PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

dec9be5d7623bfb01463ca78be5f847f3b7dae8e

1,494

py

Python

tests/transformations/state_fusion_node_merge_test.py

targetsm/dace

297b12804a334df8cc6fad5250d5fb0cce20dc6e

[ "BSD-3-Clause" ]

null

tests/transformations/state_fusion_node_merge_test.py

targetsm/dace

297b12804a334df8cc6fad5250d5fb0cce20dc6e

[ "BSD-3-Clause" ]

null

tests/transformations/state_fusion_node_merge_test.py

targetsm/dace

297b12804a334df8cc6fad5250d5fb0cce20dc6e

[ "BSD-3-Clause" ]

null

# Copyright 2019-2020 ETH Zurich and the DaCe authors. All rights reserved. import dace import numpy as np N = dace.symbol('N') @dace.program def test1(A: dace.float64[N], B:dace.float64[N]): for i in dace.map[0:N]: with dace.tasklet: input << A[i] out >> A[i] out = input + 42 B[:] = A[:] for i in dace.map[0:N]: with dace.tasklet: input << A[i] out >> A[i] out = input + 43 @dace.program def test2(C: dace.float32[1], E: dace.float32[1], F:dace.float32[1]): with dace.tasklet: ci << C[0] co >> C[0] co = ci + 1 with dace.tasklet: c << C[0] e >> E[0] e = c with dace.tasklet: c << C[0] f >> F[0] f = c def relative_error(val, ref): return np.linalg.norm(val - ref) / np.linalg.norm(ref) if __name__ == '__main__': N.set(42) A = np.random.rand(N.get()).astype(np.float64) B = np.random.rand(N.get()).astype(np.float64) A_ref = A + 42 + 43 B_ref = A + 42 test1(A, B) assert(relative_error(A, A_ref) < 1e-12) assert(relative_error(B, B_ref) < 1e-12) C = np.random.rand(1).astype(np.float32) E = np.random.rand(1).astype(np.float32) F = np.random.rand(1).astype(np.float32) C_ref = np.random.rand(1).astype(np.float32) C_ref[:] = C[:] + 1 test2(C, E, F) assert(C[0] == C_ref[0]) assert(E[0] == C_ref[0]) assert(F[0] == C_ref[0])

23.34375

75

0.53012

2d3eca997ff33f0fc9408971a510324c3b811a23

594

py

Python

catalog/bindings/gmd/linear_cs.py

NIVANorge/s-enda-playground

56ae0a8978f0ba8a5546330786c882c31e17757a

[ "Apache-2.0" ]

null

catalog/bindings/gmd/linear_cs.py

NIVANorge/s-enda-playground

56ae0a8978f0ba8a5546330786c882c31e17757a

[ "Apache-2.0" ]

null

catalog/bindings/gmd/linear_cs.py

NIVANorge/s-enda-playground

56ae0a8978f0ba8a5546330786c882c31e17757a

[ "Apache-2.0" ]

null

from dataclasses import dataclass from bindings.gmd.linear_cstype import LinearCstype __NAMESPACE__ = "http://www.opengis.net/gml" @dataclass class LinearCs(LinearCstype): """gml:LinearCS is a one-dimensional coordinate system that consists of the points that lie on the single axis described. The associated coordinate is the distance – with or without offset – from the specified datum to the point along the axis. A LinearCS shall have one gml:axis property element. """ class Meta: name = "LinearCS" namespace = "http://www.opengis.net/gml"

29.7

79

0.727273

c2de6617e443011642cea3d79b7e54d2cde87b20

693

py

Python

init_db.py

Samar-Bons/Flask_Blog

f765cd7c124fc9249596c7606ed417a325aa5f53

[ "MIT" ]

null

init_db.py

Samar-Bons/Flask_Blog

f765cd7c124fc9249596c7606ed417a325aa5f53

[ "MIT" ]

null

init_db.py

Samar-Bons/Flask_Blog

f765cd7c124fc9249596c7606ed417a325aa5f53

[ "MIT" ]

null

#Module to initialize a db named "database.db" which will store #post IDs, time_created, Title, and Post content import sqlite3 connection = sqlite3.connect('database.db') # Executing schema.sql script to initialize our db with open('schema.sql') as f: connection.executescript(f.read()) # Prepopulating the database for developmental purposes cur = connection.cursor() cur.execute("INSERT INTO posts (title, content) VALUES (?, ?)", ('First Post', 'Content for the first post') ) cur.execute("INSERT INTO posts (title, content) VALUES (?, ?)", ('Second Post', 'Content for the second post') ) connection.commit() connection.close()

27.72

64

0.681097

6cbc39d8b2c0f6c2e561a77d724782f6f5f6e212

4,278

py

Python

relationships/templatetags/relationship_tags.py

SalahAdDin/django-relationships

a1f98e5b3d0f8e9b12b616d3c3d4153f7f7b06bc

[ "MIT" ]

null

relationships/templatetags/relationship_tags.py

SalahAdDin/django-relationships

a1f98e5b3d0f8e9b12b616d3c3d4153f7f7b06bc

[ "MIT" ]

null

relationships/templatetags/relationship_tags.py

SalahAdDin/django-relationships

a1f98e5b3d0f8e9b12b616d3c3d4153f7f7b06bc

[ "MIT" ]

null

from django import template from django.urls import reverse try: from django.db.models.loading import get_model except ImportError: from django.apps import apps get_model = apps.get_model from django.template import TemplateSyntaxError from django.utils.functional import wraps from django.utils.translation import ugettext as _ from relationships.models import RelationshipStatus from relationships.utils import ( positive_filter, negative_filter ) register = template.Library() @register.simple_tag def relationship(from_user, to_user, status): """ Determine if a certain type of relationship exists between two users. The ``status`` parameter must be a slug matching either the from_slug, to_slug or symmetrical_slug of a RelationshipStatus. Example:: {% relationship from_user to_user "friends" as felas %} {% relationship from_user to_user "blocking" as blocked %} {% if felas %} Here are pictures of me drinking alcohol {% elif blocked %} damn seo experts {% else %} Sorry coworkers {% endif %} """ requested_status = status.replace('"', '') # strip quotes if from_user.is_anonymous() or to_user.is_anonymous(): return False try: status = RelationshipStatus.objects.by_slug(requested_status) except RelationshipStatus.DoesNotExist: raise template.TemplateSyntaxError('RelationshipStatus not found') if status.from_slug == requested_status: val = from_user.relationships.exists(to_user, status) elif status.to_slug == requested_status: val = to_user.relationships.exists(from_user, status) else: val = from_user.relationships.exists(to_user, status, symmetrical=True) if val: return True return False @register.filter def add_relationship_url(user, status): """ Generate a url for adding a relationship on a given user. ``user`` is a User object, and ``status`` is either a relationship_status object or a string denoting a RelationshipStatus Usage:: href="{{ user|add_relationship_url:"following" }}" """ if isinstance(status, RelationshipStatus): status = status.from_slug return reverse('relationship_add', args=[user.username, status]) @register.filter def remove_relationship_url(user, status): """ Generate a url for removing a relationship on a given user. ``user`` is a User object, and ``status`` is either a relationship_status object or a string denoting a RelationshipStatus Usage:: href="{{ user|remove_relationship_url:"following" }}" """ if isinstance(status, RelationshipStatus): status = status.from_slug return reverse('relationship_remove', args=[user.username, status]) def positive_filter_decorator(func): def inner(qs, user): if isinstance(qs, basestring): model = get_model(*qs.split('.')) if not model: return [] qs = model._default_manager.all() if user.is_anonymous(): return qs.none() return func(qs, user) inner._decorated_function = getattr(func, '_decorated_function', func) return wraps(func)(inner) def negative_filter_decorator(func): def inner(qs, user): if isinstance(qs, basestring): model = get_model(*qs.split('.')) if not model: return [] qs = model._default_manager.all() if user.is_anonymous(): return qs return func(qs, user) inner._decorated_function = getattr(func, '_decorated_function', func) return wraps(func)(inner) @register.filter @positive_filter_decorator def friend_content(qs, user): return positive_filter(qs, user.relationships.friends()) @register.filter @positive_filter_decorator def following_content(qs, user): return positive_filter(qs, user.relationships.following()) @register.filter @positive_filter_decorator def followers_content(qs, user): return positive_filter(qs, user.relationships.followers()) @register.filter @negative_filter_decorator def unblocked_content(qs, user): return negative_filter(qs, user.relationships.blocking())

28.711409

79

0.687705

a0169a974f81264426a0688fb07602fbf79e8796

1,564

py

Python

ipylivebash/tests/test_runner.py

benlau/ipylivebash

1d935efecf69ebc55983722dd9b811c5f80b1f88

[ "BSD-3-Clause-Clear", "BSD-3-Clause" ]

6

2022-01-02T19:33:05.000Z

2022-03-02T02:14:20.000Z

ipylivebash/tests/test_runner.py

benlau/ipylivebash

1d935efecf69ebc55983722dd9b811c5f80b1f88

[ "BSD-3-Clause-Clear", "BSD-3-Clause" ]

1

2022-01-17T18:08:25.000Z

2022-01-29T22:35:10.000Z

ipylivebash/tests/test_runner.py

benlau/ipylivebash

1d935efecf69ebc55983722dd9b811c5f80b1f88

[ "BSD-3-Clause-Clear", "BSD-3-Clause" ]

null

from unittest.mock import MagicMock import time from ..runner import Runner, run_script, execute_script_in_thread ECHO_SCRIPT = "echo 123" def test_runner_parse_args(): runner = Runner("--help".split()) assert runner.args.print_help is True assert runner.args.keep_cell_output is False runner = Runner("--keep-cell-output".split()) assert runner.args.keep_cell_output is True def test_runner_run(): runner = Runner("") runner.log_view = MagicMock() runner.run(ECHO_SCRIPT) runner.log_view.write_message.assert_called_once_with("123\n") def test_runner_run_confirm(): runner = Runner("--ask-confirm".split()) runner.run(ECHO_SCRIPT) def test_runner_run_notify(): runner = Runner("--notify".split()) runner.run(ECHO_SCRIPT) def test_run_script(): run_script(ECHO_SCRIPT) def test_execute_script_in_thread(): proxy = execute_script_in_thread(ECHO_SCRIPT) messages = [] while True: proxy.acquire() is_finished = proxy.is_finished if len(proxy.messages) > 0: messages = messages + proxy.messages proxy.messages = [] proxy.release() if is_finished: break time.sleep(0.1) assert messages == ["123\n"] def test_log(): runner = Runner("--save output.txt".split()) runner.log_file = MagicMock() runner.run(ECHO_SCRIPT) runner.log_file.open.assert_called_once_with() runner.log_file.write_message.assert_called_once_with("123\n") runner.log_file.close.assert_called_once_with()

25.225806

66

0.688619

16a4c9190e5df021753712d2f42b13f6c31b0fed

16,678

py

Python

facet/configuration.py

edponce/FACET

0dca2d728813a4865e72b2e8fd6b114a0c63d5b0

[ "MIT" ]

2

2020-10-16T19:27:21.000Z

2021-11-04T15:06:54.000Z

facet/configuration.py

edponce/FACET

0dca2d728813a4865e72b2e8fd6b114a0c63d5b0

[ "MIT" ]

24

2020-07-13T01:49:36.000Z

2020-10-30T21:54:00.000Z

facet/configuration.py

edponce/FACET

0dca2d728813a4865e72b2e8fd6b114a0c63d5b0

[ "MIT" ]

null

import os import re import copy import yaml import json import pyparsing import configparser from typing import ( Any, Dict, Union, Iterable, ) __all__ = ['Configuration'] # Boolean and None states are case-insensitive BOOLEAN_STATES = { True: ('true', 'yes', 'on', 'accept', 'enable'), False: ('false', 'no', 'off', 'reject', 'disable'), } NONE_STATES = ('none', 'null', 'nul', 'nil') LEFT_BRACKETS = '([{<' RIGHT_BRACKETS = ')]}>' LPAREN, LBRACK, LBRACE, LANGLE = map(pyparsing.Suppress, LEFT_BRACKETS) RPAREN, RBRACK, RBRACE, RANGLE = map(pyparsing.Suppress, RIGHT_BRACKETS) BRACKETS = LEFT_BRACKETS + RIGHT_BRACKETS QUOTES = '\'\"' SGLQUOTE, DBLQUOTE = map(pyparsing.Suppress, QUOTES) DELIMITERS = ':,=' COLON, COMMA, EQUAL = map(pyparsing.Suppress, DELIMITERS) PUNCTUATIONS = BRACKETS + QUOTES + DELIMITERS # Integral, real, and scientific numbers numberToken = pyparsing.pyparsing_common.number # Boolean values boolToken = pyparsing.oneOf( ' '.join(BOOLEAN_STATES[True] + BOOLEAN_STATES[False]), caseless=True, asKeyword=True, ) boolToken.setParseAction(lambda token: token[0] in BOOLEAN_STATES[True]) # None values noneToken = pyparsing.oneOf( ' '.join(NONE_STATES), caseless=True, asKeyword=True, ) noneToken.setParseAction(pyparsing.replaceWith(None)) # Quoted strings quotedToken = pyparsing.quotedString(QUOTES) quotedToken.setParseAction(pyparsing.removeQuotes) # Unquoted strings rawToken = pyparsing.Word(pyparsing.printables, excludeChars=PUNCTUATIONS) # Key/value pairs kvToken = pyparsing.Forward() # Iterables: key/value, list, tuple, dict, set kvIterToken = pyparsing.Forward() listToken = pyparsing.Forward() tupleToken = pyparsing.Forward() dictToken = pyparsing.Forward() setToken = pyparsing.Forward() # Parsers: scalar and all # Order matters based on the following rules: # Key/value tokens are first literals # Numbers before other scalar literals # Iterables are last literals # bool/none before raw/quoted to prioritize keyword comparison # kvIterToken as first iterable to prioritize kvToken comparison # dictToken before setToken to resolve '{}' as a dictionary pyparser_scalars = ( numberToken | boolToken | noneToken | quotedToken | rawToken ) pyparser = ( kvToken | pyparser_scalars | kvIterToken | listToken | tupleToken | dictToken | setToken ) # Key/value pairs: '[key1=val1, key2=val2, ...]' # Key can only be scalar literals kvToken <<= pyparsing.Group(pyparser_scalars + EQUAL + pyparser) kvToken.setParseAction(lambda token: dict(token.asList())) kvIterToken <<= ( ( LPAREN + pyparsing.delimitedList(kvToken) + pyparsing.Optional(COMMA) + RPAREN ) ^ ( LBRACK + pyparsing.delimitedList(kvToken) + pyparsing.Optional(COMMA) + RBRACK ) ^ ( LBRACE + pyparsing.delimitedList(kvToken) + pyparsing.Optional(COMMA) + RBRACE ) ^ ( LANGLE + pyparsing.delimitedList(kvToken) + pyparsing.Optional(COMMA) + RANGLE ) ) kvIterToken.setParseAction(lambda token: { k: v for d in token.asList() for k, v in d.items() }) listToken <<= ( LBRACK + pyparsing.Optional(pyparsing.delimitedList(pyparser)) + pyparsing.Optional(COMMA) + RBRACK ) listToken.setParseAction(lambda token: [token.asList()]) # Tuples: '(val1, ...)' or '<val1, ...>' tupleToken <<= ( ( LPAREN + pyparsing.Optional(pyparsing.delimitedList(pyparser)) + pyparsing.Optional(COMMA) + RPAREN ) ^ ( LANGLE + pyparsing.Optional(pyparsing.delimitedList(pyparser)) + pyparsing.Optional(COMMA) + RANGLE ) ) tupleToken.setParseAction(lambda token: tuple(token)) dictEntry = pyparsing.Group(pyparser + COLON + pyparser) dictToken <<= ( LBRACE + pyparsing.Optional(pyparsing.delimitedList(dictEntry)) + pyparsing.Optional(COMMA) + RBRACE ) dictToken.setParseAction(lambda token: dict(token.asList())) setToken <<= ( LBRACE + pyparsing.Optional(pyparsing.delimitedList(pyparser)) + pyparsing.Optional(COMMA) + RBRACE ) setToken.setParseAction(lambda token: set(token)) # Non-enclosed, non-quoted CSV rawCSVToken = ( pyparsing.Optional(pyparser) + COMMA + pyparsing.Optional(pyparser) ) class Configuration: def __init__(self, regex=r'\${{([^}]+)}}'): self.regex = regex def expand_envvars(self, obj): """Recursively expand user and environment variables in common data structures.""" # NOTE: To support values of the forms key:value and key=value pairs, # 'parse_string' does not recurses, so let the result be checked for # instances of data structures that need subsequent parsing. if isinstance(obj, str): obj = os.path.expandvars(os.path.expanduser(obj)) if isinstance(obj, dict): # NOTE: Keys are not modified obj = {k: self.expand_envvars(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple, set)): obj = type(obj)([self.expand_envvars(v) for v in obj]) return obj def interpolate(self, obj: dict): """Recursively interpolate dictionary values based on a regular expression. Dictionary is traversed in a depth-first manner and supports out-of-order chained interpolations. Interpolation values are represented by keys enclosed in '${{...}}'. An arbitrary number of consecutive keys can be specified by delimiting them with a colon, ':'. Note that keys can be strings for dictionaries or indices for indexable iterables. Example: ${{key}} - substitute current value with dict[key] ${{key1:key2}} - substitute current value with dict[key1][key2] ${{key:2}} - substitute current value with dict[key][2] """ regex = re.compile(self.regex) # Get handle to root of object, so that we can do a forward search # for interpolation. root = obj def _interpolate(obj: dict): if isinstance(obj, dict): for k, v in obj.items(): obj[k] = _interpolate(v) elif isinstance(obj, (list, tuple, set)): obj = type(obj)([_interpolate(v) for v in obj]) elif isinstance(obj, str): match = regex.fullmatch(obj) if match: # Do not modify handle to root value = root for key in match.group(1).split(':'): if isinstance(value, dict): value = value[key] else: # Assume value is an iterable and key the index value = value[int(key)] obj = copy.deepcopy(_interpolate(value)) return obj return _interpolate(obj) def parse_string(self, string, protect_numerics=True): """Parse a string based on a grammar and resolve environment variables. Args: protect_numerics (bool): If set, string-based numbers are not parsed to numeric types. """ # Get copy of original string, to return it in case of invalid/error orig_string = string # Case: Protect string numerics (do not parse) if protect_numerics: try: float(string) except ValueError: pass else: return string # Case: Expand user and environment variables # NOTE: Even though environment variables should have been resolved # prior to these parsing actions, for values with single-line composite # values (e.g., CSV of key=value pairs), we resolve those internal # values as well. string = self.expand_envvars(string) # Case: Non-enclosed CSV --> Convert to a tuple try: rawCSVToken.parseString(string) except pyparsing.ParseException: pass else: string = '(' + string + ')' try: # NOTE: Parser does not parses correctly a value that begins with # numbers followed by other characters, e.g., value=2a. return pyparser.parseString(string, parseAll=True)[0] except pyparsing.ParseException: return orig_string def parse(self, obj, **kwargs): """Recursively parse data structures.""" # NOTE: Given that ConfigParser may be applied before this parsing and # ConfigParser only allows a single level of key/value pairs, nested # structures are stringified, and 'parse_string' does not recurses, so # we let the result data to be checked for instances of data structures # that need subsequent parsing. if isinstance(obj, str): obj = self.parse_string(obj, **kwargs) if isinstance(obj, dict): # NOTE: Keys are not modified obj = {k: self.parse(v, **kwargs) for k, v in obj.items()} elif isinstance(obj, (list, tuple, set)): obj = type(obj)([self.parse(v, **kwargs) for v in obj]) return obj def fix_configparser(self, obj: Any, **kwargs): """Recursively parse/fix data structures previously parsed by ConfigParser. Given that ConfigParser only allows a single level of key/value pairs, nested structures are stringified, so we apply regex transformations to conform multi-line values for 'parse_string()'. """ if isinstance(obj, str): # Fix first nested element in mulitline values obj = re.sub(r'=\s*', '=', obj.strip()) # Change nested elements in same level to be a CSV string obj = re.sub(r'\n', ',', obj.strip()) # Quote interpolation strings obj = re.sub(fr'["\']?({self.regex})["\']?', r'"\1"', obj) obj = self.parse_string(obj, **kwargs) # Enable numeric protection for subsequent 'parse_string()' kwargs['protect_numerics'] = True if isinstance(obj, dict): obj = { k: self.fix_configparser(v, **kwargs) for k, v in obj.items() } elif isinstance(obj, (list, tuple, set)): obj = type(obj)([ self.fix_configparser(v, **kwargs) for v in obj ]) return obj def parse_with_configparser(self, config: Union[str, Dict[str, Any]]): """Parse with ConfigParser. Args: config (str, Dict[str, Any]): Configuration file/data to parse. """ parser = configparser.ConfigParser( delimiters=('=',), allow_no_value=True, ) # Leave options (keys) unchanged, do not lowercase parser.optionxform = lambda option: option # Expand environment variables config = self.expand_envvars(config) # Parse data if isinstance(config, str): if os.path.isfile(config): filename = config with open(filename) as fd: config = fd.read() # NOTE: Expand environment variables from loaded data config = self.expand_envvars(config) parser.read_string(config) elif isinstance(config, dict): parser.read_dict(config) else: raise ValueError('invalid configuration data for ConfigParser') # Convert ConfigParser to dict (include defaults, if available) config = { section: dict(parser[section]) for section in parser.sections() } if len(parser.defaults()) > 0: config[parser.default_section] = dict(parser.defaults()) # Fix multi-line options return self.fix_configparser(config, protect_numerics=False) def load_from_string(self, data: str): """Parse a string into a dictionary. Returns: Dict[str:Any]: Configuration mapping """ data = data.strip() try: config = json.loads(data) except json.decoder.JSONDecodeError: try: config = yaml.safe_load(data) except yaml.parser.ParserError: try: config = self.parse_with_configparser(data) except Exception: raise ValueError('invalid configuration string') return config def load_from_file(self, filename: str, file_type: str = None): """Load configuration data from YAML/JSON/INI file. Args: file_type (str): Explicit file format for file, ignore extension. Returns: dict[str:Any]: Configuration mapping None: If error/invalid occurs during parsing """ filename = self.expand_envvars(filename) if not file_type: _, ext = os.path.splitext(filename) file_type = ext[1:] file_type = file_type.lower() if file_type in ('yaml', 'yml'): with open(filename) as fd: config = yaml.safe_load(fd) elif file_type in ('json',): with open(filename) as fd: config = json.load(fd) elif file_type in ('ini', 'cfg', 'conf'): config = self.parse_with_configparser(filename) else: raise ValueError(f"invalid configuration file type, '{file_type}'") return config def load( self, config: Union[str, Dict[Any, Any]], *, keys: Union[str, Iterable[str]] = None, file_type: str = None, delimiter: str = ':', ): """Load/parse configuration from a file/string/mapping into a mapping. Note: * The transformations are idempotent with regards to the resulting configuration mapping. Args: config (str, Dict[Any, Any]): Configuration file/data to parse. File can embed keys via format: 'file.conf:key1:key2:...' Embedded keys supersede parameter keys. String formats can be dictionary-like or comma-delimited key=value pairs. keys (str, Iterable[str]): Extract only data from configuration corresponding to key. If multiple keys are provided, they are used (in order) to update the resulting configuration. file_type (str): Explicit file format for file, ignore extension. delimiter (str): Delimiter symbol between file name and keys. Returns: dict[str:Any]: Configuration mapping """ if isinstance(config, str): config = self.expand_envvars(config) filename, *tmp_keys = config.split(delimiter) if os.path.isfile(filename): # Embedded keys supersede parameter keys if len(tmp_keys) > 0: keys = tmp_keys config = self.load_from_file(filename, file_type=file_type) # If only a single configuration block, then use it if not keys and len(config) == 1: keys = list(config.keys()) # NOTE: Consider a string with no data structure related symbols # to be a filename. The symbols are based on the general parser. elif filename and not any(x in filename for x in PUNCTUATIONS): raise ValueError( f"configuration file does not exists, '{filename}'" ) else: config = self.load_from_string(config) elif isinstance(config, dict): config = self.expand_envvars(config) elif config is not None: raise ValueError( f"invalid configuration data type, '{type(config)}'" ) if not config: return {} # Normalize strings and types based on a grammar config = self.parse(config) # Interpolate/substitute values config = self.interpolate(config) # Filter configuration based on keys if keys: if isinstance(keys, str): keys = [keys] filtered_config = {} for key in keys: filtered_config.update(config[key]) config = filtered_config return config

32.447471

79

0.597254

66981e06e9c908c9cb8573a8e7c5d4a9b8632902

6,293

py

Python

docs/conf.py

timgates42/pyttsx

1a84ee33971951b1ea18f2708061a5d19ef94018

[ "FSFAP" ]

160

2016-10-04T22:45:36.000Z

2022-02-10T06:41:56.000Z

docs/conf.py

simz089s/pyttsx

4ad1e84fdefee4eed290fdc966573cb57d0b0079

[ "FSFAP" ]

27

2016-10-04T02:45:18.000Z

2022-03-09T15:15:54.000Z

docs/conf.py

simz089s/pyttsx

4ad1e84fdefee4eed290fdc966573cb57d0b0079

[ "FSFAP" ]

58

2016-10-06T16:53:43.000Z

2021-10-21T22:17:35.000Z

# -*- coding: utf-8 -*- # # pyttsx documentation build configuration file, created by # sphinx-quickstart on Sun Nov 1 09:40:19 2009. # # 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 sys, os # 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.append(os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [] # 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 = 'index' # General information about the project. project = u'pyttsx' copyright = u'2009, 2013 Peter Parente' # 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 = '1.2' # The full version, including alpha/beta/rc tags. release = '1.2' # 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'] # 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. #html_theme_options = {} # 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 = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # 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 = None # 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'] # 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 = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # 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 # 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 = 'pyttsxdoc' # -- 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', 'pyttsx.tex', u'pyttsx Documentation', u'Peter Parente', '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

32.271795

80

0.722708

dd09270e73d492b8977b90e570294b345e62791b

2,500

py

Python

test/selenium/src/tests/test_unified_mapper.py

j0gurt/ggrc-core

84662dc85aa8864c907eabe70b8efccf92298a1f

[ "ECL-2.0", "Apache-2.0" ]

1

2019-01-04T10:55:14.000Z

test/selenium/src/tests/test_unified_mapper.py

farcry4998/ggrc-core

c469039dabb55033c1b379850feb19e8dda2e2a1

[ "ECL-2.0", "Apache-2.0" ]

null

test/selenium/src/tests/test_unified_mapper.py

farcry4998/ggrc-core

c469039dabb55033c1b379850feb19e8dda2e2a1

[ "ECL-2.0", "Apache-2.0" ]

null

# Copyright (C) 2018 Google Inc. # Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file> """Unified mapper tests.""" # pylint: disable=no-self-use # pylint: disable=invalid-name import pytest from lib import base, users from lib.entities import entities_factory from lib.entities.entity import Representation from lib.service import webui_service, rest_facade class TestProgramPage(base.Test): """Tests of unified mapper.""" @pytest.mark.smoke_tests def test_destructive_mapping_controls_to_program_via_unified_mapper( self, new_program_rest, new_controls_rest, selenium ): """Check if Controls can be mapped to Program from Controls widget under Program page via unified mapper. Preconditions: - Program, Controls created via REST API. """ expected_controls = [ expected_control.repr_ui() for expected_control in new_controls_rest] controls_ui_service = webui_service.ControlsService(selenium) controls_ui_service.map_objs_via_tree_view( src_obj=new_program_rest, dest_objs=expected_controls) actual_controls_tab_count = controls_ui_service.get_count_objs_from_tab( src_obj=new_program_rest) assert len(expected_controls) == actual_controls_tab_count actual_controls = controls_ui_service.get_list_objs_from_tree_view( src_obj=new_program_rest) # 'actual_controls': created_at, updated_at, custom_attributes (None) self.general_equal_assert( sorted(expected_controls), sorted(actual_controls), *Representation.tree_view_attrs_to_exclude) def test_create_and_map_control(self, program, selenium): """Test that control can be created and mapped using Unified mapper.""" controls_service = webui_service.ControlsService(selenium) controls_service.open_widget_of_mapped_objs( program).tree_view.open_map().click_create_and_map_obj() control = entities_factory.ControlsFactory().create() controls_service.submit_obj_modal(control) tree_view_control = controls_service.get_list_objs_from_tree_view( program)[0] actual_control = controls_service.get_obj_from_info_page(tree_view_control) rest_control = rest_facade.get_obj(actual_control) control.update_attrs( created_at=rest_control.created_at, updated_at=rest_control.updated_at, modified_by=users.current_user(), slug=rest_control.slug).repr_ui() self.general_equal_assert(control, actual_control, "custom_attributes")

42.372881

79

0.7744

cb8b8168abd406ac3172b2ed5c46a4d6031fcaab

2,559

py

Python

project/drupal.py

thomasdiluccio/template-builder

effb471d7644c7d589a2ed8f6c70be887bdae5d5

[ "MIT" ]

null

project/drupal.py

thomasdiluccio/template-builder

effb471d7644c7d589a2ed8f6c70be887bdae5d5

[ "MIT" ]

null

project/drupal.py

thomasdiluccio/template-builder

effb471d7644c7d589a2ed8f6c70be887bdae5d5

[ "MIT" ]

null

from . import BaseProject from .remote import RemoteProject import json from collections import OrderedDict class Drupal7_vanilla(BaseProject): version = '7.67' @property def update(self): return super(Drupal7_vanilla, self).update + [ "wget https://ftp.drupal.org/files/projects/drupal-{0}.tar.gz && tar xzvf drupal-{0}.tar.gz -C {1}".format(self.version, self.builddir), "rm drupal-{0}.tar.gz".format(self.version), "rm -rf {0}public || true".format(self.builddir), "mv {0}drupal-{1} {0}public".format(self.builddir, self.version), ] class Drupal8(RemoteProject): major_version = '8.9' remote = 'https://github.com/drupal/recommended-project.git' @property def platformify(self): return super(Drupal8, self).platformify + [ # 'cd {0} && composer update -W'.format(self.builddir) + self.composer_defaults() 'cd {0} && composer require platformsh/config-reader drush/drush drupal/console drupal/redis'.format(self.builddir) + self.composer_defaults(), # 'cd {0} && composer update -W'.format(self.builddir) + self.composer_defaults() ] class Drupal9(RemoteProject): # This can have a common base with Drupal 8 eventually, once modules are updated. major_version = "9.3" remote = 'https://github.com/drupal/recommended-project.git' @property def platformify(self): return super(Drupal9, self).platformify + [ 'cd {0} && composer require platformsh/config-reader drush/drush drupal/redis'.format(self.builddir) + self.composer_defaults() ] class Drupal8_multisite(Drupal8): pass class Drupal8_opigno(Drupal8): major_version = '2' remote = 'https://bitbucket.org/opigno/opigno-composer.git' class Drupal8_govcms8(RemoteProject): major_version = '1' remote = 'https://github.com/govCMS/govCMS8-project.git' @property def platformify(self): return super(Drupal8_govcms8, self).platformify + [ # GovCMS comes with a pre-made lock file that pins symfony/filesystem at v4, but # drupal/console only works with the 3.x version, and therefore will fail. # It should work to remove the lock file first, but for some reason that is still failing. # For now, just skip installing console on GovCMS. I don't know if anyone uses it anyway. 'cd {0} && composer require platformsh/config-reader drush/drush drupal/redis'.format(self.builddir) + self.composer_defaults(), ]

40.619048

156

0.669793

92235ee751f5bdc387e7ba6c286e1e30c5739a8b

4,964

py

Python

adamw.py

imics-lab/tts-gan

872213938a68ca63570f9f1df0ccc8e179d73ab2

[ "Apache-2.0" ]

14

2022-01-25T09:57:59.000Z

2022-03-28T04:27:12.000Z

adamw.py

imics-lab/tts-gan

872213938a68ca63570f9f1df0ccc8e179d73ab2

[ "Apache-2.0" ]

1

2022-03-11T07:11:32.000Z

2022-03-11T15:09:21.000Z

adamw.py

imics-lab/tts-gan

872213938a68ca63570f9f1df0ccc8e179d73ab2

[ "Apache-2.0" ]

3

2022-02-11T15:22:41.000Z

2022-03-27T11:10:58.000Z

""" AdamW Optimizer Impl copied from PyTorch master """ import math import torch from torch.optim.optimizer import Optimizer class AdamW(Optimizer): r"""Implements AdamW algorithm. The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay coefficient (default: 1e-2) amsgrad (boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ (default: False) .. _Adam\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 .. _Decoupled Weight Decay Regularization: https://arxiv.org/abs/1711.05101 .. _On the Convergence of Adam and Beyond: https://openreview.net/forum?id=ryQu7f-RZ """ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-2, amsgrad=False): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad) super(AdamW, self).__init__(params, defaults) def __setstate__(self, state): super(AdamW, self).__setstate__(state) for group in self.param_groups: group.setdefault('amsgrad', False) def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue # Perform stepweight decay p.data.mul_(1 - group['lr'] * group['weight_decay']) # Perform optimization step grad = p.grad.data if grad.is_sparse: raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') amsgrad = group['amsgrad'] state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p.data) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p.data) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state['max_exp_avg_sq'] = torch.zeros_like(p.data) exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] if amsgrad: max_exp_avg_sq = state['max_exp_avg_sq'] beta1, beta2 = group['betas'] state['step'] += 1 bias_correction1 = 1 - beta1 ** state['step'] bias_correction2 = 1 - beta2 ** state['step'] # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(1 - beta1, grad) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) if amsgrad: # Maintains the maximum of all 2nd moment running avg. till now torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) # Use the max. for normalizing running avg. of gradient denom = (max_exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) else: denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) step_size = group['lr'] / bias_correction1 p.data.addcdiv_(-step_size, exp_avg, denom) return loss

42.42735

116

0.577357

7e5f7ea4df406d8c403e706764e541c8a3f2cc3f

4,129

py

Python

qiling/qiling/cc/__init__.py

mrTavas/owasp-fstm-auto

6e9ff36e46d885701c7419db3eca15f12063a7f3

[ "CC0-1.0" ]

2

2021-05-05T12:03:01.000Z

2021-06-04T14:27:15.000Z

qiling/qiling/cc/__init__.py

mrTavas/owasp-fstm-auto

6e9ff36e46d885701c7419db3eca15f12063a7f3

[ "CC0-1.0" ]

null

qiling/qiling/cc/__init__.py

mrTavas/owasp-fstm-auto

6e9ff36e46d885701c7419db3eca15f12063a7f3

[ "CC0-1.0" ]

2

2021-05-05T12:03:09.000Z

2021-06-04T14:27:21.000Z

#!/usr/bin/env python3 # # Cross Platform and Multi Architecture Advanced Binary Emulation Framework from typing import Callable, Tuple from qiling import Qiling class QlCC: """Calling convention base class. """ def __init__(self, ql: Qiling) -> None: """Initialize a calling convention instance. Args: ql: qiling instance """ self.ql = ql @staticmethod def getNumSlots(argbits: int) -> int: """Get the number of slots allocated for an argument of width `argbits`. """ raise NotImplementedError def getRawParam(self, slot: int, argbits: int = None) -> int: """Read a value of native size from the specified argument slot. Note that argument slots and argument indexes are not the same. Though they often correlate to each other, some implementations might use more than one slot to represent a sigle argument. Args: slot: argument slot to access argbits: argument size in bits (default: arch native size) Returns: raw value """ raise NotImplementedError def setRawParam(self, slot: int, value: int, argbits: int = None) -> None: """Replace the value in the specified argument slot. Note that argument slots and argument indexes are not the same. Though they often correlate to each other, some implementations might use more than one slot to represent a sigle argument. Args: slot: argument slot to access value: new raw value to write argbits: argument size in bits (default: arch native size) """ raise NotImplementedError def getReturnValue(self) -> int: """Get function return value. """ raise NotImplementedError def setReturnValue(self, val: int) -> None: """Set function return value. Args: val: a value to set """ raise NotImplementedError def unwind(self, nslots: int) -> int: """Unwind frame and return from function call. Args: nslots: number of arg slots used Returns: return address """ raise NotImplementedError class QlCommonBaseCC(QlCC): """Calling convention base class that implements parameters access through both registers and the stack. The extending class is resopnsible to implement the rest of the QlCC interface. """ _argregs = () _shadow = 0 def __init__(self, ql: Qiling, retreg: int): super().__init__(ql) # native address size in bytes self._asize = self.ql.pointersize # return value register self._retreg = retreg def __access_param(self, index: int, stack_access: Callable, reg_access: Callable) -> Tuple[Callable, int]: """[private] Generic accessor to function call parameters by their index. This method will determine whether the parameter should be accessed on the stack or in a register, and return the appropriate accessor along with the location to access (either a register id or stack address) Args: index: parameter index to access stack_access: stack accessor method (either read or write) reg_access: regs accessor method (either read or write) Returns: a tuple of the accessor method to use and the location to access """ if index >= len(self._argregs): raise IndexError(f'tried to access arg {index}, but only {len(self._argregs) - 1} args are supported') reg = self._argregs[index] # should arg be read from a reg or the stack? if reg is None: # get matching stack item si = index - self._argregs.index(None) # skip return address and shadow space return stack_access, (1 + self._shadow + si) * self._asize else: return reg_access, reg def getRawParam(self, index: int, argbits: int = None) -> int: read, loc = self.__access_param(index, self.ql.stack_read, self.ql.reg.read) mask = (0 if argbits is None else (1 << argbits)) - 1 return read(loc) & mask def setRawParam(self, index: int, value: int, argbits: int = None) -> None: write, loc = self.__access_param(index, self.ql.stack_write, self.ql.reg.write) mask = (0 if argbits is None else (1 << argbits)) - 1 write(loc, value & mask) def getReturnValue(self) -> int: return self.ql.reg.read(self._retreg) def setReturnValue(self, value: int) -> None: self.ql.reg.write(self._retreg, value)

27.344371

108

0.717365

0592bb492266682f5fb2d4e5e66ce2ac965b453e

33,976

py

Python

couchjs/scons/scons-local-2.0.1/SCons/Builder.py

Gussy/bigcouch

9e67d3f754186ce8368503509ae041a2847f2b7c

[ "Apache-2.0" ]

73

2015-03-19T04:04:52.000Z

2021-08-16T10:45:11.000Z

couchjs/scons/scons-local-2.0.1/SCons/Builder.py

Gussy/bigcouch

9e67d3f754186ce8368503509ae041a2847f2b7c

[ "Apache-2.0" ]

5

2016-04-26T13:19:25.000Z

2017-03-11T14:11:22.000Z

couchjs/scons/scons-local-2.0.1/SCons/Builder.py

Gussy/bigcouch

9e67d3f754186ce8368503509ae041a2847f2b7c

[ "Apache-2.0" ]

13

2015-03-27T05:21:42.000Z

2017-05-22T11:45:30.000Z

"""SCons.Builder Builder object subsystem. A Builder object is a callable that encapsulates information about how to execute actions to create a target Node (file) from source Nodes (files), and how to create those dependencies for tracking. The main entry point here is the Builder() factory method. This provides a procedural interface that creates the right underlying Builder object based on the keyword arguments supplied and the types of the arguments. The goal is for this external interface to be simple enough that the vast majority of users can create new Builders as necessary to support building new types of files in their configurations, without having to dive any deeper into this subsystem. The base class here is BuilderBase. This is a concrete base class which does, in fact, represent the Builder objects that we (or users) create. There is also a proxy that looks like a Builder: CompositeBuilder This proxies for a Builder with an action that is actually a dictionary that knows how to map file suffixes to a specific action. This is so that we can invoke different actions (compilers, compile options) for different flavors of source files. Builders and their proxies have the following public interface methods used by other modules: __call__() THE public interface. Calling a Builder object (with the use of internal helper methods) sets up the target and source dependencies, appropriate mapping to a specific action, and the environment manipulation necessary for overridden construction variable. This also takes care of warning about possible mistakes in keyword arguments. add_emitter() Adds an emitter for a specific file suffix, used by some Tool modules to specify that (for example) a yacc invocation on a .y can create a .h *and* a .c file. add_action() Adds an action for a specific file suffix, heavily used by Tool modules to add their specific action(s) for turning a source file into an object file to the global static and shared object file Builders. There are the following methods for internal use within this module: _execute() The internal method that handles the heavily lifting when a Builder is called. This is used so that the __call__() methods can set up warning about possible mistakes in keyword-argument overrides, and *then* execute all of the steps necessary so that the warnings only occur once. get_name() Returns the Builder's name within a specific Environment, primarily used to try to return helpful information in error messages. adjust_suffix() get_prefix() get_suffix() get_src_suffix() set_src_suffix() Miscellaneous stuff for handling the prefix and suffix manipulation we use in turning source file names into target file names. """ # # Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010 The SCons Foundation # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY # KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. __revision__ = "src/engine/SCons/Builder.py 5134 2010/08/16 23:02:40 bdeegan" import collections import SCons.Action from SCons.Debug import logInstanceCreation from SCons.Errors import InternalError, UserError import SCons.Executor import SCons.Memoize import SCons.Node import SCons.Node.FS import SCons.Util import SCons.Warnings class _Null(object): pass _null = _Null def match_splitext(path, suffixes = []): if suffixes: matchsuf = [S for S in suffixes if path[-len(S):] == S] if matchsuf: suf = max([(len(_f),_f) for _f in matchsuf])[1] return [path[:-len(suf)], path[-len(suf):]] return SCons.Util.splitext(path) class DictCmdGenerator(SCons.Util.Selector): """This is a callable class that can be used as a command generator function. It holds on to a dictionary mapping file suffixes to Actions. It uses that dictionary to return the proper action based on the file suffix of the source file.""" def __init__(self, dict=None, source_ext_match=1): SCons.Util.Selector.__init__(self, dict) self.source_ext_match = source_ext_match def src_suffixes(self): return list(self.keys()) def add_action(self, suffix, action): """Add a suffix-action pair to the mapping. """ self[suffix] = action def __call__(self, target, source, env, for_signature): if not source: return [] if self.source_ext_match: suffixes = self.src_suffixes() ext = None for src in map(str, source): my_ext = match_splitext(src, suffixes)[1] if ext and my_ext != ext: raise UserError("While building `%s' from `%s': Cannot build multiple sources with different extensions: %s, %s" % (repr(list(map(str, target))), src, ext, my_ext)) ext = my_ext else: ext = match_splitext(str(source[0]), self.src_suffixes())[1] if not ext: #return ext raise UserError("While building `%s': " "Cannot deduce file extension from source files: %s" % (repr(list(map(str, target))), repr(list(map(str, source))))) try: ret = SCons.Util.Selector.__call__(self, env, source, ext) except KeyError, e: raise UserError("Ambiguous suffixes after environment substitution: %s == %s == %s" % (e.args[0], e.args[1], e.args[2])) if ret is None: raise UserError("While building `%s' from `%s': Don't know how to build from a source file with suffix `%s'. Expected a suffix in this list: %s." % \ (repr(list(map(str, target))), repr(list(map(str, source))), ext, repr(list(self.keys())))) return ret class CallableSelector(SCons.Util.Selector): """A callable dictionary that will, in turn, call the value it finds if it can.""" def __call__(self, env, source): value = SCons.Util.Selector.__call__(self, env, source) if callable(value): value = value(env, source) return value class DictEmitter(SCons.Util.Selector): """A callable dictionary that maps file suffixes to emitters. When called, it finds the right emitter in its dictionary for the suffix of the first source file, and calls that emitter to get the right lists of targets and sources to return. If there's no emitter for the suffix in its dictionary, the original target and source are returned. """ def __call__(self, target, source, env): emitter = SCons.Util.Selector.__call__(self, env, source) if emitter: target, source = emitter(target, source, env) return (target, source) class ListEmitter(collections.UserList): """A callable list of emitters that calls each in sequence, returning the result. """ def __call__(self, target, source, env): for e in self.data: target, source = e(target, source, env) return (target, source) # These are a common errors when calling a Builder; # they are similar to the 'target' and 'source' keyword args to builders, # so we issue warnings when we see them. The warnings can, of course, # be disabled. misleading_keywords = { 'targets' : 'target', 'sources' : 'source', } class OverrideWarner(collections.UserDict): """A class for warning about keyword arguments that we use as overrides in a Builder call. This class exists to handle the fact that a single Builder call can actually invoke multiple builders. This class only emits the warnings once, no matter how many Builders are invoked. """ def __init__(self, dict): collections.UserDict.__init__(self, dict) if __debug__: logInstanceCreation(self, 'Builder.OverrideWarner') self.already_warned = None def warn(self): if self.already_warned: return for k in self.keys(): if k in misleading_keywords: alt = misleading_keywords[k] msg = "Did you mean to use `%s' instead of `%s'?" % (alt, k) SCons.Warnings.warn(SCons.Warnings.MisleadingKeywordsWarning, msg) self.already_warned = 1 def Builder(**kw): """A factory for builder objects.""" composite = None if 'generator' in kw: if 'action' in kw: raise UserError("You must not specify both an action and a generator.") kw['action'] = SCons.Action.CommandGeneratorAction(kw['generator'], {}) del kw['generator'] elif 'action' in kw: source_ext_match = kw.get('source_ext_match', 1) if 'source_ext_match' in kw: del kw['source_ext_match'] if SCons.Util.is_Dict(kw['action']): composite = DictCmdGenerator(kw['action'], source_ext_match) kw['action'] = SCons.Action.CommandGeneratorAction(composite, {}) kw['src_suffix'] = composite.src_suffixes() else: kw['action'] = SCons.Action.Action(kw['action']) if 'emitter' in kw: emitter = kw['emitter'] if SCons.Util.is_String(emitter): # This allows users to pass in an Environment # variable reference (like "$FOO") as an emitter. # We will look in that Environment variable for # a callable to use as the actual emitter. var = SCons.Util.get_environment_var(emitter) if not var: raise UserError("Supplied emitter '%s' does not appear to refer to an Environment variable" % emitter) kw['emitter'] = EmitterProxy(var) elif SCons.Util.is_Dict(emitter): kw['emitter'] = DictEmitter(emitter) elif SCons.Util.is_List(emitter): kw['emitter'] = ListEmitter(emitter) result = BuilderBase(**kw) if not composite is None: result = CompositeBuilder(result, composite) return result def _node_errors(builder, env, tlist, slist): """Validate that the lists of target and source nodes are legal for this builder and environment. Raise errors or issue warnings as appropriate. """ # First, figure out if there are any errors in the way the targets # were specified. for t in tlist: if t.side_effect: raise UserError("Multiple ways to build the same target were specified for: %s" % t) if t.has_explicit_builder(): if not t.env is None and not t.env is env: action = t.builder.action t_contents = action.get_contents(tlist, slist, t.env) contents = action.get_contents(tlist, slist, env) if t_contents == contents: msg = "Two different environments were specified for target %s,\n\tbut they appear to have the same action: %s" % (t, action.genstring(tlist, slist, t.env)) SCons.Warnings.warn(SCons.Warnings.DuplicateEnvironmentWarning, msg) else: msg = "Two environments with different actions were specified for the same target: %s" % t raise UserError(msg) if builder.multi: if t.builder != builder: msg = "Two different builders (%s and %s) were specified for the same target: %s" % (t.builder.get_name(env), builder.get_name(env), t) raise UserError(msg) # TODO(batch): list constructed each time! if t.get_executor().get_all_targets() != tlist: msg = "Two different target lists have a target in common: %s (from %s and from %s)" % (t, list(map(str, t.get_executor().get_all_targets())), list(map(str, tlist))) raise UserError(msg) elif t.sources != slist: msg = "Multiple ways to build the same target were specified for: %s (from %s and from %s)" % (t, list(map(str, t.sources)), list(map(str, slist))) raise UserError(msg) if builder.single_source: if len(slist) > 1: raise UserError("More than one source given for single-source builder: targets=%s sources=%s" % (list(map(str,tlist)), list(map(str,slist)))) class EmitterProxy(object): """This is a callable class that can act as a Builder emitter. It holds on to a string that is a key into an Environment dictionary, and will look there at actual build time to see if it holds a callable. If so, we will call that as the actual emitter.""" def __init__(self, var): self.var = SCons.Util.to_String(var) def __call__(self, target, source, env): emitter = self.var # Recursively substitute the variable. # We can't use env.subst() because it deals only # in strings. Maybe we should change that? while SCons.Util.is_String(emitter) and emitter in env: emitter = env[emitter] if callable(emitter): target, source = emitter(target, source, env) elif SCons.Util.is_List(emitter): for e in emitter: target, source = e(target, source, env) return (target, source) def __cmp__(self, other): return cmp(self.var, other.var) class BuilderBase(object): """Base class for Builders, objects that create output nodes (files) from input nodes (files). """ if SCons.Memoize.use_memoizer: __metaclass__ = SCons.Memoize.Memoized_Metaclass memoizer_counters = [] def __init__(self, action = None, prefix = '', suffix = '', src_suffix = '', target_factory = None, source_factory = None, target_scanner = None, source_scanner = None, emitter = None, multi = 0, env = None, single_source = 0, name = None, chdir = _null, is_explicit = 1, src_builder = None, ensure_suffix = False, **overrides): if __debug__: logInstanceCreation(self, 'Builder.BuilderBase') self._memo = {} self.action = action self.multi = multi if SCons.Util.is_Dict(prefix): prefix = CallableSelector(prefix) self.prefix = prefix if SCons.Util.is_Dict(suffix): suffix = CallableSelector(suffix) self.env = env self.single_source = single_source if 'overrides' in overrides: SCons.Warnings.warn(SCons.Warnings.DeprecatedBuilderKeywordsWarning, "The \"overrides\" keyword to Builder() creation has been deprecated;\n" +\ "\tspecify the items as keyword arguments to the Builder() call instead.") overrides.update(overrides['overrides']) del overrides['overrides'] if 'scanner' in overrides: SCons.Warnings.warn(SCons.Warnings.DeprecatedBuilderKeywordsWarning, "The \"scanner\" keyword to Builder() creation has been deprecated;\n" "\tuse: source_scanner or target_scanner as appropriate.") del overrides['scanner'] self.overrides = overrides self.set_suffix(suffix) self.set_src_suffix(src_suffix) self.ensure_suffix = ensure_suffix self.target_factory = target_factory self.source_factory = source_factory self.target_scanner = target_scanner self.source_scanner = source_scanner self.emitter = emitter # Optional Builder name should only be used for Builders # that don't get attached to construction environments. if name: self.name = name self.executor_kw = {} if not chdir is _null: self.executor_kw['chdir'] = chdir self.is_explicit = is_explicit if src_builder is None: src_builder = [] elif not SCons.Util.is_List(src_builder): src_builder = [ src_builder ] self.src_builder = src_builder def __nonzero__(self): raise InternalError("Do not test for the Node.builder attribute directly; use Node.has_builder() instead") def get_name(self, env): """Attempts to get the name of the Builder. Look at the BUILDERS variable of env, expecting it to be a dictionary containing this Builder, and return the key of the dictionary. If there's no key, then return a directly-configured name (if there is one) or the name of the class (by default).""" try: index = list(env['BUILDERS'].values()).index(self) return list(env['BUILDERS'].keys())[index] except (AttributeError, KeyError, TypeError, ValueError): try: return self.name except AttributeError: return str(self.__class__) def __cmp__(self, other): return cmp(self.__dict__, other.__dict__) def splitext(self, path, env=None): if not env: env = self.env if env: suffixes = self.src_suffixes(env) else: suffixes = [] return match_splitext(path, suffixes) def _adjustixes(self, files, pre, suf, ensure_suffix=False): if not files: return [] result = [] if not SCons.Util.is_List(files): files = [files] for f in files: if SCons.Util.is_String(f): f = SCons.Util.adjustixes(f, pre, suf, ensure_suffix) result.append(f) return result def _create_nodes(self, env, target = None, source = None): """Create and return lists of target and source nodes. """ src_suf = self.get_src_suffix(env) target_factory = env.get_factory(self.target_factory) source_factory = env.get_factory(self.source_factory) source = self._adjustixes(source, None, src_suf) slist = env.arg2nodes(source, source_factory) pre = self.get_prefix(env, slist) suf = self.get_suffix(env, slist) if target is None: try: t_from_s = slist[0].target_from_source except AttributeError: raise UserError("Do not know how to create a target from source `%s'" % slist[0]) except IndexError: tlist = [] else: splitext = lambda S: self.splitext(S,env) tlist = [ t_from_s(pre, suf, splitext) ] else: target = self._adjustixes(target, pre, suf, self.ensure_suffix) tlist = env.arg2nodes(target, target_factory, target=target, source=source) if self.emitter: # The emitter is going to do str(node), but because we're # being called *from* a builder invocation, the new targets # don't yet have a builder set on them and will look like # source files. Fool the emitter's str() calls by setting # up a temporary builder on the new targets. new_targets = [] for t in tlist: if not t.is_derived(): t.builder_set(self) new_targets.append(t) orig_tlist = tlist[:] orig_slist = slist[:] target, source = self.emitter(target=tlist, source=slist, env=env) # Now delete the temporary builders that we attached to any # new targets, so that _node_errors() doesn't do weird stuff # to them because it thinks they already have builders. for t in new_targets: if t.builder is self: # Only delete the temporary builder if the emitter # didn't change it on us. t.builder_set(None) # Have to call arg2nodes yet again, since it is legal for # emitters to spit out strings as well as Node instances. tlist = env.arg2nodes(target, target_factory, target=orig_tlist, source=orig_slist) slist = env.arg2nodes(source, source_factory, target=orig_tlist, source=orig_slist) return tlist, slist def _execute(self, env, target, source, overwarn={}, executor_kw={}): # We now assume that target and source are lists or None. if self.src_builder: source = self.src_builder_sources(env, source, overwarn) if self.single_source and len(source) > 1 and target is None: result = [] if target is None: target = [None]*len(source) for tgt, src in zip(target, source): if not tgt is None: tgt = [tgt] if not src is None: src = [src] result.extend(self._execute(env, tgt, src, overwarn)) return SCons.Node.NodeList(result) overwarn.warn() tlist, slist = self._create_nodes(env, target, source) # Check for errors with the specified target/source lists. _node_errors(self, env, tlist, slist) # The targets are fine, so find or make the appropriate Executor to # build this particular list of targets from this particular list of # sources. executor = None key = None if self.multi: try: executor = tlist[0].get_executor(create = 0) except (AttributeError, IndexError): pass else: executor.add_sources(slist) if executor is None: if not self.action: fmt = "Builder %s must have an action to build %s." raise UserError(fmt % (self.get_name(env or self.env), list(map(str,tlist)))) key = self.action.batch_key(env or self.env, tlist, slist) if key: try: executor = SCons.Executor.GetBatchExecutor(key) except KeyError: pass else: executor.add_batch(tlist, slist) if executor is None: executor = SCons.Executor.Executor(self.action, env, [], tlist, slist, executor_kw) if key: SCons.Executor.AddBatchExecutor(key, executor) # Now set up the relevant information in the target Nodes themselves. for t in tlist: t.cwd = env.fs.getcwd() t.builder_set(self) t.env_set(env) t.add_source(slist) t.set_executor(executor) t.set_explicit(self.is_explicit) return SCons.Node.NodeList(tlist) def __call__(self, env, target=None, source=None, chdir=_null, **kw): # We now assume that target and source are lists or None. # The caller (typically Environment.BuilderWrapper) is # responsible for converting any scalar values to lists. if chdir is _null: ekw = self.executor_kw else: ekw = self.executor_kw.copy() ekw['chdir'] = chdir if kw: if 'srcdir' in kw: def prependDirIfRelative(f, srcdir=kw['srcdir']): import os.path if SCons.Util.is_String(f) and not os.path.isabs(f): f = os.path.join(srcdir, f) return f if not SCons.Util.is_List(source): source = [source] source = list(map(prependDirIfRelative, source)) del kw['srcdir'] if self.overrides: env_kw = self.overrides.copy() env_kw.update(kw) else: env_kw = kw else: env_kw = self.overrides env = env.Override(env_kw) return self._execute(env, target, source, OverrideWarner(kw), ekw) def adjust_suffix(self, suff): if suff and not suff[0] in [ '.', '_', '$' ]: return '.' + suff return suff def get_prefix(self, env, sources=[]): prefix = self.prefix if callable(prefix): prefix = prefix(env, sources) return env.subst(prefix) def set_suffix(self, suffix): if not callable(suffix): suffix = self.adjust_suffix(suffix) self.suffix = suffix def get_suffix(self, env, sources=[]): suffix = self.suffix if callable(suffix): suffix = suffix(env, sources) return env.subst(suffix) def set_src_suffix(self, src_suffix): if not src_suffix: src_suffix = [] elif not SCons.Util.is_List(src_suffix): src_suffix = [ src_suffix ] self.src_suffix = [callable(suf) and suf or self.adjust_suffix(suf) for suf in src_suffix] def get_src_suffix(self, env): """Get the first src_suffix in the list of src_suffixes.""" ret = self.src_suffixes(env) if not ret: return '' return ret[0] def add_emitter(self, suffix, emitter): """Add a suffix-emitter mapping to this Builder. This assumes that emitter has been initialized with an appropriate dictionary type, and will throw a TypeError if not, so the caller is responsible for knowing that this is an appropriate method to call for the Builder in question. """ self.emitter[suffix] = emitter def add_src_builder(self, builder): """ Add a new Builder to the list of src_builders. This requires wiping out cached values so that the computed lists of source suffixes get re-calculated. """ self._memo = {} self.src_builder.append(builder) def _get_sdict(self, env): """ Returns a dictionary mapping all of the source suffixes of all src_builders of this Builder to the underlying Builder that should be called first. This dictionary is used for each target specified, so we save a lot of extra computation by memoizing it for each construction environment. Note that this is re-computed each time, not cached, because there might be changes to one of our source Builders (or one of their source Builders, and so on, and so on...) that we can't "see." The underlying methods we call cache their computed values, though, so we hope repeatedly aggregating them into a dictionary like this won't be too big a hit. We may need to look for a better way to do this if performance data show this has turned into a significant bottleneck. """ sdict = {} for bld in self.get_src_builders(env): for suf in bld.src_suffixes(env): sdict[suf] = bld return sdict def src_builder_sources(self, env, source, overwarn={}): sdict = self._get_sdict(env) src_suffixes = self.src_suffixes(env) lengths = list(set(map(len, src_suffixes))) def match_src_suffix(name, src_suffixes=src_suffixes, lengths=lengths): node_suffixes = [name[-l:] for l in lengths] for suf in src_suffixes: if suf in node_suffixes: return suf return None result = [] for s in SCons.Util.flatten(source): if SCons.Util.is_String(s): match_suffix = match_src_suffix(env.subst(s)) if not match_suffix and not '.' in s: src_suf = self.get_src_suffix(env) s = self._adjustixes(s, None, src_suf)[0] else: match_suffix = match_src_suffix(s.name) if match_suffix: try: bld = sdict[match_suffix] except KeyError: result.append(s) else: tlist = bld._execute(env, None, [s], overwarn) # If the subsidiary Builder returned more than one # target, then filter out any sources that this # Builder isn't capable of building. if len(tlist) > 1: tlist = [t for t in tlist if match_src_suffix(t.name)] result.extend(tlist) else: result.append(s) source_factory = env.get_factory(self.source_factory) return env.arg2nodes(result, source_factory) def _get_src_builders_key(self, env): return id(env) memoizer_counters.append(SCons.Memoize.CountDict('get_src_builders', _get_src_builders_key)) def get_src_builders(self, env): """ Returns the list of source Builders for this Builder. This exists mainly to look up Builders referenced as strings in the 'BUILDER' variable of the construction environment and cache the result. """ memo_key = id(env) try: memo_dict = self._memo['get_src_builders'] except KeyError: memo_dict = {} self._memo['get_src_builders'] = memo_dict else: try: return memo_dict[memo_key] except KeyError: pass builders = [] for bld in self.src_builder: if SCons.Util.is_String(bld): try: bld = env['BUILDERS'][bld] except KeyError: continue builders.append(bld) memo_dict[memo_key] = builders return builders def _subst_src_suffixes_key(self, env): return id(env) memoizer_counters.append(SCons.Memoize.CountDict('subst_src_suffixes', _subst_src_suffixes_key)) def subst_src_suffixes(self, env): """ The suffix list may contain construction variable expansions, so we have to evaluate the individual strings. To avoid doing this over and over, we memoize the results for each construction environment. """ memo_key = id(env) try: memo_dict = self._memo['subst_src_suffixes'] except KeyError: memo_dict = {} self._memo['subst_src_suffixes'] = memo_dict else: try: return memo_dict[memo_key] except KeyError: pass suffixes = [env.subst(x) for x in self.src_suffix] memo_dict[memo_key] = suffixes return suffixes def src_suffixes(self, env): """ Returns the list of source suffixes for all src_builders of this Builder. This is essentially a recursive descent of the src_builder "tree." (This value isn't cached because there may be changes in a src_builder many levels deep that we can't see.) """ sdict = {} suffixes = self.subst_src_suffixes(env) for s in suffixes: sdict[s] = 1 for builder in self.get_src_builders(env): for s in builder.src_suffixes(env): if s not in sdict: sdict[s] = 1 suffixes.append(s) return suffixes class CompositeBuilder(SCons.Util.Proxy): """A Builder Proxy whose main purpose is to always have a DictCmdGenerator as its action, and to provide access to the DictCmdGenerator's add_action() method. """ def __init__(self, builder, cmdgen): if __debug__: logInstanceCreation(self, 'Builder.CompositeBuilder') SCons.Util.Proxy.__init__(self, builder) # cmdgen should always be an instance of DictCmdGenerator. self.cmdgen = cmdgen self.builder = builder __call__ = SCons.Util.Delegate('__call__') def add_action(self, suffix, action): self.cmdgen.add_action(suffix, action) self.set_src_suffix(self.cmdgen.src_suffixes()) def is_a_Builder(obj): """"Returns True iff the specified obj is one of our Builder classes. The test is complicated a bit by the fact that CompositeBuilder is a proxy, not a subclass of BuilderBase. """ return (isinstance(obj, BuilderBase) or isinstance(obj, CompositeBuilder) or callable(obj)) # Local Variables: # tab-width:4 # indent-tabs-mode:nil # End: # vim: set expandtab tabstop=4 shiftwidth=4:

38.697039

186

0.606222

763f7db739ce9fd6b00a655edf9a4fefe2c7e095

6,793

py

Python

panel/widgets/base.py

govinda18/panel

d2b70d9a0a4433d427c627e70328d0bc8621d78b

[ "BSD-3-Clause" ]

2

2018-08-23T16:50:40.000Z

2018-08-23T20:01:45.000Z

panel/widgets/base.py

pyviz/pyviz_panels

120019e4318ac51bc2b9d0a1b2eb2239c8a0c9ad

[ "BSD-3-Clause" ]

null

panel/widgets/base.py

pyviz/pyviz_panels

120019e4318ac51bc2b9d0a1b2eb2239c8a0c9ad

[ "BSD-3-Clause" ]

null

""" Defines the Widget base class which provides bi-directional communication between the rendered dashboard and the Widget parameters. """ from __future__ import annotations import math from typing import ( TYPE_CHECKING, Any, Callable, List, Mapping, Optional, Tuple, ) import param # type: ignore from ..layout import Row from ..reactive import Reactive from ..viewable import Layoutable, Viewable if TYPE_CHECKING: from bokeh.document import Document from bokeh.model import Model from pyviz_comms import Comm from ..layout import Panel class Widget(Reactive): """ Widgets allow syncing changes in bokeh widget models with the parameters on the Widget instance. """ disabled = param.Boolean(default=False, doc=""" Whether the widget is disabled.""") name = param.String(default='') height = param.Integer(default=None, bounds=(0, None)) width = param.Integer(default=None, bounds=(0, None)) margin = param.Parameter(default=(5, 10), doc=""" Allows to create additional space around the component. May be specified as a two-tuple of the form (vertical, horizontal) or a four-tuple (top, right, bottom, left).""") _rename: Mapping[str, str | None] = {'name': 'title'} # Whether the widget supports embedding _supports_embed: bool = False # Declares the Bokeh model type of the widget _widget_type: 'Model' = None __abstract = True def __init__(self, **params): if 'name' not in params: params['name'] = '' if '_supports_embed' in params: self._supports_embed = params.pop('_supports_embed') if '_param_pane' in params: self._param_pane = params.pop('_param_pane') else: self._param_pane = None super().__init__(**params) @classmethod def from_param(cls, parameter: param.Parameter, **params) -> Viewable: """ Construct a widget from a Parameter and link the two bi-directionally. Parameters ---------- parameter: param.Parameter A parameter to create the widget from. params: dict Keyword arguments to be passed to the widget constructor Returns ------- Widget instance linked to the supplied parameter """ from ..param import Param layout = Param( parameter, widgets={parameter.name: dict(type=cls, **params)}, display_threshold=-math.inf ) return layout[0] def _get_model( self, doc: 'Document', root: Optional['Model'] = None, parent: Optional['Model'] = None, comm: Optional['Comm'] = None ) -> 'Model': model = self._widget_type(**self._process_param_change(self._init_params())) if root is None: root = model # Link parameters and bokeh model values = self.param.values() properties = self._filter_properties(list(self._process_param_change(values))) self._models[root.ref['id']] = (model, parent) self._link_props(model, properties, doc, root, comm) return model def _filter_properties(self, properties: List[str]) -> List[str]: ignored = list(Layoutable.param)+['loading'] return [p for p in properties if p not in ignored] def _get_embed_state( self, root: 'Model', values: Optional[List[Any]] = None, max_opts: int = 3 ) -> Tuple['Widget', 'Model', List[Any], Callable[['Model'], Any], str, str]: """ Returns the bokeh model and a discrete set of value states for the widget. Arguments --------- root: bokeh.model.Model The root model of the widget values: list (optional) An explicit list of value states to embed max_opts: int The maximum number of states the widget should return Returns ------- widget: panel.widget.Widget The Panel widget instance to modify to effect state changes model: bokeh.model.Model The bokeh model to record the current value state on values: list A list of value states to explore. getter: callable A function that returns the state value given the model on_change: string The name of the widget property to attach a callback on js_getter: string JS snippet that returns the state value given the model """ class CompositeWidget(Widget): """ A baseclass for widgets which are made up of two or more other widgets """ _composite_type: 'Panel' = Row __abstract = True def __init__(self, **params): super().__init__(**params) layout_params = [p for p in Layoutable.param if p != 'name'] layout = {p: getattr(self, p) for p in layout_params if getattr(self, p) is not None} if layout.get('width', self.width) is None and not 'sizing_mode' in layout: layout['sizing_mode'] = 'stretch_width' self._composite = self._composite_type(**layout) self._models = self._composite._models self.param.watch(self._update_layout_params, layout_params) def _update_layout_params(self, *events: param.parameterized.Event) -> None: updates = {event.name: event.new for event in events} self._composite.param.update(**updates) def select( self, selector: Optional[type | Callable[['Viewable'], bool]] = None ) -> List[Viewable]: """ Iterates over the Viewable and any potential children in the applying the Selector. Arguments --------- selector: type or callable or None The selector allows selecting a subset of Viewables by declaring a type or callable function to filter by. Returns ------- viewables: list(Viewable) """ objects = super().select(selector) for obj in self._composite.objects: objects += obj.select(selector) return objects def _cleanup(self, root: 'Model') -> None: self._composite._cleanup(root) super()._cleanup(root) def _get_model( self, doc: 'Document', root: Optional['Model'] = None, parent: Optional['Model'] = None, comm: Optional['Comm'] = None ) -> 'Model': model = self._composite._get_model(doc, root, parent, comm) if root is None: root = parent = model self._models[root.ref['id']] = (model, parent) return model def __contains__(self, object: Any) -> bool: return object in self._composite.objects @property def _synced_params(self) -> List[str]: return []

32.194313

86

0.620639

4acdf127cec504c1e290947f0b6f88de0c63f82c

9,788

py

Python

tests/testflows/rbac/tests/syntax/revoke_role.py

taleh007/ClickHouse

c94ee3151d698a77c8d32f4b59b2b2678f0a9246

[ "Apache-2.0" ]

null

tests/testflows/rbac/tests/syntax/revoke_role.py

taleh007/ClickHouse

c94ee3151d698a77c8d32f4b59b2b2678f0a9246

[ "Apache-2.0" ]

null

tests/testflows/rbac/tests/syntax/revoke_role.py

taleh007/ClickHouse

c94ee3151d698a77c8d32f4b59b2b2678f0a9246

[ "Apache-2.0" ]

1

2021-04-22T15:27:46.000Z

from contextlib import contextmanager from testflows.core import * import rbac.helper.errors as errors from rbac.requirements import * @TestFeature @Name("revoke role") @Args(format_description=False) def feature(self, node="clickhouse1"): """Check revoke query syntax. ```sql REVOKE [ON CLUSTER cluster_name] [ADMIN OPTION FOR] role [,...] FROM {user | role | CURRENT_USER} [,...] | ALL | ALL EXCEPT {user_name | role_name | CURRENT_USER} [,...] ``` """ node = self.context.cluster.node(node) @contextmanager def setup(users=2,roles=2): try: with Given("I have some users"): for i in range(users): node.query(f"CREATE USER OR REPLACE user{i}") with And("I have some roles"): for i in range(roles): node.query(f"CREATE ROLE OR REPLACE role{i}") yield finally: with Finally("I drop the users"): for i in range(users): node.query(f"DROP USER IF EXISTS user{i}") with And("I drop the roles"): for i in range(roles): node.query(f"DROP ROLE IF EXISTS role{i}") with Scenario("I revoke a role from a user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(): with When("I revoke a role"): node.query("REVOKE role0 FROM user0") with Scenario("I revoke a nonexistent role from user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(1,0): with When("I revoke nonexistent role from a user"): exitcode, message = errors.role_not_found_in_disk(name="role0") node.query("REVOKE role0 FROM user0", exitcode=exitcode, message=message) # with nonexistent object name, REVOKE assumes type role (treats user0 as role) with Scenario("I revoke a role from a nonexistent user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(0,1): with When("I revoke role from a nonexistent user"): exitcode, message = errors.role_not_found_in_disk(name="user0") node.query("REVOKE role0 FROM user0", exitcode=exitcode, message=message) # with nonexistent object name, REVOKE assumes type role (treats user0 as role) with Scenario("I revoke a role from ALL EXCEPT nonexistent user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(0,1): with When("I revoke role from a nonexistent user"): exitcode, message = errors.role_not_found_in_disk(name="user0") node.query("REVOKE role0 FROM ALL EXCEPT user0", exitcode=exitcode, message=message) with Scenario("I revoke a nonexistent role from a nonexistent user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(0,0): with When("I revoke nonexistent role from a nonexistent user"): exitcode, message = errors.role_not_found_in_disk(name="role0") node.query("REVOKE role0 FROM user0", exitcode=exitcode, message=message) with Scenario("I revoke a role from multiple users", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(): with When("I revoke a role from multiple users"): node.query("REVOKE role0 FROM user0, user1") with Scenario("I revoke multiple roles from multiple users", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(): node.query("REVOKE role0, role1 FROM user0, user1") #user is default, expect exception with Scenario("I revoke a role from default user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_Role_Keywords("1.0")]): with setup(): with When("I revoke a role from default user"): exitcode, message = errors.cannot_update_default() node.query("REVOKE role0 FROM CURRENT_USER", exitcode=exitcode, message=message) #user is user0 with Scenario("I revoke a role from current user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_Role_Keywords("1.0")]): with setup(): with When("I revoke a role from current user"): node.query("REVOKE role0 FROM CURRENT_USER", settings = [("user","user0")]) #user is default, expect exception with Scenario("I revoke a role from all", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_Role_Keywords("1.0")]): with setup(): with When("I revoke a role from all"): exitcode, message = errors.cannot_update_default() node.query("REVOKE role0 FROM ALL", exitcode=exitcode, message=message) #user is default, expect exception with Scenario("I revoke multiple roles from all", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_Role_Keywords("1.0")]): with setup(): with When("I revoke multiple roles from all"): exitcode, message = errors.cannot_update_default() node.query("REVOKE role0, role1 FROM ALL", exitcode=exitcode, message=message) with Scenario("I revoke a role from all but current user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_Role_Keywords("1.0")]): with setup(): with When("I revoke a role from all except current"): node.query("REVOKE role0 FROM ALL EXCEPT CURRENT_USER") with Scenario("I revoke a role from all but default user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_Role_Keywords("1.0")]): with setup(): with When("I revoke a role from all except default"): node.query("REVOKE role0 FROM ALL EXCEPT default", settings = [("user","user0")]) with Scenario("I revoke multiple roles from all but default user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_Role_Keywords("1.0")]): with setup(): with When("I revoke multiple roles from all except default"): node.query("REVOKE role0, role1 FROM ALL EXCEPT default", settings = [("user","user0")]) with Scenario("I revoke a role from a role", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(): with When("I revoke a role from a role"): node.query("REVOKE role0 FROM role1") with Scenario("I revoke a role from a role and a user", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0")]): with setup(): with When("I revoke a role from multiple roles"): node.query("REVOKE role0 FROM role1, user0") with Scenario("I revoke a role from a user on cluster", requirements=[ RQ_SRS_006_RBAC_Revoke_Role_Cluster("1.0")]): with Given("I have a role and a user on a cluster"): node.query("CREATE USER OR REPLACE user0 ON CLUSTER sharded_cluster") node.query("CREATE ROLE OR REPLACE role0 ON CLUSTER sharded_cluster") with When("I revoke a role from user on a cluster"): node.query("REVOKE ON CLUSTER sharded_cluster role0 FROM user0") with Finally("I drop the user and role"): node.query("DROP USER IF EXISTS user0 ON CLUSTER sharded_cluster") node.query("DROP ROLE IF EXISTS role0 ON CLUSTER sharded_cluster") with Scenario("I revoke a role on fake cluster, throws exception", requirements=[ RQ_SRS_006_RBAC_Revoke_Role_Cluster("1.0")]): with When("I revoke a role from user on a cluster"): exitcode, message = errors.cluster_not_found("fake_cluster") node.query("REVOKE ON CLUSTER fake_cluster role0 FROM user0", exitcode=exitcode, message=message) with Scenario("I revoke multiple roles from multiple users on cluster", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_Role_Cluster("1.0")]): with Given("I have multiple roles and multiple users on a cluster"): for i in range(2): node.query(f"CREATE USER OR REPLACE user{i} ON CLUSTER sharded_cluster") node.query(f"CREATE ROLE OR REPLACE role{i} ON CLUSTER sharded_cluster") with When("I revoke multiple roles from multiple users on cluster"): node.query("REVOKE ON CLUSTER sharded_cluster role0, role1 FROM user0, user1") with Finally("I drop the roles and users"): for i in range(2): node.query(f"DROP USER IF EXISTS user{i} ON CLUSTER sharded_cluster") node.query(f"DROP ROLE IF EXISTS role{i} ON CLUSTER sharded_cluster") with Scenario("I revoke admin option for role from a user", requirements=[ RQ_SRS_006_RBAC_Revoke_AdminOption("1.0")]): with setup(): with When("I revoke admin option for role from a user"): node.query("REVOKE ADMIN OPTION FOR role0 FROM user0") with Scenario("I revoke admin option for multiple roles from multiple users", requirements=[ RQ_SRS_006_RBAC_Revoke_Role("1.0"), RQ_SRS_006_RBAC_Revoke_AdminOption("1.0")]): with setup(): with When("I revoke admin option for multiple roles from multiple users"): node.query("REVOKE ADMIN OPTION FOR role0, role1 FROM user0, user1")

49.434343

109

0.627401

ba921d0b758fb1b369523509665d96049df0fa86

1,640

py

Python

test/run/test_ostree.py

jmikovic/osbuild

1476b88dcf1efa0221101b7b412bbe7934c6d2c6

[ "Apache-2.0" ]

null

test/run/test_ostree.py

jmikovic/osbuild

1476b88dcf1efa0221101b7b412bbe7934c6d2c6

[ "Apache-2.0" ]

null

test/run/test_ostree.py

jmikovic/osbuild

1476b88dcf1efa0221101b7b412bbe7934c6d2c6

[ "Apache-2.0" ]

null

# # Runtime / Integration Tests for ostree pipelines # import os import tempfile import unittest from .. import test @unittest.skipUnless(test.TestBase.have_test_data(), "no test-data access") @unittest.skipUnless(test.TestBase.can_bind_mount(), "root-only") class TestOSTree(test.TestBase): def setUp(self): self.osbuild = test.OSBuild(self) def test_ostree(self): with self.osbuild as osb: with tempfile.TemporaryDirectory(dir="/var/tmp") as temp_dir: # Build a container manifest = os.path.join(self.locate_test_data(), "manifests/fedora-ostree-container.json") osb.compile_file(manifest, output_dir=temp_dir, checkpoints=["build", "ostree-tree", "ostree-commit"], exports=["container"]) oci_archive = os.path.join(temp_dir, "container", "fedora-container.tar") self.assertTrue(os.path.exists(oci_archive)) # build a bootable ISO manifest = os.path.join(self.locate_test_data(), "manifests/fedora-ostree-bootiso.json") osb.compile_file(manifest, output_dir=temp_dir, checkpoints=["build", "ostree-tree", "ostree-commit"], exports=["bootiso"]) bootiso = os.path.join(temp_dir, "bootiso", "fedora-ostree-boot.iso") self.assertTrue(os.path.exists(bootiso))

38.139535

89

0.545122

53659adb0c4013151a1dd85bc73afcf46db07b10

5,178

py

Python

swagger_client/models/dimension.py

BruceNL/pdf-stamp---1.0

d89a5f3bfddb77661588311188fe4ff310b781ee

[ "Apache-2.0" ]

null

swagger_client/models/dimension.py

BruceNL/pdf-stamp---1.0

d89a5f3bfddb77661588311188fe4ff310b781ee

[ "Apache-2.0" ]

null

swagger_client/models/dimension.py

BruceNL/pdf-stamp---1.0

d89a5f3bfddb77661588311188fe4ff310b781ee

[ "Apache-2.0" ]

null

# coding: utf-8 """ PDF stamper The PDF Stamper API enables the possibility to add both static and dynamic stamps on existing PDFs. The stamps can consist of one or more barcode, hyperlink, image, line or text elements. The flow is generally as follows: 1. Make a configuration containing the stamp information 2. Create a job specifying the desired configuration 3. Add one or more PDF files to the job 4. Start the job for processing 5. Retrieve the processed files Full API Documentation: https://docs.sphereon.com/api/pdf-stamper/1.0 Interactive testing: A web based test console is available in the Sphereon API Store at https://store.sphereon.com OpenAPI spec version: 1.0 Contact: dev@sphereon.com Generated by: https://github.com/swagger-api/swagger-codegen.git Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from pprint import pformat from six import iteritems import re class Dimension(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self, unit=None, width=None, height=None): """ Dimension - a model defined in Swagger :param dict swaggerTypes: The key is attribute name and the value is attribute type. :param dict attributeMap: The key is attribute name and the value is json key in definition. """ self.swagger_types = { 'unit': 'str', 'width': 'float', 'height': 'float' } self.attribute_map = { 'unit': 'unit', 'width': 'width', 'height': 'height' } self._unit = unit self._width = width self._height = height @property def unit(self): """ Gets the unit of this Dimension. :return: The unit of this Dimension. :rtype: str """ return self._unit @unit.setter def unit(self, unit): """ Sets the unit of this Dimension. :param unit: The unit of this Dimension. :type: str """ allowed_values = ["pt"] if unit not in allowed_values: raise ValueError( "Invalid value for `unit` ({0}), must be one of {1}" .format(unit, allowed_values) ) self._unit = unit @property def width(self): """ Gets the width of this Dimension. :return: The width of this Dimension. :rtype: float """ return self._width @width.setter def width(self, width): """ Sets the width of this Dimension. :param width: The width of this Dimension. :type: float """ self._width = width @property def height(self): """ Gets the height of this Dimension. :return: The height of this Dimension. :rtype: float """ return self._height @height.setter def height(self, height): """ Sets the height of this Dimension. :param height: The height of this Dimension. :type: float """ self._height = height def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

28.141304

636

0.566628

3ea11de396cd2e58caab31ecb3c3b0e263414dc6

5,034

py

Python

apps/src/tax_credit.py

xgerrmann/geld-en-zo

d041c9812164b8c693d4ee86c1f897db05973df0

[ "MIT" ]

null

apps/src/tax_credit.py

xgerrmann/geld-en-zo

d041c9812164b8c693d4ee86c1f897db05973df0

[ "MIT" ]

null

apps/src/tax_credit.py

xgerrmann/geld-en-zo

d041c9812164b8c693d4ee86c1f897db05973df0

[ "MIT" ]

null

import dash_core_components as dcc import dash_html_components as html import plotly.graph_objects as go from dash.dependencies import Input, Output from pfinsim.taxes import Taxes import pfinsim from common import app, default_salary input_salary = default_salary def plot_tax_credits(taxes, selected_year): fig = go.Figure() gross_incomes = range(0, 120000, 10) discount_work = [] discount_general = [] discount_total = [] for gross_income in gross_incomes: work_discount = taxes.calc_work_tax_discount(gross_income) general_discount = taxes.calc_general_tax_discount(gross_income) total_discount = work_discount + general_discount discount_work.append(work_discount) discount_general.append(general_discount) discount_total.append(total_discount) fig.add_trace(go.Scatter( x=list(gross_incomes), y=discount_work, mode='lines', name='Arbeidskorting' )) fig.add_trace(go.Scatter( x=list(gross_incomes), y=discount_general, mode='lines', name='Algemene heffingskorting' )) fig.add_trace(go.Scatter( x=list(gross_incomes), y=discount_total, mode='lines', name='Totaal' )) fig.update_layout(legend=dict( orientation="v", yanchor="top", y=-0.2, xanchor="right", x=1, bgcolor='rgba(0,0,0,0)' ), plot_bgcolor='rgba(0,0,0,0)', title={ 'text': f"Heffingskortingen vs inkomen {selected_year}", 'y': 0.99, 'x': 0.5, 'xanchor': 'center', 'yanchor': 'top'}, margin=dict(l=0, r=0, t=30, b=0), xaxis={ 'linecolor': '#BCCCDC', 'showgrid': False, 'fixedrange': True, 'showspikes': True, 'spikethickness': 2, 'spikedash': "dot", 'spikecolor': "#999999", 'spikemode': "across" }, yaxis={ 'linecolor': '#BCCCDC', 'showgrid': False, 'fixedrange': True, 'range': [0, 7000] }, font=dict( size=16, ) ) fig.update_yaxes(title="Hoogte korting [€]") fig.update_xaxes(title="Inkomen [€]") return dcc.Graph(figure=fig, id='tax_credit_graph', config={'displayModeBar': False}) def tax_credit_app(pathname): available_years = list(pfinsim.common.load_settings()['taxes'].keys()) available_years.sort(reverse=True) selected_year = available_years[0] tax_settings = pfinsim.common.load_settings()['taxes'][selected_year] taxes = Taxes(tax_settings) return html.Div(children=[ html.Div(children=[plot_tax_credits(taxes, selected_year)], id='tax_credit_plot_div'), dcc.Dropdown( id='tax_credit_year_selection', options=[{'label': year, 'value': year} for year in available_years], value=selected_year ), html.Div( [html.H1('Bereken eigen situatie'), html.Div([ html.Label(children=['Bruto jaarinkomen'], className='input_label'), dcc.Input(id="salary_input", type="number", value=default_salary, min=0, max=10000000, placeholder=default_salary) ], id="input_div"), html.Div(id='output')], id="input_form" ), ]) @app.callback(Output(component_id='tax_credit_plot_div', component_property='children'), Input(component_id='tax_credit_year_selection', component_property='value')) def update_tax_credit_plot(selected_year): taxes = Taxes(pfinsim.common.load_settings()['taxes'][selected_year]) return plot_tax_credits(taxes, selected_year) @app.callback( Output(component_id='output', component_property='children'), [Input(component_id='salary_input', component_property='value'), Input(component_id='tax_credit_year_selection', component_property='value')], ) def determine_taxable_income(salary, selected_year): taxes = Taxes(pfinsim.common.load_settings()['taxes'][selected_year]) if salary is None: salary = 0 global input_salary if input_salary != salary: input_salary = salary work_tax_credit = taxes.calc_work_tax_discount(input_salary) general_tax_credit = taxes.calc_general_tax_discount(input_salary) total_tax_credit = work_tax_credit + general_tax_credit return ( html.Table( [ html.Tbody([ html.Tr(children=[html.Td('Arbeidskorting'), html.Td(f'{work_tax_credit:.2f} €', className="align_right")]), html.Tr(children=[html.Td('Algemene heffingskorting', className='border_bottom'), html.Td(f'{general_tax_credit:.2f} €', className="align_right border_bottom")]), html.Tr(children=[html.Td('Totaal heffingskortingen'), html.Td(f'{total_tax_credit:.2f} €', className="align_right bottom_row")]), ]) ] ) )

31.4625

118

0.623758

7716a37ee4386ea5a419ddc40b21d32a7de36ea7

18,105

py

Python

tests/unit/lms/services/canvas_api/client_test.py

hypothesis/lms

722dac444dc1e73298eea5193f871f3ddefe46fd

[ "BSD-2-Clause" ]

38

2017-12-30T23:49:53.000Z

2022-02-15T21:07:49.000Z

tests/unit/lms/services/canvas_api/client_test.py

hypothesis/lms

722dac444dc1e73298eea5193f871f3ddefe46fd

[ "BSD-2-Clause" ]

1,733

2017-11-09T18:46:05.000Z

2022-03-31T11:05:50.000Z

tests/unit/lms/services/canvas_api/client_test.py

hypothesis/lms

722dac444dc1e73298eea5193f871f3ddefe46fd

[ "BSD-2-Clause" ]

10

2018-07-11T17:12:46.000Z

2022-01-07T20:00:23.000Z

from unittest.mock import create_autospec, sentinel import pytest from h_matchers import Any from pyramid.registry import Registry from lms.events import FilesDiscoveredEvent from lms.services import CanvasAPIError, CanvasAPIServerError, OAuth2TokenError from lms.services.canvas_api.client import CanvasAPIClient from tests import factories class TestCanvasAPIClientGetToken: # This is the only test where we fake out the underlying class, because # this _one_ call is just a pass through. def test_get_token(self, canvas_api_client, authenticated_client): token = canvas_api_client.get_token(sentinel.authorization_code) authenticated_client.get_token.assert_called_once_with( sentinel.authorization_code ) assert token == authenticated_client.get_token.return_value @pytest.fixture def authenticated_client(self, patch): return patch("lms.services.canvas_api._authenticated.AuthenticatedClient") @pytest.mark.usefixtures("http_session", "oauth_token") class TestCanvasAPIClient: def test_authenticated_users_sections(self, canvas_api_client, http_session): sections = [{"id": 1, "name": "name_1"}, {"id": 2, "name": "name_2"}] http_session.send.return_value = factories.requests.Response( status_code=200, json_data={"sections": sections} ) response = canvas_api_client.authenticated_users_sections("COURSE_ID") assert response == sections http_session.send.assert_called_once_with( Any.request( "GET", url=Any.url.with_path("api/v1/courses/COURSE_ID").with_query( {"include[]": "sections"} ), ), timeout=Any(), ) def test_authenticated_users_sections_deduplicates_sections( self, canvas_api_client, http_session ): http_session.send.return_value = factories.requests.Response( status_code=200, json_data={ "sections": [{"id": 1, "name": "name"}, {"id": 1, "name": "name"}] }, ) sections = canvas_api_client.authenticated_users_sections("course_id") assert sections == [{"id": 1, "name": "name"}] def test_authenticated_users_sections_raises_CanvasAPIError_with_conflicting_duplicates( self, canvas_api_client, http_session ): http_session.send.return_value = factories.requests.Response( status_code=200, json_data={ "sections": [{"id": 1, "name": "name"}, {"id": 1, "name": "DIFFERENT"}] }, ) with pytest.raises(CanvasAPIError): canvas_api_client.authenticated_users_sections("course_id") def test_course_sections(self, canvas_api_client, http_session): sections = [ {"id": 101, "name": "name_1"}, {"id": 102, "name": "name_2"}, ] sections_with_noise = [ dict(section, unexpected="ignored") for section in sections ] http_session.send.return_value = factories.requests.Response( status_code=200, json_data=sections_with_noise ) response = canvas_api_client.course_sections("COURSE_ID") assert response == sections http_session.send.assert_called_once_with( Any.request( "GET", url=Any.url.with_path("api/v1/courses/COURSE_ID/sections") ), timeout=Any(), ) def test_course_sections_deduplicates_sections( self, canvas_api_client, http_session ): http_session.send.return_value = factories.requests.Response( status_code=200, json_data=[{"id": 1, "name": "name"}, {"id": 1, "name": "name"}], ) sections = canvas_api_client.course_sections("course_id") assert sections == [{"id": 1, "name": "name"}] def test_course_sections_raises_CanvasAPIError_with_conflicting_duplicates( self, canvas_api_client, http_session ): http_session.send.return_value = factories.requests.Response( status_code=200, json_data=[{"id": 1, "name": "name"}, {"id": 1, "name": "DIFFERENT"}], ) with pytest.raises(CanvasAPIError): canvas_api_client.course_sections("course_id") def test_course_sections_raises_CanvasAPIError_with_too_few_returned( self, canvas_api_client, http_session ): http_session.send.return_value = factories.requests.Response( status_code=200, json_data=[] ) with pytest.raises(CanvasAPIError): canvas_api_client.course_sections("dummy") def test_group_categories_list(self, canvas_api_client, http_session): group_categories = [ {"id": 1, "name": "Group category 1"}, {"id": 2, "name": "Group category 2"}, ] http_session.send.return_value = factories.requests.Response( status_code=200, json_data=group_categories ) response = canvas_api_client.course_group_categories("COURSE_ID") assert response == group_categories http_session.send.assert_called_once_with( Any.request( "GET", url=Any.url.with_path( "api/v1/courses/COURSE_ID/group_categories" ).with_query({"per_page": Any.string()}), ), timeout=Any(), ) @pytest.mark.parametrize( "only_own_groups,include_users", [(True, False), (False, True)] ) def test_course_groups( self, only_own_groups, include_users, canvas_api_client, http_session ): groups = [ { "id": 1, "name": "Group 1", "description": "Group 1", "group_category_id": 1, }, { "id": 2, "name": "Group 2", "description": "Group 2", "group_category_id": 1, }, ] http_session.send.return_value = factories.requests.Response( status_code=200, json_data=groups ) response = canvas_api_client.course_groups( "COURSE_ID", only_own_groups=only_own_groups, include_users=include_users ) assert response == groups expected_params = { "per_page": Any.string(), "only_own_groups": str(only_own_groups), } expected_timeout = Any() if include_users: expected_params["include[]"] = "users" expected_timeout = (31, 31) # pylint:disable=redefined-variable-type http_session.send.assert_called_once_with( Any.request( "GET", url=Any.url.with_path("api/v1/courses/COURSE_ID/groups").with_query( expected_params ), ), timeout=expected_timeout, ) @pytest.mark.usefixtures("list_groups_response") def test_current_user_groups(self, canvas_api_client): course_id = 1 group_category_id = 1 response = canvas_api_client.current_user_groups(course_id, group_category_id) assert len(response) == 1 assert response[0]["group_category_id"] == group_category_id @pytest.mark.usefixtures("list_groups_response") def test_current_user_groups_no_group_category(self, canvas_api_client): course_id = 1 response = canvas_api_client.current_user_groups( course_id, group_category_id=None ) assert len(response) == 2 @pytest.mark.usefixtures("list_groups_response") def test_current_user_groups_empty(self, canvas_api_client): course_id = 1 group_category_id = 10000 response = canvas_api_client.current_user_groups(course_id, group_category_id) assert not response @pytest.mark.usefixtures("list_groups_with_users_response") def test_user_groups_none_match_group_category_id(self, canvas_api_client): course_id = 1 user_id = 10000 group_category_id = 10000 response = canvas_api_client.user_groups(course_id, user_id, group_category_id) assert not response @pytest.mark.usefixtures("list_groups_with_users_response") def test_user_groups_none_match_user_id(self, canvas_api_client): course_id = 1 user_id = 10000 group_category_id = 2 response = canvas_api_client.user_groups(course_id, user_id, group_category_id) assert not response @pytest.mark.usefixtures("list_groups_with_users_response") def test_user_groups_no_group_category(self, canvas_api_client): course_id = 1 user_id = 1 response = canvas_api_client.user_groups(course_id, user_id) assert len(response) == 1 assert user_id in [u["id"] for u in response[0]["users"]] @pytest.mark.usefixtures("list_groups_with_users_response") def test_user_groups(self, canvas_api_client): course_id = 1 user_id = 1 group_category_id = 2 response = canvas_api_client.user_groups(course_id, user_id, group_category_id) assert len(response) == 1 assert user_id in [u["id"] for u in response[0]["users"]] assert response[0]["group_category_id"] == group_category_id @pytest.mark.usefixtures("list_groups_response") def test_group_category_groups(self, canvas_api_client, http_session): response = canvas_api_client.group_category_groups("GROUP_CATEGORY") assert len(response) == 2 http_session.send.assert_called_once_with( Any.request( "GET", url=Any.url.with_path( "api/v1/group_categories/GROUP_CATEGORY/groups" ).with_query({"per_page": Any.string()}), ), timeout=Any(), ) def test_users_sections(self, canvas_api_client, http_session): http_session.send.return_value = factories.requests.Response( status_code=200, json_data={ "enrollments": [ {"course_section_id": 101, "unexpected": "ignored"}, {"course_section_id": 102, "unexpected": "ignored"}, ] }, ) response = canvas_api_client.users_sections("USER_ID", "COURSE_ID") assert response == [{"id": 101}, {"id": 102}] http_session.send.assert_called_once_with( Any.request( "GET", url=Any.url.with_path( "api/v1/courses/COURSE_ID/users/USER_ID" ).with_query({"include[]": "enrollments"}), ), timeout=Any(), ) def test_users_sections_deduplicates_sections( self, canvas_api_client, http_session ): http_session.send.return_value = factories.requests.Response( status_code=200, json_data={ "enrollments": [{"course_section_id": 1}, {"course_section_id": 1}] }, ) sections = canvas_api_client.users_sections("user_id", "course_id") assert sections == [{"id": 1}] def test_list_files(self, canvas_api_client, http_session): files = [ { "display_name": "display_name_1", "id": 1, "updated_at": "updated_at_1", "size": 12345, }, { "display_name": "display_name_1", "id": 2, "updated_at": "updated_at_1", "size": 12345, }, ] files_with_noise = [dict(file, unexpected="ignored") for file in files] http_session.send.return_value = factories.requests.Response( status_code=200, json_data=files_with_noise ) response = canvas_api_client.list_files("COURSE_ID") assert response == files http_session.send.assert_called_once_with( Any.request( "GET", url=Any.url.with_path("api/v1/courses/COURSE_ID/files").with_query( { "content_types[]": "application/pdf", "per_page": Any.string(), "sort": "position", } ), ), timeout=Any(), ) def test_list_duplicate_files(self, canvas_api_client, http_session): files = [ { "display_name": "display_name_1", "id": 1, "updated_at": "updated_at_1", "size": 12345, }, { "display_name": "display_name_1", "id": 1, "updated_at": "updated_at_1", "size": 12345, }, ] http_session.send.return_value = factories.requests.Response( status_code=200, json_data=files ) response = canvas_api_client.list_files("COURSE_ID") assert response == [files[0]] def test_list_files_emits_event(self, canvas_api_client, http_session, registry): # pylint: disable=protected-access canvas_api_client._request.registry = registry files = [ { "id": i, "display_name": "display_name_{i}", "updated_at": "updated_at_{i}", "size": 1000 + i, } for i in range(2) ] http_session.send.return_value = factories.requests.Response( status_code=200, json_data=files ) canvas_api_client.list_files("COURSE_ID") canvas_api_client._request.registry.notify.assert_called_once_with( FilesDiscoveredEvent( request=canvas_api_client._request, values=[ { "type": "canvas_file", "course_id": "COURSE_ID", "lms_id": file["id"], "name": file["display_name"], "size": file["size"], } for file in files ], ) ) def test_public_url(self, canvas_api_client, http_session): http_session.send.return_value = factories.requests.Response( status_code=200, json_data={"public_url": "public_url_value"} ) response = canvas_api_client.public_url("FILE_ID") assert response == "public_url_value" http_session.send.assert_called_once_with( Any.request( "GET", url=Any.url.with_path("api/v1/files/FILE_ID/public_url") ), timeout=Any(), ) @pytest.fixture def registry(self): return create_autospec(Registry, instance=True, spec_set=True) @pytest.fixture def list_groups_response(self, http_session): http_session.send.return_value = factories.requests.Response( json_data=[ { "id": 1, "name": "Group 1", "description": "Group 1", "group_category_id": 1, }, { "id": 2, "name": "Group 2", "description": "Group 2", "group_category_id": 2, }, ], status_code=200, ) @pytest.fixture def list_groups_with_users_response(self, http_session): http_session.send.return_value = factories.requests.Response( json_data=[ { "id": 1, "name": "Group 1", "description": "Group 1", "group_category_id": 1, "users": [], }, { "id": 2, "name": "Group 2", "description": "Group 2", "group_category_id": 2, "users": [{"id": 1}], }, ], status_code=200, ) class TestMetaBehavior: def test_methods_require_access_token(self, data_method, oauth2_token_service): oauth2_token_service.get.side_effect = OAuth2TokenError( "We don't have a Canvas API access token for this user" ) with pytest.raises(OAuth2TokenError): data_method() @pytest.mark.usefixtures("oauth_token") def test_methods_raise_CanvasAPIServerError_if_the_response_json_has_the_wrong_format( self, data_method, http_session ): http_session.send.return_value = factories.requests.Response( status_code=200, json_data={} ) with pytest.raises(CanvasAPIServerError): data_method() @pytest.mark.usefixtures("oauth_token") def test_methods_raise_CanvasAPIServerError_if_the_response_is_invalid_json( self, data_method, http_session ): http_session.send.return_value = factories.requests.Response( status_code=200, raw="[broken json" ) with pytest.raises(CanvasAPIServerError): data_method() methods = { "authenticated_users_sections": ["course_id"], "course_sections": ["course_id"], "course_group_categories": ["course_id"], "users_sections": ["user_id", "course_id"], "list_files": ["course_id"], "public_url": ["file_id"], } @pytest.fixture(params=tuple(methods.items()), ids=tuple(methods.keys())) def data_method(self, request, canvas_api_client): method, args = request.param return lambda: getattr(canvas_api_client, method)(*args) @pytest.fixture def canvas_api_client(authenticated_client, pyramid_request): return CanvasAPIClient(authenticated_client, pyramid_request)

33.715084

92

0.582381

aeb113e20f94b2fe958b6d8514394f09972b0412

3,230

py

Python

flower_dictionary.py

larry-x/flower-dictionary

def60dee0de5b4cfdc7c199f864563dcec88071c

[ "MIT" ]

null

flower_dictionary.py

larry-x/flower-dictionary

def60dee0de5b4cfdc7c199f864563dcec88071c

[ "MIT" ]

null

flower_dictionary.py

larry-x/flower-dictionary

def60dee0de5b4cfdc7c199f864563dcec88071c

[ "MIT" ]

null

#! /usr/bin/env python3 # Flower_Dictionary: originally a program that allows you to catalog new flower names on an alien planet and save them to a txt file. # There are just two fields: the first field is limited to 20 characters and the second field is limited to 80 characters. # It can be used to keep reminders or short diary entries. It can also be used as a phone book. # However, as this is my first python project the original code to catalog flowers on an alien planet will be kept. import re, sys, os, logging logging.basicConfig(level=logging.DEBUG, format='%(levelname)s - %(message)s') logging.info('Welcome to the program.') book = {} flower_path = None planet_name = None def entry_prompt(): global flower_path global planet_name while True: print('What planet are you updating the flower log for? (File name please)') dr_name = os.getcwd() planet_name = input() flower_path = os.path.join(dr_name, planet_name + '.txt') if planet_name and os.path.exists(flower_path): validity() else: new_file() def new_file(): while True: print('The file for planet "%s" does not exist. Create new file?' % planet_name) answer = input() if planet_name and answer.lower() == 'yes': new_file = open(flower_path, 'w') new_file.write('This is a flower log for planet %s.'.center(150) % planet_name + '\n\n') new_file.close() validity() elif answer.lower() == 'no': entry_prompt() else: print('Please type "yes" or "no".') continue def validity(): while True: print('Please name this new flower. You can use letters and numbers, but cannot start with a number.') trythis = input() trythis_reg = re.compile(r'^[a-zA-Z][a-zA-Z0-9]+$') trythis_match = trythis_reg.search(trythis) if trythis_match == None: print('Sorry, no special characters or spaces allowed, and start with a letter. \n') continue elif len(trythis) < 3 or len(trythis) > 15: print('Sorry, the name must be at least 3 characters, but no more than 15 characters.') continue else: print('Lovely. Now let\'s have a quick description of this flower. Please be brief.') trythat = input() while len(trythat) == 0 or len(trythat) > 80: print('Your description must not exceed 80 characters but must be at least one character.') trythat = input() book.update({trythis : trythat}) addanother() def addanother(): print('Do you have more entries to make? Answer "yes" or "no".') answer = input() if answer.lower() == 'yes': validity() elif answer.lower() == 'no': edit_file() else: addanother() def edit_file(): global flower_path c_file = open(flower_path, 'a') for n, d in book.items(): c_file.write('{:30s} :: {} \n'.format(n, d)) c_file.close() content = open(flower_path, 'r') print(content.read()) content.close() sys.exit() entry_prompt()

35.108696

133

0.608978

dfa21df37fd35efc4af2eb4a946c07b5e931850d

10,119

py

Python

website/apps/monster/migrations/0002_auto__add_monsterinvite.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

61

2015-11-10T17:13:46.000Z

2021-08-06T17:58:30.000Z

website/apps/monster/migrations/0002_auto__add_monsterinvite.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

13

2015-11-11T07:49:41.000Z

2021-06-09T03:45:31.000Z

website/apps/monster/migrations/0002_auto__add_monsterinvite.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

18

2015-11-11T04:50:04.000Z

2021-08-20T00:57:11.000Z

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'MonsterInvite' db.create_table('monster_monsterinvite', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('monster_part', self.gf('django.db.models.fields.related.ForeignKey')(related_name='invites', to=orm['monster.MonsterPart'])), ('timestamp', self.gf('canvas.util.UnixTimestampField')(default=None, null=True)), ('used_by', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['auth.User'], null=True)), )) db.send_create_signal('monster', ['MonsterInvite']) def backwards(self, orm): # Deleting model 'MonsterInvite' db.delete_table('monster_monsterinvite') models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '254', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'canvas.category': { 'Meta': {'object_name': 'Category'}, 'description': ('django.db.models.fields.CharField', [], {'max_length': '140'}), 'founded': ('django.db.models.fields.FloatField', [], {'default': '1298956320'}), 'founder': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "'founded_groups'", 'null': 'True', 'blank': 'True', 'to': "orm['auth.User']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'moderators': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "'moderated_categories'", 'symmetrical': 'False', 'to': "orm['auth.User']"}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '20'}), 'visibility': ('django.db.models.fields.IntegerField', [], {'default': '0'}) }, 'canvas.comment': { 'Meta': {'object_name': 'Comment'}, 'anonymous': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'author': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "'comments'", 'null': 'True', 'blank': 'True', 'to': "orm['auth.User']"}), 'category': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "'comments'", 'null': 'True', 'blank': 'True', 'to': "orm['canvas.Category']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'ip': ('django.db.models.fields.IPAddressField', [], {'default': "'0.0.0.0'", 'max_length': '15'}), 'judged': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'ot_hidden': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'parent_comment': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "'replies'", 'null': 'True', 'blank': 'True', 'to': "orm['canvas.Comment']"}), 'parent_content': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'comments'", 'null': 'True', 'to': "orm['canvas.Content']"}), 'replied_comment': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['canvas.Comment']", 'null': 'True', 'blank': 'True'}), 'reply_content': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'used_in_comments'", 'null': 'True', 'to': "orm['canvas.Content']"}), 'reply_text': ('django.db.models.fields.CharField', [], {'max_length': '2000', 'blank': 'True'}), 'score': ('django.db.models.fields.FloatField', [], {'default': '0', 'db_index': 'True'}), 'timestamp': ('canvas.util.UnixTimestampField', [], {'default': '0'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '140', 'blank': 'True'}), 'visibility': ('django.db.models.fields.IntegerField', [], {'default': '0'}) }, 'canvas.content': { 'Meta': {'object_name': 'Content'}, 'alpha': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'animated': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'id': ('django.db.models.fields.CharField', [], {'max_length': '40', 'primary_key': 'True'}), 'ip': ('django.db.models.fields.IPAddressField', [], {'default': "'0.0.0.0'", 'max_length': '15'}), 'remix_of': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'remixes'", 'null': 'True', 'to': "orm['canvas.Content']"}), 'remix_text': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '1000', 'blank': 'True'}), 'source_url': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '4000', 'blank': 'True'}), 'stamps_used': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'used_as_stamp'", 'blank': 'True', 'to': "orm['canvas.Content']"}), 'timestamp': ('canvas.util.UnixTimestampField', [], {}), 'url_mapping': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['canvas.ContentUrlMapping']", 'null': 'True', 'blank': 'True'}), 'visibility': ('django.db.models.fields.IntegerField', [], {'default': '0'}) }, 'canvas.contenturlmapping': { 'Meta': {'object_name': 'ContentUrlMapping'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, 'canvas_auth.user': { 'Meta': {'object_name': 'User', 'db_table': "'auth_user'", '_ormbases': ['auth.User'], 'proxy': 'True'} }, 'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'monster.monsterinvite': { 'Meta': {'object_name': 'MonsterInvite'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'monster_part': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'invites'", 'to': "orm['monster.MonsterPart']"}), 'timestamp': ('canvas.util.UnixTimestampField', [], {'default': 'None', 'null': 'True'}), 'used_by': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']", 'null': 'True'}) }, 'monster.monsterpart': { 'Meta': {'object_name': 'MonsterPart'}, 'comment': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'monster_parts'", 'to': "orm['canvas.Comment']"}), 'hint_slice': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['canvas.Content']", 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'top': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "'bottoms'", 'null': 'True', 'blank': 'True', 'to': "orm['monster.MonsterPart']"}) } } complete_apps = ['monster']

77.244275

195

0.567349

53d402920ea27e179e57794f99b0be93555a7369

733

py

Python

var/spack/repos/builtin/packages/fastjson/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/fastjson/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

8

2021-11-09T20:28:40.000Z

2022-03-15T03:26:33.000Z

var/spack/repos/builtin/packages/fastjson/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2019-02-08T20:37:20.000Z

2019-03-31T15:19:26.000Z

# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack.package import * class Fastjson(MavenPackage): """Fastjson is a Java library that can be used to convert Java Objects into their JSON representation. It can also be used to convert a JSON string to an equivalent Java object. """ homepage = "https://github.com/alibaba/fastjson/wiki" url = "https://github.com/alibaba/fastjson/archive/1.2.68.tar.gz" version('1.2.68', sha256='0b3f5308830e5e5abacf9dc8e4115c20153c1cdabec228c3eca48a48c9d5f4d7') depends_on('java@8', type=('build', 'run'))

36.65

96

0.735334

bd97323b99c2df3d394de6a3931a32a0fe43d57b

32,500

py

Python

example/sockeye/source/sockeye/encoder.py

rah9eu/p3

530628be7b7a8dd3e6199c3bebebdbf104005e5f

[ "Apache-2.0" ]

22

2019-02-20T12:42:20.000Z

2021-12-25T06:09:46.000Z

example/sockeye/source/sockeye/encoder.py

rah9eu/p3

530628be7b7a8dd3e6199c3bebebdbf104005e5f

[ "Apache-2.0" ]

4

2019-04-01T07:36:04.000Z

2022-03-24T03:11:26.000Z

example/sockeye/source/sockeye/encoder.py

rah9eu/p3

530628be7b7a8dd3e6199c3bebebdbf104005e5f

[ "Apache-2.0" ]

7

2019-03-20T16:04:37.000Z

2021-04-28T18:40:11.000Z

# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You may not # use this file except in compliance with the License. A copy of the License # is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is distributed on # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either # express or implied. See the License for the specific language governing # permissions and limitations under the License. """ Encoders for sequence-to-sequence models. """ import logging from math import ceil, floor from abc import ABC, abstractmethod from typing import Callable, List, Optional, Tuple import mxnet as mx from sockeye.config import Config from . import constants as C from . import rnn from . import transformer from . import utils logger = logging.getLogger(__name__) def get_encoder(config: Config, fused: bool, embed_weight: Optional[mx.sym.Symbol] = None): if isinstance(config, RecurrentEncoderConfig): return get_recurrent_encoder(config, fused, embed_weight) elif isinstance(config, transformer.TransformerConfig): return get_transformer_encoder(config, embed_weight) else: raise ValueError("Unsupported encoder configuration") class RecurrentEncoderConfig(Config): """ Recurrent encoder configuration. :param vocab_size: Source vocabulary size. :param num_embed: Size of embedding layer. :param embed_dropout: Dropout probability on embedding layer. :param rnn_config: RNN configuration. :param conv_config: Optional configuration for convolutional embedding. :param reverse_input: Reverse embedding sequence before feeding into RNN. """ def __init__(self, vocab_size: int, num_embed: int, embed_dropout: float, rnn_config: rnn.RNNConfig, conv_config: Optional['ConvolutionalEmbeddingConfig'] = None, reverse_input: bool = False) -> None: super().__init__() self.vocab_size = vocab_size self.num_embed = num_embed self.embed_dropout = embed_dropout self.rnn_config = rnn_config self.conv_config = conv_config self.reverse_input = reverse_input def get_recurrent_encoder(config: RecurrentEncoderConfig, fused: bool, embed_weight: Optional[mx.sym.Symbol] = None) -> 'Encoder': """ Returns a recurrent encoder with embedding, batch2time-major conversion, and bidirectional RNN. If num_layers > 1, adds additional uni-directional RNNs. :param config: Configuration for recurrent encoder. :param fused: Whether to use FusedRNNCell (CuDNN). Only works with GPU context. :param embed_weight: Optionally use an existing embedding matrix instead of creating a new one. :return: Encoder instance. """ # TODO give more control on encoder architecture encoders = list() # type: List[Encoder] encoders.append(Embedding(num_embed=config.num_embed, vocab_size=config.vocab_size, prefix=C.SOURCE_EMBEDDING_PREFIX, dropout=config.embed_dropout, embed_weight=embed_weight)) if config.conv_config is not None: encoders.append(ConvolutionalEmbeddingEncoder(config.conv_config)) encoders.append(BatchMajor2TimeMajor()) if config.reverse_input: encoders.append(ReverseSequence()) if config.rnn_config.residual: utils.check_condition(config.rnn_config.first_residual_layer >= 2, "Residual connections on the first encoder layer are not supported") encoder_class = FusedRecurrentEncoder if fused else RecurrentEncoder # One layer bi-directional RNN: encoders.append(BiDirectionalRNNEncoder(rnn_config=config.rnn_config.copy(num_layers=1), prefix=C.BIDIRECTIONALRNN_PREFIX, layout=C.TIME_MAJOR)) if config.rnn_config.num_layers > 1: # Stacked uni-directional RNN: # Because we already have a one layer bi-rnn we reduce the num_layers as well as the first_residual_layer. remaining_rnn_config = config.rnn_config.copy(num_layers=config.rnn_config.num_layers - 1, first_residual_layer=config.rnn_config.first_residual_layer - 1) encoders.append(encoder_class(rnn_config=remaining_rnn_config, prefix=C.STACKEDRNN_PREFIX, layout=C.TIME_MAJOR)) return EncoderSequence(encoders) def get_transformer_encoder(config: transformer.TransformerConfig, embed_weight: Optional[mx.sym.Symbol] = None) -> 'Encoder': """ Returns a Transformer encoder, consisting of an embedding layer with positional encodings and a TransformerEncoder instance. :param config: Configuration for transformer encoder. :param embed_weight: Optionally use an existing embedding matrix instead of creating a new one. :return: Encoder instance. """ encoders = list() # type: List[Encoder] encoders.append(Embedding(num_embed=config.model_size, vocab_size=config.vocab_size, prefix=C.SOURCE_EMBEDDING_PREFIX, dropout=config.dropout_residual, embed_weight=embed_weight, add_positional_encoding=config.positional_encodings)) if config.conv_config is not None: encoders.append(ConvolutionalEmbeddingEncoder(config.conv_config)) encoders.append(TransformerEncoder(config)) encoders.append(BatchMajor2TimeMajor()) return EncoderSequence(encoders) class Encoder(ABC): """ Generic encoder interface. """ @abstractmethod def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: """ Encodes data given sequence lengths of individual examples and maximum sequence length. :param data: Input data. :param data_length: Vector with sequence lengths. :param seq_len: Maximum sequence length. :return: Encoded versions of input data (data, data_length, seq_len). """ pass def get_num_hidden(self) -> int: """ Return the representation size of this encoder. """ raise NotImplementedError() def get_rnn_cells(self) -> List[mx.rnn.BaseRNNCell]: """ Returns a list of RNNCells used by this encoder. """ return [] def get_encoded_seq_len(self, seq_len: int) -> int: """ Returns the size of the encoded sequence. """ return seq_len class BatchMajor2TimeMajor(Encoder): """ Converts batch major data to time major. """ def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: """ Encodes data given sequence lengths of individual examples and maximum sequence length. :param data: Input data. :param data_length: Vector with sequence lengths. :param seq_len: Maximum sequence length. :return: Encoded versions of input data (data, data_length, seq_len). """ with mx.AttrScope(__layout__=C.TIME_MAJOR): return mx.sym.swapaxes(data=data, dim1=0, dim2=1), data_length, seq_len class ReverseSequence(Encoder): """ Reverses the input sequence. Requires time-major layout. """ def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: data = mx.sym.SequenceReverse(data=data, sequence_length=data_length, use_sequence_length=True) return data, data_length, seq_len class Embedding(Encoder): """ Thin wrapper around MXNet's Embedding symbol. Works with both time- and batch-major data layouts. :param num_embed: Embedding size. :param vocab_size: Source vocabulary size. :param prefix: Name prefix for symbols of this encoder. :param dropout: Dropout probability. :param embed_weight: Optionally use an existing embedding matrix instead of creating a new one. :param add_positional_encoding: If true, adds positional encodings to embedding. """ def __init__(self, num_embed: int, vocab_size: int, prefix: str, dropout: float, embed_weight: Optional[mx.sym.Symbol] = None, add_positional_encoding: bool = False) -> None: self.num_embed = num_embed self.vocab_size = vocab_size self.prefix = prefix self.dropout = dropout if embed_weight is not None: self.embed_weight = embed_weight else: self.embed_weight = mx.sym.Variable(prefix + "weight") self.add_positional_encoding = add_positional_encoding def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: """ Encodes data given sequence lengths of individual examples and maximum sequence length. :param data: Input data. :param data_length: Vector with sequence lengths. :param seq_len: Maximum sequence length. :return: Encoded versions of input data (data, data_length, seq_len). """ embedding = mx.sym.Embedding(data=data, input_dim=self.vocab_size, weight=self.embed_weight, output_dim=self.num_embed, name=self.prefix + "embed") if self.add_positional_encoding: embedding = mx.sym.broadcast_add(embedding, self.get_positional_encoding(length=seq_len, depth=self.num_embed, name="%spositional_encodings" % self.prefix), name='%sadd_positional_encodings' % self.prefix) if self.dropout > 0: embedding = mx.sym.Dropout(data=embedding, p=self.dropout, name="source_embed_dropout") return embedding, data_length, seq_len @staticmethod def get_positional_encoding(length: int, depth: int, name: str) -> mx.sym.Symbol: """ Returns symbol initialized with positional encodings as in Vaswani et al. :param length: Maximum sequence length :param depth: Depth. :param name: Symbol name. :return: Symbol(1, length, depth) """ return mx.sym.BlockGrad(mx.symbol.Custom(length=length, depth=depth, name=name, op_type='positional_encodings')) def get_num_hidden(self) -> int: """ Return the representation size of this encoder. """ return self.num_embed class EncoderSequence(Encoder): """ A sequence of encoders is itself an encoder. :param encoders: List of encoders. """ def __init__(self, encoders: List[Encoder]) -> None: self.encoders = encoders def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: """ Encodes data given sequence lengths of individual examples and maximum sequence length. :param data: Input data. :param data_length: Vector with sequence lengths. :param seq_len: Maximum sequence length. :return: Encoded versions of input data (data, data_length, seq_len). """ for encoder in self.encoders: data, data_length, seq_len = encoder.encode(data, data_length, seq_len) return data, data_length, seq_len def get_num_hidden(self) -> int: """ Return the representation size of this encoder. """ if isinstance(self.encoders[-1], BatchMajor2TimeMajor): utils.check_condition(len(self.encoders) > 1, "Cannot return num_hidden from a BatchMajor2TimeMajor encoder only") return self.encoders[-2].get_num_hidden() else: return self.encoders[-1].get_num_hidden() def get_rnn_cells(self) -> List[mx.rnn.BaseRNNCell]: """ Returns a list of RNNCells used by this encoder. """ cells = [] for encoder in self.encoders: for cell in encoder.get_rnn_cells(): cells.append(cell) return cells def get_encoded_seq_len(self, seq_len: int) -> int: """ Returns the size of the encoded sequence. """ for encoder in self.encoders: seq_len = encoder.get_encoded_seq_len(seq_len) return seq_len class RecurrentEncoder(Encoder): """ Uni-directional (multi-layered) recurrent encoder. :param rnn_config: RNN configuration. :param prefix: Prefix. :param layout: Data layout. """ def __init__(self, rnn_config: rnn.RNNConfig, prefix: str = C.STACKEDRNN_PREFIX, layout: str = C.TIME_MAJOR) -> None: self.rnn_config = rnn_config self.layout = layout self.rnn = rnn.get_stacked_rnn(rnn_config, prefix) def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: """ Encodes data given sequence lengths of individual examples and maximum sequence length. :param data: Input data. :param data_length: Vector with sequence lengths. :param seq_len: Maximum sequence length. :return: Encoded versions of input data (data, data_length, seq_len). """ outputs, _ = self.rnn.unroll(seq_len, inputs=data, merge_outputs=True, layout=self.layout) return outputs, data_length, seq_len def get_rnn_cells(self): """ Returns RNNCells used in this encoder. """ return [self.rnn] def get_num_hidden(self): """ Return the representation size of this encoder. """ return self.rnn_config.num_hidden class FusedRecurrentEncoder(RecurrentEncoder): """ Uni-directional (multi-layered) recurrent encoder. :param rnn_config: RNN configuration. :param prefix: Prefix. :param layout: Data layout. """ def __init__(self, rnn_config: rnn.RNNConfig, prefix: str = C.STACKEDRNN_PREFIX, layout: str = C.TIME_MAJOR) -> None: super().__init__(rnn_config, prefix, layout) logger.warning("%s: FusedRNNCell uses standard MXNet Orthogonal initializer w/ rand_type=uniform", prefix) self.rnn = mx.rnn.FusedRNNCell(self.rnn_config.num_hidden, num_layers=self.rnn_config.num_layers, mode=self.rnn_config.cell_type, bidirectional=False, dropout=self.rnn_config.dropout, forget_bias=self.rnn_config.forget_bias, prefix=prefix) class BiDirectionalRNNEncoder(Encoder): """ An encoder that runs a forward and a reverse RNN over input data. States from both RNNs are concatenated together. :param rnn_config: RNN configuration. :param prefix: Prefix. :param layout: Data layout. :param encoder_class: Recurrent encoder class to use. """ def __init__(self, rnn_config: rnn.RNNConfig, prefix=C.BIDIRECTIONALRNN_PREFIX, layout=C.TIME_MAJOR, encoder_class: Callable = RecurrentEncoder) -> None: utils.check_condition(rnn_config.num_hidden % 2 == 0, "num_hidden must be a multiple of 2 for BiDirectionalRNNEncoders.") self.rnn_config = rnn_config self.internal_rnn_config = rnn_config.copy(num_hidden=rnn_config.num_hidden // 2) if layout[0] == 'N': logger.warning("Batch-major layout for encoder input. Consider using time-major layout for faster speed") # time-major layout as _encode needs to swap layout for SequenceReverse self.forward_rnn = encoder_class(rnn_config=self.internal_rnn_config, prefix=prefix + C.FORWARD_PREFIX, layout=C.TIME_MAJOR) self.reverse_rnn = encoder_class(rnn_config=self.internal_rnn_config, prefix=prefix + C.REVERSE_PREFIX, layout=C.TIME_MAJOR) self.layout = layout self.prefix = prefix def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: """ Encodes data given sequence lengths of individual examples and maximum sequence length. :param data: Input data. :param data_length: Vector with sequence lengths. :param seq_len: Maximum sequence length. :return: Encoded versions of input data (data, data_length, seq_len). """ if self.layout[0] == 'N': data = mx.sym.swapaxes(data=data, dim1=0, dim2=1) data = self._encode(data, data_length, seq_len) if self.layout[0] == 'N': data = mx.sym.swapaxes(data=data, dim1=0, dim2=1) return data, data_length, seq_len def _encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> mx.sym.Symbol: """ Bidirectionally encodes time-major data. """ # (seq_len, batch_size, num_embed) data_reverse = mx.sym.SequenceReverse(data=data, sequence_length=data_length, use_sequence_length=True) # (seq_length, batch, cell_num_hidden) hidden_forward, _, _ = self.forward_rnn.encode(data, data_length, seq_len) # (seq_length, batch, cell_num_hidden) hidden_reverse, _, _ = self.reverse_rnn.encode(data_reverse, data_length, seq_len) # (seq_length, batch, cell_num_hidden) hidden_reverse = mx.sym.SequenceReverse(data=hidden_reverse, sequence_length=data_length, use_sequence_length=True) # (seq_length, batch, 2 * cell_num_hidden) hidden_concat = mx.sym.concat(hidden_forward, hidden_reverse, dim=2, name="%s_rnn" % self.prefix) return hidden_concat def get_num_hidden(self) -> int: """ Return the representation size of this encoder. """ return self.rnn_config.num_hidden def get_rnn_cells(self) -> List[mx.rnn.BaseRNNCell]: """ Returns a list of RNNCells used by this encoder. """ return self.forward_rnn.get_rnn_cells() + self.reverse_rnn.get_rnn_cells() class TransformerEncoder(Encoder): """ Non-recurrent encoder based on the transformer architecture in: Attention Is All You Need, Figure 1 (left) Vaswani et al. (https://arxiv.org/pdf/1706.03762.pdf). :param config: Configuration for transformer encoder. :param prefix: Name prefix for operations in this encoder. """ def __init__(self, config: transformer.TransformerConfig, prefix: str = C.TRANSFORMER_ENCODER_PREFIX) -> None: self.config = config self.prefix = prefix self.layers = [transformer.TransformerEncoderBlock( config, prefix="%s%d_" % (prefix, i)) for i in range(config.num_layers)] def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: """ Encodes data given sequence lengths of individual examples and maximum sequence length. :param data: Input data. :param data_length: Vector with sequence lengths. :param seq_len: Maximum sequence length. :return: Encoded versions of input data data, data_length, seq_len. """ for i, layer in enumerate(self.layers): # (batch_size, seq_len, config.model_size) data = layer(data, data_length, seq_len) return data, data_length, seq_len def get_num_hidden(self) -> int: """ Return the representation size of this encoder. """ return self.config.model_size class ConvolutionalEmbeddingConfig(Config): """ Convolutional embedding encoder configuration. :param num_embed: Input embedding size. :param output_dim: Output segment embedding size. :param max_filter_width: Maximum filter width for convolutions. :param num_filters: Number of filters of each width. :param pool_stride: Stride for pooling layer after convolutions. :param num_highway_layers: Number of highway layers for segment embeddings. :param dropout: Dropout probability. """ def __init__(self, num_embed: int, output_dim: int = None, max_filter_width: int = 8, num_filters: Tuple[int, ...] = (200, 200, 250, 250, 300, 300, 300, 300), pool_stride: int = 5, num_highway_layers: int = 4, dropout: float = 0.0, add_positional_encoding: bool = False) -> None: super().__init__() self.num_embed = num_embed self.output_dim = output_dim self.max_filter_width = max_filter_width self.num_filters = num_filters self.pool_stride = pool_stride self.num_highway_layers = num_highway_layers self.dropout = dropout self.add_positional_encoding = add_positional_encoding if self.output_dim is None: self.output_dim = sum(self.num_filters) class ConvolutionalEmbeddingEncoder(Encoder): """ An encoder developed to map a sequence of character embeddings to a shorter sequence of segment embeddings using convolutional, pooling, and highway layers. More generally, it maps a sequence of input embeddings to a sequence of span embeddings. * "Fully Character-Level Neural Machine Translation without Explicit Segmentation" Jason Lee; Kyunghyun Cho; Thomas Hofmann (https://arxiv.org/pdf/1610.03017.pdf) :param config: Convolutional embedding config. :param prefix: Name prefix for symbols of this encoder. """ def __init__(self, config: ConvolutionalEmbeddingConfig, prefix: str = C.CHAR_SEQ_ENCODER_PREFIX) -> None: utils.check_condition(len(config.num_filters) == config.max_filter_width, "num_filters must have max_filter_width elements.") self.num_embed = config.num_embed self.output_dim = config.output_dim self.max_filter_width = config.max_filter_width self.num_filters = config.num_filters[:] self.pool_stride = config.pool_stride self.num_highway_layers = config.num_highway_layers self.prefix = prefix self.dropout = config.dropout self.add_positional_encoding = config.add_positional_encoding self.conv_weight = {filter_width: mx.sym.Variable("%s%s%d%s" % (self.prefix, "conv_", filter_width, "_weight")) for filter_width in range(1, self.max_filter_width + 1)} self.conv_bias = {filter_width: mx.sym.Variable("%s%s%d%s" % (self.prefix, "conv_", filter_width, "_bias")) for filter_width in range(1, self.max_filter_width + 1)} self.project_weight = mx.sym.Variable(self.prefix + "project_weight") self.project_bias = mx.sym.Variable(self.prefix + "project_bias") self.gate_weight = [mx.sym.Variable("%s%s%d%s" % (self.prefix, "gate_", i, "_weight")) for i in range(self.num_highway_layers)] self.gate_bias = [mx.sym.Variable("%s%s%d%s" % (self.prefix, "gate_", i, "_bias")) for i in range(self.num_highway_layers)] self.transform_weight = [mx.sym.Variable("%s%s%d%s" % (self.prefix, "transform_", i, "_weight")) for i in range(self.num_highway_layers)] self.transform_bias = [mx.sym.Variable("%s%s%d%s" % (self.prefix, "transform_", i, "_bias")) for i in range(self.num_highway_layers)] def encode(self, data: mx.sym.Symbol, data_length: mx.sym.Symbol, seq_len: int) -> Tuple[mx.sym.Symbol, mx.sym.Symbol, int]: """ Encodes data given sequence lengths of individual examples and maximum sequence length. :param data: Input data. :param data_length: Vector with sequence lengths. :param seq_len: Maximum sequence length. :return: Encoded versions of input data data, data_length, seq_len. """ total_num_filters = sum(self.num_filters) encoded_seq_len = self.get_encoded_seq_len(seq_len) # (batch_size, channel=1, seq_len, num_embed) data = mx.sym.Reshape(data=data, shape=(-1, 1, seq_len, self.num_embed)) # Convolution filters of width 1..N conv_outputs = [] for filter_width, num_filter in enumerate(self.num_filters, 1): # "half" padding: output length == input length pad_before = ceil((filter_width - 1) / 2) pad_after = floor((filter_width - 1) / 2) # (batch_size, channel=1, seq_len + (filter_width - 1), num_embed) padded = mx.sym.pad(data=data, mode="constant", constant_value=0, pad_width=(0, 0, 0, 0, pad_before, pad_after, 0, 0)) # (batch_size, num_filter, seq_len, num_scores=1) conv = mx.sym.Convolution(data=padded, # cudnn_tune="off", kernel=(filter_width, self.num_embed), num_filter=num_filter, weight=self.conv_weight[filter_width], bias=self.conv_bias[filter_width]) conv = mx.sym.Activation(data=conv, act_type="relu") conv_outputs.append(conv) # (batch_size, total_num_filters, seq_len, num_scores=1) conv_concat = mx.sym.concat(*conv_outputs, dim=1) # Max pooling with stride uncovered = seq_len % self.pool_stride if uncovered > 0: pad_after = self.pool_stride - uncovered # (batch_size, total_num_filters, seq_len + pad_to_final_stride, num_scores=1) conv_concat = mx.sym.pad(data=conv_concat, mode="constant", constant_value=0, pad_width=(0, 0, 0, 0, 0, pad_after, 0, 0)) # (batch_size, total_num_filters, seq_len/stride, num_scores=1) pool = mx.sym.Pooling(data=conv_concat, pool_type="max", kernel=(self.pool_stride, 1), stride=(self.pool_stride, 1)) # (batch_size, total_num_filters, seq_len/stride) pool = mx.sym.reshape(data=pool, shape=(-1, total_num_filters, encoded_seq_len)) # (batch_size, seq_len/stride, total_num_filters) pool = mx.sym.swapaxes(data=pool, dim1=1, dim2=2) if self.dropout > 0: pool = mx.sym.Dropout(data=pool, p=self.dropout) # Raw segment embeddings reshaped for highway network # (batch_size * seq_len/stride, total_num_filters) seg_embedding = mx.sym.Reshape(data=pool, shape=(-3, total_num_filters)) # Projection layer if requested output dimension is different from total number of filters # (TransformerEncoder compatibility, not in original paper) if self.output_dim != total_num_filters: # (batch_size * seq_len/stride, outut_dim) seg_embedding = mx.sym.FullyConnected(data=seg_embedding, num_hidden=self.output_dim, weight=self.project_weight, bias=self.project_bias) seg_embedding = mx.sym.Activation(data=seg_embedding, act_type="relu") if self.dropout > 0: seg_embedding = mx.sym.Dropout(data=seg_embedding, p=self.dropout) # Highway network for i in range(self.num_highway_layers): # Gate gate = mx.sym.FullyConnected(data=seg_embedding, num_hidden=self.output_dim, weight=self.gate_weight[i], bias=self.gate_bias[i]) gate = mx.sym.Activation(data=gate, act_type="sigmoid") if self.dropout > 0: gate = mx.sym.Dropout(data=gate, p=self.dropout) # Transform transform = mx.sym.FullyConnected(data=seg_embedding, num_hidden=self.output_dim, weight=self.transform_weight[i], bias=self.transform_bias[i]) transform = mx.sym.Activation(data=transform, act_type="relu") if self.dropout > 0: transform = mx.sym.Dropout(data=transform, p=self.dropout) # Connection seg_embedding = gate * transform + (1 - gate) * seg_embedding # (batch_size, seq_len/stride, outut_dim) aka # (batch_size, encoded_seq_len, num_segment_emded) seg_embedding = mx.sym.Reshape(data=seg_embedding, shape=(-1, encoded_seq_len, self.output_dim)) # If specified, add positional encodings to segment embeddings # (TransformerEncoder compatibility, not in original paper) if self.add_positional_encoding: seg_embedding = mx.sym.broadcast_add(seg_embedding, Embedding.get_positional_encoding( length=encoded_seq_len, depth=self.output_dim, name="%spositional_encodings" % self.prefix), name='%sadd_positional_encodings' % self.prefix) # Dropout on final segment embeddings if self.dropout > 0: seg_embedding = mx.sym.Dropout(data=seg_embedding, p=self.dropout) # Ceiling function isn't differentiable so this will throw errors if we # attempt to compute gradients. Fortunately we aren't updating inputs # so we can just block the backward pass here. encoded_data_length = mx.sym.BlockGrad(mx.sym.ceil(data_length / self.pool_stride)) return seg_embedding, encoded_data_length, encoded_seq_len def get_num_hidden(self) -> int: """ Return the representation size of this encoder. """ return self.output_dim def get_encoded_seq_len(self, seq_len: int) -> int: """ Returns the size of the encoded sequence. """ return int(ceil(seq_len / self.pool_stride))

42.539267

119

0.604185

319e38e1776b41e5c512ef361a49a7d8bed04377

997

py

Python

boot-hid.py

dhylands/upy-examples

90cca32f0c6c65c33967da9ac1a998e731c60d91

[ "MIT" ]

78

2015-01-15T23:24:21.000Z

2022-02-25T09:24:58.000Z

boot-hid.py

dhylands/upy-examples

90cca32f0c6c65c33967da9ac1a998e731c60d91

[ "MIT" ]

1

2015-02-04T00:51:52.000Z

boot-hid.py

dhylands/upy-examples

90cca32f0c6c65c33967da9ac1a998e731c60d91

[ "MIT" ]

26

2015-02-03T21:26:33.000Z

2022-02-21T02:57:46.000Z

# boot.py -- run on boot-up # # This is some common initialization that I like to keep around. import pyb import micropython import sys #pyb.main('main.py') # main script to run after this one #pyb.usb_mode('CDC') # act as a serial only #pyb.usb_mode('CDC+MSC') # act as a serial and a storage device pyb.usb_mode('CDC+HID') # act as a serial device and a mouse def bl(): pyb.bootloader() def pins(): for pin_name in dir(pyb.Pin.board): pin = pyb.Pin(pin_name) print('{:10s} {:s}'.format(pin_name, str(pin))) def af(): for pin_name in dir(pyb.Pin.board): pin = pyb.Pin(pin_name) print('{:10s} {:s}'.format(pin_name, str(pin.af_list()))) def init(): if True: uart = pyb.UART(6,115200) pyb.repl_uart(uart) print("REPL is also on UART 6 (Y1=Tx Y2=Rx)") if True: bufsize = 100 print("Setting alloc_emergency_exception_buf to", bufsize) micropython.alloc_emergency_exception_buf(bufsize) init()

26.945946

67

0.641926

834d4c93301bbd137e67de86cba380ce2969050a

2,796

py

Python

data/cirq_new/cirq_program/startCirq_pragma773.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/cirq_new/cirq_program/startCirq_pragma773.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/cirq_new/cirq_program/startCirq_pragma773.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 5/15/20 4:49 PM # @File : grover.py # qubit number=4 # total number=19 import cirq import cirq.google as cg from typing import Optional import sys from math import log2 import numpy as np class Opty(cirq.PointOptimizer): def optimization_at( self, circuit: 'cirq.Circuit', index: int, op: 'cirq.Operation' ) -> Optional[cirq.PointOptimizationSummary]: if (isinstance(op, cirq.ops.GateOperation) and isinstance(op.gate, cirq.CZPowGate)): return cirq.PointOptimizationSummary( clear_span=1, clear_qubits=op.qubits, new_operations=[ cirq.CZ(*op.qubits), cirq.X.on_each(*op.qubits), cirq.X.on_each(*op.qubits), ] ) #thatsNoCode def make_circuit(n: int, input_qubit): c = cirq.Circuit() # circuit begin c.append(cirq.H.on(input_qubit[0])) # number=1 c.append(cirq.H.on(input_qubit[1])) # number=2 c.append(cirq.Y.on(input_qubit[2])) # number=13 c.append(cirq.H.on(input_qubit[1])) # number=7 c.append(cirq.H.on(input_qubit[2])) # number=3 c.append(cirq.H.on(input_qubit[3])) # number=4 c.append(cirq.H.on(input_qubit[0])) # number=10 c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=11 c.append(cirq.H.on(input_qubit[0])) # number=12 c.append(cirq.CNOT.on(input_qubit[3],input_qubit[0])) # number=6 c.append(cirq.SWAP.on(input_qubit[1],input_qubit[0])) # number=8 c.append(cirq.H.on(input_qubit[1])) # number=16 c.append(cirq.SWAP.on(input_qubit[1],input_qubit[0])) # number=9 c.append(cirq.Y.on(input_qubit[1])) # number=14 c.append(cirq.Y.on(input_qubit[1])) # number=15 c.append(cirq.Y.on(input_qubit[3])) # number=17 c.append(cirq.Y.on(input_qubit[3])) # number=18 # circuit end c.append(cirq.measure(*input_qubit, key='result')) return c def bitstring(bits): return ''.join(str(int(b)) for b in bits) if __name__ == '__main__': qubit_count = 4 input_qubits = [cirq.GridQubit(i, 0) for i in range(qubit_count)] circuit = make_circuit(qubit_count,input_qubits) circuit = cg.optimized_for_sycamore(circuit, optimizer_type='sqrt_iswap') circuit_sample_count =4000 simulator = cirq.Simulator() result = simulator.run(circuit, repetitions=circuit_sample_count) frequencies = result.histogram(key='result', fold_func=bitstring) writefile = open("../data/startCirq_pragma773.csv","w+") print(format(frequencies),file=writefile) print("results end", file=writefile) print(circuit.__len__(), file=writefile) print(circuit,file=writefile) writefile.close()

32.894118

92

0.643777

fe1d9841a03e601b108132654f1df7937f543a90

533

py

Python

scripts/rpc/pmem.py

ykirichok/spdk

db7f82baf819740025da0ba271745e89ba682f47

[ "BSD-3-Clause" ]

null

scripts/rpc/pmem.py

ykirichok/spdk

db7f82baf819740025da0ba271745e89ba682f47

[ "BSD-3-Clause" ]

null

scripts/rpc/pmem.py

ykirichok/spdk

db7f82baf819740025da0ba271745e89ba682f47

[ "BSD-3-Clause" ]

2

2019-01-30T16:18:59.000Z

2020-05-27T15:41:37.000Z

def create_pmem_pool(args): num_blocks = (args.total_size * 1024 * 1024) / args.block_size params = {'pmem_file': args.pmem_file, 'num_blocks': num_blocks, 'block_size': args.block_size} return args.client.call('create_pmem_pool', params) def pmem_pool_info(args): params = {'pmem_file': args.pmem_file} return args.client.call('pmem_pool_info', params) def delete_pmem_pool(args): params = {'pmem_file': args.pmem_file} return args.client.call('delete_pmem_pool', params)

31.352941

66

0.684803

a854c012226cff8495d0aea0f51b22dcf71c1bea

4,929

py

Python

pandas/core/config_init.py

seberg/pandas

223a1fdd8286f77f1d5c3b3c589816af627d295b

[ "BSD-2-Clause" ]

null

pandas/core/config_init.py

seberg/pandas

223a1fdd8286f77f1d5c3b3c589816af627d295b

[ "BSD-2-Clause" ]

null

pandas/core/config_init.py

seberg/pandas

223a1fdd8286f77f1d5c3b3c589816af627d295b

[ "BSD-2-Clause" ]

null

import pandas.core.config as cf from pandas.core.config import is_int,is_bool,is_text,is_float from pandas.core.format import detect_console_encoding """ This module is imported from the pandas package __init__.py file in order to ensure that the core.config options registered here will be available as soon as the user loads the package. if register_option is invoked inside specific modules, they will not be registered until that module is imported, which may or may not be a problem. If you need to make sure options are available even before a certain module is imported, register them here rather then in the module. """ ########################################### # options from the "print" namespace pc_precision_doc=""" : int Floating point output precision (number of significant digits). This is only a suggestion """ pc_colspace_doc=""" : int Default space for DataFrame columns, defaults to 12 """ pc_max_rows_doc=""" : int This sets the maximum number of rows pandas should output when printing out various output. For example, this value determines whether the repr() for a dataframe prints out fully or just an summary repr. """ pc_max_cols_doc=""" : int max_rows and max_columns are used in __repr__() methods to decide if to_string() or info() is used to render an object to a string. Either one, or both can be set to 0 (experimental). Pandas will figure out how big the terminal is and will not display more rows or/and columns that can fit on it. """ pc_nb_repr_h_doc=""" : boolean When True (default), IPython notebook will use html representation for pandas objects (if it is available). """ pc_date_dayfirst_doc=""" : boolean When True, prints and parses dates with the day first, eg 20/01/2005 """ pc_date_yearfirst_doc=""" : boolean When True, prints and parses dates with the year first, eg 2005/01/20 """ pc_pprint_nest_depth=""" : int Defaults to 3. Controls the number of nested levels to process when pretty-printing """ pc_multi_sparse_doc=""" : boolean Default True, "sparsify" MultiIndex display (don't display repeated elements in outer levels within groups) """ pc_encoding_doc=""" : str/unicode Defaults to the detected encoding of the console. Specifies the encoding to be used for strings returned by to_string, these are generally strings meant to be displayed on the console. """ float_format_doc=""" : callable The callable should accept a floating point number and return a string with the desired format of the number. This is used in some places like SeriesFormatter. See core.format.EngFormatter for an example. """ max_colwidth_doc=""" : int The maximum width in characters of a column in the repr of a pandas data structure. When the column overflows, a "..." placeholder is embedded in the output. """ colheader_justify_doc=""" : 'left'/'right' Controls the justification of column headers. used by DataFrameFormatter. """ pc_expand_repr_doc=""" : boolean Default False Whether to print out the full DataFrame repr for wide DataFrames across multiple lines. If False, the summary representation is shown. """ pc_line_width_doc=""" : int Default 80 When printing wide DataFrames, this is the width of each line. """ with cf.config_prefix('print'): cf.register_option('precision', 7, pc_precision_doc, validator=is_int) cf.register_option('float_format', None, float_format_doc) cf.register_option('column_space', 12, validator=is_int) cf.register_option('max_rows', 100, pc_max_rows_doc, validator=is_int) cf.register_option('max_colwidth', 50, max_colwidth_doc, validator=is_int) cf.register_option('max_columns', 20, pc_max_cols_doc, validator=is_int) cf.register_option('colheader_justify', 'right', colheader_justify_doc, validator=is_text) cf.register_option('notebook_repr_html', True, pc_nb_repr_h_doc, validator=is_bool) cf.register_option('date_dayfirst', False, pc_date_dayfirst_doc, validator=is_bool) cf.register_option('date_yearfirst', False, pc_date_yearfirst_doc, validator=is_bool) cf.register_option('pprint_nest_depth', 3, pc_pprint_nest_depth, validator=is_int) cf.register_option('multi_sparse', True, pc_multi_sparse_doc, validator=is_bool) cf.register_option('encoding', detect_console_encoding(), pc_encoding_doc, validator=is_text) cf.register_option('expand_frame_repr', True, pc_expand_repr_doc) cf.register_option('line_width', 80, pc_line_width_doc) tc_interactive_doc=""" : boolean Default False Whether to simulate interactive mode for purposes of testing """ with cf.config_prefix('test'): cf.register_option('interactive', False, tc_interactive_doc)

33.080537

78

0.722256

b82e5e935117f34bc652a92eb79f7d65ba2ec48b

49,033

py

Python

gui/qt/__init__.py

stevehromov/Electron-Cash

1d4910d03d547b28c71070672df7ce2ebb67c5dd

[ "MIT" ]

1

2020-06-09T04:01:43.000Z

gui/qt/__init__.py

baby636/Electron-Cash

2deab3a3b15c9194949199bac662401803c10227

[ "MIT" ]

18

2019-11-14T03:55:32.000Z

2020-10-25T10:53:35.000Z

gui/qt/__init__.py

stevehromov/Electron-Cash

1d4910d03d547b28c71070672df7ce2ebb67c5dd

[ "MIT" ]

3

2019-02-21T07:57:57.000Z

2020-01-09T15:03:17.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # Electrum - lightweight Bitcoin client # Copyright (C) 2012 thomasv@gitorious # # Permission is hereby granted, free of charge, to any person # obtaining a copy of this software and associated documentation files # (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import gc, os, platform, shutil, signal, sys, traceback try: import PyQt5 except Exception: if sys.platform.startswith('win'): msg = ("\n\nError: Could not import PyQt5.\n" "If you are running the release .exe, this is a bug (please" " contact the developers in that case).\n" "If you are running from source, then you may try this from the command-line:\n\n" " python -m pip install pyqt5\n\n") elif sys.platform.startswith('darw'): msg = ("\n\nError: Could not import PyQt5.\n" "If you are running the release .app, this is a bug (please" " contact the developers in that case).\n" "If you are running from source, then you may try this from the command-line:\n\n" " python3 -m pip install --user -I pyqt5\n\n") else: msg = ("\n\nError: Could not import PyQt5.\n" "You may try:\n\n" " python3 -m pip install --user -I pyqt5\n\n" "Or, if on Linux Ubuntu, Debian, etc:\n\n" " sudo apt-get install python3-pyqt5\n\n") sys.exit(msg) from PyQt5.QtGui import * from PyQt5.QtWidgets import * from PyQt5.QtCore import * from electroncash.i18n import _, set_language from electroncash.plugins import run_hook from electroncash import WalletStorage from electroncash.util import (UserCancelled, PrintError, print_error, standardize_path, finalization_print_error, Weak, get_new_wallet_name, Handlers) from electroncash import version from electroncash.address import Address from .installwizard import InstallWizard, GoBack from . import icons # This needs to be imported once app-wide then the :icons/ namespace becomes available for Qt icon filenames. from .util import * # * needed for plugins from .main_window import ElectrumWindow from .network_dialog import NetworkDialog from .exception_window import Exception_Hook from .update_checker import UpdateChecker class ElectrumGui(QObject, PrintError): new_window_signal = pyqtSignal(str, object) update_available_signal = pyqtSignal(bool) cashaddr_toggled_signal = pyqtSignal() # app-wide signal for when cashaddr format is toggled. This used to live in each ElectrumWindow instance but it was recently refactored to here. cashaddr_status_button_hidden_signal = pyqtSignal(bool) # app-wide signal for when cashaddr toggle button is hidden from the status bar shutdown_signal = pyqtSignal() # signal for requesting an app-wide full shutdown do_in_main_thread_signal = pyqtSignal(object, object, object) instance = None def __init__(self, config, daemon, plugins): super(__class__, self).__init__() # QObject init assert __class__.instance is None, "ElectrumGui is a singleton, yet an instance appears to already exist! FIXME!" __class__.instance = self set_language(config.get('language')) self.config = config self.daemon = daemon self.plugins = plugins self.windows = [] self._setup_do_in_main_thread_handler() # Uncomment this call to verify objects are being properly # GC-ed when windows are closed #if daemon.network: # from electroncash.util import DebugMem # from electroncash.wallet import Abstract_Wallet # from electroncash.verifier import SPV # from electroncash.synchronizer import Synchronizer # daemon.network.add_jobs([DebugMem([Abstract_Wallet, SPV, Synchronizer, # ElectrumWindow], interval=5)]) call_after_app = self._pre_and_post_app_setup() try: self.app = QApplication(sys.argv) finally: call_after_app() self._load_fonts() # this needs to be done very early, before the font engine loads fonts.. out of paranoia self._exit_if_required_pyqt_is_missing() # This may immediately exit the app if missing required PyQt5 modules, so it should also be done early. self.new_version_available = None self._set_icon() self.app.installEventFilter(self) self.timer = QTimer(self); self.timer.setSingleShot(False); self.timer.setInterval(500) #msec self.gc_timer = QTimer(self); self.gc_timer.setSingleShot(True); self.gc_timer.timeout.connect(ElectrumGui.gc); self.gc_timer.setInterval(500) #msec self.nd = None self._last_active_window = None # we remember the last activated ElectrumWindow as a Weak.ref Address.show_cashaddr(self.is_cashaddr()) # Dark Theme -- ideally set this before any widgets are created. self.set_dark_theme_if_needed() # / # Wallet Password Cache # wallet -> (password, QTimer) map for some plugins (like CashShuffle) # that need wallet passwords to operate, and we don't want to prompt # for pw twice right after the InstallWizard runs (see #106). # Entries in this map are deleted after 10 seconds by the QTimer (which # also deletes itself) self._wallet_password_cache = Weak.KeyDictionary() # / self.update_checker = UpdateChecker() self.update_checker_timer = QTimer(self); self.update_checker_timer.timeout.connect(self.on_auto_update_timeout); self.update_checker_timer.setSingleShot(False) self.update_checker.got_new_version.connect(self.on_new_version) # init tray self.dark_icon = self.config.get("dark_icon", False) self.tray = QSystemTrayIcon(self.tray_icon(), self) self.tray.setToolTip('Electron Cash') self.tray.activated.connect(self.tray_activated) self.build_tray_menu() self.tray.show() self.new_window_signal.connect(self.start_new_window) if self.has_auto_update_check(): self._start_auto_update_timer(first_run = True) self.app.focusChanged.connect(self.on_focus_change) # track last window the user interacted with self.shutdown_signal.connect(self.close, Qt.QueuedConnection) run_hook('init_qt', self) # We did this once already in the set_dark_theme call, but we do this # again here just in case some plugin modified the color scheme. ColorScheme.update_from_widget(QWidget()) self._check_and_warn_qt_version() def __del__(self): stale = True if __class__.instance is self: stale = False __class__.instance = None print_error("[{}] finalized{}".format(__class__.__name__, ' (stale instance)' if stale else '')) if hasattr(super(), '__del__'): super().__del__() def _setup_do_in_main_thread_handler(self): ''' Sets up "do_in_main_thread" handler mechanism for Qt GUI. ''' self.do_in_main_thread_signal.connect(self._do_in_main_thread_handler_slot) orig_handler = Handlers.do_in_main_thread weakSelf = Weak.ref(self) def my_do_in_main_thread_handler(func, *args, **kwargs): strongSelf = weakSelf() if strongSelf: # We are still alive, emit the signal which will be handled # in the main thread. strongSelf.do_in_main_thread_signal.emit(func, args, kwargs) else: # We died. Uninstall this handler, invoke original handler. Handlers.do_in_main_thread = orig_handler orig_handler(func, *args, **kwargs) Handlers.do_in_main_thread = my_do_in_main_thread_handler def _do_in_main_thread_handler_slot(self, func, args, kwargs): ''' Hooked in to util.Handlers.do_in_main_thread via the do_in_main_thread_signal. This ensures that there is an app-wide mechanism for posting invocations to the main thread. Currently CashFusion uses this mechanism, but other code may as well. ''' func(*args, **kwargs) def _pre_and_post_app_setup(self): ''' Call this before instantiating the QApplication object. It sets up some platform-specific miscellany that need to happen before the QApplication is constructed. A function is returned. This function *must* be called after the QApplication is constructed. ''' callables = [] def call_callables(): for func in callables: func() ret = call_callables if hasattr(QGuiApplication, 'setDesktopFileName'): QGuiApplication.setDesktopFileName('electron-cash.desktop') if self.windows_qt_use_freetype: # Use FreeType for font rendering on Windows. This fixes rendering # of the Schnorr sigil and allows us to load the Noto Color Emoji # font if needed. os.environ['QT_QPA_PLATFORM'] = 'windows:fontengine=freetype' QCoreApplication.setAttribute(Qt.AA_X11InitThreads) if hasattr(Qt, "AA_ShareOpenGLContexts"): QCoreApplication.setAttribute(Qt.AA_ShareOpenGLContexts) if sys.platform not in ('darwin',) and hasattr(Qt, "AA_EnableHighDpiScaling"): # The below only applies to non-macOS. On macOS this setting is # never used (because it is implicitly auto-negotiated by the OS # in a differernt way). # # qt_disable_highdpi will be set to None by default, or True if # specified on command-line. The command-line override is intended # to supporess high-dpi mode just for this run for testing. # # The more permanent setting is qt_enable_highdpi which is the GUI # preferences option, so we don't enable highdpi if it's explicitly # set to False in the GUI. # # The default on Linux, Windows, etc is to enable high dpi disable_scaling = self.config.get('qt_disable_highdpi', False) enable_scaling = self.config.get('qt_enable_highdpi', True) if not disable_scaling and enable_scaling: QCoreApplication.setAttribute(Qt.AA_EnableHighDpiScaling) if hasattr(Qt, "AA_UseHighDpiPixmaps"): QCoreApplication.setAttribute(Qt.AA_UseHighDpiPixmaps) # macOS Mojave "font rendering looks terrible on PyQt5.11" workaround. # See: https://old.reddit.com/r/apple/comments/9leavs/fix_mojave_font_rendering_issues_on_a_perapp_basis/ # This affects PyQt 5.11 (which is what we ship in the macOS El Capitan # .dmg). We apply the workaround and also warn the user to not use # the El Capitan compatibility .dmg. if sys.platform in ('darwin',) and self.qt_version() < (5, 12): # macOS hacks. On Mojave with PyQt <5.12 the font rendering is terrible. # As a workaround we need to temporarily set this 'defaults' keys # which we immediately disable after the QApplication is started. try: ver = tuple(int(a) for a in platform.mac_ver()[0].split('.')) except (TypeError, ValueError): self.print_error("WARNING: Cannot parse platform.mac_ver", f"'{platform.mac_ver()[0]}'") ver = None if ver and ver >= (10, 14): from electroncash.utils import macos self.print_error("Mojave+ with PyQt<5.12 detected; applying CGFontRenderingFontSmoothingDisabled workaround...") bundle = macos.get_bundle_identifier() os.system(f'defaults write {bundle} CGFontRenderingFontSmoothingDisabled -bool NO') def undo_hack(): os.system(f'defaults delete {bundle} CGFontRenderingFontSmoothingDisabled') self.print_error("Mojave+ font rendering workaround applied.") #msg = _("Mojave or newer system detected, however you are running the " # "El Capitan compatibility release of Electron Cash. " # "Font and graphics rendering may be affected." # "\n\nPlease obtain the latest non-compatibility version " # "of Electron Cash for MacOS.") #QMessageBox.warning(None, _("Warning"), msg) # this works even if app is not exec_() yet. callables.append(undo_hack) return ret def _exit_if_required_pyqt_is_missing(self): ''' Will check if required PyQt5 modules are present and if not, display an error message box to the user and immediately quit the app. This is because some Linux systems break up PyQt5 into multiple subpackages, and for instance PyQt5 QtSvg is its own package, and it may be missing. ''' try: from PyQt5 import QtSvg except ImportError: # Closes #1436 -- Some "Run from source" Linux users lack QtSvg # (partial PyQt5 install) msg = _("A required Qt module, QtSvg was not found. Please fully install all of PyQt5 5.12 or above to resolve this issue.") if sys.platform == 'linux': msg += "\n\n" + _("On Linux, you may try:\n\n python3 -m pip install --user -I pyqt5") if shutil.which('apt'): msg += "\n\n" + _("On Debian-based distros, you can run:\n\n sudo apt install python3-pyqt5.qtsvg") QMessageBox.critical(None, _("QtSvg Missing"), msg) # this works even if app is not exec_() yet. self.app.exit(1) sys.exit(msg) def is_dark_theme_available(self): if sys.platform in ('darwin',): # On OSX, qdarkstyle is kind of broken. We instead rely on Mojave # dark mode if (built in to the OS) for this facility, which the # user can set outside of this application. return False try: import qdarkstyle except: return False return True def set_dark_theme_if_needed(self): if sys.platform in ('darwin',): # On OSX, qdarkstyle is kind of broken. We instead rely on Mojave # dark mode if (built in to the OS) for this facility, which the # user can set outside of this application. use_dark_theme = False else: use_dark_theme = self.config.get('qt_gui_color_theme', 'default') == 'dark' darkstyle_ver = None if use_dark_theme: try: import qdarkstyle self.app.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5()) try: darkstyle_ver = version.normalize_version(qdarkstyle.__version__) except (ValueError, IndexError, TypeError, NameError, AttributeError) as e: self.print_error("Warning: Could not determine qdarkstyle version:", repr(e)) except BaseException as e: use_dark_theme = False self.print_error('Error setting dark theme: {}'.format(repr(e))) # Apply any necessary stylesheet patches. For now this only does anything # if the version is < 2.6.8. # 2.6.8+ seems to have fixed all the issues (for now!) from . import style_patcher style_patcher.patch(dark=use_dark_theme, darkstyle_ver=darkstyle_ver) # Even if we ourselves don't set the dark theme, # the OS/window manager/etc might set *a dark theme*. # Hence, try to choose colors accordingly: ColorScheme.update_from_widget(QWidget(), force_dark=use_dark_theme) def get_cached_password(self, wallet): ''' Passwords in the cache only live for a very short while (10 seconds) after wallet window creation, and only if it's a new window. This mechanism is a convenience for plugins that need access to the wallet password and it would make for poor UX for the user to enter their password twice when opening a new window ''' entry = self._wallet_password_cache.get(wallet) if entry: return entry[0] def _expire_cached_password(self, weakWallet): ''' Timer callback, called after 10 seconds. ''' wallet = weakWallet() if isinstance(weakWallet, Weak.ref) else weakWallet if wallet: entry = self._wallet_password_cache.pop(wallet, None) if entry: timer = entry[1] timer.stop(); timer.deleteLater() def _cache_password(self, wallet, password): self._expire_cached_password(wallet) if password is None: return timer = QTimer() # NB a top-level parentless QObject will get delete by Python when its Python refct goes to 0, which is what we want here. Future programmers: Do not give this timer a parent! self._wallet_password_cache[wallet] = (password, timer) weakWallet = Weak.ref(wallet) weakSelf = Weak.ref(self) def timeout(): slf = weakSelf() slf and slf._expire_cached_password(weakWallet) timer.setSingleShot(True); timer.timeout.connect(timeout); timer.start(10000) # 10 sec def cache_password(self, wallet, password): self._cache_password(wallet, password) def _set_icon(self): icon = None if sys.platform == 'darwin': # on macOS, in "running from source" mode, we want to set the app # icon, otherwise we get the generic Python icon. # In non-running-from-source mode, macOS will get the icon from # the .app bundle Info.plist spec (which ends up being # electron.icns). However, in .app mode, Qt will not know about # this icon and won't be able to use it for e.g. the About dialog. # In the latter case the branch below will tell Qt to use # electron-cash.svg as the "window icon". icon = QIcon("electron.icns") if os.path.exists("electron.icns") else None if not icon: # Set this on all other platforms (and macOS built .app) as it can # only help and never harm, and is always available. icon = QIcon(":icons/electron-cash.svg") if icon: self.app.setWindowIcon(icon) @staticmethod def qt_version() -> tuple: ''' Returns a 3-tuple of the form (major, minor, revision) eg (5, 12, 4) for the current Qt version derived from the QT_VERSION global provided by Qt. ''' return ( (QT_VERSION >> 16) & 0xff, (QT_VERSION >> 8) & 0xff, QT_VERSION & 0xff ) def _load_fonts(self): ''' All apologies for the contorted nature of this platform code. Fonts on Windows & Linux are .. a sensitive situation. :) ''' # Only load the emoji font on Linux and Windows if sys.platform not in ('linux', 'win32', 'cygwin'): return # TODO: Check if we already have the needed emojis # TODO: Allow the user to download a full color emoji set linux_font_config_file = os.path.join(os.path.dirname(__file__), 'data', 'fonts.xml') emojis_ttf_name = 'ecsupplemental_lnx.ttf' emojis_ttf_path = os.path.join(os.path.dirname(__file__), 'data', emojis_ttf_name) did_set_custom_fontconfig = False if (sys.platform == 'linux' and self.linux_qt_use_custom_fontconfig # method-backed property, checks config settings and not os.environ.get('FONTCONFIG_FILE') and os.path.exists('/etc/fonts/fonts.conf') and os.path.exists(linux_font_config_file) and os.path.exists(emojis_ttf_path) and self.qt_version() >= (5, 12)): # doing this on Qt < 5.12 causes harm and makes the whole app render fonts badly # On Linux, we override some fontconfig rules by loading our own # font config XML file. This makes it so that our custom emojis and # other needed glyphs are guaranteed to get picked up first, # regardless of user font config. Without this some Linux systems # had black and white or missing emoji glyphs. We only do this if # the user doesn't have their own fontconfig file in env and # also as a sanity check, if they have the system # /etc/fonts/fonts.conf file in the right place. os.environ['FONTCONFIG_FILE'] = linux_font_config_file did_set_custom_fontconfig = True if sys.platform in ('win32', 'cygwin'): env_var = os.environ.get('QT_QPA_PLATFORM') if not env_var or 'windows:fontengine=freetype' not in env_var.lower(): # not set up to use freetype, so loading the .ttf would fail. # abort early. return del env_var # use a different .ttf file on Windows emojis_ttf_name = 'ecsupplemental_win.ttf' emojis_ttf_path = os.path.join(os.path.dirname(__file__), 'data', emojis_ttf_name) if QFontDatabase.addApplicationFont(emojis_ttf_path) < 0: self.print_error('Failed to add unicode emoji font to application fonts:', emojis_ttf_path) if did_set_custom_fontconfig: self.print_error('Deleting custom (fonts.xml) FONTCONFIG_FILE env. var') del os.environ['FONTCONFIG_FILE'] def _check_and_warn_qt_version(self): if sys.platform == 'linux' and self.qt_version() < (5, 12): msg = _("Electron Cash on Linux requires PyQt5 5.12+.\n\n" "You have version {version_string} installed.\n\n" "Please upgrade otherwise you may experience " "font rendering issues with emojis and other unicode " "characters used by Electron Cash.").format(version_string=QT_VERSION_STR) QMessageBox.warning(None, _("PyQt5 Upgrade Needed"), msg) # this works even if app is not exec_() yet. def eventFilter(self, obj, event): ''' This event filter allows us to open bitcoincash: URIs on macOS ''' if event.type() == QEvent.FileOpen: if len(self.windows) >= 1: self.windows[0].pay_to_URI(event.url().toString()) return True return False def build_tray_menu(self): ''' Rebuild the tray menu by tearing it down and building it new again ''' m_old = self.tray.contextMenu() if m_old is not None: # Tray does NOT take ownership of menu, so we are tasked with # deleting the old one. Note that we must delete the old one rather # than just clearing it because otherwise the old sub-menus stick # around in Qt. You can try calling qApp.topLevelWidgets() to # convince yourself of this. Doing it this way actually cleans-up # the menus and they do not leak. m_old.clear() m_old.deleteLater() # C++ object and its children will be deleted later when we return to the event loop m = QMenu() m.setObjectName("SysTray.QMenu") self.tray.setContextMenu(m) destroyed_print_error(m) for window in self.windows: submenu = m.addMenu(window.wallet.basename()) submenu.addAction(_("Show/Hide"), window.show_or_hide) submenu.addAction(_("Close"), window.close) m.addAction(_("Dark/Light"), self.toggle_tray_icon) m.addSeparator() m.addAction(_("&Check for updates..."), lambda: self.show_update_checker(None)) m.addSeparator() m.addAction(_("Exit Electron Cash"), self.close) self.tray.setContextMenu(m) def tray_icon(self): if self.dark_icon: return QIcon(':icons/electron_dark_icon.svg') else: return QIcon(':icons/electron_light_icon.svg') def toggle_tray_icon(self): self.dark_icon = not self.dark_icon self.config.set_key("dark_icon", self.dark_icon, True) self.tray.setIcon(self.tray_icon()) def tray_activated(self, reason): if reason == QSystemTrayIcon.DoubleClick: if all([w.is_hidden() for w in self.windows]): for w in self.windows: w.bring_to_top() else: for w in self.windows: w.hide() def close(self): for window in list(self.windows): window.close() def new_window(self, path, uri=None): # Use a signal as can be called from daemon thread self.new_window_signal.emit(path, uri) def show_network_dialog(self, parent, *, jumpto : str = ''): if self.warn_if_no_network(parent): return if self.nd: self.nd.on_update() run_hook("on_network_dialog", self.nd) self.nd.show() self.nd.raise_() if jumpto: self.nd.jumpto(jumpto) return self.nd = NetworkDialog(self.daemon.network, self.config) run_hook("on_network_dialog", self.nd) self.nd.show() if jumpto: self.nd.jumpto(jumpto) def create_window_for_wallet(self, wallet): w = ElectrumWindow(self, wallet) self.windows.append(w) finalization_print_error(w, "[{}] finalized".format(w.diagnostic_name())) self.build_tray_menu() run_hook('on_new_window', w) return w def get_wallet_folder(self): ''' may raise FileNotFoundError ''' return os.path.dirname(os.path.abspath(self.config.get_wallet_path())) def get_new_wallet_path(self): ''' may raise FileNotFoundError ''' wallet_folder = self.get_wallet_folder() filename = get_new_wallet_name(wallet_folder) full_path = os.path.join(wallet_folder, filename) return full_path def on_focus_change(self, ignored, new_focus_widget): ''' Remember the last wallet window that was activated because start_new_window uses this information. We store the ElectrumWindow in a weak reference so that we don't interfere with its gc when it is closed.''' if not new_focus_widget: return if isinstance(new_focus_widget, QWidget): window = QWidget.window(new_focus_widget) # call base class because some widgets may actually override 'window' with Python attributes. if isinstance(window, ElectrumWindow): self._last_active_window = Weak.ref(window) def start_new_window(self, path, uri): '''Raises the window for the wallet if it is open. Otherwise opens the wallet and creates a new window for it. `path=None` is a special usage which will raise the last activated window or open the 'last wallet' if no windows are open.''' if not path: if not self.windows: # This branch is taken if nothing is currently open but # path == None, in which case set path=last wallet self.config.open_last_wallet() path = self.config.get_wallet_path() elif self._last_active_window: # This branch is taken if we have windows open and we have # _last_active_window defined, in which case we specify # that this window should be activated by setting path # so that the for loop below will trigger on this window. w = self._last_active_window() # weak ref -> strong ref if w and w in self.windows: # check ref still alive # this will cause the last active window to be used in the # for loop below path = w.wallet.storage.path # NB: path may still be None here if it came in as None from args and # if the above logic couldn't select a window to use -- in which case # we'll end up picking self.windows[0] path = path and standardize_path(path) # just make sure some plugin didn't give us a symlink for w in self.windows: if not path or w.wallet.storage.path == path: path = w.wallet.storage.path # remember path in case it was None w.bring_to_top() break else: try: if not self.windows: self.warn_if_no_secp(relaxed=True) try: wallet = self.daemon.load_wallet(path, None) except BaseException as e: self.print_error(repr(e)) if self.windows: # *Not* starting up. Propagate exception out to present # error message box to user. raise e # We're just starting up, so we are tolerant of bad wallets # and just want to proceed to the InstallWizard so the user # can either specify a different wallet or create a new one. # (See issue #1189 where before they would get stuck) path = self.get_new_wallet_path() # give up on this unknown wallet and try a new name.. note if things get really bad this will raise FileNotFoundError and the app aborts here. wallet = None # fall thru to wizard if not wallet: storage = WalletStorage(path, manual_upgrades=True) wizard = InstallWizard(self.config, self.app, self.plugins, storage) try: wallet, password = wizard.run_and_get_wallet() or (None, None) except UserCancelled: pass except GoBack as e: self.print_error('[start_new_window] Exception caught (GoBack)', e) finally: wizard.terminate() del wizard gc.collect() # wizard sticks around in memory sometimes, otherwise :/ if not wallet: return wallet.start_threads(self.daemon.network) self.daemon.add_wallet(wallet) self._cache_password(wallet, password) except BaseException as e: traceback.print_exc(file=sys.stdout) if '2fa' in str(e): self.warning(title=_('Error'), message = '2FA wallets for Bitcoin Cash are currently unsupported by <a href="https://api.trustedcoin.com/#/">TrustedCoin</a>. Follow <a href="https://github.com/Electron-Cash/Electron-Cash/issues/41#issuecomment-357468208">this guide</a> in order to recover your funds.') else: self.warning(title=_('Error'), message = 'Cannot load wallet:\n' + str(e), icon=QMessageBox.Critical) return w = self.create_window_for_wallet(wallet) if uri: w.pay_to_URI(uri) w.bring_to_top() w.setWindowState(w.windowState() & ~Qt.WindowMinimized | Qt.WindowActive) # this will activate the window w.activateWindow() return w def close_window(self, window): self.windows.remove(window) self.build_tray_menu() # save wallet path of last open window run_hook('on_close_window', window) # GC on ElectrumWindows takes forever to actually happen due to the # circular reference zoo they create around them (they end up stuck in # generation 2 for a long time before being collected). The below # schedules a more comprehensive GC to happen in the very near future. # This mechanism takes on the order of 40-100ms to execute (depending # on hardware) but frees megabytes of memory after closing a window # (which itslef is a relatively infrequent UI event, so it's # an acceptable tradeoff). self.gc_schedule() if not self.windows: self.config.save_last_wallet(window.wallet) # NB: We see if we should quit the app after the last wallet # window is closed, even if a network dialog or some other window is # open. It was bizarre behavior to keep the app open when # things like a transaction dialog or the network dialog were still # up. self._quit_after_last_window() # central point that checks if we should quit. #window.deleteLater() # <--- This has the potential to cause bugs (esp. with misbehaving plugins), so commented-out. The object gets deleted anyway when Python GC kicks in. Forcing a delete may risk python to have a dangling reference to a deleted C++ object. def gc_schedule(self): ''' Schedule garbage collection to happen in the near future. Note that rapid-fire calls to this re-start the timer each time, thus only the last call takes effect (it's rate-limited). ''' self.gc_timer.start() # start/re-start the timer to fire exactly once in timeInterval() msecs @staticmethod def gc(): ''' self.gc_timer timeout() slot ''' gc.collect() def init_network(self): # Show network dialog if config does not exist if self.daemon.network: if self.config.get('auto_connect') is None: wizard = InstallWizard(self.config, self.app, self.plugins, None) wizard.init_network(self.daemon.network) wizard.terminate() def on_new_version(self, newver): ''' Called by the auto update check mechanism to notify that a new version is available. We propagate the signal out using our own update_available_signal as well as post a message to the system tray. ''' self.new_version_available = newver self.update_available_signal.emit(True) self.notify(_("A new version of Electron Cash is available: {}").format(newver)) def show_update_checker(self, parent, *, skip_check = False): if self.warn_if_no_network(parent): return self.update_checker.show() self.update_checker.raise_() if not skip_check: self.update_checker.do_check() def on_auto_update_timeout(self): if not self.daemon.network: # auto-update-checking never is done in offline mode self.print_error("Offline mode; update check skipped") elif not self.update_checker.did_check_recently(): # make sure auto-check doesn't happen right after a manual check. self.update_checker.do_check() if self.update_checker_timer.first_run: self._start_auto_update_timer(first_run = False) def _start_auto_update_timer(self, *, first_run = False): self.update_checker_timer.first_run = bool(first_run) if first_run: interval = 10.0*1e3 # do it very soon (in 10 seconds) else: interval = 4.0*3600.0*1e3 # once every 4 hours (in ms) self.update_checker_timer.start(interval) self.print_error("Auto update check: interval set to {} seconds".format(interval//1e3)) def _stop_auto_update_timer(self): self.update_checker_timer.stop() self.print_error("Auto update check: disabled") def warn_if_cant_import_qrreader(self, parent, show_warning=True): ''' Checks it QR reading from camera is possible. It can fail on a system lacking QtMultimedia. This can be removed in the future when we are unlikely to encounter Qt5 installations that are missing QtMultimedia ''' try: from .qrreader import QrReaderCameraDialog except ImportError as e: if show_warning: self.warning(parent=parent, title=_("QR Reader Error"), message=_("QR reader failed to load. This may " "happen if you are using an older version " "of PyQt5.<br><br>Detailed error: ") + str(e), rich_text=True) return True return False def warn_if_no_network(self, parent): if not self.daemon.network: self.warning(message=_('You are using Electron Cash in offline mode; restart Electron Cash if you want to get connected'), title=_('Offline'), parent=parent, rich_text=True) return True return False def warn_if_no_secp(self, parent=None, message=None, icon=QMessageBox.Warning, relaxed=False): ''' Returns True if it DID warn: ie if there's no secp and ecc operations are slow, otherwise returns False if we have secp. Pass message (rich text) to provide a custom message. Note that the URL link to the HOWTO will always be appended to the custom message.''' from electroncash import ecc_fast has_secp = ecc_fast.is_using_fast_ecc() if has_secp: return False # When relaxwarn is set return True without showing the warning from electroncash import get_config if relaxed and get_config().cmdline_options["relaxwarn"]: return True # else.. howto_url='https://github.com/Electron-Cash/Electron-Cash/blob/master/contrib/secp_HOWTO.md#libsecp256k1-0-for-electron-cash' template = ''' <html><body> <p> {message} <p> {url_blurb} </p> <p><a href="{url}">Electron Cash Secp Mini-HOWTO</a></p> </body></html> ''' msg = template.format( message = message or _("Electron Cash was unable to find the secp256k1 library on this system. Elliptic curve cryptography operations will be performed in slow Python-only mode."), url=howto_url, url_blurb = _("Please visit this page for instructions on how to correct the situation:") ) self.warning(parent=parent, title=_("Missing libsecp256k1"), message=msg, rich_text=True) return True def warning(self, title, message, icon = QMessageBox.Warning, parent = None, rich_text=False): if not isinstance(icon, QMessageBox.Icon): icon = QMessageBox.Warning if isinstance(parent, MessageBoxMixin): parent.msg_box(title=title, text=message, icon=icon, parent=None, rich_text=rich_text) else: parent = parent if isinstance(parent, QWidget) else None d = QMessageBoxMixin(icon, title, message, QMessageBox.Ok, parent) if not rich_text: d.setTextFormat(Qt.PlainText) d.setTextInteractionFlags(Qt.TextSelectableByMouse) else: d.setTextFormat(Qt.AutoText) d.setTextInteractionFlags(Qt.TextSelectableByMouse|Qt.LinksAccessibleByMouse) d.setWindowModality(Qt.WindowModal if parent else Qt.ApplicationModal) d.exec_() d.setParent(None) def lin_win_maybe_show_highdpi_caveat_msg(self, parent): ''' Called from main_window.py -- tells user once and only once about the high DPI mode and its caveats on Linux only. Is a no-op otherwise. ''' is_win = sys.platform[:3] in ('win', 'cyg') is_lin = sys.platform in ('linux',) if not is_win and not is_lin: return if (hasattr(Qt, "AA_EnableHighDpiScaling") and self.app.testAttribute(Qt.AA_EnableHighDpiScaling) # first run check: and self.config.get('qt_enable_highdpi', None) is None and (is_lin # we can't check pixel ratio on linux as apparently it's unreliable, so always show this message on linux # on some windows systems running in highdpi causes # glitches to the QMessageBox windows, so we need # to also warn Windows users that they can turn this off, # but only if they actually are using a high dpi display or (is_win and hasattr(QScreen, 'devicePixelRatio') and any(s.devicePixelRatio() > 1.0 # do they have any screens that are high dpi? for s in self.app.screens()) ))): # write to the config key to immediately suppress this warning in # the future -- it only appears on first-run if key was None self.config.set_key('qt_enable_highdpi', True) if is_lin: msg = (_("Automatic high DPI scaling has been enabled for Electron Cash, which should result in improved graphics quality.") + "\n\n" + _("However, on some esoteric Linux systems, this mode may cause disproportionately large status bar icons.") + "\n\n" + _("If that is the case for you, then you may disable automatic DPI scaling in the preferences, under 'General'.")) else: # is_win msg = (_("Automatic high DPI scaling has been enabled for Electron Cash, which should result in improved graphics quality.") + "\n\n" + _("However, on some Windows systems, bugs in Qt may result in minor graphics glitches in system 'message box' dialogs.") + "\n\n" + _("If that is the case for you, then you may disable automatic DPI scaling in the preferences, under 'General'.")) parent.show_message( title = _('Automatic High DPI'), msg = msg) def has_auto_update_check(self): return bool(self.config.get('auto_update_check', True)) def set_auto_update_check(self, b): was, b = self.has_auto_update_check(), bool(b) if was != b: self.config.set_key('auto_update_check', b, save=True) if b: self._start_auto_update_timer() else: self._stop_auto_update_timer() def _quit_after_last_window(self): if any(1 for w in self.windows if isinstance(w, ElectrumWindow) and not w.cleaned_up): # We can get here if we have some top-level ElectrumWindows that # are "minimized to tray" (hidden). "lastWindowClosed "is emitted # if there are no *visible* windows. If we actually have hidden # app windows (because the user hid them), then we want to *not* # quit the app. https://doc.qt.io/qt-5/qguiapplication.html#lastWindowClosed # This check and early return fixes issue #1727. return qApp.quit() def notify(self, message): ''' Display a message in the system tray popup notification. On macOS this is the GROWL thing. On Windows it's a balloon popup from the system tray. On Linux it's usually a banner in the top of the screen.''' if self.tray: try: # this requires Qt 5.9 self.tray.showMessage("Electron Cash", message, QIcon(":icons/electron-cash.svg"), 20000) except TypeError: self.tray.showMessage("Electron Cash", message, QSystemTrayIcon.Information, 20000) def is_cashaddr(self): return bool(self.config.get('show_cashaddr', True)) def toggle_cashaddr(self, on = None): was = self.is_cashaddr() if on is None: on = not was else: on = bool(on) self.config.set_key('show_cashaddr', on) Address.show_cashaddr(on) if was != on: self.cashaddr_toggled_signal.emit() def is_cashaddr_status_button_hidden(self): return bool(self.config.get('hide_cashaddr_button', False)) def set_cashaddr_status_button_hidden(self, b): b = bool(b) was = self.is_cashaddr_status_button_hidden() if was != b: self.config.set_key('hide_cashaddr_button', bool(b)) self.cashaddr_status_button_hidden_signal.emit(b) @property def windows_qt_use_freetype(self): ''' Returns True iff we are windows and we are set to use freetype as the font engine. This will always return false on platforms where the question doesn't apply. This config setting defaults to True for Windows < Win10 and False otherwise. It is only relevant when using the Qt GUI, however. ''' if sys.platform not in ('win32', 'cygwin'): return False try: winver = float(platform.win32_ver()[0]) # '7', '8', '8.1', '10', etc except (AttributeError, ValueError, IndexError): # We can get here if cygwin, which has an empty win32_ver tuple # in some cases. # In that case "assume windows 10" and just proceed. Cygwin users # can always manually override this setting from GUI prefs. winver = 10 # setting defaults to on for Windows < Win10 return bool(self.config.get('windows_qt_use_freetype', winver < 10)) @windows_qt_use_freetype.setter def windows_qt_use_freetype(self, b): if self.config.is_modifiable('windows_qt_use_freetype') and sys.platform in ('win32', 'cygwin'): self.config.set_key('windows_qt_use_freetype', bool(b)) @property def linux_qt_use_custom_fontconfig(self): ''' Returns True iff we are Linux and we are set to use the fonts.xml fontconfig override, False otherwise. This config setting defaults to True for all Linux, but only is relevant to Qt GUI. ''' return bool(sys.platform in ('linux',) and self.config.get('linux_qt_use_custom_fontconfig', True)) @linux_qt_use_custom_fontconfig.setter def linux_qt_use_custom_fontconfig(self, b): if self.config.is_modifiable('linux_qt_use_custom_fontconfig') and sys.platform in ('linux',): self.config.set_key('linux_qt_use_custom_fontconfig', bool(b)) def main(self): try: self.init_network() except UserCancelled: return except GoBack: return except BaseException as e: traceback.print_exc(file=sys.stdout) return self.timer.start() self.config.open_last_wallet() path = self.config.get_wallet_path() if not self.start_new_window(path, self.config.get('url')): return signal.signal(signal.SIGINT, lambda signum, frame: self.shutdown_signal.emit()) self.app.setQuitOnLastWindowClosed(False) # we want to control this in our slot (since we support non-visible, backgrounded windows via the systray show/hide facility) self.app.lastWindowClosed.connect(self._quit_after_last_window) def clean_up(): # Just in case we get an exception as we exit, uninstall the Exception_Hook Exception_Hook.uninstall() # Shut down the timer cleanly self.timer.stop() self.gc_timer.stop() self._stop_auto_update_timer() # clipboard persistence. see http://www.mail-archive.com/pyqt@riverbankcomputing.com/msg17328.html event = QEvent(QEvent.Clipboard) self.app.sendEvent(self.app.clipboard(), event) self.tray.hide() self.app.aboutToQuit.connect(clean_up) Exception_Hook(self.config) # This wouldn't work anyway unless the app event loop is active, so we must install it once here and no earlier. # main loop self.app.exec_() # on some platforms the exec_ call may not return, so use clean_up()

49.880977

323

0.629923

23a527da58027b1912d6d6d4d8a3e5cf2cdf81f8

942

py

Python

2015/day_23.py

nabiirah/advent-of-code

9c7e7cae437c024aa05d9cb7f9211fd47f5226a2

[ "MIT" ]

24

2020-12-08T20:07:52.000Z

2022-01-18T20:08:06.000Z

2015/day_23.py

nestorhf/advent-of-code

1bb827e9ea85e03e0720e339d10b3ed8c44d8f27

[ "MIT" ]

null

2015/day_23.py

nestorhf/advent-of-code

1bb827e9ea85e03e0720e339d10b3ed8c44d8f27

[ "MIT" ]

10

2020-12-04T10:04:15.000Z

2022-02-21T22:22:26.000Z

"""Advent of Code Day 23 - Opening the Turing Lock""" with open('inputs/day_23.txt') as f: instructions = [line.strip() for line in f] registers = {'a': 1, 'b': 0} # Change a to 0 for Part One pos = 0 while pos < len(instructions): parsed = instructions[pos].split(' ') if len(parsed) == 3: if parsed[0] == 'jio': if registers[parsed[1].strip(',')] == 1: pos += int(parsed[2]) continue elif parsed[0] == 'jie': if registers[parsed[1].strip(',')] % 2 == 0: pos += int(parsed[2]) continue elif parsed[0] == 'hlf': registers[parsed[1]] /= 2 elif parsed[0] == 'tpl': registers[parsed[1]] *= 3 elif parsed[0] == 'inc': registers[parsed[1]] += 1 elif parsed[0] == 'jmp': pos += int(parsed[1]) continue pos += 1 print("Value of Register b =", registers['b'])

24.789474

61

0.501062

d0cf6c9f1c42fd1aa8a337b2c6b20cc176e70401

28,375

py

Python

train_R3D_CatNet.py

villawang/CatNet

6f4c3bce7106e55588eecfa9c1ed04bc52bba222

[ "MIT" ]

12

2020-04-21T21:49:43.000Z

2022-03-16T05:38:52.000Z

train_R3D_CatNet.py

villawang/CatNet

6f4c3bce7106e55588eecfa9c1ed04bc52bba222

[ "MIT" ]

3

2020-10-06T23:39:04.000Z

2021-12-09T05:37:44.000Z

train_R3D_CatNet.py

villawang/CatNet

6f4c3bce7106e55588eecfa9c1ed04bc52bba222

[ "MIT" ]

2

2020-04-21T14:59:30.000Z

2020-08-11T03:34:31.000Z

import numpy as np import pickle import os from PIL import Image import time from tqdm import tqdm, trange import shutil from random import randint import argparse import glob import pdb import random import math import time import argparse import matplotlib.pyplot as plt from copy import deepcopy from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt import sys sys.path.append(os.path.join(os.getcwd(), 'dataset')) import torchvision.transforms as transforms import torchvision.models as models import torch.nn as nn import torch.optim as optim import torch from torch.autograd import Variable from torch.optim import lr_scheduler from torch.utils.data import Dataset, DataLoader, RandomSampler, ConcatDataset from torchsummary import summary from models import resnext from model import generate_model from opts import parse_opts import utils from spatial_transforms import * from temporal_transforms import * import dataset_class import warnings import os # os.environ['CUDA_VISIBLE_DEVICES']='3' warnings.filterwarnings("ignore") args = parse_opts() annot_dir = 'dataset' save_dir = 'output_CatNet/{}-{}'.format(args.arch, args.n_frames_per_clip) if not os.path.exists(save_dir): os.makedirs(save_dir) model_dir = 'models/task0_model' os.environ['CUDA_VISIBLE_DEVICES']=args.cuda_id device = 'cuda:0' if isinstance(eval(args.cuda_id), int): device_ids = [eval(args.cuda_id)] else: device_ids = [i for i in eval(args.cuda_id)] class iCaRL(object): def __init__(self, net, K): self.net = net self.K = K # number of cached exemplar sets for all observed classes self.exemplar_sets = [] # list contains exemplar sets for different classes self.exemplar_labels = [] self.criterion = nn.CrossEntropyLoss() def increment_classes(self, num_AddedClasses): """Add n classes in the final fc layer""" in_features = self.net.module.fc.in_features out_features = self.net.module.fc.out_features weight = self.net.module.fc.weight.data # self.net.module.fc = nn.Linear(in_features, out_features+num_AddedClasses, bias=False) self.net.module.fc = nn.Linear(in_features, out_features+num_AddedClasses) self.net.module.fc.weight.data[:out_features] = weight self.net.module.fc.to(device) def update_representation(self, exemplar_dataset, new_class_dataset): print('Updating representation........') # DO NOT CHANGE batch size HERE!!!!!!!!!!!!!!! exemplar_loader = torch.utils.data.DataLoader(exemplar_dataset, batch_size=1, num_workers=args.num_workers) # exemplar_dataset_UpdateLables contains stored network predicted label and new class label # replace the ground truth label with the predicted labels by the stored model exemplar_dataset_UpdateLables = [] for data in exemplar_loader: inputs, labels = data inputs = inputs.to(device, non_blocking=True).float() probs, logits = self.net(inputs) update_labels = int(probs.max(1)[1].detach().cpu().item()) exemplar_dataset_UpdateLables.append((inputs[0].detach().cpu(), update_labels)) D_dataset = ConcatDataset([dataset_class.make_dataset_instance(exemplar_dataset_UpdateLables)]+new_class_dataset) D_loader = DataLoader(D_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=True) del inputs torch.cuda.empty_cache() return D_loader def reduce_exemplar_sets(self, m): for y, P_y in enumerate(self.exemplar_sets): self.exemplar_sets[y] = P_y[:m] self.exemplar_labels[y] = self.exemplar_labels[y][:m] def train(self, new_class_train_dataset, new_class_test_dataset, added_class_ids, dataloader_val): # representation step exemplar_dataset = self.combine_dataset_with_exemplars(self.exemplar_sets, self.exemplar_labels) self.net.eval() D_loader = self.update_representation(exemplar_dataset, new_class_train_dataset) self.net.train() self.increment_classes(len(added_class_ids)) # training learning_rate = 1e-3 lr_steps = [6] num_epochs = 12 step = 0 self.optimizer = torch.optim.SGD(self.net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-3, dampening=0.9) train_loss = utils.AverageMeter() train_acc = utils.AverageMeter() val_loss = utils.AverageMeter() val_acc = utils.AverageMeter() print('Start training.......') for epoch in trange(num_epochs): train_loss.reset() train_acc.reset() for data in D_loader: inputs, labels = data inputs = inputs.to(device, non_blocking=True).float() labels = labels.to(device, non_blocking=True) probs, logits = self.net(inputs) self.optimizer.zero_grad() loss_ = self.criterion(logits, labels) loss_.backward() self.optimizer.step() train_loss.update(loss_.item()) if step % 100 == 0: loss_val, acc_val_old, acc_val_new, acc_val = self.val_model(dataloader_val) train_logger.log({ 'num_classes': self.net.module.fc.out_features, 'train_loss': train_loss.val, 'val_loss': loss_val, 'val_acc_old': acc_val_old, 'val_acc_new': acc_val_new, 'val_acc': acc_val, 'lr': self.optimizer.param_groups[0]['lr'] }) print('train loss: {:.3f}'.format(train_loss.avg), 'val loss: {:.3f}'.format(loss_val), 'acc val old: {:.3f}'.format(acc_val_old), 'acc val new: {:.3f}'.format(acc_val_new), 'acc val mean: {:.3f}'.format(acc_val)) step += 1 utils.save_checkpoint(self.net, self.optimizer, step, save_dir, '{}-{}-{}.pth'.format(args.arch, args.modality, self.net.module.fc.out_features)) utils.adjust_learning_rate(learning_rate, self.optimizer, epoch, lr_steps) # print('Updating the exemplar sets.......') # m = int(self.K / self.net.module.fc.out_features) # shrink the exemplar sets for old class # self.reduce_exemplar_sets(m) new_class_dataloader = [] ###########################Dirty way to construct the dataloader list. No idea why the second method does not work......##################################################### for i in range(len(new_class_test_dataset)): new_class_dataloader.append(DataLoader(new_class_test_dataset[i], batch_size=args.batch_size, num_workers=args.num_workers)) # new_class_dataloader = [DataLoader(new_class_dataset[i], batch_size=16, # num_workers=4) for i in range(len(new_class_dataset))] ############################################################################################# # attach the new class representation to exemplar sets self.net.eval() for dataloader_class in new_class_dataloader: self.construct_exemplar_set(dataloader_class) self.net.train() # need to be saved for testing phase # exemplar_dataset = self.combine_dataset_with_exemplars(self.exemplar_sets, self.exemplar_labels) with open(os.path.join(save_dir, '{}_ExemplarSet_{}.pkl'.format(args.modality, self.net.module.fc.out_features)), 'wb') as f: pickle.dump(self.exemplar_sets, f) del inputs, labels, probs, logits torch.cuda.empty_cache() def val_model(self, dataloader): self.net.eval() acc = utils.AverageMeter() acc_class = utils.AverageMeter() acc_class_cache = [] loss_val = utils.AverageMeter() for class_i, dataloader_i in enumerate(dataloader): acc_class.reset() for data in dataloader_i: inputs, labels = data inputs = inputs.to(device, non_blocking=True).float() labels = labels.to(device, non_blocking=True) probs, logits = self.net(inputs) val_loss_ = self.criterion(logits, labels) acc.update(utils.calculate_accuracy(probs, labels)) acc_class.update(utils.calculate_accuracy(probs, labels)) loss_val.update(val_loss_.item()) acc_class_cache.append(acc_class.avg) self.net.train() return loss_val.avg, acc_class_cache[0], acc_class_cache[1], acc.avg # function prepare exemplar dataloader for update_representation def combine_dataset_with_exemplars(self, exemplar_sets, exemplar_labels): exemplar_dataset = [] for y, P_y in enumerate(exemplar_sets): for i in range(P_y.size(0)): exemplar_dataset.append((P_y[i], exemplar_labels[y][i])) return exemplar_dataset def construct_exemplar_set(self, dataloader): """Construct an exemplar set for videos frames set Args: dataloader: dataloader containing videos frames of a class """ # m = int(self.K / self.net.module.fc.out_features) m = int(self.K / 40) # assert m <= len(dataloader) * 16 features = [] frames = [] for data in dataloader: inputs, labels = data inputs = inputs.to(device, non_blocking=True).float() labels = labels.to(device, non_blocking=True) feature = feature_extractor(self.net, inputs).detach().cpu() feature = feature/torch.norm(feature, p=2, dim=1, keepdim=True) features.append(feature) frames.append(inputs.detach().cpu()) features = torch.cat(features) class_mean = torch.mean(features, dim=0, keepdim=True) class_mean = class_mean/torch.norm(class_mean, p=2, dim=1, keepdim=True) frames = torch.cat(frames) exemplar_set = [] exemplar_features = [] # list of Variables of shape (feature_size,) for k in range(m): if k == 1: S = torch.cat(exemplar_features).view(1,-1) elif k == 0: S = 0. else: S = torch.sum(torch.stack(exemplar_features), dim=0, keepdim=True) phi = features mu = class_mean mu_p = 1/(k+1) * (phi + S) selected_indice = torch.argmin(torch.sqrt(torch.sum((mu - mu_p)**2, dim=1))).item() exemplar_set.append(frames[selected_indice:selected_indice+1]) exemplar_features.append(features[selected_indice]) exemplar_set = torch.cat(exemplar_set) exemplar_features = torch.cat(exemplar_features) exemplar_label = torch.tensor([labels[0]]*m).long() self.exemplar_sets.append(exemplar_set) self.exemplar_labels.append(exemplar_label) del inputs torch.cuda.empty_cache() def compute_mean(net, exemplar_sets): # prior knowledge of the statistics in the exemplar dataset exemplar_means = [] for P_y in exemplar_sets: loader = torch.utils.data.DataLoader(P_y, batch_size=args.batch_size, num_workers=args.num_workers) features = [] for inputs in loader: feature = feature_extractor(net, inputs.to(device,non_blocking=True).float()).detach().cpu() feature = feature / torch.norm(feature, p=2, dim=1, keepdim=True) # batch_size * feature_size features.append(feature) features = torch.cat(features) # batch_size * feature_size mu_y = torch.mean(features, dim=0, keepdim=True) # 1 * feature_size mu_y = mu_y/torch.norm(mu_y, p=2, dim=1, keepdim=True) exemplar_means.append(mu_y) # save gpu memory del feature torch.cuda.empty_cache() exemplar_means = torch.cat(exemplar_means) # (n_classes, feature_size) return exemplar_means def feature_extractor(net, x): """Classify images by neares-means-of-exemplars Args: x: input video batch Returns: feature: Tensor of extracted features (batch_size,) """ net_FeatureExtractor = nn.Sequential(*list([i for i in net.children()][0].children())[:-2]) feature = net_FeatureExtractor(x) feature = feature.view(feature.size(0), -1) return feature def test(model, load_dir, ExemplarSet_file, checkpoint_file, dataloader, class_id1, class_id2): in_features = model.module.fc.in_features out_features = model.module.fc.out_features # model.module.fc = nn.Linear(in_features, len(class_id1)+len(class_id2), bias=False) model.module.fc = nn.Linear(in_features, len(class_id1)+len(class_id2)) model.to(device) print('Start testing for class {}.....'.format(range(class_id1[0], class_id2[-1]))) print('Model {} and exemplar sets {} loaded'.format(checkpoint_file, ExemplarSet_file)) ExemplarSet_file = os.path.join(load_dir, ExemplarSet_file) checkpoint_file = os.path.join(load_dir, checkpoint_file) with open(ExemplarSet_file, 'rb') as f: exemplar_sets = pickle.load(f) checkpoint = torch.load(checkpoint_file) # model = checkpoint['model'] model.load_state_dict(checkpoint['state_dict']) model.eval() print('Computing exemplar means for {} classes........'.format(len(exemplar_sets))) exemplar_means = compute_mean(model, exemplar_sets) acc = utils.AverageMeter() acc_class = utils.AverageMeter() acc_class_cache = [] for class_i, dataloader_i in enumerate(dataloader): acc_class.reset() for data in dataloader_i: inputs, labels = data inputs = inputs.to(device, non_blocking=True).float() preds = classfier(model, exemplar_means, inputs) acc.update(utils.calculate_accuracy_ForIcarl(preds, labels)) acc_class.update(utils.calculate_accuracy_ForIcarl(preds, labels)) acc_class_cache.append(acc_class.avg) print('Accuracy for old classes:') print(acc_class_cache[0]) print('Accuracy for new classes:') print(acc_class_cache[1]) print('Mean accuracy') print(acc.avg) acc.reset() acc_class.reset() acc_class_cache = [] for class_i, dataloader_i in enumerate(dataloader): acc_class.reset() for data in dataloader_i: inputs, labels = data inputs = inputs.to(device, non_blocking=True).float() labels = labels.to(device, non_blocking=True) probs, logits = model(inputs) acc.update(utils.calculate_accuracy(probs, labels)) acc_class.update(utils.calculate_accuracy(probs, labels)) acc_class_cache.append(acc_class.avg) print('\n') print('Accuracy for old classes:') print(acc_class_cache[0]) print('Accuracy for new classes:') print(acc_class_cache[1]) print('Mean accuracy') print(acc.avg) del inputs, labels, probs, logits, model torch.cuda.empty_cache() def classfier(net, exemplar_means, x): """Classify images by neares-means-of-exemplars Args: x: input video batch Returns: preds: Tensor of size (batch_size,) """ batch_size = x.size(0) # feature = self.feature_extractor(x).detach().cpu() # (batch_size, feature_size) # feature = feature / torch.norm(feature, p=2, dim=1, keepdim=True) # feature_extractor = nn.Sequential(*list([i for i in net.children()][0].children())[:-2]) feature = feature_extractor(net, x).detach().cpu() feature = feature / torch.norm(feature, p=2, dim=1, keepdim=True) feature = feature.unsqueeze(2) # (batch_size, feature_size, 1) exemplar_means = torch.stack([exemplar_means] * batch_size) # (batch_size, n_classes, feature_size) exemplar_means = exemplar_means.transpose(1, 2) feature = feature.expand_as(exemplar_means) # (batch_size, feature_size, n_classes) dists = (feature - exemplar_means).pow(2).sum(1) #(batch_size, n_classes) _, preds = dists.min(1) return preds def get_confusion_matrix(model, load_dir, ExemplarSet_file, checkpoint_file, dataloader, class_id): in_features = model.module.fc.in_features out_features = model.module.fc.out_features # model.module.fc = nn.Linear(in_features, len(class_id1)+len(class_id2), bias=False) model.module.fc = nn.Linear(in_features, len(class_id)) model.to(device) print('Model {} and exemplar sets {} loaded'.format(checkpoint_file, ExemplarSet_file)) ExemplarSet_file = os.path.join(load_dir, ExemplarSet_file) checkpoint_file = os.path.join(load_dir, checkpoint_file) with open(ExemplarSet_file, 'rb') as f: exemplar_sets = pickle.load(f) checkpoint = torch.load(checkpoint_file) # model = checkpoint['model'] model.load_state_dict(checkpoint['state_dict']) model.eval() print('Computing exemplar means for {} classes........'.format(len(exemplar_sets))) exemplar_means = compute_mean(model, exemplar_sets) labels_all = [] preds_exemplar_all = [] preds_softmax_all = [] # for class_i, dataloader_i in enumerate(dataloader): for data in tqdm(dataloader): inputs, labels = data inputs = inputs.to(device, non_blocking=True).float() labels = labels.to(device, non_blocking=True) preds_exemplar = classfier(model, exemplar_means, inputs) probs, logits = model(inputs) pred_softmax = probs.max(1)[1] labels_all.append(labels.detach().cpu()) preds_exemplar_all.append(preds_exemplar.detach().cpu()) preds_softmax_all.append(pred_softmax.detach().cpu()) preds_exemplar_all = torch.cat(preds_exemplar_all) preds_softmax_all = torch.cat(preds_softmax_all) labels_all = torch.cat(labels_all) preds_exemplar_all = preds_exemplar_all.numpy() preds_softmax_all = preds_softmax_all.numpy() labels_all = labels_all.numpy() C_exemplar = confusion_matrix(labels_all,preds_exemplar_all) C_softmax = confusion_matrix(labels_all,preds_softmax_all) plt.figure() plt.matshow(C_exemplar) plt.ylabel('True Label') plt.xlabel('Predicated Label') plt.savefig(os.path.join(load_dir, 'confusion_matrix_exemplar.jpg')) plt.figure() plt.matshow(C_softmax) plt.ylabel('True Label') plt.xlabel('Predicated Label') plt.savefig(os.path.join(load_dir, 'confusion_matrix_softmax.jpg')) if __name__ == '__main__': # keep shuffling be constant every time seed = 1 torch.manual_seed(seed) # torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # if you are using multi-GPU. # activitynet mean value mean = [114.7, 107.7, 99.4] norm_method = Normalize(mean, [1, 1, 1]) scales = [args.initial_scale] for i in range(1, args.n_scales): scales.append(scales[-1] * args.scale_step) trans_train = Compose([ Scale([112,112]), MultiScaleRandomCrop(scales, [112,112]), SpatialElasticDisplacement(), # RandomHorizontalFlip(), ToTensor(1), norm_method ]) temporal_transform_train = Compose([ TemporalRandomCrop(args.n_frames_per_clip) ]) trans_test = Compose([ Scale([112,112]), CenterCrop([112, 112]), ToTensor(1), norm_method ]) temporal_transform_test = Compose([ TemporalCenterCrop(args.n_frames_per_clip) ]) net, parameters = generate_model(args) checkpoint = utils.load_checkpoint(model_dir, '{}-{}-{}.pth'.format(args.arch, args.n_frames_per_clip, args.modality)) net.load_state_dict(checkpoint['state_dict']) # # # set fine tune parameters: Conv5_x and fc layer from original paper # for param in net.module.parameters(): # param.requires_grad = False # for named_child in net.module.named_children(): # if named_child[0] == 'fc' or named_child[0] == 'layer4' or named_child[0] == 'layer3': # # if named_child[0] == 'fc': # for param in named_child[1].parameters(): # param.requires_grad = True net_train = deepcopy(net) net_train.to(device) net_test = deepcopy(net) icarl = iCaRL(net_train, 2000) # load dataset if args.is_train: train_logger = utils.Logger(os.path.join(save_dir, '{}.log'.format(args.modality)), ['num_classes', 'train_loss', 'val_loss', 'val_acc_old', 'val_acc_new','val_acc', 'lr']) class_id1 = [i for i in range(1, 41)] # initial learned class print('Preparing initial exemplar sets........') print('Loading the initial class training data..... class {}'.format(range(class_id1[0], class_id1[-1]))) # class_id1 dataloader for creating the exemplar set dataset_init = [dataset_class.dataset_video_class(annot_dir, 'train_plus_val', class_id = [class_id1[i]], n_frames_per_clip=args.n_frames_per_clip, img_size=(args.w, args.h), reverse=False, transform=trans_test, temporal_transform = temporal_transform_test, modality = args.modality) for i in range(len(class_id1))] dataloader_init = [DataLoader(dataset_init[i], batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) for i in range(len(class_id1))] icarl.net.eval() for dataloader_init_class in tqdm(dataloader_init): icarl.construct_exemplar_set(dataloader_init_class) icarl.net.train() class_step = 5 # incremental steps for new class for incremenral_class in trange(41, 84, class_step): if incremenral_class == range(41, 84, class_step)[-1]: class_id2 = [i for i in range(incremenral_class, 84)] else: class_id2 = [i for i in range(incremenral_class, incremenral_class+class_step)] class_all = class_id1 + class_id2 print('Loading new class training data..... class {}'.format(range(class_id2[0], class_id2[-1]))) # this dataset is used for preparing the exemplar set. DO NOT use any augmentation rules e.g. transforms dataset_new = [dataset_class.dataset_video_class(annot_dir, 'train_plus_val', class_id = [class_id2[i]], n_frames_per_clip=args.n_frames_per_clip, img_size=(args.w, args.h), reverse=False, transform=trans_test, temporal_transform = temporal_transform_test, modality = args.modality) for i in range(len(class_id2))] # new train dataset does not use augmentation (otherwise may causes some unstable issues) dataset_new_train = [dataset_class.dataset_video_class(annot_dir, 'train_plus_val', class_id = [class_id2[i]], n_frames_per_clip=args.n_frames_per_clip, img_size=(args.w, args.h), reverse=False, transform=trans_test, temporal_transform = temporal_transform_test, modality = args.modality) for i in range(len(class_id2))] print('Loading validating data..... class {}'.format(range(class_all[0], class_all[-1]))) print('Loading testing data..... class_id1 {} class_id2 {}'.format(range(class_id1[0], class_id1[-1]), range(class_id2[0], class_id2[-1]) )) dataset_test = [dataset_class.dataset_video_class(annot_dir, 'test', class_id = class_id, n_frames_per_clip=args.n_frames_per_clip, img_size=(args.w, args.h), reverse=False, transform=trans_test, temporal_transform = temporal_transform_test, modality = args.modality) for class_id in [class_id1, class_id2]] dataloader_test = [DataLoader(dataset_test[i], batch_size=args.batch_size_val, num_workers=args.num_workers,pin_memory=True) for i in range(len(dataset_test))] icarl.train(dataset_new_train, dataset_new, class_id2, dataloader_test) test(net_test, save_dir, '{}_ExemplarSet_{}.pkl'.format(args.modality, len(class_all)), '{}-{}-{}.pth'.format(args.arch, args.modality, len(class_all)), dataloader_test, class_id1, class_id2) class_id1 = deepcopy(class_all) # update learned class pdb.set_trace() else: class_id1 = [i for i in range(1, 41)] class_id2 = [i for i in range(41, 46)] # class_all = class_id1 + class_id2 class_all = [i for i in range(1, 84)] print('Loading validating data.....') dataset_test = dataset_class.dataset_video_class(annot_dir, 'test', class_id = class_all, n_frames_per_clip=args.n_frames_per_clip, img_size=(args.w, args.h), reverse=False, transform=trans_test, temporal_transform = temporal_transform_test, modality = args.modality) dataloader_test = DataLoader(dataset_test, batch_size=16, num_workers=args.num_workers,pin_memory=True) test(net_test, save_dir, '{}_ExemplarSet_{}.pkl'.format(args.modality, len(class_all)), 'resnext-101-{}-{}.pth'.format(args.modality, len(class_all)), dataloader_test, class_id1, class_id2) # get_confusion_matrix(net_test, save_dir, # '{}_ExemplarSet_{}.pkl'.format(args.modality, len(class_all)), # 'resnext-101-{}-{}.pth'.format(args.modality, len(class_all)), # dataloader_test, class_all)

45.766129

182

0.588828

bdd5e8e129828164920a6c6fbd77cf246b3947f6

7,388

py

Python

Yoyo/test.py

teabao/AI-introduction

0a608326f627446011f723201b8f705ad7c77e8c

[ "MIT" ]

null

Yoyo/test.py

teabao/AI-introduction

0a608326f627446011f723201b8f705ad7c77e8c

[ "MIT" ]

1

2021-05-19T08:34:44.000Z

Yoyo/test.py

teabao/AI-introduction

0a608326f627446011f723201b8f705ad7c77e8c

[ "MIT" ]

1

2021-05-19T08:23:28.000Z

import argparse import gym import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim # from drawnow import drawnow import matplotlib.pyplot as plt last_score_plot = [-100] avg_score_plot = [-100] def draw_fig(): plt.title('reward') plt.plot(last_score_plot, '-') plt.plot(avg_score_plot, 'r-') parser = argparse.ArgumentParser( description='PyTorch PPO solution of MountainCarContinuous-v0') parser.add_argument('--gamma', type=float, default=0.99) parser.add_argument('--actor_lr', type=float, default=1e-3) parser.add_argument('--critic_lr', type=float, default=1e-3) parser.add_argument('--clip_epsilon', type=float, default=0.2) parser.add_argument('--gae_lambda', type=float, default=0.97) parser.add_argument('--batch_size', type=int, default=10000) parser.add_argument('--max_episode', type=int, default=100) cfg = parser.parse_args() env = gym.make('MountainCarContinuous-v0') class running_state: def __init__(self, state): self.len = 1 self.running_mean = state self.running_std = state ** 2 def update(self, state): self.len += 1 old_mean = self.running_mean.copy() self.running_mean[...] = old_mean + (state - old_mean) / self.len self.running_std[...] = self.running_std + \ (state - old_mean) * (state - self.running_mean) def mean(self): return self.running_mean def std(self): return np.sqrt(self.running_std / (self.len - 1)) class Actor(nn.Module): def __init__(self): super(Actor, self).__init__() self.fc1 = nn.Linear(2, 64) self.fc2 = nn.Linear(64, 64) self.fc_mean = nn.Linear(64, 1) self.fc_log_std = nn.Linear(64, 1) def forward(self, x): x = F.elu(self.fc1(x)) x = F.elu(self.fc2(x)) action_mean = self.fc_mean(x) action_std = torch.exp(self.fc_log_std(x)) return action_mean.squeeze(), action_std.squeeze() def get_action(state): action_mean, action_std = actor(state) action_dist = torch.distributions.Normal(action_mean, action_std) action = action_dist.sample() return action.item() def synchronize_actors(): for target_param, param in zip(actor_old.parameters(), actor.parameters()): target_param.data.copy_(param.data) def update_actor(state, action, advantage): mean_old, std_old = actor_old(state) action_dist_old = torch.distributions.Normal(mean_old, std_old) action_log_probs_old = action_dist_old.log_prob(action) mean, std = actor(state) print(mean.size(), std.size()) action_dist = torch.distributions.Normal(mean, std) action_log_probs = action_dist.log_prob(action) # update old actor before update current actor synchronize_actors() r_theta = torch.exp(action_log_probs - action_log_probs_old) surrogate1 = r_theta * advantage surrogate2 = torch.clamp(r_theta, 1.0 - cfg.clip_epsilon, 1.0 + cfg.clip_epsilon) * advantage loss = -torch.min(surrogate1, surrogate2).mean() entropy = action_dist.entropy() loss = torch.mean(loss - 1e-2 * entropy) actor_optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm(actor.parameters(), 40) actor_optimizer.step() return class Critic(nn.Module): def __init__(self): super(Critic, self).__init__() self.fc1 = nn.Linear(2, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, 1) def forward(self, x): x = F.elu(self.fc1(x)) x = F.elu(self.fc2(x)) value = self.fc3(x) return value.squeeze() def get_state_value(state): state_value = critic(state) return state_value def update_critic(state, target): state_value = critic(state) loss = F.mse_loss(state_value, target) critic_optimizer.zero_grad() loss.backward() critic_optimizer.step() return actor = Actor() actor_old = Actor() critic = Critic() actor_optimizer = optim.Adam(actor.parameters(), lr=cfg.actor_lr) critic_optimizer = optim.Adam(critic.parameters(), lr=cfg.critic_lr) def main(): state = env.reset() state_stat = running_state(state) for i in range(cfg.max_episode): start_time = time.perf_counter() episode_score = 0 episode = 0 memory = [] with torch.no_grad(): while len(memory) < cfg.batch_size: episode += 1 state = env.reset() state_stat.update(state) state = np.clip((state - state_stat.mean()) / (state_stat.std() + 1e-6), -10., 10.) for s in range(1000): # env.render() action = get_action(torch.tensor(state).float()[None, :]) next_state, reward, done, _ = env.step([action]) state_stat.update(next_state) next_state = np.clip( (next_state - state_stat.mean()) / (state_stat.std() + 1e-6), -10., 10.) memory.append([state, action, reward, next_state, done]) state = next_state episode_score += reward if done: break state_batch, \ action_batch, \ reward_batch, \ next_state_batch, \ done_batch = map(lambda x: np.array( x).astype(np.float32), zip(*memory)) state_batch = torch.tensor(state_batch).float() values = get_state_value(state_batch).detach().cpu().numpy() returns = np.zeros(action_batch.shape) deltas = np.zeros(action_batch.shape) advantages = np.zeros(action_batch.shape) prev_return = 0 prev_value = 0 prev_advantage = 0 for i in reversed(range(reward_batch.shape[0])): returns[i] = reward_batch[i] + cfg.gamma * \ prev_return * (1 - done_batch[i]) # generalized advantage estimation deltas[i] = reward_batch[i] + cfg.gamma * \ prev_value * (1 - done_batch[i]) - values[i] advantages[i] = deltas[i] + cfg.gamma * \ cfg.gae_lambda * prev_advantage * (1 - done_batch[i]) prev_return = returns[i] prev_value = values[i] prev_advantage = advantages[i] advantages = (advantages - advantages.mean()) / advantages.std() advantages = torch.tensor(advantages).float() action_batch = torch.tensor(action_batch).float() returns = torch.tensor(returns).float() # using discounted reward as target q-value to update critic update_critic(state_batch, returns) update_actor(state_batch, action_batch, advantages) episode_score /= episode print('last_score {:5f}, steps {}, ({:2f} sec/eps)'. format(episode_score, len(memory), time.perf_counter() - start_time)) avg_score_plot.append(avg_score_plot[-1] * 0.99 + episode_score * 0.01) last_score_plot.append(episode_score) # drawnow(draw_fig) env.close() if __name__ == '__main__': main()

31.172996

96

0.606253

e3faddd5e69246c4361590b6fe169ceb1ca329cb

871

py

Python

myaxf/urls.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

myaxf/urls.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

myaxf/urls.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

from django.conf.urls import url from django.contrib import admin from .views import * urlpatterns = [ url(r'^home/', home, name='home'), url(r'^market/', market, name='market'), url(r'^cart/', cart, name='cart'), url(r'^mine/', mine, name='mine'), url(r'^market_with_params/(\d+)/(\d+)/(\d+)', market_with_params, name='market_params'), url(r'^register&', RegisterAPI.as_view(), name='register'), url(r'^login$', LoginAPI.as_view(), name='login'), url(r'^logout$', LogoutAPI.as_view(), name='logout'), url(r'^confirm/(.*)', confirm), url(r'^check_uname$', check_uname), url(r'^cart_api$', CartAPI.as_view()), url(r'^cart_status$', CartStatusAPI.as_view()), url(r'^cart_all_status$', CartAllStatusAPI.as_view()), url(r'^cart_item$', CartItemAPI.as_view()), url(r'^order$', OrderAPI.as_view(), name='order') ]

37.869565

92

0.63031

916477a6c7c827adc33128660cf93d7aa22ae997

1,804

py

Python

tests/test_transpile.py

blthree/sqlglot

c3130584db6d767575854ba0d57da37e026863c9

[ "MIT" ]

null

tests/test_transpile.py

blthree/sqlglot

c3130584db6d767575854ba0d57da37e026863c9

[ "MIT" ]

null

tests/test_transpile.py

blthree/sqlglot

c3130584db6d767575854ba0d57da37e026863c9

[ "MIT" ]

null

import os import unittest from sqlglot import transpile class TestTranspile(unittest.TestCase): file_dir = os.path.dirname(__file__) fixtures_dir = os.path.join(file_dir, 'fixtures') def test_comments(self): sql = transpile('SELECT 1 FROM foo -- comment')[0] self.assertEqual(sql, 'SELECT 1 FROM foo') sql = transpile('SELECT 1 /* inline */ FROM foo -- comment')[0] self.assertEqual(sql, 'SELECT 1 FROM foo') sql = transpile( """ SELECT 1 -- comment FROM foo -- comment """ )[0] self.assertEqual(sql, 'SELECT 1 FROM foo') sql = transpile( """ SELECT 1 /* big comment like this */ FROM foo -- comment """ )[0] self.assertEqual(sql, 'SELECT 1 FROM foo') def test_if(self): sql = transpile('SELECT IF(a > 1, 1, 0) FROM foo')[0] self.assertEqual(sql, 'SELECT CASE WHEN a > 1 THEN 1 ELSE 0 END FROM foo') sql = transpile('SELECT IF(a > 1, 1) FROM foo')[0] self.assertEqual(sql, 'SELECT CASE WHEN a > 1 THEN 1 END FROM foo') def test_identity(self): with open(os.path.join(self.fixtures_dir, 'identity.sql')) as f: for sql in f: self.assertEqual(transpile(sql)[0], sql.strip()) def test_pretty(self): with open(os.path.join(self.fixtures_dir, 'pretty.sql')) as f: lines = f.read().split(';') size = len(lines) for i in range(0, size, 2): if i + 1 < size: sql = lines[i] pretty = lines[i + 1].strip() generated = transpile(sql, pretty=True)[0] self.assertEqual(generated, pretty)

32.214286

82

0.533259

74ee6db22e3da5e381be4b98c9bad3a7e31977c0

1,403

py

Python

corehq/apps/accounting/migrations/0009_make_billingaccount_name_unique.py

kkrampa/commcare-hq

d64d7cad98b240325ad669ccc7effb07721b4d44

[ "BSD-3-Clause" ]

1

2020-05-05T13:10:01.000Z

corehq/apps/accounting/migrations/0009_make_billingaccount_name_unique.py

kkrampa/commcare-hq

d64d7cad98b240325ad669ccc7effb07721b4d44

[ "BSD-3-Clause" ]

1

2019-12-09T14:00:14.000Z

corehq/apps/accounting/migrations/0009_make_billingaccount_name_unique.py

MaciejChoromanski/commcare-hq

fd7f65362d56d73b75a2c20d2afeabbc70876867

[ "BSD-3-Clause" ]

5

2015-11-30T13:12:45.000Z

2019-07-01T19:27:07.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.10.7 on 2017-08-01 21:47 from __future__ import unicode_literals from __future__ import absolute_import from django.db import migrations, models from django.db.models import Count from corehq.apps.accounting.utils import get_account_name_from_default_name def _make_existing_billing_account_names_unique(apps, schema_editor): BillingAccount = apps.get_model('accounting', 'BillingAccount') counts_by_name = BillingAccount.objects.values('name').annotate(Count('name')) duplicated_names = [ count_by_name['name'] for count_by_name in counts_by_name if count_by_name['name__count'] > 1 ] for duplicated_name in duplicated_names: for billing_account in BillingAccount.objects.filter(name=duplicated_name).order_by('date_created')[1:]: billing_account.name = get_account_name_from_default_name(billing_account.name) billing_account.save() class Migration(migrations.Migration): dependencies = [ ('accounting', '0008_update_report_builder_included_feature_numbers'), ] operations = [ migrations.RunPython(_make_existing_billing_account_names_unique), migrations.AlterField( model_name='billingaccount', name='name', field=models.CharField(db_index=True, max_length=200, unique=True), ), ]

32.627907

112

0.72345

3d8420383691bad5db348fe0cf21b10f0da91759

6,684

py

Python

test_code/test_main_unspacer.py

JKamlah/ocromore

f9d302eff234478c98e03740adf6bbeeafe7db8d

[ "Apache-2.0" ]

16

2018-04-20T11:11:18.000Z

2020-01-14T11:11:59.000Z

test_code/test_main_unspacer.py

JKamlah/ocromore

f9d302eff234478c98e03740adf6bbeeafe7db8d

[ "Apache-2.0" ]

2

2019-09-16T06:22:02.000Z

2020-08-27T23:11:49.000Z

test_code/test_main_unspacer.py

JKamlah/ocromore

f9d302eff234478c98e03740adf6bbeeafe7db8d

[ "Apache-2.0" ]

4

2019-07-18T18:01:05.000Z

2020-07-10T18:44:09.000Z

""" This is the starting file for testing unspacing capabilities (was developed before msa_algorithm) generates unspaced and line height adapted files from different ocr results and compares them to ground truth """ from n_dist_keying.hocr_line_normalizer import HocrLineNormalizer from n_dist_keying.hocr_bbox_comparator import HocrBBoxComparator from n_dist_keying.hocr_line_height import LineHeightCalculator from n_dist_keying.textfile_generator import TextFileGenerator from ocr_validation.ocr_validator import OCRvalidator from ocr_validation.visualization_handler import VisualizationHandler USE_REFSPACING = True # instead of unspacing algorithm use the respacing algorithm DISPLAY_DIFFERENCES = True IGNORE_LINEFEED = False IGNORE_WHITESPACE = False # base files (input) filepath_ground_truth = "./Testfiles/oneprof.gt.txt" filepath_ocropus = "../Testfiles/oneprof_ocropus.html" filepath_tesseract = "../Testfiles/oneprof_tesseract_sure.html" filepath_abbyy = "../Testfiles/oneprof_abbyy_tables_ok.hocr.html" filepath_tesseract_txt = "./Testfiles/oneprof_tesseract_sure.txt" # textfiles which get generated filepath_ocropus_txt = "./Testfiles/oneprof_ocropus_spaced.txt" #ocropus without lhi adaption filepath_abbyy_txt = "./Testfiles/oneprof_abbyy.txt" filepath_ocropus_lha_txt = "./Testfiles/oneprof_ocropus_spaced_lha.txt" filepath_abbyy_lha_txt = "./Testfiles/oneprof_abbyy_lha.txt" # textfiles which get generated for final spacing comparison filepath_ocropus_unspaced_lha = "./Testfiles/oneprof_ocropus_unspaced_lha.txt" filepath_abbyy_unspaced_lha = "./Testfiles/oneprof_abbyy_unspaced_lha.txt" hocr_comparator = HocrBBoxComparator() ocrolist = hocr_comparator.get_ocropus_boxes(filepath_ocropus) tesslist = hocr_comparator.get_tesseract_boxes(filepath_tesseract) abbylist = hocr_comparator.get_abbyy_boxes(filepath_abbyy) hocr_normalizer = HocrLineNormalizer() ocrolist_normalized = hocr_normalizer.normalize_ocropus_list(ocrolist) abbylist_normalized = hocr_normalizer.normalize_abbyy_list(abbylist) tesslist_normalized = hocr_normalizer.normalize_tesseract_list(tesslist) lh_calculator = LineHeightCalculator() lhi_abbyy_normalized = lh_calculator.calculate_line_distance_information(abbylist_normalized, False, True, "abbyy_normalized") lhi_tesseract_normalized = lh_calculator.calculate_line_distance_information(tesslist_normalized, False, True, "tesseract_normalized") lhi_ocropus_normalized = lh_calculator.calculate_line_distance_information(ocrolist_normalized, False, True, "ocropus_normalized") tfg = TextFileGenerator() tfg.create_file(lhi_abbyy_normalized, abbylist_normalized, filepath_abbyy_lha_txt) tfg2 = TextFileGenerator() tfg2.create_file(lhi_ocropus_normalized, ocrolist_normalized, filepath_ocropus_lha_txt) tfg3 = TextFileGenerator() tfg3.create_file(None, ocrolist_normalized, filepath_ocropus_txt, False) tfg4 = TextFileGenerator() tfg4.create_file(None, abbylist_normalized, filepath_abbyy_txt, False) base_ocr_lists = [] base_ocr_lists.append(abbylist_normalized) base_ocr_lists.append(tesslist_normalized) base_ocr_lists.append(ocrolist_normalized) ocr_comparison = hocr_comparator.compare_lists(base_ocr_lists) ocr_comparison.add_line_information(lhi_abbyy_normalized) ocr_comparison.add_line_information(lhi_tesseract_normalized) ocr_comparison.add_line_information(lhi_ocropus_normalized) ocr_comparison.sort_set() print("Print mean||decision||abbyy||tesseract||ocropus|||| without unspacing-------------------") ocr_comparison.print_sets(False) if not USE_REFSPACING: ocr_comparison.unspace_list(2, 1) # unspace ocropus with tesseract as unspacing template ocr_comparison.unspace_list(0, 1) # unspace abbyy with tesseract as unspacing template else: ocr_comparison.refspace_list(2, 1) # refspace ocropus with tesseract as reference spacing template ocr_comparison.refspace_list(0, 1) # refspace abbyy with tesseract as reference spacing template print("Print mean||decision||abbyy||tesseract||ocropus|||| ocropus and abbyy unspaced--------------------") ocr_comparison.print_sets(False) ocr_comparison.save_n_distance_keying_results_to_file("./Testfiles/oneprof_keying_result.txt", True) ocr_comparison.save_dataset_to_file(filepath_ocropus_unspaced_lha, 2, True) ocr_comparison.save_dataset_to_file(filepath_abbyy_unspaced_lha, 0, True) ocr_validator = OCRvalidator() print("Comparison of the unspaced files and spaced files to groundtruth--------------") print("Refspacing is: ", USE_REFSPACING) ocr_validator.set_groundtruth(filepath_ground_truth) # plain file comparison print("Plain file comparison---(tesseract is lha by default)--") ocr_validator.set_ocr_file(filepath_ocropus_txt) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) ocr_validator.set_ocr_file(filepath_abbyy_txt) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) ocr_validator.set_ocr_file(filepath_tesseract_txt) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) # lha file comparison print("LHA file comparison---(tesseract is lha by default)-------------") ocr_validator.set_ocr_file(filepath_ocropus_lha_txt) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) ocr_validator.set_ocr_file(filepath_abbyy_lha_txt) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) ocr_validator.set_ocr_file(filepath_tesseract_txt) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) # unspaced + lha file comparsion print("Unspaced + LHA file comparison---(tesseract is lha and us by default)-------------") ocr_validator.set_ocr_file(filepath_ocropus_unspaced_lha) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) ocr_validator.set_ocr_file(filepath_abbyy_unspaced_lha) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) ocr_validator.set_ocr_file(filepath_tesseract_txt) ocr_validator.compare_ocrolib_edist(IGNORE_LINEFEED, IGNORE_WHITESPACE) if DISPLAY_DIFFERENCES: pyc_handler = VisualizationHandler() # the lha visual comparison #pyc_handler.show_file_comparison(filepath_ocropus_lha_txt, filepath_ocropus_txt) #pyc_handler.show_file_comparison(filepath_abbyy_lha_txt, filepath_abbyy_txt) pyc_handler.show_file_comparison(filepath_ground_truth, filepath_ocropus_unspaced_lha) # mind this is the line height adapted text, generated by this file pyc_handler.show_file_comparison(filepath_ground_truth, filepath_abbyy_unspaced_lha) #pyc_handler.show_file_comparison(filepath_ground_truth, filepath_abbyy_lha_txt) #pyc_handler.show_file_comparison(filepath_ground_truth, filepath_abbyy_unspaced_lha)

45.469388

134

0.844405

44568a63b711952a8695aab87de40ae8c7b3c03d

12,632

py

Python

mailchimp_marketing_asyncio/models/open_activity.py

john-parton/mailchimp-asyncio

3865ca0867bec8f537dc1e3256aa3a160c00f8a2

[ "Apache-2.0" ]

null

mailchimp_marketing_asyncio/models/open_activity.py

john-parton/mailchimp-asyncio

3865ca0867bec8f537dc1e3256aa3a160c00f8a2

[ "Apache-2.0" ]

null

mailchimp_marketing_asyncio/models/open_activity.py

john-parton/mailchimp-asyncio

3865ca0867bec8f537dc1e3256aa3a160c00f8a2

[ "Apache-2.0" ]

1

2022-03-09T14:52:22.000Z

# coding: utf-8 """ Mailchimp Marketing API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 3.0.74 Contact: apihelp@mailchimp.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class OpenActivity(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'campaign_id': 'str', 'list_id': 'str', 'list_is_active': 'bool', 'contact_status': 'str', 'email_id': 'str', 'email_address': 'str', 'merge_fields': 'dict(str, object)', 'vip': 'bool', 'opens_count': 'int', 'opens': 'list[MemberActivity1]', 'links': 'list[ResourceLink]' } attribute_map = { 'campaign_id': 'campaign_id', 'list_id': 'list_id', 'list_is_active': 'list_is_active', 'contact_status': 'contact_status', 'email_id': 'email_id', 'email_address': 'email_address', 'merge_fields': 'merge_fields', 'vip': 'vip', 'opens_count': 'opens_count', 'opens': 'opens', 'links': '_links' } def __init__(self, campaign_id=None, list_id=None, list_is_active=None, contact_status=None, email_id=None, email_address=None, merge_fields=None, vip=None, opens_count=None, opens=None, links=None): # noqa: E501 """OpenActivity - a model defined in Swagger""" # noqa: E501 self._campaign_id = None self._list_id = None self._list_is_active = None self._contact_status = None self._email_id = None self._email_address = None self._merge_fields = None self._vip = None self._opens_count = None self._opens = None self._links = None self.discriminator = None if campaign_id is not None: self.campaign_id = campaign_id if list_id is not None: self.list_id = list_id if list_is_active is not None: self.list_is_active = list_is_active if contact_status is not None: self.contact_status = contact_status if email_id is not None: self.email_id = email_id if email_address is not None: self.email_address = email_address if merge_fields is not None: self.merge_fields = merge_fields if vip is not None: self.vip = vip if opens_count is not None: self.opens_count = opens_count if opens is not None: self.opens = opens if links is not None: self.links = links @property def campaign_id(self): """Gets the campaign_id of this OpenActivity. # noqa: E501 The unique id for the campaign. # noqa: E501 :return: The campaign_id of this OpenActivity. # noqa: E501 :rtype: str """ return self._campaign_id @campaign_id.setter def campaign_id(self, campaign_id): """Sets the campaign_id of this OpenActivity. The unique id for the campaign. # noqa: E501 :param campaign_id: The campaign_id of this OpenActivity. # noqa: E501 :type: str """ self._campaign_id = campaign_id @property def list_id(self): """Gets the list_id of this OpenActivity. # noqa: E501 The unique id for the list. # noqa: E501 :return: The list_id of this OpenActivity. # noqa: E501 :rtype: str """ return self._list_id @list_id.setter def list_id(self, list_id): """Sets the list_id of this OpenActivity. The unique id for the list. # noqa: E501 :param list_id: The list_id of this OpenActivity. # noqa: E501 :type: str """ self._list_id = list_id @property def list_is_active(self): """Gets the list_is_active of this OpenActivity. # noqa: E501 The status of the list used, namely if it's deleted or disabled. # noqa: E501 :return: The list_is_active of this OpenActivity. # noqa: E501 :rtype: bool """ return self._list_is_active @list_is_active.setter def list_is_active(self, list_is_active): """Sets the list_is_active of this OpenActivity. The status of the list used, namely if it's deleted or disabled. # noqa: E501 :param list_is_active: The list_is_active of this OpenActivity. # noqa: E501 :type: bool """ self._list_is_active = list_is_active @property def contact_status(self): """Gets the contact_status of this OpenActivity. # noqa: E501 The status of the member, namely if they are subscribed, unsubscribed, deleted, non-subscribed, transactional, pending, or need reconfirmation. # noqa: E501 :return: The contact_status of this OpenActivity. # noqa: E501 :rtype: str """ return self._contact_status @contact_status.setter def contact_status(self, contact_status): """Sets the contact_status of this OpenActivity. The status of the member, namely if they are subscribed, unsubscribed, deleted, non-subscribed, transactional, pending, or need reconfirmation. # noqa: E501 :param contact_status: The contact_status of this OpenActivity. # noqa: E501 :type: str """ self._contact_status = contact_status @property def email_id(self): """Gets the email_id of this OpenActivity. # noqa: E501 The MD5 hash of the lowercase version of the list member's email address. # noqa: E501 :return: The email_id of this OpenActivity. # noqa: E501 :rtype: str """ return self._email_id @email_id.setter def email_id(self, email_id): """Sets the email_id of this OpenActivity. The MD5 hash of the lowercase version of the list member's email address. # noqa: E501 :param email_id: The email_id of this OpenActivity. # noqa: E501 :type: str """ self._email_id = email_id @property def email_address(self): """Gets the email_address of this OpenActivity. # noqa: E501 Email address for a subscriber. # noqa: E501 :return: The email_address of this OpenActivity. # noqa: E501 :rtype: str """ return self._email_address @email_address.setter def email_address(self, email_address): """Sets the email_address of this OpenActivity. Email address for a subscriber. # noqa: E501 :param email_address: The email_address of this OpenActivity. # noqa: E501 :type: str """ self._email_address = email_address @property def merge_fields(self): """Gets the merge_fields of this OpenActivity. # noqa: E501 A dictionary of merge fields where the keys are the merge tags. See the [Merge Fields documentation](https://mailchimp.com/developer/marketing/docs/merge-fields/#structure) for more about the structure. # noqa: E501 :return: The merge_fields of this OpenActivity. # noqa: E501 :rtype: dict(str, object) """ return self._merge_fields @merge_fields.setter def merge_fields(self, merge_fields): """Sets the merge_fields of this OpenActivity. A dictionary of merge fields where the keys are the merge tags. See the [Merge Fields documentation](https://mailchimp.com/developer/marketing/docs/merge-fields/#structure) for more about the structure. # noqa: E501 :param merge_fields: The merge_fields of this OpenActivity. # noqa: E501 :type: dict(str, object) """ self._merge_fields = merge_fields @property def vip(self): """Gets the vip of this OpenActivity. # noqa: E501 [VIP status](https://mailchimp.com/help/designate-and-send-to-vip-contacts/) for subscriber. # noqa: E501 :return: The vip of this OpenActivity. # noqa: E501 :rtype: bool """ return self._vip @vip.setter def vip(self, vip): """Sets the vip of this OpenActivity. [VIP status](https://mailchimp.com/help/designate-and-send-to-vip-contacts/) for subscriber. # noqa: E501 :param vip: The vip of this OpenActivity. # noqa: E501 :type: bool """ self._vip = vip @property def opens_count(self): """Gets the opens_count of this OpenActivity. # noqa: E501 The total number of times the this campaign was opened by the list member. # noqa: E501 :return: The opens_count of this OpenActivity. # noqa: E501 :rtype: int """ return self._opens_count @opens_count.setter def opens_count(self, opens_count): """Sets the opens_count of this OpenActivity. The total number of times the this campaign was opened by the list member. # noqa: E501 :param opens_count: The opens_count of this OpenActivity. # noqa: E501 :type: int """ self._opens_count = opens_count @property def opens(self): """Gets the opens of this OpenActivity. # noqa: E501 An array of timestamps for each time a list member opened the campaign. If a list member opens an email multiple times, this will return a separate timestamp for each open event. # noqa: E501 :return: The opens of this OpenActivity. # noqa: E501 :rtype: list[MemberActivity1] """ return self._opens @opens.setter def opens(self, opens): """Sets the opens of this OpenActivity. An array of timestamps for each time a list member opened the campaign. If a list member opens an email multiple times, this will return a separate timestamp for each open event. # noqa: E501 :param opens: The opens of this OpenActivity. # noqa: E501 :type: list[MemberActivity1] """ self._opens = opens @property def links(self): """Gets the links of this OpenActivity. # noqa: E501 A list of link types and descriptions for the API schema documents. # noqa: E501 :return: The links of this OpenActivity. # noqa: E501 :rtype: list[ResourceLink] """ return self._links @links.setter def links(self, links): """Sets the links of this OpenActivity. A list of link types and descriptions for the API schema documents. # noqa: E501 :param links: The links of this OpenActivity. # noqa: E501 :type: list[ResourceLink] """ self._links = links def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(OpenActivity, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, OpenActivity): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

31.738693

224

0.611146

4e8ab5b14c699aa7e0402c4b0789cd8027c3135e

138

py

Python

urlcf/exceptions.py

alkorgun/urlcf

3d8752ac94b4f57a07f8de2381ffbe53f815ebfb

[ "MIT" ]

1

2017-07-06T02:10:23.000Z

urlcf/exceptions.py

alkorgun/urlcf

3d8752ac94b4f57a07f8de2381ffbe53f815ebfb

[ "MIT" ]

null

urlcf/exceptions.py

alkorgun/urlcf

3d8752ac94b4f57a07f8de2381ffbe53f815ebfb

[ "MIT" ]

null

""" Created on Nov 17, 2016 @author: alkorgun """ class UrlcfError(Exception): pass class UIDNotFoundError(UrlcfError): pass

9.857143

35

0.688406

c34bc02ae30160d892fdafb80e7caeec298e7547

1,075

py

Python

h2o-py/h2o/model/__init__.py

kernelrich/h2o-3

16bd6be6d0ac22b037cb55b4c647e63e2b112e1e

[ "Apache-2.0" ]

2

2020-09-23T14:23:55.000Z

2020-09-23T19:26:30.000Z

h2o-py/h2o/model/__init__.py

kernelrich/h2o-3

16bd6be6d0ac22b037cb55b4c647e63e2b112e1e

[ "Apache-2.0" ]

1

2021-04-06T13:07:04.000Z

h2o-py/h2o/model/__init__.py

kernelrich/h2o-3

16bd6be6d0ac22b037cb55b4c647e63e2b112e1e

[ "Apache-2.0" ]

1

2020-04-17T13:06:26.000Z

from .autoencoder import H2OAutoEncoderModel from .binomial import H2OBinomialModel from .clustering import H2OClusteringModel from .confusion_matrix import ConfusionMatrix from .dim_reduction import H2ODimReductionModel from .metrics_base import MetricsBase from .model_base import ModelBase from .model_future import H2OModelFuture from .multinomial import H2OMultinomialModel from .ordinal import H2OOrdinalModel from .regression import H2ORegressionModel from .segment_models import H2OSegmentModels from .metrics_base import H2OAutoEncoderModelMetrics from .metrics_base import H2OBinomialModelMetrics from .metrics_base import H2OClusteringModelMetrics from .metrics_base import H2ODimReductionModelMetrics from .metrics_base import H2OMultinomialModelMetrics from .metrics_base import H2OOrdinalModelMetrics from .metrics_base import H2ORegressionModelMetrics __all__ = ["H2OAutoEncoderModel", "H2OBinomialModel", "H2OClusteringModel", "ConfusionMatrix", "H2ODimReductionModel", "MetricsBase", "ModelBase", "H2OModelFuture", "H2OSegmentModels"]

44.791667

81

0.853023

5c4a167ff55b871ef9c337f494458d7b118f4d2b

1,490

py

Python

soundrts/worldplayercomputer2.py

fcnjd/soundrts

3492503d0f4712a31c662d57434ddf8a852d0816

[ "BSD-3-Clause" ]

1

2021-12-28T20:57:20.000Z

soundrts/worldplayercomputer2.py

fcnjd/soundrts

3492503d0f4712a31c662d57434ddf8a852d0816

[ "BSD-3-Clause" ]

1

2018-02-17T10:41:18.000Z

soundrts/worldplayercomputer2.py

fcnjd/soundrts

3492503d0f4712a31c662d57434ddf8a852d0816

[ "BSD-3-Clause" ]

null

from worldorders import ORDERS_DICT from worldplayerbase import Player orders = sorted(ORDERS_DICT.keys()) # sort to avoid desync orders.remove("enter") # later orders.remove("stop") orders.remove("attack") orders.remove("patrol") orders.remove("wait") orders.remove("auto_explore") orders.remove("auto_attack") orders.remove("block") orders.remove("join_group") orders.remove("cancel_building") orders.remove("cancel_training") orders.remove("cancel_upgrading") orders.remove("rallying_point") def _id(x): return x.id class Computer2(Player): name = ["ai2"] def __init__(self, *args, **kargs): Player.__init__(self, *args) def __repr__(self): return "Computer2(%s)" % self.client def _random_order(self, unit, targets): order = ORDERS_DICT[self.world.random.choice(orders)] args = order.nb_args menu = order.menu(unit, strict=True) if menu: order = self.world.random.choice(menu).split() if args: order.append(self.world.random.choice(targets).id) return order def play(self): # sort to avoid desync targets = list(self.perception) + list(self.memory) targets = [x for x in targets if not getattr(x, "is_an_exit", False)] targets.sort(key=_id) for u in self.units: if not u.orders: order = self._random_order(u, targets) if order: u.take_order(order)

27.592593

77

0.637584

ea3c994b779a9c5e2c64762029f80fa5cc0998a9

8,570

py

Python

Project 6 Capstone Project/airflow/dag.py

senthilvel-dev/UD_DE

536cb92f47743b7a52fd62b91845992943965411

[ "MIT" ]

42

2020-05-22T01:24:33.000Z

2022-03-28T14:51:13.000Z

Project 6 Capstone Project/airflow/dag.py

senthilvel-dev/UD_DE

536cb92f47743b7a52fd62b91845992943965411

[ "MIT" ]

9

2020-12-17T21:21:08.000Z

2022-03-29T22:29:16.000Z

Project 6 Capstone Project/airflow/dag.py

vineeths96/Data-Engineering-Nanodegree

4b588607ca654db7e8a2469ab113d4323a1fc079

[ "MIT" ]

30

2020-05-22T01:19:31.000Z

2022-03-13T20:06:28.000Z

import datetime from pathlib import Path from airflow import DAG from airflow.models import Variable from airflow.operators.bash_operator import BashOperator from airflow.operators.dummy_operator import DummyOperator from airflow.operators.postgres_operator import PostgresOperator from airflow.operators.s3_to_redshift_operator import S3ToRedshiftTransfer from airflow.operators.check_operator import CheckOperator, ValueCheckOperator # DAG default argument dictionary default_args = { 'owner': 'vineeths', 'start_date': datetime.datetime(2020, 2, 1), 'depends_on_past': False, 'catchup': False, 'email_on_retry': False, 'retries': 3, 'retry_delay': datetime.timedelta(minutes=5), 'aws_conn_id': 'aws', 'postgres_conn_id': 'redshift', 'redshift_conn_id': 'redshift', 'params': { 's3_bucket': 'udacity-twitter-stream', 's3_region': 'us-west-2', 's3_json_path': None, 'redshift_schema': 'public', 'aws_iam_role': Variable.get("aws_iam_role") } } # Define DAG with template SQL path with DAG(dag_id='capstone_dag', description='ETL data from S3 into AWS Redshift', default_args=default_args, schedule_interval='@daily', template_searchpath=str(Path(__file__).parent.parent.joinpath('sql'))) as dag: # Dummy start task start = DummyOperator(task_id='start_execution') # Create AWS Redshift cluster create_cluster = BashOperator( task_id='create_redshift_cluster', bash_command='python aws_cluster_create.py' ) # Upload static datasets to S3 bucket upload_data_s3 = BashOperator( task_id='upload_data_to_s3', bash_command='python upload_to_s3.py' ) # Stream tweet data to AWS Kineses stream_tweet = BashOperator( task_id='stream_data_to_kinesis', bash_command='python stream_tweets.py' ) # Stream historical tweet data to AWS Kineses stream_historical_tweet = BashOperator( task_id='stream_batch_data_to_kinesis', bash_command='python search_tweets.py' ) # Create the tables in AWS Redshift cluster create_tables = PostgresOperator( task_id='create_tables', sql='create_tables.sql' ) # Stage tweets data in Redshift stage_tweets = S3ToRedshiftTransfer( task_id='stage_tweets', schema='{{ params.redshift_schema }}', table='staging_tweets', s3_bucket='{{ params.s3_bucket }}', s3_key='twitter_feed', copy_options=['COMPUPDATE OFF', 'STATUPDATE OFF', 'TRUNCATECOLUMNS'] ) # Stage happiness data in Redshift stage_happiness = S3ToRedshiftTransfer( task_id='stage_happiness', schema='{{ params.redshift_schema }}', table='staging_happiness', s3_bucket='{{ params.s3_bucket }}', s3_key='happiness', copy_options=['COMPUPDATE OFF', 'STATUPDATE OFF', 'TRUNCATECOLUMNS'] ) # Stage temperature data in Redshift stage_temperature = S3ToRedshiftTransfer( task_id='stage_temperature', schema='{{ params.redshift_schema }}', table='staging_temperature', s3_bucket='{{ params.s3_bucket }}', s3_key='temperature', copy_options=['COMPUPDATE OFF', 'STATUPDATE OFF', 'TRUNCATECOLUMNS'] ) # Runs a basic data quality check to ensure data is inserted check_tweets = CheckOperator( task_id='quality_check_staging_tweets_table', sql='SELECT COUNT(*) FROM public.staging_tweets', conn_id='{{ redshift_conn_id }}' ) # Runs a basic data quality check to ensure data is inserted check_happiness = ValueCheckOperator( task_id='quality_check_staging_happiness_table', sql='SELECT COUNT(*) FROM public.staging_happiness', pass_value=154, conn_id='{{ redshift_conn_id }}' ) # Runs a basic data quality check to ensure data is inserted check_temperature = ValueCheckOperator( task_id='quality_check_staging_temperature_table', sql='SELECT COUNT(*) FROM public.staging_temperature', pass_value=8599212, conn_id='{{ redshift_conn_id }}' ) # Load user table from staging tables load_users_table = PostgresOperator( task_id='load_users_table', sql='users_insert.sql' ) # Load sources table from staging tables load_sources_table = PostgresOperator( task_id='load_sources_table', sql='sources_insert.sql' ) # Load happiness table from staging tables load_happiness_table = PostgresOperator( task_id='load_happiness_table', sql='happiness_insert.sql' ) # Load temperature table from staging tables load_temperature_table = PostgresOperator( task_id='load_temperature_table', sql='temperature_insert.sql' ) # Load time table from staging tables load_time_table = PostgresOperator( task_id='load_time_table', sql='time_insert.sql' ) # Load tweets table from staging tables load_tweets_table = PostgresOperator( task_id='load_tweets_fact_table', sql='tweets_insert.sql' ) # Runs a basic data quality check to ensure data is inserted check_users_dim = CheckOperator( task_id='quality_check_users_table', sql='SELECT COUNT(*) FROM public.users', conn_id='{{ redshift_conn_id }}' ) # Runs a basic data quality check to ensure data is inserted check_sources_dim = CheckOperator( task_id='quality_check_sources_table', sql='SELECT COUNT(*) FROM public.sources', conn_id='{{ redshift_conn_id }}' ) # Runs a basic data quality check to ensure data is inserted check_happiness_dim = CheckOperator( task_id='quality_check_dim_happiness_table', sql='SELECT COUNT(*) FROM public.happiness', conn_id='{{ redshift_conn_id }}' ) # Runs a basic data quality check to ensure data is inserted check_temperature_dim = CheckOperator( task_id='quality_check_dim_temperature_table', sql='SELECT COUNT(*) FROM public.temperature', conn_id='{{ redshift_conn_id }}' ) # Runs a basic data quality check to ensure data is inserted check_time = CheckOperator( task_id='quality_check_time_table', sql='SELECT COUNT(*) FROM public.time', conn_id='{{ redshift_conn_id }}' ) # Runs a basic data quality check to ensure data is inserted check_tweets_fact = CheckOperator( task_id='quality_check_fact_tweets_table', sql='SELECT COUNT(*) FROM public.tweets', conn_id='{{ redshift_conn_id }}' ) # Destroy AWS Redshift cluster destroy_cluster = BashOperator( task_id='destroy_redshift_cluster', bash_command='python aws_cluster_destroy.py' ) # Dummy end task end = DummyOperator(task_id='stop_execution') # Setup task dependencies """ # Stream real time tweets (Uncomment this block and comment next block) start >> upload_data_s3 >> stream_tweet >> create_tables >> [stage_tweets, stage_happiness, stage_temperature] """ # Stream historical tweets start >> create_cluster >> upload_data_s3 >> stream_historical_tweet >> create_tables >> [stage_tweets, stage_happiness, stage_temperature] [stage_tweets >> check_tweets, stage_happiness >> check_happiness, stage_temperature >> check_temperature] >> load_tweets_table >> check_tweets_fact >> [load_users_table, load_sources_table, load_happiness_table, load_temperature_table, load_time_table] [load_users_table >> check_users_dim, load_sources_table >> check_sources_dim, load_happiness_table >> check_happiness_dim, load_temperature_table >> check_temperature_dim, load_time_table >> check_time] >> destroy_cluster >> end

35.857741

114

0.632439

017370f2a0f8f69bf4b6af62cf06e1be1d5bded3

13,177

py

Python

thrift/test/py/TestClientServer.py

fakeNetflix/facebook-repo-fbthrift

24f2357142d1da8c89f4cabc6cb144d83749b3c6

[ "Apache-2.0" ]

2

2021-06-29T13:42:22.000Z

2021-09-06T10:57:34.000Z

thrift/test/py/TestClientServer.py

fakeNetflix/facebook-repo-fbthrift

24f2357142d1da8c89f4cabc6cb144d83749b3c6

[ "Apache-2.0" ]

null

thrift/test/py/TestClientServer.py

fakeNetflix/facebook-repo-fbthrift

24f2357142d1da8c89f4cabc6cb144d83749b3c6

[ "Apache-2.0" ]

5

2021-06-29T13:42:26.000Z

2022-02-08T02:41:34.000Z

#!/usr/bin/env python # This starts up a bunch of servers, one for each of the server type # and socket typecombinations we have. It then runs through the tests # for each server, which entails connecting to calling a method on the # server, asserting something about that method, and then closing the # connection from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os.path import sys from subprocess import Popen import time import unittest import string import socket import errno import ssl as SSL from ThriftTest import ThriftTest, SecondService from ThriftTest.ttypes import * from thrift.transport import TTransport from thrift.transport.THeaderTransport import ( THeaderTransport, TRANSFORM, CLIENT_TYPE, ) from thrift.transport import TSocket, TSSLSocket from thrift.protocol import ( TBinaryProtocol, THeaderProtocol, TMultiplexedProtocol, TCompactProtocol ) _servers = [] _ports = {} try: from thrift.protocol import fastproto except ImportError: fastproto = None def start_server(server_type, ssl, server_header, server_context, multiple, port): server_path = os.path.dirname(sys.argv[0]) if sys.version_info[0] >= 3: server_bin = os.path.join(server_path, 'py3_test_server.par') else: server_bin = os.path.join(server_path, 'python_test_server.par') args = [server_bin, '--port', str(port)] if ssl: args.append('--ssl') if server_header: args.append('--header') if server_context: args.append('--context') if multiple: args.append('--multiple') args.append(server_type) stdout = None stderr = None if sys.stdout.isatty(): stdout = sys.stdout stderr = sys.stderr return Popen(args, stdout=stdout, stderr=stderr) def isConnectionRefused(e): if sys.version_info[0] >= 3: return isinstance(e, ConnectionRefusedError) else: return e[0] == errno.ECONNREFUSED def wait_for_server(port, timeout, ssl=False): end = time.time() + timeout while time.time() < end: try: sock = socket.socket() sock.settimeout(end - time.time()) if ssl: sock = SSL.wrap_socket(sock) sock.connect(('localhost', port)) return True except socket.timeout: return False except socket.error as e: if not isConnectionRefused(e): raise finally: sock.close() time.sleep(0.1) return False class AbstractTest(object): @classmethod def setUpClass(cls): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.setsockopt( socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) sock.bind(('0.0.0.0', 0)) port = sock.getsockname()[1] server = start_server( cls.server_type, cls.ssl, cls.server_header, cls.server_context, cls.multiple, port) if not wait_for_server(port, 5.0, ssl=cls.ssl): msg = "Failed to start " + cls.server_type if cls.ssl: msg += " using ssl" if cls.server_header: msg += " using header protocol" if cls.server_context: msg += " using context" raise Exception(msg) cls._port = port cls._server = server @classmethod def tearDownClass(cls): cls._server.kill() cls._server.wait() def bytes_comp(self, seq1, seq2): if not isinstance(seq1, bytes): seq1 = seq1.encode('utf-8') if not isinstance(seq2, bytes): seq2 = seq2.encode('utf-8') self.assertEquals(seq1, seq2) def setUp(self): if self.ssl: self.socket = TSSLSocket.TSSLSocket("localhost", self._port) else: self.socket = TSocket.TSocket("localhost", self._port) self.transport = TTransport.TBufferedTransport(self.socket) self.protocol = self.protocol_factory.getProtocol(self.transport) if isinstance(self, HeaderAcceleratedCompactTest): self.protocol.trans.set_protocol_id( THeaderProtocol.THeaderProtocol.T_COMPACT_PROTOCOL) self.protocol.reset_protocol() self.transport.open() self.client = ThriftTest.Client(self.protocol) if self.multiple: p = TMultiplexedProtocol.TMultiplexedProtocol(self.protocol, "ThriftTest") self.client = ThriftTest.Client(p) p = TMultiplexedProtocol.TMultiplexedProtocol(self.protocol, "SecondService") self.client2 = SecondService.Client(p) else: self.client = ThriftTest.Client(self.protocol) self.client2 = None def tearDown(self): self.transport.close() def testVoid(self): self.client.testVoid() def testString(self): self.bytes_comp(self.client.testString('Python'), 'Python') def testByte(self): self.assertEqual(self.client.testByte(63), 63) def testI32(self): self.assertEqual(self.client.testI32(-1), -1) self.assertEqual(self.client.testI32(0), 0) def testI64(self): self.assertEqual(self.client.testI64(-34359738368), -34359738368) def testDouble(self): self.assertEqual(self.client.testDouble(-5.235098235), -5.235098235) def testFloat(self): self.assertEqual(self.client.testFloat(-5.25), -5.25) def testStruct(self): x = Xtruct() x.string_thing = "Zero" x.byte_thing = 1 x.i32_thing = -3 x.i64_thing = -5 y = self.client.testStruct(x) self.bytes_comp(y.string_thing, "Zero") self.assertEqual(y.byte_thing, 1) self.assertEqual(y.i32_thing, -3) self.assertEqual(y.i64_thing, -5) def testException(self): self.client.testException('Safe') try: self.client.testException('Xception') self.fail("should have gotten exception") except Xception as x: self.assertEqual(x.errorCode, 1001) self.bytes_comp(x.message, 'Xception') # noqa try: self.client.testException("throw_undeclared") self.fail("should have thrown exception") except Exception: # type is undefined pass def testOneway(self): start = time.time() self.client.testOneway(1) end = time.time() self.assertTrue(end - start < 0.2, "oneway sleep took %f sec" % (end - start)) def testblahBlah(self): if self.client2: self.assertEqual(self.client2.blahBlah(), None) class NormalBinaryTest(AbstractTest): protocol_factory = TBinaryProtocol.TBinaryProtocolFactory() class AcceleratedBinaryTest(AbstractTest): protocol_factory = TBinaryProtocol.TBinaryProtocolAcceleratedFactory() class HeaderBase(AbstractTest): protocol_factory = THeaderProtocol.THeaderProtocolFactory( True, [CLIENT_TYPE.HEADER, CLIENT_TYPE.FRAMED_DEPRECATED, CLIENT_TYPE.UNFRAMED_DEPRECATED, CLIENT_TYPE.HTTP_SERVER] ) class HeaderTest(HeaderBase): def testZlibCompression(self): htrans = self.protocol.trans if isinstance(htrans, THeaderTransport): htrans.add_transform(TRANSFORM.ZLIB) self.testStruct() def testSnappyCompression(self): htrans = self.protocol.trans if isinstance(htrans, THeaderTransport): htrans.add_transform(TRANSFORM.SNAPPY) self.testStruct() def testMultipleCompression(self): htrans = self.protocol.trans if isinstance(htrans, THeaderTransport): htrans.add_transform(TRANSFORM.ZLIB) htrans.add_transform(TRANSFORM.SNAPPY) self.testStruct() def testKeyValueHeader(self): htrans = self.protocol.trans if isinstance(htrans, THeaderTransport): # Try just persistent header htrans.set_persistent_header("permanent", "true") self.client.testString('test') headers = htrans.get_headers() self.assertTrue("permanent" in headers) self.assertEquals(headers["permanent"], "true") # Try with two transient headers htrans.set_header("transient1", "true") htrans.set_header("transient2", "true") self.client.testString('test') headers = htrans.get_headers() self.assertTrue("permanent" in headers) self.assertEquals(headers["permanent"], "true") self.assertTrue("transient1" in headers) self.assertEquals(headers["transient1"], "true") self.assertTrue("transient2" in headers) self.assertEquals(headers["transient2"], "true") # Add one, update one and delete one transient header htrans.set_header("transient2", "false") htrans.set_header("transient3", "true") self.client.testString('test') headers = htrans.get_headers() self.assertTrue("permanent" in headers) self.assertEquals(headers["permanent"], "true") self.assertTrue("transient1" not in headers) self.assertTrue("transient2" in headers) self.assertEquals(headers["transient2"], "false") self.assertTrue("transient3" in headers) self.assertEquals(headers["transient3"], "true") class HeaderAcceleratedCompactTest(HeaderBase): pass class HeaderFramedCompactTest(HeaderBase): def setUp(self): self.socket = TSocket.TSocket("localhost", self._port) self.transport = TTransport.TFramedTransport(self.socket) self.protocol = TCompactProtocol.TCompactProtocol(self.transport) self.transport.open() self.client = ThriftTest.Client(self.protocol) self.client2 = None class HeaderFramedBinaryTest(HeaderBase): def setUp(self): self.socket = TSocket.TSocket("localhost", self._port) self.transport = TTransport.TFramedTransport(self.socket) self.protocol = TBinaryProtocol.TBinaryProtocol(self.transport) self.transport.open() self.client = ThriftTest.Client(self.protocol) self.client2 = None class HeaderUnframedCompactTest(HeaderBase): def setUp(self): self.socket = TSocket.TSocket("localhost", self._port) self.transport = TTransport.TBufferedTransport(self.socket) self.protocol = TCompactProtocol.TCompactProtocol(self.transport) self.transport.open() self.client = ThriftTest.Client(self.protocol) self.client2 = None class HeaderUnframedBinaryTest(HeaderBase): def setUp(self): self.socket = TSocket.TSocket("localhost", self._port) self.transport = TTransport.TBufferedTransport(self.socket) self.protocol = TBinaryProtocol.TBinaryProtocol(self.transport) self.transport.open() self.client = ThriftTest.Client(self.protocol) self.client2 = None def camelcase(s): if not s[0].isupper(): if sys.version_info[0] >= 3: s = ''.join([x.capitalize() for x in s.split('_')]) else: s = ''.join(map(string.capitalize, s.split('_'))) return s def class_name_mixin(k, v): mixin = camelcase(k) if isinstance(v, bool): mixin += 'On' if v else 'Off' else: mixin += camelcase(v) return mixin def new_test_class(cls, vars): template = "" name = cls.__name__ for k, v in sorted(vars.items()): name += class_name_mixin(k, v) template += " {} = {!r}\n".format(k, v) template = "class {}(cls, unittest.TestCase):\n".format(name) + template exec(template) return locals()[name] def add_test_classes(module): classes = [] for server_context in (True, False): for multiple in (True, False): config1 = { 'server_type': "TCppServer", 'ssl': False, 'server_header': False, 'server_context': server_context, 'multiple': multiple, } classes.append(new_test_class(NormalBinaryTest, config1)) classes.append(new_test_class(AcceleratedBinaryTest, config1)) config2 = { 'server_type': "TCppServer", 'ssl': False, 'server_header': False, 'server_context': False, 'multiple': False, } if fastproto is not None: classes.append(new_test_class(HeaderAcceleratedCompactTest, config2)) for header in (HeaderFramedCompactTest, HeaderFramedBinaryTest, HeaderUnframedCompactTest, HeaderUnframedBinaryTest): classes.append(new_test_class(header, config2)) for cls in classes: setattr(module, cls.__name__, cls) add_test_classes(sys.modules[__name__])

32.139024

77

0.629658

455700a4c6c5eaf6ed7bafea602e7dfcd0d4a927

175

py

Python

lab_assignment/lab_bla/linux_mac/sample/vector.py

caru1613/introduction_to_python_TEAMLAB_MOOC

e0ac95f7a6b889e7d18b7bdaaab49820e73d5477

[ "MIT" ]

null

lab_assignment/lab_bla/linux_mac/sample/vector.py

caru1613/introduction_to_python_TEAMLAB_MOOC

e0ac95f7a6b889e7d18b7bdaaab49820e73d5477

[ "MIT" ]

null

lab_assignment/lab_bla/linux_mac/sample/vector.py

caru1613/introduction_to_python_TEAMLAB_MOOC

e0ac95f7a6b889e7d18b7bdaaab49820e73d5477

[ "MIT" ]

null

u = [2,2] v = [2,3] z = [3,5] #result = [] #for i in range(len(u)): # result.append(u[i] + v[i] + z[i]) #print(result) result = [sum(t) for t in zip(u,v,z)] print(result)

15.909091

38

0.525714

569545181dde173594c5bf4600d92d70feec418b

5,430

py

Python

tests/3_2_0/blueprints/test_blueprint_having_ahv_helper/blueprint.py

tuxtof/calm-dsl

5af67435d8304b97e170a690068f2d5975e9bfe6

[ "Apache-2.0" ]

37

2019-12-23T15:23:20.000Z

2022-03-15T11:12:11.000Z

tests/3_2_0/blueprints/test_blueprint_having_ahv_helper/blueprint.py

tuxtof/calm-dsl

5af67435d8304b97e170a690068f2d5975e9bfe6

[ "Apache-2.0" ]

144

2020-03-09T11:22:09.000Z

2022-03-28T21:34:09.000Z

tests/3_2_0/blueprints/test_blueprint_having_ahv_helper/blueprint.py

abhijeetkaurav1st/calm-dsl

6487a896967b3fd667b9320e2ad3a397c9960497

[ "Apache-2.0" ]

46

2020-01-23T14:28:04.000Z

2022-03-09T04:17:10.000Z

import json from calm.dsl.builtins import AhvVmDisk, AhvVmNic, AhvVmGC from calm.dsl.builtins import ref, AhvVmResources, AhvVm, Ref, Metadata from calm.dsl.builtins import vm_disk_package, read_local_file, basic_cred from calm.dsl.builtins import Service, Package, Substrate from calm.dsl.builtins import Deployment, Profile, Blueprint from calm.dsl.builtins import CalmVariable, CalmTask, action CENTOS_KEY = read_local_file(".tests/keys/centos") CENTOS_PUBLIC_KEY = read_local_file(".tests/keys/centos_pub") DSL_CONFIG = json.loads(read_local_file(".tests/config.json")) CENTOS_CI = DSL_CONFIG["AHV"]["IMAGES"]["DISK"]["CENTOS_7_CLOUD_INIT"] NETWORK1 = DSL_CONFIG["AHV"]["NETWORK"]["VLAN1211"] # TODO change network constants # project constants PROJECT = DSL_CONFIG["PROJECTS"]["PROJECT1"] PROJECT_NAME = PROJECT["NAME"] ENV_NAME = PROJECT["ENVIRONMENTS"][0]["NAME"] ACCOUNT_NAME = PROJECT["ACCOUNTS"]["NUTANIX_PC"][0]["NAME"] Centos = basic_cred("centos", CENTOS_KEY, name="Centos", type="KEY", default=True) Era_Disk = vm_disk_package( name="era_disk", config={ # By default image type is set to DISK_IMAGE "image": { "source": "http://download.nutanix.com/era/1.1.1/ERA-Server-build-1.1.1-340d9db1118eac81219bec98507d4982045d8799.qcow2" } }, ) Virtio_CdRom = vm_disk_package( name="virtio_cdrom", config={ "image": { "type": "ISO_IMAGE", "source": "http://10.40.64.33/GoldImages/NuCalm/ISO/Nutanix-VirtIO-1.1.4.iso", } }, ) class AhvVmService(Service): """Sample mysql service""" ENV = CalmVariable.Simple("DEV") class AhvVmPackage(Package): """Example package with variables, install tasks and link to service""" foo = CalmVariable.Simple("bar") services = [ref(AhvVmService)] @action def __install__(): CalmTask.Exec.ssh(name="Task1", script="echo @@{foo}@@") class AhvVmPackage2(AhvVmPackage): pass class MyAhvVmResources(AhvVmResources): memory = 4 vCPUs = 2 cores_per_vCPU = 1 disks = [ AhvVmDisk(CENTOS_CI), ] nics = [AhvVmNic(NETWORK1)] guest_customization = AhvVmGC.CloudInit( config={ "users": [ { "name": "centos", "ssh-authorized-keys": [CENTOS_PUBLIC_KEY], "sudo": ["ALL=(ALL) NOPASSWD:ALL"], } ] } ) serial_ports = {0: False, 1: False, 2: True, 3: True} class MyAhvVm(AhvVm): resources = MyAhvVmResources categories = {"AppFamily": "Backup", "AppType": "Default"} class AhvVmSubstrate(Substrate): """AHV VM config given by reading a spec file""" provider_spec = MyAhvVm account = Ref.Account(ACCOUNT_NAME) class MyAhvVmResources2(AhvVmResources): memory = 4 vCPUs = 2 cores_per_vCPU = 1 disks = [ AhvVmDisk(CENTOS_CI), AhvVmDisk.CdRom.Ide.emptyCdRom(), ] nics = [AhvVmNic(NETWORK1)] guest_customization = AhvVmGC.CloudInit( config={ "users": [ { "name": "centos", "ssh-authorized-keys": [CENTOS_PUBLIC_KEY], "sudo": ["ALL=(ALL) NOPASSWD:ALL"], } ] } ) serial_ports = {0: False, 1: False, 2: True, 3: True} class MyAhvVm2(AhvVm): resources = MyAhvVmResources2 class AhvVmSubstrate2(Substrate): """AHV VM config given by reading a spec file""" provider_spec = MyAhvVm2 account = Ref.Account(ACCOUNT_NAME) class AhvVmDeployment(Deployment): """Sample deployment pulling in service and substrate references""" packages = [ref(AhvVmPackage)] substrate = ref(AhvVmSubstrate) class AhvVmDeployment2(Deployment): """Sample deployment pulling in service and substrate references""" packages = [ref(AhvVmPackage2)] substrate = ref(AhvVmSubstrate2) class AhvVmProfile(Profile): """Sample application profile with variables""" nameserver = CalmVariable.Simple("10.40.64.15", label="Local DNS resolver") foo1 = CalmVariable.Simple("bar1", runtime=True) foo2 = CalmVariable.Simple("bar2", runtime=True) deployments = [AhvVmDeployment] environments = [Ref.Environment(name=ENV_NAME)] @action def test_profile_action(): """Sample description for a profile action""" CalmTask.Exec.ssh(name="Task5", script='echo "Hello"', target=ref(AhvVmService)) class AhvVmProfile2(Profile): """Sample application profile with variables""" nameserver = CalmVariable.Simple("10.40.64.15", label="Local DNS resolver") foo1 = CalmVariable.Simple("bar1", runtime=True) foo2 = CalmVariable.Simple("bar2", runtime=True) deployments = [AhvVmDeployment2] environments = [Ref.Environment(name=ENV_NAME)] @action def test_profile_action(): """Sample description for a profile action""" CalmTask.Exec.ssh(name="Task5", script='echo "Hello"', target=ref(AhvVmService)) class AhvBlueprint(Blueprint): """Sample Bp that used ahv_vm_helpers""" credentials = [Centos] services = [AhvVmService] packages = [AhvVmPackage, AhvVmPackage2, Era_Disk, Virtio_CdRom] substrates = [AhvVmSubstrate, AhvVmSubstrate2] profiles = [AhvVmProfile, AhvVmProfile2] class BpMetadata(Metadata): project = Ref.Project(PROJECT_NAME)

26.748768

131

0.653775

93726d765b24143bd88950ebfcd1da8d281de6b5

866

py

Python

csuibot/utils/__init__.py

agungputrap/bribot

20cb68508b8e2c354c7cb3d527c0997183bd46aa

[ "Apache-2.0" ]

null

csuibot/utils/__init__.py

agungputrap/bribot

20cb68508b8e2c354c7cb3d527c0997183bd46aa

[ "Apache-2.0" ]

null

csuibot/utils/__init__.py

agungputrap/bribot

20cb68508b8e2c354c7cb3d527c0997183bd46aa

[ "Apache-2.0" ]

null

from csuibot.utils import zodiac as z def lookup_zodiac(month, day): zodiacs = [ z.Aries(), z.Taurus(), z.Gemini(), z.Cancer(), z.Leo(), z.Virgo(), z.Libra(), z.Scorpio(), z.Sagittarius(), z.Capricorn(), z.Aquarius(), z.Pisces() ] for zodiac in zodiacs: if zodiac.date_includes(month, day): return zodiac.name else: return 'Unknown zodiac' def lookup_chinese_zodiac(year): num_zodiacs = 12 zodiacs = { 0: 'rat', 1: 'buffalo', 2: 'tiger', 3: 'rabbit', 4: 'dragon', 5: 'snake', 6: 'horse', 7: 'goat', 8: 'monkey' } ix = (year - 4) % num_zodiacs try: return zodiacs[ix] except KeyError: return 'Unknown zodiac'

18.826087

44

0.473441

2573a3cea4554bd2d7faa6c8d0a1ab25c5cd04f5

17,466

py

Python

tests/test_mbgdml_datasets.py

keithgroup/mbGDML

a68b2a41c26c8e7e8e2f4527939c4564402f36bc

[ "MIT" ]

6

2020-08-03T03:44:33.000Z

2022-02-24T21:50:03.000Z

tests/test_mbgdml_datasets.py

keithgroup/mbGDML

a68b2a41c26c8e7e8e2f4527939c4564402f36bc

[ "MIT" ]

null

tests/test_mbgdml_datasets.py

keithgroup/mbGDML

a68b2a41c26c8e7e8e2f4527939c4564402f36bc

[ "MIT" ]

1

2022-02-25T03:03:40.000Z

#!/usr/bin/env python # MIT License # # Copyright (c) 2020-2021, Alex M. Maldonado # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """Tests for `mbgdml` package.""" import pytest import numpy as np import mbgdml.data as data from mbgdml import criteria from mbgdml import utils # Must be run from mbGDML root directory. rset_path_140h2o = './tests/data/structuresets/140h2o.sphere.gfn2.md.500k.prod1.npz' dset_dir = './tests/data/datasets' molecule_sizes = { 'h2o': 3, 'mecn': 6, 'meoh': 6 } def trim_140h2o_rset(): """Trims the 140h2o structure set to make tests easier. """ n_R = 3 # Number of structures to keep. n_entities = 5 # Number of molecules to keep in each structure. molecule_size = molecule_sizes['h2o'] # Number of atoms in a water molecule. rset = data.structureSet(rset_path_140h2o) assert rset.type == 's' assert rset.md5 == '8726c482c19cdf7889cd1e62b9e9c8e1' # Trims and checks z. rset.z = rset.z[:n_entities*molecule_size] z = np.array([8, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1]) assert np.all(rset.z == z) # Trims and checks R. rset.R = rset.R[:n_R, :molecule_size*n_entities] r_2 = np.array([ [ 6.07124359, 0.7619846, 0.58984577], [ 6.47807882, -0.18138608, 0.67938893], [ 5.14951519, 0.76914325, 0.66198299], [-4.28204826, -3.57395445, 0.81850038], [-4.33819343, -4.29134079, 0.12722189], [-4.33829705, -2.80167393, 0.40818626], [-2.82371582, -3.52131402, -4.12086561], [-2.96180787, -4.46433929, -3.79287547], [-1.85909245, -3.46817877, -4.3649756,], [ 6.24586283, -1.76605224, 0.72883595], [ 5.51074538, -2.26847206, 1.21432844], [ 6.92768826, -2.3359825, 0.25592583], [-2.44826194, -6.14429515, -3.37660252], [-2.19536627, -6.12210888, -2.51171765], [-2.65953004, -7.04099688, -3.59504014] ]) assert np.allclose(rset.R[2], r_2) assert rset.z.shape[0] == rset.R.shape[1] # Trims and checks entity_ids. rset.entity_ids = rset.entity_ids[:n_entities*molecule_size] entity_ids = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]) assert np.all(rset.entity_ids == entity_ids) # Trims and checks comp_ids rset.comp_ids = rset.comp_ids[:n_entities] comp_ids = np.array([ ['0', 'h2o'], ['1', 'h2o'], ['2', 'h2o'], ['3', 'h2o'], ['4', 'h2o'] ]) assert np.all(rset.comp_ids == comp_ids) # Confirms changes with MD5. assert rset.md5 == 'da254c95956709d1a00512f1ac7c0bbb' return rset def dset_sample_structures( dset, data, quantity, size, selected_rset_id, r_criteria, z_slice, cutoff, center_structures, sampling_updates ): """Generic sampling function. """ dset.sample_structures( data, quantity, size, selected_rset_id=selected_rset_id, criteria=r_criteria, z_slice=z_slice, cutoff=cutoff, center_structures=center_structures, sampling_updates=sampling_updates ) return dset def check_R_with_rset(dset, rset, centered): """Uses structure information from Rset_info to check structure coordinates. Parameters ---------- dset : :obj:`mbgdml.data.dataset.dataSet` The data set. rset : :obj:`mbgdml.data.structureset.structureSet` The structure set. centered : :obj:`bool` If the dset coordinates were centered with respect to the cluster's center of mass. """ z_dset = dset.z R_dset = dset.R Rset_info = dset.Rset_info R_rset = rset.R rset_entity_ids = rset.entity_ids for i_r_dset in range(len(dset.R)): r_dset = R_dset[i_r_dset] r_rset_info = Rset_info[i_r_dset] i_r_rset = r_rset_info[1] r_rset_entity_ids = r_rset_info[2:] r_slice_rset = utils.get_R_slice(r_rset_entity_ids, rset_entity_ids) r_rset = R_rset[i_r_rset][r_slice_rset] if centered == True: r_rset = utils.center_structures(z_dset, r_rset) assert np.allclose(r_dset, r_rset, atol=5.1e-07, rtol=0) def test_rset_sampling_all_2mers_normal(): """Sampling all dimers (2mers) from trimmed 140h2o structure set. """ rset = trim_140h2o_rset() ### NORMAL SAMPLING ### dset = data.dataSet() dset.name = '140h2o.sphere.gfn2.md.500k.prod1' dset = dset_sample_structures( dset, rset, 'all', 2, None, None, np.array([]), np.array([]), False, False ) # Checking properties. assert dset.Rset_md5 == {0: 'da254c95956709d1a00512f1ac7c0bbb'} assert dset.Rset_info.shape == (30, 4) assert np.all(dset.Rset_info[:, :1] == np.zeros((30,))) assert np.all( dset.Rset_info[:, 1] == np.array( [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) ) assert dset.Rset_info.shape == np.unique(dset.Rset_info, axis=0).shape assert np.all(dset.entity_ids == np.array([0, 0, 0, 1, 1, 1])) assert np.all(dset.comp_ids == np.array([['0', 'h2o'], ['1', 'h2o']])) assert np.all(dset.z == np.array([8, 1, 1, 8, 1, 1])) # Checking R. assert dset.R.shape == (30, 6, 3) rset_info_r_check = np.array([0, 1, 1, 4]) r_index = np.where( np.all(dset.Rset_info == rset_info_r_check, axis=1) )[0][0] r_check = np.array([ [-4.27804369, -3.56574992, 0.81519167], [-4.3569076, -4.2647005, 0.1558876], [-4.35184085, -2.82879184, 0.39925437], [-2.44708832, -6.14572336, -3.36929742], [-2.18964657, -6.13868747, -2.48473228], [-2.64909444, -7.04677952, -3.60878085] ]) assert np.allclose(dset.R[r_index], r_check) # Checking E. assert dset.E.shape == (30,) assert np.all(np.isnan(dset.E)) # Checking F assert dset.F.shape == (30, 6, 3) assert np.all(np.isnan(dset.F)) def test_rset_sampling_all_2mers_ignore_duplicate(): rset = trim_140h2o_rset() dset = data.dataSet() dset.name = '140h2o.sphere.gfn2.md.500k.prod1' dset = dset_sample_structures( dset, rset, 'all', 2, None, None, np.array([]), np.array([]), False, False ) dset_duplicate = dset_sample_structures( dset, rset, 'all', 2, None, None, np.array([]), np.array([]), False, False ) assert dset_duplicate.Rset_md5 == {0: 'da254c95956709d1a00512f1ac7c0bbb'} assert dset_duplicate.Rset_info.shape == (30, 4) assert np.all(dset.entity_ids == np.array([0, 0, 0, 1, 1, 1])) assert np.all(dset.comp_ids == np.array([['0', 'h2o'], ['1', 'h2o']])) assert dset_duplicate.R.shape == (30, 6, 3) print('yes') def test_rset_sampling_all_2mers_centering(): rset = trim_140h2o_rset() dset = data.dataSet() dset.name = '140h2o.sphere.gfn2.md.500k.prod1' dset = dset_sample_structures( dset, rset, 'all', 2, None, None, np.array([]), np.array([]), False, False ) centered_R = utils.center_structures(dset.z, dset.R) dset_centered = data.dataSet() dset_centered.name = '140h2o.sphere.gfn2.md.500k.prod1-centered' dset_centered = dset_sample_structures( dset_centered, rset, 'all', 2, None, None, np.array([]), np.array([]), True, False ) assert np.allclose(centered_R, dset_centered.R) def test_rset_sampling_all_2mers_criteria(): rset = trim_140h2o_rset() dset_centered = data.dataSet() dset_centered.name = '140h2o.sphere.gfn2.md.500k.prod1-centered' dset_centered = dset_sample_structures( dset_centered, rset, 'all', 2, None, None, np.array([]), np.array([]), True, False ) dset_criteria = data.dataSet() dset_criteria.name = '140h2o.sphere.gfn2.md.500k.prod1-criteria' dset_criteria = dset_sample_structures( dset_criteria, rset, 'all', 2, None, criteria.cm_distance_sum, np.array([]), np.array([6.0]), True, False ) Rset_info_accpetable_criteria = np.array([ [0,0,0,3], [0,0,1,2], [0,0,1,4], [0,0,2,4], [0,1,0,3], [0,1,1,2], [0,1,1,4], [0,1,2,4], [0,2,0,3], [0,2,1,2], [0,2,1,4], [0,2,2,4] ]) assert np.array_equal(dset_criteria.Rset_info, Rset_info_accpetable_criteria) def test_dset_default_attributes(): dset = data.dataSet() assert isinstance(dset.Rset_md5, dict) assert len(dset.Rset_md5) == 0 assert dset.Rset_info.shape == (1, 0) assert dset.criteria == '' assert dset.z_slice.shape == (0,) assert dset.cutoff.shape == (0,) assert dset.z.shape == (0,) assert dset.R.shape == (1, 1, 0) assert dset.E.shape == (0,) assert dset.F.shape == (1, 1, 0) assert dset.entity_ids.shape == (0,) assert dset.comp_ids.shape == (1, 0) try: dset.md5 except AttributeError: pass def test_rset_sampling_num_2mers_criteria(): rset = trim_140h2o_rset() dset = data.dataSet() dset.name = '140h2o.sphere.gfn2.md.500k.prod1' dset = dset_sample_structures( dset, rset, 5, 2, None, criteria.cm_distance_sum, np.array([]), np.array([6.0]), True, False ) assert isinstance(dset.criteria, str) assert dset.criteria in criteria.__dict__ assert dset.z_slice.shape == (0,) assert dset.cutoff.shape == (1,) assert np.array_equal(dset.cutoff, np.array([6.])) assert dset.r_unit == 'Angstrom' assert np.array_equal(dset.z, np.array([8, 1, 1, 8, 1, 1])) assert dset.R.shape == (5, 6, 3) assert dset.E.shape == (5,) assert dset.F.shape == (5, 6, 3) assert dset.Rset_md5 == {0: 'da254c95956709d1a00512f1ac7c0bbb'} assert np.array_equal(dset.entity_ids, np.array([0, 0, 0, 1, 1, 1])) assert np.array_equal(dset.comp_ids, np.array([['0', 'h2o'], ['1', 'h2o']])) check_R_with_rset(dset, rset, True) def test_rset_sampling_num_2mers_additional(): rset = trim_140h2o_rset() dset = data.dataSet() dset.name = '140h2o.sphere.gfn2.md.500k.prod1' dset = dset_sample_structures( dset, rset, 5, 2, None, criteria.cm_distance_sum, np.array([]), np.array([6.0]), True, False ) # Ensure energies and forces are not overwritten i_test = 1 e_test = -47583.29857 dset.E[i_test] = e_test f_test = np.array([ [4.4, 2.8, 6.0], [-3.65, 34.0, 2.3], [4.4, 2.8, 6.0], [-3.65, 34.0, 2.3], [4.4, 2.8, 6.0], [-3.65, 34.0, 2.3], ]) dset.F[i_test] = f_test dset = dset_sample_structures( dset, rset, 5, 2, None, criteria.cm_distance_sum, np.array([]), np.array([6.0]), True, False ) assert dset.Rset_md5 == {0: 'da254c95956709d1a00512f1ac7c0bbb'} assert np.array_equal(dset.entity_ids, np.array([0, 0, 0, 1, 1, 1])) assert np.array_equal(dset.comp_ids, np.array([['0', 'h2o'], ['1', 'h2o']])) assert np.array_equal(dset.z, np.array([8, 1, 1, 8, 1, 1])) assert dset.R.shape == (10, 6, 3) assert dset.E.shape == (10,) assert np.allclose(dset.E[i_test], e_test) assert dset.F.shape == (10, 6, 3) assert np.allclose(dset.F[i_test], f_test) check_R_with_rset(dset, rset, True) def test_dset_sampling_all_2mers_after_3mers(): rset = trim_140h2o_rset() dset = data.dataSet() dset.name = '140h2o.sphere.gfn2.md.500k.prod1' dset = dset_sample_structures( dset, rset, 'all', 3, None, None, np.array([]), np.array([]), True, False ) dset_from_dset = data.dataSet() dset_from_dset = dset_sample_structures( dset_from_dset, dset, 'all', 2, None, criteria.cm_distance_sum, np.array([]), np.array([6.0]), True, False ) assert np.array_equal(dset_from_dset.entity_ids, np.array([0, 0, 0, 1, 1, 1])) assert np.array_equal( dset_from_dset.comp_ids, np.array([['0', 'h2o'], ['1', 'h2o']]) ) assert dset_from_dset.Rset_md5 == {0: 'da254c95956709d1a00512f1ac7c0bbb'} assert dset_from_dset.Rset_info.shape == (12, 4) # Same as test_rset_sampling_all_2mers_criteria, but organized to match # the 3mer then 2mer sampling. Rset_info_accpetable_criteria = np.array([ [0,0,1,2], [0,0,0,3], [0,0,1,4], [0,0,2,4], [0,1,1,2], [0,1,0,3], [0,1,1,4], [0,1,2,4], [0,2,1,2], [0,2,0,3], [0,2,1,4], [0,2,2,4] ]) assert np.array_equal(dset_from_dset.Rset_info, Rset_info_accpetable_criteria) assert dset_from_dset.R.shape == (12, 6, 3) assert dset_from_dset.E.shape == (12,) assert dset_from_dset.F.shape == (12, 6, 3) assert dset_from_dset.criteria == 'cm_distance_sum' assert np.array_equal(dset_from_dset.cutoff, np.array([6.0])) def test_sample_dset_same_size(): """ """ dset_h2o_2body_path = f'{dset_dir}/2h2o/140h2o.sphere.gfn2.md.500k.prod1.3h2o.dset.2h2o-dset.mb.npz' dset_h2o_2body = data.dataSet(dset_h2o_2body_path) # Trim dset_h2o_2body to 50 structures remaining = 50 for key in ['Rset_info', 'E', 'R', 'F']: setattr(dset_h2o_2body, key, getattr(dset_h2o_2body, key)[:remaining]) dset_h2o_2body_cm_6 = data.dataSet() dset_h2o_2body_cm_6.name = '140h2o.sphere.gfn2.md.500k.prod1.3h2o.dset.2h2o-dset.mb-cm.6' dset_h2o_2body_cm_6 = dset_sample_structures( dset_h2o_2body_cm_6, dset_h2o_2body, 'all', 2, None, criteria.cm_distance_sum, np.array([]), np.array([6.0]), True, False ) assert dset_h2o_2body_cm_6.theory == 'mp2.def2tzvp.frozencore' assert dset_h2o_2body_cm_6.criteria == 'cm_distance_sum' assert np.array_equal(dset_h2o_2body_cm_6.z_slice, np.array([])) assert np.array_equal(dset_h2o_2body_cm_6.cutoff, np.array([6.0])) assert np.array_equal(dset_h2o_2body_cm_6.entity_ids, np.array([0, 0, 0, 1, 1, 1])) assert np.array_equal( dset_h2o_2body_cm_6.comp_ids, np.array([['0', 'h2o'], ['1', 'h2o']]) ) assert dset_h2o_2body_cm_6.centered == True assert dset_h2o_2body_cm_6.r_unit == 'Angstrom' # 8726c482c19cdf7889cd1e62b9e9c8e1 is the MD5 has for the full 140h2o rset. assert dset_h2o_2body_cm_6.Rset_md5 == {0: '8726c482c19cdf7889cd1e62b9e9c8e1'} assert np.array_equal(dset_h2o_2body_cm_6.z, np.array([8, 1, 1, 8, 1, 1])) rset = data.structureSet(rset_path_140h2o) check_R_with_rset(dset_h2o_2body_cm_6, rset, True) # Checking energies and forces. dset_Rset_info = dset_h2o_2body_cm_6.Rset_info dset_E = dset_h2o_2body_cm_6.E dset_F = dset_h2o_2body_cm_6.F dset_sample_Rset_info = dset_h2o_2body.Rset_info dset_sample_E = dset_h2o_2body.E dset_sample_F = dset_h2o_2body.F for i_r in range(len(dset_h2o_2body_cm_6.R)): i_r_dset_sample = np.where( np.all(dset_sample_Rset_info == dset_Rset_info[i_r], axis=1) )[0][0] assert np.allclose(dset_E[i_r], dset_sample_E[i_r_dset_sample]) assert np.allclose(dset_F[i_r], dset_sample_F[i_r_dset_sample]) def test_sample_dset_1mers_multiple_rsets(): """ """ dset_4h2o_lit_path = f'{dset_dir}/4h2o/4h2o.temelso.etal-dset.npz' dset_4h2o_lit_dset = data.dataSet(dset_4h2o_lit_path) # Sample all 1mers dset_1mers = data.dataSet() dset_1mers = dset_sample_structures( dset_1mers, dset_4h2o_lit_dset, 'all', 1, None, None, np.array([]), np.array([]), True, False ) # Checking data set Rset_info = np.array([ [0,0,0], [0,0,1], [0,0,2], [0,0,3], [1,0,0], [1,0,1], [1,0,2], [1,0,3], [2,0,0], [2,0,1], [2,0,2], [2,0,3] ]) assert np.array_equal(dset_1mers.Rset_info, Rset_info) Rset_md5 = {0: '92dd31a90a3d2a443023d9d708010a4f', 1: '5593ef822ede64f6011ece82d6702ff9', 2: '33098027b401c38efcb5f05fa33c93ad'} assert dset_1mers.Rset_md5 == Rset_md5 assert np.array_equal(dset_1mers.entity_ids, np.array([0, 0, 0])) assert np.array_equal(dset_1mers.comp_ids, np.array([['0', 'h2o']])) assert dset_1mers.centered == True assert dset_1mers.r_unit == 'Angstrom' assert np.array_equal(dset_1mers.z, np.array([8, 1, 1])) assert dset_1mers.R.shape == (12, 3, 3) r_3 = np.array([ [-0.02947763, -0.0325826, -0.05004315], [ 0.93292237, 0.1104174, 0.10365685], [-0.46497763, 0.4068174, 0.69075685] ]) assert np.allclose(dset_1mers.R[3], r_3) assert dset_1mers.E.shape == (12,) for e in dset_1mers.E: assert np.isnan(e) assert dset_1mers.F.shape == (12, 3, 3) for f in dset_1mers.F.flatten(): assert np.isnan(f)

36.236515

132

0.639643

206f36006525514f6654193efce08900327616b0

326

py

Python

lib/utils.py

MusaTamzid05/simple_transfer_learning_lib

adef55fc277268011e526596ad517b68c59c43fb

[ "MIT" ]

null

lib/utils.py

MusaTamzid05/simple_transfer_learning_lib

adef55fc277268011e526596ad517b68c59c43fb

[ "MIT" ]

null

lib/utils.py

MusaTamzid05/simple_transfer_learning_lib

adef55fc277268011e526596ad517b68c59c43fb

[ "MIT" ]

null

import tensorflow as tf def limit_gpu(memory = 2048): gpus = tf.config.list_physical_devices('GPU') if gpus: try: tf.config.set_logical_device_configuration( gpus[0], [tf.config.LogicalDeviceConfiguration(memory_limit = memory)]) except RuntimeError as e: print(e)

27.166667

74

0.647239

a6a894239da5504e0612ddb5d578f76fa7bd942e

1,429

py

Python

images/python_analysis/src/venv/lib/python3.7/site-packages/elasticsearch/_async/client/graph.py

Jael24/TB_ElasticStack

f9aad11eda69045140a90f28739b558bf077d877

[ "MIT" ]

2

2021-05-01T05:40:55.000Z

2021-06-25T13:34:46.000Z

images/python_analysis/src/venv/lib/python3.7/site-packages/elasticsearch/_async/client/graph.py

Jael24/TB_ElasticStack

f9aad11eda69045140a90f28739b558bf077d877

[ "MIT" ]

2

2021-02-22T14:55:01.000Z

2021-03-23T12:42:33.000Z

images/python_analysis/src/venv/lib/python3.7/site-packages/elasticsearch/_async/client/graph.py

Jael24/TB_ElasticStack

f9aad11eda69045140a90f28739b558bf077d877

[ "MIT" ]

1

2021-03-23T12:24:52.000Z

# Licensed to Elasticsearch B.V under one or more agreements. # Elasticsearch B.V licenses this file to you under the Apache 2.0 License. # See the LICENSE file in the project root for more information from .utils import NamespacedClient, query_params, _make_path, SKIP_IN_PATH class GraphClient(NamespacedClient): @query_params("routing", "timeout") async def explore(self, index, body=None, doc_type=None, params=None, headers=None): """ Explore extracted and summarized information about the documents and terms in an index. `<https://www.elastic.co/guide/en/elasticsearch/reference/7.8/graph-explore-api.html>`_ :arg index: A comma-separated list of index names to search; use `_all` or empty string to perform the operation on all indices :arg body: Graph Query DSL :arg doc_type: A comma-separated list of document types to search; leave empty to perform the operation on all types :arg routing: Specific routing value :arg timeout: Explicit operation timeout """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'index'.") return await self.transport.perform_request( "POST", _make_path(index, doc_type, "_graph", "explore"), params=params, headers=headers, body=body, )

42.029412

95

0.669699

6c7e7c64ca5d2041343ebe9528cb6a52563ef2c6

1,089

py

Python

libcst/_nodes/tests/test_trailing_whitespace.py

mvismonte/LibCST

fc430343b5f7e0c30d6d0ca30b5251b74bd3890d

[ "MIT" ]

1

2021-08-12T11:52:00.000Z

libcst/_nodes/tests/test_trailing_whitespace.py

mvismonte/LibCST

fc430343b5f7e0c30d6d0ca30b5251b74bd3890d

[ "MIT" ]

1

2019-08-21T18:58:13.000Z

libcst/_nodes/tests/test_trailing_whitespace.py

mvismonte/LibCST

fc430343b5f7e0c30d6d0ca30b5251b74bd3890d

[ "MIT" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # pyre-strict import libcst as cst from libcst._nodes.tests.base import CSTNodeTest from libcst.testing.utils import data_provider class TrailingWhitespaceTest(CSTNodeTest): @data_provider( ( (cst.TrailingWhitespace(), "\n"), (cst.TrailingWhitespace(whitespace=cst.SimpleWhitespace(" ")), " \n"), (cst.TrailingWhitespace(comment=cst.Comment("# comment")), "# comment\n"), (cst.TrailingWhitespace(newline=cst.Newline("\r\n")), "\r\n"), ( cst.TrailingWhitespace( whitespace=cst.SimpleWhitespace(" "), comment=cst.Comment("# comment"), newline=cst.Newline("\r\n"), ), " # comment\r\n", ), ) ) def test_valid(self, node: cst.CSTNode, code: str) -> None: self.validate_node(node, code)

35.129032

88

0.580349

6077d0e195ac760235da3b81067f2a87a604f836

94,700

py

Python

python/mxnet/ndarray/ndarray.py

yinscapital/incubator-mxnet

4c0df6249d03841f5eb30e1428aa25fc230fed30

[ "Apache-2.0" ]

null

python/mxnet/ndarray/ndarray.py

yinscapital/incubator-mxnet

4c0df6249d03841f5eb30e1428aa25fc230fed30

[ "Apache-2.0" ]

null

python/mxnet/ndarray/ndarray.py

yinscapital/incubator-mxnet

4c0df6249d03841f5eb30e1428aa25fc230fed30

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # coding: utf-8 # pylint: disable=too-many-lines, protected-access # pylint: disable=import-error, no-name-in-module, undefined-variable """NDArray API of MXNet.""" from __future__ import absolute_import from __future__ import division try: from __builtin__ import slice as py_slice except ImportError: from builtins import slice as py_slice import ctypes import warnings import operator import numpy as np from ..base import _LIB, numeric_types, integer_types from ..base import c_array, mx_real_t from ..base import mx_uint, NDArrayHandle, check_call from ..base import ctypes2buffer from ..context import Context from . import _internal from . import op from .op import NDArrayBase __all__ = ["NDArray", "concatenate", "_DTYPE_NP_TO_MX", "_DTYPE_MX_TO_NP", "_GRAD_REQ_MAP", "ones", "add", "arange", "divide", "equal", "full", "greater", "greater_equal", "imdecode", "lesser", "lesser_equal", "maximum", "minimum", "moveaxis", "modulo", "multiply", "not_equal", "onehot_encode", "power", "subtract", "true_divide", "waitall", "_new_empty_handle"] _STORAGE_TYPE_UNDEFINED = -1 _STORAGE_TYPE_DEFAULT = 0 _STORAGE_TYPE_ROW_SPARSE = 1 _STORAGE_TYPE_CSR = 2 # pylint: disable= no-member _DTYPE_NP_TO_MX = { None: -1, np.float32: 0, np.float64: 1, np.float16: 2, np.uint8: 3, np.int32: 4, np.int8: 5, np.int64: 6, } _DTYPE_MX_TO_NP = { -1: None, 0: np.float32, 1: np.float64, 2: np.float16, 3: np.uint8, 4: np.int32, 5: np.int8, 6: np.int64, } _STORAGE_TYPE_STR_TO_ID = { 'undefined': _STORAGE_TYPE_UNDEFINED, 'default': _STORAGE_TYPE_DEFAULT, 'row_sparse': _STORAGE_TYPE_ROW_SPARSE, 'csr': _STORAGE_TYPE_CSR, } _STORAGE_TYPE_ID_TO_STR = { _STORAGE_TYPE_UNDEFINED: 'undefined', _STORAGE_TYPE_DEFAULT: 'default', _STORAGE_TYPE_ROW_SPARSE: 'row_sparse', _STORAGE_TYPE_CSR: 'csr', } _GRAD_REQ_MAP = { 'null': 0, 'write': 1, 'add': 3 } # pylint: enable= no-member def _new_empty_handle(): """Returns a new empty handle. Empty handle can be used to hold a result. Returns ------- handle A new empty `NDArray` handle. """ hdl = NDArrayHandle() check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl))) return hdl def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t): """Return a new handle with specified shape and context. Empty handle is only used to hold results. Returns ------- handle A new empty `NDArray` handle. """ hdl = NDArrayHandle() check_call(_LIB.MXNDArrayCreateEx( c_array(mx_uint, shape), mx_uint(len(shape)), ctypes.c_int(ctx.device_typeid), ctypes.c_int(ctx.device_id), ctypes.c_int(int(delay_alloc)), ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])), ctypes.byref(hdl))) return hdl def waitall(): """Wait for all async operations to finish in MXNet. This function is used for benchmarking only. """ check_call(_LIB.MXNDArrayWaitAll()) def _storage_type(handle): storage_type = ctypes.c_int(0) check_call(_LIB.MXNDArrayGetStorageType(handle, ctypes.byref(storage_type))) return storage_type.value class NDArray(NDArrayBase): """An array object representing a multidimensional, homogeneous array of fixed-size items. """ __slots__ = [] # make numpy functions return NDArray instead of numpy object array __array_priority__ = 1000.0 # pylint: disable= no-member, undefined-variable def __repr__(self): """Returns a string representation of the array.""" shape_info = 'x'.join(['%d' % x for x in self.shape]) return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()), self.__class__.__name__, shape_info, self.context) def __reduce__(self): return NDArray, (None,), self.__getstate__() def __add__(self, other): """x.__add__(y) <=> x+y <=> mx.nd.add(x, y) """ return add(self, other) def __iadd__(self, other): """x.__iadd__(y) <=> x+=y """ if not self.writable: raise ValueError('trying to add to a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_add(self, other, out=self) elif isinstance(other, numeric_types): return _internal._plus_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other))) def __radd__(self, other): return self.__add__(other) def __sub__(self, other): """x.__sub__(y) <=> x-y <=> mx.nd.subtract(x, y) """ return subtract(self, other) def __isub__(self, other): """x.__isub__(y) <=> x-=y """ if not self.writable: raise ValueError('trying to subtract from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_sub(self, other, out=self) elif isinstance(other, numeric_types): return _internal._minus_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other))) def __rsub__(self, other): """x.__rsub__(y) <=> y-x <=> mx.nd.subtract(y, x) """ return subtract(other, self) def __mul__(self, other): """x.__mul__(y) <=> x*y <=> mx.nd.multiply(x, y) """ return multiply(self, other) def __neg__(self): """x.__neg__(y) <=> -x """ return _internal._mul_scalar(self, -1.0) def __imul__(self, other): """x.__imul__(y) <=> x*=y """ if not self.writable: raise ValueError('trying to multiply to a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_mul(self, other, out=self) elif isinstance(other, numeric_types): return _internal._mul_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other))) def __rmul__(self, other): return self.__mul__(other) def __div__(self, other): """x.__div__(y) <=> x/y <=> mx.nd.divide(x, y) """ return divide(self, other) def __rdiv__(self, other): """x.__rdiv__(y) <=> y/x <=> mx.nd.divide(y, x) """ return divide(other, self) def __idiv__(self, other): """x.__rdiv__(y) <=> x/=y """ if not self.writable: raise ValueError('trying to divide from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_div(self, other, out=self) elif isinstance(other, numeric_types): return _internal._div_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other))) def __truediv__(self, other): return divide(self, other) def __rtruediv__(self, other): return divide(other, self) def __itruediv__(self, other): return self.__idiv__(other) def __mod__(self, other): """x.__mod__(y) <=> x%y <=> mx.nd.modulo(x, y) """ return modulo(self, other) def __rmod__(self, other): """x.__rmod__(y) <=> y%x <=> mx.nd.modulo(y, x) """ return modulo(other, self) def __imod__(self, other): """x.__rmod__(y) <=> x%=y """ if not self.writable: raise ValueError('trying to take modulo from a readonly NDArray') if isinstance(other, NDArray): return op.broadcast_mod(self, other, out=self) elif isinstance(other, numeric_types): return _internal._mod_scalar(self, float(other), out=self) else: raise TypeError('type %s not supported' % str(type(other))) def __pow__(self, other): """x.__pow__(y) <=> x**y <=> mx.nd.power(x,y) """ return power(self, other) def __rpow__(self, other): """x.__pow__(y) <=> y**x <=> mx.nd.power(y,x) """ return power(other, self) def __eq__(self, other): """x.__eq__(y) <=> x==y <=> mx.nd.equal(x, y) """ return equal(self, other) def __ne__(self, other): """x.__ne__(y) <=> x!=y <=> mx.nd.not_equal(x, y) """ return not_equal(self, other) def __gt__(self, other): """x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y) """ return greater(self, other) def __ge__(self, other): """x.__ge__(y) <=> x>=y <=> mx.nd.greater_equal(x, y) """ return greater_equal(self, other) def __lt__(self, other): """x.__lt__(y) <=> x<y <=> mx.nd.lesser(x, y) """ return lesser(self, other) def __le__(self, other): """x.__le__(y) <=> x<=y <=> mx.nd.less_equal(x, y) """ return lesser_equal(self, other) def __bool__(self): raise ValueError("The truth value of an NDArray is ambiguous. " \ "Please convert to number with asscalar() first.") __nonzero__ = __bool__ def __len__(self): """Number of element along the first axis.""" return self.shape[0] def __getstate__(self): handle = self.handle this = {'handle' : None} if handle is not None: length = ctypes.c_size_t() cptr = ctypes.POINTER(ctypes.c_char)() check_call(_LIB.MXNDArraySaveRawBytes(self.handle, ctypes.byref(length), ctypes.byref(cptr))) this['handle'] = ctypes2buffer(cptr, length.value) return this def __setstate__(self, state): # pylint: disable=assigning-non-slot handle = state['handle'] if handle is not None: buf = handle handle = NDArrayHandle() ptr = (ctypes.c_char * len(buf)).from_buffer(buf) length = ctypes.c_size_t(len(buf)) check_call(_LIB.MXNDArrayLoadFromRawBytes(ptr, length, ctypes.byref(handle))) self.handle = handle else: self.handle = None def __setitem__(self, key, value): """x.__setitem__(i, y) <=> x[i]=y Set self[key] to value. Parameters ---------- key : int, slice or tuple The indexing key. value : scalar, NDArray or numpy.ndarray The value to set. Examples -------- >>> x = mx.nd.zeros((2,3)) >>> x[:] = 1 >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> x[:,1:2] = 2 >>> x.asnumpy() array([[ 1., 2., 1.], [ 1., 2., 1.]], dtype=float32) >>> x[1:2,1:] = 3 >>> x.asnumpy() array([[ 1., 2., 1.], [ 1., 3., 3.]], dtype=float32) >>> x[1:,0:2] = mx.nd.zeros((1,2)) >>> x.asnumpy() array([[ 1., 2., 1.], [ 0., 0., 3.]], dtype=float32) >>> x[1,2] = 4 >>> x.asnumpy() array([[ 1., 2., 1.], [ 0., 0., 4.]], dtype=float32) """ # pylint: disable=too-many-branches if not self.writable: raise ValueError('Cannot assign to readonly NDArray') if isinstance(key, integer_types): sliced_arr = self._at(key) sliced_arr[:] = value return elif isinstance(key, py_slice): if key.step is not None: raise ValueError('NDArray only supports slicing with step size 1') if key.start is not None or key.stop is not None: sliced_arr = self._slice(key.start, key.stop) sliced_arr[:] = value return if isinstance(value, NDArray): if value.handle is not self.handle: value.copyto(self) elif isinstance(value, numeric_types): _internal._set_value(float(value), out=self) elif isinstance(value, (np.ndarray, np.generic)): self._sync_copyfrom(value) else: raise TypeError( 'NDArray does not support assignment with %s of type %s'%( str(value), str(type(value)))) elif isinstance(key, tuple): # multi-dimension indexing my_shape = self.shape assert len(key) <= len(my_shape), \ "Indexing dimensions exceed array dimensions, %d vs %d"%( len(key), len(my_shape)) begin = [0 for _ in my_shape] end = [x for x in my_shape] expand = [] for i, slice_i in enumerate(key): if isinstance(slice_i, integer_types): assert slice_i < my_shape[i] begin[i] = slice_i end[i] = slice_i + 1 expand.append(i) elif isinstance(slice_i, py_slice): # only support continuous slicing assert slice_i.step is None, \ "NDArray only supports slicing with step size 1." begin[i] = slice_i.start or 0 end[i] = slice_i.stop or my_shape[i] assert begin[i] < end[i] assert end[i] <= my_shape[i] else: raise ValueError( "NDArray does not support slicing with key %s of type %s."%( str(slice_i), str(type(slice_i)))) if isinstance(value, NDArray): value = value.as_in_context(self.context) self._slice_assign(value, begin, end, expand) elif isinstance(value, numeric_types): _internal._crop_assign_scalar(self, out=self, begin=begin, end=end, scalar=value) elif isinstance(value, (np.ndarray, np.generic)): value = array(value, ctx=self.context, dtype=self.dtype) self._slice_assign(value, begin, end, expand) else: raise TypeError( 'NDArray does not support assignment with %s of type %s'%( str(value), str(type(value)))) else: raise ValueError( "NDArray does not support slicing with key %s of type %s."%( str(key), str(type(key)))) # pylint: enable=too-many-branches def _slice_assign(self, value, begin, end, expand): vshape = list(value.shape) if expand and len(vshape) != len(begin): if len(expand) + len(vshape) != len(begin): sshape = [e - b for e, b in zip(end, begin)] for i in reversed(expand): sshape.pop(i) raise ValueError( "Cannot assign NDArray with shape %s to NDArray slice with " \ "shape %s"%(str(vshape), str(sshape))) for i in expand: vshape.insert(i, 1) value = value.reshape(vshape) _internal._crop_assign(self, value, out=self, begin=begin, end=end) def __getitem__(self, key): """x.__getitem__(i) <=> x[i] Returns a sliced view of this array. Parameters ---------- key : int or slice, or array like Indexing key. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> x[1].asnumpy() array([ 3., 4., 5.], dtype=float32) >>> y = x[0:1] >>> y[:] = 2 >>> x.asnumpy() array([[ 2., 2., 2.], [ 3., 4., 5.]], dtype=float32) """ # multi-dimensional slicing is not supported yet if isinstance(key, integer_types): if key > self.shape[0] - 1: raise IndexError( 'index {} is out of bounds for axis 0 with size {}'.format( key, self.shape[0])) return self._at(key) elif isinstance(key, py_slice): if key.step is not None: raise ValueError("NDArray only supports slicing with step size 1.") if key.start is not None or key.stop is not None: return self._slice(key.start, key.stop) return self elif isinstance(key, tuple): shape = self.shape assert len(key) > 0, "Cannot slice with empty indices" assert len(shape) >= len(key), \ "Slicing dimensions exceeds array dimensions, %d vs %d"%( len(key), len(shape)) if isinstance(key[0], (NDArray, np.ndarray, list)): indices = [] dtype = 'int32' for idx_i in key: if not isinstance(idx_i, NDArray): idx_i = array(idx_i, ctx=self.context, dtype=dtype) else: dtype = idx_i.dtype indices.append(idx_i) indices = op.stack(*indices) return op.gather_nd(self, indices) else: oshape = [] begin = [] end = [] i = -1 for i, slice_i in enumerate(key): if isinstance(slice_i, integer_types): begin.append(slice_i) end.append(slice_i+1) elif isinstance(slice_i, py_slice): if slice_i.step is not None: raise ValueError("NDArray only supports slicing with step size 1.") begin.append(0 if slice_i.start is None else slice_i.start) end.append(shape[i] if slice_i.stop is None else slice_i.stop) oshape.append(end[i] - begin[i]) else: raise ValueError( "NDArray does not support slicing with key %s of type %s."%( str(slice_i), str(type(slice_i)))) oshape.extend(shape[i+1:]) if len(oshape) == 0: oshape.append(1) return op.slice(self, begin, end).reshape(oshape) else: raise ValueError( "NDArray does not support slicing with key %s of type %s."%( str(key), str(type(key)))) def _sync_copyfrom(self, source_array): """Performs a synchronized copy from the `source_array` to the current array. This is called through ``x[:] = source_array``, where the `source_array` is a `numpy.ndarray` or array-like object. This function blocks until all the pending read/write operations with respect to the current `NDArray` are finished and carry out the copy operation to the current NDArray. Parameters ---------- source_array : array_like The data source we would like to copy from. Example ------- >>> a = mx.nd.array([1, 2]) >>> a.asnumpy() array([ 1., 2.], dtype=float32) >>> a[:] = np.array([3, 4]) >> a.asnumpy() array([ 3., 4.], dtype=float32) """ if not isinstance(source_array, np.ndarray): try: source_array = np.array(source_array, dtype=self.dtype) except: raise TypeError('array must consist of array-like data,' + 'type %s is not supported' % str(type(array))) source_array = np.ascontiguousarray(source_array, dtype=self.dtype) if source_array.shape != self.shape: raise ValueError('Shape inconsistent: expected %s vs got %s'%( str(self.shape), str(source_array.shape))) check_call(_LIB.MXNDArraySyncCopyFromCPU( self.handle, source_array.ctypes.data_as(ctypes.c_void_p), ctypes.c_size_t(source_array.size))) def _slice(self, start, stop): """Returns a sliced NDArray that shares memory with the current one. This is called through ``x[start:stop]``. Parameters ---------- start : int Starting inclusive index of slice in the first dim. stop : int Finishing exclusive index of slice in the first dim. Returns ------- `NDArray` sharing the memory with the current one sliced from start to stop in the first dim. Examples: >>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]]) >>> a[1:2].asnumpy() array([[ 3., 4.]], dtype=float32) >>> a[1:1].asnumpy() array([], shape=(0, 2), dtype=float32) """ handle = NDArrayHandle() if start is None: start = mx_uint(0) elif start < 0: length = self.shape[0] start += length assert start >= 0, "Slicing start %d exceeds limit of %d"%(start-length, length) start = mx_uint(start) else: start = mx_uint(start) if stop is None: stop = mx_uint(self.shape[0]) elif stop < 0: length = self.shape[0] stop += length assert stop >= 0, "Slicing end %d exceeds limit of %d"%(stop-length, length) stop = mx_uint(stop) else: stop = mx_uint(stop) check_call(_LIB.MXNDArraySlice( self.handle, start, stop, ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable) def _at(self, idx): """Returns a view of the array sliced at `idx` in the first dim. This is called through ``x[idx]``. Parameters ---------- idx : int index for slicing the `NDArray` in the first dim. Returns ------- NDArray `NDArray` sharing the memory with the current one sliced at `idx` in the first dim. Examples -------- >>> a = mx.nd.array([[1,2], [3, 4]]) >>> a[1].asnumpy() array([ 3., 4.], dtype=float32) >>> b = mx.nd.array([1, 2, 3, 4]) >>> b[0].asnumpy() array([ 1.], dtype=float32) """ handle = NDArrayHandle() idx = mx_uint(idx) check_call(_LIB.MXNDArrayAt( self.handle, idx, ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable) def reshape(self, shape): """Returns a **view** of this array with a new shape without altering any data. Parameters ---------- shape : tuple of int The new shape should not change the array size, namely ``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``. One dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions. 0 Dimensions in shape will be copied from original shape, i.e. if x.shape == (3, 4, 5), x.reshape((0, 20)).shape will be (3, 20). Returns ------- NDArray An array with desired shape that shares data with this array. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> y = x.reshape((3,2)) >>> y.asnumpy() array([[ 0., 1.], [ 2., 3.], [ 4., 5.]], dtype=float32) >>> y = x.reshape((3,-1)) >>> y.asnumpy() array([[ 0., 1.], [ 2., 3.], [ 4., 5.]], dtype=float32) >>> y[:] = -1 >>> x.asnumpy() array([[-1., -1., -1.], [-1., -1., -1.]], dtype=float32) """ handle = NDArrayHandle() # Actual reshape check_call(_LIB.MXNDArrayReshape(self.handle, len(shape), c_array(ctypes.c_int, shape), ctypes.byref(handle))) return NDArray(handle=handle, writable=self.writable) def zeros_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`zeros_like`. The arguments are the same as for :py:func:`zeros_like`, with this array as data. """ return op.zeros_like(self, *args, **kwargs) def ones_like(self, *args, **kwargs): """Convenience fluent method for :py:func:`ones_like`. The arguments are the same as for :py:func:`ones_like`, with this array as data. """ return op.ones_like(self, *args, **kwargs) def broadcast_axes(self, *args, **kwargs): """Convenience fluent method for :py:func:`broadcast_axes`. The arguments are the same as for :py:func:`broadcast_axes`, with this array as data. """ return op.broadcast_axes(self, *args, **kwargs) def repeat(self, *args, **kwargs): """Convenience fluent method for :py:func:`repeat`. The arguments are the same as for :py:func:`repeat`, with this array as data. """ return op.repeat(self, *args, **kwargs) def pad(self, *args, **kwargs): """Convenience fluent method for :py:func:`pad`. The arguments are the same as for :py:func:`pad`, with this array as data. """ return op.pad(self, *args, **kwargs) def swapaxes(self, *args, **kwargs): """Convenience fluent method for :py:func:`swapaxes`. The arguments are the same as for :py:func:`swapaxes`, with this array as data. """ return op.swapaxes(self, *args, **kwargs) def split(self, *args, **kwargs): """Convenience fluent method for :py:func:`split`. The arguments are the same as for :py:func:`split`, with this array as data. """ return op.split(self, *args, **kwargs) def slice(self, *args, **kwargs): """Convenience fluent method for :py:func:`slice`. The arguments are the same as for :py:func:`slice`, with this array as data. """ return op.slice(self, *args, **kwargs) def slice_axis(self, *args, **kwargs): """Convenience fluent method for :py:func:`slice_axis`. The arguments are the same as for :py:func:`slice_axis`, with this array as data. """ return op.slice_axis(self, *args, **kwargs) def take(self, *args, **kwargs): """Convenience fluent method for :py:func:`take`. The arguments are the same as for :py:func:`take`, with this array as data. """ return op.take(self, *args, **kwargs) def one_hot(self, *args, **kwargs): """Convenience fluent method for :py:func:`one_hot`. The arguments are the same as for :py:func:`one_hot`, with this array as data. """ return op.one_hot(self, *args, **kwargs) def pick(self, *args, **kwargs): """Convenience fluent method for :py:func:`pick`. The arguments are the same as for :py:func:`pick`, with this array as data. """ return op.pick(self, *args, **kwargs) def sort(self, *args, **kwargs): """Convenience fluent method for :py:func:`sort`. The arguments are the same as for :py:func:`sort`, with this array as data. """ return op.sort(self, *args, **kwargs) def topk(self, *args, **kwargs): """Convenience fluent method for :py:func:`topk`. The arguments are the same as for :py:func:`topk`, with this array as data. """ return op.topk(self, *args, **kwargs) def argsort(self, *args, **kwargs): """Convenience fluent method for :py:func:`argsort`. The arguments are the same as for :py:func:`argsort`, with this array as data. """ return op.argsort(self, *args, **kwargs) def argmax(self, *args, **kwargs): """Convenience fluent method for :py:func:`argmax`. The arguments are the same as for :py:func:`argmax`, with this array as data. """ return op.argmax(self, *args, **kwargs) def argmin(self, *args, **kwargs): """Convenience fluent method for :py:func:`argmin`. The arguments are the same as for :py:func:`argmin`, with this array as data. """ return op.argmin(self, *args, **kwargs) def clip(self, *args, **kwargs): """Convenience fluent method for :py:func:`clip`. The arguments are the same as for :py:func:`clip`, with this array as data. """ return op.clip(self, *args, **kwargs) def abs(self, *args, **kwargs): """Convenience fluent method for :py:func:`abs`. The arguments are the same as for :py:func:`abs`, with this array as data. """ return op.abs(self, *args, **kwargs) def sign(self, *args, **kwargs): """Convenience fluent method for :py:func:`sign`. The arguments are the same as for :py:func:`sign`, with this array as data. """ return op.sign(self, *args, **kwargs) def flatten(self, *args, **kwargs): """Convenience fluent method for :py:func:`flatten`. The arguments are the same as for :py:func:`flatten`, with this array as data. """ return op.flatten(self, *args, **kwargs) def expand_dims(self, *args, **kwargs): """Convenience fluent method for :py:func:`expand_dims`. The arguments are the same as for :py:func:`expand_dims`, with this array as data. """ return op.expand_dims(self, *args, **kwargs) def tile(self, *args, **kwargs): """Convenience fluent method for :py:func:`tile`. The arguments are the same as for :py:func:`tile`, with this array as data. """ return op.tile(self, *args, **kwargs) def transpose(self, *args, **kwargs): """Convenience fluent method for :py:func:`transpose`. The arguments are the same as for :py:func:`transpose`, with this array as data. """ return op.transpose(self, *args, **kwargs) def flip(self, *args, **kwargs): """Convenience fluent method for :py:func:`flip`. The arguments are the same as for :py:func:`flip`, with this array as data. """ return op.flip(self, *args, **kwargs) def sum(self, *args, **kwargs): """Convenience fluent method for :py:func:`sum`. The arguments are the same as for :py:func:`sum`, with this array as data. """ return op.sum(self, *args, **kwargs) def nansum(self, *args, **kwargs): """Convenience fluent method for :py:func:`nansum`. The arguments are the same as for :py:func:`nansum`, with this array as data. """ return op.nansum(self, *args, **kwargs) def prod(self, *args, **kwargs): """Convenience fluent method for :py:func:`prod`. The arguments are the same as for :py:func:`prod`, with this array as data. """ return op.prod(self, *args, **kwargs) def nanprod(self, *args, **kwargs): """Convenience fluent method for :py:func:`nanprod`. The arguments are the same as for :py:func:`nanprod`, with this array as data. """ return op.nanprod(self, *args, **kwargs) def mean(self, *args, **kwargs): """Convenience fluent method for :py:func:`mean`. The arguments are the same as for :py:func:`mean`, with this array as data. """ return op.mean(self, *args, **kwargs) def max(self, *args, **kwargs): """Convenience fluent method for :py:func:`max`. The arguments are the same as for :py:func:`max`, with this array as data. """ return op.max(self, *args, **kwargs) def min(self, *args, **kwargs): """Convenience fluent method for :py:func:`min`. The arguments are the same as for :py:func:`min`, with this array as data. """ return op.min(self, *args, **kwargs) def norm(self, *args, **kwargs): """Convenience fluent method for :py:func:`norm`. The arguments are the same as for :py:func:`norm`, with this array as data. """ return op.norm(self, *args, **kwargs) def round(self, *args, **kwargs): """Convenience fluent method for :py:func:`round`. The arguments are the same as for :py:func:`round`, with this array as data. """ return op.round(self, *args, **kwargs) def rint(self, *args, **kwargs): """Convenience fluent method for :py:func:`rint`. The arguments are the same as for :py:func:`rint`, with this array as data. """ return op.rint(self, *args, **kwargs) def fix(self, *args, **kwargs): """Convenience fluent method for :py:func:`fix`. The arguments are the same as for :py:func:`fix`, with this array as data. """ return op.fix(self, *args, **kwargs) def floor(self, *args, **kwargs): """Convenience fluent method for :py:func:`floor`. The arguments are the same as for :py:func:`floor`, with this array as data. """ return op.floor(self, *args, **kwargs) def ceil(self, *args, **kwargs): """Convenience fluent method for :py:func:`ceil`. The arguments are the same as for :py:func:`ceil`, with this array as data. """ return op.ceil(self, *args, **kwargs) def trunc(self, *args, **kwargs): """Convenience fluent method for :py:func:`trunc`. The arguments are the same as for :py:func:`trunc`, with this array as data. """ return op.trunc(self, *args, **kwargs) def sin(self, *args, **kwargs): """Convenience fluent method for :py:func:`sin`. The arguments are the same as for :py:func:`sin`, with this array as data. """ return op.sin(self, *args, **kwargs) def cos(self, *args, **kwargs): """Convenience fluent method for :py:func:`cos`. The arguments are the same as for :py:func:`cos`, with this array as data. """ return op.cos(self, *args, **kwargs) def tan(self, *args, **kwargs): """Convenience fluent method for :py:func:`tan`. The arguments are the same as for :py:func:`tan`, with this array as data. """ return op.tan(self, *args, **kwargs) def arcsin(self, *args, **kwargs): """Convenience fluent method for :py:func:`arcsin`. The arguments are the same as for :py:func:`arcsin`, with this array as data. """ return op.arcsin(self, *args, **kwargs) def arccos(self, *args, **kwargs): """Convenience fluent method for :py:func:`arccos`. The arguments are the same as for :py:func:`arccos`, with this array as data. """ return op.arccos(self, *args, **kwargs) def arctan(self, *args, **kwargs): """Convenience fluent method for :py:func:`arctan`. The arguments are the same as for :py:func:`arctan`, with this array as data. """ return op.arctan(self, *args, **kwargs) def degrees(self, *args, **kwargs): """Convenience fluent method for :py:func:`degrees`. The arguments are the same as for :py:func:`degrees`, with this array as data. """ return op.degrees(self, *args, **kwargs) def radians(self, *args, **kwargs): """Convenience fluent method for :py:func:`radians`. The arguments are the same as for :py:func:`radians`, with this array as data. """ return op.radians(self, *args, **kwargs) def sinh(self, *args, **kwargs): """Convenience fluent method for :py:func:`sinh`. The arguments are the same as for :py:func:`sinh`, with this array as data. """ return op.sinh(self, *args, **kwargs) def cosh(self, *args, **kwargs): """Convenience fluent method for :py:func:`cosh`. The arguments are the same as for :py:func:`cosh`, with this array as data. """ return op.cosh(self, *args, **kwargs) def tanh(self, *args, **kwargs): """Convenience fluent method for :py:func:`tanh`. The arguments are the same as for :py:func:`tanh`, with this array as data. """ return op.tanh(self, *args, **kwargs) def arcsinh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arcsinh`. The arguments are the same as for :py:func:`arcsinh`, with this array as data. """ return op.arcsinh(self, *args, **kwargs) def arccosh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arccosh`. The arguments are the same as for :py:func:`arccosh`, with this array as data. """ return op.arccosh(self, *args, **kwargs) def arctanh(self, *args, **kwargs): """Convenience fluent method for :py:func:`arctanh`. The arguments are the same as for :py:func:`arctanh`, with this array as data. """ return op.arctanh(self, *args, **kwargs) def exp(self, *args, **kwargs): """Convenience fluent method for :py:func:`exp`. The arguments are the same as for :py:func:`exp`, with this array as data. """ return op.exp(self, *args, **kwargs) def expm1(self, *args, **kwargs): """Convenience fluent method for :py:func:`expm1`. The arguments are the same as for :py:func:`expm1`, with this array as data. """ return op.expm1(self, *args, **kwargs) def log(self, *args, **kwargs): """Convenience fluent method for :py:func:`log`. The arguments are the same as for :py:func:`log`, with this array as data. """ return op.log(self, *args, **kwargs) def log10(self, *args, **kwargs): """Convenience fluent method for :py:func:`log10`. The arguments are the same as for :py:func:`log10`, with this array as data. """ return op.log10(self, *args, **kwargs) def log2(self, *args, **kwargs): """Convenience fluent method for :py:func:`log2`. The arguments are the same as for :py:func:`log2`, with this array as data. """ return op.log2(self, *args, **kwargs) def log1p(self, *args, **kwargs): """Convenience fluent method for :py:func:`log1p`. The arguments are the same as for :py:func:`log1p`, with this array as data. """ return op.log1p(self, *args, **kwargs) def sqrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`sqrt`. The arguments are the same as for :py:func:`sqrt`, with this array as data. """ return op.sqrt(self, *args, **kwargs) def rsqrt(self, *args, **kwargs): """Convenience fluent method for :py:func:`rsqrt`. The arguments are the same as for :py:func:`rsqrt`, with this array as data. """ return op.rsqrt(self, *args, **kwargs) def square(self, *args, **kwargs): """Convenience fluent method for :py:func:`square`. The arguments are the same as for :py:func:`square`, with this array as data. """ return op.square(self, *args, **kwargs) # pylint: disable= undefined-variable def broadcast_to(self, shape): """Broadcasts the input array to a new shape. Broadcasting is only allowed on axes with size 1. The new shape cannot change the number of dimensions. For example, you could broadcast from shape (2, 1) to (2, 3), but not from shape (2, 3) to (2, 3, 3). Parameters ---------- shape : tuple of int The shape of the desired array. Returns ------- NDArray A NDArray with the desired shape that is not sharing data with this array, even if the new shape is the same as ``self.shape``. Examples -------- >>> x = mx.nd.arange(0,3).reshape((1,3,1)) >>> x.asnumpy() array([[[ 0.], [ 1.], [ 2.]]], dtype=float32) >>> y = x.broadcast_to((2,3,3)) >>> y.asnumpy() array([[[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]], <BLANKLINE> [[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]]], dtype=float32) """ cur_shape = self.shape err_str = 'operands could not be broadcast together with remapped shapes' \ '[original->remapped]: {} and requested shape {}'.format(cur_shape, shape) if len(shape) < len(cur_shape): raise ValueError(err_str) cur_shape = (1,) * (len(shape) - len(cur_shape)) + cur_shape cur_shape_arr = np.array(cur_shape) broadcasting_axes = np.nonzero(cur_shape_arr != np.array(shape)) if (cur_shape_arr[broadcasting_axes] != 1).any(): raise ValueError(err_str) if cur_shape != self.shape: return op.broadcast_to(self.reshape(cur_shape), shape=shape) else: return op.broadcast_to(self, shape=tuple(shape)) # pylint: enable= undefined-variable def wait_to_read(self): """Waits until all previous write operations on the current array are finished. This method guarantees that all previous write operations that pushed into the backend engine for execution are actually finished. Examples -------- >>> import time >>> tic = time.time() >>> a = mx.nd.ones((1000,1000)) >>> b = mx.nd.dot(a, a) >>> print(time.time() - tic) # doctest: +SKIP 0.003854036331176758 >>> b.wait_to_read() >>> print(time.time() - tic) # doctest: +SKIP 0.0893700122833252 """ check_call(_LIB.MXNDArrayWaitToRead(self.handle)) @property def ndim(self): """Returns the number of dimensions of this array Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.ndim 1 >>> x = mx.nd.array([[1, 2], [3, 4]]) >>> x.ndim 2 """ return len(self.shape) @property def shape(self): """Tuple of array dimensions. Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.shape (4L,) >>> y = mx.nd.zeros((2, 3, 4)) >>> y.shape (2L, 3L, 4L) """ ndim = mx_uint() pdata = ctypes.POINTER(mx_uint)() check_call(_LIB.MXNDArrayGetShape( self.handle, ctypes.byref(ndim), ctypes.byref(pdata))) return tuple(pdata[:ndim.value]) @property def size(self): """Number of elements in the array. Equivalent to the product of the array's dimensions. Examples -------- >>> import numpy as np >>> x = mx.nd.zeros((3, 5, 2)) >>> x.size 30 >>> np.prod(x.shape) 30 """ size = 1 for i in self.shape: size *= i return size @property def context(self): """Device context of the array. Examples -------- >>> x = mx.nd.array([1, 2, 3, 4]) >>> x.context cpu(0) >>> type(x.context) <class 'mxnet.context.Context'> >>> y = mx.nd.zeros((2,3), mx.gpu(0)) >>> y.context gpu(0) """ dev_typeid = ctypes.c_int() dev_id = ctypes.c_int() check_call(_LIB.MXNDArrayGetContext( self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id))) return Context(Context.devtype2str[dev_typeid.value], dev_id.value) @property def dtype(self): """Data-type of the array's elements. Returns ------- numpy.dtype This NDArray's data type. Examples -------- >>> x = mx.nd.zeros((2,3)) >>> x.dtype <type 'numpy.float32'> >>> y = mx.nd.zeros((2,3), dtype='int32') >>> y.dtype <type 'numpy.int32'> """ mx_dtype = ctypes.c_int() check_call(_LIB.MXNDArrayGetDType( self.handle, ctypes.byref(mx_dtype))) return _DTYPE_MX_TO_NP[mx_dtype.value] @property def stype(self): """Storage-type of the array. """ return _STORAGE_TYPE_ID_TO_STR[_storage_type(self.handle)] @property # pylint: disable= invalid-name, undefined-variable def T(self): """Returns a copy of the array with axes transposed. Equivalent to ``mx.nd.transpose(self)`` except that self is returned if ``self.ndim < 2``. Unlike ``numpy.ndarray.T``, this function returns a copy rather than a view of the array unless ``self.ndim < 2``. Examples -------- >>> x = mx.nd.arange(0,6).reshape((2,3)) >>> x.asnumpy() array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> x.T.asnumpy() array([[ 0., 3.], [ 1., 4.], [ 2., 5.]], dtype=float32) """ if len(self.shape) < 2: return self return op.transpose(self) # pylint: enable= invalid-name, undefined-variable @property def _fresh_grad(self): """Whether this array's corresponding gradient array (registered via `autograd.mark_variables`) has been updated by `autograd.backward` since last reset. `_fresh_grad` need to be manually set to False after consuming gradient (usually after updating this array). """ out = ctypes.c_int() check_call(_LIB.MXNDArrayGetGradState(self.handle, ctypes.byref(out))) return out.value @_fresh_grad.setter def _fresh_grad(self, state): check_call(_LIB.MXNDArraySetGradState(self.handle, ctypes.c_int(state))) def asnumpy(self): """Returns a ``numpy.ndarray`` object with value copied from this array. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.asnumpy() >>> type(y) <type 'numpy.ndarray'> >>> y array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> z = mx.nd.ones((2,3), dtype='int32') >>> z.asnumpy() array([[1, 1, 1], [1, 1, 1]], dtype=int32) """ data = np.empty(self.shape, dtype=self.dtype) check_call(_LIB.MXNDArraySyncCopyToCPU( self.handle, data.ctypes.data_as(ctypes.c_void_p), ctypes.c_size_t(data.size))) return data def asscalar(self): """Returns a scalar whose value is copied from this array. This function is equivalent to ``self.asnumpy()[0]``. This NDArray must have shape (1,). Examples -------- >>> x = mx.nd.ones((1,), dtype='int32') >>> x.asscalar() 1 >>> type(x.asscalar()) <type 'numpy.int32'> """ if self.shape != (1,): raise ValueError("The current array is not a scalar") return self.asnumpy()[0] def astype(self, dtype): """Returns a copy of the array after casting to a specified type. Parameters ---------- dtype : numpy.dtype or str The type of the returned array. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array after casting to the specified type. Examples -------- >>> x = mx.nd.zeros((2,3), dtype='float32') >>> y = x.astype('int32') >>> y.dtype <type 'numpy.int32'> """ res = empty(self.shape, ctx=self.context, dtype=dtype) self.copyto(res) return res def copyto(self, other): """Copies the value of this array to another array. If ``other`` is a ``NDArray`` object, then ``other.shape`` and ``self.shape`` should be the same. This function copies the value from ``self`` to ``other``. If ``other`` is a context, a new ``NDArray`` will be first created on the target context, and the value of ``self`` is copied. Parameters ---------- other : NDArray or Context The destination array or context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array. If ``other`` is an ``NDArray``, then the return value and ``other`` will point to the same ``NDArray``. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.zeros((2,3), mx.gpu(0)) >>> z = x.copyto(y) >>> z is y True >>> y.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.copyto(mx.gpu(0)) <NDArray 2x3 @gpu(0)> """ if isinstance(other, NDArray): if other.handle is self.handle: warnings.warn('You are attempting to copy an array to itself', RuntimeWarning) return return _internal._copyto(self, out=other) elif isinstance(other, Context): hret = NDArray(_new_alloc_handle(self.shape, other, True, self.dtype)) return _internal._copyto(self, out=hret) else: raise TypeError('copyto does not support type ' + str(type(other))) def copy(self): """Makes a copy of this ``NDArray``, keeping the same context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The copied array Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.copy() >>> y.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) """ return self.copyto(self.context) def as_in_context(self, context): """Returns an array on the target device with the same value as this array. If the target context is the same as ``self.context``, then ``self`` is returned. Otherwise, a copy is made. Parameters ---------- context : Context The target context. Returns ------- NDArray, CSRNDArray or RowSparseNDArray The target array. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.as_in_context(mx.cpu()) >>> y is x True >>> z = x.as_in_context(mx.gpu(0)) >>> z is x False """ if self.context == context: return self return self.copyto(context) def attach_grad(self, grad_req='write', stype=None): """Attach a gradient buffer to this NDArray, so that `backward` can compute gradient with respect to it. Parameters ---------- grad_req : {'write', 'add', 'null'} How gradient will be accumulated. - 'write': gradient will be overwritten on every backward. - 'add': gradient will be added to existing value on every backward. - 'null': do not compute gradient for this NDArray. stype : str, optional The storage type of the gradient array. Defaults to the same stype of this NDArray. """ from . import zeros as _zeros if stype is not None: grad = _zeros(self.shape, stype=stype) else: grad = op.zeros_like(self) # pylint: disable=undefined-variable grad_req = _GRAD_REQ_MAP[grad_req] check_call(_LIB.MXAutogradMarkVariables( 1, ctypes.pointer(self.handle), ctypes.pointer(mx_uint(grad_req)), ctypes.pointer(grad.handle))) @property def grad(self): """Returns gradient buffer attached to this NDArray.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl))) if hdl.value is None: return None return _ndarray_cls(hdl) def detach(self): """Returns a new NDArray, detached from the current graph.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl))) return _ndarray_cls(hdl) def backward(self, out_grad=None, retain_graph=False, train_mode=True): """Compute the gradients of this NDArray w.r.t variables. Parameters ---------- out_grad : NDArray, optional Gradient with respect to head. retain_graph : bool, optional Whether to retain the computaion graph for another backward pass on the same graph. By default the computaion history is cleared. train_mode : bool, optional Whether to compute gradient for training or inference. """ if out_grad is None: ograd_handles = [NDArrayHandle(0)] else: ograd_handles = [out_grad.handle] check_call(_LIB.MXAutogradBackwardEx( 1, c_array(NDArrayHandle, [self.handle]), c_array(NDArrayHandle, ograd_handles), 0, ctypes.c_void_p(0), ctypes.c_int(retain_graph), ctypes.c_int(0), ctypes.c_int(train_mode), ctypes.c_void_p(0), ctypes.c_void_p(0))) def tostype(self, stype): """Return a copy of the array with chosen storage type. See Also ---------- :meth:`mxnet.ndarray.cast_storage`. Returns ------- NDArray, CSRNDArray or RowSparseNDArray A copy of the array with the chosen storage stype """ return op.cast_storage(self, stype=stype) def onehot_encode(indices, out): """One-hot encoding indices into matrix out. .. note:: `onehot_encode` is deprecated. Use `one_hot` instead. """ # pylint: disable= no-member, protected-access return _internal._onehot_encode(indices, out, out=out) # pylint: enable= no-member, protected-access def ones(shape, ctx=None, dtype=None, **kwargs): """Returns a new array filled with all ones, with the given shape and type. Parameters ---------- shape : int or tuple of int or list of int The shape of the empty array. ctx : Context, optional An optional device context. Defaults to the current default context (``mxnet.Context.default_ctx``). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray A new array of the specified shape filled with all ones. Examples -------- >>> mx.nd.ones(1).asnumpy() array([ 1.], dtype=float32) >>> mx.nd.ones((1,2), mx.gpu(0)) <NDArray 1x2 @gpu(0)> >>> mx.nd.ones((1,2), dtype='float16').asnumpy() array([[ 1., 1.]], dtype=float16) """ # pylint: disable= unused-argument if ctx is None: ctx = Context.default_ctx dtype = mx_real_t if dtype is None else dtype # pylint: disable= no-member, protected-access return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs) # pylint: enable= no-member, protected-access def full(shape, val, ctx=None, dtype=mx_real_t, out=None): """Returns a new array of given shape and type, filled with the given value `val`. Parameters -------- shape : int or tuple of int The shape of the new array. val : scalar Fill value. ctx : Context, optional Device context (default is the current default context). dtype : `str` or `numpy.dtype`, optional The data type of the returned `NDArray`. The default datatype is `float32`. out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray `NDArray` filled with `val`, with the given shape, ctx, and dtype. Examples -------- >>> mx.nd.full(1, 2.0).asnumpy() array([ 2.], dtype=float32) >>> mx.nd.full((1, 2), 2.0, mx.gpu(0)) <NDArray 1x2 @gpu(0)> >>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy() array([[ 2., 2.]], dtype=float16) """ out = empty(shape, ctx, dtype) if out is None else out out[:] = val return out def array(source_array, ctx=None, dtype=None): """Creates an array from any object exposing the array interface. Parameters ---------- source_array : array_like An object exposing the array interface, an object whose `__array__` method returns an array, or any (nested) sequence. ctx : Context, optional Device context (default is the current default context). dtype : str or numpy.dtype, optional The data type of the output array. The default dtype is ``source_array.dtype`` if `source_array` is an `NDArray`, `float32` otherwise. Returns ------- NDArray An `NDArray` with the same contents as the `source_array`. """ if isinstance(source_array, NDArray): dtype = source_array.dtype if dtype is None else dtype else: dtype = mx_real_t if dtype is None else dtype if not isinstance(source_array, np.ndarray): try: source_array = np.array(source_array, dtype=dtype) except: raise TypeError('source_array must be array like object') arr = empty(source_array.shape, ctx, dtype) arr[:] = source_array return arr def moveaxis(tensor, source, destination): """Moves the `source` axis into the `destination` position while leaving the other axes in their original order Parameters ---------- tensor : mx.nd.array The array which axes should be reordered source : int Original position of the axes to move. destination : int Destination position for each of the original axes. Returns ------- result : mx.nd.array Array with moved axes. Examples -------- >>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]]) >>> mx.nd.moveaxis(X, 0, 1).shape (3L, 2L) """ axes = list(range(tensor.ndim)) try: axes.pop(source) except IndexError: raise ValueError('Source should verify 0 <= source < tensor.ndim' 'Got %d' % source) try: axes.insert(destination, source) except IndexError: raise ValueError('Destination should verify 0 <= destination < tensor.ndim' 'Got %d' % destination) return op.transpose(tensor, axes) # pylint: disable= no-member, protected-access, too-many-arguments, redefined-outer-name def arange(start, stop=None, step=1.0, repeat=1, ctx=None, dtype=mx_real_t): """Returns evenly spaced values within a given interval. Values are generated within the half-open interval [`start`, `stop`). In other words, the interval includes `start` but excludes `stop`. The function is similar to the built-in Python function `range` and to `numpy.arange`, but returns an `NDArray`. Parameters ---------- start : float, optional Start of interval. The default start value is 0. stop : float End of interval. step : float, optional Spacing between values. The default step size is 1. repeat : int, optional Number of times to repeat each element. The default repeat count is 1. ctx : Context, optional Device context. Default context is the current default context. dtype : str or numpy.dtype, optional The data type of the `NDArray`. The default datatype is `np.float32`. Returns ------- NDArray `NDArray` of evenly spaced values in the specified range. Examples -------- >>> mx.nd.arange(3).asnumpy() array([ 0., 1., 2.], dtype=float32) >>> mx.nd.arange(2, 6).asnumpy() array([ 2., 3., 4., 5.], dtype=float32) >>> mx.nd.arange(2, 6, step=2).asnumpy() array([ 2., 4.], dtype=float32) >>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy() array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32) >>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy() array([2, 2, 2, 4, 4, 4], dtype=int32) """ if ctx is None: ctx = Context.default_ctx return _internal._arange(start=start, stop=stop, step=step, repeat=repeat, dtype=dtype, ctx=str(ctx)) # pylint: enable= no-member, protected-access, too-many-arguments #pylint: disable= too-many-arguments, no-member, protected-access def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None): """ Helper function for element-wise operation. The function will perform numpy-like broadcasting if needed and call different functions. Parameters -------- lhs : NDArray or numeric value Left-hand side operand. rhs : NDArray or numeric value Right-hand operand, fn_array : function Function to be called if both lhs and rhs are of ``NDArray`` type. fn_scalar : function Function to be called if both lhs and rhs are numeric values. lfn_scalar : function Function to be called if lhs is ``NDArray`` while rhs is numeric value rfn_scalar : function Function to be called if lhs is numeric value while rhs is ``NDArray``; if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar Returns -------- NDArray result array """ if isinstance(lhs, numeric_types): if isinstance(rhs, numeric_types): return fn_scalar(lhs, rhs) else: if rfn_scalar is None: # commutative function return lfn_scalar(rhs, float(lhs)) else: return rfn_scalar(rhs, float(lhs)) elif isinstance(rhs, numeric_types): return lfn_scalar(lhs, float(rhs)) elif isinstance(rhs, NDArray): return fn_array(lhs, rhs) else: raise TypeError('type %s not supported' % str(type(rhs))) #pylint: enable= too-many-arguments, no-member, protected-access def add(lhs, rhs): """Returns element-wise sum of the input arrays with broadcasting. Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and ``mx.nd.broadcast_plus(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be added. rhs : scalar or array Second array to be added. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise sum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x+2).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) >>> (x+y).asnumpy() array([[ 1., 1., 1.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.add(x,y).asnumpy() array([[ 1., 1., 1.], [ 2., 2., 2.]], dtype=float32) >>> (z + y).asnumpy() array([[ 0., 1.], [ 1., 2.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_add, operator.add, _internal._plus_scalar, None) # pylint: enable= no-member, protected-access def subtract(lhs, rhs): """Returns element-wise difference of the input arrays with broadcasting. Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and ``mx.nd.broadcast_minus(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be subtracted. rhs : scalar or array Second array to be subtracted. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise difference of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x-2).asnumpy() array([[-1., -1., -1.], [-1., -1., -1.]], dtype=float32) >>> (x-y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.subtract(x,y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z-y).asnumpy() array([[ 0., 1.], [-1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_sub, operator.sub, _internal._minus_scalar, _internal._rminus_scalar) # pylint: enable= no-member, protected-access def multiply(lhs, rhs): """Returns element-wise product of the input arrays with broadcasting. Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be multiplied. rhs : scalar or array Second array to be multiplied. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise multiplication of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x*2).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> (x*y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.multiply(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z*y).asnumpy() array([[ 0., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_mul, operator.mul, _internal._mul_scalar, None) # pylint: enable= no-member, protected-access def divide(lhs, rhs): """Returns element-wise division of the input arrays with broadcasting. Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array in division. rhs : scalar or array Second array in division. The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise division of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3))*6 >>> y = mx.nd.ones((2,1))*2 >>> x.asnumpy() array([[ 6., 6., 6.], [ 6., 6., 6.]], dtype=float32) >>> y.asnumpy() array([[ 2.], [ 2.]], dtype=float32) >>> x/2 <NDArray 2x3 @cpu(0)> >>> (x/3).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> (x/y).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) >>> mx.nd.divide(x,y).asnumpy() array([[ 3., 3., 3.], [ 3., 3., 3.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_div, operator.truediv, _internal._div_scalar, _internal._rdiv_scalar) # pylint: enable= no-member, protected-access def modulo(lhs, rhs): """Returns element-wise modulo of the input arrays with broadcasting. Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array in modulo. rhs : scalar or array Second array in modulo. The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise modulo of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3))*6 >>> y = mx.nd.ones((2,1))*4 >>> x.asnumpy() array([[ 6., 6., 6.], [ 6., 6., 6.]], dtype=float32) >>> y.asnumpy() array([[ 4.], [ 4.]], dtype=float32) >>> x%5 <NDArray 2x3 @cpu(0)> >>> (x%5).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x%y).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.modulo(x,y).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_mod, operator.mod, _internal._mod_scalar, _internal._rmod_scalar) # pylint: enable= no-member, protected-access def power(base, exp): """Returns result of first array elements raised to powers from second array, element-wise with broadcasting. Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- base : scalar or NDArray The base array exp : scalar or NDArray The exponent array. If ``base.shape != exp.shape``, they must be broadcastable to a common shape. Returns -------- NDArray The bases in x raised to the exponents in y. Examples -------- >>> x = mx.nd.ones((2,3))*2 >>> y = mx.nd.arange(1,3).reshape((2,1)) >>> z = mx.nd.arange(1,3).reshape((2,1)) >>> x.asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> y.asnumpy() array([[ 1.], [ 2.]], dtype=float32) >>> z.asnumpy() array([[ 1.], [ 2.]], dtype=float32) >>> (x**2).asnumpy() array([[ 4., 4., 4.], [ 4., 4., 4.]], dtype=float32) >>> (x**y).asnumpy() array([[ 2., 2., 2.], [ 4., 4., 4.]], dtype=float32) >>> mx.nd.power(x,y).asnumpy() array([[ 2., 2., 2.], [ 4., 4., 4.]], dtype=float32) >>> (z**y).asnumpy() array([[ 1.], [ 4.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( base, exp, op.broadcast_power, operator.pow, _internal._power_scalar, _internal._rpower_scalar) # pylint: enable= no-member, protected-access def maximum(lhs, rhs): """Returns element-wise maximum of the input arrays with broadcasting. Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise maximum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> mx.nd.maximum(x, 2).asnumpy() array([[ 2., 2., 2.], [ 2., 2., 2.]], dtype=float32) >>> mx.nd.maximum(x, y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.maximum(y, z).asnumpy() array([[ 0., 1.], [ 1., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_maximum, lambda x, y: x if x > y else y, _internal._maximum_scalar, None) # pylint: enable= no-member, protected-access def minimum(lhs, rhs): """Returns element-wise minimum of the input arrays with broadcasting. Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray The element-wise minimum of the input arrays. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> mx.nd.minimum(x, 2).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.minimum(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.minimum(z, y).asnumpy() array([[ 0., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_minimum, lambda x, y: x if x < y else y, _internal._minimum_scalar, None) # pylint: enable= no-member, protected-access def equal(lhs, rhs): """Returns the result of element-wise **equal to** (==) comparison operation with broadcasting. For each element in input arrays, return 1(true) if corresponding elements are same, otherwise return 0(false). Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x == 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x == y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.equal(x,y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z == y).asnumpy() array([[ 1., 0.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_equal, lambda x, y: 1 if x == y else 0, _internal._equal_scalar, None) # pylint: enable= no-member, protected-access def not_equal(lhs, rhs): """Returns the result of element-wise **not equal to** (!=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if corresponding elements are different, otherwise return 0(false). Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (z == y).asnumpy() array([[ 1., 0.], [ 0., 1.]], dtype=float32) >>> (x != 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x != y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.not_equal(x, y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z != y).asnumpy() array([[ 0., 1.], [ 1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_not_equal, lambda x, y: 1 if x != y else 0, _internal._not_equal_scalar, None) # pylint: enable= no-member, protected-access def greater(lhs, rhs): """Returns the result of element-wise **greater than** (>) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are greater than rhs, otherwise return 0(false). Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x > 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x > y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.greater(x, y).asnumpy() array([[ 1., 1., 1.], [ 0., 0., 0.]], dtype=float32) >>> (z > y).asnumpy() array([[ 0., 1.], [ 0., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_greater, lambda x, y: 1 if x > y else 0, _internal._greater_scalar, _internal._lesser_scalar) # pylint: enable= no-member, protected-access def greater_equal(lhs, rhs): """Returns the result of element-wise **greater than or equal to** (>=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs, otherwise return 0(false). Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x >= 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x >= y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.greater_equal(x, y).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (z >= y).asnumpy() array([[ 1., 1.], [ 0., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_greater_equal, lambda x, y: 1 if x >= y else 0, _internal._greater_equal_scalar, _internal._lesser_equal_scalar) # pylint: enable= no-member, protected-access def lesser(lhs, rhs): """Returns the result of element-wise **lesser than** (<) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are less than rhs, otherwise return 0(false). Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x < 1).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (x < y).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> mx.nd.lesser(x, y).asnumpy() array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> (z < y).asnumpy() array([[ 0., 0.], [ 1., 0.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_lesser, lambda x, y: 1 if x < y else 0, _internal._lesser_scalar, _internal._greater_scalar) # pylint: enable= no-member, protected-access def lesser_equal(lhs, rhs): """Returns the result of element-wise **lesser than or equal to** (<=) comparison operation with broadcasting. For each element in input arrays, return 1(true) if lhs elements are lesser than equal to rhs, otherwise return 0(false). Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``. .. note:: If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape. Parameters ---------- lhs : scalar or array First array to be compared. rhs : scalar or array Second array to be compared. If ``lhs.shape != rhs.shape``, they must be broadcastable to a common shape. Returns ------- NDArray Output array of boolean values. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = mx.nd.arange(2).reshape((2,1)) >>> z = mx.nd.arange(2).reshape((1,2)) >>> x.asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> y.asnumpy() array([[ 0.], [ 1.]], dtype=float32) >>> z.asnumpy() array([[ 0., 1.]], dtype=float32) >>> (x <= 1).asnumpy() array([[ 1., 1., 1.], [ 1., 1., 1.]], dtype=float32) >>> (x <= y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> mx.nd.lesser_equal(x, y).asnumpy() array([[ 0., 0., 0.], [ 1., 1., 1.]], dtype=float32) >>> (z <= y).asnumpy() array([[ 1., 0.], [ 1., 1.]], dtype=float32) """ # pylint: disable= no-member, protected-access return _ufunc_helper( lhs, rhs, op.broadcast_lesser_equal, lambda x, y: 1 if x <= y else 0, _internal._lesser_equal_scalar, _internal._greater_equal_scalar) # pylint: enable= no-member, protected-access def true_divide(lhs, rhs): """This function is similar to :meth:`divide`. """ return divide(lhs, rhs) def concatenate(arrays, axis=0, always_copy=True): """DEPRECATED, use ``concat`` instead Parameters ---------- arrays : list of `NDArray` Arrays to be concatenate. They must have identical shape except the first dimension. They also must have the same data type. axis : int The axis along which to concatenate. always_copy : bool Default `True`. When not `True`, if the arrays only contain one `NDArray`, that element will be returned directly, avoid copying. Returns ------- NDArray An `NDArray` that lives on the same context as `arrays[0].context`. """ assert isinstance(arrays, list) assert len(arrays) > 0 assert isinstance(arrays[0], NDArray) if not always_copy and len(arrays) == 1: return arrays[0] shape_axis = arrays[0].shape[axis] shape_rest1 = arrays[0].shape[0:axis] shape_rest2 = arrays[0].shape[axis+1:] dtype = arrays[0].dtype for arr in arrays[1:]: shape_axis += arr.shape[axis] assert shape_rest1 == arr.shape[0:axis] assert shape_rest2 == arr.shape[axis+1:] assert dtype == arr.dtype ret_shape = shape_rest1 + (shape_axis,) + shape_rest2 ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype) idx = 0 begin = [0 for _ in ret_shape] end = list(ret_shape) for arr in arrays: if axis == 0: ret[idx:idx+arr.shape[0]] = arr else: begin[axis] = idx end[axis] = idx+arr.shape[axis] # pylint: disable=no-member,protected-access _internal._crop_assign(ret, arr, out=ret, begin=tuple(begin), end=tuple(end)) # pylint: enable=no-member,protected-access idx += arr.shape[axis] return ret # pylint: disable=redefined-outer-name def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None): """DEPRECATED, use mx.img instead Parameters ---------- str_img : str Binary image data clip_rect : iterable of 4 int Clip decoded image to rectangle (x0, y0, x1, y1). out : NDArray Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w). index : int Output decoded image to i-th slice of 4 dimensional buffer. channels : int Number of channels to output. Decode to grey scale when channels = 1. mean : NDArray Subtract mean from decode image before outputing. """ # pylint: disable= no-member, protected-access, too-many-arguments if mean is None: mean = NDArray(_new_empty_handle()) if out is None: return _internal._imdecode(mean, index, clip_rect[0], clip_rect[1], clip_rect[2], clip_rect[3], channels, len(str_img), str_img=str_img) else: return _internal._imdecode(mean, index, clip_rect[0], clip_rect[1], clip_rect[2], clip_rect[3], channels, len(str_img), str_img=str_img, out=out) def zeros(shape, ctx=None, dtype=None, **kwargs): """Returns a new array filled with all zeros, with the given shape and type. Parameters ---------- shape : int or tuple of int The shape of the empty array. ctx : Context, optional An optional device context (default is the current default context). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). out : NDArray, optional The output NDArray (default is `None`). Returns ------- NDArray A created array Examples -------- >>> mx.nd.zeros(1).asnumpy() array([ 0.], dtype=float32) >>> mx.nd.zeros((1,2), mx.gpu(0)) <NDArray 1x2 @gpu(0)> >>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy() array([[ 0., 0.]], dtype=float16) """ # pylint: disable= unused-argument if ctx is None: ctx = Context.default_ctx dtype = mx_real_t if dtype is None else dtype # pylint: disable= no-member, protected-access return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, **kwargs) # pylint: enable= no-member, protected-access def empty(shape, ctx=None, dtype=None): """Returns a new array of given shape and type, without initializing entries. Parameters ---------- shape : int or tuple of int The shape of the empty array. ctx : Context, optional An optional device context (default is the current default context). dtype : str or numpy.dtype, optional An optional value type (default is `float32`). Returns ------- NDArray A created array. """ if isinstance(shape, int): shape = (shape, ) if ctx is None: ctx = Context.default_ctx if dtype is None: dtype = mx_real_t return NDArray(handle=_new_alloc_handle(shape, ctx, False, dtype))

31.917762

99

0.54528

ba3a2db20ed39479f4d40ec01c58b455b5d59feb

1,275

py

Python

ship.py

favmatteo/navicella-cavallo

2a785da894399b912e6c35d49298db9bd8aecbd7

[ "MIT" ]

5

2021-11-09T22:04:48.000Z

2022-02-08T12:32:31.000Z

ship.py

favmatteo/navicella-cavallo

2a785da894399b912e6c35d49298db9bd8aecbd7

[ "MIT" ]

1

2021-11-11T11:16:13.000Z

2021-11-11T17:26:47.000Z

ship.py

favmatteo/navicella-cavallo

2a785da894399b912e6c35d49298db9bd8aecbd7

[ "MIT" ]

2

2021-11-10T10:06:47.000Z

2022-02-08T12:29:44.000Z

import settings """Classe astratta che rappresenta una navicella""" class Ship: def __init__(self, x, y): """ Inizializza gli attributi della classe Ship """ self.x = x self.y = y self.ship_img = None self.laser_img = None def draw(self, window): window.blit(self.ship_img, (self.x, self.y)) def get_width(self): return self.ship_img.get_width() def get_height(self): return self.ship_img.get_height() """ Classe Player che eredita dalla classe Ship""" class PlayerShip(Ship): def __init__(self, x, y): """ Inizializza gli attributi della classe PlayerShip """ super().__init__(x, y) self.cool_down_counter = 0 self.ship_img = settings.YELLOW_SPACE_SHIP """Classe Nemico""" class EnemyShip(Ship): def __init__(self, x, y, color): """ Inizializza gli attributi della classe EnemyShip """ super().__init__(x, y) self.colors = { "red": settings.RED_SPACE_SHIP, "green": settings.GREEN_SPACE_SHIP, "blue": settings.BLUE_SPACE_SHIP, } self.ship_img = self.colors.get(color, self.colors[ 'red']) def move(self): self.y += settings.ENEMY_SPEED

23.181818

65

0.603922

82ff8ee9b54f6081d26d30c863eaefde479bd6fd

1,525

py

Python

python3/543.diameter-of-binary-tree.248946473.ac.py

Diego-Zulu/leetcode_answers

ad435df1bd95fb2c6e17d2d9ff349282c98ee0f4

[ "MIT" ]

null

python3/543.diameter-of-binary-tree.248946473.ac.py

Diego-Zulu/leetcode_answers

ad435df1bd95fb2c6e17d2d9ff349282c98ee0f4

[ "MIT" ]

null

python3/543.diameter-of-binary-tree.248946473.ac.py

Diego-Zulu/leetcode_answers

ad435df1bd95fb2c6e17d2d9ff349282c98ee0f4

[ "MIT" ]

null

# # @lc app=leetcode id=543 lang=python3 # # [543] Diameter of Binary Tree # # https://leetcode.com/problems/diameter-of-binary-tree/description/ # # algorithms # Easy (47.98%) # Likes: 2733 # Dislikes: 176 # Total Accepted: 308.1K # Total Submissions: 642K # Testcase Example: '[1,2,3,4,5]' # # # Given a binary tree, you need to compute the length of the diameter of the # tree. The diameter of a binary tree is the length of the longest path between # any two nodes in a tree. This path may or may not pass through the root. # # # # Example: # Given a binary tree # # ⁠ 1 # ⁠ / \ # ⁠ 2 3 # ⁠ / \ # ⁠ 4 5 # # # # Return 3, which is the length of the path [4,2,1,3] or [5,2,1,3]. # # # Note: # The length of path between two nodes is represented by the number of edges # between them. # # # @lc code=start # Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None def diameterOfBinaryTreeAux(root, result): if root is None: return 0 left_path = 1 + diameterOfBinaryTreeAux(root.left, result) right_path = 1 + diameterOfBinaryTreeAux(root.right, result) result[0] = max(result[0], left_path + right_path - 2) return max(left_path, right_path) class Solution: def diameterOfBinaryTree(self, root: TreeNode) -> int: result = [0] diameterOfBinaryTreeAux(root, result) return result.pop() # @lc code=end

23.461538

79

0.630164

0091861d3bbc3164a84bbec6f99ee0ab32808ad0

1,958

py

Python

astropath/utilities/units/core.py

AstroPathJHU/AstroPathPipeline

3b0d9cdad18b84e55d00724f89f09bc7f7464c98

[ "Apache-2.0" ]

14

2021-06-14T05:05:58.000Z

2022-01-15T14:07:30.000Z

astropath/utilities/units/core.py

AstroPathJHU/AstroPathPipeline

3b0d9cdad18b84e55d00724f89f09bc7f7464c98

[ "Apache-2.0" ]

null

astropath/utilities/units/core.py

AstroPathJHU/AstroPathPipeline

3b0d9cdad18b84e55d00724f89f09bc7f7464c98

[ "Apache-2.0" ]

2

2021-08-14T16:22:50.000Z

2022-01-16T17:47:07.000Z

import abc, methodtools currentmodule = None class UnitsError(Exception): pass class Distance: def __new__(self, *args, **kwargs): return currentmodule.Distance(*args, **kwargs) def onepixel(pscale): return Distance(pixels=1, pscale=pscale) def onemicron(pscale): return Distance(microns=1, pscale=pscale) class ThingWithPscale(abc.ABC): @property @abc.abstractmethod def pscale(self): return self.__pscale @pscale.setter def pscale(self, pscale): object.__setattr__(self, "_ThingWithPscale__pscale", pscale) @methodtools.lru_cache() @property def onepixel(self): return onepixel(pscale=self.pscale) @methodtools.lru_cache() @property def onemicron(self): return onemicron(pscale=self.pscale) class ThingWithQpscale(abc.ABC): @property @abc.abstractmethod def qpscale(self): return self.__qpscale @qpscale.setter def qpscale(self, qpscale): object.__setattr__(self, "_ThingWithQpscale__qpscale", qpscale) @methodtools.lru_cache() @property def oneqppixel(self): return onepixel(pscale=self.qpscale) @methodtools.lru_cache() @property def oneqpmicron(self): return onemicron(pscale=self.qpscale) class ThingWithApscale(abc.ABC): @property @abc.abstractmethod def apscale(self): return self.__apscale @apscale.setter def apscale(self, apscale): object.__setattr__(self, "_ThingWithApscale__apscale", apscale) @methodtools.lru_cache() @property def oneappixel(self): return onepixel(pscale=self.apscale) @methodtools.lru_cache() @property def oneapmicron(self): return onemicron(pscale=self.apscale) class ThingWithImscale(abc.ABC): @property @abc.abstractmethod def imscale(self): pass @imscale.setter def imscale(self, imscale): object.__setattr__(self, "_ThingWithImscale__imscale", imscale) @property def oneimpixel(self): return onepixel(pscale=self.imscale) @property def oneimmicron(self): return onemicron(pscale=self.imscale)

27.577465

93

0.755363

72066d210c04ec6e8a86e8afac14680c0da51efe

403

py

Python

setup.py

urielka/pydds-rti-xml

15ad015420a966284203afa4e115e2871565d4ad

[ "MIT" ]

null

setup.py

urielka/pydds-rti-xml

15ad015420a966284203afa4e115e2871565d4ad

[ "MIT" ]

null

setup.py

urielka/pydds-rti-xml

15ad015420a966284203afa4e115e2871565d4ad

[ "MIT" ]

1

2018-06-29T06:39:52.000Z

from setuptools import setup, find_packages import os setup(name='pydds-rti-xml', version='0.2.0', description='Python wrapper for RTI DDS with XML application support', author='Uriel Katz', author_email='uriel.katz@gmail.com', url='https://github.com/urielka/pydds-rti-xml', include_package_data=True, packages = find_packages(), py_modules=['dds'], )

26.866667

76

0.672457

daff1307f9ed9f8bbc3337df298f90d55c7b880e

537

py

Python

rx3/core/operators/publishvalue.py

samiur/RxPY

ea6b3554ab06cfc70e28b532c0a54b910b6ee470

[ "MIT" ]

null

rx3/core/operators/publishvalue.py

samiur/RxPY

ea6b3554ab06cfc70e28b532c0a54b910b6ee470

[ "MIT" ]

null

rx3/core/operators/publishvalue.py

samiur/RxPY

ea6b3554ab06cfc70e28b532c0a54b910b6ee470

[ "MIT" ]

null

from typing import Any, Callable, Optional from rx3 import operators as ops from rx3.core import Observable from rx3.subject import BehaviorSubject from rx3.core.typing import Mapper def _publish_value(initial_value: Any, mapper: Optional[Mapper] = None) -> Callable[[Observable], Observable]: if mapper: def subject_factory(scheduler): return BehaviorSubject(initial_value) return ops.multicast(subject_factory=subject_factory, mapper=mapper) return ops.multicast(BehaviorSubject(initial_value))

33.5625

110

0.769088

066426649ff57fc38e87551fc2b4e62e6bf45bc9

7,022

py

Python

src/utils.py

asafjo23/outliers-detection

dd78e3161c02a2f8ae685ef31ad796787e305591

[ "MIT" ]

null

src/utils.py

asafjo23/outliers-detection

dd78e3161c02a2f8ae685ef31ad796787e305591

[ "MIT" ]

4

2022-03-28T08:53:39.000Z

2022-03-28T09:00:27.000Z

src/utils.py

asafjo23/outliers-detection

dd78e3161c02a2f8ae685ef31ad796787e305591

[ "MIT" ]

null

import numpy as np import pandas as pd from pandas import DataFrame, Series from scipy.sparse import csr_matrix from torch import Tensor, sum, LongTensor, FloatTensor, histc from sklearn.metrics.pairwise import cosine_similarity from collections import namedtuple from typing import Tuple, Mapping from torch.nn import Parameter from src.data_set import RatingsDataset from src.model import MF class ProcColumn: def __init__(self, column: Series): uniq = column.unique() self._name_2_index = {o: i for i, o in enumerate(uniq)} self._idx_2_name = {i: e for i, e in enumerate(self._name_2_index.keys())} self.encoded_col = np.array([self._name_2_index[x] for x in column]) def get_index(self, name: str) -> int: return self._name_2_index[name] def get_name(self, index: int) -> str: return self._idx_2_name[index] class DataProcessor: def __init__(self, original_df: DataFrame): self.min_rating = min(original_df.rating.values) self.max_rating = max(original_df.rating.values) ( self.ratings_by_user, self.histograms_by_users, self.item_to_index_rating, ) = self.data_process(original_df=original_df) def data_process(self, original_df: DataFrame) -> Tuple: """ This function creates the original ratings embedding for each user and saves mapping from index to item place in the rating. In addition, it also creates the original histogram of the ratings of the user. :param original_df: original dataframe :return: Tuple of ratings_by_users, histograms_by_users, item_to_index_rating """ ratings_by_users, histograms_by_users, item_to_index_rating = {}, {}, {} items_grouped_by_users = original_df.groupby("user_id") for user_id, group in items_grouped_by_users: ratings_as_tensor = Tensor(group.rating.values) ratings_by_users[user_id] = Parameter(ratings_as_tensor, requires_grad=False) histograms_by_users[user_id] = histc( ratings_as_tensor, bins=self.max_rating, min=self.min_rating, max=self.max_rating ) item_to_index_rating[user_id] = { row.item_id: i for i, row in enumerate(group.itertuples()) } return ratings_by_users, histograms_by_users, item_to_index_rating class DataConverter: def __init__(self, original_df: DataFrame, n_random_users: int, n_ratings_per_random_user: int): assert list(original_df.columns) == ["user_id", "item_id", "rating"] original_data = original_df.copy() self.min_rating = min(original_df.rating.values) self.max_rating = max(original_df.rating.values) if n_random_users > 0: random_users = self.create_random_users( original_df=original_data, number_of_users_to_add=n_random_users, n_ratings_per_random_user=n_ratings_per_random_user, ) original_data = pd.concat([original_data, random_users], ignore_index=True) self._user_original_id_to_encoded_id = ProcColumn(original_data.user_id) self._item_original_id_to_encoded_id = ProcColumn(original_data.item_id) self.original_df = original_data self.encoded_df = original_data.copy() self.encoded_df.user_id = self._user_original_id_to_encoded_id.encoded_col self.encoded_df.item_id = self._item_original_id_to_encoded_id.encoded_col self.n_users = self.original_df.user_id.nunique() self.n_item = self.original_df.item_id.nunique() def get_original_user_id(self, encoded_id: int) -> str: return self._user_original_id_to_encoded_id.get_name(index=encoded_id) def get_original_item_id(self, encoded_id: int) -> str: return self._item_original_id_to_encoded_id.get_name(index=encoded_id) def get_encoded_user_ids(self) -> np.ndarray: return self._user_original_id_to_encoded_id.encoded_col def get_encoded_item_ids(self) -> np.ndarray: return self._item_original_id_to_encoded_id.encoded_col def create_random_users( self, original_df: DataFrame, number_of_users_to_add: int, n_ratings_per_random_user: int ) -> DataFrame: assert list(original_df.columns) == ["user_id", "item_id", "rating"] Row = namedtuple("Row", ["user_id", "item_id", "rating"]) random_data = [] original_num_of_users = original_df.user_id.nunique() for i in range(original_num_of_users, original_num_of_users + number_of_users_to_add): for _ in range(n_ratings_per_random_user): random_song_id = np.random.choice(original_df.item_id.values) random_rating = np.random.randint(self.min_rating, self.max_rating) random_data.append( Row( user_id=f"random_guy_{i}", item_id=random_song_id, rating=random_rating, ) ) return DataFrame(random_data, columns=["user_id", "item_id", "rating"]) def create_dataset(data_converter: DataConverter): users_tensor = LongTensor(data_converter.encoded_df.user_id.values) items_tensor = LongTensor(data_converter.encoded_df.item_id.values) ratings_tensor = FloatTensor(data_converter.encoded_df.rating.values) return RatingsDataset( users_tensor=users_tensor, items_tensor=items_tensor, ratings_tensor=ratings_tensor, ) def mine_outliers(model: MF, data_converter: DataConverter) -> Mapping: optimized_user_embeddings = np.array(model.user_factors.weight.data) c_similarity = cosine_similarity(optimized_user_embeddings) similarities = c_similarity.sum(axis=1) c_similarity_scores = { data_converter.get_original_user_id(i): score for i, score in enumerate(similarities) } return c_similarity_scores def classical_outliers_mining(data_converter: DataConverter) -> Mapping: """ This function tries to identify who the outliers are by using cosine similarities between all users ratings vectors. :return mapping of outliers to score: """ sparse_matrix = csr_matrix( (data_converter.n_users, data_converter.n_item), dtype=np.float64 ).toarray() items_group_by_users = data_converter.encoded_df.groupby("user_id") for key, group in items_group_by_users: total_sum = sum(group.rating.values) non_zeros = len(group.rating.values) for row in group.itertuples(): sparse_matrix[row.user_id][row.item_id] = row.rating - total_sum / non_zeros sparse_matrix[key] /= total_sum c_similarity = cosine_similarity(sparse_matrix) similarities = c_similarity.sum(axis=1) c_similarity_scores = { data_converter.get_original_user_id(i): score for i, score in enumerate(similarities) } return c_similarity_scores

41.305882

100

0.697664

85e19c9cc95d2e3e452e7b929ebadf08162bf3e5

2,469

py

Python

cogs/verify.py

xspo-oky/iris-test

a4cc15cd590c4ba9b78276565281ca6984be7bef

[ "MIT" ]

null

cogs/verify.py

xspo-oky/iris-test

a4cc15cd590c4ba9b78276565281ca6984be7bef

[ "MIT" ]

null

cogs/verify.py

xspo-oky/iris-test

a4cc15cd590c4ba9b78276565281ca6984be7bef

[ "MIT" ]

null

import discord from discord.ext import commands import config import asyncio from discord.utils import get import random class Verify(commands.Cog): def __init__(self, client): self.client = client @commands.Cog.listener() async def on_message(self, message): if message.channel.id == config.verifyChannel and not message.content.startswith(".verify "): if message.author == self.client.user: pass else: await message.delete() @commands.command() async def verify(self, ctx, *args): if args == () and ctx.channel.id == config.verifyChannel: msg = await ctx.send("Missing arguments") await asyncio.sleep(2) await msg.delete() await ctx.message.delete() elif ctx.channel.id != config.verifyChannel or args == (): msg = await ctx.send("This command can't be used in this channel.") await asyncio.sleep(2) await msg.delete() await ctx.message.delete() elif ctx.channel.id == config.verifyChannel and args[0] != config.verifycode: msg = await ctx.send("Incorrect code.") await asyncio.sleep(2) await msg.delete() await ctx.message.delete() else: if ctx.channel.id == config.verifyChannel and args[0] == config.verifycode: await ctx.message.delete() member = ctx.message.author role = discord.utils.get(member.guild.roles, id=config.verifyrole) await member.add_roles(role) channel = self.client.get_channel(config.verifylogs) embed=discord.Embed( color=0x0066ff, description=f"**{member}** verified and got **{role}** role") embed.set_footer(text=f"User ID: {ctx.message.author.id}") await channel.send(embed=embed) @commands.command() async def verifymessage(self, ctx): channel = self.client.get_channel(config.verifyChannel) embed=discord.Embed( title="Verification:", color=0x0066ff, ) embed.add_field(name="To confirm that you are not a bot, please enter:", value=f"`.verify {config.verifycode}`") embed.set_footer(text=config.default_footer) await channel.send(embed=embed) @verify.error async def verify_error(self, ctx, error): if isinstance(error, discord.ext.commands.errors.MissingRequiredArgument): await ctx.send("Missing required arguments.") await ctx.message.delete() else: print(error) def setup(client): client.add_cog(Verify(client))

34.291667

116

0.669097

72bfc5ba377adecf88c8721fbcab413e202c929a

669

py

Python

URI Online Judge/Practice/question1049.py

kushagra1212/Competitive-Programming

5b68774c617d6abdf1b29893b1b13d47f62161e8

[ "MIT" ]

994

2017-02-28T06:13:47.000Z

2022-03-31T10:49:00.000Z

Practice/UriOnlineJudge/question1049.py

devesh17m/Competitive-Programming

2d459dc8dc5ac628d94700b739988b0ea364cb71

[ "MIT" ]

16

2018-01-01T02:59:55.000Z

2021-11-22T12:49:16.000Z

Practice/UriOnlineJudge/question1049.py

devesh17m/Competitive-Programming

2d459dc8dc5ac628d94700b739988b0ea364cb71

[ "MIT" ]

325

2017-06-15T03:32:43.000Z

2022-03-28T22:43:42.000Z

# coding: utf-8 classificacao = raw_input() tipo = raw_input() especie = raw_input() if (classificacao == "vertebrado"): if(tipo == "ave"): if (especie == "carnivoro"): print("aguia") elif(especie == "onivoro"): print("pomba") elif (tipo == "mamifero"): if (especie == "onivoro"): print("homem") elif(especie == "herbivoro"): print("vaca") elif(classificacao == "invertebrado"): if(tipo == "inseto"): if (especie == "hematofago"): print ("pulga") elif(especie == "herbivoro"): print ("lagarta") elif(tipo == "anelideo"): if (especie == "hematofago"): print("sanguessuga") elif(especie == "onivoro"): print("minhoca")

20.90625

38

0.605381

3ad1db2daf37d07310381dbbb814eddb95195e0c

859

py

Python

setup.py

nielsdrost/pymt

ae39bf807428827a6904202bf4d3b927daa255ea

[ "MIT" ]

null

setup.py

nielsdrost/pymt

ae39bf807428827a6904202bf4d3b927daa255ea

[ "MIT" ]

null

setup.py

nielsdrost/pymt

ae39bf807428827a6904202bf4d3b927daa255ea

[ "MIT" ]

null

from setuptools import setup, find_packages import versioneer def read_requirements(): import os path = os.path.dirname(os.path.abspath(__file__)) requirements_file = os.path.join(path, "requirements.txt") try: with open(requirements_file, "r") as req_fp: requires = req_fp.read().split() except IOError: return [] else: return [require.split() for require in requires] setup( name="pymt", version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), description="The CSDMS Python Modeling Toolkit", author="Eric Hutton", author_email="huttone@colorado.edu", url="http://csdms.colorado.edu", setup_requires=["setuptools"], packages=find_packages(exclude=("tests*",)), entry_points={"console_scripts": ["cmt-config=cmt.cmd.cmt_config:main"]}, )

26.84375

77

0.675204

777063b782c6df91ae36400652cb0dcd4d91ed40

6,064

py

Python

pirates/piratesgui/PDialog.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

81

2018-04-08T18:14:24.000Z

2022-01-11T07:22:15.000Z

pirates/piratesgui/PDialog.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

4

2018-09-13T20:41:22.000Z

2022-01-08T06:57:00.000Z

pirates/piratesgui/PDialog.py

Willy5s/Pirates-Online-Rewritten

7434cf98d9b7c837d57c181e5dabd02ddf98acb7

[ "BSD-3-Clause" ]

26

2018-05-26T12:49:27.000Z

2021-09-11T09:11:59.000Z

from pandac.PandaModules import TextNode from direct.gui.DirectGui import * from direct.directnotify import DirectNotifyGlobal from otp.otpgui import OTPDialog from pirates.piratesbase import PLocalizer from pirates.piratesgui import BorderFrame from pirates.piratesgui import PiratesGuiGlobals class PDialog(DirectDialog): loadedAssets = False checkButton = None cancelButton = None def __init__(self, parent=None, style=OTPDialog.NoButtons, giveMouse=True, **kw): self.style = style if not self.loadedAssets: buttons = loader.loadModel('models/gui/lookout_gui') self.checkButton = (buttons.find('**/lookout_submit'), buttons.find('**/lookout_submit_down'), buttons.find('**/lookout_submit_over')) self.cancelButton = (buttons.find('**/lookout_close_window'), buttons.find('**/lookout_close_window_down'), buttons.find('**/lookout_close_window_over')) for button in self.checkButton: button.setScale(0.2) button.flattenStrong() for button in self.cancelButton: button.setScale(0.2) button.flattenStrong() self.loadedAssets = True buttons.removeNode() if self.style == OTPDialog.TwoChoiceCustom: buttonImage = [ self.checkButton, self.cancelButton] buttonValue = [DGG.DIALOG_OK, DGG.DIALOG_CANCEL] if 'buttonText' in kw: buttonText = kw['buttonText'] del kw['buttonText'] else: buttonText = [ PLocalizer.DialogOK, PLocalizer.DialogCancel] elif self.style == OTPDialog.TwoChoice: buttonImage = [ self.checkButton, self.cancelButton] buttonText = [PLocalizer.DialogOK, PLocalizer.DialogCancel] buttonValue = [ DGG.DIALOG_OK, DGG.DIALOG_CANCEL] elif self.style == OTPDialog.YesNo: buttonImage = [ self.checkButton, self.cancelButton] buttonText = [PLocalizer.DialogYes, PLocalizer.DialogNo] buttonValue = [ DGG.DIALOG_OK, DGG.DIALOG_CANCEL] elif self.style == OTPDialog.Acknowledge: buttonImage = [ self.checkButton] buttonText = [PLocalizer.DialogOK] buttonValue = [DGG.DIALOG_OK] elif self.style == OTPDialog.CancelOnly: buttonImage = [ self.cancelButton] buttonText = [PLocalizer.DialogCancel] buttonValue = [DGG.DIALOG_CANCEL] elif self.style == OTPDialog.NoButtons: buttonImage = [] buttonText = [] buttonValue = [] else: self.notify.error('No such style as: ' + str(self.style)) self.borderFrame = BorderFrame.BorderFrame(borderScale=0.5) optiondefs = ( ( 'image', self.borderFrame, None), ('relief', None, None), ('buttonImageList', buttonImage, DGG.INITOPT), ('buttonTextList', buttonText, DGG.INITOPT), ('buttonValueList', buttonValue, DGG.INITOPT), ('buttonPadSF', 2.2, DGG.INITOPT), ('title_text', '', None), ('title_text_font', DGG.getDefaultFont(), None), ('title_text_wordwrap', 12, None), ('title_text_scale', PiratesGuiGlobals.TextScaleTitleSmall, None), ('title_text_fg', PiratesGuiGlobals.TextFG1, None), ('title_text_shadow', PiratesGuiGlobals.TextShadow, None), ('title_text_align', TextNode.ACenter, None), ('text_font', DGG.getDefaultFont(), None), ('text_wordwrap', 12, None), ('text_scale', PiratesGuiGlobals.TextScaleLarge, None), ('text_fg', PiratesGuiGlobals.TextFG1, None), ('text_shadow', PiratesGuiGlobals.TextShadow, None), ('text_align', TextNode.ALeft, None), ('button_pad', (0, 0), None), ('button_relief', None, None), ('button_text_pos', (0, -0.08), None), ('button_text_fg', PiratesGuiGlobals.TextFG1, None), ('button_text_shadow', PiratesGuiGlobals.TextShadow, None), ('button_text_scale', PiratesGuiGlobals.TextScaleLarge, None), ('fadeScreen', 0.5, None), ('image_color', (1, 1, 1, 1), None), ('destroyedCallback', None, None)) self.defineoptions(kw, optiondefs) DirectDialog.__init__(self, parent) def cleanupBorderFrame(): self.borderFrame.destroy() self.postInitialiseFuncList.append(cleanupBorderFrame) self.createcomponent('title', (), None, DirectLabel, (self,), relief=None, text_pos=(0, 0.16)) self.initialiseoptions(PDialog) self.setBin('gui-fixed', 1) def destroy(self): if self['destroyedCallback']: self['destroyedCallback']() DirectDialog.destroy(self) class PGlobalDialog(PDialog): notify = DirectNotifyGlobal.directNotify.newCategory('PGlobalDialog') def __init__(self, message='', doneEvent=None, style=OTPDialog.NoButtons, okButtonText=PLocalizer.DialogOK, cancelButtonText=PLocalizer.DialogCancel, **kw): if doneEvent == None and style != OTPDialog.NoButtons: self.notify.error('Boxes with buttons must specify a doneEvent.') self.__doneEvent = doneEvent if style == OTPDialog.NoButtons: buttonText = [] elif style == OTPDialog.Acknowledge: buttonText = [ okButtonText] elif style == OTPDialog.CancelOnly: buttonText = [ cancelButtonText] else: buttonText = [ okButtonText, cancelButtonText] optiondefs = (('dialogName', 'globalDialog', DGG.INITOPT), ('buttonTextList', buttonText, DGG.INITOPT), ('text', message, None), ('command', self.handleButton, None)) self.defineoptions(kw, optiondefs) PDialog.__init__(self, style=style) self.initialiseoptions(PGlobalDialog) def handleButton(self, value): if value == DGG.DIALOG_OK: self.doneStatus = 'ok' messenger.send(self.__doneEvent) elif value == DGG.DIALOG_CANCEL: self.doneStatus = 'cancel' messenger.send(self.__doneEvent)

50.533333

1,215

0.64215

9fc2fb585ab81aff14a51d613814ecebc401f77f

30,543

py

Python

numba/tests/test_typedlist.py

Rubtsowa/numba

6d7cddb9084d843e12bcdf9c443bc9cce56aa02f

[ "BSD-2-Clause" ]

null

numba/tests/test_typedlist.py

Rubtsowa/numba

6d7cddb9084d843e12bcdf9c443bc9cce56aa02f

[ "BSD-2-Clause" ]

null

numba/tests/test_typedlist.py

Rubtsowa/numba

6d7cddb9084d843e12bcdf9c443bc9cce56aa02f

[ "BSD-2-Clause" ]

null

from __future__ import print_function, absolute_import, division from itertools import product import numpy as np from numba import njit from numba import int32, float32, types, prange from numba import jitclass, typeof from numba.typed import List, Dict from numba.utils import IS_PY3 from .support import (TestCase, MemoryLeakMixin, unittest, override_config, forbid_codegen) from numba.unsafe.refcount import get_refcount from .test_parfors import skip_unsupported as parfors_skip_unsupported skip_py2 = unittest.skipUnless(IS_PY3, reason='not supported in py2') def to_tl(l): """ Convert cpython list to typed-list. """ tl = List.empty_list(int32) for k in l: tl.append(k) return tl class TestTypedList(MemoryLeakMixin, TestCase): def test_basic(self): l = List.empty_list(int32) # len self.assertEqual(len(l), 0) # append l.append(0) # len self.assertEqual(len(l), 1) # setitem l.append(0) l.append(0) l[0] = 10 l[1] = 11 l[2] = 12 # getitem self.assertEqual(l[0], 10) self.assertEqual(l[1], 11) self.assertEqual(l[2], 12) self.assertEqual(l[-3], 10) self.assertEqual(l[-2], 11) self.assertEqual(l[-1], 12) # __iter__ # the default __iter__ from MutableSequence will raise an IndexError # via __getitem__ and thus leak an exception, so this shouldn't for i in l: pass # contains self.assertTrue(10 in l) self.assertFalse(0 in l) # count l.append(12) self.assertEqual(l.count(0), 0) self.assertEqual(l.count(10), 1) self.assertEqual(l.count(12), 2) # pop self.assertEqual(len(l), 4) self.assertEqual(l.pop(), 12) self.assertEqual(len(l), 3) self.assertEqual(l.pop(1), 11) self.assertEqual(len(l), 2) # extend l.extend((100, 200, 300)) self.assertEqual(len(l), 5) self.assertEqual(list(l), [10, 12, 100, 200, 300]) # insert l.insert(0, 0) self.assertEqual(list(l), [0, 10, 12, 100, 200, 300]) l.insert(3, 13) self.assertEqual(list(l), [0, 10, 12, 13, 100, 200, 300]) l.insert(100, 400) self.assertEqual(list(l), [0, 10, 12, 13, 100, 200, 300, 400]) # remove l.remove(0) l.remove(400) l.remove(13) self.assertEqual(list(l), [10, 12, 100, 200, 300]) # clear l.clear() self.assertEqual(len(l), 0) self.assertEqual(list(l), []) # reverse l.extend(tuple(range(10, 20))) l.reverse() self.assertEqual(list(l), list(range(10, 20))[::-1]) # copy new = l.copy() self.assertEqual(list(new), list(range(10, 20))[::-1]) # equal self.assertEqual(l, new) # not equal new[-1] = 42 self.assertNotEqual(l, new) # index self.assertEqual(l.index(15), 4) def test_unsigned_access(self): L = List.empty_list(int32) ui32_0 = types.uint32(0) ui32_1 = types.uint32(1) ui32_2 = types.uint32(2) # insert L.append(types.uint32(10)) L.append(types.uint32(11)) L.append(types.uint32(12)) self.assertEqual(len(L), 3) # getitem self.assertEqual(L[ui32_0], 10) self.assertEqual(L[ui32_1], 11) self.assertEqual(L[ui32_2], 12) # setitem L[ui32_0] = 123 L[ui32_1] = 456 L[ui32_2] = 789 self.assertEqual(L[ui32_0], 123) self.assertEqual(L[ui32_1], 456) self.assertEqual(L[ui32_2], 789) # index ui32_123 = types.uint32(123) ui32_456 = types.uint32(456) ui32_789 = types.uint32(789) self.assertEqual(L.index(ui32_123), 0) self.assertEqual(L.index(ui32_456), 1) self.assertEqual(L.index(ui32_789), 2) # delitem L.__delitem__(ui32_2) del L[ui32_1] self.assertEqual(len(L), 1) self.assertEqual(L[ui32_0], 123) # pop L.append(2) L.append(3) L.append(4) self.assertEqual(len(L), 4) self.assertEqual(L.pop(), 4) self.assertEqual(L.pop(ui32_2), 3) self.assertEqual(L.pop(ui32_1), 2) self.assertEqual(L.pop(ui32_0), 123) @parfors_skip_unsupported def test_unsigned_prange(self): @njit(parallel=True) def foo(a): r = types.uint64(3) s = types.uint64(0) for i in prange(r): s = s + a[i] return s a = List.empty_list(types.uint64) a.append(types.uint64(12)) a.append(types.uint64(1)) a.append(types.uint64(7)) self.assertEqual(foo(a), 20) def test_compiled(self): @njit def producer(): l = List.empty_list(int32) l.append(23) return l @njit def consumer(l): return l[0] l = producer() val = consumer(l) self.assertEqual(val, 23) def test_getitem_slice(self): """ Test getitem using a slice. This tests suffers from combinatorial explosion, so we parametrize it and compare results against the regular list in a quasi fuzzing approach. """ # initialize regular list rl = list(range(10, 20)) # initialize typed list tl = List.empty_list(int32) for i in range(10, 20): tl.append(i) # define the ranges start_range = list(range(-20, 30)) stop_range = list(range(-20, 30)) step_range = [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5] # check that they are the same initially self.assertEqual(rl, list(tl)) # check that copy by slice works, no start, no stop, no step self.assertEqual(rl[:], list(tl[:])) # start only for sa in start_range: self.assertEqual(rl[sa:], list(tl[sa:])) # stop only for so in stop_range: self.assertEqual(rl[:so], list(tl[:so])) # step only for se in step_range: self.assertEqual(rl[::se], list(tl[::se])) # start and stop for sa, so in product(start_range, stop_range): self.assertEqual(rl[sa:so], list(tl[sa:so])) # start and step for sa, se in product(start_range, step_range): self.assertEqual(rl[sa::se], list(tl[sa::se])) # stop and step for so, se in product(stop_range, step_range): self.assertEqual(rl[:so:se], list(tl[:so:se])) # start, stop and step for sa, so, se in product(start_range, stop_range, step_range): self.assertEqual(rl[sa:so:se], list(tl[sa:so:se])) def test_setitem_slice(self): """ Test setitem using a slice. This tests suffers from combinatorial explosion, so we parametrize it and compare results against the regular list in a quasi fuzzing approach. """ def setup(start=10, stop=20): # initialize regular list rl_ = list(range(start, stop)) # intialize typed list tl_ = List.empty_list(int32) # populate typed list for i in range(start, stop): tl_.append(i) # check they are the same self.assertEqual(rl_, list(tl_)) return rl_, tl_ ### Simple slicing ### # assign to itself rl, tl = setup() rl[:], tl[:] = rl, tl self.assertEqual(rl, list(tl)) # extend self rl, tl = setup() rl[len(rl):], tl[len(tl):] = rl, tl self.assertEqual(rl, list(tl)) # prepend self rl, tl = setup() rl[:0], tl[:0] = rl, tl self.assertEqual(rl, list(tl)) # partial assign to self, with equal length rl, tl = setup() rl[3:5], tl[3:5] = rl[6:8], tl[6:8] self.assertEqual(rl, list(tl)) # partial assign to self, with larger slice rl, tl = setup() rl[3:5], tl[3:5] = rl[6:9], tl[6:9] self.assertEqual(rl, list(tl)) # partial assign to self, with smaller slice rl, tl = setup() rl[3:5], tl[3:5] = rl[6:7], tl[6:7] self.assertEqual(rl, list(tl)) # extend rl, tl = setup() rl[len(rl):] = list(range(110, 120)) tl[len(tl):] = to_tl(range(110,120)) self.assertEqual(rl, list(tl)) # extend empty rl, tl = setup(0, 0) rl[len(rl):] = list(range(110, 120)) tl[len(tl):] = to_tl(range(110,120)) self.assertEqual(rl, list(tl)) # extend singleton rl, tl = setup(0, 1) rl[len(rl):] = list(range(110, 120)) tl[len(tl):] = to_tl(range(110,120)) self.assertEqual(rl, list(tl)) # prepend rl, tl = setup() rl[:0], tl[:0] = list(range(110, 120)), to_tl(range(110,120)) self.assertEqual(rl, list(tl)) # prepend empty rl, tl = setup(0,0) rl[:0], tl[:0] = list(range(110, 120)), to_tl(range(110,120)) self.assertEqual(rl, list(tl)) # prepend singleton rl, tl = setup(0,1) rl[:0], tl[:0] = list(range(110, 120)), to_tl(range(110,120)) self.assertEqual(rl, list(tl)) # simple equal length assignment, just replace rl, tl = setup() rl[1:3], tl[1:3] = [100, 200], to_tl([100, 200]) self.assertEqual(rl, list(tl)) # slice for assignment is larger, need to replace and insert rl, tl = setup() rl[1:3], tl[1:3] = [100, 200, 300, 400], to_tl([100, 200, 300, 400]) self.assertEqual(rl, list(tl)) # slice for assignment is smaller, need to replace and delete rl, tl = setup() rl[1:3], tl[1:3] = [100], to_tl([100]) self.assertEqual(rl, list(tl)) # slice for assignment is smaller and item is empty, need to delete rl, tl = setup() rl[1:3], tl[1:3] = [], to_tl([]) self.assertEqual(rl, list(tl)) # Synonym for clear rl, tl = setup() rl[:], tl[:] = [], to_tl([]) self.assertEqual(rl, list(tl)) ### Extended slicing ### # replace every second element rl, tl = setup() rl[::2], tl[::2] = [100,200,300,400,500], to_tl([100,200,300,400,500]) self.assertEqual(rl, list(tl)) # replace every second element, backwards rl, tl = setup() rl[::-2], tl[::-2] = [100,200,300,400,500], to_tl([100,200,300,400,500]) self.assertEqual(rl, list(tl)) # reverse assign to itself rl, tl = setup() rl[::-1], tl[::-1] = rl, tl self.assertEqual(rl, list(tl)) def test_setitem_slice_value_error(self): self.disable_leak_check() tl = List.empty_list(int32) for i in range(10,20): tl.append(i) assignment = List.empty_list(int32) for i in range(1, 4): assignment.append(i) with self.assertRaises(ValueError) as raises: tl[8:3:-1] = assignment self.assertIn( "length mismatch for extended slice and sequence", str(raises.exception), ) def test_delitem_slice(self): """ Test delitem using a slice. This tests suffers from combinatorial explosion, so we parametrize it and compare results against the regular list in a quasi fuzzing approach. """ def setup(start=10, stop=20): # initialize regular list rl_ = list(range(start, stop)) # intialize typed list tl_ = List.empty_list(int32) # populate typed list for i in range(start, stop): tl_.append(i) # check they are the same self.assertEqual(rl_, list(tl_)) return rl_, tl_ # define the ranges start_range = list(range(-20, 30)) stop_range = list(range(-20, 30)) step_range = [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5] rl, tl = setup() # check that they are the same initially self.assertEqual(rl, list(tl)) # check that deletion of the whole list by slice works del rl[:] del tl[:] self.assertEqual(rl, list(tl)) # start only for sa in start_range: rl, tl = setup() del rl[sa:] del tl[sa:] self.assertEqual(rl, list(tl)) # stop only for so in stop_range: rl, tl = setup() del rl[:so] del tl[:so] self.assertEqual(rl, list(tl)) # step only for se in step_range: rl, tl = setup() del rl[::se] del tl[::se] self.assertEqual(rl, list(tl)) # start and stop for sa, so in product(start_range, stop_range): rl, tl = setup() del rl[sa:so] del tl[sa:so] self.assertEqual(rl, list(tl)) # start and step for sa, se in product(start_range, step_range): rl, tl = setup() del rl[sa::se] del tl[sa::se] self.assertEqual(rl, list(tl)) # stop and step for so, se in product(stop_range, step_range): rl, tl = setup() del rl[:so:se] del tl[:so:se] self.assertEqual(rl, list(tl)) # start, stop and step for sa, so, se in product(start_range, stop_range, step_range): rl, tl = setup() del rl[sa:so:se] del tl[sa:so:se] self.assertEqual(rl, list(tl)) def test_list_create_no_jit_using_empty_list(self): with override_config('DISABLE_JIT', True): with forbid_codegen(): l = List.empty_list(types.int32) self.assertEqual(type(l), list) def test_list_create_no_jit_using_List(self): with override_config('DISABLE_JIT', True): with forbid_codegen(): l = List() self.assertEqual(type(l), list) class TestAllocation(MemoryLeakMixin, TestCase): def test_allocation(self): # kwarg version for i in range(16): tl = List.empty_list(types.int32, allocated=i) self.assertEqual(tl._allocated(), i) # posarg version for i in range(16): tl = List.empty_list(types.int32, i) self.assertEqual(tl._allocated(), i) def test_growth_and_shrinkage(self): tl = List.empty_list(types.int32) growth_before = {0: 0, 4:4, 8:8, 16:16} growth_after = {0: 4, 4:8, 8:16, 16:25} for i in range(17): if i in growth_before: self.assertEqual(growth_before[i], tl._allocated()) tl.append(i) if i in growth_after: self.assertEqual(growth_after[i], tl._allocated()) shrink_before = {17: 25, 12:25, 9:18, 6:12, 4:8, 3:6, 2:5, 1:4} shrink_after = {17: 25, 12:18, 9:12, 6:8, 4:6, 3:5, 2:4, 1:0} for i in range(17, 0, -1): if i in shrink_before: self.assertEqual(shrink_before[i], tl._allocated()) tl.pop() if i in shrink_after: self.assertEqual(shrink_after[i], tl._allocated()) class TestExtend(MemoryLeakMixin, TestCase): def test_extend_other(self): @njit def impl(other): l = List.empty_list(types.int32) for x in range(10): l.append(x) l.extend(other) return l other = List.empty_list(types.int32) for x in range(10): other.append(x) expected = impl.py_func(other) got = impl(other) self.assertEqual(expected, got) def test_extend_self(self): @njit def impl(): l = List.empty_list(types.int32) for x in range(10): l.append(x) l.extend(l) return l expected = impl.py_func() got = impl() self.assertEqual(expected, got) def test_extend_tuple(self): @njit def impl(): l = List.empty_list(types.int32) for x in range(10): l.append(x) l.extend((100,200,300)) return l expected = impl.py_func() got = impl() self.assertEqual(expected, got) @njit def cmp(a, b): return a < b, a <= b, a == b, a != b, a >= b, a > b class TestComparisons(MemoryLeakMixin, TestCase): def _cmp_dance(self, expected, pa, pb, na, nb): # interpreter with regular list self.assertEqual(cmp.py_func(pa, pb), expected) # interpreter with typed-list py_got = cmp.py_func(na, nb) self.assertEqual(py_got, expected) # compiled with typed-list jit_got = cmp(na, nb) self.assertEqual(jit_got, expected) def test_empty_vs_empty(self): pa, pb = [], [] na, nb = to_tl(pa), to_tl(pb) expected = False, True, True, False, True, False self._cmp_dance(expected, pa, pb, na, nb) def test_empty_vs_singleton(self): pa, pb = [], [0] na, nb = to_tl(pa), to_tl(pb) expected = True, True, False, True, False, False self._cmp_dance(expected, pa, pb, na, nb) def test_singleton_vs_empty(self): pa, pb = [0], [] na, nb = to_tl(pa), to_tl(pb) expected = False, False, False, True, True, True self._cmp_dance(expected, pa, pb, na, nb) def test_singleton_vs_singleton_equal(self): pa, pb = [0], [0] na, nb = to_tl(pa), to_tl(pb) expected = False, True, True, False, True, False self._cmp_dance(expected, pa, pb, na, nb) def test_singleton_vs_singleton_less_than(self): pa, pb = [0], [1] na, nb = to_tl(pa), to_tl(pb) expected = True, True, False, True, False, False self._cmp_dance(expected, pa, pb, na, nb) def test_singleton_vs_singleton_greater_than(self): pa, pb = [1], [0] na, nb = to_tl(pa), to_tl(pb) expected = False, False, False, True, True, True self._cmp_dance(expected, pa, pb, na, nb) def test_equal(self): pa, pb = [1, 2, 3], [1, 2, 3] na, nb = to_tl(pa), to_tl(pb) expected = False, True, True, False, True, False self._cmp_dance(expected, pa, pb, na, nb) def test_first_shorter(self): pa, pb = [1, 2], [1, 2, 3] na, nb = to_tl(pa), to_tl(pb) expected = True, True, False, True, False, False self._cmp_dance(expected, pa, pb, na, nb) def test_second_shorter(self): pa, pb = [1, 2, 3], [1, 2] na, nb = to_tl(pa), to_tl(pb) expected = False, False, False, True, True, True self._cmp_dance(expected, pa, pb, na, nb) def test_first_less_than(self): pa, pb = [1, 2, 2], [1, 2, 3] na, nb = to_tl(pa), to_tl(pb) expected = True, True, False, True, False, False self._cmp_dance(expected, pa, pb, na, nb) def test_first_greater_than(self): pa, pb = [1, 2, 3], [1, 2, 2] na, nb = to_tl(pa), to_tl(pb) expected = False, False, False, True, True, True self._cmp_dance(expected, pa, pb, na, nb) def test_equals_non_list(self): l = to_tl([1, 2, 3]) self.assertFalse(any(cmp.py_func(l, 1))) self.assertFalse(any(cmp(l, 1))) class TestListInferred(TestCase): def test_simple_refine_append(self): @njit def foo(): l = List() l.append(1) return l expected = foo.py_func() got = foo() self.assertEqual(expected, got) self.assertEqual(list(got), [1]) self.assertEqual(typeof(got).item_type, typeof(1)) def test_simple_refine_insert(self): @njit def foo(): l = List() l.insert(0, 1) return l expected = foo.py_func() got = foo() self.assertEqual(expected, got) self.assertEqual(list(got), [1]) self.assertEqual(typeof(got).item_type, typeof(1)) def test_refine_extend_list(self): @njit def foo(): a = List() b = List() for i in range(3): b.append(i) a.extend(b) return a expected = foo.py_func() got = foo() self.assertEqual(expected, got) self.assertEqual(list(got), [0, 1, 2]) self.assertEqual(typeof(got).item_type, typeof(1)) def test_refine_extend_set(self): @njit def foo(): l = List() l.extend((0, 1, 2)) return l expected = foo.py_func() got = foo() self.assertEqual(expected, got) self.assertEqual(list(got), [0, 1, 2]) self.assertEqual(typeof(got).item_type, typeof(1)) def test_refine_list_extend_iter(self): @njit def foo(): l = List() d = Dict() d[0] = 0 # d.keys() provides a DictKeysIterableType l.extend(d.keys()) return l got = foo() self.assertEqual(0, got[0]) class TestListRefctTypes(MemoryLeakMixin, TestCase): @skip_py2 def test_str_item(self): @njit def foo(): l = List.empty_list(types.unicode_type) for s in ("a", "ab", "abc", "abcd"): l.append(s) return l l = foo() expected = ["a", "ab", "abc", "abcd"] for i, s in enumerate(expected): self.assertEqual(l[i], s) self.assertEqual(list(l), expected) # Test insert replacement l[3] = 'uxyz' self.assertEqual(l[3], 'uxyz') # Test list growth nelem = 100 for i in range(4, nelem): l.append(str(i)) self.assertEqual(l[i], str(i)) @skip_py2 def test_str_item_refcount_replace(self): @njit def foo(): # use some tricks to make ref-counted unicode i, j = 'ab', 'c' a = i + j m, n = 'zy', 'x' z = m + n l = List.empty_list(types.unicode_type) l.append(a) # This *should* dec' a and inc' z thus tests that items that are # replaced are also dec'ed. l[0] = z ra, rz = get_refcount(a), get_refcount(z) return l, ra, rz l, ra, rz = foo() self.assertEqual(l[0], "zyx") self.assertEqual(ra, 1) self.assertEqual(rz, 2) @skip_py2 def test_dict_as_item_in_list(self): @njit def foo(): l = List.empty_list(Dict.empty(int32, int32)) d = Dict.empty(int32, int32) d[0] = 1 # This increments the refcount for d l.append(d) return get_refcount(d) c = foo() self.assertEqual(2, c) @skip_py2 def test_dict_as_item_in_list_multi_refcount(self): @njit def foo(): l = List.empty_list(Dict.empty(int32, int32)) d = Dict.empty(int32, int32) d[0] = 1 # This increments the refcount for d, twice l.append(d) l.append(d) return get_refcount(d) c = foo() self.assertEqual(3, c) @skip_py2 def test_list_as_value_in_dict(self): @njit def foo(): d = Dict.empty(int32, List.empty_list(int32)) l = List.empty_list(int32) l.append(0) # This increments the refcount for l d[0] = l return get_refcount(l) c = foo() self.assertEqual(2, c) @skip_py2 def test_list_as_item_in_list(self): nested_type = types.ListType(types.int32) @njit def foo(): la = List.empty_list(nested_type) lb = List.empty_list(types.int32) lb.append(1) la.append(lb) return la expected = foo.py_func() got = foo() self.assertEqual(expected, got) @skip_py2 def test_array_as_item_in_list(self): nested_type = types.Array(types.float64, 1, 'C') @njit def foo(): l = List.empty_list(nested_type) a = np.zeros((1,)) l.append(a) return l expected = foo.py_func() got = foo() # Need to compare the nested arrays self.assertTrue(np.all(expected[0] == got[0])) @skip_py2 def test_jitclass_as_item_in_list(self): spec = [ ('value', int32), # a simple scalar field ('array', float32[:]), # an array field ] @jitclass(spec) class Bag(object): def __init__(self, value): self.value = value self.array = np.zeros(value, dtype=np.float32) @property def size(self): return self.array.size def increment(self, val): for i in range(self.size): self.array[i] += val return self.array @njit def foo(): l = List() l.append(Bag(21)) l.append(Bag(22)) l.append(Bag(23)) return l expected = foo.py_func() got = foo() def bag_equal(one, two): # jitclasses couldn't override __eq__ at time of writing self.assertEqual(one.value, two.value) np.testing.assert_allclose(one.array, two.array) [bag_equal(a, b) for a, b in zip(expected, got)] @skip_py2 def test_storage_model_mismatch(self): # https://github.com/numba/numba/issues/4520 # check for storage model mismatch in refcount ops generation lst = List() ref = [ ("a", True, "a"), ("b", False, "b"), ("c", False, "c"), ] # populate for x in ref: lst.append(x) # test for i, x in enumerate(ref): self.assertEqual(lst[i], ref[i]) @skip_py2 def test_equals_on_list_with_dict_for_equal_lists(self): # https://github.com/numba/numba/issues/4879 a, b = List(), Dict() b["a"] = 1 a.append(b) c, d = List(), Dict() d["a"] = 1 c.append(d) self.assertEqual(a, c) @skip_py2 def test_equals_on_list_with_dict_for_unequal_dicts(self): # https://github.com/numba/numba/issues/4879 a, b = List(), Dict() b["a"] = 1 a.append(b) c, d = List(), Dict() d["a"] = 2 c.append(d) self.assertNotEqual(a, c) @skip_py2 def test_equals_on_list_with_dict_for_unequal_lists(self): # https://github.com/numba/numba/issues/4879 a, b = List(), Dict() b["a"] = 1 a.append(b) c, d, e = List(), Dict(), Dict() d["a"] = 1 e["b"] = 2 c.append(d) c.append(e) self.assertNotEqual(a, c) class TestListSort(MemoryLeakMixin, TestCase): def setUp(self): super(TestListSort, self).setUp() np.random.seed(0) def make(self, ctor, data): lst = ctor() lst.extend(data) return lst def make_both(self, data): return { 'py': self.make(list, data), 'nb': self.make(List, data), } def test_sort_no_args(self): def udt(lst): lst.sort() return lst for nelem in [13, 29, 127]: my_lists = self.make_both(np.random.randint(0, nelem, nelem)) self.assertEqual(list(udt(my_lists['nb'])), udt(my_lists['py'])) def test_sort_all_args(self): def udt(lst, key, reverse): lst.sort(key=key, reverse=reverse) return lst possible_keys = [ lambda x: -x, # negative lambda x: 1 / (1 + x), # make float lambda x: (x, -x), # tuple lambda x: x, # identity ] possible_reverse = [True, False] for key, reverse in product(possible_keys, possible_reverse): my_lists = self.make_both(np.random.randint(0, 100, 23)) msg = "case for key={} reverse={}".format(key, reverse) self.assertEqual( list(udt(my_lists['nb'], key=key, reverse=reverse)), udt(my_lists['py'], key=key, reverse=reverse), msg=msg, ) def test_sort_dispatcher_key(self): def udt(lst, key): lst.sort(key=key) return lst my_lists = self.make_both(np.random.randint(0, 100, 31)) py_key = lambda x: x + 1 nb_key = njit(lambda x: x + 1) # test typedlist with jitted function self.assertEqual( list(udt(my_lists['nb'], key=nb_key)), udt(my_lists['py'], key=py_key), ) # test typedlist with and without jitted function self.assertEqual( list(udt(my_lists['nb'], key=nb_key)), list(udt(my_lists['nb'], key=py_key)), ) def test_sort_in_jit_w_lambda_key(self): @njit def udt(lst): lst.sort(key=lambda x: -x) return lst lst = self.make(List, np.random.randint(0, 100, 31)) self.assertEqual(udt(lst), udt.py_func(lst)) def test_sort_in_jit_w_global_key(self): @njit def keyfn(x): return -x @njit def udt(lst): lst.sort(key=keyfn) return lst lst = self.make(List, np.random.randint(0, 100, 31)) self.assertEqual(udt(lst), udt.py_func(lst)) def test_sort_on_arrays(self): @njit def foo(lst): lst.sort(key=lambda arr: np.sum(arr)) return lst arrays = [np.random.random(3) for _ in range(10)] my_lists = self.make_both(arrays) self.assertEqual( list(foo(my_lists['nb'])), foo.py_func(my_lists['py']), )

29.827148

80

0.528042

4d5d10f090adbbffaaa58792e17aa0f290ae1ab8

371

py

Python

tests/test_modules/test_ADOdin/test_adodin_blocks.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

tests/test_modules/test_ADOdin/test_adodin_blocks.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

tests/test_modules/test_ADOdin/test_adodin_blocks.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

from mock import Mock from malcolm.testutil import ChildTestCase from malcolm.modules.ADOdin.blocks import odin_runnable_block class TestADOdinBlocks(ChildTestCase): def test_odin_detector_runnable_block(self): self.create_child_block( odin_runnable_block, Mock(), mri_prefix="mri_prefix", pv_prefix="pv_prefix", config_dir="/tmp")

30.916667

78

0.757412

8ffa61871dece7c31eb6c5f5e5f9b2ec161d9ef9

2,343

py

Python

docs/conf.py

Lesson-ThienHi/thienhi_shop

1c595d70299e1fcce12c3610e27b66c89bbadda6

[ "MIT" ]

null

docs/conf.py

Lesson-ThienHi/thienhi_shop

1c595d70299e1fcce12c3610e27b66c89bbadda6

[ "MIT" ]

2

2022-03-30T06:34:29.000Z

2022-03-31T06:34:49.000Z

docs/conf.py

Lesson-ThienHi/thienhi_shop

1c595d70299e1fcce12c3610e27b66c89bbadda6

[ "MIT" ]

null

# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # 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. import os import sys import django if os.getenv("READTHEDOCS", default=False) == "True": sys.path.insert(0, os.path.abspath("..")) os.environ["DJANGO_READ_DOT_ENV_FILE"] = "True" os.environ["USE_DOCKER"] = "no" else: sys.path.insert(0, os.path.abspath("/app")) os.environ["DATABASE_URL"] = "sqlite:///readthedocs.db" os.environ["CELERY_BROKER_URL"] = os.getenv("REDIS_URL", "redis://redis:6379") os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.local") django.setup() # -- Project information ----------------------------------------------------- project = "shop_thienhi" copyright = """2022, ThienHi""" author = "ThienHi" # -- General configuration --------------------------------------------------- # 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", ] # Add any paths that contain templates here, relative to this directory. # templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "alabaster" # 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"]

36.609375

79

0.668374

5d3a7a4c828e00572f691871f5959cb379c716a9

3,021

py

Python

tests/test_cli.py

OasisLMF/OEDtransform

688a9cf90a7f11ee19f5f48fcbe1cb93962ea67d

[ "BSD-3-Clause" ]

1

2022-03-18T14:53:27.000Z

tests/test_cli.py

OasisLMF/OpenDataTransform

688a9cf90a7f11ee19f5f48fcbe1cb93962ea67d

[ "BSD-3-Clause" ]

25

2021-08-05T16:17:24.000Z

2022-03-29T16:28:35.000Z

tests/test_cli.py

OasisLMF/OEDtransform

688a9cf90a7f11ee19f5f48fcbe1cb93962ea67d

[ "BSD-3-Clause" ]

1

2022-01-21T14:39:20.000Z

from unittest import mock import yaml from click.testing import CliRunner from hypothesis import given from hypothesis.strategies import fixed_dictionaries, just, one_of, tuples from converter.cli import cli from converter.config import Config from converter.mapping import BaseMapping from converter.runner import BaseRunner from tests.config.test_config import config_file class FakeRunner(BaseRunner): def transform(self, extractor, mapping): return [] class FakeMapping(BaseMapping): def get_transformations(self): return [] def options(): return tuples( one_of(fixed_dictionaries({"foo": just({"file": "bizz"})}), just({})), one_of( fixed_dictionaries({"converter_foo_env": just("fizz")}), just({}), ), one_of(tuples(just("foo.args"), just("fuzz")), just(())), just( { "runner": {"path": "tests.test_cli.FakeRunner"}, "mapping": { "path": "tests.test_cli.FakeMapping", "options": {"input_format": "A", "output_format": "B"}, }, "extractor": {"path": "tests.connector.fakes.FakeConnector"}, "loader": {"path": "tests.connector.fakes.FakeConnector"}, }, ), ) @given(opts=options()) def test_show_config(opts): conf, env, argv, ovr = opts argv_dict = dict([argv] if argv else []) argv = ["-o", *argv] if argv else [] with config_file({**conf, **ovr}) as f, mock.patch("os.environ", env): expected_conf = Config( config_path=f, env=env, argv=argv_dict, overrides=ovr, ) runner = CliRunner() result = runner.invoke(cli, [*argv, "--config", f, "show-config"]) assert result.exit_code == 0 assert yaml.load(result.output, yaml.SafeLoader) == expected_conf @given(opts=options()) def test_run(opts): conf, env, argv, ovr = opts argv_dict = dict([argv] if argv else []) argv = ["-o", *argv] if argv else [] with config_file({**conf, **ovr}) as f, mock.patch( "converter.cli.Controller" ) as mock_controller, mock.patch("os.environ", env): expected_conf = Config( config_path=f, env=env, argv=argv_dict, overrides=ovr, ) runner = CliRunner() result = runner.invoke(cli, [*argv, "--config", f, "run"]) assert result.exit_code == 0 mock_controller.assert_called_once_with(expected_conf) def test_run_raises___exception_is_logged(): expected_exception = Exception("Some Error") with mock.patch("logging.exception") as mock_exception_logger, mock.patch( "converter.cli.Controller", side_effect=expected_exception ): runner = CliRunner() result = runner.invoke(cli, ["run"]) mock_exception_logger.assert_called_once_with(expected_exception) assert result.exit_code == 1

28.771429

78

0.602119

3a9ffb2008dcbe723586d855dd457ac4f001297d

1,087

py

Python

pyNastran/gui/dev/save_load.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

293

2015-03-22T20:22:01.000Z

2022-03-14T20:28:24.000Z

pyNastran/gui/dev/save_load.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

512

2015-03-14T18:39:27.000Z

2022-03-31T16:15:43.000Z

pyNastran/gui/dev/save_load.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

136

2015-03-19T03:26:06.000Z

2022-03-25T22:14:54.000Z

import json data = { 'completed_in': 0.074, 'max_id': 264043230692245504, 'max_id_str': '264043230692245504', 'next_page': '?page=2&max_id=264043230692245504&q=python&rpp=5', 'page': 1, 'query': 'python', 'refresh_url': '?since_id=264043230692245504&q=python', 'results': [ {'created_at': 'Thu, 01 Nov 2012 16:36:26 +0000', 'from_user': 'cat', }, {'created_at': 'Thu, 01 Nov 2012 16:36:14 +0000', 'from_user': 'cat', }, {'created_at': 'Thu, 01 Nov 2012 16:36:13 +0000', 'from_user': 'cat', }, {'created_at': 'Thu, 01 Nov 2012 16:36:07 +0000', 'from_user': 'cat', }, {'created_at': 'Thu, 01 Nov 2012 16:36:04 +0000', 'from_user': 'cat', }, ], 'results_per_page': 5, 'since_id': 0, 'since_id_str': '0' } #print(json.dumps(data, indent=4)) with open('f.json', 'wb') as f: json.dump(data, f, indent=4) #parsed_json = json.loads(json_string) with open('f.json', 'r') as f2: data2 = json.load(f2) print(data2)

27.175

68

0.546458

9549cdbee430a202bf0c02a19fc0dd13d6b22edf

42

py

Python

IDE/Missup.py

Xpliot/IDE

7b9b3debf8d5369f8ac6747fb46f5be25d3cfca1

[ "MIT" ]

null

IDE/Missup.py

Xpliot/IDE

7b9b3debf8d5369f8ac6747fb46f5be25d3cfca1

[ "MIT" ]

null

IDE/Missup.py

Xpliot/IDE

7b9b3debf8d5369f8ac6747fb46f5be25d3cfca1

[ "MIT" ]

null

def add(a,b): return a+b add(54+23)

8.4

14

0.547619

055431ee8ee3ac14676fbfaf01642c79a03e6139

678

py

Python

django_redux/engine.py

fcurella/django_redux

0215fcd09cd02049778e98d77467d4900c62b8c2

[ "BSD-3-Clause" ]

42

2017-02-15T13:39:41.000Z

2019-06-24T17:59:35.000Z

django_redux/engine.py

channels-frontend/django_redux

0215fcd09cd02049778e98d77467d4900c62b8c2

[ "BSD-3-Clause" ]

3

2019-08-19T19:35:23.000Z

2022-02-26T16:46:54.000Z

django_redux/engine.py

channels-frontend/django_redux

0215fcd09cd02049778e98d77467d4900c62b8c2

[ "BSD-3-Clause" ]

4

2017-06-28T18:35:21.000Z

2019-01-20T14:49:29.000Z

from channels.layers import get_channel_layer async def send_action(group_name, action): """ Convenience method to dispatch redux actions from channels. Usage:: send_action("group_name", { "type": "MY_ACTION", "payload": { "id": 1, "name": "Lorem", } }) """ channel_layer = get_channel_layer() data = { 'type': "redux.action", 'action': action, } await channel_layer.group_send( group_name, data ) def action(action_type): def wrap(func): func.action_type = action_type return func return wrap

18.833333

63

0.538348

40502351eaf29688fab9e182e67fd1cd214d5167

196

py

Python

rhea/cores/usbext/fpgalink/__init__.py

meetps/rhea

f8a9a08fb5e14c5c4488ef68a2dff4d18222c2c0

[ "MIT" ]

1

2022-03-16T23:56:09.000Z

rhea/cores/usbext/fpgalink/__init__.py

meetps/rhea

f8a9a08fb5e14c5c4488ef68a2dff4d18222c2c0

[ "MIT" ]

null

rhea/cores/usbext/fpgalink/__init__.py

meetps/rhea

f8a9a08fb5e14c5c4488ef68a2dff4d18222c2c0

[ "MIT" ]

null

from __future__ import absolute_import from . import _fpgalink_fx2 as fpgalink from ._fpgalink_fx2 import get_interfaces from ._fpgalink_fx2 import fpgalink_fx2 from ._fl_convert import convert

24.5

41

0.857143

f0c8340e9b6f928085a100a3d56bf3541cf1d7f3

900

py

Python

telegram_gcloner/handlers/cancel.py

Selena-of/ClonBot

22a291a8a56c6bb33596f7c9cecd2857e78ec55e

[ "MIT" ]

79

2021-04-06T16:14:37.000Z

2022-03-31T06:35:50.000Z

telegram_gcloner/handlers/cancel.py

Selena-of/ClonBot

22a291a8a56c6bb33596f7c9cecd2857e78ec55e

[ "MIT" ]

1

2021-05-01T14:03:21.000Z

2021-11-18T18:29:44.000Z

telegram_gcloner/handlers/cancel.py

Selena-of/ClonBot

22a291a8a56c6bb33596f7c9cecd2857e78ec55e

[ "MIT" ]

184

2021-04-06T16:14:38.000Z

2022-03-31T06:35:52.000Z

#!/usr/bin/python3 # -*- coding: utf-8 -*- import logging from telegram.ext import Dispatcher, CallbackQueryHandler from utils.helper import alert_users from utils.restricted import restricted logger = logging.getLogger(__name__) def init(dispatcher: Dispatcher): """Provide handlers initialization.""" dispatcher.add_handler(CallbackQueryHandler(cancel, pattern=r'^cancel$')) @restricted def cancel(update, context): query = update.callback_query if query.message.chat_id < 0 and \ (not query.message.reply_to_message or query.from_user.id != query.message.reply_to_message.from_user.id): alert_users(context, update.effective_user, 'invalid caller', query.data) query.answer(text='Yo-he!', show_alert=True) return # query.message.edit_reply_markup(reply_markup=None) query.message.delete()

31.034483

82

0.703333

1e03eadfb95d1b9182301855a5865086b87ad09d

598

py

Python

tests/_testsite/urls.py

kapt-labs/django-cross-site-urls-gitlab

b0d7415ee0f86e64c71d50ccea73f9c4a46e8768

[ "BSD-3-Clause" ]

1

2016-03-01T20:49:18.000Z

tests/_testsite/urls.py

kapt-labs/django-cross-site-urls-gitlab

b0d7415ee0f86e64c71d50ccea73f9c4a46e8768

[ "BSD-3-Clause" ]

null

tests/_testsite/urls.py

kapt-labs/django-cross-site-urls-gitlab

b0d7415ee0f86e64c71d50ccea73f9c4a46e8768

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.conf.urls import include from django.conf.urls.i18n import i18n_patterns from django.conf.urls import url from django.contrib import admin from django.contrib.staticfiles.urls import staticfiles_urlpatterns # Local application / specific library imports from cross_site_urls.conf import settings as cross_site_settings admin.autodiscover() urlpatterns = i18n_patterns( '', url((r'^{}').format(cross_site_settings.DEFAULT_API_URL), include('cross_site_urls.urls')), ) urlpatterns += staticfiles_urlpatterns()

27.181818

95

0.794314

f8d4f57addaa672900b4eb6bed569aa75c240016

18,467

py

Python

scripts/audio_thread_log_viewer/viewer_c3.py

TinkerBoard-Android/external-adhd

ffc625793b55ea7f8e511b9362a88b6ba080c983

[ "BSD-1-Clause", "BSD-3-Clause" ]

1

2021-03-08T11:49:58.000Z

scripts/audio_thread_log_viewer/viewer_c3.py

TinkerBoard-Android/external-adhd

ffc625793b55ea7f8e511b9362a88b6ba080c983

[ "BSD-1-Clause", "BSD-3-Clause" ]

1

2021-03-23T18:17:08.000Z

scripts/audio_thread_log_viewer/viewer_c3.py

TinkerBoard-Android/external-adhd

ffc625793b55ea7f8e511b9362a88b6ba080c983

[ "BSD-1-Clause", "BSD-3-Clause" ]

1

2016-10-19T08:33:40.000Z

#!/usr/bin/python # # Copyright 2016 The Chromium OS Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. # """Generates an HTML file with plot of buffer level in the audio thread log.""" import argparse import collections import logging import string import time page_content = string.Template(""" <html meta charset="UTF8"> <head>  <link href="https://rawgit.com/masayuki0812/c3/master/c3.css" rel="stylesheet" type="text/css">  <script src="https://d3js.org/d3.v4.min.js" charset="utf-8"></script> <script src="https://rawgit.com/masayuki0812/c3/master/c3.js" charset="utf-8"></script> <style type="text/css"> .c3-grid text { fill: grey; } .event_log_box { font-family: 'Courier New', Courier, 'Lucida Sans Typewriter', 'Lucida Typewriter', monospace; font-size: 20px; font-style: normal; font-variant: normal; font-weight: 300; line-height: 26.4px; white-space: pre; height:50%; width:48%; border:1px solid #ccc; overflow:auto; } .checkbox { font-size: 30px; border: 2px; } .device { font-size: 15px; } .stream{ font-size: 15px; } .fetch{ } .wake{ } </style> <script type="text/javascript"> var selected = null; draw_chart = function() { var chart = c3.generate({ data: { x: 'time', columns: [ ['time', $times], ['buffer_level', $buffer_levels], ], type: 'bar', types: { buffer_level: 'line', }, onclick: function (d, i) { elm = document.getElementById(d.x.toFixed(9)); if (selected) selected.style.color = ''; if (elm === null) { console.error("Can not find element by ID %s", d.x.toFixed(9)); return; } elm.style.color = 'blue'; elm.scrollIntoView(); selected = elm; }, }, zoom: { enabled: true, }, grid: { x: { lines: [ $grids, ], }, }, axis: { y: {min: 0, max: $max_y}, }, }); }; logs = `$logs`; put_logs = function () { document.getElementById('logs').innerHTML = logs; }; set_initial_checkbox_value = function () { document.getElementById('device').checked = true; document.getElementById('stream').checked = true; document.getElementById('fetch').checked = true; document.getElementById('wake').checked = true; } window.onload = function() { draw_chart(); put_logs(); set_initial_checkbox_value(); }; function handleClick(checkbox) { var class_name = checkbox.id; var elements = document.getElementsByClassName(class_name); var i; if (checkbox.checked) { display_value = "block"; } else { display_value = "none" } console.log("change " + class_name + " to " + display_value); for (i = 0; i < elements.length; i++) { elements[i].style.display = display_value; } } </script> </head> <body> <div id="chart" style="height:50%; width:100%" ></div> <div style="margin:0 auto"; class="checkbox"> <label><input type="checkbox" onclick="handleClick(this);" id="device">Show device removed/added event</label> <label><input type="checkbox" onclick="handleClick(this);" id="stream">Show stream removed/added event</label> <label><input type="checkbox" onclick="handleClick(this);" id="fetch">Show fetch event</label> <label><input type="checkbox" onclick="handleClick(this);" id="wake">Show wake by num_fds=1 event</label> </div> <div class="event_log_box", id="logs", style="float:left;"></div> <textarea class="event_log_box", id="text", style="float:right;"></textarea> </body> </html> """) def StrToTimestamp(s): """Converts a time string to a timestamp. @param s: A time string like "2019-07-02T15:30:46.684190644". @returns: Returns a timestamp string like "55846.684190644". """ fmt = "%Y-%m-%dT%H:%M:%S" t = time.strptime(s[:-10], fmt) # Ignore date to avoid a long timestamp. ts = t.tm_hour * 3600 + t.tm_min * 60 + t.tm_sec return "{:d}.{}".format(ts, s[-9:]) Tag = collections.namedtuple('Tag', ['time', 'text', 'position', 'class_name']) """ The tuple for tags shown on the plot on certain time. text is the tag to show, position is the tag position, which is one of 'start', 'middle', 'end', class_name is one of 'device', 'stream', 'fetch', and 'wake' which will be their CSS class name. """ class EventData(object): """The base class of an event.""" def __init__(self, time, name): """Initializes an EventData. @param time: A string for event time. @param name: A string for event name. """ self.time = time self.name = name self._text = None self._position = None self._class_name = None def GetTag(self): """Gets the tag for this event. @returns: A Tag object. Returns None if no need to show tag. """ if self._text: return Tag( time=self.time, text=self._text, position=self._position, class_name=self._class_name) return None class DeviceEvent(EventData): """Class for device event.""" def __init__(self, time, name, device): """Initializes a DeviceEvent. @param time: A string for event time. @param name: A string for event name. @param device: A string for device index. """ super(DeviceEvent, self).__init__(time, name) self.device = device self._position = 'start' self._class_name = 'device' class DeviceRemovedEvent(DeviceEvent): """Class for device removed event.""" def __init__(self, time, name, device): """Initializes a DeviceRemovedEvent. @param time: A string for event time. @param name: A string for event name. @param device: A string for device index. """ super(DeviceRemovedEvent, self).__init__(time, name, device) self._text = 'Removed Device %s' % self.device class DeviceAddedEvent(DeviceEvent): """Class for device added event.""" def __init__(self, time, name, device): """Initializes a DeviceAddedEvent. @param time: A string for event time. @param name: A string for event name. @param device: A string for device index. """ super(DeviceAddedEvent, self).__init__(time, name, device) self._text = 'Added Device %s' % self.device class LevelEvent(DeviceEvent): """Class for device event with buffer level.""" def __init__(self, time, name, device, level): """Initializes a LevelEvent. @param time: A string for event time. @param name: A string for event name. @param device: A string for device index. @param level: An int for buffer level. """ super(LevelEvent, self).__init__(time, name, device) self.level = level class StreamEvent(EventData): """Class for event with stream.""" def __init__(self, time, name, stream): """Initializes a StreamEvent. @param time: A string for event time. @param name: A string for event name. @param stream: A string for stream id. """ super(StreamEvent, self).__init__(time, name) self.stream = stream self._class_name = 'stream' class FetchStreamEvent(StreamEvent): """Class for stream fetch event.""" def __init__(self, time, name, stream): """Initializes a FetchStreamEvent. @param time: A string for event time. @param name: A string for event name. @param stream: A string for stream id. """ super(FetchStreamEvent, self).__init__(time, name, stream) self._text = 'Fetch %s' % self.stream self._position = 'end' self._class_name = 'fetch' class StreamAddedEvent(StreamEvent): """Class for stream added event.""" def __init__(self, time, name, stream): """Initializes a StreamAddedEvent. @param time: A string for event time. @param name: A string for event name. @param stream: A string for stream id. """ super(StreamAddedEvent, self).__init__(time, name, stream) self._text = 'Add stream %s' % self.stream self._position = 'middle' class StreamRemovedEvent(StreamEvent): """Class for stream removed event.""" def __init__(self, time, name, stream): """Initializes a StreamRemovedEvent. @param time: A string for event time. @param name: A string for event name. @param stream: A string for stream id. """ super(StreamRemovedEvent, self).__init__(time, name, stream) self._text = 'Remove stream %s' % self.stream self._position = 'middle' class WakeEvent(EventData): """Class for wake event.""" def __init__(self, time, name, num_fds): """Initializes a WakeEvent. @param time: A string for event time. @param name: A string for event name. @param num_fds: A string for number of fd that wakes audio thread up. """ super(WakeEvent, self).__init__(time, name) self._position = 'middle' self._class_name = 'wake' if num_fds != '0': self._text = 'num_fds %s' % num_fds class C3LogWriter(object): """Class to handle event data and fill an HTML page using c3.js library""" def __init__(self): """Initializes a C3LogWriter.""" self.times = [] self.buffer_levels = [] self.tags = [] self.max_y = 0 def AddEvent(self, event): """Digests an event. Add a tag if this event needs to be shown on grid. Add a buffer level data into buffer_levels if this event has buffer level. @param event: An EventData object. """ tag = event.GetTag() if tag: self.tags.append(tag) if isinstance(event, LevelEvent): self.times.append(event.time) self.buffer_levels.append(str(event.level)) if event.level > self.max_y: self.max_y = event.level logging.debug('add data for a level event %s: %s', event.time, event.level) if (isinstance(event, DeviceAddedEvent) or isinstance(event, DeviceRemovedEvent)): self.times.append(event.time) self.buffer_levels.append('null') def _GetGrids(self): """Gets the content to be filled for grids. @returns: A str for grid with format: '{value: time1, text: "tag1", position: "position1"}, {value: time1, text: "tag1"},...' """ grids = [] for tag in self.tags: content = ('{value: %s, text: "%s", position: "%s", ' 'class: "%s"}') % ( tag.time, tag.text, tag.position, tag.class_name) grids.append(content) grids_joined = ', '.join(grids) return grids_joined def FillPage(self, page_template): """Fills in the page template with content. @param page_template: A string for HTML page content with variables to be filled. @returns: A string for filled page. """ times = ', '.join(self.times) buffer_levels = ', '.join(self.buffer_levels) grids = self._GetGrids() filled = page_template.safe_substitute( times=times, buffer_levels=buffer_levels, grids=grids, max_y=str(self.max_y)) return filled class EventLogParser(object): """Class for event log parser.""" def __init__(self): """Initializes an EventLogParse.""" self.parsed_events = [] def AddEventLog(self, event_log): """Digests a line of event log. @param event_log: A line for event log. """ event = self._ParseOneLine(event_log) if event: self.parsed_events.append(event) def GetParsedEvents(self): """Gets the list of parsed events. @returns: A list of parsed EventData. """ return self.parsed_events def _ParseOneLine(self, line): """Parses one line of event log. Split a line like 2019-07-02T15:30:46.683829810 cras atlog WRITE_STREAMS_FETCH_STREAM id:1e0000 cbth:512 delay:1136 into time, name, and props where time = '54946.683829810' name = 'WRITE_STREAMS_FETCH_STREAM' props = { 'id': 0, 'cb_th': 512, 'delay': 1136 } @param line: A line of event log. @returns: A EventData object. """ line_split = line.split() time, name = StrToTimestamp(line_split[0]), line_split[3] logging.debug('time: %s, name: %s', time, name) props = {} for index in xrange(4, len(line_split)): key, value = line_split[index].split(':')[:2] props[key] = value logging.debug('props: %s', props) return self._CreateEventData(time, name, props) def _CreateEventData(self, time, name, props): """Creates an EventData based on event name. @param time: A string for event time. @param name: A string for event name. @param props: A dict for event properties. @returns: A EventData object. """ if name == 'WRITE_STREAMS_FETCH_STREAM': return FetchStreamEvent(time, name, stream=props['id']) if name == 'STREAM_ADDED': return StreamAddedEvent(time, name, stream=props['id']) if name == 'STREAM_REMOVED': return StreamRemovedEvent(time, name, stream=props['id']) if name in ['FILL_AUDIO', 'SET_DEV_WAKE']: return LevelEvent( time, name, device=props['dev'], level=int(props['hw_level'])) if name == 'DEV_ADDED': return DeviceAddedEvent(time, name, device=props['dev']) if name == 'DEV_REMOVED': return DeviceRemovedEvent(time, name, device=props['dev']) if name == 'WAKE': return WakeEvent(time, name, num_fds=props['num_fds']) return None class AudioThreadLogParser(object): """Class for audio thread log parser.""" def __init__(self, path): """Initializes an AudioThreadLogParser. @param path: The audio thread log file path. """ self.path = path self.content = None def Parse(self): """Prases the audio thread logs. @returns: A list of event log lines. """ logging.debug('Using file: %s', self.path) with open(self.path, 'r') as f: self.content = f.read().splitlines() # Event logs starting at two lines after 'Audio Thread Event Log'. index_start = self.content.index('Audio Thread Event Log:') + 2 # If input is from audio_diagnostic result, use aplay -l line to find # the end of audio thread event logs. try: index_end = self.content.index('=== aplay -l ===') except ValueError: logging.debug( 'Can not find aplay line. This is not from diagnostic') index_end = len(self.content) event_logs = self.content[index_start:index_end] logging.info('Parsed %s log events', len(event_logs)) return event_logs def FillLogs(self, page_template): """Fills the HTML page template with contents for audio thread logs. @param page_template: A string for HTML page content with log variable to be filled. @returns: A string for filled page. """ logs = [] for s in self.content: if 'atlog' in s: time = StrToTimestamp(s.split()[0]) logs.append('<label id="{}">{}</label>'.format(time, s)) else: logs.append(s) logs = '\n'.join(logs) return page_template.substitute(logs=logs) def ParseArgs(): """Parses the arguments. @returns: The namespace containing parsed arguments. """ parser = argparse.ArgumentParser( description='Draw time chart from audio thread log', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('FILE', type=str, help='The audio thread log file') parser.add_argument('-o', type=str, dest='output', default='view.html', help='The output HTML file') parser.add_argument('-d', dest='debug', action='store_true', default=False, help='Show debug message') return parser.parse_args() def Main(): """The Main program.""" options = ParseArgs() logging.basicConfig( format='%(asctime)s:%(levelname)s:%(message)s', level=logging.DEBUG if options.debug else logging.INFO) # Gets lines of event logs. audio_thread_log_parser = AudioThreadLogParser(options.FILE) event_logs = audio_thread_log_parser.Parse() # Parses event logs into events. event_log_parser = EventLogParser() for event_log in event_logs: event_log_parser.AddEventLog(event_log) events = event_log_parser.GetParsedEvents() # Reads in events in preparation of filling HTML template. c3_writer = C3LogWriter() for event in events: c3_writer.AddEvent(event) # Fills in buffer level chart. page_content_with_chart = c3_writer.FillPage(page_content) # Fills in audio thread log into text box. page_content_with_chart_and_logs = audio_thread_log_parser.FillLogs( string.Template(page_content_with_chart)) with open(options.output, 'w') as f: f.write(page_content_with_chart_and_logs) if __name__ == '__main__': Main()

30.523967

116

0.586722

6ee75387f7debd95d17fe3c3b04f51af21a58b44

66

py

Python

tokenfe/task/tests.py

jameem-pixel/ticketproject

d8ae4c3a0dc924bc99d8ba50eaa722935710ef93

[ "Apache-2.0" ]

null

tokenfe/task/tests.py

jameem-pixel/ticketproject

d8ae4c3a0dc924bc99d8ba50eaa722935710ef93

[ "Apache-2.0" ]

null

tokenfe/task/tests.py

jameem-pixel/ticketproject

d8ae4c3a0dc924bc99d8ba50eaa722935710ef93

[ "Apache-2.0" ]

1

2022-01-22T18:58:20.000Z

from django.test import TestCase # Create your tests here.

16.5

33

0.712121

ca880b599c44d8861e1b3e9e7a2aab49c55c473e

31,913

py

Python

time_series/Lib/site-packages/statsmodels/discrete/count_model.py

Evans-colon/Time_Series_Analysis_Forcasting

cf7229d8558013ffcff0780b5ba7a2e2b95d7253

[ "MIT" ]

null

time_series/Lib/site-packages/statsmodels/discrete/count_model.py

Evans-colon/Time_Series_Analysis_Forcasting

cf7229d8558013ffcff0780b5ba7a2e2b95d7253

[ "MIT" ]

5

2022-02-13T14:38:04.000Z

2022-02-15T00:13:07.000Z

time_series/Lib/site-packages/statsmodels/discrete/count_model.py

Evans-colon/Time_Series_Analysis_Forcasting

cf7229d8558013ffcff0780b5ba7a2e2b95d7253

[ "MIT" ]

4

2022-02-04T22:58:27.000Z

2022-02-14T19:29:18.000Z

__all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson", "ZeroInflatedNegativeBinomialP"] import warnings import numpy as np import statsmodels.base.model as base import statsmodels.base.wrapper as wrap import statsmodels.regression.linear_model as lm from statsmodels.discrete.discrete_model import (DiscreteModel, CountModel, Poisson, Logit, CountResults, L1CountResults, Probit, _discrete_results_docs, _validate_l1_method, GeneralizedPoisson, NegativeBinomialP) from statsmodels.distributions import zipoisson, zigenpoisson, zinegbin from statsmodels.tools.numdiff import approx_fprime, approx_hess from statsmodels.tools.decorators import cache_readonly from statsmodels.tools.sm_exceptions import ConvergenceWarning from statsmodels.compat.pandas import Appender _doc_zi_params = """ exog_infl : array_like or None Explanatory variables for the binary inflation model, i.e. for mixing probability model. If None, then a constant is used. offset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. inflation : {'logit', 'probit'} The model for the zero inflation, either Logit (default) or Probit """ class GenericZeroInflated(CountModel): __doc__ = """ Generic Zero Inflated Model %(params)s %(extra_params)s Attributes ---------- endog : ndarray A reference to the endogenous response variable exog : ndarray A reference to the exogenous design. exog_infl : ndarray A reference to the zero-inflated exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : _doc_zi_params + base._missing_param_doc} def __init__(self, endog, exog, exog_infl=None, offset=None, inflation='logit', exposure=None, missing='none', **kwargs): super(GenericZeroInflated, self).__init__(endog, exog, offset=offset, exposure=exposure, missing=missing, **kwargs) if exog_infl is None: self.k_inflate = 1 self._no_exog_infl = True self.exog_infl = np.ones((endog.size, self.k_inflate), dtype=np.float64) else: self.exog_infl = exog_infl self.k_inflate = exog_infl.shape[1] self._no_exog_infl = False if len(exog.shape) == 1: self.k_exog = 1 else: self.k_exog = exog.shape[1] self.infl = inflation if inflation == 'logit': self.model_infl = Logit(np.zeros(self.exog_infl.shape[0]), self.exog_infl) self._hessian_inflate = self._hessian_logit elif inflation == 'probit': self.model_infl = Probit(np.zeros(self.exog_infl.shape[0]), self.exog_infl) self._hessian_inflate = self._hessian_probit else: raise ValueError("inflation == %s, which is not handled" % inflation) self.inflation = inflation self.k_extra = self.k_inflate if len(self.exog) != len(self.exog_infl): raise ValueError('exog and exog_infl have different number of' 'observation. `missing` handling is not supported') infl_names = ['inflate_%s' % i for i in self.model_infl.data.param_names] self.exog_names[:] = infl_names + list(self.exog_names) self.exog_infl = np.asarray(self.exog_infl, dtype=np.float64) self._init_keys.extend(['exog_infl', 'inflation']) self._null_drop_keys = ['exog_infl'] def loglike(self, params): """ Loglikelihood of Generic Zero Inflated model. Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ----- .. math:: \\ln L=\\sum_{y_{i}=0}\\ln(w_{i}+(1-w_{i})*P_{main\\_model})+ \\sum_{y_{i}>0}(\\ln(1-w_{i})+L_{main\\_model}) where P - pdf of main model, L - loglike function of main model. """ return np.sum(self.loglikeobs(params)) def loglikeobs(self, params): """ Loglikelihood for observations of Generic Zero Inflated model. Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike : ndarray The log likelihood for each observation of the model evaluated at `params`. See Notes for definition. Notes ----- .. math:: \\ln L=\\ln(w_{i}+(1-w_{i})*P_{main\\_model})+ \\ln(1-w_{i})+L_{main\\_model} where P - pdf of main model, L - loglike function of main model. for observations :math:`i=1,...,n` """ params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] y = self.endog w = self.model_infl.predict(params_infl) w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) llf_main = self.model_main.loglikeobs(params_main) zero_idx = np.nonzero(y == 0)[0] nonzero_idx = np.nonzero(y)[0] llf = np.zeros_like(y, dtype=np.float64) llf[zero_idx] = (np.log(w[zero_idx] + (1 - w[zero_idx]) * np.exp(llf_main[zero_idx]))) llf[nonzero_idx] = np.log(1 - w[nonzero_idx]) + llf_main[nonzero_idx] return llf @Appender(DiscreteModel.fit.__doc__) def fit(self, start_params=None, method='bfgs', maxiter=35, full_output=1, disp=1, callback=None, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs): if start_params is None: offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None start_params = self._get_start_params() if callback is None: # work around perfect separation callback #3895 callback = lambda *x: x mlefit = super(GenericZeroInflated, self).fit(start_params=start_params, maxiter=maxiter, disp=disp, method=method, full_output=full_output, callback=callback, **kwargs) zipfit = self.result_class(self, mlefit._results) result = self.result_class_wrapper(zipfit) if cov_kwds is None: cov_kwds = {} result._get_robustcov_results(cov_type=cov_type, use_self=True, use_t=use_t, **cov_kwds) return result @Appender(DiscreteModel.fit_regularized.__doc__) def fit_regularized(self, start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4, qc_tol=0.03, **kwargs): _validate_l1_method(method) if np.size(alpha) == 1 and alpha != 0: k_params = self.k_exog + self.k_inflate alpha = alpha * np.ones(k_params) extra = self.k_extra - self.k_inflate alpha_p = alpha[:-(self.k_extra - extra)] if (self.k_extra and np.size(alpha) > 1) else alpha if start_params is None: offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0) if np.size(offset) == 1 and offset == 0: offset = None start_params = self.model_main.fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=0, callback=callback, alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params start_params = np.append(np.ones(self.k_inflate), start_params) cntfit = super(CountModel, self).fit_regularized( start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol, size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs) discretefit = self.result_class_reg(self, cntfit) return self.result_class_reg_wrapper(discretefit) def score_obs(self, params): """ Generic Zero Inflated model score (gradient) vector of the log-likelihood Parameters ---------- params : array_like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` """ params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] y = self.endog w = self.model_infl.predict(params_infl) w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) score_main = self.model_main.score_obs(params_main) llf_main = self.model_main.loglikeobs(params_main) llf = self.loglikeobs(params) zero_idx = np.nonzero(y == 0)[0] nonzero_idx = np.nonzero(y)[0] mu = self.model_main.predict(params_main) dldp = np.zeros((self.exog.shape[0], self.k_exog), dtype=np.float64) dldw = np.zeros_like(self.exog_infl, dtype=np.float64) dldp[zero_idx,:] = (score_main[zero_idx].T * (1 - (w[zero_idx]) / np.exp(llf[zero_idx]))).T dldp[nonzero_idx,:] = score_main[nonzero_idx] if self.inflation == 'logit': dldw[zero_idx,:] = (self.exog_infl[zero_idx].T * w[zero_idx] * (1 - w[zero_idx]) * (1 - np.exp(llf_main[zero_idx])) / np.exp(llf[zero_idx])).T dldw[nonzero_idx,:] = -(self.exog_infl[nonzero_idx].T * w[nonzero_idx]).T elif self.inflation == 'probit': return approx_fprime(params, self.loglikeobs) return np.hstack((dldw, dldp)) def score(self, params): return self.score_obs(params).sum(0) def _hessian_main(self, params): pass def _hessian_logit(self, params): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] y = self.endog w = self.model_infl.predict(params_infl) w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) score_main = self.model_main.score_obs(params_main) llf_main = self.model_main.loglikeobs(params_main) llf = self.loglikeobs(params) zero_idx = np.nonzero(y == 0)[0] nonzero_idx = np.nonzero(y)[0] hess_arr = np.zeros((self.k_inflate, self.k_exog + self.k_inflate)) pmf = np.exp(llf) #d2l/dw2 for i in range(self.k_inflate): for j in range(i, -1, -1): hess_arr[i, j] = (( self.exog_infl[zero_idx, i] * self.exog_infl[zero_idx, j] * (w[zero_idx] * (1 - w[zero_idx]) * ((1 - np.exp(llf_main[zero_idx])) * (1 - 2 * w[zero_idx]) * np.exp(llf[zero_idx]) - (w[zero_idx] - w[zero_idx]**2) * (1 - np.exp(llf_main[zero_idx]))**2) / pmf[zero_idx]**2)).sum() - (self.exog_infl[nonzero_idx, i] * self.exog_infl[nonzero_idx, j] * w[nonzero_idx] * (1 - w[nonzero_idx])).sum()) #d2l/dpdw for i in range(self.k_inflate): for j in range(self.k_exog): hess_arr[i, j + self.k_inflate] = -(score_main[zero_idx, j] * w[zero_idx] * (1 - w[zero_idx]) * self.exog_infl[zero_idx, i] / pmf[zero_idx]).sum() return hess_arr def _hessian_probit(self, params): pass def hessian(self, params): """ Generic Zero Inflated model Hessian matrix of the loglikelihood Parameters ---------- params : array_like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- """ hess_arr_main = self._hessian_main(params) hess_arr_infl = self._hessian_inflate(params) if hess_arr_main is None or hess_arr_infl is None: return approx_hess(params, self.loglike) dim = self.k_exog + self.k_inflate hess_arr = np.zeros((dim, dim)) hess_arr[:self.k_inflate,:] = hess_arr_infl hess_arr[self.k_inflate:,self.k_inflate:] = hess_arr_main tri_idx = np.triu_indices(self.k_exog + self.k_inflate, k=1) hess_arr[tri_idx] = hess_arr.T[tri_idx] return hess_arr def predict(self, params, exog=None, exog_infl=None, exposure=None, offset=None, which='mean'): """ Predict response variable of a count model given exogenous variables. Parameters ---------- params : array_like The parameters of the model exog : ndarray, optional A reference to the exogenous design. If not assigned, will be used exog from fitting. exog_infl : ndarray, optional A reference to the zero-inflated exogenous design. If not assigned, will be used exog from fitting. offset : ndarray, optional Offset is added to the linear prediction with coefficient equal to 1. exposure : ndarray, optional Log(exposure) is added to the linear prediction with coefficient equal to 1. If exposure is specified, then it will be logged by the method. The user does not need to log it first. which : str, optional Define values that will be predicted. 'mean', 'mean-main', 'linear', 'mean-nonzero', 'prob-zero, 'prob', 'prob-main' Default is 'mean'. Notes ----- """ no_exog = False if exog is None: no_exog = True exog = self.exog if exog_infl is None: if no_exog: exog_infl = self.exog_infl else: if self._no_exog_infl: exog_infl = np.ones((len(exog), 1)) else: exog_infl = np.asarray(exog_infl) if exog_infl.ndim == 1 and self.k_inflate == 1: exog_infl = exog_infl[:, None] if exposure is None: if no_exog: exposure = getattr(self, 'exposure', 0) else: exposure = 0 else: exposure = np.log(exposure) if offset is None: if no_exog: offset = getattr(self, 'offset', 0) else: offset = 0 params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] prob_main = 1 - self.model_infl.predict(params_infl, exog_infl) lin_pred = np.dot(exog, params_main[:self.exog.shape[1]]) + exposure + offset # Refactor: This is pretty hacky, # there should be an appropriate predict method in model_main # this is just prob(y=0 | model_main) tmp_exog = self.model_main.exog tmp_endog = self.model_main.endog tmp_offset = getattr(self.model_main, 'offset', ['no']) tmp_exposure = getattr(self.model_main, 'exposure', ['no']) self.model_main.exog = exog self.model_main.endog = np.zeros((exog.shape[0])) self.model_main.offset = offset self.model_main.exposure = exposure llf = self.model_main.loglikeobs(params_main) self.model_main.exog = tmp_exog self.model_main.endog = tmp_endog # tmp_offset might be an array with elementwise equality testing if len(tmp_offset) == 1 and tmp_offset[0] == 'no': del self.model_main.offset else: self.model_main.offset = tmp_offset if len(tmp_exposure) == 1 and tmp_exposure[0] == 'no': del self.model_main.exposure else: self.model_main.exposure = tmp_exposure # end hack prob_zero = (1 - prob_main) + prob_main * np.exp(llf) if which == 'mean': return prob_main * np.exp(lin_pred) elif which == 'mean-main': return np.exp(lin_pred) elif which == 'linear': return lin_pred elif which == 'mean-nonzero': return prob_main * np.exp(lin_pred) / (1 - prob_zero) elif which == 'prob-zero': return prob_zero elif which == 'prob-main': return prob_main elif which == 'prob': return self._predict_prob(params, exog, exog_infl, exposure, offset) else: raise ValueError('which = %s is not available' % which) class ZeroInflatedPoisson(GenericZeroInflated): __doc__ = """ Poisson Zero Inflated Model %(params)s %(extra_params)s Attributes ---------- endog : ndarray A reference to the endogenous response variable exog : ndarray A reference to the exogenous design. exog_infl : ndarray A reference to the zero-inflated exogenous design. """ % {'params' : base._model_params_doc, 'extra_params' : _doc_zi_params + base._missing_param_doc} def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', missing='none', **kwargs): super(ZeroInflatedPoisson, self).__init__(endog, exog, offset=offset, inflation=inflation, exog_infl=exog_infl, exposure=exposure, missing=missing, **kwargs) self.model_main = Poisson(self.endog, self.exog, offset=offset, exposure=exposure) self.distribution = zipoisson self.result_class = ZeroInflatedPoissonResults self.result_class_wrapper = ZeroInflatedPoissonResultsWrapper self.result_class_reg = L1ZeroInflatedPoissonResults self.result_class_reg_wrapper = L1ZeroInflatedPoissonResultsWrapper def _hessian_main(self, params): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] y = self.endog w = self.model_infl.predict(params_infl) w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) score = self.score(params) zero_idx = np.nonzero(y == 0)[0] nonzero_idx = np.nonzero(y)[0] mu = self.model_main.predict(params_main) hess_arr = np.zeros((self.k_exog, self.k_exog)) coeff = (1 + w[zero_idx] * (np.exp(mu[zero_idx]) - 1)) #d2l/dp2 for i in range(self.k_exog): for j in range(i, -1, -1): hess_arr[i, j] = (( self.exog[zero_idx, i] * self.exog[zero_idx, j] * mu[zero_idx] * (w[zero_idx] - 1) * (1 / coeff - w[zero_idx] * mu[zero_idx] * np.exp(mu[zero_idx]) / coeff**2)).sum() - (mu[nonzero_idx] * self.exog[nonzero_idx, i] * self.exog[nonzero_idx, j]).sum()) return hess_arr def _predict_prob(self, params, exog, exog_infl, exposure, offset): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1)) if len(exog_infl.shape) < 2: transform = True w = np.atleast_2d( self.model_infl.predict(params_infl, exog_infl))[:, None] else: transform = False w = self.model_infl.predict(params_infl, exog_infl)[:, None] w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) mu = self.model_main.predict(params_main, exog, offset=offset)[:, None] result = self.distribution.pmf(counts, mu, w) return result[0] if transform else result def _get_start_params(self): start_params = self.model_main.fit(disp=0, method="nm").params start_params = np.append(np.ones(self.k_inflate) * 0.1, start_params) return start_params class ZeroInflatedGeneralizedPoisson(GenericZeroInflated): __doc__ = """ Zero Inflated Generalized Poisson Model %(params)s %(extra_params)s Attributes ---------- endog : ndarray A reference to the endogenous response variable exog : ndarray A reference to the exogenous design. exog_infl : ndarray A reference to the zero-inflated exogenous design. p : scalar P denotes parametrizations for ZIGP regression. """ % {'params' : base._model_params_doc, 'extra_params' : _doc_zi_params + """p : float dispersion power parameter for the GeneralizedPoisson model. p=1 for ZIGP-1 and p=2 for ZIGP-2. Default is p=2 """ + base._missing_param_doc} def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', p=2, missing='none', **kwargs): super(ZeroInflatedGeneralizedPoisson, self).__init__(endog, exog, offset=offset, inflation=inflation, exog_infl=exog_infl, exposure=exposure, missing=missing, **kwargs) self.model_main = GeneralizedPoisson(self.endog, self.exog, offset=offset, exposure=exposure, p=p) self.distribution = zigenpoisson self.k_exog += 1 self.k_extra += 1 self.exog_names.append("alpha") self.result_class = ZeroInflatedGeneralizedPoissonResults self.result_class_wrapper = ZeroInflatedGeneralizedPoissonResultsWrapper self.result_class_reg = L1ZeroInflatedGeneralizedPoissonResults self.result_class_reg_wrapper = L1ZeroInflatedGeneralizedPoissonResultsWrapper def _get_init_kwds(self): kwds = super(ZeroInflatedGeneralizedPoisson, self)._get_init_kwds() kwds['p'] = self.model_main.parameterization + 1 return kwds def _predict_prob(self, params, exog, exog_infl, exposure, offset): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] p = self.model_main.parameterization counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1)) if len(exog_infl.shape) < 2: transform = True w = np.atleast_2d( self.model_infl.predict(params_infl, exog_infl))[:, None] else: transform = False w = self.model_infl.predict(params_infl, exog_infl)[:, None] w[w == 1.] = np.nextafter(1, 0) mu = self.model_main.predict(params_main, exog, exposure=exposure, offset=offset)[:, None] result = self.distribution.pmf(counts, mu, params_main[-1], p, w) return result[0] if transform else result def _get_start_params(self): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=ConvergenceWarning) start_params = ZeroInflatedPoisson(self.endog, self.exog, exog_infl=self.exog_infl).fit(disp=0).params start_params = np.append(start_params, 0.1) return start_params class ZeroInflatedNegativeBinomialP(GenericZeroInflated): __doc__ = """ Zero Inflated Generalized Negative Binomial Model %(params)s %(extra_params)s Attributes ---------- endog : ndarray A reference to the endogenous response variable exog : ndarray A reference to the exogenous design. exog_infl : ndarray A reference to the zero-inflated exogenous design. p : scalar P denotes parametrizations for ZINB regression. p=1 for ZINB-1 and p=2 for ZINB-2. Default is p=2 """ % {'params' : base._model_params_doc, 'extra_params' : _doc_zi_params + """p : float dispersion power parameter for the NegativeBinomialP model. p=1 for ZINB-1 and p=2 for ZINM-2. Default is p=2 """ + base._missing_param_doc} def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None, inflation='logit', p=2, missing='none', **kwargs): super(ZeroInflatedNegativeBinomialP, self).__init__(endog, exog, offset=offset, inflation=inflation, exog_infl=exog_infl, exposure=exposure, missing=missing, **kwargs) self.model_main = NegativeBinomialP(self.endog, self.exog, offset=offset, exposure=exposure, p=p) self.distribution = zinegbin self.k_exog += 1 self.k_extra += 1 self.exog_names.append("alpha") self.result_class = ZeroInflatedNegativeBinomialResults self.result_class_wrapper = ZeroInflatedNegativeBinomialResultsWrapper self.result_class_reg = L1ZeroInflatedNegativeBinomialResults self.result_class_reg_wrapper = L1ZeroInflatedNegativeBinomialResultsWrapper def _get_init_kwds(self): kwds = super(ZeroInflatedNegativeBinomialP, self)._get_init_kwds() kwds['p'] = self.model_main.parameterization return kwds def _predict_prob(self, params, exog, exog_infl, exposure, offset): params_infl = params[:self.k_inflate] params_main = params[self.k_inflate:] p = self.model_main.parameterization counts = np.arange(0, np.max(self.endog)+1) if len(exog_infl.shape) < 2: transform = True w = np.atleast_2d( self.model_infl.predict(params_infl, exog_infl))[:, None] else: transform = False w = self.model_infl.predict(params_infl, exog_infl)[:, None] w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps) mu = self.model_main.predict(params_main, exog, exposure=exposure, offset=offset)[:, None] result = self.distribution.pmf(counts, mu, params_main[-1], p, w) return result[0] if transform else result def _get_start_params(self): with warnings.catch_warnings(): warnings.simplefilter("ignore", category=ConvergenceWarning) start_params = self.model_main.fit(disp=0, method='nm').params start_params = np.append(np.zeros(self.k_inflate), start_params) return start_params class ZeroInflatedPoissonResults(CountResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Zero Inflated Poisson", "extra_attr": ""} @cache_readonly def _dispersion_factor(self): mu = self.predict(which='linear') w = 1 - self.predict() / np.exp(self.predict(which='linear')) return (1 + w * np.exp(mu)) def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Not yet implemented for Zero Inflated Models """ raise NotImplementedError("not yet implemented for zero inflation") class L1ZeroInflatedPoissonResults(L1CountResults, ZeroInflatedPoissonResults): pass class ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(ZeroInflatedPoissonResultsWrapper, ZeroInflatedPoissonResults) class L1ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1ZeroInflatedPoissonResultsWrapper, L1ZeroInflatedPoissonResults) class ZeroInflatedGeneralizedPoissonResults(CountResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Zero Inflated Generalized Poisson", "extra_attr": ""} @cache_readonly def _dispersion_factor(self): p = self.model.model_main.parameterization alpha = self.params[self.model.k_inflate:][-1] mu = np.exp(self.predict(which='linear')) w = 1 - self.predict() / mu return ((1 + alpha * mu**p)**2 + w * mu) def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Not yet implemented for Zero Inflated Models """ raise NotImplementedError("not yet implemented for zero inflation") class L1ZeroInflatedGeneralizedPoissonResults(L1CountResults, ZeroInflatedGeneralizedPoissonResults): pass class ZeroInflatedGeneralizedPoissonResultsWrapper( lm.RegressionResultsWrapper): pass wrap.populate_wrapper(ZeroInflatedGeneralizedPoissonResultsWrapper, ZeroInflatedGeneralizedPoissonResults) class L1ZeroInflatedGeneralizedPoissonResultsWrapper( lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1ZeroInflatedGeneralizedPoissonResultsWrapper, L1ZeroInflatedGeneralizedPoissonResults) class ZeroInflatedNegativeBinomialResults(CountResults): __doc__ = _discrete_results_docs % { "one_line_description": "A results class for Zero Inflated Generalized Negative Binomial", "extra_attr": ""} @cache_readonly def _dispersion_factor(self): p = self.model.model_main.parameterization alpha = self.params[self.model.k_inflate:][-1] mu = np.exp(self.predict(which='linear')) w = 1 - self.predict() / mu return (1 + alpha * mu**(p-1) + w * mu) def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False): """Get marginal effects of the fitted model. Not yet implemented for Zero Inflated Models """ raise NotImplementedError("not yet implemented for zero inflation") class L1ZeroInflatedNegativeBinomialResults(L1CountResults, ZeroInflatedNegativeBinomialResults): pass class ZeroInflatedNegativeBinomialResultsWrapper( lm.RegressionResultsWrapper): pass wrap.populate_wrapper(ZeroInflatedNegativeBinomialResultsWrapper, ZeroInflatedNegativeBinomialResults) class L1ZeroInflatedNegativeBinomialResultsWrapper( lm.RegressionResultsWrapper): pass wrap.populate_wrapper(L1ZeroInflatedNegativeBinomialResultsWrapper, L1ZeroInflatedNegativeBinomialResults)

38.264988

98

0.59681

07b529e7a720807fc3252b20de3680d9007dab6e

343

py

Python

test/word_store/test_construction.py

AndrewWasHere/passphrase_generator

8db3b833401036fc6db4ed2b9dc7980e5fec525c

[ "BSD-3-Clause" ]

null

test/word_store/test_construction.py

AndrewWasHere/passphrase_generator

8db3b833401036fc6db4ed2b9dc7980e5fec525c

[ "BSD-3-Clause" ]

null

test/word_store/test_construction.py

AndrewWasHere/passphrase_generator

8db3b833401036fc6db4ed2b9dc7980e5fec525c

[ "BSD-3-Clause" ]

null

""" Copyright 2017, Andrew Lin All rights reserved. This software is licensed under the BSD 3-Clause License. See LICENSE.txt at the root of the project or https://opensource.org/licenses/BSD-3-Clause """ from passphrase.word_store import WordStore def test_nominal(): words = WordStore() assert len(list(words.iter_words())) == 0

21.4375

57

0.74344

7526daa6f0b263e555f30adc5e6af3be286474cf

7,647

py

Python

vis_sv/vis_sv_z1000_L1.py

danforthcenter/plantcv-dev-scripts

57ebc9a031b7141b8965c927c3b7b01ba6504dc1

[ "MIT" ]

null

vis_sv/vis_sv_z1000_L1.py

danforthcenter/plantcv-dev-scripts

57ebc9a031b7141b8965c927c3b7b01ba6504dc1

[ "MIT" ]

null

vis_sv/vis_sv_z1000_L1.py

danforthcenter/plantcv-dev-scripts

57ebc9a031b7141b8965c927c3b7b01ba6504dc1

[ "MIT" ]

null

#!/usr/bin/env python import sys, traceback import cv2 import numpy as np import argparse import string import plantcv as pcv ### Parse command-line arguments def options(): parser = argparse.ArgumentParser(description="Imaging processing with opencv") parser.add_argument("-i", "--image", help="Input image file.", required=True) parser.add_argument("-m", "--roi", help="Input region of interest file.", required=False) parser.add_argument("-o", "--outdir", help="Output directory for image files.", required=True) parser.add_argument("-D", "--debug", help="Turn on debug, prints intermediate images.", action="store_true") args = parser.parse_args() return args ### Main pipeline def main(): # Get options args = options() # Read image img, path, filename = pcv.readimage(args.image) #roi = cv2.imread(args.roi) # Pipeline step device = 0 # Convert RGB to HSV and extract the Saturation channel device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug) # Threshold the Saturation image device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, args.debug) # Median Filter device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug) device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug) # Fill small objects device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug) # Convert RGB to LAB and extract the Blue channel device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug) # Threshold the blue image device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug) device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug) # Fill small objects device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug) # Join the thresholded saturation and blue-yellow images device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug) # Apply Mask (for vis images, mask_color=white) device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug) # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug) device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug) # Threshold the green-magenta and blue images device, maskeda_thresh = pcv.binary_threshold(masked_a, 122, 255, 'dark', device, args.debug) device, maskedb_thresh = pcv.binary_threshold(masked_b, 133, 255, 'light', device, args.debug) # Join the thresholded saturation and blue-yellow images (OR) device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug) device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug) # Fill small objects device, ab_fill = pcv.fill(ab, ab_cnt, 200, device, args.debug) # Apply mask (for vis images, mask_color=white) device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', device, args.debug) # Select area with black bars and find overlapping plant material device, roi1, roi_hierarchy1= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 0, 0,-1900,0) device, id_objects1,obj_hierarchy1 = pcv.find_objects(masked2, ab_fill, device, args.debug) device,roi_objects1, hierarchy1, kept_mask1, obj_area1 = pcv.roi_objects(masked2,'cutto',roi1,roi_hierarchy1,id_objects1,obj_hierarchy1,device, args.debug) device, masked3 = pcv.apply_mask(masked2, kept_mask1, 'white', device, args.debug) device, masked_a1 = pcv.rgb2gray_lab(masked3, 'a', device, args.debug) device, masked_b1 = pcv.rgb2gray_lab(masked3, 'b', device, args.debug) device, maskeda_thresh1 = pcv.binary_threshold(masked_a1, 122, 255, 'dark', device, args.debug) device, maskedb_thresh1 = pcv.binary_threshold(masked_b1, 170, 255, 'light', device, args.debug) device, ab1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device, args.debug) device, ab_cnt1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device, args.debug) device, ab_fill1 = pcv.fill(ab1, ab_cnt1, 300, device, args.debug) device, roi2, roi_hierarchy2= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 1900, 0,0,0) device, id_objects2,obj_hierarchy2 = pcv.find_objects(masked2, ab_fill, device, args.debug) device,roi_objects2, hierarchy2, kept_mask2, obj_area2 = pcv.roi_objects(masked2,'cutto',roi2,roi_hierarchy2,id_objects2,obj_hierarchy2,device, args.debug) device, masked4 = pcv.apply_mask(masked2, kept_mask2, 'white', device, args.debug) device, masked_a2 = pcv.rgb2gray_lab(masked4, 'a', device, args.debug) device, masked_b2 = pcv.rgb2gray_lab(masked4, 'b', device, args.debug) device, maskeda_thresh2 = pcv.binary_threshold(masked_a2, 122, 255, 'dark', device, args.debug) device, maskedb_thresh2 = pcv.binary_threshold(masked_b2, 170, 255, 'light', device, args.debug) device, ab2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device, args.debug) device, ab_cnt2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device, args.debug) device, ab_fill2 = pcv.fill(ab2, ab_cnt2, 200, device, args.debug) device, ab_cnt3 = pcv.logical_or(ab_fill1, ab_fill2, device, args.debug) device, masked3 = pcv.apply_mask(masked2, ab_cnt3, 'white', device, args.debug) # Identify objects device, id_objects3,obj_hierarchy3 = pcv.find_objects(masked2, ab_fill, device, args.debug) # Define ROI device, roi3, roi_hierarchy3= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 500, 0,-450,-530) # Decide which objects to keep and combine with objects overlapping with black bars device,roi_objects3, hierarchy3, kept_mask3, obj_area1 = pcv.roi_objects(img,'cutto',roi3,roi_hierarchy3,id_objects3,obj_hierarchy3,device, args.debug) device, kept_mask4_1 = pcv.logical_or(ab_cnt3, kept_mask3, device, args.debug) device, kept_cnt = pcv.logical_or(ab_cnt3, kept_mask3, device, args.debug) device, kept_mask4 = pcv.fill(kept_mask4_1, kept_cnt, 200, device, args.debug) device, masked5 = pcv.apply_mask(masked2, kept_mask4, 'white', device, args.debug) device, id_objects4,obj_hierarchy4 = pcv.find_objects(masked5, kept_mask4, device, args.debug) device, roi4, roi_hierarchy4= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,False, 0, 0,0,0) device,roi_objects4, hierarchy4, kept_mask4, obj_area = pcv.roi_objects(img,'partial',roi4,roi_hierarchy4,id_objects4,obj_hierarchy4,device, args.debug) # Object combine kept objects device, obj, mask = pcv.object_composition(img, roi_objects4, hierarchy4, device, args.debug) ############## Analysis ################ # Find shape properties, output shape image (optional) device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,args.outdir+'/'+filename) # Shape properties relative to user boundary line (optional) device, boundary_header,boundary_data, boundary_img1= pcv.analyze_bound(img, args.image,obj, mask, 950, device,args.debug,args.outdir+'/'+filename) # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional) device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, kept_mask4, 256, device, args.debug,'all','rgb','v','img',300,args.outdir+'/'+filename) # Output shape and color data pcv.print_results(args.image, shape_header, shape_data) pcv.print_results(args.image, color_header, color_data) pcv.print_results(args.image, boundary_header, boundary_data) if __name__ == '__main__': main()#!/usr/bin/env python

52.737931

168

0.741729

7a5ed1592d9b05c8a304a27c939a38020c2b340f

2,114

py

Python

warpseq/model/instrument.py

simian-terminal/warpseq

f61e68d1e6a9ad15a5e0c899237be784bcff7093

[ "Apache-2.0" ]

3

2021-01-22T01:20:20.000Z

2022-03-10T20:58:42.000Z

warpseq/model/instrument.py

simian-terminal/warpseq

f61e68d1e6a9ad15a5e0c899237be784bcff7093

[ "Apache-2.0" ]

1

2020-08-11T07:21:05.000Z

warpseq/model/instrument.py

simianterminal/warpseq

f61e68d1e6a9ad15a5e0c899237be784bcff7093

[ "Apache-2.0" ]

null

# ------------------------------------------------------------------ # Warp Sequencer # (C) 2020 Michael DeHaan <michael@michaeldehaan.net> & contributors # Apache2 Licensed # ------------------------------------------------------------------ # an instrument adds a channel number to a MIDI device and has some # parameters around supported note ranges. It can also be muted. from .base import NewReferenceObject from .device import Device class Instrument(NewReferenceObject): __slots__ = [ 'name', 'channel', 'device', 'min_octave', 'base_octave', 'max_octave', 'default_velocity', 'muted' ] def __init__(self, name=None, channel=None, device=None, min_octave=0, base_octave=3, max_octave=10, default_velocity=120, muted=False, obj_id=None): self.name = name self.channel = int(channel) self.device = device self.min_octave = min_octave self.base_octave = base_octave self.max_octave = max_octave self.default_velocity = default_velocity self.muted = muted self.obj_id = obj_id super(Instrument,self).__init__() def to_dict(self): result = dict( obj_id = self.obj_id, name = self.name, channel = self.channel, min_octave = self.min_octave, base_octave = self.base_octave, max_octave = self.max_octave, default_velocity = self.default_velocity, muted = self.muted ) if self.device: result['device'] = self.device.obj_id else: result['device'] = None return result @classmethod def from_dict(cls, song, data): return Instrument( obj_id = data['obj_id'], name = data['name'], channel = int(data['channel']), device = song.find_device(data['device']), min_octave = data['min_octave'], base_octave = data['base_octave'], max_octave = data['max_octave'], default_velocity = data['default_velocity'], muted = data['muted'] )

34.096774

153

0.569536

375630a1da1c56e360aae35470702f17b9ba31a7

2,806

py

Python

lib/exabgp/bgp/message/update/attribute/bgpls/prefix/srprefix.py

fser/exabgp

9a41b5f833a00a4d56b1a38f73858d62685065dd

[ "BSD-3-Clause" ]

2

2018-02-07T14:49:11.000Z

2021-09-08T15:31:51.000Z

lib/exabgp/bgp/message/update/attribute/bgpls/prefix/srprefix.py

fser/exabgp

9a41b5f833a00a4d56b1a38f73858d62685065dd

[ "BSD-3-Clause" ]

null

lib/exabgp/bgp/message/update/attribute/bgpls/prefix/srprefix.py

fser/exabgp

9a41b5f833a00a4d56b1a38f73858d62685065dd

[ "BSD-3-Clause" ]

1

2018-12-19T18:02:59.000Z

# encoding: utf-8 """ srprefix.py Created by Evelio Vila Copyright (c) 2014-2017 Exa Networks. All rights reserved. """ import json from struct import unpack from exabgp.vendoring import six from exabgp.vendoring.bitstring import BitArray from exabgp.bgp.message.notification import Notify from exabgp.bgp.message.update.attribute.bgpls.linkstate import LINKSTATE, LsGenericFlags # draft-gredler-idr-bgp-ls-segment-routing-ext-03 # 0 1 2 3 # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # | Type | Length | # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # | Flags | Algorithm | Reserved | # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # | SID/Index/Label (variable) | # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ @LINKSTATE.register() class SrPrefix(object): TLV = 1158 def __init__ (self, flags, sids, sr_algo): self.flags = flags self.sids = sids self.sr_algo = sr_algo def __repr__ (self): return "prefix_flags: %s, sids: %s" % (self.flags, self.sids) @classmethod def unpack (cls,data,length): # We only support IS-IS flags for now. flags = LsGenericFlags.unpack(data[0:1],LsGenericFlags.ISIS_SR_FLAGS) # # Parse Algorithm sr_algo = six.indexbytes(data, 1) # Move pointer 4 bytes: Flags(1) + Algorithm(1) + Reserved(2) data = data[4:] # SID/Index/Label: according to the V and L flags, it contains # either: # * A 3 octet local label where the 20 rightmost bits are used for # encoding the label value. In this case the V and L flags MUST # be set. # # * A 4 octet index defining the offset in the SID/Label space # advertised by this router using the encodings defined in # Section 3.1. In this case V and L flags MUST be unset. sids = [] while data: if flags.flags['V'] and flags.flags['L']: b = BitArray(bytes=data[:3]) sid = b.unpack('uintbe:24')[0] data = data[3:] elif (not flags.flags['V']) and \ (not flags.flags['L']): if len(data) != 4: # Cisco IOS XR Software, Version 6.1.1.19I is not # correctly setting the flags raise Notify(3,5, "SID/Label size doesn't match V and L flag state") sid = unpack('!I', data[:4])[0] data = data[4:] sids.append(sid) return cls(flags=flags, sids=sids, sr_algo=sr_algo) def json (self,compact=None): return ', '.join(['"sr-prefix-flags": {}'.format(self.flags.json()), '"sids": {}'.format(json.dumps(self.sids)), '"sr-algorithm": {}'.format(json.dumps(self.sr_algo))])

35.518987

89

0.556664

617b37d997a3cd28c041baae49d52cf26bc290f1

220

py

Python

arrays/first_not_repeating_char.py

nickaigi/automatic-dollop

eb8222475c7871c1d5710242c5aed8c70ea0d2c8

[ "Unlicense" ]

null

arrays/first_not_repeating_char.py

nickaigi/automatic-dollop

eb8222475c7871c1d5710242c5aed8c70ea0d2c8

[ "Unlicense" ]

null

arrays/first_not_repeating_char.py

nickaigi/automatic-dollop

eb8222475c7871c1d5710242c5aed8c70ea0d2c8

[ "Unlicense" ]

null

def first_not_repeating_character(s): for c in s: if s.index(c) == s.rindex(c): return c return '_' if __name__ == '__main__': s = 'abacabad' print(first_not_repeating_character(s))

20

43

0.604545

d86b1b6c0bb04edd6cec2877caf04b99efefba37

1,629

py

Python

tests/neox_args/test_neoxargs_usage.py

igor0/gpt-neox

3ad61952c290669d3741c01f767d41fdee5215c5

[ "Apache-2.0" ]

1,871

2020-12-22T14:44:29.000Z

2022-03-31T14:21:40.000Z

tests/neox_args/test_neoxargs_usage.py

igor0/gpt-neox

3ad61952c290669d3741c01f767d41fdee5215c5

[ "Apache-2.0" ]

300

2020-12-23T17:51:43.000Z

2022-03-30T17:34:42.000Z

tests/neox_args/test_neoxargs_usage.py

igor0/gpt-neox

3ad61952c290669d3741c01f767d41fdee5215c5

[ "Apache-2.0" ]

235

2020-12-23T19:45:19.000Z

2022-03-31T20:33:47.000Z

""" plausibility check for the usage of neox_args in the megatron codebase """ import pytest import re from ..common import get_root_directory @pytest.mark.cpu def test_neoxargs_usage(): """" checks for code pieces of the pattern "args.*" and verifies that such used arg is defined in NeoXArgs """ from megatron.neox_arguments import NeoXArgs declared_all = True neox_args_attributes = set(NeoXArgs.__dataclass_fields__.keys()) # we exlude a number of properties (implemented with the @property decorator) or functions that we know exists exclude = set(['params_dtype', 'deepspeed_config', 'get', 'pop', 'get_deepspeed_main_args', 'optimizer["params"]', 'attention_config[layer_number]', 'adlr_autoresume_object', 'update_value', 'all_config', 'tensorboard_writer', 'tokenizer', 'train_batch_size]']) # test file by file for filename in (get_root_directory() / "megatron").glob('**/*.py'): if filename.name in ["text_generation_utils.py", "train_tokenizer.py"]: continue # load file with open(filename, 'r') as f: file_contents = f.read() # find args matches matches = list(re.findall(r"(?<=args\.).{2,}?(?=[\s\n(){}+-/*;:,=])", file_contents)) if len(matches) == 0: continue # compare for match in matches: if match not in neox_args_attributes and match not in exclude: print(f"(arguments used not found in neox args): {filename.name}: {match}", flush=True) declared_all = False assert declared_all, "all arguments used in code defined in NeoXArgs"

38.785714

265

0.666053

a2b9d5671ae53cbefb3e73e2c4ecc597b6ae1fc8

1,209

py

Python

13_async_development/sample_code/8_queued_no_waits.py

gdia/The-Complete-Python-Course

ed375b65242249bc749c3e292a6149f8528b9dcf

[ "MIT" ]

29

2019-09-02T21:15:59.000Z

2022-01-14T02:20:05.000Z

13_async_development/sample_code/8_queued_no_waits.py

gdia/The-Complete-Python-Course

ed375b65242249bc749c3e292a6149f8528b9dcf

[ "MIT" ]

2

2020-08-20T05:48:36.000Z

2021-06-02T03:16:31.000Z

13_async_development/sample_code/8_queued_no_waits.py

gdia/The-Complete-Python-Course

ed375b65242249bc749c3e292a6149f8528b9dcf

[ "MIT" ]

38

2019-10-20T14:29:12.000Z

2022-03-27T19:50:05.000Z

import time import random import queue from threading import Thread # still needed for daemon threads from concurrent.futures import ThreadPoolExecutor counter = 0 job_queue = queue.Queue() counter_queue = queue.Queue() def increment_manager(): global counter while True: increment = counter_queue.get() # this waits until an item is available and locks the queue old_counter = counter counter = old_counter + increment job_queue.put((f'New counter value {counter}', '------------')) counter_queue.task_done() # this unlocks the queue # printer_manager and increment_manager run continuously because of the `daemon` flag. Thread(target=increment_manager, daemon=True).start() def printer_manager(): while True: for line in job_queue.get(): print(line) job_queue.task_done() # printer_manager and increment_manager run continuously because of the `daemon` flag. Thread(target=printer_manager, daemon=True).start() def increment_counter(): counter_queue.put(1) with ThreadPoolExecutor(max_workers=10) as pool: [pool.submit(increment_counter) for x in range(10)] counter_queue.join() # wait for counter_queue to be empty job_queue.join() # wait for job_queue to be empty

26.866667

94

0.762614

85d195d594a710336938dcda953fb7fbeae1bb79

3,782

py

Python

prodmanager/settings.py

georgiawang5332/prodmanager

5dbff0789deb0c4f696149345ded3e338c6f59ec

[ "MIT" ]

null

prodmanager/settings.py

georgiawang5332/prodmanager

5dbff0789deb0c4f696149345ded3e338c6f59ec

[ "MIT" ]

null

prodmanager/settings.py

georgiawang5332/prodmanager

5dbff0789deb0c4f696149345ded3e338c6f59ec

[ "MIT" ]

null

""" Django settings for prodmanager project. Generated by 'django-admin startproject' using Django 3.1. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path import os # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve(strict=True).parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'g2f2j02l)q(*z31g@()&-neg2o4d_lg%fqvv@nhztk&twb5px9' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'dashboard', 'django_filters', 'milt_sear', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'prodmanager.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', # 'DIRS': [BASE_DIR / 'templates'], 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'prodmanager.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': 'filt_db', 'USER': 'postgres', 'PASSWORD': 'postgres5', 'HOST': 'localhost', 'PORT': '5432', 'DISABLE_SERVER_SIDE_CURSORS': True, # 游標問題: https://stackoverflow.com/questions/19069722/psycopg2-operationalerror-cursor-does-not-exist } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ # 非必要、方便做語言選單用的 from django.utils.translation import gettext_lazy as _ LANGUAGE_CODE = 'zh-Hant' LANGUAGES = ( ('en', _('English')), ('zh-hant', _('Traditional Chinese')), ) TIME_ZONE = 'Asia/Taipei' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles') # Extra places for collectstatic to find static files. STATICFILES_DIRS = ( os.path.join(BASE_DIR, 'static'), ) # STATICFILES_DIRS = [ BASE_DIR / 'static'] MEDIA_ROOT = os.path.join(BASE_DIR, "static/dist/media_cdn") MEDIA_URL = '/images/'

26.447552

104

0.724484

f7954dc876348518f9d557b91d1e863ed39cd3dc

2,682

py

Python

shop/shipping/modifiers.py

r4co0n/django-shop

69cbddba27066568920cce177511c95824bfe45b

[ "BSD-3-Clause" ]

null

shop/shipping/modifiers.py

r4co0n/django-shop

69cbddba27066568920cce177511c95824bfe45b

[ "BSD-3-Clause" ]

5

2021-03-19T16:04:13.000Z

2022-01-13T03:35:33.000Z

shop/shipping/modifiers.py

r4co0n/django-shop

69cbddba27066568920cce177511c95824bfe45b

[ "BSD-3-Clause" ]

1

2020-07-06T05:06:05.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.utils import timezone from django.utils.translation import ugettext_lazy as _ from shop.modifiers.base import BaseCartModifier class ShippingModifier(BaseCartModifier): """ Base class for all shipping modifiers. """ def get_choice(self): """ :returns: A tuple consisting of 'value, label' used by the shipping form dialog to render the available shipping choices. """ raise NotImplemented("{} must implement method `get_choice()`.".format(self.__class__)) def is_active(self, shipping_modifier): """ :returns: ``True`` if this shipping modifier is active. """ return shipping_modifier == self.identifier def is_disabled(self, cart): """ Hook method to be overridden by the concrete shipping modifier. Shall be used to temporarily disable a shipping method, in case the cart does not fulfill certain criteria, for instance an undeliverable destination address. :returns: ``True`` if this shipping modifier is disabled for the current cart. """ return False def update_render_context(self, context): """ Hook to update the rendering context with shipping specific data. """ from shop.models.cart import CartModel if 'shipping_modifiers' not in context: context['shipping_modifiers'] = {} try: cart = CartModel.objects.get_from_request(context['request']) if self.is_active(cart.extra.get('shipping_modifier')): cart.update(context['request']) data = cart.extra_rows[self.identifier].data data.update(modifier=self.identifier) context['shipping_modifiers']['initial_row'] = data except (KeyError, CartModel.DoesNotExist): pass def ship_the_goods(self, delivery): """ Hook to be overridden by the active shipping modifier. It should be used to perform the shipping request. """ delivery.shipped_at = timezone.now() class SelfCollectionModifier(ShippingModifier): """ This modifiers has not influence on the cart final. It can be used, to enable the customer to pick up the products in the shop. """ identifier = 'self-collection' def get_choice(self): return (self.identifier, _("Self-collection")) def ship_the_goods(self, delivery): if not delivery.shipping_id: delivery.shipping_id = str(delivery.id) super(SelfCollectionModifier, self).ship_the_goods(delivery)

35.289474

98

0.65399

f77a7e41120a37ce5a2f05432c49db923c877116

1,486

py

Python

simpleh5/tests/test_simpleh5_search_util.py

ssolari/simpleh5

0c82395f5144634221419ff347db23ef4d34cd04

[ "MIT" ]

null

simpleh5/tests/test_simpleh5_search_util.py

ssolari/simpleh5

0c82395f5144634221419ff347db23ef4d34cd04

[ "MIT" ]

null

simpleh5/tests/test_simpleh5_search_util.py

ssolari/simpleh5

0c82395f5144634221419ff347db23ef4d34cd04

[ "MIT" ]

null

import unittest import os from simpleh5.utilities.search_utilities import _build_search_string class TestBuildString(unittest.TestCase): def test_string_single(self): query = ['strs', '==', 'abc'] match_string, uservars = _build_search_string(query) self.assertEqual(match_string, "(n0==b'abc')") self.assertDictEqual(uservars, {'n0': 'strs'}) def test_string_single_unicode(self): query = ( ('strs', '==', '£') ) match_string, uservars = _build_search_string(query) self.assertEqual(match_string, "(n0==b'\\xc2\\xa3')") self.assertDictEqual(uservars, {'n0': 'strs'}) def test_string_compound(self): query = [ [('strs', '==', 'abc'), ('strs', '==', '£')], ['nums', '>', 1.3] ] match_string, uservars = _build_search_string(query) self.assertEqual(match_string, "((n0==b'abc')|(n0==b'\\xc2\\xa3'))&(n1>1.3)") self.assertDictEqual(uservars, {'n0': 'strs', 'n1': 'nums'}) def test_string_double_compound(self): # meaningless logic query = [ [('strs', '!=', 'abc'), ('strs', '!=', 'cba')], ('strs', '==', '£') ] match_string, uservars = _build_search_string(query) self.assertEqual(match_string, "((n0!=b'abc')|(n0!=b'cba'))&(n0==b'\\xc2\\xa3')") self.assertDictEqual(uservars, {'n0': 'strs'}) if __name__ == "__main__": unittest.main()

30.326531

89

0.560565

9cd5b7957823e02e9cd7b6b090f21f29c565b07a

802

py

Python

api/source/models/option.py

1pkg/ReRe

83f77d2cece0fb5f6d7b86a395fcca7d4e16459f

[ "MIT" ]

1

2019-12-17T10:31:48.000Z

api/source/models/option.py

c-pkg/ReRe

83f77d2cece0fb5f6d7b86a395fcca7d4e16459f

[ "MIT" ]

null

api/source/models/option.py

c-pkg/ReRe

83f77d2cece0fb5f6d7b86a395fcca7d4e16459f

[ "MIT" ]

1

2019-04-29T08:19:36.000Z

from base import Alchemy class Option(Alchemy.Model): __tablename__ = 'option' id = Alchemy.Column( Alchemy.Integer, nullable=False, primary_key=True, ) name = Alchemy.Column( Alchemy.String, nullable=False, unique=True, ) description = Alchemy.Column( Alchemy.String, nullable=False, ) link = Alchemy.Column( Alchemy.String, nullable=False, unique=True, ) source = Alchemy.Column( Alchemy.String, nullable=False, ) subjects = Alchemy.relationship( 'Subject', backref='option', passive_deletes=True, ) answers = Alchemy.relationship( 'Answer', backref='option', passive_deletes=True, )

19.560976

36

0.562344

ebfa2f6b03db0b30d80056918a698118b714de25

253

py

Python

tests/exceptions/source/others/handler_formatting_with_context_manager.py

joshgordon/loguru

9777f4bec8b03ef074635269224baa3fd263fd09

[ "MIT" ]

2

2020-01-20T10:03:24.000Z

2020-02-05T03:38:25.000Z

tests/exceptions/source/others/handler_formatting_with_context_manager.py

joshgordon/loguru

9777f4bec8b03ef074635269224baa3fd263fd09

[ "MIT" ]

1

2021-01-31T07:28:51.000Z

2021-02-21T08:38:53.000Z

tests/exceptions/source/others/handler_formatting_with_context_manager.py

joshgordon/loguru

9777f4bec8b03ef074635269224baa3fd263fd09

[ "MIT" ]

3

2020-02-05T03:38:28.000Z

2020-02-11T02:34:56.000Z

import sys from loguru import logger logger.remove() logger.add( sys.stderr, format="{name} {file.name} {function} {line}", diagnose=False, backtrace=False, colorize=False, ) def a(): with logger.catch(): 1 / 0 a()

12.047619

50

0.604743

cd7b6b0a2f2c2d1cd94a529ea300dcfba5c891e3

1,150

py

Python

dataxHWSp2021/HW2_CoreConcepts/student/tests/q3b.py

UCBerkeley-SCET/DataX-Berkeley

f912d22c838b511d3ada4ecfa3548afd80437b74

[ "Apache-2.0" ]

28

2020-06-15T23:53:36.000Z

2022-03-19T09:27:02.000Z

dataxHWSp2021/HW2_CoreConcepts/student/tests/q3b.py

UCBerkeley-SCET/DataX-Berkeley

f912d22c838b511d3ada4ecfa3548afd80437b74

[ "Apache-2.0" ]

4

2020-06-24T22:20:31.000Z

2022-02-28T01:37:36.000Z

dataxHWSp2021/HW2_CoreConcepts/student/tests/q3b.py

UCBerkeley-SCET/DataX-Berkeley

f912d22c838b511d3ada4ecfa3548afd80437b74

[ "Apache-2.0" ]

78

2020-06-19T09:41:01.000Z

2022-02-05T00:13:29.000Z

test = { 'name': 'q3b', 'points': 3, 'suites': [ { 'cases': [ { 'code': '>>> ' 'print(np.mean(train_scores,axis=1).round(3))\n' '[0.954 0.97 0.979 0.985 0.988 ' '0.989 0.988 0.988 0.992 ' '0.993]\n', 'hidden': False, 'locked': False}, { 'code': '>>> ' 'print(np.mean(val_scores,axis=1).round(3))\n' '[0.947 0.958 0.969 0.976 0.976 ' '0.974 0.969 0.971 0.965 ' '0.958]\n', 'hidden': False, 'locked': False}], 'scored': True, 'setup': '', 'teardown': '', 'type': 'doctest'}]}

54.761905

95

0.229565

1f69739cac447b4837daed3ea17b01ac125a4367

797

py

Python

devday/talk/migrations/0042_delete_attendee_feedback_with_attendee.py

jenslauterbach/devday_website

a827c9237e656842542eff07ec9fa7b39716a0ee

[ "CC-BY-4.0", "BSD-3-Clause" ]

6

2018-09-30T20:18:01.000Z

2020-03-12T09:03:38.000Z

devday/talk/migrations/0042_delete_attendee_feedback_with_attendee.py

jenslauterbach/devday_website

a827c9237e656842542eff07ec9fa7b39716a0ee

[ "CC-BY-4.0", "BSD-3-Clause" ]

260

2018-09-30T14:17:57.000Z

2022-03-04T13:48:34.000Z

devday/talk/migrations/0042_delete_attendee_feedback_with_attendee.py

jenslauterbach/devday_website

a827c9237e656842542eff07ec9fa7b39716a0ee

[ "CC-BY-4.0", "BSD-3-Clause" ]

9

2018-09-30T13:17:21.000Z

2020-10-03T12:55:05.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.20 on 2019-04-29 12:05 from __future__ import unicode_literals from django.db import migrations, models import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ("attendee", "0009_auto_20181020_0802"), ("event", "0007_event_voting_open"), ("talk", "0041_drop_unique_constraint_on_talkslot"), ] operations = [ migrations.AlterField( model_name="attendeefeedback", name="attendee", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to="attendee.Attendee", verbose_name="Attendee", ), ) ]

26.566667

60

0.588457

f9cdee0ce7a58c8128d3676b25266565f52c51d3

537

py

Python

easyrules/support/reader/rule_definition_reader.py

wffzxyl/easyrules

8874718fec629435c69ce360cd43a281da162627

[ "MIT" ]

1

2020-10-03T12:34:01.000Z

easyrules/support/reader/rule_definition_reader.py

wffzxyl/easyrules

8874718fec629435c69ce360cd43a281da162627

[ "MIT" ]

null

easyrules/support/reader/rule_definition_reader.py

wffzxyl/easyrules

8874718fec629435c69ce360cd43a281da162627

[ "MIT" ]

null

# coding: utf-8 """ Rules engine interface. """ from abc import ABCMeta, abstractmethod from typing import List from easyrules.utils import exception_handler class RuleDefinitionReader(metaclass=ABCMeta): @exception_handler @abstractmethod def read(self, file_path: str) -> List: """ Read a list of rule definitions from a structured file. The file is expected to contain a collection of rule definitions even for a single rule. :return: a list of rule definitions """ pass

25.571429

96

0.6946

f6247a0962f97c4e574a50a886fbfb77814fd561

15,474

py

Python

PythonNetwork/venv/Lib/site-packages/mysql/connector/django/introspection.py

Moldovandreii/RepetitionCount

b5715b0948b609fde0ce05d45023b7d4cfd635e7

[ "FTL" ]

1

2021-04-09T15:23:40.000Z

PythonNetwork/venv/Lib/site-packages/mysql/connector/django/introspection.py

Moldovandreii/RepetitionCount

b5715b0948b609fde0ce05d45023b7d4cfd635e7

[ "FTL" ]

null

PythonNetwork/venv/Lib/site-packages/mysql/connector/django/introspection.py

Moldovandreii/RepetitionCount

b5715b0948b609fde0ce05d45023b7d4cfd635e7

[ "FTL" ]

1

2021-03-28T18:09:09.000Z

# Copyright (c) 2020, Oracle and/or its affiliates. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License, version 2.0, as # published by the Free Software Foundation. # # This program is also distributed with certain software (including # but not limited to OpenSSL) that is licensed under separate terms, # as designated in a particular file or component or in included license # documentation. The authors of MySQL hereby grant you an # additional permission to link the program and your derivative works # with the separately licensed software that they have included with # MySQL. # # Without limiting anything contained in the foregoing, this file, # which is part of MySQL Connector/Python, is also subject to the # Universal FOSS Exception, version 1.0, a copy of which can be found at # http://oss.oracle.com/licenses/universal-foss-exception. # # 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, version 2.0, for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, Inc., # 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA from collections import namedtuple import sqlparse from mysql.connector.constants import FieldType from django.db.backends.base.introspection import ( BaseDatabaseIntrospection, FieldInfo as BaseFieldInfo, TableInfo, ) from django.db.models import Index from django.utils.datastructures import OrderedSet FieldInfo = namedtuple('FieldInfo', BaseFieldInfo._fields + ('extra', 'is_unsigned', 'has_json_constraint')) InfoLine = namedtuple('InfoLine', 'col_name data_type max_len num_prec num_scale extra column_default is_unsigned') class DatabaseIntrospection(BaseDatabaseIntrospection): data_types_reverse = { FieldType.BLOB: 'TextField', FieldType.DECIMAL: 'DecimalField', FieldType.NEWDECIMAL: 'DecimalField', FieldType.DATE: 'DateField', FieldType.DATETIME: 'DateTimeField', FieldType.DOUBLE: 'FloatField', FieldType.FLOAT: 'FloatField', FieldType.INT24: 'IntegerField', FieldType.JSON: 'JSONField', FieldType.LONG: 'IntegerField', FieldType.LONGLONG: 'BigIntegerField', FieldType.SHORT: 'SmallIntegerField', FieldType.STRING: 'CharField', FieldType.TIME: 'TimeField', FieldType.TIMESTAMP: 'DateTimeField', FieldType.TINY: 'IntegerField', FieldType.TINY_BLOB: 'TextField', FieldType.MEDIUM_BLOB: 'TextField', FieldType.LONG_BLOB: 'TextField', FieldType.VAR_STRING: 'CharField', } def get_field_type(self, data_type, description): field_type = super().get_field_type(data_type, description) if 'auto_increment' in description.extra: if field_type == 'IntegerField': return 'AutoField' elif field_type == 'BigIntegerField': return 'BigAutoField' elif field_type == 'SmallIntegerField': return 'SmallAutoField' if description.is_unsigned: if field_type == 'BigIntegerField': return 'PositiveBigIntegerField' elif field_type == 'IntegerField': return 'PositiveIntegerField' elif field_type == 'SmallIntegerField': return 'PositiveSmallIntegerField' # JSON data type is an alias for LONGTEXT in MariaDB, use check # constraints clauses to introspect JSONField. if description.has_json_constraint: return 'JSONField' return field_type def get_table_list(self, cursor): """Return a list of table and view names in the current database.""" cursor.execute("SHOW FULL TABLES") return [TableInfo(row[0], {'BASE TABLE': 't', 'VIEW': 'v'}.get(row[1])) for row in cursor.fetchall()] def get_table_description(self, cursor, table_name): """ Return a description of the table with the DB-API cursor.description interface." """ json_constraints = {} if self.connection.mysql_is_mariadb and self.connection.features.can_introspect_json_field: # JSON data type is an alias for LONGTEXT in MariaDB, select # JSON_VALID() constraints to introspect JSONField. cursor.execute(""" SELECT c.constraint_name AS column_name FROM information_schema.check_constraints AS c WHERE c.table_name = %s AND LOWER(c.check_clause) = 'json_valid(`' + LOWER(c.constraint_name) + '`)' AND c.constraint_schema = DATABASE() """, [table_name]) json_constraints = {row[0] for row in cursor.fetchall()} # information_schema database gives more accurate results for some figures: # - varchar length returned by cursor.description is an internal length, # not visible length (#5725) # - precision and scale (for decimal fields) (#5014) # - auto_increment is not available in cursor.description cursor.execute(""" SELECT column_name, data_type, character_maximum_length, numeric_precision, numeric_scale, extra, column_default, CASE WHEN column_type LIKE '%% unsigned' THEN 1 ELSE 0 END AS is_unsigned FROM information_schema.columns WHERE table_name = %s AND table_schema = DATABASE()""", [table_name]) field_info = {line[0]: InfoLine(*line) for line in cursor.fetchall()} cursor.execute("SELECT * FROM %s LIMIT 1" % self.connection.ops.quote_name(table_name)) def to_int(i): return int(i) if i is not None else i fields = [] for line in cursor.description: info = field_info[line[0]] name, type_code, display_size = line[:3] fields.append(FieldInfo( name, type_code, display_size, to_int(info.max_len) or line[3], to_int(info.num_prec) or line[4], to_int(info.num_scale) or line[5], line[6], info.column_default, info.extra, info.is_unsigned, line[0] in json_constraints )) return fields def get_sequences(self, cursor, table_name, table_fields=()): for field_info in self.get_table_description(cursor, table_name): if 'auto_increment' in field_info.extra: # MySQL allows only one auto-increment column per table. return [{'table': table_name, 'column': field_info.name}] return [] def get_relations(self, cursor, table_name): """ Return a dictionary of {field_name: (field_name_other_table, other_table)} representing all relationships to the given table. """ constraints = self.get_key_columns(cursor, table_name) relations = {} for my_fieldname, other_table, other_field in constraints: relations[my_fieldname] = (other_field, other_table) return relations def get_key_columns(self, cursor, table_name): """ Return a list of (column_name, referenced_table_name, referenced_column_name) for all key columns in the given table. """ key_columns = [] cursor.execute(""" SELECT column_name, referenced_table_name, referenced_column_name FROM information_schema.key_column_usage WHERE table_name = %s AND table_schema = DATABASE() AND referenced_table_name IS NOT NULL AND referenced_column_name IS NOT NULL""", [table_name]) key_columns.extend(cursor.fetchall()) return key_columns def get_indexes(self, cursor, table_name): cursor.execute("SHOW INDEX FROM {0}" "".format(self.connection.ops.quote_name(table_name))) # Do a two-pass search for indexes: on first pass check which indexes # are multicolumn, on second pass check which single-column indexes # are present. rows = list(cursor.fetchall()) multicol_indexes = set() for row in rows: if row[3] > 1: multicol_indexes.add(row[2]) indexes = {} for row in rows: if row[2] in multicol_indexes: continue if row[4] not in indexes: indexes[row[4]] = {'primary_key': False, 'unique': False} # It's possible to have the unique and PK constraints in # separate indexes. if row[2] == 'PRIMARY': indexes[row[4]]['primary_key'] = True if not row[1]: indexes[row[4]]['unique'] = True return indexes def get_primary_key_column(self, cursor, table_name): """ Returns the name of the primary key column for the given table """ for column in self.get_indexes(cursor, table_name).items(): if column[1]['primary_key']: return column[0] return None def get_storage_engine(self, cursor, table_name): """ Retrieve the storage engine for a given table. Return the default storage engine if the table doesn't exist. """ cursor.execute( "SELECT engine " "FROM information_schema.tables " "WHERE table_name = %s", [table_name]) result = cursor.fetchone() if not result: return self.connection.features._mysql_storage_engine return result[0] def _parse_constraint_columns(self, check_clause, columns): check_columns = OrderedSet() statement = sqlparse.parse(check_clause)[0] tokens = (token for token in statement.flatten() if not token.is_whitespace) for token in tokens: if ( token.ttype == sqlparse.tokens.Name and self.connection.ops.quote_name(token.value) == token.value and token.value[1:-1] in columns ): check_columns.add(token.value[1:-1]) return check_columns def get_constraints(self, cursor, table_name): """ Retrieve any constraints or keys (unique, pk, fk, check, index) across one or more columns. """ constraints = {} # Get the actual constraint names and columns name_query = """ SELECT kc.`constraint_name`, kc.`column_name`, kc.`referenced_table_name`, kc.`referenced_column_name` FROM information_schema.key_column_usage AS kc WHERE kc.table_schema = DATABASE() AND kc.table_name = %s ORDER BY kc.`ordinal_position` """ cursor.execute(name_query, [table_name]) for constraint, column, ref_table, ref_column in cursor.fetchall(): if constraint not in constraints: constraints[constraint] = { 'columns': OrderedSet(), 'primary_key': False, 'unique': False, 'index': False, 'check': False, 'foreign_key': (ref_table, ref_column) if ref_column else None, } constraints[constraint]['columns'].add(column) # Now get the constraint types type_query = """ SELECT c.constraint_name, c.constraint_type FROM information_schema.table_constraints AS c WHERE c.table_schema = DATABASE() AND c.table_name = %s """ cursor.execute(type_query, [table_name]) for constraint, kind in cursor.fetchall(): if kind.lower() == "primary key": constraints[constraint]['primary_key'] = True constraints[constraint]['unique'] = True elif kind.lower() == "unique": constraints[constraint]['unique'] = True # Add check constraints. if self.connection.features.can_introspect_check_constraints: unnamed_constraints_index = 0 columns = {info.name for info in self.get_table_description(cursor, table_name)} if self.connection.mysql_is_mariadb: type_query = """ SELECT c.constraint_name, c.check_clause FROM information_schema.check_constraints AS c WHERE c.constraint_schema = DATABASE() AND c.table_name = %s """ else: type_query = """ SELECT cc.constraint_name, cc.check_clause FROM information_schema.check_constraints AS cc, information_schema.table_constraints AS tc WHERE cc.constraint_schema = DATABASE() AND tc.table_schema = cc.constraint_schema AND cc.constraint_name = tc.constraint_name AND tc.constraint_type = 'CHECK' AND tc.table_name = %s """ cursor.execute(type_query, [table_name]) for constraint, check_clause in cursor.fetchall(): constraint_columns = self._parse_constraint_columns(check_clause, columns) # Ensure uniqueness of unnamed constraints. Unnamed unique # and check columns constraints have the same name as # a column. if set(constraint_columns) == {constraint}: unnamed_constraints_index += 1 constraint = '__unnamed_constraint_%s__' % unnamed_constraints_index constraints[constraint] = { 'columns': constraint_columns, 'primary_key': False, 'unique': False, 'index': False, 'check': True, 'foreign_key': None, } # Now add in the indexes cursor.execute("SHOW INDEX FROM %s" % self.connection.ops.quote_name(table_name)) for table, non_unique, index, colseq, column, type_ in [x[:5] + (x[10],) for x in cursor.fetchall()]: if index not in constraints: constraints[index] = { 'columns': OrderedSet(), 'primary_key': False, 'unique': False, 'check': False, 'foreign_key': None, } constraints[index]['index'] = True constraints[index]['type'] = Index.suffix if type_ == 'BTREE' else type_.lower() constraints[index]['columns'].add(column) # Convert the sorted sets to lists for constraint in constraints.values(): constraint['columns'] = list(constraint['columns']) return constraints

43.711864

115

0.597131

0ccdb4b618383146e0de733c5d407498a806594a

114

py

Python

snake/snake/__init__.py

JoyPang123/RL-Explore-with-Own-made-Env

4f527236571ea8a8d700d2d56c1718f2126d2d8d

[ "MIT" ]

1

2021-12-07T07:37:40.000Z

snake/snake/__init__.py

JoyPang123/RL-Explore-with-Own-made-Env

4f527236571ea8a8d700d2d56c1718f2126d2d8d

[ "MIT" ]

null

snake/snake/__init__.py

JoyPang123/RL-Explore-with-Own-made-Env

4f527236571ea8a8d700d2d56c1718f2126d2d8d

[ "MIT" ]

null

from gym.envs.registration import register register( id='snake-v0', entry_point='snake.envs:SnakeEnv', )

16.285714

42

0.719298

b91e2edf9a6bf573759a03f1b8cee7f96e3e60d7

1,863

py

Python

Visualization/styled_layer_descriptors.py

monocilindro/qgis-earthengine-examples

82aea8926d34ed3f4ad4a4a345ddbd225819d28f

[ "MIT" ]

646

2019-12-03T06:09:03.000Z

2022-03-28T03:37:08.000Z

Visualization/styled_layer_descriptors.py

csaybar/qgis-earthengine-examples

ba8942683834d2847ff3246bdd1859b36e50fe44

[ "MIT" ]

10

2019-12-30T03:42:44.000Z

2021-05-22T07:34:07.000Z

Visualization/styled_layer_descriptors.py

csaybar/qgis-earthengine-examples

ba8942683834d2847ff3246bdd1859b36e50fe44

[ "MIT" ]

219

2019-12-06T02:20:53.000Z

2022-03-30T15:14:27.000Z

import ee from ee_plugin import Map cover = ee.Image('MODIS/051/MCD12Q1/2012_01_01').select('Land_Cover_Type_1') # Define an SLD style of discrete intervals to apply to the image. sld_intervals = \ '<RasterSymbolizer>' + \ ' <ColorMap type="intervals" extended="false" >' + \ '<ColorMapEntry color="#aec3d4" quantity="0" label="Water"/>' + \ '<ColorMapEntry color="#152106" quantity="1" label="Evergreen Needleleaf Forest"/>' + \ '<ColorMapEntry color="#225129" quantity="2" label="Evergreen Broadleaf Forest"/>' + \ '<ColorMapEntry color="#369b47" quantity="3" label="Deciduous Needleleaf Forest"/>' + \ '<ColorMapEntry color="#30eb5b" quantity="4" label="Deciduous Broadleaf Forest"/>' + \ '<ColorMapEntry color="#387242" quantity="5" label="Mixed Deciduous Forest"/>' + \ '<ColorMapEntry color="#6a2325" quantity="6" label="Closed Shrubland"/>' + \ '<ColorMapEntry color="#c3aa69" quantity="7" label="Open Shrubland"/>' + \ '<ColorMapEntry color="#b76031" quantity="8" label="Woody Savanna"/>' + \ '<ColorMapEntry color="#d9903d" quantity="9" label="Savanna"/>' + \ '<ColorMapEntry color="#91af40" quantity="10" label="Grassland"/>' + \ '<ColorMapEntry color="#111149" quantity="11" label="Permanent Wetland"/>' + \ '<ColorMapEntry color="#cdb33b" quantity="12" label="Cropland"/>' + \ '<ColorMapEntry color="#cc0013" quantity="13" label="Urban"/>' + \ '<ColorMapEntry color="#33280d" quantity="14" label="Crop, Natural Veg. Mosaic"/>' + \ '<ColorMapEntry color="#d7cdcc" quantity="15" label="Permanent Snow, Ice"/>' + \ '<ColorMapEntry color="#f7e084" quantity="16" label="Barren, Desert"/>' + \ '<ColorMapEntry color="#6f6f6f" quantity="17" label="Tundra"/>' + \ '</ColorMap>' + \ '</RasterSymbolizer>' Map.addLayer(cover.sldStyle(sld_intervals), {}, 'IGBP classification styled')

62.1

91

0.66774

ef5d7eb08a00d544a732b7adb80dadd78a39fbf0

2,954

py

Python

Testscripts/Linux/BVT-VERIFY-UUID-FSTAB.py

senugala/LISAv2

42edd3e40fb0ee847bdd915343c4c064256ede4c

[ "Apache-2.0" ]

null

Testscripts/Linux/BVT-VERIFY-UUID-FSTAB.py

senugala/LISAv2

42edd3e40fb0ee847bdd915343c4c064256ede4c

[ "Apache-2.0" ]

null

Testscripts/Linux/BVT-VERIFY-UUID-FSTAB.py

senugala/LISAv2

42edd3e40fb0ee847bdd915343c4c064256ede4c

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the Apache License. from azuremodules import * import sys import time import re import linecache def RunTest(): UpdateState("TestRunning") uuid_from_demesg = 0 dmsg_dev_count = 0 output = JustRun("dmesg") output = output.lower() filter_condition_dmesg = r'.*root=UUID=(.*?) .*' filter_condition_fstab = r'.*UUID=(.*?)[ \t]+\/[ \t]+' if (DetectDistro()[0] == 'opensuse' or DetectDistro()[0] == 'SUSE'or DetectDistro()[0] == 'sles'): filter_condition_dmesg = r'.*root=/dev/disk/by-uuid/(.*?) .*' filter_condition_fstab = r'.*/dev/disk/by-uuid/(.*?)[ \t]+\/[ \t]+' dmsg_dev_count = output.count('command line:.*root=/dev/sd') outputlist = re.split("\n", output) for line in outputlist: matchObj = re.match(filter_condition_dmesg, line, re.M|re.I) if matchObj: uuid_from_demesg = matchObj.group(1) uuid_from_fstab = 0 fstab_dev_count = 0 fstab_dev_count = output = JustRun("cat /etc/fstab") fstab_dev_count = output.count('/dev/sd') outputlist = re.split("\n", output) for line in outputlist: matchObj = re.match(filter_condition_fstab, line, re.M|re.I) #matchObj = re.match( r'.*UUID=(.*?)[ \t]*/ .*', line, re.M|re.I) if matchObj: uuid_from_fstab = matchObj.group(1) if(uuid_from_demesg and uuid_from_fstab and (uuid_from_demesg == uuid_from_fstab) and (dmsg_dev_count == 0) and (fstab_dev_count == 0)): ResultLog.info('PASS') #print "UUID are valid and matched" elif (DetectDistro()[0] == 'coreos'): output = JustRun("dmesg | grep root") if ("root=LABEL" in output): RunLog.info('CoreOS uses disk labels to specify drives.') ResultLog.info('PASS') else: RunLog.info('root partition is not mounted using LABEL in dmesg.') ResultLog.info('FAIL') elif(DetectDistro()[0] == 'ubuntu' and fstab_dev_count == 1): if (uuid_from_demesg != 0 and uuid_from_fstab != 0 and uuid_from_demesg == uuid_from_fstab and dmsg_dev_count == 0): ResultLog.info('PASS') else: ResultLog.info('FAIL') else: if (uuid_from_demesg == 0): RunLog.info('/ partition is not mounted using UUID in dmesg.') if (uuid_from_fstab == 0): RunLog.info('/ partition is not mounted using UUID in /etc/fstab.') if (uuid_from_demesg != uuid_from_fstab): RunLog.info(' UUID is not same in dmesg and /etc/fstab.') if (dmsg_dev_count != 0): RunLog.info('Found disks mounted without using UUID in dmesg.') if (fstab_dev_count != 0): RunLog.info('Found disks mounted without using UUID in /etc/fstab.') ResultLog.info('FAIL') UpdateState("TestCompleted") RunTest()

38.363636

140

0.61239

69d6fdf642e999bacb659a1db08c335d0680dc96

437

py

Python

experiment/util.py

tom-andersson/neuralprocesses

7696dc1c8bbe922fb2a1ba18fe0cdda041fc9cfd

[ "MIT" ]

null

experiment/util.py

tom-andersson/neuralprocesses

7696dc1c8bbe922fb2a1ba18fe0cdda041fc9cfd

[ "MIT" ]

null

experiment/util.py

tom-andersson/neuralprocesses

7696dc1c8bbe922fb2a1ba18fe0cdda041fc9cfd

[ "MIT" ]

null

import lab as B __all__ = ["with_err"] def with_err(vals, and_lower=False, and_upper=False): """Print the mean value of a list of values with error.""" vals = B.to_numpy(vals) mean = B.mean(vals) err = 1.96 * B.std(vals) / B.sqrt(B.length(vals)) res = f"{mean:10.5f} +- {err:10.5f}" if and_lower: res += f" ({mean - err:10.5f})" if and_upper: res += f" ({mean + err:10.5f})" return res

25.705882

62

0.574371

a855f5aed2d28386615e30726f906c83f7f34de0

2,745

py

Python

sdk/python/pulumi_azure_native/domainregistration/v20180201/list_domain_recommendations.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/domainregistration/v20180201/list_domain_recommendations.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/domainregistration/v20180201/list_domain_recommendations.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs __all__ = [ 'ListDomainRecommendationsResult', 'AwaitableListDomainRecommendationsResult', 'list_domain_recommendations', ] @pulumi.output_type class ListDomainRecommendationsResult: """ Collection of domain name identifiers. """ def __init__(__self__, next_link=None, value=None): if next_link and not isinstance(next_link, str): raise TypeError("Expected argument 'next_link' to be a str") pulumi.set(__self__, "next_link", next_link) if value and not isinstance(value, list): raise TypeError("Expected argument 'value' to be a list") pulumi.set(__self__, "value", value) @property @pulumi.getter(name="nextLink") def next_link(self) -> str: """ Link to next page of resources. """ return pulumi.get(self, "next_link") @property @pulumi.getter def value(self) -> Sequence['outputs.NameIdentifierResponse']: """ Collection of resources. """ return pulumi.get(self, "value") class AwaitableListDomainRecommendationsResult(ListDomainRecommendationsResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return ListDomainRecommendationsResult( next_link=self.next_link, value=self.value) def list_domain_recommendations(keywords: Optional[str] = None, max_domain_recommendations: Optional[int] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListDomainRecommendationsResult: """ Collection of domain name identifiers. :param str keywords: Keywords to be used for generating domain recommendations. :param int max_domain_recommendations: Maximum number of recommendations. """ __args__ = dict() __args__['keywords'] = keywords __args__['maxDomainRecommendations'] = max_domain_recommendations if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:domainregistration/v20180201:listDomainRecommendations', __args__, opts=opts, typ=ListDomainRecommendationsResult).value return AwaitableListDomainRecommendationsResult( next_link=__ret__.next_link, value=__ret__.value)

34.3125

170

0.685246

b47da3782c873de9cc822c18a93afdfcfdb98d0f

40,775

py

Python

thonny/tktextext.py

Natureshadow/thonny

3dfc846829968ae8294b61bbdf154278850b29df

[ "MIT" ]

null

thonny/tktextext.py

Natureshadow/thonny

3dfc846829968ae8294b61bbdf154278850b29df

[ "MIT" ]

null

thonny/tktextext.py

Natureshadow/thonny

3dfc846829968ae8294b61bbdf154278850b29df

[ "MIT" ]

null

# coding=utf-8 """Extensions for tk.Text""" import platform import time import tkinter as tk import traceback from logging import exception from tkinter import TclError, messagebox from tkinter import font as tkfont from tkinter import ttk class TweakableText(tk.Text): """Allows intercepting Text commands at Tcl-level""" def __init__(self, master=None, cnf={}, read_only=False, **kw): super().__init__(master=master, cnf=cnf, **kw) self._read_only = read_only self._suppress_events = False self._original_widget_name = self._w + "_orig" self.tk.call("rename", self._w, self._original_widget_name) self.tk.createcommand(self._w, self._dispatch_tk_operation) self._tk_proxies = {} self._original_insert = self._register_tk_proxy_function("insert", self.intercept_insert) self._original_delete = self._register_tk_proxy_function("delete", self.intercept_delete) self._original_mark = self._register_tk_proxy_function("mark", self.intercept_mark) def _register_tk_proxy_function(self, operation, function): self._tk_proxies[operation] = function setattr(self, operation, function) def original_function(*args): self.tk.call((self._original_widget_name, operation) + args) return original_function def _dispatch_tk_operation(self, operation, *args): f = self._tk_proxies.get(operation) try: if f: return f(*args) else: return self.tk.call((self._original_widget_name, operation) + args) except TclError as e: # Some Tk internal actions (eg. paste and cut) can cause this error if ( str(e).lower() == '''text doesn't contain any characters tagged with "sel"''' and operation in ["delete", "index", "get"] and args in [("sel.first", "sel.last"), ("sel.first",)] ): pass else: exception( "[_dispatch_tk_operation] operation: " + operation + ", args:" + repr(args) ) # traceback.print_exc() return "" # Taken from idlelib.WidgetRedirector def set_read_only(self, value): self._read_only = value def is_read_only(self): return self._read_only def set_content(self, chars): self.direct_delete("1.0", tk.END) self.direct_insert("1.0", chars) def set_insertwidth(self, new_width): """Change cursor width NB! Need to be careful with setting text["insertwidth"]! My first straightforward solution caused unexplainable infinite loop of insertions and deletions in the text (Repro: insert a line and a word, select that word and then do Ctrl-Z). This solution seems safe but be careful! """ if self._suppress_events: return if self["insertwidth"] != new_width: old_suppress = self._suppress_events try: self._suppress_events = True self.config(insertwidth=new_width) finally: self._suppress_events = old_suppress def intercept_mark(self, *args): self.direct_mark(*args) def intercept_insert(self, index, chars, tags=None, **kw): assert isinstance(chars, str) if chars >= "\uf704" and chars <= "\uf70d": # Function keys F1..F10 in Mac cause these pass elif self.is_read_only(): self.bell() else: self.direct_insert(index, chars, tags, **kw) def intercept_delete(self, index1, index2=None, **kw): if index1 == "sel.first" and index2 == "sel.last" and not self.has_selection(): return if self.is_read_only(): self.bell() elif self._is_erroneous_delete(index1, index2): pass else: self.direct_delete(index1, index2, **kw) def _is_erroneous_delete(self, index1, index2): """Paste can cause deletes where index1 is sel.start but text has no selection. This would cause errors""" return index1.startswith("sel.") and not self.has_selection() def direct_mark(self, *args): self._original_mark(*args) if args[:2] == ("set", "insert") and not self._suppress_events: self.event_generate("<<CursorMove>>") def index_sel_first(self): # Tk will give error without this check if self.tag_ranges("sel"): return self.index("sel.first") else: return None def index_sel_last(self): if self.tag_ranges("sel"): return self.index("sel.last") else: return None def has_selection(self): return len(self.tag_ranges("sel")) > 0 def get_selection_indices(self): # If a selection is defined in the text widget, return (start, # end) as Tkinter text indices, otherwise return (None, None) if self.has_selection(): return self.index("sel.first"), self.index("sel.last") else: return None, None def direct_insert(self, index, chars, tags=None, **kw): self._original_insert(index, chars, tags, **kw) if not self._suppress_events: self.event_generate("<<TextChange>>") def direct_delete(self, index1, index2=None, **kw): self._original_delete(index1, index2, **kw) if not self._suppress_events: self.event_generate("<<TextChange>>") class EnhancedText(TweakableText): """Text widget with extra navigation and editing aids. Provides more comfortable deletion, indentation and deindentation, and undo handling. Not specific to Python code. Most of the code is adapted from idlelib.EditorWindow. """ def __init__( self, master=None, style="Text", tag_current_line=False, indent_with_tabs=False, replace_tabs=False, cnf={}, **kw ): # Parent class shouldn't autoseparate # TODO: take client provided autoseparators value into account kw["autoseparators"] = False self._style = style self._original_options = kw.copy() super().__init__(master=master, cnf=cnf, **kw) self.tabwidth = 8 # See comments in idlelib.editor.EditorWindow self.indent_width = 4 self.indent_with_tabs = indent_with_tabs self.replace_tabs = replace_tabs self._last_event_kind = None self._last_key_time = None self._bind_editing_aids() self._bind_movement_aids() self._bind_selection_aids() self._bind_undo_aids() self._bind_mouse_aids() self._ui_theme_change_binding = self.bind( "<<ThemeChanged>>", self._reload_theme_options, True ) self._initial_configuration = self.configure() self._regular_insertwidth = self["insertwidth"] self._reload_theme_options() self._should_tag_current_line = tag_current_line if tag_current_line: self.bind("<<CursorMove>>", self._tag_current_line, True) self.bind("<<TextChange>>", self._tag_current_line, True) self._tag_current_line() def _bind_mouse_aids(self): if _running_on_mac(): self.bind("<Button-2>", self.on_secondary_click) self.bind("<Control-Button-1>", self.on_secondary_click) else: self.bind("<Button-3>", self.on_secondary_click) def _bind_editing_aids(self): def if_not_readonly(fun): def dispatch(event): if not self.is_read_only(): return fun(event) else: return "break" return dispatch self.bind("<Control-BackSpace>", if_not_readonly(self.delete_word_left), True) self.bind("<Control-Delete>", if_not_readonly(self.delete_word_right), True) self.bind("<Control-d>", self._redirect_ctrld, True) self.bind("<Control-t>", self._redirect_ctrlt, True) self.bind("<BackSpace>", if_not_readonly(self.perform_smart_backspace), True) self.bind("<Return>", if_not_readonly(self.perform_return), True) self.bind("<KP_Enter>", if_not_readonly(self.perform_return), True) self.bind("<Tab>", if_not_readonly(self.perform_tab), True) try: # Is needed on eg. Ubuntu with Estonian keyboard self.bind("<ISO_Left_Tab>", if_not_readonly(self.perform_tab), True) except Exception: pass if platform.system() == "Windows": self.bind("<KeyPress>", self._insert_untypable_characters_on_windows, True) def _bind_movement_aids(self): self.bind("<Home>", self.perform_smart_home, True) self.bind("<Left>", self.move_to_edge_if_selection(0), True) self.bind("<Right>", self.move_to_edge_if_selection(1), True) self.bind("<Next>", self.perform_page_down, True) self.bind("<Prior>", self.perform_page_up, True) def _bind_selection_aids(self): self.bind("<Command-a>" if _running_on_mac() else "<Control-a>", self.select_all, True) def _bind_undo_aids(self): self.bind("<<Undo>>", self._on_undo, True) self.bind("<<Redo>>", self._on_redo, True) self.bind("<<Cut>>", self._on_cut, True) self.bind("<<Copy>>", self._on_copy, True) self.bind("<<Paste>>", self._on_paste, True) self.bind("<FocusIn>", self._on_get_focus, True) self.bind("<FocusOut>", self._on_lose_focus, True) self.bind("<Key>", self._on_key_press, True) self.bind("<1>", self._on_mouse_click, True) self.bind("<2>", self._on_mouse_click, True) self.bind("<3>", self._on_mouse_click, True) def _redirect_ctrld(self, event): # I want to disable the deletion effect of Ctrl-D in the text but still # keep the event for other purposes self.event_generate("<<CtrlDInText>>") return "break" def _redirect_ctrlt(self, event): # I want to disable the swap effect of Ctrl-T in the text but still # keep the event for other purposes self.event_generate("<<CtrlTInText>>") return "break" def tag_reset(self, tag_name): empty_conf = {key: "" for key in self.tag_configure(tag_name)} self.tag_configure(empty_conf) def select_lines(self, first_line, last_line): self.tag_remove("sel", "1.0", tk.END) self.tag_add("sel", "%s.0" % first_line, "%s.end" % last_line) def delete_word_left(self, event): self.event_generate("<Meta-Delete>") self.edit_separator() return "break" def delete_word_right(self, event): self.event_generate("<Meta-d>") self.edit_separator() return "break" def perform_smart_backspace(self, event): self._log_keypress_for_undo(event) text = self first, last = self.get_selection_indices() if first and last: text.delete(first, last) text.mark_set("insert", first) return "break" # Delete whitespace left, until hitting a real char or closest # preceding virtual tab stop. chars = text.get("insert linestart", "insert") if chars == "": if text.compare("insert", ">", "1.0"): # easy: delete preceding newline text.delete("insert-1c") else: text.bell() # at start of buffer return "break" if ( chars.strip() != "" ): # there are non-whitespace chars somewhere to the left of the cursor # easy: delete preceding real char text.delete("insert-1c") self._log_keypress_for_undo(event) return "break" # Ick. It may require *inserting* spaces if we back up over a # tab character! This is written to be clear, not fast. have = len(chars.expandtabs(self.tabwidth)) assert have > 0 want = ((have - 1) // self.indent_width) * self.indent_width # Debug prompt is multilined.... # if self.context_use_ps1: # last_line_of_prompt = sys.ps1.split('\n')[-1] # else: last_line_of_prompt = "" ncharsdeleted = 0 while 1: if chars == last_line_of_prompt: break chars = chars[:-1] ncharsdeleted = ncharsdeleted + 1 have = len(chars.expandtabs(self.tabwidth)) if have <= want or chars[-1] not in " \t": break text.delete("insert-%dc" % ncharsdeleted, "insert") if have < want: text.insert("insert", " " * (want - have)) return "break" def perform_midline_tab(self, event=None): "autocompleter can put its magic here" # by default return self.perform_smart_tab(event) def perform_smart_tab(self, event=None): self._log_keypress_for_undo(event) # if intraline selection: # delete it # elif multiline selection: # do indent-region # else: # indent one level first, last = self.get_selection_indices() if first and last: if index2line(first) != index2line(last): return self.indent_region(event) self.delete(first, last) self.mark_set("insert", first) prefix = self.get("insert linestart", "insert") raw, effective = classifyws(prefix, self.tabwidth) if raw == len(prefix): # only whitespace to the left self._reindent_to(effective + self.indent_width) else: # tab to the next 'stop' within or to right of line's text: if self.indent_with_tabs: pad = "\t" else: effective = len(prefix.expandtabs(self.tabwidth)) n = self.indent_width pad = " " * (n - effective % n) self.insert("insert", pad) self.see("insert") return "break" def get_cursor_position(self): return map(int, self.index("insert").split(".")) def get_line_count(self): return list(map(int, self.index("end-1c").split(".")))[0] def perform_return(self, event): self.insert("insert", "\n") return "break" def perform_page_down(self, event): # if last line is visible then go to last line # (by default it doesn't move then) try: last_visible_idx = self.index("@0,%d" % self.winfo_height()) row, _ = map(int, last_visible_idx.split(".")) line_count = self.get_line_count() if row == line_count or row == line_count - 1: # otherwise tk doesn't show last line self.mark_set("insert", "end") except Exception: traceback.print_exc() def perform_page_up(self, event): # if first line is visible then go there # (by default it doesn't move then) try: first_visible_idx = self.index("@0,0") row, _ = map(int, first_visible_idx.split(".")) if row == 1: self.mark_set("insert", "1.0") except Exception: traceback.print_exc() def compute_smart_home_destination_index(self): """Is overridden in shell""" line = self.get("insert linestart", "insert lineend") for insertpt in range(len(line)): if line[insertpt] not in (" ", "\t"): break else: insertpt = len(line) lineat = int(self.index("insert").split(".")[1]) if insertpt == lineat: insertpt = 0 return "insert linestart+" + str(insertpt) + "c" def perform_smart_home(self, event): if (event.state & 4) != 0 and event.keysym == "Home": # state&4==Control. If <Control-Home>, use the Tk binding. return None dest = self.compute_smart_home_destination_index() if (event.state & 1) == 0: # shift was not pressed self.tag_remove("sel", "1.0", "end") else: if not self.index_sel_first(): # there was no previous selection self.mark_set("my_anchor", "insert") else: if self.compare(self.index_sel_first(), "<", self.index("insert")): self.mark_set("my_anchor", "sel.first") # extend back else: self.mark_set("my_anchor", "sel.last") # extend forward first = self.index(dest) last = self.index("my_anchor") if self.compare(first, ">", last): first, last = last, first self.tag_remove("sel", "1.0", "end") self.tag_add("sel", first, last) self.mark_set("insert", dest) self.see("insert") return "break" def move_to_edge_if_selection(self, edge_index): """Cursor move begins at start or end of selection When a left/right cursor key is pressed create and return to Tkinter a function which causes a cursor move from the associated edge of the selection. """ def move_at_edge(event): if ( self.has_selection() and (event.state & 5) == 0 ): # no shift(==1) or control(==4) pressed try: self.mark_set("insert", ("sel.first+1c", "sel.last-1c")[edge_index]) except tk.TclError: pass return move_at_edge def perform_tab(self, event=None): self._log_keypress_for_undo(event) if event.state & 0x0001: # shift is pressed (http://stackoverflow.com/q/32426250/261181) return self.dedent_region(event) else: # check whether there are letters before cursor on this line index = self.index("insert") left_text = self.get(index + " linestart", index) if left_text.strip() == "" or self.has_selection(): return self.perform_smart_tab(event) else: return self.perform_midline_tab(event) def indent_region(self, event=None): return self._change_indentation(True) def dedent_region(self, event=None): return self._change_indentation(False) def _change_indentation(self, increase=True): head, tail, chars, lines = self._get_region() # Text widget plays tricks if selection ends on last line # and content doesn't end with empty line, text_last_line = index2line(self.index("end-1c")) sel_last_line = index2line(tail) if sel_last_line >= text_last_line: while not self.get(head, "end").endswith("\n\n"): self.insert("end", "\n") for pos in range(len(lines)): line = lines[pos] if line: raw, effective = classifyws(line, self.tabwidth) if increase: effective = effective + self.indent_width else: effective = max(effective - self.indent_width, 0) lines[pos] = self._make_blanks(effective) + line[raw:] self._set_region(head, tail, chars, lines) return "break" def select_all(self, event): self.tag_remove("sel", "1.0", tk.END) self.tag_add("sel", "1.0", tk.END) def set_read_only(self, value): if value == self.is_read_only(): return TweakableText.set_read_only(self, value) self._reload_theme_options() if self._should_tag_current_line: self._tag_current_line() def _reindent_to(self, column): # Delete from beginning of line to insert point, then reinsert # column logical (meaning use tabs if appropriate) spaces. if self.compare("insert linestart", "!=", "insert"): self.delete("insert linestart", "insert") if column: self.insert("insert", self._make_blanks(column)) def _get_region(self): first, last = self.get_selection_indices() if first and last: head = self.index(first + " linestart") tail = self.index(last + "-1c lineend +1c") else: head = self.index("insert linestart") tail = self.index("insert lineend +1c") chars = self.get(head, tail) lines = chars.split("\n") return head, tail, chars, lines def _set_region(self, head, tail, chars, lines): newchars = "\n".join(lines) if newchars == chars: self.bell() return self.tag_remove("sel", "1.0", "end") self.mark_set("insert", head) self.delete(head, tail) self.insert(head, newchars) self.tag_add("sel", head, "insert") def _log_keypress_for_undo(self, e): if e is None: return # NB! this may not execute if the event is cancelled in another handler event_kind = self._get_event_kind(e) if ( event_kind != self._last_event_kind or e.char in ("\r", "\n", " ", "\t") or e.keysym in ["Return", "KP_Enter"] or time.time() - self._last_key_time > 2 ): self.edit_separator() self._last_event_kind = event_kind self._last_key_time = time.time() def _get_event_kind(self, event): if event.keysym in ("BackSpace", "Delete"): return "delete" elif event.char: return "insert" else: # eg. e.keysym in ("Left", "Up", "Right", "Down", "Home", "End", "Prior", "Next"): return "other_key" def _make_blanks(self, n): # Make string that displays as n leading blanks. if self.indent_with_tabs: ntabs, nspaces = divmod(n, self.tabwidth) return "\t" * ntabs + " " * nspaces else: return " " * n def _on_undo(self, e): self._last_event_kind = "undo" def _on_redo(self, e): self._last_event_kind = "redo" def _on_cut(self, e): self._last_event_kind = "cut" self.edit_separator() def _on_copy(self, e): self._last_event_kind = "copy" self.edit_separator() def _on_paste(self, e): if self.is_read_only(): return try: if self.has_selection(): self.direct_delete("sel.first", "sel.last") except Exception: pass self._last_event_kind = "paste" self.edit_separator() self.see("insert") self.after_idle(lambda: self.see("insert")) def _on_get_focus(self, e): self._last_event_kind = "get_focus" self.edit_separator() def _on_lose_focus(self, e): self._last_event_kind = "lose_focus" self.edit_separator() def _on_key_press(self, e): return self._log_keypress_for_undo(e) def _on_mouse_click(self, event): self.edit_separator() def _tag_current_line(self, event=None): self.tag_remove("current_line", "1.0", "end") # Let's show current line only with readable text # (this fits well with Thonny debugger, # otherwise debugger focus box and current line interact in an ugly way) if self._should_tag_current_line and not self.is_read_only(): # we may be on the same line as with prev event but tag needs extension lineno = int(self.index("insert").split(".")[0]) self.tag_add("current_line", str(lineno) + ".0", str(lineno + 1) + ".0") def on_secondary_click(self, event=None): "Use this for invoking context menu" self.focus_set() if event: self.mark_set("insert", "@%d,%d" % (event.x, event.y)) def _reload_theme_options(self, event=None): style = ttk.Style() states = [] if self.is_read_only(): states.append("readonly") # Following crashes when a combobox is focused # if self.focus_get() == self: # states.append("focus") if "background" not in self._initial_configuration: background = style.lookup(self._style, "background", states) if background: self.configure(background=background) if "foreground" not in self._initial_configuration: foreground = style.lookup(self._style, "foreground", states) if foreground: self.configure(foreground=foreground) self.configure(insertbackground=foreground) def _insert_untypable_characters_on_windows(self, event): if event.state == 131084: # AltGr or Ctrl+Alt lang_id = get_keyboard_language() char = _windows_altgr_chars_by_lang_id_and_keycode.get(lang_id, {}).get( event.keycode, None ) if char is not None: self.insert("insert", char) def destroy(self): self.unbind("<<ThemeChanged>>", self._ui_theme_change_binding) super().destroy() def direct_insert(self, index, chars, tags=None, **kw): chars = self.check_convert_tabs_to_spaces(chars) super().direct_insert(index, chars, tags, **kw) def check_convert_tabs_to_spaces(self, chars): if not self.replace_tabs: return chars tab_count = chars.count("\t") if tab_count == 0: return chars else: if messagebox.askyesno( "Convert tabs to spaces?", "Thonny (according to Python recommendation) uses spaces for indentation, " + "but the text you are about to insert/open contains %d tab characters. " % tab_count + "To avoid confusion, it's better to convert them into spaces (unless you know they should be kept as tabs).\n\n" + "Do you want me to replace each tab with %d spaces?\n\n" % self.indent_width, parent=tk._default_root, ): return chars.expandtabs(self.indent_width) else: return chars class TextFrame(ttk.Frame): "Decorates text with scrollbars, line numbers and print margin" def __init__( self, master, line_numbers=False, line_length_margin=0, first_line_number=1, text_class=EnhancedText, horizontal_scrollbar=True, vertical_scrollbar=True, vertical_scrollbar_class=ttk.Scrollbar, horizontal_scrollbar_class=ttk.Scrollbar, vertical_scrollbar_style=None, horizontal_scrollbar_style=None, borderwidth=0, relief="sunken", gutter_background="#e0e0e0", gutter_foreground="#999999", **text_options ): ttk.Frame.__init__(self, master=master, borderwidth=borderwidth, relief=relief) final_text_options = { "borderwidth": 0, "insertwidth": 2, "spacing1": 0, "spacing3": 0, "highlightthickness": 0, "inactiveselectbackground": "gray", "padx": 5, "pady": 5, } final_text_options.update(text_options) self.text = text_class(self, **final_text_options) self.text.grid(row=0, column=2, sticky=tk.NSEW) self._gutter = tk.Text( self, width=5, padx=0, pady=5, highlightthickness=0, bd=0, takefocus=False, font=self.text["font"], background="#e0e0e0", foreground=gutter_foreground, selectbackground=gutter_background, selectforeground=gutter_foreground, cursor="arrow", state="disabled", undo=False, wrap="none", ) self._gutter_is_gridded = False self._gutter.bind("<Double-Button-1>", self.on_gutter_double_click, True), self._gutter.bind("<ButtonRelease-1>", self.on_gutter_click, True) self._gutter.bind("<Button-1>", self.on_gutter_click, True) self._gutter.bind("<Button1-Motion>", self.on_gutter_motion, True) self._gutter["yscrollcommand"] = self._gutter_scroll # need tags for justifying and rmargin self._gutter.tag_configure("content", justify="right", rmargin=3) # gutter will be gridded later assert first_line_number is not None self._first_line_number = first_line_number self.set_gutter_visibility(line_numbers) if vertical_scrollbar: self._vbar = vertical_scrollbar_class( self, orient=tk.VERTICAL, style=vertical_scrollbar_style ) self._vbar.grid(row=0, column=3, sticky=tk.NSEW) self._vbar["command"] = self._vertical_scroll self.text["yscrollcommand"] = self._vertical_scrollbar_update if horizontal_scrollbar: self._hbar = horizontal_scrollbar_class( self, orient=tk.HORIZONTAL, style=horizontal_scrollbar_style ) self._hbar.grid(row=1, column=0, sticky=tk.NSEW, columnspan=3) self._hbar["command"] = self._horizontal_scroll self.text["xscrollcommand"] = self._horizontal_scrollbar_update self.columnconfigure(2, weight=1) self.rowconfigure(0, weight=1) self._recommended_line_length = line_length_margin margin_line_color = ttk.Style().lookup("Gutter", "background", default="LightGray") self._margin_line = tk.Canvas( self.text, borderwidth=0, width=1, height=2000, highlightthickness=0, background=margin_line_color, ) self.update_margin_line() self.text.bind("<<TextChange>>", self._text_changed, True) self.text.bind("<<CursorMove>>", self._cursor_moved, True) self._ui_theme_change_binding = self.bind( "<<ThemeChanged>>", self._reload_theme_options, True ) self._reload_theme_options() # TODO: add context menu? def focus_set(self): self.text.focus_set() def set_gutter_visibility(self, value): if value and not self._gutter_is_gridded: self._gutter.grid(row=0, column=0, sticky=tk.NSEW) self._gutter_is_gridded = True elif not value and self._gutter_is_gridded: self._gutter.grid_forget() self._gutter_is_gridded = False else: return """ # insert first line number (NB! Without trailing linebreak. See update_gutter) self._gutter.config(state="normal") self._gutter.delete("1.0", "end") for content, tags in self.compute_gutter_line(self._first_line_number): self._gutter.insert("end", content, ("content",) + tags) self._gutter.config(state="disabled") """ self.update_gutter(True) def set_line_length_margin(self, value): self._recommended_line_length = value self.update_margin_line() def _text_changed(self, event): self.update_gutter() def _cursor_moved(self, event): self._update_gutter_active_line() def _vertical_scrollbar_update(self, *args): self._vbar.set(*args) self._gutter.yview(tk.MOVETO, args[0]) self.text.event_generate("<<VerticalScroll>>") def _gutter_scroll(self, *args): try: self._vbar.set(*args) self.text.yview(tk.MOVETO, args[0]) except TclError: pass def _horizontal_scrollbar_update(self, *args): self._hbar.set(*args) self.update_margin_line() def _vertical_scroll(self, *args): self.text.yview(*args) self._gutter.yview(*args) self.text.event_generate("<<VerticalScroll>>") def _horizontal_scroll(self, *args): self.text.xview(*args) self.update_margin_line() def update_gutter(self, clean=False): if clean: self._gutter.config(state="normal") self._gutter.delete("1.0", "end") # need to add first item separately, because Text can't report 0 rows for content, tags in self.compute_gutter_line(self._first_line_number): self._gutter.insert("end-1c", content, tags + ("content",)) self._gutter.config(state="disabled") text_line_count = int(self.text.index("end").split(".")[0]) gutter_line_count = int(self._gutter.index("end").split(".")[0]) if text_line_count != gutter_line_count: self._gutter.config(state="normal") # NB! Text acts weird with last symbol # (don't really understand whether it automatically keeps a newline there or not) # Following seems to ensure both Text-s have same height if text_line_count > gutter_line_count: delta = text_line_count - gutter_line_count start = gutter_line_count + self._first_line_number - 1 if not clean and text_line_count > 10 and gutter_line_count < 3: # probably initial load, do bulk insert parts = [] for i in range(start, start + delta): parts.append("\n") for content, tags in self.compute_gutter_line(i, plain=True): parts.append(content) self._gutter.insert("end-1c", "".join(parts), ("content",) + tags) else: for i in range(start, start + delta): self._gutter.insert("end-1c", "\n", ("content",)) for content, tags in self.compute_gutter_line(i): self._gutter.insert("end-1c", content, ("content",) + tags) else: self._gutter.delete(line2index(text_line_count) + "-1c", "end-1c") self._gutter.config(state="disabled") # synchronize gutter scroll position with text # https://mail.python.org/pipermail/tkinter-discuss/2010-March/002197.html first, _ = self.text.yview() self._gutter.yview_moveto(first) self._update_gutter_active_line() if text_line_count > 9998: self._gutter.configure(width=7) elif text_line_count > 998: self._gutter.configure(width=6) def _update_gutter_active_line(self): self._gutter.tag_remove("active", "1.0", "end") insert = self.text.index("insert") self._gutter.tag_add("active", insert + " linestart", insert + " lineend") def compute_gutter_line(self, lineno, plain=False): yield str(lineno), () def update_margin_line(self): if self._recommended_line_length == 0: self._margin_line.place_forget() else: try: self.text.update_idletasks() # How far left has text been scrolled first_visible_idx = self.text.index("@0,0") first_visible_col = int(first_visible_idx.split(".")[1]) bbox = self.text.bbox(first_visible_idx) first_visible_col_x = bbox[0] margin_line_visible_col = self._recommended_line_length - first_visible_col delta = first_visible_col_x except Exception: # fall back to ignoring scroll position margin_line_visible_col = self._recommended_line_length delta = 0 if margin_line_visible_col > -1: x = ( get_text_font(self.text).measure((margin_line_visible_col - 1) * "M") + delta + self.text["padx"] ) else: x = -10 # print(first_visible_col, first_visible_col_x) self._margin_line.place(y=-10, x=x) def on_gutter_click(self, event=None): try: linepos = self._gutter.index("@%s,%s" % (event.x, event.y)).split(".")[0] self.text.mark_set("insert", "%s.0" % linepos) self._gutter.mark_set("gutter_selection_start", "%s.0" % linepos) if ( event.type == "4" ): # In Python 3.6 you can use tk.EventType.ButtonPress instead of "4" self.text.tag_remove("sel", "1.0", "end") except tk.TclError: exception("on_gutter_click") def on_gutter_double_click(self, event=None): try: self._gutter.mark_unset("gutter_selection_start") self.text.tag_remove("sel", "1.0", "end") self._gutter.tag_remove("sel", "1.0", "end") except tk.TclError: exception("on_gutter_click") def on_gutter_motion(self, event=None): try: if "gutter_selection_start" not in self._gutter.mark_names(): return linepos = int(self._gutter.index("@%s,%s" % (event.x, event.y)).split(".")[0]) gutter_selection_start = int(self._gutter.index("gutter_selection_start").split(".")[0]) self.text.select_lines( min(gutter_selection_start, linepos), max(gutter_selection_start - 1, linepos - 1) ) self.text.mark_set("insert", "%s.0" % linepos) except tk.TclError: exception("on_gutter_motion") def _reload_theme_options(self, event=None): style = ttk.Style() background = style.lookup("GUTTER", "background") if background: self._gutter.configure(background=background, selectbackground=background) self._margin_line.configure(background=background) foreground = style.lookup("GUTTER", "foreground") if foreground: self._gutter.configure(foreground=foreground, selectforeground=foreground) def destroy(self): self.unbind("<<ThemeChanged>>", self._ui_theme_change_binding) super().destroy() def get_text_font(text): font = text["font"] if isinstance(font, str): return tkfont.nametofont(font) else: return font def classifyws(s, tabwidth): raw = effective = 0 for ch in s: if ch == " ": raw = raw + 1 effective = effective + 1 elif ch == "\t": raw = raw + 1 effective = (effective // tabwidth + 1) * tabwidth else: break return raw, effective def index2line(index): return int(float(index)) def line2index(line): return str(float(line)) def fixwordbreaks(root): # Adapted from idlelib.EditorWindow (Python 3.4.2) # Modified to include non-ascii chars # Make sure that Tk's double-click and next/previous word # operations use our definition of a word (i.e. an identifier) root.tk.call("tcl_wordBreakAfter", "a b", 0) # make sure word.tcl is loaded root.tk.call("set", "tcl_wordchars", r"\w") root.tk.call("set", "tcl_nonwordchars", r"\W") def rebind_control_a(root): # Tk 8.6 has <<SelectAll>> event but 8.5 doesn't # http://stackoverflow.com/questions/22907200/remap-default-keybinding-in-tkinter def control_a(event): widget = event.widget if isinstance(widget, tk.Text): widget.tag_remove("sel", "1.0", "end") widget.tag_add("sel", "1.0", "end") root.bind_class("Text", "<Control-a>", control_a) def _running_on_mac(): return tk._default_root.call("tk", "windowingsystem") == "aqua" def get_keyboard_language(): # https://stackoverflow.com/a/42047820/261181 if platform.system() != "Windows": raise NotImplementedError("Can provide keyboard language only on Windows") import ctypes user32 = ctypes.WinDLL("user32", use_last_error=True) curr_window = user32.GetForegroundWindow() thread_id = user32.GetWindowThreadProcessId(curr_window, 0) # Made up of 0xAAABBBB, AAA = HKL (handle object) & BBBB = language ID klid = user32.GetKeyboardLayout(thread_id) # Language ID -> low 10 bits, Sub-language ID -> high 6 bits # Extract language ID from KLID lid = klid & (2 ** 16 - 1) return lid _windows_altgr_chars_by_lang_id_and_keycode = { # https://docs.microsoft.com/en-us/windows/desktop/intl/language-identifier-constants-and-strings 0x0425: {191: "^"} # AltGr+Ä }

36.14805

130

0.591637

78504e4682d31f1e857a409dd9b39c0ecdb6c924

7,299

py

Python

yt/visualization/volume_rendering/transfer_function_helper.py

themousepotato/yt

6befef2bc0427250fd62395962599be41b193e65

[ "BSD-3-Clause-Clear" ]

null

yt/visualization/volume_rendering/transfer_function_helper.py

themousepotato/yt

6befef2bc0427250fd62395962599be41b193e65

[ "BSD-3-Clause-Clear" ]

null

yt/visualization/volume_rendering/transfer_function_helper.py

themousepotato/yt

6befef2bc0427250fd62395962599be41b193e65

[ "BSD-3-Clause-Clear" ]

null

import matplotlib import numpy as np from distutils.version import LooseVersion from yt.funcs import mylog from yt.data_objects.profiles import create_profile from yt.visualization.volume_rendering.transfer_functions import \ ColorTransferFunction from io import BytesIO class TransferFunctionHelper: r"""A transfer function helper. This attempts to help set up a good transfer function by finding bounds, handling linear/log options, and displaying the transfer function combined with 1D profiles of rendering quantity. Parameters ---------- ds: A Dataset instance A static output that is currently being rendered. This is used to help set up data bounds. Notes ----- """ profiles = None def __init__(self, ds): self.ds = ds self.field = None self.log = False self.tf = None self.bounds = None self.grey_opacity = False self.profiles = {} def set_bounds(self, bounds=None): """ Set the bounds of the transfer function. Parameters ---------- bounds: array-like, length 2, optional A length 2 list/array in the form [min, max]. These should be the raw values and not the logarithm of the min and max. If bounds is None, the bounds of the data are calculated from all of the data in the dataset. This can be slow for very large datasets. """ if bounds is None: bounds = self.ds.h.all_data().quantities['Extrema'](self.field, non_zero=True) bounds = [b.ndarray_view() for b in bounds] self.bounds = bounds # Do some error checking. assert(len(self.bounds) == 2) if self.log: assert(self.bounds[0] > 0.0) assert(self.bounds[1] > 0.0) return def set_field(self, field): """ Set the field to be rendered Parameters ---------- field: string The field to be rendered. """ if field != self.field: self.log = self.ds._get_field_info(field).take_log self.field = field def set_log(self, log): """ Set whether or not the transfer function should be in log or linear space. Also modifies the ds.field_info[field].take_log attribute to stay in sync with this setting. Parameters ---------- log: boolean Sets whether the transfer function should use log or linear space. """ self.log = log def build_transfer_function(self): """ Builds the transfer function according to the current state of the TransferFunctionHelper. Parameters ---------- None Returns ------- A ColorTransferFunction object. """ if self.bounds is None: mylog.info('Calculating data bounds. This may take a while.' + ' Set the TransferFunctionHelper.bounds to avoid this.') self.set_bounds() if self.log: mi, ma = np.log10(self.bounds[0]), np.log10(self.bounds[1]) else: mi, ma = self.bounds self.tf = ColorTransferFunction((mi, ma), grey_opacity=self.grey_opacity, nbins=512) return self.tf def setup_default(self): """Setup a default colormap Creates a ColorTransferFunction including 10 gaussian layers whose colors sample the 'spectral' colormap. Also attempts to scale the transfer function to produce a natural contrast ratio. """ if LooseVersion(matplotlib.__version__) < LooseVersion('2.0.0'): colormap_name = 'spectral' else: colormap_name = 'nipy_spectral' self.tf.add_layers(10, colormap=colormap_name) factor = self.tf.funcs[-1].y.size / self.tf.funcs[-1].y.sum() self.tf.funcs[-1].y *= 2*factor def plot(self, fn=None, profile_field=None, profile_weight=None): """ Save the current transfer function to a bitmap, or display it inline. Parameters ---------- fn: string, optional Filename to save the image to. If None, the returns an image to an IPython session. Returns ------- If fn is None, will return an image to an IPython notebook. """ from yt.visualization._mpl_imports import FigureCanvasAgg from matplotlib.figure import Figure if self.tf is None: self.build_transfer_function() self.setup_default() tf = self.tf if self.log: xfunc = np.logspace xmi, xma = np.log10(self.bounds[0]), np.log10(self.bounds[1]) else: xfunc = np.linspace # Need to strip units off of the bounds to avoid a recursion error # in matplotlib 1.3.1 xmi, xma = [np.float64(b) for b in self.bounds] x = xfunc(xmi, xma, tf.nbins) y = tf.funcs[3].y w = np.append(x[1:]-x[:-1], x[-1]-x[-2]) colors = np.array([tf.funcs[0].y, tf.funcs[1].y, tf.funcs[2].y, np.ones_like(x)]).T fig = Figure(figsize=[6, 3]) canvas = FigureCanvasAgg(fig) ax = fig.add_axes([0.2, 0.2, 0.75, 0.75]) ax.bar(x, tf.funcs[3].y, w, edgecolor=[0.0, 0.0, 0.0, 0.0], log=self.log, color=colors, bottom=[0]) if profile_field is not None: try: prof = self.profiles[self.field] except KeyError: self.setup_profile(profile_field, profile_weight) prof = self.profiles[self.field] try: prof[profile_field] except KeyError: prof.add_fields([profile_field]) # Strip units, if any, for matplotlib 1.3.1 xplot = np.array(prof.x) yplot = np.array(prof[profile_field]*tf.funcs[3].y.max() / prof[profile_field].max()) ax.plot(xplot, yplot, color='w', linewidth=3) ax.plot(xplot, yplot, color='k') ax.set_xscale({True: 'log', False: 'linear'}[self.log]) ax.set_xlim(x.min(), x.max()) ax.set_xlabel(self.ds._get_field_info(self.field).get_label()) ax.set_ylabel(r'$\mathrm{alpha}$') ax.set_ylim(y.max()*1.0e-3, y.max()*2) if fn is None: from IPython.core.display import Image f = BytesIO() canvas.print_figure(f) f.seek(0) img = f.read() return Image(img) else: fig.savefig(fn) def setup_profile(self, profile_field=None, profile_weight=None): if profile_field is None: profile_field = 'cell_volume' prof = create_profile(self.ds.all_data(), self.field, profile_field, n_bins=128, extrema={self.field: self.bounds}, weight_field=profile_weight, logs = {self.field: self.log}) self.profiles[self.field] = prof return

32.730942

90

0.561173

8462b49b868aa8141b29e19a0be853634906efa9

85

py

Python

vagas/apps.py

rodgeraraujo/cv-base

c6bbd19f4586a3c4d065f486c4de6a92e26c1c9b

[ "MIT" ]

1

2019-08-21T20:10:21.000Z

vagas/apps.py

rodgeraraujo/cv-base

c6bbd19f4586a3c4d065f486c4de6a92e26c1c9b

[ "MIT" ]

8

2019-10-02T19:44:19.000Z

2022-02-10T08:30:05.000Z

vagas/apps.py

rodgeraraujo/cv-base

c6bbd19f4586a3c4d065f486c4de6a92e26c1c9b

[ "MIT" ]

4

2019-09-17T01:00:55.000Z

2019-10-02T20:07:36.000Z

from django.apps import AppConfig class VagasConfig(AppConfig): name = "vagas"

14.166667

33

0.741176

27d49470797debb25f38cafee39cf075973b7b18

5,380

py

Python

python/benchmarks/extensions_25percent_1teams.py

tbvanderwoude/research-project

1c5cb09a424a0db83f18dae3278ecb7ad6866d2c

[ "Apache-2.0", "MIT" ]

3

2021-08-23T18:00:44.000Z

2022-02-09T06:00:50.000Z

python/benchmarks/extensions_25percent_1teams.py

tbvanderwoude/research-project

1c5cb09a424a0db83f18dae3278ecb7ad6866d2c

[ "Apache-2.0", "MIT" ]

null

python/benchmarks/extensions_25percent_1teams.py

tbvanderwoude/research-project

1c5cb09a424a0db83f18dae3278ecb7ad6866d2c

[ "Apache-2.0", "MIT" ]

1

2021-06-15T11:38:45.000Z

from multiprocessing import Pool from typing import Optional from mapfmclient import Problem from tqdm import tqdm from python.benchmarks.extensions_25percent_3teams import read_from_file from python.benchmarks.graph_times import graph_results from python.benchmarks.inmatch_vs_prematch_75percent_1teams import output_data from python.benchmarks.map import MapGenerator import pathlib from python.benchmarks.parse_map import MapParser from python.benchmarks.run_with_timeout import run_with_timeout from python.mstar.rewrite import Config, MatchingStrategy from python.mstar.rewrite.config import GigaByte from python.solvers.configurable_mstar_solver import ConfigurableMStar this_dir = pathlib.Path(__file__).parent.absolute() name = "extensions_25percent_1teams_maps" processes = 1 def generate_maps(): path = this_dir / name try: path.mkdir(parents=True) except FileExistsError: print("maps already generated") return for i in tqdm(range(1, 16)): tqdm.write(f"generating {path}") map_generator = MapGenerator(path) map_generator.generate_even_batch( 200, # number of maps 20, 20, # size i, # number of agents 1, # number of teams prefix=name, min_goal_distance=0, open_factor=0.65, max_neighbors=3, ) def run(config: Config, bm_name: str): batchdir = this_dir / name parser = MapParser(batchdir) fname = batchdir / f"results_{bm_name}.txt" if fname.exists(): print(f"data exists for {bm_name}") return fname, bm_name # num agents : solutions results: dict[int, list[Optional[float]]] = {} all_problems = [[i[1] for i in parser.parse_batch(n.name)] for n in batchdir.iterdir() if n.is_dir()] all_problems.sort(key=lambda i: len(i[0].goals)) with Pool(processes) as p: for problems in tqdm(all_problems): num_agents = len(problems[0].goals) partname = pathlib.Path(str(fname) + f".{num_agents}agents") if partname.exists(): print(f"found data for part {num_agents}") results[num_agents] = read_from_file(partname, num_agents) continue if num_agents <= 1 or sum(1 for i in results[num_agents - 1] if i is not None) != 0: sols_inmatch = run_with_timeout(p, ConfigurableMStar( config ), problems, 2 * 60) tqdm.write(f"{bm_name} with {num_agents} agents: {sols_inmatch}") results[num_agents] = sols_inmatch else: results[num_agents] = [None for i in range(len(problems))] output_data(partname, results) tqdm.write(str(results)) output_data(fname, results) return fname, bm_name def main(): batchdir = this_dir / name generate_maps() files: list[tuple[pathlib.Path, str]] = [] files.append(run( Config( operator_decomposition=False, precompute_paths=False, precompute_heuristic=False, collision_avoidance_table=False, recursive=False, matching_strategy=MatchingStrategy.Prematch, max_memory_usage=3 * GigaByte, debug=False, report_expansions=True, ), "no extensions" )) files.append(run( Config( operator_decomposition=False, precompute_paths=False, precompute_heuristic=False, collision_avoidance_table=False, recursive=False, matching_strategy=MatchingStrategy.PruningPrematch, max_memory_usage=3 * GigaByte, debug=False, report_expansions=True, ), "pruning" )) files.append(run( Config( operator_decomposition=False, precompute_paths=False, precompute_heuristic=False, collision_avoidance_table=False, recursive=False, matching_strategy=MatchingStrategy.SortedPruningPrematch, max_memory_usage=3 * GigaByte, debug=False, report_expansions=True, ), "pruning and sorting" )) files.append(run( Config( operator_decomposition=False, precompute_paths=False, precompute_heuristic=True, collision_avoidance_table=False, recursive=False, matching_strategy=MatchingStrategy.SortedPruningPrematch, max_memory_usage=3 * GigaByte, debug=False, report_expansions=True, ), "precomputed heuristic" )) files.append(run( Config( operator_decomposition=True, precompute_paths=False, precompute_heuristic=True, collision_avoidance_table=False, recursive=False, matching_strategy=MatchingStrategy.SortedPruningPrematch, max_memory_usage=3 * GigaByte, debug=False, report_expansions=True, ), "operator decomposition" )) graph_results( *files, batchdir / f"{name}", save=True, bounds=False, legend=False, ) if __name__ == '__main__': main()

29.081081

105

0.6171

df3f328e28f8bca7b4d88d903cd87578fe2ff3a2

578

py

Python

code/digit_factorials/sol_34.py

bhavinjawade/project-euler-solutions

56bf6a282730ed4b9b875fa081cf4509d9939d98

[ "Apache-2.0" ]

2

2020-07-16T08:16:32.000Z

2020-10-01T07:16:48.000Z

code/digit_factorials/sol_34.py

Psingh12354/project-euler-solutions

56bf6a282730ed4b9b875fa081cf4509d9939d98

[ "Apache-2.0" ]

null

code/digit_factorials/sol_34.py

Psingh12354/project-euler-solutions

56bf6a282730ed4b9b875fa081cf4509d9939d98

[ "Apache-2.0" ]

1

2021-05-07T18:06:08.000Z

# -*- coding: utf-8 -*- ''' File name: code\digit_factorials\sol_34.py Author: Vaidic Joshi Date created: Oct 20, 2018 Python Version: 3.x ''' # Solution to Project Euler Problem #34 :: Digit factorials # # For more information see: # https://projecteuler.net/problem=34 # Problem Statement ''' 145 is a curious number, as 1! + 4! + 5! = 1 + 24 + 120 = 145. Find the sum of all numbers which are equal to the sum of the factorial of their digits. Note: as 1! = 1 and 2! = 2 are not sums they are not included. ''' # Solution # Solution Approach ''' '''

21.407407

88

0.653979

aed956091a2c754e19d546901f2a83bf1ddf4038

1,300

py

Python

doc/generate-doc.py

ponty/pyunpack

09184e1f35ce8b805c672b6f7ebf06974d4ba34c

[ "BSD-2-Clause" ]

74

2015-03-19T20:32:04.000Z

2022-03-18T02:29:01.000Z

doc/generate-doc.py

ponty/pyunpack

09184e1f35ce8b805c672b6f7ebf06974d4ba34c

[ "BSD-2-Clause" ]

11

2016-01-21T11:29:21.000Z

2022-01-24T19:26:50.000Z

doc/generate-doc.py

ponty/pyunpack

09184e1f35ce8b805c672b6f7ebf06974d4ba34c

[ "BSD-2-Clause" ]

9

2015-11-30T02:24:27.000Z

2021-07-05T20:14:46.000Z

import glob import logging import os from easyprocess import EasyProcess from entrypoint2 import entrypoint commands = """ python3 -m pyunpack.cli --help """ commands = commands.strip().splitlines() def empty_dir(dir): files = glob.glob(os.path.join(dir, "*")) for f in files: os.remove(f) @entrypoint def main(): gendir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "gen") logging.info("gendir: %s", gendir) os.makedirs(gendir, exist_ok=True) empty_dir(gendir) try: os.chdir("gen") for cmd in commands: logging.info("cmd: %s", cmd) fname_base = cmd.replace(" ", "_") fname = fname_base + ".txt" logging.info("cmd: %s", cmd) print("file name: %s" % fname) with open(fname, "w") as f: f.write("$ " + cmd + "\n") p = EasyProcess(cmd).call() f.write(p.stdout) if p.stderr and p.stdout: f.write("\n") f.write(p.stderr) finally: os.chdir("..") embedme = EasyProcess(["npx", "embedme", "../README.md"]) embedme.call() print(embedme.stdout) assert embedme.return_code == 0 assert not "but file does not exist" in embedme.stdout

25

76

0.555385

6fdc7319b512cca35163df8683c7cbb1bab4957f

1,215

py

Python

Walker.py

ey3lock3r/The-Nature-of-Code

cca3a0359a46570b1cf0b02315be8cee1728a01a

[ "MIT" ]

null

Walker.py

ey3lock3r/The-Nature-of-Code

cca3a0359a46570b1cf0b02315be8cee1728a01a

[ "MIT" ]

null

Walker.py

ey3lock3r/The-Nature-of-Code

cca3a0359a46570b1cf0b02315be8cee1728a01a

[ "MIT" ]

null

import matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np import noise map = lambda n, start1, stop1, start2, stop2: ((n-start1)/(stop1-start1))*(stop2-start2)+start2 class Walker(): def __init__(self): self.fig, self.ax = plt.subplots(figsize=(8, 5), subplot_kw=dict(aspect="equal", adjustable='datalim', anchor='C')) self.fig.set_dpi(100) self.w = 320 self.h = 180 self.ax.set_xlim((-self.w,self.w)) self.ax.set_ylim((-self.h,self.h)) self.n1 = 0 self.n2 = 10000 def step(self, time): n1 = noise.pnoise1(self.n1) n2 = noise.pnoise1(self.n2) x = map(n1, 0, 1, 0, self.w) y = map(n2, 0, 1, 0, self.h) # print('x={} y={}'.format(x,y)) self.point.center = (x, y) self.n1 += 0.01 self.n2 += 0.01 return [self.point] def display(self): self.point = plt.Circle((0, 0), 10, color='red', alpha=1) self.ax.add_patch(self.point) ani = animation.FuncAnimation(self.fig, self.step, frames=500, interval=40, blit=True) plt.show() agent = Walker() agent.display()

31.973684

124

0.561317

bf540c2d9004cbfba99d2117e9e8bfc7eed95f5c

75

py

Python

instance/config.py

cherucole/News.io

6a00132acf37b39649d2954d43433a2eb4a414ac

[ "Unlicense", "MIT" ]

null

instance/config.py

cherucole/News.io

6a00132acf37b39649d2954d43433a2eb4a414ac

[ "Unlicense", "MIT" ]

null

instance/config.py

cherucole/News.io

6a00132acf37b39649d2954d43433a2eb4a414ac

[ "Unlicense", "MIT" ]

null

NEWS_API_KEY='34c58192d7aa4ba89c5f8fcca69719a8' SECRET_KEY = 'mySecretKey'

25

47

0.866667