSimilarity(\n", " tfidf[[dictionary.doc2bow(document) for document in documents]],\n", " num_features=len(dictionary))\n", " similarities = index[query]\n", " return similarities\n", "\n", "def softcossim(query, documents):\n", " # Compute Soft Cosine Measure between the query and the documents.\n", " query = tfidf[dictionary.doc2bow(query)]\n", " index = SoftCosineSimilarity(\n", " tfidf[[dictionary.doc2bow(document) for document in documents]],\n", " similarity_matrix)\n", " similarities = index[query]\n", " return similarities\n", "\n", "def wmd_gensim(query, documents):\n", " # Compute Word Mover's Distance as implemented in POT\n", " # between the query and the documents.\n", " index = WmdSimilarity(documents, w2v_model)\n", " similarities = index[query]\n", " return similarities\n", "\n", "def wmd_relax(query, documents):\n", " # Compute Word Mover's Distance as implemented in WMD by Source{d}\n", " # between the query and the documents.\n", " words = [word for word in set(chain(query, documents)) if word in w2v_model.wv]\n", " indices, words = zip(sorted((\n", " (index, word) for (index, _), word in zip(dictionary.doc2bow(words), words))))\n", " query = dict(tfidf[dictionary.doc2bow(query)])\n", " query = [\n", " (new_index, query[dict_index])\n", " for new_index, dict_index in enumerate(indices)\n", " if dict_index in query]\n", " documents = [dict(tfidf[dictionary.doc2bow(document)]) for document in documents]\n", " documents = [[\n", " (new_index, document[dict_index])\n", " for new_index, dict_index in enumerate(indices)\n", " if dict_index in document] for document in documents]\n", " embeddings = np.array([w2v_model.wv[word] for word in words], dtype=np.float32)\n", " nbow = dict(((index, list(chain([None], zip(document)))) for index, document in enumerate(documents)))\n", " nbow[\"query\"] = tuple([None] + list(zip(query)))\n", " distances = WMD(embeddings, nbow, vocabulary_min=1).nearest_neighbors(\"query\")\n", " similarities = [-distance for _, distance in sorted(distances)]\n", " return similarities\n", "\n", "strategies = {\n", " \"cossim\" : cossim,\n", " \"softcossim\": softcossim,\n", " \"wmd-gensim\": wmd_gensim,\n", " \"wmd-relax\": wmd_relax}\n", "\n", "def evaluate(split, strategy):\n", " # Perform a single round of evaluation.\n", " results = []\n", " start_time = time()\n", " for query, documents, relevance in split:\n", " similarities = strategies[strategy](query, documents)\n", " assert len(similarities) == len(documents)\n", " precision = [\n", " (num_correct + 1) / (num_total + 1) for num_correct, num_total in enumerate(\n", " num_total for num_total, (_, relevant) in enumerate(\n", " sorted(zip(similarities, relevance), reverse=True)) if relevant)]\n", " average_precision = np.mean(precision) if precision else 0.0\n", " results.append(average_precision)\n", " return (np.mean(results) 100, time() - start_time)\n", "\n", "def crossvalidate(args):\n", " # Perform a cross-validation.\n", " dataset, strategy = args\n", " test_data = np.array(list(produce_test_data(dataset)))\n", " kf = KFold(n_splits=10)\n", " samples = []\n", " for _, test_index in kf.split(test_data):\n", " samples.append(evaluate(test_data[test_index], strategy))\n", " return (np.mean(samples, axis=0), np.std(samples, axis=0))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2.54 s, sys: 1.19 s, total: 3.73 s\n", "Wall time: 1min 18s\n" ] } ], "source": [ "%%time\n", "from multiprocessing import Pool\n", "\n", "args_list = [\n", " (dataset, technique)\n", " for dataset in (\"2016-test\", \"2017-test\")\n", " for technique in (\"softcossim\", \"wmd-gensim\", \"wmd-relax\", \"cossim\")]\n", "with Pool() as pool:\n", " results = pool.map(crossvalidate, args_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The table below shows the pointwise estimates of means and standard variances for MAP scores and elapsed times. Baselines and winners for each year are displayed in bold. We can see that the Soft Cosine Measure gives a strong performance on both the 2016 and the 2017 dataset." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from IPython.display import display, Markdown\n", "\n", "output = []\n", "baselines = [\n", " ((\"2016-test\", \"Winner (UH-PRHLT-primary)\"), ((76.70, 0), (0, 0))),\n", " ((\"2016-test\", \"Baseline 1 (IR)\"), ((74.75, 0), (0, 0))),\n", " ((\"2016-test\", \"Baseline 2 (random)\"), ((46.98, 0), (0, 0))),\n", " ((\"2017-test\", \"Winner (SimBow-primary)\"), ((47.22, 0), (0, 0))),\n", " ((\"2017-test\", \"Baseline 1 (IR)\"), ((41.85, 0), (0, 0))),\n", " ((\"2017-test\", \"Baseline 2 (random)\"), ((29.81, 0), (0, 0)))]\n", "table_header = [\"Dataset \| Strategy \| MAP score \| Elapsed time (sec)\", \":---\|:---\|:---\|---:\"]\n", "for row, ((dataset, technique), ((mean_map_score, mean_duration), (std_map_score, std_duration))) \\\n", " in enumerate(sorted(chain(zip(args_list, results), baselines), key=lambda x: (x[0][0], -x[1][0][0]))):\n", " if row % (len(strategies) + 3) == 0:\n", " output.extend(chain([\"\\n\"], table_header))\n", " map_score = \"%.02f ±%.02f\" % (mean_map_score, std_map_score)\n", " duration = \"%.02f ±%.02f\" % (mean_duration, std_duration) if mean_duration else \"\"\n", " output.append(\"%s\|%s\|%s\|%s\" % (dataset, technique, map_score, duration))\n", "\n", "display(Markdown('\\n'.join(output)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dataset \| Strategy \| MAP score \| Elapsed time (sec)\n", ":---\|:---\|:---\|---:\n", "2016-test\|softcossim\|78.52 ±11.18\|6.00 ±0.79\n", "2016-test\|Winner (UH-PRHLT-primary)\|76.70 ±0.00\|\n", "2016-test\|cossim\|76.45 ±10.40\|0.64 ±0.08\n", "2016-test\|wmd-gensim\|76.23 ±11.42\|5.37 ±0.64\n", "2016-test\|Baseline 1 (IR)\|74.75 ±0.00\|\n", "2016-test\|wmd-relax\|71.05 ±11.06\|1.11 ±0.09\n", "2016-test\|Baseline 2 (random)\|46.98 ±0.00\|\n", "\n", "\n", "Dataset \| Strategy \| MAP score \| Elapsed time (sec)\n", ":---\|:---\|:---\|---:\n", "2017-test\|Winner (SimBow-primary)\|47.22 ±0.00\|\n", "2017-test\|softcossim\|45.88 ±16.22\|7.08 ±1.49\n", "2017-test\|cossim\|44.38 ±14.71\|0.74 ±0.10\n", "2017-test\|wmd-gensim\|44.06 ±15.92\|6.20 ±0.87\n", "2017-test\|wmd-relax\|43.52 ±16.30\|1.30 ±0.18\n", "2017-test\|Baseline 1 (IR)\|41.85 ±0.00\|\n", "2017-test\|Baseline 2 (random)\|29.81 ±0.00\|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "1. Grigori Sidorov et al. Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model, 2014. ([link to PDF](http://www.scielo.org.mx/pdf/cys/v18n3/v18n3a7.pdf))\n", "2. Delphine Charlet and Geraldine Damnati, SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering, 2017. ([link to PDF](http://www.aclweb.org/anthology/S17-2051))\n", "3. Vít Novotný. Implementation Notes for the Soft Cosine Measure, 2018. ([link to PDF](https://arxiv.org/pdf/1808.09407))\n", "4. Thomas Mikolov et al. Efficient Estimation of Word Representations in Vector Space, 2013. ([link to PDF](https://arxiv.org/pdf/1301.3781.pdf))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }	24,079	Python	.py	591	36.1489	619	0.616473	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,943	topic_coherence_model_selection.ipynb	piskvorky_gensim/docs/notebooks/topic_coherence_model_selection.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Performing Model Selection Using Topic Coherence\n", "\n", "This notebook will perform topic modeling on the 20 Newsgroups corpus using LDA. We will perform model selection (over the number of topics) using topic coherence as our evaluation metric. This will showcase some of the features of the topic coherence pipeline implemented in `gensim`. In particular, we will see several features of the `CoherenceModel`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "import os\n", "import re\n", "import logging\n", "from collections import OrderedDict\n", "\n", "from gensim.corpora import TextCorpus, MmCorpus\n", "from gensim import utils, models\n", "\n", "logging.basicConfig(level=logging.ERROR) # disable warning logging" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the Dataset\n", "\n", "The 20 Newsgroups dataset consists of 20 different newsgroup (forum discussion) groups, with many posts per group. The original data is available here: http://qwone.com/~jason/20Newsgroups/. However, `sklearn` also provides a wrapper around this data, so we'll use that for simplicity. It takes care of downloading the text and loading them into memory.\n", "\n", "The documents are in the newsgroup format, which includes some headers, quoting of previous messages in the thread, and possibly PGP signature blocks. The code below builds on the `TextCorpus` preprocessing to handle the newsgroup-specific text parsing. By default, `TextCorpus` preprocessing performs asciifolding and lowercases all text, then tokenizes by pulling out contiguous sequences of alphabetic characters, then discards stopwords and tokens less than length 3." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from sklearn import datasets\n", "\n", "\n", "class NewsgroupCorpus(TextCorpus):\n", " \"\"\"Parse 20 Newsgroups dataset.\"\"\"\n", " \n", " def __init__(self, args, kwargs):\n", " super(NewsgroupCorpus, self).__init__(\n", " datasets.fetch_20newsgroups(subset='all'), args, *kwargs)\n", "\n", " def getstream(self):\n", " for doc in self.input.data:\n", " yield doc # already unicode\n", "\n", " def preprocess_text(self, text):\n", " body = extract_body(text)\n", " return super(NewsgroupCorpus, self).preprocess_text(body)\n", "\n", " \n", "def extract_body(text):\n", " return strip_newsgroup_header(\n", " strip_newsgroup_footer(\n", " strip_newsgroup_quoting(text)))\n", "\n", "\n", "def strip_newsgroup_header(text):\n", " \"\"\"Given text in \"news\" format, strip the headers, by removing everything\n", " before the first blank line.\n", " \"\"\"\n", " _before, _blankline, after = text.partition('\\n\\n')\n", " return after\n", "\n", "\n", "_QUOTE_RE = re.compile(r'(writes in\|writes:\|wrote:\|says:\|said:'\n", " r'\|^In article\|^Quoted from\|^\\\|\|^>)')\n", "def strip_newsgroup_quoting(text):\n", " \"\"\"Given text in \"news\" format, strip lines beginning with the quote\n", " characters > or \|, plus lines that often introduce a quoted section\n", " (for example, because they contain the string 'writes:'.)\n", " \"\"\"\n", " good_lines = [line for line in text.split('\\n')\n", " if not _QUOTE_RE.search(line)]\n", " return '\\n'.join(good_lines)\n", "\n", "\n", "_PGP_SIG_BEGIN = \"-----BEGIN PGP SIGNATURE-----\"\n", "def strip_newsgroup_footer(text):\n", " \"\"\"Given text in \"news\" format, attempt to remove a signature block.\"\"\"\n", " try:\n", " return text[:text.index(_PGP_SIG_BEGIN)]\n", " except ValueError:\n", " return text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading the Dataset\n", "\n", "Now that we have defined the necessary code for preprocessing the dataset, let's load it up and serialize it into Matrix Market format. We'll do this because we want to train LDA on it with several different parameter settings, and this will allow us to avoid repeating the preprocessing." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18846\n", "Dictionary(23775 unique tokens: ['actually', 'bashers', 'beat', 'better', 'bit']...)\n", "CPU times: user 36.8 s, sys: 191 ms, total: 37 s\n", "Wall time: 38.1 s\n" ] } ], "source": [ "%%time\n", "\n", "corpus = NewsgroupCorpus()\n", "corpus.dictionary.filter_extremes(no_below=5, no_above=0.8)\n", "dictionary = corpus.dictionary\n", "print(len(corpus))\n", "print(dictionary)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 39 s, sys: 243 ms, total: 39.2 s\n", "Wall time: 40.2 s\n" ] } ], "source": [ "%%time\n", "\n", "mm_path = '20_newsgroups.mm'\n", "MmCorpus.serialize(mm_path, corpus, id2word=dictionary)\n", "mm_corpus = MmCorpus(mm_path) # load back in to use for LDA training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the Models\n", "\n", "Our goal is to determine which number of topics produces the most coherent topics for the 20 Newsgroups corpus. The corpus contains 18,846 documents. If we used 100 topics and the documents were evenly distributed among topics, we'd have clusters of ~188 documents. This seems like a reasonable upper bound. In this case, the corpus actually has categories, which we show below. There are 20 of these (hence the name of the dataset), so we'll use 20 as our lower bound for the number of topics.\n", "\n", "One could argue that we already know the model should have 20 topics. I'll argue there may be additional categorizations within each newsgroup and we might hope to capture those by using more topics. We'll step by increments of 10 from 20 to 100." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alt.atheism\n", "comp.graphics\n", "comp.os.ms-windows.misc\n", "comp.sys.ibm.pc.hardware\n", "comp.sys.mac.hardware\n", "comp.windows.x\n", "misc.forsale\n", "rec.autos\n", "rec.motorcycles\n", "rec.sport.baseball\n", "rec.sport.hockey\n", "sci.crypt\n", "sci.electronics\n", "sci.med\n", "sci.space\n", "soc.religion.christian\n", "talk.politics.guns\n", "talk.politics.mideast\n", "talk.politics.misc\n", "talk.religion.misc\n" ] } ], "source": [ "print('\\n'.join(corpus.input.target_names))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training LDA(k=20)\n", "Training LDA(k=30)\n" ] } ], "source": [ "%%time\n", "\n", "trained_models = OrderedDict()\n", "for num_topics in range(20, 101, 10):\n", " print(\"Training LDA(k=%d)\" % num_topics)\n", " lda = models.LdaMulticore(\n", " mm_corpus, id2word=dictionary, num_topics=num_topics, workers=4,\n", " passes=10, iterations=100, random_state=42, eval_every=None,\n", " alpha='asymmetric', # shown to be better than symmetric in most cases\n", " decay=0.5, offset=64 # best params from Hoffman paper\n", " )\n", " trained_models[num_topics] = lda" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Some useful utility functions in case you want to save your models.\n", "\n", "home = os.path.expanduser('~/')\n", "models_dir = os.path.join(home, 'workshop', 'nlp', 'models') # use whatever directory you prefer\n", "\n", "def save_models(named_models):\n", " for num_topics, model in named_models.items():\n", " model_path = os.path.join(models_dir, 'lda-newsgroups-k%d.lda' % num_topics)\n", " model.save(model_path, separately=False)\n", "\n", " \n", "def load_models():\n", " trained_models = OrderedDict()\n", " for num_topics in range(20, 101, 10):\n", " model_path = os.path.join(models_dir, 'lda-newsgroups-k%d.lda' % num_topics)\n", " print(\"Loading LDA(k=%d) from %s\" % (num_topics, model_path))\n", " trained_models[num_topics] = models.LdaMulticore.load(model_path)\n", "\n", " return trained_models" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# save_models(trained_models)\n", "# trained_models = load_models()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation Using Coherence\n", "\n", "Now we get to the heart of this notebook. In this section, we'll evaluate each of our LDA models using topic coherence. Coherence is a measure of how interpretable the topics are to humans. It is based on the representation of topics as the top-N most probable words for a particular topic. More specifically, given the topic-term matrix for LDA, we sort each topic from highest to lowest term weights and then select the first N terms.\n", "\n", "Coherence essentially measures how similar these words are to each other. There are various methods for doing this, most of which have been explored in the paper [\"Exploring the Space of Topic Coherence Measures\"](https://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf). The authors performed a comparative analysis of various methods, correlating them to human judgements. The method named \"c_v\" coherence was found to be the most highly correlated. This and several of the other methods have been implemented in `gensim.models.CoherenceModel`. We will use this to perform our evaluations.\n", "\n", "The \"c_v\" coherence method makes an expensive pass over the corpus, accumulating term occurrence and co-occurrence counts. It only accumulates counts for the terms in the lists of top-N terms for each topic. In order to ensure we only need to make one pass, we'll construct a \"super topic\" from the top-N lists of each of the models. This will consist of a single topic with all the relevant terms from all the models. We choose 20 as N." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "# Now estimate the probabilities for the CoherenceModel.\n", "# This performs a single pass over the reference corpus, accumulating\n", "# the necessary statistics for all of the models at once.\n", "cm = models.CoherenceModel.for_models(\n", " trained_models.values(), dictionary, texts=corpus.get_texts(), coherence='c_v')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "coherence_estimates = cm.compare_models(trained_models.values())\n", "coherences = dict(zip(trained_models.keys(), coherence_estimates))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def print_coherence_rankings(coherences):\n", " avg_coherence = \\\n", " [(num_topics, avg_coherence)\n", " for num_topics, (_, avg_coherence) in coherences.items()]\n", " ranked = sorted(avg_coherence, key=lambda tup: tup[1], reverse=True)\n", " print(\"Ranked by average '%s' coherence:\\n\" % cm.coherence)\n", " for item in ranked:\n", " print(\"num_topics=%d:\\t%.4f\" % item)\n", " print(\"\\nBest: %d\" % ranked[0][0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print_coherence_rankings(coherences)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Results so Far\n", "\n", "So far in this notebook, we have used `gensim`'s `CoherenceModel` to perform model selection over the number of topics for LDA. We found that for the 20 Newsgroups corpus, 30 topics is best, followed by 20. We showcased the ability of the coherence pipeline to evaluate individual aggregated model coherence (Note that the individual topic coherence is also computed by `cm.compare_models`). We also demonstrated how to avoid repeated passes over the corpus, estimating the term similarity probabilities for all relevant terms just once. Topic coherence is a powerful alternative to evaluation using perplexity on a held-out document set. It is appropriate to use whenever the objective of the topic modeling is to present the topics as top-N lists for human consumption.\n", "\n", "Note that coherence calculations are generally much more accurate when a larger reference corpus is used to estimate the probabilities. In this case, we used the same corpus as for our modeling, which is relatively small at only 20,000 documents. A better reference corpus is the full Wikipedia corpus. The motivated explorer of this notebook is encouraged to download that corpus (see [Experiments on the English Wikipedia](https://radimrehurek.com/gensim/wiki.html)) and use it for probability estimation.\n", "\n", "Next we'll look at another method of coherence evaluation using distributed word embeddings." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Evaluating Coherence with Word2Vec\n", "\n", "The fact that \"c_v\" coherence uses distributional semantics to evalaute word similarity motivates the use of Word2Vec for coherence evaluation. This idea is explored further in an appendix at the end of the notebook. The `CoherenceModel` implemented in `gensim` also supports this, so let's look at a few examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "class TextsIterable(object):\n", " \"\"\"Wrap a TextCorpus in something that yields texts from its __iter__.\n", " \n", " It's necessary to use this because the Word2Vec model is built by scanning\n", " over the texts several times. Passing in corpus.get_texts() would result in\n", " an empty iterable on passes after the first.\n", " \"\"\"\n", " \n", " def __init__(self, corpus):\n", " self.corpus = corpus\n", " \n", " def __iter__(self):\n", " return self.corpus.get_texts()\n", "\n", "\n", "cm = models.CoherenceModel.for_models(\n", " trained_models.values(), dictionary, texts=TextsIterable(corpus), coherence='c_w2v')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "coherence_estimates = cm.compare_models(trained_models.values())\n", "coherences = dict(zip(trained_models.keys(), coherence_estimates))\n", "print_coherence_rankings(coherences)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Using pre-trained word vectors for coherence evaluation.\n", "\n", "Whoa! These results are completely different from those of the \"c_v\" method, and \"c_w2v\" is saying the models we thought were best are actually some of the worst! So what happened here?\n", "\n", "The same note must be made for Word2Vec (\"c_w2v\") that we made for \"c_v\": results are more accurate when a larger reference corpus is used. Except for \"c_w2v\", this is actually _way, way_ more important. Distributional word embedding techniques such as Word2Vec are fitting a probability distribution with a large number of parameters, and doing that takes a lot of data.\n", "\n", "Luckily, there are a variety of pre-trained word vectors [freely available for download](http://ahogrammer.com/2017/01/20/the-list-of-pretrained-word-embeddings/). Below we demonstrate using word vectors trained on ~100 billion words from Google News, [available at this link](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing). Note that this file is 1.5G, so downloading it can take quite some time. It is also quite slow to load and ends up occupying about 3.35G in memory (this load time is included in the timing below). There is no need to use such a large set of word vectors for this evaluation; this one is just readily available." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "models_dir = os.path.join(home, 'workshop', 'nlp', 'models')\n", "vectors_path = os.path.join(models_dir, 'GoogleNews-vectors-negative300.bin.gz')\n", "keyed_vectors = models.KeyedVectors.load_word2vec_format(vectors_path, binary=True)\n", "\n", "# still need to estimate_probabilities, but corpus is not scanned\n", "cm = models.CoherenceModel.for_models(\n", " trained_models.values(), dictionary, texts=corpus.get_texts(),\n", " coherence='c_w2v', keyed_vectors=keyed_vectors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "coherence_estimates = cm.compare_models(trained_models.values())\n", "coherences = dict(zip(trained_models.keys(), coherence_estimates))\n", "print_coherence_rankings(coherences)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Watch out for Out-of-Vocabulary (OOV) terms!\n", "\n", "At first glance, it might seem like the \"c_w2v\" coherence with the GoogleNews vectors is a great improvement on the \"c_v\" method; it certainly improves on training on the newsgroups corpus. However, if you run the above code with logging enabled, you'll notice a TON of warning messages stating something like \"3 terms for topic 10 not in word2vec model.\" This is a real gotcha to watch out for! In this case, we might suspect there is significant mismatch because all the coherence measures are so similar (within about 0.02 of each other).\n", "\n", "When using pre-trained word vectors, there is likely to be some vocabulary mismatch. So unless the corpus you're modeling on was included in the training data for the vectors, you need to watch out for this. In the results above, it is easy to diagnose because all of the models have very similar coherence rankings. You can use the function below to dig in and see exactly how bad the issue is." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from gensim import utils\n", "\n", "\n", "def report_on_oov_terms(cm, topic_models):\n", " \"\"\"OOV = out-of-vocabulary\"\"\"\n", " topics_as_topn_terms = [\n", " models.CoherenceModel.top_topics_as_word_lists(model, dictionary)\n", " for model in topic_models\n", " ]\n", "\n", " oov_words = cm._accumulator.not_in_vocab(topics_as_topn_terms)\n", " print('number of oov words: %d' % len(oov_words))\n", " \n", " for num_topics, words in zip(trained_models.keys(), topics_as_topn_terms):\n", " oov_words = cm._accumulator.not_in_vocab(words)\n", " print('number of oov words for num_topics=%d: %d' % (num_topics, len(oov_words)))\n", "\n", "report_on_oov_terms(cm, trained_models.values())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yikes!* That's a lot of terms that are being ignored when calculating the coherence metrics. So these results are not really reliable. Let's use a different set of pre-trained word vectors. I trained these on a recent Wikipedia dump using skip-gram negative sampling (SGNS) with a context window of 5 and 300 dimensions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "vectors_path = os.path.join(models_dir, 'wiki-en_sgns5-w5-s300.bin.gz')\n", "keyed_vectors = models.KeyedVectors.load_word2vec_format(vectors_path, binary=True)\n", "\n", "# still need to estimate_probabilities, but corpus is not scanned\n", "cm = models.CoherenceModel.for_models(\n", " trained_models.values(), dictionary, texts=corpus.get_texts(),\n", " coherence='c_w2v', keyed_vectors=keyed_vectors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%%time\n", "\n", "coherence_estimates = cm.compare_models(trained_models.values())\n", "coherences = dict(zip(trained_models.keys(), coherence_estimates))\n", "print_coherence_rankings(coherences)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "report_on_oov_terms(cm, trained_models.values())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Looks like we've now restored order\n", "\n", "The results with the Wikipedia-trained word vectors are much better because we have less terms OOV. The \"c_w2v\" evalution is now agreeing with \"c_v\" on the best two models, and the rest of the ordering is generally similar. Note that the \"c_w2v\" values should not be compared directly to those produced by the \"c_v\" method. Only the ranking of models is comparable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Appendix: Why Word2Vec for Coherence?\n", "\n", "The \"c_v\" coherence method drags a sliding window across all documents in the corpus to accumulate co-occurrence statistics. Similarity is calculated using normalized pointwise mutual information (PMI) values estimated from these statistics. More specifically, each word is represented by a vector of its NPMI with every other word in its top-N topic list. These vectors are then used to compute (cosine) similarity between words. The restriction to the other words in the top-N list was found to produce better results than using the entire vocabulary and other methods of reducing the vocabulary (see section 3.2.2 of http://www.aclweb.org/anthology/W13-0102).\n", "\n", "The fact that a reduced space is superior for these metrics indicates there is noise getting in the way. The \"c_v\" method can be seen as constructing an NPMI matrix between words. The vector of NPMI values for a particular word can then be looked up by indexing the row or column corresponding to that word's `Dictionary` ID. The reduction to the \"topic word space\" can then be achieved by using a mask to select out the top-N topic words. If we are constructing an NPMI matrix between words, then discarding some elements to reduce noise, why not factorize the matrix instead? Dimensionality reduction techniques such as SVD do a great job of reducing noise along with dimensionality, while also providing a compressed representation to work with.\n", "\n", "[Recent work](https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization) has shown that Word2Vec (trained with Skip-Gram Negative Sampling (SGNS)) is actually implicitly factorizing a PMI matrix shifted by a positive constant. [A subsequent paper](http://dl.acm.org/citation.cfm?id=2914720) compared Word2Vec to a few different PMI-based metrics and showed that it found coherence values that correlated more strongly with human judgements." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	25,071	Python	.py	575	39.08	780	0.647657	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,944	Any2Vec_Filebased.ipynb	piskvorky_gensim/docs/notebooks/Any2Vec_Filebased.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2Vec File-based Training: API Tutorial\n", "\n", "This tutorial introduces a new file-based training mode for `gensim.models.{Word2Vec, FastText, Doc2Vec}`* which leads to (much) faster training on machines with many cores. Below we demonstrate how to use this new mode, with Python examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In this tutorial\n", "\n", "1. We will show how to use the new training mode on Word2Vec, FastText and Doc2Vec.\n", "2. Evaluate the performance of file-based training on the English Wikipedia and compare it to the existing queue-based training.\n", "3. Show that model quality (analogy accuracies on `question-words.txt`) are almost the same for both modes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Motivation\n", "\n", "The original implementation of Word2Vec training in Gensim is already super fast (covered in [this blog series](https://rare-technologies.com/word2vec-in-python-part-two-optimizing/), see also [benchmarks against other implementations in Tensorflow, DL4J, and C](https://rare-technologies.com/machine-learning-hardware-benchmarks/)) and flexible, allowing you to train on arbitrary Python streams. We had to jump through [some serious hoops](https://www.youtube.com/watch?v=vU4TlwZzTfU) to make it so, avoiding the Global Interpreter Lock (the dreaded GIL, the main bottleneck for any serious high performance computation in Python).\n", "\n", "The end result worked great for modest machines (< 8 cores), but for higher-end servers, the GIL reared its ugly head again. Simply managing the input stream iterators and worker queues, which has to be done in Python holding the GIL, was becoming the bottleneck. Simply put, the Python implementation didn't scale linearly with cores, as the original C implementation by Tomáš Mikolov did." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![scaling of word2vec file-based training](word2vec_file_scaling.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We decided to change that. After [much](https://github.com/RaRe-Technologies/gensim/pull/2127) [experimentation](https://github.com/RaRe-Technologies/gensim/pull/2048#issuecomment-401494412) and [benchmarking](https://persiyanov.github.io/jekyll/update/2018/05/28/gsoc-first-weeks.html), including some pretty [hardcore outlandish ideas](https://github.com/RaRe-Technologies/gensim/pull/2127#issuecomment-405937741), we figured there's no way around the GIL limitations—not at the level of fine-tuned performance needed here. Remember, we're talking >500k words (training instances) per second, using highly optimized C code. Way past the naive \"vectorize with NumPy arrays\" territory.\n", "\n", "So we decided to introduce a new code path, which has less flexibility in favour of more performance. We call this code path `file-based training`, and it's realized by passing a new `corpus_file` parameter to training. The existing `sentences` parameter (queue-based training) is still available, and you can continue using without any change: there's full backward compatibility." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How it works\n", "\n", "<style>\n", ".rendered_html tr, .rendered_html th, .rendered_html td {\n", " text-align: \"left\";\n", "}\n", "</style>\n", "\n", "\| code path \| input parameter \| advantages \| disadvantages\n", "\| :-------- \| :-------- \| :--------- \| :----------- \|\n", "\| queue-based training (existing) \| `sentences` (Python iterable) \| Input can be generated dynamically from any storage, or even on-the-fly. \| Scaling plateaus after 8 cores. \|\n", "\| file-based training (new) \| `corpus_file` (file on disk) \| Scales linearly with CPU cores. \| Training corpus must be serialized to disk in a specific format. \|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you specify `corpus_file`, the model will read and process different portions of the file with different workers. The entire bulk of work is done outside of GIL, using no Python structures at all. The workers update the same weight matrix, but otherwise there's no communication, each worker munches on its data portion completely independently. This is the same approach the original C tool uses. \n", "\n", "Training with `corpus_file` yields a significant performance boost: for example, in the experiment belows training is 3.7x faster with 32 workers in comparison to training with `sentences` argument. It even outperforms the original Word2Vec C tool in terms of words/sec processing speed on high-core machines.\n", "\n", "The limitation of this approach is that `corpus_file` argument accepts a path to your corpus file, which must be stored on disk in a specific format. The format is simply the well-known [gensim.models.word2vec.LineSentence](https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.LineSentence): one sentence per line, with words separated by spaces." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to use it\n", "\n", "You only need to:\n", "\n", "1. Save your corpus in the LineSentence format to disk (you may use [gensim.utils.save_as_line_sentence(your_corpus, your_corpus_file)](https://radimrehurek.com/gensim/utils.html#gensim.utils.save_as_line_sentence) for convenience).\n", "2. Change `sentences=your_corpus` argument to `corpus_file=your_corpus_file` in `Word2Vec.__init__`, `Word2Vec.build_vocab`, `Word2Vec.train` calls.\n", "\n", "\n", "A short Word2Vec example:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "import gensim\n", "import gensim.downloader as api\n", "from gensim.utils import save_as_line_sentence\n", "from gensim.models.word2vec import Word2Vec\n", "\n", "print(gensim.models.word2vec.CORPUSFILE_VERSION) # must be >= 0, i.e. optimized compiled version\n", "\n", "corpus = api.load(\"text8\")\n", "save_as_line_sentence(corpus, \"my_corpus.txt\")\n", "\n", "model = Word2Vec(corpus_file=\"my_corpus.txt\", iter=5, size=300, workers=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's prepare the full Wikipedia dataset as training corpus\n", "\n", "We load wikipedia dump from `gensim-data`, perform text preprocessing with Gensim functions, and finally save processed corpus in LineSentence format." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "CORPUS_FILE = 'wiki-en-20171001.txt'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from gensim.parsing.preprocessing import preprocess_string\n", "\n", "def processed_corpus():\n", " raw_corpus = api.load('wiki-english-20171001')\n", " for article in raw_corpus:\n", " # concatenate all section titles and texts of each Wikipedia article into a single \"sentence\"\n", " doc = '\\n'.join(itertools.chain.from_iterable(zip(article['section_titles'], article['section_texts'])))\n", " yield preprocess_string(doc)\n", "\n", "# serialize the preprocessed corpus into a single file on disk, using memory-efficient streaming\n", "save_as_line_sentence(processed_corpus(), CORPUS_FILE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Word2Vec\n", "\n", "We train two models:\n", "* With `sentences` argument\n", "* With `corpus_file` argument\n", "\n", "\n", "Then, we compare the timings and accuracy on `question-words.txt`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from gensim.models.word2vec import LineSentence\n", "import time\n", "\n", "start_time = time.time()\n", "model_sent = Word2Vec(sentences=LineSentence(CORPUS_FILE), iter=5, size=300, workers=32)\n", "sent_time = time.time() - start_time\n", "\n", "start_time = time.time()\n", "model_corp_file = Word2Vec(corpus_file=CORPUS_FILE, iter=5, size=300, workers=32)\n", "file_time = time.time() - start_time" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training model with `sentences` took 9494.237 seconds\n", "Training model with `corpus_file` took 2566.170 seconds\n" ] } ], "source": [ "print(\"Training model with `sentences` took {:.3f} seconds\".format(sent_time))\n", "print(\"Training model with `corpus_file` took {:.3f} seconds\".format(file_time))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Training with `corpus_file` took 3.7x less time!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's compare the accuracies:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from gensim.test.utils import datapath" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/persiyanov/gensim/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Word analogy accuracy with `sentences`: 75.4%\n", "Word analogy accuracy with `corpus_file`: 74.8%\n" ] } ], "source": [ "model_sent_accuracy = model_sent.wv.evaluate_word_analogies(datapath('questions-words.txt'))[0]\n", "print(\"Word analogy accuracy with `sentences`: {:.1f}%\".format(100.0 * model_sent_accuracy))\n", "\n", "model_corp_file_accuracy = model_corp_file.wv.evaluate_word_analogies(datapath('questions-words.txt'))[0]\n", "print(\"Word analogy accuracy with `corpus_file`: {:.1f}%\".format(100.0 * model_corp_file_accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The accuracies are approximately the same." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FastText" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Short example:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import gensim.downloader as api\n", "from gensim.utils import save_as_line_sentence\n", "from gensim.models.fasttext import FastText\n", "\n", "corpus = api.load(\"text8\")\n", "save_as_line_sentence(corpus, \"my_corpus.txt\")\n", "\n", "model = FastText(corpus_file=\"my_corpus.txt\", iter=5, size=300, workers=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Let's compare the timings" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from gensim.models.word2vec import LineSentence\n", "from gensim.models.fasttext import FastText\n", "import time\n", "\n", "start_time = time.time()\n", "model_corp_file = FastText(corpus_file=CORPUS_FILE, iter=5, size=300, workers=32)\n", "file_time = time.time() - start_time\n", "\n", "start_time = time.time()\n", "model_sent = FastText(sentences=LineSentence(CORPUS_FILE), iter=5, size=300, workers=32)\n", "sent_time = time.time() - start_time" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training model with `sentences` took 17963.283 seconds\n", "Training model with `corpus_file` took 10725.931 seconds\n" ] } ], "source": [ "print(\"Training model with `sentences` took {:.3f} seconds\".format(sent_time))\n", "print(\"Training model with `corpus_file` took {:.3f} seconds\".format(file_time))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see a 1.67x performance boost!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Now, accuracies:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/persiyanov/gensim/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Word analogy accuracy with `sentences`: 64.2%\n", "Word analogy accuracy with `corpus_file`: 66.2%\n" ] } ], "source": [ "from gensim.test.utils import datapath\n", "\n", "model_sent_accuracy = model_sent.wv.evaluate_word_analogies(datapath('questions-words.txt'))[0]\n", "print(\"Word analogy accuracy with `sentences`: {:.1f}%\".format(100.0 * model_sent_accuracy))\n", "\n", "model_corp_file_accuracy = model_corp_file.wv.evaluate_word_analogies(datapath('questions-words.txt'))[0]\n", "print(\"Word analogy accuracy with `corpus_file`: {:.1f}%\".format(100.0 * model_corp_file_accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Doc2Vec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Short example:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import gensim.downloader as api\n", "from gensim.utils import save_as_line_sentence\n", "from gensim.models.doc2vec import Doc2Vec\n", "\n", "corpus = api.load(\"text8\")\n", "save_as_line_sentence(corpus, \"my_corpus.txt\")\n", "\n", "model = Doc2Vec(corpus_file=\"my_corpus.txt\", epochs=5, vector_size=300, workers=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Let's compare the timings" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from gensim.models.doc2vec import Doc2Vec, TaggedLineDocument\n", "import time\n", "\n", "start_time = time.time()\n", "model_corp_file = Doc2Vec(corpus_file=CORPUS_FILE, epochs=5, vector_size=300, workers=32)\n", "file_time = time.time() - start_time\n", "\n", "start_time = time.time()\n", "model_sent = Doc2Vec(documents=TaggedLineDocument(CORPUS_FILE), epochs=5, vector_size=300, workers=32)\n", "sent_time = time.time() - start_time" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training model with `sentences` took 20427.949 seconds\n", "Training model with `corpus_file` took 3085.256 seconds\n" ] } ], "source": [ "print(\"Training model with `sentences` took {:.3f} seconds\".format(sent_time))\n", "print(\"Training model with `corpus_file` took {:.3f} seconds\".format(file_time))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A 6.6x speedup!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Accuracies:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/persiyanov/gensim/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n", " if np.issubdtype(vec.dtype, np.int):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Word analogy accuracy with `sentences`: 71.7%\n", "Word analogy accuracy with `corpus_file`: 67.8%\n" ] } ], "source": [ "from gensim.test.utils import datapath\n", "\n", "model_sent_accuracy = model_sent.wv.evaluate_word_analogies(datapath('questions-words.txt'))[0]\n", "print(\"Word analogy accuracy with `sentences`: {:.1f}%\".format(100.0 * model_sent_accuracy))\n", "\n", "model_corp_file_accuracy = model_corp_file.wv.evaluate_word_analogies(datapath('questions-words.txt'))[0]\n", "print(\"Word analogy accuracy with `corpus_file`: {:.1f}%\".format(100.0 * model_corp_file_accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TL;DR: Conclusion\n", "\n", "In case your training corpus already lives on disk, you lose nothing by switching to the new `corpus_file` training mode. Training will be much faster.\n", "\n", "In case your corpus is generated dynamically, you can either serialize it to disk first with `gensim.utils.save_as_line_sentence` (and then use the fast `corpus_file`), or if that's not possible continue using the existing `sentences` training mode.\n", "\n", "------\n", "\n", "This new code branch was created by [@persiyanov](https://github.com/persiyanov) as a Google Summer of Code 2018 project in the [RARE Student Incubator](https://rare-technologies.com/incubator/).\n", "\n", "Questions, comments? Use our Gensim [mailing list](https://groups.google.com/g/gensim) and [twitter](https://twitter.com/gensim_py). Happy training!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	18,975	Python	.py	550	30.067273	696	0.629608	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,945	nmslibtutorial.ipynb	piskvorky_gensim/docs/notebooks/nmslibtutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Similarity Queries using Nmslib Tutorial" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial is about using the ([Non-Metric Space Library (NMSLIB)](https://github.com/nmslib/nmslib \"Link to nmslib repo\")) library for similarity queries with a Word2Vec model built with gensim.\n", "\n", "## Why use Nmslib?\n", "The current implementation for finding k nearest neighbors in a vector space in gensim has linear complexity via brute force in the number of indexed documents, although with extremely low constant factors. The retrieved results are exact, which is an overkill in many applications: approximate results retrieved in sub-linear time may be enough. Nmslib can find approximate nearest neighbors much faster.\n", "Compared to annoy, nmslib has more parameteres to control the build and query time and accuracy. Nmslib can achieve faster and more accurate nearest neighbors search than annoy. This figure shows a comparison between annoy and nmslib indexer with differents parameters. This shows nmslib is better than annoy.\n", "![nmslib.png](nmslib.png)\n", "\n", "## Prerequisites\n", "Additional libraries needed for this tutorial:\n", "- nmslib\n", "- annoy\n", "- psutil\n", "- matplotlib\n", "\n", "## Outline\n", "1. Download Text8 Corpus\n", "2. Build Word2Vec Model\n", "3. Construct NmslibIndex with model & make a similarity query\n", "4. Verify & Evaluate performance\n", "5. Evaluate relationship of parameters to initialization/query time and accuracy, compared with annoy\n", "6. Work with Google's word2vec C formats" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPython 3.6.0\n", "IPython 7.5.0\n", "\n", "gensim 3.7.3\n", "numpy 1.16.2\n", "scipy 1.2.1\n", "psutil 5.6.3\n", "matplotlib 3.1.0\n", "\n", "compiler : GCC 4.2.1 Compatible Apple LLVM 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 18.2.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "# pip install watermark\n", "%reload_ext watermark\n", "%watermark -v -m -p gensim,numpy,scipy,psutil,matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Download Text8 Corpus" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2019-06-27 13:48:42-- https://mattmahoney.net/dc/text8.zip\n", "Resolving mattmahoney.net... 67.195.197.75\n", "Connecting to mattmahoney.net\|67.195.197.75\|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 31344016 (30M) [application/zip]\n", "Saving to: 'text8.zip'\n", "\n", "text8.zip 100%[=====================>] 29.89M 327KB/s in 98s \n", "\n", "2019-06-27 13:50:21 (313 KB/s) - 'text8.zip' saved [31344016/31344016]\n", "\n", "Archive: text8.zip\n", " inflating: text8 \n" ] } ], "source": [ "import os.path\n", "if not os.path.isfile('text8'):\n", " !wget -c https://mattmahoney.net/dc/text8.zip\n", " !unzip text8.zip" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import & Set up Logging\n", "I'm not going to set up logging due to the verbose input displaying in notebooks, but if you want that, uncomment the lines in the cell below." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "LOGS = False\n", "\n", "if LOGS:\n", " import logging\n", " logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Build Word2Vec Model" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Word2Vec(vocab=71290, size=100, alpha=0.05)\n" ] } ], "source": [ "from gensim.models import Word2Vec, KeyedVectors\n", "from gensim.models.word2vec import Text8Corpus\n", "\n", "# Using params from Word2Vec_FastText_Comparison\n", "\n", "params = {\n", " 'alpha': 0.05,\n", " 'size': 100,\n", " 'window': 5,\n", " 'iter': 5,\n", " 'min_count': 5,\n", " 'sample': 1e-4,\n", " 'sg': 1,\n", " 'hs': 0,\n", " 'negative': 5\n", "}\n", "\n", "model = Word2Vec(Text8Corpus('text8'), params)\n", "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See the [Word2Vec tutorial](word2vec.ipynb) for how to initialize and save this model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Comparing the traditional implementation, Annoy and Nmslib approximation" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Set up the model and vector that we are using in the comparison\n", "from gensim.similarities.index import AnnoyIndexer\n", "from gensim.similarities.nmslib import NmslibIndexer\n", "\n", "model.init_sims()\n", "annoy_index = AnnoyIndexer(model, 300)\n", "nmslib_index = NmslibIndexer(model, {'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}, {'efSearch': 10})" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('the', 1.0), ('of', 0.700629323720932), ('in', 0.6932138800621033), ('which', 0.6132444441318512), ('for', 0.6099657416343689)]\n", "[('the', 0.9999999701976776), ('of', 0.91037717461586), ('in', 0.9058823883533478), ('a', 0.8834112882614136), ('and', 0.8790014386177063)]\n", "[('the', 1.0000001192092896), ('of', 0.82075434923172), ('in', 0.811764657497406), ('a', 0.7668224573135376), ('and', 0.7580028772354126)]\n" ] } ], "source": [ "# Dry run to make sure both indices are fully in RAM\n", "vector = model.wv.syn0norm[0]\n", "print(model.most_similar([vector], topn=5, indexer=annoy_index))\n", "print(model.most_similar([vector], topn=5, indexer=nmslib_index))\n", "print(model.most_similar([vector], topn=5))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def avg_query_time(annoy_index=None, queries=1000):\n", " \"\"\"\n", " Average query time of a most_similar method over 1000 random queries,\n", " uses annoy if given an indexer\n", " \"\"\"\n", " total_time = 0\n", " for _ in range(queries):\n", " rand_vec = model.wv.syn0norm[np.random.randint(0, len(model.wv.vocab))]\n", " start_time = time.clock()\n", " model.most_similar([rand_vec], topn=5, indexer=annoy_index)\n", " total_time += time.clock() - start_time\n", " return total_time / queries" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gensim (s/query):\t0.00560\n", "Annoy (s/query):\t0.00099\n", "Nmslib (s/query):\t0.00046\n", "\n", "Nmslib is 12.16 times faster on average on this particular run\n", "\n", "Nmslib is 2.15 times faster on average than annoy on this particular run\n" ] } ], "source": [ "queries = 10000\n", "\n", "gensim_time = avg_query_time(queries=queries)\n", "annoy_time = avg_query_time(annoy_index, queries=queries)\n", "nmslib_time = avg_query_time(nmslib_index, queries=queries)\n", "print(\"Gensim (s/query):\\t{0:.5f}\".format(gensim_time))\n", "print(\"Annoy (s/query):\\t{0:.5f}\".format(annoy_time))\n", "print(\"Nmslib (s/query):\\t{0:.5f}\".format(nmslib_time))\n", "speed_improvement_gensim = gensim_time / nmslib_time\n", "speed_improvement_annoy = annoy_time / nmslib_time\n", "print (\"\\nNmslib is {0:.2f} times faster on average on this particular run\".format(speed_improvement_gensim))\n", "print (\"\\nNmslib is {0:.2f} times faster on average than annoy on this particular run\".format(speed_improvement_annoy))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Construct Nmslib Index with model & make a similarity query\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating an indexer\n", "An instance of `NmslibIndexer` needs to be created in order to use Nmslib in gensim. The `NmslibIndexer` class is located in `gensim.similarities.nmslib`\n", "\n", "`NmslibIndexer()` takes three parameters:\n", "\n", "`model`: A `Word2Vec` or `Doc2Vec` model\n", "\n", "`index_params`: Parameters for building nmslib indexer. `index_params` effects the build time and the index size. The example is `{'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}`. Increasing the value of `M` and `efConstruction` improves the accuracy of search. However this also leads to longer indexing times. `indexThreadQty` is the number of thread. \n", "\n", "`query_time_params`**: Parameters for querying on nmslib indexer. `query_time_params` effects the query time and the search accuracy. The example is `{'efSearch': 100}`. A larger `efSearch` will give more accurate results, but larger query time. \n", "\n", "More information can be found [here](https://github.com/nmslib/nmslib/blob/master/manual/methods.md). The relationship between parameters, build/query time, and accuracy will be investigated later in the tutorial. \n", "\n", "Now that we are ready to make a query, lets find the top 5 most similar words to \"science\" in the Text8 corpus. To make a similarity query we call `Word2Vec.most_similar` like we would traditionally, but with an added parameter, `indexer`. The only supported indexerers in gensim as of now are Annoy and Nmslib. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Approximate Neighbors\n", "('science', 1.0000000596046448)\n", "('fiction', 0.8769421577453613)\n", "('protoscience', 0.8432635962963104)\n", "('multidisciplinary', 0.835610032081604)\n", "('sciences', 0.8348604440689087)\n", "('astrobiology', 0.8340338170528412)\n", "('actuarial', 0.8339103162288666)\n", "('interdisciplinary', 0.8327268362045288)\n", "('xenobiology', 0.8318319618701935)\n", "('criminology', 0.8261869251728058)\n", "('futurists', 0.82555091381073)\n", "\n", "Normal (not nmslib-indexed) Neighbors\n", "('science', 0.9999998807907104)\n", "('fiction', 0.7538841962814331)\n", "('protoscience', 0.6865270733833313)\n", "('multidisciplinary', 0.6712199449539185)\n", "('sciences', 0.6697208881378174)\n", "('astrobiology', 0.6680675148963928)\n", "('actuarial', 0.6678205132484436)\n", "('interdisciplinary', 0.6654534339904785)\n", "('xenobiology', 0.663663923740387)\n", "('vernor', 0.6569585800170898)\n", "('criminology', 0.652373731136322)\n" ] } ], "source": [ "# Building nmslib indexer\n", "nmslib_index = NmslibIndexer(model, {'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}, {'efSearch': 10})\n", "# Derive the vector for the word \"science\" in our model\n", "vector = model[\"science\"]\n", "# The instance of AnnoyIndexer we just created is passed \n", "approximate_neighbors = model.most_similar([vector], topn=11, indexer=nmslib_index)\n", "\n", "# Neatly print the approximate_neighbors and their corresponding cosine similarity values\n", "print(\"Approximate Neighbors\")\n", "for neighbor in approximate_neighbors:\n", " print(neighbor)\n", "\n", "normal_neighbors = model.most_similar([vector], topn=11)\n", "print(\"\\nNormal (not nmslib-indexed) Neighbors\")\n", "for neighbor in normal_neighbors:\n", " print(neighbor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Analyzing the results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The closer the cosine similarity of a vector is to 1, the more similar that word is to our query, which was the vector for \"science\". In this case the results are almostly same." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Verify & Evaluate performance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Persisting Indexes\n", "You can save and load your indexes from/to disk to prevent having to construct them each time. This will create two files on disk, _fname_ and _fname.d_. Both files are needed to correctly restore all attributes. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "fname = '/tmp/mymodel.index'\n", "\n", "# Persist index to disk\n", "nmslib_index.save(fname)\n", "\n", "# Load index back\n", "if os.path.exists(fname):\n", " nmslib_index2 = NmslibIndexer.load(fname)\n", " nmslib_index2.model = model" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('science', 1.0000000596046448)\n", "('fiction', 0.8769421577453613)\n", "('protoscience', 0.8432635962963104)\n", "('multidisciplinary', 0.835610032081604)\n", "('sciences', 0.8348604440689087)\n", "('astrobiology', 0.8340338170528412)\n", "('actuarial', 0.8339103162288666)\n", "('interdisciplinary', 0.8327268362045288)\n", "('xenobiology', 0.8318319618701935)\n", "('criminology', 0.8261869251728058)\n", "('futurists', 0.82555091381073)\n" ] } ], "source": [ "# Results should be identical to above\n", "vector = model[\"science\"]\n", "approximate_neighbors2 = model.most_similar([vector], topn=11, indexer=nmslib_index2)\n", "for neighbor in approximate_neighbors2:\n", " print(neighbor)\n", " \n", "assert approximate_neighbors == approximate_neighbors2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be sure to use the same model at load that was used originally, otherwise you will get unexpected behaviors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Save memory by memory-mapping indices saved to disk\n", "\n", "Nmslib library has a useful feature that indices can be memory-mapped from disk. It saves memory when the same index is used by several processes.\n", "\n", "Below are two snippets of code. First one has a separate index for each process. The second snipped shares the index between two processes via memory-mapping. The second example uses less total RAM as it is shared." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Remove verbosity from code below (if logging active)\n", "\n", "if LOGS:\n", " logging.disable(logging.CRITICAL)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "from multiprocessing import Process\n", "import psutil" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Bad Example: Two processes load the Word2vec model from disk and create there own Nmslib indices from that model. " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process Id: 36262\n", "\n", "Memory used by process 36262: pmem(rss=600539136, vms=6052294656, pfaults=170185, pageins=2)\n", "---\n", "Process Id: 36263\n", "\n", "Memory used by process 36263: pmem(rss=600539136, vms=6052294656, pfaults=170155, pageins=0)\n", "---\n", "CPU times: user 547 ms, sys: 308 ms, total: 856 ms\n", "Wall time: 59.2 s\n" ] } ], "source": [ "%%time\n", "\n", "model.save('/tmp/mymodel.pkl')\n", "\n", "def f(process_id):\n", " print('Process Id: {}'.format(os.getpid()))\n", " process = psutil.Process(os.getpid())\n", " new_model = Word2Vec.load('/tmp/mymodel.pkl')\n", " vector = new_model[\"science\"]\n", " nmslib_index = NmslibIndexer(new_model, {'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}, {'efSearch': 10})\n", " approximate_neighbors = new_model.most_similar([vector], topn=5, indexer=nmslib_index)\n", " print('\\nMemory used by process {}: {}\\n---'.format(os.getpid(), process.memory_info()))\n", "\n", "# Creating and running two parallel process to share the same index file.\n", "p1 = Process(target=f, args=('1',))\n", "p1.start()\n", "p1.join()\n", "p2 = Process(target=f, args=('2',))\n", "p2.start()\n", "p2.join()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Good example. Two processes load both the Word2vec model and index from disk and memory-map the index\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process Id: 36265\n", "\n", "Memory used by process 36265: pmem(rss=417345536, vms=5981540352, pfaults=129428, pageins=0)\n", "---\n", "Process Id: 36266\n", "\n", "Memory used by process 36266: pmem(rss=417579008, vms=5981540352, pfaults=129528, pageins=0)\n", "---\n", "CPU times: user 581 ms, sys: 266 ms, total: 847 ms\n", "Wall time: 3.74 s\n" ] } ], "source": [ "%%time\n", "\n", "model.save('/tmp/mymodel.pkl')\n", "\n", "def f(process_id):\n", " print('Process Id: {}'.format(os.getpid()))\n", " process = psutil.Process(os.getpid())\n", " new_model = Word2Vec.load('/tmp/mymodel.pkl')\n", " vector = new_model[\"science\"]\n", " nmslib_index = NmslibIndexer.load('/tmp/mymodel.index')\n", " nmslib_index.model = new_model\n", " approximate_neighbors = new_model.most_similar([vector], topn=5, indexer=nmslib_index)\n", " print('\\nMemory used by process {}: {}\\n---'.format(os.getpid(), process.memory_info()))\n", "\n", "# Creating and running two parallel process to share the same index file.\n", "p1 = Process(target=f, args=('1',))\n", "p1.start()\n", "p1.join()\n", "p2 = Process(target=f, args=('2',))\n", "p2.start()\n", "p2.join()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Evaluate relationship of parameters to initialization/query time and accuracy, compared with annoy\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Build dataset of Initialization times and accuracy measures" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "exact_results = [element[0] for element in model.most_similar([model.wv.syn0norm[0]], topn=100)]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# For calculating query time\n", "queries = 1000" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def create_evaluation_graph(x_values, y_values_init, y_values_accuracy, y_values_query, param_name):\n", " plt.figure(1, figsize=(12, 6))\n", " plt.subplot(231)\n", " plt.plot(x_values, y_values_init)\n", " plt.title(\"{} vs initalization time\".format(param_name))\n", " plt.ylabel(\"Initialization time (s)\")\n", " plt.xlabel(param_name)\n", " plt.subplot(232)\n", " plt.plot(x_values, y_values_accuracy)\n", " plt.title(\"{} vs accuracy\".format(param_name))\n", " plt.ylabel(\"% accuracy\")\n", " plt.xlabel(param_name)\n", " plt.tight_layout()\n", " plt.subplot(233)\n", " plt.plot(y_values_init, y_values_accuracy)\n", " plt.title(\"Initialization time vs accuracy\")\n", " plt.ylabel(\"% accuracy\")\n", " plt.xlabel(\"Initialization time (s)\")\n", " plt.tight_layout()\n", " plt.subplot(234)\n", " plt.plot(x_values, y_values_query)\n", " plt.title(\"{} vs query time\".format(param_name))\n", " plt.ylabel(\"query time\")\n", " plt.xlabel(param_name)\n", " plt.tight_layout()\n", " plt.subplot(235)\n", " plt.plot(y_values_query, y_values_accuracy)\n", " plt.title(\"query time vs accuracy\")\n", " plt.ylabel(\"% accuracy\")\n", " plt.xlabel(\"query time (s)\")\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def evaluate_nmslib_performance(parameter, is_parameter_query, parameter_start, parameter_end, parameter_step):\n", " nmslib_x_values = []\n", " nmslib_y_values_init = []\n", " nmslib_y_values_accuracy = []\n", " nmslib_y_values_query = []\n", " index_params = {'M': 100, 'indexThreadQty': 10, 'efConstruction': 100, 'post': 0}\n", " query_params = {'efSearch': 100}\n", " \n", " for x in range(parameter_start, parameter_end, parameter_step):\n", " nmslib_x_values.append(x)\n", " start_time = time.time()\n", " if is_parameter_query:\n", " query_params[parameter] = x\n", " else:\n", " index_params[parameter] = x\n", " nmslib_index = NmslibIndexer(model\n", " , index_params\n", " , query_params)\n", " nmslib_y_values_init.append(time.time() - start_time)\n", " approximate_results = model.most_similar([model.wv.syn0norm[0]], topn=100, indexer=nmslib_index)\n", " top_words = [result[0] for result in approximate_results]\n", " nmslib_y_values_accuracy.append(len(set(top_words).intersection(exact_results)))\n", " nmslib_y_values_query.append(avg_query_time(nmslib_index, queries=queries))\n", " create_evaluation_graph(nmslib_x_values,\n", " nmslib_y_values_init, \n", " nmslib_y_values_accuracy, \n", " nmslib_y_values_query, \n", " parameter)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Evaluate nmslib indexer, changing the parameter M\n", "evaluate_nmslib_performance(\"M\", False, 50, 401, 50)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Evaluate nmslib indexer, changing the parameter efConstruction\n", "evaluate_nmslib_performance(\"efConstruction\", False, 50, 1001, 100)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Evaluate nmslib indexer, changing the parameter efSearch\n", "evaluate_nmslib_performance(\"efSearch\", True, 50, 401, 100)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Evaluate annoy indexer, changing the parameter num_tree\n", "annoy_x_values = []\n", "annoy_y_values_init = []\n", "annoy_y_values_accuracy = []\n", "annoy_y_values_query = []\n", "\n", "for x in range(100, 401, 50):\n", " annoy_x_values.append(x)\n", " start_time = time.time()\n", " annoy_index = AnnoyIndexer(model, x)\n", " annoy_y_values_init.append(time.time() - start_time)\n", " approximate_results = model.most_similar([model.wv.syn0norm[0]], topn=100, indexer=annoy_index)\n", " top_words = [result[0] for result in approximate_results]\n", " annoy_y_values_accuracy.append(len(set(top_words).intersection(exact_results)))\n", " annoy_y_values_query.append(avg_query_time(annoy_index, queries=queries))\n", "create_evaluation_graph(annoy_x_values,\n", " annoy_y_values_init, \n", " annoy_y_values_accuracy, \n", " annoy_y_values_query, \n", " \"num_tree\")" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# nmslib indexer changing the parameter M, efConstruction, efSearch\n", "nmslib_y_values_init = []\n", "nmslib_y_values_accuracy = []\n", "nmslib_y_values_query = []\n", "\n", "for M in [100, 200]:\n", " for efConstruction in [100, 200]:\n", " for efSearch in [100, 200]:\n", " start_time = time.time()\n", " nmslib_index = NmslibIndexer(model, \n", " {'M': M, 'indexThreadQty': 10, 'efConstruction': efConstruction, 'post': 0},\n", " {'efSearch': efSearch})\n", " nmslib_y_values_init.append(time.time() - start_time)\n", " approximate_results = model.most_similar([model.wv.syn0norm[0]], topn=100, indexer=nmslib_index)\n", " top_words = [result[0] for result in approximate_results]\n", " nmslib_y_values_accuracy.append(len(set(top_words).intersection(exact_results)))\n", " nmslib_y_values_query.append(avg_query_time(nmslib_index, queries=queries))\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make a comparison between annoy and nmslib indexer\n", "plt.figure(1, figsize=(12, 6))\n", "plt.subplot(121)\n", "plt.scatter(nmslib_y_values_init, nmslib_y_values_accuracy, label=\"nmslib\", color='r', marker='o')\n", "plt.scatter(annoy_y_values_init, annoy_y_values_accuracy, label=\"annoy\", color='b', marker='x')\n", "plt.legend()\n", "plt.title(\"Initialization time vs accuracy. Upper left is better.\")\n", "plt.ylabel(\"% accuracy\")\n", "plt.xlabel(\"Initialization time (s)\")\n", "plt.subplot(122)\n", "plt.scatter(nmslib_y_values_query, nmslib_y_values_accuracy, label=\"nmslib\", color='r', marker='o')\n", "plt.scatter(annoy_y_values_query, annoy_y_values_accuracy, label=\"annoy\", color='b', marker='x')\n", "plt.legend()\n", "plt.title(\"Query time vs accuracy. Upper left is better.\")\n", "plt.ylabel(\"% accuracy\")\n", "plt.xlabel(\"Query time (s)\")\n", "plt.xlim(min(nmslib_y_values_query+annoy_y_values_query), max(nmslib_y_values_query+annoy_y_values_query))\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Work with Google word2vec files\n", "\n", "Our model can be exported to a word2vec C format. There is a binary and a plain text word2vec format. Both can be read with a variety of other software, or imported back into gensim as a `KeyedVectors` object." ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "# To export our model as text\n", "model.wv.save_word2vec_format('/tmp/vectors.txt', binary=False)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "71290 100\n", "the -0.20548718 0.19478682 -0.15149663 -0.31142342 0.014471135 -0.17996445 -0.07373469 0.09573618 -0.06328416 0.15571225 0.021413572 -0.12776679 -0.16940169 0.15807933 -0.21688043 0.074471496 0.08091913 0.07911484 0.31909388 -0.12297766 0.16993207 -0.02962172 0.08481803 0.12566781 0.02949822 -0.009697897 0.10780254 -0.102594994 -0.03935867 0.2679534 -0.061677158 -0.26071545 0.16285498 0.051780242 -0.1697231 0.24037386 0.0726078 -0.090416454 0.0776138 -0.06611322 0.057015926 0.07859522 -0.1910579 0.2974446 -0.033308737 -0.07360004 0.10797568 0.3595622 0.26797494 -0.062491674 0.21733648 -0.08524646 -0.06860078 -0.01714756 0.1305319 -0.09754544 -0.11249808 0.27328265 0.0041686473 -0.09874534 -0.30283058 0.111191 -0.026302295 -0.095534325 -0.0907799 -0.09120328 0.00068672217 0.31802058 -0.03345536 0.103762306 0.068564445 0.07402255 0.013657822 0.020439595 0.14985266 -0.13516407 0.36674532 0.0077319355 0.24709526 0.07666927 -0.11271039 0.02220251 -0.0235162 0.06409378 -0.10098407 0.23384795 0.094924204 0.061178204 0.19992544 0.29211295 0.004201066 0.12897494 -0.07837112 0.06269808 0.18545003 0.10452479 0.093281575 -0.24360427 0.01996208 0.35048977\n", "of -0.19580448 0.209091 -0.099757686 -0.21272708 0.022300478 -0.21946296 -0.19841547 0.09413904 0.08382151 0.20281556 0.12914163 -0.062828615 -0.24957581 0.29098126 -0.12465087 0.09803499 0.08116001 0.06726196 0.2615518 -0.035177294 0.06914535 -0.09427601 0.0011476864 0.23290296 0.0034714406 0.009549841 0.03912403 0.11168332 -0.10166585 0.34404838 -0.08050078 -0.3933344 0.17402974 -0.15764657 -0.21314052 0.20686424 0.034598276 0.16018851 0.14357902 0.046236232 0.075825214 0.029642927 -0.059190348 0.4163471 -0.1367429 -0.017528763 0.19181107 0.2601198 -0.020112848 -0.23402186 0.2841525 -0.10974068 -0.002565893 0.00070757867 0.13032512 -0.002393167 -0.14120881 0.22138755 0.027622899 0.06942904 -0.39498508 0.1133777 0.19053803 -0.062439334 -0.025348661 -0.11142109 0.015062763 0.3285828 -0.09184951 0.2661699 -0.11710489 -0.15770112 -0.12773664 0.15360866 0.08832063 -0.20914252 0.32392043 -0.023845093 0.3131217 0.08974748 -0.11354328 -0.2037927 -0.06780317 0.20184614 -0.13539118 0.2029387 0.07701099 -0.048417546 0.09797926 0.284204 0.036153372 0.17912139 -0.118080124 -0.025121484 0.10947146 0.09291596 0.1244357 0.006804844 0.025120731 0.28958535\n" ] } ], "source": [ "from smart_open import open\n", "# View the first 3 lines of the exported file\n", "\n", "# The first line has the total number of entries and the vector dimension count. \n", "# The next lines have a key (a string) followed by its vector.\n", "with open('/tmp/vectors.txt') as myfile:\n", " for i in range(3):\n", " print(myfile.readline().strip())" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# To import a word2vec text model\n", "wv = KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# To export our model as binary\n", "model.wv.save_word2vec_format('/tmp/vectors.bin', binary=True)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "# To import a word2vec binary model\n", "wv = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# To create and save Nmslib Index from a loaded `KeyedVectors` object \n", "nmslib_index = NmslibIndexer(wv, \n", " {'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}, {'efSearch': 100})\n", "nmslib_index.save('/tmp/mymodel.index')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Approximate Neighbors\n", "('cat', 1.0)\n", "('cats', 0.8335747122764587)\n", "('meow', 0.8298084139823914)\n", "('leopardus', 0.8215900659561157)\n", "('albino', 0.8207193315029144)\n", "('poodle', 0.8168523907661438)\n", "('saimiri', 0.8167656660079956)\n", "('squirrel', 0.8138661682605743)\n", "('sighthound', 0.8134040832519531)\n", "('proboscis', 0.8130573630332947)\n", "('eared', 0.812840610742569)\n", "\n", "Normal (not Nmslib-indexed) Neighbors\n", "('cat', 1.0)\n", "('cats', 0.6671494245529175)\n", "('meow', 0.6596168279647827)\n", "('leopardus', 0.6431801319122314)\n", "('albino', 0.6414386034011841)\n", "('poodle', 0.633704662322998)\n", "('saimiri', 0.633531391620636)\n", "('squirrel', 0.6277321577072144)\n", "('sighthound', 0.6268081665039062)\n", "('proboscis', 0.6261147260665894)\n", "('eared', 0.6256811618804932)\n" ] } ], "source": [ "# Load and test the saved word vectors and saved nmslib index\n", "wv = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True)\n", "nmslib_index = NmslibIndexer.load('/tmp/mymodel.index')\n", "nmslib_index.model = wv\n", "\n", "vector = wv[\"cat\"]\n", "approximate_neighbors = wv.most_similar([vector], topn=11, indexer=nmslib_index)\n", "# Neatly print the approximate_neighbors and their corresponding cosine similarity values\n", "print(\"Approximate Neighbors\")\n", "for neighbor in approximate_neighbors:\n", " print(neighbor)\n", "\n", "normal_neighbors = wv.most_similar([vector], topn=11)\n", "print(\"\\nNormal (not Nmslib-indexed) Neighbors\")\n", "for neighbor in normal_neighbors:\n", " print(neighbor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Recap\n", "In this notebook we used the Nmslib module to build an indexed approximation of our word embeddings. To do so, we did the following steps:\n", "1. Download Text8 Corpus\n", "2. Build Word2Vec Model\n", "3. Construct NmslibIndex with model & make a similarity query\n", "4. Verify & Evaluate performance\n", "5. Evaluate relationship of parameters to initialization/query time and accuracy, compared with annoy\n", "6. Work with Google's word2vec C formats" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	301,877	Python	.py	1,076	275.740706	71,948	0.923039	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,946	Training_visualizations.ipynb	piskvorky_gensim/docs/notebooks/Training_visualizations.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup Visdom\n", "\n", "Install it with:\n", "\n", "`pip install visdom`\n", "\n", "Start the server:\n", "\n", "`python -m visdom.server`\n", "\n", "Visdom now can be accessed at http://localhost:8097 in the browser.\n", "\n", "\n", "# LDA Training Visualization\n", "\n", "Knowing about the progress and performance of a model, as we train them, could be very helpful in understanding itâ€™s learning process and makes it easier to debug and optimize them. In this notebook, we will learn how to visualize training statistics for LDA topic model in gensim. To monitor the training, a list of Metrics is passed to the LDA function call for plotting their values live as the training progresses. \n", "\n", "\n", "<img src=\"visdom_graph.png\">\n", "\n", "\n", "Let's plot the training stats for an LDA model being trained on kaggle's [fake news dataset](https://www.kaggle.com/mrisdal/fake-news). We will use the four evaluation metrics available for topic models in gensim: Coherence, Perplexity, Topic diff and Convergence. (using separate hold_out and test corpus for evaluating the perplexity)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] File b'fake.csv' does not exist: b'fake.csv'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-de0530cf9fd8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdf_fake\u001b[0m \u001b[0;34m=\u001b[0m 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infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/envs/gensim/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 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\u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'fake.csv' does not exist: b'fake.csv'" ] } ], "source": [ "from gensim.models import ldamodel\n", "from gensim.corpora import Dictionary\n", "import pandas as pd\n", "import re\n", "from gensim.parsing.preprocessing import remove_stopwords, strip_punctuation\n", "\n", "import numpy as np\n", "\n", "df_fake = pd.read_csv('fake.csv')\n", "df_fake[['title', 'text', 'language']].head()\n", "df_fake = df_fake.loc[(pd.notnull(df_fake.text)) & (df_fake.language == 'english')]\n", "\n", "# remove stopwords and punctuations\n", "def preprocess(row):\n", " return strip_punctuation(remove_stopwords(row.lower()))\n", " \n", "df_fake['text'] = df_fake['text'].apply(preprocess)\n", "\n", "# Convert data to required input format by LDA\n", "texts = []\n", "for line in df_fake.text:\n", " lowered = line.lower()\n", " words = re.findall(r'\\w+', lowered, flags = re.UNICODE \| re.LOCALE)\n", " texts.append(words)\n", "\n", "dictionary = Dictionary(texts)\n", "\n", "training_texts = texts[:5000]\n", "holdout_texts = texts[5000:7500]\n", "test_texts = texts[7500:10000]\n", "\n", "training_corpus = [dictionary.doc2bow(text) for text in training_texts]\n", "holdout_corpus = [dictionary.doc2bow(text) for text in holdout_texts]\n", "test_corpus = [dictionary.doc2bow(text) for text in test_texts]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", "\n", "# define perplexity callback for hold_out and test corpus\n", "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", title=\"Perplexity (hold_out)\")\n", "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"visdom\", title=\"Perplexity (test)\")\n", "\n", "# define other remaining metrics available\n", "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", title=\"Coherence (u_mass)\")\n", "ch_cv = CoherenceMetric(corpus=training_corpus, texts=training_texts, coherence=\"c_v\", logger=\"visdom\", title=\"Coherence (c_v)\")\n", "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", title=\"Diff (kullback_leibler)\")\n", "convergence_kl = ConvergenceMetric(distance=\"jaccard\", logger=\"visdom\", title=\"Convergence (jaccard)\")\n", "\n", "callbacks = [pl_holdout, pl_test, ch_umass, ch_cv, diff_kl, convergence_kl]\n", "\n", "# training LDA model\n", "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, num_topics=35, passes=50, chunksize=1500, iterations=200, alpha='auto', callbacks=callbacks)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the model is set for training, you can open http://localhost:8097 to see the training progress." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# to get a metric value on a trained model\n", "print(CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\").get_value(model=model))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The four types of graphs which are plotted for LDA:\n", "\n", "Coherence*\n", "\n", "Coherence measures are generally based on the idea of computing the sum of pairwise scores of top n* words w<sub>1</sub>, ...,w<sub>n</sub> used to describe the topic. There are four coherence measures available in gensim: `u_mass, c_v, c_uci, c_npmi`. A good model will generate coherent topics, i.e., topics with high topic coherence scores. Good topics can be described by a short label based on the topic terms they spit out. \n", "\n", "<img src=\"Coherence.gif\">\n", "\n", "Now, this graph along with the others explained below, can be used to decide if it's time to stop the training. We can see if the value stops changing after some epochs and that we are able to get the highest possible coherence of our model. \n", "\n", "\n", "Perplexity\n", "\n", "Perplexity is a measurement of how well a probability distribution or probability model predicts a sample. In LDA, topics are described by a probability distribution over vocabulary words. So, perplexity can be used to evaluate the topic-term distribution output by LDA.\n", "\n", "<img src=\"Perplexity.gif\">\n", "\n", "For a good model, perplexity should be low.\n", "\n", "\n", "Topic Difference\n", "\n", "Topic Diff calculates the distance between two LDA models. This distance is calculated based on the topics, by either using their probability distribution over vocabulary words (kullback_leibler, hellinger) or by simply using the common vocabulary words between the topics from both model.\n", "\n", "<img src=\"Diff.gif\">\n", "\n", "In the heatmap, X-axis define the Epoch no. and Y-axis define the distance between identical topics from consecutive epochs. For ex. a particular cell in the heatmap with values (x=3, y=5, z=0.4) represent the distance(=0.4) between the topic 5 from 3rd epoch and topic 5 from 2nd epoch. With increasing epochs, the distance between the identical topics should decrease.\n", " \n", " \n", "Convergence\n", "\n", "Convergence is the sum of the difference between all the identical topics from two consecutive epochs. It is basically the sum of column values in the heatmap above.\n", "\n", "<img src=\"Convergence.gif\">\n", "\n", "The model is said to be converged when the convergence value stops descending with increasing epochs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training Logs\n", "\n", "We can also log the metric values after every epoch to the shell apart from visualizing them in Visdom. The only change is to define `logger=\"shell\"` instead of `\"visdom\"` in the input callbacks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import logging\n", "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", "\n", "logging.basicConfig(level=logging.INFO)\n", "logger = logging.getLogger(__name__)\n", "logger.setLevel(logging.DEBUG)\n", "\n", "# define perplexity callback for hold_out and test corpus\n", "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"shell\", title=\"Perplexity (hold_out)\")\n", "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"shell\", title=\"Perplexity (test)\")\n", "\n", "# define other remaining metrics available\n", "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"shell\", title=\"Coherence (u_mass)\")\n", "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"shell\", title=\"Diff (kullback_leibler)\")\n", "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"shell\", title=\"Convergence (jaccard)\")\n", "\n", "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", "\n", "# training LDA model\n", "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, num_topics=35, passes=2, eval_every=None, callbacks=callbacks)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The metric values can also be accessed from the model instance for custom uses." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.metrics" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	18,511	Python	.py	254	68.314961	1,710	0.683354	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,947	Corpora_and_Vector_Spaces.ipynb	piskvorky_gensim/docs/notebooks/Corpora_and_Vector_Spaces.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,948	deepir.ipynb	piskvorky_gensim/docs/notebooks/deepir.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Deep Inverse Regression with Yelp reviews\n", "\n", "In this note we'll use [gensim](https://radimrehurek.com/gensim/) to turn the Word2Vec machinery into a document classifier, as in [Document Classification by Inversion of Distributed Language Representations](http://arxiv.org/pdf/1504.07295v3) from ACL 2015." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data and prep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, download to the same directory as this note the data from the [Yelp recruiting contest](https://www.kaggle.com/c/yelp-recruiting) on [kaggle](https://www.kaggle.com/):\n", "* https://www.kaggle.com/c/yelp-recruiting/download/yelp_training_set.zip\n", "* https://www.kaggle.com/c/yelp-recruiting/download/yelp_test_set.zip\n", "\n", "You'll need to sign-up for kaggle.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can then unpack the data and grab the information we need. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tutorial Requirements:\n", "1. gensim (and all of its own requirements)\n", "1. pandas\n", "1. matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# ### uncomment below if you want...\n", "# ## ... copious amounts of logging info\n", "# import logging\n", "# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", "# rootLogger = logging.getLogger()\n", "# rootLogger.setLevel(logging.INFO)\n", "# ## ... or auto-reload of gensim during development\n", "# %load_ext autoreload\n", "# %autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we define a super simple parser" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import re\n", "\n", "contractions = re.compile(r\"'\|-\|\\\"\")\n", "# all non alphanumeric\n", "symbols = re.compile(r'(\\W+)', re.U)\n", "# single character removal\n", "singles = re.compile(r'(\\s\\S\\s)', re.I\|re.U)\n", "# separators (any whitespace)\n", "seps = re.compile(r'\\s+')\n", "\n", "# cleaner (order matters)\n", "def clean(text): \n", " text = text.lower()\n", " text = contractions.sub('', text)\n", " text = symbols.sub(r' \\1 ', text)\n", " text = singles.sub(' ', text)\n", " text = seps.sub(' ', text)\n", " return text\n", "\n", "# sentence splitter\n", "alteos = re.compile(r'([!\\?])')\n", "def sentences(l):\n", " l = alteos.sub(r' \\1 .', l).rstrip(\"(\\.)\\n\")\n", " return l.split(\".\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And put everything together in a review generator that provides tokenized sentences and the number of stars for every review." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from zipfile import ZipFile\n", "import json\n", "\n", "def YelpReviews(label):\n", " with ZipFile(\"yelp_%s_set.zip\"%label, 'r') as zf:\n", " with zf.open(\"yelp_%s_set/yelp_%s_set_review.json\"%(label,label)) as f:\n", " for line in f:\n", " if type(line) is bytes:\n", " line = line.decode('utf-8')\n", " rev = json.loads(line)\n", " yield {'y':rev['stars'],\\\n", " 'x':[clean(s).split() for s in sentences(rev['text'])]}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'x': [['nice', 'place', 'big', 'patio'],\n", " ['now', 'offering', 'live', 'sketch', 'comedy'],\n", " ['wednesday',\n", " 'november',\n", " '17th',\n", " 'see',\n", " 'local',\n", " 'troupe',\n", " 'th',\n", " 'sic',\n", " 'sense',\n", " 'in',\n", " 'their',\n", " '2nd',\n", " 'annual',\n", " 'holiday',\n", " 'show'],\n", " ['lighter', 'snappier', 'take', 'on', 'the', 'holiday', 'times'],\n", " ['not', 'for', 'the', 'easily', 'offended'],\n", " ['sketches',\n", " 'include',\n", " 'the',\n", " 'scariest',\n", " 'holloween',\n", " 'costume',\n", " 'the',\n", " 'first',\n", " 'thanksgiving',\n", " 'and',\n", " 'who',\n", " 'shot',\n", " 'santa',\n", " 'claus'],\n", " ['as', 'well', 'as', 'the', 'infectious', 'song', 'mama', 'christmas']],\n", " 'y': 5}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "try:\n", " next(YelpReviews(\"test\"))\n", "except FileNotFoundError:\n", " raise ValueError(\"SKIP: Please download the yelp_test_set.zip\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, since the files are small we'll just read everything into in-memory lists. It takes a minute ..." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "229907 training reviews\n" ] } ], "source": [ "revtrain = list(YelpReviews(\"training\"))\n", "print(len(revtrain), \"training reviews\")\n", "\n", "## and shuffle just in case they are ordered\n", "import numpy as np\n", "np.random.shuffle(revtrain)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, write a function to generate sentences -- ordered lists of words -- from reviews that have certain star ratings" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def StarSentences(reviews, stars=[1,2,3,4,5]):\n", " for r in reviews:\n", " if r['y'] in stars:\n", " for s in r['x']:\n", " yield s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Word2Vec modeling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We fit out-of-the-box Word2Vec" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Word2Vec(vocab=0, size=100, alpha=0.025)\n" ] } ], "source": [ "from gensim.models import Word2Vec\n", "import multiprocessing\n", "\n", "## create a w2v learner \n", "basemodel = Word2Vec(\n", " workers=multiprocessing.cpu_count(), # use your cores\n", " iter=3, # iter = sweeps of SGD through the data; more is better\n", " hs=1, negative=0 # we only have scoring for the hierarchical softmax setup\n", " )\n", "print(basemodel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build vocab from all sentences (you could also pre-train the base model from a neutral or un-labeled vocabulary)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "basemodel.build_vocab(StarSentences(revtrain))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will _deep_ copy each base model and do star-specific training. This is where the big computations happen..." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 stars ( 246207 )\n", "2 stars ( 295371 )\n", "3 stars ( 437718 )\n", "4 stars ( 883235 )\n", "5 stars ( 799704 )\n" ] } ], "source": [ "from copy import deepcopy\n", "starmodels = [deepcopy(basemodel) for i in range(5)]\n", "for i in range(5):\n", " slist = list(StarSentences(revtrain, [i+1]))\n", " print(i+1, \"stars (\", len(slist), \")\")\n", " starmodels[i].train( slist, total_examples=len(slist) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inversion of the distributed representations\n", "\n", "At this point, we have 5 different word2vec language representations. Each 'model' has been trained conditional (i.e., limited to) text from a specific star rating. We will apply Bayes rule to go from _p(text\|stars)_ to _p(stars\|text)_." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For any new sentence we can obtain its _likelihood_ (lhd; actually, the composite likelihood approximation; see the paper) using the [score](https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec.score) function in the `word2vec` class. We get the likelihood for each sentence in the first test review, then convert to a probability over star ratings. Every sentence in the review is evaluated separately and the final star rating of the review is an average vote of all the sentences. This is all in the following handy wrapper." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "docprob takes two lists\n", " docs: a list of documents, each of which is a list of sentences\n", "* models: the candidate word2vec models (each potential class)\n", "\n", "it returns the array of class probabilities. Everything is done in-memory.\n", "\"\"\"\n", "\n", "import pandas as pd # for quick summing within doc\n", "\n", "def docprob(docs, mods):\n", " # score() takes a list [s] of sentences here; could also be a sentence generator\n", " sentlist = [s for d in docs for s in d]\n", " # the log likelihood of each sentence in this review under each w2v representation\n", " llhd = np.array( [ m.score(sentlist, len(sentlist)) for m in mods ] )\n", " # now exponentiate to get likelihoods, \n", " lhd = np.exp(llhd - llhd.max(axis=0)) # subtract row max to avoid numeric overload\n", " # normalize across models (stars) to get sentence-star probabilities\n", " prob = pd.DataFrame( (lhd/lhd.sum(axis=0)).transpose() )\n", " # and finally average the sentence probabilities to get the review probability\n", " prob[\"doc\"] = [i for i,d in enumerate(docs) for s in d]\n", " prob = prob.groupby(\"doc\").mean()\n", " return prob" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test set example\n", "\n", "As an example, we apply the inversion on the full test set. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# read in the test set\n", "revtest = list(YelpReviews(\"test\"))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# get the probs (note we give docprob a list of lists of words, plus the models)\n", "probs = docprob( [r['x'] for r in revtest], starmodels )" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import matplotlib" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1bde1f3c8>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZbZT0dnd/cdeVe9vuiZJ+IumI2uQo88nMrlF6o+L/me/PBoD5UqSn+l+VDmPUkpm9QtLT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\nAwNRxRNiOUkSjWc9WrXfl3a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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "probpos = pd.DataFrame({\"out-of-sample prob positive\":probs[[3,4]].sum(axis=1), \n", " \"true stars\":[r['y'] for r in revtest]})\n", "probpos.boxplot(\"out-of-sample prob positive\",by=\"true stars\", figsize=(12,5))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	32,646	Python	.py	471	64.66879	19,521	0.791049	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,949	dtm_example.ipynb	piskvorky_gensim/docs/notebooks/dtm_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DTM Example\n", "\n", "In this example we will present a sample usage of the DTM wrapper. Prior to using this you need to compile the [DTM code](https://github.com/magsilva/dtm) yourself or use one of the [binaries](https://github.com/magsilva/dtm/tree/master/bin).\n", "\n", "This tutorial is on Windows. Running it on Linux and OSX is the same.\n", "\n", "In this example we will use a small already processed corpus. To see how to get a dataset to this stage please take a look at [Gensim Tutorials](https://radimrehurek.com/gensim/tutorial.html)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import logging\n", "import os\n", "from gensim import corpora, utils\n", "from gensim.models.wrappers.dtmmodel import DtmModel\n", "import numpy as np\n", "\n", "if not os.environ.get('DTM_PATH', None):\n", " raise ValueError(\"SKIP: You need to set the DTM path\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we wil setup logging" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "logger = logging.getLogger()\n", "logger.setLevel(logging.DEBUG)\n", "logging.debug(\"test\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets load a set of documents" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "documents = [[u'senior', u'studios', u'studios', u'studios', u'creators', u'award', u'mobile', u'currently', u'challenges', u'senior', u'summary', u'senior', u'motivated', u'creative', u'senior', u'performs', u'engineering', u'tasks', u'infrastructure', u'focusing', u'primarily', u'programming', u'interaction', u'designers', u'engineers', u'leadership', u'teams', u'teams', u'crews', u'responsibilities', u'engineering', u'quality', u'functional', u'functional', u'teams', u'organizing', u'prioritizing', u'technical', u'decisions', u'engineering', u'participates', u'participates', u'reviews', u'participates', u'hiring', u'conducting', u'interviews', u'feedback', u'departments', u'define', u'focusing', u'engineering', u'teams', u'crews', u'facilitate', u'engineering', u'departments', u'deadlines', u'milestones', u'typically', u'spends', u'designing', u'developing', u'updating', u'bugs', u'mentoring', u'engineers', u'define', u'schedules', u'milestones', u'participating', u'reviews', u'interviews', u'sized', u'teams', u'interacts', u'disciplines', u'knowledge', u'skills', u'knowledge', u'knowledge', u'xcode', u'scripting', u'debugging', u'skills', u'skills', u'knowledge', u'disciplines', u'animation', u'networking', u'expertise', u'competencies', u'oral', u'skills', u'management', u'skills', u'proven', u'effectively', u'teams', u'deadline', u'environment', u'bachelor', u'minimum', u'shipped', u'leadership', u'teams', u'location', u'resumes', u'jobs', u'candidates', u'openings', u'jobs'], [u'maryland', u'client', u'producers', u'electricity', u'operates', u'storage', u'utility', u'retail', u'customers', u'engineering', u'consultant', u'maryland', u'summary', u'technical', u'technology', u'departments', u'expertise', u'maximizing', u'output', u'reduces', u'operating', u'participates', u'areas', u'engineering', u'conducts', u'testing', u'solve', u'supports', u'environmental', u'understands', u'objectives', u'operates', u'responsibilities', u'handles', u'complex', u'engineering', u'aspects', u'monitors', u'quality', u'proficiency', u'optimization', u'recommendations', u'supports', u'personnel', u'troubleshooting', u'commissioning', u'startup', u'shutdown', u'supports', u'procedure', u'operating', u'units', u'develops', u'simulations', u'troubleshooting', u'tests', u'enhancing', u'solving', u'develops', u'estimates', u'schedules', u'scopes', u'understands', u'technical', u'management', u'utilize', u'routine', u'conducts', u'hazards', u'utilizing', u'hazard', u'operability', u'methodologies', u'participates', u'startup', u'reviews', u'pssr', u'participate', u'teams', u'participate', u'regulatory', u'audits', u'define', u'scopes', u'budgets', u'schedules', u'technical', u'management', u'environmental', u'awareness', u'interfacing', u'personnel', u'interacts', u'regulatory', u'departments', u'input', u'objectives', u'identifying', u'introducing', u'concepts', u'solutions', u'peers', u'customers', u'coworkers', u'knowledge', u'skills', u'engineering', u'quality', u'engineering', u'commissioning', u'startup', u'knowledge', u'simulators', u'technologies', u'knowledge', u'engineering', u'techniques', u'disciplines', u'leadership', u'skills', u'proven', u'engineers', u'oral', u'skills', u'technical', u'skills', u'analytically', u'solve', u'complex', u'interpret', u'proficiency', u'simulation', u'knowledge', u'applications', u'manipulate', u'applications', u'engineering', u'calculations', u'programs', u'matlab', u'excel', u'independently', u'environment', u'proven', u'skills', u'effectively', u'multiple', u'tasks', u'planning', u'organizational', u'management', u'skills', u'rigzone', u'jobs', u'developer', u'exceptional', u'strategies', u'junction', u'exceptional', u'strategies', u'solutions', u'solutions', u'biggest', u'insurers', u'operates', u'investment'], [u'vegas', u'tasks', u'electrical', u'contracting', u'expertise', u'virtually', u'electrical', u'developments', u'institutional', u'utilities', u'technical', u'experts', u'relationships', u'credibility', u'contractors', u'utility', u'customers', u'customer', u'relationships', u'consistently', u'innovations', u'profile', u'construct', u'envision', u'dynamic', u'complex', u'electrical', u'management', u'grad', u'internship', u'electrical', u'engineering', u'infrastructures', u'engineers', u'documented', u'management', u'engineering', u'quality', u'engineering', u'electrical', u'engineers', u'complex', u'distribution', u'grounding', u'estimation', u'testing', u'procedures', u'voltage', u'engineering', u'troubleshooting', u'installation', u'documentation', u'bsee', u'certification', u'electrical', u'voltage', u'cabling', u'electrical', u'engineering', u'candidates', u'electrical', u'internships', u'oral', u'skills', u'organizational', u'prioritization', u'skills', u'skills', u'excel', u'cadd', u'calculation', u'autocad', u'mathcad', u'skills', u'skills', u'customer', u'relationships', u'solving', u'ethic', u'motivation', u'tasks', u'budget', u'affirmative', u'diversity', u'workforce', u'gender', u'orientation', u'disability', u'disabled', u'veteran', u'vietnam', u'veteran', u'qualifying', u'veteran', u'diverse', u'candidates', u'respond', u'developing', u'workplace', u'reflects', u'diversity', u'communities', u'reviews', u'electrical', u'contracting', u'southwest', u'electrical', u'contractors'], [u'intern', u'electrical', u'engineering', u'idexx', u'laboratories', u'validating', u'idexx', u'integrated', u'hardware', u'entails', u'planning', u'debug', u'validation', u'engineers', u'validation', u'methodologies', u'healthcare', u'platforms', u'brightest', u'solve', u'challenges', u'innovation', u'technology', u'idexx', u'intern', u'idexx', u'interns', u'supplement', u'interns', u'teams', u'roles', u'competitive', u'interns', u'idexx', u'interns', u'participate', u'internships', u'mentors', u'seminars', u'topics', u'leadership', u'workshops', u'relevant', u'planning', u'topics', u'intern', u'presentations', u'mixers', u'applicants', u'ineligible', u'laboratory', u'compliant', u'idexx', u'laboratories', u'healthcare', u'innovation', u'practicing', u'veterinarians', u'diagnostic', u'technology', u'idexx', u'enhance', u'veterinarians', u'efficiency', u'economically', u'idexx', u'worldwide', u'diagnostic', u'tests', u'tests', u'quality', u'headquartered', u'idexx', u'laboratories', u'employs', u'customers', u'qualifications', u'applicants', u'idexx', u'interns', u'potential', u'demonstrated', u'portfolio', u'recommendation', u'resumes', u'marketing', u'location', u'americas', u'verification', u'validation', u'schedule', u'overtime', u'idexx', u'laboratories', u'reviews', u'idexx', u'laboratories', u'nasdaq', u'healthcare', u'innovation', u'practicing', u'veterinarians'], [u'location', u'duration', u'temp', u'verification', u'validation', u'tester', u'verification', u'validation', u'middleware', u'specifically', u'testing', u'applications', u'clinical', u'laboratory', u'regulated', u'environment', u'responsibilities', u'complex', u'hardware', u'testing', u'clinical', u'analyzers', u'laboratory', u'graphical', u'interfaces', u'complex', u'sample', u'sequencing', u'protocols', u'developers', u'correction', u'tracking', u'tool', u'timely', u'troubleshoot', u'testing', u'functional', u'manual', u'automated', u'participate', u'ongoing', u'testing', u'coverage', u'planning', u'documentation', u'testing', u'validation', u'corrections', u'monitor', u'implementation', u'recurrence', u'operating', u'statistical', u'quality', u'testing', u'global', u'multi', u'teams', u'travel', u'skills', u'concepts', u'waterfall', u'agile', u'methodologies', u'debugging', u'skills', u'complex', u'automated', u'instrumentation', u'environment', u'hardware', u'mechanical', u'components', u'tracking', u'lifecycle', u'management', u'quality', u'organize', u'define', u'priorities', u'organize', u'supervision', u'aggressive', u'deadlines', u'ambiguity', u'analyze', u'complex', u'situations', u'concepts', u'technologies', u'verbal', u'skills', u'effectively', u'technical', u'clinical', u'diverse', u'strategy', u'clinical', u'chemistry', u'analyzer', u'laboratory', u'middleware', u'basic', u'automated', u'testing', u'biomedical', u'engineering', u'technologists', u'laboratory', u'technology', u'availability', u'click', u'attach'], [u'scientist', u'linux', u'asrc', u'scientist', u'linux', u'asrc', u'technology', u'solutions', u'subsidiary', u'asrc', u'engineering', u'technology', u'contracts', u'multiple', u'agencies', u'scientists', u'engineers', u'management', u'personnel', u'allows', u'solutions', u'complex', u'aeronautics', u'aviation', u'management', u'aviation', u'engineering', u'hughes', u'technical', u'technical', u'aviation', u'evaluation', u'engineering', u'management', u'technical', u'terminal', u'surveillance', u'programs', u'currently', u'scientist', u'travel', u'responsibilities', u'develops', u'technology', u'modifies', u'technical', u'complex', u'reviews', u'draft', u'conformity', u'completeness', u'testing', u'interface', u'hardware', u'regression', u'impact', u'reliability', u'maintainability', u'factors', u'standardization', u'skills', u'travel', u'programming', u'linux', u'environment', u'cisco', u'knowledge', u'terminal', u'environment', u'clearance', u'clearance', u'input', u'output', u'digital', u'automatic', u'terminal', u'management', u'controller', u'termination', u'testing', u'evaluating', u'policies', u'procedure', u'interface', u'installation', u'verification', u'certification', u'core', u'avionic', u'programs', u'knowledge', u'procedural', u'testing', u'interfacing', u'hardware', u'regression', u'impact', u'reliability', u'maintainability', u'factors', u'standardization', u'missions', u'asrc', u'subsidiaries', u'affirmative', u'employers', u'applicants', u'disability', u'veteran', u'technology', u'location', u'airport', u'bachelor', u'schedule', u'travel', u'contributor', u'management', u'asrc', u'reviews'], [u'technical', u'solarcity', u'niche', u'vegas', u'overview', u'resolving', u'customer', u'clients', u'expanding', u'engineers', u'developers', u'responsibilities', u'knowledge', u'planning', u'adapt', u'dynamic', u'environment', u'inventive', u'creative', u'solarcity', u'lifecycle', u'responsibilities', u'technical', u'analyzing', u'diagnosing', u'troubleshooting', u'customers', u'ticketing', u'console', u'escalate', u'knowledge', u'engineering', u'timely', u'basic', u'phone', u'functionality', u'customer', u'tracking', u'knowledgebase', u'rotation', u'configure', u'deployment', u'sccm', u'technical', u'deployment', u'deploy', u'hardware', u'solarcity', u'bachelor', u'knowledge', u'dell', u'laptops', u'analytical', u'troubleshooting', u'solving', u'skills', u'knowledge', u'databases', u'preferably', u'server', u'preferably', u'monitoring', u'suites', u'documentation', u'procedures', u'knowledge', u'entries', u'verbal', u'skills', u'customer', u'skills', u'competitive', u'solar', u'package', u'insurance', u'vacation', u'savings', u'referral', u'eligibility', u'equity', u'performers', u'solarcity', u'affirmative', u'diversity', u'workplace', u'applicants', u'orientation', u'disability', u'veteran', u'careerrookie'], [u'embedded', u'exelis', u'junction', u'exelis', u'embedded', u'acquisition', u'networking', u'capabilities', u'classified', u'customer', u'motivated', u'develops', u'tests', u'innovative', u'solutions', u'minimal', u'supervision', u'paced', u'environment', u'enjoys', u'assignments', u'interact', u'multi', u'disciplined', u'challenging', u'focused', u'embedded', u'developments', u'spanning', u'engineering', u'lifecycle', u'specification', u'enhancement', u'applications', u'embedded', u'freescale', u'applications', u'android', u'platforms', u'interface', u'customers', u'developers', u'refine', u'specifications', u'architectures', u'java', u'programming', u'scripts', u'python', u'debug', u'debugging', u'emulators', u'regression', u'revisions', u'specialized', u'setups', u'capabilities', u'subversion', u'technical', u'documentation', u'multiple', u'engineering', u'techexpousa', u'reviews'], [u'modeler', u'semantic', u'modeling', u'models', u'skills', u'ontology', u'resource', u'framework', u'schema', u'technologies', u'hadoop', u'warehouse', u'oracle', u'relational', u'artifacts', u'models', u'dictionaries', u'models', u'interface', u'specifications', u'documentation', u'harmonization', u'mappings', u'aligned', u'coordinate', u'technical', u'peer', u'reviews', u'stakeholder', u'communities', u'impact', u'domains', u'relationships', u'interdependencies', u'models', u'define', u'analyze', u'legacy', u'models', u'corporate', u'databases', u'architectural', u'alignment', u'customer', u'expertise', u'harmonization', u'modeling', u'modeling', u'consulting', u'stakeholders', u'quality', u'models', u'storage', u'agile', u'specifically', u'focus', u'modeling', u'qualifications', u'bachelors', u'accredited', u'modeler', u'encompass', u'evaluation', u'skills', u'knowledge', u'modeling', u'techniques', u'resource', u'framework', u'schema', u'technologies', u'unified', u'modeling', u'technologies', u'schemas', u'ontologies', u'sybase', u'knowledge', u'skills', u'interpersonal', u'skills', u'customers', u'clearance', u'applicants', u'eligibility', u'classified', u'clearance', u'polygraph', u'techexpousa', u'solutions', u'partnership', u'solutions', u'integration'], [u'technologies', u'junction', u'develops', u'maintains', u'enhances', u'complex', u'diverse', u'intensive', u'analytics', u'algorithm', u'manipulation', u'management', u'documented', u'individually', u'reviews', u'tests', u'components', u'adherence', u'resolves', u'utilizes', u'methodologies', u'environment', u'input', u'components', u'hardware', u'offs', u'reuse', u'cots', u'gots', u'synthesis', u'components', u'tasks', u'individually', u'analyzes', u'modifies', u'debugs', u'corrects', u'integrates', u'operating', u'environments', u'develops', u'queries', u'databases', u'repositories', u'recommendations', u'improving', u'documentation', u'develops', u'implements', u'algorithms', u'functional', u'assists', u'developing', u'executing', u'procedures', u'components', u'reviews', u'documentation', u'solutions', u'analyzing', u'conferring', u'users', u'engineers', u'analyzing', u'investigating', u'areas', u'adapt', u'hardware', u'mathematical', u'models', u'predict', u'outcome', u'implement', u'complex', u'database', u'repository', u'interfaces', u'queries', u'bachelors', u'accredited', u'substituted', u'bachelors', u'firewalls', u'ipsec', u'vpns', u'technology', u'administering', u'servers', u'apache', u'jboss', u'tomcat', u'developing', u'interfaces', u'firefox', u'internet', u'explorer', u'operating', u'mainframe', u'linux', u'solaris', u'virtual', u'scripting', u'programming', u'oriented', u'programming', u'ajax', u'script', u'procedures', u'cobol', u'cognos', u'fusion', u'focus', u'html', u'java', u'java', u'script', u'jquery', u'perl', u'visual', u'basic', u'powershell', u'cots', u'cots', u'oracle', u'apex', u'integration', u'competitive', u'package', u'bonus', u'corporate', u'equity', u'tuition', u'reimbursement', u'referral', u'bonus', u'holidays', u'insurance', u'flexible', u'disability', u'insurance', u'technologies', u'disability', u'accommodation', u'recruiter', u'techexpousa']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This corpus contains 10 documents. Now lets say we would like to model this with DTM.\n", "To do this we have to define the time steps\n", "each document belongs to. In this case the first 3 documents were collected at the same time, while the last 7 were collected \n", "a month later, and we wish to see how the topics change from month to month.\n", "For this we will define the `time_seq`, which contains the time slice definition." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "time_seq = [3, 7] # first 3 documents are from time slice one \n", "# and the other 7 are from the second time slice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple corpus wrapper to load a premade corpus. You can use this with your own data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "class DTMcorpus(corpora.textcorpus.TextCorpus):\n", "\n", " def get_texts(self):\n", " return self.input\n", "\n", " def __len__(self):\n", " return len(self.input)\n", "\n", "corpus = DTMcorpus(documents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now we have to generate the path to DTM executable, here I have already set an ENV variable for the DTM_HOME" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# path to dtm home folder\n", "dtm_home = os.environ.get('DTM_HOME', \"dtm-master\")\n", "# path to the binary. on my PC the executable file is dtm-master/bin/dtm\n", "dtm_path = os.path.join(dtm_home, 'bin', 'dtm') if dtm_home else None\n", "# you can also copy the path down directly. Change this variable to your DTM executable before running.\n", "dtm_path = \"/home/bhargav/dtm/main\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is basically all we need to be able to invoke the Training. \n", "\n", "If ```initialize_lda=True``` then DTM will create a LDA model first and store it in initial-lda-ss.dat.\n", "If you already have itial-lda-ss.dat in the DTM folder then you can save time and re-use it with ```initialize_lda=False```. If the file is missing then DTM wil exit with an error." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "model = DtmModel(dtm_path, corpus, time_seq, num_topics=2,\n", " id2word=corpus.dictionary, initialize_lda=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything worked we should be able to print out the topics" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "topics = model.show_topic(topicid=1, time=1, num_words=10)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0.023565028919164586, 'skills'),\n", " (0.02308969736545094, 'engineering'),\n", " (0.019616329462533579, 'idexx'),\n", " (0.0194313503731963, 'testing'),\n", " (0.01858957362093603, 'technical'),\n", " (0.017685337300946517, 'electrical'),\n", " (0.017483543705882995, 'management'),\n", " (0.015310984365058886, 'complex'),\n", " (0.014032951915032212, 'knowledge'),\n", " (0.012958700085355939, 'technology')]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Document-Topic proportions " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll attempt to find the Document-Topic proportions. We will use the gamma class variable of the model to do the same. Gamma is a matrix such that gamma[5,10] is the proportion of the 10th topic in document 5.\n", "\n", "To find, say, the topic proportions in Document 1, we do the following:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Distribution of Topic 0 0.562498\n", "Distribution of Topic 1 0.437502\n" ] } ], "source": [ "doc_number = 1\n", "num_topics = 2\n", "\n", "for i in range(0, num_topics):\n", " print (\"Distribution of Topic %d %f\" % (i, model.gamma_[doc_number, i]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DIM Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The DTM wrapper in Gensim also has the capacity to run in Document Influence Model mode. The Model is described in [this](http://www.umiacs.umd.edu/~jbg/nips_tm_workshop/30.pdf) paper. What it allows you to do is find the 'influence' of a certain document on a particular topic. It is primarily used in identifying the scientific impact of research papers through the capability of that document's keywords influencing a topic. \n", "\n", "'Influence' can be naively thought of like this - if more of a particular document's words appear in subsequent evolution of a topic, that document is understood to have influenced that topic more.\n", "\n", "To run it in this mode, we now call `DtmModel` again, but with the `model` parameter set as `fixed`. \n", "\n", "Note that running it in this mode will also generate the DTM topics similar to running plain DTM, but with added information on document influence." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "model = DtmModel(dtm_path, corpus, time_seq, num_topics=2,\n", " id2word=corpus.dictionary, initialize_lda=True, model='fixed')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The main difference between the DTM and DIM models are the addition of Influence files for each time-slice, which is interpreted with the `influences_time` variable. \n", "\n", "To find, say, the influence of Document 2 on Topic 2 in Time-Slice 1, we do the following:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0061833357763878861" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "document_no = 1 #document 2\n", "topic_no = 1 #topic number 2\n", "time_slice = 0 #time slice 1\n", "\n", "model.influences_time[time_slice][document_no][topic_no]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Differences between DTM and DIM mode." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are not too many differences in DTM and DIM apart from the Document Influence information which is generated by running it in DIM mode. The topics generated by both the models are also more or less similar.\n", "\n", "As for running times, with smaller corpuses of less than 2000 documents, time taken for the two models is roughly the same, but for larger corpuses DIM mode takes significantly more time - usually 1.5 or 2 times as how long DTM would take.\n", "\n", "For examples of use-cases of both, the following resources might be helpful:\n", "\n", "[Modeling Musical Influence with Topic Models](http://jmlr.org/proceedings/papers/v28/shalit13.pdf)\n", "\n", "[A Language-based Approach to Measuring Scholarly Impact](https://www.cs.princeton.edu/~blei/papers/GerrishBlei2010.pdf)\n", "\n", "[Studying the history of ideas using topic models](http://web.stanford.edu/~jurafsky/hallemnlp08.pdf)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	25,703	Python	.py	354	68.268362	15,267	0.668034	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,950	doc2vec-lee.ipynb	piskvorky_gensim/docs/notebooks/doc2vec-lee.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,951	gensim Quick Start.ipynb	piskvorky_gensim/docs/notebooks/gensim Quick Start.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,952	topic_coherence-movies.ipynb	piskvorky_gensim/docs/notebooks/topic_coherence-movies.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Benchmark testing of coherence pipeline on Movies dataset\n", "## How to find how well coherence measure matches your manual annotators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Introduction__: For the validation of any model adapted from a paper, it is of utmost importance that the results of benchmark testing on the datasets listed in the paper match between the actual implementation (palmetto) and gensim. This coherence pipeline has been implemented from the work done by Roeder et al. The paper can be found [here](http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf).\n", "\n", "__Approach__ :\n", "1. In this notebook, we'll use the Movies dataset mentioned in the paper. This dataset along with the topics on which the coherence is calculated and the gold (human) ratings on these topics can be found [here](http://139.18.2.164/mroeder/palmetto/datasets/).\n", "2. We will then calculate the coherence on these topics using the pipeline implemented in gensim.\n", "3. Once we have all our coherence values on these topics we will calculate the correlation with the human ratings using pearson's r.\n", "4. We will compare this final correlation value with the values listed in the paper and see if the pipeline is working as expected." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "import re\n", "import os\n", "\n", "from scipy.stats import pearsonr\n", "from datetime import datetime\n", "\n", "from gensim.models import CoherenceModel\n", "from gensim.corpora.dictionary import Dictionary\n", "from smart_open import smart_open" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Download the dataset (`movie.zip`) and gold standard data (`topicsMovie.txt` and `goldMovie.txt`) from the link and plug in the locations below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "base_dir = os.path.join(os.path.expanduser('~'), \"workshop/nlp/data/\")\n", "data_dir = os.path.join(base_dir, 'wiki-movie-subset')\n", "if not os.path.exists(data_dir):\n", " raise ValueError(\"SKIP: Please download the movie corpus.\")\n", "\n", "ref_dir = os.path.join(base_dir, 'reference')\n", "topics_path = os.path.join(ref_dir, 'topicsMovie.txt')\n", "human_scores_path = os.path.join(ref_dir, 'goldMovie.txt')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROGRESS: 10000/125384, preprocessed 9916, discarded 84\n", "PROGRESS: 20000/125384, preprocessed 19734, discarded 266\n", "PROGRESS: 30000/125384, preprocessed 29648, discarded 352\n", "PROGRESS: 50000/125384, preprocessed 37074, discarded 12926\n", "PROGRESS: 60000/125384, preprocessed 47003, discarded 12997\n", "PROGRESS: 70000/125384, preprocessed 56961, discarded 13039\n", "PROGRESS: 80000/125384, preprocessed 66891, discarded 13109\n", "PROGRESS: 90000/125384, preprocessed 76784, discarded 13216\n", "PROGRESS: 100000/125384, preprocessed 86692, discarded 13308\n", "PROGRESS: 110000/125384, preprocessed 96593, discarded 13407\n", "PROGRESS: 120000/125384, preprocessed 106522, discarded 13478\n", "CPU times: user 19.8 s, sys: 9.55 s, total: 29.4 s\n", "Wall time: 44.9 s\n" ] } ], "source": [ "%%time\n", "\n", "texts = []\n", "file_num = 0\n", "preprocessed = 0\n", "listing = os.listdir(data_dir)\n", "\n", "for fname in listing:\n", " file_num += 1\n", " if 'disambiguation' in fname:\n", " continue # discard disambiguation and redirect pages\n", " elif fname.startswith('File_'):\n", " continue # discard images, gifs, etc.\n", " elif fname.startswith('Category_'):\n", " continue # discard category articles\n", " \n", " # Not sure how to identify portal and redirect pages,\n", " # as well as pages about a single year.\n", " # As a result, this preprocessing differs from the paper.\n", " \n", " with smart_open(os.path.join(data_dir, fname), 'rb') as f:\n", " for line in f:\n", " # lower case all words\n", " lowered = line.lower()\n", " #remove punctuation and split into seperate words\n", " words = re.findall(r'\\w+', lowered, flags = re.UNICODE \| re.LOCALE)\n", " texts.append(words)\n", " \n", " preprocessed += 1\n", " if file_num % 10000 == 0:\n", " print('PROGRESS: %d/%d, preprocessed %d, discarded %d' % (\n", " file_num, len(listing), preprocessed, (file_num - preprocessed)))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 26s, sys: 1.1 s, total: 1min 27s\n", "Wall time: 1min 27s\n" ] } ], "source": [ "%%time\n", "\n", "dictionary = Dictionary(texts)\n", "corpus = [dictionary.doc2bow(text) for text in texts]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross validate the numbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to the paper the number of documents should be 108,952 with a vocabulary of 1,625,124. The difference is because of a difference in preprocessing. However the results obtained are still very similar." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "111637\n", "Dictionary(756837 unique tokens: [u'verplank', u'mdbg', u'shatzky', u'duelcity', u'dulcitone']...)\n" ] } ], "source": [ "print(len(corpus))\n", "print(dictionary)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "100" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topics = [] # list of 100 topics\n", "with smart_open(topics_path, 'rb') as f:\n", " topics = [line.split() for line in f if line]\n", "len(topics)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "100" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "human_scores = []\n", "with smart_open(human_scores_path, 'rb') as f:\n", " for line in f:\n", " human_scores.append(float(line.strip()))\n", "len(human_scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deal with any vocabulary mismatch." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Topics with out-of-vocab terms: 72\n" ] } ], "source": [ "# We first need to filter out any topics that contain terms not in our dictionary\n", "# These may occur as a result of preprocessing steps differing from those used to\n", "# produce the reference topics. In this case, this only occurs in one topic.\n", "invalid_topic_indices = set(\n", " i for i, topic in enumerate(topics)\n", " if any(t not in dictionary.token2id for t in topic)\n", ")\n", "print(\"Topics with out-of-vocab terms: %s\" % ', '.join(map(str, invalid_topic_indices)))\n", "usable_topics = [topic for i, topic in enumerate(topics) if i not in invalid_topic_indices]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Start off with u_mass coherence measure." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculated u_mass coherence for 99 topics\n", "CPU times: user 7.22 s, sys: 141 ms, total: 7.36 s\n", "Wall time: 7.38 s\n" ] } ], "source": [ "%%time\n", "\n", "cm = CoherenceModel(topics=usable_topics, corpus=corpus, dictionary=dictionary, coherence='u_mass')\n", "u_mass = cm.get_coherence_per_topic()\n", "print(\"Calculated u_mass coherence for %d topics\" % len(u_mass))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Start c_v coherence measure\n", "This is expected to take much more time since `c_v` uses a sliding window to perform probability estimation and uses the cosine similarity indirect confirmation measure." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculated c_v coherence for 99 topics\n", "CPU times: user 38.5 s, sys: 5.52 s, total: 44 s\n", "Wall time: 13min 8s\n" ] } ], "source": [ "%%time\n", "\n", "cm = CoherenceModel(topics=usable_topics, texts=texts, dictionary=dictionary, coherence='c_v')\n", "c_v = cm.get_coherence_per_topic()\n", "print(\"Calculated c_v coherence for %d topics\" % len(c_v))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Start c_uci and c_npmi coherence measures\n", "c_v and c_uci and c_npmi all use the boolean sliding window approach of estimating probabilities. Since the `CoherenceModel` caches the accumulated statistics, calculation of c_uci and c_npmi are practically free after calculating c_v coherence. These two methods are simpler and were shown to correlate less with human judgements than c_v but more so than u_mass." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculated c_uci coherence for 99 topics\n", "CPU times: user 95 ms, sys: 8.87 ms, total: 104 ms\n", "Wall time: 97.2 ms\n" ] } ], "source": [ "%%time\n", "\n", "cm.coherence = 'c_uci'\n", "c_uci = cm.get_coherence_per_topic()\n", "print(\"Calculated c_uci coherence for %d topics\" % len(c_uci))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculated c_npmi coherence for 99 topics\n", "CPU times: user 192 ms, sys: 6.38 ms, total: 198 ms\n", "Wall time: 194 ms\n" ] } ], "source": [ "%%time\n", "\n", "cm.coherence = 'c_npmi'\n", "c_npmi = cm.get_coherence_per_topic()\n", "print(\"Calculated c_npmi coherence for %d topics\" % len(c_npmi))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "99" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_scores = [\n", " score for i, score in enumerate(human_scores)\n", " if i not in invalid_topic_indices\n", "]\n", "len(final_scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [values in the paper](http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf) were:\n", "\n", "__`u_mass` correlation__ : 0.093\n", "\n", "__`c_v` correlation__ : 0.548\n", "\n", "__`c_uci` correlation__ : 0.473\n", "\n", "__`c_npmi` correlation__ : 0.438\n", "\n", "Our values are also very similar to these values which is good. This validates the correctness of our pipeline, as we can reasonably attribute the differences to differences in preprocessing." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.158529392277\n", "0.530450687702\n", "0.406162050908\n", "0.46002144316\n" ] } ], "source": [ "for our_scores in (u_mass, c_v, c_uci, c_npmi):\n", " print(pearsonr(our_scores, final_scores)[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Where do we go now?\n", "\n", "- The time required for completing all of these operations can be improved a lot by cythonising them.\n", "- Preprocessing can be improved for this notebook by following the exact process mentioned in the reference paper. Specifically: _All corpora as well as the complete Wikipedia used as reference corpus are preprocessed using lemmatization and stop word removal. Additionally, we removed portal and category articles, redirection and disambiguation pages as well as articles about single years._ Note: we tried lemmatizing and found that significantly more of the reference topics had out-of-vocabulary terms." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	14,245	Python	.py	477	25.188679	515	0.59079	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,953	nmf_tutorial.ipynb	piskvorky_gensim/docs/notebooks/nmf_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gensim Tutorial on Online Non-Negative Matrix Factorization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebooks explains basic ideas behind the open source NMF implementation in [Gensim](https://github.com/RaRe-Technologies/gensim), including code examples for applying NMF to text processing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What's in this tutorial?\n", "\n", "1. [Introduction: Why NMF?](#1.-Introduction-to-NMF)\n", "2. [Code example on 20 Newsgroups](#2.-Code-example:-NMF-on-20-Newsgroups)\n", "3. [Benchmarks against Sklearn's NMF and Gensim's LDA](#3.-Benchmarks)\n", "4. [Large-scale NMF training on the English Wikipedia (sparse text vectors)](#4.-NMF-on-English-Wikipedia)\n", "5. [NMF on face decomposition (dense image vectors)](#5.-And-now-for-something-completely-different:-Face-decomposition-from-images)" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "# 1. Introduction to NMF" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "## What's in a name?\n", "\n", "Gensim's Online Non-Negative Matrix Factorization (NMF, NNMF, ONMF) implementation is based on [Renbo Zhao, Vincent Y. F. Tan: Online Nonnegative Matrix Factorization with Outliers, 2016](https://arxiv.org/abs/1604.02634) and is optimized for extremely large, sparse, streamed inputs. Such inputs happen in NLP with unsupervised training on massive text corpora.\n", "\n", "* Why Online? Because corpora and datasets in modern ML can be very large, and RAM is limited. Unlike batch algorithms, online algorithms learn iteratively, streaming through the available training examples, without loading the entire dataset into RAM or requiring random-access to the data examples.\n", "\n", "* Why Non-Negative? Because non-negativity leads to more interpretable, sparse \"human-friendly\" topics. This is in contrast to e.g. SVD (another popular matrix factorization method with [super-efficient implementation in Gensim](https://radimrehurek.com/gensim/models/lsimodel.html)), which produces dense negative factors and thus harder-to-interpret topics.\n", "\n", "* Matrix factorizations are the corner stone of modern machine learning. They can be used either directly (recommendation systems, bi-clustering, image compression, topic modeling…) or as internal routines in more complex deep learning algorithms." ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "## How ONNMF works\n", "\n", "Terminology:\n", "- `corpus` is a stream of input documents = training examples\n", "- `batch` is a chunk of input corpus, a word-document matrix mini-batch that fits in RAM\n", "- `W` is a word-topic matrix (to be learned; stored in the resulting model)\n", "- `h` is a topic-document matrix (to be learned; not stored, but rather inferred for documents on-the-fly)\n", "- `A`, `B` - matrices that accumulate information from consecutive chunks. `A = h.dot(ht)`, `B = v.dot(ht)`.\n", "\n", "The idea behind the algorithm is as follows:\n", "\n", "```\n", " Initialize W, A and B matrices\n", "\n", " for batch in input corpus batches:\n", " infer h:\n", " do coordinate gradient descent step to find h that minimizes \|\|batch - Wh\|\| in L2 norm\n", "\n", " bound h so that it is non-negative\n", "\n", " update A and B:\n", " A = h.dot(ht)\n", " B = batch.dot(ht)\n", "\n", " update W:\n", " do gradient descent step to find W that minimizes \|\|0.5trace(WtWA) - trace(WtB)\|\| in L2 norm\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Code example: NMF on 20 Newsgroups" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's import the models we'll be using throughout this tutorial (`numpy==1.14.2`, `matplotlib==3.0.2`, `pandas==0.24.1`, `sklearn==0.19.1`, `gensim==3.7.1`) and set up logging at INFO level.\n", "\n", "Gensim uses logging generously to inform users what's going on. Eyeballing the logs is a good sanity check, to make sure everything is working as expected.\n", "\n", "Only `numpy` and `gensim` are actually needed to train and use NMF. The other imports are used only to make our life a little easier in this tutorial." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import logging\n", "import time\n", "from contextlib import contextmanager\n", "import os\n", "from multiprocessing import Process\n", "import psutil\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from numpy.random import RandomState\n", "from sklearn import decomposition\n", "from sklearn.cluster import MiniBatchKMeans\n", "from sklearn.datasets import fetch_olivetti_faces\n", "from sklearn.decomposition.nmf import NMF as SklearnNmf\n", "from sklearn.linear_model import LogisticRegressionCV\n", "from sklearn.metrics import f1_score\n", "\n", "import gensim.downloader\n", "from gensim import matutils, utils\n", "from gensim.corpora import Dictionary\n", "from gensim.models import CoherenceModel, LdaModel, TfidfModel, LsiModel\n", "from gensim.models.basemodel import BaseTopicModel\n", "from gensim.models.nmf import Nmf as GensimNmf\n", "from gensim.parsing.preprocessing import preprocess_string\n", "\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset preparation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's load the notorious [20 Newsgroups dataset](http://qwone.com/~jason/20Newsgroups/) from Gensim's [repository of pre-trained models and corpora](https://github.com/RaRe-Technologies/gensim-data):" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "newsgroups = gensim.downloader.load('20-newsgroups')\n", "\n", "categories = [\n", " 'alt.atheism',\n", " 'comp.graphics',\n", " 'rec.motorcycles',\n", " 'talk.politics.mideast',\n", " 'sci.space'\n", "]\n", "\n", "categories = {name: idx for idx, name in enumerate(categories)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a train/test split:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "random_state = RandomState(42)\n", "\n", "trainset = np.array([\n", " {\n", " 'data': doc['data'],\n", " 'target': categories[doc['topic']],\n", " }\n", " for doc in newsgroups\n", " if doc['topic'] in categories and doc['set'] == 'train'\n", "])\n", "random_state.shuffle(trainset)\n", "\n", "testset = np.array([\n", " {\n", " 'data': doc['data'],\n", " 'target': categories[doc['topic']],\n", " }\n", " for doc in newsgroups\n", " if doc['topic'] in categories and doc['set'] == 'test'\n", "])\n", "random_state.shuffle(testset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use very [simple preprocessing with stemming](https://radimrehurek.com/gensim/parsing/preprocessing.html#gensim.parsing.preprocessing.preprocess_string) to tokenize each document. YMMV; in your application, use whatever preprocessing makes sense in your domain. Correctly preparing the input has [major impact](https://en.wikipedia.org/wiki/Garbage_in,_garbage_out) on any subsequent ML training." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "train_documents = [preprocess_string(doc['data']) for doc in trainset]\n", "test_documents = [preprocess_string(doc['data']) for doc in testset]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dictionary compilation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a mapping between tokens and their ids. Another option would be a [HashDictionary](https://radimrehurek.com/gensim/corpora/hashdictionary.html), saving ourselves one pass over the training documents." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 15:57:16,471 : INFO : adding document #0 to Dictionary(0 unique tokens: [])\n", "2019-05-06 15:57:16,781 : INFO : built Dictionary(25279 unique tokens: ['sketch', 'addario', 'foyer', 'labratsat', 'reclaim']...) from 2819 documents (total 435328 corpus positions)\n", "2019-05-06 15:57:16,809 : INFO : discarding 18198 tokens: [('batka', 1), ('batkaj', 1), ('beatl', 1), ('ccmail', 3), ('dayton', 4), ('edu', 1785), ('inhibit', 1), ('jbatka', 1), ('line', 2748), ('organ', 2602)]...\n", "2019-05-06 15:57:16,810 : INFO : keeping 7081 tokens which were in no less than 5 and no more than 1409 (=50.0%) documents\n", "2019-05-06 15:57:16,821 : INFO : resulting dictionary: Dictionary(7081 unique tokens: ['colost', 'choke', 'editor', 'china', 'piss']...)\n" ] } ], "source": [ "dictionary = Dictionary(train_documents)\n", "dictionary.filter_extremes(no_below=5, no_above=0.5, keep_n=20000) # filter out too in/frequent tokens" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create training corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's vectorize the training corpus into the bag-of-words format. We'll train LDA on a BOW and NMFs on an TF-IDF corpus:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "tfidf = TfidfModel(dictionary=dictionary)\n", "\n", "train_corpus = [\n", " dictionary.doc2bow(document)\n", " for document\n", " in train_documents\n", "]\n", "\n", "test_corpus = [\n", " dictionary.doc2bow(document)\n", " for document\n", " in test_documents\n", "]\n", "\n", "train_corpus_tfidf = list(tfidf[train_corpus])\n", "\n", "test_corpus_tfidf = list(tfidf[test_corpus])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we simply stored the bag-of-words vectors into a `list`, but Gensim accepts [any iterable](https://radimrehurek.com/gensim/tut1.html#corpus-streaming-one-document-at-a-time) as input, including streamed ones. To learn more about memory-efficient input iterables, see our [Data Streaming in Python: Generators, Iterators, Iterables](https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/) tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NMF Model Training\n", "\n", "The API works in the same way as other Gensim models, such as [LdaModel](https://radimrehurek.com/gensim/models/ldamodel.html) or [LsiModel](https://radimrehurek.com/gensim/models/lsimodel.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notable model parameters:\n", "\n", "- `kappa` float, optional\n", "\n", " Gradient descent step size.\n", " Larger value makes the model train faster, but could lead to non-convergence if set too large.\n", " \n", "- `w_max_iter` int, optional\n", "\n", " Maximum number of iterations to train W per each batch.\n", " \n", "- `w_stop_condition` float, optional\n", "\n", " If the error difference gets smaller than this, training of ``W`` stops for the current batch.\n", " \n", "- `h_r_max_iter` int, optional\n", "\n", " Maximum number of iterations to train h per each batch.\n", " \n", "- `h_r_stop_condition` float, optional\n", "\n", " If the error difference gets smaller than this, training of ``h`` stops for the current batch." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learn an NMF model with 5 topics:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 15:57:18,565 : INFO : running NMF training, 5 topics, 5 passes over the supplied corpus of 2819 documents, evaluating l2 norm every 2819 documents\n", "2019-05-06 15:57:18,581 : INFO : PROGRESS: pass 0, at document #1000/2819\n", "2019-05-06 15:57:18,604 : INFO : W error: -11.552583824232896\n", "2019-05-06 15:57:18,619 : INFO : PROGRESS: pass 0, at document #2000/2819\n", "2019-05-06 15:57:18,627 : INFO : W error: -13.74803744073488\n", "2019-05-06 15:57:18,639 : INFO : PROGRESS: pass 0, at document #2819/2819\n", "2019-05-06 15:57:18,735 : INFO : L2 norm: 28.141396382337142\n", "2019-05-06 15:57:18,770 : INFO : topic #0 (0.395): 0.011\"isra\" + 0.010\"israel\" + 0.006\"arab\" + 0.006\"jew\" + 0.004\"palestinian\" + 0.004\"henri\" + 0.003\"toronto\" + 0.003\"question\" + 0.003\"kill\" + 0.003\"hernlem\"\n", "2019-05-06 15:57:18,771 : INFO : topic #1 (0.352): 0.008\"space\" + 0.005\"access\" + 0.005\"nasa\" + 0.004\"pat\" + 0.003\"digex\" + 0.003\"orbit\" + 0.003\"shuttl\" + 0.003\"data\" + 0.003\"graphic\" + 0.003\"com\"\n", "2019-05-06 15:57:18,772 : INFO : topic #2 (0.378): 0.012\"armenian\" + 0.006\"turkish\" + 0.004\"greek\" + 0.004\"peopl\" + 0.004\"armenia\" + 0.004\"turk\" + 0.004\"argic\" + 0.004\"bike\" + 0.003\"serdar\" + 0.003\"turkei\"\n", "2019-05-06 15:57:18,772 : INFO : topic #3 (0.412): 0.010\"moral\" + 0.006\"keith\" + 0.004\"anim\" + 0.003\"jake\" + 0.003\"boni\" + 0.003\"instinct\" + 0.003\"act\" + 0.003\"think\" + 0.003\"object\" + 0.003\"caltech\"\n", "2019-05-06 15:57:18,773 : INFO : topic #4 (0.428): 0.009\"islam\" + 0.008\"god\" + 0.006\"livesei\" + 0.006\"muslim\" + 0.005\"imag\" + 0.005\"sgi\" + 0.005\"jaeger\" + 0.004\"jon\" + 0.004\"solntz\" + 0.004\"wpd\"\n", "2019-05-06 15:57:18,776 : INFO : W error: -14.346700852834074\n", "2019-05-06 15:57:18,791 : INFO : PROGRESS: pass 1, at document #1000/2819\n", "2019-05-06 15:57:18,797 : INFO : W error: -15.92349117004869\n", "2019-05-06 15:57:18,811 : INFO : PROGRESS: pass 1, at document #2000/2819\n", "2019-05-06 15:57:18,816 : INFO : W error: -17.04437515427273\n", "2019-05-06 15:57:18,829 : INFO : PROGRESS: pass 1, at document #2819/2819\n", "2019-05-06 15:57:18,922 : INFO : L2 norm: 28.021861174968205\n", "2019-05-06 15:57:18,956 : INFO : topic #0 (0.341): 0.014\"israel\" + 0.013\"isra\" + 0.008\"arab\" + 0.007\"jew\" + 0.005\"palestinian\" + 0.004\"lebanes\" + 0.003\"peac\" + 0.003\"henri\" + 0.003\"attack\" + 0.003\"polici\"\n", "2019-05-06 15:57:18,956 : INFO : topic #1 (0.252): 0.008\"space\" + 0.005\"nasa\" + 0.004\"access\" + 0.003\"pat\" + 0.003\"orbit\" + 0.003\"digex\" + 0.003\"launch\" + 0.003\"shuttl\" + 0.003\"graphic\" + 0.003\"com\"\n", "2019-05-06 15:57:18,957 : INFO : topic #2 (0.295): 0.020\"armenian\" + 0.010\"turkish\" + 0.006\"armenia\" + 0.006\"turk\" + 0.006\"argic\" + 0.006\"serdar\" + 0.005\"greek\" + 0.005\"turkei\" + 0.004\"peopl\" + 0.004\"genocid\"\n", "2019-05-06 15:57:18,958 : INFO : topic #3 (0.345): 0.013\"moral\" + 0.011\"keith\" + 0.006\"object\" + 0.005\"caltech\" + 0.004\"schneider\" + 0.004\"anim\" + 0.004\"jake\" + 0.004\"allan\" + 0.004\"boni\" + 0.004\"cco\"\n", "2019-05-06 15:57:18,959 : INFO : topic #4 (0.375): 0.011\"islam\" + 0.011\"god\" + 0.006\"livesei\" + 0.006\"sgi\" + 0.006\"jaeger\" + 0.005\"muslim\" + 0.005\"jon\" + 0.005\"imag\" + 0.005\"religion\" + 0.005\"solntz\"\n", "2019-05-06 15:57:18,961 : INFO : W error: -17.08829704968913\n", "2019-05-06 15:57:18,975 : INFO : PROGRESS: pass 2, at document #1000/2819\n", "2019-05-06 15:57:18,981 : INFO : W error: -17.73961065930116\n", "2019-05-06 15:57:18,996 : INFO : PROGRESS: pass 2, at document #2000/2819\n", "2019-05-06 15:57:19,001 : INFO : W error: -18.289085863153712\n", "2019-05-06 15:57:19,013 : INFO : PROGRESS: pass 2, at document #2819/2819\n", "2019-05-06 15:57:19,107 : INFO : L2 norm: 28.001900396967052\n", "2019-05-06 15:57:19,141 : INFO : topic #0 (0.341): 0.014\"israel\" + 0.014\"isra\" + 0.009\"arab\" + 0.008\"jew\" + 0.005\"palestinian\" + 0.004\"lebanes\" + 0.004\"peac\" + 0.003\"attack\" + 0.003\"polici\" + 0.003\"lebanon\"\n", "2019-05-06 15:57:19,142 : INFO : topic #1 (0.230): 0.007\"space\" + 0.005\"nasa\" + 0.004\"access\" + 0.003\"orbit\" + 0.003\"pat\" + 0.003\"launch\" + 0.003\"digex\" + 0.003\"gov\" + 0.003\"com\" + 0.003\"graphic\"\n", "2019-05-06 15:57:19,142 : INFO : topic #2 (0.284): 0.021\"armenian\" + 0.011\"turkish\" + 0.007\"armenia\" + 0.007\"turk\" + 0.007\"argic\" + 0.006\"serdar\" + 0.006\"turkei\" + 0.005\"greek\" + 0.005\"genocid\" + 0.004\"soviet\"\n", "2019-05-06 15:57:19,143 : INFO : topic #3 (0.346): 0.015\"moral\" + 0.013\"keith\" + 0.006\"object\" + 0.006\"caltech\" + 0.005\"schneider\" + 0.005\"allan\" + 0.005\"cco\" + 0.004\"anim\" + 0.004\"jake\" + 0.003\"boni\"\n", "2019-05-06 15:57:19,144 : INFO : topic #4 (0.365): 0.012\"islam\" + 0.011\"god\" + 0.006\"livesei\" + 0.006\"sgi\" + 0.006\"jaeger\" + 0.005\"muslim\" + 0.005\"jon\" + 0.005\"religion\" + 0.004\"solntz\" + 0.004\"wpd\"\n", "2019-05-06 15:57:19,146 : INFO : W error: -18.220202313431095\n", "2019-05-06 15:57:19,160 : INFO : PROGRESS: pass 3, at document #1000/2819\n", "2019-05-06 15:57:19,165 : INFO : W error: -18.590446221955172\n", "2019-05-06 15:57:19,180 : INFO : PROGRESS: pass 3, at document #2000/2819\n", "2019-05-06 15:57:19,185 : INFO : W error: -18.936998738726114\n", "2019-05-06 15:57:19,197 : INFO : PROGRESS: pass 3, at document #2819/2819\n", "2019-05-06 15:57:19,291 : INFO : L2 norm: 27.993018072469805\n", "2019-05-06 15:57:19,324 : INFO : topic #0 (0.348): 0.015\"israel\" + 0.014\"isra\" + 0.009\"arab\" + 0.008\"jew\" + 0.005\"palestinian\" + 0.004\"lebanes\" + 0.004\"peac\" + 0.003\"attack\" + 0.003\"lebanon\" + 0.003\"polici\"\n", "2019-05-06 15:57:19,325 : INFO : topic #1 (0.221): 0.007\"space\" + 0.005\"nasa\" + 0.003\"access\" + 0.003\"orbit\" + 0.003\"pat\" + 0.003\"launch\" + 0.003\"gov\" + 0.003\"digex\" + 0.003\"com\" + 0.002\"graphic\"\n", "2019-05-06 15:57:19,325 : INFO : topic #2 (0.281): 0.022\"armenian\" + 0.011\"turkish\" + 0.007\"armenia\" + 0.007\"turk\" + 0.007\"argic\" + 0.007\"serdar\" + 0.006\"turkei\" + 0.005\"greek\" + 0.005\"genocid\" + 0.005\"soviet\"\n", "2019-05-06 15:57:19,326 : INFO : topic #3 (0.349): 0.016\"moral\" + 0.014\"keith\" + 0.007\"object\" + 0.006\"caltech\" + 0.006\"schneider\" + 0.005\"allan\" + 0.005\"cco\" + 0.004\"anim\" + 0.004\"natur\" + 0.003\"think\"\n", "2019-05-06 15:57:19,327 : INFO : topic #4 (0.365): 0.012\"islam\" + 0.012\"god\" + 0.006\"sgi\" + 0.006\"livesei\" + 0.006\"jaeger\" + 0.005\"muslim\" + 0.005\"religion\" + 0.005\"atheist\" + 0.005\"jon\" + 0.005\"atheism\"\n", "2019-05-06 15:57:19,328 : INFO : W error: -18.84541532652092\n", "2019-05-06 15:57:19,342 : INFO : PROGRESS: pass 4, at document #1000/2819\n", "2019-05-06 15:57:19,347 : INFO : W error: -19.091058241700402\n", "2019-05-06 15:57:19,362 : INFO : PROGRESS: pass 4, at document #2000/2819\n", "2019-05-06 15:57:19,367 : INFO : W error: -19.338453391122066\n", "2019-05-06 15:57:19,378 : INFO : PROGRESS: pass 4, at document #2819/2819\n", "2019-05-06 15:57:19,473 : INFO : L2 norm: 27.988158841345246\n", "2019-05-06 15:57:19,506 : INFO : topic #0 (0.352): 0.015\"israel\" + 0.014\"isra\" + 0.009\"arab\" + 0.008\"jew\" + 0.005\"palestinian\" + 0.004\"lebanes\" + 0.004\"peac\" + 0.003\"attack\" + 0.003\"lebanon\" + 0.003\"polici\"\n", "2019-05-06 15:57:19,507 : INFO : topic #1 (0.210): 0.007\"space\" + 0.005\"nasa\" + 0.003\"access\" + 0.003\"orbit\" + 0.003\"launch\" + 0.003\"pat\" + 0.003\"gov\" + 0.003\"com\" + 0.002\"alaska\" + 0.002\"graphic\"\n", "2019-05-06 15:57:19,508 : INFO : topic #2 (0.282): 0.023\"armenian\" + 0.011\"turkish\" + 0.008\"armenia\" + 0.007\"argic\" + 0.007\"turk\" + 0.007\"serdar\" + 0.006\"turkei\" + 0.005\"greek\" + 0.005\"genocid\" + 0.005\"soviet\"\n", "2019-05-06 15:57:19,509 : INFO : topic #3 (0.353): 0.016\"moral\" + 0.015\"keith\" + 0.007\"object\" + 0.007\"caltech\" + 0.006\"schneider\" + 0.005\"allan\" + 0.005\"cco\" + 0.004\"anim\" + 0.004\"natur\" + 0.004\"goal\"\n", "2019-05-06 15:57:19,509 : INFO : topic #4 (0.367): 0.012\"god\" + 0.012\"islam\" + 0.006\"jaeger\" + 0.006\"sgi\" + 0.006\"livesei\" + 0.005\"muslim\" + 0.005\"atheist\" + 0.005\"religion\" + 0.005\"atheism\" + 0.004\"jon\"\n", "2019-05-06 15:57:19,511 : INFO : W error: -19.245120389312117\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.52 s, sys: 1.84 s, total: 3.36 s\n", "Wall time: 947 ms\n" ] } ], "source": [ "%%time\n", "\n", "nmf = GensimNmf(\n", " corpus=train_corpus_tfidf,\n", " num_topics=5,\n", " id2word=dictionary,\n", " chunksize=1000,\n", " passes=5,\n", " eval_every=10,\n", " minimum_probability=0,\n", " random_state=0,\n", " kappa=1,\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "W = nmf.get_topics().T\n", "\n", "dense_test_corpus = matutils.corpus2dense(\n", " test_corpus_tfidf,\n", " num_terms=W.shape[0],\n", ")\n", "\n", "if isinstance(nmf, SklearnNmf):\n", " H = nmf.transform(dense_test_corpus.T).T\n", "else:\n", " H = np.zeros((nmf.num_topics, len(test_corpus_tfidf)))\n", " for bow_id, bow in enumerate(test_corpus_tfidf):\n", " for topic_id, word_count in nmf[bow]:\n", " H[topic_id, bow_id] = word_count" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.105176733465657" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linalg.norm(W.dot(H))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "43.312817" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linalg.norm(dense_test_corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### View the learned topics" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0,\n", " '0.015\"israel\" + 0.014\"isra\" + 0.009\"arab\" + 0.008\"jew\" + 0.005\"palestinian\" + 0.004\"lebanes\" + 0.004\"peac\" + 0.004\"attack\" + 0.004\"lebanon\" + 0.003\"polici\"'),\n", " (1,\n", " '0.007\"space\" + 0.005\"nasa\" + 0.003\"access\" + 0.003\"orbit\" + 0.003\"launch\" + 0.003\"pat\" + 0.003\"gov\" + 0.003\"com\" + 0.002\"alaska\" + 0.002\"moon\"'),\n", " (2,\n", " '0.023\"armenian\" + 0.011\"turkish\" + 0.008\"armenia\" + 0.007\"argic\" + 0.007\"turk\" + 0.007\"serdar\" + 0.006\"turkei\" + 0.005\"greek\" + 0.005\"genocid\" + 0.005\"soviet\"'),\n", " (3,\n", " '0.016\"moral\" + 0.015\"keith\" + 0.007\"object\" + 0.007\"caltech\" + 0.006\"schneider\" + 0.005\"allan\" + 0.005\"cco\" + 0.004\"anim\" + 0.004\"natur\" + 0.004\"goal\"'),\n", " (4,\n", " '0.012\"god\" + 0.012\"islam\" + 0.006\"jaeger\" + 0.006\"sgi\" + 0.005\"livesei\" + 0.005\"muslim\" + 0.005\"atheist\" + 0.005\"religion\" + 0.005\"atheism\" + 0.004\"rushdi\"')]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nmf.show_topics()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation measure: Coherence\n", "\n", "[Topic coherence](http://qpleple.com/topic-coherence-to-evaluate-topic-models/) measures how often do most frequent tokens from each topic co-occur in one document. Larger is better." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 15:57:20,590 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n" ] }, { "data": { "text/plain": [ "-4.1310114675795875" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CoherenceModel(\n", " model=nmf,\n", " corpus=test_corpus_tfidf,\n", " coherence='u_mass'\n", ").get_coherence()" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "## Topic inference on new documents\n", "\n", "With the NMF model trained, let's fetch one news document not seen during training, and infer its topic vector." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From: spl@ivem.ucsd.edu (Steve Lamont)\n", "Subject: Re: RGB to HVS, and back\n", "Organization: University of Calif., San Diego/Microscopy and Imaging Resource\n", "Lines: 18\n", "Distribution: world\n", "NNTP-Posting-Host: ivem.ucsd.edu\n", "\n", "In article <ltu4buINNe7j@caspian.usc.edu> zyeh@caspian.usc.edu (zhenghao yeh) writes:\n", ">\|> See Foley, van Dam, Feiner, and Hughes, _Computer Graphics: Principles\n", ">\|> and Practice, Second Edition_.\n", ">\|> \n", ">\|> [If people would read* this book, 75 percent of the questions in this\n", ">\|> froup would disappear overnight...]\n", ">\|> \n", ">\tNot really. I think it is less than 10%.\n", "\n", "Nah... I figure most people would be so busy reading that they wouldn't\n", "have time to post. :-) :-) :-)\n", "\n", "\t\t\t\t\t\t\tspl\n", "-- \n", "Steve Lamont, SciViGuy -- (619) 534-7968 -- spl@szechuan.ucsd.edu\n", "San Diego Microscopy and Imaging Resource/UC San Diego/La Jolla, CA 92093-0608\n", "\"Until I meet you, then, in Upper Hell\n", "Convulsed, foaming immortal blood: farewell\" - J. Berryman, \"A Professor's Song\"\n", "\n", "====================================================================================================\n", "Topics: [(0, 0.10199317206513686), (1, 0.39976628221371285), (2, 0.1428263926167706), (3, 0.0333080734922002), (4, 0.3221060796121796)]\n" ] } ], "source": [ "print(testset[0]['data'])\n", "print('=' * 100)\n", "print(\"Topics: {}\".format(nmf[test_corpus[0]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Word topic inference\n", "\n", "Similarly, we can inspect the topic distribution assigned to a vocabulary term:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Word: actual\n", "Topics: [(0, 0.15401782844659068), (1, 0.2829834256007429), (2, 0.04354905106817273), (3, 0.25783766798021135), (4, 0.26161202690428226)]\n" ] } ], "source": [ "word = dictionary[0]\n", "print(\"Word: {}\".format(word))\n", "print(\"Topics: {}\".format(nmf.get_term_topics(word)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Internal NMF state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Density is a fraction of non-zero elements in a matrix." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "def density(matrix):\n", " return (matrix > 0).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Term-topic matrix of shape `(words, topics)`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Density: 0.6872475639034035\n" ] } ], "source": [ "print(\"Density: {}\".format(density(nmf._W)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Topic-document matrix for the last batch of shape `(topics, batch)`" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Density: 0.6615384615384615\n" ] } ], "source": [ "print(\"Density: {}\".format(density(nmf._h)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Benchmarks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gensim NMF vs Sklearn NMF vs Gensim LDA\n", "\n", "We'll run these three unsupervised models on the [20newsgroups](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html) dataset.\n", "\n", "20 Newsgroups also contains labels for each document, which will allow us to evaluate the trained models on an \"upstream\" classification task, using the unsupervised document topics as input features." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Metrics\n", "\n", "We'll track these metrics as we train and test NMF on the 20-newsgroups corpus we created above:\n", "- `train time` - time to train a model\n", "- `mean_ram` - mean RAM consumption during training\n", "- `max_ram` - maximum RAM consumption during training\n", "- `train time` - time to train a model.\n", "- `coherence` - coherence score (larger is better).\n", "- `l2_norm` - L2 norm of `v - Wh` (less is better, not defined for LDA).\n", "- `f1` - [F1 score](https://en.wikipedia.org/wiki/F1_score) on the task of news topic classification (larger is better)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "fixed_params = dict(\n", " chunksize=1000,\n", " num_topics=5,\n", " id2word=dictionary,\n", " passes=5,\n", " eval_every=10,\n", " minimum_probability=0,\n", " random_state=0,\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "@contextmanager\n", "def measure_ram(output, tick=5):\n", " def _measure_ram(pid, output, tick=tick):\n", " py = psutil.Process(pid)\n", " with open(output, 'w') as outfile:\n", " while True:\n", " memory = py.memory_info().rss\n", " outfile.write(\"{}\\n\".format(memory))\n", " outfile.flush()\n", " time.sleep(tick)\n", "\n", " pid = os.getpid()\n", " p = Process(target=_measure_ram, args=(pid, output, tick))\n", " p.start()\n", " yield\n", " p.terminate()\n", "\n", "\n", "def get_train_time_and_ram(func, name, tick=5):\n", " memprof_filename = \"{}.memprof\".format(name)\n", "\n", " start = time.time()\n", "\n", " with measure_ram(memprof_filename, tick=tick):\n", " result = func()\n", "\n", " elapsed_time = pd.to_timedelta(time.time() - start, unit='s').round('ms')\n", "\n", " memprof_df = pd.read_csv(memprof_filename, squeeze=True)\n", "\n", " mean_ram = \"{} MB\".format(\n", " int(memprof_df.mean() // 2 20),\n", " )\n", "\n", " max_ram = \"{} MB\".format(int(memprof_df.max() // 2 20))\n", "\n", " return elapsed_time, mean_ram, max_ram, result\n", "\n", "\n", "def get_f1(model, train_corpus, X_test, y_train, y_test):\n", " if isinstance(model, SklearnNmf):\n", " dense_train_corpus = matutils.corpus2dense(\n", " train_corpus,\n", " num_terms=model.components_.shape[1],\n", " )\n", " X_train = model.transform(dense_train_corpus.T)\n", " else:\n", " X_train = np.zeros((len(train_corpus), model.num_topics))\n", " for bow_id, bow in enumerate(train_corpus):\n", " for topic_id, word_count in model[bow]:\n", " X_train[bow_id, topic_id] = word_count\n", "\n", " log_reg = LogisticRegressionCV(multi_class='multinomial', cv=5)\n", " log_reg.fit(X_train, y_train)\n", "\n", " pred_labels = log_reg.predict(X_test)\n", "\n", " return f1_score(y_test, pred_labels, average='micro')\n", "\n", "def get_sklearn_topics(model, top_n=5):\n", " topic_probas = model.components_.T\n", " topic_probas = topic_probas / topic_probas.sum(axis=0)\n", "\n", " sparsity = np.zeros(topic_probas.shape[1])\n", "\n", " for row in topic_probas:\n", " sparsity += (row == 0)\n", "\n", " sparsity /= topic_probas.shape[1]\n", "\n", " topic_probas = topic_probas[:, sparsity.argsort()[::-1]][:, :top_n]\n", "\n", " token_indices = topic_probas.argsort(axis=0)[:-11:-1, :]\n", " topic_probas.sort(axis=0)\n", " topic_probas = topic_probas[:-11:-1, :]\n", "\n", " topics = []\n", "\n", " for topic_idx in range(topic_probas.shape[1]):\n", " tokens = [\n", " model.id2word[token_idx]\n", " for token_idx\n", " in token_indices[:, topic_idx]\n", " ]\n", " topic = (\n", " '{}\"{}\"'.format(round(proba, 3), token)\n", " for proba, token\n", " in zip(topic_probas[:, topic_idx], tokens)\n", " )\n", " topic = \" + \".join(topic)\n", " topics.append((topic_idx, topic))\n", "\n", " return topics\n", "\n", "def get_metrics(model, test_corpus, train_corpus=None, y_train=None, y_test=None, dictionary=None):\n", " if isinstance(model, SklearnNmf):\n", " model.get_topics = lambda: model.components_\n", " model.show_topics = lambda top_n: get_sklearn_topics(model, top_n)\n", " model.id2word = dictionary\n", "\n", " W = model.get_topics().T\n", "\n", " dense_test_corpus = matutils.corpus2dense(\n", " test_corpus,\n", " num_terms=W.shape[0],\n", " )\n", "\n", " if isinstance(model, SklearnNmf):\n", " H = model.transform(dense_test_corpus.T).T\n", " else:\n", " H = np.zeros((model.num_topics, len(test_corpus)))\n", " for bow_id, bow in enumerate(test_corpus):\n", " for topic_id, word_count in model[bow]:\n", " H[topic_id, bow_id] = word_count\n", "\n", " l2_norm = None\n", "\n", " if not isinstance(model, LdaModel):\n", " pred_factors = W.dot(H)\n", "\n", " l2_norm = np.linalg.norm(pred_factors - dense_test_corpus)\n", " l2_norm = round(l2_norm, 4)\n", "\n", " f1 = None\n", "\n", " if train_corpus and y_train and y_test:\n", " f1 = get_f1(model, train_corpus, H.T, y_train, y_test)\n", " f1 = round(f1, 4)\n", "\n", " model.normalize = True\n", "\n", " coherence = CoherenceModel(\n", " model=model,\n", " corpus=test_corpus,\n", " coherence='u_mass'\n", " ).get_coherence()\n", " coherence = round(coherence, 4)\n", "\n", " topics = model.show_topics(5)\n", "\n", " model.normalize = False\n", "\n", " return dict(\n", " coherence=coherence,\n", " l2_norm=l2_norm,\n", " f1=f1,\n", " topics=topics,\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run the models" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "tm_metrics = pd.DataFrame(columns=['model', 'train_time', 'coherence', 'l2_norm', 'f1', 'topics'])\n", "\n", "y_train = [doc['target'] for doc in trainset]\n", "y_test = [doc['target'] for doc in testset]\n", "\n", "# LDA metrics\n", "row = {}\n", "row['model'] = 'lda'\n", "row['train_time'], row['mean_ram'], row['max_ram'], lda = get_train_time_and_ram(\n", " lambda: LdaModel(\n", " corpus=train_corpus,\n", " fixed_params,\n", " ),\n", " 'lda',\n", " 0.1,\n", ")\n", "row.update(get_metrics(\n", " lda, test_corpus, train_corpus, y_train, y_test,\n", "))\n", "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)\n", "\n", "# LSI metrics\n", "row = {}\n", "row['model'] = 'lsi'\n", "row['train_time'], row['mean_ram'], row['max_ram'], lsi = get_train_time_and_ram(\n", " lambda: LsiModel(\n", " corpus=train_corpus_tfidf,\n", " num_topics=5,\n", " id2word=dictionary,\n", " chunksize=2000,\n", " ),\n", " 'lsi',\n", " 0.1,\n", ")\n", "row.update(get_metrics(\n", " lsi, test_corpus_tfidf, train_corpus_tfidf, y_train, y_test,\n", "))\n", "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)\n", "\n", "# Sklearn NMF metrics\n", "row = {}\n", "row['model'] = 'sklearn_nmf'\n", "train_csc_corpus_tfidf = matutils.corpus2csc(train_corpus_tfidf, len(dictionary)).T\n", "row['train_time'], row['mean_ram'], row['max_ram'], sklearn_nmf = get_train_time_and_ram(\n", " lambda: SklearnNmf(n_components=5, random_state=42).fit(train_csc_corpus_tfidf),\n", " 'sklearn_nmf',\n", " 0.1,\n", ")\n", "row.update(get_metrics(\n", " sklearn_nmf, test_corpus_tfidf, train_corpus_tfidf, y_train, y_test, dictionary,\n", "))\n", "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)\n", "\n", "# Gensim NMF metrics\n", "row = {}\n", "row['model'] = 'gensim_nmf'\n", "row['train_time'], row['mean_ram'], row['max_ram'], gensim_nmf = get_train_time_and_ram(\n", " lambda: GensimNmf(\n", " normalize=False,\n", " corpus=train_corpus_tfidf,\n", " fixed_params\n", " ),\n", " 'gensim_nmf',\n", " 0.1,\n", ")\n", "row.update(get_metrics(\n", " gensim_nmf, test_corpus_tfidf, train_corpus_tfidf, y_train, y_test,\n", "))\n", "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)\n", "tm_metrics.replace(np.nan, '-', inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmark results" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " <th>train_time</th>\n", " <th>coherence</th>\n", " <th>l2_norm</th>\n", " <th>f1</th>\n", " <th>max_ram</th>\n", " <th>mean_ram</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>lda</td>\n", " <td>00:00:08.862000</td>\n", " <td>-2.1054</td>\n", " <td>-</td>\n", " <td>0.7511</td>\n", " <td>366 MB</td>\n", " <td>366 MB</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>lsi</td>\n", " <td>00:00:00.332000</td>\n", " <td>-5.7010</td>\n", " <td>42.4642</td>\n", " <td>0.8587</td>\n", " <td>381 MB</td>\n", " <td>379 MB</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>sklearn_nmf</td>\n", " <td>00:00:00.166000</td>\n", " <td>-3.1835</td>\n", " <td>42.4759</td>\n", " <td>0.7889</td>\n", " <td>378 MB</td>\n", " <td>378 MB</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>gensim_nmf</td>\n", " <td>00:00:00.954000</td>\n", " <td>-4.1310</td>\n", " <td>42.5487</td>\n", " <td>0.8065</td>\n", " <td>379 MB</td>\n", " <td>379 MB</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model train_time coherence l2_norm f1 max_ram mean_ram\n", "0 lda 00:00:08.862000 -2.1054 - 0.7511 366 MB 366 MB\n", "1 lsi 00:00:00.332000 -5.7010 42.4642 0.8587 381 MB 379 MB\n", "2 sklearn_nmf 00:00:00.166000 -3.1835 42.4759 0.7889 378 MB 378 MB\n", "3 gensim_nmf 00:00:00.954000 -4.1310 42.5487 0.8065 379 MB 379 MB" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tm_metrics.drop('topics', axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Main insights\n", "\n", "- LDA has the best coherence of all models.\n", "- LSI has the best l2 norm and f1 performance on downstream task (it's factors aren't non-negative though).\n", "- Gensim NMF, Sklearn NMF and LSI has a bit larger memory footprint than that of LDA.\n", "- Gensim NMF, Sklearn NMF and LSI are much faster than LDA." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Learned topics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's inspect the 5 topics learned by each of the three models:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lda:\n", "(0, '0.013\"space\" + 0.008\"imag\" + 0.007\"nasa\" + 0.006\"graphic\" + 0.006\"program\" + 0.005\"launch\" + 0.005\"file\" + 0.005\"com\" + 0.005\"new\" + 0.004\"orbit\"')\n", "(1, '0.015\"com\" + 0.007\"like\" + 0.007\"nntp\" + 0.007\"host\" + 0.006\"know\" + 0.006\"univers\" + 0.005\"henri\" + 0.005\"work\" + 0.005\"bit\" + 0.005\"think\"')\n", "(2, '0.014\"armenian\" + 0.011\"peopl\" + 0.009\"turkish\" + 0.007\"jew\" + 0.007\"said\" + 0.006\"right\" + 0.005\"know\" + 0.005\"kill\" + 0.005\"isra\" + 0.005\"turkei\"')\n", "(3, '0.012\"com\" + 0.010\"israel\" + 0.009\"bike\" + 0.006\"isra\" + 0.006\"dod\" + 0.005\"like\" + 0.005\"ride\" + 0.005\"host\" + 0.005\"nntp\" + 0.005\"motorcycl\"')\n", "(4, '0.011\"god\" + 0.008\"peopl\" + 0.007\"think\" + 0.006\"exist\" + 0.006\"univers\" + 0.005\"com\" + 0.005\"believ\" + 0.005\"islam\" + 0.005\"moral\" + 0.005\"christian\"')\n", "\n", "lsi:\n", "(0, '0.157\"armenian\" + 0.118\"israel\" + 0.110\"peopl\" + 0.110\"isra\" + 0.105\"space\" + 0.098\"com\" + 0.097\"god\" + 0.084\"jew\" + 0.082\"think\" + 0.081\"turkish\"')\n", "(1, '-0.476\"armenian\" + -0.231\"turkish\" + -0.157\"armenia\" + -0.151\"argic\" + -0.149\"serdar\" + -0.145\"turk\" + 0.128\"space\" + -0.117\"turkei\" + -0.111\"genocid\" + 0.107\"nasa\"')\n", "(2, '0.295\"israel\" + -0.291\"armenian\" + 0.273\"isra\" + 0.182\"arab\" + 0.157\"jew\" + -0.143\"space\" + -0.134\"turkish\" + -0.112\"nasa\" + 0.106\"jake\" + 0.103\"boni\"')\n", "(3, '0.274\"keith\" + 0.252\"moral\" + -0.235\"israel\" + 0.213\"god\" + -0.213\"isra\" + 0.165\"livesei\" + -0.143\"arab\" + 0.123\"sgi\" + 0.118\"caltech\" + 0.114\"islam\"')\n", "(4, '0.240\"henri\" + -0.215\"bike\" + 0.210\"space\" + 0.167\"toronto\" + 0.158\"nasa\" + 0.148\"moral\" + 0.143\"keith\" + -0.142\"graphic\" + 0.128\"alaska\" + 0.125\"orbit\"')\n", "\n", "sklearn_nmf:\n", "(0, '0.027\"armenian\" + 0.013\"turkish\" + 0.009\"armenia\" + 0.009\"argic\" + 0.009\"serdar\" + 0.008\"turk\" + 0.007\"turkei\" + 0.006\"genocid\" + 0.006\"soviet\" + 0.006\"zuma\"')\n", "(1, '0.015\"israel\" + 0.014\"isra\" + 0.01\"arab\" + 0.008\"jew\" + 0.005\"palestinian\" + 0.005\"jake\" + 0.005\"boni\" + 0.004\"lebanes\" + 0.004\"peac\" + 0.004\"adam\"')\n", "(2, '0.011\"god\" + 0.01\"keith\" + 0.01\"moral\" + 0.006\"islam\" + 0.006\"livesei\" + 0.006\"atheist\" + 0.005\"atheism\" + 0.005\"caltech\" + 0.004\"religion\" + 0.004\"object\"')\n", "(3, '0.011\"space\" + 0.008\"nasa\" + 0.008\"henri\" + 0.006\"orbit\" + 0.005\"toronto\" + 0.005\"alaska\" + 0.005\"launch\" + 0.005\"moon\" + 0.004\"gov\" + 0.004\"access\"')\n", "(4, '0.005\"bike\" + 0.005\"graphic\" + 0.004\"file\" + 0.004\"imag\" + 0.003\"com\" + 0.003\"ride\" + 0.003\"thank\" + 0.003\"program\" + 0.003\"motorcycl\" + 0.002\"look\"')\n", "\n", "gensim_nmf:\n", "(0, '0.015\"israel\" + 0.014\"isra\" + 0.009\"arab\" + 0.008\"jew\" + 0.005\"palestinian\" + 0.004\"lebanes\" + 0.004\"peac\" + 0.004\"attack\" + 0.004\"lebanon\" + 0.003\"polici\"')\n", "(1, '0.007\"space\" + 0.005\"nasa\" + 0.003\"access\" + 0.003\"orbit\" + 0.003\"launch\" + 0.003\"pat\" + 0.003\"gov\" + 0.003\"com\" + 0.002\"alaska\" + 0.002\"moon\"')\n", "(2, '0.023\"armenian\" + 0.011\"turkish\" + 0.008\"armenia\" + 0.007\"argic\" + 0.007\"turk\" + 0.007\"serdar\" + 0.006\"turkei\" + 0.005\"greek\" + 0.005\"genocid\" + 0.005\"soviet\"')\n", "(3, '0.016\"moral\" + 0.015\"keith\" + 0.007\"object\" + 0.007\"caltech\" + 0.006\"schneider\" + 0.005\"allan\" + 0.005\"cco\" + 0.004\"anim\" + 0.004\"natur\" + 0.004\"goal\"')\n", "(4, '0.012\"god\" + 0.012\"islam\" + 0.006\"jaeger\" + 0.006\"sgi\" + 0.005\"livesei\" + 0.005\"muslim\" + 0.005\"atheist\" + 0.005\"religion\" + 0.005\"atheism\" + 0.004\"rushdi\"')\n" ] } ], "source": [ "def compare_topics(tm_metrics):\n", " for _, row in tm_metrics.iterrows():\n", " print('\\n{}:'.format(row.model))\n", " print(\"\\n\".join(str(topic) for topic in row.topics))\n", " \n", "compare_topics(tm_metrics)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subjectively, Gensim and Sklearn NMFs are on par with each other, LDA and LSI look a bit worse." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. NMF on English Wikipedia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This section shows how to train an NMF model on a large text corpus, the entire English Wikipedia: 2.6 billion words, in 23.1 million article sections across 5 million Wikipedia articles.\n", "\n", "The data preprocessing takes a while, and we'll be comparing multiple models, so reserve about 3 hours* and some 20 GB of disk space to go through the following notebook cells in full. You'll need `gensim>=3.7.1`, `numpy`, `tqdm`, `pandas`, `psutils`, `joblib` and `sklearn`." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Re-import modules from scratch, so that this Section doesn't rely on any previous cells.\n", "import itertools\n", "import json\n", "import logging\n", "import time\n", "import os\n", "\n", "from smart_open import smart_open\n", "import psutil\n", "import numpy as np\n", "import scipy.sparse\n", "from contextlib import contextmanager, contextmanager, contextmanager\n", "from multiprocessing import Process\n", "from tqdm import tqdm, tqdm_notebook\n", "import joblib\n", "import pandas as pd\n", "from sklearn.decomposition.nmf import NMF as SklearnNmf\n", "\n", "import gensim.downloader\n", "from gensim import matutils\n", "from gensim.corpora import MmCorpus, Dictionary\n", "from gensim.models import LdaModel, LdaMulticore, CoherenceModel\n", "from gensim.models.nmf import Nmf as GensimNmf\n", "from gensim.utils import simple_preprocess\n", "\n", "tqdm.pandas()\n", "\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the Wikipedia dump" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the [gensim.downloader](https://github.com/RaRe-Technologies/gensim-data) to download a parsed Wikipedia dump (6.1 GB disk space):" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "data = gensim.downloader.load(\"wiki-english-20171001\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the titles and sections of the first Wikipedia article, as a little sanity check:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Article: 'Anarchism'\n", "\n", "Section title: 'Introduction'\n", "Section text: '''Anarchism''' is a political philosophy that advocates self-governed societies based on volun…\n", "\n", "Section title: 'Etymology and terminology'\n", "Section text: The word ''anarchism'' is composed from the word ''anarchy'' and the suffix ''-ism'', themselves d…\n", "\n", "Section title: 'History'\n", "Section text: ===Origins=== Woodcut from a Diggers document by William Everard The earliest anarchist themes ca…\n", "\n", "Section title: 'Anarchist schools of thought'\n", "Section text: Portrait of philosopher Pierre-Joseph Proudhon (1809–1865) by Gustave Courbet. Proudhon was the pri…\n", "\n", "Section title: 'Internal issues and debates'\n", "Section text: consistent with anarchist values is a controversial subject among anarchists. Anarchism is a philo…\n", "\n", "Section title: 'Topics of interest'\n", "Section text: Intersecting and overlapping between various schools of thought, certain topics of interest and inte…\n", "\n", "Section title: 'Criticisms'\n", "Section text: Criticisms of anarchism include moral criticisms and pragmatic criticisms. Anarchism is often evalu…\n", "\n", "Section title: 'See also'\n", "Section text: * Anarchism by country…\n", "\n", "Section title: 'References'\n", "Section text: …\n", "\n", "Section title: 'Further reading'\n", "Section text: * Barclay, Harold, ''People Without Government: An Anthropology of Anarchy'' (2nd ed.), Left Bank Bo…\n", "\n", "Section title: 'External links'\n", "Section text: * …\n", "\n" ] } ], "source": [ "data = gensim.downloader.load(\"wiki-english-20171001\")\n", "article = next(iter(data))\n", "\n", "print(\"Article: %r\\n\" % article['title'])\n", "for section_title, section_text in zip(article['section_titles'], article['section_texts']):\n", " print(\"Section title: %r\" % section_title)\n", " print(\"Section text: %s…\\n\" % section_text[:100].replace('\\n', ' ').strip())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a Python generator function that streams through the downloaded Wikipedia dump and preprocesses (tokenizes, lower-cases) each article:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "def wikidump2tokens(articles):\n", " \"\"\"Stream through the Wikipedia dump, yielding a list of tokens for each article.\"\"\"\n", " for article in articles:\n", " article_section_texts = [\n", " \" \".join([title, text])\n", " for title, text\n", " in zip(article['section_titles'], article['section_texts'])\n", " ]\n", " article_tokens = simple_preprocess(\" \".join(article_section_texts))\n", " yield article_tokens" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a word-to-id mapping, in order to vectorize texts. Makes a full pass over the Wikipedia corpus, takes ~3.5 hours:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 15:57:45,000 : INFO : loading Dictionary object from wiki.dict\n", "2019-05-06 15:57:45,031 : INFO : loaded wiki.dict\n" ] } ], "source": [ "if os.path.exists('wiki.dict'):\n", " # If we already stored the Dictionary in a previous run, simply load it, to save time.\n", " dictionary = Dictionary.load('wiki.dict')\n", "else:\n", " dictionary = Dictionary(wikidump2tokens(data))\n", " # Keep only the 30,000 most frequent vocabulary terms, after filtering away terms\n", " # that are too frequent/too infrequent.\n", " dictionary.filter_extremes(no_below=5, no_above=0.5, keep_n=30000)\n", " dictionary.save('wiki.dict')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Store preprocessed Wikipedia as bag-of-words sparse matrix in MatrixMarket format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When training NMF with a single pass over the input corpus (\"online\"), we simply vectorize each raw text straight from the input storage:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "vector_stream = (dictionary.doc2bow(article) for article in wikidump2tokens(data))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the purposes of this tutorial though, we'll serialize (\"cache\") the vectorized bag-of-words vectors to disk, to `wiki.mm` file in MatrixMarket format. The reason is, we'll be re-using the vectorized articles multiple times, for different models for our benchmarks, and also shuffling them, so it makes sense to amortize the vectorization time by persisting the resulting vectors to disk." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, let's stream through the preprocessed sparse Wikipedia bag-of-words matrix while storing it to disk. This step takes about 3 hours* and needs 38 GB of disk space:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "class RandomSplitCorpus(MmCorpus):\n", " \"\"\"\n", " Use the fact that MmCorpus supports random indexing, and create a streamed\n", " corpus in shuffled order, including a train/test split for evaluation.\n", " \"\"\"\n", " def __init__(self, random_seed=42, testset=False, testsize=1000, args, kwargs):\n", " super().__init__(args, kwargs)\n", "\n", " random_state = np.random.RandomState(random_seed)\n", " \n", " self.indices = random_state.permutation(range(self.num_docs))\n", " test_nnz = sum(len(self[doc_idx]) for doc_idx in self.indices[:testsize])\n", " \n", " if testset:\n", " self.indices = self.indices[:testsize]\n", " self.num_docs = testsize\n", " self.num_nnz = test_nnz\n", " else:\n", " self.indices = self.indices[testsize:]\n", " self.num_docs -= testsize\n", " self.num_nnz -= test_nnz\n", "\n", " def __iter__(self):\n", " for doc_id in self.indices:\n", " yield self[doc_id]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [], "source": [ "if not os.path.exists('wiki.mm'):\n", " MmCorpus.serialize('wiki.mm', vector_stream, progress_cnt=100000)\n", "\n", "if not os.path.exists('wiki_tfidf.mm'):\n", " MmCorpus.serialize('wiki_tfidf.mm', tfidf[MmCorpus('wiki.mm')], progress_cnt=100000)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 15:57:45,584 : INFO : loaded corpus index from wiki.mm.index\n", "2019-05-06 15:57:45,585 : INFO : initializing cython corpus reader from wiki.mm\n", "2019-05-06 15:57:45,586 : INFO : accepted corpus with 4924894 documents, 30000 features, 820242695 non-zero entries\n", "2019-05-06 15:57:49,102 : INFO : loaded corpus index from wiki.mm.index\n", "2019-05-06 15:57:49,103 : INFO : initializing cython corpus reader from wiki.mm\n", "2019-05-06 15:57:49,103 : INFO : accepted corpus with 4924894 documents, 30000 features, 820242695 non-zero entries\n", "2019-05-06 15:57:51,552 : INFO : loaded corpus index from wiki_tfidf.mm.index\n", "2019-05-06 15:57:51,553 : INFO : initializing cython corpus reader from wiki_tfidf.mm\n", "2019-05-06 15:57:51,554 : INFO : accepted corpus with 4924661 documents, 30000 features, 820007548 non-zero entries\n", "2019-05-06 15:57:55,680 : INFO : loaded corpus index from wiki_tfidf.mm.index\n", "2019-05-06 15:57:55,681 : INFO : initializing cython corpus reader from wiki_tfidf.mm\n", "2019-05-06 15:57:55,682 : INFO : accepted corpus with 4924661 documents, 30000 features, 820007548 non-zero entries\n" ] } ], "source": [ "# Load back the vectors as two lazily-streamed train/test iterables.\n", "train_corpus = RandomSplitCorpus(\n", " random_seed=42, testset=False, testsize=10000, fname='wiki.mm',\n", ")\n", "test_corpus = RandomSplitCorpus(\n", " random_seed=42, testset=True, testsize=10000, fname='wiki.mm',\n", ")\n", "\n", "train_corpus_tfidf = RandomSplitCorpus(\n", " random_seed=42, testset=False, testsize=10000, fname='wiki_tfidf.mm',\n", ")\n", "test_corpus_tfidf = RandomSplitCorpus(\n", " random_seed=42, testset=True, testsize=10000, fname='wiki_tfidf.mm',\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save preprocessed Wikipedia in scipy.sparse format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is only needed to run the Sklearn NMF on Wikipedia, for comparison in the benchmarks below. Sklearn expects in-memory scipy sparse input, not on-the-fly vector streams. Needs additional ~2 GB of disk space.\n", "\n", "\n", "Skip this step if you don't need the Sklearn's NMF benchmark, and only want to run Gensim's NMF." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "if not os.path.exists('wiki_train_csr.npz'):\n", " scipy.sparse.save_npz(\n", " 'wiki_train_csr.npz',\n", " matutils.corpus2csc(train_corpus_tfidf, len(dictionary)).T,\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Metrics\n", "\n", "We'll track these metrics as we train and test NMF on the Wikipedia corpus we created above:\n", "- `train time` - time to train a model\n", "- `mean_ram` - mean RAM consumption during training\n", "- `max_ram` - maximum RAM consumption during training\n", "- `train time` - time to train a model.\n", "- `coherence` - coherence score (larger is better).\n", "- `l2_norm` - L2 norm of `v - Wh` (less is better, not defined for LDA)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a dataframe in which we'll store the recorded metrics:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "tm_metrics = pd.DataFrame(columns=[\n", " 'model', 'train_time', 'mean_ram', 'max_ram', 'coherence', 'l2_norm', 'topics',\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define common parameters, to be shared by all evaluated models:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "params = dict(\n", " chunksize=2000,\n", " num_topics=50,\n", " id2word=dictionary,\n", " passes=1,\n", " eval_every=10,\n", " minimum_probability=0,\n", " random_state=42,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train Gensim NMF model and record its metrics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Wikipedia training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train Gensim NMF model and record its metrics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "row = {}\n", "row['model'] = 'gensim_nmf'\n", "row['train_time'], row['mean_ram'], row['max_ram'], nmf = get_train_time_and_ram(\n", " lambda: GensimNmf(normalize=False, corpus=train_corpus_tfidf, params),\n", " 'gensim_nmf',\n", " 1,\n", ")" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 16:23:44,657 : INFO : saving Nmf object under gensim_nmf.model, separately None\n", "2019-05-06 16:23:44,767 : INFO : saved gensim_nmf.model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'max_ram': '774 MB', 'train_time': Timedelta('0 days 00:25:46.617000'), 'model': 'gensim_nmf', 'mean_ram': '771 MB'}\n" ] } ], "source": [ "print(row)\n", "nmf.save('gensim_nmf.model')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 16:23:44,772 : INFO : loading Nmf object from gensim_nmf.model\n", "2019-05-06 16:23:44,871 : INFO : loading id2word recursively from gensim_nmf.model.id2word.* with mmap=None\n", "2019-05-06 16:23:44,872 : INFO : loaded gensim_nmf.model\n", "2019-05-06 16:24:47,126 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n", "2019-05-06 16:24:47,272 : INFO : CorpusAccumulator accumulated stats from 2000 documents\n", "2019-05-06 16:24:47,424 : INFO : CorpusAccumulator accumulated stats from 3000 documents\n", "2019-05-06 16:24:47,573 : INFO : CorpusAccumulator accumulated stats from 4000 documents\n", "2019-05-06 16:24:47,726 : INFO : CorpusAccumulator accumulated stats from 5000 documents\n", "2019-05-06 16:24:47,880 : INFO : CorpusAccumulator accumulated stats from 6000 documents\n", "2019-05-06 16:24:48,027 : INFO : CorpusAccumulator accumulated stats from 7000 documents\n", "2019-05-06 16:24:48,168 : INFO : CorpusAccumulator accumulated stats from 8000 documents\n", "2019-05-06 16:24:48,319 : INFO : CorpusAccumulator accumulated stats from 9000 documents\n", "2019-05-06 16:24:48,472 : INFO : CorpusAccumulator accumulated stats from 10000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'mean_ram': '771 MB', 'topics': [(21, '0.009\"his\" + 0.005\"that\" + 0.005\"him\" + 0.004\"had\" + 0.003\"they\" + 0.003\"who\" + 0.003\"her\" + 0.003\"but\" + 0.003\"king\" + 0.003\"were\"'), (39, '0.005\"are\" + 0.005\"or\" + 0.004\"be\" + 0.004\"that\" + 0.003\"can\" + 0.003\"used\" + 0.003\"this\" + 0.002\"have\" + 0.002\"such\" + 0.002\"which\"'), (45, '0.092\"apelor\" + 0.087\"bucurești\" + 0.050\"river\" + 0.046\"cadastrul\" + 0.046\"hidrologie\" + 0.046\"meteorologie\" + 0.046\"institutul\" + 0.046\"române\" + 0.045\"româniei\" + 0.045\"rîurile\"'), (28, '0.066\"gmina\" + 0.065\"poland\" + 0.065\"voivodeship\" + 0.046\"village\" + 0.045\"administrative\" + 0.042\"lies\" + 0.037\"approximately\" + 0.036\"east\" + 0.031\"west\" + 0.030\"county\"'), (34, '0.087\"romanized\" + 0.084\"iran\" + 0.067\"rural\" + 0.067\"province\" + 0.066\"census\" + 0.060\"families\" + 0.055\"village\" + 0.049\"county\" + 0.047\"population\" + 0.043\"district\"')], 'model': 'gensim_nmf', 'coherence': -2.1071, 'l2_norm': 94.9686, 'train_time': Timedelta('0 days 00:25:46.617000'), 'max_ram': '774 MB', 'f1': None}\n" ] } ], "source": [ "nmf = GensimNmf.load('gensim_nmf.model')\n", "row.update(get_metrics(nmf, test_corpus_tfidf))\n", "print(row)\n", "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train Gensim LSI model and record its metrics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "row = {}\n", "row['model'] = 'lsi'\n", "row['train_time'], row['mean_ram'], row['max_ram'], lsi = get_train_time_and_ram(\n", " lambda: LsiModel(\n", " corpus=train_corpus_tfidf,\n", " chunksize=2000,\n", " num_topics=50,\n", " id2word=dictionary,\n", " ),\n", " 'lsi',\n", " 1,\n", ")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 17:34:51,929 : INFO : saving Projection object under lsi.model.projection, separately None\n", "2019-05-06 17:34:52,010 : INFO : saved lsi.model.projection\n", "2019-05-06 17:34:52,011 : INFO : saving LsiModel object under lsi.model, separately None\n", "2019-05-06 17:34:52,012 : INFO : not storing attribute projection\n", "2019-05-06 17:34:52,012 : INFO : not storing attribute dispatcher\n", "2019-05-06 17:34:52,022 : INFO : saved lsi.model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'max_ram': '896 MB', 'train_time': Timedelta('0 days 01:10:03.206000'), 'model': 'lsi', 'mean_ram': '882 MB'}\n" ] } ], "source": [ "print(row)\n", "lsi.save('lsi.model')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 17:34:52,027 : INFO : loading LsiModel object from lsi.model\n", "2019-05-06 17:34:52,039 : INFO : loading id2word recursively from lsi.model.id2word.* with mmap=None\n", "2019-05-06 17:34:52,039 : INFO : setting ignored attribute projection to None\n", "2019-05-06 17:34:52,040 : INFO : setting ignored attribute dispatcher to None\n", "2019-05-06 17:34:52,040 : INFO : loaded lsi.model\n", "2019-05-06 17:34:52,041 : INFO : loading LsiModel object from lsi.model.projection\n", "2019-05-06 17:34:52,115 : INFO : loaded lsi.model.projection\n", "2019-05-06 17:35:03,515 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n", "2019-05-06 17:35:03,650 : INFO : CorpusAccumulator accumulated stats from 2000 documents\n", "2019-05-06 17:35:03,791 : INFO : CorpusAccumulator accumulated stats from 3000 documents\n", "2019-05-06 17:35:03,929 : INFO : CorpusAccumulator accumulated stats from 4000 documents\n", "2019-05-06 17:35:04,071 : INFO : CorpusAccumulator accumulated stats from 5000 documents\n", "2019-05-06 17:35:04,211 : INFO : CorpusAccumulator accumulated stats from 6000 documents\n", "2019-05-06 17:35:04,347 : INFO : CorpusAccumulator accumulated stats from 7000 documents\n", "2019-05-06 17:35:04,478 : INFO : CorpusAccumulator accumulated stats from 8000 documents\n", "2019-05-06 17:35:04,622 : INFO : CorpusAccumulator accumulated stats from 9000 documents\n", "2019-05-06 17:35:04,758 : INFO : CorpusAccumulator accumulated stats from 10000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'mean_ram': '882 MB', 'topics': [(0, '0.260\"he\" + 0.172\"his\" + 0.131\"she\" + 0.111\"her\" + 0.105\"that\" + 0.094\"district\" + 0.092\"were\" + 0.087\"school\" + 0.077\"had\" + 0.077\"film\"'), (1, '-0.430\"district\" + -0.308\"village\" + -0.270\"population\" + -0.264\"census\" + -0.263\"romanized\" + -0.255\"iran\" + -0.247\"rural\" + -0.232\"county\" + -0.221\"province\" + -0.204\"families\"'), (2, '-0.287\"league\" + -0.275\"he\" + -0.215\"football\" + 0.212\"album\" + -0.183\"season\" + -0.180\"team\" + -0.169\"club\" + -0.161\"cup\" + -0.136\"played\" + 0.128\"song\"'), (3, '-0.216\"poland\" + -0.214\"gmina\" + 0.213\"romanized\" + -0.213\"voivodeship\" + 0.209\"iran\" + 0.208\"album\" + 0.180\"rural\" + -0.156\"administrative\" + -0.155\"east\" + -0.154\"lies\"'), (4, '-0.260\"album\" + -0.252\"gmina\" + -0.252\"poland\" + -0.250\"voivodeship\" + -0.171\"administrative\" + -0.161\"lies\" + -0.152\"song\" + -0.136\"chart\" + -0.135\"approximately\" + -0.129\"village\"')], 'model': 'lsi', 'coherence': -3.9198, 'l2_norm': 94.5983, 'train_time': Timedelta('0 days 01:10:03.206000'), 'max_ram': '896 MB', 'f1': None}\n" ] } ], "source": [ "lsi = LsiModel.load('lsi.model')\n", "row.update(get_metrics(lsi, test_corpus_tfidf))\n", "print(row)\n", "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train Gensim LDA and record its metrics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "row = {}\n", "row['model'] = 'lda'\n", "row['train_time'], row['mean_ram'], row['max_ram'], lda = get_train_time_and_ram(\n", " lambda: LdaModel(corpus=train_corpus, *params),\n", " 'lda',\n", " 1,\n", ")" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 18:56:04,893 : INFO : saving LdaState object under lda.model.state, separately None\n", "2019-05-06 18:56:04,956 : INFO : saved lda.model.state\n", "2019-05-06 18:56:04,977 : INFO : saving LdaModel object under lda.model, separately ['expElogbeta', 'sstats']\n", "2019-05-06 18:56:04,979 : INFO : not storing attribute state\n", "2019-05-06 18:56:04,980 : INFO : not storing attribute id2word\n", "2019-05-06 18:56:04,981 : INFO : storing np array 'expElogbeta' to lda.model.expElogbeta.npy\n", "2019-05-06 18:56:04,992 : INFO : not storing attribute dispatcher\n", "2019-05-06 18:56:04,995 : INFO : saved lda.model\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'max_ram': '864 MB', 'train_time': Timedelta('0 days 01:20:59.941000'), 'model': 'lda', 'mean_ram': '862 MB'}\n" ] } ], "source": [ "print(row)\n", "lda.save('lda.model')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 18:56:05,002 : INFO : loading LdaModel object from lda.model\n", "2019-05-06 18:56:05,005 : INFO : loading expElogbeta from lda.model.expElogbeta.npy with mmap=None\n", "2019-05-06 18:56:05,007 : INFO : setting ignored attribute state to None\n", "2019-05-06 18:56:05,008 : INFO : setting ignored attribute id2word to None\n", "2019-05-06 18:56:05,008 : INFO : setting ignored attribute dispatcher to None\n", "2019-05-06 18:56:05,009 : INFO : loaded lda.model\n", "2019-05-06 18:56:05,009 : INFO : loading LdaState object from lda.model.state\n", "2019-05-06 18:56:05,039 : INFO : loaded lda.model.state\n", "2019-05-06 18:56:20,341 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n", "2019-05-06 18:56:20,466 : INFO : CorpusAccumulator accumulated stats from 2000 documents\n", "2019-05-06 18:56:20,595 : INFO : CorpusAccumulator accumulated stats from 3000 documents\n", "2019-05-06 18:56:20,715 : INFO : CorpusAccumulator accumulated stats from 4000 documents\n", "2019-05-06 18:56:20,848 : INFO : CorpusAccumulator accumulated stats from 5000 documents\n", "2019-05-06 18:56:20,973 : INFO : CorpusAccumulator accumulated stats from 6000 documents\n", "2019-05-06 18:56:21,100 : INFO : CorpusAccumulator accumulated stats from 7000 documents\n", "2019-05-06 18:56:21,226 : INFO : CorpusAccumulator accumulated stats from 8000 documents\n", "2019-05-06 18:56:21,351 : INFO : CorpusAccumulator accumulated stats from 9000 documents\n", "2019-05-06 18:56:21,483 : INFO : CorpusAccumulator accumulated stats from 10000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'mean_ram': '862 MB', 'topics': [(11, '0.066\"de\" + 0.034\"art\" + 0.030\"french\" + 0.028\"la\" + 0.022\"france\" + 0.019\"paris\" + 0.017\"le\" + 0.016\"museum\" + 0.013\"van\" + 0.013\"saint\"'), (45, '0.033\"new\" + 0.027\"states\" + 0.025\"united\" + 0.023\"york\" + 0.023\"american\" + 0.023\"county\" + 0.021\"state\" + 0.017\"city\" + 0.014\"california\" + 0.012\"washington\"'), (40, '0.028\"radio\" + 0.025\"show\" + 0.021\"tv\" + 0.020\"television\" + 0.016\"news\" + 0.015\"station\" + 0.014\"channel\" + 0.012\"fm\" + 0.012\"network\" + 0.011\"media\"'), (28, '0.064\"university\" + 0.018\"research\" + 0.015\"college\" + 0.014\"institute\" + 0.013\"science\" + 0.011\"professor\" + 0.010\"has\" + 0.010\"international\" + 0.009\"national\" + 0.009\"society\"'), (20, '0.179\"he\" + 0.123\"his\" + 0.015\"born\" + 0.014\"after\" + 0.013\"him\" + 0.011\"who\" + 0.011\"career\" + 0.010\"had\" + 0.010\"later\" + 0.009\"where\"')], 'model': 'lda', 'coherence': -1.7641, 'l2_norm': None, 'train_time': Timedelta('0 days 01:20:59.941000'), 'max_ram': '864 MB', 'f1': None}\n" ] } ], "source": [ "lda = LdaModel.load('lda.model')\n", "row.update(get_metrics(lda, test_corpus))\n", "print(row)\n", "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train Sklearn NMF and record its metrics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Careful!* Sklearn loads the entire input Wikipedia matrix into RAM. Even though the matrix is sparse, you'll need FIXME GB of free RAM to run the cell below." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'max_ram': '18177 MB', 'train_time': Timedelta('0 days 00:47:41.026000'), 'model': 'sklearn_nmf', 'mean_ram': '12872 MB'}\n" ] }, { "data": { "text/plain": [ "['sklearn_nmf.joblib']" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "row = {}\n", "row['model'] = 'sklearn_nmf'\n", "sklearn_nmf = SklearnNmf(n_components=50, tol=1e-2, random_state=42)\n", "row['train_time'], row['mean_ram'], row['max_ram'], sklearn_nmf = get_train_time_and_ram(\n", " lambda: sklearn_nmf.fit(scipy.sparse.load_npz('wiki_train_csr.npz')),\n", " 'sklearn_nmf',\n", " 10,\n", ")\n", "print(row)\n", "joblib.dump(sklearn_nmf, 'sklearn_nmf.joblib')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-05-06 19:44:22,696 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n", "2019-05-06 19:44:22,841 : INFO : CorpusAccumulator accumulated stats from 2000 documents\n", "2019-05-06 19:44:22,994 : INFO : CorpusAccumulator accumulated stats from 3000 documents\n", "2019-05-06 19:44:23,142 : INFO : CorpusAccumulator accumulated stats from 4000 documents\n", "2019-05-06 19:44:23,297 : INFO : CorpusAccumulator accumulated stats from 5000 documents\n", "2019-05-06 19:44:23,448 : INFO : CorpusAccumulator accumulated stats from 6000 documents\n", "2019-05-06 19:44:23,595 : INFO : CorpusAccumulator accumulated stats from 7000 documents\n", "2019-05-06 19:44:23,737 : INFO : CorpusAccumulator accumulated stats from 8000 documents\n", "2019-05-06 19:44:23,889 : INFO : CorpusAccumulator accumulated stats from 9000 documents\n", "2019-05-06 19:44:24,038 : INFO : CorpusAccumulator accumulated stats from 10000 documents\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'mean_ram': '12872 MB', 'topics': [(0, '0.067\"gmina\" + 0.067\"poland\" + 0.066\"voivodeship\" + 0.047\"administrative\" + 0.043\"lies\" + 0.038\"approximately\" + 0.037\"east\" + 0.032\"west\" + 0.031\"county\" + 0.03\"regional\"'), (1, '0.098\"district\" + 0.077\"romanized\" + 0.075\"iran\" + 0.071\"rural\" + 0.062\"census\" + 0.061\"province\" + 0.054\"families\" + 0.048\"population\" + 0.043\"county\" + 0.031\"village\"'), (2, '0.095\"apelor\" + 0.09\"bucurești\" + 0.047\"cadastrul\" + 0.047\"hidrologie\" + 0.047\"meteorologie\" + 0.047\"institutul\" + 0.047\"române\" + 0.047\"româniei\" + 0.047\"rîurile\" + 0.046\"river\"'), (3, '0.097\"commune\" + 0.05\"department\" + 0.045\"communes\" + 0.03\"insee\" + 0.029\"france\" + 0.018\"population\" + 0.015\"saint\" + 0.014\"region\" + 0.01\"town\" + 0.01\"french\"'), (4, '0.148\"township\" + 0.05\"county\" + 0.018\"townships\" + 0.018\"unincorporated\" + 0.015\"community\" + 0.011\"indiana\" + 0.01\"census\" + 0.01\"creek\" + 0.009\"pennsylvania\" + 0.009\"illinois\"')], 'model': 'sklearn_nmf', 'coherence': -2.0476, 'l2_norm': 94.8459, 'train_time': Timedelta('0 days 00:47:41.026000'), 'max_ram': '18177 MB', 'f1': None}\n" ] } ], "source": [ "sklearn_nmf = joblib.load('sklearn_nmf.joblib')\n", "row.update(get_metrics(\n", " sklearn_nmf, test_corpus_tfidf, dictionary=dictionary,\n", "))\n", "print(row)\n", "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Wikipedia results" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " <th>train_time</th>\n", " <th>mean_ram</th>\n", " <th>max_ram</th>\n", " <th>coherence</th>\n", " <th>l2_norm</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>gensim_nmf</td>\n", " <td>00:25:46.617000</td>\n", " <td>771 MB</td>\n", " <td>774 MB</td>\n", " <td>-2.1071</td>\n", " <td>94.9686</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>lsi</td>\n", " <td>01:10:03.206000</td>\n", " <td>882 MB</td>\n", " <td>896 MB</td>\n", " <td>-3.9198</td>\n", " <td>94.5983</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>lda</td>\n", " <td>01:20:59.941000</td>\n", " <td>862 MB</td>\n", " <td>864 MB</td>\n", " <td>-1.7641</td>\n", " <td>-</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>sklearn_nmf</td>\n", " <td>00:47:41.026000</td>\n", " <td>12872 MB</td>\n", " <td>18177 MB</td>\n", " <td>-2.0476</td>\n", " <td>94.8459</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model train_time mean_ram max_ram coherence l2_norm\n", "0 gensim_nmf 00:25:46.617000 771 MB 774 MB -2.1071 94.9686\n", "1 lsi 01:10:03.206000 882 MB 896 MB -3.9198 94.5983\n", "2 lda 01:20:59.941000 862 MB 864 MB -1.7641 -\n", "3 sklearn_nmf 00:47:41.026000 12872 MB 18177 MB -2.0476 94.8459" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tm_metrics.replace(np.nan, '-', inplace=True)\n", "tm_metrics.drop(['topics', 'f1'], axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Insights\n", "\n", "Gensim's online NMF outperforms all other models in terms of speed and memory foorprint size.\n", "\n", "Compared to Sklearn's NMF:\n", "\n", "- 2x faster.\n", "\n", "- Uses ~20x less memory.\n", "\n", " About 8GB of Sklearn's RAM comes from the in-memory input matrices, which, in contrast to Gensim NMF, cannot be streamed iteratively. But even if we forget about the huge input size, Sklearn NMF uses about 2-8 GB of RAM – significantly more than Gensim NMF or LDA.\n", "\n", "- L2 norm and coherence are a bit worse.\n", "\n", "Compared to Gensim's LSI:\n", "\n", "- 3x faster\n", "- Better coherence but slightly worse l2 norm.\n", "\n", "Compared to Gensim's LDA, Gensim NMF also gives superior results:\n", "\n", "- 3x faster\n", "- Coherence is worse than LDA's." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Learned Wikipedia topics" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "gensim_nmf:\n", "(21, '0.009\"his\" + 0.005\"that\" + 0.005\"him\" + 0.004\"had\" + 0.003\"they\" + 0.003\"who\" + 0.003\"her\" + 0.003\"but\" + 0.003\"king\" + 0.003\"were\"')\n", "(39, '0.005\"are\" + 0.005\"or\" + 0.004\"be\" + 0.004\"that\" + 0.003\"can\" + 0.003\"used\" + 0.003\"this\" + 0.002\"have\" + 0.002\"such\" + 0.002\"which\"')\n", "(45, '0.092\"apelor\" + 0.087\"bucurești\" + 0.050\"river\" + 0.046\"cadastrul\" + 0.046\"hidrologie\" + 0.046\"meteorologie\" + 0.046\"institutul\" + 0.046\"române\" + 0.045\"româniei\" + 0.045\"rîurile\"')\n", "(28, '0.066\"gmina\" + 0.065\"poland\" + 0.065\"voivodeship\" + 0.046\"village\" + 0.045\"administrative\" + 0.042\"lies\" + 0.037\"approximately\" + 0.036\"east\" + 0.031\"west\" + 0.030\"county\"')\n", "(34, '0.087\"romanized\" + 0.084\"iran\" + 0.067\"rural\" + 0.067\"province\" + 0.066\"census\" + 0.060\"families\" + 0.055\"village\" + 0.049\"county\" + 0.047\"population\" + 0.043\"district\"')\n", "\n", "lsi:\n", "(0, '0.260\"he\" + 0.172\"his\" + 0.131\"she\" + 0.111\"her\" + 0.105\"that\" + 0.094\"district\" + 0.092\"were\" + 0.087\"school\" + 0.077\"had\" + 0.077\"film\"')\n", "(1, '-0.430\"district\" + -0.308\"village\" + -0.270\"population\" + -0.264\"census\" + -0.263\"romanized\" + -0.255\"iran\" + -0.247\"rural\" + -0.232\"county\" + -0.221\"province\" + -0.204\"families\"')\n", "(2, '-0.287\"league\" + -0.275\"he\" + -0.215\"football\" + 0.212\"album\" + -0.183\"season\" + -0.180\"team\" + -0.169\"club\" + -0.161\"cup\" + -0.136\"played\" + 0.128\"song\"')\n", "(3, '-0.216\"poland\" + -0.214\"gmina\" + 0.213\"romanized\" + -0.213\"voivodeship\" + 0.209\"iran\" + 0.208\"album\" + 0.180\"rural\" + -0.156\"administrative\" + -0.155\"east\" + -0.154\"lies\"')\n", "(4, '-0.260\"album\" + -0.252\"gmina\" + -0.252\"poland\" + -0.250\"voivodeship\" + -0.171\"administrative\" + -0.161\"lies\" + -0.152\"song\" + -0.136\"chart\" + -0.135\"approximately\" + -0.129\"village\"')\n", "\n", "lda:\n", "(11, '0.066\"de\" + 0.034\"art\" + 0.030\"french\" + 0.028\"la\" + 0.022\"france\" + 0.019\"paris\" + 0.017\"le\" + 0.016\"museum\" + 0.013\"van\" + 0.013\"saint\"')\n", "(45, '0.033\"new\" + 0.027\"states\" + 0.025\"united\" + 0.023\"york\" + 0.023\"american\" + 0.023\"county\" + 0.021\"state\" + 0.017\"city\" + 0.014\"california\" + 0.012\"washington\"')\n", "(40, '0.028\"radio\" + 0.025\"show\" + 0.021\"tv\" + 0.020\"television\" + 0.016\"news\" + 0.015\"station\" + 0.014\"channel\" + 0.012\"fm\" + 0.012\"network\" + 0.011\"media\"')\n", "(28, '0.064\"university\" + 0.018\"research\" + 0.015\"college\" + 0.014\"institute\" + 0.013\"science\" + 0.011\"professor\" + 0.010\"has\" + 0.010\"international\" + 0.009\"national\" + 0.009\"society\"')\n", "(20, '0.179\"he\" + 0.123\"his\" + 0.015\"born\" + 0.014\"after\" + 0.013\"him\" + 0.011\"who\" + 0.011\"career\" + 0.010\"had\" + 0.010\"later\" + 0.009\"where\"')\n", "\n", "sklearn_nmf:\n", "(0, '0.067\"gmina\" + 0.067\"poland\" + 0.066\"voivodeship\" + 0.047\"administrative\" + 0.043\"lies\" + 0.038\"approximately\" + 0.037\"east\" + 0.032\"west\" + 0.031\"county\" + 0.03\"regional\"')\n", "(1, '0.098\"district\" + 0.077\"romanized\" + 0.075\"iran\" + 0.071\"rural\" + 0.062\"census\" + 0.061\"province\" + 0.054\"families\" + 0.048\"population\" + 0.043\"county\" + 0.031\"village\"')\n", "(2, '0.095\"apelor\" + 0.09\"bucurești\" + 0.047\"cadastrul\" + 0.047\"hidrologie\" + 0.047\"meteorologie\" + 0.047\"institutul\" + 0.047\"române\" + 0.047\"româniei\" + 0.047\"rîurile\" + 0.046\"river\"')\n", "(3, '0.097\"commune\" + 0.05\"department\" + 0.045\"communes\" + 0.03\"insee\" + 0.029\"france\" + 0.018\"population\" + 0.015\"saint\" + 0.014\"region\" + 0.01\"town\" + 0.01\"french\"')\n", "(4, '0.148\"township\" + 0.05\"county\" + 0.018\"townships\" + 0.018\"unincorporated\" + 0.015\"community\" + 0.011\"indiana\" + 0.01\"census\" + 0.01\"creek\" + 0.009\"pennsylvania\" + 0.009\"illinois\"')\n" ] } ], "source": [ "def compare_topics(tm_metrics):\n", " for _, row in tm_metrics.iterrows():\n", " print('\\n{}:'.format(row.model))\n", " print(\"\\n\".join(str(topic) for topic in row.topics))\n", " \n", "compare_topics(tm_metrics)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems all four models successfully learned useful topics from the Wikipedia corpus." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. And now for something completely different: Face decomposition from images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The NMF algorithm in Gensim is optimized for extremely large (sparse) text corpora, but it will also work on vectors from other domains!\n", "\n", "Let's compare our model to other factorization algorithms on dense image vectors and check out the results.\n", "\n", "To do that we'll patch sklearn's [Faces Dataset Decomposition](https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sklearn wrapper\n", "Let's create an Scikit-learn wrapper in order to run Gensim NMF on images." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "import logging\n", "import time\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from numpy.random import RandomState\n", "from sklearn import decomposition\n", "from sklearn.cluster import MiniBatchKMeans\n", "from sklearn.datasets import fetch_olivetti_faces\n", "from sklearn.decomposition.nmf import NMF as SklearnNmf\n", "from sklearn.linear_model import LogisticRegressionCV\n", "from sklearn.metrics import f1_score\n", "from sklearn.model_selection import ParameterGrid\n", "\n", "import gensim.downloader\n", "from gensim import matutils\n", "from gensim.corpora import Dictionary\n", "from gensim.models import CoherenceModel, LdaModel, LdaMulticore\n", "from gensim.models.nmf import Nmf as GensimNmf\n", "from gensim.parsing.preprocessing import preprocess_string\n", "\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "from sklearn.base import BaseEstimator, TransformerMixin\n", "import scipy.sparse as sparse\n", "\n", "\n", "class NmfWrapper(BaseEstimator, TransformerMixin):\n", " def __init__(self, bow_matrix, kwargs):\n", " self.corpus = sparse.csc.csc_matrix(bow_matrix)\n", " self.nmf = GensimNmf(kwargs)\n", "\n", " def fit(self, X):\n", " self.nmf.update(self.corpus)\n", "\n", " @property\n", " def components_(self):\n", " return self.nmf.get_topics()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modified face decomposition notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adapted from the excellent [Scikit-learn tutorial](https://github.com/scikit-learn/scikit-learn/blob/master/examples/decomposition/plot_faces_decomposition.py) (BSD license):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Turn off the logger due to large number of info messages during training" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "gensim.models.nmf.logger.propagate = False" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================\n", "Faces dataset decompositions\n", "============================\n", "\n", "This example applies to :ref:`olivetti_faces` different unsupervised\n", "matrix decomposition (dimension reduction) methods from the module\n", ":py:mod:`sklearn.decomposition` (see the documentation chapter\n", ":ref:`decompositions`) .\n", "\n", "\n", "Dataset consists of 400 faces\n", "Extracting the top 6 Eigenfaces - PCA using randomized SVD...\n", "done in 0.025s\n", "Extracting the top 6 Non-negative components - NMF (Sklearn)...\n", "done in 0.171s\n", "Extracting the top 6 Non-negative components - NMF (Gensim)...\n", "done in 0.582s\n", "Extracting the top 6 Independent components - FastICA...\n", "done in 0.097s\n", "Extracting the top 6 Sparse comp. - MiniBatchSparsePCA...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anotherbugmaster/.local/lib/python3.5/site-packages/sklearn/decomposition/sparse_pca.py:405: DeprecationWarning: normalize_components=False is a backward-compatible setting that implements a non-standard definition of sparse PCA. This compatibility mode will be removed in 0.22.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "done in 0.694s\n", "Extracting the top 6 MiniBatchDictionaryLearning...\n", "done in 0.479s\n", "Extracting the top 6 Cluster centers - MiniBatchKMeans...\n", "done in 0.087s\n", "Extracting the top 6 Factor Analysis components - FA...\n", "done in 0.048s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/anotherbugmaster/.local/lib/python3.5/site-packages/sklearn/decomposition/factor_analysis.py:238: ConvergenceWarning: FactorAnalysis did not converge. You might want to increase the number of iterations.\n", " ConvergenceWarning)\n" ] }, { "data": { "image/png": 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yjm1FadYlK/8bZJJdNr6IKEUhKXofXeLh+yjNIu3X1EWZhIk05HPLdYbUlvSbOad9WBrHRgmvNo9cn/7s2LGjNy7Y1Be/+MVBVeLq6mqPMcTxIUUy11yTfkb1l/ebPUn/aePLX/7yzJjjPqCvzogBxw4xLj+Gv+PeYZ5gNb6GXD/bu66q8vspk959f7FH6Jvfmxkr4Bhnds5KYv9ranAfbzzfxz4EZ01xvbjf2OOMbUjF58y69uwYMt3430NmhBoLnMeE4hof75uPJZ7r2qhanyPYzzVtQdamayx8j/o+9P/HccLQWU/+lrq1Zv1XVlbmVuU2htfQ0NDQsBA4JoaHbj6T7Gr2kJoR3M+Pv7nxOzu3ZixGwsskYCQDl1YyvXTtOs70arZEqWMxNYYH4jhdD06f6Zvb5zLQhtu3ODfawpyx+hpkNoIXvehFkjqG9+yzzw7q52O77uggdfNEf2t7J9MOuAbB+z+0Vzday8hC3Z44j7Qe51nq5tDH5QxX6u8Dt9ll+9u1DVzfWRuIc+I2YuD3YmwDiZvr8HxwFh+ldI6NdsTaXi6laHl5uXftOK/0gblzVuFrTbtx/P7p9vp5njuZ9sSPYY79HhhyQPNznCHPY/9zTUnmT+F98HvCfSKyZ5Zfn2P8mRzHA/wY2kBTE/sE+8v6XUNjeA0NDQ0NC4H2wmtoaGhoWAhsSqUZ6wpJfVVJhprBPNLfzKEgazdzZqnF/NA+6pToROB9ceOtO0Bk13G3cFc9uhorgxuaM7Wrq6eAq+Hi9WgnGnUjsvXimBhOkR0b59Fdy/ft21d1WpHGczdPDE7NuE/b8RqELuAYwrH0E9WcO6hI9VAZV7dFFSNwVSPrxHVR60nd3LrzRm39s/WpOUdljjyoW/2e4BjWOAvZ8ZAc5pW9xFyceOKJ03NQQdMX1E++/+I8+v0x5HRw9OhRPfHEE9OQGA85iO25E5HPdVShuSMI/eV7n4t4bi3er/Z7NlZXOWZOJLXQrFqcbkRNRezP0bh3MlNDvJ7v4XhdN7u489k86nc3/7D/4j3vppqtW7cOPnciGsNraGhoaFgIbIrhSbNv2lpAY/zNz8uMnc5A3IGiJiHH79wRwCWgzEED1FykYx+d/Xn7PoboMu0u3TVjeYSzHJiKS6FI69kacA7GXWc0Q8H4NXfxOHeM68ILL5Qk/emf/umGTis+13He6INL5Xz/pS99SVIn2UndvmIeNvqMc8137A2C4d0VP+4dWK2Pc4hx19h+bb9FFu3OKX7dIckWyboWEuSON/EYpHOX7LkecxWPhW37vYJDSdw7p5xyiqSODR49erTKilZXV/X4449Pr717925JsyEyzhT8HsuSFriDhDtXDN3zfp/4/Zeds1H41ZATSS3Rhe+L+NzxdmuML55T08g5AxvSQjizA64Vkfpr4IkjfA9L3fMshqJs9NwBjeE1NDQ0NCwENm3DW1tb6+nsh972NcknSlr+FvccjW7Pitdzhuduu+7GH3/zfsOI+IzswwPBa9Joxg6RRGqphLL+1ALnGQfSkgfES938Pf300zO/uYSXBWH7/PF9lg+R3+LcDNliov0XRCnQbQq0jx6ffRfTdvncuQTsbUX25HY+1yywR6OLPizT18NZQWaj9vl3yXtIU1LTWAzZtZkvZyOe4ioLMWCfMz5PWlBLeSZ18wULdBtO7ANrkCWvAOwb7IYeBhH/7/thKJyCa3raOQ9az+4fT3iwUThP7JP32fddlqrRr1tLSJCx0FoatCHm50zP5yKziXraM9fIZXPimiXfq4wr88Hgty1btrTA84aGhoaGhohNMTwCQF1iy6Q9D+L2YPUo2bvUgOTF37AYZx/xO5fSYIuZFOOeQC41ZQl5PQjZvfNqjCgbe83jzoN943g8iSvAjpFVMfC/mXPsJtmc+Hi9r3GtXYLfSNJaX1/vrQe6es6PY3QbAGON6+LzTfv0zT07I9PH6w/W5mPnelEy9/mvJQTO2nPW6ew804pkezEiYx+cz76jXRiS25Di2runnXs983v00vTvfA9k7Jo+RW3RkGfj2tpaL91UTN9X83x0Jp4xSfrAnvH+Z4kC/H6g/aG0gTV731B6MLfRegKImvYg+86f11lwfC2hQa3aROyfJ4Fwb+As+YMHwTtDzzQAQ/faRmgMr6GhoaFhIbBphrd169ZeaZqhOA6PxeGtnyXkdSnMvaaw3USWAUNAWndvLKTB6NmH5M4xnow08/jxmByXoj3eK0ofHiPk+mn3oov/53p4wLmkzblZQm2XhNzuENkKv9Eex9Q8ZiOQjOfRpTvDp9SHNJs+KP7GfqBv0YbHtRmL959+n3vuuZKkiy66aHrurbfeKkm6//77Z9pCqnzxi1/cu56zFfYV+5CE6meeeWZv7M5cv+7rvk6SdO+990rq4hmj1Myx/MY+gE1RqiuW7CLlG3s/1iuMfw+lv3K7uUv4ca3oE2PnnvAkwvF6rCn33lAC4NFopPX19el9y9gzL2rfn37vZUm9XRvgc5ExT+a2Fsvntnapz/Cc3WTaIe+Lj3coxaA/BzK7m8OfLxnrjMfFZ4iX/PJ5dc2G1O2jWqxtNi7X3kXN0UZoDK+hoaGhYSFwTF6aQ8l8nUV41gJ+j295Z3QunXscVpSakGzd3kNxQNqI+n7vq0tnrj+Wcgkx9t1Zb3Yd93SiLT5jbJPb9bx9L50TJRwkYbd51WwT8druueZzksXQxKTU83pLDXmIIcEj/cGeHn30UUkdo5D6Hnv039k7DOmlL33p9NwrrrhCknTnnXdKkm6++WZJfVtOTHrsjJ4+sjcff/zx3rjoE/NNn5CE+T2bE1gujIJP1tbL4kjS5ZdfLqm7x1w74Tb4rNSPZ1FiLlgbWGS8ttvGYXyZjfpjH/uYpI55X3XVVYNJ1bdu3TqNgYxjBZ48mU8YOB7L2bPK94rHf2b3J3vQGRBj9GTL8Tv3fK0lMZc6NuPM1VlaxnBr5Zlq/gdxDoCP3RlZ1H7U4gsZp7PG2I57TPv6xXM8hrd5aTY0NDQ0NBjaC6+hoaGhYSGwaZXm6urqICWeNjyhm1BhVDNDVas90NiNyLSVpZZy921PghypfnSpjm3UAiXj+X6sqyuh5H6NCPqKWoTPrOYTc+EqTFedZG7p9LGWvieuG8dwHVdBZ2vtqr+lpaVBx4MsLVmm8nnwwQcldepB+nb++edLmlUxulqavcE4mD9UgKS0iqBfX/jCF2b+9kQIEajvUHF6QuOHHnpoeixqO3dWoM/cG1lQt9eW8/llXGefffb0Ow/jcRU6yJzOPDFvLS1aTOvF9diTqI9JAcYaxD3K3vnLv/zL6TFRPRaxvLys3bt3T80Ubi7xtuPY3IQSj+MedceWWg3C2D93UvMK4cwfznSxPcDa8smeivcE57hzWq3GXZwT3ytDiZ8B13FVPePw52zcq7SHWpTrePL1GBrkTjLuYJU52Hi9zMz5qobG8BoaGhoaFgKbTh6Ni7A07ArrhnFnEJEpuEu5BxsiiSEJR4nBww5qQY9ZsLJLE7XUQrG/tWTV7vIbJWM3SiM9YUhHioqONx6Y61KNXyeWo3GW6+wsSzjt7s3urJBJWkjpcd2GgoezgNPIaj/5yU9Kkk477TRJ0mWXXTZttzZW5oV2PEyB7++77z5J0iOPPDI9FybipZ6QZt1AL/W1DjAdxuPOUpJ0zz33SJJe/epXz4zDg7pdGyL1HUFYBxx5YKyxX3fddZck6frrr5/p04033iip7/w1lASCOXAnlujA4Vob9hCOLWecccZMn6Xuvn3yyScljZNy1xJhr6ysaNeuXT03+8zZxpmBO6Jk1bYZv6fG8jCS2D93pvD14X6KTBhNDvcLf7M+GQsFzlg9IUU2J/6bO6Bl2hjGijOga9U8cD+yKw+V8mQAQ1odf+6w3/z+iu279mkeNIbX0NDQ0LAQ2HTg+ZYtW3quo5n9iLcwb2jYzFCpDaQxl3SQKggEjuzJXV3dLX3Pnj0z/Yn/dzuP26SirdCv50mDOZa5ifp+tzlgm0K6dQlT6gdi0gYSPSwoC8J96qmnJPXd3D38IkrpjJk18ITKmQ3PGcKhQ4eqAaCllFRK+9znPjf9jjAB9gz9QxJ2phz7AHtgbmPoQrze7/zO70y/I0SBtfLg8aFkvqwlx7okCquRurmkHbf7cX2OixoMZyh+PQ9xkDp73lve8hZJHaODaRLeATKm5H/7XETblDMg1s3LLcW2GSPzuW/fvqotZnl5Wbt27eoxhPgccI2IF0HOAqXdNufrALsdStDtwdQ+rriW9M0D870sVqYlqSUAAJkmC/izxMMHot2PMfPJXHjaP7fpxT55KSlPMhBZoocsuG+CFxCQOg1IPGbeNGON4TU0NDQ0LASOKXl0rdii1E+1g3SJ9Jylt4GluJcS7dMGklGUSGCOXibD7TFRikUi5TckO097FRme25FqJe/5HpYldUzOwXg98Dhezz2RkJppM0uu6ozR53zI88ltrs5ysxIpGRNyHD16VE888cT0749//OOSZgOY6RfHwdJYJ6S92CeOce9MwDhc6pS6gHNnwO5hHMflzN73GRJr9CTFFnneeefNnAOjfeyxx2b6E7URMHr3JHTWlCVUR7vh2gcPhI/wQGc/1gvDSv1gb67DfUvqtJik20u8bN++verhu7y8rJNPPnlQivcEEMyL2/ajVoP7nrXjnnWmmd1j3tdaQoDI8DyJvBdKzQrAAi8x5mAeIxPyZ5MnPs+KIgP3vPVE8Vm6ONrBJslvaCXQvkS7ptsvgZejyn7L/EE2QmN4DQ0NDQ0LgWPy0gSZDQ+JgLc6Ul18q0uz6YGQXnhjw154+/OJhBzZE153NXsB39MfqWMV6ILpG+Pg+sSDSZ3nG8fASt2ukMUIIdkxZv52thBtEhyD3dJjt1xizWxrziy99EuW9NvLDXlS6Sjl0kdY9vr6etVLc//+/frwhz88jU8juXLcF9iY2EOsLf3mepmnHUwIqb1WkDOOmWOdBfp1omTszJ75cEYemSR7hXRa7D/2JPYSxhm9NJkf90L0gsQR7IkPfvCDM+NhDWmfPhIvF8fKeLg3PXFzJlU7i+a62JtJDSbNesvW2gNLS0szzwufi3hNT5/mnpFRy8CzKSY/53pSt5asV3aPuWbHYx0jc4H1uzdobQxS3+vTfRRA5k3tfazFrWX3rB/DnEQP6fi91GfTjJP2uUcjy0bzx95wbVdWUNm90Lds2TLoHT7T37mOamhoaGho+CrHphleKaVnL4tvV8+O4SVPkFSijcOlcmwetEub2H3i257fkEyRXvnk+rDEeKz3CSkD9kESXkl6//vfPx2/JL3hDW+Q1E9sjUSM/UTqmKJ7SSKhIL3G0jX011mnM2Yk1qi7Z27POeecmT67HTLaVJhHvvNsHVlCbTBPxoP19XUdOnRoaq96+9vfLmk2Do85Y11OP/30mTayTCRua4zSo9S36cX+uz0WeImSLF6ReWHPsIeZtyiBu/cln8wF+yCWygFIvIzLM+24diT2+4EHHpDU7RW35XisbDyG/VdjbfEcjw11ezB9jPZaNBdkWllbW6tK6aPRSIcPH+6VGotwj27G6DG2Wayf20Xdc5AyTllpIY7hby9PlXlrewyvJ4SP46Mv7ufgMXUenyf1i6kCt+Vl5/gna+cJ1TObPnPMvc2zCzYXNRjAY6Nr8c5SP96zJY9uaGhoaGgwPC8bHv+PGRSQCNwW5KUiouSDDQhGgp0n2oakLvr/6quvnp7rsW0eC3T33XdL6piepF6ZEVgNEglSRrQbXHDBBZI6Wx7FQ+kTtgLir6LuHhvG3r17JXVeYRxLrsUo2SElI9khWSMFvuY1r5HUxZUxV9K41IokfeITn5DUSYVIVrQR7Wcek+TxOEh0kbk4m3r22WerLG/Xrl1685vfPJ0LZxCxP6wh8+9epbGECfMEi6afSOUPP/xw2p8IxuRxhey7mBfVCwxzD3hR4biW7A1nh35vZHFYnl2Gda71VepnwOF6Ucsh9VlRPNelcPKMOvuROhsgfXIvV/ZUnEfaZ/1OOumkasYMsjvV7LIZ3JbOZ7wv6Sd9gMWwR91DMbIIj/NzuyznxGcIvzmjdA/YCPczyErsxP5QoVbyAAAgAElEQVRED19ntR53l2Vc8QxV3E+e8QfEvVPLUcwcsHezeEz3c/B9Fs9hz0f7abPhNTQ0NDQ0BLQXXkNDQ0PDQuCYnFY86WpM44TaxB0Z3Nge3ZKh7ahNMHKiRkQV6OmjpM7w7ymYcAD59Kc/LWnWMAtNJgUTVJzrMp5Ikz3sgXZdtYW6MqqPvCQNf3MM14sG9UsuuURSp1657bbbJHWqO1zcUZNEBw/mB3UDaj1Ut6gpokqT/3vYgycDiOfUys1kWF9f18GDB3upy6KzBXPsKhcv4xJVmqjTGP+f/dmfSerWAVV2FlYB+I39xbkeeiJ16nAPC+FYN7pL3Xq4s4WXYskqkLO/ua47NmQu7vSB9lEleVhMlqaK/zMXrMG1114rqZvnaJLwcjqeDJ224j3BOfTttNNOq5YiksbPj1rasziWWrJoDwmQ+inQXLXMXvHnXWzX73/mmPXA5BHboy8cw/OP7+P6s1Zcx/evq3WzBPQeOB/TwsXxx/M5x0MAuN+ytXJHF55NtMVzL6p5GTP3gIddsM+jqtbfKUNJCxyN4TU0NDQ0LAQ2XQB2bW1tKkHC0rJSOJ6sFVZF0HhWBBCJAEmAtpAqKfFy//33T8/l2kgpMCLOxXkhuuAjgcCsPPEvUmeUltywTdLj17/+9ZI6SYXxRYnEEzMzHiQfjo0SsIclwEZJ04TTDH2PEv7tt98uSbr00ktnxoG0yZpERlZzGPDA+jiP7IOYJHaoAOzhw4d7KcCitOmlflxq9j5l/YZpsYZRwpZm14X2mQ/2gycvz8rCcK6n3qLvL3/5y6fnfOpTn5LUSfIe3O0u9PF+gh15wU/AvotrSR9oj/YJYWE+vTxWbAcGF4PFY5/jvLuGInNmkmbvJ9aHfT60d6TxWngqw8hu+I3192QSmfMDGiX2IM8f5jSGTkmz+9BDmzxonOtHRxQvbI3TmhcPzpKjb1TMNQtBqKUq8xSNkVF6IgV/fns6suio4s8InoXsZ/Z/DF5nf7EP/PmQJbimv4S2nHjiiWmoSobG8BoaGhoaFgKbYnirq6t68skney7fV1555fQYL+LqrsRZAmOOhZ1xjgdZw4w8mJi+SZ2EQJoql3YjkF74hA1wTpTo+P/5558/0z7w1F/xeownJsqN46LPkUnAZj1oGIYZ06tJs0yJeaLPSJK0zzjj9ZDS+WSOIxOXZgPFGU9M3zRkxyulTKVo5jyOw93pAf11G0i8thfkdLtLZgvwtGl+rkvPUj8MgXM9eB1bYhyrhyN44ueM4TBmD50BSM0xPZi3w98ewjCUYo5juJ4nVo4s1F3xfT6z8ldu6z548OCGSYDdRpSFSHk6Kw85iedwbcaIBsYL87omI47NtSV870kGMsCOYDlZKjvg7Jbr8Zml4PJgdNcgcP0sGJ9z3L7syR/i3uH/7Fm392WJAzzBPXD7Y7yOB/cvLS01G15DQ0NDQ0PEphje4cOHde+9904lOi/TIPWlCt7UnhopSohIQX4Okg4FK72sfWwPSYe+4XHpSZBjXxxcNwuuRcIlXRPB8QSiwzo8BZPU6Zo9HRjHuD1A6iRglzbpu+v2owSEVMa8MR7mnL9jmR23K7mdxNlQbB/WuWfPnkGGt7S01CscmZVtcqnVWVzsN2OhPaR0PxYmFKVLt20gmXrqorgPkIq5jl8Pdv3nf/7n03NgqCQ/d6nWtQ+ZbZX1YP/xN2nw4v72RLyeJLmW6NivLXXr7cw/rhv7zTUZgL0V9w59YhwPPfRQqrmhTxkLyUoUeeJiX9M4Zo5hD3niYo71orL0SeoXTHYWFdeW//Mc8CTyvu/iNWsMxu+j7Nno7MztznF/u0akZhvLtBIeyO6JpjMG73NbSzydleiaN9g8ojG8hoaGhoaFwKZteE8//fTUu4kEylFS9hLtHsfhCZvj+Z6CiWORzpFMMimG35AY8CB1XbTUsSf6itSObdKlQqlLbEwCa9KgIcXQd5hljJfh2i95yUtmrus2lHg9pEAkG/fg8oTQMT7Oi2C6tMR44/V87j3uhzFEr0cYAwzvhBNOqKZ7Wl5e1s6dO3sesFFKc7uAp8/yPkrdetdSLoFMAq7FEdKGx9ZJ/YTTXqQYDUNMtwez87mpeRBm0jzXZZ29lNbQOGqelrG8CvBE137/uKYmHuN7x9lpZHisLZqSp556KrVdgZi0nnmLjNBttLUi1XHNfR583/kaR7bjCfRhic5y4znYtDy+z+//yGbdXul7iHPc9hrhMYn0CXbqdnqp7yfh9yL3XeZRyjr6HGT2TN8b7qXpyb/jOUP3Sw2N4TU0NDQ0LAQ2HYd35MiR6dsX21R8+yKF85ZHMiQGxaUqqe8l5BIIkkEmkURPHamTQJ2ZREmYYz1xKZ9IDvEcWOGrXvUqSdJb3/pWSf0SL4zvk5/8ZG88njzW7SNRUnFJ0ZPV+hzFc5Ho3GtuKFMFa4qtklhIzyAT2QDMIWauiWWRIpDQ3SM1rqVnKeFYZ2JZglzX67udgvmM9j+3OWTJlONxUp8BeVYWGHD08MUbMGaGiH0a8tZ0Ox9aCGx5vofiOZ4w2ec3i8d09uTef9m6+Vw7y86K73IszwWPtXSsra31mEjmCQ3op2cZyUovcQ95jJm3EeeJsbhmwZN9ZzY8b2PII9Hv2VrC8WzvuFbDbXZ8xrl3LYv7ENQSUkv97FpeDi2La2XOaywtm3vPSHT06NGWPLqhoaGhoSGivfAaGhoaGhYCm1Jpbt++XZdeeqluuukmSX3jtNRXT6Ky8JRF8ThPveUJWVFLOhWPx/LpbvQgc/VGDeW03QPR4//f8Y53SOrX8/KgaMIVpC55sCfQ5hxX2Upd0uMsGXEcnzsFxet4LTAfX6ZaQKXpKhNUNNHwzLVxRNm/f/9g1fN4PnMSHSpQrfh6u6okqjj5zhMAe+C8q6mkbr7dISCmSpNmg/q5HuvPuaz7zTffLKlT90vdPHkYioeAuPo1/p/r0AYOEJ7kOY7DVWWu9smSVdeCuz3YN57jjmOuQsvqS7I+nsYtAykNvd/xHA8LqAXzR5W8J4ugf/48yMI4PNzJ79MsXKi23u6sMpQejPY98DxTCXoaOFfdZwmgPZSBc11164lEpG5NXf05tA9dZe5z5H/HvsRA/abSbGhoaGhoCDgmpxWYURYU6Cl++M3TRWVMAYnbXfKRhHijZ8ZjgEQQy5f49byEiEtYOJd8/vOfn56DRE3JIs5BKmR8tB2Tqt5yyy2SOqnfpRmvaix1TiO11ETuRBClNZesmAtYkJftiHCJlfFkqbmQ6GnvyJEj1fRQa2trOnDgwNTp58Ybb5QkvelNb5oeA3upSbWZodylYt9/noopC0uosQskybiX3PmFdSaZt5fgkbq5dIcjmKxL51mJFw+sJ8CdcIjYR3eVd6cld3TI7t/sN2l4vznbYLyZBsPTrb3qVa+altxylFK0ZcuWQSm+VlooqyK/0TGu3ciccWopvlzDld0PtFfbm1l6MHcE8fG4FiReB3iokScJif316vWANY1rCVwL5eWiQPy75ujiDlUZg0XrtnXr1g01S6AxvIaGhoaGhcCmGV7Ul2bBw0gL7hLvabsim3EG50GdHuwbA1Q94a+XlskkklpRWqRkbFIkcJak7/zO75zpay3okX5EewX2Ksr1IGF56Y1oz4L1eQJeZ3aZZFMLzHRJPEpk7rrOJ8dmLtN+nTPPPHNamsixvr6uZ599Vt/0Td8kqUuufOedd06PoQQS7MnTNmXSntslPFB/SLJ3adkl7SydGmsFA8eG5raOyMy94Kon83VNRmQFHgZAn5zpER4jdSEKhEN4YnUfb9wHWRKE2IYXnpX6iRroP98z/hjuwf+vueYaSWPNSVacN0PG1n0vujbI93E8xu9ht19me6hWeqfWRryOn5ul2fOxArdp+T0R55A9w37wUCee0TFUx4PU3QbuCa7j9bxPnjSc+yg+v13bBZzxRdAOe56E/vOgMbyGhoaGhoXAMRWAdW+/LNWX2+ywgWXlgWoJSmvl6zN9vafpcu+vKJHWJCukDJgd0qckvfKVr5w5p5aWirkhQDj+Hw/ICy+8cObYWsBzRM2OlRXkdBsB7XrS2ji/fm23J2ReZ27nOe2006q2FAB7fs973iNJ+s3f/M3pb+wnyhnx91BguHvjeUoktytkyW6dRXki8qyIJ1Ixv3lgc2T4rqHwIOVaKaYIZyN8nnXWWZJmU5mxRpTvwnbs+929K6W+tyNw78Csb84KYKHZfqO9K664QtLYrlNjS6UULS0t9eYvKw9UsyF7CjrazfrvGGJePqdug4psxp9NzI+3n91j/tx0W+HQGDyBg6chiyW60JCxN9E6uR2TNuP97kzZ2ZunD4tjHUoc7m2Tjo7EIF/+8pdn2hxCY3gNDQ0NDQuBTTG8lZUV7d69e+q96MUopX5peOwILvln3kS8xV3KrMWTxHZcWh1Kn+RSKu2RcJq/SY4tdVIqUoXHtnEOHkgkVJakb//2b5ckfexjH5vpq6dTypiy23uAxypmdlQ8CN2DFMQ2ndE5C8gYXsZQhxK5Li0t9ebr+uuvn/5+ww03SOo8OF/2spdJ6uaU8URpzmMNa5Iu142Mz5l9LYYrSy2GVHzJJZdI6jzGvJin1K1VLTGzM42sOLLbbj1WjLhNqWN7SM14kJLw3PdOxmBq91xWrob22F/EJLqtOjIJmDLnbqQdWFpa6rGn2IcsfVkcoz9bpH55oFqcYmZjc5sgbWEXY3yxj2i5PA7OyxBl8BRzvmaZBzvgWNgt/gEgSyLPs9DtwUNemrV7z/dwZHOZb0D8PmPzvHdiqsbmpdnQ0NDQ0BCwKYa3tLSkE044YZp5whNCS92bGikvemNGRGnGJQP3uPMEylFK84h/PxeJJJ7j0hEM4v7775fUsY7oNYlU4RknXC/PnMQ4PGL37rnnHkmdBI5XopdOiuNAGqrZSUDmuerxX7V5juewLl4kN9OlezHfHTt2VMvzcD1nFZFlvuIVr5DUFU/lEy8s5jSuH/11CbcmAWf9d68yt7nFTCvsFUo9wey8MGy8JzzuE9QSXWexgjVPUi+hFdvhWGzSeMRiI0Vqz5hLraSMx6bFc5C4+fR9GLPPwDZhPUPsppSi5eXlXtaXzLvUbV5D9jEAi/G4XPeqzZKt02/GzP7g98suu2x6Dloi9oMzPfeNiP33bDDsGdcSZGzdGVctw0zsG33wcl787kWlpXpGF5+/7H3B9fy54PdmPCY+x+YtEdQYXkNDQ0PDQqC98BoaGhoaFgKbUmmWUrSysjKlkk888YSkWfUaVBRVJn97CqhMnQbcyM/fWYVmV9d5YDjUP0tYihris5/9rKROTYXKJ57jCYXdOOyOCFEtgRoA9cYnPvEJSeNAbalT/0b3d3dVdxWTp86KlJ523JXY05RlCYe9ArGrzuK6oc5BNVYLL8mu6W7VETir3HvvvZKkRx99VJJ07rnnSppNTeThL0OVn73/rnJxt+ksaTDrihoMtTdOI64Siqi5+ruTR1Sh+vi4r1ApMY9xbdnX3CfMH9/jeMVnDEuoVeF2NXi8Z+kL+4CgfNSWqDL5Xeonod66dWs1LGBtbU3PPvtsNSWgVA8TGHJm8N88yYOr0+I+8Dqf/rc7Bkl9VR/w+zSraccnpoZamFLcd+7IxTg8nCyaL9wZp6aOzEJpas/i7Jno1+Oe8/ALfx7F/8d3TFNpNjQ0NDQ0BGyK4ZEAGCcMnDxgRlInLW6UzipKOW6YraXayYJ6hyRPKa8IDjPFiI+0cuWVV86MIUrNjAPDM9IMUrOnCYvSI78RgI6Dw969eyV1wbcx4bAn6XU3dGcykT04u6059kRk0pePI/ZH6lerrrEqMBqNes4XWWka+odTD+vgzCv2Adbne8mdWeI8eQiG9yML+aAvMZ2a1JemM7Z72mmnSer2DA4BjAHWGOfcHWhYU+452Fs06rvGwEvXuJNEHL8zSl+nGEYAWH/YrrNe+pNJ4UPJgeMxR48erZaLimOppcIbKoVUS8zMnMLe4j3iCQg8uJv1j2wdRuKJwP1ZGfvoTmvOvDx9V0wizlp6G+w7EPvIWNkztOHXzZzT/B5wJj7kbOQhQF5RPmqEmB9Cv0opjeE1NDQ0NDREbIrhra+v6+DBg1Omcscdd0ialSpwj3b9rUuVEV6eZ9o5k5K9YKvU1/ny6QHoMfXSAw88IKlL6kzJGmx3XiJH6gcNw8boC1IS+vAY0sBvSC/YqAiwRjrMglRdZ89c+dxktgp30fag5ayIp0v0tYTKsT0kt0OHDlVZHvbfIRboyQI87IEQgMhCYviH1LEnTzSehRg4G/eCxr7W8RhYi5e/4jPaRVgrvvM0eJ7AIUrALi2zZ7mvGG/GQhmfJ41mDB5MLPVt1TUNTdSysAZ+f9aSv8d2XYORYWlpSccdd9xgkuVaEuKsBBZwm1aNkbA+cU09EbLPG+wjakTQLHmKPLfHZunIPHm3B62jHYj3htsivVhyltbLCz67jW2I4QFn7UNr4nvS94H3OQKGfMUVV0yTK2yExvAaGhoaGhYCm2J40lg64e2O11xkT0g2zhTc2yZKPu4B6JL2kPTndilPweP2uvgd5XoIePZg0thHD4SslaxBasvGBy666CJJ3dzs2bNH0mw6Msq+uGTn3nNZMKd7s7ke3PX+sd8A1u6eVxH8hvR18ODBQYa3ZcuWqk0ituOep7SZJTFwXb9Lvu5lOk/CYY6FsWRewZ4ui/niXoiB1Kwddq/MszaOJfbH2SafMFX2cmbDo094Y+I96QHcsR+e0ICxu20ySvhub/bCs17QObYXvRlr9/loNNLq6mpvLaNWo5bE2VlM5mXsDMSZHwwvrmmtUCpjRUMTtQNe1gZ2znxl+4J2fV38uQPDyzRojMPtmuyTuL/5vxd85m/XdMV9V9MOuf/BkB3d93tm1/Zxffd3f7d+93d/t/d7hsbwGhoaGhoWApuOw1teXu6lQIpSDG9k3up4armdZKYTk/bcq9Bj+DIvw1pJCjwgP/3pT0vqPP6kLj7oda97naROwnKdd9SHuz4aTye34SBVY2uROkkOiYrrXXXVVZI6ZkNRVKlLp4VN1G0pLq1FCdDtmZ6OLJN2acc9uHyNo5TrUtj+/fs3tOHVEjVL3d6gD7WEwJGNuleh21/dxpLBbZwuVUeJ1Nkz3qHMAfsusgbO51juCVgAyDQLrrFw79ws3RrryzzWkoZn+4B9XvOIzLwq3YuWPet7NUs4vVHcZITHRcY5dhseY3MmFveBeyA6G/Tfs7I2zCXsmecMaxz9ANxmd/fdd0vqtAKuPZK6+4519nRnPv44x7VYXfYdz6MIv9dgrLBc9gp/Z8WYPd6wVkw4u66XdWPOoxbRtQ733ntv1cvc0RheQ0NDQ8NC4JgYHpKIv42lTiKA4SBlIqEgCUXPTpeskUyQiIZi7XizIz0Tl4RthSwP2M2kLn4QyRcG5iXvY2aIWqJhPhkD/aDN+BvzhXTE+Ij/u+mmm6bnfOADH5Akvf71r5fUSWMuqbokHvtai8fL7AJuP/NYxMxWiKTFsUM2PIfbBiJo12N0XGqX+vPh/XTpMrMjAWc3WaJu+ouUzLzDavg+SrG0y71AAUsvMJtJqV4k1jNSZDZj1vK8886bmQOX+LPiyLUitEPlgdy+7cVxaT+ynSxB/EaxVM42s6witfV3L9p4jLNCn69sLzH/PCPw8OaTZ0ucT9ga57BnSCqfeYPSB/aKZ3pyppnZYznHk1e7vTsbM2yU53qWdcbBb6y3xwpnrNC1UhwLs4tzz3gozbV///5WHqihoaGhoSHimLw0PWtFZFyeQ5O3PW9qmFfGLmAxnuPNJd8YS1XzlsQjjjbjORQ3vOuuu2b6Rnwenm/vfve7p+fQPsyV9pBeXAKPYH6Q0jgGSSuz6dx8882SunlEmsEz1m2hUeLCnuT5Cn294jm1PH8w1iwfJ+MYso9FlFI2LOMz9JvPtbedteGI48sydkh9lhP3N5oKZ1Yeg5ZJwF4MGW89j6HK8mLSntsVPQNK7IszOC9e7DZyqZ7r1PdFli2De83nPtMOwDbcHpMB+2+Nzce2na0xp1k8l+8jj0/ztjNvXTRIfHp8WtyXzprwJYDp8dwh3lTqnjOwP18X/vaSQ1K3j2i/ZpfNngMO1sv3aBZT5yWy/LkT55H967Gjfv9EPxH6zfP6+OOPbza8hoaGhoaGiPbCa2hoaGhYCGy64vnxxx/fcwWPNNsN1tBY0pE5zZU6VUKNanvYQlT5cA6UH7UAqkfURagxpU61g0oDtQHB6Zwb1R9ve9vbZn5D1edqlixtF+pJ5o0Acz49MXVsh/EwJ1SvJjAdNVlcAw/k52/G4wmv43g8DMFLDUV1FXPO2u7cuXOwRND6+novlVBUH7k60NUrqEri3LoqoxbUnRnoXY3r4TaMPabRcrUMbXCsp1uLv+GwhdqG8Tz88MMzcxHnxFPLsd6c69XSpX4ldfYZ84bzTBYIXCsH5MhUw8D75AkQ4nfzJP1dW1vTvn37pun6slAGf3a4k8pQNfGNEqd7UgOpUxN64nQ3w2RhPN4u36PijM5y3P+YXRiPq/39eSvNPk8iPOA+e377b6wTJivGF/cO//dPkKmv/RjGwbiz4+gLjo833XRTmnosQ2N4DQ0NDQ0LgU2HJWzbtq2XbDVzD0bycSeFrHyOB/O66z/sAykHhib1GReMzsupRFZ4zTXXSOqMw0gKngIqXuejH/3ozG9IyS7pMD6CSqXOOYFxeRJhmFIMZWBOkNI4xxPaZpK4ux/TV2cQUeLGccbXx93gY6JjvjvrrLMkjaXemls7cAearOwHThdcyxlEFijte8eZScZUfO48MS97JjOyO5P0FElxf7s7NnvSg5Nx6Ir7gHM8MNedPmIwvhf49H3tZWKGXLq971nCAJ8nZ3jOnOP/Y8hOje2Rls7TT2XaBA9p8iQSWUgLcO1DLc1aBHu0FvAe4UzXQxe8QHPsk6e581AJZ5hSt488VZo/B7LiqhzLc5y+eUhaXDPXOnjSB0+wHc/3feXrFc+h33EftPJADQ0NDQ0NAZtieMvLyzr++ON77CJKWs5aPMA0KyvvJTCQXjgGfS7SRQwxcLdjJAGkJoJvSdUVv8N2csstt8yMw91rGbvUMUj65vYmfo9la3BZdomSucK9Ns4Jx6LXZ94efPBBSZ30xvexHBHu4YQ5OPvJ7Fxue3QW78HfUjdfztCHgITqduDYP2deQwVsfWwu9Xs6qsgWvX3GgQ3FE1DH67lk7RJwtPs5C3TGynpx/agxcXssYJ970u/YJ+8La4fNFcS0Tb6fPRjfS/9kffJ73YPkIzyhQ4aVlRXt3r27V0A0rrUnCfC19aB4/3/sd7yulNv/eHZwvzM2L/KalZbiN54RziRjknTXdvn96Enlo3bAbWauyeJYEiFIfeaIVoA+sUfZU5lds8a2soQXvp/oozPM+GzxBO7HH398Y3gNDQ0NDQ0Rm7bhbd26dfqWR0KKb2wkBN7UrkPnTT1UqNB126QFGwpWdrsYUjPejFFqov2XvOQlkjopDLubFxGVupRBNS9Nxu0JoqVOwvJ0WkiFWYJUpD+YG3ayM888U1JnX0TCzLylYNm1cktxTmoMr1bCJvY/Sp9DknoppSdxxxRz3j9ndJkU5zYzZ4dZGSIfM+vvxYszj1sPfncW6OOT+kVoPUkw1/cUdLFd1tCT9mYJer3/7EW3kyC9R80Ddm1v3z0j4/i8XWdZQx6fsd3a3lldXdWXvvSlHquOzx3uLeaS9XFWERMmezo97kNPZsEejXPM/c+xaKHQ5vCMwTNX6ntUY8MF9DWWCfProd1ir/KcoK/Ru5F9xNz4veD+DvEc+sZzB7gvRuZ56+NxLUXGzD3Jt3tkx3tiqOTTRmgMr6GhoaFhIbAphre2tqb9+/dPpc0sFqPmNZR5BAGXvrA9ubecSwNSX1rhrY+kR5tREkFKcSkNaQymBauTOqaFBEKSanT5LtXSdryeS9xIcsxJTC2GhAND9dg9ypB4wuvYHl54HsuVSVrOqpn7LFUaGGJCDop4uqdatAE4C3NvLJdUYzvO8NwG5bY3qdsjzLvbqTK7oEuttUTMmdbD40trjCazM7pUO5Q83GMEAYycc9mH0abnyYo9vivbO5ltVer2g0vvsY/x3Jod5tChQ/r85z8/vR+zQsB+bfd4zEo98WzwZxVjh02zT6I9Dj8A9iQetmheGGv0N3D7HuvB37DDqB3yNIS1+yfzZnR/CS+myjlx/ekjDM9Tlg3d427n38gmH+Gp05iD7B50lrm8vNxseA0NDQ0NDRGbZngHDhyYkUCkXAfscUkemxHf2Oih+XSpMssIAdzLB6mt5vkZ/3/vvfdK6tspkPAuu+yy6TlIR7TvUh9ShyfPljrpjz56nI/HLEqdrQ5G59dB8so8+5wZwfQ8i0JkBUiBsB2XxrKsFJ5IeSMpa2lpqeepGpmQsxdHxiTdxuUZFzxuLl6PfZbFP8W2o9Tstiz3wPTMHlI3Z7VitG7bjZ6w7g1MG5yTMUofj3v0sWeyc5Hs0VB4bFrmuepr4LGCQ5I97Q15+K6uruqpp56a9uWCCy6Y6Vu8hq+Ls91MO+CsyZl3VjCVOcSWhp2P5wTMLnpNOtP350KWzcgzxnicnydwj5qlWtForucemHEOvFhtbb/H+fTyU35OpmWhL7THeDL/AEB/4/07lOEpojG8hoaGhoaFQHvhNTQ0NDQsBI4pLMFr3UWVCFQU+u+qQA9ojudnhmWuK/Wr+0odbXa1ICoFT20mSXv27JHUuWC72zafUXVLcCbfocqAkvM944zJqmtqAq8EHVVRMeg9jtNr3GUqJvpPH93Q7Kl+pE6V4MZ/d0zJ1i2qjYbSQy0tLfXSGmVGcP/OqyFn/XPVh6fLygLCUY14VXHG4OmcYt/cASVLFkc8/3wAACAASURBVAxcre7GfAz2HkQc++QOIH69uF/cJED7uMXz6WExUrd/3enHHQ+GQjVcZeuOFXGMUfVY2zvLy8s6+eSTp+ejtsucVzZyVorXcFOJJw3nk+tkyZi9FqCfE/cO8+xqaneEi9fhHNbQU2+5qjmq3xkXfWLdvbJ7vOe5PzzdmSdN8GdyRK22JsieITgB8dz053p8NjK3MaHBPEkvpMbwGhoaGhoWBJtieKPRSEeOHOlVd45ptDzBK29jN2zHt78nFXVjJ1JFVlLE3aSRfJEUcGWOxlyYnRvkaQvDPaV44rEwVo7BWO2lS6Kk5UH4WSJm/xuHGlyGGR+hE264jQzWr+PM0gNupU5SY3zu6JC50NMexx4+fLjqau9Vq0GUzGqJpd1RJEqV7mjgqcs8IXRkBUjS7BlPNO4JjqXOAYg1dYadVc2uSePA91/UmLgR3x1faDu6v3tpJ/YV+x5nBZfepW4t2c9eoitj+r7fnNFm94Q7shx//PFVRrCysqJTTjll2i57NLLaGlvzcWUaBQ8x8fAUnm9RO8C12QeeZN3XS+o0LoQa+Z5xDUOEawX8PspStDFW1t+TVDCGLDzJHdH8ulmCBcBc1NL8xXsQZsx37M3bbrtt5jqR9fozb96QBKkxvIaGhoaGBcGmGB7w1DtRivGkn7zt3caW2QD8jU1b7mYdz3V3XS/eyu8xhZVL/Z7Elc/7779/eg5hAkg4uCp7W874Yl+chXhQZZT86QOuy0iOnjLJQx2kfkJbt88565Y66Z/5cvtsnD/g7tV33nlnj7UCSrxkKb7iMRG1xM9x/d1m43PqqYqyxLwutXph4Dh2dwN3u1IWHO92JE+8PJTUu5aWK6ahi/2Q+loA5o8yVXwiTUdJHEmaveOB7x4wHs/PyinFNrJk5bE0Ui0cZWlpSccdd1zP7gvbjv1mrG4Dz1Kw1QKja1qNyLwZI3PNXnENSWS1XgaMsbtGK6KWTpHvPeQg0yzw6SkFM40Cz+kak+NYfo8hNG4n9STisLn4bGSd+I55pYQbmq54jmt6WuB5Q0NDQ0ODYVMMb2lpaYZtZTYJZ1yehiw7B6nBPdJcUuDaSKhSJ+UhccA2kKyQhKMEgITDJ8HdLonG1GLY7DgHaYXxIvm7LSyOuVa4Eokv2ghgFbBP1/N7ctoocTO3XqTRy5DEtfRikR7I6jZYqWOFMOEnnniiyuBGo5FGo9FgORjfK5nNVpqdP/rpDNtZdGZzQCpH8nYmxvekfJI6+wt9ZF24HuWbotYjSqdSv0gpc5bZKOkvx9LHIbsf+/bcc8+d+Y2962w9svIhVhMR95uPg73pLDhjrozjs5/9bC9pAKDwtNv6Y7+99I0nk3Dv5vh/Z6Sc4zauoSTFzC1jdK1O/K2WFMOTjMe+ANrzPer7IoJnld8T7s0bUQvkdw/vjJX7sexH0iFmacK4x1hbT7QRz8mSPcybQLoxvIaGhoaGhcAxMTyXaodicpB87rrrLkmdhBx1vw4kj1oKM/S69Cl+unTm5TSkTtpzLz2PA4zJnL0oradRcs/H6FXkXnpZ/Is0q++HcbluHkkWL1TGG9MeedFIt5E6S4xj97/pB8dGVkii3GiTqOnS19bW9Mwzz0wZa2RADmf2Lr3Gvvq6u/ckc+oMUOqn1oIt00fGFUu8sDfQHLhnJ2wulmlBO8Ce4Ldrr71WUqex8LmWOpu0F8JkX7MvYqkZ9jrHcl0v+cJ+uOOOO6bnunere8E6Y8qOdSaW2SY5h+vt2bNncE9E7QB9iHvRPTfpg8d9Rgbk2gZn017kNLIZruN2eZ53ntRc6qeYc3u8e7lG+Bwyb8TWZWy1ptlx+3/Gjrw8mNsQ+T1qdJwVenJq+pppd9yzmE8v2ST1k25v3749LS6coTG8hoaGhoaFwKYY3vr6uo4ePdqLeYklf1yaQPLCFoRkFPXvSJ5ezNDZGxJwjDly70/3HspiW/j/3r17Z/rCeNzjLrbLdTwLB5KOJ1uN50a7XmwLaQZpPrZDH2EZtMtcYLOMsZC+Lm4zYE7i+Jwpue2L8SGlSbNecly3xvBWV1f19NNPV7ObxGt5uRn3zhsqPprFJcbvo0SKVAkrYw6Zaxh+5uFLe9432ozaCbeL0q5nSfH9EOfE4UwrahTc9uk2G7QBXixZ6tu8azawyCRqhV59D2XetVzv8ccfH0wavrKy0lv/rNyQszT64rGwUrd2bjN2VusMLJ7jdnLWMss2wt73kl6Z7db76GNnzfy5F9fF579WxDeOq6Z582TVbnfO+uyZpPzZEvtf8zr3wttSN4/RT6R5aTY0NDQ0NARsOtPKc889N2VAxPHEt6u/8T0uy+O84jmuS3ePJPcYlDoJEQnB4+9gVbEf2XdSxxyxscTfuaZnj/BPj8+T+mzQJW+kwjgnsCeOgckhySOFkn8vMgq8TpFqazE8keHRLtd1Cdbz/sU5qcWKRRw9elSPPvrolD25bc3bztpzKTCOze07znI8U4jUScnMHWNkXjwvotTPpOJ2MrfpxD6yHoyLfehliDLm4uvg8VLR7ueem5yDbc/zgEb7n9t9vU2PrZL6XnOZp6Cfw3z98R//cXqsI8vTmnkXcv+5RoL15z6K/fEYM9af9RgqsusMzPdfZjP00mLuHRzX372e/Z7wOYjPHddyeT5MEJ8DPh7P1uKet/H6XvyafeUexpFF1ryA3SYatWOcEzUjLZdmQ0NDQ0NDQHvhNTQ0NDQsBDYdlnDiiSdO3dGhz9FhAurrSWfdzT2eA7IqurEtDJfRRZXfaBcVp6seI42++uqrJUmXXnqppL4RH9VJrFYc50DqjLeuwvKEylJHx93wy3juvPPOmb+lbt4IyPSyIFB9+hjd4FHr0CdP5kqfY1kYNxYzDi/vlBnFo3tzzXh86NAh3XHHHVMV0yWXXCIpr3jO2mXu7FwHeAokr5TsqrjYf3d04Lq1hMBSX5UJ2PdusI9j9PJTWTC0t+3qKMbjezWqvtxZwZNI+70R1aGo2TxFms9fts4cQ189rCj+TbKCL3zhC9Pr1lTi7rSSOXm4kwOhS4wDNX88B/Wmt+fqOg+vkPpJwz2VXbZ3acdVfD7uof1dK6+VlQnzPepr5qptqZ+cnHvAE2v4mOL1eCbxnHUnxLgPPFG3O6HxezyHvrFXh547jsbwGhoaGhoWAsdUHsiDuqMbNZInkjxSFG93mEg0nGJMR7rwwo4eBBmlZ/qAJHfBBRdIkl7xilfMfB+vR9+QGjx4l+vjECJ1jNSZgzvWZEGVLr0gCcHezj//fEnSAw88MD3nc5/7nKR+SQ/6gWEY5hWlNCR5Zy7OFiKz8GB05hXJOXMy8cDtIaeV1dVVPfPMM9NxEfycubf73+7UFI9zxx8v9eOStqd1k/rSshdxjVItx7j7ee0z9smlcQ/yzkrvuLNQbU4ivESSr3+WABowLi+27Aw6WwNAu4zbg8El6YYbbpg5djOu5SCyC+6Hc845R1I/GTpjjmE19C+GOcXvAXMdk1fQb0/j56w2S6dWS5LugftS/fnijm+ZI5onrXDHOzQZ0TnPnf5qYQieZFzqJ2Pg+ebao7jvsmQSUn/fxXllrSNDbgyvoaGhoaEhYNPlgUopvbQvkeHxpn744Ycl9VmA/y11UoVLQEg6bnuI0iVSCuV7rrjiCkkd84ERRanJ9dQuHTCe2EeXtDiXY+hjVvjRbWa04X+TgkfqmCrSDDYJtzfS12hvdDuSlwfKEjzzHZKb25nchiB1knuUoocCpU844YRpGi3CHwhtidf0MAdnPnEtPfyAv92W6oH1Ut/12stdZUzY59YDf13ijsdwrmsd3GaUBfN6ELTbjuK6eGknv46Xo4rXYz95sndAm5kt1xn6ULJyTzKxkYS+tLTUk/pjsmnm//LLL5fUaZIoJOq2aal/L9GeJ3dwLVI8159rntIus8e5jdrZe8aendHV7I5xH3hYEud6maL4rEKj5M8oxse6sZZRYwLD4xnsqcuyROfA73W/X2NiBQ8nmzdxtNQYXkNDQ0PDgmDTNrwsgWqUzpwBeVFA91iT+l6MSGFIIJ5aLEoxSFKUnvDisW6/iH3z/rtEFKVYL/eB5OHlkJA+MsmuZusAkXkxVuwGJN2mLU+DFO0L9NXbp69ZGRIkVk+z5p6MMM445lqgccTKyopOP/103X777ZL6Xq4RWeLd2Kcokbpk6JK1M8C4Li7FOsPL0izV0p3xvc9xPNZLujjDdC9HqZ+g3dfFEx3HMTtTdE9CkKXbcjbF9bIAfm/HA+mz67zjHe+QJP3cz/2cpLG9PGO2XGvr1q1piSeAhoeSSHhler/j+sNWnAG5TY/voye0J292/4JMs+TB6Nzvfv9kGoXamrqdLj5DvFgxnzzn/NkV/8997mzMvetjf7i2p1D0EkPx2e/3uJeNymx47m3ebHgNDQ0NDQ2GTTG8Q4cO6c4775yyqZh0Frh9yKUnJIQsMTPMBLbCm9yZWIxXc+8oJCFPzBolEdfJuxSLh1eUPrz8D5II3yMJcd2oc3advdt9smKKtBftHlK/NIbb3qSOXfCdsxH3eo3fMedeZDNLFJ6lF6ph69atOuuss6YMDykw9sHtYp6mycupxLG5BO8xbllJpqzESfw+S+Y7lBw7+8zarc2be+tlxzrLyeLwWLta8mYvlRPnxG1QwO+viFrSb48DpDiuJF122WWSpHe+852SpPe+972pBy3jOHTo0LRvbqOUOg9rbMOxtJfU7Z14n3h6Nrfpu1YnPkNgQBzDs5D7n+/jPDrbjOVtauPyfeQMzwtOR/brSeK9tBB9zGyhHqvp57o3qtR5t3taRN8zQx6+Nc1MljDetYfzoDG8hoaGhoaFwKYY3pEjR/TQQw9N4ysyKcbtVW5Tc8+k+H+Xyt2byPW78diaPSzzSHRJ13XOSCjRXuWZJ5AUORddPl6I2BRiezBXznVbYWQUzIlne3GvMI8Zi3115uqephkLdQmPNc68AWtlYDKsrKzoRS960bQdWDrZGCJc5+82j7h3nNm5ncRjD6NtzefO7SGZx6VL2M6mXIqPffPkzX4v0Ha8nzLbTPw7Y0W+LjUpOivb46yP67stJ57jNjs0MnhqX3PNNZI6Lz6py7Ry5ZVXShozvd///d/vjYX2d+zYMV1bz1gTQX9j9hipe5ZErRTMyu2jgL+z0kLsK/d0ZA74Pl7PPWvd8zlLjg7cQ92TmEfNCxgqOxTbjKgl7nfPXh+T1DE8z2BT87qO7frfmc0dMMeMuWb7zdAYXkNDQ0PDQqC98BoaGhoaFgKbDjxfX1/XQw89JKlzlY/pelxNh4MIx9RUJBGk9EId4Mlvo0EadYO7fNN+po7whKioB9xtN6rOnJ5Dq6HvtUTAsU/ugOLql8zxxNVr7sjhaYLisR5A7SEhmfuzX3+okrtXVK85HUhdAmDWFJVWVCezZzwA2IPgs6B+dzioJebN0oR5QP5QYDZwxyOvnRZV7B5CgqqcOfXwh8yo76p7D6WI+9sN/X59bzuqmFz9XVvTLDyJT8ZDsoSLL75Y0qyaHweW8847b/pbLbxl27ZtOv/88wdDTPg/fcA5jnuKOY994FifQ851NXncq67y8+cPv8f7kvX2+nu1GpvxWI6pqalrDiLxN1dhZ+YgT0rOOvs9kakaubdrCfyzdas5tLj6N96DzKObbuZBY3gNDQ0NDQuBTTG8Uoq2bNnSM5hG438tiBZpCgeOLLWYB8YimZB+6pFHHun1icTLLgnw9ncpKvbbAyW9JEbsI5Ib7SIFcl13XonXIwgWqaXG3qLUnDkUxD65wZ2STVJnPPYkwi5ZZpWVkbhog3HT5yixOpuqGce59tra2nS9brzxRkkdC5C6tFBI4x7I7GEqsT8eoFtzWorSZa2kizugZKETtbRZfMZzvP1aCMU8bM2Dbp1hRNSCxocq1Xv6tlry4jh+2kXyJjE0mhruedLlSV1ZoD179kiSLrrool7/wZYtW3T66acPJtn2McI2eHZwnSzxgDse1aq9xzV1xyp3eMruX7+XPJGCO8TFY9h39NUdUrKkAjWnMmd2mVOW77taYve4Bux9r3yOJjDTKPh+riVhj9dnn/lzYh40htfQ0NDQsBDYtA1vNBr1iq3GN7azNaQX3sLOTOL5Lj17WjBYVHzbIw15QmvXNUcJ2O0gziBoK5YpQoLEZudJlilLgm0iSj4exO12zEzf7ymLaumP+D26fAOkvb1790rq2zei7cglRHdzZu7j9+7CPqRLp7QUNhTKBGEPlrr0cPQT1uou5plUSbKAWh+GWLSnMnNbXuaCXyuE6kkG4jWdYTkr87R8caxuU6kFrcffPNjepXIvNZT1zUNCsuTRsClCcj7zmc9I6pKhs//iuLgnuF9OPfXUNFifa5122mmDJZh8ntgzlKEi4cFQuR7G5ImtMzumh05l4VaxDanP5Dz9YhaCUkuMXNOmZGnpvM/O2rLwBN8HtXs8s6MyX/MwMN+rzjCz8TuTXF9fn5vlNYbX0NDQ0LAQOCaGx9sYiSUrTePpeZDWd+3aNfO91A+8RYqETbk3W5RuYUVIdDWbTWQmHpzsxU05JzIuPEO9AKJLyQQXZ+makAY96TJ9j16c7hXlbNBTc8VzCeqGJXrqJA+SjX0BjN3TEw0lil5bWxuUtNbW1qbrTyqouHeQ9mHLjMnTusVrsJasD+3Vgnjj3qnZNFy6jdejT860nBVG9uyStTM5byOCvrj3nLOOoWKurLPvu8xm5fYr92R1NiR1mpA/+ZM/mTmW/YcNj/tZ6p4DPBceeeSRdPyM7aSTTurtrYzluP2VZNL0G7YpdfY997x2j8+h8jPOSDyZQcZa6VuWNMLP8bVyz+Fa2jipzuR9D8V59+dMjVVnqfo8wUJM3OHH+nc1++lQyax4bzSG19DQ0NDQELDp8kD8k/KEpUgI/MbbHskbqSmW4PCUQe4J5sVPI/NCSsKG5TFBXiwwXs+LQnrqpxjvRd9Iq+Zs1L00I3vyooqAc+lrlNI5x70BnaVlkp17KNIX9yjLYiGRyjxJdeZh5Xr3IaytrengwYPTNcRe98ADD0yPwQ7nMVSMNYu7cV0/TM9TitW8zeJ3PpecE9fSE3D790Op82r2WLcvZuviEqx7BWbnDEnj8fvIFr1P7knKsdGTkHsZBnfKKadI6pgfezmyefqPB/MzzzxTtUvhHe7zlhXzdS0AfYDp4TkqdVoG99IEHqc3VCiXY9h3QzGONTtYVnC45mHpv2faCO93LYVahKc59L66zS2LN+U5Gj3H4zgjap7eQ16ivuejtnAjNIbX0NDQ0LAQOKZMK5mNC3hRRbd5uFeT1EmAHmflCZIzrym3Lbndyr3MpE4C8RIogGOjlyZ9cG9Qt4dxvSj5MCecg1RLW1kSVJe0a4lSM8ZRK6rodp4sGa7HCHkB0iFd+UZMb3V1dXo+TDnG4TFGpHC8/NyWl3kk1rQDLnHHufF5cmk28wZ0m5bDs/ZkcEbnUnW07TAuz8Lhca5xTtwO47GjQ/Gffh23c/N9LA9z6623zlyHuCvOQdKPbJ7sKzDyWPIrQ5Z9ZIjNsGaM+dJLL5UkffjDH56ewzz72GrZbaLdkv+7h6979g55wLp2ImNxvt9qWY0y1uv7NyuC621tlLXEvbmz66HFgfHzzOd5mj1DfN7myZ7Cc+HAgQPNhtfQ0NDQ0BDRXngNDQ0NDQuBTas0pb6BNjqGQJfdYO1Vq6OK0dWd/IZ6DfWB/x2PdRWWU/9oZHfVhddZQ8URVZ7Q8zPOOENSR71dLZKpFlD1QMFRBXsAJU4b8TtcumnPA8IzQ38trduQ2sBVpozX0zkNpS4aCksYjUZaXV3tuYDHdWHt+CSQGfdx1j0G2TMvrpaK143fx/lzhwNXT2Xu4+5EwN8ck6l+mUNPWecJF9zJROrm26tigyw9mKsqa3snu54nUHY1GN/HfXDLLbfMjJ01ZVysX0y+jNqL651++umDIS/r6+tVh434f1cB0ibXi/PkSbtdxejq5Kj6c5WmOxwNhUx43UNXS2dOWf5ZS+eXOSAN1aOLfY6/ZU4psf3MTFJLyoAjHM/ObN08NMfvo0zF6c5486AxvIaGhoaGhcCmwxJWV1enEkkW1M3bFqM2Uq2Xl4lSsxtrn3jiCUldcKqngMrcm2kfZoLLMedGtgZzwMHApYhLLrlEknTTTTdNz0FCrAX+4pqNET7+7iEGzmBoO5ZZ4jcvLQQLHCpHxG+wQ1yyPUF0dHTxAHZPdJtJhy4NRqcUB67l7jDBGkud4wIsAibMXPJ9lszZJWyXMjPmzZjcKcrDNzIJ0sdOP7K0Ye5k4e3WpOn4m6edcueVeG6t3AzMmDXmnskSQdecgPie1HBZu846SBodnxPsL9jgrl270vHTh7W1tUGGlzmJxLGjccJhLPbH96zPrQd/S/2SVaw/c+D7IbZXqyKe3TseFuLJAxzxueMMv8bwhs5xrVDt+9gnZ3o8k4cSHTij87nK7kHmZGlpaW6W1xheQ0NDQ8NCYNMM78iRIz3WFKUNGI7b1mAmsJgs8Bx4KiRYBtJgVtYGVuhMEpdoXNxjX1wP7YHUUeJijKQmch0+Ugz2iocffnh6LmyT8kbOXJCQo2Ts5Y1go7AR1oDjYHGxL8yNn5O5WXsYidsMMl26S6SllKqktbS0NOPKzhrGYr4wPE8h5+WoYjJp9oanbfOCrLEfwBmJ2zE9IXGEJzYHWfozZ3C1EI/se/7PODO7m5QHEzPHrslwFpwFDzuzB+zrGE7itjtAcgEC0eNccQ8SInTw4MHBsJaY8CILS4jHxU/mib7RF6l/39Xc9z00Jx7jZYnYU5lGoZYIwhMcZOnB3O7rv2c2NU+z5jZ3t1VKfX8D4LZRD5bPxgWrxoaXJUmo2ercjp6Fd2wmhGF67txHNjQ0NDQ0fBWjbKZ4XinlSUkPbHhgwyLj3NFodKp/2fZOwxxoe6fhWJHuHcemXngNDQ0NDQ1frWgqzYaGhoaGhUB74TU0NDQ0LATaC6+hoaGhYSHQXngNDQ0NDQuBTcXhbdu2bXTCCScMlrz3DA2gVvQwO6ZWXn4or9pGmOecY2n3hWjr+Vz3r+o6NWemGHfjWUaee+45HT58WKurq70Lbd++fbRjx47BGLChTBq1/mdxb/HvoTEP5f3c6NyNkJ07FD82T7+y346lj8cyvqxUkv/m8V3ztB/jsJ566ikdOHCgd9LKyspo69atvZizOBfE84F55tozPNWeN0Oo5Vb1awydO098obc/T99qeTjnOedYft+ovFbWD4/r4/1B7CixuVm5rZhN6ejRo+lzx7GpF96OHTv0xje+sVfXKL68PFjTU+6waWOQqm+8WgLYoUDD2mbNkuvWUKsb93zh1/Yktf59/H9tsw797fPpD6Da9eN3noQ7S6RMfbMHH3xQ0nhzfuYzn+m1KY3X+9u+7dt04YUXSuoC9D1YWerXOPSag1kQqj8IPIDVg1XjbyALFo5t+f+zY4ZedF59fZ5K8X5P+OfQWvr6e+B71mf66KnavP34wCURgNfh84dYllCZJAzLy8v6yZ/8yf4EaLzul1122bSGIgkMYr3KV77ylZL6yQM8lV2cc0+IXquans1xbb9tJlED4Pr0Pc5TFuAd4XsonuvB8bVnYAw8r6Uhq1Wbj2162jH/m37ExBGcT0ISngec8yu/8iuSpA9+8IPTczifvu3evVtf+MIX0rE5mkqzoaGhoWEhsCmGV0rR0tJSNbmq1E+55Il4s/Q5nrDWUZOipL4kshk1xEa/D0nrG1H/LLFtDRn7AF7KyM/JWAnfoRbwNFtZ1W7vg7ORLGm2S26ZBB/7dPTo0V5pqaw9319gqIo0qKWHy5i+S8+eUiybp9paDjFyzvHSPjUGFpN6DyXjrsGTu9MG8+vsPV7D2UyNFcR58JIu7Adni3GtOTbug9pYV1ZWdMopp0wrqT/66KOSpIsuumh6jK9lTUuTrV8txZvPT7Z3/L4ZUudupELP7nXacS2XJyn3hOHZeGosPZsT2nMTlc9F3NM+dn+GZOfA1vy5Q0L96667TpJ0ww039MbDsS21WENDQ0NDg2FTDG9tbU379++fvn2Hkix7guRamYn4W01qcukiSiS1gp81J4bsO2eHWR83whDzOxYJayNHgJqtKv4/k6giol0gJnaO7Tr7iWWWmJ+YMHeIPUftAGwzYzPOklzKjPPl7KFm0/NrxP8POUNI+dw6Wx5yXvA5rNmia3aaeKzvlUzKdabsNkR+Z+7jdZG4fU7YQ9zfMbkwc8ExtXI3MXm0l9wZsrEvLy9r165dvSTLMSF8jeE4W8u0EJ482hNPz5OkeihpuvfFzxkqAFvT7PhezcoF1bQOPgeZzdDH5/egP+fjd76mPp/Z89vnnr358pe/XJJ09dVXT8/Zs2fPTHvHHXfc3M/qxvAaGhoaGhYC7YXX0NDQ0LAQ2JRKUxpTUNxnMycTVwO4s4pXYZb67stOwWsOCPG7jdz454nHGVJTDLllb7b9jeJV4v9r6rYhd2TWB1UJoQSuyohjohJ0dPWO18vGx7FUVn/22Wc3rGnmDi6xXXdkQW1GP70mXISrRlwdmlVO9vmoqbaz63l7Q84StTgrbz9TT/qxtXPnifvz+wc1dgw1qd2nvhaZ0wr9Z53cjBH3m6s/vf5axNLSko477rhpzcNdu3ZJkk488cTpMdRcA1FVHvuWPTv4rVaxO7sH3MQwj5NM7Rni90Q0QdRUiiALD/Br155VHnIUz6k5r3nYUgTPHZ8/H1+cE98rqNR9vb7lW75les5DDz0kqatteN999w06zEU0htfQ0NDQsBDYFMNbWlrStm3bqq7yUl8icPfWzG3XpYmag8aQO/1GoQZDGT1AzT0568uxZCTYyLCauSM7y41ZTeI5UcJF0gJUl3ZJPEp2VEnH9RvpmetlQfkEicZA0hqQKkLt0AAAIABJREFU0l0SjdJsrcoyYA/Fc2AkXqndA6czRwp37fbruiQc/+9Sa62tiBpbGwpwdkeDWpiFV3aXZh2MYlvucBPPZY494NzZWmRQtMt3zLXv4bhuhMx4GxlKKdqyZct0L15zzTWS8rXc6DkQmQn9OXDggKRuzDBfZzPRuSdm+YjH0kYtxCoeWwtlyJ47YCNnrNjHWqgR42YNsucOqDkdDiUtYP74ZB/i7BjB+awBzx2uh1MMziuS9Hu/93uSxgHnkvTII4/MHZrQGF5DQ0NDw0Jg04HnKysr07evu9PWvpP6DCFKsUgasBakJD49RU2UomvpyFyqifBjaynM4rm1oOiNUk3Fc12CYxxcJ47Lg4Oxk+3fv3+mr/z+zDPPTM9FKkJyQ3pCAs+kYHIRPvLII5K6OeH78847b2YssQ9IZTGw3LG0tKTt27dXGYvUzYunoXOWEZkpx/o5tM/3ngjB/x+v73s3shlnqG47zNayZjt1yR6mnDEX3zN+T0SWjUTt81ZzF4+s3dt19sk9GtkjtjX2nbNp2owMz+/1Ukr1XhqNRlpdXZ3ajLE3R7bG/13b4H1D2xHBfHCfPPHEE5L6Nr19+/b1znWGf+qp46LbsJu49mhEPEWeaw2yRB5u36uFOjFeqVsz5obnLM8QNEFxf7ttjvvH++a23dhH1oD1oo/07Zxzzpmew2++ZwHjPPvss6ffXXLJJZK6BAQ7duxoYQkNDQ0NDQ0Rx+SlCTLPN0+jxFseySGTtLAfudTCWx8GgQ44k+yQHlzCz6QywHWQGPH+QuKJ0hkSW4191Lwq42/OAmBlSLtkBpc6acy9zZwZcW6029EHl9a4LnPFuKVuvfCA4hjG/dhjj0ma9ZYi4StsYNu2bYOMd/v27YNp1NxrzW0pjCtKwC6Fu4TIHkK6jnPijKrm+ZrZDPkuejjG68fvXSNS8yT1NFIRHvhPn2Hg0WPRg/sZu+8/Z1lSnxGzr3ycUftR85R2xhzn3gOMN8L6+rrOPffcmT4+/PDD09+d/dM/7n+OHWJcgH7yXKKP8R5zezLt8lzLkqKzn7GTM+/uARux0d70dYqB4G47hcFyjj+T43U8VZv7VzDP0avb7ZZu08s0Z2eeeaakLpm8Pz/cHixJL33pSyV1Wq/NoDG8hoaGhoaFwKYZntTX0UcJGMkDiRB9Md/DHKK05Lpf9+gckoC4jpeQQSLgeplXGUAKRPJCQo6ej57OCMmG792LKfOWArQLa+Iz6t+dQbhNxXX5mWcXY0bCQurMmAtMgb6yFhyDFHjrrbdOz/nmb/5mSbN6/iGGt7y8PJ2vIVsXY6W/tO823ThGjz10r8nMs9i91hizS/xxbumLrwuM3NclXpNjOddtbCBLBO42ZD6JRYr14DwFl4PfWdNs73gqLrfDxXMYO1I/mhLXPsR7gu+Yz8OHD1e9p5eWlnT88cdP70/6ENPhuV0MW/T9998/07fIhLmHOYff0FzQ/t69eyXlnpfOwBgj93hk6+7xjIaFOcjsWLVEzF5KiO+j96t7lzJv7sUd7XC+zq5d4VjmhrWOv/l4OJb5jXsVFg37ZE3Q5jHnvEck6YorrpAk/cEf/MFM3+ZBY3gNDQ0NDQuBY2J4IIs5gqU8+eSTkjopD4knKxUCPEEt0kW0bUm5HdG95dwzKNog3AMRSRemhaQfGR7tcw4SFSwk88oCLv0hMTJHWTwUkhrSmNsmnWVndi36iiTpiYEjw+McGCzrg8ca+vLoDerlh0466aSqtEUcnvc3Mm/G5pKhFxaN59Af1qrmZcb3sX/OGLk+Er73g3HEc92Gm9mkXKIGbnfKYqyc2TEeL4LpcW3S7FrFfjAXaDaQrqXuXqA9rof3IXMT9w7t0D6FWjk3y3LCmGNml5p2YNu2bbrgggt6NtfITPg/9xb79iUveYmk7l7IvIyJ52KOWW/mALtc9AfgeeJjr3moRngZKC+SHZk5//fCtv68ybJfsa7uqe73U9Y3167BuJwFR7bmsbDMr8frxnVmr1DAlcTQrInbZCVNbbkcc999981VTFlqDK+hoaGhYUHQXngNDQ0NDQuBTak0R6ORjhw5Uq1hJPWpvasyOTaq7zwdT83FPAsidyrvbrQgU5egUoByo6ZArRPVYxhnXdXoNcX4O/YdKu8OO25wjqoMVBb0oVZLjb7jvCB1Lr78hjrCnYKi+oPf3FUalQPq3mg8pm+oOZaWlgbrgu3YsaNnBI/7APWFq7uZLwzcUT3tLtZumPc0XpmDFXNN33F4cLWbVHekydKQAXeYcZU93zOGeD/RNw9VYL8RfBtdy1kjPrm+7zv2alRpMreuymLfc2xUJ7oa1FVZWZJid2QZSg21detWnXXWWdUQnXhtzATs5zPOOENS5/4eVYxcm376HLvaGrVu7D/X9QD37B6rqd1q6up4vocYeHgAwLFH6quuUfeiovVkz7EPqCwxcfD8o298xnPd6cyf9VlqMb/X77vvPknSPffcI6kLQYjXob1LL71UkvSBD3wgTWadoTG8hoaGhoaFwKYZ3uHDh3uG9EyqdddbpBvcTrOEtZ482F1VM+O3M6xaYtYh47E7DTCe6MLshmzgQbweNB+P8XM9aDgGqyIp8ukOBxzLuZkB2h1enJVk5Y/caI1hmDWIjjzuVj/kHuyJxz2dVvw/v+F4wPxlqZCcNbPuzDWfzgDjd+545GnI4jk+T7BpJGCviC7VQ0lq5YIiE2BfueMTc8D4o0MFLIe1gum5tiALqGYN2JuMx9NSxb3j9yBMgns/q7Q9FGrkWF5e1sknn9xzXov3C6yc/Ur6KtYlCwHxvehgXBkz82dHDK+QuvnKyqD5Oey7oWeUJwd3pxieA/EZyrqyD/hkzzCuLLG+X3ee5NHuQOjB6Z40Qermib3C9ZydxrVmXNdee+20TxslDQeN4TU0NDQ0LAQ2zfCyxM3xLY+kwXG8mXmDoxOODMj1uEhJDz744MzfSAxRF+wBsh48nOnSa2VakCpgljEQ3OFJdvnbbTzxN+8zkt1FF10kaVaKow8wPOxXuJp7qZcszIP2kbDoG+O94447pse6RMr8uW0P3b7ULweTlf8BpZSZ34eKqqK/dwbkkmnsQ43JOSuMNkjG6uvMWL3EUISndHLbVky5VCufVJNKsxI29J8+M172RVYeilASnxNYoycXjmDvMC4+a3bWCBgm12f+Yioo7C9nnXWWpLp9S+qSR7sNOq4lv5Fk2IP8mb+oHfDwBg97YW49qUCEJ4D3zzi3Xm7Iy3Vl97KnLGT+fc28XJHUT6vGs8O1EPEc9g4snfmDOXtwedzbnirPGayHWMQ5YDwcy/WyFH6sD/a9iy++eMaGPYTG8BoaGhoaFgKbLg+0vLw8lUgynTNvfJgAb3kYXpbU2W0ASFpeSBCJDo9BqS/1E2iKxIN0FiUA905yVoKECtOIx8CS/NPtj1FqYp6QUpC0sJMgaUXp0wO9XT/ukmuU+CiQSUkfJCvm/POf/7ykWYZBX2KQdYQH2Er9kiEbBZ5v3bq1F2Qf+8264kXmczoEZ7xoAZzlRrbG3uF67BGX2uM53n8POOfvaP/NPJPjse5hnKUW8yTp2LMyLQTtujSOPQtvQ9hVvB7ry5y496Gz+niOs15PkpAl+0XjM2TLI2mBe2RHJsTYPFGxe3ZmbJaxeh/cfh7vafa8sz7XDmQe7MxL7f7PUsK5v0St/FrsI6yfeadPtD+UysyTR3uasqFC2DwTOZbnKeOMnqXOev3ZD+Jzh3mivW/8xm/U3Xff3etHhsbwGhoaGhoWAptieOvr6zpy5EgvUWuUxN1biDe3xzbFt7x7w8EmiKFBevPip/GYCy+8UJL0zne+c6YtpLSPfOQj03M+8YlPSOpLL55yLMa0oAcn3g0pmb9hEPQ1Sp/0AUkLFsxcYSuIUoxLzTBX4oluv/12SR1bi+wBhnfZZZfNtMF1b7vtNkmzTJljmE+kNOYCRnHjjTdOzyHFD+tx2mmnpaVtQJQGPe2UVC99xFwSExjtiF6YFEmYY1gf5icyIsYEi3b7CxJyZm9mvV3azNKReQoztx3zd1bg2OcCz1VnO1Hi9xgx/n7ggQdmzrn++uslzXrAuU3cWWKW3o3reGo+t/PFPepscGVlZTAWDy/f2G7cB64B8XvavXZjf/hkDtkP3K/EOkYmjBcoyalhqtwvzGmcJ9aQPcizwllhXEv3OndPcvf8zbw0ma/4PPO+AfdJoH32Hc9g7qt4b9AHngt4MPNM4XqxNBzHsF6ecJxnZNwbPE+Zr2uvvVa/8Ru/0RtLhsbwGhoaGhoWAptieGtra9q/f3+v3ExMIEoBUWx2XhKev6N3Ty0RMizq8ssvl9SPx5I6by/ac8kOCSGWiPfMF14OCCkwetpxPlIZ0qV7rXmGijhPjJPPmoen1C9oC7uB6dF3vKmuu+666bmeDLtWMiljlIzdmQN9I8mrJN11112SpJe97GWSpDe/+c1VGyDX9/idaD+oFULl2nhuxawyzANjZT5uvvlmSd06IBFjT4jtu03o/PPPl9R5JsZz+D+SLnufv7OiorUYKme07okp9TOI+N50b0qpm1NsuPQRxoIU/cpXvlJSx/gl6aabbpo5lvvJGR8MR+rmmHu+lvg82rM8gfJQLBXe4e6JmCWE9799bjMPX2dW7Gs0JVwvFpyF0ZEZJO5JqWOJkXHx7GCPsC94dnk8cOyjZ6txrVhm/2PdeXY4280KXXv2JPcSBTwrYW9SPfk+n8xnhGe7Yh97gds4LvrEHn3ta1+bFtzN0BheQ0NDQ8NCYNNxeEePHp2+7WFgUfLh/14ixGPPoq3H40KQtJAM8MBBiop2H76D1XzoQx+SJF155ZWSOgkhMhOXklz/jwQUWSgSLe2h1/c8e7EYKkAax/6GjdKz0UQmwDx6gUSkKL5Hirvzzjun58JUsO/Rvn/G2C366OvlGWuihMw63HLLLZKkb/3Wb03znYLRaNRj/BHOuJh/pDfmHluU1NmGP/vZz0rqmBCMnHNYn6iNYC9yLNdxFk08qNTZMN3jDYYHw8SmHK/jnm7unQmixA2LYb1ZO2I3hzK7MA6kcPYsoJhvlI6vuuoqSZ2mBjsnn+wp5j2ez/7mb1/jaIdxm+c8mTKcFcb2PK8v+9QZUNRqoOmArbkmiblnXNETkD3BOvC88wLYUeNB36IHr9TXDmUeiTWmxZ7imRX3AQyfNlh/+uEsOF7HM67QN/YSz6HIsjmX+4l7jXn71Kc+JUm64IILenPCPvAMU+4vEsE8XXjhhYO+AxGN4TU0NDQ0LATaC6+hoaGhYSGwKZXm0tKStm/fPnU7hWZGoz4U1x1Q3DU7UlBXo7ljiyc0jqpGaDpqCNSFToFRCcXreXkQ1B+oOqLKrxYUynXcwSEGumPU9wTTtWBZqV8NG5WSVw9GPRDnhPZRs9EX5hHnj0suuWR6DmP3YGhAP6JzhAcy33TTTWnlbTAajXpB8DEQ2J2I6Pf/39659Vh2XWV7dFV3px2jlkOwiALGMXbbHBILKVIUzhJccYmEQIH/RX4Bf4DLSJFAlhzhBJzEJORkIDgxhLSIEnfittPd+7uInr3fevaYq6r86bvgq/HeVNWudZhrrrnWHu84vIN5Y32kixHXG9twPNwqzA9zkslETubhGrmHXQdy5gyXHtdusYJ09bCNxaFdAgByTkh+YKy4iSgaZ13m/eK+kzLPOsftxr0mTT3XOWPDjQc4L/c3hZxxLTEHuFL9/GYSGPeBZ3Gr4zmw+HLOE9e4cmG6zCfHkOOqOqyZl156qaoOzwsuz6rjpDVLzHWiz6wjxopr0+100sVt+UaLZVhCL92hhHtwu7qlFM9vns/tjpxQx5zh2szzMSesVe4tyVEcu2s2wPWxL8+P2yFVHcvGXVQ4umoY3mAwGAyuCC7F8G7evFlPPfXU3rokESS/5d2CxAKtXQsUgMXj9iwuzMUyqTpYWBYPdRuTZEAE/m3FYOlgpeV5ODfBWhfkWhA2rVUKLWFyFFtaLigtLa6LOcHSJykCC4tjZhEuVhJWOOzWLZSSrbrNEP+zoG1KKbno/969e0sR4N1uV48ePTpKgumCzZyLa3a5SBbMMpdYni5xcTJOlxrP+VhDTnhIYDWzL3PLPWTNJJPwnLg0gzFxfVmYyxqFYbl4l/u+lZZtEWfWzJalDVIYvOrAcJMpMxaX97Avc5aFz4x7JaztMV2/fr2V6wJY+2YRzD0sLZM+LA/H88KYaD/D/GXpgd8ZPJeWo+uEwF2WwPnd4qrquNzC0mhmevls8Dtrh+eUz3mX5fn8zrXXi+eJ+cznd9W6imfUiVYJNwR2S7UuyZGfW+8dYxjeYDAYDK4ELsXwHnvssXrxxReP0mq3LEVLLzlel9sAF6o6TtG13LB4sOMkaSlZNJV4EhYJFnBadFgcWPCWPeNYXdkFsRTHNCxw21kp7OM2GlhnnK9r4gnLXbUw6URjHQsxU0/LHqaMBbnb7TZFgGkgnMgWL9w75pQxwKKYt2QKbEt8qhPezX1z3XG/+YyxwBZgKjlmF5a7rRJzkkzCRcJmUWa9eS+dys5YiF2ybwozIwPFdTn13+IJeX0cj32IP/r68j7zWQrCVx3uJ//P5wmmwHPy4MGDzVjMw4cPl41081otPMG4/ezlNhZKdmyIn3lf2MZSWKuGrVWHZ5VnyM+ci/yrjuXm2NbvXIuMVx23VYOV0VaHe8x6yTG54bAFEMwe8zyWObOMXOfV4XpchtEVx9sz8tZbb7XfQR2G4Q0Gg8HgSuBSDO/09PQMu+ObO62ZlZ/d2Uv5fywCrAi+wbGm+btrxLiylj2OtADsayZWRAwH6zOzt/DRu6EtlqMzSDOm5iakji90zSLZJ7PY8trNGjJ70ozRWa5dpqwLy93wE0sWP3zVcVuTLXmohw8f1g9/+MP9vNlnX3WwZl3IbMmxvP+Wm2P++clcd2zN8Ql+mhlnLNeFv9wf5qLLJHammb0ejul12YrcF7elgdkls4XtYo3DkO0x4R5nhqdjqzwDzB/PSO7j++a4OYwmx+j46XkNYHNtdTFvF5w7u7HLHeDcjoNx/K2sUYvgM0++h/kecDa2pQY7b9Qq58FegU6qz+859uEdDtNLEXnec2475dZGILORuQ63MMss4Kqzcmt+B67aH3UMjvX14x//eGJ4g8FgMBgkLsXwfvzjH9crr7xSv/d7v1dVxy0cqo6z+ZxRw7d+ZpW5Oadjdraa8tvcFrf98vxMi5TjwKjMgLqMLsZETMN1avYrJwuBIWBxu12P5XvyOJzHbKCTBfJYHLMzch5tEXO/sP74G7miqoO178zcFXa73VGMJb0DfGZrjiwv5iJr/czSLT5rVtDFGBk36xCGtCWE7exjy4QlO7C3wU0vHb/o2gO5RtGNZruGw9TDEVtjbrC4WY/J9N1Q2JmqnKdrf2QGzjYZ5wFeMw8ePNhcPw8fPjxax+kRcQNRx6k7z5PPZ9bptdI9a6v741ZGOUbHIH1dHbP0e2XVaDnB8T03Xoe806oO72V7RLrGuTmePB5gjKw712bnNma7zt7tzsNzcv/+/WF4g8FgMBgkLt0e6N69e0dMLGMczrBjm616G6wgZ9i5fY5ZW/4POKbTffNbTYRjuO1RZmVZLLjLdKzq1RI4HtlQjlly3rSaHVOz1ee5yHlYxS+A2U9ua+aaKjpVB/ZbdbACncnZYbfb1YMHD47iI7mPLd0Vq0Hst+qwzmBnVtrxPHUKP5zX2WOdpW9W5jVL7CPnvKvny23MjLZiU4wZds0aynY9zJcbgJJZ7Lq/ZJRY4z4+6Bies0B55lf75pgyI3K1fmg87f93bM3P40rdpup4nrzO/Mx1z4vF0P0+yhiXY2l+DvmZXg97XFbxxa1M+c7rlGPvPD38z8+N3yXdWJ2n4XZOub4dg+Re8M7cqtvO2PhF1VaG4Q0Gg8HgSmC+8AaDwWBwJXApl+ajR4/q3r17R4kTSZWRHev6QSWSgpqOuzDbFDkp8coNtiUPtSplsCxUFmT6Ojiv3a0dBberwkKzlr/K/R1sd8C5c/N6Pp0WvxItzvNZsJnrdHFxjvW8ovOHDx8euTC65I7cp+rY1ZgBdMbFvbMQ8FbigV2xTgTy51XHQr+WmLJbLH9fuZYtmp7zsEpOsKRe3hdKPzqRh9yXZIX8P2sTcWwLOrAu8lpWCQ2+zi4Z4zzB6KpDH85VT7jueE7c6mCXssMUW4lvPp+TY5wwkr+nmzOP2z0/fp+dVzKR63s1Jt4H3NPch/tvUQbPeSfwsCqd8Pu8K0VyeYfnOsfYCQKMS3MwGAwGg8Clk1beeuutvVVJAsOrr76634ZO4xQwOnBpizi3sXXs9O0ty87WuRMAkiXY0iGhBusCZpdBd+SsVskjWwFul2+QQk8QG+u8kyMyQz0vmSX3Oc/KzX0tc8acWyotu42bcd++fXvJ8na73Zn0YSzE7p66sNhF+JlMZCk5j81p3Gk58j+urWN0+Xn+z0kKzJdLaRJdok6OvbNSfR632QJZKGw5LcC8WqauK4Mwo3Ch9VaauEtqfIzuWs9LK3/48OGSXecYDM95t9787rDXxgkqVes59HXlPOHRMZN0wliXRAK8rr3ecvuV+AbPTyfKwbuId9/Kc8E++TytSkL8XHWye557z2O3T17nMLzBYDAYDAKXYnhVP/tmpy0Q6eGf+9zn9v+35bFiF10cbtXkcD/YxnpeWeWOK6UF4CJHjod1g7RUFsev/PmrdOSuxQf7ILVEyj+sKf3ibnfj61hZflXHMj2rwufO4vb9A118yUz5wx/+cMtSOf6DBw/2DLKTN7OVam9AJxq8sgRtkbJPssNVrMbrsWsp5JT2VVw4j9tJo3X/zzlxoT7zB/PuJKXYZlVu4XF0DJZi/1Xblo6RrUoAzKDzPBcppEawYBVjzf23YsT5/xzfKi6+xdY8DyuG2rVO4zn3fTGb8u85hvSq5LE6AQLfZ3uJsrUaLZL8nFryC2+YGwnkPn5ndN6J8+5X9/1xWe9AYhjeYDAYDK4ELsXwrl+/Xr/wC79Q//AP/1BVVZ/61Keq6pCZWVX1ne98p6qOMx4t/JwMwplA/K/Lisrtq45jhCu/+FYWI751RHaJFSXD85jM8GyZdFl6AKklrD8zvaqq559/vqqOrSSzts7CXInSehzJKM1mbJV3bUnYhnn6xV/8xeU9u3btWt28efNo/DnHZnS+T9212kq3XJst384atBW9JeK8ap+C9Yp3INseWWDc1+N72Vn4PD9Y1ith8KrjZst+RnwPkpWvCpxXx6g6Lih2bKiLwXPfM3az1Tw4pcc8lvzd98wZgltYFYJ3x7Y0mrfpMjJZB85w9DrIewmTZyzEav0eZZ1nprdZtEW9mZtsLcX7G4bH8ez9sCB+1XELuJX4Q+cFWjHxLs5otnmRTN/9cS+85WAwGAwG/4txKYZ37dq1unXr1j7LEGHh3//9399v89JLL1XVwbLBmnCriK6Og20tmAy2WguZkdjySThbiTFhCeGXTrayagNzkaxNW4ocl4aYnPcb3/jGfh9887TIWfnSu9ZC/O76qC05slUsjDmy4HXVofUJbUbu3r27mRF6enp6JFHUxePMajqLfgVLfpk9dZ6F82Ti8nPGz77Mh5lwJ0u3klwym8q4CPEV35/VmPM8Zje2mp1FmWMwK1jFjnN/Mzxi4p3HxLG7d95559xMO1/zSrKtu9aO4a9E6b2GO+FxnofVeu9qE1krrCHYEdfBmsnscNfz4Y3yPh1rck2tY4fcj5xH3oV+f/vZ60SlWbeO4XKMznPDuC1l5jXbZQVn5vBFWd4wvMFgMBhcCVxaaeUnP/nJvn3Kyy+/XFVVf/qnf7rf5nd/93erquq1116rqnX7ns46W2VLuQlqJ0LLt70ZGP/POiUY6t27d6vqYK3YyugaY9p6XjG9zuLwMZgDlDEYV1XVG2+8cWZMbOPs1y6jbBWHwWoiLtAprYBVa6FsD/Tss8+eGdvnP//5Mz79LWBddlYzcNygyy61Jbiqi7PlmNuujrElVu77wPm4rmyJY0ULsw6ux/WGeXx+YuFjrbuuMY+3ElY30+yEx50Zu1XH5jjpqiVYNkPdalxqoLSylQltVnERtr6qEfb4u2xW7tUq43brfMwHcV6YcOfJ8LnZ10LU3bPBtrzn2BYRcZ7lVOlxVq69RX6+8vrN2j12t5GqOvbqreJyyXr93D722GOb6ycxDG8wGAwGVwLzhTcYDAaDK4FLuTRxLeByIY3/s5/97H6bO3fuVNWhKN2SVSRjdIkndiVAUy1DlfQVmmwJLCg3Y8ziSn6HvjuYvCV75YSaVVf2bp9VWQDXm+Ud//Iv/1JVVV/72teq6lAITLkH19sJwNo159IGu8Py99UYO/ce5ybZZqunGanlq9T1HJ/LVJzs00k8+VrtcmGseb7zEo+2EpBWa9Xu9xw3WCVHbLnscWUCC5DnfbEYubtv2+3c3TMnQWz1QXNxPNviwuwSXnytW4kHu92uHj16dOSiz2teCY9vFZOfF4ZweU+eg3eI3cTMucsTqg4uTMoAEPDgc5I+sgO5SzFw7WVSVNXxus/jfvGLXzzzN+9ojo3LM6/D72S71jsBeicQ8o5yMmCuA0IAdnd6fWTpBNuyzi/S/R0MwxsMBoPBlcClpcVgeVWHb1iEoqsOpQoIS8PogBlZ1cEq4qetMwc000LA0nGwNTsp53mrjhMPYE1OcMiArFmaE1zMPrfkgSy82kkNPf3001V1KEanIJSfWEvdvra0Mwmiqm/x4rR9J2V07U64juzcvgoeP3r0qO7fv3+UgJJW80qAm2s028xtzLxWQs2dnJqZnEsxkhG5ZMXlGqzrrqXQqkv1qo1PgoQmkq8sPZed6TkfiTNY0WaanWXs+70qAM45WSXjmC1slR2cnp5uppY/evToQpIWQrHeAAAgAElEQVRSHstKTqsbj5m3f+b7wIXnwMXVneQbTAvvE/eFtZP3xYyKY7AO7HHK+wIrcnkPx8xEPuC1aHa+JUjPue2N4Jh8nnPGXDjpxn/n2lgJBFwEw/AGg8FgcCVw6cLz69evH0kGZUo03/Kk1TvVHGszi1C7uEce19Z6l4JvtoS1RCwxLS03XsUStp88435YNGxrpmepsY7p2NJ1qncWdZM6zNy4cB+LPpvUgpWUj4u+c4wraTFbcsnYV+nHKzx8+HB/PZ0IMddoZr8See6wsri7diaMwUW0WJ1YwLlWM76S+zBfnWjBqrCZtcR5HGPNfS0dRXyM64It5Hm4LsbEMVhnXWulLATfmqNOjH0l39XFQt0ya0v6a7fbtex3S6B7S8x7hfMYXrInx5X9jHWNr+05eOGFF6rqcE95tvJaHYPmuMTdWCtdgThz+7GPfezMeSgro5wovQOwQpdkdB6Zqv6ZZz37/dPFQnnXWijCMbxs7HwR4YEVhuENBoPB4Erg0gzv5s2bR1lenYir40Z861PkmOwJ+Fvdcl6O9eXvWADEDrGesXLSAmb8jt1ZricFgLHcsMKwsFdtgjqfM+d1hlPn77clTwNagIVHbCfn05akLSEzPZ+76lhg1sKzeVzO94EPfGApHs053FIorVnWCOfk2m1pd80gzWpWjUvTuoRRcl7uN3FSruXjH//4fh/uv1ko64wxJ0vrxA9yDuyVSMFhzsO6ZvzPPffcmeuG+VcdLHbOZ9bJWuratawY3lZ80dfHejCj7OJZ/Hzf+953boYv6Ji+77cl+Lpj29PiHIHVzzzPSry5k040C+W+2NuRGZiODYN8n+Wx0hvBO9EskPceY86ibot6r1padevC2/o95ByNhPMK7N3pGDO4iCwdGIY3GAwGgyuB9xTDc4PMLf+6sxbN1qrW1pEtE//d/c+ySlgOKZ/j7DuLR8OaUurLNYFY8q4f6eYEyxcW5lgVbDT3cazE2Uvsw5wl63brDrOermWKs6LMXLnnyeCcqfahD31ok+HlWLp5cgyXbbeahfraLMi71WoKy5a1wTrgp7NEq44ZHvefdQgTy/XNeLG4XUNlCa5kgp7/ZNNVh7lPy56sacckHQ9mDWd8ZJWdCzj/Vjsq7gHn4Zhdw9ZVTWKC947ZfOdZWrGyLrbnmtBVU2X2yUzvVexuJeuWx3eNo9+n3XPJ+mL+WWer1mr5mT1m9r51gvAem71C3fvAbNfenK4RML+7ltPxvq5+km1/9KMfbXogEsPwBoPBYHAlcOk6vKpja3nLSjez42e2QOF3LA+zDGdr5jnM7BzLwVrO2hCOC+PCAsaaIPMx6wsd48IqxvLgGhhjWulkPhF3++53v3tmn2eeeaaqzlpXrtFa1ZNZEaHqOBPWVtmWKszKkrO1lmO6iNLByclJvf/9719adHk838M8hq+1E+nN47s5aY7f7N8Wfteux+eBaRNjgAHaiq46fm4szAvjzH1tueJ1cIwqrXTG6+NarBi2kDFDZyz7HneMjPvEeWyV85wls3FbpS1cu3atbty4cRSPy3vhOjsr4GzV+K2aSLv+N2s4zejs6dlioY6TrpqtJuy5OC/zNo+zyh0Aud7I3HRWpmvfung6Y/DaN0PuYsaee6+LTn2IMX3/+9+/cMbmMLzBYDAYXAm8J4bn+EWnp2a9RmtPZszB1omzFt3EtfNT28LDesWazXpAqxJg+bIN7CytBiwa2BqZfFw7PnWQcRq3EmGssE5qFpOFOuvKlpWtxZwTW0erBqoXbamR23ZxGNjNG2+8sbS0drtd3b9//8jPn2Myw1plmaYVy++OJ7puDGaUrJG1wWewG366FUpev+uwYHadvp/jbLbwk2FVnX2erOvKsb73ve+duf6co2SkVQfmZV1Z1nl6FqjRMsvxs94prfh/nJdjpOXvDNWLwHO/1YLL22y1h1q1g+K+d+dxo+FVE9y8Ps7tFj/Acec8t9sB8c70eyKZvq8TuDY6nyd7B1wj6nnNa3D7MTO6LpPUrJZ97Q3p2DX7bDWeNobhDQaDweBKYL7wBoPBYHAlcOmO5/fv3z9yOSYlhpbb5ebyhK7lCpTYrga3fumEoP3Taf1d12rcDYw5i3er+iQS9sX9aJcaLoVM9f7gBz9YVcdBcNyjuKeyANTp7Z0cWHctec0OTls+rAuo23W66p7eXc+Wa4EWL6BLI/baAbiEmetOmsjp8i4FsZBuno8Uf1yZdnkj4J3bWmg673dVn4zjBKCVlFW6iRgj643rslssC5GZW5e7MEaSp1jvWX7jJCwnkHVF2Oxj1/N5IgQXxaNHj+qtt946cgl3LbFWIu8dVqLuzPWqnCO3XaXDO3yRY7KYM248C6vn9XjeXeriUq6q4xIgwPl5Z7lMojufXY6sj7x+kv3Yx2GerojeiXV24XehD9/bBw8eTOH5YDAYDAaJSzG8hw8f1v/8z//UL/3SL1XVsVXFNlUH68UW15Zl5ICzA+Z8+2fwe3V8t7PpAsGMkYQDJw90ac8uLHVxNGPFuq46NG91Wj3WDVZazgnssitGzevxPFf1KcN5/hUDzP+5mBjkMZ32nMXBxsnJSRvA78bnYlczrmRpbubr4m3YC2wmrVnuu5OmuHdY+K+//vp+Hxdts68bgqZ1azboFivMMcdImThKWL71rW9V1aFUhrng+jrvgAWFnWDDMUjAynMjP0aaepcMYVgYwKn6mcjFHHfttIx333233nzzzX2D5E6g3W3A/Ixvte1iW+4tbNnJHfl+WMmRsWadNFV1XLoE+JvnI9mKvRpOAHJZVh7b8n1mQayZTsrObdfMFi1TlmNE2o415Gcl31VmeGbkXbNqy8jdu3fvQuLgVcPwBoPBYHBFcOkY3ttvv30Uz0pLy5Iwti63YMbhwnOsmjzWquDTBagdu7DVhLXRCRub2a2at2LNpJ/azNWpy5aJ6uZkxfS6mOiKyTk1e+u+rYq+u5KQVdmDkdZ11/TUxc5YoGZ+yRSwTolL8ZM4jIt6u/iB1x0MiHFk+5QvfOELZ7blGLShIlZ4586d/TYweK9Ns2lYaTKuV155paqqvvzlL1fVganCPmENHaO0eMGqiWfOJwySuWc+YXpuj1R1LAzRyU5V9U2f83larZ8HDx7U3bt39y2zthp/8hwy52ad3Tksfs0cM7fMddcw2XPNXHZlPPYSuTSD8215lszSed90whCW8nLuBWs3y6HsIXOs2F6JjF1zf2gCztrhc8dg89o5rwXUYYU59/Zu/fCHPxxpscFgMBgMEv9X4tGdX9zSVPZ121LpjmM2sZIaY0weY3eMtJpsDXWZjlVn4z1ml84q8hiz+Nd+aMcrusaIzFvX2DHRtUpZzYkZ35aQLlhlweb1XBSPHj06KhpNqx/rmLnO7MEcb8YciLfAimBjxIhgPmTKdhlpnJc54PhdFhtzBwvAOv7mN79ZVVX//u//XlVVn/jEJ/b7wIoszOz7QJbb1772tf2+r7322pnr67LxDJgKxyeWAitgXrsGzs8++2xVHVgNP7/yla9UVdXzzz9/5pqqDvcLC95ZdC6SznN2GZfGbrert99++yjzu8v09jNt78pWpjDxSwQJXNRvr0duY5Fvr/O8Zj+PzlHIOBbnXolzWGghY7l+B3tsFu2vOhbJ4Bg8V9xj1kUnWmCRdOcU5PV5DrhOe+a61kwps3bRrN9heIPBYDC4Erg0w7t169b+G7sTyPW3uDOEOkvOLMV+a3/eZYU67rYllGxG6UwuW6xVx409V/Vm/L8TcbXf31mOnSyXfeq2VDtrd5XRueXntnSVY5Vbck4pMLxiojTxtJXezZOBpdi1cYHhEXvinn3kIx+pquM1mgzVEnLE32xd5riwYhm35bNgZLC1qkMMw3VJXrtIzL355pv7bRj/b/3Wb50Zk2u6MrOTsbhJKKzMVnMnacd65+//+I//qKoDk82sTdcmWiB+S9g4PUKrtXP9+vV68skn99fI2LpGwH7feL4ya9JxNo7vmDeMJQXhXTPqNkH8P993rs3zmLmX+Zyu3mds6/devkNWtXQrCbWqA7u1gD4/Wf/evuoQ53X2sxl/9/7ms2wInGPvPIJs+/M///MXyhGpGoY3GAwGgyuCSzG8k5OTunXr1t6KcKZV/m4fsP24HdPbatZZte2HX1k6rtnIz2zpOKO0U2fBanG9iBlfXh8WlGsQ3VQ2sWqeaLbYZVyuVFmcuZpj5DPG6ho4j7nq2EK9efPmuW1YrEjTsU7HSy1gu9WIEyuTY6QF6vOxDfu4XRCZlxlnBLAX9oHlMG/J8IjrEUtzzDYVI6rOzvFTTz1VVQd26Fgqc0O9XtVx7MTi0ezr7M3cxlnHsFTmJpkzY3MmM393sSl7DLbWzc2bN+sjH/nI/hqZk+4d4qy/lZB6gvsBMzGLBzl+5p2MROb8V3/1V8+MI6/LXg17T7rmumafzoh2i6kOKyFtN8muOsSKUX9yXJ3r6t7RrnnmufL9z3ldKXI5Vtll5nfzdR6G4Q0Gg8HgSmC+8AaDwWBwJXBpl+Zjjz22p9ddEonTg1ddirtec8aqA3W6pZzosZL86Y6zSo6xqyGPsxKltdRYXpPpugPRdgVVnU0vzm0Zm4tWcx6cTt9dT15T7uN+W1vuNruvz+ttlu4k34P8zKK6FgSnxKDq4GrDhcjaJMnC7rW8ZlxxuBotqtsl0XA83DVO23aKedUh4I/rCHcRc4pkFokpKWLO/9iWBBPuD3OR95+x4Iby9ZCcsyVLxxicoIY7kfKEnAtS1UkccrLUViLXO++8syxNoOM58/ibv/mbZ46R53CYYOueOmnFrm3muitbsuQX84/L1z0V83hOhmKMduNx7Xl81jfvGReTJ+x+tIBH1++R33HZ233o0pq8Psbo9w7Psd2XCZetcYxObITPeOZff/31tmSkwzC8wWAwGFwJvKfCc2DWk7/zLb5K1OjSdV2I63261GLv48Jw/u5SmG1pYTl0cmhYKQ7A+nq2AukrptexULcMWYlkm0lXHSxUszUz2C44bitwldJcdbAy87rOkxfDms2CVWBvgFkm++QcYzVzTTATs9wuvRkmwnxxzeyD6DfMImHZJq7biQ9VVb/xG79RVYdyA+4pY3zmmWeq6lCsTup/1YGFwspIKmHMnVg51jhzw7a+jk4Q2u2BvGYobYBZ5/9I5GANsT62RMr5ucXw7t27Vy+99NK+bIPr6oSLVx3OmRPuT47Lxc+sMxfwJ8ODWfOZJQX9zsrzrFoWrTwxuY89Ltwn1m4mE6261puB5bvx6aefrqrDvWPtwN7dOinvLet5JQTuAvgcA1iJfndeQBKGvvnNb56Rx9vCMLzBYDAYXAlciuFV/ezb2+wmfc5Oa7d1Z3aTv5u1uKi623eVduwyggT78z/LNXWi2Ja+sWi1mW0n4mqLyiy4kxazGK3Tj7tGk+yLdWZW2rVoshWLpWipobzX9ptvCQADxmk2UHVcdmLxaM6XbIaiYGInzI/jV8Q+UkwA1uT4FNdMLCxZARa1YygWVMdSzuPDAhwvfeGFF6rqUAaRYtWrJp0rkfSqw33mOhgzYr6ANl85n8wjx4XJMTddQb/Zrdd1l8Lu53RLWuztt9+u11577aig3oX8Ccfsttal2SDPOGuFeexEj/ECcO+YA86f7wF7A1ZNXXMuLAdojw73wSUg3fV15VZVZ8tuYPBcM6Ug9oJ1jIv3Dc+kY7idR8vs2szfZVlVBy8Oguo/+MEPRjx6MBgMBoPEpRnetWvXjuS10npyzMSNWEFaGc50sj+W/3cxQ8fwbCF02YCOR1k4uRN+9TYukncm11ZDS2dJdfE/GIT/x5iwpjq25jYt/hx0BfxmrM567TJXu/iecXJyUrdv394zLcafkliObXRxN/+NRUrMbNW806yj6rDObI1z3o4V8pkz6ziWW0xVHcubwUxcUM/Yc3uyTf08OTs558Ss3yLRWMj87ETELS5BVmZmyAKvN+I8jsV2VjjneffddzdZXtVhjinkp8i76jgu7bXTeXpWa93SaH72qg5MDgYMq+Fntw9rxs9N92z5mr2vvTj8zPP5HWVPFnOyld9gLxvrgevM58y5CGZpINeBM3sd5+N8ef9effXVqjo0Rb5x48a5niUwDG8wGAwGVwLvKUtzS6jTIsRmfHksw9s4m43zplXhOhU3/mQ8yRotUeXr6RiLY1z47rs4SI6j6sAKVvVdbqOS29o6ct1dx/As2mrf+VbDVu+zyqLK41yE4V27dq1OTk728QIsu66+xkyPv9k31wmxpd/5nd+pqqrPfe5zZ47PT1hVxsI8h8TOsF45Xza5/Nd//deqOtxftrEEU943mBTMJGN0VQdmx+d5fRzXGYN8Tlwu1x/sI9lT1eH+sC33Mlk288Tz45q6TjydsRD/g40wb107Ku4p8/juu+8uJaJOT0/riSee2M8985nto2BjHt95coVVB3bkbV3jlkzIa8QxKB8jr9nSctwfxpFrh/vOOrbQvFtNJbh3/LQAfucJ8vuE8dtz4czvvI5VnaHr8aqO2xu5+TLHSolAWlVlzHUY3mAwGAwGgUszvBs3bhyxjq71DpaBazOczZZwXYzjf1jP6et3K3pqnajRcMwo97F/2OKuOUZne33961+vqoM6A758jplWs0VOzYS6TDJbbK63MgvKTKtVnU/XXsf7uM4HYGl1otiZdbqytN566636+7//+/qDP/iDqjpYwFaUyXOsBLo7hRgal1rUF1ZFrC/jcY7DOabbqQJZUcdtTNgnm51aYBhL26zDccAcG7V5/E0sjXWdHgwrXJzHcjJmyDxxzWRlOmMxvSywD3tijE59iH3u3r27VFwib8Bix53XhmfZ3iBnruYYVq2wVk2Rq46ZEPvae9LF1l0P5+ax+Uwwt7DaVTYi6y/fA/zOWvQYHa+tOqxnC95bWYY1k+9VzyfHZe102ehWg3HWMZ/zHFcd3r1se/v27c2cicQwvMFgMBhcCVw6S7PqWJewa72z0jDsKuKdzYP17JqPrp2O281gPX/729+uqoNuYcbUXAPkpq32W+c+jIHzcVxULFwvlXALHjPltJp9PqxDzksMAUu/q/ty3Z3rcdI6M7t2zWPXaNZaoFt+9J/85Cf16quv1p/8yZ+c2SebnVonlHXAPe10AxkD1ixMj32YJ6uoVB0z1C7GUHV27XBc3xdnB3Zs/Vd+5Veqquq555478znZZpwnGRfZp6iMYP0T6+haF60yhbH07TlJxuw6U9RMzGyThfBMW2eW56dTy+DcMNfvfe97yxY3p6en9fjjj++vFcbMPFZVvfzyy1V1uD88F6xnjp3vH67NjYAdX+TznOuuNVrVMfNPrJpSOy7XMUq2tZIL+3aeBcfSrPO7pTPMu8Hre6WmU3X8fnP+BteVa8fxZBgt94bj//M///N+H+5hNt2dGN5gMBgMBoH5whsMBoPBlcClk1ZOT0+PqHC6/lwQbfrujsTdtqv0ZLfzyX35DLcRLgfKB3CDVJ1NKKk6uAncpifdFXYHmEJD9aHbWWJg0Wu7gOx6yG0tTm3JNEuQVR3uQVdsm+ftWmqsOqxvpTBnwsvKtbDb7er+/ftHMlSk5Fcd7gtuGQL1pCSTUJGuJXfvJomIa+d6+JmCwxaN5qdlnHIenW7O3+4IjWum6jjl2q5s5o/rzbIFzmNRX+aok16ydJ5djMBlGd312bXNnOXacZmP10pXrsI1fvGLX6yqn7metxIy7t+/v08M+7d/+7eqOluKwXPA/yyR1707nOpvMBdsl65mv6NchmV5xKrj9xzn9Ti6ZBy343GIqJNS5H92AbrMIl2oq0QdxubSilxTLs1xuMHPVX7mInx+/tM//dOZv3Pc6b69KIbhDQaDweBK4D2JR9tCTYvbckZO4ugEoJ3+bdFgrPIuIcBBY6wLmnqStECBeNWxRWUL362Acmxc10rUFaQl6bZDZm9uqphjM7CoSGbYEua1Zef057S8XQKwanuU1qctxYu0B4IJY51lmyASjRgL8+bWJF1Q38kCbvXCuHOtOoXdLZFcSJvnduKRyywyAYM1yDr2enfKf66DTL3Ofczich1YLLxrA1PVF56vkpS4Lu5BjtHMzkkqXodVh2Qlisdv3LixKS326NGj/bV2TIhkpVdeeaWqDkwfDw/3o3vGmH/mwSnxrAvWbh7Hz7BZU9cc2+8BJ3XkGJ0I5PcMc9IVrbsMyfff5839s21TjtUejE4I2mO1iH3Oib16nA/PDx6ATow/3z+TtDIYDAaDQeA9xfAstppWswsXVwKtaW3Y0nCsy40kuwaCfObmp5alyvNY1Ndt5bESqw5W4Mpasq89LY5V81aui5+Z/r6KoTEOWMNWTNTFqBwTS7WzilbX5aLf/GxLtsnHxrJH+Ddb78DGiGWZ6TmOmmMw43L8qitpcXsoH9Nxizw3cwwr4HPmNmMpLgewSLWZTV6fJZxWxcsZS3GsziIQLpJP5rUSJ95a31sNmvN6k5HB7LpYV4fT09OjmDHC2lUHiblvfOMbVXWI5cHwOjFnt3Yyq2G8sJlkwqxb4stuz+MygoRl2sz8Mm2fbd2s2ELX3VpyjgDr0Iw84fvA3PBMWlg731kuaXDxv9lqgrlmXfDMc95kkl5vF33/VA3DGwwGg8EVwaUY3m63qwcPHhyxtq5w1ZYIVkZXeG5LwP5iLJ6uEaOFkrHCusaoYJWJ5JhDWnTOXuOnGZat0Kq+SWeO0Qw29+GnhWC3rJoutpbX2wnOrhplOnaXsUVbs7vdbhmHQZbOTVeTZcPwHAfz2HLclkDibxfkYmnn+TyXZk9d+xSLiLOtM3/zPF4jZnj2RnTxOI9/JZqQ2zpebuHxrZihs+bM7JIVW6jZz2knHwdryhZCK/Hxk5OTunXr1p5NcN9ef/31/TY00SVW/4//+I9VdSjqh+nlORgfa9JM2MXXubb5H3NtuTPmJ9m2n0Mz7C7j1uIIK49ZF6f18S1I4PuWv1t2jHXQNUX2+djHbYi4f7kv18FxYXhk5DJWmF7unzHjieENBoPBYBC4dAzv5OSklZkC1MbY92oLsas5A47HuZ4kLRLLWvl8tt4TjmU5XpItKfjMbTLchgh0bM3sA1bAdXWi2BZ1dsZlJ8ZtSTEL6HbSQqsWP8wN1lnXHghcv359aWnB8FgXnCezNLnfyEJ997vfPTNe7mWOkblbjXsrzsh9WDXK3JJTcx2eYzjd/QCu93McrmviyTGc8ZbySsCMwXFNGFe3DszwvM68thLOynNMPmsgGUMyxq21c/369b1l/9///d9VdVZQmGvh/fPrv/7rVXWI6TFftD/KczvTlnXh68h76rVoxsq6To+IPQjMpXMK0jvAcZlvYverjOVcB16LXg9+RqqOmR33iWNx3qwzBfZkrTJ+c054B5OhTXY944DxbXn1zmscnBiGNxgMBoMrgUvH8Ha73d5i+/CHP1xVvUWKJfWd73ynqo6zifIb21a4RUZhBVjAXQakazwcN0grwLFBrBUsOqyotHxdT2g1Fmd8ZqzSWWCOFXZNXIEzFW1xY4l1MSPHUlxTk9dnBskcY4FZNDavJy3jlbVF81dbjGmROmuS+AgWvducVB1nJDrj0vVXnVqGW8vkmL2PW9hYJN3i0vk/szVnNTojMuE6UNekdrVUzmrODNsVXIPm+GnHmB3n4XyujczYDXGzrHVbxfB2u92ZtUr2dMZ1OBdrhqxNrpn6PNpTcdy8ZsYNi2HceGJS2YXzOduU+li3LcuxWaXFOQw5t9l8uOq4ptdz3L0bPa9eH7mPn3sLTzumn8+br4u1YhHu3O7LX/7ymfMSg2Vemb9cO/4OGfHowWAwGAyE+cIbDAaDwZXAe+qHB/XHPfDaa6/t/wflNRV377R0wdhNYuoLJXZRYtW6FxdUvEt0cf8zC+MiNJ374E6DWjsJgmNx7ExT7zqD53l9jPydeVzt08mS2T2JOwI3RVdQDewiI10Y18Mv//Iv77clwNy5RozT09O6ffv23qXJWLriflxV7khPElG6N/gf23K/7TbsSlq8VjgG95qx5lq1YO3KTZkucN93FwA7ASrdYCmuneex9Fy60FcJVS414L51YsUWgGbMnYi4k69W0oAJwgiEPp544okLFZ9XHZc+VR3myeUouMjoi0n/vapjKTmug+My1zyDuQ7Y1vJdrGsnJlUdz4sFrblvnfBAN+9Vh/Xn+5O/+13lJLpu7VhCsZNKW43V5RVOVOvejYj+IxBPwhpj7Vyn6SK+aOLKMLzBYDAYXAlciuE9fPiwfvCDH9QnP/nJqqr66Ec/WlVVX/3qV/fbuLAcC/7u3btV1af4Om3VQUkYHj+7di3AyStuV1R1XIyMJeLWPp1YrNPgGauTMdLyWZVkuIi9K4q2/NEqaaIrHnaSjBlLN48OUlO4C7tKhgMTvnPnTlUdins7nJ6e1hNPPLFfB1h5uQ/3w0XW7kyd98UF2CuRZf5OKSQXaLsgvyvmZQ6976p9U9Ux+7PlvWLteR4nQXismZhgYWnPG/eWe5rlN5bQcwd0LPKcR45rYWnuBYk8rKXchvfD7du3lwyPkhYnlSX7Zf5JGrEX49d+7deOxgDbYwwrEQmuvUsMcrG1xTNyra6k/ix00bEVe70shNGJgLgkiznpvBDAknk8gy5p4PNcB5zPjNnvnxSCJvHR3iYLq2fpGmvyIp4lYxjeYDAYDK4ELl14fvPmzfrjP/7jqjpOkU1gpWCd/+d//ueZbTsJHFs+trTsJ899zGrchLBrt2Nm6UaZaaWvrC9feyeQ6rR0fjL2rsUL4/f5XBDssoyq47lYCVDn9VnAmuJvrGnGk/539kfE90c/+tGyrdGtW7fqzp07+3R01sNzzz2334b5YHz8jQUMU8C/n9dqa3Ul55Xjc1zC7NmWZO7PZ7ADp7hvFfVbkNctX/Kem0EAi4aAHxkAAAjaSURBVBh0bN2C2hYi4F5kCQ+szN4Hr/8cj5sfu1SHa8hCcVL8uaePP/74siwBWFou59ixetigmwpnzJiSKYuS23vDOuxa/bhQ3yVBuXbMnl0mYi9S1eHd4XIUtzDjWHkvVyU6Xqv57DiG7+a3LibP9xxzzVwwFnshMr+DdWtPgj2FWX5kb8qqYXiHYXiDwWAwuBK4FMP74Ac/WH/1V3+1j7vwjZ7+VWcT8e2OGOgbb7xRVWetj1XLE8sZwT66olcsBMfYtvzUwEyvi8NgibpNiuWZOsvHxeO+Tkv/5FiAhVjZ1zGkHAtwK5FO3s3F6ViMf/7nf15VVX/zN39TVWetdGcs3rt3ry2e59xPP/10vfrqq1V1YIXJuBiDY5pYs51ElWPDMAdb5dynXKsWJXfMjvNsSUqtWFTeD1vpbj+zKvbObd1+yMIKybwtIed2VDBLrPdOds2MwvOX51tJ9HEfWUtZKI4Hgay884qHd7vd0XOS4+bYHJf3jGOtGTMmfsR7xcXUjpt3rX787rJsW2YUM/+sUTO6TnoLWPaM8ziLsZPds1i+BSgyd8CyZ47p+R2ZzIssep4bzsu9Yb7zHhBz5b5ZLLqTsnNW661bt6bwfDAYDAaDxKUY3vve9766c+fO3tL5+te/XlVns6UcA8AywG/LvmntuU7H7Ixv906ax/5iW96d/93ZcY4duLaq6thKsuXrfTu/+EqeydlNCSw3rLBVFmpa3DAvx3+cHZhWEUwJSSasdGJtf/Znf1ZVVZ/+9Kf3+3jOLa+WeOyxx+rFF1+sL3zhC1V1EPVNEVq367HEWCdNhBVroV9n6bm2ruowZ85IW/3M43kN2ZrONWUBbjMKrqebPzfVJKOS9e06rbx2njHmBCZGTRrHTIvbLYRcO9WxNbewcm1kbgtgOcnIt6TF3n333SPPRLJ1syLG4izmZAp4nSxOb2+Hs53zOG547Zh7jtGMdxXDS0+PPVQWdeb4zGN6XRijWafl9jJexvvbMmd+3zkeXHV4hzD3PCu8311bnL/7XeyGAh27zuzqqcMbDAaDwSBw6SzN69ev7y034jFpIaTAatVxrQk++zfffHO/jVtNuE0PVgDbpYVvH73hNjtcR+7j+FsnyNv5yLtjgGSU/M+Zdlbg6DLtbC078wor1CLK3ZhWYsWJF198saoObADrHFbwl3/5l/tt//qv/7qqztY6rWqpbt68WU899dS+Zu/zn/98VZ3NuHz22Werap3xxlyk9YdSB14Gt1oym+6sdK8/ewWSFThG6xqtrl7SLVUcQ2NfzptWvesiuWfcH9ZUWsAc16yTukksceImGWdyVqtjhq7HqjpWqGEtcqxOuYj3APf08ccfX64dxKO9nvOaYb6MhdiQG0LnGDg3ikGuoeP/FrOvOlb0IfvTTX0zo9zKLmaUrumtOtxv5paxuMbN3rDuM9ah2Vm+s12ry7yxDfNsBaDclvNwPdwLx+SrDpmybON6PP7OOTHbOy+798y2F95yMBgMBoP/xZgvvMFgMBhcCbwnlybUFUHhBPQcKr5y9eHSqDoIFENbLTBtt1S6CZxwsBLm7VLLu2A011l1liq7eBhavSrD6Hqa2S266jmWv6+2cYF9ujKc7s4cWAiW1O2qqr/4i7+oqkPSyt/93d9VVdXHPvaxqqr6zGc+U1Vn79tv//ZvV1XVSy+9VFU/c6WtyhIQj37hhReq6uDmIvGp6iD0C1y+0blv+Qy3HO5CF2TjZunKRTg++yI5xTxll2xLmTmJxULNVYd+fhR6O/GJsVpOKbfxPXVPx0zasPuRa8d9zNg4T65Vy485acbrLo/j8AIuM9LV812AG5FwxZNPPtmWRzCGrit3wm5crtku4M49DSxtZxm1dOM6EcgJV10YY1Xq4SSy7llmHycF8v+tEhML23sNdW5Xxsrat8QXz0pKi5EE1PW6zPNmqIh9eG+vkr+6Up0sT7ho8fkwvMFgMBhcCVya4d24cWOfqk4iQ1oVzz//fFUdp/Y6PTi//bFaLHVkaSIneWzB58vEGorIsXy6lGWjY2Hd+VzcXXXcjsX7dFaZmcJKpJjP00pzYSZWLveCn3neZ555pqoOc8K94Hr+8A//sKqq/vZv/3a/D0kmL7/88n6fzvpmDu7du7dvAwLTS4YHAyFNnmP5fue4M+EirxmL14XbuS+BeCxSAvEwMrObPN+K4XdCw8wlVrHl51ibLirOa3fxuAUPcm2ZpcGmAUwaq70TRbaEmkWLM7XcDIJtuC6uG2u+6sDwsgRp9Wztdrt65513ls9C1YFpMP9ORCOpJN87zJP3YQ6cMt8lIvm+ONkjmbCfO7cdYp11rczMMi3x1bVBW3mwfC+7//kYTl7q3sUWjOB6mXv+TwF61bHcnss8QCfRlt8lU3g+GAwGg0HgUgzv5OSkfu7nfq6+9KUvVdXBIuliT26Jk8c4GoQKjYnL8a1O+rStmqqDxWOfNpYcx8x9XOhrS2eL6QFbsxaR7SwOizpvtfZwc9KV/BmWV+7rmJ1jEexLvC7Pwz5YZTT3RTD8j/7oj/b7YLnTJgq5sA4//elP67/+67/2x4VRZnkKDI+4m2NCWJfJuJgP7qXLYpgfF9vmcTgGBbLMEx6MZFzEMLHOWV/cS9hUsg/HUvmb2JYZRJZJ+L473telzHNcx4xgWIydee6KyF1C40ajnRyVnyMXPMPuq469K+fh9PR0f78Y9xbjYix4c0AKNDhGyzxQtuF3VQpkrFqadWsUcM38z8XqjK0Tked/Lr9gjNzzjP9abpF7x/kt0Oxz53HxbHgt55ywDWsUTw3vMn4yvzl+5pHnt2t7Bsz+Tk5OhuENBoPBYJC4dlFJlqqqa9eufb+qvv3/bjiD/w/w9G63e9IfztoZXACzdgbvFe3aMS71hTcYDAaDwf9WjEtzMBgMBlcC84U3GAwGgyuB+cIbDAaDwZXAfOENBoPB4EpgvvAGg8FgcCUwX3iDwWAwuBKYL7zBYDAYXAnMF95gMBgMrgTmC28wGAwGVwL/BxfldYutiQgNAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 144x162.72 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x325.44 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "============================\n", "Faces dataset decompositions\n", "============================\n", "\n", "This example applies to :ref:`olivetti_faces` different unsupervised\n", "matrix decomposition (dimension reduction) methods from the module\n", ":py:mod:`sklearn.decomposition` (see the documentation chapter\n", ":ref:`decompositions`) .\n", "\n", "\"\"\"\n", "print(__doc__)\n", "\n", "# Authors: Vlad Niculae, Alexandre Gramfort\n", "# License: BSD 3 claus\n", "\n", "n_row, n_col = 2, 3\n", "n_components = n_row * n_col\n", "image_shape = (64, 64)\n", "rng = RandomState(0)\n", "\n", "# #############################################################################\n", "# Load faces data\n", "dataset = fetch_olivetti_faces(shuffle=True, random_state=rng)\n", "faces = dataset.data\n", "\n", "n_samples, n_features = faces.shape\n", "\n", "# global centering\n", "faces_centered = faces - faces.mean(axis=0)\n", "\n", "# local centering\n", "faces_centered -= faces_centered.mean(axis=1).reshape(n_samples, -1)\n", "\n", "print(\"Dataset consists of %d faces\" % n_samples)\n", "\n", "\n", "def plot_gallery(title, images, n_col=n_col, n_row=n_row):\n", " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", " plt.suptitle(title, size=16)\n", " for i, comp in enumerate(images):\n", " plt.subplot(n_row, n_col, i + 1)\n", " vmax = max(comp.max(), -comp.min())\n", " plt.imshow(comp.reshape(image_shape), cmap=plt.cm.gray,\n", " interpolation='nearest',\n", " vmin=-vmax, vmax=vmax)\n", " plt.xticks(())\n", " plt.yticks(())\n", " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)\n", "\n", "\n", "# #############################################################################\n", "# List of the different estimators, whether to center and transpose the\n", "# problem, and whether the transformer uses the clustering API.\n", "estimators = [\n", " ('Eigenfaces - PCA using randomized SVD',\n", " decomposition.PCA(n_components=n_components, svd_solver='randomized',\n", " whiten=True),\n", " True),\n", "\n", " ('Non-negative components - NMF (Sklearn)',\n", " decomposition.NMF(n_components=n_components, init='nndsvda', tol=5e-3),\n", " False),\n", "\n", " ('Non-negative components - NMF (Gensim)',\n", " NmfWrapper(\n", " bow_matrix=faces.T,\n", " chunksize=3,\n", " eval_every=400,\n", " passes=2,\n", " id2word={idx: idx for idx in range(faces.shape[1])},\n", " num_topics=n_components,\n", " minimum_probability=0,\n", " random_state=42,\n", " ),\n", " False),\n", "\n", " ('Independent components - FastICA',\n", " decomposition.FastICA(n_components=n_components, whiten=True),\n", " True),\n", "\n", " ('Sparse comp. - MiniBatchSparsePCA',\n", " decomposition.MiniBatchSparsePCA(n_components=n_components, alpha=0.8,\n", " n_iter=100, batch_size=3,\n", " random_state=rng),\n", " True),\n", "\n", " ('MiniBatchDictionaryLearning',\n", " decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,\n", " n_iter=50, batch_size=3,\n", " random_state=rng),\n", " True),\n", "\n", " ('Cluster centers - MiniBatchKMeans',\n", " MiniBatchKMeans(n_clusters=n_components, tol=1e-3, batch_size=20,\n", " max_iter=50, random_state=rng),\n", " True),\n", "\n", " ('Factor Analysis components - FA',\n", " decomposition.FactorAnalysis(n_components=n_components, max_iter=2),\n", " True),\n", "]\n", "\n", "# #############################################################################\n", "# Plot a sample of the input data\n", "\n", "plot_gallery(\"First centered Olivetti faces\", faces_centered[:n_components])\n", "\n", "# #############################################################################\n", "# Do the estimation and plot it\n", "\n", "for name, estimator, center in estimators:\n", " print(\"Extracting the top %d %s...\" % (n_components, name))\n", " t0 = time.time()\n", " data = faces\n", " if center:\n", " data = faces_centered\n", " estimator.fit(data)\n", " train_time = (time.time() - t0)\n", " print(\"done in %0.3fs\" % train_time)\n", " if hasattr(estimator, 'cluster_centers_'):\n", " components_ = estimator.cluster_centers_\n", " else:\n", " components_ = estimator.components_\n", "\n", " # Plot an image representing the pixelwise variance provided by the\n", " # estimator e.g its noise_variance_ attribute. The Eigenfaces estimator,\n", " # via the PCA decomposition, also provides a scalar noise_variance_\n", " # (the mean of pixelwise variance) that cannot be displayed as an image\n", " # so we skip it.\n", " if (hasattr(estimator, 'noise_variance_') and\n", " estimator.noise_variance_.ndim > 0): # Skip the Eigenfaces case\n", " plot_gallery(\"Pixelwise variance\",\n", " estimator.noise_variance_.reshape(1, -1), n_col=1,\n", " n_row=1)\n", " plot_gallery('%s - Train time %.1fs' % (name, train_time),\n", " components_[:n_components])\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, Gensim's NMF implementation is slower than Sklearn's on dense vectors, while achieving comparable quality." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "\n", "Gensim NMF is an extremely fast and memory-optimized model. Use it to obtain interpretable topics, as an alternative to SVD / LDA.\n", "\n", "---\n", "\n", "The NMF implementation in Gensim was created by [Timofey Yefimov](https://github.com/anotherbugmaster/) as a part of his [RARE Technologies Student Incubator](https://rare-technologies.com/incubator/) graduation project." ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".py", "format_name": "percent", "format_version": "1.1", "jupytext_version": "0.8.3" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	537,415	Python	.py	2,710	193.193727	57,956	0.88425	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,954	distance_metrics.ipynb	piskvorky_gensim/docs/notebooks/distance_metrics.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,955	topic_methods.ipynb	piskvorky_gensim/docs/notebooks/topic_methods.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# New Term Topics Methods and Document Coloring" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "from gensim.corpora import Dictionary\n", "from gensim.models import ldamodel\n", "import numpy\n", "%matplotlib inline\n", "\n", "import logging\n", "logging.basicConfig(level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're setting up our corpus now. We want to show off the new `get_term_topics` and `get_document_topics` functionalities, and a good way to do so is to play around with words which might have different meanings in different context.\n", "\n", "The word `bank` is a good candidate here, where it can mean either the financial institution or a river bank.\n", "In the toy corpus presented, there are 11 documents, 5 `river` related and 6 `finance` related. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "import gensim.downloader\n", "corpus = gensim.downloader.load(\"20-newsgroups\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:smart_open.smart_open_lib:this function is deprecated, use smart_open.open instead\n" ] } ], "source": [ "import collections\n", "from gensim.parsing.preprocessing import preprocess_string\n", "\n", "texts = [\n", " preprocess_string(text['data'])\n", " for text in corpus\n", " if text['topic'] in ('soc.religion.christian', 'talk.politics.guns')\n", "]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.corpora.dictionary:adding document #0 to Dictionary(0 unique tokens: [])\n", "INFO:gensim.corpora.dictionary:built Dictionary(17455 unique tokens: ['accept', 'action', 'adulter', 'adulteri', 'adventur']...) from 1907 documents (total 318505 corpus positions)\n", "INFO:gensim.corpora.dictionary:discarding 14137 tokens: [('accept', 259), ('adventur', 6), ('annia', 3), ('believ', 649), ('bibl', 280), ('calvinist', 5), ('cannanit', 4), ('case', 317), ('chastis', 4), ('christian', 527)]...\n", "INFO:gensim.corpora.dictionary:keeping 3318 tokens which were in no less than 10 and no more than 190 (=10.0%) documents\n", "INFO:gensim.corpora.dictionary:resulting dictionary: Dictionary(3318 unique tokens: ['action', 'adulter', 'adulteri', 'affect', 'andi']...)\n" ] } ], "source": [ "dictionary = Dictionary(texts)\n", "dictionary.filter_extremes(no_above=0.1, no_below=10)\n", "corpus = [dictionary.doc2bow(text) for text in texts]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set up the LDA model in the corpus. We set the number of topics to be 2, and expect to see one which is to do with river banks, and one to do with financial banks. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:gensim.models.ldamodel:using asymmetric alpha [0.63060194, 0.36939806]\n", "INFO:gensim.models.ldamodel:using symmetric eta at 0.5\n", "INFO:gensim.models.ldamodel:using serial LDA version on this node\n", "INFO:gensim.models.ldamodel:running online (single-pass) LDA training, 2 topics, 1 passes over the supplied corpus of 1907 documents, updating model once every 1907 documents, evaluating perplexity every 1907 documents, iterating 50x with a convergence threshold of 0.001000\n", "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n", "INFO:gensim.models.ldamodel:-8.511 per-word bound, 364.7 perplexity estimate based on a held-out corpus of 1907 documents with 180557 words\n", "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #1907/1907\n", "INFO:gensim.models.ldamodel:topic #0 (0.631): 0.004\"homosexu\" + 0.004\"hell\" + 0.003\"paul\" + 0.002\"firearm\" + 0.002\"batf\" + 0.002\"cathol\" + 0.002\"natur\" + 0.002\"crime\" + 0.002\"lord\" + 0.002\"shall\"\n", "INFO:gensim.models.ldamodel:topic #1 (0.369): 0.004\"firearm\" + 0.004\"homosexu\" + 0.003\"file\" + 0.003\"author\" + 0.003\"paul\" + 0.003\"scriptur\" + 0.002\"stratu\" + 0.002\"amend\" + 0.002\"koresh\" + 0.002\"crimin\"\n", "INFO:gensim.models.ldamodel:topic diff=0.737151, rho=1.000000\n" ] } ], "source": [ "numpy.random.seed(1) # setting random seed to get the same results each time.\n", "model = ldamodel.LdaModel(corpus, id2word=dictionary, num_topics=2, alpha='asymmetric', minimum_probability=1e-8)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0,\n", " '0.004\"homosexu\" + 0.004\"hell\" + 0.003\"paul\" + 0.002\"firearm\" + 0.002\"batf\" + 0.002\"cathol\" + 0.002\"natur\" + 0.002\"crime\" + 0.002\"lord\" + 0.002\"shall\"'),\n", " (1,\n", " '0.004\"firearm\" + 0.004\"homosexu\" + 0.003\"file\" + 0.003\"author\" + 0.003\"paul\" + 0.003\"scriptur\" + 0.002\"stratu\" + 0.002\"amend\" + 0.002\"koresh\" + 0.002\"crimin\"')]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.show_topics()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And like we expected, the LDA model has given us near perfect results. Bank is the most influential word in both the topics, as we can see. The other words help define what kind of bank we are talking about. Let's now see where our new methods fit in." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### get_term_topics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `get_term_topics` returns the odds of that particular word belonging to a particular topic. \n", "A few examples:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, 0.0035053839), (1, 0.0011557308)]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.get_term_topics('hell')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Makes sense, the value for it belonging to `topic_0` is a lot more." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, 0.002482554), (1, 0.0036967357)]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.get_term_topics('firearm')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This also works out well, the word finance is more likely to be in topic_1 to do with financial banks." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, 0.000701838), (1, 0.0006635987)]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.get_term_topics('car')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And this is particularly interesting. Since the word bank is likely to be in both the topics, the values returned are also very similar." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### get_document_topics and Document Word-Topic Coloring" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`get_document_topics` is an already existing gensim functionality which uses the `inference` function to get the sufficient statistics and figure out the topic distribution of the document.\n", "\n", "The addition to this is the ability for us to now know the topic distribution for each word in the document. \n", "Let us test this with two different documents which have the word bank in it, one in the finance context and one in the river context.\n", "\n", "The `get_document_topics` method returns (along with the standard document topic proprtion) the word_type followed by a list sorted with the most likely topic ids, when `per_word_topics` is set as true." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "bow_water = ['bank','water','bank']\n", "bow_finance = ['bank','finance','bank']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, [0, 1]), (3, [0, 1])]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bow = model.id2word.doc2bow(bow_water) # convert to bag of words format first\n", "doc_topics, word_topics, phi_values = model.get_document_topics(bow, per_word_topics=True)\n", "\n", "word_topics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now what does that output mean? It means that like `word_type 1`, our `word_type` `3`, which is the word `bank`, is more likely to be in `topic_0` than `topic_1`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You must have noticed that while we unpacked into `doc_topics` and `word_topics`, there is another variable - `phi_values`. Like the name suggests, phi_values contains the phi values for each topic for that particular word, scaled by feature length. Phi is essentially the probability of that word in that document belonging to a particular topic. The next few lines should illustrate this. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, [(0, 1.8300905), (1, 0.16990812)]),\n", " (3, [(0, 0.8581231), (1, 0.14187533)])]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi_values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This means that `word_type` 0 has the following phi_values for each of the topics. \n", "What is intresting to note is `word_type` 3 - because it has 2 occurences (i.e, the word `bank` appears twice in the bow), we can see that the scaling by feature length is very evident. The sum of the phi_values is 2, and not 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we know exactly what `get_document_topics` does, let us now do the same with our second document, `bow_finance`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, [0, 1]), (10, [0, 1])]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bow = model.id2word.doc2bow(bow_finance) # convert to bag of words format first\n", "doc_topics, word_topics, phi_values = model.get_document_topics(bow, per_word_topics=True)\n", "\n", "word_topics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And lo and behold, because the word bank is now used in the financial context, it immedietly swaps to being more likely associated with `topic_1`.\n", "\n", "We've seen quite clearly that based on the context, the most likely topic associated with a word can change. \n", "This differs from our previous method, `get_term_topics`, where it is a 'static' topic distribution. \n", "\n", "It must also be noted that because the gensim implementation of LDA uses Variational Bayes sampling, a `word_type` in a document is only given one topic distribution. For example, the sentence 'the bank by the river bank' is likely to be assigned to `topic_0`, and each of the bank word instances have the same distribution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### get_document_topics for entire corpus\n", "\n", "You can get `doc_topics`, `word_topics` and `phi_values` for all the documents in the corpus in the following manner :" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New Document \n", "\n", "Document topics: [(0, 0.73633265), (1, 0.26366737)]\n", "Word topics: [(0, [0, 1]), (1, [0, 1]), (2, [0, 1]), (3, [0, 1])]\n", "Phi values: [(0, [(0, 0.8527051), (1, 0.14729421)]), (1, [(0, 0.795473), (1, 0.20452519)]), (2, [(0, 0.7709577), (1, 0.2290356)]), (3, [(0, 0.76475203), (1, 0.23524645)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.7539386), (1, 0.2460614)]\n", "Word topics: [(1, [0, 1]), (3, [0, 1]), (4, [0, 1]), (5, [0, 1]), (6, [0, 1])]\n", "Phi values: [(1, [(0, 0.80670816), (1, 0.19329017)]), (3, [(0, 0.77720207), (1, 0.22279654)]), (4, [(0, 0.90712374), (1, 0.092870995)]), (5, [(0, 0.7294175), (1, 0.27057865)]), (6, [(0, 0.805549), (1, 0.19444753)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.17039819), (1, 0.82960176)]\n", "Word topics: [(0, [1, 0]), (3, [1, 0]), (5, [1, 0]), (7, [1, 0])]\n", "Phi values: [(0, [(0, 0.15414648), (1, 0.8458525)]), (3, [(0, 0.09283445), (1, 0.90716416)]), (5, [(0, 0.073286586), (1, 0.92671037)]), (7, [(0, 0.031027067), (1, 0.96896887)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.8758601), (1, 0.124139935)]\n", "Word topics: [(0, [0, 1]), (1, [0, 1]), (3, [0, 1]), (8, [0, 1])]\n", "Phi values: [(0, [(0, 1.9142816), (1, 0.085716955)]), (1, [(0, 0.9375137), (1, 0.06248462)]), (3, [(0, 0.92614746), (1, 0.073851064)]), (8, [(0, 0.97765744), (1, 0.022337979)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.1712567), (1, 0.8287433)]\n", "Word topics: [(1, [1, 0]), (3, [1, 0]), (6, [1, 0]), (9, [1, 0])]\n", "Phi values: [(1, [(0, 0.11006477), (1, 0.8899334)]), (3, [(0, 0.09368856), (1, 0.9063102)]), (6, [(0, 0.109341085), (1, 0.8906553)]), (9, [(0, 0.04248068), (1, 0.9575148)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.16402602), (1, 0.8359739)]\n", "Word topics: [(0, [1, 0]), (10, [1, 0]), (11, [1, 0]), (12, [1, 0])]\n", "Phi values: [(0, [(0, 0.14435525), (1, 0.85564375)]), (10, [(0, 0.07960652), (1, 0.92039144)]), (11, [(0, 0.07176139), (1, 0.92823654)]), (12, [(0, 0.023786588), (1, 0.97620964)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.80524194), (1, 0.19475807)]\n", "Word topics: [(0, [0, 1]), (11, [0, 1]), (13, [0, 1])]\n", "Phi values: [(0, [(0, 0.9217699), (1, 0.07822937)]), (11, [(0, 0.84373295), (1, 0.1562643)]), (13, [(0, 0.95610404), (1, 0.043893255)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.65283513), (1, 0.3471648)]\n", "Word topics: [(0, [0, 1]), (10, [0, 1])]\n", "Phi values: [(0, [(0, 0.79454756), (1, 0.20545153)]), (10, [(0, 0.6647264), (1, 0.33527094)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.7917557), (1, 0.20824426)]\n", "Word topics: [(0, [0, 1]), (10, [0, 1]), (11, [0, 1]), (14, [0, 1])]\n", "Phi values: [(0, [(0, 0.9003985), (1, 0.099600814)]), (10, [(0, 0.8225217), (1, 0.17747577)]), (11, [(0, 0.80554056), (1, 0.19445676)]), (14, [(0, 0.9310509), (1, 0.0689471)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.80841804), (1, 0.19158193)]\n", "Word topics: [(13, [0, 1]), (14, [0, 1])]\n", "Phi values: [(13, [(0, 0.9664923), (1, 0.033504896)]), (14, [(0, 0.95886075), (1, 0.041137177)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topics: [(0, 0.84000635), (1, 0.15999362)]\n", "Word topics: [(0, [0, 1]), (14, [0, 1]), (15, [0, 1])]\n", "Phi values: [(0, [(0, 0.94806826), (1, 0.051930968)]), (14, [(0, 0.9646261), (1, 0.035372045)]), (15, [(0, 0.9475713), (1, 0.052422963)])]\n", " \n", "-------------- \n", "\n" ] } ], "source": [ "all_topics = model.get_document_topics(corpus, per_word_topics=True)\n", "\n", "for doc_topics, word_topics, phi_values in all_topics:\n", " print('New Document \\n')\n", " print('Document topics:', doc_topics)\n", " print('Word topics:', word_topics)\n", " print('Phi values:', phi_values)\n", " print(\" \")\n", " print('-------------- \\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In case you want to store `doc_topics`, `word_topics` and `phi_values` for all the documents in the corpus in a variable and later access details of a particular document using its index, it can be done in the following manner:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "topics = model.get_document_topics(corpus, per_word_topics=True)\n", "all_topics = [(doc_topics, word_topics, word_phis) for doc_topics, word_topics, word_phis in topics]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, I can access details of a particular document, say Document #3, as follows: " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Document topic: [(0, 0.84000635), (1, 0.15999362)] \n", "\n", "Word topic: [(0, [1, 0]), (3, [1, 0]), (5, [1, 0]), (7, [1, 0])] \n", "\n", "Phi value: [(0, [(0, 0.1540126), (1, 0.8459863)]), (3, [(0, 0.09274801), (1, 0.90725076)]), (5, [(0, 0.07321687), (1, 0.9267802)]), (7, [(0, 0.030996205), (1, 0.96899974)])]\n" ] } ], "source": [ "doc_topic, word_topics, phi_values = all_topics[2]\n", "print('Document topic:', doc_topics, \"\\n\")\n", "print('Word topic:', word_topics, \"\\n\")\n", "print('Phi value:', phi_values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can print details for all the documents (as shown above), in the following manner:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New Document \n", "\n", "Document topic: [(0, 0.7339641), (1, 0.26603588)]\n", "Word topic: [(0, [0, 1]), (1, [0, 1]), (2, [0, 1]), (3, [0, 1])]\n", "Phi value: [(0, [(0, 0.850583), (1, 0.14941624)]), (1, [(0, 0.7927268), (1, 0.20727131)]), (2, [(0, 0.7679785), (1, 0.23201483)]), (3, [(0, 0.76171696), (1, 0.23828152)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.7540561), (1, 0.24594393)]\n", "Word topic: [(1, [0, 1]), (3, [0, 1]), (4, [0, 1]), (5, [0, 1]), (6, [0, 1])]\n", "Phi value: [(1, [(0, 0.8068402), (1, 0.19315805)]), (3, [(0, 0.77734876), (1, 0.2226498)]), (4, [(0, 0.9071951), (1, 0.09279961)]), (5, [(0, 0.7295848), (1, 0.27041143)]), (6, [(0, 0.80568177), (1, 0.1943148)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.17031126), (1, 0.8296888)]\n", "Word topic: [(0, [1, 0]), (3, [1, 0]), (5, [1, 0]), (7, [1, 0])]\n", "Phi value: [(0, [(0, 0.1540126), (1, 0.8459863)]), (3, [(0, 0.09274801), (1, 0.90725076)]), (5, [(0, 0.07321687), (1, 0.9267802)]), (7, [(0, 0.030996205), (1, 0.96899974)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.87583715), (1, 0.12416287)]\n", "Word topic: [(0, [0, 1]), (1, [0, 1]), (3, [0, 1]), (8, [0, 1])]\n", "Phi value: [(0, [(0, 1.9142504), (1, 0.08574835)]), (1, [(0, 0.9374913), (1, 0.062507026)]), (3, [(0, 0.9261214), (1, 0.07387724)]), (8, [(0, 0.97764915), (1, 0.022346335)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.17121859), (1, 0.8287814)]\n", "Word topic: [(1, [1, 0]), (3, [1, 0]), (6, [1, 0]), (9, [1, 0])]\n", "Phi value: [(1, [(0, 0.11002095), (1, 0.8899771)]), (3, [(0, 0.093650565), (1, 0.906348)]), (6, [(0, 0.109297544), (1, 0.8906989)]), (9, [(0, 0.04246249), (1, 0.95753306)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.16401243), (1, 0.83598757)]\n", "Word topic: [(0, [1, 0]), (10, [1, 0]), (11, [1, 0]), (12, [1, 0])]\n", "Phi value: [(0, [(0, 0.14433442), (1, 0.85566455)]), (10, [(0, 0.07959415), (1, 0.92040366)]), (11, [(0, 0.07175015), (1, 0.9282477)]), (12, [(0, 0.023782672), (1, 0.9762135)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.8052399), (1, 0.19476008)]\n", "Word topic: [(0, [0, 1]), (11, [0, 1]), (13, [0, 1])]\n", "Phi value: [(0, [(0, 0.9217683), (1, 0.07823093)]), (11, [(0, 0.84373015), (1, 0.15626718)]), (13, [(0, 0.95610315), (1, 0.043894168)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.6528616), (1, 0.34713835)]\n", "Word topic: [(0, [0, 1]), (10, [0, 1])]\n", "Phi value: [(0, [(0, 0.7945762), (1, 0.20542288)]), (10, [(0, 0.6647654), (1, 0.3352318)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.79167444), (1, 0.20832555)]\n", "Word topic: [(0, [0, 1]), (10, [0, 1]), (11, [0, 1]), (14, [0, 1])]\n", "Phi value: [(0, [(0, 0.9003313), (1, 0.09966788)]), (10, [(0, 0.82241255), (1, 0.17758493)]), (11, [(0, 0.80542344), (1, 0.19457388)]), (14, [(0, 0.931003), (1, 0.06899511)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.8083355), (1, 0.19166456)]\n", "Word topic: [(13, [0, 1]), (14, [0, 1])]\n", "Phi value: [(13, [(0, 0.9664568), (1, 0.033540305)]), (14, [(0, 0.9588177), (1, 0.04118031)])]\n", " \n", "-------------- \n", "\n", "New Document \n", "\n", "Document topic: [(0, 0.8399969), (1, 0.1600031)]\n", "Word topic: [(0, [0, 1]), (14, [0, 1]), (15, [0, 1])]\n", "Phi value: [(0, [(0, 0.9480616), (1, 0.051937718)]), (14, [(0, 0.9646214), (1, 0.03537672)]), (15, [(0, 0.9475646), (1, 0.052429777)])]\n", " \n", "-------------- \n", "\n" ] } ], "source": [ "for doc in all_topics:\n", " print('New Document \\n')\n", " print('Document topic:', doc[0])\n", " print('Word topic:', doc[1])\n", " print('Phi value:', doc[2])\n", " print(\" \")\n", " print('-------------- \\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Coloring topic-terms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These methods can come in handy when we want to color the words in a corpus or a document. If we wish to color the words in a corpus (i.e, color all the words in the dictionary of the corpus), then `get_term_topics` would be a better choice. If not, `get_document_topics` would do the trick." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now attempt to color these words and plot it using `matplotlib`. \n", "This is just one way to go about plotting words - there are more and better ways.\n", "\n", "[WordCloud](https://github.com/amueller/word_cloud) is such a python package which also does this.\n", "\n", "For our simple illustration, let's keep `topic_1` as red, and `topic_0` as blue." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# this is a sample method to color words. Like mentioned before, there are many ways to do this.\n", "\n", "def color_words(model, doc):\n", " import matplotlib.pyplot as plt\n", " import matplotlib.patches as patches\n", " \n", " # make into bag of words\n", " doc = model.id2word.doc2bow(doc)\n", " # get word_topics\n", " doc_topics, word_topics, phi_values = model.get_document_topics(doc, per_word_topics=True)\n", "\n", " # color-topic matching\n", " topic_colors = { 1:'red', 0:'blue'}\n", " \n", " # set up fig to plot\n", " fig = plt.figure()\n", " ax = fig.add_axes([0,0,1,1])\n", "\n", " # a sort of hack to make sure the words are well spaced out.\n", " word_pos = 1/len(doc)\n", " \n", " # use matplotlib to plot words\n", " for word, topics in word_topics:\n", " ax.text(word_pos, 0.8, model.id2word[word],\n", " horizontalalignment='center',\n", " verticalalignment='center',\n", " fontsize=20, color=topic_colors[topics[0]], # choose just the most likely topic\n", " transform=ax.transAxes)\n", " word_pos += 0.2 # to move the word for the next iter\n", "\n", " ax.set_axis_off()\n", " plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us revisit our old examples to show some examples of document coloring" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# our river bank document\n", "\n", "bow_water = ['bank','water','bank']\n", "color_words(model, bow_water)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bow_finance = ['bank','finance','bank']\n", "color_words(model, bow_finance)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is fun to note here is that while bank was colored blue in our first example, it is now red because of the financial context - something which the numbers proved to us before." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# sample doc with a somewhat even distribution of words among the likely topics\n", "\n", "doc = ['bank', 'water', 'bank', 'finance', 'money','sell','river','fast','tree']\n", "color_words(model, doc)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the document word coloring is done just the way we expected. :)\n", "\n", "## Word-coloring a dictionary\n", "\n", "We can do the same for the entire vocabulary, statically. The only difference would be in using `get_term_topics`, and iterating over the dictionary.\n", "\n", "We will use a modified version of the coloring code when passing an entire dictionary." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def color_words_dict(model, dictionary):\n", " import matplotlib.pyplot as plt\n", " import matplotlib.patches as patches\n", "\n", " word_topics = []\n", " for word_id in dictionary:\n", " word = str(dictionary[word_id])\n", " # get_term_topics returns static topics, as mentioned before\n", " probs = model.get_term_topics(word)\n", " # we are creating word_topics which is similar to the one created by get_document_topics\n", " try:\n", " if probs[0][1] >= probs[1][1]:\n", " word_topics.append((word_id, [0, 1]))\n", " else:\n", " word_topics.append((word_id, [1, 0]))\n", " # this in the case only one topic is returned\n", " except IndexError:\n", " word_topics.append((word_id, [probs[0][0]]))\n", " \n", " # color-topic matching\n", " topic_colors = { 1:'red', 0:'blue'}\n", " \n", " # set up fig to plot\n", " fig = plt.figure()\n", " ax = fig.add_axes([0,0,1,1])\n", "\n", " # a sort of hack to make sure the words are well spaced out.\n", " word_pos = 1/len(doc)\n", " \n", " # use matplotlib to plot words\n", " for word, topics in word_topics:\n", " ax.text(word_pos, 0.8, model.id2word[word],\n", " horizontalalignment='center',\n", " verticalalignment='center',\n", " fontsize=20, color=topic_colors[topics[0]], # choose just the most likely topic\n", " transform=ax.transAxes)\n", " word_pos += 0.2 # to move the word for the next iter\n", "\n", " ax.set_axis_off()\n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "color_words_dict(model, dictionary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the red words are to do with finance, and the blue ones are to do with water. \n", "\n", "You can also notice that some words, like mud, shore and borrow seem to be incorrectly colored - however, they are correctly colored according to the LDA model used for coloring. A small corpus means that the LDA algorithm might not assign 'ideal' topic proportions to each word. Fine tuning the model and having a larger corpus would improve the model, and improve the results of the word coloring." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	65,706	Python	.py	942	64.677282	15,496	0.750602	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,956	Varembed.ipynb	piskvorky_gensim/docs/notebooks/Varembed.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# VarEmbed Tutorial\n", "\n", "Varembed is a word embedding model incorporating morphological information, capturing shared sub-word features. Unlike previous work that constructs word embeddings directly from morphemes, varembed combines morphological and distributional information in a unified probabilistic framework. Varembed thus yields improvements on intrinsic word similarity evaluations. Check out the original paper, [arXiv:1608.01056](https://arxiv.org/abs/1608.01056) accepted in [EMNLP 2016](http://www.emnlp2016.net/accepted-papers.html).\n", "\n", "Varembed is now integrated into [Gensim](https://radimrehurek.com/gensim/) providing ability to load already trained varembed models into gensim with additional functionalities over word vectors already present in gensim.\n", "\n", "# This Tutorial\n", "\n", "In this tutorial you will learn how to train, load and evaluate varembed model on your data.\n", "\n", "# Train Model\n", "\n", "The authors provide their code to train a varembed model. Checkout the repository [MorphologicalPriorsForWordEmbeddings](https://github.com/rguthrie3/MorphologicalPriorsForWordEmbeddings) for to train a varembed model. You'll need to use that code if you want to train a model. \n", "\n", "# Load Varembed Model\n", "\n", "Now that you have an already trained varembed model, you can easily load the varembed word vectors directly into Gensim. <br>\n", "For that, you need to provide the path to the word vectors pickle file generated after you train the model and run the script to [package varembed embeddings](https://github.com/rguthrie3/MorphologicalPriorsForWordEmbeddings/blob/master/package_embeddings.py) provided in the [varembed source code repository](https://github.com/rguthrie3/MorphologicalPriorsForWordEmbeddings).\n", "\n", "We'll use a varembed model trained on [Lee Corpus](https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/test/test_data/lee.cor) as the vocabulary, which is already available in gensim.\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/misha/git/gensim/docs/notebooks\n" ] }, { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '../../gensim/test/test_data/varembed_leecorpus_vectors.pkl'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-2-3653006df438>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvector_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'../../gensim/test/test_data/varembed_leecorpus_vectors.pkl'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m~/envs/gensim/lib/python3.7/site-packages/smart_open/smart_open_lib.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(uri, mode, buffering, encoding, errors, newline, closefd, opener, ignore_ext, transport_params)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0mbuffering\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbuffering\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 308\u001b[0m )\n\u001b[1;32m 309\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m 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'../../gensim/test/test_data/varembed_leecorpus_vectors.pkl'\n", "model = varembed.VarEmbed.load_varembed_format(vectors=vector_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This loads a varembed model into Gensim. Also if you want to load with morphemes added into the varembed vectors, you just need to also provide the path to the trained morfessor model binary as an argument. This works as an optional parameter, if not provided, it would just load the varembed vectors without morphemes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "morfessor_file = '../../gensim/test/test_data/varembed_leecorpus_morfessor.bin'\n", "model_with_morphemes = varembed.VarEmbed.load_varembed_format(vectors=vector_file, morfessor_model=morfessor_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This helps load trained varembed models into Gensim. Now you can use this for any of the Keyed Vector functionalities, like 'most_similar', 'similarity' and so on, already provided in gensim. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.most_similar('government')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.similarity('peace', 'grim')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "In this tutorial, we learnt how to load already trained varembed models vectors into gensim and easily use and evaluate it. That's it!\n", "\n", "# Resources\n", "\n", " [Varembed Source Code](https://github.com/rguthrie3/MorphologicalPriorsForWordEmbeddings)\n", "* [Gensim](https://radimrehurek.com/gensim/)\n", "* [Lee Corpus](https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/test/test_data/lee.cor)\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	12,731	Python	.py	148	81.635135	1,631	0.701264	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,957	test_notebooks.py	piskvorky_gensim/docs/notebooks/test_notebooks.py	import os import sys import tempfile from glob import glob import nbformat from nbconvert.preprocessors import ExecutePreprocessor from nbconvert.preprocessors.execute import CellExecutionError "from smart_open import smart_open\n", def _notebook_run(path): """Execute a notebook via nbconvert and collect output. :returns (parsed nb object, execution errors) """ kernel_name = 'python%d' % sys.version_info[0] this_file_directory = os.path.dirname(__file__) errors = [] with tempfile.NamedTemporaryFile(suffix=".ipynb", mode='wt') as fout: with smart_open(path, 'rb') as f: nb = nbformat.read(f, as_version=4) nb.metadata.get('kernelspec', {})['name'] = kernel_name ep = ExecutePreprocessor(kernel_name=kernel_name, timeout=10) try: ep.preprocess(nb, {'metadata': {'path': this_file_directory}}) except CellExecutionError as e: if "SKIP" in e.traceback: print(str(e.traceback).split("\n")[-2]) else: raise e except RuntimeError as e: print(e) finally: nbformat.write(nb, fout) return nb, errors def test_notebooks(): for notebook in glob("*.ipynb"): if " " in notebook: continue print("Testing {}".format(notebook)) nb, errors = _notebook_run(notebook) assert errors == []	1,477	Python	.py	39	29.102564	78	0.61049	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,958	Topics_and_Transformations.ipynb	piskvorky_gensim/docs/notebooks/Topics_and_Transformations.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,959	Similarity_Queries.ipynb	piskvorky_gensim/docs/notebooks/Similarity_Queries.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,960	word2vec.ipynb	piskvorky_gensim/docs/notebooks/word2vec.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,961	translation_matrix.ipynb	piskvorky_gensim/docs/notebooks/translation_matrix.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Translation Matrix Tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is it ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose we are given a set of word pairs and their associated vector representaion $\\{x_{i},z_{i}\\}_{i=1}^{n}$, where $x_{i} \\in R^{d_{1}}$ is the distibuted representation of word $i$ in the source language, and ${z_{i} \\in R^{d_{2}}}$ is the vector representation of its translation. Our goal is to find a transformation matrix $W$ such that $Wx_{i}$ approximates $z_{i}$. In practice, $W$ can be learned by the following optimization prolem:\n", "\n", "<center>$\\min \\limits_{W} \\sum \\limits_{i=1}^{n} \|\|Wx_{i}-z_{i}\|\|^{2}$</center>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resources" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tomas Mikolov, Quoc V Le, Ilya Sutskever. 2013. [Exploiting Similarities among Languages for Machine Translation](https://arxiv.org/pdf/1309.4168.pdf)\n", "\n", "Georgiana Dinu, Angelikie Lazaridou and Marco Baroni. 2014. [Improving zero-shot learning by mitigating the hubness problem](https://arxiv.org/pdf/1309.4168.pdf)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "from gensim import utils\n", "from gensim.models import translation_matrix\n", "from gensim.models import KeyedVectors\n", "import smart_open" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this tutorial, we'll train our model using the English -> Italian word pairs from the OPUS collection. This corpus contains 5000 word pairs. Each word pair is English word with corresponding Italian word.\n", "\n", "Dataset download: \n", "\n", "[OPUS_en_it_europarl_train_5K.txt](https://pan.baidu.com/s/1nuIuQoT)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!rm 1nuIuQoT" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('for', 'per'), ('that', 'che'), ('with', 'con'), ('are', 'are'), ('are', 'sono'), ('this', 'questa'), ('this', 'questo'), ('you', 'lei'), ('not', 'non'), ('which', 'che')]\n" ] } ], "source": [ "train_file = \"OPUS_en_it_europarl_train_5K.txt\"\n", "\n", "with smart_open.open(train_file, \"r\") as f:\n", " word_pair = [tuple(utils.to_unicode(line).strip().split()) for line in f]\n", "print(word_pair[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial uses 300-dimensional vectors of English words as source and vectors of Italian words as target. (Those vector trained by the word2vec toolkit with cbow. The context window was set 5 words to either side of the target,\n", "the sub-sampling option was set to 1e-05 and estimate the probability of a target word with the negative sampling method, drawing 10 samples from the noise distribution)\n", "\n", "Download dataset:\n", "\n", "[EN.200K.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt](https://pan.baidu.com/s/1nv3bYel)\n", "\n", "[IT.200K.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt](https://pan.baidu.com/s/1boP0P7D)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Load the source language word vector\n", "source_word_vec_file = \"EN.200K.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt\"\n", "source_word_vec = KeyedVectors.load_word2vec_format(source_word_vec_file, binary=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Load the target language word vector\n", "target_word_vec_file = \"IT.200K.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt\"\n", "target_word_vec = KeyedVectors.load_word2vec_format(target_word_vec_file, binary=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train the translation matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "transmat = translation_matrix.TranslationMatrix(source_word_vec, target_word_vec, word_pair)\n", "transmat.train(word_pair)\n", "print(\"the shape of translation matrix is: \", transmat.translation_matrix.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prediction Time: For any given new word, we can map it to the other language space by computing $z = Wx$, then we find the word whose representation is closet to z in the target language space, using cosine similarity as the distance metric." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Part one:\n", "Let's look at some vocabulary of numbers translation. We use English words (one, two, three, four and five) as test." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# The pair is in the form of (English, Italian), we can see whether the translated word is correct\n", "words = [(\"one\", \"uno\"), (\"two\", \"due\"), (\"three\", \"tre\"), (\"four\", \"quattro\"), (\"five\", \"cinque\")]\n", "source_word, target_word = zip(words)\n", "translated_word = transmat.translate(source_word, 5, )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "for k, v in translated_word.iteritems():\n", " print(\"word \", k, \" and translated word\", v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Part two:\n", "Let's look at some vocabulary of fruits translation. We use English words (apple, orange, grape, banana and mango) as test." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "words = [(\"apple\", \"mela\"), (\"orange\", \"arancione\"), (\"grape\", \"acino\"), (\"banana\", \"banana\"), (\"mango\", \"mango\")]\n", "source_word, target_word = zip(words)\n", "translated_word = transmat.translate(source_word, 5)\n", "for k, v in translated_word.iteritems():\n", " print(\"word \", k, \" and translated word\", v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Part three:\n", "Let's look at some vocabulary of animals translation. We use English words (dog, pig, cat, horse and bird) as test." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "words = [(\"dog\", \"cane\"), (\"pig\", \"maiale\"), (\"cat\", \"gatto\"), (\"fish\", \"cavallo\"), (\"birds\", \"uccelli\")]\n", "source_word, target_word = zip(words)\n", "translated_word = transmat.translate(source_word, 5)\n", "for k, v in translated_word.iteritems():\n", " print(\"word \", k, \" and translated word\", v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Creation Time for the Translation Matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Testing the creation time, we extracted more word pairs from a dictionary built from Europarl([Europara, en-it](http://opus.lingfil.uu.se/)). We obtain about 20K word pairs and their corresponding word vectors or you can download from this: [word_dict.pkl](https://pan.baidu.com/s/1dF8HUX7)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "word_dict = \"word_dict.pkl\"\n", "with smart_open.open(word_dict, \"r\") as f:\n", " word_pair = pickle.load(f)\n", "print(\"the length of word pair \", len(word_pair))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import time\n", "\n", "test_case = 10\n", "word_pair_length = len(word_pair)\n", "step = word_pair_length / test_case\n", "\n", "duration = []\n", "sizeofword = []\n", "\n", "for idx in range(0, test_case):\n", " sub_pair = word_pair[: (idx + 1) step]\n", "\n", " startTime = time.time()\n", " transmat = translation_matrix.TranslationMatrix(source_word_vec, target_word_vec, sub_pair)\n", " transmat.train(sub_pair)\n", " endTime = time.time()\n", " \n", " sizeofword.append(len(sub_pair))\n", " duration.append(endTime - startTime)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import plotly\n", "from plotly.graph_objs import Scatter, Layout\n", "\n", "plotly.offline.init_notebook_mode(connected=True)\n", "\n", "plotly.offline.iplot({\n", " \"data\": [Scatter(x=sizeofword, y=duration)],\n", " \"layout\": Layout(title=\"time for creation\"),\n", "}, filename=\"tm_creation_time.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will see a two dimensional coordination whose horizontal axis is the size of corpus and vertical axis is the time to train a translation matrix (the unit is second). As the size of corpus increases, the time increases linearly." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Linear Relationship Between Languages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To have a better understanding of the principles behind, we visualized the word vectors using PCA, we noticed that the vector representations of similar words in different languages were related by a linear transformation." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "\n", "import plotly\n", "from plotly.graph_objs import Scatter, Layout, Figure\n", "plotly.offline.init_notebook_mode(connected=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "words = [(\"one\", \"uno\"), (\"two\", \"due\"), (\"three\", \"tre\"), (\"four\", \"quattro\"), (\"five\", \"cinque\")]\n", "en_words_vec = [source_word_vec[item[0]] for item in words]\n", "it_words_vec = [target_word_vec[item[1]] for item in words]\n", "\n", "en_words, it_words = zip(words)\n", "\n", "pca = PCA(n_components=2)\n", "new_en_words_vec = pca.fit_transform(en_words_vec)\n", "new_it_words_vec = pca.fit_transform(it_words_vec)\n", "\n", "# remove the code, use the plotly for ploting instead\n", "# fig = plt.figure()\n", "# fig.add_subplot(121)\n", "# plt.scatter(new_en_words_vec[:, 0], new_en_words_vec[:, 1])\n", "# for idx, item in enumerate(en_words):\n", "# plt.annotate(item, xy=(new_en_words_vec[idx][0], new_en_words_vec[idx][1]))\n", "\n", "# fig.add_subplot(122)\n", "# plt.scatter(new_it_words_vec[:, 0], new_it_words_vec[:, 1])\n", "# for idx, item in enumerate(it_words):\n", "# plt.annotate(item, xy=(new_it_words_vec[idx][0], new_it_words_vec[idx][1]))\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# you can also using plotly lib to plot in one figure\n", "trace1 = Scatter(\n", " x = new_en_words_vec[:, 0],\n", " y = new_en_words_vec[:, 1],\n", " mode = 'markers+text',\n", " text = en_words,\n", " textposition = 'top'\n", ")\n", "trace2 = Scatter(\n", " x = new_it_words_vec[:, 0],\n", " y = new_it_words_vec[:, 1],\n", " mode = 'markers+text',\n", " text = it_words,\n", " textposition = 'top'\n", ")\n", "layout = Layout(\n", " showlegend = False\n", ")\n", "data = [trace1, trace2]\n", "\n", "fig = Figure(data=data, layout=layout)\n", "plot_url = plotly.offline.iplot(fig, filename='relatie_position_for_number.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure shows that the word vectors for English number one to five and the corresponding Italian words uno to cinque have similar geometric arrangements. So the relationship between vector spaces that represent these two languages can be captured by linear mapping. \n", "If we know the translation of one to four from English to Italian, we can learn the transformation matrix that can help us to translate five or other numbers to the Italian word." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "words = [(\"one\", \"uno\"), (\"two\", \"due\"), (\"three\", \"tre\"), (\"four\", \"quattro\"), (\"five\", \"cinque\")]\n", "en_words, it_words = zip(words)\n", "en_words_vec = [source_word_vec[item[0]] for item in words]\n", "it_words_vec = [target_word_vec[item[1]] for item in words]\n", "\n", "# Translate the English word five to Italian word\n", "translated_word = transmat.translate([en_words[4]], 3)\n", "print(\"translation of five: \", translated_word)\n", "\n", "# the translated words of five\n", "for item in translated_word[en_words[4]]:\n", " it_words_vec.append(target_word_vec[item])\n", "\n", "pca = PCA(n_components=2)\n", "new_en_words_vec = pca.fit_transform(en_words_vec)\n", "new_it_words_vec = pca.fit_transform(it_words_vec)\n", "\n", "# remove the code, use the plotly for ploting instead\n", "# fig = plt.figure()\n", "# fig.add_subplot(121)\n", "# plt.scatter(new_en_words_vec[:, 0], new_en_words_vec[:, 1])\n", "# for idx, item in enumerate(en_words):\n", "# plt.annotate(item, xy=(new_en_words_vec[idx][0], new_en_words_vec[idx][1]))\n", "\n", "# fig.add_subplot(122)\n", "# plt.scatter(new_it_words_vec[:, 0], new_it_words_vec[:, 1])\n", "# for idx, item in enumerate(it_words):\n", "# plt.annotate(item, xy=(new_it_words_vec[idx][0], new_it_words_vec[idx][1]))\n", "# # annote for the translation of five, the red text annotation is the translation of five\n", "# for idx, item in enumerate(translated_word[en_words[4]]):\n", "# plt.annotate(item, xy=(new_it_words_vec[idx + 5][0], new_it_words_vec[idx + 5][1]),\n", "# xytext=(new_it_words_vec[idx + 5][0] + 0.1, new_it_words_vec[idx + 5][1] + 0.1),\n", "# color=\"red\",\n", "# arrowprops=dict(facecolor='red', shrink=0.1, width=1, headwidth=2),)\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trace1 = Scatter(\n", " x = new_en_words_vec[:, 0],\n", " y = new_en_words_vec[:, 1],\n", " mode = 'markers+text',\n", " text = en_words,\n", " textposition = 'top'\n", ")\n", "trace2 = Scatter(\n", " x = new_it_words_vec[:, 0],\n", " y = new_it_words_vec[:, 1],\n", " mode = 'markers+text',\n", " text = it_words,\n", " textposition = 'top'\n", ")\n", "layout = Layout(\n", " showlegend = False,\n", " annotations = [dict(\n", " x = new_it_words_vec[5][0],\n", " y = new_it_words_vec[5][1],\n", " text = translated_word[en_words[4]][0],\n", " arrowcolor = \"black\",\n", " arrowsize = 1.5,\n", " arrowwidth = 1,\n", " arrowhead = 0.5\n", " ), dict(\n", " x = new_it_words_vec[6][0],\n", " y = new_it_words_vec[6][1],\n", " text = translated_word[en_words[4]][1],\n", " arrowcolor = \"black\",\n", " arrowsize = 1.5,\n", " arrowwidth = 1,\n", " arrowhead = 0.5\n", " ), dict(\n", " x = new_it_words_vec[7][0],\n", " y = new_it_words_vec[7][1],\n", " text = translated_word[en_words[4]][2],\n", " arrowcolor = \"black\",\n", " arrowsize = 1.5,\n", " arrowwidth = 1,\n", " arrowhead = 0.5\n", " )]\n", ")\n", "data = [trace1, trace2]\n", "\n", "fig = Figure(data=data, layout=layout)\n", "plot_url = plotly.offline.iplot(fig, filename='relatie_position_for_numbers.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You probably will see that two kind of different color nodes, one for the English and the other for the Italian. For the translation of word `five`, we return `top 3` similar words `[u'cinque', u'quattro', u'tre']`. We can easily see that the translation is convincing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see some animal words, the figure shows that most of the words also have similar geometric arrangements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "words = [(\"dog\", \"cane\"), (\"pig\", \"maiale\"), (\"cat\", \"gatto\"), (\"horse\", \"cavallo\"), (\"birds\", \"uccelli\")]\n", "en_words_vec = [source_word_vec[item[0]] for item in words]\n", "it_words_vec = [target_word_vec[item[1]] for item in words]\n", "\n", "en_words, it_words = zip(words)\n", "\n", "# remove the code, use the plotly for ploting instead\n", "# pca = PCA(n_components=2)\n", "# new_en_words_vec = pca.fit_transform(en_words_vec)\n", "# new_it_words_vec = pca.fit_transform(it_words_vec)\n", "\n", "# fig = plt.figure()\n", "# fig.add_subplot(121)\n", "# plt.scatter(new_en_words_vec[:, 0], new_en_words_vec[:, 1])\n", "# for idx, item in enumerate(en_words):\n", "# plt.annotate(item, xy=(new_en_words_vec[idx][0], new_en_words_vec[idx][1]))\n", "\n", "# fig.add_subplot(122)\n", "# plt.scatter(new_it_words_vec[:, 0], new_it_words_vec[:, 1])\n", "# for idx, item in enumerate(it_words):\n", "# plt.annotate(item, xy=(new_it_words_vec[idx][0], new_it_words_vec[idx][1]))\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trace1 = Scatter(\n", " x = new_en_words_vec[:, 0],\n", " y = new_en_words_vec[:, 1],\n", " mode = 'markers+text',\n", " text = en_words,\n", " textposition = 'top'\n", ")\n", "trace2 = Scatter(\n", " x = new_it_words_vec[:, 0],\n", " y = new_it_words_vec[:, 1],\n", " mode = 'markers+text',\n", " text = it_words,\n", " textposition ='top'\n", ")\n", "layout = Layout(\n", " showlegend = False\n", ")\n", "data = [trace1, trace2]\n", "\n", "fig = Figure(data=data, layout=layout)\n", "plot_url = plotly.offline.iplot(fig, filename='relatie_position_for_animal.html')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "words = [(\"dog\", \"cane\"), (\"pig\", \"maiale\"), (\"cat\", \"gatto\"), (\"horse\", \"cavallo\"), (\"birds\", \"uccelli\")]\n", "en_words, it_words = zip(words)\n", "en_words_vec = [source_word_vec[item[0]] for item in words]\n", "it_words_vec = [target_word_vec[item[1]] for item in words]\n", "\n", "# Translate the English word birds to Italian word\n", "translated_word = transmat.translate([en_words[4]], 3)\n", "print(\"translation of birds: \", translated_word)\n", "\n", "# the translated words of birds\n", "for item in translated_word[en_words[4]]:\n", " it_words_vec.append(target_word_vec[item])\n", "\n", "pca = PCA(n_components=2)\n", "new_en_words_vec = pca.fit_transform(en_words_vec)\n", "new_it_words_vec = pca.fit_transform(it_words_vec)\n", "\n", "# # remove the code, use the plotly for ploting instead\n", "# fig = plt.figure()\n", "# fig.add_subplot(121)\n", "# plt.scatter(new_en_words_vec[:, 0], new_en_words_vec[:, 1])\n", "# for idx, item in enumerate(en_words):\n", "# plt.annotate(item, xy=(new_en_words_vec[idx][0], new_en_words_vec[idx][1]))\n", "\n", "# fig.add_subplot(122)\n", "# plt.scatter(new_it_words_vec[:, 0], new_it_words_vec[:, 1])\n", "# for idx, item in enumerate(it_words):\n", "# plt.annotate(item, xy=(new_it_words_vec[idx][0], new_it_words_vec[idx][1]))\n", "# # annote for the translation of five, the red text annotation is the translation of five\n", "# for idx, item in enumerate(translated_word[en_words[4]]):\n", "# plt.annotate(item, xy=(new_it_words_vec[idx + 5][0], new_it_words_vec[idx + 5][1]),\n", "# xytext=(new_it_words_vec[idx + 5][0] + 0.1, new_it_words_vec[idx + 5][1] + 0.1),\n", "# color=\"red\",\n", "# arrowprops=dict(facecolor='red', shrink=0.1, width=1, headwidth=2),)\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trace1 = Scatter(\n", " x = new_en_words_vec[:, 0],\n", " y = new_en_words_vec[:, 1],\n", " mode = 'markers+text',\n", " text = en_words,\n", " textposition = 'top'\n", ")\n", "trace2 = Scatter(\n", " x = new_it_words_vec[:5, 0],\n", " y = new_it_words_vec[:5, 1],\n", " mode = 'markers+text',\n", " text = it_words[:5],\n", " textposition = 'top'\n", ")\n", "layout = Layout(\n", " showlegend = False,\n", " annotations = [dict(\n", " x = new_it_words_vec[5][0],\n", " y = new_it_words_vec[5][1],\n", " text = translated_word[en_words[4]][0],\n", " arrowcolor = \"black\",\n", " arrowsize = 1.5,\n", " arrowwidth = 1,\n", " arrowhead = 0.5\n", " ), dict(\n", " x = new_it_words_vec[6][0],\n", " y = new_it_words_vec[6][1],\n", " text = translated_word[en_words[4]][1],\n", " arrowcolor = \"black\",\n", " arrowsize = 1.5,\n", " arrowwidth = 1,\n", " arrowhead = 0.5\n", " ), dict(\n", " x = new_it_words_vec[7][0],\n", " y = new_it_words_vec[7][1],\n", " text = translated_word[en_words[4]][2],\n", " arrowcolor = \"black\",\n", " arrowsize = 1.5,\n", " arrowwidth = 1,\n", " arrowhead = 0.5\n", " )]\n", ")\n", "data = [trace1, trace2]\n", "\n", "fig = Figure(data=data, layout=layout)\n", "plot_url = plotly.offline.iplot(fig, filename='relatie_position_for_animal.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You probably will see that two kind of different color nodes, one for the English and the other for the Italian. For the translation of word `birds`, we return `top 3` similar words `[u'uccelli', u'garzette', u'iguane']`. We can easily see that the animals' words translation is also convincing as the numbers." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Tranlation Matrix Revisit \n", "## Warning: this part is unstable/experimental, it requires more experimentation and will change soon!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As dicussion in this [PR](https://github.com/RaRe-Technologies/gensim/pull/1434), Translation Matrix not only can used to translate the words from one source language to another target lanuage, but also to translate new document vectors back to old model space.\n", "\n", "For example, if we have trained 15k documents using doc2vec (we called this as model1), and we are going to train new 35k documents using doc2vec (we called this as model2). So we can include those 15k documents as reference documents into the new 35k documents. Then we can get 15k document vectors from model1 and 50k document vectors from model2, but both of the two models have vectors for those 15k documents. We can use those vectors to build a mapping from model1 to model2. Finally, with this relation, we can back-map the model2's vector to model1. Therefore, 35k document vectors are learned using this method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we use the IMDB dataset as example. For more information about this dataset, please refer to [this](http://ai.stanford.edu/~amaas/data/sentiment/). And some of code are borrowed from this [notebook](http://localhost:8888/notebooks/docs/notebooks/doc2vec-IMDB.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import gensim\n", "from gensim.models.doc2vec import TaggedDocument\n", "from gensim.models import Doc2Vec\n", "from collections import namedtuple\n", "import smart_open\n", "\n", "def read_sentimentDocs():\n", " SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment')\n", "\n", " alldocs = [] # will hold all docs in original order\n", " with smart_open.open('aclImdb/alldata-id.txt', encoding='utf-8') as alldata:\n", " for line_no, line in enumerate(alldata):\n", " tokens = gensim.utils.to_unicode(line).split()\n", " words = tokens[1:]\n", " tags = [line_no] # `tags = [tokens[0]]` would also work at extra memory cost\n", " split = ['train','test','extra','extra'][line_no // 25000] # 25k train, 25k test, 25k extra\n", " sentiment = [1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no // 12500] # [12.5K pos, 12.5K neg]*2 then unknown\n", " alldocs.append(SentimentDocument(words, tags, split, sentiment))\n", "\n", " train_docs = [doc for doc in alldocs if doc.split == 'train']\n", " test_docs = [doc for doc in alldocs if doc.split == 'test']\n", " doc_list = alldocs[:] # for reshuffling per pass\n", "\n", " print('%d docs: %d train-sentiment, %d test-sentiment' % (len(doc_list), len(train_docs), len(test_docs)))\n", "\n", " return train_docs, test_docs, doc_list\n", "\n", "train_docs, test_docs, doc_list = read_sentimentDocs()\n", "\n", "small_corpus = train_docs[:15000]\n", "large_corpus = train_docs + test_docs\n", "\n", "print(len(train_docs), len(test_docs), len(doc_list), len(small_corpus), len(large_corpus))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we train two Doc2vec model, the parameters can be determined by yourself. We trained on 15k documents for the `model1` and 50k documents for the `model2`. But you should mix some documents which from the 15k document in `model` to the `model2`, as discussed before. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# for the computer performance limited, didn't run on the notebook. \n", "# You do can trained on the server and save the model to the disk.\n", "import multiprocessing\n", "from random import shuffle\n", "\n", "cores = multiprocessing.cpu_count()\n", "model1 = Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores)\n", "model2 = Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores)\n", "\n", "small_train_docs = train_docs[:15000]\n", "# train for small corpus\n", "model1.build_vocab(small_train_docs)\n", "for epoch in range(50):\n", " shuffle(small_train_docs)\n", " model1.train(small_train_docs, total_examples=len(small_train_docs), epochs=1)\n", "model.save(\"small_doc_15000_iter50.bin\")\n", "\n", "large_train_docs = train_docs + test_docs\n", "# train for large corpus\n", "model2.build_vocab(large_train_docs)\n", "for epoch in range(50):\n", " shuffle(large_train_docs)\n", " model2.train(large_train_docs, total_examples=len(train_docs), epochs=1)\n", "# save the model\n", "model2.save(\"large_doc_50000_iter50.bin\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the IMDB training dataset, we train an classifier on the train data which has 25k documents with positive and negative label. Then using this classifier to predict the test data, we see what accuracy can be achieved by the document vectors learned by different methods." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "def test_classifier_error(train, train_label, test, test_label):\n", " classifier = LogisticRegression()\n", " classifier.fit(train, train_label)\n", " score = classifier.score(test, test_label)\n", " print(\"the classifier score :\", score)\n", " return score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the experiment one, we use the vector which learned by the Doc2vec method.To evalute those document vector, we use split those 50k document into two part, one for training and the other for testing." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#you can change the data folder\n", "basedir = \"/home/robotcator/doc2vec\"\n", "\n", "model2 = Doc2Vec.load(os.path.join(basedir, \"large_doc_50000_iter50.bin\"))\n", "m2 = []\n", "for i in range(len(large_corpus)):\n", " m2.append(model2.docvecs[large_corpus[i].tags])\n", "\n", "train_array = np.zeros((25000, 100))\n", "train_label = np.zeros((25000, 1))\n", "test_array = np.zeros((25000, 100))\n", "test_label = np.zeros((25000, 1))\n", "\n", "for i in range(12500):\n", " train_array[i] = m2[i]\n", " train_label[i] = 1\n", "\n", " train_array[i + 12500] = m2[i + 12500]\n", " train_label[i + 12500] = 0\n", "\n", " test_array[i] = m2[i + 25000]\n", " test_label[i] = 1\n", "\n", " test_array[i + 12500] = m2[i + 37500]\n", " test_label[i + 12500] = 0\n", "\n", "print(\"The vectors are learned by doc2vec method\")\n", "test_classifier_error(train_array, train_label, test_array, test_label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the experiment two, the document vectors are learned by the back-mapping method, which has a linear mapping for the `model1` and `model2`. Using this method like translation matrix for the word translation, If we provide the vector for the addtional 35k document vector in `model2`, we can infer this vector for the `model1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from gensim.models import translation_matrix\n", "# you can change the data folder\n", "basedir = \"/home/robotcator/doc2vec\"\n", "\n", "model1 = Doc2Vec.load(os.path.join(basedir, \"small_doc_15000_iter50.bin\"))\n", "model2 = Doc2Vec.load(os.path.join(basedir, \"large_doc_50000_iter50.bin\"))\n", "\n", "l = model1.docvecs.count\n", "l2 = model2.docvecs.count\n", "m1 = np.array([model1.docvecs[large_corpus[i].tags].flatten() for i in range(l)])\n", "\n", "# learn the mapping bettween two model\n", "model = translation_matrix.BackMappingTranslationMatrix(large_corpus[:15000], model1, model2)\n", "model.train(large_corpus[:15000])\n", "\n", "for i in range(l, l2):\n", " infered_vec = model.infer_vector(model2.docvecs[large_corpus[i].tags])\n", " m1 = np.vstack((m1, infered_vec.flatten()))\n", "\n", "train_array = np.zeros((25000, 100))\n", "train_label = np.zeros((25000, 1))\n", "test_array = np.zeros((25000, 100))\n", "test_label = np.zeros((25000, 1))\n", "\n", "# because those document, 25k documents are postive label, 25k documents are negative label\n", "for i in range(12500):\n", " train_array[i] = m1[i]\n", " train_label[i] = 1\n", "\n", " train_array[i + 12500] = m1[i + 12500]\n", " train_label[i + 12500] = 0\n", "\n", " test_array[i] = m1[i + 25000]\n", " test_label[i] = 1\n", "\n", " test_array[i + 12500] = m1[i + 37500]\n", " test_label[i + 12500] = 0\n", "\n", "print(\"The vectors are learned by back-mapping method\")\n", "test_classifier_error(train_array, train_label, test_array, test_label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see that, the vectors learned by back-mapping method performed not bad but still need to be improved." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visulization\n", " we pick some documents and extract the vector both from `model1` and `model2`, we can see that they also share the similar geometric arrangment." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "\n", "import plotly\n", "from plotly.graph_objs import Scatter, Layout, Figure\n", "plotly.offline.init_notebook_mode(connected=True)\n", "\n", "m1_part = m1[14995: 15000]\n", "m2_part = m2[14995: 15000]\n", "\n", "m1_part = np.array(m1_part).reshape(len(m1_part), 100)\n", "m2_part = np.array(m2_part).reshape(len(m2_part), 100)\n", "\n", "pca = PCA(n_components=2)\n", "reduced_vec1 = pca.fit_transform(m1_part)\n", "reduced_vec2 = pca.fit_transform(m2_part)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trace1 = Scatter(\n", " x = reduced_vec1[:, 0],\n", " y = reduced_vec1[:, 1],\n", " mode = 'markers+text',\n", " text = ['doc' + str(i) for i in range(len(reduced_vec1))],\n", " textposition = 'top'\n", ")\n", "trace2 = Scatter(\n", " x = reduced_vec2[:, 0],\n", " y = reduced_vec2[:, 1],\n", " mode = 'markers+text',\n", " text = ['doc' + str(i) for i in range(len(reduced_vec1))],\n", " textposition ='top'\n", ")\n", "layout = Layout(\n", " showlegend = False\n", ")\n", "data = [trace1, trace2]\n", "\n", "fig = Figure(data=data, layout=layout)\n", "plot_url = plotly.offline.iplot(fig, filename='doc_vec_vis')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m1_part = m1[14995: 15002]\n", "m2_part = m2[14995: 15002]\n", "\n", "m1_part = np.array(m1_part).reshape(len(m1_part), 100)\n", "m2_part = np.array(m2_part).reshape(len(m2_part), 100)\n", "\n", "pca = PCA(n_components=2)\n", "reduced_vec1 = pca.fit_transform(m1_part)\n", "reduced_vec2 = pca.fit_transform(m2_part)\n", "\n", "trace1 = Scatter(\n", " x = reduced_vec1[:, 0],\n", " y = reduced_vec1[:, 1],\n", " mode = 'markers+text',\n", " text = ['sdoc' + str(i) for i in range(len(reduced_vec1))],\n", " textposition = 'top'\n", ")\n", "trace2 = Scatter(\n", " x = reduced_vec2[:, 0],\n", " y = reduced_vec2[:, 1],\n", " mode = 'markers+text',\n", " text = ['tdoc' + str(i) for i in range(len(reduced_vec1))],\n", " textposition ='top'\n", ")\n", "layout = Layout(\n", " showlegend = False\n", ")\n", "data = [trace1, trace2]\n", "\n", "fig = Figure(data=data, layout=layout)\n", "plot_url = plotly.offline.iplot(fig, filename='doc_vec_vis')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You probably will see kinds of colors point. One for the `model1`, the `sdoc0` to `sdoc4` document vector are learned by Doc2vec and `sdoc5` and `sdoc6` are learned by back-mapping. One for the `model2`, the `tdoc0` to `tdoc6` are learned by Doc2vec. We can see that some of points learned from the back-mapping method still have the relative position with the point learned by Doc2vec." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.2 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.2" }, "vscode": { "interpreter": { "hash": "901b79e026e03396fd1ffa7133844e9ea80e258ce34c66e1aabb5896bcb18463" } } }, "nbformat": 4, "nbformat_minor": 1 }	37,933	Python	.py	1,051	31.660324	626	0.57627	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,962	gensim_news_classification.ipynb	piskvorky_gensim/docs/notebooks/gensim_news_classification.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	626	Python	.py	34	14.970588	64	0.559122	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,963	Tensorboard_visualizations.ipynb	piskvorky_gensim/docs/notebooks/Tensorboard_visualizations.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorBoard Visualizations\n", "\n", "\n", "In this tutorial, we will learn how to visualize different types of NLP based Embeddings via TensorBoard. TensorBoard is a data visualization framework for visualizing and inspecting the TensorFlow runs and graphs. We will use a built-in Tensorboard visualizer called Embedding Projector in this tutorial. It lets you interactively visualize and analyze high-dimensional data like embeddings.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read Data \n", "\n", "For this tutorial, a transformed MovieLens dataset<sup>[1]</sup> is used. You can download the final prepared csv from [here](https://github.com/parulsethi/DocViz/blob/master/movie_plots.csv)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>MovieID</th>\n", " <th>Titles</th>\n", " <th>Plots</th>\n", " <th>Genres</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>Toy Story (1995)</td>\n", " <td>A little boy named Andy loves to be in his roo...</td>\n", " <td>animation</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>Jumanji (1995)</td>\n", " <td>When two kids find and play a magical board ga...</td>\n", " <td>fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>Grumpier Old Men (1995)</td>\n", " <td>Things don't seem to change much in Wabasha Co...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>6</td>\n", " <td>Heat (1995)</td>\n", " <td>Hunters and their prey--Neil and his professio...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>7</td>\n", " <td>Sabrina (1995)</td>\n", " <td>An ugly duckling having undergone a remarkable...</td>\n", " <td>romance</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>9</td>\n", " <td>Sudden Death (1995)</td>\n", " <td>Some terrorists kidnap the Vice President of t...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>10</td>\n", " <td>GoldenEye (1995)</td>\n", " <td>James Bond teams up with the lone survivor of ...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>15</td>\n", " <td>Cutthroat Island (1995)</td>\n", " <td>Morgan Adams and her slave, William Shaw, are ...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>17</td>\n", " <td>Sense and Sensibility (1995)</td>\n", " <td>When Mr. Dashwood dies, he must leave the bulk...</td>\n", " <td>romance</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>18</td>\n", " <td>Four Rooms (1995)</td>\n", " <td>This movie features the collaborative director...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>19</td>\n", " <td>Ace Ventura: When Nature Calls (1995)</td>\n", " <td>Ace Ventura, emerging from self-imposed exile ...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>29</td>\n", " <td>City of Lost Children, The (Cité des enfants p...</td>\n", " <td>Krank (Daniel Emilfork), who cannot dream, kid...</td>\n", " <td>sci-fi</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>32</td>\n", " <td>Twelve Monkeys (a.k.a. 12 Monkeys) (1995)</td>\n", " <td>In a future world devastated by disease, a con...</td>\n", " <td>sci-fi</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>34</td>\n", " <td>Babe (1995)</td>\n", " <td>Farmer Hoggett wins a runt piglet at a local f...</td>\n", " <td>fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>39</td>\n", " <td>Clueless (1995)</td>\n", " <td>A rich high school student tries to boost a ne...</td>\n", " <td>romance</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>44</td>\n", " <td>Mortal Kombat (1995)</td>\n", " <td>Based on the popular video game of the same na...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>48</td>\n", " <td>Pocahontas (1995)</td>\n", " <td>Capt. John Smith leads a rag-tag band of Engli...</td>\n", " <td>animation</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>50</td>\n", " <td>Usual Suspects, The (1995)</td>\n", " <td>Following a truck hijack in New York, five con...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>57</td>\n", " <td>Home for the Holidays (1995)</td>\n", " <td>After losing her job, making out with her soon...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>69</td>\n", " <td>Friday (1995)</td>\n", " <td>Two homies, Smokey and Craig, smoke a dope dea...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>70</td>\n", " <td>From Dusk Till Dawn (1996)</td>\n", " <td>Two criminals and their hostages unknowingly s...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>76</td>\n", " <td>Screamers (1995)</td>\n", " <td>(SIRIUS 6B, Year 2078) On a distant mining pla...</td>\n", " <td>sci-fi</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>82</td>\n", " <td>Antonia's Line (Antonia) (1995)</td>\n", " <td>In an anonymous Dutch village, a sturdy, stron...</td>\n", " <td>fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>88</td>\n", " <td>Black Sheep (1996)</td>\n", " <td>Comedy about the prospective Washington State ...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>95</td>\n", " <td>Broken Arrow (1996)</td>\n", " <td>\"Broken Arrow\" is the term used to describe a ...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>104</td>\n", " <td>Happy Gilmore (1996)</td>\n", " <td>A rejected hockey player puts his skills to th...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>105</td>\n", " <td>Bridges of Madison County, The (1995)</td>\n", " <td>Photographer Robert Kincaid wanders into the l...</td>\n", " <td>romance</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>110</td>\n", " <td>Braveheart (1995)</td>\n", " <td>When his secret bride is executed for assaulti...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>141</td>\n", " <td>Birdcage, The (1996)</td>\n", " <td>Armand Goldman owns a popular drag nightclub i...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>145</td>\n", " <td>Bad Boys (1995)</td>\n", " <td>Marcus Burnett is a hen-pecked family man. Mik...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1813</th>\n", " <td>122902</td>\n", " <td>Fantastic Four (2015)</td>\n", " <td>FANTASTIC FOUR, a contemporary re-imagining of...</td>\n", " <td>sci-fi</td>\n", " </tr>\n", " <tr>\n", " <th>1814</th>\n", " <td>127098</td>\n", " <td>Louis C.K.: Live at The Comedy Store (2015)</td>\n", " <td>Comedian Louis C.K. performs live at the Comed...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1815</th>\n", " <td>127158</td>\n", " <td>Tig (2015)</td>\n", " <td>An intimate, mixed media documentary that foll...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1816</th>\n", " <td>127202</td>\n", " <td>Me and Earl and the Dying Girl (2015)</td>\n", " <td>Seventeen-year-old Greg has managed to become ...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1817</th>\n", " <td>129354</td>\n", " <td>Focus (2015)</td>\n", " <td>In the midst of veteran con man Nicky's latest...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>1818</th>\n", " <td>129428</td>\n", " <td>The Second Best Exotic Marigold Hotel (2015)</td>\n", " <td>The Second Best Exotic Marigold Hotel is the e...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1819</th>\n", " <td>129937</td>\n", " <td>Run All Night (2015)</td>\n", " <td>Professional Brooklyn hitman Jimmy Conlon is m...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>1820</th>\n", " <td>130490</td>\n", " <td>Insurgent (2015)</td>\n", " <td>One choice can transform you-or it can destroy...</td>\n", " <td>sci-fi</td>\n", " </tr>\n", " <tr>\n", " <th>1821</th>\n", " <td>130520</td>\n", " <td>Home (2015)</td>\n", " <td>An alien on the run from his own people makes ...</td>\n", " <td>animation</td>\n", " </tr>\n", " <tr>\n", " <th>1822</th>\n", " <td>130634</td>\n", " <td>Furious 7 (2015)</td>\n", " <td>Dominic and his crew thought they'd left the c...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>1823</th>\n", " <td>131013</td>\n", " <td>Get Hard (2015)</td>\n", " <td>Kevin Hart plays the role of Darnell--a family...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1824</th>\n", " <td>132046</td>\n", " <td>Tomorrowland (2015)</td>\n", " <td>Bound by a shared destiny, a bright, optimisti...</td>\n", " <td>sci-fi</td>\n", " </tr>\n", " <tr>\n", " <th>1825</th>\n", " <td>132480</td>\n", " <td>The Age of Adaline (2015)</td>\n", " <td>A young woman, born at the turn of the 20th ce...</td>\n", " <td>romance</td>\n", " </tr>\n", " <tr>\n", " <th>1826</th>\n", " <td>132488</td>\n", " <td>Lovesick (2014)</td>\n", " <td>Lovesick is the comic tale of Charlie Darby (M...</td>\n", " <td>fantasy</td>\n", " </tr>\n", " <tr>\n", " <th>1827</th>\n", " <td>132796</td>\n", " <td>San Andreas (2015)</td>\n", " <td>In San Andreas, California is experiencing a s...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>1828</th>\n", " <td>132961</td>\n", " <td>Far from the Madding Crowd (2015)</td>\n", " <td>In Victorian England, the independent and head...</td>\n", " <td>romance</td>\n", " </tr>\n", " <tr>\n", " <th>1829</th>\n", " <td>133195</td>\n", " <td>Hitman: Agent 47 (2015)</td>\n", " <td>An assassin teams up with a woman to help her ...</td>\n", " <td>action</td>\n", " </tr>\n", " <tr>\n", " <th>1830</th>\n", " <td>133645</td>\n", " <td>Carol (2015)</td>\n", " <td>In an adaptation of Patricia Highsmith's semin...</td>\n", " <td>romance</td>\n", " </tr>\n", " <tr>\n", " <th>1831</th>\n", " <td>134130</td>\n", " <td>The Martian (2015)</td>\n", " <td>During a manned mission to Mars, Astronaut Mar...</td>\n", " <td>sci-fi</td>\n", " </tr>\n", " <tr>\n", " <th>1832</th>\n", " <td>134368</td>\n", " <td>Spy (2015)</td>\n", " <td>A desk-bound CIA analyst volunteers to go unde...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1833</th>\n", " <td>134783</td>\n", " <td>Entourage (2015)</td>\n", " <td>Movie star Vincent Chase, together with his bo...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1834</th>\n", " <td>134853</td>\n", " <td>Inside Out (2015)</td>\n", " <td>After young Riley is uprooted from her Midwest...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1835</th>\n", " <td>135518</td>\n", " <td>Self/less (2015)</td>\n", " <td>A dying real estate mogul transfers his consci...</td>\n", " <td>sci-fi</td>\n", " </tr>\n", " <tr>\n", " <th>1836</th>\n", " <td>135861</td>\n", " <td>Ted 2 (2015)</td>\n", " <td>Months after John's divorce, Ted and Tami-Lynn...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1837</th>\n", " <td>135887</td>\n", " <td>Minions (2015)</td>\n", " <td>Ever since the dawn of time, the Minions have ...</td>\n", " <td>comedy</td>\n", " </tr>\n", " <tr>\n", " <th>1838</th>\n", " <td>136016</td>\n", " <td>The Good Dinosaur (2015)</td>\n", " <td>In a world where dinosaurs and humans live sid...</td>\n", " <td>animation</td>\n", " </tr>\n", " <tr>\n", " <th>1839</th>\n", " <td>139855</td>\n", " <td>Anomalisa (2015)</td>\n", " <td>Michael Stone, an author that specializes in c...</td>\n", " <td>animation</td>\n", " </tr>\n", " <tr>\n", " <th>1840</th>\n", " <td>142997</td>\n", " <td>Hotel Transylvania 2 (2015)</td>\n", " <td>The Drac pack is back for an all-new monster c...</td>\n", " <td>animation</td>\n", " </tr>\n", " <tr>\n", " <th>1841</th>\n", " <td>145935</td>\n", " <td>Peanuts Movie, The (2015)</td>\n", " <td>Charlie Brown, Lucy, Snoopy, and the whole gan...</td>\n", " <td>animation</td>\n", " </tr>\n", " <tr>\n", " <th>1842</th>\n", " <td>149406</td>\n", " <td>Kung Fu Panda 3 (2016)</td>\n", " <td>Continuing his \"legendary adventures of awesom...</td>\n", " <td>comedy</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1843 rows × 4 columns</p>\n", "</div>" ], "text/plain": [ " MovieID Titles \\\n", "0 1 Toy Story (1995) \n", "1 2 Jumanji (1995) \n", "2 3 Grumpier Old Men (1995) \n", "3 6 Heat (1995) \n", "4 7 Sabrina (1995) \n", "5 9 Sudden Death (1995) \n", "6 10 GoldenEye (1995) \n", "7 15 Cutthroat Island (1995) \n", "8 17 Sense and Sensibility (1995) \n", "9 18 Four Rooms (1995) \n", "10 19 Ace Ventura: When Nature Calls (1995) \n", "11 29 City of Lost Children, The (Cité des enfants p... \n", "12 32 Twelve Monkeys (a.k.a. 12 Monkeys) (1995) \n", "13 34 Babe (1995) \n", "14 39 Clueless (1995) \n", "15 44 Mortal Kombat (1995) \n", "16 48 Pocahontas (1995) \n", "17 50 Usual Suspects, The (1995) \n", "18 57 Home for the Holidays (1995) \n", "19 69 Friday (1995) \n", "20 70 From Dusk Till Dawn (1996) \n", "21 76 Screamers (1995) \n", "22 82 Antonia's Line (Antonia) (1995) \n", "23 88 Black Sheep (1996) \n", "24 95 Broken Arrow (1996) \n", "25 104 Happy Gilmore (1996) \n", "26 105 Bridges of Madison County, The (1995) \n", "27 110 Braveheart (1995) \n", "28 141 Birdcage, The (1996) \n", "29 145 Bad Boys (1995) \n", "... ... ... \n", "1813 122902 Fantastic Four (2015) \n", "1814 127098 Louis C.K.: Live at The Comedy Store (2015) \n", "1815 127158 Tig (2015) \n", "1816 127202 Me and Earl and the Dying Girl (2015) \n", "1817 129354 Focus (2015) \n", "1818 129428 The Second Best Exotic Marigold Hotel (2015) \n", "1819 129937 Run All Night (2015) \n", "1820 130490 Insurgent (2015) \n", "1821 130520 Home (2015) \n", "1822 130634 Furious 7 (2015) \n", "1823 131013 Get Hard (2015) \n", "1824 132046 Tomorrowland (2015) \n", "1825 132480 The Age of Adaline (2015) \n", "1826 132488 Lovesick (2014) \n", "1827 132796 San Andreas (2015) \n", "1828 132961 Far from the Madding Crowd (2015) \n", "1829 133195 Hitman: Agent 47 (2015) \n", "1830 133645 Carol (2015) \n", "1831 134130 The Martian (2015) \n", "1832 134368 Spy (2015) \n", "1833 134783 Entourage (2015) \n", "1834 134853 Inside Out (2015) \n", "1835 135518 Self/less (2015) \n", "1836 135861 Ted 2 (2015) \n", "1837 135887 Minions (2015) \n", "1838 136016 The Good Dinosaur (2015) \n", "1839 139855 Anomalisa (2015) \n", "1840 142997 Hotel Transylvania 2 (2015) \n", "1841 145935 Peanuts Movie, The (2015) \n", "1842 149406 Kung Fu Panda 3 (2016) \n", "\n", " Plots Genres \n", "0 A little boy named Andy loves to be in his roo... animation \n", "1 When two kids find and play a magical board ga... fantasy \n", "2 Things don't seem to change much in Wabasha Co... comedy \n", "3 Hunters and their prey--Neil and his professio... action \n", "4 An ugly duckling having undergone a remarkable... romance \n", "5 Some terrorists kidnap the Vice President of t... action \n", "6 James Bond teams up with the lone survivor of ... action \n", "7 Morgan Adams and her slave, William Shaw, are ... action \n", "8 When Mr. Dashwood dies, he must leave the bulk... romance \n", "9 This movie features the collaborative director... comedy \n", "10 Ace Ventura, emerging from self-imposed exile ... comedy \n", "11 Krank (Daniel Emilfork), who cannot dream, kid... sci-fi \n", "12 In a future world devastated by disease, a con... sci-fi \n", "13 Farmer Hoggett wins a runt piglet at a local f... fantasy \n", "14 A rich high school student tries to boost a ne... romance \n", "15 Based on the popular video game of the same na... action \n", "16 Capt. John Smith leads a rag-tag band of Engli... animation \n", "17 Following a truck hijack in New York, five con... comedy \n", "18 After losing her job, making out with her soon... comedy \n", "19 Two homies, Smokey and Craig, smoke a dope dea... comedy \n", "20 Two criminals and their hostages unknowingly s... action \n", "21 (SIRIUS 6B, Year 2078) On a distant mining pla... sci-fi \n", "22 In an anonymous Dutch village, a sturdy, stron... fantasy \n", "23 Comedy about the prospective Washington State ... comedy \n", "24 \"Broken Arrow\" is the term used to describe a ... action \n", "25 A rejected hockey player puts his skills to th... comedy \n", "26 Photographer Robert Kincaid wanders into the l... romance \n", "27 When his secret bride is executed for assaulti... action \n", "28 Armand Goldman owns a popular drag nightclub i... comedy \n", "29 Marcus Burnett is a hen-pecked family man. Mik... action \n", "... ... ... \n", "1813 FANTASTIC FOUR, a contemporary re-imagining of... sci-fi \n", "1814 Comedian Louis C.K. performs live at the Comed... comedy \n", "1815 An intimate, mixed media documentary that foll... comedy \n", "1816 Seventeen-year-old Greg has managed to become ... comedy \n", "1817 In the midst of veteran con man Nicky's latest... action \n", "1818 The Second Best Exotic Marigold Hotel is the e... comedy \n", "1819 Professional Brooklyn hitman Jimmy Conlon is m... action \n", "1820 One choice can transform you-or it can destroy... sci-fi \n", "1821 An alien on the run from his own people makes ... animation \n", "1822 Dominic and his crew thought they'd left the c... action \n", "1823 Kevin Hart plays the role of Darnell--a family... comedy \n", "1824 Bound by a shared destiny, a bright, optimisti... sci-fi \n", "1825 A young woman, born at the turn of the 20th ce... romance \n", "1826 Lovesick is the comic tale of Charlie Darby (M... fantasy \n", "1827 In San Andreas, California is experiencing a s... action \n", "1828 In Victorian England, the independent and head... romance \n", "1829 An assassin teams up with a woman to help her ... action \n", "1830 In an adaptation of Patricia Highsmith's semin... romance \n", "1831 During a manned mission to Mars, Astronaut Mar... sci-fi \n", "1832 A desk-bound CIA analyst volunteers to go unde... comedy \n", "1833 Movie star Vincent Chase, together with his bo... comedy \n", "1834 After young Riley is uprooted from her Midwest... comedy \n", "1835 A dying real estate mogul transfers his consci... sci-fi \n", "1836 Months after John's divorce, Ted and Tami-Lynn... comedy \n", "1837 Ever since the dawn of time, the Minions have ... comedy \n", "1838 In a world where dinosaurs and humans live sid... animation \n", "1839 Michael Stone, an author that specializes in c... animation \n", "1840 The Drac pack is back for an all-new monster c... animation \n", "1841 Charlie Brown, Lucy, Snoopy, and the whole gan... animation \n", "1842 Continuing his \"legendary adventures of awesom... comedy \n", "\n", "[1843 rows x 4 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gensim\n", "import pandas as pd\n", "import smart_open\n", "import random\n", "from smart_open import smart_open\n", "\n", "# read data\n", "dataframe = pd.read_csv('movie_plots.csv')\n", "dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Visualizing Doc2Vec\n", "In this part, we will learn about visualizing Doc2Vec Embeddings aka [Paragraph Vectors](https://arxiv.org/abs/1405.4053) via TensorBoard. The input documents for training will be the synopsis of movies, on which Doc2Vec model is trained. \n", "\n", "<img src=\"Tensorboard.png\">\n", "\n", "The visualizations will be a scatterplot as seen in the above image, where each datapoint is labelled by the movie title and colored by it's corresponding genre. You can also visit this [Projector link](http://projector.tensorflow.org/?config=https://raw.githubusercontent.com/parulsethi/DocViz/master/movie_plot_config.json) which is configured with my embeddings for the above mentioned dataset. \n", "\n", "\n", "## Preprocess Text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we define a function to read the training documents, pre-process each document using a simple gensim pre-processing tool (i.e., tokenize text into individual words, remove punctuation, set to lowercase, etc), and return a list of words. Also, to train the model, we'll need to associate a tag/number with each document of the training corpus. In our case, the tag is simply the zero-based line number." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def read_corpus(documents):\n", " for i, plot in enumerate(documents):\n", " yield gensim.models.doc2vec.TaggedDocument(gensim.utils.simple_preprocess(plot, max_len=30), [i])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_corpus = list(read_corpus(dataframe.Plots))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the training corpus." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[TaggedDocument(words=['little', 'boy', 'named', 'andy', 'loves', 'to', 'be', 'in', 'his', 'room', 'playing', 'with', 'his', 'toys', 'especially', 'his', 'doll', 'named', 'woody', 'but', 'what', 'do', 'the', 'toys', 'do', 'when', 'andy', 'is', 'not', 'with', 'them', 'they', 'come', 'to', 'life', 'woody', 'believes', 'that', 'he', 'has', 'life', 'as', 'toy', 'good', 'however', 'he', 'must', 'worry', 'about', 'andy', 'family', 'moving', 'and', 'what', 'woody', 'does', 'not', 'know', 'is', 'about', 'andy', 'birthday', 'party', 'woody', 'does', 'not', 'realize', 'that', 'andy', 'mother', 'gave', 'him', 'an', 'action', 'figure', 'known', 'as', 'buzz', 'lightyear', 'who', 'does', 'not', 'believe', 'that', 'he', 'is', 'toy', 'and', 'quickly', 'becomes', 'andy', 'new', 'favorite', 'toy', 'woody', 'who', 'is', 'now', 'consumed', 'with', 'jealousy', 'tries', 'to', 'get', 'rid', 'of', 'buzz', 'then', 'both', 'woody', 'and', 'buzz', 'are', 'now', 'lost', 'they', 'must', 'find', 'way', 'to', 'get', 'back', 'to', 'andy', 'before', 'he', 'moves', 'without', 'them', 'but', 'they', 'will', 'have', 'to', 'pass', 'through', 'ruthless', 'toy', 'killer', 'sid', 'phillips'], tags=[0]),\n", " TaggedDocument(words=['when', 'two', 'kids', 'find', 'and', 'play', 'magical', 'board', 'game', 'they', 'release', 'man', 'trapped', 'for', 'decades', 'in', 'it', 'and', 'host', 'of', 'dangers', 'that', 'can', 'only', 'be', 'stopped', 'by', 'finishing', 'the', 'game'], tags=[1])]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_corpus[:2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the Doc2Vec Model\n", "We'll instantiate a Doc2Vec model with a vector size with 50 words and iterating over the training corpus 55 times. We set the minimum word count to 2 in order to give higher frequency words more weighting. Model accuracy can be improved by increasing the number of iterations but this generally increases the training time. Small datasets with short documents, like this one, can benefit from more training passes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5168238" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = gensim.models.doc2vec.Doc2Vec(size=50, min_count=2, iter=55)\n", "model.build_vocab(train_corpus)\n", "model.train(train_corpus, total_examples=model.corpus_count, epochs=model.iter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we'll save the document embedding vectors per doctag." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.save_word2vec_format('doc_tensor.w2v', doctag_vec=True, word_vec=False) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare the Input files for Tensorboard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tensorboard takes two Input files. One containing the embedding vectors and the other containing relevant metadata. We'll use a gensim script to directly convert the embedding file saved in word2vec format above to the tsv format required in Tensorboard." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-04-20 02:23:05,284 : MainThread : INFO : running ../../gensim/scripts/word2vec2tensor.py -i doc_tensor.w2v -o movie_plot\n", "2017-04-20 02:23:05,286 : MainThread : INFO : loading projection weights from doc_tensor.w2v\n", "2017-04-20 02:23:05,464 : MainThread : INFO : loaded (1843, 50) matrix from doc_tensor.w2v\n", "2017-04-20 02:23:05,578 : MainThread : INFO : 2D tensor file saved to movie_plot_tensor.tsv\n", "2017-04-20 02:23:05,579 : MainThread : INFO : Tensor metadata file saved to movie_plot_metadata.tsv\n", "2017-04-20 02:23:05,581 : MainThread : INFO : finished running word2vec2tensor.py\n" ] } ], "source": [ "%run ../../gensim/scripts/word2vec2tensor.py -i doc_tensor.w2v -o movie_plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The script above generates two files, `movie_plot_tensor.tsv` which contain the embedding vectors and `movie_plot_metadata.tsv` containing doctags. But, these doctags are simply the unique index values and hence are not really useful to interpret what the document was while visualizing. So, we will overwrite `movie_plot_metadata.tsv` to have a custom metadata file with two columns. The first column will be for the movie titles and the second for their corresponding genres." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with smart_open('movie_plot_metadata.tsv','w') as w:\n", " w.write('Titles\\tGenres\\n')\n", " for i,j in zip(dataframe.Titles, dataframe.Genres):\n", " w.write(\"%s\\t%s\\n\" % (i,j))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Now you can go to http://projector.tensorflow.org/ and upload the two files by clicking on Load data in the left panel.\n", "\n", "For demo purposes I have uploaded the Doc2Vec embeddings generated from the model trained above [here](https://github.com/parulsethi/DocViz). You can access the Embedding projector configured with these uploaded embeddings at this [link](http://projector.tensorflow.org/?config=https://raw.githubusercontent.com/parulsethi/DocViz/master/movie_plot_config.json)." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Using Tensorboard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the visualization purpose, the multi-dimensional embeddings that we get from the Doc2Vec model above, needs to be downsized to 2 or 3 dimensions. So that we basically end up with a new 2d or 3d embedding which tries to preserve information from the original multi-dimensional embedding. As these vectors are reduced to a much smaller dimension, the exact cosine/euclidean distances between them are not preserved, but rather relative, and hence as you’ll see below the nearest similarity results may change.\n", "\n", "TensorBoard has two popular dimensionality reduction methods for visualizing the embeddings and also provides a custom method based on text searches:\n", "\n", "- Principal Component Analysis: PCA aims at exploring the global structure in data, and could end up losing the local similarities between neighbours. It maximizes the total variance in the lower dimensional subspace and hence, often preserves the larger pairwise distances better than the smaller ones. See an intuition behind it in this nicely explained [answer](https://stats.stackexchange.com/questions/176672/what-is-meant-by-pca-preserving-only-large-pairwise-distances) on stackexchange.\n", "\n", "\n", "- T-SNE: The idea of T-SNE is to place the local neighbours close to each other, and almost completely ignoring the global structure. It is useful for exploring local neighborhoods and finding local clusters. But the global trends are not represented accurately and the separation between different groups is often not preserved (see the t-sne plots of our data below which testify the same).\n", "\n", "\n", "- Custom Projections: This is a custom method based on the text searches you define for different directions. It could be useful for finding meaningful directions in the vector space, for example, female to male, currency to country etc.\n", "\n", "You can refer to this [doc](https://www.tensorflow.org/get_started/embedding_viz) for instructions on how to use and navigate through different panels available in TensorBoard." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize using PCA\n", "\n", "The Embedding Projector computes the top 10 principal components. The menu at the left panel lets you project those components onto any combination of two or three. \n", "<img src=\"pca.png\">\n", "The above plot was made using the first two principal components with total variance covered being 36.5%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Visualize using T-SNE\n", "\n", "Data is visualized by animating through every iteration of the t-sne algorithm. The t-sne menu at the left lets you adjust the value of it's two hyperparameters. The first one is Perplexity, which is basically a measure of information. It may be viewed as a knob that sets the number of effective nearest neighbors<sup>[2]</sup>. The second one is learning rate that defines how quickly an algorithm learns on encountering new examples/data points.\n", "\n", "<img src=\"tsne.png\">\n", "\n", "The above plot was generated with perplexity 8, learning rate 10 and iteration 500. Though the results could vary on successive runs, and you may not get the exact plot as above with same hyperparameter settings. But some small clusters will start forming as above, with different orientations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Visualizing LDA\n", "\n", "In this part, we will see how to visualize LDA in Tensorboard. We will be using the Document-topic distribution as the embedding vector of a document. Basically, we treat topics as the dimensions and the value in each dimension represents the topic proportion of that topic in the document.\n", "\n", "## Preprocess Text\n", "\n", "We use the movie Plots as our documents in corpus and remove rare words and common words based on their document frequency. Below we remove words that appear in less than 2 documents or in more than 30% of the documents." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import re\n", "from gensim.parsing.preprocessing import remove_stopwords, strip_punctuation\n", "from gensim.models import ldamodel\n", "from gensim.corpora.dictionary import Dictionary\n", "\n", "# read data\n", "dataframe = pd.read_csv('movie_plots.csv')\n", "\n", "# remove stopwords and punctuations\n", "def preprocess(row):\n", " return strip_punctuation(remove_stopwords(row.lower()))\n", " \n", "dataframe['Plots'] = dataframe['Plots'].apply(preprocess)\n", "\n", "# Convert data to required input format by LDA\n", "texts = []\n", "for line in dataframe.Plots:\n", " lowered = line.lower()\n", " words = re.findall(r'\\w+', lowered, flags = re.UNICODE \| re.LOCALE)\n", " texts.append(words)\n", "# Create a dictionary representation of the documents.\n", "dictionary = Dictionary(texts)\n", "\n", "# Filter out words that occur less than 2 documents, or more than 30% of the documents.\n", "dictionary.filter_extremes(no_below=2, no_above=0.3)\n", "# Bag-of-words representation of the documents.\n", "corpus = [dictionary.doc2bow(text) for text in texts]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train LDA Model\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Set training parameters.\n", "num_topics = 10\n", "chunksize = 2000\n", "passes = 50\n", "iterations = 200\n", "eval_every = None\n", "\n", "# Train model\n", "model = ldamodel.LdaModel(corpus=corpus, id2word=dictionary, chunksize=chunksize, alpha='auto', eta='auto', iterations=iterations, num_topics=num_topics, passes=passes, eval_every=eval_every)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can refer to [this notebook](lda_training_tips.ipynb) also before training the LDA model. It contains tips and suggestions for pre-processing the text data, and how to train the LDA model to get good results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Doc-Topic distribution\n", "\n", "Now we will use `get_document_topics` which infers the topic distribution of a document. It basically returns a list of (topic_id, topic_probability) for each document in the input corpus." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, 0.00029626785677659928),\n", " (1, 0.99734244187457377),\n", " (2, 0.00031813940693891458),\n", " (3, 0.00031573036467256674),\n", " (4, 0.00033277056023999966),\n", " (5, 0.00023981837072288835),\n", " (6, 0.00033113374640540293),\n", " (7, 0.00027953838669809549),\n", " (8, 0.0002706215262517565),\n", " (9, 0.00027353790672011199)]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get document topics\n", "all_topics = model.get_document_topics(corpus, minimum_probability=0)\n", "all_topics[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above output shows the topic distribution of first document in the corpus as a list of (topic_id, topic_probability).\n", "\n", "Now, using the topic distribution of a document as it's vector embedding, we will plot all the documents in our corpus using Tensorboard." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare the Input files for Tensorboard\n", "\n", "Tensorboard takes two input files, one containing the embedding vectors and the other containing relevant metadata. As described above we will use the topic distribution of documents as their embedding vector. Metadata file will consist of Movie titles with their genres." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# create file for tensors\n", "with smart_open('doc_lda_tensor.tsv','w') as w:\n", " for doc_topics in all_topics:\n", " for topics in doc_topics:\n", " w.write(str(topics[1])+ \"\\t\")\n", " w.write(\"\\n\")\n", " \n", "# create file for metadata\n", "with smart_open('doc_lda_metadata.tsv','w') as w:\n", " w.write('Titles\\tGenres\\n')\n", " for j, k in zip(dataframe.Titles, dataframe.Genres):\n", " w.write(\"%s\\t%s\\n\" % (j, k))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Now you can go to http://projector.tensorflow.org/ and upload these two files by clicking on Load data in the left panel.\n", "\n", "For demo purposes I have uploaded the LDA doc-topic embeddings generated from the model trained above [here](https://github.com/parulsethi/LdaProjector/). You can also access the Embedding projector configured with these uploaded embeddings at this [link](http://projector.tensorflow.org/?config=https://raw.githubusercontent.com/parulsethi/LdaProjector/master/doc_lda_config.json)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize using PCA\n", "\n", "The Embedding Projector computes the top 10 principal components. The menu at the left panel lets you project those components onto any combination of two or three.\n", "<img src=\"doc_lda_pca.png\">\n", "From PCA, we get a simplex (tetrahedron in this case) where each data point represent a document. These data points are colored according to their Genres which were given in the Movie dataset. \n", "\n", "As we can see there are a lot of points which cluster at the corners of the simplex. This is primarily due to the sparsity of vectors we are using. The documents at the corners primarily belongs to a single topic (hence, large weight in a single dimension and other dimensions have approximately zero weight.) You can modify the metadata file as explained below to see the dimension weights along with the Movie title.\n", "\n", "Now, we will append the topics with highest probability (topic_id, topic_probability) to the document's title, in order to explore what topics do the cluster corners or edges dominantly belong to. For this, we just need to overwrite the metadata file as below:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tensors = []\n", "for doc_topics in all_topics:\n", " doc_tensor = []\n", " for topic in doc_topics:\n", " if round(topic[1], 3) > 0:\n", " doc_tensor.append((topic[0], float(round(topic[1], 3))))\n", " # sort topics according to highest probabilities\n", " doc_tensor = sorted(doc_tensor, key=lambda x: x[1], reverse=True)\n", " # store vectors to add in metadata file\n", " tensors.append(doc_tensor[:5])\n", "\n", "# overwrite metadata file\n", "i=0\n", "with smart_open('doc_lda_metadata.tsv','w') as w:\n", " w.write('Titles\\tGenres\\n')\n", " for j,k in zip(dataframe.Titles, dataframe.Genres):\n", " w.write(\"%s\\t%s\\n\" % (''.join((str(j), str(tensors[i]))),k))\n", " i+=1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we upload the previous tensor file \"doc_lda_tensor.tsv\" and this new metadata file to http://projector.tensorflow.org/ .\n", "<img src=\"topic_with_coordinate.png\">\n", "Voila! Now we can click on any point to see it's top topics with their probabilty in that document, along with the title. As we can see in the above example, \"Beverly hill cops\" primarily belongs to the 0th and 1st topic as they have the highest probability amongst all.\n", "\n", "\n", "\n", "## Visualize using T-SNE\n", "\n", "In T-SNE, the data is visualized by animating through every iteration of the t-sne algorithm. The t-sne menu at the left lets you adjust the value of it's two hyperparameters. The first one is Perplexity, which is basically a measure of information. It may be viewed as a knob that sets the number of effective nearest neighbors[2]. The second one is learning rate that defines how quickly an algorithm learns on encountering new examples/data points.\n", "\n", "Now, as the topic distribution of a document is used as it’s embedding vector, t-sne ends up forming clusters of documents belonging to same topics. In order to understand and interpret about the theme of those topics, we can use `show_topic()` to explore the terms that the topics consisted of.\n", "\n", "<img src=\"doc_lda_tsne.png\">\n", "\n", "The above plot was generated with perplexity 11, learning rate 10 and iteration 1100. Though the results could vary on successive runs, and you may not get the exact plot as above even with same hyperparameter settings. But some small clusters will start forming as above, with different orientations.\n", "\n", "I named some clusters above based on the genre of it's movies and also using the `show_topic()` to see relevant terms of the topic which was most prevalent in a cluster. Most of the clusters had documents belonging dominantly to a single topic. For ex. The cluster with movies belonging primarily to topic 0 could be named Fantasy/Romance based on terms displayed below for topic 0. You can play with the visualization yourself on this [link](http://projector.tensorflow.org/?config=https://raw.githubusercontent.com/parulsethi/LdaProjector/master/doc_lda_config.json) and try to conclude a label for clusters based on movies it has and dominant topic. You can see the top 5 topics of every point by hovering over it.\n", "\n", "Now, we can notice that there are more than 10 clusters in the above image, whereas we trained our model for `num_topics=10`. It's because there are few clusters, which has documents belonging to more than one topic with an approximately close topic probability values." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('life', 0.0069577926389817156),\n", " ('world', 0.006240163206609986),\n", " ('man', 0.0058828040298109794),\n", " ('young', 0.0053747678629860532),\n", " ('family', 0.005083746467542196),\n", " ('love', 0.0048691281379952146),\n", " ('new', 0.004097644507005606),\n", " ('t', 0.0037446821043766597),\n", " ('time', 0.0037022423231064822),\n", " ('finds', 0.0036129806190553109),\n", " ('woman', 0.0031742920620375422),\n", " ('earth', 0.0031692677510459484),\n", " ('help', 0.0031061538189201504),\n", " ('it', 0.0028658594310878023),\n", " ('years', 0.00272218005397741)]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.show_topic(topicid=0, topn=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can even use pyLDAvis to deduce topics more efficiently. It provides a deeper inspection of the terms highly associated with each individual topic. For this, it uses a measure called relevance of a term to a topic that allows users to flexibly rank terms best suited for a meaningful topic interpretation. It's weight parameter called λ can be adjusted to display useful terms which could help in differentiating topics efficiently." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/parul/.virtualenvs/gensim3/lib/python3.4/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n", " topic_term_dists = topic_term_dists.ix[topic_order]\n" ] }, { "data": { "text/html": [ "\n", "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n", "\n", "\n", "<div id=\"ldavis_el2617146404026722199990525\"></div>\n", "<script type=\"text/javascript\">\n", "\n", "var ldavis_el2617146404026722199990525_data = {\"topic.order\": [2, 6, 5, 10, 3, 7, 9, 1, 8, 4], \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"tinfo\": {\"loglift\": [30.0, 29.0, 28.0, 27.0, 26.0, 25.0, 24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 1.8363, 1.772, 1.761, 1.7218, 1.7098, 1.7052, 1.7003, 1.6791, 1.6733, 1.6733, 1.6733, 1.6733, 1.6733, 1.6733, 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0.11202000528310457, 0.1206369287664203, 0.11202000528310457, 0.09478615831647311, 0.07755231134984163, 0.12925385224973604, 0.0689353878665259, 0.06031846438321015, 0.18807568813707873, 0.10920523827314249, 0.09100436522761873, 0.07280349218209499, 0.07887044986393624, 0.10313828059130124, 0.09707132290946, 0.13954002668234874, 0.04853566145473, 0.06673653450025374, 0.13193752826481736, 0.09595456601077626, 0.05997160375673516, 0.13193752826481736, 0.15592616976751142, 0.16792049051885846, 0.08396024525942923, 0.04797728300538813, 0.05997160375673516, 0.05997160375673516, 0.1309989850881487, 0.10718098779939438, 0.09527198915501722, 0.11512032022897914, 0.10321132158460199, 0.13893831751773345, 0.11115065401418676, 0.06748432565147053, 0.06748432565147053, 0.059544993221885764, 0.8503651401798237, 0.17737438291927263, 0.7094975316770905, 0.16689278657690218, 0.6675711463076087]}, \"mdsDat\": {\"y\": [0.011125894575601931, -0.0035114776037454072, -0.03680971389836211, 0.00934490889749367, 0.016511358878256447, 0.028161438079822885, -0.010895677116577018, -0.010367571215838069, 0.0006086003054567231, -0.004167760902109017], \"x\": [-0.05034221283890668, 0.020489565324999705, -0.003375676839805856, 0.0048863221133020615, 0.0031166219078171335, 0.016972321016802763, 0.002911116748704508, 0.004774791405761274, 0.003410874185421603, -0.0028437230240966247], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [14.785007274114479, 10.596420593691231, 10.522205961625293, 10.463378007783705, 10.45625150947458, 10.351211454142542, 9.945160926631893, 9.88225362171537, 6.83953035229849, 6.158580298522415]}, \"R\": 30, \"lambda.step\": 0.01};\n", "\n", "function LDAvis_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(LDAvis) !== \"undefined\"){\n", " // already loaded: just create the visualization\n", " !function(LDAvis){\n", " new LDAvis(\"#\" + \"ldavis_el2617146404026722199990525\", ldavis_el2617146404026722199990525_data);\n", " }(LDAvis);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/LDAvis\n", " require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n", " new LDAvis(\"#\" + \"ldavis_el2617146404026722199990525\", ldavis_el2617146404026722199990525_data);\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & LDAvis\n", " LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n", " LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n", " new LDAvis(\"#\" + \"ldavis_el2617146404026722199990525\", ldavis_el2617146404026722199990525_data);\n", " })\n", " });\n", "}\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pyLDAvis.gensim\n", "\n", "viz = pyLDAvis.gensim.prepare(model, corpus, dictionary)\n", "pyLDAvis.display(viz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The weight parameter λ can be viewed as a knob to adjust the ranks of the terms based on whether they are simply ranked according to their probability in the topic (λ=1) or are normalized by their marginal probability across the corpus (λ=0). Setting λ=1 could result in similar ranking of terms for large no. of topics hence making it difficult to differentiate between them, and setting λ=0 ranks terms solely based on their exclusiveness to current topic which could result in such rare terms that occur in only a single topic and hence the topics may remain difficult to interpret. [(Sievert and Shirley 2014)](https://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf) suggested the optimal value of λ=0.6 based on a user study." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "\n", "We learned about visualizing the Document Embeddings and LDA Doc-topic distributions through Tensorboard's Embedding Projector. It is a useful tool for visualizing different types of data for example, word embeddings, document embeddings or the gene expressions and biological sequences. It just needs an input of 2D tensors and then you can explore your data using provided algorithms. You can also perform nearest neighbours search to find most similar data points to your query point.\n", "\n", "# References\n", " 1. https://grouplens.org/datasets/movielens/\n", " 2. https://lvdmaaten.github.io/tsne/\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	186,617	Python	.py	1,284	139.060748	128,449	0.63606	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,964	online_w2v_tutorial.ipynb	piskvorky_gensim/docs/notebooks/online_w2v_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Online word2vec tutorial\n", "\n", "So far, word2vec cannot increase the size of vocabulary after initial training. To handle unknown words, not in word2vec vocaburary, you must retrain updated documents over again.\n", "\n", "In this tutorial, we introduce gensim new feature, online vocaburary update. This additional feature overcomes the unknown word problems. Despite after initial training, we can continuously add new vocaburary to the pre-trained word2vec model using this online feature.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from gensim.corpora.wikicorpus import WikiCorpus\n", "from gensim.models.word2vec import Word2Vec, LineSentence\n", "from pprint import pprint\n", "from copy import deepcopy\n", "from multiprocessing import cpu_count\n", "from smart_open import smart_open" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download wikipedia dump files\n", "\n", "We use the past and the current version of wiki dump files as online training." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "wget https://dumps.wikimedia.org/archive/2010/2010-11/enwiki/20101011/enwiki-20101011-pages-articles.xml.bz2\n", "wget https://dumps.wikimedia.org/enwiki/20160820/enwiki-20160820-pages-articles.xml.bz2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert two wikipedia dump files\n", "To avoid alert when convert old verision of wikipedia dump, you should download alternative wikicorpus.py in my repo." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "old, new = [WikiCorpus('enwiki-{}-pages-articles.xml.bz2'.format(ymd)) for ymd in ['20101011', '20160820']]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def write_wiki(wiki, name, titles = []):\n", " with smart_open('{}.wiki'.format(name), 'wb') as f:\n", " wiki.metadata = True\n", " for text, (page_id, title) in wiki.get_texts():\n", " if title not in titles:\n", " f.write(b' '.join(text)+b'\\n')\n", " titles.append(title)\n", " return titles" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "old_titles = write_wiki(old, 'old')\n", "all_titles = write_wiki(new, 'new', old_titles)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "oldwiki, newwiki = [LineSentence(f+'.wiki') for f in ['old', 'new']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initial training\n", "At first we train word2vec using \"enwiki-20101011-pages-articles.xml.bz2\". After that, we update model using \"enwiki-20160820-pages-articles.xml.bz2\"." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4h 39min 57s, sys: 1min 28s, total: 4h 41min 25s\n", "Wall time: 1h 32min 43s\n" ] } ], "source": [ "%%time\n", "model = Word2Vec(oldwiki, min_count = 0, workers=cpu_count())\n", "# model = Word2Vec.load('oldmodel')\n", "oldmodel = deepcopy(model)\n", "oldmodel.save('oldmodel')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Japanese new idol group, [\"Babymetal\"](https://en.wikipedia.org/wiki/Babymetal), weren't known worldwide in 2010, so that the word, \"babymetal\", is not in oldmodel vocaburary.\n", "Note: In recent years, they became the famous idol group not only in Japan. They won many music awards and run world tour." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"word 'babymetal' not in vocabulary\"\n" ] } ], "source": [ "try:\n", " print(oldmodel.most_similar('babymetal'))\n", "except KeyError as e:\n", " print(e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Online update\n", "To use online word2vec feature, set update=True when you use build_vocab using new documents." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2h 53min 51s, sys: 1min 1s, total: 2h 54min 52s\n", "Wall time: 57min 24s\n" ] } ], "source": [ "%%time\n", "model.build_vocab(newwiki, update=True)\n", "model.train(newwiki, total_examples=model.corpus_count, epochs=model.iter)\n", "model.save('newmodel')\n", "# model = Word2Vec.load('newmodel')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Model Comparison\n", "By the online training, the size of vocaburaries are increased about 3 millions." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The vocabulary size of the oldmodel is 6161170\n", "The vocabulary size of the model is 8469444\n" ] } ], "source": [ "for m in ['oldmodel', 'model']:\n", " print('The vocabulary size of the', m, 'is', len(eval(m).wv.vocab))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### After online training, the word, \"babymetal\", is added in model. This word is simillar with rock and metal bands." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('espairsray', 0.7539531588554382),\n", " ('crossfaith', 0.7476214170455933),\n", " ('mucc', 0.7363666296005249),\n", " ('girugamesh', 0.7309226989746094),\n", " ('flumpool', 0.7182492017745972),\n", " ('gackt', 0.715751051902771),\n", " ('jpop', 0.7055245637893677),\n", " ('kuroyume', 0.7049269676208496),\n", " ('ellegarden', 0.7018687725067139),\n", " ('tigertailz', 0.701062023639679)]\n" ] } ], "source": [ "try:\n", " pprint(model.most_similar('babymetal'))\n", "except KeyError as e:\n", " print(e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The word, \"Zootopia\", become disney movie through the years.\n", "In the past, the word, \"Zootopia\", was used just for an annual summer concert put on by New York top-40 radio station Z100, so that the word, \"zootopia\", is simillar with music festival.\n", "\n", "In 2016, Zootopia is a American 3D computer-animated comedy film released by Walt Disney Pictures. As a result, the word, \"zootopia\", was often used as Animation films." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The count of the word,zootopia, is 24 in oldmodel\n", "[('itsekseni', 0.655870258808136),\n", " ('baverstam', 0.6502687931060791),\n", " ('hachnosas', 0.6450551748275757),\n", " ('carrantouhill', 0.631106436252594),\n", " ('bugasan', 0.6258121728897095),\n", " ('lollapolooza', 0.6192305088043213),\n", " ('hutuz', 0.6134281754493713),\n", " ('soulico', 0.6122198104858398),\n", " ('kabungwe', 0.6060466766357422),\n", " ('prischo√ühalle', 0.6056506633758545)]\n", "\n", "The count of the word,zootopia, is 257 in model\n", "[('incredibles', 0.7643648386001587),\n", " ('antz', 0.7575620412826538),\n", " ('spaceballs', 0.7434272766113281),\n", " ('pagemaster', 0.730089545249939),\n", " ('beetlejuice', 0.7257461547851562),\n", " ('coneheads', 0.7239412069320679),\n", " ('tarzan', 0.7139339447021484),\n", " ('catscratch', 0.7124171257019043),\n", " ('boxtrolls', 0.7024375796318054),\n", " ('aristocats', 0.7005465030670166)]\n", "\n" ] } ], "source": [ "w = 'zootopia'\n", "for m in ['oldmodel', 'model']:\n", " print('The count of the word,'+w+', is', eval(m).wv.vocab[w].count, 'in', m)\n", " pprint(eval(m).most_similar(w))\n", " print('')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	9,441	Python	.py	335	23.602985	277	0.579179	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,965	topic_coherence_tutorial.ipynb	piskvorky_gensim/docs/notebooks/topic_coherence_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration of the topic coherence pipeline in Gensim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will be using the `u_mass` and `c_v` coherence for two different LDA models: a \"good\" and a \"bad\" LDA model. The good LDA model will be trained over 50 iterations and the bad one for 1 iteration. Hence in theory, the good LDA model will be able come up with better or more human-understandable topics. Therefore the coherence measure output for the good LDA model should be more (better) than that for the bad LDA model. This is because, simply, the good LDA model usually comes up with better topics that are more human interpretable." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "import os\n", "import logging\n", "import json\n", "import warnings\n", "\n", "try:\n", " raise ImportError\n", " import pyLDAvis.gensim\n", " CAN_VISUALIZE = True\n", " pyLDAvis.enable_notebook()\n", " from IPython.display import display\n", "except ImportError:\n", " ValueError(\"SKIP: please install pyLDAvis\")\n", " CAN_VISUALIZE = False\n", "\n", "import numpy as np\n", "\n", "from gensim.models import CoherenceModel, LdaModel, HdpModel\n", "from gensim.models.wrappers import LdaVowpalWabbit, LdaMallet\n", "from gensim.corpora import Dictionary\n", "\n", "warnings.filterwarnings('ignore') # To ignore all warnings that arise here to enhance clarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As stated in table 2 from [this](http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf) paper, this corpus essentially has two classes of documents. First five are about human-computer interaction and the other four are about graphs. We will be setting up two LDA models. One with 50 iterations of training and the other with just 1. Hence the one with 50 iterations (\"better\" model) should be able to capture this underlying pattern of the corpus better than the \"bad\" LDA model. Therefore, in theory, our topic coherence for the good LDA model should be greater than the one for the bad LDA model." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "texts = [['human', 'interface', 'computer'],\n", " ['survey', 'user', 'computer', 'system', 'response', 'time'],\n", " ['eps', 'user', 'interface', 'system'],\n", " ['system', 'human', 'system', 'eps'],\n", " ['user', 'response', 'time'],\n", " ['trees'],\n", " ['graph', 'trees'],\n", " ['graph', 'minors', 'trees'],\n", " ['graph', 'minors', 'survey']]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dictionary = Dictionary(texts)\n", "corpus = [dictionary.doc2bow(text) for text in texts]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up two topic models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll be setting up two different LDA Topic models. A good one and bad one. To build a \"good\" topic model, we'll simply train it using more iterations than the bad one. Therefore the `u_mass` coherence should in theory be better for the good model than the bad one since it would be producing more \"human-interpretable\" topics." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "goodLdaModel = LdaModel(corpus=corpus, id2word=dictionary, iterations=50, num_topics=2)\n", "badLdaModel = LdaModel(corpus=corpus, id2word=dictionary, iterations=1, num_topics=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using U_Mass Coherence" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "goodcm = CoherenceModel(model=goodLdaModel, corpus=corpus, dictionary=dictionary, coherence='u_mass')\n", "badcm = CoherenceModel(model=badLdaModel, corpus=corpus, dictionary=dictionary, coherence='u_mass')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### View the pipeline parameters for one coherence model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following are the pipeline parameters for `u_mass` coherence. By pipeline parameters, we mean the functions being used to calculate segmentation, probability estimation, confirmation measure and aggregation as shown in figure 1 in [this](http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf) paper." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coherence_Measure(seg=<function s_one_pre at 0x7f6b0a12ed90>, prob=<function p_boolean_document at 0x7f6b0a12eea0>, conf=<function log_conditional_probability at 0x7f6b09c326a8>, aggr=<function arithmetic_mean at 0x7f6b09c32f28>)\n" ] } ], "source": [ "print(goodcm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpreting the topics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we will see below using LDA visualization, the better model comes up with two topics composed of the following words:\n", "1. goodLdaModel:\n", " - __Topic 1__: More weightage assigned to words such as \"system\", \"user\", \"eps\", \"interface\" etc which captures the first set of documents.\n", " - __Topic 2__: More weightage assigned to words such as \"graph\", \"trees\", \"survey\" which captures the topic in the second set of documents.\n", "2. badLdaModel:\n", " - __Topic 1__: More weightage assigned to words such as \"system\", \"user\", \"trees\", \"graph\" which doesn't make the topic clear enough.\n", " - __Topic 2__: More weightage assigned to words such as \"system\", \"trees\", \"graph\", \"user\" which is similar to the first topic. Hence both topics are not human-interpretable.\n", "\n", "Therefore, the topic coherence for the goodLdaModel should be greater for this than the badLdaModel since the topics it comes up with are more human-interpretable. We will see this using `u_mass` and `c_v` topic coherence measures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize topic models" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "if CAN_VISUALIZE:\n", " prepared = pyLDAvis.gensim.prepare(goodLdaModel, corpus, dictionary)\n", " display(pyLDAvis.display(prepared))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "if CAN_VISUALIZE:\n", " prepared = pyLDAvis.gensim.prepare(badLdaModel, corpus, dictionary)\n", " display(pyLDAvis.display(prepared))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-14.695344054692296\n", "-14.722989402972397\n" ] } ], "source": [ "print(goodcm.get_coherence())\n", "print(badcm.get_coherence())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using C_V coherence" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "goodcm = CoherenceModel(model=goodLdaModel, texts=texts, dictionary=dictionary, coherence='c_v')\n", "badcm = CoherenceModel(model=badLdaModel, texts=texts, dictionary=dictionary, coherence='c_v')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pipeline parameters for C_V coherence" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coherence_Measure(seg=<function s_one_set at 0x7f6b0a12ee18>, prob=<function p_boolean_sliding_window at 0x7f6b0a1421e0>, conf=<function cosine_similarity at 0x7f6b09c328c8>, aggr=<function arithmetic_mean at 0x7f6b09c32f28>)\n" ] } ], "source": [ "print(goodcm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Print coherence values" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.3838413553737203\n", "0.3838413553737203\n" ] } ], "source": [ "print(goodcm.get_coherence())\n", "print(badcm.get_coherence())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Support for wrappers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This API supports gensim's _ldavowpalwabbit_ and _ldamallet_ wrappers as input parameter to `model`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Replace with path to your Vowpal Wabbit installation\n", "vw_path = '/usr/local/bin/vw'\n", "\n", "# Replace with path to your Mallet installation\n", "home = os.path.expanduser('~')\n", "mallet_path = os.path.join(home, 'mallet-2.0.8', 'bin', 'mallet')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '/usr/local/bin/vw': '/usr/local/bin/vw'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-14-9421c07a3fe9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLdaVowpalWabbit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvw_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorpus\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_topics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid2word\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmodel2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLdaVowpalWabbit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvw_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorpus\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_topics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid2word\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/git/gensim/gensim/models/wrappers/ldavowpalwabbit.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vw_path, corpus, num_topics, id2word, chunksize, passes, alpha, eta, decay, offset, gamma_threshold, random_seed, cleanup_files, tmp_prefix)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcorpus\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m 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%s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 850\u001b[0m proc = subprocess.Popen(cmd, stdout=subprocess.PIPE,\n\u001b[0;32m--> 851\u001b[0;31m stderr=subprocess.STDOUT)\n\u001b[0m\u001b[1;32m 852\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 853\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Vowpal Wabbit output: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.7/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors, text)\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[0mc2pread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc2pwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 774\u001b[0m \u001b[0merrread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 775\u001b[0;31m restore_signals, start_new_session)\n\u001b[0m\u001b[1;32m 776\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[0;31m# Cleanup if the child failed starting.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.7/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, start_new_session)\u001b[0m\n\u001b[1;32m 1520\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrno_num\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0merrno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENOENT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1521\u001b[0m \u001b[0merr_msg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m': '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1522\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1523\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/usr/local/bin/vw': '/usr/local/bin/vw'" ] } ], "source": [ "model1 = LdaVowpalWabbit(vw_path, corpus=corpus, num_topics=2, id2word=dictionary, passes=50)\n", "model2 = LdaVowpalWabbit(vw_path, corpus=corpus, num_topics=2, id2word=dictionary, passes=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cm1 = CoherenceModel(model=model1, corpus=corpus, coherence='u_mass')\n", "cm2 = CoherenceModel(model=model2, corpus=corpus, coherence='u_mass')\n", "print(cm1.get_coherence())\n", "print(cm2.get_coherence())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model1 = LdaMallet(mallet_path, corpus=corpus, num_topics=2, id2word=dictionary, iterations=50)\n", "model2 = LdaMallet(mallet_path, corpus=corpus, num_topics=2, id2word=dictionary, iterations=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cm1 = CoherenceModel(model=model1, texts=texts, coherence='c_v')\n", "cm2 = CoherenceModel(model=model2, texts=texts, coherence='c_v')\n", "print(cm1.get_coherence())\n", "print(cm2.get_coherence())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Support for other topic models\n", "The gensim topics coherence pipeline can be used with other topics models too. Only the tokenized `topics` should be made available for the pipeline. Eg. with the gensim HDP model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hm = HdpModel(corpus=corpus, id2word=dictionary)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# To get the topic words from the model\n", "topics = []\n", "for topic_id, topic in hm.show_topics(num_topics=10, formatted=False):\n", " topic = [word for word, _ in topic]\n", " topics.append(topic)\n", "topics[:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Initialize CoherenceModel using `topics` parameter\n", "cm = CoherenceModel(topics=topics, corpus=corpus, dictionary=dictionary, coherence='u_mass')\n", "cm.get_coherence()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hence as we can see, the `u_mass` and `c_v` coherence for the good LDA model is much more (better) than that for the bad LDA model. This is because, simply, the good LDA model usually comes up with better topics that are more human interpretable. The badLdaModel however fails to decipher between these two topics and comes up with topics which are not clear to a human. The `u_mass` and `c_v` topic coherences capture this wonderfully by giving the interpretability of these topics a number as we can see above. Hence this coherence measure can be used to compare difference topic models based on their human-interpretability." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	21,938	Python	.py	473	42.156448	1,532	0.648777	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,966	WMD_tutorial.ipynb	piskvorky_gensim/docs/notebooks/WMD_tutorial.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Finding similar documents with Word2Vec and WMD \n", "\n", "Word Mover's Distance is a promising new tool in machine learning that allows us to submit a query and return the most relevant documents. For example, in a blog post [OpenTable](http://tech.opentable.com/2015/08/11/navigating-themes-in-restaurant-reviews-with-word-movers-distance/) use WMD on restaurant reviews. Using this approach, they are able to mine different aspects of the reviews. In part 2 of this tutorial, we show how you can use Gensim's `WmdSimilarity` to do something similar to what OpenTable did. In part 1 shows how you can compute the WMD distance between two documents using `wmdistance`. Part 1 is optional if you want use `WmdSimilarity`, but is also useful in it's own merit.\n", "\n", "First, however, we go through the basics of what WMD is.\n", "\n", "## Word Mover's Distance basics\n", "\n", "WMD is a method that allows us to assess the \"distance\" between two documents in a meaningful way, even when they have no words in common. It uses [word2vec](https://rare-technologies.com/word2vec-tutorial/) [4] vector embeddings of words. It been shown to outperform many of the state-of-the-art methods in k-nearest neighbors classification [3].\n", "\n", "WMD is illustrated below for two very similar sentences (illustration taken from [Vlad Niculae's blog](http://vene.ro/blog/word-movers-distance-in-python.html)). The sentences have no words in common, but by matching the relevant words, WMD is able to accurately measure the (dis)similarity between the two sentences. The method also uses the bag-of-words representation of the documents (simply put, the word's frequencies in the documents), noted as $d$ in the figure below. The intuition behind the method is that we find the minimum \"traveling distance\" between documents, in other words the most efficient way to \"move\" the distribution of document 1 to the distribution of document 2.\n", "\n", "<img src='https://vene.ro/images/wmd-obama.png' height='600' width='600'>\n", "\n", "\n", "This method was introduced in the article \"From Word Embeddings To Document Distances\" by Matt Kusner et al. ([link to PDF](http://jmlr.org/proceedings/papers/v37/kusnerb15.pdf)). It is inspired by the \"Earth Mover's Distance\", and employs a solver of the \"transportation problem\".\n", "\n", "In this tutorial, we will learn how to use Gensim's WMD functionality, which consists of the `wmdistance` method for distance computation, and the `WmdSimilarity` class for corpus based similarity queries.\n", "\n", "> Note:\n", ">\n", "> If you use this software, please consider citing [1], [2] and [3].\n", ">\n", "\n", "## Running this notebook\n", "\n", "You can download this [iPython Notebook](http://ipython.org/notebook.html), and run it on your own computer, provided you have installed Gensim, POT, NLTK, and downloaded the necessary data.\n", "\n", "The notebook was run on an Ubuntu machine with an Intel core i7-4770 CPU 3.40GHz (8 cores) and 32 GB memory. Running the entire notebook on this machine takes about 3 minutes.\n", "\n", "## Part 1: Computing the Word Mover's Distance\n", "\n", "To use WMD, we need some word embeddings first of all. You could train a word2vec (see tutorial [here](https://rare-technologies.com/word2vec-tutorial/)) model on some corpus, but we will start by downloading some pre-trained word2vec embeddings. Download the GoogleNews-vectors-negative300.bin.gz embeddings [here](https://code.google.com/archive/p/word2vec/) (warning: 1.5 GB, file is not needed for part 2). Training your own embeddings can be beneficial, but to simplify this tutorial, we will be using pre-trained embeddings at first.\n", "\n", "Let's take some sentences to compute the distance between." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from time import time\n", "start_nb = time()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Initialize logging.\n", "import logging\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')\n", "\n", "sentence_obama = 'Obama speaks to the media in Illinois'\n", "sentence_president = 'The president greets the press in Chicago'\n", "sentence_obama = sentence_obama.lower().split()\n", "sentence_president = sentence_president.lower().split()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These sentences have very similar content, and as such the WMD should be low. Before we compute the WMD, we want to remove stopwords (\"the\", \"to\", etc.), as these do not contribute a lot to the information in the sentences." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to /home/misha/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n" ] } ], "source": [ "# Import and download stopwords from NLTK.\n", "from nltk.corpus import stopwords\n", "from nltk import download\n", "download('stopwords') # Download stopwords list.\n", "\n", "# Remove stopwords.\n", "stop_words = stopwords.words('english')\n", "sentence_obama = [w for w in sentence_obama if w not in stop_words]\n", "sentence_president = [w for w in sentence_president if w not in stop_words]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, as mentioned earlier, we will be using some downloaded pre-trained embeddings. We load these into a Gensim Word2Vec model class. Note that the embeddings we have chosen here require a lot of memory." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2019-06-17 00:50:23,614 : WARNING : this function is deprecated, use smart_open.open instead\n" ] }, { "data": { "text/plain": [ "<gensim.models.keyedvectors.Word2VecKeyedVectors at 0x7f8aac8cc208>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gensim.downloader as api\n", "api.load('word2vec-google-news-300')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-06-17 09:33:25,029 : WARNING : this function is deprecated, use smart_open.open instead\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Cell took 154.82 seconds to run.\n" ] } ], "source": [ "start = time()\n", "import os\n", "\n", "# from gensim.models import KeyedVectors\n", "# if not os.path.exists('/data/w2v_googlenews/GoogleNews-vectors-negative300.bin.gz'):\n", "# raise ValueError(\"SKIP: You need to download the google news model\")\n", "# \n", "# model = KeyedVectors.load_word2vec_format('/data/w2v_googlenews/GoogleNews-vectors-negative300.bin.gz', binary=True)\n", "model = api.load('word2vec-google-news-300')\n", "\n", "print('Cell took %.2f seconds to run.' % (time() - start))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So let's compute WMD using the `wmdistance` method." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "distance = 3.3741\n" ] } ], "source": [ "distance = model.wmdistance(sentence_obama, sentence_president)\n", "print('distance = %.4f' % distance)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try the same thing with two completely unrelated sentences. Notice that the distance is larger." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "distance = 4.3802\n" ] } ], "source": [ "sentence_orange = 'Oranges are my favorite fruit'\n", "sentence_orange = sentence_orange.lower().split()\n", "sentence_orange = [w for w in sentence_orange if w not in stop_words]\n", "\n", "distance = model.wmdistance(sentence_obama, sentence_orange)\n", "print('distance = %.4f' % distance)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normalizing word2vec vectors\n", "\n", "When using the `wmdistance` method, it is beneficial to normalize the word2vec vectors first, so they all have equal length. To do this, simply call `model.init_sims(replace=True)` and Gensim will take care of that for you.\n", "\n", "Usually, one measures the distance between two word2vec vectors using the cosine distance (see [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity)), which measures the angle between vectors. WMD, on the other hand, uses the Euclidean distance. The Euclidean distance between two vectors might be large because their lengths differ, but the cosine distance is small because the angle between them is small; we can mitigate some of this by normalizing the vectors.\n", "\n", "Note that normalizing the vectors can take some time, especially if you have a large vocabulary and/or large vectors.\n", "\n", "Usage is illustrated in the example below. It just so happens that the vectors we have downloaded are already normalized, so it won't do any difference in this case." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "distance: %r 1.0174646259300113\n", "Cell took 3.15 seconds to run.\n" ] } ], "source": [ "# Normalizing word2vec vectors.\n", "start = time()\n", "\n", "model.init_sims(replace=True) # Normalizes the vectors in the word2vec class.\n", "\n", "distance = model.wmdistance(sentence_obama, sentence_president) # Compute WMD as normal.\n", "print('distance: %r', distance)\n", "print('Cell took %.2f seconds to run.' %(time() - start))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Similarity queries using `WmdSimilarity`\n", "\n", "You can use WMD to get the most similar documents to a query, using the `WmdSimilarity` class. Its interface is similar to what is described in the [Similarity Queries](https://radimrehurek.com/gensim/tut3.html) Gensim tutorial.\n", "\n", "> Important note:\n", ">\n", "> WMD is a measure of distance. The similarities in `WmdSimilarity` are simply the negative distance. Be careful not to confuse distances and similarities. Two similar documents will have a high similarity score and a small distance; two very different documents will have low similarity score, and a large distance.\n", "\n", "### Yelp data\n", "\n", "Let's try similarity queries using some real world data. For that we'll be using Yelp reviews, available at http://www.yelp.com/dataset_challenge. Specifically, we will be using reviews of a single restaurant, namely the [Mon Ami Gabi](http://en.yelp.be/biz/mon-ami-gabi-las-vegas-2).\n", "\n", "To get the Yelp data, you need to register by name and email address. The data is 775 MB.\n", "\n", "This time around, we are going to train the Word2Vec embeddings on the data ourselves. One restaurant is not enough to train Word2Vec properly, so we use 6 restaurants for that, but only run queries against one of them. In addition to the Mon Ami Gabi, mentioned above, we will be using:\n", "\n", "* [Earl of Sandwich](http://en.yelp.be/biz/earl-of-sandwich-las-vegas).\n", "* [Wicked Spoon](http://en.yelp.be/biz/wicked-spoon-las-vegas).\n", "* [Serendipity 3](http://en.yelp.be/biz/serendipity-3-las-vegas).\n", "* [Bacchanal Buffet](http://en.yelp.be/biz/bacchanal-buffet-las-vegas-7).\n", "* [The Buffet](http://en.yelp.be/biz/the-buffet-las-vegas-6).\n", "\n", "The restaurants we chose were those with the highest number of reviews in the Yelp dataset. Incidentally, they all are on the Las Vegas Boulevard. The corpus we trained Word2Vec on has 18957 documents (reviews), and the corpus we used for `WmdSimilarity` has 4137 documents.\n", "\n", "Below a JSON file with Yelp reviews is read line by line, the text is extracted, tokenized, and stopwords and punctuation are removed.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Pre-processing a document.\n", "\n", "from nltk import word_tokenize\n", "download('punkt') # Download data for tokenizer.\n", "\n", "def preprocess(doc):\n", " doc = doc.lower() # Lower the text.\n", " doc = word_tokenize(doc) # Split into words.\n", " doc = [w for w in doc if not w in stop_words] # Remove stopwords.\n", " doc = [w for w in doc if w.isalpha()] # Remove numbers and punctuation.\n", " return doc" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "start = time()\n", "\n", "import json\n", "from smart_open import smart_open\n", "\n", "# Business IDs of the restaurants.\n", "ids = ['4bEjOyTaDG24SY5TxsaUNQ', '2e2e7WgqU1BnpxmQL5jbfw', 'zt1TpTuJ6y9n551sw9TaEg',\n", " 'Xhg93cMdemu5pAMkDoEdtQ', 'sIyHTizqAiGu12XMLX3N3g', 'YNQgak-ZLtYJQxlDwN-qIg']\n", "\n", "w2v_corpus = [] # Documents to train word2vec on (all 6 restaurants).\n", "wmd_corpus = [] # Documents to run queries against (only one restaurant).\n", "documents = [] # wmd_corpus, with no pre-processing (so we can see the original documents).\n", "with smart_open('/data/yelp_academic_dataset_review.json', 'rb') as data_file:\n", " for line in data_file:\n", " json_line = json.loads(line)\n", " \n", " if json_line['business_id'] not in ids:\n", " # Not one of the 6 restaurants.\n", " continue\n", " \n", " # Pre-process document.\n", " text = json_line['text'] # Extract text from JSON object.\n", " text = preprocess(text)\n", " \n", " # Add to corpus for training Word2Vec.\n", " w2v_corpus.append(text)\n", " \n", " if json_line['business_id'] == ids[0]:\n", " # Add to corpus for similarity queries.\n", " wmd_corpus.append(text)\n", " documents.append(json_line['text'])\n", "\n", "print 'Cell took %.2f seconds to run.' %(time() - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is a plot with a histogram of document lengths and includes the average document length as well. Note that these are the pre-processed documents, meaning stopwords are removed, punctuation is removed, etc. Document lengths have a high impact on the running time of WMD, so when comparing running times with this experiment, the number of documents in query corpus (about 4000) and the length of the documents (about 62 words on average) should be taken into account." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "# Document lengths.\n", "lens = [len(doc) for doc in wmd_corpus]\n", "\n", "# Plot.\n", "plt.rc('figure', figsize=(8,6))\n", "plt.rc('font', size=14)\n", "plt.rc('lines', linewidth=2)\n", "plt.rc('axes', color_cycle=('#377eb8','#e41a1c','#4daf4a',\n", " '#984ea3','#ff7f00','#ffff33'))\n", "# Histogram.\n", "plt.hist(lens, bins=20)\n", "plt.hold(True)\n", "# Average length.\n", "avg_len = sum(lens) / float(len(lens))\n", "plt.axvline(avg_len, color='#e41a1c')\n", "plt.hold(False)\n", "plt.title('Histogram of document lengths.')\n", "plt.xlabel('Length')\n", "plt.text(100, 800, 'mean = %.2f' % avg_len)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to initialize the similarity class with a corpus and a word2vec model (which provides the embeddings and the `wmdistance` method itself)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Train Word2Vec on all the restaurants.\n", "model = Word2Vec(w2v_corpus, workers=3, size=100)\n", "\n", "# Initialize WmdSimilarity.\n", "from gensim.similarities import WmdSimilarity\n", "num_best = 10\n", "instance = WmdSimilarity(wmd_corpus, model, num_best=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `num_best` parameter decides how many results the queries return. Now let's try making a query. The output is a list of indeces and similarities of documents in the corpus, sorted by similarity.\n", "\n", "Note that the output format is slightly different when `num_best` is `None` (i.e. not assigned). In this case, you get an array of similarities, corresponding to each of the documents in the corpus.\n", "\n", "The query below is taken directly from one of the reviews in the corpus. Let's see if there are other reviews that are similar to this one." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "start = time()\n", "\n", "sent = 'Very good, you should seat outdoor.'\n", "query = preprocess(sent)\n", "\n", "sims = instance[query] # A query is simply a \"look-up\" in the similarity class.\n", "\n", "print 'Cell took %.2f seconds to run.' %(time() - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The query and the most similar documents, together with the similarities, are printed below. We see that the retrieved documents are discussing the same thing as the query, although using different words. The query talks about getting a seat \"outdoor\", while the results talk about sitting \"outside\", and one of them says the restaurant has a \"nice view\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Print the query and the retrieved documents, together with their similarities.\n", "print 'Query:'\n", "print sent\n", "for i in range(num_best):\n", " print\n", " print 'sim = %.4f' % sims[i][1]\n", " print documents[sims[i][0]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try a different query, also taken directly from one of the reviews in the corpus." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "start = time()\n", "\n", "sent = 'I felt that the prices were extremely reasonable for the Strip'\n", "query = preprocess(sent)\n", "\n", "sims = instance[query] # A query is simply a \"look-up\" in the similarity class.\n", "\n", "print 'Query:'\n", "print sent\n", "for i in range(num_best):\n", " print\n", " print 'sim = %.4f' % sims[i][1]\n", " print documents[sims[i][0]]\n", "\n", "print '\\nCell took %.2f seconds to run.' %(time() - start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time around, the results are more straight forward; the retrieved documents basically contain the same words as the query.\n", "\n", "`WmdSimilarity` normalizes the word embeddings by default (using `init_sims()`, as explained before), but you can overwrite this behaviour by calling `WmdSimilarity` with `normalize_w2v_and_replace=False`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print 'Notebook took %.2f seconds to run.' %(time() - start_nb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "1. * RÃ©mi Flamary et al. POT: Python Optimal Transport, 2021.\n", "* Matt Kusner et al. From Embeddings To Document Distances, 2015.\n", "* Thomas Mikolov et al. Efficient Estimation of Word Representations in Vector Space, 2013." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }	21,841	Python	.py	554	34.891697	717	0.632546	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,967	poincare_numpy.patch	piskvorky_gensim/docs/notebooks/poincare/poincare_numpy.patch	diff --git a/poincare.py b/poincare.py index ecae36e..f85bf22 100644 --- a/poincare.py +++ b/poincare.py @@ -1,160 +1,169 @@ +import argparse +import csv import nltk from nltk.corpus import wordnet as wn from math import * +import pickle import random +import re import numpy as np -import matplotlib.pyplot as plt -import matplotlib.lines as mlines +import time +from collections import defaultdict +from smart_open import smart_open + STABILITY = 0.00001 # to avoid overflow while dividing -network = {} # representation of network (here it is hierarchical) -last_level = 4 +# network: the actual network of which node is connected to whom +network = defaultdict(list) # representation of network (here it is hierarchical) -# plots the embedding of all the nodes of network -def plotall(ii): - fig = plt.figure() - # plot all the nodes - for a in emb: - plt.plot(emb[a][0], emb[a][1], marker = 'o', color = [levelOfNode[a]/(last_level+1),levelOfNode[a]/(last_level+1),levelOfNode[a]/(last_level+1)]) - # plot the relationship, black line means root level relationship - # consecutive relationship lines fade out in color + +def load_wordnet(wordnet_path): + with smart_open(wordnet_path, 'r') as f: + reader = csv.reader(f, delimiter='\t') + for row in reader: + assert len(row) == 2, 'Hypernym pair has more than two items' + network[row[0]].append(row[1]) + +def main(input_file, output_file, embedding_size, num_epochs, num_negs, lr): + print('Creating wordnet dataset') + load_wordnet(input_file) + print('Created wordnet dataset') + + # embedding of nodes of network + emb = {} + + # Randomly uniform distribution for a in network: for b in network[a]: - plt.plot([emb[a][0], emb[b][0]], [emb[a][1], emb[b][1]], color = [levelOfNode[a]/(last_level+1),levelOfNode[a]/(last_level+1),levelOfNode[a]/(last_level+1)]) - # plt.show() - fig.savefig(str(last_level) + '_' + str(ii) + '.png', dpi=fig.dpi) + emb[b] = np.random.uniform(low=-0.001, high=0.001, size=(embedding_size,)) + emb[a] = np.random.uniform(low=-0.001, high=0.001, size=(embedding_size,)) -# network: the actual network of which node is connected to whom -# levelOfNode: level of the node in hierarchical data -levelOfNode = {} - -# recursive function to popoulate the hyponyms of a root node in `network` -# synset: the root node -# last_level: the level till which we consider the hyponyms -def get_hyponyms(synset, level): - if (level == last_level): - levelOfNode[str(synset)] = level - return - # BFS - if not str(synset) in network: - network[str(synset)] = [str(s) for s in synset.hyponyms()] - levelOfNode[str(synset)] = level - for hyponym in synset.hyponyms(): - get_hyponyms(hyponym, level + 1) - -mammal = wn.synset('mammal.n.01') -get_hyponyms(mammal, 0) -levelOfNode[str(mammal)] = 0 - -# embedding of nodes of network -emb = {} - -# Randomly uniform distribution -for a in network: - for b in network[a]: - emb[b] = np.random.uniform(low=-0.001, high=0.001, size=(2,)) - emb[a] = np.random.uniform(low=-0.001, high=0.001, size=(2,)) - -vocab = list(emb.keys()) -random.shuffle(vocab) - -# the leave nodes are not connected to anything -for a in emb: - if not a in network: - network[a] = [] - - -# Partial derivative as given in the paper wrt theta -def partial_der(theta, x, gamma): #eqn4 - alpha = (1.0-np.dot(theta, theta)) - norm_x = np.dot(x, x) - beta = (1-norm_x) - gamma = gamma - return 4.0/(beta * sqrt(gammagamma - 1) + STABILITY)((norm_x- 2np.dot(theta, x)+1)/(pow(alpha,2)+STABILITY)theta - x/(alpha + STABILITY)) - -lr = 0.01 - -# the update equation as given in the paper -def update(emb, error_): #eqn5 - try: - update = lrpow((1 - np.dot(emb,emb)), 2)error_/4 - emb = emb - update - if (np.dot(emb, emb) >= 1): - emb = emb/sqrt(np.dot(emb, emb)) - STABILITY - return emb - except Exception as e: - print (e) - -# Distance in poincare disk model -def dist(vec1, vec2): # eqn1 - return 1 + 2np.dot(vec1 - vec2, vec1 - vec2)/ \ - ((1-np.dot(vec1, vec1))(1-np.dot(vec2, vec2)) + STABILITY) - -num_negs = 5 - -# The plot of initialized embeddings -plotall("init") - -for epoch in range(200): - # pos2 is related to pos1 - # negs are not related to pos1 - for pos1 in vocab: - if not network[pos1]: # a leaf node - continue - pos2 = random.choice(network[pos1]) # pos2 and pos1 are related - dist_p_init = dist(emb[pos1], emb[pos2]) # distance between the related nodes - if (dist_p_init > 700): # this causes overflow, so I clipped it here - print ("got one very high") # if you have reached this zone, the training is unstable now - dist_p_init = 700 - elif (dist_p_init < -700): - print ("got one very high") - dist_p_init = -700 - dist_p = cosh(dist_p_init) # this is the actual distance, it is always positive - # print ("distance between related nodes", dist_p) - negs = [] # pairs of not related nodes, the first node in the pair is `pos1` - dist_negs_init = [] # distances without taking cosh on it (for not related nodes) - dist_negs = [] # distances with taking cosh on it (for not related nodes) - while (len(negs) < num_negs): - neg1 = pos1 - neg2 = random.choice(vocab) - if not (neg2 in network[neg1] or neg1 in network[neg2] or neg2 == neg1): # neg2 should not be related to neg1 and vice versa - dist_neg_init = dist(emb[neg1], emb[neg2]) - if (dist_neg_init > 700 or dist_neg_init < -700): # already dist is good, leave it - continue - negs.append([neg1, neg2]) - dist_neg = cosh(dist_neg_init) - dist_negs_init.append(dist_neg_init) # saving it for faster computation - dist_negs.append(dist_neg) - # print ("distance between non related nodes", dist_neg) - loss_den = 0.0 - # eqn6 - for dist_neg in dist_negs: - loss_den += exp(-1dist_neg) - loss = -1dist_p - log(loss_den + STABILITY) - # derivative of loss wrt positive relation [d(u, v)] - der_p = -1 - der_negs = [] - # derivative of loss wrt negative relation [d(u, v')] - for dist_neg in dist_negs: - der_negs.append(exp(-1dist_neg)/(loss_den + STABILITY)) - # derivative of loss wrt pos1 - der_p_pos1 = der_p partial_der(emb[pos1], emb[pos2], dist_p_init) - # derivative of loss wrt pos2 - der_p_pos2 = der_p * partial_der(emb[pos2], emb[pos1], dist_p_init) - der_negs_final = [] - for (der_neg, neg, dist_neg_init) in zip(der_negs, negs, dist_negs_init): - # derivative of loss wrt second element of the pair in neg - der_neg1 = der_neg * partial_der(emb[neg[1]], emb[neg[0]], dist_neg_init) - # derivative of loss wrt first element of the pair in neg - der_neg0 = der_neg * partial_der(emb[neg[0]], emb[neg[1]], dist_neg_init) - der_negs_final.append([der_neg0, der_neg1]) - # update embeddings now - emb[pos1] = update(emb[pos1], -1der_p_pos1) - emb[pos2] = update(emb[pos2], -1der_p_pos2) - for (neg, der_neg) in zip(negs, der_negs_final): - emb[neg[0]] = update(emb[neg[0]], -1der_neg[0]) - emb[neg[1]] = update(emb[neg[1]], -1der_neg[1]) - # plot the embeddings - if ((epoch)%20 == 0): - plotall(epoch+1) \ No newline at end of file + vocab = list(emb.keys()) + random.shuffle(vocab) + + # the leave nodes are not connected to anything + for a in emb: + if not a in network: + network[a] = [] + + # Partial derivative as given in the paper wrt theta + def partial_der(theta, x, gamma): #eqn4 + alpha = (1.0-np.dot(theta, theta)) + norm_x = np.dot(x, x) + beta = (1-norm_x) + gamma = gamma + return 4.0/(beta * sqrt(gammagamma - 1) + STABILITY)((norm_x- 2np.dot(theta, x)+1)/(pow(alpha,2)+STABILITY)theta - x/(alpha + STABILITY)) + + # the update equation as given in the paper + def update(emb, error_): #eqn5 + try: + update = lrpow((1 - np.dot(emb,emb)), 2)error_/4 + emb = emb - update + if (np.dot(emb, emb) >= 1): + emb = emb/sqrt(np.dot(emb, emb)) - STABILITY + return emb + except Exception as e: + print (e) + + # Distance in poincare disk model + def dist(vec1, vec2): # eqn1 + return 1 + 2np.dot(vec1 - vec2, vec1 - vec2)/ \ + ((1-np.dot(vec1, vec1))(1-np.dot(vec2, vec2)) + STABILITY) + + + # The plot of initialized embeddings + # plotall("init") + + last_time = time.time() + for epoch in range(num_epochs): + # pos2 is related to pos1 + # negs are not related to pos1 + for pos1 in vocab: + if not network[pos1]: # a leaf node + continue + pos2 = random.choice(network[pos1]) # pos2 and pos1 are related + dist_p_init = dist(emb[pos1], emb[pos2]) # distance between the related nodes + if (dist_p_init > 700): # this causes overflow, so I clipped it here + print ("got one very high") # if you have reached this zone, the training is unstable now + dist_p_init = 700 + elif (dist_p_init < -700): + print ("got one very high") + dist_p_init = -700 + dist_p = cosh(dist_p_init) # this is the actual distance, it is always positive + # print ("distance between related nodes", dist_p) + negs = [] # pairs of not related nodes, the first node in the pair is `pos1` + dist_negs_init = [] # distances without taking cosh on it (for not related nodes) + dist_negs = [] # distances with taking cosh on it (for not related nodes) + while (len(negs) < num_negs): + neg1 = pos1 + neg2 = random.choice(vocab) + if not (neg2 in network[neg1] or neg1 in network[neg2] or neg2 == neg1): # neg2 should not be related to neg1 and vice versa + dist_neg_init = dist(emb[neg1], emb[neg2]) + if (dist_neg_init > 700 or dist_neg_init < -700): # already dist is good, leave it + continue + negs.append([neg1, neg2]) + dist_neg = cosh(dist_neg_init) + dist_negs_init.append(dist_neg_init) # saving it for faster computation + dist_negs.append(dist_neg) + # print ("distance between non related nodes", dist_neg) + loss_den = 0.0 + # eqn6 + for dist_neg in dist_negs: + loss_den += exp(-1dist_neg) + loss = -1dist_p - log(loss_den + STABILITY) + # derivative of loss wrt positive relation [d(u, v)] + der_p = -1 + der_negs = [] + # derivative of loss wrt negative relation [d(u, v')] + for dist_neg in dist_negs: + der_negs.append(exp(-1dist_neg)/(loss_den + STABILITY)) + # derivative of loss wrt pos1 + der_p_pos1 = der_p partial_der(emb[pos1], emb[pos2], dist_p_init) + # derivative of loss wrt pos2 + der_p_pos2 = der_p * partial_der(emb[pos2], emb[pos1], dist_p_init) + der_negs_final = [] + for (der_neg, neg, dist_neg_init) in zip(der_negs, negs, dist_negs_init): + # derivative of loss wrt second element of the pair in neg + der_neg1 = der_neg * partial_der(emb[neg[1]], emb[neg[0]], dist_neg_init) + # derivative of loss wrt first element of the pair in neg + der_neg0 = der_neg * partial_der(emb[neg[0]], emb[neg[1]], dist_neg_init) + der_negs_final.append([der_neg0, der_neg1]) + # update embeddings now + emb[pos1] = update(emb[pos1], -1der_p_pos1) + emb[pos2] = update(emb[pos2], -1der_p_pos2) + for (neg, der_neg) in zip(negs, der_negs_final): + emb[neg[0]] = update(emb[neg[0]], -1der_neg[0]) + emb[neg[1]] = update(emb[neg[1]], -1der_neg[1]) + print('Epoch #%d, time taken: %.2f seconds' % (epoch + 1, time.time() - last_time)) + last_time = time.time() + pickle.dump(emb, smart_open(output_file, 'wb')) + + +if __name__ == "__main__": + # check and process cmdline input + parser = argparse.ArgumentParser() + parser.add_argument( + '-i', '--input-file', required=True, + help="Input tsv file containing relation pairs") + parser.add_argument( + '-o', '--output-file', required=True, + help="Where to save the trained model") + parser.add_argument( + '-d', '--dimensions', required=True, type=int, + help="Dimensionality of the trained vectors") + parser.add_argument( + '-e', '--epochs', required=True, type=int, + help="Number of epochs to train the model for") + parser.add_argument( + '-l', '--learning-rate', required=True, type=float, + help="Learning rate to use for training the model") + parser.add_argument( + '-n', '--num-negative', required=True, type=int, + help="Number of negative samples to use for each node") + args = parser.parse_args() + + main( + args.input_file, args.output_file, args.dimensions, + args.epochs, args.num_negative, args.learning_rate, + ) \ No newline at end of file	13,875	Python	.py	320	42.271875	170	0.590094	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,968	conf.py	piskvorky_gensim/docs/src/conf.py	# -- coding: utf-8 -- # # gensim documentation build configuration file, created by # sphinx-quickstart on Wed Mar 17 13:42:21 2010. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import os import sys # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('../..')) # -- General configuration ----------------------------------------------------- html_theme = 'sphinx_rtd_theme' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.imgmath', 'sphinxcontrib.programoutput', 'sphinx_gallery.gen_gallery', ] autoclass_content = "both" napoleon_google_docstring = False # Disable support for google-style docstring # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. # source_encoding = 'utf-8' # The master toctree document. master_doc = 'indextoc' # Additional templates that should be rendered to pages, maps page names to # template names. html_additional_pages = {'index': './_templates/indexcontent.html'} # General information about the project. project = u'gensim' copyright = u'2009-now' # The version info for the project you're documenting, acts as replacement for # \|version\| and \|release\|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '4.3.3' # The full version, including alpha/beta/rc tags. release = '4.3.3' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing \|today\|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. # unused_docs = [] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = ['_build'] exclude_patterns = ['gallery/README.rst'] # The reST default role (used for this markup: `text`) to use for all documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. # html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # main_colour = "#ffbbbb" html_theme_options = { # "rightsidebar": "false", # "stickysidebar": "true", # "bodyfont": "'Lucida Grande', 'Lucida Sans Unicode', 'Geneva', 'Verdana', 'sans-serif'", # "headfont": "'Lucida Grande', 'Lucida Sans Unicode', 'Geneva', 'Verdana', 'sans-serif'", # "sidebarbgcolor": "#ababab", # "footerbgcolor": "#771111", # "relbarbgcolor": "#993333", # "sidebartextcolor": "#000000", # "sidebarlinkcolor": "#330000", # "codebgcolor": "#fffff0", # "headtextcolor": "#000080", # "headbgcolor": "#f0f0ff", # "bgcolor": "#ffffff", } # Add any paths that contain custom themes here, relative to this directory. html_theme_path = ['.'] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". html_title = "gensim" # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = '' # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = '_static/favicon.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # These paths are either relative to html_static_path # or fully qualified paths (eg. https://...) # html_css_files = [ # 'erp/css/global.css', # 'erp/css/structure.css', # 'erp/css/erp2.css', # 'erp/css/custom.css', # ] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. html_sidebars = {} # {'index': ['download.html', 'globaltoc.html', 'searchbox.html', 'indexsidebar.html']} # html_sidebars = {'index': ['globaltoc.html', 'searchbox.html']} # If false, no module index is generated. # html_use_modindex = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. html_split_index = False # If true, links to the reST sources are added to the pages. html_show_sourcelink = False html_domain_indices = False # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = '' # Output file base name for HTML help builder. htmlhelp_basename = 'gensimdoc' html_show_sphinx = False # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). # latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). # latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [('index', 'gensim.tex', u'gensim Documentation', u'Radim Řehůřek', 'manual')] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. latex_use_parts = False # Additional stuff for the LaTeX preamble. # latex_preamble = '' # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_use_modindex = True suppress_warnings = ['image.nonlocal_uri', 'ref.citation', 'ref.footnote'] def sort_key(source_dir): """Sorts tutorials and guides in a predefined order. If the predefined order doesn't include the filename we're looking for, fallback to alphabetical order. """ core_order = [ 'run_core_concepts.py', 'run_corpora_and_vector_spaces.py', 'run_topics_and_transformations.py', 'run_similarity_queries.py', ] tutorials_order = [ 'run_word2vec.py', 'run_doc2vec_lee.py', 'run_fasttext.py', 'run_ensemblelda.py', 'run_annoy.py', 'run_lda.py', 'run_wmd.py', 'run_scm.py', ] howto_order = [ 'run_downloader_api.py', 'run_binder.py', 'run_doc.py', 'run_doc2vec_imdb.py', 'run_news_classification.py', 'run_compare_lda.py', ] order = core_order + tutorials_order + howto_order files = sorted(os.listdir(source_dir)) def key(arg): try: return order.index(arg) except ValueError: return files.index(arg) return key import sphinx_gallery.sorting sphinx_gallery_conf = { 'examples_dirs': 'gallery', # path to your example scripts 'gallery_dirs': 'auto_examples', # path where to save gallery generated examples 'show_memory': True, 'filename_pattern': 'run', 'subsection_order': sphinx_gallery.sorting.ExplicitOrder( [ 'gallery/core', 'gallery/tutorials', 'gallery/howtos', 'gallery/other', ], ), 'within_subsection_order': sort_key, }	9,350	Python	.py	231	37.640693	107	0.700895	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,969	check_gallery.py	piskvorky_gensim/docs/src/check_gallery.py	"""Check that the cached gallery files are up to date. If they are stale, then Sphinx will attempt to rebuild them from source. When running the documentation build on CI, we want to avoid rebuilding the gallery, because that takes too long. Instead, we use this script to warn the author of the PR that they need to rebuild the docs themselves. """ import hashlib import os import sys def different(path1, path2): with open(path1) as fin: f1 = fin.read() with open(path2) as fin: f2 = fin.read() return f1 != f2 curr_dir = os.path.dirname(__file__) docs_dir = os.path.dirname(curr_dir) src_dir = os.path.dirname(docs_dir) stale = [] for root, dirs, files in os.walk(os.path.join(curr_dir, 'gallery')): for f in files: if f.endswith('.py'): source_path = os.path.join(root, f) cache_path = source_path.replace('docs/src/gallery/', 'docs/src/auto_examples/') rel_source_path = os.path.relpath(source_path, src_dir) rel_cache_path = os.path.relpath(cache_path, src_dir) # # We check two things: # # 1) Actual file content # 2) MD5 checksums # # We check 1) because that's the part that matters to the user - # it's what will appear in the documentation. We check 2) because # that's what Sphinx Gallery relies on to decide what it needs to # rebuild. In practice, only one of these checks is necessary, # but we run them both because it's trivial. # if different(source_path, cache_path): stale.append(f"{rel_source_path} != {rel_cache_path}") continue actual_md5 = hashlib.md5() with open(source_path, 'rb') as fin: actual_md5.update(fin.read()) md5_path = cache_path + '.md5' with open(md5_path) as fin: expected_md5 = fin.read() if actual_md5.hexdigest() != expected_md5: stale.append(f"{rel_source_path} md5 != {rel_cache_path}.md5") if stale: stale = '\n'.join(stale) print(f"""The gallery cache appears stale. Use Github Actions to rebuild the documentation. See the following links for more info: https://github.com/RaRe-Technologies/gensim/actions/workflows/build-docs.yml https://github.com/RaRe-Technologies/gensim/wiki/Rebuilding-documentation Alternatively, rebuild the documentation locally using the following commands from the gensim root subdirectory: pip install -e .[docs] make -C docs/src html and then run `git add docs/src/auto_examples` to update the cache. Stale files: {stale} """, file=sys.stderr) sys.exit(1)	2,772	Python	.py	63	36.174603	112	0.641397	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,970	run_doc.py	piskvorky_gensim/docs/src/gallery/howtos/run_doc.py	r""" How to Author Gensim Documentation ================================== How to author documentation for Gensim. """ ############################################################################### # Background # ---------- # # Gensim is a large project with a wide range of functionality. # Unfortunately, not all of this functionality is documented well, and some of it is not documented at all. # Without good documentation, users are unable to unlock Gensim's full potential. # Therefore, authoring new documentation and improving existing documentation is of great value to the Gensim project. # # If you implement new functionality in Gensim, please include helpful documentation. # By "helpful", we mean that your documentation answers questions that Gensim users may have. # For example: # # - What is this new functionality? # - Why is it important? # - How is it relevant to Gensim? # - What can I do with it? What are some real-world applications? # - How do I use it to achieve those things? # - ... and others (if you can think of them, please add them here) # # Before you author documentation, I suggest reading # `"What nobody tells you about documentation" <https://www.divio.com/blog/documentation/>`__ # or watching its `accompanying video <https://www.youtube.com/watch?v=t4vKPhjcMZg>`__ # (or even both, if you're really keen). # # The summary of the above presentation is: there are four distinct kinds of documentation, and you really need them all: # # 1. Tutorials # 2. Howto guides # 3. Explanations # 4. References # # Each kind has its own intended audience, purpose, and writing style. # When you make a PR with new functionality, please consider authoring each kind of documentation. # At the very least, you will (indirectly) author reference documentation through module, class and function docstrings. # # Mechanisms # ---------- # # We keep our documentation as individual Python scripts. # These scripts live under :file:`docs/src/gallery` in one of several subdirectories: # # - core: core tutorials. We try to keep this part small, avoid putting stuff here. # - tutorials: tutorials. # - howtos: howto guides. # # Pick a subdirectory and save your script under it. # Prefix the name of the script with ``run_``: this way, the the documentation builder will run your script each time it builds our docs. # # The contents of the script are straightforward. # At the very top, you need a docstring describing what your script does. r""" Title ===== Brief description. """ ############################################################################### # The title is what will show up in the gallery. # Keep this short and descriptive. # # The description will appear as a tooltip in the gallery. # When people mouse-over the title, they will see the description. # Keep this short too. # ############################################################################### # The rest of the script is Python, formatted in a special way so that Sphinx Gallery can parse it. # The most important properties of this format are: # # - Sphinx Gallery will split your script into blocks # - A block can be Python source or RST-formatted comments # - To indicate that a block is in RST, prefix it with a line of 80 hash (#) characters. # - All other blocks will be interpreted as Python source # # Read `this link <https://sphinx-gallery.github.io/syntax.html>`__ for more details. # If you need further examples, check out other ``gensim`` tutorials and guides. # All of them (including this one!) have a download link at the bottom of the page, which exposes the Python source they were generated from. # # You should be able to run your script directly from the command line:: # # python myscript.py # # and it should run to completion without error, occasionally printing stuff to standard output. # ############################################################################### # Authoring Workflow # ------------------ # # There are several ways to author documentation. # The simplest and most straightforward is to author your ``script.py`` from scratch. # You'll have the following cycle: # # 1. Make changes # 2. Run ``python script.py`` # 3. Check standard output, standard error and return code # 4. If everything works well, stop. # 5. Otherwise, go back to step 1). # # If the above is not your cup of tea, you can also author your documentation as a Jupyter notebook. # This is a more flexible approach that enables you to tweak parts of the documentation and re-run them as necessary. # # Once you're happy with the notebook, convert it to a script.py. # There's a helpful `script <https://github.com/RaRe-Technologies/gensim/blob/develop/docs/src/tools/to_python.py>`__ that will do it for you. # To use it:: # # python to_python.py < notebook.ipynb > script.py # # You may have to touch up the resulting ``script.py``. # More specifically: # # - Update the title # - Update the description # - Fix any issues that the markdown-to-RST converter could not deal with # # Once your script.py works, put it in a suitable subdirectory. # Please don't include your original Jupyter notebook in the repository - we won't be using it. ############################################################################### # Correctness # ----------- # # Incorrect documentation can be worse than no documentation at all. # Take the following steps to ensure correctness: # # - Run Python's doctest module on your docstrings # - Run your documentation scripts from scratch, removing any temporary files/results # # Using data in your documentation # -------------------------------- # # Some parts of the documentation require real-world data to be useful. # For example, you may need more than just a toy example to demonstrate the benefits of one model over another. # This subsection provides some tips for including data in your documentation. # # If possible, use data available via Gensim's # `downloader API <https://radimrehurek.com/gensim/gensim_numfocus/auto_examples/010_tutorials/run_downloader_api.html>`__. # This will reduce the risk of your documentation becoming obsolete because required data is no longer available. # # Use the smallest possible dataset: avoid making people unnecessarily load large datasets and models. # This will make your documentation faster to run and easier for people to use (they can modify your examples and re-run them quickly). # # Finalizing your contribution # ---------------------------- # # First, get Sphinx Gallery to build your documentation:: # # make --directory docs/src html # # This can take a while if your documentation uses a large dataset, or if you've changed many other tutorials or guides. # Once this completes successfully, open ``docs/auto_examples/index.html`` in your browser. # You should see your new tutorial or guide in the gallery. # # Once your documentation script is working correctly, it's time to add it to the git repository:: # # git add docs/src/gallery/tutorials/run_example.py # git add docs/src/auto_examples/tutorials/run_example.{py,py.md5,rst,ipynb} # git add docs/src/auto_examples/howtos/sg_execution_times.rst # git commit -m "enter a helpful commit message here" # git push origin branchname # # .. Note:: # You may be wondering what all those other files are. # Sphinx Gallery puts a copy of your Python script in ``auto_examples/tutorials``. # The .md5 contains MD5 hash of the script to enable easy detection of modifications. # Gallery also generates .rst (RST for Sphinx) and .ipynb (Jupyter notebook) files from the script. # Finally, ``sg_execution_times.rst`` contains the time taken to run each example. # # Finally, open a PR at `github <https://github.com/RaRe-Technologies/gensim>`__. # One of our friendly maintainers will review it, make suggestions, and eventually merge it. # Your documentation will then appear in the `gallery <https://radimrehurek.com/gensim/auto_examples/index.html>`__, # alongside the rest of the examples. Thanks a lot!	8,054	Python	.py	174	45.241379	142	0.713542	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,971	run_doc2vec_imdb.py	piskvorky_gensim/docs/src/gallery/howtos/run_doc2vec_imdb.py	r""" How to reproduce the doc2vec 'Paragraph Vector' paper ===================================================== Shows how to reproduce results of the "Distributed Representation of Sentences and Documents" paper by Le and Mikolov using Gensim. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # Introduction # ------------ # # This guide shows you how to reproduce the results of the paper by `Le and # Mikolov 2014 <https://arxiv.org/pdf/1405.4053.pdf>`_ using Gensim. While the # entire paper is worth reading (it's only 9 pages), we will be focusing on # Section 3.2: "Beyond One Sentence - Sentiment Analysis with the IMDB # dataset". # # This guide follows the following steps: # # #. Load the IMDB dataset # #. Train a variety of Doc2Vec models on the dataset # #. Evaluate the performance of each model using a logistic regression # #. Examine some of the results directly: # # When examining results, we will look for answers for the following questions: # # #. Are inferred vectors close to the precalculated ones? # #. Do close documents seem more related than distant ones? # #. Do the word vectors show useful similarities? # #. Are the word vectors from this dataset any good at analogies? # # Load corpus # ----------- # # Our data for the tutorial will be the `IMDB archive # <http://ai.stanford.edu/~amaas/data/sentiment/>`_. # If you're not familiar with this dataset, then here's a brief intro: it # contains several thousand movie reviews. # # Each review is a single line of text containing multiple sentences, for example: # # ``` # One of the best movie-dramas I have ever seen. We do a lot of acting in the # church and this is one that can be used as a resource that highlights all the # good things that actors can do in their work. I highly recommend this one, # especially for those who have an interest in acting, as a "must see." # ``` # # These reviews will be the documents that we will work with in this tutorial. # There are 100 thousand reviews in total. # # #. 25k reviews for training (12.5k positive, 12.5k negative) # #. 25k reviews for testing (12.5k positive, 12.5k negative) # #. 50k unlabeled reviews # # Out of 100k reviews, 50k have a label: either positive (the reviewer liked # the movie) or negative. # The remaining 50k are unlabeled. # # Our first task will be to prepare the dataset. # # More specifically, we will: # # #. Download the tar.gz file (it's only 84MB, so this shouldn't take too long) # #. Unpack it and extract each movie review # #. Split the reviews into training and test datasets # # First, let's define a convenient datatype for holding data for a single document: # # * words: The text of the document, as a ``list`` of words. # * tags: Used to keep the index of the document in the entire dataset. # * split: one of ``train``\ , ``test`` or ``extra``. Determines how the document will be used (for training, testing, etc). # * sentiment: either 1 (positive), 0 (negative) or None (unlabeled document). # # This data type is helpful for later evaluation and reporting. # In particular, the ``index`` member will help us quickly and easily retrieve the vectors for a document from a model. # import collections SentimentDocument = collections.namedtuple('SentimentDocument', 'words tags split sentiment') ############################################################################### # We can now proceed with loading the corpus. import io import re import tarfile import os.path import smart_open import gensim.utils def download_dataset(url='http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'): fname = url.split('/')[-1] if os.path.isfile(fname): return fname # Download the file to local storage first. try: kwargs = { 'compression': smart_open.compression.NO_COMPRESSION } fin = smart_open.open(url, "rb", kwargs) except (AttributeError, TypeError): kwargs = { 'ignore_ext': True } fin = smart_open.open(url, "rb", kwargs) if fin: with smart_open.open(fname, 'wb', *kwargs) as fout: while True: buf = fin.read(io.DEFAULT_BUFFER_SIZE) if not buf: break fout.write(buf) fin.close() return fname def create_sentiment_document(name, text, index): _, split, sentiment_str, _ = name.split('/') sentiment = {'pos': 1.0, 'neg': 0.0, 'unsup': None}[sentiment_str] if sentiment is None: split = 'extra' tokens = gensim.utils.to_unicode(text).split() return SentimentDocument(tokens, [index], split, sentiment) def extract_documents(): fname = download_dataset() index = 0 with tarfile.open(fname, mode='r:gz') as tar: for member in tar.getmembers(): if re.match(r'aclImdb/(train\|test)/(pos\|neg\|unsup)/\d+_\d+.txt$', member.name): member_bytes = tar.extractfile(member).read() member_text = member_bytes.decode('utf-8', errors='replace') assert member_text.count('\n') == 0 yield create_sentiment_document(member.name, member_text, index) index += 1 alldocs = list(extract_documents()) ############################################################################### # Here's what a single document looks like. print(alldocs[27]) ############################################################################### # Extract our documents and split into training/test sets. train_docs = [doc for doc in alldocs if doc.split == 'train'] test_docs = [doc for doc in alldocs if doc.split == 'test'] print(f'{len(alldocs)} docs: {len(train_docs)} train-sentiment, {len(test_docs)} test-sentiment') ############################################################################### # Set-up Doc2Vec Training & Evaluation Models # ------------------------------------------- # # We approximate the experiment of Le & Mikolov `"Distributed Representations # of Sentences and Documents" # <http://cs.stanford.edu/~quocle/paragraph_vector.pdf>`_ with guidance from # Mikolov's `example go.sh # <https://groups.google.com/g/word2vec-toolkit/c/Q49FIrNOQRo/m/J6KG8mUj45sJ>`_:: # # ./word2vec -train ../alldata-id.txt -output vectors.txt -cbow 0 -size 100 -window 10 -negative 5 -hs 0 -sample 1e-4 -threads 40 -binary 0 -iter 20 -min-count 1 -sentence-vectors 1 # # We vary the following parameter choices: # # 100-dimensional vectors, as the 400-d vectors of the paper take a lot of # memory and, in our tests of this task, don't seem to offer much benefit # * Similarly, frequent word subsampling seems to decrease sentiment-prediction # accuracy, so it's left out # * ``cbow=0`` means skip-gram which is equivalent to the paper's 'PV-DBOW' # mode, matched in gensim with ``dm=0`` # * Added to that DBOW model are two DM models, one which averages context # vectors (\ ``dm_mean``\ ) and one which concatenates them (\ ``dm_concat``\ , # resulting in a much larger, slower, more data-hungry model) # * A ``min_count=2`` saves quite a bit of model memory, discarding only words # that appear in a single doc (and are thus no more expressive than the # unique-to-each doc vectors themselves) # import multiprocessing from collections import OrderedDict import gensim.models.doc2vec assert gensim.models.doc2vec.FAST_VERSION > -1, "This will be painfully slow otherwise" from gensim.models.doc2vec import Doc2Vec common_kwargs = dict( vector_size=100, epochs=20, min_count=2, sample=0, workers=multiprocessing.cpu_count(), negative=5, hs=0, ) simple_models = [ # PV-DBOW plain Doc2Vec(dm=0, common_kwargs), # PV-DM w/ default averaging; a higher starting alpha may improve CBOW/PV-DM modes Doc2Vec(dm=1, window=10, alpha=0.05, comment='alpha=0.05', common_kwargs), # PV-DM w/ concatenation - big, slow, experimental mode # window=5 (both sides) approximates paper's apparent 10-word total window size Doc2Vec(dm=1, dm_concat=1, window=5, *common_kwargs), ] for model in simple_models: model.build_vocab(alldocs) print(f"{model} vocabulary scanned & state initialized") models_by_name = OrderedDict((str(model), model) for model in simple_models) ############################################################################### # Le and Mikolov note that combining a paragraph vector from Distributed Bag of # Words (DBOW) and Distributed Memory (DM) improves performance. We will # follow, pairing the models together for evaluation. Here, we concatenate the # paragraph vectors obtained from each model with the help of a thin wrapper # class included in a gensim test module. (Note that this a separate, later # concatenation of output-vectors than the kind of input-window-concatenation # enabled by the ``dm_concat=1`` mode above.) # from gensim.test.test_doc2vec import ConcatenatedDoc2Vec models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[0], simple_models[1]]) models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[0], simple_models[2]]) ############################################################################### # Predictive Evaluation Methods # ----------------------------- # # Given a document, our ``Doc2Vec`` models output a vector representation of the document. # How useful is a particular model? # In case of sentiment analysis, we want the output vector to reflect the sentiment in the input document. # So, in vector space, positive documents should be distant from negative documents. # # We train a logistic regression from the training set: # # - regressors (inputs): document vectors from the Doc2Vec model # - target (outpus): sentiment labels # # So, this logistic regression will be able to predict sentiment given a document vector. # # Next, we test our logistic regression on the test set, and measure the rate of errors (incorrect predictions). # If the document vectors from the Doc2Vec model reflect the actual sentiment well, the error rate will be low. # # Therefore, the error rate of the logistic regression is indication of how well* the given Doc2Vec model represents documents as vectors. # We can then compare different ``Doc2Vec`` models by looking at their error rates. # import numpy as np import statsmodels.api as sm from random import sample def logistic_predictor_from_data(train_targets, train_regressors): """Fit a statsmodel logistic predictor on supplied data""" logit = sm.Logit(train_targets, train_regressors) predictor = logit.fit(disp=0) # print(predictor.summary()) return predictor def error_rate_for_model(test_model, train_set, test_set): """Report error rate on test_doc sentiments, using supplied model and train_docs""" train_targets = [doc.sentiment for doc in train_set] train_regressors = [test_model.dv[doc.tags[0]] for doc in train_set] train_regressors = sm.add_constant(train_regressors) predictor = logistic_predictor_from_data(train_targets, train_regressors) test_regressors = [test_model.dv[doc.tags[0]] for doc in test_set] test_regressors = sm.add_constant(test_regressors) # Predict & evaluate test_predictions = predictor.predict(test_regressors) corrects = sum(np.rint(test_predictions) == [doc.sentiment for doc in test_set]) errors = len(test_predictions) - corrects error_rate = float(errors) / len(test_predictions) return (error_rate, errors, len(test_predictions), predictor) ############################################################################### # Bulk Training & Per-Model Evaluation # ------------------------------------ # # Note that doc-vector training is occurring on all documents of the dataset, # which includes all TRAIN/TEST/DEV docs. Because the native document-order # has similar-sentiment documents in large clumps – which is suboptimal for # training – we work with once-shuffled copy of the training set. # # We evaluate each model's sentiment predictive power based on error rate, and # the evaluation is done for each model. # # (On a 4-core 2.6Ghz Intel Core i7, these 20 passes training and evaluating 3 # main models takes about an hour.) # from collections import defaultdict error_rates = defaultdict(lambda: 1.0) # To selectively print only best errors achieved ############################################################################### # from random import shuffle shuffled_alldocs = alldocs[:] shuffle(shuffled_alldocs) for model in simple_models: print(f"Training {model}") model.train(shuffled_alldocs, total_examples=len(shuffled_alldocs), epochs=model.epochs) print(f"\nEvaluating {model}") err_rate, err_count, test_count, predictor = error_rate_for_model(model, train_docs, test_docs) error_rates[str(model)] = err_rate print("\n%f %s\n" % (err_rate, model)) for model in [models_by_name['dbow+dmm'], models_by_name['dbow+dmc']]: print(f"\nEvaluating {model}") err_rate, err_count, test_count, predictor = error_rate_for_model(model, train_docs, test_docs) error_rates[str(model)] = err_rate print(f"\n{err_rate} {model}\n") ############################################################################### # Achieved Sentiment-Prediction Accuracy # -------------------------------------- # Compare error rates achieved, best-to-worst print("Err_rate Model") for rate, name in sorted((rate, name) for name, rate in error_rates.items()): print(f"{rate} {name}") ############################################################################### # In our testing, contrary to the results of the paper, on this problem, # PV-DBOW alone performs as good as anything else. Concatenating vectors from # different models only sometimes offers a tiny predictive improvement – and # stays generally close to the best-performing solo model included. # # The best results achieved here are just around 10% error rate, still a long # way from the paper's reported 7.42% error rate. # # (Other trials not shown, with larger vectors and other changes, also don't # come close to the paper's reported value. Others around the net have reported # a similar inability to reproduce the paper's best numbers. The PV-DM/C mode # improves a bit with many more training epochs – but doesn't reach parity with # PV-DBOW.) # ############################################################################### # Examining Results # ----------------- # # Let's look for answers to the following questions: # # #. Are inferred vectors close to the precalculated ones? # #. Do close documents seem more related than distant ones? # #. Do the word vectors show useful similarities? # #. Are the word vectors from this dataset any good at analogies? # ############################################################################### # Are inferred vectors close to the precalculated ones? # ----------------------------------------------------- doc_id = np.random.randint(len(simple_models[0].dv)) # Pick random doc; re-run cell for more examples print(f'for doc {doc_id}...') for model in simple_models: inferred_docvec = model.infer_vector(alldocs[doc_id].words) print(f'{model}:\n {model.dv.most_similar([inferred_docvec], topn=3)}') ############################################################################### # (Yes, here the stored vector from 20 epochs of training is usually one of the # closest to a freshly-inferred vector for the same words. Defaults for # inference may benefit from tuning for each dataset or model parameters.) # ############################################################################### # Do close documents seem more related than distant ones? # ------------------------------------------------------- import random doc_id = np.random.randint(len(simple_models[0].dv)) # pick random doc, re-run cell for more examples model = random.choice(simple_models) # and a random model sims = model.dv.most_similar(doc_id, topn=len(model.dv)) # get all similar documents print(f'TARGET ({doc_id}): «{" ".join(alldocs[doc_id].words)}»\n') print(f'SIMILAR/DISSIMILAR DOCS PER MODEL {model}%s:\n') for label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]: s = sims[index] i = sims[index][0] words = ' '.join(alldocs[i].words) print(f'{label} {s}: «{words}»\n') ############################################################################### # Somewhat, in terms of reviewer tone, movie genre, etc... the MOST # cosine-similar docs usually seem more like the TARGET than the MEDIAN or # LEAST... especially if the MOST has a cosine-similarity > 0.5. Re-run the # cell to try another random target document. # ############################################################################### # Do the word vectors show useful similarities? # --------------------------------------------- # import random word_models = simple_models[:] def pick_random_word(model, threshold=10): # pick a random word with a suitable number of occurences while True: word = random.choice(model.wv.index_to_key) if model.wv.get_vecattr(word, "count") > threshold: return word target_word = pick_random_word(word_models[0]) # or uncomment below line, to just pick a word from the relevant domain: # target_word = 'comedy/drama' for model in word_models: print(f'target_word: {repr(target_word)} model: {model} similar words:') for i, (word, sim) in enumerate(model.wv.most_similar(target_word, topn=10), 1): print(f' {i}. {sim:.2f} {repr(word)}') print() ############################################################################### # Do the DBOW words look meaningless? That's because the gensim DBOW model # doesn't train word vectors – they remain at their random initialized values – # unless you ask with the ``dbow_words=1`` initialization parameter. Concurrent # word-training slows DBOW mode significantly, and offers little improvement # (and sometimes a little worsening) of the error rate on this IMDB # sentiment-prediction task, but may be appropriate on other tasks, or if you # also need word-vectors. # # Words from DM models tend to show meaningfully similar words when there are # many examples in the training data (as with 'plot' or 'actor'). (All DM modes # inherently involve word-vector training concurrent with doc-vector training.) # ############################################################################### # Are the word vectors from this dataset any good at analogies? # ------------------------------------------------------------- from gensim.test.utils import datapath questions_filename = datapath('questions-words.txt') # Note: this analysis takes many minutes for model in word_models: score, sections = model.wv.evaluate_word_analogies(questions_filename) correct, incorrect = len(sections[-1]['correct']), len(sections[-1]['incorrect']) print(f'{model}: {float(correct*100)/(correct+incorrect):0.2f}%% correct ({correct} of {correct+incorrect}') ############################################################################### # Even though this is a tiny, domain-specific dataset, it shows some meager # capability on the general word analogies – at least for the DM/mean and # DM/concat models which actually train word vectors. (The untrained # random-initialized words of the DBOW model of course fail miserably.) #	19,497	Python	.py	389	47.493573	185	0.656858	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,972	run_compare_lda.py	piskvorky_gensim/docs/src/gallery/howtos/run_compare_lda.py	r""" How to Compare LDA Models ========================= Demonstrates how you can visualize and compare trained topic models. """ # sphinx_gallery_thumbnail_number = 2 import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # # First, clean up the 20 Newsgroups dataset. We will use it to fit LDA. # --------------------------------------------------------------------- # from string import punctuation from nltk import RegexpTokenizer from nltk.stem.porter import PorterStemmer from nltk.corpus import stopwords from sklearn.datasets import fetch_20newsgroups newsgroups = fetch_20newsgroups() eng_stopwords = set(stopwords.words('english')) tokenizer = RegexpTokenizer(r'\s+', gaps=True) stemmer = PorterStemmer() translate_tab = {ord(p): u" " for p in punctuation} def text2tokens(raw_text): """Split the raw_text string into a list of stemmed tokens.""" clean_text = raw_text.lower().translate(translate_tab) tokens = [token.strip() for token in tokenizer.tokenize(clean_text)] tokens = [token for token in tokens if token not in eng_stopwords] stemmed_tokens = [stemmer.stem(token) for token in tokens] return [token for token in stemmed_tokens if len(token) > 2] # skip short tokens dataset = [text2tokens(txt) for txt in newsgroups['data']] # convert a documents to list of tokens from gensim.corpora import Dictionary dictionary = Dictionary(documents=dataset, prune_at=None) dictionary.filter_extremes(no_below=5, no_above=0.3, keep_n=None) # use Dictionary to remove un-relevant tokens dictionary.compactify() d2b_dataset = [dictionary.doc2bow(doc) for doc in dataset] # convert list of tokens to bag of word representation ############################################################################### # # Second, fit two LDA models. # --------------------------- # from gensim.models import LdaMulticore num_topics = 15 lda_fst = LdaMulticore( corpus=d2b_dataset, num_topics=num_topics, id2word=dictionary, workers=4, eval_every=None, passes=10, batch=True, ) lda_snd = LdaMulticore( corpus=d2b_dataset, num_topics=num_topics, id2word=dictionary, workers=4, eval_every=None, passes=20, batch=True, ) ############################################################################### # # Time to visualize, yay! # ----------------------- # # We use two slightly different visualization methods depending on how you're running this tutorial. # If you're running via a Jupyter notebook, then you'll get a nice interactive Plotly heatmap. # If you're viewing the static version of the page, you'll get a similar matplotlib heatmap, but it won't be interactive. # def plot_difference_plotly(mdiff, title="", annotation=None): """Plot the difference between models. Uses plotly as the backend.""" import plotly.graph_objs as go import plotly.offline as py annotation_html = None if annotation is not None: annotation_html = [ [ "+++ {}<br>--- {}".format(", ".join(int_tokens), ", ".join(diff_tokens)) for (int_tokens, diff_tokens) in row ] for row in annotation ] data = go.Heatmap(z=mdiff, colorscale='RdBu', text=annotation_html) layout = go.Layout(width=950, height=950, title=title, xaxis=dict(title="topic"), yaxis=dict(title="topic")) py.iplot(dict(data=[data], layout=layout)) def plot_difference_matplotlib(mdiff, title="", annotation=None): """Helper function to plot difference between models. Uses matplotlib as the backend.""" import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(18, 14)) data = ax.imshow(mdiff, cmap='RdBu_r', origin='lower') plt.title(title) plt.colorbar(data) try: get_ipython() import plotly.offline as py except Exception: # # Fall back to matplotlib if we're not in a notebook, or if plotly is # unavailable for whatever reason. # plot_difference = plot_difference_matplotlib else: py.init_notebook_mode() plot_difference = plot_difference_plotly ############################################################################### # # Gensim can help you visualise the differences between topics. For this purpose, you can use the ``diff()`` method of LdaModel. # # ``diff()`` returns a matrix with distances mdiff and a matrix with annotations annotation. Read the docstring for more detailed info. # # In each mdiff[i][j] cell you'll find a distance between topic_i from the first model and topic_j from the second model. # # In each annotation[i][j] cell you'll find [tokens from intersection, tokens from difference between topic_i from first model and topic_j from the second model. # print(LdaMulticore.diff.__doc__) ############################################################################### # # Case 1: How topics within ONE model correlate with each other. # -------------------------------------------------------------- # ############################################################################### # # Short description: # # * x-axis - topic; # # * y-axis - topic; # # .. role:: raw-html-m2r(raw) # :format: html # # * :raw-html-m2r:`<span style="color:red">almost red cell</span>` - strongly decorrelated topics; # # .. role:: raw-html-m2r(raw) # :format: html # # * :raw-html-m2r:`<span style="color:blue">almost blue cell</span>` - strongly correlated topics. # # In an ideal world, we would like to see different topics decorrelated between themselves. # In this case, our matrix would look like this: # import numpy as np mdiff = np.ones((num_topics, num_topics)) np.fill_diagonal(mdiff, 0.) plot_difference(mdiff, title="Topic difference (one model) in ideal world") ############################################################################### # # Unfortunately, in real life, not everything is so good, and the matrix looks different. # ############################################################################### # # Short description (interactive annotations only): # # * ``+++ make, world, well`` - words from the intersection of topics = present in both topics; # # * ``--- money, day, still`` - words from the symmetric difference of topics = present in one topic but not the other. # mdiff, annotation = lda_fst.diff(lda_fst, distance='jaccard', num_words=50) plot_difference(mdiff, title="Topic difference (one model) [jaccard distance]", annotation=annotation) ############################################################################### # # If you compare a model with itself, you want to see as many red elements as # possible (except on the diagonal). With this picture, you can look at the # "not very red elements" and understand which topics in the model are very # similar and why (you can read annotation if you move your pointer to cell). # # Jaccard is a stable and robust distance function, but sometimes not sensitive # enough. Let's try to use the Hellinger distance instead. # mdiff, annotation = lda_fst.diff(lda_fst, distance='hellinger', num_words=50) plot_difference(mdiff, title="Topic difference (one model)[hellinger distance]", annotation=annotation) ############################################################################### # # You see that everything has become worse, but remember that everything depends on the task. # # Choose a distance function that matches your upstream task better: what kind of "similarity" is # relevant to you. From my (Ivan's) experience, Jaccard is fine. # ############################################################################### # # Case 2: How topics from DIFFERENT models correlate with each other. # ------------------------------------------------------------------- # ############################################################################### # # Sometimes, we want to look at the patterns between two different models and compare them. # # You can do this by constructing a matrix with the difference. # mdiff, annotation = lda_fst.diff(lda_snd, distance='jaccard', num_words=50) plot_difference(mdiff, title="Topic difference (two models)[jaccard distance]", annotation=annotation) ############################################################################### # # Looking at this matrix, you can find similar and different topics between the two models. # The plot also includes relevant tokens describing the topics' intersection and difference. #	8,569	Python	.py	189	42.89418	177	0.622494	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,973	run_downloader_api.py	piskvorky_gensim/docs/src/gallery/howtos/run_downloader_api.py	r""" How to download pre-trained models and corpora ============================================== Demonstrates simple and quick access to common corpora and pretrained models. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # One of Gensim's features is simple and easy access to common data. # The `gensim-data <https://github.com/RaRe-Technologies/gensim-data>`_ project stores a # variety of corpora and pretrained models. # Gensim has a :py:mod:`gensim.downloader` module for programmatically accessing this data. # This module leverages a local cache (in user's home folder, by default) that # ensures data is downloaded at most once. # # This tutorial: # # * Downloads the text8 corpus, unless it is already on your local machine # * Trains a Word2Vec model from the corpus (see :ref:`sphx_glr_auto_examples_tutorials_run_doc2vec_lee.py` for a detailed tutorial) # * Leverages the model to calculate word similarity # * Demonstrates using the API to load other models and corpora # # Let's start by importing the api module. # import gensim.downloader as api ############################################################################### # # Now, let's download the text8 corpus and load it as a Python object # that supports streamed access. # corpus = api.load('text8') ############################################################################### # In this case, our corpus is an iterable. # If you look under the covers, it has the following definition: import inspect print(inspect.getsource(corpus.__class__)) ############################################################################### # For more details, look inside the file that defines the Dataset class for your particular resource. # print(inspect.getfile(corpus.__class__)) ############################################################################### # # With the corpus has been downloaded and loaded, let's use it to train a word2vec model. # from gensim.models.word2vec import Word2Vec model = Word2Vec(corpus) ############################################################################### # # Now that we have our word2vec model, let's find words that are similar to 'tree'. # print(model.wv.most_similar('tree')) ############################################################################### # # You can use the API to download several different corpora and pretrained models. # Here's how to list all resources available in gensim-data: # import json info = api.info() print(json.dumps(info, indent=4)) ############################################################################### # There are two types of data resources: corpora and models. print(info.keys()) ############################################################################### # Let's have a look at the available corpora: for corpus_name, corpus_data in sorted(info['corpora'].items()): print( '%s (%d records): %s' % ( corpus_name, corpus_data.get('num_records', -1), corpus_data['description'][:40] + '...', ) ) ############################################################################### # ... and the same for models: for model_name, model_data in sorted(info['models'].items()): print( '%s (%d records): %s' % ( model_name, model_data.get('num_records', -1), model_data['description'][:40] + '...', ) ) ############################################################################### # # If you want to get detailed information about a model/corpus, use: # fake_news_info = api.info('fake-news') print(json.dumps(fake_news_info, indent=4)) ############################################################################### # # Sometimes, you do not want to load a model into memory. Instead, you can request # just the filesystem path to the model. For that, use: # print(api.load('glove-wiki-gigaword-50', return_path=True)) ############################################################################### # # If you want to load the model to memory, then: # model = api.load("glove-wiki-gigaword-50") model.most_similar("glass") ############################################################################### # # For corpora, the corpus is never loaded to memory, all corpora are iterables wrapped in # a special class ``Dataset``, with an ``__iter__`` method. #	4,515	Python	.py	105	40.580952	132	0.534125	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,974	run_corpora_and_vector_spaces.py	piskvorky_gensim/docs/src/gallery/core/run_corpora_and_vector_spaces.py	r""" Corpora and Vector Spaces ========================= Demonstrates transforming text into a vector space representation. Also introduces corpus streaming and persistence to disk in various formats. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # First, letâ€™s create a small corpus of nine short documents [1]_: # # .. _second example: # # From Strings to Vectors # ------------------------ # # This time, let's start from documents represented as strings: # documents = [ "Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in trees", "Graph minors IV Widths of trees and well quasi ordering", "Graph minors A survey", ] ############################################################################### # This is a tiny corpus of nine documents, each consisting of only a single sentence. # # First, let's tokenize the documents, remove common words (using a toy stoplist) # as well as words that only appear once in the corpus: from pprint import pprint # pretty-printer from collections import defaultdict # remove common words and tokenize stoplist = set('for a of the and to in'.split()) texts = [ [word for word in document.lower().split() if word not in stoplist] for document in documents ] # remove words that appear only once frequency = defaultdict(int) for text in texts: for token in text: frequency[token] += 1 texts = [ [token for token in text if frequency[token] > 1] for text in texts ] pprint(texts) ############################################################################### # Your way of processing the documents will likely vary; here, I only split on whitespace # to tokenize, followed by lowercasing each word. In fact, I use this particular # (simplistic and inefficient) setup to mimic the experiment done in Deerwester et al.'s # original LSA article [1]_. # # The ways to process documents are so varied and application- and language-dependent that I # decided to not constrain them by any interface. Instead, a document is represented # by the features extracted from it, not by its "surface" string form: how you get to # the features is up to you. Below I describe one common, general-purpose approach (called # :dfn:`bag-of-words`), but keep in mind that different application domains call for # different features, and, as always, it's `garbage in, garbage out <https://en.wikipedia.org/wiki/Garbage_In,_Garbage_Out>`_... # # To convert documents to vectors, we'll use a document representation called # `bag-of-words <https://en.wikipedia.org/wiki/Bag_of_words>`_. In this representation, # each document is represented by one vector where each vector element represents # a question-answer pair, in the style of: # # - Question: How many times does the word `system` appear in the document? # - Answer: Once. # # It is advantageous to represent the questions only by their (integer) ids. The mapping # between the questions and ids is called a dictionary: from gensim import corpora dictionary = corpora.Dictionary(texts) dictionary.save('/tmp/deerwester.dict') # store the dictionary, for future reference print(dictionary) ############################################################################### # Here we assigned a unique integer id to all words appearing in the corpus with the # :class:`gensim.corpora.dictionary.Dictionary` class. This sweeps across the texts, collecting word counts # and relevant statistics. In the end, we see there are twelve distinct words in the # processed corpus, which means each document will be represented by twelve numbers (ie., by a 12-D vector). # To see the mapping between words and their ids: print(dictionary.token2id) ############################################################################### # To actually convert tokenized documents to vectors: new_doc = "Human computer interaction" new_vec = dictionary.doc2bow(new_doc.lower().split()) print(new_vec) # the word "interaction" does not appear in the dictionary and is ignored ############################################################################### # The function :func:`doc2bow` simply counts the number of occurrences of # each distinct word, converts the word to its integer word id # and returns the result as a sparse vector. The sparse vector ``[(0, 1), (1, 1)]`` # therefore reads: in the document `"Human computer interaction"`, the words `computer` # (id 0) and `human` (id 1) appear once; the other ten dictionary words appear (implicitly) zero times. corpus = [dictionary.doc2bow(text) for text in texts] corpora.MmCorpus.serialize('/tmp/deerwester.mm', corpus) # store to disk, for later use print(corpus) ############################################################################### # By now it should be clear that the vector feature with ``id=10`` stands for the question "How many # times does the word `graph` appear in the document?" and that the answer is "zero" for # the first six documents and "one" for the remaining three. # # .. _corpus_streaming_tutorial: # # Corpus Streaming -- One Document at a Time # ------------------------------------------- # # Note that `corpus` above resides fully in memory, as a plain Python list. # In this simple example, it doesn't matter much, but just to make things clear, # let's assume there are millions of documents in the corpus. Storing all of them in RAM won't do. # Instead, let's assume the documents are stored in a file on disk, one document per line. Gensim # only requires that a corpus must be able to return one document vector at a time: # from smart_open import open # for transparently opening remote files class MyCorpus: def __iter__(self): for line in open('https://radimrehurek.com/mycorpus.txt'): # assume there's one document per line, tokens separated by whitespace yield dictionary.doc2bow(line.lower().split()) ############################################################################### # The full power of Gensim comes from the fact that a corpus doesn't have to be # a ``list``, or a ``NumPy`` array, or a ``Pandas`` dataframe, or whatever. # Gensim accepts any object that, when iterated over, successively yields # documents. # This flexibility allows you to create your own corpus classes that stream the # documents directly from disk, network, database, dataframes... The models # in Gensim are implemented such that they don't require all vectors to reside # in RAM at once. You can even create the documents on the fly! ############################################################################### # Download the sample `mycorpus.txt file here <https://radimrehurek.com/mycorpus.txt>`_. The assumption that # each document occupies one line in a single file is not important; you can mold # the `__iter__` function to fit your input format, whatever it is. # Walking directories, parsing XML, accessing the network... # Just parse your input to retrieve a clean list of tokens in each document, # then convert the tokens via a dictionary to their ids and yield the resulting sparse vector inside `__iter__`. corpus_memory_friendly = MyCorpus() # doesn't load the corpus into memory! print(corpus_memory_friendly) ############################################################################### # Corpus is now an object. We didn't define any way to print it, so `print` just outputs address # of the object in memory. Not very useful. To see the constituent vectors, let's # iterate over the corpus and print each document vector (one at a time): for vector in corpus_memory_friendly: # load one vector into memory at a time print(vector) ############################################################################### # Although the output is the same as for the plain Python list, the corpus is now much # more memory friendly, because at most one vector resides in RAM at a time. Your # corpus can now be as large as you want. # # Similarly, to construct the dictionary without loading all texts into memory: # collect statistics about all tokens dictionary = corpora.Dictionary(line.lower().split() for line in open('https://radimrehurek.com/mycorpus.txt')) # remove stop words and words that appear only once stop_ids = [ dictionary.token2id[stopword] for stopword in stoplist if stopword in dictionary.token2id ] once_ids = [tokenid for tokenid, docfreq in dictionary.dfs.items() if docfreq == 1] dictionary.filter_tokens(stop_ids + once_ids) # remove stop words and words that appear only once dictionary.compactify() # remove gaps in id sequence after words that were removed print(dictionary) ############################################################################### # And that is all there is to it! At least as far as bag-of-words representation is concerned. # Of course, what we do with such a corpus is another question; it is not at all clear # how counting the frequency of distinct words could be useful. As it turns out, it isn't, and # we will need to apply a transformation on this simple representation first, before # we can use it to compute any meaningful document vs. document similarities. # Transformations are covered in the next tutorial # (:ref:`sphx_glr_auto_examples_core_run_topics_and_transformations.py`), # but before that, let's briefly turn our attention to corpus persistency. # # .. _corpus-formats: # # Corpus Formats # --------------- # # There exist several file formats for serializing a Vector Space corpus (~sequence of vectors) to disk. # `Gensim` implements them via the streaming corpus interface mentioned earlier: # documents are read from (resp. stored to) disk in a lazy fashion, one document at # a time, without the whole corpus being read into main memory at once. # # One of the more notable file formats is the `Market Matrix format <http://math.nist.gov/MatrixMarket/formats.html>`_. # To save a corpus in the Matrix Market format: # # create a toy corpus of 2 documents, as a plain Python list corpus = [[(1, 0.5)], []] # make one document empty, for the heck of it corpora.MmCorpus.serialize('/tmp/corpus.mm', corpus) ############################################################################### # Other formats include `Joachim's SVMlight format <http://svmlight.joachims.org/>`_, # `Blei's LDA-C format <https://github.com/blei-lab/lda-c>`_ and # `GibbsLDA++ format <https://gibbslda.sourceforge.net/>`_. corpora.SvmLightCorpus.serialize('/tmp/corpus.svmlight', corpus) corpora.BleiCorpus.serialize('/tmp/corpus.lda-c', corpus) corpora.LowCorpus.serialize('/tmp/corpus.low', corpus) ############################################################################### # Conversely, to load a corpus iterator from a Matrix Market file: corpus = corpora.MmCorpus('/tmp/corpus.mm') ############################################################################### # Corpus objects are streams, so typically you won't be able to print them directly: print(corpus) ############################################################################### # Instead, to view the contents of a corpus: # one way of printing a corpus: load it entirely into memory print(list(corpus)) # calling list() will convert any sequence to a plain Python list ############################################################################### # or # another way of doing it: print one document at a time, making use of the streaming interface for doc in corpus: print(doc) ############################################################################### # The second way is obviously more memory-friendly, but for testing and development # purposes, nothing beats the simplicity of calling ``list(corpus)``. # # To save the same Matrix Market document stream in Blei's LDA-C format, corpora.BleiCorpus.serialize('/tmp/corpus.lda-c', corpus) ############################################################################### # In this way, `gensim` can also be used as a memory-efficient I/O format conversion tool: # just load a document stream using one format and immediately save it in another format. # Adding new formats is dead easy, check out the `code for the SVMlight corpus # <https://github.com/piskvorky/gensim/blob/develop/gensim/corpora/svmlightcorpus.py>`_ for an example. # # Compatibility with NumPy and SciPy # ---------------------------------- # # Gensim also contains `efficient utility functions <https://radimrehurek.com/gensim/matutils.html>`_ # to help converting from/to numpy matrices import gensim import numpy as np numpy_matrix = np.random.randint(10, size=[5, 2]) # random matrix as an example corpus = gensim.matutils.Dense2Corpus(numpy_matrix) # numpy_matrix = gensim.matutils.corpus2dense(corpus, num_terms=number_of_corpus_features) ############################################################################### # and from/to `scipy.sparse` matrices import scipy.sparse scipy_sparse_matrix = scipy.sparse.random(5, 2) # random sparse matrix as example corpus = gensim.matutils.Sparse2Corpus(scipy_sparse_matrix) scipy_csc_matrix = gensim.matutils.corpus2csc(corpus) ############################################################################### # What Next # --------- # # Read about :ref:`sphx_glr_auto_examples_core_run_topics_and_transformations.py`. # # References # ---------- # # For a complete reference (Want to prune the dictionary to a smaller size? # Optimize converting between corpora and NumPy/SciPy arrays?), see the :ref:`apiref`. # # .. [1] This is the same corpus as used in # `Deerwester et al. (1990): Indexing by Latent Semantic Analysis <http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf>`_, Table 2. import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('run_corpora_and_vector_spaces.png') imgplot = plt.imshow(img) _ = plt.axis('off')	14,277	Python	.py	259	53.467181	132	0.665664	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,975	run_core_concepts.py	piskvorky_gensim/docs/src/gallery/core/run_core_concepts.py	r""" Core Concepts ============= This tutorial introduces Documents, Corpora, Vectors and Models: the basic concepts and terms needed to understand and use gensim. """ import pprint ############################################################################### # The core concepts of ``gensim`` are: # # 1. :ref:`core_concepts_document`: some text. # 2. :ref:`core_concepts_corpus`: a collection of documents. # 3. :ref:`core_concepts_vector`: a mathematically convenient representation of a document. # 4. :ref:`core_concepts_model`: an algorithm for transforming vectors from one representation to another. # # Let's examine each of these in slightly more detail. # # .. _core_concepts_document: # # Document # -------- # # In Gensim, a document is an object of the `text sequence type <https://docs.python.org/3.7/library/stdtypes.html#text-sequence-type-str>`_ (commonly known as ``str`` in Python 3). # A document could be anything from a short 140 character tweet, a single # paragraph (i.e., journal article abstract), a news article, or a book. # document = "Human machine interface for lab abc computer applications" ############################################################################### # .. _core_concepts_corpus: # # Corpus # ------ # # A corpus is a collection of :ref:`core_concepts_document` objects. # Corpora serve two roles in Gensim: # # 1. Input for training a :ref:`core_concepts_model`. # During training, the models use this training corpus to look for common # themes and topics, initializing their internal model parameters. # # Gensim focuses on unsupervised models so that no human intervention, # such as costly annotations or tagging documents by hand, is required. # # 2. Documents to organize. # After training, a topic model can be used to extract topics from new # documents (documents not seen in the training corpus). # # Such corpora can be indexed for # :ref:`sphx_glr_auto_examples_core_run_similarity_queries.py`, # queried by semantic similarity, clustered etc. # # Here is an example corpus. # It consists of 9 documents, where each document is a string consisting of a single sentence. # text_corpus = [ "Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in trees", "Graph minors IV Widths of trees and well quasi ordering", "Graph minors A survey", ] ############################################################################### # # .. Important:: # The above example loads the entire corpus into memory. # In practice, corpora may be very large, so loading them into memory may be impossible. # Gensim intelligently handles such corpora by streaming them one document at a time. # See :ref:`corpus_streaming_tutorial` for details. # # This is a particularly small example of a corpus for illustration purposes. # Another example could be a list of all the plays written by Shakespeare, list # of all wikipedia articles, or all tweets by a particular person of interest. # # After collecting our corpus, there are typically a number of preprocessing # steps we want to undertake. We'll keep it simple and just remove some # commonly used English words (such as 'the') and words that occur only once in # the corpus. In the process of doing so, we'll tokenize our data. # Tokenization breaks up the documents into words (in this case using space as # a delimiter). # # .. Important:: # There are better ways to perform preprocessing than just lower-casing and # splitting by space. Effective preprocessing is beyond the scope of this # tutorial: if you're interested, check out the # :py:func:`gensim.utils.simple_preprocess` function. # # Create a set of frequent words stoplist = set('for a of the and to in'.split(' ')) # Lowercase each document, split it by white space and filter out stopwords texts = [[word for word in document.lower().split() if word not in stoplist] for document in text_corpus] # Count word frequencies from collections import defaultdict frequency = defaultdict(int) for text in texts: for token in text: frequency[token] += 1 # Only keep words that appear more than once processed_corpus = [[token for token in text if frequency[token] > 1] for text in texts] pprint.pprint(processed_corpus) ############################################################################### # Before proceeding, we want to associate each word in the corpus with a unique # integer ID. We can do this using the :py:class:`gensim.corpora.Dictionary` # class. This dictionary defines the vocabulary of all words that our # processing knows about. # from gensim import corpora dictionary = corpora.Dictionary(processed_corpus) print(dictionary) ############################################################################### # Because our corpus is small, there are only 12 different tokens in this # :py:class:`gensim.corpora.Dictionary`. For larger corpuses, dictionaries that # contains hundreds of thousands of tokens are quite common. # ############################################################################### # .. _core_concepts_vector: # # Vector # ------ # # To infer the latent structure in our corpus we need a way to represent # documents that we can manipulate mathematically. One approach is to represent # each document as a vector of features. # For example, a single feature may be thought of as a question-answer pair: # # 1. How many times does the word splonge appear in the document? Zero. # 2. How many paragraphs does the document consist of? Two. # 3. How many fonts does the document use? Five. # # The question is usually represented only by its integer id (such as `1`, `2` and `3`). # The representation of this document then becomes a series of pairs like ``(1, 0.0), (2, 2.0), (3, 5.0)``. # This is known as a dense vector, because it contains an explicit answer to each of the above questions. # # If we know all the questions in advance, we may leave them implicit # and simply represent the document as ``(0, 2, 5)``. # This sequence of answers is the vector for our document (in this case a 3-dimensional dense vector). # For practical purposes, only questions to which the answer is (or # can be converted to) a single floating point number are allowed in Gensim. # # In practice, vectors often consist of many zero values. # To save memory, Gensim omits all vector elements with value 0.0. # The above example thus becomes ``(2, 2.0), (3, 5.0)``. # This is known as a sparse vector or bag-of-words vector. # The values of all missing features in this sparse representation can be unambiguously resolved to zero, ``0.0``. # # Assuming the questions are the same, we can compare the vectors of two different documents to each other. # For example, assume we are given two vectors ``(0.0, 2.0, 5.0)`` and ``(0.1, 1.9, 4.9)``. # Because the vectors are very similar to each other, we can conclude that the documents corresponding to those vectors are similar, too. # Of course, the correctness of that conclusion depends on how well we picked the questions in the first place. # # Another approach to represent a document as a vector is the bag-of-words # model. # Under the bag-of-words model each document is represented by a vector # containing the frequency counts of each word in the dictionary. # For example, assume we have a dictionary containing the words # ``['coffee', 'milk', 'sugar', 'spoon']``. # A document consisting of the string ``"coffee milk coffee"`` would then # be represented by the vector ``[2, 1, 0, 0]`` where the entries of the vector # are (in order) the occurrences of "coffee", "milk", "sugar" and "spoon" in # the document. The length of the vector is the number of entries in the # dictionary. One of the main properties of the bag-of-words model is that it # completely ignores the order of the tokens in the document that is encoded, # which is where the name bag-of-words comes from. # # Our processed corpus has 12 unique words in it, which means that each # document will be represented by a 12-dimensional vector under the # bag-of-words model. We can use the dictionary to turn tokenized documents # into these 12-dimensional vectors. We can see what these IDs correspond to: # pprint.pprint(dictionary.token2id) ############################################################################### # For example, suppose we wanted to vectorize the phrase "Human computer # interaction" (note that this phrase was not in our original corpus). We can # create the bag-of-word representation for a document using the ``doc2bow`` # method of the dictionary, which returns a sparse representation of the word # counts: # new_doc = "Human computer interaction" new_vec = dictionary.doc2bow(new_doc.lower().split()) print(new_vec) ############################################################################### # The first entry in each tuple corresponds to the ID of the token in the # dictionary, the second corresponds to the count of this token. # # Note that "interaction" did not occur in the original corpus and so it was # not included in the vectorization. Also note that this vector only contains # entries for words that actually appeared in the document. Because any given # document will only contain a few words out of the many words in the # dictionary, words that do not appear in the vectorization are represented as # implicitly zero as a space saving measure. # # We can convert our entire original corpus to a list of vectors: # bow_corpus = [dictionary.doc2bow(text) for text in processed_corpus] pprint.pprint(bow_corpus) ############################################################################### # Note that while this list lives entirely in memory, in most applications you # will want a more scalable solution. Luckily, ``gensim`` allows you to use any # iterator that returns a single document vector at a time. See the # documentation for more details. # # .. Important:: # The distinction between a document and a vector is that the former is text, # and the latter is a mathematically convenient representation of the text. # Sometimes, people will use the terms interchangeably: for example, given # some arbitrary document ``D``, instead of saying "the vector that # corresponds to document ``D``", they will just say "the vector ``D``" or # the "document ``D``". This achieves brevity at the cost of ambiguity. # # As long as you remember that documents exist in document space, and that # vectors exist in vector space, the above ambiguity is acceptable. # # .. Important:: # Depending on how the representation was obtained, two different documents # may have the same vector representations. # # .. _core_concepts_model: # # Model # ----- # # Now that we have vectorized our corpus we can begin to transform it using # models. We use model as an abstract term referring to a transformation from # one document representation to another. In ``gensim`` documents are # represented as vectors so a model can be thought of as a transformation # between two vector spaces. The model learns the details of this # transformation during training, when it reads the training # :ref:`core_concepts_corpus`. # # One simple example of a model is `tf-idf # <https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_. The tf-idf model # transforms vectors from the bag-of-words representation to a vector space # where the frequency counts are weighted according to the relative rarity of # each word in the corpus. # # Here's a simple example. Let's initialize the tf-idf model, training it on # our corpus and transforming the string "system minors": # from gensim import models # train the model tfidf = models.TfidfModel(bow_corpus) # transform the "system minors" string words = "system minors".lower().split() print(tfidf[dictionary.doc2bow(words)]) ############################################################################### # The ``tfidf`` model again returns a list of tuples, where the first entry is # the token ID and the second entry is the tf-idf weighting. Note that the ID # corresponding to "system" (which occurred 4 times in the original corpus) has # been weighted lower than the ID corresponding to "minors" (which only # occurred twice). # # You can save trained models to disk and later load them back, either to # continue training on new training documents or to transform new documents. # # ``gensim`` offers a number of different models/transformations. # For more, see :ref:`sphx_glr_auto_examples_core_run_topics_and_transformations.py`. # # Once you've created the model, you can do all sorts of cool stuff with it. # For example, to transform the whole corpus via TfIdf and index it, in # preparation for similarity queries: # from gensim import similarities index = similarities.SparseMatrixSimilarity(tfidf[bow_corpus], num_features=12) ############################################################################### # and to query the similarity of our query document ``query_document`` against every document in the corpus: query_document = 'system engineering'.split() query_bow = dictionary.doc2bow(query_document) sims = index[tfidf[query_bow]] print(list(enumerate(sims))) ############################################################################### # How to read this output? # Document 3 has a similarity score of 0.718=72%, document 2 has a similarity score of 42% etc. # We can make this slightly more readable by sorting: for document_number, score in sorted(enumerate(sims), key=lambda x: x[1], reverse=True): print(document_number, score) ############################################################################### # Summary # ------- # # The core concepts of ``gensim`` are: # # 1. :ref:`core_concepts_document`: some text. # 2. :ref:`core_concepts_corpus`: a collection of documents. # 3. :ref:`core_concepts_vector`: a mathematically convenient representation of a document. # 4. :ref:`core_concepts_model`: an algorithm for transforming vectors from one representation to another. # # We saw these concepts in action. # First, we started with a corpus of documents. # Next, we transformed these documents to a vector space representation. # After that, we created a model that transformed our original vector representation to TfIdf. # Finally, we used our model to calculate the similarity between some query document and all documents in the corpus. # # What Next? # ---------- # # There's still much more to learn about :ref:`sphx_glr_auto_examples_core_run_corpora_and_vector_spaces.py`. import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('run_core_concepts.png') imgplot = plt.imshow(img) _ = plt.axis('off')	15,054	Python	.py	304	48.233553	183	0.708571	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,976	run_topics_and_transformations.py	piskvorky_gensim/docs/src/gallery/core/run_topics_and_transformations.py	r""" Topics and Transformations =========================== Introduces transformations and demonstrates their use on a toy corpus. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # In this tutorial, I will show how to transform documents from one vector representation # into another. This process serves two goals: # # 1. To bring out hidden structure in the corpus, discover relationships between # words and use them to describe the documents in a new and # (hopefully) more semantic way. # 2. To make the document representation more compact. This both improves efficiency # (new representation consumes less resources) and efficacy (marginal data # trends are ignored, noise-reduction). # # Creating the Corpus # ------------------- # # First, we need to create a corpus to work with. # This step is the same as in the previous tutorial; # if you completed it, feel free to skip to the next section. from collections import defaultdict from gensim import corpora documents = [ "Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in trees", "Graph minors IV Widths of trees and well quasi ordering", "Graph minors A survey", ] # remove common words and tokenize stoplist = set('for a of the and to in'.split()) texts = [ [word for word in document.lower().split() if word not in stoplist] for document in documents ] # remove words that appear only once frequency = defaultdict(int) for text in texts: for token in text: frequency[token] += 1 texts = [ [token for token in text if frequency[token] > 1] for text in texts ] dictionary = corpora.Dictionary(texts) corpus = [dictionary.doc2bow(text) for text in texts] ############################################################################### # # Creating a transformation # ++++++++++++++++++++++++++ # # The transformations are standard Python objects, typically initialized by means of # a :dfn:`training corpus`: # from gensim import models tfidf = models.TfidfModel(corpus) # step 1 -- initialize a model ############################################################################### # We used our old corpus from tutorial 1 to initialize (train) the transformation model. Different # transformations may require different initialization parameters; in case of TfIdf, the # "training" consists simply of going through the supplied corpus once and computing document frequencies # of all its features. Training other models, such as Latent Semantic Analysis or Latent Dirichlet # Allocation, is much more involved and, consequently, takes much more time. # # .. note:: # Transformations always convert between two specific vector # spaces. The same vector space (= the same set of feature ids) must be used for training # as well as for subsequent vector transformations. Failure to use the same input # feature space, such as applying a different string preprocessing, using different # feature ids, or using bag-of-words input vectors where TfIdf vectors are expected, will # result in feature mismatch during transformation calls and consequently in either # garbage output and/or runtime exceptions. # # # Transforming vectors # +++++++++++++++++++++ # # From now on, ``tfidf`` is treated as a read-only object that can be used to convert # any vector from the old representation (bag-of-words integer counts) to the new representation # (TfIdf real-valued weights): doc_bow = [(0, 1), (1, 1)] print(tfidf[doc_bow]) # step 2 -- use the model to transform vectors ############################################################################### # Or to apply a transformation to a whole corpus: corpus_tfidf = tfidf[corpus] for doc in corpus_tfidf: print(doc) ############################################################################### # In this particular case, we are transforming the same corpus that we used # for training, but this is only incidental. Once the transformation model has been initialized, # it can be used on any vectors (provided they come from the same vector space, of course), # even if they were not used in the training corpus at all. This is achieved by a process called # folding-in for LSA, by topic inference for LDA etc. # # .. note:: # Calling ``model[corpus]`` only creates a wrapper around the old ``corpus`` # document stream -- actual conversions are done on-the-fly, during document iteration. # We cannot convert the entire corpus at the time of calling ``corpus_transformed = model[corpus]``, # because that would mean storing the result in main memory, and that contradicts gensim's objective of memory-indepedence. # If you will be iterating over the transformed ``corpus_transformed`` multiple times, and the # transformation is costly, :ref:`serialize the resulting corpus to disk first <corpus-formats>` and continue # using that. # # Transformations can also be serialized, one on top of another, in a sort of chain: lsi_model = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2) # initialize an LSI transformation corpus_lsi = lsi_model[corpus_tfidf] # create a double wrapper over the original corpus: bow->tfidf->fold-in-lsi ############################################################################### # Here we transformed our Tf-Idf corpus via `Latent Semantic Indexing <https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ # into a latent 2-D space (2-D because we set ``num_topics=2``). Now you're probably wondering: what do these two latent # dimensions stand for? Let's inspect with :func:`models.LsiModel.print_topics`: lsi_model.print_topics(2) ############################################################################### # (the topics are printed to log -- see the note at the top of this page about activating # logging) # # It appears that according to LSI, "trees", "graph" and "minors" are all related # words (and contribute the most to the direction of the first topic), while the # second topic practically concerns itself with all the other words. As expected, # the first five documents are more strongly related to the second topic while the # remaining four documents to the first topic: # both bow->tfidf and tfidf->lsi transformations are actually executed here, on the fly for doc, as_text in zip(corpus_lsi, documents): print(doc, as_text) ############################################################################### # Model persistency is achieved with the :func:`save` and :func:`load` functions: import os import tempfile with tempfile.NamedTemporaryFile(prefix='model-', suffix='.lsi', delete=False) as tmp: lsi_model.save(tmp.name) # same for tfidf, lda, ... loaded_lsi_model = models.LsiModel.load(tmp.name) os.unlink(tmp.name) ############################################################################### # The next question might be: just how exactly similar are those documents to each other? # Is there a way to formalize the similarity, so that for a given input document, we can # order some other set of documents according to their similarity? Similarity queries # are covered in the next tutorial (:ref:`sphx_glr_auto_examples_core_run_similarity_queries.py`). # # .. _transformations: # # Available transformations # -------------------------- # # Gensim implements several popular Vector Space Model algorithms: # # * `Term Frequency * Inverse Document Frequency, Tf-Idf <https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_ # expects a bag-of-words (integer values) training corpus during initialization. # During transformation, it will take a vector and return another vector of the # same dimensionality, except that features which were rare in the training corpus # will have their value increased. # It therefore converts integer-valued vectors into real-valued ones, while leaving # the number of dimensions intact. It can also optionally normalize the resulting # vectors to (Euclidean) unit length. # # .. sourcecode:: pycon # # model = models.TfidfModel(corpus, normalize=True) # # * `Okapi Best Matching, Okapi BM25 <https://en.wikipedia.org/wiki/Okapi_BM25>`_ # expects a bag-of-words (integer values) training corpus during initialization. # During transformation, it will take a vector and return another vector of the # same dimensionality, except that features which were rare in the training corpus # will have their value increased. It therefore converts integer-valued # vectors into real-valued ones, while leaving the number of dimensions intact. # # Okapi BM25 is the standard ranking function used by search engines to estimate # the relevance of documents to a given search query. # # .. sourcecode:: pycon # # model = models.OkapiBM25Model(corpus) # # * `Latent Semantic Indexing, LSI (or sometimes LSA) <https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ # transforms documents from either bag-of-words or (preferrably) TfIdf-weighted space into # a latent space of a lower dimensionality. For the toy corpus above we used only # 2 latent dimensions, but on real corpora, target dimensionality of 200--500 is recommended # as a "golden standard" [1]_. # # .. sourcecode:: pycon # # model = models.LsiModel(tfidf_corpus, id2word=dictionary, num_topics=300) # # LSI training is unique in that we can continue "training" at any point, simply # by providing more training documents. This is done by incremental updates to # the underlying model, in a process called `online training`. Because of this feature, the # input document stream may even be infinite -- just keep feeding LSI new documents # as they arrive, while using the computed transformation model as read-only in the meanwhile! # # .. sourcecode:: pycon # # model.add_documents(another_tfidf_corpus) # now LSI has been trained on tfidf_corpus + another_tfidf_corpus # lsi_vec = model[tfidf_vec] # convert some new document into the LSI space, without affecting the model # # model.add_documents(more_documents) # tfidf_corpus + another_tfidf_corpus + more_documents # lsi_vec = model[tfidf_vec] # # See the :mod:`gensim.models.lsimodel` documentation for details on how to make # LSI gradually "forget" old observations in infinite streams. If you want to get dirty, # there are also parameters you can tweak that affect speed vs. memory footprint vs. numerical # precision of the LSI algorithm. # # `gensim` uses a novel online incremental streamed distributed training algorithm (quite a mouthful!), # which I published in [5]_. `gensim` also executes a stochastic multi-pass algorithm # from Halko et al. [4]_ internally, to accelerate in-core part # of the computations. # See also :ref:`wiki` for further speed-ups by distributing the computation across # a cluster of computers. # # * `Random Projections, RP <http://www.cis.hut.fi/ella/publications/randproj_kdd.pdf>`_ aim to # reduce vector space dimensionality. This is a very efficient (both memory- and # CPU-friendly) approach to approximating TfIdf distances between documents, by throwing in a little randomness. # Recommended target dimensionality is again in the hundreds/thousands, depending on your dataset. # # .. sourcecode:: pycon # # model = models.RpModel(tfidf_corpus, num_topics=500) # # * `Latent Dirichlet Allocation, LDA <https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_ # is yet another transformation from bag-of-words counts into a topic space of lower # dimensionality. LDA is a probabilistic extension of LSA (also called multinomial PCA), # so LDA's topics can be interpreted as probability distributions over words. These distributions are, # just like with LSA, inferred automatically from a training corpus. Documents # are in turn interpreted as a (soft) mixture of these topics (again, just like with LSA). # # .. sourcecode:: pycon # # model = models.LdaModel(corpus, id2word=dictionary, num_topics=100) # # `gensim` uses a fast implementation of online LDA parameter estimation based on [2]_, # modified to run in :ref:`distributed mode <distributed>` on a cluster of computers. # # * `Hierarchical Dirichlet Process, HDP <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_ # is a non-parametric bayesian method (note the missing number of requested topics): # # .. sourcecode:: pycon # # model = models.HdpModel(corpus, id2word=dictionary) # # `gensim` uses a fast, online implementation based on [3]_. # The HDP model is a new addition to `gensim`, and still rough around its academic edges -- use with care. # # Adding new :abbr:`VSM (Vector Space Model)` transformations (such as different weighting schemes) is rather trivial; # see the :ref:`apiref` or directly the `Python code <https://github.com/piskvorky/gensim/blob/develop/gensim/models/tfidfmodel.py>`_ # for more info and examples. # # It is worth repeating that these are all unique, incremental implementations, # which do not require the whole training corpus to be present in main memory all at once. # With memory taken care of, I am now improving :ref:`distributed`, # to improve CPU efficiency, too. # If you feel you could contribute by testing, providing use-cases or code, see the `Gensim Developer guide <https://github.com/RaRe-Technologies/gensim/wiki/Developer-page>`__. # # What Next? # ---------- # # Continue on to the next tutorial on :ref:`sphx_glr_auto_examples_core_run_similarity_queries.py`. # # References # ---------- # # .. [1] Bradford. 2008. An empirical study of required dimensionality for large-scale latent semantic indexing applications. # # .. [2] Hoffman, Blei, Bach. 2010. Online learning for Latent Dirichlet Allocation. # # .. [3] Wang, Paisley, Blei. 2011. Online variational inference for the hierarchical Dirichlet process. # # .. [4] Halko, Martinsson, Tropp. 2009. Finding structure with randomness. # # .. [5] Řehůřek. 2011. Subspace tracking for Latent Semantic Analysis. import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('run_topics_and_transformations.png') imgplot = plt.imshow(img) _ = plt.axis('off')	14,611	Python	.py	279	50.989247	177	0.718711	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,977	run_similarity_queries.py	piskvorky_gensim/docs/src/gallery/core/run_similarity_queries.py	r""" Similarity Queries ================== Demonstrates querying a corpus for similar documents. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # # Creating the Corpus # ------------------- # # First, we need to create a corpus to work with. # This step is the same as in the previous tutorial; # if you completed it, feel free to skip to the next section. from collections import defaultdict from gensim import corpora documents = [ "Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in trees", "Graph minors IV Widths of trees and well quasi ordering", "Graph minors A survey", ] # remove common words and tokenize stoplist = set('for a of the and to in'.split()) texts = [ [word for word in document.lower().split() if word not in stoplist] for document in documents ] # remove words that appear only once frequency = defaultdict(int) for text in texts: for token in text: frequency[token] += 1 texts = [ [token for token in text if frequency[token] > 1] for text in texts ] dictionary = corpora.Dictionary(texts) corpus = [dictionary.doc2bow(text) for text in texts] ############################################################################### # Similarity interface # -------------------- # # In the previous tutorials on # :ref:`sphx_glr_auto_examples_core_run_corpora_and_vector_spaces.py` # and # :ref:`sphx_glr_auto_examples_core_run_topics_and_transformations.py`, # we covered what it means to create a corpus in the Vector Space Model and how # to transform it between different vector spaces. A common reason for such a # charade is that we want to determine similarity between pairs of # documents, or the similarity between a specific document and a set of # other documents (such as a user query vs. indexed documents). # # To show how this can be done in gensim, let us consider the same corpus as in the # previous examples (which really originally comes from Deerwester et al.'s # `"Indexing by Latent Semantic Analysis" <http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf>`_ # seminal 1990 article). # To follow Deerwester's example, we first use this tiny corpus to define a 2-dimensional # LSI space: from gensim import models lsi = models.LsiModel(corpus, id2word=dictionary, num_topics=2) ############################################################################### # For the purposes of this tutorial, there are only two things you need to know about LSI. # First, it's just another transformation: it transforms vectors from one space to another. # Second, the benefit of LSI is that enables identifying patterns and relationships between terms (in our case, words in a document) and topics. # Our LSI space is two-dimensional (`num_topics = 2`) so there are two topics, but this is arbitrary. # If you're interested, you can read more about LSI here: `Latent Semantic Indexing <https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_: # # Now suppose a user typed in the query `"Human computer interaction"`. We would # like to sort our nine corpus documents in decreasing order of relevance to this query. # Unlike modern search engines, here we only concentrate on a single aspect of possible # similarities---on apparent semantic relatedness of their texts (words). No hyperlinks, # no random-walk static ranks, just a semantic extension over the boolean keyword match: doc = "Human computer interaction" vec_bow = dictionary.doc2bow(doc.lower().split()) vec_lsi = lsi[vec_bow] # convert the query to LSI space print(vec_lsi) ############################################################################### # In addition, we will be considering `cosine similarity <https://en.wikipedia.org/wiki/Cosine_similarity>`_ # to determine the similarity of two vectors. Cosine similarity is a standard measure # in Vector Space Modeling, but wherever the vectors represent probability distributions, # `different similarity measures <https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence#Symmetrised_divergence>`_ # may be more appropriate. # # Initializing query structures # ++++++++++++++++++++++++++++++++ # # To prepare for similarity queries, we need to enter all documents which we want # to compare against subsequent queries. In our case, they are the same nine documents # used for training LSI, converted to 2-D LSA space. But that's only incidental, we # might also be indexing a different corpus altogether. from gensim import similarities index = similarities.MatrixSimilarity(lsi[corpus]) # transform corpus to LSI space and index it ############################################################################### # .. warning:: # The class :class:`similarities.MatrixSimilarity` is only appropriate when the whole # set of vectors fits into memory. For example, a corpus of one million documents # would require 2GB of RAM in a 256-dimensional LSI space, when used with this class. # # Without 2GB of free RAM, you would need to use the :class:`similarities.Similarity` class. # This class operates in fixed memory, by splitting the index across multiple files on disk, called shards. # It uses :class:`similarities.MatrixSimilarity` and :class:`similarities.SparseMatrixSimilarity` internally, # so it is still fast, although slightly more complex. # # Index persistency is handled via the standard :func:`save` and :func:`load` functions: index.save('/tmp/deerwester.index') index = similarities.MatrixSimilarity.load('/tmp/deerwester.index') ############################################################################### # This is true for all similarity indexing classes (:class:`similarities.Similarity`, # :class:`similarities.MatrixSimilarity` and :class:`similarities.SparseMatrixSimilarity`). # Also in the following, `index` can be an object of any of these. When in doubt, # use :class:`similarities.Similarity`, as it is the most scalable version, and it also # supports adding more documents to the index later. # # Performing queries # ++++++++++++++++++ # # To obtain similarities of our query document against the nine indexed documents: sims = index[vec_lsi] # perform a similarity query against the corpus print(list(enumerate(sims))) # print (document_number, document_similarity) 2-tuples ############################################################################### # Cosine measure returns similarities in the range `<-1, 1>` (the greater, the more similar), # so that the first document has a score of 0.99809301 etc. # # With some standard Python magic we sort these similarities into descending # order, and obtain the final answer to the query `"Human computer interaction"`: sims = sorted(enumerate(sims), key=lambda item: -item[1]) for doc_position, doc_score in sims: print(doc_score, documents[doc_position]) ############################################################################### # The thing to note here is that documents no. 2 (``"The EPS user interface management system"``) # and 4 (``"Relation of user perceived response time to error measurement"``) would never be returned by # a standard boolean fulltext search, because they do not share any common words with ``"Human # computer interaction"``. However, after applying LSI, we can observe that both of # them received quite high similarity scores (no. 2 is actually the most similar!), # which corresponds better to our intuition of # them sharing a "computer-human" related topic with the query. In fact, this semantic # generalization is the reason why we apply transformations and do topic modelling # in the first place. # # Where next? # ------------ # # Congratulations, you have finished the tutorials -- now you know how gensim works :-) # To delve into more details, you can browse through the :ref:`apiref`, # see the :ref:`wiki` or perhaps check out :ref:`distributed` in `gensim`. # # Gensim is a fairly mature package that has been used successfully by many individuals and companies, both for rapid prototyping and in production. # That doesn't mean it's perfect though: # # * there are parts that could be implemented more efficiently (in C, for example), or make better use of parallelism (multiple machines cores) # * new algorithms are published all the time; help gensim keep up by `discussing them <https://groups.google.com/g/gensim>`_ and `contributing code <https://github.com/piskvorky/gensim/wiki/Developer-page>`_ # * your feedback is most welcome and appreciated (and it's not just the code!): # `bug reports <https://github.com/piskvorky/gensim/issues>`_ or # `user stories and general questions <https://groups.google.com/g/gensim>`_. # # Gensim has no ambition to become an all-encompassing framework, across all NLP (or even Machine Learning) subfields. # Its mission is to help NLP practitioners try out popular topic modelling algorithms # on large datasets easily, and to facilitate prototyping of new algorithms for researchers. import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('run_similarity_queries.png') imgplot = plt.imshow(img) _ = plt.axis('off')	9,565	Python	.py	170	54.729412	208	0.711268	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,978	run_word2vec.py	piskvorky_gensim/docs/src/gallery/tutorials/run_word2vec.py	r""" Word2Vec Model ============== Introduces Gensim's Word2Vec model and demonstrates its use on the `Lee Evaluation Corpus <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`_. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # In case you missed the buzz, Word2Vec is a widely used algorithm based on neural # networks, commonly referred to as "deep learning" (though word2vec itself is rather shallow). # Using large amounts of unannotated plain text, word2vec learns relationships # between words automatically. The output are vectors, one vector per word, # with remarkable linear relationships that allow us to do things like: # # * vec("king") - vec("man") + vec("woman") =~ vec("queen") # * vec("Montreal Canadiens") – vec("Montreal") + vec("Toronto") =~ vec("Toronto Maple Leafs"). # # Word2vec is very useful in `automatic text tagging # <https://github.com/RaRe-Technologies/movie-plots-by-genre>`_\ , recommender # systems and machine translation. # # This tutorial: # # #. Introduces ``Word2Vec`` as an improvement over traditional bag-of-words # #. Shows off a demo of ``Word2Vec`` using a pre-trained model # #. Demonstrates training a new model from your own data # #. Demonstrates loading and saving models # #. Introduces several training parameters and demonstrates their effect # #. Discusses memory requirements # #. Visualizes Word2Vec embeddings by applying dimensionality reduction # # Review: Bag-of-words # -------------------- # # .. Note:: Feel free to skip these review sections if you're already familiar with the models. # # You may be familiar with the `bag-of-words model # <https://en.wikipedia.org/wiki/Bag-of-words_model>`_ from the # :ref:`core_concepts_vector` section. # This model transforms each document to a fixed-length vector of integers. # For example, given the sentences: # # - ``John likes to watch movies. Mary likes movies too.`` # - ``John also likes to watch football games. Mary hates football.`` # # The model outputs the vectors: # # - ``[1, 2, 1, 1, 2, 1, 1, 0, 0, 0, 0]`` # - ``[1, 1, 1, 1, 0, 1, 0, 1, 2, 1, 1]`` # # Each vector has 10 elements, where each element counts the number of times a # particular word occurred in the document. # The order of elements is arbitrary. # In the example above, the order of the elements corresponds to the words: # ``["John", "likes", "to", "watch", "movies", "Mary", "too", "also", "football", "games", "hates"]``. # # Bag-of-words models are surprisingly effective, but have several weaknesses. # # First, they lose all information about word order: "John likes Mary" and # "Mary likes John" correspond to identical vectors. There is a solution: bag # of `n-grams <https://en.wikipedia.org/wiki/N-gram>`__ # models consider word phrases of length n to represent documents as # fixed-length vectors to capture local word order but suffer from data # sparsity and high dimensionality. # # Second, the model does not attempt to learn the meaning of the underlying # words, and as a consequence, the distance between vectors doesn't always # reflect the difference in meaning. The ``Word2Vec`` model addresses this # second problem. # # Introducing: the ``Word2Vec`` Model # ----------------------------------- # # ``Word2Vec`` is a more recent model that embeds words in a lower-dimensional # vector space using a shallow neural network. The result is a set of # word-vectors where vectors close together in vector space have similar # meanings based on context, and word-vectors distant to each other have # differing meanings. For example, ``strong`` and ``powerful`` would be close # together and ``strong`` and ``Paris`` would be relatively far. # # The are two versions of this model and :py:class:`~gensim.models.word2vec.Word2Vec` # class implements them both: # # 1. Skip-grams (SG) # 2. Continuous-bag-of-words (CBOW) # # .. Important:: # Don't let the implementation details below scare you. # They're advanced material: if it's too much, then move on to the next section. # # The `Word2Vec Skip-gram <http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model>`__ # model, for example, takes in pairs (word1, word2) generated by moving a # window across text data, and trains a 1-hidden-layer neural network based on # the synthetic task of given an input word, giving us a predicted probability # distribution of nearby words to the input. A virtual `one-hot # <https://en.wikipedia.org/wiki/One-hot>`__ encoding of words # goes through a 'projection layer' to the hidden layer; these projection # weights are later interpreted as the word embeddings. So if the hidden layer # has 300 neurons, this network will give us 300-dimensional word embeddings. # # Continuous-bag-of-words Word2vec is very similar to the skip-gram model. It # is also a 1-hidden-layer neural network. The synthetic training task now uses # the average of multiple input context words, rather than a single word as in # skip-gram, to predict the center word. Again, the projection weights that # turn one-hot words into averageable vectors, of the same width as the hidden # layer, are interpreted as the word embeddings. # ############################################################################### # Word2Vec Demo # ------------- # # To see what ``Word2Vec`` can do, let's download a pre-trained model and play # around with it. We will fetch the Word2Vec model trained on part of the # Google News dataset, covering approximately 3 million words and phrases. Such # a model can take hours to train, but since it's already available, # downloading and loading it with Gensim takes minutes. # # .. Important:: # The model is approximately 2GB, so you'll need a decent network connection # to proceed. Otherwise, skip ahead to the "Training Your Own Model" section # below. # # You may also check out an `online word2vec demo # <https://radimrehurek.com/2014/02/word2vec-tutorial/#app>`_ where you can try # this vector algebra for yourself. That demo runs ``word2vec`` on the # entire Google News dataset, of about 100 billion words. # import gensim.downloader as api wv = api.load('word2vec-google-news-300') ############################################################################### # A common operation is to retrieve the vocabulary of a model. That is trivial: for index, word in enumerate(wv.index_to_key): if index == 10: break print(f"word #{index}/{len(wv.index_to_key)} is {word}") ############################################################################### # We can easily obtain vectors for terms the model is familiar with: # vec_king = wv['king'] ############################################################################### # Unfortunately, the model is unable to infer vectors for unfamiliar words. # This is one limitation of Word2Vec: if this limitation matters to you, check # out the FastText model. # try: vec_cameroon = wv['cameroon'] except KeyError: print("The word 'cameroon' does not appear in this model") ############################################################################### # Moving on, ``Word2Vec`` supports several word similarity tasks out of the # box. You can see how the similarity intuitively decreases as the words get # less and less similar. # pairs = [ ('car', 'minivan'), # a minivan is a kind of car ('car', 'bicycle'), # still a wheeled vehicle ('car', 'airplane'), # ok, no wheels, but still a vehicle ('car', 'cereal'), # ... and so on ('car', 'communism'), ] for w1, w2 in pairs: print('%r\t%r\t%.2f' % (w1, w2, wv.similarity(w1, w2))) ############################################################################### # Print the 5 most similar words to "car" or "minivan" print(wv.most_similar(positive=['car', 'minivan'], topn=5)) ############################################################################### # Which of the below does not belong in the sequence? print(wv.doesnt_match(['fire', 'water', 'land', 'sea', 'air', 'car'])) ############################################################################### # Training Your Own Model # ----------------------- # # To start, you'll need some data for training the model. For the following # examples, we'll use the `Lee Evaluation Corpus # <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`_ # (which you `already have # <https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/test/test_data/lee_background.cor>`_ # if you've installed Gensim). # # This corpus is small enough to fit entirely in memory, but we'll implement a # memory-friendly iterator that reads it line-by-line to demonstrate how you # would handle a larger corpus. # from gensim.test.utils import datapath from gensim import utils class MyCorpus: """An iterator that yields sentences (lists of str).""" def __iter__(self): corpus_path = datapath('lee_background.cor') for line in open(corpus_path): # assume there's one document per line, tokens separated by whitespace yield utils.simple_preprocess(line) ############################################################################### # If we wanted to do any custom preprocessing, e.g. decode a non-standard # encoding, lowercase, remove numbers, extract named entities... All of this can # be done inside the ``MyCorpus`` iterator and ``word2vec`` doesn’t need to # know. All that is required is that the input yields one sentence (list of # utf8 words) after another. # # Let's go ahead and train a model on our corpus. Don't worry about the # training parameters much for now, we'll revisit them later. # import gensim.models sentences = MyCorpus() model = gensim.models.Word2Vec(sentences=sentences) ############################################################################### # Once we have our model, we can use it in the same way as in the demo above. # # The main part of the model is ``model.wv``\ , where "wv" stands for "word vectors". # vec_king = model.wv['king'] ############################################################################### # Retrieving the vocabulary works the same way: for index, word in enumerate(wv.index_to_key): if index == 10: break print(f"word #{index}/{len(wv.index_to_key)} is {word}") ############################################################################### # Storing and loading models # -------------------------- # # You'll notice that training non-trivial models can take time. Once you've # trained your model and it works as expected, you can save it to disk. That # way, you don't have to spend time training it all over again later. # # You can store/load models using the standard gensim methods: # import tempfile with tempfile.NamedTemporaryFile(prefix='gensim-model-', delete=False) as tmp: temporary_filepath = tmp.name model.save(temporary_filepath) # # The model is now safely stored in the filepath. # You can copy it to other machines, share it with others, etc. # # To load a saved model: # new_model = gensim.models.Word2Vec.load(temporary_filepath) ############################################################################### # which uses pickle internally, optionally ``mmap``\ ‘ing the model’s internal # large NumPy matrices into virtual memory directly from disk files, for # inter-process memory sharing. # # In addition, you can load models created by the original C tool, both using # its text and binary formats:: # # model = gensim.models.KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False) # # using gzipped/bz2 input works too, no need to unzip # model = gensim.models.KeyedVectors.load_word2vec_format('/tmp/vectors.bin.gz', binary=True) # ############################################################################### # Training Parameters # ------------------- # # ``Word2Vec`` accepts several parameters that affect both training speed and quality. # # min_count # --------- # # ``min_count`` is for pruning the internal dictionary. Words that appear only # once or twice in a billion-word corpus are probably uninteresting typos and # garbage. In addition, there’s not enough data to make any meaningful training # on those words, so it’s best to ignore them: # # default value of min_count=5 model = gensim.models.Word2Vec(sentences, min_count=10) ############################################################################### # # vector_size # ----------- # # ``vector_size`` is the number of dimensions (N) of the N-dimensional space that # gensim Word2Vec maps the words onto. # # Bigger size values require more training data, but can lead to better (more # accurate) models. Reasonable values are in the tens to hundreds. # # The default value of vector_size is 100. model = gensim.models.Word2Vec(sentences, vector_size=200) ############################################################################### # workers # ------- # # ``workers`` , the last of the major parameters (full list `here # <https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec>`_) # is for training parallelization, to speed up training: # # default value of workers=3 (tutorial says 1...) model = gensim.models.Word2Vec(sentences, workers=4) ############################################################################### # The ``workers`` parameter only has an effect if you have `Cython # <http://cython.org/>`_ installed. Without Cython, you’ll only be able to use # one core because of the `GIL # <https://wiki.python.org/moin/GlobalInterpreterLock>`_ (and ``word2vec`` # training will be `miserably slow # <https://rare-technologies.com/word2vec-in-python-part-two-optimizing/>`_\ ). # ############################################################################### # Memory # ------ # # At its core, ``word2vec`` model parameters are stored as matrices (NumPy # arrays). Each array is #vocabulary (controlled by the ``min_count`` parameter) # times vector size (the ``vector_size`` parameter) of floats (single precision aka 4 bytes). # # Three such matrices are held in RAM (work is underway to reduce that number # to two, or even one). So if your input contains 100,000 unique words, and you # asked for layer ``vector_size=200``\ , the model will require approx. # ``100,00020043 bytes = ~229MB``. # # There’s a little extra memory needed for storing the vocabulary tree (100,000 words would # take a few megabytes), but unless your words are extremely loooong strings, memory # footprint will be dominated by the three matrices above. # ############################################################################### # Evaluating # ---------- # # ``Word2Vec`` training is an unsupervised task, there’s no good way to # objectively evaluate the result. Evaluation depends on your end application. # # Google has released their testing set of about 20,000 syntactic and semantic # test examples, following the “A is to B as C is to D” task. It is provided in # the 'datasets' folder. # # For example a syntactic analogy of comparative type is ``bad:worse;good:?``. # There are total of 9 types of syntactic comparisons in the dataset like # plural nouns and nouns of opposite meaning. # # The semantic questions contain five types of semantic analogies, such as # capital cities (``Paris:France;Tokyo:?``) or family members # (``brother:sister;dad:?``). # ############################################################################### # Gensim supports the same evaluation set, in exactly the same format: # model.wv.evaluate_word_analogies(datapath('questions-words.txt')) ############################################################################### # # This ``evaluate_word_analogies`` method takes an `optional parameter # <https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.KeyedVectors.evaluate_word_analogies>`_ # ``restrict_vocab`` which limits which test examples are to be considered. # ############################################################################### # In the December 2016 release of Gensim we added a better way to evaluate semantic similarity. # # By default it uses an academic dataset WS-353 but one can create a dataset # specific to your business based on it. It contains word pairs together with # human-assigned similarity judgments. It measures the relatedness or # co-occurrence of two words. For example, 'coast' and 'shore' are very similar # as they appear in the same context. At the same time 'clothes' and 'closet' # are less similar because they are related but not interchangeable. # model.wv.evaluate_word_pairs(datapath('wordsim353.tsv')) ############################################################################### # .. Important:: # Good performance on Google's or WS-353 test set doesn’t mean word2vec will # work well in your application, or vice versa. It’s always best to evaluate # directly on your intended task. For an example of how to use word2vec in a # classifier pipeline, see this `tutorial # <https://github.com/RaRe-Technologies/movie-plots-by-genre>`_. # ############################################################################### # Online training / Resuming training # ----------------------------------- # # Advanced users can load a model and continue training it with more sentences # and `new vocabulary words <online_w2v_tutorial.ipynb>`_: # model = gensim.models.Word2Vec.load(temporary_filepath) more_sentences = [ ['Advanced', 'users', 'can', 'load', 'a', 'model', 'and', 'continue', 'training', 'it', 'with', 'more', 'sentences'], ] model.build_vocab(more_sentences, update=True) model.train(more_sentences, total_examples=model.corpus_count, epochs=model.epochs) # cleaning up temporary file import os os.remove(temporary_filepath) ############################################################################### # You may need to tweak the ``total_words`` parameter to ``train()``, # depending on what learning rate decay you want to simulate. # # Note that it’s not possible to resume training with models generated by the C # tool, ``KeyedVectors.load_word2vec_format()``. You can still use them for # querying/similarity, but information vital for training (the vocab tree) is # missing there. # ############################################################################### # Training Loss Computation # ------------------------- # # The parameter ``compute_loss`` can be used to toggle computation of loss # while training the Word2Vec model. The computed loss is stored in the model # attribute ``running_training_loss`` and can be retrieved using the function # ``get_latest_training_loss`` as follows : # # instantiating and training the Word2Vec model model_with_loss = gensim.models.Word2Vec( sentences, min_count=1, compute_loss=True, hs=0, sg=1, seed=42, ) # getting the training loss value training_loss = model_with_loss.get_latest_training_loss() print(training_loss) ############################################################################### # Benchmarks # ---------- # # Let's run some benchmarks to see effect of the training loss computation code # on training time. # # We'll use the following data for the benchmarks: # # #. Lee Background corpus: included in gensim's test data # #. Text8 corpus. To demonstrate the effect of corpus size, we'll look at the # first 1MB, 10MB, 50MB of the corpus, as well as the entire thing. # import io import os import gensim.models.word2vec import gensim.downloader as api import smart_open def head(path, size): with smart_open.open(path) as fin: return io.StringIO(fin.read(size)) def generate_input_data(): lee_path = datapath('lee_background.cor') ls = gensim.models.word2vec.LineSentence(lee_path) ls.name = '25kB' yield ls text8_path = api.load('text8').fn labels = ('1MB', '10MB', '50MB', '100MB') sizes = (1024 * 2, 10 * 1024 ** 2, 50 * 1024 ** 2, 100 * 1024 ** 2) for l, s in zip(labels, sizes): ls = gensim.models.word2vec.LineSentence(head(text8_path, s)) ls.name = l yield ls input_data = list(generate_input_data()) ############################################################################### # We now compare the training time taken for different combinations of input # data and model training parameters like ``hs`` and ``sg``. # # For each combination, we repeat the test several times to obtain the mean and # standard deviation of the test duration. # # Temporarily reduce logging verbosity logging.root.level = logging.ERROR import time import numpy as np import pandas as pd train_time_values = [] seed_val = 42 sg_values = [0, 1] hs_values = [0, 1] fast = True if fast: input_data_subset = input_data[:3] else: input_data_subset = input_data for data in input_data_subset: for sg_val in sg_values: for hs_val in hs_values: for loss_flag in [True, False]: time_taken_list = [] for i in range(3): start_time = time.time() w2v_model = gensim.models.Word2Vec( data, compute_loss=loss_flag, sg=sg_val, hs=hs_val, seed=seed_val, ) time_taken_list.append(time.time() - start_time) time_taken_list = np.array(time_taken_list) time_mean = np.mean(time_taken_list) time_std = np.std(time_taken_list) model_result = { 'train_data': data.name, 'compute_loss': loss_flag, 'sg': sg_val, 'hs': hs_val, 'train_time_mean': time_mean, 'train_time_std': time_std, } print("Word2vec model #%i: %s" % (len(train_time_values), model_result)) train_time_values.append(model_result) train_times_table = pd.DataFrame(train_time_values) train_times_table = train_times_table.sort_values( by=['train_data', 'sg', 'hs', 'compute_loss'], ascending=[False, False, True, False], ) print(train_times_table) ############################################################################### # # Visualising Word Embeddings # --------------------------- # # The word embeddings made by the model can be visualised by reducing # dimensionality of the words to 2 dimensions using tSNE. # # Visualisations can be used to notice semantic and syntactic trends in the data. # # Example: # # * Semantic: words like cat, dog, cow, etc. have a tendency to lie close by # * Syntactic: words like run, running or cut, cutting lie close together. # # Vector relations like vKing - vMan = vQueen - vWoman can also be noticed. # # .. Important:: # The model used for the visualisation is trained on a small corpus. Thus # some of the relations might not be so clear. # from sklearn.decomposition import IncrementalPCA # inital reduction from sklearn.manifold import TSNE # final reduction import numpy as np # array handling def reduce_dimensions(model): num_dimensions = 2 # final num dimensions (2D, 3D, etc) # extract the words & their vectors, as numpy arrays vectors = np.asarray(model.wv.vectors) labels = np.asarray(model.wv.index_to_key) # fixed-width numpy strings # reduce using t-SNE tsne = TSNE(n_components=num_dimensions, random_state=0) vectors = tsne.fit_transform(vectors) x_vals = [v[0] for v in vectors] y_vals = [v[1] for v in vectors] return x_vals, y_vals, labels x_vals, y_vals, labels = reduce_dimensions(model) def plot_with_plotly(x_vals, y_vals, labels, plot_in_notebook=True): from plotly.offline import init_notebook_mode, iplot, plot import plotly.graph_objs as go trace = go.Scatter(x=x_vals, y=y_vals, mode='text', text=labels) data = [trace] if plot_in_notebook: init_notebook_mode(connected=True) iplot(data, filename='word-embedding-plot') else: plot(data, filename='word-embedding-plot.html') def plot_with_matplotlib(x_vals, y_vals, labels): import matplotlib.pyplot as plt import random random.seed(0) plt.figure(figsize=(12, 12)) plt.scatter(x_vals, y_vals) # # Label randomly subsampled 25 data points # indices = list(range(len(labels))) selected_indices = random.sample(indices, 25) for i in selected_indices: plt.annotate(labels[i], (x_vals[i], y_vals[i])) try: get_ipython() except Exception: plot_function = plot_with_matplotlib else: plot_function = plot_with_plotly plot_function(x_vals, y_vals, labels) ############################################################################### # Conclusion # ---------- # # In this tutorial we learned how to train word2vec models on your custom data # and also how to evaluate it. Hope that you too will find this popular tool # useful in your Machine Learning tasks! # # Links # ----- # # - API docs: :py:mod:`gensim.models.word2vec` # - `Original C toolkit and word2vec papers by Google <https://code.google.com/archive/p/word2vec/>`_. #	25,529	Python	.py	579	41.336788	126	0.642739	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,979	run_lda.py	piskvorky_gensim/docs/src/gallery/tutorials/run_lda.py	r""" LDA Model ========= Introduces Gensim's LDA model and demonstrates its use on the NIPS corpus. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # The purpose of this tutorial is to demonstrate how to train and tune an LDA model. # # In this tutorial we will: # # * Load input data. # * Pre-process that data. # * Transform documents into bag-of-words vectors. # * Train an LDA model. # # This tutorial will not: # # * Explain how Latent Dirichlet Allocation works # * Explain how the LDA model performs inference # * Teach you all the parameters and options for Gensim's LDA implementation # # If you are not familiar with the LDA model or how to use it in Gensim, I (Olavur Mortensen) # suggest you read up on that before continuing with this tutorial. Basic # understanding of the LDA model should suffice. Examples: # # * `Introduction to Latent Dirichlet Allocation <http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation>`_ # * Gensim tutorial: :ref:`sphx_glr_auto_examples_core_run_topics_and_transformations.py` # * Gensim's LDA model API docs: :py:class:`gensim.models.LdaModel` # # I would also encourage you to consider each step when applying the model to # your data, instead of just blindly applying my solution. The different steps # will depend on your data and possibly your goal with the model. # # Data # ---- # # I have used a corpus of NIPS papers in this tutorial, but if you're following # this tutorial just to learn about LDA I encourage you to consider picking a # corpus on a subject that you are familiar with. Qualitatively evaluating the # output of an LDA model is challenging and can require you to understand the # subject matter of your corpus (depending on your goal with the model). # # NIPS (Neural Information Processing Systems) is a machine learning conference # so the subject matter should be well suited for most of the target audience # of this tutorial. You can download the original data from Sam Roweis' # `website <http://www.cs.nyu.edu/~roweis/data.html>`_. The code below will # also do that for you. # # .. Important:: # The corpus contains 1740 documents, and not particularly long ones. # So keep in mind that this tutorial is not geared towards efficiency, and be # careful before applying the code to a large dataset. # import io import os.path import re import tarfile import smart_open def extract_documents(url='https://cs.nyu.edu/~roweis/data/nips12raw_str602.tgz'): with smart_open.open(url, "rb") as file: with tarfile.open(fileobj=file) as tar: for member in tar.getmembers(): if member.isfile() and re.search(r'nipstxt/nips\d+/\d+\.txt', member.name): member_bytes = tar.extractfile(member).read() yield member_bytes.decode('utf-8', errors='replace') docs = list(extract_documents()) ############################################################################### # So we have a list of 1740 documents, where each document is a Unicode string. # If you're thinking about using your own corpus, then you need to make sure # that it's in the same format (list of Unicode strings) before proceeding # with the rest of this tutorial. # print(len(docs)) print(docs[0][:500]) ############################################################################### # Pre-process and vectorize the documents # --------------------------------------- # # As part of preprocessing, we will: # # * Tokenize (split the documents into tokens). # * Lemmatize the tokens. # * Compute bigrams. # * Compute a bag-of-words representation of the data. # # First we tokenize the text using a regular expression tokenizer from NLTK. We # remove numeric tokens and tokens that are only a single character, as they # don't tend to be useful, and the dataset contains a lot of them. # # .. Important:: # # This tutorial uses the nltk library for preprocessing, although you can # replace it with something else if you want. # # Tokenize the documents. from nltk.tokenize import RegexpTokenizer # Split the documents into tokens. tokenizer = RegexpTokenizer(r'\w+') for idx in range(len(docs)): docs[idx] = docs[idx].lower() # Convert to lowercase. docs[idx] = tokenizer.tokenize(docs[idx]) # Split into words. # Remove numbers, but not words that contain numbers. docs = [[token for token in doc if not token.isnumeric()] for doc in docs] # Remove words that are only one character. docs = [[token for token in doc if len(token) > 1] for doc in docs] ############################################################################### # We use the WordNet lemmatizer from NLTK. A lemmatizer is preferred over a # stemmer in this case because it produces more readable words. Output that is # easy to read is very desirable in topic modelling. # # Download the WordNet data from nltk import download download('wordnet') # Lemmatize the documents. from nltk.stem.wordnet import WordNetLemmatizer lemmatizer = WordNetLemmatizer() docs = [[lemmatizer.lemmatize(token) for token in doc] for doc in docs] ############################################################################### # We find bigrams in the documents. Bigrams are sets of two adjacent words. # Using bigrams we can get phrases like "machine_learning" in our output # (spaces are replaced with underscores); without bigrams we would only get # "machine" and "learning". # # Note that in the code below, we find bigrams and then add them to the # original data, because we would like to keep the words "machine" and # "learning" as well as the bigram "machine_learning". # # .. Important:: # Computing n-grams of large dataset can be very computationally # and memory intensive. # # Compute bigrams. from gensim.models import Phrases # Add bigrams and trigrams to docs (only ones that appear 20 times or more). bigram = Phrases(docs, min_count=20) for idx in range(len(docs)): for token in bigram[docs[idx]]: if '_' in token: # Token is a bigram, add to document. docs[idx].append(token) ############################################################################### # We remove rare words and common words based on their document frequency. # Below we remove words that appear in less than 20 documents or in more than # 50% of the documents. Consider trying to remove words only based on their # frequency, or maybe combining that with this approach. # # Remove rare and common tokens. from gensim.corpora import Dictionary # Create a dictionary representation of the documents. dictionary = Dictionary(docs) # Filter out words that occur less than 20 documents, or more than 50% of the documents. dictionary.filter_extremes(no_below=20, no_above=0.5) ############################################################################### # Finally, we transform the documents to a vectorized form. We simply compute # the frequency of each word, including the bigrams. # # Bag-of-words representation of the documents. corpus = [dictionary.doc2bow(doc) for doc in docs] ############################################################################### # Let's see how many tokens and documents we have to train on. # print('Number of unique tokens: %d' % len(dictionary)) print('Number of documents: %d' % len(corpus)) ############################################################################### # Training # -------- # # We are ready to train the LDA model. We will first discuss how to set some of # the training parameters. # # First of all, the elephant in the room: how many topics do I need? There is # really no easy answer for this, it will depend on both your data and your # application. I have used 10 topics here because I wanted to have a few topics # that I could interpret and "label", and because that turned out to give me # reasonably good results. You might not need to interpret all your topics, so # you could use a large number of topics, for example 100. # # ``chunksize`` controls how many documents are processed at a time in the # training algorithm. Increasing chunksize will speed up training, at least as # long as the chunk of documents easily fit into memory. I've set ``chunksize = # 2000``, which is more than the amount of documents, so I process all the # data in one go. Chunksize can however influence the quality of the model, as # discussed in Hoffman and co-authors [2], but the difference was not # substantial in this case. # # ``passes`` controls how often we train the model on the entire corpus. # Another word for passes might be "epochs". ``iterations`` is somewhat # technical, but essentially it controls how often we repeat a particular loop # over each document. It is important to set the number of "passes" and # "iterations" high enough. # # I suggest the following way to choose iterations and passes. First, enable # logging (as described in many Gensim tutorials), and set ``eval_every = 1`` # in ``LdaModel``. When training the model look for a line in the log that # looks something like this:: # # 2016-06-21 15:40:06,753 - gensim.models.ldamodel - DEBUG - 68/1566 documents converged within 400 iterations # # If you set ``passes = 20`` you will see this line 20 times. Make sure that by # the final passes, most of the documents have converged. So you want to choose # both passes and iterations to be high enough for this to happen. # # We set ``alpha = 'auto'`` and ``eta = 'auto'``. Again this is somewhat # technical, but essentially we are automatically learning two parameters in # the model that we usually would have to specify explicitly. # # Train LDA model. from gensim.models import LdaModel # Set training parameters. num_topics = 10 chunksize = 2000 passes = 20 iterations = 400 eval_every = None # Don't evaluate model perplexity, takes too much time. # Make an index to word dictionary. temp = dictionary[0] # This is only to "load" the dictionary. id2word = dictionary.id2token model = LdaModel( corpus=corpus, id2word=id2word, chunksize=chunksize, alpha='auto', eta='auto', iterations=iterations, num_topics=num_topics, passes=passes, eval_every=eval_every ) ############################################################################### # We can compute the topic coherence of each topic. Below we display the # average topic coherence and print the topics in order of topic coherence. # # Note that we use the "Umass" topic coherence measure here (see # :py:func:`gensim.models.ldamodel.LdaModel.top_topics`), Gensim has recently # obtained an implementation of the "AKSW" topic coherence measure (see # accompanying blog post, https://rare-technologies.com/what-is-topic-coherence/). # # If you are familiar with the subject of the articles in this dataset, you can # see that the topics below make a lot of sense. However, they are not without # flaws. We can see that there is substantial overlap between some topics, # others are hard to interpret, and most of them have at least some terms that # seem out of place. If you were able to do better, feel free to share your # methods on the blog at https://rare-technologies.com/lda-training-tips/ ! # top_topics = model.top_topics(corpus) # Average topic coherence is the sum of topic coherences of all topics, divided by the number of topics. avg_topic_coherence = sum([t[1] for t in top_topics]) / num_topics print('Average topic coherence: %.4f.' % avg_topic_coherence) from pprint import pprint pprint(top_topics) ############################################################################### # Things to experiment with # ------------------------- # # * ``no_above`` and ``no_below`` parameters in ``filter_extremes`` method. # * Adding trigrams or even higher order n-grams. # * Consider whether using a hold-out set or cross-validation is the way to go for you. # * Try other datasets. # # Where to go from here # --------------------- # # * Check out a RaRe blog post on the AKSW topic coherence measure (https://rare-technologies.com/what-is-topic-coherence/). # * pyLDAvis (https://pyldavis.readthedocs.io/en/latest/index.html). # * Read some more Gensim tutorials (https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials). # * If you haven't already, read [1] and [2] (see references). # # References # ---------- # # 1. "Latent Dirichlet Allocation", Blei et al. 2003. # 2. "Online Learning for Latent Dirichlet Allocation", Hoffman et al. 2010. #	12,616	Python	.py	275	44.145455	128	0.695041	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,980	run_scm.py	piskvorky_gensim/docs/src/gallery/tutorials/run_scm.py	r""" Soft Cosine Measure =================== Demonstrates using Gensim's implemenation of the SCM. """ ############################################################################### # Soft Cosine Measure (SCM) is a promising new tool in machine learning that # allows us to submit a query and return the most relevant documents. This # tutorial introduces SCM and shows how you can compute the SCM similarities # between two documents using the ``inner_product`` method. # # Soft Cosine Measure basics # -------------------------- # # Soft Cosine Measure (SCM) is a method that allows us to assess the similarity # between two documents in a meaningful way, even when they have no words in # common. It uses a measure of similarity between words, which can be derived # [2] using [word2vec][] [4] vector embeddings of words. It has been shown to # outperform many of the state-of-the-art methods in the semantic text # similarity task in the context of community question answering [2]. # # # SCM is illustrated below for two very similar sentences. The sentences have # no words in common, but by modeling synonymy, SCM is able to accurately # measure the similarity between the two sentences. The method also uses the # bag-of-words vector representation of the documents (simply put, the word's # frequencies in the documents). The intution behind the method is that we # compute standard cosine similarity assuming that the document vectors are # expressed in a non-orthogonal basis, where the angle between two basis # vectors is derived from the angle between the word2vec embeddings of the # corresponding words. # import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('scm-hello.png') imgplot = plt.imshow(img) plt.axis('off') plt.show() ############################################################################### # This method was perhaps first introduced in the article “Soft Measure and # Soft Cosine Measure: Measure of Features in Vector Space Model” by Grigori # Sidorov, Alexander Gelbukh, Helena Gomez-Adorno, and David Pinto. # # In this tutorial, we will learn how to use Gensim's SCM functionality, which # consists of the ``inner_product`` method for one-off computation, and the # ``SoftCosineSimilarity`` class for corpus-based similarity queries. # # .. Important:: # If you use Gensim's SCM functionality, please consider citing [1], [2] and [3]. # # Computing the Soft Cosine Measure # --------------------------------- # To use SCM, you need some existing word embeddings. # You could train your own Word2Vec model, but that is beyond the scope of this tutorial # (check out :ref:`sphx_glr_auto_examples_tutorials_run_word2vec.py` if you're interested). # For this tutorial, we'll be using an existing Word2Vec model. # # Let's take some sentences to compute the distance between. # # Initialize logging. import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) sentence_obama = 'Obama speaks to the media in Illinois' sentence_president = 'The president greets the press in Chicago' sentence_orange = 'Oranges are my favorite fruit' ############################################################################### # The first two sentences sentences have very similar content, and as such the # SCM should be high. By contrast, the third sentence is unrelated to the first # two and the SCM should be low. # # Before we compute the SCM, we want to remove stopwords ("the", "to", etc.), # as these do not contribute a lot to the information in the sentences. # # Import and download stopwords from NLTK. from nltk.corpus import stopwords from nltk import download download('stopwords') # Download stopwords list. stop_words = stopwords.words('english') def preprocess(sentence): return [w for w in sentence.lower().split() if w not in stop_words] sentence_obama = preprocess(sentence_obama) sentence_president = preprocess(sentence_president) sentence_orange = preprocess(sentence_orange) ############################################################################### # Next, we will build a dictionary and a TF-IDF model, and we will convert the # sentences to the bag-of-words format. # from gensim.corpora import Dictionary documents = [sentence_obama, sentence_president, sentence_orange] dictionary = Dictionary(documents) sentence_obama = dictionary.doc2bow(sentence_obama) sentence_president = dictionary.doc2bow(sentence_president) sentence_orange = dictionary.doc2bow(sentence_orange) from gensim.models import TfidfModel documents = [sentence_obama, sentence_president, sentence_orange] tfidf = TfidfModel(documents) sentence_obama = tfidf[sentence_obama] sentence_president = tfidf[sentence_president] sentence_orange = tfidf[sentence_orange] ############################################################################### # Now, as mentioned earlier, we will be using some downloaded pre-trained # embeddings. We load these into a Gensim Word2Vec model class and we build # a term similarity mextrix using the embeddings. # # .. Important:: # The embeddings we have chosen here require a lot of memory. # import gensim.downloader as api model = api.load('word2vec-google-news-300') from gensim.similarities import SparseTermSimilarityMatrix, WordEmbeddingSimilarityIndex termsim_index = WordEmbeddingSimilarityIndex(model) termsim_matrix = SparseTermSimilarityMatrix(termsim_index, dictionary, tfidf) ############################################################################### # So let's compute SCM using the ``inner_product`` method. # similarity = termsim_matrix.inner_product(sentence_obama, sentence_president, normalized=(True, True)) print('similarity = %.4f' % similarity) ############################################################################### # Let's try the same thing with two completely unrelated sentences. # Notice that the similarity is smaller. # similarity = termsim_matrix.inner_product(sentence_obama, sentence_orange, normalized=(True, True)) print('similarity = %.4f' % similarity) ############################################################################### # # References # ---------- # # 1. Grigori Sidorov et al. Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model, 2014. # 2. Delphine Charlet and Geraldine Damnati, SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering, 2017. # 3. Vít Novotný. Implementation Notes for the Soft Cosine Measure, 2018. # 4. Tomáš Mikolov et al. Efficient Estimation of Word Representations in Vector Space, 2013. #	6,658	Python	.py	133	48.744361	165	0.703094	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,981	run_doc2vec_lee.py	piskvorky_gensim/docs/src/gallery/tutorials/run_doc2vec_lee.py	r""" Doc2Vec Model ============= Introduces Gensim's Doc2Vec model and demonstrates its use on the `Lee Corpus <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`__. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # Doc2Vec is a :ref:`core_concepts_model` that represents each # :ref:`core_concepts_document` as a :ref:`core_concepts_vector`. This # tutorial introduces the model and demonstrates how to train and assess it. # # Here's a list of what we'll be doing: # # 0. Review the relevant models: bag-of-words, Word2Vec, Doc2Vec # 1. Load and preprocess the training and test corpora (see :ref:`core_concepts_corpus`) # 2. Train a Doc2Vec :ref:`core_concepts_model` model using the training corpus # 3. Demonstrate how the trained model can be used to infer a :ref:`core_concepts_vector` # 4. Assess the model # 5. Test the model on the test corpus # # Review: Bag-of-words # -------------------- # # .. Note:: Feel free to skip these review sections if you're already familiar with the models. # # You may be familiar with the `bag-of-words model # <https://en.wikipedia.org/wiki/Bag-of-words_model>`_ from the # :ref:`core_concepts_vector` section. # This model transforms each document to a fixed-length vector of integers. # For example, given the sentences: # # - ``John likes to watch movies. Mary likes movies too.`` # - ``John also likes to watch football games. Mary hates football.`` # # The model outputs the vectors: # # - ``[1, 2, 1, 1, 2, 1, 1, 0, 0, 0, 0]`` # - ``[1, 1, 1, 1, 0, 1, 0, 1, 2, 1, 1]`` # # Each vector has 10 elements, where each element counts the number of times a # particular word occurred in the document. # The order of elements is arbitrary. # In the example above, the order of the elements corresponds to the words: # ``["John", "likes", "to", "watch", "movies", "Mary", "too", "also", "football", "games", "hates"]``. # # Bag-of-words models are surprisingly effective, but have several weaknesses. # # First, they lose all information about word order: "John likes Mary" and # "Mary likes John" correspond to identical vectors. There is a solution: bag # of `n-grams <https://en.wikipedia.org/wiki/N-gram>`__ # models consider word phrases of length n to represent documents as # fixed-length vectors to capture local word order but suffer from data # sparsity and high dimensionality. # # Second, the model does not attempt to learn the meaning of the underlying # words, and as a consequence, the distance between vectors doesn't always # reflect the difference in meaning. The ``Word2Vec`` model addresses this # second problem. # # Review: ``Word2Vec`` Model # -------------------------- # # ``Word2Vec`` is a more recent model that embeds words in a lower-dimensional # vector space using a shallow neural network. The result is a set of # word-vectors where vectors close together in vector space have similar # meanings based on context, and word-vectors distant to each other have # differing meanings. For example, ``strong`` and ``powerful`` would be close # together and ``strong`` and ``Paris`` would be relatively far. # # Gensim's :py:class:`~gensim.models.word2vec.Word2Vec` class implements this model. # # With the ``Word2Vec`` model, we can calculate the vectors for each word in a document. # But what if we want to calculate a vector for the entire document\ ? # We could average the vectors for each word in the document - while this is quick and crude, it can often be useful. # However, there is a better way... # # Introducing: Paragraph Vector # ----------------------------- # # .. Important:: In Gensim, we refer to the Paragraph Vector model as ``Doc2Vec``. # # Le and Mikolov in 2014 introduced the `Doc2Vec algorithm <https://cs.stanford.edu/~quocle/paragraph_vector.pdf>`__, # which usually outperforms such simple-averaging of ``Word2Vec`` vectors. # # The basic idea is: act as if a document has another floating word-like # vector, which contributes to all training predictions, and is updated like # other word-vectors, but we will call it a doc-vector. Gensim's # :py:class:`~gensim.models.doc2vec.Doc2Vec` class implements this algorithm. # # There are two implementations: # # 1. Paragraph Vector - Distributed Memory (PV-DM) # 2. Paragraph Vector - Distributed Bag of Words (PV-DBOW) # # .. Important:: # Don't let the implementation details below scare you. # They're advanced material: if it's too much, then move on to the next section. # # PV-DM is analogous to Word2Vec CBOW. The doc-vectors are obtained by training # a neural network on the synthetic task of predicting a center word based an # average of both context word-vectors and the full document's doc-vector. # # PV-DBOW is analogous to Word2Vec SG. The doc-vectors are obtained by training # a neural network on the synthetic task of predicting a target word just from # the full document's doc-vector. (It is also common to combine this with # skip-gram testing, using both the doc-vector and nearby word-vectors to # predict a single target word, but only one at a time.) # # Prepare the Training and Test Data # ---------------------------------- # # For this tutorial, we'll be training our model using the `Lee Background # Corpus # <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`_ # included in gensim. This corpus contains 314 documents selected from the # Australian Broadcasting Corporation’s news mail service, which provides text # e-mails of headline stories and covers a number of broad topics. # # And we'll test our model by eye using the much shorter `Lee Corpus # <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`_ # which contains 50 documents. # import os import gensim # Set file names for train and test data test_data_dir = os.path.join(gensim.__path__[0], 'test', 'test_data') lee_train_file = os.path.join(test_data_dir, 'lee_background.cor') lee_test_file = os.path.join(test_data_dir, 'lee.cor') ############################################################################### # Define a Function to Read and Preprocess Text # --------------------------------------------- # # Below, we define a function to: # # - open the train/test file (with latin encoding) # - read the file line-by-line # - pre-process each line (tokenize text into individual words, remove punctuation, set to lowercase, etc) # # The file we're reading is a corpus. # Each line of the file is a document. # # .. Important:: # To train the model, we'll need to associate a tag/number with each document # of the training corpus. In our case, the tag is simply the zero-based line # number. # import smart_open def read_corpus(fname, tokens_only=False): with smart_open.open(fname, encoding="iso-8859-1") as f: for i, line in enumerate(f): tokens = gensim.utils.simple_preprocess(line) if tokens_only: yield tokens else: # For training data, add tags yield gensim.models.doc2vec.TaggedDocument(tokens, [i]) train_corpus = list(read_corpus(lee_train_file)) test_corpus = list(read_corpus(lee_test_file, tokens_only=True)) ############################################################################### # Let's take a look at the training corpus # print(train_corpus[:2]) ############################################################################### # And the testing corpus looks like this: # print(test_corpus[:2]) ############################################################################### # Notice that the testing corpus is just a list of lists and does not contain # any tags. # ############################################################################### # Training the Model # ------------------ # # Now, we'll instantiate a Doc2Vec model with a vector size with 50 dimensions and # iterating over the training corpus 40 times. We set the minimum word count to # 2 in order to discard words with very few occurrences. (Without a variety of # representative examples, retaining such infrequent words can often make a # model worse!) Typical iteration counts in the published `Paragraph Vector paper <https://cs.stanford.edu/~quocle/paragraph_vector.pdf>`__ # results, using 10s-of-thousands to millions of docs, are 10-20. More # iterations take more time and eventually reach a point of diminishing # returns. # # However, this is a very very small dataset (300 documents) with shortish # documents (a few hundred words). Adding training passes can sometimes help # with such small datasets. # model = gensim.models.doc2vec.Doc2Vec(vector_size=50, min_count=2, epochs=40) ############################################################################### # Build a vocabulary model.build_vocab(train_corpus) ############################################################################### # Essentially, the vocabulary is a list (accessible via # ``model.wv.index_to_key``) of all of the unique words extracted from the training corpus. # Additional attributes for each word are available using the ``model.wv.get_vecattr()`` method, # For example, to see how many times ``penalty`` appeared in the training corpus: # print(f"Word 'penalty' appeared {model.wv.get_vecattr('penalty', 'count')} times in the training corpus.") ############################################################################### # Next, train the model on the corpus. # In the usual case, where Gensim installation found a BLAS library for optimized # bulk vector operations, this training on this tiny 300 document, ~60k word corpus # should take just a few seconds. (More realistic datasets of tens-of-millions # of words or more take proportionately longer.) If for some reason a BLAS library # isn't available, training uses a fallback approach that takes 60x-120x longer, # so even this tiny training will take minutes rather than seconds. (And, in that # case, you should also notice a warning in the logging letting you know there's # something worth fixing.) So, be sure your installation uses the BLAS-optimized # Gensim if you value your time. # model.train(train_corpus, total_examples=model.corpus_count, epochs=model.epochs) ############################################################################### # Now, we can use the trained model to infer a vector for any piece of text # by passing a list of words to the ``model.infer_vector`` function. This # vector can then be compared with other vectors via cosine similarity. # vector = model.infer_vector(['only', 'you', 'can', 'prevent', 'forest', 'fires']) print(vector) ############################################################################### # Note that ``infer_vector()`` does not take a string, but rather a list of # string tokens, which should have already been tokenized the same way as the # ``words`` property of original training document objects. # # Also note that because the underlying training/inference algorithms are an # iterative approximation problem that makes use of internal randomization, # repeated inferences of the same text will return slightly different vectors. # ############################################################################### # Assessing the Model # ------------------- # # To assess our new model, we'll first infer new vectors for each document of # the training corpus, compare the inferred vectors with the training corpus, # and then returning the rank of the document based on self-similarity. # Basically, we're pretending as if the training corpus is some new unseen data # and then seeing how they compare with the trained model. The expectation is # that we've likely overfit our model (i.e., all of the ranks will be less than # 2) and so we should be able to find similar documents very easily. # Additionally, we'll keep track of the second ranks for a comparison of less # similar documents. # ranks = [] second_ranks = [] for doc_id in range(len(train_corpus)): inferred_vector = model.infer_vector(train_corpus[doc_id].words) sims = model.dv.most_similar([inferred_vector], topn=len(model.dv)) rank = [docid for docid, sim in sims].index(doc_id) ranks.append(rank) second_ranks.append(sims[1]) ############################################################################### # Let's count how each document ranks with respect to the training corpus # # NB. Results vary between runs due to random seeding and very small corpus import collections counter = collections.Counter(ranks) print(counter) ############################################################################### # Basically, greater than 95% of the inferred documents are found to be most # similar to itself and about 5% of the time it is mistakenly most similar to # another document. Checking the inferred-vector against a # training-vector is a sort of 'sanity check' as to whether the model is # behaving in a usefully consistent manner, though not a real 'accuracy' value. # # This is great and not entirely surprising. We can take a look at an example: # print('Document ({}): «{}»\n'.format(doc_id, ' '.join(train_corpus[doc_id].words))) print(u'SIMILAR/DISSIMILAR DOCS PER MODEL %s:\n' % model) for label, index in [('MOST', 0), ('SECOND-MOST', 1), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]: print(u'%s %s: «%s»\n' % (label, sims[index], ' '.join(train_corpus[sims[index][0]].words))) ############################################################################### # Notice above that the most similar document (usually the same text) is has a # similarity score approaching 1.0. However, the similarity score for the # second-ranked documents should be significantly lower (assuming the documents # are in fact different) and the reasoning becomes obvious when we examine the # text itself. # # We can run the next cell repeatedly to see a sampling other target-document # comparisons. # # Pick a random document from the corpus and infer a vector from the model import random doc_id = random.randint(0, len(train_corpus) - 1) # Compare and print the second-most-similar document print('Train Document ({}): «{}»\n'.format(doc_id, ' '.join(train_corpus[doc_id].words))) sim_id = second_ranks[doc_id] print('Similar Document {}: «{}»\n'.format(sim_id, ' '.join(train_corpus[sim_id[0]].words))) ############################################################################### # Testing the Model # ----------------- # # Using the same approach above, we'll infer the vector for a randomly chosen # test document, and compare the document to our model by eye. # # Pick a random document from the test corpus and infer a vector from the model doc_id = random.randint(0, len(test_corpus) - 1) inferred_vector = model.infer_vector(test_corpus[doc_id]) sims = model.dv.most_similar([inferred_vector], topn=len(model.dv)) # Compare and print the most/median/least similar documents from the train corpus print('Test Document ({}): «{}»\n'.format(doc_id, ' '.join(test_corpus[doc_id]))) print(u'SIMILAR/DISSIMILAR DOCS PER MODEL %s:\n' % model) for label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]: print(u'%s %s: «%s»\n' % (label, sims[index], ' '.join(train_corpus[sims[index][0]].words))) ############################################################################### # Conclusion # ---------- # # Let's review what we've seen in this tutorial: # # 0. Review the relevant models: bag-of-words, Word2Vec, Doc2Vec # 1. Load and preprocess the training and test corpora (see :ref:`core_concepts_corpus`) # 2. Train a Doc2Vec :ref:`core_concepts_model` model using the training corpus # 3. Demonstrate how the trained model can be used to infer a :ref:`core_concepts_vector` # 4. Assess the model # 5. Test the model on the test corpus # # That's it! Doc2Vec is a great way to explore relationships between documents. # # Additional Resources # -------------------- # # If you'd like to know more about the subject matter of this tutorial, check out the links below. # # * `Word2Vec Paper <https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf>`_ # * `Doc2Vec Paper <https://cs.stanford.edu/~quocle/paragraph_vector.pdf>`_ # * `Dr. Michael D. Lee's Website <http://faculty.sites.uci.edu/mdlee>`_ # * `Lee Corpus <http://faculty.sites.uci.edu/mdlee/similarity-data/>`__ # * `IMDB Doc2Vec Tutorial <doc2vec-IMDB.ipynb>`_ #	16,619	Python	.py	330	48.836364	139	0.674689	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,982	run_fasttext.py	piskvorky_gensim/docs/src/gallery/tutorials/run_fasttext.py	r""" FastText Model ============== Introduces Gensim's fastText model and demonstrates its use on the Lee Corpus. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # Here, we'll learn to work with fastText library for training word-embedding # models, saving & loading them and performing similarity operations & vector # lookups analogous to Word2Vec. ############################################################################### # # When to use fastText? # --------------------- # # The main principle behind `fastText <https://github.com/facebookresearch/fastText>`_ is that the # morphological structure of a word carries important information about the meaning of the word. # Such structure is not taken into account by traditional word embeddings like Word2Vec, which # train a unique word embedding for every individual word. # This is especially significant for morphologically rich languages (German, Turkish) in which a # single word can have a large number of morphological forms, each of which might occur rarely, # thus making it hard to train good word embeddings. # # # fastText attempts to solve this by treating each word as the aggregation of its subwords. # For the sake of simplicity and language-independence, subwords are taken to be the character ngrams # of the word. The vector for a word is simply taken to be the sum of all vectors of its component char-ngrams. # # # According to a detailed comparison of Word2Vec and fastText in # `this notebook <https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Word2Vec_FastText_Comparison.ipynb>`__, # fastText does significantly better on syntactic tasks as compared to the original Word2Vec, # especially when the size of the training corpus is small. Word2Vec slightly outperforms fastText # on semantic tasks though. The differences grow smaller as the size of the training corpus increases. # # # fastText can obtain vectors even for out-of-vocabulary (OOV) words, by summing up vectors for its # component char-ngrams, provided at least one of the char-ngrams was present in the training data. # ############################################################################### # # Training models # --------------- # ############################################################################### # # For the following examples, we'll use the Lee Corpus (which you already have if you've installed Gensim) for training our model. # # # from pprint import pprint as print from gensim.models.fasttext import FastText from gensim.test.utils import datapath # Set file names for train and test data corpus_file = datapath('lee_background.cor') model = FastText(vector_size=100) # build the vocabulary model.build_vocab(corpus_file=corpus_file) # train the model model.train( corpus_file=corpus_file, epochs=model.epochs, total_examples=model.corpus_count, total_words=model.corpus_total_words, ) print(model) ############################################################################### # # Training hyperparameters # ^^^^^^^^^^^^^^^^^^^^^^^^ # ############################################################################### # # Hyperparameters for training the model follow the same pattern as Word2Vec. FastText supports the following parameters from the original word2vec: # # - model: Training architecture. Allowed values: `cbow`, `skipgram` (Default `cbow`) # - vector_size: Dimensionality of vector embeddings to be learnt (Default 100) # - alpha: Initial learning rate (Default 0.025) # - window: Context window size (Default 5) # - min_count: Ignore words with number of occurrences below this (Default 5) # - loss: Training objective. Allowed values: `ns`, `hs`, `softmax` (Default `ns`) # - sample: Threshold for downsampling higher-frequency words (Default 0.001) # - negative: Number of negative words to sample, for `ns` (Default 5) # - epochs: Number of epochs (Default 5) # - sorted_vocab: Sort vocab by descending frequency (Default 1) # - threads: Number of threads to use (Default 12) # # # In addition, fastText has three additional parameters: # # - min_n: min length of char ngrams (Default 3) # - max_n: max length of char ngrams (Default 6) # - bucket: number of buckets used for hashing ngrams (Default 2000000) # # # Parameters ``min_n`` and ``max_n`` control the lengths of character ngrams that each word is broken down into while training and looking up embeddings. If ``max_n`` is set to 0, or to be lesser than ``min_n``\ , no character ngrams are used, and the model effectively reduces to Word2Vec. # # # # To bound the memory requirements of the model being trained, a hashing function is used that maps ngrams to integers in 1 to K. For hashing these character sequences, the `Fowler-Noll-Vo hashing function <http://www.isthe.com/chongo/tech/comp/fnv>`_ (FNV-1a variant) is employed. # ############################################################################### # # Note: You can continue to train your model while using Gensim's native implementation of fastText. # ############################################################################### # # Saving/loading models # --------------------- # ############################################################################### # # Models can be saved and loaded via the ``load`` and ``save`` methods, just like # any other model in Gensim. # # Save a model trained via Gensim's fastText implementation to temp. import tempfile import os with tempfile.NamedTemporaryFile(prefix='saved_model_gensim-', delete=False) as tmp: model.save(tmp.name, separately=[]) # Load back the same model. loaded_model = FastText.load(tmp.name) print(loaded_model) os.unlink(tmp.name) # demonstration complete, don't need the temp file anymore ############################################################################### # # The ``save_word2vec_format`` is also available for fastText models, but will # cause all vectors for ngrams to be lost. # As a result, a model loaded in this way will behave as a regular word2vec model. # ############################################################################### # # Word vector lookup # ------------------ # # # All information necessary for looking up fastText words (incl. OOV words) is # contained in its ``model.wv`` attribute. # # If you don't need to continue training your model, you can export & save this `.wv` # attribute and discard `model`, to save space and RAM. # wv = model.wv print(wv) # # FastText models support vector lookups for out-of-vocabulary words by summing up character ngrams belonging to the word. # print('night' in wv.key_to_index) ############################################################################### # print('nights' in wv.key_to_index) ############################################################################### # print(wv['night']) ############################################################################### # print(wv['nights']) ############################################################################### # # Similarity operations # --------------------- # ############################################################################### # # Similarity operations work the same way as word2vec. Out-of-vocabulary words can also be used, provided they have at least one character ngram present in the training data. # print("nights" in wv.key_to_index) ############################################################################### # print("night" in wv.key_to_index) ############################################################################### # print(wv.similarity("night", "nights")) ############################################################################### # # Syntactically similar words generally have high similarity in fastText models, since a large number of the component char-ngrams will be the same. As a result, fastText generally does better at syntactic tasks than Word2Vec. A detailed comparison is provided `here <Word2Vec_FastText_Comparison.ipynb>`_. # ############################################################################### # # Other similarity operations # ^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # The example training corpus is a toy corpus, results are not expected to be good, for proof-of-concept only print(wv.most_similar("nights")) ############################################################################### # print(wv.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant'])) ############################################################################### # print(wv.doesnt_match("breakfast cereal dinner lunch".split())) ############################################################################### # print(wv.most_similar(positive=['baghdad', 'england'], negative=['london'])) ############################################################################### # print(wv.evaluate_word_analogies(datapath('questions-words.txt'))) ############################################################################### # Word Movers distance # ^^^^^^^^^^^^^^^^^^^^ # # You'll need the optional ``POT`` library for this section, ``pip install POT``. # # Let's start with two sentences: sentence_obama = 'Obama speaks to the media in Illinois'.lower().split() sentence_president = 'The president greets the press in Chicago'.lower().split() ############################################################################### # Remove their stopwords. # from gensim.parsing.preprocessing import STOPWORDS sentence_obama = [w for w in sentence_obama if w not in STOPWORDS] sentence_president = [w for w in sentence_president if w not in STOPWORDS] ############################################################################### # Compute the Word Movers Distance between the two sentences. distance = wv.wmdistance(sentence_obama, sentence_president) print(f"Word Movers Distance is {distance} (lower means closer)") ############################################################################### # That's all! You've made it to the end of this tutorial. # import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('fasttext-logo-color-web.png') imgplot = plt.imshow(img) _ = plt.axis('off')	10,335	Python	.py	222	45.252252	306	0.589083	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,983	run_annoy.py	piskvorky_gensim/docs/src/gallery/tutorials/run_annoy.py	r""" Fast Similarity Queries with Annoy and Word2Vec =============================================== Introduces the Annoy library for similarity queries on top of vectors learned by Word2Vec. """ LOGS = False # Set to True if you want to see progress in logs. if LOGS: import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # The `Annoy "Approximate Nearest Neighbors Oh Yeah" # <https://github.com/spotify/annoy>`_ library enables similarity queries with # a Word2Vec model. The current implementation for finding k nearest neighbors # in a vector space in Gensim has linear complexity via brute force in the # number of indexed documents, although with extremely low constant factors. # The retrieved results are exact, which is an overkill in many applications: # approximate results retrieved in sub-linear time may be enough. Annoy can # find approximate nearest neighbors much faster. # # Outline # ------- # # 1. Download Text8 Corpus # 2. Train the Word2Vec model # 3. Construct AnnoyIndex with model & make a similarity query # 4. Compare to the traditional indexer # 5. Persist indices to disk # 6. Save memory by via memory-mapping indices saved to disk # 7. Evaluate relationship of ``num_trees`` to initialization time and accuracy # 8. Work with Google's word2vec C formats # ############################################################################### # 1. Download Text8 corpus # ------------------------ import gensim.downloader as api text8_path = api.load('text8', return_path=True) print("Using corpus from", text8_path) ############################################################################### # 2. Train the Word2Vec model # --------------------------- # # For more details, see :ref:`sphx_glr_auto_examples_tutorials_run_word2vec.py`. from gensim.models import Word2Vec, KeyedVectors from gensim.models.word2vec import Text8Corpus # Using params from Word2Vec_FastText_Comparison params = { 'alpha': 0.05, 'vector_size': 100, 'window': 5, 'epochs': 5, 'min_count': 5, 'sample': 1e-4, 'sg': 1, 'hs': 0, 'negative': 5, } model = Word2Vec(Text8Corpus(text8_path), *params) wv = model.wv print("Using trained model", wv) ############################################################################### # 3. Construct AnnoyIndex with model & make a similarity query # ------------------------------------------------------------ # # An instance of ``AnnoyIndexer`` needs to be created in order to use Annoy in Gensim. # The ``AnnoyIndexer`` class is located in ``gensim.similarities.annoy``. # # ``AnnoyIndexer()`` takes two parameters: # # model: A ``Word2Vec`` or ``Doc2Vec`` model. # * num_trees: A positive integer. ``num_trees`` effects the build # time and the index size. A larger value will give more accurate results, # but larger indexes. More information on what trees in Annoy do can be found # `here <https://github.com/spotify/annoy#how-does-it-work>`__. The relationship # between ``num_trees``\ , build time, and accuracy will be investigated later # in the tutorial. # # Now that we are ready to make a query, lets find the top 5 most similar words # to "science" in the Text8 corpus. To make a similarity query we call # ``Word2Vec.most_similar`` like we would traditionally, but with an added # parameter, ``indexer``. # # Apart from Annoy, Gensim also supports the NMSLIB indexer. NMSLIB is a similar library to # Annoy â€“ both support fast, approximate searches for similar vectors. # from gensim.similarities.annoy import AnnoyIndexer # 100 trees are being used in this example annoy_index = AnnoyIndexer(model, 100) # Derive the vector for the word "science" in our model vector = wv["science"] # The instance of AnnoyIndexer we just created is passed approximate_neighbors = wv.most_similar([vector], topn=11, indexer=annoy_index) # Neatly print the approximate_neighbors and their corresponding cosine similarity values print("Approximate Neighbors") for neighbor in approximate_neighbors: print(neighbor) normal_neighbors = wv.most_similar([vector], topn=11) print("\nExact Neighbors") for neighbor in normal_neighbors: print(neighbor) ############################################################################### # The closer the cosine similarity of a vector is to 1, the more similar that # word is to our query, which was the vector for "science". There are some # differences in the ranking of similar words and the set of words included # within the 10 most similar words. ############################################################################### # 4. Compare to the traditional indexer # ------------------------------------- # Set up the model and vector that we are using in the comparison annoy_index = AnnoyIndexer(model, 100) # Dry run to make sure both indexes are fully in RAM normed_vectors = wv.get_normed_vectors() vector = normed_vectors[0] wv.most_similar([vector], topn=5, indexer=annoy_index) wv.most_similar([vector], topn=5) import time import numpy as np def avg_query_time(annoy_index=None, queries=1000): """Average query time of a most_similar method over 1000 random queries.""" total_time = 0 for _ in range(queries): rand_vec = normed_vectors[np.random.randint(0, len(wv))] start_time = time.process_time() wv.most_similar([rand_vec], topn=5, indexer=annoy_index) total_time += time.process_time() - start_time return total_time / queries queries = 1000 gensim_time = avg_query_time(queries=queries) annoy_time = avg_query_time(annoy_index, queries=queries) print("Gensim (s/query):\t{0:.5f}".format(gensim_time)) print("Annoy (s/query):\t{0:.5f}".format(annoy_time)) speed_improvement = gensim_time / annoy_time print ("\nAnnoy is {0:.2f} times faster on average on this particular run".format(speed_improvement)) ############################################################################### # This speedup factor is by no means constant and will vary greatly from # run to run and is particular to this data set, BLAS setup, Annoy # parameters(as tree size increases speedup factor decreases), machine # specifications, among other factors. # # .. Important:: # Initialization time for the annoy indexer was not included in the times. # The optimal knn algorithm for you to use will depend on how many queries # you need to make and the size of the corpus. If you are making very few # similarity queries, the time taken to initialize the annoy indexer will be # longer than the time it would take the brute force method to retrieve # results. If you are making many queries however, the time it takes to # initialize the annoy indexer will be made up for by the incredibly fast # retrieval times for queries once the indexer has been initialized # # .. Important:: # Gensim's 'most_similar' method is using numpy operations in the form of # dot product whereas Annoy's method isnt. If 'numpy' on your machine is # using one of the BLAS libraries like ATLAS or LAPACK, it'll run on # multiple cores (only if your machine has multicore support ). Check `SciPy # Cookbook # <http://scipy-cookbook.readthedocs.io/items/ParallelProgramming.html>`_ # for more details. # ############################################################################### # 5. Persisting indices to disk # ----------------------------- # # You can save and load your indexes from/to disk to prevent having to # construct them each time. This will create two files on disk, fname and # fname.d. Both files are needed to correctly restore all attributes. Before # loading an index, you will have to create an empty AnnoyIndexer object. # fname = '/tmp/mymodel.index' # Persist index to disk annoy_index.save(fname) # Load index back import os.path if os.path.exists(fname): annoy_index2 = AnnoyIndexer() annoy_index2.load(fname) annoy_index2.model = model # Results should be identical to above vector = wv["science"] approximate_neighbors2 = wv.most_similar([vector], topn=11, indexer=annoy_index2) for neighbor in approximate_neighbors2: print(neighbor) assert approximate_neighbors == approximate_neighbors2 ############################################################################### # Be sure to use the same model at load that was used originally, otherwise you # will get unexpected behaviors. # ############################################################################### # 6. Save memory via memory-mapping indexes saved to disk # ------------------------------------------------------- # # Annoy library has a useful feature that indices can be memory-mapped from # disk. It saves memory when the same index is used by several processes. # # Below are two snippets of code. First one has a separate index for each # process. The second snipped shares the index between two processes via # memory-mapping. The second example uses less total RAM as it is shared. # # Remove verbosity from code below (if logging active) if LOGS: logging.disable(logging.CRITICAL) from multiprocessing import Process import os import psutil ############################################################################### # Bad example: two processes load the Word2vec model from disk and create their # own Annoy index from that model. # model.save('/tmp/mymodel.pkl') def f(process_id): print('Process Id: {}'.format(os.getpid())) process = psutil.Process(os.getpid()) new_model = Word2Vec.load('/tmp/mymodel.pkl') vector = new_model.wv["science"] annoy_index = AnnoyIndexer(new_model, 100) approximate_neighbors = new_model.wv.most_similar([vector], topn=5, indexer=annoy_index) print('\nMemory used by process {}: {}\n---'.format(os.getpid(), process.memory_info())) # Create and run two parallel processes to share the same index file. p1 = Process(target=f, args=('1',)) p1.start() p1.join() p2 = Process(target=f, args=('2',)) p2.start() p2.join() ############################################################################### # Good example: two processes load both the Word2vec model and index from disk # and memory-map the index. # model.save('/tmp/mymodel.pkl') def f(process_id): print('Process Id: {}'.format(os.getpid())) process = psutil.Process(os.getpid()) new_model = Word2Vec.load('/tmp/mymodel.pkl') vector = new_model.wv["science"] annoy_index = AnnoyIndexer() annoy_index.load('/tmp/mymodel.index') annoy_index.model = new_model approximate_neighbors = new_model.wv.most_similar([vector], topn=5, indexer=annoy_index) print('\nMemory used by process {}: {}\n---'.format(os.getpid(), process.memory_info())) # Creating and running two parallel process to share the same index file. p1 = Process(target=f, args=('1',)) p1.start() p1.join() p2 = Process(target=f, args=('2',)) p2.start() p2.join() ############################################################################### # 7. Evaluate relationship of ``num_trees`` to initialization time and accuracy # ----------------------------------------------------------------------------- # import matplotlib.pyplot as plt ############################################################################### # Build dataset of initialization times and accuracy measures: # exact_results = [element[0] for element in wv.most_similar([normed_vectors[0]], topn=100)] x_values = [] y_values_init = [] y_values_accuracy = [] for x in range(1, 300, 10): x_values.append(x) start_time = time.time() annoy_index = AnnoyIndexer(model, x) y_values_init.append(time.time() - start_time) approximate_results = wv.most_similar([normed_vectors[0]], topn=100, indexer=annoy_index) top_words = [result[0] for result in approximate_results] y_values_accuracy.append(len(set(top_words).intersection(exact_results))) ############################################################################### # Plot results: plt.figure(1, figsize=(12, 6)) plt.subplot(121) plt.plot(x_values, y_values_init) plt.title("num_trees vs initalization time") plt.ylabel("Initialization time (s)") plt.xlabel("num_trees") plt.subplot(122) plt.plot(x_values, y_values_accuracy) plt.title("num_trees vs accuracy") plt.ylabel("%% accuracy") plt.xlabel("num_trees") plt.tight_layout() plt.show() ############################################################################### # From the above, we can see that the initialization time of the annoy indexer # increases in a linear fashion with num_trees. Initialization time will vary # from corpus to corpus. In the graph above we used the (tiny) Lee corpus. # # Furthermore, in this dataset, the accuracy seems logarithmically related to # the number of trees. We see an improvement in accuracy with more trees, but # the relationship is nonlinear. # ############################################################################### # 7. Work with Google's word2vec files # ------------------------------------ # # Our model can be exported to a word2vec C format. There is a binary and a # plain text word2vec format. Both can be read with a variety of other # software, or imported back into Gensim as a ``KeyedVectors`` object. # # To export our model as text wv.save_word2vec_format('/tmp/vectors.txt', binary=False) from smart_open import open # View the first 3 lines of the exported file # The first line has the total number of entries and the vector dimension count. # The next lines have a key (a string) followed by its vector. with open('/tmp/vectors.txt', encoding='utf8') as myfile: for i in range(3): print(myfile.readline().strip()) # To import a word2vec text model wv = KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False) # To export a model as binary wv.save_word2vec_format('/tmp/vectors.bin', binary=True) # To import a word2vec binary model wv = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True) # To create and save Annoy Index from a loaded `KeyedVectors` object (with 100 trees) annoy_index = AnnoyIndexer(wv, 100) annoy_index.save('/tmp/mymodel.index') # Load and test the saved word vectors and saved Annoy index wv = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True) annoy_index = AnnoyIndexer() annoy_index.load('/tmp/mymodel.index') annoy_index.model = wv vector = wv["cat"] approximate_neighbors = wv.most_similar([vector], topn=11, indexer=annoy_index) # Neatly print the approximate_neighbors and their corresponding cosine similarity values print("Approximate Neighbors") for neighbor in approximate_neighbors: print(neighbor) normal_neighbors = wv.most_similar([vector], topn=11) print("\nExact Neighbors") for neighbor in normal_neighbors: print(neighbor) ############################################################################### # Recap # ----- # # In this notebook we used the Annoy module to build an indexed approximation # of our word embeddings. To do so, we did the following steps: # # 1. Download Text8 Corpus # 2. Train Word2Vec Model # 3. Construct AnnoyIndex with model & make a similarity query # 4. Persist indices to disk # 5. Save memory by via memory-mapping indices saved to disk # 6. Evaluate relationship of ``num_trees`` to initialization time and accuracy # 7. Work with Google's word2vec C formats #	15,445	Python	.py	344	43.06686	101	0.661128	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,984	run_wmd.py	piskvorky_gensim/docs/src/gallery/tutorials/run_wmd.py	r""" Word Mover's Distance ===================== Demonstrates using Gensim's implemenation of the WMD. """ ############################################################################### # Word Mover's Distance (WMD) is a promising new tool in machine learning that # allows us to submit a query and return the most relevant documents. This # tutorial introduces WMD and shows how you can compute the WMD distance # between two documents using ``wmdistance``. # # WMD Basics # ---------- # # WMD enables us to assess the "distance" between two documents in a meaningful # way even when they have no words in common. It uses `word2vec # <https://rare-technologies.com/word2vec-tutorial/>`_ [4] vector embeddings of # words. It been shown to outperform many of the state-of-the-art methods in # k-nearest neighbors classification [3]. # # WMD is illustrated below for two very similar sentences (illustration taken # from `Vlad Niculae's blog # <http://vene.ro/blog/word-movers-distance-in-python.html>`_). The sentences # have no words in common, but by matching the relevant words, WMD is able to # accurately measure the (dis)similarity between the two sentences. The method # also uses the bag-of-words representation of the documents (simply put, the # word's frequencies in the documents), noted as $d$ in the figure below. The # intuition behind the method is that we find the minimum "traveling distance" # between documents, in other words the most efficient way to "move" the # distribution of document 1 to the distribution of document 2. # # Image from https://vene.ro/images/wmd-obama.png import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('wmd-obama.png') imgplot = plt.imshow(img) plt.axis('off') plt.show() ############################################################################### # This method was introduced in the article "From Word Embeddings To Document # Distances" by Matt Kusner et al. (\ `link to PDF # <http://jmlr.org/proceedings/papers/v37/kusnerb15.pdf>`_\ ). It is inspired # by the "Earth Mover's Distance", and employs a solver of the "transportation # problem". # # In this tutorial, we will learn how to use Gensim's WMD functionality, which # consists of the ``wmdistance`` method for distance computation, and the # ``WmdSimilarity`` class for corpus based similarity queries. # # .. Important:: # If you use Gensim's WMD functionality, please consider citing [1] and [2]. # # Computing the Word Mover's Distance # ----------------------------------- # # To use WMD, you need some existing word embeddings. # You could train your own Word2Vec model, but that is beyond the scope of this tutorial # (check out :ref:`sphx_glr_auto_examples_tutorials_run_word2vec.py` if you're interested). # For this tutorial, we'll be using an existing Word2Vec model. # # Let's take some sentences to compute the distance between. # # Initialize logging. import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) sentence_obama = 'Obama speaks to the media in Illinois' sentence_president = 'The president greets the press in Chicago' ############################################################################### # These sentences have very similar content, and as such the WMD should be low. # Before we compute the WMD, we want to remove stopwords ("the", "to", etc.), # as these do not contribute a lot to the information in the sentences. # # Import and download stopwords from NLTK. from nltk.corpus import stopwords from nltk import download download('stopwords') # Download stopwords list. stop_words = stopwords.words('english') def preprocess(sentence): return [w for w in sentence.lower().split() if w not in stop_words] sentence_obama = preprocess(sentence_obama) sentence_president = preprocess(sentence_president) ############################################################################### # Now, as mentioned earlier, we will be using some downloaded pre-trained # embeddings. We load these into a Gensim Word2Vec model class. # # .. Important:: # The embeddings we have chosen here require a lot of memory. # import gensim.downloader as api model = api.load('word2vec-google-news-300') ############################################################################### # So let's compute WMD using the ``wmdistance`` method. # distance = model.wmdistance(sentence_obama, sentence_president) print('distance = %.4f' % distance) ############################################################################### # Let's try the same thing with two completely unrelated sentences. Notice that the distance is larger. # sentence_orange = preprocess('Oranges are my favorite fruit') distance = model.wmdistance(sentence_obama, sentence_orange) print('distance = %.4f' % distance) ############################################################################### # References # ---------- # # 1. Rémi Flamary et al. POT: Python Optimal Transport, 2021. # 2. Matt Kusner et al. From Embeddings To Document Distances, 2015. # 3. Tomáš Mikolov et al. Efficient Estimation of Word Representations in Vector Space, 2013. #	5,165	Python	.py	109	46.183486	103	0.674871	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,985	run_ensemblelda.py	piskvorky_gensim/docs/src/gallery/tutorials/run_ensemblelda.py	r""" Ensemble LDA ============ Introduces Gensim's EnsembleLda model """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # This tutorial will explain how to use the EnsembleLDA model class. # # EnsembleLda is a method of finding and generating stable topics from the results of multiple topic models, # it can be used to remove topics from your results that are noise and are not reproducible. # ############################################################################### # Corpus # ------ # We will use the gensim downloader api to get a small corpus for training our ensemble. # # The preprocessing is similar to :ref:`sphx_glr_auto_examples_tutorials_run_word2vec.py`, # so it won't be explained again in detail. # import gensim.downloader as api from gensim.corpora import Dictionary from nltk.stem.wordnet import WordNetLemmatizer from nltk import download download('wordnet') lemmatizer = WordNetLemmatizer() docs = api.load('text8') dictionary = Dictionary() for doc in docs: dictionary.add_documents([[lemmatizer.lemmatize(token) for token in doc]]) dictionary.filter_extremes(no_below=20, no_above=0.5) corpus = [dictionary.doc2bow(doc) for doc in docs] ############################################################################### # Training # -------- # # Training the ensemble works very similar to training a single model, # # You can use any model that is based on LdaModel, such as LdaMulticore, to train the Ensemble. # In experiments, LdaMulticore showed better results. # from gensim.models import LdaModel topic_model_class = LdaModel ############################################################################### # Any arbitrary number of models can be used, but it should be a multiple of your workers so that the # load can be distributed properly. In this example, 4 processes will train 8 models each. # ensemble_workers = 4 num_models = 8 ############################################################################### # After training all the models, some distance computations are required which can take quite some # time as well. You can speed this up by using workers for that as well. # distance_workers = 4 ############################################################################### # All other parameters that are unknown to EnsembleLda are forwarded to each LDA Model, such as # num_topics = 20 passes = 2 ############################################################################### # Now start the training # # Since 20 topics were trained on each of the 8 models, we expect there to be 160 different topics. # The number of stable topics which are clustered from all those topics is smaller. # from gensim.models import EnsembleLda ensemble = EnsembleLda( corpus=corpus, id2word=dictionary, num_topics=num_topics, passes=passes, num_models=num_models, topic_model_class=LdaModel, ensemble_workers=ensemble_workers, distance_workers=distance_workers ) print(len(ensemble.ttda)) print(len(ensemble.get_topics())) ############################################################################### # Tuning # ------ # # Different from LdaModel, the number of resulting topics varies greatly depending on the clustering parameters. # # You can provide those in the ``recluster()`` function or the ``EnsembleLda`` constructor. # # Play around until you get as many topics as you desire, which however may reduce their quality. # If your ensemble doesn't have enough topics to begin with, you should make sure to make it large enough. # # Having an epsilon that is smaller than the smallest distance doesn't make sense. # Make sure to chose one that is within the range of values in ``asymmetric_distance_matrix``. # import numpy as np shape = ensemble.asymmetric_distance_matrix.shape without_diagonal = ensemble.asymmetric_distance_matrix[~np.eye(shape[0], dtype=bool)].reshape(shape[0], -1) print(without_diagonal.min(), without_diagonal.mean(), without_diagonal.max()) ensemble.recluster(eps=0.09, min_samples=2, min_cores=2) print(len(ensemble.get_topics())) ############################################################################### # Increasing the Size # ------------------- # # If you have some models lying around that were trained on a corpus based on the same dictionary, # they are compatible and you can add them to the ensemble. # # By setting num_models of the EnsembleLda constructor to 0 you can also create an ensemble that is # entirely made out of your existing topic models with the following method. # # Afterwards the number and quality of stable topics might be different depending on your added topics and parameters. # from gensim.models import LdaMulticore model1 = LdaMulticore( corpus=corpus, id2word=dictionary, num_topics=9, passes=4, ) model2 = LdaModel( corpus=corpus, id2word=dictionary, num_topics=11, passes=2, ) # add_model supports various types of input, check out its docstring ensemble.add_model(model1) ensemble.add_model(model2) ensemble.recluster() print(len(ensemble.ttda)) print(len(ensemble.get_topics()))	5,237	Python	.py	130	38.523077	118	0.66647	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,986	__init__.py	piskvorky_gensim/docs/src/sphinx_rtd_theme/__init__.py	""" Sphinx Read the Docs theme. From https://github.com/ryan-roemer/sphinx-bootstrap-theme. """ from os import path import sphinx __version__ = '0.5.0' __version_full__ = __version__ def get_html_theme_path(): """Return list of HTML theme paths.""" cur_dir = path.abspath(path.dirname(path.dirname(__file__))) return cur_dir # See http://www.sphinx-doc.org/en/stable/theming.html#distribute-your-theme-as-a-python-package def setup(app): if sphinx.version_info >= (1, 6, 0): # Register the theme that can be referenced without adding a theme path app.add_html_theme('sphinx_rtd_theme', path.abspath(path.dirname(__file__))) if sphinx.version_info >= (1, 8, 0): # Add Sphinx message catalog for newer versions of Sphinx # See http://www.sphinx-doc.org/en/master/extdev/appapi.html#sphinx.application.Sphinx.add_message_catalog rtd_locale_path = path.join(path.abspath(path.dirname(__file__)), 'locale') app.add_message_catalog('sphinx', rtd_locale_path) return {'parallel_read_safe': True, 'parallel_write_safe': True}	1,101	Python	.py	23	43.26087	114	0.695408	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,987	run_doc.py	piskvorky_gensim/docs/src/auto_examples/howtos/run_doc.py	r""" How to Author Gensim Documentation ================================== How to author documentation for Gensim. """ ############################################################################### # Background # ---------- # # Gensim is a large project with a wide range of functionality. # Unfortunately, not all of this functionality is documented well, and some of it is not documented at all. # Without good documentation, users are unable to unlock Gensim's full potential. # Therefore, authoring new documentation and improving existing documentation is of great value to the Gensim project. # # If you implement new functionality in Gensim, please include helpful documentation. # By "helpful", we mean that your documentation answers questions that Gensim users may have. # For example: # # - What is this new functionality? # - Why is it important? # - How is it relevant to Gensim? # - What can I do with it? What are some real-world applications? # - How do I use it to achieve those things? # - ... and others (if you can think of them, please add them here) # # Before you author documentation, I suggest reading # `"What nobody tells you about documentation" <https://www.divio.com/blog/documentation/>`__ # or watching its `accompanying video <https://www.youtube.com/watch?v=t4vKPhjcMZg>`__ # (or even both, if you're really keen). # # The summary of the above presentation is: there are four distinct kinds of documentation, and you really need them all: # # 1. Tutorials # 2. Howto guides # 3. Explanations # 4. References # # Each kind has its own intended audience, purpose, and writing style. # When you make a PR with new functionality, please consider authoring each kind of documentation. # At the very least, you will (indirectly) author reference documentation through module, class and function docstrings. # # Mechanisms # ---------- # # We keep our documentation as individual Python scripts. # These scripts live under :file:`docs/src/gallery` in one of several subdirectories: # # - core: core tutorials. We try to keep this part small, avoid putting stuff here. # - tutorials: tutorials. # - howtos: howto guides. # # Pick a subdirectory and save your script under it. # Prefix the name of the script with ``run_``: this way, the the documentation builder will run your script each time it builds our docs. # # The contents of the script are straightforward. # At the very top, you need a docstring describing what your script does. r""" Title ===== Brief description. """ ############################################################################### # The title is what will show up in the gallery. # Keep this short and descriptive. # # The description will appear as a tooltip in the gallery. # When people mouse-over the title, they will see the description. # Keep this short too. # ############################################################################### # The rest of the script is Python, formatted in a special way so that Sphinx Gallery can parse it. # The most important properties of this format are: # # - Sphinx Gallery will split your script into blocks # - A block can be Python source or RST-formatted comments # - To indicate that a block is in RST, prefix it with a line of 80 hash (#) characters. # - All other blocks will be interpreted as Python source # # Read `this link <https://sphinx-gallery.github.io/syntax.html>`__ for more details. # If you need further examples, check out other ``gensim`` tutorials and guides. # All of them (including this one!) have a download link at the bottom of the page, which exposes the Python source they were generated from. # # You should be able to run your script directly from the command line:: # # python myscript.py # # and it should run to completion without error, occasionally printing stuff to standard output. # ############################################################################### # Authoring Workflow # ------------------ # # There are several ways to author documentation. # The simplest and most straightforward is to author your ``script.py`` from scratch. # You'll have the following cycle: # # 1. Make changes # 2. Run ``python script.py`` # 3. Check standard output, standard error and return code # 4. If everything works well, stop. # 5. Otherwise, go back to step 1). # # If the above is not your cup of tea, you can also author your documentation as a Jupyter notebook. # This is a more flexible approach that enables you to tweak parts of the documentation and re-run them as necessary. # # Once you're happy with the notebook, convert it to a script.py. # There's a helpful `script <https://github.com/RaRe-Technologies/gensim/blob/develop/docs/src/tools/to_python.py>`__ that will do it for you. # To use it:: # # python to_python.py < notebook.ipynb > script.py # # You may have to touch up the resulting ``script.py``. # More specifically: # # - Update the title # - Update the description # - Fix any issues that the markdown-to-RST converter could not deal with # # Once your script.py works, put it in a suitable subdirectory. # Please don't include your original Jupyter notebook in the repository - we won't be using it. ############################################################################### # Correctness # ----------- # # Incorrect documentation can be worse than no documentation at all. # Take the following steps to ensure correctness: # # - Run Python's doctest module on your docstrings # - Run your documentation scripts from scratch, removing any temporary files/results # # Using data in your documentation # -------------------------------- # # Some parts of the documentation require real-world data to be useful. # For example, you may need more than just a toy example to demonstrate the benefits of one model over another. # This subsection provides some tips for including data in your documentation. # # If possible, use data available via Gensim's # `downloader API <https://radimrehurek.com/gensim/gensim_numfocus/auto_examples/010_tutorials/run_downloader_api.html>`__. # This will reduce the risk of your documentation becoming obsolete because required data is no longer available. # # Use the smallest possible dataset: avoid making people unnecessarily load large datasets and models. # This will make your documentation faster to run and easier for people to use (they can modify your examples and re-run them quickly). # # Finalizing your contribution # ---------------------------- # # First, get Sphinx Gallery to build your documentation:: # # make --directory docs/src html # # This can take a while if your documentation uses a large dataset, or if you've changed many other tutorials or guides. # Once this completes successfully, open ``docs/auto_examples/index.html`` in your browser. # You should see your new tutorial or guide in the gallery. # # Once your documentation script is working correctly, it's time to add it to the git repository:: # # git add docs/src/gallery/tutorials/run_example.py # git add docs/src/auto_examples/tutorials/run_example.{py,py.md5,rst,ipynb} # git add docs/src/auto_examples/howtos/sg_execution_times.rst # git commit -m "enter a helpful commit message here" # git push origin branchname # # .. Note:: # You may be wondering what all those other files are. # Sphinx Gallery puts a copy of your Python script in ``auto_examples/tutorials``. # The .md5 contains MD5 hash of the script to enable easy detection of modifications. # Gallery also generates .rst (RST for Sphinx) and .ipynb (Jupyter notebook) files from the script. # Finally, ``sg_execution_times.rst`` contains the time taken to run each example. # # Finally, open a PR at `github <https://github.com/RaRe-Technologies/gensim>`__. # One of our friendly maintainers will review it, make suggestions, and eventually merge it. # Your documentation will then appear in the `gallery <https://radimrehurek.com/gensim/auto_examples/index.html>`__, # alongside the rest of the examples. Thanks a lot!	8,054	Python	.py	174	45.241379	142	0.713542	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,988	run_doc2vec_imdb.py	piskvorky_gensim/docs/src/auto_examples/howtos/run_doc2vec_imdb.py	r""" How to reproduce the doc2vec 'Paragraph Vector' paper ===================================================== Shows how to reproduce results of the "Distributed Representation of Sentences and Documents" paper by Le and Mikolov using Gensim. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # Introduction # ------------ # # This guide shows you how to reproduce the results of the paper by `Le and # Mikolov 2014 <https://arxiv.org/pdf/1405.4053.pdf>`_ using Gensim. While the # entire paper is worth reading (it's only 9 pages), we will be focusing on # Section 3.2: "Beyond One Sentence - Sentiment Analysis with the IMDB # dataset". # # This guide follows the following steps: # # #. Load the IMDB dataset # #. Train a variety of Doc2Vec models on the dataset # #. Evaluate the performance of each model using a logistic regression # #. Examine some of the results directly: # # When examining results, we will look for answers for the following questions: # # #. Are inferred vectors close to the precalculated ones? # #. Do close documents seem more related than distant ones? # #. Do the word vectors show useful similarities? # #. Are the word vectors from this dataset any good at analogies? # # Load corpus # ----------- # # Our data for the tutorial will be the `IMDB archive # <http://ai.stanford.edu/~amaas/data/sentiment/>`_. # If you're not familiar with this dataset, then here's a brief intro: it # contains several thousand movie reviews. # # Each review is a single line of text containing multiple sentences, for example: # # ``` # One of the best movie-dramas I have ever seen. We do a lot of acting in the # church and this is one that can be used as a resource that highlights all the # good things that actors can do in their work. I highly recommend this one, # especially for those who have an interest in acting, as a "must see." # ``` # # These reviews will be the documents that we will work with in this tutorial. # There are 100 thousand reviews in total. # # #. 25k reviews for training (12.5k positive, 12.5k negative) # #. 25k reviews for testing (12.5k positive, 12.5k negative) # #. 50k unlabeled reviews # # Out of 100k reviews, 50k have a label: either positive (the reviewer liked # the movie) or negative. # The remaining 50k are unlabeled. # # Our first task will be to prepare the dataset. # # More specifically, we will: # # #. Download the tar.gz file (it's only 84MB, so this shouldn't take too long) # #. Unpack it and extract each movie review # #. Split the reviews into training and test datasets # # First, let's define a convenient datatype for holding data for a single document: # # * words: The text of the document, as a ``list`` of words. # * tags: Used to keep the index of the document in the entire dataset. # * split: one of ``train``\ , ``test`` or ``extra``. Determines how the document will be used (for training, testing, etc). # * sentiment: either 1 (positive), 0 (negative) or None (unlabeled document). # # This data type is helpful for later evaluation and reporting. # In particular, the ``index`` member will help us quickly and easily retrieve the vectors for a document from a model. # import collections SentimentDocument = collections.namedtuple('SentimentDocument', 'words tags split sentiment') ############################################################################### # We can now proceed with loading the corpus. import io import re import tarfile import os.path import smart_open import gensim.utils def download_dataset(url='http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'): fname = url.split('/')[-1] if os.path.isfile(fname): return fname # Download the file to local storage first. try: kwargs = { 'compression': smart_open.compression.NO_COMPRESSION } fin = smart_open.open(url, "rb", kwargs) except (AttributeError, TypeError): kwargs = { 'ignore_ext': True } fin = smart_open.open(url, "rb", kwargs) if fin: with smart_open.open(fname, 'wb', *kwargs) as fout: while True: buf = fin.read(io.DEFAULT_BUFFER_SIZE) if not buf: break fout.write(buf) fin.close() return fname def create_sentiment_document(name, text, index): _, split, sentiment_str, _ = name.split('/') sentiment = {'pos': 1.0, 'neg': 0.0, 'unsup': None}[sentiment_str] if sentiment is None: split = 'extra' tokens = gensim.utils.to_unicode(text).split() return SentimentDocument(tokens, [index], split, sentiment) def extract_documents(): fname = download_dataset() index = 0 with tarfile.open(fname, mode='r:gz') as tar: for member in tar.getmembers(): if re.match(r'aclImdb/(train\|test)/(pos\|neg\|unsup)/\d+_\d+.txt$', member.name): member_bytes = tar.extractfile(member).read() member_text = member_bytes.decode('utf-8', errors='replace') assert member_text.count('\n') == 0 yield create_sentiment_document(member.name, member_text, index) index += 1 alldocs = list(extract_documents()) ############################################################################### # Here's what a single document looks like. print(alldocs[27]) ############################################################################### # Extract our documents and split into training/test sets. train_docs = [doc for doc in alldocs if doc.split == 'train'] test_docs = [doc for doc in alldocs if doc.split == 'test'] print(f'{len(alldocs)} docs: {len(train_docs)} train-sentiment, {len(test_docs)} test-sentiment') ############################################################################### # Set-up Doc2Vec Training & Evaluation Models # ------------------------------------------- # # We approximate the experiment of Le & Mikolov `"Distributed Representations # of Sentences and Documents" # <http://cs.stanford.edu/~quocle/paragraph_vector.pdf>`_ with guidance from # Mikolov's `example go.sh # <https://groups.google.com/g/word2vec-toolkit/c/Q49FIrNOQRo/m/J6KG8mUj45sJ>`_:: # # ./word2vec -train ../alldata-id.txt -output vectors.txt -cbow 0 -size 100 -window 10 -negative 5 -hs 0 -sample 1e-4 -threads 40 -binary 0 -iter 20 -min-count 1 -sentence-vectors 1 # # We vary the following parameter choices: # # 100-dimensional vectors, as the 400-d vectors of the paper take a lot of # memory and, in our tests of this task, don't seem to offer much benefit # * Similarly, frequent word subsampling seems to decrease sentiment-prediction # accuracy, so it's left out # * ``cbow=0`` means skip-gram which is equivalent to the paper's 'PV-DBOW' # mode, matched in gensim with ``dm=0`` # * Added to that DBOW model are two DM models, one which averages context # vectors (\ ``dm_mean``\ ) and one which concatenates them (\ ``dm_concat``\ , # resulting in a much larger, slower, more data-hungry model) # * A ``min_count=2`` saves quite a bit of model memory, discarding only words # that appear in a single doc (and are thus no more expressive than the # unique-to-each doc vectors themselves) # import multiprocessing from collections import OrderedDict import gensim.models.doc2vec assert gensim.models.doc2vec.FAST_VERSION > -1, "This will be painfully slow otherwise" from gensim.models.doc2vec import Doc2Vec common_kwargs = dict( vector_size=100, epochs=20, min_count=2, sample=0, workers=multiprocessing.cpu_count(), negative=5, hs=0, ) simple_models = [ # PV-DBOW plain Doc2Vec(dm=0, common_kwargs), # PV-DM w/ default averaging; a higher starting alpha may improve CBOW/PV-DM modes Doc2Vec(dm=1, window=10, alpha=0.05, comment='alpha=0.05', common_kwargs), # PV-DM w/ concatenation - big, slow, experimental mode # window=5 (both sides) approximates paper's apparent 10-word total window size Doc2Vec(dm=1, dm_concat=1, window=5, *common_kwargs), ] for model in simple_models: model.build_vocab(alldocs) print(f"{model} vocabulary scanned & state initialized") models_by_name = OrderedDict((str(model), model) for model in simple_models) ############################################################################### # Le and Mikolov note that combining a paragraph vector from Distributed Bag of # Words (DBOW) and Distributed Memory (DM) improves performance. We will # follow, pairing the models together for evaluation. Here, we concatenate the # paragraph vectors obtained from each model with the help of a thin wrapper # class included in a gensim test module. (Note that this a separate, later # concatenation of output-vectors than the kind of input-window-concatenation # enabled by the ``dm_concat=1`` mode above.) # from gensim.test.test_doc2vec import ConcatenatedDoc2Vec models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[0], simple_models[1]]) models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[0], simple_models[2]]) ############################################################################### # Predictive Evaluation Methods # ----------------------------- # # Given a document, our ``Doc2Vec`` models output a vector representation of the document. # How useful is a particular model? # In case of sentiment analysis, we want the output vector to reflect the sentiment in the input document. # So, in vector space, positive documents should be distant from negative documents. # # We train a logistic regression from the training set: # # - regressors (inputs): document vectors from the Doc2Vec model # - target (outpus): sentiment labels # # So, this logistic regression will be able to predict sentiment given a document vector. # # Next, we test our logistic regression on the test set, and measure the rate of errors (incorrect predictions). # If the document vectors from the Doc2Vec model reflect the actual sentiment well, the error rate will be low. # # Therefore, the error rate of the logistic regression is indication of how well* the given Doc2Vec model represents documents as vectors. # We can then compare different ``Doc2Vec`` models by looking at their error rates. # import numpy as np import statsmodels.api as sm from random import sample def logistic_predictor_from_data(train_targets, train_regressors): """Fit a statsmodel logistic predictor on supplied data""" logit = sm.Logit(train_targets, train_regressors) predictor = logit.fit(disp=0) # print(predictor.summary()) return predictor def error_rate_for_model(test_model, train_set, test_set): """Report error rate on test_doc sentiments, using supplied model and train_docs""" train_targets = [doc.sentiment for doc in train_set] train_regressors = [test_model.dv[doc.tags[0]] for doc in train_set] train_regressors = sm.add_constant(train_regressors) predictor = logistic_predictor_from_data(train_targets, train_regressors) test_regressors = [test_model.dv[doc.tags[0]] for doc in test_set] test_regressors = sm.add_constant(test_regressors) # Predict & evaluate test_predictions = predictor.predict(test_regressors) corrects = sum(np.rint(test_predictions) == [doc.sentiment for doc in test_set]) errors = len(test_predictions) - corrects error_rate = float(errors) / len(test_predictions) return (error_rate, errors, len(test_predictions), predictor) ############################################################################### # Bulk Training & Per-Model Evaluation # ------------------------------------ # # Note that doc-vector training is occurring on all documents of the dataset, # which includes all TRAIN/TEST/DEV docs. Because the native document-order # has similar-sentiment documents in large clumps – which is suboptimal for # training – we work with once-shuffled copy of the training set. # # We evaluate each model's sentiment predictive power based on error rate, and # the evaluation is done for each model. # # (On a 4-core 2.6Ghz Intel Core i7, these 20 passes training and evaluating 3 # main models takes about an hour.) # from collections import defaultdict error_rates = defaultdict(lambda: 1.0) # To selectively print only best errors achieved ############################################################################### # from random import shuffle shuffled_alldocs = alldocs[:] shuffle(shuffled_alldocs) for model in simple_models: print(f"Training {model}") model.train(shuffled_alldocs, total_examples=len(shuffled_alldocs), epochs=model.epochs) print(f"\nEvaluating {model}") err_rate, err_count, test_count, predictor = error_rate_for_model(model, train_docs, test_docs) error_rates[str(model)] = err_rate print("\n%f %s\n" % (err_rate, model)) for model in [models_by_name['dbow+dmm'], models_by_name['dbow+dmc']]: print(f"\nEvaluating {model}") err_rate, err_count, test_count, predictor = error_rate_for_model(model, train_docs, test_docs) error_rates[str(model)] = err_rate print(f"\n{err_rate} {model}\n") ############################################################################### # Achieved Sentiment-Prediction Accuracy # -------------------------------------- # Compare error rates achieved, best-to-worst print("Err_rate Model") for rate, name in sorted((rate, name) for name, rate in error_rates.items()): print(f"{rate} {name}") ############################################################################### # In our testing, contrary to the results of the paper, on this problem, # PV-DBOW alone performs as good as anything else. Concatenating vectors from # different models only sometimes offers a tiny predictive improvement – and # stays generally close to the best-performing solo model included. # # The best results achieved here are just around 10% error rate, still a long # way from the paper's reported 7.42% error rate. # # (Other trials not shown, with larger vectors and other changes, also don't # come close to the paper's reported value. Others around the net have reported # a similar inability to reproduce the paper's best numbers. The PV-DM/C mode # improves a bit with many more training epochs – but doesn't reach parity with # PV-DBOW.) # ############################################################################### # Examining Results # ----------------- # # Let's look for answers to the following questions: # # #. Are inferred vectors close to the precalculated ones? # #. Do close documents seem more related than distant ones? # #. Do the word vectors show useful similarities? # #. Are the word vectors from this dataset any good at analogies? # ############################################################################### # Are inferred vectors close to the precalculated ones? # ----------------------------------------------------- doc_id = np.random.randint(len(simple_models[0].dv)) # Pick random doc; re-run cell for more examples print(f'for doc {doc_id}...') for model in simple_models: inferred_docvec = model.infer_vector(alldocs[doc_id].words) print(f'{model}:\n {model.dv.most_similar([inferred_docvec], topn=3)}') ############################################################################### # (Yes, here the stored vector from 20 epochs of training is usually one of the # closest to a freshly-inferred vector for the same words. Defaults for # inference may benefit from tuning for each dataset or model parameters.) # ############################################################################### # Do close documents seem more related than distant ones? # ------------------------------------------------------- import random doc_id = np.random.randint(len(simple_models[0].dv)) # pick random doc, re-run cell for more examples model = random.choice(simple_models) # and a random model sims = model.dv.most_similar(doc_id, topn=len(model.dv)) # get all similar documents print(f'TARGET ({doc_id}): «{" ".join(alldocs[doc_id].words)}»\n') print(f'SIMILAR/DISSIMILAR DOCS PER MODEL {model}%s:\n') for label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]: s = sims[index] i = sims[index][0] words = ' '.join(alldocs[i].words) print(f'{label} {s}: «{words}»\n') ############################################################################### # Somewhat, in terms of reviewer tone, movie genre, etc... the MOST # cosine-similar docs usually seem more like the TARGET than the MEDIAN or # LEAST... especially if the MOST has a cosine-similarity > 0.5. Re-run the # cell to try another random target document. # ############################################################################### # Do the word vectors show useful similarities? # --------------------------------------------- # import random word_models = simple_models[:] def pick_random_word(model, threshold=10): # pick a random word with a suitable number of occurences while True: word = random.choice(model.wv.index_to_key) if model.wv.get_vecattr(word, "count") > threshold: return word target_word = pick_random_word(word_models[0]) # or uncomment below line, to just pick a word from the relevant domain: # target_word = 'comedy/drama' for model in word_models: print(f'target_word: {repr(target_word)} model: {model} similar words:') for i, (word, sim) in enumerate(model.wv.most_similar(target_word, topn=10), 1): print(f' {i}. {sim:.2f} {repr(word)}') print() ############################################################################### # Do the DBOW words look meaningless? That's because the gensim DBOW model # doesn't train word vectors – they remain at their random initialized values – # unless you ask with the ``dbow_words=1`` initialization parameter. Concurrent # word-training slows DBOW mode significantly, and offers little improvement # (and sometimes a little worsening) of the error rate on this IMDB # sentiment-prediction task, but may be appropriate on other tasks, or if you # also need word-vectors. # # Words from DM models tend to show meaningfully similar words when there are # many examples in the training data (as with 'plot' or 'actor'). (All DM modes # inherently involve word-vector training concurrent with doc-vector training.) # ############################################################################### # Are the word vectors from this dataset any good at analogies? # ------------------------------------------------------------- from gensim.test.utils import datapath questions_filename = datapath('questions-words.txt') # Note: this analysis takes many minutes for model in word_models: score, sections = model.wv.evaluate_word_analogies(questions_filename) correct, incorrect = len(sections[-1]['correct']), len(sections[-1]['incorrect']) print(f'{model}: {float(correct*100)/(correct+incorrect):0.2f}%% correct ({correct} of {correct+incorrect}') ############################################################################### # Even though this is a tiny, domain-specific dataset, it shows some meager # capability on the general word analogies – at least for the DM/mean and # DM/concat models which actually train word vectors. (The untrained # random-initialized words of the DBOW model of course fail miserably.) #	19,497	Python	.py	389	47.493573	185	0.656858	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,989	run_compare_lda.py	piskvorky_gensim/docs/src/auto_examples/howtos/run_compare_lda.py	r""" How to Compare LDA Models ========================= Demonstrates how you can visualize and compare trained topic models. """ # sphinx_gallery_thumbnail_number = 2 import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # # First, clean up the 20 Newsgroups dataset. We will use it to fit LDA. # --------------------------------------------------------------------- # from string import punctuation from nltk import RegexpTokenizer from nltk.stem.porter import PorterStemmer from nltk.corpus import stopwords from sklearn.datasets import fetch_20newsgroups newsgroups = fetch_20newsgroups() eng_stopwords = set(stopwords.words('english')) tokenizer = RegexpTokenizer(r'\s+', gaps=True) stemmer = PorterStemmer() translate_tab = {ord(p): u" " for p in punctuation} def text2tokens(raw_text): """Split the raw_text string into a list of stemmed tokens.""" clean_text = raw_text.lower().translate(translate_tab) tokens = [token.strip() for token in tokenizer.tokenize(clean_text)] tokens = [token for token in tokens if token not in eng_stopwords] stemmed_tokens = [stemmer.stem(token) for token in tokens] return [token for token in stemmed_tokens if len(token) > 2] # skip short tokens dataset = [text2tokens(txt) for txt in newsgroups['data']] # convert a documents to list of tokens from gensim.corpora import Dictionary dictionary = Dictionary(documents=dataset, prune_at=None) dictionary.filter_extremes(no_below=5, no_above=0.3, keep_n=None) # use Dictionary to remove un-relevant tokens dictionary.compactify() d2b_dataset = [dictionary.doc2bow(doc) for doc in dataset] # convert list of tokens to bag of word representation ############################################################################### # # Second, fit two LDA models. # --------------------------- # from gensim.models import LdaMulticore num_topics = 15 lda_fst = LdaMulticore( corpus=d2b_dataset, num_topics=num_topics, id2word=dictionary, workers=4, eval_every=None, passes=10, batch=True, ) lda_snd = LdaMulticore( corpus=d2b_dataset, num_topics=num_topics, id2word=dictionary, workers=4, eval_every=None, passes=20, batch=True, ) ############################################################################### # # Time to visualize, yay! # ----------------------- # # We use two slightly different visualization methods depending on how you're running this tutorial. # If you're running via a Jupyter notebook, then you'll get a nice interactive Plotly heatmap. # If you're viewing the static version of the page, you'll get a similar matplotlib heatmap, but it won't be interactive. # def plot_difference_plotly(mdiff, title="", annotation=None): """Plot the difference between models. Uses plotly as the backend.""" import plotly.graph_objs as go import plotly.offline as py annotation_html = None if annotation is not None: annotation_html = [ [ "+++ {}<br>--- {}".format(", ".join(int_tokens), ", ".join(diff_tokens)) for (int_tokens, diff_tokens) in row ] for row in annotation ] data = go.Heatmap(z=mdiff, colorscale='RdBu', text=annotation_html) layout = go.Layout(width=950, height=950, title=title, xaxis=dict(title="topic"), yaxis=dict(title="topic")) py.iplot(dict(data=[data], layout=layout)) def plot_difference_matplotlib(mdiff, title="", annotation=None): """Helper function to plot difference between models. Uses matplotlib as the backend.""" import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(18, 14)) data = ax.imshow(mdiff, cmap='RdBu_r', origin='lower') plt.title(title) plt.colorbar(data) try: get_ipython() import plotly.offline as py except Exception: # # Fall back to matplotlib if we're not in a notebook, or if plotly is # unavailable for whatever reason. # plot_difference = plot_difference_matplotlib else: py.init_notebook_mode() plot_difference = plot_difference_plotly ############################################################################### # # Gensim can help you visualise the differences between topics. For this purpose, you can use the ``diff()`` method of LdaModel. # # ``diff()`` returns a matrix with distances mdiff and a matrix with annotations annotation. Read the docstring for more detailed info. # # In each mdiff[i][j] cell you'll find a distance between topic_i from the first model and topic_j from the second model. # # In each annotation[i][j] cell you'll find [tokens from intersection, tokens from difference between topic_i from first model and topic_j from the second model. # print(LdaMulticore.diff.__doc__) ############################################################################### # # Case 1: How topics within ONE model correlate with each other. # -------------------------------------------------------------- # ############################################################################### # # Short description: # # * x-axis - topic; # # * y-axis - topic; # # .. role:: raw-html-m2r(raw) # :format: html # # * :raw-html-m2r:`<span style="color:red">almost red cell</span>` - strongly decorrelated topics; # # .. role:: raw-html-m2r(raw) # :format: html # # * :raw-html-m2r:`<span style="color:blue">almost blue cell</span>` - strongly correlated topics. # # In an ideal world, we would like to see different topics decorrelated between themselves. # In this case, our matrix would look like this: # import numpy as np mdiff = np.ones((num_topics, num_topics)) np.fill_diagonal(mdiff, 0.) plot_difference(mdiff, title="Topic difference (one model) in ideal world") ############################################################################### # # Unfortunately, in real life, not everything is so good, and the matrix looks different. # ############################################################################### # # Short description (interactive annotations only): # # * ``+++ make, world, well`` - words from the intersection of topics = present in both topics; # # * ``--- money, day, still`` - words from the symmetric difference of topics = present in one topic but not the other. # mdiff, annotation = lda_fst.diff(lda_fst, distance='jaccard', num_words=50) plot_difference(mdiff, title="Topic difference (one model) [jaccard distance]", annotation=annotation) ############################################################################### # # If you compare a model with itself, you want to see as many red elements as # possible (except on the diagonal). With this picture, you can look at the # "not very red elements" and understand which topics in the model are very # similar and why (you can read annotation if you move your pointer to cell). # # Jaccard is a stable and robust distance function, but sometimes not sensitive # enough. Let's try to use the Hellinger distance instead. # mdiff, annotation = lda_fst.diff(lda_fst, distance='hellinger', num_words=50) plot_difference(mdiff, title="Topic difference (one model)[hellinger distance]", annotation=annotation) ############################################################################### # # You see that everything has become worse, but remember that everything depends on the task. # # Choose a distance function that matches your upstream task better: what kind of "similarity" is # relevant to you. From my (Ivan's) experience, Jaccard is fine. # ############################################################################### # # Case 2: How topics from DIFFERENT models correlate with each other. # ------------------------------------------------------------------- # ############################################################################### # # Sometimes, we want to look at the patterns between two different models and compare them. # # You can do this by constructing a matrix with the difference. # mdiff, annotation = lda_fst.diff(lda_snd, distance='jaccard', num_words=50) plot_difference(mdiff, title="Topic difference (two models)[jaccard distance]", annotation=annotation) ############################################################################### # # Looking at this matrix, you can find similar and different topics between the two models. # The plot also includes relevant tokens describing the topics' intersection and difference. #	8,569	Python	.py	189	42.89418	177	0.622494	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,990	run_downloader_api.py	piskvorky_gensim/docs/src/auto_examples/howtos/run_downloader_api.py	r""" How to download pre-trained models and corpora ============================================== Demonstrates simple and quick access to common corpora and pretrained models. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # One of Gensim's features is simple and easy access to common data. # The `gensim-data <https://github.com/RaRe-Technologies/gensim-data>`_ project stores a # variety of corpora and pretrained models. # Gensim has a :py:mod:`gensim.downloader` module for programmatically accessing this data. # This module leverages a local cache (in user's home folder, by default) that # ensures data is downloaded at most once. # # This tutorial: # # * Downloads the text8 corpus, unless it is already on your local machine # * Trains a Word2Vec model from the corpus (see :ref:`sphx_glr_auto_examples_tutorials_run_doc2vec_lee.py` for a detailed tutorial) # * Leverages the model to calculate word similarity # * Demonstrates using the API to load other models and corpora # # Let's start by importing the api module. # import gensim.downloader as api ############################################################################### # # Now, let's download the text8 corpus and load it as a Python object # that supports streamed access. # corpus = api.load('text8') ############################################################################### # In this case, our corpus is an iterable. # If you look under the covers, it has the following definition: import inspect print(inspect.getsource(corpus.__class__)) ############################################################################### # For more details, look inside the file that defines the Dataset class for your particular resource. # print(inspect.getfile(corpus.__class__)) ############################################################################### # # With the corpus has been downloaded and loaded, let's use it to train a word2vec model. # from gensim.models.word2vec import Word2Vec model = Word2Vec(corpus) ############################################################################### # # Now that we have our word2vec model, let's find words that are similar to 'tree'. # print(model.wv.most_similar('tree')) ############################################################################### # # You can use the API to download several different corpora and pretrained models. # Here's how to list all resources available in gensim-data: # import json info = api.info() print(json.dumps(info, indent=4)) ############################################################################### # There are two types of data resources: corpora and models. print(info.keys()) ############################################################################### # Let's have a look at the available corpora: for corpus_name, corpus_data in sorted(info['corpora'].items()): print( '%s (%d records): %s' % ( corpus_name, corpus_data.get('num_records', -1), corpus_data['description'][:40] + '...', ) ) ############################################################################### # ... and the same for models: for model_name, model_data in sorted(info['models'].items()): print( '%s (%d records): %s' % ( model_name, model_data.get('num_records', -1), model_data['description'][:40] + '...', ) ) ############################################################################### # # If you want to get detailed information about a model/corpus, use: # fake_news_info = api.info('fake-news') print(json.dumps(fake_news_info, indent=4)) ############################################################################### # # Sometimes, you do not want to load a model into memory. Instead, you can request # just the filesystem path to the model. For that, use: # print(api.load('glove-wiki-gigaword-50', return_path=True)) ############################################################################### # # If you want to load the model to memory, then: # model = api.load("glove-wiki-gigaword-50") model.most_similar("glass") ############################################################################### # # For corpora, the corpus is never loaded to memory, all corpora are iterables wrapped in # a special class ``Dataset``, with an ``__iter__`` method. #	4,515	Python	.py	105	40.580952	132	0.534125	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,991	run_compare_lda.ipynb	piskvorky_gensim/docs/src/auto_examples/howtos/run_compare_lda.ipynb	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nHow to Compare LDA Models\n=========================\n\nDemonstrates how you can visualize and compare trained topic models.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# sphinx_gallery_thumbnail_number = 2\nimport logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, clean up the 20 Newsgroups dataset. We will use it to fit LDA.\n---------------------------------------------------------------------\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from string import punctuation\nfrom nltk import RegexpTokenizer\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.corpus import stopwords\nfrom sklearn.datasets import fetch_20newsgroups\n\n\nnewsgroups = fetch_20newsgroups()\neng_stopwords = set(stopwords.words('english'))\n\ntokenizer = RegexpTokenizer(r'\\s+', gaps=True)\nstemmer = PorterStemmer()\ntranslate_tab = {ord(p): u\" \" for p in punctuation}\n\ndef text2tokens(raw_text):\n \"\"\"Split the raw_text string into a list of stemmed tokens.\"\"\"\n clean_text = raw_text.lower().translate(translate_tab)\n tokens = [token.strip() for token in tokenizer.tokenize(clean_text)]\n tokens = [token for token in tokens if token not in eng_stopwords]\n stemmed_tokens = [stemmer.stem(token) for token in tokens]\n return [token for token in stemmed_tokens if len(token) > 2] # skip short tokens\n\ndataset = [text2tokens(txt) for txt in newsgroups['data']] # convert a documents to list of tokens\n\nfrom gensim.corpora import Dictionary\ndictionary = Dictionary(documents=dataset, prune_at=None)\ndictionary.filter_extremes(no_below=5, no_above=0.3, keep_n=None) # use Dictionary to remove un-relevant tokens\ndictionary.compactify()\n\nd2b_dataset = [dictionary.doc2bow(doc) for doc in dataset] # convert list of tokens to bag of word representation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Second, fit two LDA models.\n---------------------------\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.models import LdaMulticore\nnum_topics = 15\n\nlda_fst = LdaMulticore(\n corpus=d2b_dataset, num_topics=num_topics, id2word=dictionary,\n workers=4, eval_every=None, passes=10, batch=True,\n)\n\nlda_snd = LdaMulticore(\n corpus=d2b_dataset, num_topics=num_topics, id2word=dictionary,\n workers=4, eval_every=None, passes=20, batch=True,\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time to visualize, yay!\n-----------------------\n\nWe use two slightly different visualization methods depending on how you're running this tutorial.\nIf you're running via a Jupyter notebook, then you'll get a nice interactive Plotly heatmap.\nIf you're viewing the static version of the page, you'll get a similar matplotlib heatmap, but it won't be interactive.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_difference_plotly(mdiff, title=\"\", annotation=None):\n \"\"\"Plot the difference between models.\n\n Uses plotly as the backend.\"\"\"\n import plotly.graph_objs as go\n import plotly.offline as py\n\n annotation_html = None\n if annotation is not None:\n annotation_html = [\n [\n \"+++ {}<br>--- {}\".format(\", \".join(int_tokens), \", \".join(diff_tokens))\n for (int_tokens, diff_tokens) in row\n ]\n for row in annotation\n ]\n\n data = go.Heatmap(z=mdiff, colorscale='RdBu', text=annotation_html)\n layout = go.Layout(width=950, height=950, title=title, xaxis=dict(title=\"topic\"), yaxis=dict(title=\"topic\"))\n py.iplot(dict(data=[data], layout=layout))\n\n\ndef plot_difference_matplotlib(mdiff, title=\"\", annotation=None):\n \"\"\"Helper function to plot difference between models.\n\n Uses matplotlib as the backend.\"\"\"\n import matplotlib.pyplot as plt\n fig, ax = plt.subplots(figsize=(18, 14))\n data = ax.imshow(mdiff, cmap='RdBu_r', origin='lower')\n plt.title(title)\n plt.colorbar(data)\n\n\ntry:\n get_ipython()\n import plotly.offline as py\nexcept Exception:\n #\n # Fall back to matplotlib if we're not in a notebook, or if plotly is\n # unavailable for whatever reason.\n #\n plot_difference = plot_difference_matplotlib\nelse:\n py.init_notebook_mode()\n plot_difference = plot_difference_plotly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gensim can help you visualise the differences between topics. For this purpose, you can use the ``diff()`` method of LdaModel.\n\n``diff()`` returns a matrix with distances mdiff and a matrix with annotations annotation. Read the docstring for more detailed info.\n\nIn each mdiff[i][j] cell you'll find a distance between topic_i from the first model and topic_j from the second model.\n\nIn each annotation[i][j] cell you'll find [tokens from intersection, tokens from difference between topic_i from first model and topic_j from the second model.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(LdaMulticore.diff.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Case 1: How topics within ONE model correlate with each other.\n--------------------------------------------------------------\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Short description:\n\n* x-axis - topic;\n\n* y-axis - topic;\n\n.. role:: raw-html-m2r(raw)\n :format: html\n\n* :raw-html-m2r:`<span style=\"color:red\">almost red cell</span>` - strongly decorrelated topics;\n\n.. role:: raw-html-m2r(raw)\n :format: html\n\n* :raw-html-m2r:`<span style=\"color:blue\">almost blue cell</span>` - strongly correlated topics.\n\nIn an ideal world, we would like to see different topics decorrelated between themselves.\nIn this case, our matrix would look like this:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n\nmdiff = np.ones((num_topics, num_topics))\nnp.fill_diagonal(mdiff, 0.)\nplot_difference(mdiff, title=\"Topic difference (one model) in ideal world\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, in real life, not everything is so good, and the matrix looks different.\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Short description (interactive annotations only):\n\n* ``+++ make, world, well`` - words from the intersection of topics = present in both topics;\n\n* ``--- money, day, still`` - words from the symmetric difference of topics = present in one topic but not the other.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mdiff, annotation = lda_fst.diff(lda_fst, distance='jaccard', num_words=50)\nplot_difference(mdiff, title=\"Topic difference (one model) [jaccard distance]\", annotation=annotation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you compare a model with itself, you want to see as many red elements as\npossible (except on the diagonal). With this picture, you can look at the\n\"not very red elements\" and understand which topics in the model are very\nsimilar and why (you can read annotation if you move your pointer to cell).\n\nJaccard is a stable and robust distance function, but sometimes not sensitive\nenough. Let's try to use the Hellinger distance instead.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mdiff, annotation = lda_fst.diff(lda_fst, distance='hellinger', num_words=50)\nplot_difference(mdiff, title=\"Topic difference (one model)[hellinger distance]\", annotation=annotation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You see that everything has become worse, but remember that everything depends on the task.\n\nChoose a distance function that matches your upstream task better: what kind of \"similarity\" is\nrelevant to you. From my (Ivan's) experience, Jaccard is fine.\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Case 2: How topics from DIFFERENT models correlate with each other.\n-------------------------------------------------------------------\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, we want to look at the patterns between two different models and compare them.\n\nYou can do this by constructing a matrix with the difference.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mdiff, annotation = lda_fst.diff(lda_snd, distance='jaccard', num_words=50)\nplot_difference(mdiff, title=\"Topic difference (two models)[jaccard distance]\", annotation=annotation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at this matrix, you can find similar and different topics between the two models.\nThe plot also includes relevant tokens describing the topics' intersection and difference.\n\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 0 }	11,395	Python	.py	233	42.201717	1,499	0.599301	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,992	run_doc.ipynb	piskvorky_gensim/docs/src/auto_examples/howtos/run_doc.ipynb	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nHow to Author Gensim Documentation\n==================================\n\nHow to author documentation for Gensim.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Background\n----------\n\nGensim is a large project with a wide range of functionality.\nUnfortunately, not all of this functionality is documented well, and some of it is not documented at all.\nWithout good documentation, users are unable to unlock Gensim's full potential.\nTherefore, authoring new documentation and improving existing documentation is of great value to the Gensim project.\n\nIf you implement new functionality in Gensim, please include helpful documentation.\nBy \"helpful\", we mean that your documentation answers questions that Gensim users may have.\nFor example:\n\n- What is this new functionality?\n- Why is it important?\n- How is it relevant to Gensim?\n- What can I do with it? What are some real-world applications?\n- How do I use it to achieve those things?\n- ... and others (if you can think of them, please add them here)\n\nBefore you author documentation, I suggest reading\n`\"What nobody tells you about documentation\" <https://www.divio.com/blog/documentation/>`__\nor watching its `accompanying video <https://www.youtube.com/watch?v=t4vKPhjcMZg>`__\n(or even both, if you're really keen).\n\nThe summary of the above presentation is: there are four distinct kinds of documentation, and you really need them all:\n\n1. Tutorials\n2. Howto guides\n3. Explanations\n4. References\n\nEach kind has its own intended audience, purpose, and writing style.\nWhen you make a PR with new functionality, please consider authoring each kind of documentation.\nAt the very least, you will (indirectly) author reference documentation through module, class and function docstrings.\n\nMechanisms\n----------\n\nWe keep our documentation as individual Python scripts.\nThese scripts live under :file:`docs/src/gallery` in one of several subdirectories:\n\n- core: core tutorials. We try to keep this part small, avoid putting stuff here.\n- tutorials: tutorials.\n- howtos: howto guides.\n\nPick a subdirectory and save your script under it.\nPrefix the name of the script with ``run_``: this way, the the documentation builder will run your script each time it builds our docs.\n\nThe contents of the script are straightforward.\nAt the very top, you need a docstring describing what your script does.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r\"\"\"\nTitle\n=====\n\nBrief description.\n\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The title is what will show up in the gallery.\nKeep this short and descriptive.\n\nThe description will appear as a tooltip in the gallery.\nWhen people mouse-over the title, they will see the description.\nKeep this short too.\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rest of the script is Python, formatted in a special way so that Sphinx Gallery can parse it.\nThe most important properties of this format are:\n\n- Sphinx Gallery will split your script into blocks\n- A block can be Python source or RST-formatted comments\n- To indicate that a block is in RST, prefix it with a line of 80 hash (#) characters.\n- All other blocks will be interpreted as Python source\n\nRead `this link <https://sphinx-gallery.github.io/syntax.html>`__ for more details.\nIf you need further examples, check out other ``gensim`` tutorials and guides.\nAll of them (including this one!) have a download link at the bottom of the page, which exposes the Python source they were generated from.\n\nYou should be able to run your script directly from the command line::\n\n python myscript.py\n\nand it should run to completion without error, occasionally printing stuff to standard output.\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Authoring Workflow\n------------------\n\nThere are several ways to author documentation.\nThe simplest and most straightforward is to author your ``script.py`` from scratch.\nYou'll have the following cycle:\n\n1. Make changes\n2. Run ``python script.py``\n3. Check standard output, standard error and return code\n4. If everything works well, stop.\n5. Otherwise, go back to step 1).\n\nIf the above is not your cup of tea, you can also author your documentation as a Jupyter notebook.\nThis is a more flexible approach that enables you to tweak parts of the documentation and re-run them as necessary.\n\nOnce you're happy with the notebook, convert it to a script.py.\nThere's a helpful `script <https://github.com/RaRe-Technologies/gensim/blob/develop/docs/src/tools/to_python.py>`__ that will do it for you.\nTo use it::\n\n python to_python.py < notebook.ipynb > script.py\n\nYou may have to touch up the resulting ``script.py``.\nMore specifically:\n\n- Update the title\n- Update the description\n- Fix any issues that the markdown-to-RST converter could not deal with\n\nOnce your script.py works, put it in a suitable subdirectory.\nPlease don't include your original Jupyter notebook in the repository - we won't be using it.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correctness\n-----------\n\nIncorrect documentation can be worse than no documentation at all.\nTake the following steps to ensure correctness:\n\n- Run Python's doctest module on your docstrings\n- Run your documentation scripts from scratch, removing any temporary files/results\n\nUsing data in your documentation\n--------------------------------\n\nSome parts of the documentation require real-world data to be useful.\nFor example, you may need more than just a toy example to demonstrate the benefits of one model over another.\nThis subsection provides some tips for including data in your documentation.\n\nIf possible, use data available via Gensim's\n`downloader API <https://radimrehurek.com/gensim/gensim_numfocus/auto_examples/010_tutorials/run_downloader_api.html>`__.\nThis will reduce the risk of your documentation becoming obsolete because required data is no longer available.\n\nUse the smallest possible dataset: avoid making people unnecessarily load large datasets and models.\nThis will make your documentation faster to run and easier for people to use (they can modify your examples and re-run them quickly).\n\nFinalizing your contribution\n----------------------------\n\nFirst, get Sphinx Gallery to build your documentation::\n\n make --directory docs/src html\n\nThis can take a while if your documentation uses a large dataset, or if you've changed many other tutorials or guides.\nOnce this completes successfully, open ``docs/auto_examples/index.html`` in your browser.\nYou should see your new tutorial or guide in the gallery.\n\nOnce your documentation script is working correctly, it's time to add it to the git repository::\n\n git add docs/src/gallery/tutorials/run_example.py\n git add docs/src/auto_examples/tutorials/run_example.{py,py.md5,rst,ipynb}\n git add docs/src/auto_examples/howtos/sg_execution_times.rst\n git commit -m \"enter a helpful commit message here\"\n git push origin branchname\n\n.. Note::\n You may be wondering what all those other files are.\n Sphinx Gallery puts a copy of your Python script in ``auto_examples/tutorials``.\n The .md5 contains MD5 hash of the script to enable easy detection of modifications.\n Gallery also generates .rst (RST for Sphinx) and .ipynb (Jupyter notebook) files from the script.\n Finally, ``sg_execution_times.rst`` contains the time taken to run each example.\n\nFinally, open a PR at `github <https://github.com/RaRe-Technologies/gensim>`__.\nOne of our friendly maintainers will review it, make suggestions, and eventually merge it.\nYour documentation will then appear in the `gallery <https://radimrehurek.com/gensim/auto_examples/index.html>`__,\nalongside the rest of the examples. Thanks a lot!\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 0 }	9,057	Python	.py	89	95.359551	2,741	0.707883	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,993	run_downloader_api.ipynb	piskvorky_gensim/docs/src/auto_examples/howtos/run_downloader_api.ipynb	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nHow to download pre-trained models and corpora\n==============================================\n\nDemonstrates simple and quick access to common corpora and pretrained models.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of Gensim's features is simple and easy access to common data.\nThe `gensim-data <https://github.com/RaRe-Technologies/gensim-data>`_ project stores a\nvariety of corpora and pretrained models.\nGensim has a :py:mod:`gensim.downloader` module for programmatically accessing this data.\nThis module leverages a local cache (in user's home folder, by default) that\nensures data is downloaded at most once.\n\nThis tutorial:\n\n* Downloads the text8 corpus, unless it is already on your local machine\n* Trains a Word2Vec model from the corpus (see `sphx_glr_auto_examples_tutorials_run_doc2vec_lee.py` for a detailed tutorial)\n* Leverages the model to calculate word similarity\n* Demonstrates using the API to load other models and corpora\n\nLet's start by importing the api module.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import gensim.downloader as api" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's download the text8 corpus and load it as a Python object\nthat supports streamed access.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "corpus = api.load('text8')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, our corpus is an iterable.\nIf you look under the covers, it has the following definition:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import inspect\nprint(inspect.getsource(corpus.__class__))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more details, look inside the file that defines the Dataset class for your particular resource.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(inspect.getfile(corpus.__class__))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the corpus has been downloaded and loaded, let's use it to train a word2vec model.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.models.word2vec import Word2Vec\nmodel = Word2Vec(corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our word2vec model, let's find words that are similar to 'tree'.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(model.wv.most_similar('tree'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use the API to download several different corpora and pretrained models.\nHere's how to list all resources available in gensim-data:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\ninfo = api.info()\nprint(json.dumps(info, indent=4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two types of data resources: corpora and models.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(info.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a look at the available corpora:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for corpus_name, corpus_data in sorted(info['corpora'].items()):\n print(\n '%s (%d records): %s' % (\n corpus_name,\n corpus_data.get('num_records', -1),\n corpus_data['description'][:40] + '...',\n )\n )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and the same for models:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for model_name, model_data in sorted(info['models'].items()):\n print(\n '%s (%d records): %s' % (\n model_name,\n model_data.get('num_records', -1),\n model_data['description'][:40] + '...',\n )\n )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to get detailed information about a model/corpus, use:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fake_news_info = api.info('fake-news')\nprint(json.dumps(fake_news_info, indent=4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, you do not want to load a model into memory. Instead, you can request\njust the filesystem path to the model. For that, use:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(api.load('glove-wiki-gigaword-50', return_path=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to load the model to memory, then:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = api.load(\"glove-wiki-gigaword-50\")\nmodel.most_similar(\"glass\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For corpora, the corpus is never loaded to memory, all corpora are iterables wrapped in\na special class ``Dataset``, with an ``__iter__`` method.\n\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 0 }	8,187	Python	.py	295	20.99322	804	0.518561	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,994	run_doc2vec_imdb.ipynb	piskvorky_gensim/docs/src/auto_examples/howtos/run_doc2vec_imdb.ipynb	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# How to reproduce the doc2vec 'Paragraph Vector' paper\n\nShows how to reproduce results of the \"Distributed Representation of Sentences and Documents\" paper by Le and Mikolov using Gensim.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n\nThis guide shows you how to reproduce the results of the paper by [Le and\nMikolov 2014](https://arxiv.org/pdf/1405.4053.pdf) using Gensim. While the\nentire paper is worth reading (it's only 9 pages), we will be focusing on\nSection 3.2: \"Beyond One Sentence - Sentiment Analysis with the IMDB\ndataset\".\n\nThis guide follows the following steps:\n\n#. Load the IMDB dataset\n#. Train a variety of Doc2Vec models on the dataset\n#. Evaluate the performance of each model using a logistic regression\n#. Examine some of the results directly:\n\nWhen examining results, we will look for answers for the following questions:\n\n#. Are inferred vectors close to the precalculated ones?\n#. Do close documents seem more related than distant ones?\n#. Do the word vectors show useful similarities?\n#. Are the word vectors from this dataset any good at analogies?\n\n## Load corpus\n\nOur data for the tutorial will be the [IMDB archive](http://ai.stanford.edu/~amaas/data/sentiment/).\nIf you're not familiar with this dataset, then here's a brief intro: it\ncontains several thousand movie reviews.\n\nEach review is a single line of text containing multiple sentences, for example:\n\n```\nOne of the best movie-dramas I have ever seen. We do a lot of acting in the\nchurch and this is one that can be used as a resource that highlights all the\ngood things that actors can do in their work. I highly recommend this one,\nespecially for those who have an interest in acting, as a \"must see.\"\n```\n\nThese reviews will be the documents that we will work with in this tutorial.\nThere are 100 thousand reviews in total.\n\n#. 25k reviews for training (12.5k positive, 12.5k negative)\n#. 25k reviews for testing (12.5k positive, 12.5k negative)\n#. 50k unlabeled reviews\n\nOut of 100k reviews, 50k have a label: either positive (the reviewer liked\nthe movie) or negative.\nThe remaining 50k are unlabeled.\n\nOur first task will be to prepare the dataset.\n\nMore specifically, we will:\n\n#. Download the tar.gz file (it's only 84MB, so this shouldn't take too long)\n#. Unpack it and extract each movie review\n#. Split the reviews into training and test datasets\n\nFirst, let's define a convenient datatype for holding data for a single document:\n\n* words: The text of the document, as a ``list`` of words.\n* tags: Used to keep the index of the document in the entire dataset.\n* split: one of ``train``\\ , ``test`` or ``extra``. Determines how the document will be used (for training, testing, etc).\n* sentiment: either 1 (positive), 0 (negative) or None (unlabeled document).\n\nThis data type is helpful for later evaluation and reporting.\nIn particular, the ``index`` member will help us quickly and easily retrieve the vectors for a document from a model.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import collections\n\nSentimentDocument = collections.namedtuple('SentimentDocument', 'words tags split sentiment')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now proceed with loading the corpus.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import io\nimport re\nimport tarfile\nimport os.path\n\nimport smart_open\nimport gensim.utils\n\ndef download_dataset(url='http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'):\n fname = url.split('/')[-1]\n\n if os.path.isfile(fname):\n return fname\n\n # Download the file to local storage first.\n try:\n kwargs = { 'compression': smart_open.compression.NO_COMPRESSION }\n fin = smart_open.open(url, \"rb\", kwargs)\n except (AttributeError, TypeError):\n kwargs = { 'ignore_ext': True }\n fin = smart_open.open(url, \"rb\", kwargs)\n if fin:\n with smart_open.open(fname, 'wb', *kwargs) as fout:\n while True:\n buf = fin.read(io.DEFAULT_BUFFER_SIZE)\n if not buf:\n break\n fout.write(buf)\n fin.close()\n\n return fname\n\ndef create_sentiment_document(name, text, index):\n _, split, sentiment_str, _ = name.split('/')\n sentiment = {'pos': 1.0, 'neg': 0.0, 'unsup': None}[sentiment_str]\n\n if sentiment is None:\n split = 'extra'\n\n tokens = gensim.utils.to_unicode(text).split()\n return SentimentDocument(tokens, [index], split, sentiment)\n\ndef extract_documents():\n fname = download_dataset()\n\n index = 0\n\n with tarfile.open(fname, mode='r:gz') as tar:\n for member in tar.getmembers():\n if re.match(r'aclImdb/(train\|test)/(pos\|neg\|unsup)/\\d+_\\d+.txt$', member.name):\n member_bytes = tar.extractfile(member).read()\n member_text = member_bytes.decode('utf-8', errors='replace')\n assert member_text.count('\\n') == 0\n yield create_sentiment_document(member.name, member_text, index)\n index += 1\n\nalldocs = list(extract_documents())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what a single document looks like.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(alldocs[27])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract our documents and split into training/test sets.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "train_docs = [doc for doc in alldocs if doc.split == 'train']\ntest_docs = [doc for doc in alldocs if doc.split == 'test']\nprint(f'{len(alldocs)} docs: {len(train_docs)} train-sentiment, {len(test_docs)} test-sentiment')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set-up Doc2Vec Training & Evaluation Models\n\nWe approximate the experiment of Le & Mikolov [\"Distributed Representations\nof Sentences and Documents\"](http://cs.stanford.edu/~quocle/paragraph_vector.pdf) with guidance from\nMikolov's [example go.sh](https://groups.google.com/g/word2vec-toolkit/c/Q49FIrNOQRo/m/J6KG8mUj45sJ)::\n\n ./word2vec -train ../alldata-id.txt -output vectors.txt -cbow 0 -size 100 -window 10 -negative 5 -hs 0 -sample 1e-4 -threads 40 -binary 0 -iter 20 -min-count 1 -sentence-vectors 1\n\nWe vary the following parameter choices:\n\n 100-dimensional vectors, as the 400-d vectors of the paper take a lot of\n memory and, in our tests of this task, don't seem to offer much benefit\n* Similarly, frequent word subsampling seems to decrease sentiment-prediction\n accuracy, so it's left out\n* ``cbow=0`` means skip-gram which is equivalent to the paper's 'PV-DBOW'\n mode, matched in gensim with ``dm=0``\n* Added to that DBOW model are two DM models, one which averages context\n vectors (\\ ``dm_mean``\\ ) and one which concatenates them (\\ ``dm_concat``\\ ,\n resulting in a much larger, slower, more data-hungry model)\n* A ``min_count=2`` saves quite a bit of model memory, discarding only words\n that appear in a single doc (and are thus no more expressive than the\n unique-to-each doc vectors themselves)\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import multiprocessing\nfrom collections import OrderedDict\n\nimport gensim.models.doc2vec\nassert gensim.models.doc2vec.FAST_VERSION > -1, \"This will be painfully slow otherwise\"\n\nfrom gensim.models.doc2vec import Doc2Vec\n\ncommon_kwargs = dict(\n vector_size=100, epochs=20, min_count=2,\n sample=0, workers=multiprocessing.cpu_count(), negative=5, hs=0,\n)\n\nsimple_models = [\n # PV-DBOW plain\n Doc2Vec(dm=0, common_kwargs),\n # PV-DM w/ default averaging; a higher starting alpha may improve CBOW/PV-DM modes\n Doc2Vec(dm=1, window=10, alpha=0.05, comment='alpha=0.05', common_kwargs),\n # PV-DM w/ concatenation - big, slow, experimental mode\n # window=5 (both sides) approximates paper's apparent 10-word total window size\n Doc2Vec(dm=1, dm_concat=1, window=5, *common_kwargs),\n]\n\nfor model in simple_models:\n model.build_vocab(alldocs)\n print(f\"{model} vocabulary scanned & state initialized\")\n\nmodels_by_name = OrderedDict((str(model), model) for model in simple_models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le and Mikolov note that combining a paragraph vector from Distributed Bag of\nWords (DBOW) and Distributed Memory (DM) improves performance. We will\nfollow, pairing the models together for evaluation. Here, we concatenate the\nparagraph vectors obtained from each model with the help of a thin wrapper\nclass included in a gensim test module. (Note that this a separate, later\nconcatenation of output-vectors than the kind of input-window-concatenation\nenabled by the ``dm_concat=1`` mode above.)\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.test.test_doc2vec import ConcatenatedDoc2Vec\nmodels_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[0], simple_models[1]])\nmodels_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[0], simple_models[2]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictive Evaluation Methods\n\nGiven a document, our ``Doc2Vec`` models output a vector representation of the document.\nHow useful is a particular model?\nIn case of sentiment analysis, we want the output vector to reflect the sentiment in the input document.\nSo, in vector space, positive documents should be distant from negative documents.\n\nWe train a logistic regression from the training set:\n\n - regressors (inputs): document vectors from the Doc2Vec model\n - target (outpus): sentiment labels\n\nSo, this logistic regression will be able to predict sentiment given a document vector.\n\nNext, we test our logistic regression on the test set, and measure the rate of errors (incorrect predictions).\nIf the document vectors from the Doc2Vec model reflect the actual sentiment well, the error rate will be low.\n\nTherefore, the error rate of the logistic regression is indication of how well* the given Doc2Vec model represents documents as vectors.\nWe can then compare different ``Doc2Vec`` models by looking at their error rates.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\nimport statsmodels.api as sm\nfrom random import sample\n\ndef logistic_predictor_from_data(train_targets, train_regressors):\n \"\"\"Fit a statsmodel logistic predictor on supplied data\"\"\"\n logit = sm.Logit(train_targets, train_regressors)\n predictor = logit.fit(disp=0)\n # print(predictor.summary())\n return predictor\n\ndef error_rate_for_model(test_model, train_set, test_set):\n \"\"\"Report error rate on test_doc sentiments, using supplied model and train_docs\"\"\"\n\n train_targets = [doc.sentiment for doc in train_set]\n train_regressors = [test_model.dv[doc.tags[0]] for doc in train_set]\n train_regressors = sm.add_constant(train_regressors)\n predictor = logistic_predictor_from_data(train_targets, train_regressors)\n\n test_regressors = [test_model.dv[doc.tags[0]] for doc in test_set]\n test_regressors = sm.add_constant(test_regressors)\n\n # Predict & evaluate\n test_predictions = predictor.predict(test_regressors)\n corrects = sum(np.rint(test_predictions) == [doc.sentiment for doc in test_set])\n errors = len(test_predictions) - corrects\n error_rate = float(errors) / len(test_predictions)\n return (error_rate, errors, len(test_predictions), predictor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bulk Training & Per-Model Evaluation\n\nNote that doc-vector training is occurring on all documents of the dataset,\nwhich includes all TRAIN/TEST/DEV docs. Because the native document-order\nhas similar-sentiment documents in large clumps \u2013 which is suboptimal for\ntraining \u2013 we work with once-shuffled copy of the training set.\n\nWe evaluate each model's sentiment predictive power based on error rate, and\nthe evaluation is done for each model.\n\n(On a 4-core 2.6Ghz Intel Core i7, these 20 passes training and evaluating 3\nmain models takes about an hour.)\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import defaultdict\nerror_rates = defaultdict(lambda: 1.0) # To selectively print only best errors achieved" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from random import shuffle\nshuffled_alldocs = alldocs[:]\nshuffle(shuffled_alldocs)\n\nfor model in simple_models:\n print(f\"Training {model}\")\n model.train(shuffled_alldocs, total_examples=len(shuffled_alldocs), epochs=model.epochs)\n\n print(f\"\\nEvaluating {model}\")\n err_rate, err_count, test_count, predictor = error_rate_for_model(model, train_docs, test_docs)\n error_rates[str(model)] = err_rate\n print(\"\\n%f %s\\n\" % (err_rate, model))\n\nfor model in [models_by_name['dbow+dmm'], models_by_name['dbow+dmc']]:\n print(f\"\\nEvaluating {model}\")\n err_rate, err_count, test_count, predictor = error_rate_for_model(model, train_docs, test_docs)\n error_rates[str(model)] = err_rate\n print(f\"\\n{err_rate} {model}\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Achieved Sentiment-Prediction Accuracy\nCompare error rates achieved, best-to-worst\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Err_rate Model\")\nfor rate, name in sorted((rate, name) for name, rate in error_rates.items()):\n print(f\"{rate} {name}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our testing, contrary to the results of the paper, on this problem,\nPV-DBOW alone performs as good as anything else. Concatenating vectors from\ndifferent models only sometimes offers a tiny predictive improvement \u2013 and\nstays generally close to the best-performing solo model included.\n\nThe best results achieved here are just around 10% error rate, still a long\nway from the paper's reported 7.42% error rate.\n\n(Other trials not shown, with larger vectors and other changes, also don't\ncome close to the paper's reported value. Others around the net have reported\na similar inability to reproduce the paper's best numbers. The PV-DM/C mode\nimproves a bit with many more training epochs \u2013 but doesn't reach parity with\nPV-DBOW.)\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examining Results\n\nLet's look for answers to the following questions:\n\n#. Are inferred vectors close to the precalculated ones?\n#. Do close documents seem more related than distant ones?\n#. Do the word vectors show useful similarities?\n#. Are the word vectors from this dataset any good at analogies?\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Are inferred vectors close to the precalculated ones?\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "doc_id = np.random.randint(len(simple_models[0].dv)) # Pick random doc; re-run cell for more examples\nprint(f'for doc {doc_id}...')\nfor model in simple_models:\n inferred_docvec = model.infer_vector(alldocs[doc_id].words)\n print(f'{model}:\\n {model.dv.most_similar([inferred_docvec], topn=3)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Yes, here the stored vector from 20 epochs of training is usually one of the\nclosest to a freshly-inferred vector for the same words. Defaults for\ninference may benefit from tuning for each dataset or model parameters.)\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Do close documents seem more related than distant ones?\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import random\n\ndoc_id = np.random.randint(len(simple_models[0].dv)) # pick random doc, re-run cell for more examples\nmodel = random.choice(simple_models) # and a random model\nsims = model.dv.most_similar(doc_id, topn=len(model.dv)) # get all similar documents\nprint(f'TARGET ({doc_id}): \u00ab{\" \".join(alldocs[doc_id].words)}\u00bb\\n')\nprint(f'SIMILAR/DISSIMILAR DOCS PER MODEL {model}%s:\\n')\nfor label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]:\n s = sims[index]\n i = sims[index][0]\n words = ' '.join(alldocs[i].words)\n print(f'{label} {s}: \u00ab{words}\u00bb\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Somewhat, in terms of reviewer tone, movie genre, etc... the MOST\ncosine-similar docs usually seem more like the TARGET than the MEDIAN or\nLEAST... especially if the MOST has a cosine-similarity > 0.5. Re-run the\ncell to try another random target document.\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Do the word vectors show useful similarities?\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import random\n\nword_models = simple_models[:]\n\ndef pick_random_word(model, threshold=10):\n # pick a random word with a suitable number of occurences\n while True:\n word = random.choice(model.wv.index_to_key)\n if model.wv.get_vecattr(word, \"count\") > threshold:\n return word\n\ntarget_word = pick_random_word(word_models[0])\n# or uncomment below line, to just pick a word from the relevant domain:\n# target_word = 'comedy/drama'\n\nfor model in word_models:\n print(f'target_word: {repr(target_word)} model: {model} similar words:')\n for i, (word, sim) in enumerate(model.wv.most_similar(target_word, topn=10), 1):\n print(f' {i}. {sim:.2f} {repr(word)}')\n print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the DBOW words look meaningless? That's because the gensim DBOW model\ndoesn't train word vectors \u2013 they remain at their random initialized values \u2013\nunless you ask with the ``dbow_words=1`` initialization parameter. Concurrent\nword-training slows DBOW mode significantly, and offers little improvement\n(and sometimes a little worsening) of the error rate on this IMDB\nsentiment-prediction task, but may be appropriate on other tasks, or if you\nalso need word-vectors.\n\nWords from DM models tend to show meaningfully similar words when there are\nmany examples in the training data (as with 'plot' or 'actor'). (All DM modes\ninherently involve word-vector training concurrent with doc-vector training.)\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Are the word vectors from this dataset any good at analogies?\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.test.utils import datapath\nquestions_filename = datapath('questions-words.txt')\n\n# Note: this analysis takes many minutes\nfor model in word_models:\n score, sections = model.wv.evaluate_word_analogies(questions_filename)\n correct, incorrect = len(sections[-1]['correct']), len(sections[-1]['incorrect'])\n print(f'{model}: {float(correct*100)/(correct+incorrect):0.2f}%% correct ({correct} of {correct+incorrect}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even though this is a tiny, domain-specific dataset, it shows some meager\ncapability on the general word analogies \u2013 at least for the DM/mean and\nDM/concat models which actually train word vectors. (The untrained\nrandom-initialized words of the DBOW model of course fail miserably.)\n\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" } }, "nbformat": 4, "nbformat_minor": 0 }	23,094	Python	.py	341	60.961877	2,813	0.651885	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,995	run_corpora_and_vector_spaces.py	piskvorky_gensim/docs/src/auto_examples/core/run_corpora_and_vector_spaces.py	r""" Corpora and Vector Spaces ========================= Demonstrates transforming text into a vector space representation. Also introduces corpus streaming and persistence to disk in various formats. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # First, letâ€™s create a small corpus of nine short documents [1]_: # # .. _second example: # # From Strings to Vectors # ------------------------ # # This time, let's start from documents represented as strings: # documents = [ "Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in trees", "Graph minors IV Widths of trees and well quasi ordering", "Graph minors A survey", ] ############################################################################### # This is a tiny corpus of nine documents, each consisting of only a single sentence. # # First, let's tokenize the documents, remove common words (using a toy stoplist) # as well as words that only appear once in the corpus: from pprint import pprint # pretty-printer from collections import defaultdict # remove common words and tokenize stoplist = set('for a of the and to in'.split()) texts = [ [word for word in document.lower().split() if word not in stoplist] for document in documents ] # remove words that appear only once frequency = defaultdict(int) for text in texts: for token in text: frequency[token] += 1 texts = [ [token for token in text if frequency[token] > 1] for text in texts ] pprint(texts) ############################################################################### # Your way of processing the documents will likely vary; here, I only split on whitespace # to tokenize, followed by lowercasing each word. In fact, I use this particular # (simplistic and inefficient) setup to mimic the experiment done in Deerwester et al.'s # original LSA article [1]_. # # The ways to process documents are so varied and application- and language-dependent that I # decided to not constrain them by any interface. Instead, a document is represented # by the features extracted from it, not by its "surface" string form: how you get to # the features is up to you. Below I describe one common, general-purpose approach (called # :dfn:`bag-of-words`), but keep in mind that different application domains call for # different features, and, as always, it's `garbage in, garbage out <https://en.wikipedia.org/wiki/Garbage_In,_Garbage_Out>`_... # # To convert documents to vectors, we'll use a document representation called # `bag-of-words <https://en.wikipedia.org/wiki/Bag_of_words>`_. In this representation, # each document is represented by one vector where each vector element represents # a question-answer pair, in the style of: # # - Question: How many times does the word `system` appear in the document? # - Answer: Once. # # It is advantageous to represent the questions only by their (integer) ids. The mapping # between the questions and ids is called a dictionary: from gensim import corpora dictionary = corpora.Dictionary(texts) dictionary.save('/tmp/deerwester.dict') # store the dictionary, for future reference print(dictionary) ############################################################################### # Here we assigned a unique integer id to all words appearing in the corpus with the # :class:`gensim.corpora.dictionary.Dictionary` class. This sweeps across the texts, collecting word counts # and relevant statistics. In the end, we see there are twelve distinct words in the # processed corpus, which means each document will be represented by twelve numbers (ie., by a 12-D vector). # To see the mapping between words and their ids: print(dictionary.token2id) ############################################################################### # To actually convert tokenized documents to vectors: new_doc = "Human computer interaction" new_vec = dictionary.doc2bow(new_doc.lower().split()) print(new_vec) # the word "interaction" does not appear in the dictionary and is ignored ############################################################################### # The function :func:`doc2bow` simply counts the number of occurrences of # each distinct word, converts the word to its integer word id # and returns the result as a sparse vector. The sparse vector ``[(0, 1), (1, 1)]`` # therefore reads: in the document `"Human computer interaction"`, the words `computer` # (id 0) and `human` (id 1) appear once; the other ten dictionary words appear (implicitly) zero times. corpus = [dictionary.doc2bow(text) for text in texts] corpora.MmCorpus.serialize('/tmp/deerwester.mm', corpus) # store to disk, for later use print(corpus) ############################################################################### # By now it should be clear that the vector feature with ``id=10`` stands for the question "How many # times does the word `graph` appear in the document?" and that the answer is "zero" for # the first six documents and "one" for the remaining three. # # .. _corpus_streaming_tutorial: # # Corpus Streaming -- One Document at a Time # ------------------------------------------- # # Note that `corpus` above resides fully in memory, as a plain Python list. # In this simple example, it doesn't matter much, but just to make things clear, # let's assume there are millions of documents in the corpus. Storing all of them in RAM won't do. # Instead, let's assume the documents are stored in a file on disk, one document per line. Gensim # only requires that a corpus must be able to return one document vector at a time: # from smart_open import open # for transparently opening remote files class MyCorpus: def __iter__(self): for line in open('https://radimrehurek.com/mycorpus.txt'): # assume there's one document per line, tokens separated by whitespace yield dictionary.doc2bow(line.lower().split()) ############################################################################### # The full power of Gensim comes from the fact that a corpus doesn't have to be # a ``list``, or a ``NumPy`` array, or a ``Pandas`` dataframe, or whatever. # Gensim accepts any object that, when iterated over, successively yields # documents. # This flexibility allows you to create your own corpus classes that stream the # documents directly from disk, network, database, dataframes... The models # in Gensim are implemented such that they don't require all vectors to reside # in RAM at once. You can even create the documents on the fly! ############################################################################### # Download the sample `mycorpus.txt file here <https://radimrehurek.com/mycorpus.txt>`_. The assumption that # each document occupies one line in a single file is not important; you can mold # the `__iter__` function to fit your input format, whatever it is. # Walking directories, parsing XML, accessing the network... # Just parse your input to retrieve a clean list of tokens in each document, # then convert the tokens via a dictionary to their ids and yield the resulting sparse vector inside `__iter__`. corpus_memory_friendly = MyCorpus() # doesn't load the corpus into memory! print(corpus_memory_friendly) ############################################################################### # Corpus is now an object. We didn't define any way to print it, so `print` just outputs address # of the object in memory. Not very useful. To see the constituent vectors, let's # iterate over the corpus and print each document vector (one at a time): for vector in corpus_memory_friendly: # load one vector into memory at a time print(vector) ############################################################################### # Although the output is the same as for the plain Python list, the corpus is now much # more memory friendly, because at most one vector resides in RAM at a time. Your # corpus can now be as large as you want. # # Similarly, to construct the dictionary without loading all texts into memory: # collect statistics about all tokens dictionary = corpora.Dictionary(line.lower().split() for line in open('https://radimrehurek.com/mycorpus.txt')) # remove stop words and words that appear only once stop_ids = [ dictionary.token2id[stopword] for stopword in stoplist if stopword in dictionary.token2id ] once_ids = [tokenid for tokenid, docfreq in dictionary.dfs.items() if docfreq == 1] dictionary.filter_tokens(stop_ids + once_ids) # remove stop words and words that appear only once dictionary.compactify() # remove gaps in id sequence after words that were removed print(dictionary) ############################################################################### # And that is all there is to it! At least as far as bag-of-words representation is concerned. # Of course, what we do with such a corpus is another question; it is not at all clear # how counting the frequency of distinct words could be useful. As it turns out, it isn't, and # we will need to apply a transformation on this simple representation first, before # we can use it to compute any meaningful document vs. document similarities. # Transformations are covered in the next tutorial # (:ref:`sphx_glr_auto_examples_core_run_topics_and_transformations.py`), # but before that, let's briefly turn our attention to corpus persistency. # # .. _corpus-formats: # # Corpus Formats # --------------- # # There exist several file formats for serializing a Vector Space corpus (~sequence of vectors) to disk. # `Gensim` implements them via the streaming corpus interface mentioned earlier: # documents are read from (resp. stored to) disk in a lazy fashion, one document at # a time, without the whole corpus being read into main memory at once. # # One of the more notable file formats is the `Market Matrix format <http://math.nist.gov/MatrixMarket/formats.html>`_. # To save a corpus in the Matrix Market format: # # create a toy corpus of 2 documents, as a plain Python list corpus = [[(1, 0.5)], []] # make one document empty, for the heck of it corpora.MmCorpus.serialize('/tmp/corpus.mm', corpus) ############################################################################### # Other formats include `Joachim's SVMlight format <http://svmlight.joachims.org/>`_, # `Blei's LDA-C format <https://github.com/blei-lab/lda-c>`_ and # `GibbsLDA++ format <https://gibbslda.sourceforge.net/>`_. corpora.SvmLightCorpus.serialize('/tmp/corpus.svmlight', corpus) corpora.BleiCorpus.serialize('/tmp/corpus.lda-c', corpus) corpora.LowCorpus.serialize('/tmp/corpus.low', corpus) ############################################################################### # Conversely, to load a corpus iterator from a Matrix Market file: corpus = corpora.MmCorpus('/tmp/corpus.mm') ############################################################################### # Corpus objects are streams, so typically you won't be able to print them directly: print(corpus) ############################################################################### # Instead, to view the contents of a corpus: # one way of printing a corpus: load it entirely into memory print(list(corpus)) # calling list() will convert any sequence to a plain Python list ############################################################################### # or # another way of doing it: print one document at a time, making use of the streaming interface for doc in corpus: print(doc) ############################################################################### # The second way is obviously more memory-friendly, but for testing and development # purposes, nothing beats the simplicity of calling ``list(corpus)``. # # To save the same Matrix Market document stream in Blei's LDA-C format, corpora.BleiCorpus.serialize('/tmp/corpus.lda-c', corpus) ############################################################################### # In this way, `gensim` can also be used as a memory-efficient I/O format conversion tool: # just load a document stream using one format and immediately save it in another format. # Adding new formats is dead easy, check out the `code for the SVMlight corpus # <https://github.com/piskvorky/gensim/blob/develop/gensim/corpora/svmlightcorpus.py>`_ for an example. # # Compatibility with NumPy and SciPy # ---------------------------------- # # Gensim also contains `efficient utility functions <https://radimrehurek.com/gensim/matutils.html>`_ # to help converting from/to numpy matrices import gensim import numpy as np numpy_matrix = np.random.randint(10, size=[5, 2]) # random matrix as an example corpus = gensim.matutils.Dense2Corpus(numpy_matrix) # numpy_matrix = gensim.matutils.corpus2dense(corpus, num_terms=number_of_corpus_features) ############################################################################### # and from/to `scipy.sparse` matrices import scipy.sparse scipy_sparse_matrix = scipy.sparse.random(5, 2) # random sparse matrix as example corpus = gensim.matutils.Sparse2Corpus(scipy_sparse_matrix) scipy_csc_matrix = gensim.matutils.corpus2csc(corpus) ############################################################################### # What Next # --------- # # Read about :ref:`sphx_glr_auto_examples_core_run_topics_and_transformations.py`. # # References # ---------- # # For a complete reference (Want to prune the dictionary to a smaller size? # Optimize converting between corpora and NumPy/SciPy arrays?), see the :ref:`apiref`. # # .. [1] This is the same corpus as used in # `Deerwester et al. (1990): Indexing by Latent Semantic Analysis <http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf>`_, Table 2. import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('run_corpora_and_vector_spaces.png') imgplot = plt.imshow(img) _ = plt.axis('off')	14,277	Python	.py	259	53.467181	132	0.665664	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,996	run_core_concepts.ipynb	piskvorky_gensim/docs/src/auto_examples/core/run_core_concepts.ipynb	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nCore Concepts\n=============\n\nThis tutorial introduces Documents, Corpora, Vectors and Models: the basic concepts and terms needed to understand and use gensim.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pprint" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The core concepts of ``gensim`` are:\n\n1. `core_concepts_document`: some text.\n2. `core_concepts_corpus`: a collection of documents.\n3. `core_concepts_vector`: a mathematically convenient representation of a document.\n4. `core_concepts_model`: an algorithm for transforming vectors from one representation to another.\n\nLet's examine each of these in slightly more detail.\n\n\nDocument\n--------\n\nIn Gensim, a document is an object of the `text sequence type <https://docs.python.org/3.7/library/stdtypes.html#text-sequence-type-str>`_ (commonly known as ``str`` in Python 3).\nA document could be anything from a short 140 character tweet, a single\nparagraph (i.e., journal article abstract), a news article, or a book.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "document = \"Human machine interface for lab abc computer applications\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nCorpus\n------\n\nA corpus is a collection of `core_concepts_document` objects.\nCorpora serve two roles in Gensim:\n\n1. Input for training a `core_concepts_model`.\n During training, the models use this training corpus to look for common\n themes and topics, initializing their internal model parameters.\n\n Gensim focuses on unsupervised models so that no human intervention,\n such as costly annotations or tagging documents by hand, is required.\n\n2. Documents to organize.\n After training, a topic model can be used to extract topics from new\n documents (documents not seen in the training corpus).\n\n Such corpora can be indexed for\n `sphx_glr_auto_examples_core_run_similarity_queries.py`,\n queried by semantic similarity, clustered etc.\n\nHere is an example corpus.\nIt consists of 9 documents, where each document is a string consisting of a single sentence.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "text_corpus = [\n \"Human machine interface for lab abc computer applications\",\n \"A survey of user opinion of computer system response time\",\n \"The EPS user interface management system\",\n \"System and human system engineering testing of EPS\",\n \"Relation of user perceived response time to error measurement\",\n \"The generation of random binary unordered trees\",\n \"The intersection graph of paths in trees\",\n \"Graph minors IV Widths of trees and well quasi ordering\",\n \"Graph minors A survey\",\n]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".. Important::\n The above example loads the entire corpus into memory.\n In practice, corpora may be very large, so loading them into memory may be impossible.\n Gensim intelligently handles such corpora by streaming them one document at a time.\n See `corpus_streaming_tutorial` for details.\n\nThis is a particularly small example of a corpus for illustration purposes.\nAnother example could be a list of all the plays written by Shakespeare, list\nof all wikipedia articles, or all tweets by a particular person of interest.\n\nAfter collecting our corpus, there are typically a number of preprocessing\nsteps we want to undertake. We'll keep it simple and just remove some\ncommonly used English words (such as 'the') and words that occur only once in\nthe corpus. In the process of doing so, we'll tokenize our data.\nTokenization breaks up the documents into words (in this case using space as\na delimiter).\n\n.. Important::\n There are better ways to perform preprocessing than just lower-casing and\n splitting by space. Effective preprocessing is beyond the scope of this\n tutorial: if you're interested, check out the\n :py:func:`gensim.utils.simple_preprocess` function.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a set of frequent words\nstoplist = set('for a of the and to in'.split(' '))\n# Lowercase each document, split it by white space and filter out stopwords\ntexts = [[word for word in document.lower().split() if word not in stoplist]\n for document in text_corpus]\n\n# Count word frequencies\nfrom collections import defaultdict\nfrequency = defaultdict(int)\nfor text in texts:\n for token in text:\n frequency[token] += 1\n\n# Only keep words that appear more than once\nprocessed_corpus = [[token for token in text if frequency[token] > 1] for text in texts]\npprint.pprint(processed_corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before proceeding, we want to associate each word in the corpus with a unique\ninteger ID. We can do this using the :py:class:`gensim.corpora.Dictionary`\nclass. This dictionary defines the vocabulary of all words that our\nprocessing knows about.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim import corpora\n\ndictionary = corpora.Dictionary(processed_corpus)\nprint(dictionary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because our corpus is small, there are only 12 different tokens in this\n:py:class:`gensim.corpora.Dictionary`. For larger corpuses, dictionaries that\ncontains hundreds of thousands of tokens are quite common.\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nVector\n------\n\nTo infer the latent structure in our corpus we need a way to represent\ndocuments that we can manipulate mathematically. One approach is to represent\neach document as a vector of features.\nFor example, a single feature may be thought of as a question-answer pair:\n\n1. How many times does the word splonge appear in the document? Zero.\n2. How many paragraphs does the document consist of? Two.\n3. How many fonts does the document use? Five.\n\nThe question is usually represented only by its integer id (such as `1`, `2` and `3`).\nThe representation of this document then becomes a series of pairs like ``(1, 0.0), (2, 2.0), (3, 5.0)``.\nThis is known as a dense vector, because it contains an explicit answer to each of the above questions.\n\nIf we know all the questions in advance, we may leave them implicit\nand simply represent the document as ``(0, 2, 5)``.\nThis sequence of answers is the vector for our document (in this case a 3-dimensional dense vector).\nFor practical purposes, only questions to which the answer is (or\ncan be converted to) a single floating point number are allowed in Gensim.\n\nIn practice, vectors often consist of many zero values.\nTo save memory, Gensim omits all vector elements with value 0.0.\nThe above example thus becomes ``(2, 2.0), (3, 5.0)``.\nThis is known as a sparse vector or bag-of-words vector.\nThe values of all missing features in this sparse representation can be unambiguously resolved to zero, ``0.0``.\n\nAssuming the questions are the same, we can compare the vectors of two different documents to each other.\nFor example, assume we are given two vectors ``(0.0, 2.0, 5.0)`` and ``(0.1, 1.9, 4.9)``.\nBecause the vectors are very similar to each other, we can conclude that the documents corresponding to those vectors are similar, too.\nOf course, the correctness of that conclusion depends on how well we picked the questions in the first place.\n\nAnother approach to represent a document as a vector is the bag-of-words\nmodel.\nUnder the bag-of-words model each document is represented by a vector\ncontaining the frequency counts of each word in the dictionary.\nFor example, assume we have a dictionary containing the words\n``['coffee', 'milk', 'sugar', 'spoon']``.\nA document consisting of the string ``\"coffee milk coffee\"`` would then\nbe represented by the vector ``[2, 1, 0, 0]`` where the entries of the vector\nare (in order) the occurrences of \"coffee\", \"milk\", \"sugar\" and \"spoon\" in\nthe document. The length of the vector is the number of entries in the\ndictionary. One of the main properties of the bag-of-words model is that it\ncompletely ignores the order of the tokens in the document that is encoded,\nwhich is where the name bag-of-words comes from.\n\nOur processed corpus has 12 unique words in it, which means that each\ndocument will be represented by a 12-dimensional vector under the\nbag-of-words model. We can use the dictionary to turn tokenized documents\ninto these 12-dimensional vectors. We can see what these IDs correspond to:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pprint.pprint(dictionary.token2id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, suppose we wanted to vectorize the phrase \"Human computer\ninteraction\" (note that this phrase was not in our original corpus). We can\ncreate the bag-of-word representation for a document using the ``doc2bow``\nmethod of the dictionary, which returns a sparse representation of the word\ncounts:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_doc = \"Human computer interaction\"\nnew_vec = dictionary.doc2bow(new_doc.lower().split())\nprint(new_vec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first entry in each tuple corresponds to the ID of the token in the\ndictionary, the second corresponds to the count of this token.\n\nNote that \"interaction\" did not occur in the original corpus and so it was\nnot included in the vectorization. Also note that this vector only contains\nentries for words that actually appeared in the document. Because any given\ndocument will only contain a few words out of the many words in the\ndictionary, words that do not appear in the vectorization are represented as\nimplicitly zero as a space saving measure.\n\nWe can convert our entire original corpus to a list of vectors:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bow_corpus = [dictionary.doc2bow(text) for text in processed_corpus]\npprint.pprint(bow_corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that while this list lives entirely in memory, in most applications you\nwill want a more scalable solution. Luckily, ``gensim`` allows you to use any\niterator that returns a single document vector at a time. See the\ndocumentation for more details.\n\n.. Important::\n The distinction between a document and a vector is that the former is text,\n and the latter is a mathematically convenient representation of the text.\n Sometimes, people will use the terms interchangeably: for example, given\n some arbitrary document ``D``, instead of saying \"the vector that\n corresponds to document ``D``\", they will just say \"the vector ``D``\" or\n the \"document ``D``\". This achieves brevity at the cost of ambiguity.\n\n As long as you remember that documents exist in document space, and that\n vectors exist in vector space, the above ambiguity is acceptable.\n\n.. Important::\n Depending on how the representation was obtained, two different documents\n may have the same vector representations.\n\n\nModel\n-----\n\nNow that we have vectorized our corpus we can begin to transform it using\nmodels. We use model as an abstract term referring to a transformation from\none document representation to another. In ``gensim`` documents are\nrepresented as vectors so a model can be thought of as a transformation\nbetween two vector spaces. The model learns the details of this\ntransformation during training, when it reads the training\n`core_concepts_corpus`.\n\nOne simple example of a model is `tf-idf\n<https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_. The tf-idf model\ntransforms vectors from the bag-of-words representation to a vector space\nwhere the frequency counts are weighted according to the relative rarity of\neach word in the corpus.\n\nHere's a simple example. Let's initialize the tf-idf model, training it on\nour corpus and transforming the string \"system minors\":\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim import models\n\n# train the model\ntfidf = models.TfidfModel(bow_corpus)\n\n# transform the \"system minors\" string\nwords = \"system minors\".lower().split()\nprint(tfidf[dictionary.doc2bow(words)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``tfidf`` model again returns a list of tuples, where the first entry is\nthe token ID and the second entry is the tf-idf weighting. Note that the ID\ncorresponding to \"system\" (which occurred 4 times in the original corpus) has\nbeen weighted lower than the ID corresponding to \"minors\" (which only\noccurred twice).\n\nYou can save trained models to disk and later load them back, either to\ncontinue training on new training documents or to transform new documents.\n\n``gensim`` offers a number of different models/transformations.\nFor more, see `sphx_glr_auto_examples_core_run_topics_and_transformations.py`.\n\nOnce you've created the model, you can do all sorts of cool stuff with it.\nFor example, to transform the whole corpus via TfIdf and index it, in\npreparation for similarity queries:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim import similarities\n\nindex = similarities.SparseMatrixSimilarity(tfidf[bow_corpus], num_features=max(tfidf.dfs) + 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and to query the similarity of our query document ``query_document`` against every document in the corpus:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "query_document = 'system engineering'.split()\nquery_bow = dictionary.doc2bow(query_document)\nsims = index[tfidf[query_bow]]\nprint(list(enumerate(sims)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How to read this output?\nDocument 3 has a similarity score of 0.718=72%, document 2 has a similarity score of 42% etc.\nWe can make this slightly more readable by sorting:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for document_number, score in sorted(enumerate(sims), key=lambda x: x[1], reverse=True):\n print(document_number, score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summary\n-------\n\nThe core concepts of ``gensim`` are:\n\n1. `core_concepts_document`: some text.\n2. `core_concepts_corpus`: a collection of documents.\n3. `core_concepts_vector`: a mathematically convenient representation of a document.\n4. `core_concepts_model`: an algorithm for transforming vectors from one representation to another.\n\nWe saw these concepts in action.\nFirst, we started with a corpus of documents.\nNext, we transformed these documents to a vector space representation.\nAfter that, we created a model that transformed our original vector representation to TfIdf.\nFinally, we used our model to calculate the similarity between some query document and all documents in the corpus.\n\nWhat Next?\n----------\n\nThere's still much more to learn about `sphx_glr_auto_examples_core_run_corpora_and_vector_spaces.py`.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimg = mpimg.imread('run_core_concepts.png')\nimgplot = plt.imshow(img)\n_ = plt.axis('off')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 0 }	18,305	Python	.py	277	59.32852	3,103	0.676448	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,997	run_topics_and_transformations.ipynb	piskvorky_gensim/docs/src/auto_examples/core/run_topics_and_transformations.ipynb	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Topics and Transformations\n\nIntroduces transformations and demonstrates their use on a toy corpus.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, I will show how to transform documents from one vector representation\ninto another. This process serves two goals:\n\n1. To bring out hidden structure in the corpus, discover relationships between\n words and use them to describe the documents in a new and\n (hopefully) more semantic way.\n2. To make the document representation more compact. This both improves efficiency\n (new representation consumes less resources) and efficacy (marginal data\n trends are ignored, noise-reduction).\n\n## Creating the Corpus\n\nFirst, we need to create a corpus to work with.\nThis step is the same as in the previous tutorial;\nif you completed it, feel free to skip to the next section.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import defaultdict\nfrom gensim import corpora\n\ndocuments = [\n \"Human machine interface for lab abc computer applications\",\n \"A survey of user opinion of computer system response time\",\n \"The EPS user interface management system\",\n \"System and human system engineering testing of EPS\",\n \"Relation of user perceived response time to error measurement\",\n \"The generation of random binary unordered trees\",\n \"The intersection graph of paths in trees\",\n \"Graph minors IV Widths of trees and well quasi ordering\",\n \"Graph minors A survey\",\n]\n\n# remove common words and tokenize\nstoplist = set('for a of the and to in'.split())\ntexts = [\n [word for word in document.lower().split() if word not in stoplist]\n for document in documents\n]\n\n# remove words that appear only once\nfrequency = defaultdict(int)\nfor text in texts:\n for token in text:\n frequency[token] += 1\n\ntexts = [\n [token for token in text if frequency[token] > 1]\n for text in texts\n]\n\ndictionary = corpora.Dictionary(texts)\ncorpus = [dictionary.doc2bow(text) for text in texts]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating a transformation\n\nThe transformations are standard Python objects, typically initialized by means of\na :dfn:`training corpus`:\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim import models\n\ntfidf = models.TfidfModel(corpus) # step 1 -- initialize a model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We used our old corpus from tutorial 1 to initialize (train) the transformation model. Different\ntransformations may require different initialization parameters; in case of TfIdf, the\n\"training\" consists simply of going through the supplied corpus once and computing document frequencies\nof all its features. Training other models, such as Latent Semantic Analysis or Latent Dirichlet\nAllocation, is much more involved and, consequently, takes much more time.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Transformations always convert between two specific vector\n spaces. The same vector space (= the same set of feature ids) must be used for training\n as well as for subsequent vector transformations. Failure to use the same input\n feature space, such as applying a different string preprocessing, using different\n feature ids, or using bag-of-words input vectors where TfIdf vectors are expected, will\n result in feature mismatch during transformation calls and consequently in either\n garbage output and/or runtime exceptions.</p></div>\n\n\n### Transforming vectors\n\nFrom now on, ``tfidf`` is treated as a read-only object that can be used to convert\nany vector from the old representation (bag-of-words integer counts) to the new representation\n(TfIdf real-valued weights):\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "doc_bow = [(0, 1), (1, 1)]\nprint(tfidf[doc_bow]) # step 2 -- use the model to transform vectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or to apply a transformation to a whole corpus:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "corpus_tfidf = tfidf[corpus]\nfor doc in corpus_tfidf:\n print(doc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this particular case, we are transforming the same corpus that we used\nfor training, but this is only incidental. Once the transformation model has been initialized,\nit can be used on any vectors (provided they come from the same vector space, of course),\neven if they were not used in the training corpus at all. This is achieved by a process called\nfolding-in for LSA, by topic inference for LDA etc.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Calling ``model[corpus]`` only creates a wrapper around the old ``corpus``\n document stream -- actual conversions are done on-the-fly, during document iteration.\n We cannot convert the entire corpus at the time of calling ``corpus_transformed = model[corpus]``,\n because that would mean storing the result in main memory, and that contradicts gensim's objective of memory-indepedence.\n If you will be iterating over the transformed ``corpus_transformed`` multiple times, and the\n transformation is costly, `serialize the resulting corpus to disk first <corpus-formats>` and continue\n using that.</p></div>\n\nTransformations can also be serialized, one on top of another, in a sort of chain:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lsi_model = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2) # initialize an LSI transformation\ncorpus_lsi = lsi_model[corpus_tfidf] # create a double wrapper over the original corpus: bow->tfidf->fold-in-lsi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we transformed our Tf-Idf corpus via `Latent Semantic Indexing <http://en.wikipedia.org/wiki/Latent_semantic_indexing>`_\ninto a latent 2-D space (2-D because we set ``num_topics=2``). Now you're probably wondering: what do these two latent\ndimensions stand for? Let's inspect with :func:`models.LsiModel.print_topics`:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lsi_model.print_topics(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(the topics are printed to log -- see the note at the top of this page about activating\nlogging)\n\nIt appears that according to LSI, \"trees\", \"graph\" and \"minors\" are all related\nwords (and contribute the most to the direction of the first topic), while the\nsecond topic practically concerns itself with all the other words. As expected,\nthe first five documents are more strongly related to the second topic while the\nremaining four documents to the first topic:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# both bow->tfidf and tfidf->lsi transformations are actually executed here, on the fly\nfor doc, as_text in zip(corpus_lsi, documents):\n print(doc, as_text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Model persistency is achieved with the :func:`save` and :func:`load` functions:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\nimport tempfile\n\nwith tempfile.NamedTemporaryFile(prefix='model-', suffix='.lsi', delete=False) as tmp:\n lsi_model.save(tmp.name) # same for tfidf, lda, ...\n\nloaded_lsi_model = models.LsiModel.load(tmp.name)\n\nos.unlink(tmp.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next question might be: just how exactly similar are those documents to each other?\nIs there a way to formalize the similarity, so that for a given input document, we can\norder some other set of documents according to their similarity? Similarity queries\nare covered in the next tutorial (`sphx_glr_auto_examples_core_run_similarity_queries.py`).\n\n\n## Available transformations\n\nGensim implements several popular Vector Space Model algorithms:\n\n* `Term Frequency * Inverse Document Frequency, Tf-Idf <http://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_\n expects a bag-of-words (integer values) training corpus during initialization.\n During transformation, it will take a vector and return another vector of the\n same dimensionality, except that features which were rare in the training corpus\n will have their value increased.\n It therefore converts integer-valued vectors into real-valued ones, while leaving\n the number of dimensions intact. It can also optionally normalize the resulting\n vectors to (Euclidean) unit length.\n\n .. sourcecode:: pycon\n\n model = models.TfidfModel(corpus, normalize=True)\n\n* `Okapi Best Matching, Okapi BM25 <https://en.wikipedia.org/wiki/Okapi_BM25>`_\n expects a bag-of-words (integer values) training corpus during initialization.\n During transformation, it will take a vector and return another vector of the\n same dimensionality, except that features which were rare in the training corpus\n will have their value increased. It therefore converts integer-valued\n vectors into real-valued ones, while leaving the number of dimensions intact.\n\n Okapi BM25 is the standard ranking function used by search engines to estimate\n the relevance of documents to a given search query.\n\n .. sourcecode:: pycon\n\n model = models.OkapiBM25Model(corpus)\n\n* `Latent Semantic Indexing, LSI (or sometimes LSA) <http://en.wikipedia.org/wiki/Latent_semantic_indexing>`_\n transforms documents from either bag-of-words or (preferrably) TfIdf-weighted space into\n a latent space of a lower dimensionality. For the toy corpus above we used only\n 2 latent dimensions, but on real corpora, target dimensionality of 200--500 is recommended\n as a \"golden standard\" [1]_.\n\n .. sourcecode:: pycon\n\n model = models.LsiModel(tfidf_corpus, id2word=dictionary, num_topics=300)\n\n LSI training is unique in that we can continue \"training\" at any point, simply\n by providing more training documents. This is done by incremental updates to\n the underlying model, in a process called `online training`. Because of this feature, the\n input document stream may even be infinite -- just keep feeding LSI new documents\n as they arrive, while using the computed transformation model as read-only in the meanwhile!\n\n .. sourcecode:: pycon\n\n model.add_documents(another_tfidf_corpus) # now LSI has been trained on tfidf_corpus + another_tfidf_corpus\n lsi_vec = model[tfidf_vec] # convert some new document into the LSI space, without affecting the model\n\n model.add_documents(more_documents) # tfidf_corpus + another_tfidf_corpus + more_documents\n lsi_vec = model[tfidf_vec]\n\n See the :mod:`gensim.models.lsimodel` documentation for details on how to make\n LSI gradually \"forget\" old observations in infinite streams. If you want to get dirty,\n there are also parameters you can tweak that affect speed vs. memory footprint vs. numerical\n precision of the LSI algorithm.\n\n `gensim` uses a novel online incremental streamed distributed training algorithm (quite a mouthful!),\n which I published in [5]_. `gensim` also executes a stochastic multi-pass algorithm\n from Halko et al. [4]_ internally, to accelerate in-core part\n of the computations.\n See also `wiki` for further speed-ups by distributing the computation across\n a cluster of computers.\n\n* `Random Projections, RP <http://www.cis.hut.fi/ella/publications/randproj_kdd.pdf>`_ aim to\n reduce vector space dimensionality. This is a very efficient (both memory- and\n CPU-friendly) approach to approximating TfIdf distances between documents, by throwing in a little randomness.\n Recommended target dimensionality is again in the hundreds/thousands, depending on your dataset.\n\n .. sourcecode:: pycon\n\n model = models.RpModel(tfidf_corpus, num_topics=500)\n\n* `Latent Dirichlet Allocation, LDA <http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_\n is yet another transformation from bag-of-words counts into a topic space of lower\n dimensionality. LDA is a probabilistic extension of LSA (also called multinomial PCA),\n so LDA's topics can be interpreted as probability distributions over words. These distributions are,\n just like with LSA, inferred automatically from a training corpus. Documents\n are in turn interpreted as a (soft) mixture of these topics (again, just like with LSA).\n\n .. sourcecode:: pycon\n\n model = models.LdaModel(corpus, id2word=dictionary, num_topics=100)\n\n `gensim` uses a fast implementation of online LDA parameter estimation based on [2]_,\n modified to run in `distributed mode <distributed>` on a cluster of computers.\n\n* `Hierarchical Dirichlet Process, HDP <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_\n is a non-parametric bayesian method (note the missing number of requested topics):\n\n .. sourcecode:: pycon\n\n model = models.HdpModel(corpus, id2word=dictionary)\n\n `gensim` uses a fast, online implementation based on [3]_.\n The HDP model is a new addition to `gensim`, and still rough around its academic edges -- use with care.\n\nAdding new :abbr:`VSM (Vector Space Model)` transformations (such as different weighting schemes) is rather trivial;\nsee the `apiref` or directly the `Python code <https://github.com/piskvorky/gensim/blob/develop/gensim/models/tfidfmodel.py>`_\nfor more info and examples.\n\nIt is worth repeating that these are all unique, incremental implementations,\nwhich do not require the whole training corpus to be present in main memory all at once.\nWith memory taken care of, I am now improving `distributed`,\nto improve CPU efficiency, too.\nIf you feel you could contribute by testing, providing use-cases or code, see the `Gensim Developer guide <https://github.com/RaRe-Technologies/gensim/wiki/Developer-page>`__.\n\n## What Next?\n\nContinue on to the next tutorial on `sphx_glr_auto_examples_core_run_similarity_queries.py`.\n\n## References\n\n.. [1] Bradford. 2008. An empirical study of required dimensionality for large-scale latent semantic indexing applications.\n\n.. [2] Hoffman, Blei, Bach. 2010. Online learning for Latent Dirichlet Allocation.\n\n.. [3] Wang, Paisley, Blei. 2011. Online variational inference for the hierarchical Dirichlet process.\n\n.. [4] Halko, Martinsson, Tropp. 2009. Finding structure with randomness.\n\n.. [5] \u0158eh\u016f\u0159ek. 2011. Subspace tracking for Latent Semantic Analysis.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimg = mpimg.imread('run_topics_and_transformations.png')\nimgplot = plt.imshow(img)\n_ = plt.axis('off')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 0 }	17,297	Python	.py	216	73.361111	6,990	0.693303	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,998	run_core_concepts.py	piskvorky_gensim/docs/src/auto_examples/core/run_core_concepts.py	r""" Core Concepts ============= This tutorial introduces Documents, Corpora, Vectors and Models: the basic concepts and terms needed to understand and use gensim. """ import pprint ############################################################################### # The core concepts of ``gensim`` are: # # 1. :ref:`core_concepts_document`: some text. # 2. :ref:`core_concepts_corpus`: a collection of documents. # 3. :ref:`core_concepts_vector`: a mathematically convenient representation of a document. # 4. :ref:`core_concepts_model`: an algorithm for transforming vectors from one representation to another. # # Let's examine each of these in slightly more detail. # # .. _core_concepts_document: # # Document # -------- # # In Gensim, a document is an object of the `text sequence type <https://docs.python.org/3.7/library/stdtypes.html#text-sequence-type-str>`_ (commonly known as ``str`` in Python 3). # A document could be anything from a short 140 character tweet, a single # paragraph (i.e., journal article abstract), a news article, or a book. # document = "Human machine interface for lab abc computer applications" ############################################################################### # .. _core_concepts_corpus: # # Corpus # ------ # # A corpus is a collection of :ref:`core_concepts_document` objects. # Corpora serve two roles in Gensim: # # 1. Input for training a :ref:`core_concepts_model`. # During training, the models use this training corpus to look for common # themes and topics, initializing their internal model parameters. # # Gensim focuses on unsupervised models so that no human intervention, # such as costly annotations or tagging documents by hand, is required. # # 2. Documents to organize. # After training, a topic model can be used to extract topics from new # documents (documents not seen in the training corpus). # # Such corpora can be indexed for # :ref:`sphx_glr_auto_examples_core_run_similarity_queries.py`, # queried by semantic similarity, clustered etc. # # Here is an example corpus. # It consists of 9 documents, where each document is a string consisting of a single sentence. # text_corpus = [ "Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in trees", "Graph minors IV Widths of trees and well quasi ordering", "Graph minors A survey", ] ############################################################################### # # .. Important:: # The above example loads the entire corpus into memory. # In practice, corpora may be very large, so loading them into memory may be impossible. # Gensim intelligently handles such corpora by streaming them one document at a time. # See :ref:`corpus_streaming_tutorial` for details. # # This is a particularly small example of a corpus for illustration purposes. # Another example could be a list of all the plays written by Shakespeare, list # of all wikipedia articles, or all tweets by a particular person of interest. # # After collecting our corpus, there are typically a number of preprocessing # steps we want to undertake. We'll keep it simple and just remove some # commonly used English words (such as 'the') and words that occur only once in # the corpus. In the process of doing so, we'll tokenize our data. # Tokenization breaks up the documents into words (in this case using space as # a delimiter). # # .. Important:: # There are better ways to perform preprocessing than just lower-casing and # splitting by space. Effective preprocessing is beyond the scope of this # tutorial: if you're interested, check out the # :py:func:`gensim.utils.simple_preprocess` function. # # Create a set of frequent words stoplist = set('for a of the and to in'.split(' ')) # Lowercase each document, split it by white space and filter out stopwords texts = [[word for word in document.lower().split() if word not in stoplist] for document in text_corpus] # Count word frequencies from collections import defaultdict frequency = defaultdict(int) for text in texts: for token in text: frequency[token] += 1 # Only keep words that appear more than once processed_corpus = [[token for token in text if frequency[token] > 1] for text in texts] pprint.pprint(processed_corpus) ############################################################################### # Before proceeding, we want to associate each word in the corpus with a unique # integer ID. We can do this using the :py:class:`gensim.corpora.Dictionary` # class. This dictionary defines the vocabulary of all words that our # processing knows about. # from gensim import corpora dictionary = corpora.Dictionary(processed_corpus) print(dictionary) ############################################################################### # Because our corpus is small, there are only 12 different tokens in this # :py:class:`gensim.corpora.Dictionary`. For larger corpuses, dictionaries that # contains hundreds of thousands of tokens are quite common. # ############################################################################### # .. _core_concepts_vector: # # Vector # ------ # # To infer the latent structure in our corpus we need a way to represent # documents that we can manipulate mathematically. One approach is to represent # each document as a vector of features. # For example, a single feature may be thought of as a question-answer pair: # # 1. How many times does the word splonge appear in the document? Zero. # 2. How many paragraphs does the document consist of? Two. # 3. How many fonts does the document use? Five. # # The question is usually represented only by its integer id (such as `1`, `2` and `3`). # The representation of this document then becomes a series of pairs like ``(1, 0.0), (2, 2.0), (3, 5.0)``. # This is known as a dense vector, because it contains an explicit answer to each of the above questions. # # If we know all the questions in advance, we may leave them implicit # and simply represent the document as ``(0, 2, 5)``. # This sequence of answers is the vector for our document (in this case a 3-dimensional dense vector). # For practical purposes, only questions to which the answer is (or # can be converted to) a single floating point number are allowed in Gensim. # # In practice, vectors often consist of many zero values. # To save memory, Gensim omits all vector elements with value 0.0. # The above example thus becomes ``(2, 2.0), (3, 5.0)``. # This is known as a sparse vector or bag-of-words vector. # The values of all missing features in this sparse representation can be unambiguously resolved to zero, ``0.0``. # # Assuming the questions are the same, we can compare the vectors of two different documents to each other. # For example, assume we are given two vectors ``(0.0, 2.0, 5.0)`` and ``(0.1, 1.9, 4.9)``. # Because the vectors are very similar to each other, we can conclude that the documents corresponding to those vectors are similar, too. # Of course, the correctness of that conclusion depends on how well we picked the questions in the first place. # # Another approach to represent a document as a vector is the bag-of-words # model. # Under the bag-of-words model each document is represented by a vector # containing the frequency counts of each word in the dictionary. # For example, assume we have a dictionary containing the words # ``['coffee', 'milk', 'sugar', 'spoon']``. # A document consisting of the string ``"coffee milk coffee"`` would then # be represented by the vector ``[2, 1, 0, 0]`` where the entries of the vector # are (in order) the occurrences of "coffee", "milk", "sugar" and "spoon" in # the document. The length of the vector is the number of entries in the # dictionary. One of the main properties of the bag-of-words model is that it # completely ignores the order of the tokens in the document that is encoded, # which is where the name bag-of-words comes from. # # Our processed corpus has 12 unique words in it, which means that each # document will be represented by a 12-dimensional vector under the # bag-of-words model. We can use the dictionary to turn tokenized documents # into these 12-dimensional vectors. We can see what these IDs correspond to: # pprint.pprint(dictionary.token2id) ############################################################################### # For example, suppose we wanted to vectorize the phrase "Human computer # interaction" (note that this phrase was not in our original corpus). We can # create the bag-of-word representation for a document using the ``doc2bow`` # method of the dictionary, which returns a sparse representation of the word # counts: # new_doc = "Human computer interaction" new_vec = dictionary.doc2bow(new_doc.lower().split()) print(new_vec) ############################################################################### # The first entry in each tuple corresponds to the ID of the token in the # dictionary, the second corresponds to the count of this token. # # Note that "interaction" did not occur in the original corpus and so it was # not included in the vectorization. Also note that this vector only contains # entries for words that actually appeared in the document. Because any given # document will only contain a few words out of the many words in the # dictionary, words that do not appear in the vectorization are represented as # implicitly zero as a space saving measure. # # We can convert our entire original corpus to a list of vectors: # bow_corpus = [dictionary.doc2bow(text) for text in processed_corpus] pprint.pprint(bow_corpus) ############################################################################### # Note that while this list lives entirely in memory, in most applications you # will want a more scalable solution. Luckily, ``gensim`` allows you to use any # iterator that returns a single document vector at a time. See the # documentation for more details. # # .. Important:: # The distinction between a document and a vector is that the former is text, # and the latter is a mathematically convenient representation of the text. # Sometimes, people will use the terms interchangeably: for example, given # some arbitrary document ``D``, instead of saying "the vector that # corresponds to document ``D``", they will just say "the vector ``D``" or # the "document ``D``". This achieves brevity at the cost of ambiguity. # # As long as you remember that documents exist in document space, and that # vectors exist in vector space, the above ambiguity is acceptable. # # .. Important:: # Depending on how the representation was obtained, two different documents # may have the same vector representations. # # .. _core_concepts_model: # # Model # ----- # # Now that we have vectorized our corpus we can begin to transform it using # models. We use model as an abstract term referring to a transformation from # one document representation to another. In ``gensim`` documents are # represented as vectors so a model can be thought of as a transformation # between two vector spaces. The model learns the details of this # transformation during training, when it reads the training # :ref:`core_concepts_corpus`. # # One simple example of a model is `tf-idf # <https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_. The tf-idf model # transforms vectors from the bag-of-words representation to a vector space # where the frequency counts are weighted according to the relative rarity of # each word in the corpus. # # Here's a simple example. Let's initialize the tf-idf model, training it on # our corpus and transforming the string "system minors": # from gensim import models # train the model tfidf = models.TfidfModel(bow_corpus) # transform the "system minors" string words = "system minors".lower().split() print(tfidf[dictionary.doc2bow(words)]) ############################################################################### # The ``tfidf`` model again returns a list of tuples, where the first entry is # the token ID and the second entry is the tf-idf weighting. Note that the ID # corresponding to "system" (which occurred 4 times in the original corpus) has # been weighted lower than the ID corresponding to "minors" (which only # occurred twice). # # You can save trained models to disk and later load them back, either to # continue training on new training documents or to transform new documents. # # ``gensim`` offers a number of different models/transformations. # For more, see :ref:`sphx_glr_auto_examples_core_run_topics_and_transformations.py`. # # Once you've created the model, you can do all sorts of cool stuff with it. # For example, to transform the whole corpus via TfIdf and index it, in # preparation for similarity queries: # from gensim import similarities index = similarities.SparseMatrixSimilarity(tfidf[bow_corpus], num_features=12) ############################################################################### # and to query the similarity of our query document ``query_document`` against every document in the corpus: query_document = 'system engineering'.split() query_bow = dictionary.doc2bow(query_document) sims = index[tfidf[query_bow]] print(list(enumerate(sims))) ############################################################################### # How to read this output? # Document 3 has a similarity score of 0.718=72%, document 2 has a similarity score of 42% etc. # We can make this slightly more readable by sorting: for document_number, score in sorted(enumerate(sims), key=lambda x: x[1], reverse=True): print(document_number, score) ############################################################################### # Summary # ------- # # The core concepts of ``gensim`` are: # # 1. :ref:`core_concepts_document`: some text. # 2. :ref:`core_concepts_corpus`: a collection of documents. # 3. :ref:`core_concepts_vector`: a mathematically convenient representation of a document. # 4. :ref:`core_concepts_model`: an algorithm for transforming vectors from one representation to another. # # We saw these concepts in action. # First, we started with a corpus of documents. # Next, we transformed these documents to a vector space representation. # After that, we created a model that transformed our original vector representation to TfIdf. # Finally, we used our model to calculate the similarity between some query document and all documents in the corpus. # # What Next? # ---------- # # There's still much more to learn about :ref:`sphx_glr_auto_examples_core_run_corpora_and_vector_spaces.py`. import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('run_core_concepts.png') imgplot = plt.imshow(img) _ = plt.axis('off')	15,054	Python	.py	304	48.233553	183	0.708571	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)
6,999	run_topics_and_transformations.py	piskvorky_gensim/docs/src/auto_examples/core/run_topics_and_transformations.py	r""" Topics and Transformations =========================== Introduces transformations and demonstrates their use on a toy corpus. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # In this tutorial, I will show how to transform documents from one vector representation # into another. This process serves two goals: # # 1. To bring out hidden structure in the corpus, discover relationships between # words and use them to describe the documents in a new and # (hopefully) more semantic way. # 2. To make the document representation more compact. This both improves efficiency # (new representation consumes less resources) and efficacy (marginal data # trends are ignored, noise-reduction). # # Creating the Corpus # ------------------- # # First, we need to create a corpus to work with. # This step is the same as in the previous tutorial; # if you completed it, feel free to skip to the next section. from collections import defaultdict from gensim import corpora documents = [ "Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in trees", "Graph minors IV Widths of trees and well quasi ordering", "Graph minors A survey", ] # remove common words and tokenize stoplist = set('for a of the and to in'.split()) texts = [ [word for word in document.lower().split() if word not in stoplist] for document in documents ] # remove words that appear only once frequency = defaultdict(int) for text in texts: for token in text: frequency[token] += 1 texts = [ [token for token in text if frequency[token] > 1] for text in texts ] dictionary = corpora.Dictionary(texts) corpus = [dictionary.doc2bow(text) for text in texts] ############################################################################### # # Creating a transformation # ++++++++++++++++++++++++++ # # The transformations are standard Python objects, typically initialized by means of # a :dfn:`training corpus`: # from gensim import models tfidf = models.TfidfModel(corpus) # step 1 -- initialize a model ############################################################################### # We used our old corpus from tutorial 1 to initialize (train) the transformation model. Different # transformations may require different initialization parameters; in case of TfIdf, the # "training" consists simply of going through the supplied corpus once and computing document frequencies # of all its features. Training other models, such as Latent Semantic Analysis or Latent Dirichlet # Allocation, is much more involved and, consequently, takes much more time. # # .. note:: # Transformations always convert between two specific vector # spaces. The same vector space (= the same set of feature ids) must be used for training # as well as for subsequent vector transformations. Failure to use the same input # feature space, such as applying a different string preprocessing, using different # feature ids, or using bag-of-words input vectors where TfIdf vectors are expected, will # result in feature mismatch during transformation calls and consequently in either # garbage output and/or runtime exceptions. # # # Transforming vectors # +++++++++++++++++++++ # # From now on, ``tfidf`` is treated as a read-only object that can be used to convert # any vector from the old representation (bag-of-words integer counts) to the new representation # (TfIdf real-valued weights): doc_bow = [(0, 1), (1, 1)] print(tfidf[doc_bow]) # step 2 -- use the model to transform vectors ############################################################################### # Or to apply a transformation to a whole corpus: corpus_tfidf = tfidf[corpus] for doc in corpus_tfidf: print(doc) ############################################################################### # In this particular case, we are transforming the same corpus that we used # for training, but this is only incidental. Once the transformation model has been initialized, # it can be used on any vectors (provided they come from the same vector space, of course), # even if they were not used in the training corpus at all. This is achieved by a process called # folding-in for LSA, by topic inference for LDA etc. # # .. note:: # Calling ``model[corpus]`` only creates a wrapper around the old ``corpus`` # document stream -- actual conversions are done on-the-fly, during document iteration. # We cannot convert the entire corpus at the time of calling ``corpus_transformed = model[corpus]``, # because that would mean storing the result in main memory, and that contradicts gensim's objective of memory-indepedence. # If you will be iterating over the transformed ``corpus_transformed`` multiple times, and the # transformation is costly, :ref:`serialize the resulting corpus to disk first <corpus-formats>` and continue # using that. # # Transformations can also be serialized, one on top of another, in a sort of chain: lsi_model = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2) # initialize an LSI transformation corpus_lsi = lsi_model[corpus_tfidf] # create a double wrapper over the original corpus: bow->tfidf->fold-in-lsi ############################################################################### # Here we transformed our Tf-Idf corpus via `Latent Semantic Indexing <https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ # into a latent 2-D space (2-D because we set ``num_topics=2``). Now you're probably wondering: what do these two latent # dimensions stand for? Let's inspect with :func:`models.LsiModel.print_topics`: lsi_model.print_topics(2) ############################################################################### # (the topics are printed to log -- see the note at the top of this page about activating # logging) # # It appears that according to LSI, "trees", "graph" and "minors" are all related # words (and contribute the most to the direction of the first topic), while the # second topic practically concerns itself with all the other words. As expected, # the first five documents are more strongly related to the second topic while the # remaining four documents to the first topic: # both bow->tfidf and tfidf->lsi transformations are actually executed here, on the fly for doc, as_text in zip(corpus_lsi, documents): print(doc, as_text) ############################################################################### # Model persistency is achieved with the :func:`save` and :func:`load` functions: import os import tempfile with tempfile.NamedTemporaryFile(prefix='model-', suffix='.lsi', delete=False) as tmp: lsi_model.save(tmp.name) # same for tfidf, lda, ... loaded_lsi_model = models.LsiModel.load(tmp.name) os.unlink(tmp.name) ############################################################################### # The next question might be: just how exactly similar are those documents to each other? # Is there a way to formalize the similarity, so that for a given input document, we can # order some other set of documents according to their similarity? Similarity queries # are covered in the next tutorial (:ref:`sphx_glr_auto_examples_core_run_similarity_queries.py`). # # .. _transformations: # # Available transformations # -------------------------- # # Gensim implements several popular Vector Space Model algorithms: # # * `Term Frequency * Inverse Document Frequency, Tf-Idf <https://en.wikipedia.org/wiki/Tf%E2%80%93idf>`_ # expects a bag-of-words (integer values) training corpus during initialization. # During transformation, it will take a vector and return another vector of the # same dimensionality, except that features which were rare in the training corpus # will have their value increased. # It therefore converts integer-valued vectors into real-valued ones, while leaving # the number of dimensions intact. It can also optionally normalize the resulting # vectors to (Euclidean) unit length. # # .. sourcecode:: pycon # # model = models.TfidfModel(corpus, normalize=True) # # * `Okapi Best Matching, Okapi BM25 <https://en.wikipedia.org/wiki/Okapi_BM25>`_ # expects a bag-of-words (integer values) training corpus during initialization. # During transformation, it will take a vector and return another vector of the # same dimensionality, except that features which were rare in the training corpus # will have their value increased. It therefore converts integer-valued # vectors into real-valued ones, while leaving the number of dimensions intact. # # Okapi BM25 is the standard ranking function used by search engines to estimate # the relevance of documents to a given search query. # # .. sourcecode:: pycon # # model = models.OkapiBM25Model(corpus) # # * `Latent Semantic Indexing, LSI (or sometimes LSA) <https://en.wikipedia.org/wiki/Latent_semantic_indexing>`_ # transforms documents from either bag-of-words or (preferrably) TfIdf-weighted space into # a latent space of a lower dimensionality. For the toy corpus above we used only # 2 latent dimensions, but on real corpora, target dimensionality of 200--500 is recommended # as a "golden standard" [1]_. # # .. sourcecode:: pycon # # model = models.LsiModel(tfidf_corpus, id2word=dictionary, num_topics=300) # # LSI training is unique in that we can continue "training" at any point, simply # by providing more training documents. This is done by incremental updates to # the underlying model, in a process called `online training`. Because of this feature, the # input document stream may even be infinite -- just keep feeding LSI new documents # as they arrive, while using the computed transformation model as read-only in the meanwhile! # # .. sourcecode:: pycon # # model.add_documents(another_tfidf_corpus) # now LSI has been trained on tfidf_corpus + another_tfidf_corpus # lsi_vec = model[tfidf_vec] # convert some new document into the LSI space, without affecting the model # # model.add_documents(more_documents) # tfidf_corpus + another_tfidf_corpus + more_documents # lsi_vec = model[tfidf_vec] # # See the :mod:`gensim.models.lsimodel` documentation for details on how to make # LSI gradually "forget" old observations in infinite streams. If you want to get dirty, # there are also parameters you can tweak that affect speed vs. memory footprint vs. numerical # precision of the LSI algorithm. # # `gensim` uses a novel online incremental streamed distributed training algorithm (quite a mouthful!), # which I published in [5]_. `gensim` also executes a stochastic multi-pass algorithm # from Halko et al. [4]_ internally, to accelerate in-core part # of the computations. # See also :ref:`wiki` for further speed-ups by distributing the computation across # a cluster of computers. # # * `Random Projections, RP <http://www.cis.hut.fi/ella/publications/randproj_kdd.pdf>`_ aim to # reduce vector space dimensionality. This is a very efficient (both memory- and # CPU-friendly) approach to approximating TfIdf distances between documents, by throwing in a little randomness. # Recommended target dimensionality is again in the hundreds/thousands, depending on your dataset. # # .. sourcecode:: pycon # # model = models.RpModel(tfidf_corpus, num_topics=500) # # * `Latent Dirichlet Allocation, LDA <https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_ # is yet another transformation from bag-of-words counts into a topic space of lower # dimensionality. LDA is a probabilistic extension of LSA (also called multinomial PCA), # so LDA's topics can be interpreted as probability distributions over words. These distributions are, # just like with LSA, inferred automatically from a training corpus. Documents # are in turn interpreted as a (soft) mixture of these topics (again, just like with LSA). # # .. sourcecode:: pycon # # model = models.LdaModel(corpus, id2word=dictionary, num_topics=100) # # `gensim` uses a fast implementation of online LDA parameter estimation based on [2]_, # modified to run in :ref:`distributed mode <distributed>` on a cluster of computers. # # * `Hierarchical Dirichlet Process, HDP <http://jmlr.csail.mit.edu/proceedings/papers/v15/wang11a/wang11a.pdf>`_ # is a non-parametric bayesian method (note the missing number of requested topics): # # .. sourcecode:: pycon # # model = models.HdpModel(corpus, id2word=dictionary) # # `gensim` uses a fast, online implementation based on [3]_. # The HDP model is a new addition to `gensim`, and still rough around its academic edges -- use with care. # # Adding new :abbr:`VSM (Vector Space Model)` transformations (such as different weighting schemes) is rather trivial; # see the :ref:`apiref` or directly the `Python code <https://github.com/piskvorky/gensim/blob/develop/gensim/models/tfidfmodel.py>`_ # for more info and examples. # # It is worth repeating that these are all unique, incremental implementations, # which do not require the whole training corpus to be present in main memory all at once. # With memory taken care of, I am now improving :ref:`distributed`, # to improve CPU efficiency, too. # If you feel you could contribute by testing, providing use-cases or code, see the `Gensim Developer guide <https://github.com/RaRe-Technologies/gensim/wiki/Developer-page>`__. # # What Next? # ---------- # # Continue on to the next tutorial on :ref:`sphx_glr_auto_examples_core_run_similarity_queries.py`. # # References # ---------- # # .. [1] Bradford. 2008. An empirical study of required dimensionality for large-scale latent semantic indexing applications. # # .. [2] Hoffman, Blei, Bach. 2010. Online learning for Latent Dirichlet Allocation. # # .. [3] Wang, Paisley, Blei. 2011. Online variational inference for the hierarchical Dirichlet process. # # .. [4] Halko, Martinsson, Tropp. 2009. Finding structure with randomness. # # .. [5] Řehůřek. 2011. Subspace tracking for Latent Semantic Analysis. import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('run_topics_and_transformations.png') imgplot = plt.imshow(img) _ = plt.axis('off')	14,611	Python	.py	279	50.989247	177	0.718711	piskvorky/gensim	15,546	4,374	408	LGPL-2.1	9/5/2024, 5:10:17 PM (Europe/Amsterdam)