ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

0c34fc08f42c8637a1d795a07180d44a6de5c252

4,946

py

Python

components/server/src/data_model/sources/jenkins.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

components/server/src/data_model/sources/jenkins.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

components/server/src/data_model/sources/jenkins.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

"""Jenkins source.""" from ..meta.entity import Color, EntityAttributeType from ..parameters import ( access_parameters, Days, FailureType, MultipleChoiceParameter, MultipleChoiceWithAdditionParameter, TestResult, ) from ..meta.source import Source def jenkins_access_parameters(*args, **kwargs): """Create Jenkins specific access parameters.""" kwargs["include"] = dict(private_token=False, landing_url=False) if "name" not in kwargs.setdefault("kwargs", {}).setdefault("url", {}): kwargs["kwargs"]["url"]["name"] = "URL to Jenkins job" kwargs["kwargs"]["password"] = dict( name="Password or API token for basic authentication", help_url="https://wiki.jenkins.io/display/JENKINS/Authenticating+scripted+clients", ) return access_parameters(*args, **kwargs) ALL_JENKINS_METRICS = ["failed_jobs", "source_up_to_dateness", "source_version", "unused_jobs"] JOB_ENTITY = dict( name="job", attributes=[ dict(name="Job name", key="name", url="url"), dict( name="Status of most recent build", key="build_status", color=dict(Success=Color.POSITIVE, Failure=Color.NEGATIVE, Aborted=Color.ACTIVE, Unstable=Color.WARNING), ), dict(name="Date of most recent build", key="build_date", type=EntityAttributeType.DATE), ], ) JENKINS = Source( name="Jenkins", description="Jenkins is an open source continuous integration/continuous deployment server.", url="https://jenkins.io/", parameters=dict( inactive_days=Days( name="Number of days without builds after which to consider CI-jobs unused.", short_name="number of days without builds", default_value="90", metrics=["unused_jobs"], ), jobs_to_include=MultipleChoiceWithAdditionParameter( name="Jobs to include (regular expressions or job names)", short_name="jobs to include", help="Jobs to include can be specified by job name or by regular expression. " "Use {parent job name}/{child job name} for the names of nested jobs.", placeholder="all", metrics=["failed_jobs", "source_up_to_dateness", "unused_jobs"], ), jobs_to_ignore=MultipleChoiceWithAdditionParameter( name="Jobs to ignore (regular expressions or job names)", short_name="jobs to ignore", help="Jobs to ignore can be specified by job name or by regular expression. " "Use {parent job name}/{child job name} for the names of nested jobs.", metrics=["failed_jobs", "source_up_to_dateness", "unused_jobs"], ), result_type=MultipleChoiceParameter( name="Build result types", short_name="result types", help="Limit which build result types to include.", placeholder="all result types", values=["Aborted", "Failure", "Not built", "Success", "Unstable"], metrics=["source_up_to_dateness"], ), failure_type=FailureType(values=["Aborted", "Failure", "Not built", "Unstable"]), **jenkins_access_parameters( ALL_JENKINS_METRICS, kwargs=dict( url=dict( name="URL", help="URL of the Jenkins instance, with port if necessary, but without path. For example, " "'https://jenkins.example.org'.", ) ), ) ), entities=dict( failed_jobs=JOB_ENTITY, source_up_to_dateness=JOB_ENTITY, unused_jobs=JOB_ENTITY, ), ) ALL_JENKINS_TEST_REPORT_METRICS = ["source_up_to_dateness", "tests"] JENKINS_TEST_REPORT = Source( name="Jenkins test report", description="A Jenkins job with test results.", url="https://plugins.jenkins.io/junit", parameters=dict( test_result=TestResult(values=["failed", "passed", "skipped"]), **jenkins_access_parameters( ALL_JENKINS_TEST_REPORT_METRICS, kwargs=dict( url=dict( help="URL to a Jenkins job with a test report generated by the JUnit plugin. For example, " "'https://jenkins.example.org/job/test' or https://jenkins.example.org/job/test/job/master' " "in case of a pipeline job." ) ), ) ), entities=dict( tests=dict( name="test", attributes=[ dict(name="Class name"), dict(name="Test case", key="name"), dict( name="Test result", color=dict(failed=Color.NEGATIVE, passed=Color.POSITIVE, skipped=Color.WARNING) ), dict(name="Number of builds the test has been failing", key="age", type=EntityAttributeType.INTEGER), ], ) ), )

38.640625

119

0.603114

0

1,978

0.399919

0c3529b1f848cd4aef129f241e7149c8c46fd8c6

3,155

py

Python

tests/test_transducer.py

dandersonw/myouji-kenchi

6a373d8626995bf5d4383dcac4fc8e6372135640

[ "MIT" ]

7

2019-10-22T10:09:12.000Z

2022-01-31T07:49:07.000Z

tests/test_transducer.py

dandersonw/myouji-kenchi

6a373d8626995bf5d4383dcac4fc8e6372135640

[ "MIT" ]

null

tests/test_transducer.py

dandersonw/myouji-kenchi

6a373d8626995bf5d4383dcac4fc8e6372135640

[ "MIT" ]

1

2021-07-09T18:10:17.000Z

import myouji_kenchi # Given that the output depends on what goes into the attested myouji file I'm # hesitant to write too many tests in the blast radius of changes to that file class TestTransducer(): nbt = myouji_kenchi.MyoujiBackTransliteration() def assert_transliteration(self, romaji, *expected_results): results = self.nbt.back_transliterate(romaji) strings = set(r[0] for r in results) assert strings == set(expected_results) def test_assorted(self): self.assert_transliteration('sa', 'サ') self.assert_transliteration('ＳＡ', 'サ') self.assert_transliteration('se', 'セ') self.assert_transliteration('shō', 'ショウ') # composed self.assert_transliteration('shō', 'ショウ') # decomposed self.assert_transliteration('sho', 'ショウ') self.assert_transliteration('syo', 'ショウ') self.assert_transliteration('ho', 'ホ', 'ホウ', 'ホオ') self.assert_transliteration('teppou', 'テッポウ') self.assert_transliteration('shibukawa', 'シブカワ') self.assert_transliteration('watamura', 'ワタムラ') self.assert_transliteration('Matsumoto', 'マツモト') self.assert_transliteration('Matumoto', 'マツモト') self.assert_transliteration('Tusima', 'ツシマ') self.assert_transliteration('IMAZU', 'イマヅ', 'イマズ') self.assert_transliteration('SATO', 'サトウ', 'サトオ', 'サト') self.assert_transliteration('Uchino', 'ウチノ', 'ウチノウ') self.assert_transliteration('Utino', 'ウチノ', 'ウチノウ') self.assert_transliteration('Chano', 'チャノ') self.assert_transliteration('Tyano', 'チャノ') self.assert_transliteration('Kojima', 'コジマ', 'コヂマ', 'コウジマ') self.assert_transliteration('Kozima', 'コジマ', 'コヂマ', 'コウジマ') self.assert_transliteration('Inuduka', 'イヌヅカ') self.assert_transliteration('Inuzuka', 'イヌヅカ', 'イヌズカ') self.assert_transliteration('Inudzuka', 'イヌヅカ') self.assert_transliteration('Betchaku', 'ベッチャク') self.assert_transliteration('Becchaku', 'ベッチャク') self.assert_transliteration('Uwozaki', 'ウヲザキ') self.assert_transliteration('Uozaki', 'ウヲザキ', 'ウオザキ') self.assert_transliteration('Nyoya', 'ニョウヤ') self.assert_transliteration('Nitta', 'ニッタ') def test_oh(self): self.assert_transliteration('Ohnishi', 'オオニシ', 'オウニシ') def test_leading_m(self): self.assert_transliteration('Sampei', 'サンペイ') self.assert_transliteration('Sanpei', 'サンペイ') def test_glottal_stop(self): self.assert_transliteration('Shinyagaito', 'シンヤガイト') self.assert_transliteration('Sinyagaito', 'シンヤガイト') self.assert_transliteration('Shin\'yagaito', 'シンヤガイト') self.assert_transliteration('Shin-yagaito', 'シンヤガイト') def test_double_i(self): self.assert_transliteration('Ishii', 'イシイ') # To be clear, 'Ishî' is almost certainly a wrong transliteration # Nevertheless, the below is the expected behavior self.assert_transliteration('Ishî', 'イシイ') self.assert_transliteration('Isî', 'イシイ') def test_bad_characters(self): self.assert_transliteration('@')

44.43662

78

0.66878

3,362

0.948111

0

1,317

0.371404

0c36ba1ececdebf56f9ae6696bc5d261578450ca

1,668

py

Python

algorithms/named/mergesort.py

thundergolfer/uni

e604d1edd8e5085f0ae1c0211015db38c07fc926

[ "MIT" ]

1

2022-01-06T04:50:09.000Z

algorithms/named/mergesort.py

thundergolfer/uni

e604d1edd8e5085f0ae1c0211015db38c07fc926

[ "MIT" ]

1

2022-01-23T06:09:21.000Z

2022-01-23T06:14:17.000Z

algorithms/named/mergesort.py

thundergolfer/uni

e604d1edd8e5085f0ae1c0211015db38c07fc926

[ "MIT" ]

null

import unittest from typing import List, Optional # Consider making generic with: # https://stackoverflow.com/a/47970232/4885590 def merge_sort(items: List[int], lo: int = 0, hi: Optional[int] = None) -> None: if hi is None: hi = len(items) - 1 if lo < hi: mid = (lo + hi) // 2 # Ranges passed are *inclusive*. merge_sort(items, lo=lo, hi=mid) merge_sort(items, lo=mid + 1, hi=hi) merge_partitions(items, lo, mid, hi) def merge_partitions(items: List[int], lo: int, mid: int, hi: int) -> None: left_part = items[lo : mid + 1] right_part = items[mid + 1 : hi + 1] i = lo left_ptr = 0 right_ptr = 0 while left_ptr < len(left_part) and right_ptr < len(right_part): if left_part[left_ptr] < right_part[right_ptr]: items[i] = left_part[left_ptr] left_ptr += 1 else: items[i] = right_part[right_ptr] right_ptr += 1 i += 1 # Only 1 of these two for-loops should ever run anything. for item in left_part[left_ptr:]: items[i] = item i += 1 for item in right_part[right_ptr:]: items[i] = item i += 1 # Idx tracking sorted insertions has reached beyond hi, so # partitions are fully merged. assert i == (hi + 1) class TestMergeSort(unittest.TestCase): def test(self): actual = [10, 5, 9, 10, 3] expected = [10, 5, 9, 10, 3] expected.sort() merge_sort(actual) self.assertEqual(actual, expected) if __name__ == "__main__": x = [10, 5, 9, 10, 3] print(x) merge_sort(x) print(x) unittest.main()

24.895522

80

0.576739

225

0.134892

0

264

0.158273

0c380570b168add317dd67b7037f3b6ec7e93c2b

392

py

Python

pages/main_page.py

thaidem/selenium-training-page-objects

1f37a2b5287a502295bb57050c95455d68c2d3eb

[ "Apache-2.0" ]

null

pages/main_page.py

thaidem/selenium-training-page-objects

1f37a2b5287a502295bb57050c95455d68c2d3eb

[ "Apache-2.0" ]

null

pages/main_page.py

thaidem/selenium-training-page-objects

1f37a2b5287a502295bb57050c95455d68c2d3eb

[ "Apache-2.0" ]

null

from selenium.webdriver.support.wait import WebDriverWait class MainPage: def __init__(self, driver): self.driver = driver self.wait = WebDriverWait(driver, 10) def open(self): self.driver.get("https://litecart.stqa.ru/en/") return self @property def product_item(self): return self.driver.find_element_by_css_selector(".product")

23.058824

67

0.670918

331

0.844388

0

105

0.267857

0

40

0.102041

0c39dce08e2639d2b8e9721a52545154b1694858

196

py

Python

src/frr/tests/lib/test_nexthop_iter.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

src/frr/tests/lib/test_nexthop_iter.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

src/frr/tests/lib/test_nexthop_iter.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

import frrtest class TestNexthopIter(frrtest.TestMultiOut): program = "./test_nexthop_iter" TestNexthopIter.onesimple("Simple test passed.") TestNexthopIter.onesimple("PRNG test passed.")

19.6

48

0.785714

80

0.408163

0

61

0.311224

0c3b154bc332d251c3e35e98a56001cf94c27a53

1,319

py

Python

setup.py

themis-project/themis-finals-checker-app-py

12e70102bcca3d6e4082d96e676e364176c0da67

[ "MIT" ]

null

setup.py

themis-project/themis-finals-checker-app-py

12e70102bcca3d6e4082d96e676e364176c0da67

[ "MIT" ]

null

setup.py

themis-project/themis-finals-checker-app-py

12e70102bcca3d6e4082d96e676e364176c0da67

[ "MIT" ]

null

# -*- coding: utf-8 -*- from setuptools import setup, find_packages import io import os about = {} about_filename = os.path.join( os.path.dirname(os.path.realpath(__file__)), 'themis', 'finals', 'checker', 'app', '__about__.py') with io.open(about_filename, 'rb') as fp: exec(fp.read(), about) setup( name='themis.finals.checker.app', version=about['__version__'], description='Themis Finals checker application', author='Alexander Pyatkin', author_email='aspyatkin@gmail.com', url='https://github.com/themis-project/themis-finals-checker-app-py', license='MIT', packages=find_packages('.'), install_requires=[ 'setuptools>=0.8', 'Flask>=0.11.1,<0.12', 'redis>=2.10.5,<2.11', 'hiredis>=0.2.0,<0.3', 'rq>=0.7.1,<0.8.0', 'requests>=2.11.0', 'python-dateutil>=2.5.3,<2.6', 'themis.finals.checker.result==1.1.0', 'raven>=5.26.0,<5.27.0', 'PyJWT>=1.5.0,<1.6.0', 'cryptography>=1.8.1,<1.9.0', 'PyYAML>=3.11' ], namespace_packages=[ 'themis', 'themis.finals', 'themis.finals.checker' ], entry_points=dict( console_scripts=[ 'themis-finals-checker-app-worker = themis.finals.checker.app:start_worker' ] ) )

26.918367

87

0.581501

0

647

0.490523

0c3b1affbabd1c858deb93d0a0302a8d675091d1

8,090

py

Python

tools/xenserver/cleanup_sm_locks.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

tools/xenserver/cleanup_sm_locks.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

tools/xenserver/cleanup_sm_locks.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

2

2017-07-20T17:31:34.000Z

2020-07-24T02:42:19.000Z

13.758503

495

0.591595

0

4,563

0.56403

0c3c5abd70c1f21c01879c6ec3f584ca3464ae2e

13,324

py

Python

clifun.py

tdimiduk/clifun

d7e5acae0a76506d9440ae86a15341b6cc1cf25e

[ "MIT" ]

1

2022-01-04T17:58:19.000Z

clifun.py

tdimiduk/clifun

d7e5acae0a76506d9440ae86a15341b6cc1cf25e

[ "MIT" ]

4

2022-01-04T17:17:33.000Z

2022-01-04T17:26:12.000Z

clifun.py

tdimiduk/clifun

d7e5acae0a76506d9440ae86a15341b6cc1cf25e

[ "MIT" ]

null

import datetime as dt import importlib.util import inspect import itertools import json import os import pathlib import sys import types import typing from typing import ( Any, Callable, Dict, Generic, Iterable, Iterator, List, Optional, Set, Tuple, Type, TypeVar, Union, ) S = TypeVar("S") T = TypeVar("T") O = TypeVar("O", Any, None) StringInterpreters = Dict[Type[T], Callable[[str], T]] def call( c: Callable[..., T], args: Optional[List[str]] = None, string_interpreters: Optional[StringInterpreters] = None, ) -> T: """ Call a function from the command line Assembles the inputs to a function from command line arguments, environment variables, and config files and call it. """ argv = sys.argv if args is None else args interpreters = ( string_interpreters if string_interpreters is not None else default_string_interpreters() ) annotated = annotate_callable(c, interpreters, []) provided_inputs = assemble_input_sources(argv) if provided_inputs.args.help: print_usage(annotated, header=True) sys.exit(0) needed_inputs = all_needed_inputs(annotated) unknown = invalid_args(provided_inputs.args.keyword.keys(), needed_inputs) if unknown: print(f"Unknown arguments: {unknown}") print_usage(annotated) sys.exit(1) resolved_inputs, missing_inputs = resolve_inputs(needed_inputs, provided_inputs) if missing_inputs: print(f"Missing arguments: {missing_inputs}") print_usage(annotated) sys.exit(1) return annotated(resolved_inputs) ################################################################################ # Interpreting strings into python types ################################################################################ def default_string_interpreters() -> StringInterpreters: return { int: int, float: float, str: str, bool: interpret_bool, dt.datetime: interpret_datetime, dt.date: interpret_date, } class InterpretationError(ValueError): def __init__(self, s: str, t: T): self.s = s self.t = t def __str__(self): return f"Could not interpret '{self.s}' as {self.t}" def interpret_bool(s: str) -> bool: """ Slightly more intuitive bool iterpretation Raw python's `bool("false")==True` since it is a non-empty string """ if s.lower() in {"t", "true", "yes", "y"}: return True elif s.lower() in {"f", "false", "no", "n"}: return False else: raise InterpretationError(s, bool) def interpret_datetime(s: str) -> dt.datetime: """ Date and time in isoformat """ if hasattr(dt.datetime, "fromisoformat"): return dt.datetime.fromisoformat(s) else: # for python 3.6 where `fromisoformat` doesn't exist import isodate # type: ignore return isodate.parse_datetime(s) def interpret_date(s: str) -> dt.date: """ Dates in YYYY-MM-DD format """ return dt.date(*[int(i) for i in s.split("-")]) def interpret_string_as_type( s: str, t: Type[T], type_converters: StringInterpreters ) -> T: try: return ( type_converters[unwrap_optional(t)](s) if is_optional(t) else type_converters[t](s) ) except KeyError: raise InterpretationError(s, t) ################################################################################ # Data classes # # these should really be dataclasses, and will be converted when clifun drops compatability # with python 3.6 ################################################################################ class Arguments: def __init__( self, positional: List[str], keyword: Dict[str, str], help: bool = False ): self.positional = positional self.keyword = keyword self.help = help class ConfigFiles: def __init__(self, configs: List[Dict[str, str]]): self.configs = configs def get(self, key: str, default: Optional[str] = None) -> Optional[str]: for config in self.configs: if key in config: return config[key] return default Annotated = Union["AnnotatedParameter", "AnnotatedCallable"] class AnnotatedCallable(Generic[T]): def __init__( self, callable: Callable[[...], T], name: str, needed_inputs: List[Annotated] ): self.callable = callable self.name = name self.needed_inputs = needed_inputs def __call__(self, inputs: Dict[str, str]): def collect(needed: Annotated): if isinstance(needed, AnnotatedParameter): value = inputs[needed.prefixed_name] if value is None: if is_optional(needed.t): return None raise ValueError( f"Somehow got None for non optional parameter {needed}" ) return needed(value) return needed(inputs) collected_inputs = { needed.name: collect(needed) for needed in self.needed_inputs } return self.callable(**collected_inputs) def __str__(self) -> str: return f"<callable: {self.name} {[str(i) for i in self.needed_inputs]}>" class AnnotatedParameter(Generic[T]): def __init__( self, parameter: inspect.Parameter, from_string: Callable[[str], T], prefix ): self.parameter = parameter self.from_string = from_string self.prefix = prefix @property def name(self): return self.parameter.name @property def prefixed_name(self): return ".".join(self.prefix + [self.name]) @property def t(self): return self.parameter.annotation @property def default(self): return self.parameter.default def __call__(self, input: Optional[str]) -> T: return self.from_string(input) def __str__(self) -> str: return f"<parameter: {self.name}: {self.t}>" class InputSources: def __init__(self, args: Arguments, config_files: ConfigFiles): self.args = args self.config_files = config_files def get(self, key: str, default: Optional[T] = None) -> Union[str, T, None]: env_value = os.environ.get(key.upper(), default) return self.args.keyword.get(key, self.config_files.get(key, env_value)) def get_value(self, value: AnnotatedParameter) -> Union[str, T, None]: return self.get(value.prefixed_name, value.default) ################################################################################ # Assemble inputs from the "outside world" ################################################################################ def assemble_input_sources(args: List[str]) -> InputSources: args_object = interpret_arguments(args) return InputSources(args_object, load_config_files(args_object.positional)) def interpret_arguments(args: Optional[List[str]] = None) -> Arguments: if args is None: args = sys.argv i = 1 keyword = {} positional = [] while i < len(args): arg = args[i] key = arg[2:] if arg in {"-h", "--help"}: return Arguments([], {}, True) if arg[:2] == "--": if len(args) < i + 2: raise ValueError(f"Missing value for argument: {key}") keyword[key] = args[i + 1] i += 2 else: positional.append(arg) i += 1 return Arguments(positional, keyword, not (keyword or positional)) def load_config_files(filenames: List[str]) -> ConfigFiles: # reverse the order so that later config files override earlier ones def load(name): if not pathlib.Path(name).exists(): raise ValueError(f"Could not find config file {name}") return json.load(open(name)) return ConfigFiles([load(name) for name in filenames[::-1]]) NOT_SPECIFIED = inspect._empty def resolve_inputs( needed_inputs: List[AnnotatedParameter], provided_inputs: InputSources ) -> Tuple[Dict[str, Optional[str]], Set[str]]: missing = set() def resolve(v): s = provided_inputs.get_value(v) if s is None: if is_optional(v.t): return None else: missing.add(v.prefixed_name) if s == NOT_SPECIFIED: missing.add(v.prefixed_name) return s collected = {value.prefixed_name: resolve(value) for value in needed_inputs} return collected, missing ################################################################################ # Input validation and help ################################################################################ def check_usage(provided_inputs, needed_inputs) -> None: check_help(provided_inputs, needed_inputs) check_invalid_args(provided_inputs, needed_inputs) def valid_args(values: List[AnnotatedParameter]) -> Set[str]: return {v.prefixed_name for v in values} def invalid_args(args, allowed_args): return set(args) - valid_args(allowed_args) def print_usage(annotated: AnnotatedCallable, header: bool = False) -> None: needed_inputs = all_needed_inputs(annotated) if header: print(f"{annotated.name}\n") doc = inspect.getdoc(annotated.callable) if doc: print(f"{doc}\n") print(f"Usage: {sys.argv[0]} [config_file] [--key: value]") print("\n".join(describe_needed(needed_inputs))) def describe_needed(needed_inputs: List[AnnotatedParameter]) -> List[str]: def desc(v): base = f" --{v.prefixed_name}: {type_to_string(v.t)}" if v.default != NOT_SPECIFIED: default = f'"{v.default}"' if isinstance(v.default, str) else v.default return f"{base} (default: {default})" return base return [desc(v) for v in needed_inputs] ################################################################################ # Determine what inputs a function needs ################################################################################ def all_needed_inputs(c: AnnotatedCallable) -> List[AnnotatedParameter]: def inner(): for needed in c.needed_inputs: if isinstance(needed, AnnotatedParameter): yield needed else: yield from all_needed_inputs(needed) return list(inner()) def inspect_parameters(t: Type[T]) -> Iterable[inspect.Parameter]: return inspect.signature(t).parameters.values() def is_optional(t: Type[T]) -> bool: return Union[t, None] == t def unwrap_optional(t: Optional[Type[T]]) -> Type[T]: if hasattr(typing, "get_args"): args = typing.get_args(t) if len(args) == 0: return t else: return args[0] # fallback for python < 3.8. May be brittle since it depends on an `_`'d interface # this should use typing.get_args, but that is not available until python 3.8 if type(t) != typing._GenericAlias: return t for s in t.__args__: # type: ignore if s != type(None): return s def type_to_string(t: Type[O]) -> str: if is_optional(t): return f"Optional[{unwrap_optional(t).__name__}]" return t.__name__ def annotate_parameter( parameter: inspect.Parameter, interpreter: StringInterpreters, prefix: List[str] ) -> Union[AnnotatedParameter, AnnotatedCallable]: if parameter.annotation == NOT_SPECIFIED: raise Exception(f"Missing type annotation for {parameter}") t = unwrap_optional(parameter.annotation) if t in interpreter: # We have found a "basic" value we know how to interpret return AnnotatedParameter(parameter, from_string=interpreter[t], prefix=prefix) # This is some kind of composite prefix = prefix + [parameter.name] return annotate_callable(parameter.annotation, interpreter, prefix, parameter.name) def annotate_callable( callable: Callable[[...], T], interpreter: StringInterpreters, prefix: List[str], name: Optional[str] = None, ) -> AnnotatedCallable[T]: needed = [ annotate_parameter(p, interpreter, prefix) for p in inspect_parameters(callable) ] return AnnotatedCallable( callable, name if name is not None else callable.__name__, needed ) ################################################################################ # Make clifun.py usable as a script to call functions in any module ################################################################################ def import_module_by_path(path: pathlib.Path) -> types.ModuleType: spec = importlib.util.spec_from_file_location(target.name, str(target)) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) return module if __name__ == "__main__": print(sys.argv) if len(sys.argv) < 3: print("Usage: clifun.py path_to_module function_name ...") sys.exit(1) target = pathlib.Path(sys.argv[1]).resolve() function_name = sys.argv[2] arguments = sys.argv[2:] module = import_module_by_path(target) function = getattr(module, function_name) print(call(function, arguments))

29.283516

120

0.590513

3,014

0.226208

309

0.023191

290

0.021765

0

2,878

0.216001

0c3c8f51e10f9073a8d53d99be68ca016464578d

2,758

py

Python

pages/views.py

joshua-hashimoto/eigo-of-the-day-django

68ec7fe4257c67689de596cf34e991a3750b7f36

[ "MIT" ]

null

pages/views.py

joshua-hashimoto/eigo-of-the-day-django

68ec7fe4257c67689de596cf34e991a3750b7f36

[ "MIT" ]

8

2021-04-08T19:45:15.000Z

2022-03-12T00:49:25.000Z

pages/views.py

joshua-hashimoto/eigo-of-the-day-django

68ec7fe4257c67689de596cf34e991a3750b7f36

[ "MIT" ]

null

import os import json import uuid from django.conf import settings from django.http import HttpResponse from django.utils.translation import ugettext_lazy as _ from django.views.generic import View import cloudinary class MarkdownImageUploader(View): """ custom image uploader for martor. """ def post(self, request, *args, **kwargs): """ called when images are uploaded to martor's markdown field. validation is from martor's documentation. it will upload images to cloudinary. Note: when there is '?' in the to be foldername the image upload will not work. """ if not request.is_ajax(): return HttpResponse(_('Invalid request!')) if 'markdown-image-upload' not in request.FILES: return HttpResponse(_('Invalid request!')) image = request.FILES['markdown-image-upload'] image_types = [ 'image/png', 'image/jpg', 'image/jpeg', 'image/pjpeg', 'image/gif' ] if image.content_type not in image_types: # return error when the image type # is not an expected type data = json.dumps({ 'status': 405, 'error': _('Bad image format.') }, cls=LazyEncoder) return HttpResponse( data, content_type='application/json', status=405) if image.size > settings.MAX_IMAGE_UPLOAD_SIZE: # return error when the image size # is over the setted MAX_IMAGE_UPLOAD_SIZE to_MB = settings.MAX_IMAGE_UPLOAD_SIZE / (1024 * 1024) data = json.dumps({ 'status': 405, 'error': _('Maximum image file is %(size) MB.') % {'size': to_MB} }, cls=LazyEncoder) return HttpResponse( data, content_type='application/json', status=405) # when the image is valid # create new name for image img_name = f'{uuid.uuid4().hex[:10]}-{image.name.replace(" ", "-")}' # assign new name to the image that is being uploaded image.name = img_name # create folder path img_folder = os.path.join( settings.MEDIA_URL, 'memo') # save image to cloudinary cloudinary_img = cloudinary.uploader.upload( image, folder=img_folder, overwrite=True) # get the saved image url from cloudinary response cloudinary_img_url = cloudinary_img['url'] # name json data to return to markdown data = json.dumps({ 'status': 200, 'link': cloudinary_img_url, 'name': image.name }) return HttpResponse(data, content_type='application/json')

34.049383

85

0.591008

2,537

0.919869

0

1,075

0.389775

0c3e673531e09903ae71e40dc82ffb45887a73df

1,776

py

Python

shc/log/in_memory.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

5

2021-07-02T21:48:45.000Z

2021-12-12T21:55:42.000Z

shc/log/in_memory.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

49

2020-09-18T20:05:55.000Z

2022-03-05T19:51:33.000Z

shc/log/in_memory.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

1

2021-12-10T14:50:43.000Z

import datetime from typing import Optional, Type, Generic, List, Tuple from ..base import T from .generic import PersistenceVariable class InMemoryPersistenceVariable(PersistenceVariable, Generic[T]): def __init__(self, type_: Type[T], keep: datetime.timedelta): super().__init__(type_, log=True) self.data: List[Tuple[datetime.datetime, T]] = [] self.keep = keep async def _write_to_log(self, value: T): self.clean_up() self.data.append((datetime.datetime.now(datetime.timezone.utc), value)) def clean_up(self) -> None: begin = datetime.datetime.now(datetime.timezone.utc) - self.keep keep_from: Optional[int] = None for i, (ts, _v) in enumerate(self.data): if ts > begin: keep_from = i break self.data = self.data[keep_from:] async def _read_from_log(self) -> Optional[T]: if not self.data: return None return self.data[-1][1] async def retrieve_log(self, start_time: datetime.datetime, end_time: datetime.datetime, include_previous: bool = False) -> List[Tuple[datetime.datetime, T]]: iterator = iter(enumerate(self.data)) try: start_index = next(i for i, (ts, _v) in iterator if ts >= start_time) except StopIteration: if include_previous and self.data: return self.data[-1:] else: return [] if include_previous and start_index > 0: start_index -= 1 try: end_index = next(i for i, (ts, _v) in iterator if ts > end_time) except StopIteration: return self.data[start_index:] return self.data[start_index:end_index]

36.244898

96

0.606419

1,638

0.922297

0

1,046

0.588964

0

0c3ec0f29f7bce414073cc341dd9839fbf5fca06

1,393

py

Python

guts/api/contrib/type_actions.py

smallwormer/stable-liberty-guts

e635b710cdd210f70e9d50c3b85fffdeb53e8f01

[ "Apache-2.0" ]

null

guts/api/contrib/type_actions.py

smallwormer/stable-liberty-guts

e635b710cdd210f70e9d50c3b85fffdeb53e8f01

[ "Apache-2.0" ]

null

guts/api/contrib/type_actions.py

smallwormer/stable-liberty-guts

e635b710cdd210f70e9d50c3b85fffdeb53e8f01

[ "Apache-2.0" ]

1

2022-03-03T05:41:31.000Z

# Copyright (c) 2015 Aptira Pty Ltd. # 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. 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 oslo_config import cfg from oslo_log import log as logging from guts.api import extensions from guts.api.openstack import wsgi CONF = cfg.CONF LOG = logging.getLogger(__name__) authorize = extensions.extension_authorizer('types', '') class TypeActionsController(wsgi.Controller): def __init__(self): super(TypeActionsController, self).__init__() class Type_actions(extensions.ExtensionDescriptor): """Enables source hypervisor type actions.""" name = "TypeActions" alias = "os-type-actions" namespace = "" updated = "" def get_controller_extensions(self): controller = TypeActionsController() extension = extensions.ControllerExtension(self, 'types', controller) return [extension]

29.638298

78

0.724336

508

0.364681

0

715

0.513281

0c4122d4f0b749136bdf171cdb6e696eecf404bd

8,579

py

Python

models/StyleTransfer/AdaIN.py

mtroym/pytorch-train

3b303b6c7e364a58cb88d7142da942a30cc2b255

[ "Apache-2.0" ]

2

2019-12-21T14:40:11.000Z

2020-05-26T09:26:52.000Z

models/StyleTransfer/AdaIN.py

mtroym/pytorch-train

3b303b6c7e364a58cb88d7142da942a30cc2b255

[ "Apache-2.0" ]

null

models/StyleTransfer/AdaIN.py

mtroym/pytorch-train

3b303b6c7e364a58cb88d7142da942a30cc2b255

[ "Apache-2.0" ]

1

2020-10-16T12:03:19.000Z

""" Author: Yiming Mao - mtroym@github Description: Transplant from "https://github.com/xunhuang1995/AdaIN-style/blob/master/train.lua" """ import functools import os from collections import OrderedDict import torch import torch.nn as nn from torchvision.models import vgg19 from datasets.utils import denorm from models.blocks import AdaptiveInstanceNorm2d from models.blocks.vgg import rename_sequential from models.helpers import init_weights class _Encoder(nn.Module): def __init__(self, pretrained=True, init_type="normal", endlayer='relu4_1', feature_hook=None): super(_Encoder, self).__init__() self.init_type = init_type self.pretrained = pretrained self.feature_hook = ["relu1_1", "relu2_1", "relu3_1", "relu4_1"] if feature_hook is None else feature_hook self.core = nn.Sequential() backbone = vgg19(pretrained=pretrained, progress=True) feature_extractor = rename_sequential(backbone.features) for name, layer in feature_extractor.named_children(): self.core.add_module(name, layer) if name == endlayer: break idx = -1 while not hasattr(self.core[idx], "out_channels"): idx -= 1 self.out_channels = self.core[idx].out_channels def init_param(self) -> None: if self.pretrained: return init_weights(self.model, self.init_type) def frozen(self): for param in self.core.parameters(): param.requires_grad = False def forward(self, inputs, feature_hook=None): if feature_hook is None: feature_hook = self.feature_hook results = OrderedDict() for name, layer in self.core.named_children(): inputs = layer(inputs) if name in feature_hook: results[name] = inputs return results class _Decoder(nn.Module): def __init__(self, enc: nn.Module, activation="relu", remove_idx=-1): super(_Decoder, self).__init__() core_list = [] nonlinear_act = nn.ReLU if activation == "lrelu": nonlinear_act = functools.partial(nn.LeakyReLU, negative_slope=0.2) for name, layer in enc.core.named_children(): if 'conv' in name: in_channels, out_channels = layer.in_channels, layer.out_channels core_list.append((activation + name.replace("conv", ""), nonlinear_act(inplace=True))) # core_list.append(("in{}".format(name.replace("conv", "")), # nn.InstanceNorm2d(num_features=in_channels))) core_list.append(("conv{}".format(name.replace("conv", "")), nn.Conv2d(out_channels, in_channels, kernel_size=3, stride=1))) core_list.append(("pad{}".format(name.replace("conv", "")), nn.ReflectionPad2d(padding=(1, 1, 1, 1)))) if 'pool' in name: core_list.append(("up{}".format(name.replace("pool", "")), nn.UpsamplingNearest2d(scale_factor=2))) self.core = rename_sequential(nn.Sequential(OrderedDict(reversed(core_list)))) # print(self) def forward(self, inputs) -> torch.Tensor: return self.core(inputs) class AdaIN: def __init__(self, opt): self.name = "AdaIN-Style model" self.opt = opt self.in_channel = opt.channel self.init_type = self.opt.init_type self.gpu_ids = opt.gpu_ids if opt.gpu_ids else [] self.device = torch.device("cuda:0" if (torch.cuda.is_available() and len(self.gpu_ids) > 0) else "cpu") self.dtype = torch.cuda.FloatTensor if self.device != torch.device("cpu") else torch.FloatTensor self.save_dir = opt.expr_dir self.encoder = _Encoder() if self.opt.freeze_enc: self.encoder.frozen() self.decoder = _Decoder(self.encoder) self.adain = AdaptiveInstanceNorm2d(num_features=self.encoder.out_channels) self.optimizer = torch.optim.Adam(self.decoder.parameters(), lr=self.opt.lr, betas=(self.opt.beta1, 0.999)) if self.opt.resume_path is not None: pass # place_holders self.inputs = None self.loss = None self.metrics = None self.current_minibatch = self.opt.batchSize if self.opt.resume_path is not None: self.load_model(self.opt.resume_path) self.cuda() def cuda(self): if torch.cuda.is_available(): self.encoder.cuda(self.device) self.decoder.cuda(self.device) self.adain.cuda(self.device) def train_decoder(self, content, style, alpha=1.0): self.optimizer.zero_grad() # find all the features with frozen VGG style_features = self.encoder(style) content_latent = self.encoder(content, feature_hook=["relu4_1"]) # Find adain trans_content = self.adain(content=content_latent["relu4_1"], style=style_features["relu4_1"]) interpolate_latent = (1.0 - alpha) * content_latent["relu4_1"] + \ alpha * trans_content transferred_image = self.decoder(interpolate_latent) transferred_features = self.encoder(transferred_image) c_loss = self.loss["content_loss"](transferred_features["relu4_1"], interpolate_latent) s_loss = self.loss["style_loss"](transferred_features, style_features) smooth_reg = self.loss["smooth_reg"](transferred_image) loss = c_loss.mean() + s_loss.mean() + smooth_reg.mean() loss.backward() self.optimizer.step() return c_loss, s_loss, smooth_reg, transferred_image def train_batch(self, inputs: dict, loss: dict, metrics: dict, niter: int = 0, epoch: int = 0) -> dict: self.inputs = inputs self.loss = loss if self.loss is None else self.loss self.metrics = metrics if self.metrics is None else self.metrics self.current_minibatch = inputs["Source"].shape[0] c_loss, s_loss, smooth_reg, transferred_image = self.train_decoder(inputs["Source"].to(self.device), inputs["Style"].to(self.device)) store_val = {"vis": {"Target": denorm(transferred_image, self.device, to_board=True), "Source": denorm(inputs["Source"], self.device, to_board=True), "Style": denorm(inputs["Style"], self.device, to_board=True)}, "loss": {"loss_content": c_loss, "loss_style": s_loss, "smooth_reg": smooth_reg, } } if epoch % 30 == 5: self.save_model(epoch, store=store_val) return store_val def predict_batch(self, inputs: dict, loss=None, metrics=None, niter=None, epoch=None): self.current_minibatch = self.opt.batchSize return { "vis": {"Target": None}, "loss": {} } def save_model(self, epoch, store): store_dict = { "epoch": epoch, "model_state_dict": { "decoder_model_state_dict": self.decoder.state_dict(), }, "optimizer_state_dict": { "decoder_optimizer_state_dict": self.optimizer.state_dict(), } } store_dict.update(store) torch.save(store_dict, os.path.join(self.opt.expr_dir, "epoch_{}.pth".format(epoch))) torch.save(store_dict, os.path.join(self.opt.expr_dir, "latest.pth".format(epoch))) def load_model(self, store_path, no_opt=False): store_dict = torch.load(store_path) self.decoder.load_state_dict(store_dict["model_state_dict"]["decoder_model_state_dict"]) if no_opt: return self.optimizer.load_state_dict(store_dict["optimizer_state_dict"]["decoder_optimizer_state_dict"]) if __name__ == '__main__': bs = 10 w, h = 128, 128 image = torch.rand((bs, 3, w, h)) # g = _Generator_ResizeConv() e = _Encoder() d = _Decoder(e) adain = AdaptiveInstanceNorm2d(e.out_channels) te = adain(e(image)["relu4_1"], e(image)["relu4_1"]) print(d) print(d(te).shape) # print(e(image).shape) # print(d(e(image)).shape) # print(.out_channels) # fak = g(z) # print(fak.shape) # print(d(fak).shape)

40.852381

115

0.602518

7,642

0.89078

0

1,152

0.134281

0c418b56746d824c2d98f37af03cc0b209cd7415

1,099

py

Python

airflow/migrations/versions/52d714495f0_job_id_indices.py

rubeshdcube/incubator-airflow

5419fbb78a2ea2388456c356d2f899ea1991b2de

[ "Apache-2.0" ]

6

2016-04-20T20:40:43.000Z

2022-02-20T10:32:00.000Z

airflow/migrations/versions/52d714495f0_job_id_indices.py

curest0x1021/incubator-airflow

e6d3160a061dbaa6042d524095dcd1cbc15e0bcd

[ "Apache-2.0" ]

13

2018-11-30T18:18:32.000Z

2021-02-19T17:04:12.000Z

airflow/migrations/versions/52d714495f0_job_id_indices.py

curest0x1021/incubator-airflow

e6d3160a061dbaa6042d524095dcd1cbc15e0bcd

[ "Apache-2.0" ]

9

2017-08-24T15:47:44.000Z

2022-02-14T03:30:49.000Z

# -*- coding: utf-8 -*- # # 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. """job_id indices Revision ID: 52d714495f0 Revises: 338e90f54d61 Create Date: 2015-10-20 03:17:01.962542 """ # revision identifiers, used by Alembic. revision = '52d714495f0' down_revision = '338e90f54d61' branch_labels = None depends_on = None from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import mysql def upgrade(): op.create_index('idx_job_state_heartbeat', 'job', ['state', 'latest_heartbeat'], unique=False) def downgrade(): op.drop_index('idx_job_state_heartbeat', table_name='job')

27.475

98

0.755232

0

815

0.741583

0c42822d78adfa0e7f0264f5d356cb0270939941

7,836

py

Python

deep_semantic_similarity_keras.py

viksit/Deep-Semantic-Similarity-Model

1dc94346801e711125fb573284a1984ce17fb90e

[ "MIT" ]

3

2016-05-26T00:04:38.000Z

2019-10-22T09:52:39.000Z

deep_semantic_similarity_keras.py

viksit/Deep-Semantic-Similarity-Model

1dc94346801e711125fb573284a1984ce17fb90e

[ "MIT" ]

null

deep_semantic_similarity_keras.py

viksit/Deep-Semantic-Similarity-Model

1dc94346801e711125fb573284a1984ce17fb90e

[ "MIT" ]

1

2019-10-22T09:59:04.000Z

# Michael A. Alcorn (malcorn@redhat.com) # An implementation of the Deep Semantic Similarity Model (DSSM) found in [1]. # [1] Shen, Y., He, X., Gao, J., Deng, L., and Mesnil, G. 2014. A latent semantic model # with convolutional-pooling structure for information retrieval. In CIKM, pp. 101-110. # http://research.microsoft.com/pubs/226585/cikm2014_cdssm_final.pdf # [2] http://research.microsoft.com/en-us/projects/dssm/ # [3] http://research.microsoft.com/pubs/238873/wsdm2015.v3.pdf import numpy as np from keras import backend from keras.layers import Input, merge from keras.layers.core import Dense, Lambda, Reshape from keras.layers.convolutional import Convolution1D from keras.models import Model def R(vects): """ Calculates the cosine similarity of two vectors. :param vects: a list of two vectors. :return: the cosine similarity of two vectors. """ (x, y) = vects return backend.dot(x, backend.transpose(y)) / (x.norm(2) * y.norm(2)) # See equation (4) LETTER_GRAM_SIZE = 3 # See section 3.2. WINDOW_SIZE = 3 # See section 3.2. TOTAL_LETTER_GRAMS = int(3 * 1e4) # Determined from data. See section 3.2. WORD_DEPTH = WINDOW_SIZE * TOTAL_LETTER_GRAMS # See equation (1). K = 300 # Dimensionality of the max-pooling layer. See section 3.4. L = 128 # Dimensionality of latent semantic space. See section 3.5. J = 4 # Number of random unclicked documents serving as negative examples for a query. See section 4. FILTER_LENGTH = 1 # We only consider one time step for convolutions. # Input tensors holding the query, positive (clicked) document, and negative (unclicked) documents. # The first dimension is None because the queries and documents can vary in length. query = Input(shape = (None, WORD_DEPTH)) pos_doc = Input(shape = (None, WORD_DEPTH)) neg_docs = [Input(shape = (None, WORD_DEPTH)) for j in range(J)] # Query model. The paper uses separate neural nets for queries and documents (see section 5.2). # In this step, we transform each word vector with WORD_DEPTH dimensions into its # convolved representation with K dimensions. K is the number of kernels/filters # being used in the operation. Essentially, the operation is taking the dot product # of a single weight matrix (W_c) with each of the word vectors (l_t) from the # query matrix (l_Q), adding a bias vector (b_c), and then applying the tanh function. # That is, h_Q = tanh(W_c • l_Q + b_c). With that being said, that's not actually # how the operation is being calculated here. To tie the weights of the weight # matrix (W_c) together, we have to use a one-dimensional convolutional layer. # Further, we have to transpose our query matrix (l_Q) so that time is the first # dimension rather than the second (as described in the paper). That is, l_Q[0, :] # represents our first word vector rather than l_Q[:, 0]. We can think of the weight # matrix (W_c) as being similarly transposed such that each kernel is a column # of W_c. Therefore, h_Q = tanh(l_Q • W_c + b_c) with l_Q, W_c, and b_c being # the transposes of the matrices described in the paper. query_conv = Convolution1D(K, FILTER_LENGTH, border_mode = "same", input_shape = (None, WORD_DEPTH), activation = "tanh")(query) # See equation (2). # Next, we apply a max-pooling layer to the convolved query matrix. Keras provides # its own max-pooling layers, but they cannot handle variable length input (as # far as I can tell). As a result, I define my own max-pooling layer here. In the # paper, the operation selects the maximum value for each row of h_Q, but, because # we're using the transpose, we're selecting the maximum value for each column. query_max = Lambda(lambda x: x.max(axis = 1), output_shape = (K,))(query_conv) # See section 3.4. # In this step, we generate the semantic vector represenation of the query. This # is a standard neural network dense layer, i.e., y = tanh(W_s • v + b_s). query_sem = Dense(L, activation = "tanh", input_dim = K)(query_max) # See section 3.5. # The document equivalent of the above query model. doc_conv = Convolution1D(K, FILTER_LENGTH, border_mode = "same", input_shape = (None, WORD_DEPTH), activation = "tanh") doc_max = Lambda(lambda x: x.max(axis = 1), output_shape = (K,)) doc_sem = Dense(L, activation = "tanh", input_dim = K) pos_doc_conv = doc_conv(pos_doc) neg_doc_convs = [doc_conv(neg_doc) for neg_doc in neg_docs] pos_doc_max = doc_max(pos_doc_conv) neg_doc_maxes = [doc_max(neg_doc_conv) for neg_doc_conv in neg_doc_convs] pos_doc_sem = doc_sem(pos_doc_max) neg_doc_sems = [doc_sem(neg_doc_max) for neg_doc_max in neg_doc_maxes] # This layer calculates the cosine similarity between the semantic representations of # a query and a document. R_layer = Lambda(R, output_shape = (1,)) # See equation (4). R_Q_D_p = R_layer([query_sem, pos_doc_sem]) # See equation (4). R_Q_D_ns = [R_layer([query_sem, neg_doc_sem]) for neg_doc_sem in neg_doc_sems] # See equation (4). concat_Rs = merge([R_Q_D_p] + R_Q_D_ns, mode = "concat") concat_Rs = Reshape((J + 1, 1))(concat_Rs) # In this step, we multiply each R(Q, D) value by gamma. In the paper, gamma is # described as a smoothing factor for the softmax function, and it's set empirically # on a held-out data set. We're going to learn gamma's value by pretending it's # a single, 1 x 1 kernel. with_gamma = Convolution1D(1, 1, border_mode = "same", input_shape = (J + 1, 1), activation = "linear")(concat_Rs) # See equation (5). # Next, we exponentiate each of the gamma x R(Q, D) values. exponentiated = Lambda(lambda x: backend.exp(x), output_shape = (J + 1,))(with_gamma) # See equation (5). exponentiated = Reshape((J + 1,))(exponentiated) # Finally, we use the softmax function to calculate the P(D+|Q). prob = Lambda(lambda x: x[0][0] / backend.sum(x[0]), output_shape = (1,))(exponentiated) # See equation (5). # We now have everything we need to define our model. model = Model(input = [query, pos_doc] + neg_docs, output = prob) model.compile(optimizer = "adadelta", loss = "binary_crossentropy") # Build a random data set. sample_size = 10 l_Qs = [] pos_l_Ds = [] for i in range(sample_size): query_len = np.random.randint(1, 10) l_Q = np.random.rand(1, query_len, WORD_DEPTH) l_Qs.append(l_Q) doc_len = np.random.randint(50, 500) l_D = np.random.rand(1, doc_len, WORD_DEPTH) pos_l_Ds.append(l_D) neg_l_Ds = [] for i in range(sample_size): possibilities = list(range(sample_size)) possibilities.remove(i) negatives = np.random.choice(possibilities, J) neg_l_Ds.append([pos_l_Ds[negative] for negative in negatives]) # Because we're using the "binary_crossentropy" loss function, we can pretend that # we're dealing with a binary classification problem and that every sample is a # member of the "1" class. y = np.ones(1) for i in range(sample_size): history = model.fit([l_Qs[i], pos_l_Ds[i]] + neg_l_Ds[i], y, nb_epoch = 1, verbose = 0) # Here, I walk through an example of how to define a function for calculating output # from the computational graph. Let's define a function that calculates R(Q, D+) # for a given query and clicked document. The function depends on two inputs, query # and pos_doc. That is, if you start at the point in the graph where R(Q, D+) is # calculated and then backtrack as far as possible, you'll end up at two different # starting points, query and pos_doc. As a result, we supply those inputs in a list # to the function. This particular function only calculates a single output, but # multiple outputs are possible (see the next example). get_R_Q_D_p = backend.function([query, pos_doc], R_Q_D_p) get_R_Q_D_p([l_Qs[0], pos_l_Ds[0]]) # A slightly more complex function. Notice that both neg_docs and the output are # lists. get_R_Q_D_ns = backend.function([query] + neg_docs, R_Q_D_ns) get_R_Q_D_ns([l_Qs[0]] + neg_l_Ds[0])

49.910828

148

0.732006

0

4,759

0.60686

0c4483174d1c4ff711dd1bd4cb802a150131d7f7

469

py

Python

posthog/migrations/0087_fix_annotation_created_at.py

avoajaugochukwu/posthog

7e7fd42b0542ebc4734aedb926df11d462e3dd4f

[ "MIT" ]

7,409

2020-02-09T23:18:10.000Z

2022-03-31T22:36:25.000Z

posthog/migrations/0087_fix_annotation_created_at.py

avoajaugochukwu/posthog

7e7fd42b0542ebc4734aedb926df11d462e3dd4f

[ "MIT" ]

5,709

2020-02-09T23:26:13.000Z

2022-03-31T20:20:01.000Z

posthog/migrations/0087_fix_annotation_created_at.py

avoajaugochukwu/posthog

7e7fd42b0542ebc4734aedb926df11d462e3dd4f

[ "MIT" ]

647

2020-02-13T17:50:55.000Z

2022-03-31T11:24:19.000Z

# Generated by Django 3.0.7 on 2020-10-14 07:46 import django.utils.timezone from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("posthog", "0086_team_session_recording_opt_in"), ] operations = [ migrations.AlterField( model_name="annotation", name="created_at", field=models.DateTimeField(default=django.utils.timezone.now, null=True), ), ]

23.45

85

0.648188

347

0.739872

0

116

0.247335

0c476cbc9139db2d5b5477a2919a3f47a83b94b5

4,723

py

Python

tumorevo/tumorfig/main.py

pedrofale/tumorevo

cf43f3854f6815c822cf4df71be82fc6dbae065b

[ "MIT" ]

2

2022-02-08T12:54:58.000Z

2022-03-04T12:21:06.000Z

tumorevo/tumorfig/main.py

pedrofale/tumorevo

cf43f3854f6815c822cf4df71be82fc6dbae065b

[ "MIT" ]

null

tumorevo/tumorfig/main.py

pedrofale/tumorevo

cf43f3854f6815c822cf4df71be82fc6dbae065b

[ "MIT" ]

null

""" Create a cartoon of a tumor given the frequencies of different genotypes. """ from .util import * import pandas as pd import matplotlib.pyplot as plt import click import os from pathlib import Path from pymuller import muller @click.command(help="Plot the evolution of a tumor.") @click.argument( "genotype-counts", type=click.Path(exists=True, dir_okay=False), ) @click.argument( "genotype-parents", type=click.Path(exists=True, dir_okay=False), ) @click.option("-c", "--cells", default=100, help="Number of cells in slice plot.") @click.option( "-r", "--average-radius", default=10, help="Average radius of circles in slice plot.", ) @click.option("--grid-file", default="", help="Path to grid file.") @click.option("--colormap", default="gnuplot", help="Colormap for genotypes.") @click.option("--dpi", default=100, help="DPI for figures.") @click.option("--plot", is_flag=True, help="Plot all the figures.") @click.option("--do-muller", is_flag=True, help="Make a Muller plot.") @click.option("--do-slice", is_flag=True, help="Make a slice plot.") @click.option("--do-tree", is_flag=True, help="Make a clone tree plot.") @click.option( "--normalize", is_flag=True, help="Normalize the abundances in the Muller plot." ) @click.option("--labels", is_flag=True, help="Annotate the clone tree plot.") @click.option( "--remove", is_flag=True, help="Remove empty clones in the clone tree plot." ) @click.option( "-o", "--output-path", default="./", help="Directory to write figures into." ) def main( genotype_counts, genotype_parents, cells, average_radius, grid_file, colormap, dpi, plot, do_muller, do_slice, do_tree, normalize, labels, remove, output_path, ): genotype_counts = pd.read_csv(genotype_counts, index_col=0) genotype_parents = pd.read_csv(genotype_parents, index_col=0, dtype=str) if grid_file != "": grid = pd.read_csv(grid_file, index_col=0, dtype=str) pop_df, anc_df, color_by = prepare_plots(genotype_counts, genotype_parents) cmap, genotypes = get_colormap(pop_df, anc_df, color_by, colormap) if plot: fig, ax_list = plt.subplots(ncols=3, sharex=False, dpi=dpi, figsize=(8, 2)) muller( pop_df, anc_df, color_by, ax=ax_list[0], colorbar=False, colormap=colormap, normalize=normalize, background_strain=False, ) plt.axis("off") if grid_file == "": plot_deme( cells, genotype_counts.iloc[-1], pop_df, anc_df, color_by, average_radius=average_radius, colormap=colormap, ax=ax_list[1], ) else: plot_grid(grid, cmap, genotypes, ax=ax_list[1]) plot_tree( genotype_parents, pop_df, anc_df, color_by, genotype_counts=genotype_counts.iloc[-1], filter_clones=remove, labels=labels, colormap=colormap, ax=ax_list[2], ) plt.show() else: Path(output_path).mkdir(parents=True, exist_ok=True) if do_muller: ax = muller( pop_df, anc_df, color_by, colorbar=False, colormap=colormap, normalize=normalize, ) plt.axis("off") plt.savefig( os.path.join(output_path, "muller.pdf"), dpi=dpi, bbox_inches="tight" ) if do_slice: if grid_file == "": ax = plot_deme( cells, genotype_counts.iloc[-1], pop_df, anc_df, color_by, average_radius=average_radius, colormap=colormap, ) else: ax = plot_grid(grid, cmap) plt.savefig( os.path.join(output_path, "slice.pdf"), dpi=dpi, bbox_inches="tight" ) if do_tree: ax = plot_tree( genotype_parents, pop_df, anc_df, color_by, genotype_counts=genotype_counts.iloc[-1], filter_clones=remove, labels=labels, colormap=colormap, ) plt.savefig( os.path.join(output_path, "tree.pdf"), dpi=dpi, bbox_inches="tight" ) if __name__ == "__main__": main()

28.624242

85

0.547957

0

4,447

0.941563

0

788

0.166843

0c486c53d8fb3bd729143cb46efffc314acc492f

23

py

Python

Aulas/aula20/src/bb_circular.py

alexNeto/data-struct

f54917247a4e75ffe15783b0a025185d2215309a

[ "BSD-2-Clause" ]

5

2017-08-25T19:24:47.000Z

2020-01-19T15:52:02.000Z

Aulas/aula20/src/bb_circular.py

alexNeto/data-struct

f54917247a4e75ffe15783b0a025185d2215309a

[ "BSD-2-Clause" ]

null

Aulas/aula20/src/bb_circular.py

alexNeto/data-struct

f54917247a4e75ffe15783b0a025185d2215309a

[ "BSD-2-Clause" ]

5

2017-09-25T14:49:43.000Z

2019-11-27T00:05:56.000Z

class Circular():

11.5

18

0.565217

17

0.73913

0

0c48b673acc0ea7efa42fafb3fba6d032e5deab7

196

py

Python

src/brouwers/online_users/urls.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

6

2015-03-03T13:23:07.000Z

2021-12-19T18:12:41.000Z

src/brouwers/online_users/urls.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

95

2015-02-07T00:55:39.000Z

2022-02-08T20:22:05.000Z

src/brouwers/online_users/urls.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

2

2016-03-22T16:53:26.000Z

2019-02-09T22:46:04.000Z

from django.conf.urls import url from .views import get_online_users, set_online app_name = 'online_users' urlpatterns = [ url(r'^so/$', set_online), url(r'^ous/$', get_online_users), ]

19.6

47

0.704082

0

31

0.158163

0c49d08e42802a84e6d6315644d21f43e88ce921

5,881

py

Python

dftb+/plot_spline.py

hsulab/DailyScripts

26b03cfb721fd66f39c86df50d2ec5866e651d6e

[ "MIT" ]

2

2020-06-08T21:39:44.000Z

2020-10-18T15:12:47.000Z

dftb+/plot_spline.py

hsulab/DailyScripts

26b03cfb721fd66f39c86df50d2ec5866e651d6e

[ "MIT" ]

null

dftb+/plot_spline.py

hsulab/DailyScripts

26b03cfb721fd66f39c86df50d2ec5866e651d6e

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import os import argparse import numpy as np import matplotlib as mpl mpl.use('Agg') #silent mode from matplotlib import pyplot as plt from mpl_toolkits.axes_grid1.inset_locator import inset_axes, InsetPosition, zoomed_inset_axes, mark_inset MAXLINE = 10000 class SplineRepulsion(): def __init__(self, npoints, cutoff, begrep, splrep, endrep): # self.npoints, self.cutoff = npoints, cutoff self.begrep = begrep self.splrep = splrep self.endrep = endrep # bounds = np.zeros(npoints+2) bounds[0], bounds[npoints+1] = 0., cutoff bounds[1:npoints] = splrep[:,0] bounds[npoints] = endrep[0] self.bounds = bounds return def calc_rep(self,r): """""" for i in range(self.npoints+1): b1, b2 = self.bounds[i], self.bounds[i+1] if b1 <= r < b2: if i == 0: rep = self.calc_begrep(r,*self.begrep) elif 0 < i < self.npoints: cur_spl = self.splrep[i-1,:] coefs = np.zeros(5) coefs[0] = cur_spl[0] coefs[1:] = cur_spl[2:] rep = self.calc_splrep(r,*coefs) elif i == self.npoints: cur_spl = self.endrep coefs = np.zeros(7) coefs[0] = cur_spl[0] coefs[1:] = cur_spl[2:] rep = self.calc_endrep(r,*coefs) break else: raise ValueError('Distance %12.8f Not in Bound...' %r) return rep @staticmethod def calc_begrep(r,a1,a2,a3): rep = np.exp(-a1*r+a2) + a3 return rep @staticmethod def calc_splrep(r,r0,c0,c1,c2,c3): rep = c0 + c1*(r-r0) + c2*(r-r0)**2 + c3*(r-r0)**3 return rep @staticmethod def calc_endrep(r,r0,c0,c1,c2,c3,c4,c5): rep = c0 + c1*(r-r0) + c2*(r-r0)**2 + c3*(r-r0)**3 \ + c4*(r-r0)**4 + c5*(r-r0)**5 return rep def read_spline(skf): # read spline data fopen = open(skf, 'r') for i in range(MAXLINE): line = fopen.readline() if line: if line.startswith('Spline'): # points and cutoff line = fopen.readline() data = line.strip().split() npoints, cutoff = int(data[0]), float(data[1]) # exp rep # exp(-a1*r+a2)+a3 line = fopen.readline() data = line.strip().split() begrep = np.array(data, dtype=float) # spline # c0+c1(r-r0)+c2(r-r0)**2+c3(r-r0)**3 splrep = [] for j in range(npoints-1): line = fopen.readline() data = line.strip().split() splrep.append(data) splrep = np.array(splrep, dtype=float) # end line = fopen.readline() data = line.strip().split() endrep = np.array(data, dtype=float) else: # end of the file break fopen.close() # init spline sprp = SplineRepulsion(npoints,cutoff,begrep,splrep,endrep) return sprp def plot_spline(skf='Pt-Pt.skf', skf2=None, rmin=1.0, pic='spl.png'): """Plot the Spline Repulsive Potential...""" # read spline, turn into spline object SP_rep1 = read_spline(skf) # generate data rs = np.linspace(0.,SP_rep1.cutoff-0.01,1000) reps = [] for r in rs: reps.append(SP_rep1.calc_rep(r)) skf_name = os.path.basename(skf).split('.')[0] rs = np.array(rs) reps = np.array(reps) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,8)) ax.set_title(r'$%s$ Spline Repulsive Potential' %skf_name, \ fontsize=24, fontweight='bold') ax.set_xlabel(r'$r$ / Bohr', fontsize=20) ax.set_ylabel(r'$V_{rep}(r)$ / Hartree', fontsize=20) skf1_curve, = ax.plot(rs, reps, \ color='g', linestyle='-', linewidth=2., \ label='Skf-1') # inset figure ax2 = plt.axes([0,0,1,1]) ip = InsetPosition(ax, [0.4,0.2,0.5,0.5]) ax2.set_axes_locator(ip) mark_inset(ax, ax2, loc1=1, loc2=3, fc="none", ec='0.5') #ax2 = zoomed_inset_axes(ax, 1, loc=4) r_min, r_max = rmin, SP_rep1.cutoff indices = np.where((rs>r_min) & (rs<r_max)) ax2.plot(rs[indices], reps[indices], color='g', linestyle='-', linewidth=2.) # skf2 for comparision if skf2: SP_rep2 = read_spline(skf2) # generate data rs = np.linspace(0.,SP_rep2.cutoff-0.01,1000) reps = [] for r in rs: reps.append(SP_rep2.calc_rep(r)) rs = np.array(rs) reps = np.array(reps) skf2_curve, = ax.plot(rs, reps, \ color='orange', linestyle='--', linewidth=2., \ label='Skf-2') ax2.plot(rs[indices], reps[indices], color='orange', linestyle='--', linewidth=2.) plt.legend(handles=[skf1_curve,skf2_curve]) else: plt.legend(handles=[skf1_curve,]) plt.savefig(pic) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-f', '--skf', required=True,\ help='Slater-Koster File') parser.add_argument('-f2', '--skf2', default=None, \ help='Second Slater-Koster File for Comparision') parser.add_argument('-p', '--pic', \ default='spl.png', help='Spline Repulsive Potential Figure') parser.add_argument('-rmin', '--radius_min', type=float,\ default=1.0, help='Minimum Radius for Zoom') args = parser.parse_args() plot_spline(args.skf, args.skf2, args.radius_min, args.pic) #plot_spline()

29.70202

106

0.530012

1,800

0.30607

0

418

0.071076

0

818

0.139092

0c4b5ba22b3ba7761012b4918404bffd6258a269

370

py

Python

network_monitor/__init__.py

brennanhfredericks/network-monitor-client

618d222bb015662c3958f0100a965f3c71b29d32

[ "MIT" ]

null

network_monitor/__init__.py

brennanhfredericks/network-monitor-client

618d222bb015662c3958f0100a965f3c71b29d32

[ "MIT" ]

null

network_monitor/__init__.py

brennanhfredericks/network-monitor-client

618d222bb015662c3958f0100a965f3c71b29d32

[ "MIT" ]

null

import argparse import netifaces import sys import signal import os import asyncio from asyncio import CancelledError, Task from typing import Optional, List, Any from .services import ( Service_Manager, Packet_Parser, Packet_Submitter, Packet_Filter, ) from .configurations import generate_configuration_template, DevConfig, load_config_from_file

16.818182

93

0.802703

0

0c4c75e50a5aeb0f4d0c50388de64676ac264483

1,516

py

Python

investing_com/cs_pattern_list.py

filipecn/maldives

f20f17d817fc3dcad7f9674753744716d1d4c821

[ "MIT" ]

1

2021-09-17T18:04:33.000Z

investing_com/cs_pattern_list.py

filipecn/maldives

f20f17d817fc3dcad7f9674753744716d1d4c821

[ "MIT" ]

null

investing_com/cs_pattern_list.py

filipecn/maldives

f20f17d817fc3dcad7f9674753744716d1d4c821

[ "MIT" ]

3

2021-09-17T18:04:43.000Z

2022-03-18T20:04:07.000Z

#!/usr/bin/py import pandas as pd import os # Holds investing.com candlestick patterns class CSPatternList: def __init__(self, path): self.data = None with os.scandir(path) as entries: for e in entries: if e.is_file() and os.path.splitext(e.path)[1] == '.csv': if self.data is None: self.data = pd.read_csv(e.path) else: self.data.append(pd.read_csv(e.path)) # imgui settings self.selected_row = 0 def draw_filter_menu(self, imgui): imgui.separator() def draw(self, imgui): imgui.begin("CS Patterns") imgui.columns(len(self.data.columns) + 1, "asodf") imgui.separator() imgui.text("") imgui.next_column() for c in self.data.columns: imgui.text(c) imgui.next_column() imgui.separator() # fill with data for i in range(10): label = str(i) clicked, _ = imgui.selectable( label=label, selected=self.selected_row == i, flags=imgui.SELECTABLE_SPAN_ALL_COLUMNS, ) if clicked: self.selected_row = i hovered = imgui.is_item_hovered() row = self.data.loc[i] imgui.next_column() for c in self.data.columns: imgui.text(row[c]) imgui.next_column() imgui.end()

29.72549

73

0.513852

1,425

0.939974

0

115

0.075858

0c4cdf64475499e51798185a532224a138493103

1,113

py

Python

simpleTest04Client_.py

LaplaceKorea/APIClient

e772482c3d9cbedee98f46a3529dca5acc254f3c

[ "MIT" ]

null

simpleTest04Client_.py

LaplaceKorea/APIClient

e772482c3d9cbedee98f46a3529dca5acc254f3c

[ "MIT" ]

null

simpleTest04Client_.py

LaplaceKorea/APIClient

e772482c3d9cbedee98f46a3529dca5acc254f3c

[ "MIT" ]

null

from LaplaceWSAPIClient import * from MarkowitzSerde import * from TargetSerde import * from Operators import * from TargetOperators import * from RLStructure import * from ClientConfig import client_config query = RLQuery("default", datetime(2021,1,1), datetime(2021,1,21), { "BankAccount": 100000.0, "MMM":1.0, "AA":1.0, "AXP":1.0, "BA":1.0, "BAC":1.0, "C":1.0, "CAT":1.0, "CVX":1.0, "DD":1.0, "DIS":1.0, "GE":1.0, "GM":1.0, "HD":1.0, "HPQ":1.0, "IBM":1.0, "JNJ":1.0, "JPM":1.0, "KO":1.0, "MCD":1.0, "MRK":1.0, "PFE":1.0, "PG":1.0, "T":1.0, "UTX":1.0, "VZ":1.0, "WMT":1.0, "XOM":1.0 }, UserTokenSerde(client_config["user"],client_config["token"])) performQueryRLQuery(client_config["wss"], query, lambda x: print("yahoo: ", x.Steps[0][0], x.Steps[0][1], x.Steps[0][203]))

27.146341

123

0.442947

0

171

0.153639

0c4fdea50a153837205a14c5c61c7d560b9d7a43

14,406

py

Python

vdisk.py

cookpan001/vdisk

1414e5c20eba3722ce99818fe48ddf0217fb25ca

[ "BSD-3-Clause" ]

1

2016-01-11T06:46:11.000Z

vdisk.py

cookpan001/vdisk

1414e5c20eba3722ce99818fe48ddf0217fb25ca

[ "BSD-3-Clause" ]

null

vdisk.py

cookpan001/vdisk

1414e5c20eba3722ce99818fe48ddf0217fb25ca

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # encoding: utf-8 # author: cookpan001 import sys import logging import time import mimetypes import urllib import urllib2 """ oauth2 client """ class OAuth2(object): ACCESS_TOKEN_URL = "https://auth.sina.com.cn/oauth2/access_token" AUTHORIZE_URL = "https://auth.sina.com.cn/oauth2/authorize" def __init__(self,app_key, app_secret, call_back_url): self.version = 1.0 self.app_key = app_key self.app_secret = app_secret self.call_back_url = call_back_url #display = default|mobile|popup def authorize(self,response_type = "code",display = "default",state = ""): data = {"client_id":self.app_key, "redirect_uri":self.call_back_url, "response_type":response_type, "display":display} if len(state) > 0: data["state"] = state return OAuth2.AUTHORIZE_URL + "?" + urllib.urlencode(data) #grant_type = authorization_code|refresh_token def access_token(self,grant_type = "authorization_code",code = "",refresh_token = ""): data = {"client_id":self.app_key, "client_secret":self.app_secret, "grant_type":grant_type} if grant_type == "authorization_code": data["code"] = code data["redirect_uri"] = self.call_back_url elif grant_type == "refresh_token": data["refresh_token"] = refresh_token try: request = urllib2.Request(OAuth2.ACCESS_TOKEN_URL) response = urllib2.urlopen(request,urllib.urlencode(data)) return response.read() except urllib2.HTTPError,e: return e.read() except urllib2.URLError,e: return e.read() """ All the responses will be a Response object """ class Response(object): BLOCK_SIZE = 8192 def __init__(self,response): self.response = response """ return a HTTPMessage object """ def headers(self): if hasattr(self.response,"info"): return self.response.info() elif hasattr(self.resopnse,"msg"): return self.resopnse.msg() """ Get the content of response ,optimized for big size resopnse. data() return a generator. Developer can use this method like this: for content in Response.data(): print content """ def data(self): while True: block = self.response.read(Response.BLOCK_SIZE) if block: yield block else: return def read(self): return self.response.read() def __str__(self): return self.response.read() """ The vdisk(weipan) client. """ class Client(object): log = logging.getLogger('api_client') API_URL = 'https://api.weipan.cn/2/' WEIBO_URL = 'https://api.weipan.cn/weibo/' UPLOAD_HOST = 'upload-vdisk.sina.com.cn' CONTENT_SAFE_URL = 'https://'+UPLOAD_HOST+'/2/' version = 1.0 def __init__(self,root = "basic"): self.timeout = 10 self.python_version_is_bigger_than_2_4 = float(sys.version[:3]) > 2.4 self.root = root def setRoot(self,root): self.root = root def get(self, host, api, queries={}): try: if isinstance(api, unicode): api = api.encode('utf-8') else: api = str(api) url = host.strip('/') + '/' + urllib.quote(api.strip('/')) queries = self.encode_queries(queries) request = urllib2.Request('%s?%s' % (url, queries)) # set timeout. if self.python_version_is_bigger_than_2_4: response = urllib2.urlopen(request, timeout=self.timeout) else: # http://stackoverflow.com/questions/2084782/timeout-for-urllib2-urlopen-in-pre-python-2-6-versions import socket socket.setdefaulttimeout(self.timeout) response = urllib2.urlopen(request) return Response(response) except urllib2.HTTPError,e: return e.read() except urllib2.URLError,e: return e.read() def post(self, host, api, data=[], files=[]): try: if isinstance(api, unicode): api = api.encode('utf-8') else: api = str(api) url = host.strip('/') + '/' + api.strip('/') if isinstance(data, dict): data = data.items() content_type, body = self.encode_multipart_formdata(data, files) request = urllib2.Request(url, data=body) request.add_header('Content-Type', content_type) request.add_header('Content-Length', str(len(body))) if self.python_version_is_bigger_than_2_4: response = urllib2.urlopen(request, timeout=self.timeout) else: import socket socket.setdefaulttimeout(self.timeout) response = urllib2.urlopen(request) return Response(response) except urllib2.HTTPError,e: return e.read() except urllib2.URLError,e: return e.read() # used by non GET or POST method. such as PUT def request(self, method,host, api, data, headers = {}, use_safe = True): import httplib if isinstance(api, unicode): api = api.encode('utf-8') else: api = str(api) if isinstance(data, dict): data = self.encode_queries(data) try: if use_safe: conn = httplib.HTTPSConnection(host) else: conn = httplib.HTTPConnection(host) conn.request(method,api,data,headers) return Response(conn.getresponse()) except httplib.HTTPException,e: print e return e.read() def get_content_type(self, filename): return mimetypes.guess_type(filename)[0] or 'application/octet-stream' def encode_multipart_formdata(self, fields, files): """ fields is a sequence of (name, value) elements for regular form fields. files is a sequence of (name, filename, value) elements for data to be uploaded as files Return (content_type, body) ready for httplib.HTTP instance """ BOUNDARY = '----------%s' % hex(int(time.time() * 1000)) CRLF = '\r\n' L = [] for (key, value) in fields: L.append('--' + BOUNDARY) L.append('Content-Disposition: form-data; name="%s"' % str(key)) L.append('') if isinstance(value, unicode): L.append(value.encode('utf-8')) else: L.append(value) for (key, filename, value) in files: L.append('--' + BOUNDARY) L.append('Content-Disposition: form-data; name="%s"; filename="%s"' % (str(key), str(filename))) L.append('Content-Type: %s' % str(self.get_content_type(filename))) L.append('Content-Length: %d' % len(value)) L.append('') L.append(value) L.append('--' + BOUNDARY + '--') L.append('') body = CRLF.join(L) content_type = 'multipart/form-data; boundary=%s' % BOUNDARY return content_type, body def encode_queries(self, queries={}, **kwargs): queries.update(kwargs) args = [] for k, v in queries.iteritems(): if isinstance(v, unicode): qv = v.encode('utf-8') else: qv = str(v) args.append('%s=%s' % (k, urllib.quote(qv))) return '&'.join(args) def account_info(self,access_token): data = self.get(Client.API_URL, 'account/info', {"access_token":access_token}) return data def metadata(self,access_token,path): data = self.get(Client.API_URL, 'metadata/' + self.root + '/' + path, {"access_token":access_token}) return data def delta(self,access_token,cursor = ''): param = {"access_token":access_token} if len(cursor) > 0: param['cursor'] = cursor data = self.get(Client.API_URL, 'delta/' + self.root, param) return data def files(self,access_token,path,rev = ''): param = {"access_token":access_token} if len(rev) > 0: param['rev'] = rev data = self.get(Client.API_URL, 'files/' + self.root + "/" + path, param) return data def revisions(self,access_token,path): data = self.get(Client.API_URL, 'revisions/' + self.root + "/" + path, {"access_token":access_token}) return data #files = {"filename":filename,"content":"file content"} def files_post(self,access_token,path,files,overwrite = "true",sha1 = "",size = "", parent_rev = ""): param = { "access_token":access_token, "overwrite":overwrite } if len(sha1) > 0: param["sha1"] = sha1 if len(size) > 0: param["size"] = size if len(parent_rev) > 0: param["parent_rev"] = parent_rev queries = self.encode_queries(param) data = self.post(Client.CONTENT_SAFE_URL, 'files/'+self.root+"/"+path+"?"+queries, [], [("file",files["filename"],files["content"])]) return data """ content should be a file object or file raw content, such as: open("./filename","rb"), "rb" is prefered. """ def files_put(self,access_token,path,content,overwrite = "true",sha1 = "",size = "", parent_rev = ""): param = { "access_token":access_token, "overwrite":overwrite } if len(sha1) > 0: param["sha1"] = sha1 if len(size) > 0: param["size"] = size if len(parent_rev) > 0: param["parent_rev"] = parent_rev data = self.request( method="PUT", host=Client.UPLOAD_HOST, api='/2/files_put/'+self.root+"/"+path+"?"+self.encode_queries(param), data=content) return data # 公开分享 def shares(self,access_token,path,cancel = "false"): data = self.post(Client.API_URL, 'shares/'+self.root+"/"+path, {"access_token":access_token, "cancel":cancel }) return data def restore(self,access_token,path,rev = ""): param = {"access_token":access_token, "path":path } if len(rev) > 0: param['rev'] = rev data = self.post(Client.API_URL, 'restore/'+self.root+"/"+path, {"access_token":access_token}) return data def search(self,access_token,path,query,file_limit = 1000,include_deleted = "false"): data = self.get(Client.API_URL, 'search/'+self.root+"/"+path, {"access_token":access_token, "path":path, "query":query, "file_limit":file_limit, "include_deleted":include_deleted }) return data def copy_ref(self,access_token,path): data = self.post(Client.API_URL, 'copy_ref/'+self.root+"/"+path, {"access_token":access_token, "path":path}) return data def media(self,access_token,path): data = self.get(Client.API_URL, 'media/'+self.root+"/"+path, {"access_token":access_token, "path":path}) return data #s:60x60,m:100x100,l:640x480,xl:1027x768 def thumbnails(self,access_token,path,size): data = self.get(Client.API_URL, 'thumbnails/'+self.root+"/"+path, {"access_token":access_token, "path":path, "size":size}) return data def fileops_copy(self,access_token,to_path,from_path = "",from_copy_ref = ""): param = {"access_token":access_token, "root":self.root, "to_path":to_path } if len(from_path) > 0: param['from_path'] = from_path if len(from_copy_ref) > 0: param['from_copy_ref'] = from_copy_ref data = self.post(Client.API_URL, 'fileops/copy', param) return data def fileops_delete(self,access_token,path): data = self.post(Client.API_URL, 'fileops/delete', {"access_token":access_token, "root":self.root, "path":path }) return data def fileops_move(self,access_token,from_path = "",to_path = ""): param = {"access_token":access_token, "root":self.root } if len(from_path) > 0: param['from_path'] = from_path if len(to_path) > 0: param['to_path'] = to_path data = self.post(Client.API_URL, 'fileops/move', param) return data def fileops_create_folder(self,access_token,path): data = self.post(Client.API_URL, 'fileops/create_folder', {"access_token":access_token, "root":self.root, "path":path }) return data def shareops_media(self,access_token,from_copy_ref): data = self.get(Client.API_URL, 'shareops/media', {"access_token":access_token, "from_copy_ref":from_copy_ref}) return data

36.470886

115

0.521102

14,158

0.982239

191

0.013251

0

2,783

0.193076

0c50ef47cd53ea48685602b6b3d98c7fea184c96

263

py

Python

setup.py

thevoxium/netspeed

9e16a49d64da90a173ef9eaf491d4245c1023105

[ "MIT" ]

null

setup.py

thevoxium/netspeed

9e16a49d64da90a173ef9eaf491d4245c1023105

[ "MIT" ]

null

setup.py

thevoxium/netspeed

9e16a49d64da90a173ef9eaf491d4245c1023105

[ "MIT" ]

null

from setuptools import setup setup( name='netspeed', version='0.1', py_modules=['netspeed'], install_requires=[ 'Click', 'pyspeedtest' ], entry_points=''' [console_scripts] netspeed=netspeed:cli ''', )

16.4375

29

0.558935

0

112

0.425856

0c51490cf6a9e00d3f171f44d583a875d050c2af

244

py

Python

store/admin.py

salemzii/ChopFast

95ea88387ecfdb56bd643970b69425b1a1c6f388

[ "MIT" ]

null

store/admin.py

salemzii/ChopFast

95ea88387ecfdb56bd643970b69425b1a1c6f388

[ "MIT" ]

null

store/admin.py

salemzii/ChopFast

95ea88387ecfdb56bd643970b69425b1a1c6f388

[ "MIT" ]

null

from django.contrib import admin from .models import (Dish, Payments, Order, Delivery, OrderItem) admin.site.register(Dish) admin.site.register(Payments) admin.site.register(Order) admin.site.register(Delivery) admin.site.register(OrderItem)

24.4

64

0.807377

0

0c517d2c976cb6c4a933b0a237cbe0bcc83aaacb

31,109

py

Python

hpedockerplugin/request_context.py

renovate-bot/python-hpedockerplugin

b7fa6b3193fa6dd42574585b4c621ff6a16babc9

[ "Apache-2.0" ]

49

2016-06-14T22:25:40.000Z

2021-04-05T05:00:59.000Z

hpedockerplugin/request_context.py

imran-ansari/python-hpedockerplugin

e2726f48ac793dc894100e3772c40ce89bfe9bb8

[ "Apache-2.0" ]

550

2016-07-25T12:01:12.000Z

2021-11-15T17:52:40.000Z

hpedockerplugin/request_context.py

imran-ansari/python-hpedockerplugin

e2726f48ac793dc894100e3772c40ce89bfe9bb8

[ "Apache-2.0" ]

96

2016-06-01T22:07:03.000Z

2021-06-22T09:05:05.000Z

import abc import json import re from collections import OrderedDict from oslo_log import log as logging import hpedockerplugin.exception as exception from hpedockerplugin.hpe import share LOG = logging.getLogger(__name__) class RequestContextBuilderFactory(object): def __init__(self, all_configs): self._all_configs = all_configs # if 'block' in all_configs: # block_configs = all_configs['block'] # backend_configs = block_configs[1] # self._vol_req_ctxt_creator = VolumeRequestContextBuilder( # backend_configs) # else: # self._vol_req_ctxt_creator = NullRequestContextBuilder( # "ERROR: Volume driver not enabled. Please provide hpe.conf " # "file to enable it") if 'file' in all_configs: file_configs = all_configs['file'] f_backend_configs = file_configs[1] self._file_req_ctxt_builder = FileRequestContextBuilder( f_backend_configs) else: self._file_req_ctxt_builder = NullRequestContextBuilder( "ERROR: File driver not enabled. Please provide hpe_file.conf " "file to enable it") def get_request_context_builder(self): return self._file_req_ctxt_builder class NullRequestContextBuilder(object): def __init__(self, msg): self._msg = msg def build_request_context(self, contents, def_backend_name): raise exception.InvalidInput(self._msg) class RequestContextBuilder(object): def __init__(self, backend_configs): self._backend_configs = backend_configs def build_request_context(self, contents, def_backend_name): LOG.info("build_request_context: Entering...") self._validate_name(contents['Name']) req_ctxt_map = self._get_build_req_ctxt_map() if 'Opts' in contents and contents['Opts']: # self._validate_mutually_exclusive_ops(contents) self._validate_dependent_opts(contents) for op_name, req_ctxt_creator in req_ctxt_map.items(): op_name = op_name.split(',') found = not (set(op_name) - set(contents['Opts'].keys())) if found: return req_ctxt_creator(contents, def_backend_name) return self._default_req_ctxt_creator(contents) @staticmethod def _validate_name(vol_name): is_valid_name = re.match("^[A-Za-z0-9]+[A-Za-z0-9_-]+$", vol_name) if not is_valid_name: msg = 'Invalid volume name: %s is passed.' % vol_name raise exception.InvalidInput(reason=msg) @staticmethod def _get_int_option(options, option_name, default_val): opt = options.get(option_name) if opt and opt != '': try: opt = int(opt) except ValueError as ex: msg = "ERROR: Invalid value '%s' specified for '%s' option. " \ "Please specify an integer value." % (opt, option_name) LOG.error(msg) raise exception.InvalidInput(msg) else: opt = default_val return opt # This method does the following: # 1. Option specified # - Some value: # -- return if valid value else exception # - Blank value: # -- Return default if provided # ELSE # -- Throw exception if value_unset_exception is set # 2. Option NOT specified # - Return default value @staticmethod def _get_str_option(options, option_name, default_val, valid_values=None, value_unset_exception=False): opt = options.get(option_name) if opt: if opt != '': opt = str(opt) if valid_values and opt.lower() not in valid_values: msg = "ERROR: Invalid value '%s' specified for '%s'" \ "option. Valid values are: %s" %\ (opt, option_name, valid_values) LOG.error(msg) raise exception.InvalidInput(msg) return opt if default_val: return default_val if value_unset_exception: return json.dumps({ 'Err': "Value not set for option: %s" % opt }) return default_val def _validate_dependent_opts(self, contents): pass # To be implemented by derived class @abc.abstractmethod def _get_build_req_ctxt_map(self): pass def _default_req_ctxt_creator(self, contents): pass @staticmethod def _validate_mutually_exclusive_ops(contents): mutually_exclusive_ops = ['virtualCopyOf', 'cloneOf', 'importVol', 'replicationGroup'] if 'Opts' in contents and contents['Opts']: received_opts = contents.get('Opts').keys() diff = set(mutually_exclusive_ops) - set(received_opts) if len(diff) < len(mutually_exclusive_ops) - 1: mutually_exclusive_ops.sort() msg = "Operations %s are mutually exclusive and cannot be " \ "specified together. Please check help for usage." % \ mutually_exclusive_ops raise exception.InvalidInput(reason=msg) @staticmethod def _check_valid_fsMode_string(value): valid_type = ['A', 'D', 'U', 'L'] valid_flag = ['f', 'd', 'p', 'i', 'S', 'F', 'g'] valid_perm1 = ['r', 'w', 'a', 'x', 'd', 'D', 't', 'T'] valid_perm2 = ['n', 'N', 'c', 'C', 'o', 'y'] valid_perm = valid_perm1 + valid_perm2 type_flag_perm = value.split(':') if len(type_flag_perm) != 3: msg = "Incorrect value passed , please check correct "\ "format and values to be passed in help" LOG.error(msg) raise exception.InvalidInput(reason=msg) vtype = type_flag_perm[0] if vtype not in valid_type: msg = "Incorrect value passed for type of a mode, please check "\ "correct format and values to be passed." LOG.error(msg) raise exception.InvalidInput(reason=msg) passed_vflag_len = len(list(type_flag_perm[1])) vflag = list(set(list(type_flag_perm[1]))) if len(vflag) < passed_vflag_len: msg = "Duplicate characters for given flag are passed. "\ "Please correct the passed flag characters for fsMode." LOG.error(msg) raise exception.InvalidInput(reason=msg) if set(vflag) - set(valid_flag): msg = "Invalid flag passed for the fsMode. Please "\ "pass the correct flag characters" LOG.error(msg) raise exception.InvalidInput(reason=msg) passed_vperm_len = len(list(type_flag_perm[2])) vperm = list(set(list(type_flag_perm[2]))) if len(vperm) < passed_vperm_len: msg = "Duplicate characters for given permission are passed. "\ "Please correct the passed permissions for fsMode." LOG.error(msg) raise exception.InvalidInput(reason=msg) if set(vperm) - set(valid_perm): msg = "Invalid characters for the permissions of fsMode are "\ "passed. Please remove the invalid characters." LOG.error(msg) raise exception.InvalidInput(reason=msg) return True def _check_is_valid_acl_string(self, fsMode): fsMode_list = fsMode.split(',') if len(fsMode_list) != 3: msg = "Passed acl string is not valid. "\ "Pass correct acl string." LOG.error(msg) raise exception.InvalidInput(reason=msg) for value in fsMode_list: self._check_valid_fsMode_string(value) return True @staticmethod def _is_valid_octal_num(fsMode): return re.match('^0[0-7]{3}$', fsMode) def _validate_fsMode(self, fsMode): is_valid_fs_mode = True if ':' in fsMode: is_valid_fs_mode = self._check_is_valid_acl_string(fsMode) else: is_valid_fs_mode = self._is_valid_octal_num(fsMode) if not is_valid_fs_mode: msg = "Invalid value passed for the fsMode." LOG.error(msg) raise exception.InvalidInput(reason=msg) @staticmethod def _validate_fsOwner(fsOwner): fsOwner_list = fsOwner.split(':') if len(fsOwner_list) != 2: msg = "Invalid value specified for fsOwner Option." LOG.error(msg) raise exception.InvalidInput(reason=msg) try: for val in fsOwner_list: int(val) except ValueError as ex: msg = "Please provide correct fsowner inforamtion. You have "\ "passed non integer values." LOG.error(msg) raise exception.InvalidInput(reason=msg) @staticmethod def _validate_opts(operation, contents, valid_opts, mandatory_opts=None): LOG.info("Validating options for operation '%s'" % operation) if 'Opts' in contents and contents['Opts']: received_opts = contents.get('Opts').keys() if mandatory_opts: diff = set(mandatory_opts) - set(received_opts) if diff: # Print options in sorted manner mandatory_opts.sort() msg = "One or more mandatory options %s are missing " \ "for operation %s" % (mandatory_opts, operation) LOG.error(msg) raise exception.InvalidInput(reason=msg) diff = set(received_opts) - set(valid_opts) if diff: diff = list(diff) diff.sort() msg = "Invalid option(s) %s specified for operation %s. " \ "Please check help for usage." % \ (diff, operation) LOG.error(msg) raise exception.InvalidInput(reason=msg) class FileRequestContextBuilder(RequestContextBuilder): def __init__(self, backend_configs): super(FileRequestContextBuilder, self).__init__(backend_configs) def _get_build_req_ctxt_map(self): build_req_ctxt_map = OrderedDict() # If share-dir is specified, file-store MUST be specified build_req_ctxt_map['filePersona,help'] = self._create_help_req_ctxt build_req_ctxt_map['filePersona'] = \ self._create_share_req_ctxt # build_req_ctxt_map['persona,cpg'] = \ # self._create_share_req_ctxt # build_req_ctxt_map['persona,cpg,size'] = \ # self._create_share_req_ctxt # build_req_ctxt_map['persona,cpg,size,fpg_name'] = \ # self._create_share_req_ctxt # build_req_ctxt_map['virtualCopyOf,shareName'] = \ # self._create_snap_req_ctxt # build_req_ctxt_map['updateShare'] = \ # self._create_update_req_ctxt return build_req_ctxt_map def _create_share_req_params(self, name, options, def_backend_name): LOG.info("_create_share_req_params: Entering...") # import pdb # pdb.set_trace() backend = self._get_str_option(options, 'backend', def_backend_name) if backend == 'DEFAULT_BLOCK': msg = 'Backend DEFAULT_BLOCK is reserved for Block ' \ 'operations. Cannot specify it for File operations' LOG.error(msg) raise exception.InvalidInput(msg) config = self._backend_configs.get(backend) if not config: raise exception.InvalidInput( 'ERROR: Backend %s is not configured for File Persona' % backend ) cpg = self._get_str_option( options, 'cpg', config.hpe3par_cpg[0] if config.hpe3par_cpg else None) if not cpg: raise exception.InvalidInput( "ERROR: CPG is not configured in hpe.conf. Please specify" "name of an existing CPG in hpe.conf and restart plugin") fpg = self._get_str_option(options, 'fpg', None) fsMode = self._get_str_option(options, 'fsMode', None) fsOwner = self._get_str_option(options, 'fsOwner', None) if fsMode: self._validate_fsMode(fsMode) if fsOwner: self._validate_fsOwner(fsOwner) if fsMode: if fsOwner is None: raise exception.InvalidInput( " ERROR: If mode bits or directory permissions" " needs to be changed then, providing fsOwner" " is mandetory") size_gib = self._get_int_option(options, 'size', 1024) # Default share size or quota in MiB which is 1TiB size = size_gib * 1024 fpg_size_gib = int(config.hpe3par_default_fpg_size) * 1024 if size_gib > fpg_size_gib: raise exception.InvalidInput( "ERROR: Share size cannot be greater than the FPG size. " "Either specify hpe3par_default_fpg_size >= %s GiB or " "specify option '-o size' < %s GiB" % (size_gib, fpg_size_gib)) # TODO: This check would be required when VFS needs to be created. # NOT HERE # if not ip_subnet and not config.hpe3par_ip_pool: # raise exception.InvalidInput( # "ERROR: Unable to create share as neither 'ipSubnet' " # "option specified not IP address pool hpe3par_ip_pool " # "configured in configuration file specified") readonly_str = self._get_str_option(options, 'readonly', 'false') readonly = str.lower(readonly_str) if readonly == 'true': readonly = True elif readonly == 'false': readonly = False else: raise exception.InvalidInput( 'ERROR: Invalid value "%s" supplied for "readonly" option. ' 'Valid values are case insensitive ["true", "false"]' % readonly_str) nfs_options = self._get_str_option(options, 'nfsOptions', None) comment = self._get_str_option(options, 'comment', None) share_details = share.create_metadata(backend, cpg, fpg, name, size, readonly=readonly, nfs_options=nfs_options, comment=comment, fsMode=fsMode, fsOwner=fsOwner) LOG.info("_create_share_req_params: %s" % share_details) return share_details def _create_share_req_ctxt(self, contents, def_backend_name): LOG.info("_create_share_req_ctxt: Entering...") valid_opts = ('backend', 'filePersona', 'cpg', 'fpg', 'size', 'mountConflictDelay', 'fsMode', 'fsOwner') mandatory_opts = ('filePersona',) self._validate_opts("create share", contents, valid_opts, mandatory_opts) share_args = self._create_share_req_params(contents['Name'], contents['Opts'], def_backend_name) ctxt = {'orchestrator': 'file', 'operation': 'create_share', 'kwargs': share_args} LOG.info("_create_share_req_ctxt: Exiting: %s" % ctxt) return ctxt def _create_help_req_ctxt(self, contents, def_backend_name): LOG.info("_create_help_req_ctxt: Entering...") valid_opts = ('filePersona', 'help', 'mountConflictDelay') self._validate_opts("create help content for share", contents, valid_opts, mandatory_opts=None) options = contents['Opts'] if options: value = self._get_str_option(options, 'help', None) if not value: return { 'orchestrator': 'file', 'operation': 'create_share_help', 'kwargs': {} } if value == 'backends': return { 'orchestrator': 'file', 'operation': 'get_backends_status', 'kwargs': {} } else: raise exception.InvalidInput( "ERROR: Invalid value %s for option 'help' specified." % value) LOG.info("_create_help_req_ctxt: Exiting...") def _create_snap_req_ctxt(self, contents): pass def _create_update_req_ctxt(self, contents): pass # TODO: This is work in progress - can be taken up later if agreed upon # class VolumeRequestContextBuilder(RequestContextBuilder): # def __init__(self, backend_configs): # super(VolumeRequestContextBuilder, self).__init__(backend_configs) # # def _get_build_req_ctxt_map(self): # build_req_ctxt_map = OrderedDict() # build_req_ctxt_map['virtualCopyOf,scheduleName'] = \ # self._create_snap_schedule_req_ctxt, # build_req_ctxt_map['virtualCopyOf,scheduleFrequency'] = \ # self._create_snap_schedule_req_ctxt # build_req_ctxt_map['virtualCopyOf,snaphotPrefix'] = \ # self._create_snap_schedule_req_ctxt # build_req_ctxt_map['virtualCopyOf'] = \ # self._create_snap_req_ctxt # build_req_ctxt_map['cloneOf'] = \ # self._create_clone_req_ctxt # build_req_ctxt_map['importVol'] = \ # self._create_import_vol_req_ctxt # build_req_ctxt_map['replicationGroup'] = \ # self._create_rcg_req_ctxt # build_req_ctxt_map['help'] = self._create_help_req_ctxt # return build_req_ctxt_map # # def _default_req_ctxt_creator(self, contents): # return self._create_vol_create_req_ctxt(contents) # # @staticmethod # def _validate_mutually_exclusive_ops(contents): # mutually_exclusive_ops = ['virtualCopyOf', 'cloneOf', 'importVol', # 'replicationGroup'] # if 'Opts' in contents and contents['Opts']: # received_opts = contents.get('Opts').keys() # diff = set(mutually_exclusive_ops) - set(received_opts) # if len(diff) < len(mutually_exclusive_ops) - 1: # mutually_exclusive_ops.sort() # msg = "Operations %s are mutually exclusive and cannot be " \ # "specified together. Please check help for usage." % \ # mutually_exclusive_ops # raise exception.InvalidInput(reason=msg) # # @staticmethod # def _validate_opts(operation, contents, valid_opts, mandatory_opts=None): # if 'Opts' in contents and contents['Opts']: # received_opts = contents.get('Opts').keys() # # if mandatory_opts: # diff = set(mandatory_opts) - set(received_opts) # if diff: # # Print options in sorted manner # mandatory_opts.sort() # msg = "One or more mandatory options %s are missing " \ # "for operation %s" % (mandatory_opts, operation) # raise exception.InvalidInput(reason=msg) # # diff = set(received_opts) - set(valid_opts) # if diff: # diff = list(diff) # diff.sort() # msg = "Invalid option(s) %s specified for operation %s. " \ # "Please check help for usage." % \ # (diff, operation) # raise exception.InvalidInput(reason=msg) # # def _create_vol_create_req_ctxt(self, contents): # valid_opts = ['compression', 'size', 'provisioning', # 'flash-cache', 'qos-name', 'fsOwner', # 'fsMode', 'mountConflictDelay', 'cpg', # 'snapcpg', 'backend'] # self._validate_opts("create volume", contents, valid_opts) # return {'operation': 'create_volume', # '_vol_orchestrator': 'volume'} # # def _create_clone_req_ctxt(self, contents): # valid_opts = ['cloneOf', 'size', 'cpg', 'snapcpg', # 'mountConflictDelay'] # self._validate_opts("clone volume", contents, valid_opts) # return {'operation': 'clone_volume', # 'orchestrator': 'volume'} # # def _create_snap_req_ctxt(self, contents): # valid_opts = ['virtualCopyOf', 'retentionHours', 'expirationHours', # 'mountConflictDelay', 'size'] # self._validate_opts("create snapshot", contents, valid_opts) # return {'operation': 'create_snapshot', # '_vol_orchestrator': 'volume'} # # def _create_snap_schedule_req_ctxt(self, contents): # valid_opts = ['virtualCopyOf', 'scheduleFrequency', 'scheduleName', # 'snapshotPrefix', 'expHrs', 'retHrs', # 'mountConflictDelay', 'size'] # mandatory_opts = ['scheduleName', 'snapshotPrefix', # 'scheduleFrequency'] # self._validate_opts("create snapshot schedule", contents, # valid_opts, mandatory_opts) # return {'operation': 'create_snapshot_schedule', # 'orchestrator': 'volume'} # # def _create_import_vol_req_ctxt(self, contents): # valid_opts = ['importVol', 'backend', 'mountConflictDelay'] # self._validate_opts("import volume", contents, valid_opts) # # # Replication enabled backend cannot be used for volume import # backend = contents['Opts'].get('backend', 'DEFAULT') # if backend == '': # backend = 'DEFAULT' # # try: # config = self._backend_configs[backend] # except KeyError: # backend_names = list(self._backend_configs.keys()) # backend_names.sort() # msg = "ERROR: Backend '%s' doesn't exist. Available " \ # "backends are %s. Please use " \ # "a valid backend name and retry." % \ # (backend, backend_names) # raise exception.InvalidInput(reason=msg) # # if config.replication_device: # msg = "ERROR: Import volume not allowed with replication " \ # "enabled backend '%s'" % backend # raise exception.InvalidInput(reason=msg) # # volname = contents['Name'] # existing_ref = str(contents['Opts']['importVol']) # manage_opts = contents['Opts'] # return {'orchestrator': 'volume', # 'operation': 'import_volume', # 'args': (volname, # existing_ref, # backend, # manage_opts)} # # def _create_rcg_req_ctxt(self, contents): # valid_opts = ['replicationGroup', 'size', 'provisioning', # 'backend', 'mountConflictDelay', 'compression'] # self._validate_opts('create replicated volume', contents, valid_opts) # # # It is possible that the user configured replication in hpe.conf # # but didn't specify any options. In that case too, this operation # # must fail asking for "replicationGroup" parameter # # Hence this validation must be done whether "Opts" is there or not # options = contents['Opts'] # backend = self._get_str_option(options, 'backend', 'DEFAULT') # create_vol_args = self._get_create_volume_args(options) # rcg_name = create_vol_args['replicationGroup'] # try: # self._validate_rcg_params(rcg_name, backend) # except exception.InvalidInput as ex: # return json.dumps({u"Err": ex.msg}) # # return {'operation': 'create_volume', # 'orchestrator': 'volume', # 'args': create_vol_args} # # def _get_fs_owner(self, options): # val = self._get_str_option(options, 'fsOwner', None) # if val: # fs_owner = val.split(':') # if len(fs_owner) != 2: # msg = "Invalid value '%s' specified for fsOwner. Please " \ # "specify a correct value." % val # raise exception.InvalidInput(msg) # return fs_owner # return None # # def _get_fs_mode(self, options): # fs_mode_str = self._get_str_option(options, 'fsMode', None) # if fs_mode_str: # try: # int(fs_mode_str) # except ValueError as ex: # msg = "Invalid value '%s' specified for fsMode. Please " \ # "specify an integer value." % fs_mode_str # raise exception.InvalidInput(msg) # # if fs_mode_str[0] != '0': # msg = "Invalid value '%s' specified for fsMode. Please " \ # "specify an octal value." % fs_mode_str # raise exception.InvalidInput(msg) # # for mode in fs_mode_str: # if int(mode) > 7: # msg = "Invalid value '%s' specified for fsMode. Please"\ # " specify an octal value." % fs_mode_str # raise exception.InvalidInput(msg) # return fs_mode_str # # def _get_create_volume_args(self, options): # ret_args = dict() # ret_args['size'] = self._get_int_option( # options, 'size', volume.DEFAULT_SIZE) # ret_args['provisioning'] = self._get_str_option( # options, 'provisioning', volume.DEFAULT_PROV, # ['full', 'thin', 'dedup']) # ret_args['flash-cache'] = self._get_str_option( # options, 'flash-cache', volume.DEFAULT_FLASH_CACHE, # ['true', 'false']) # ret_args['qos-name'] = self._get_str_option( # options, 'qos-name', volume.DEFAULT_QOS) # ret_args['compression'] = self._get_str_option( # options, 'compression', volume.DEFAULT_COMPRESSION_VAL, # ['true', 'false']) # ret_args['fsOwner'] = self._get_fs_owner(options) # ret_args['fsMode'] = self._get_fs_mode(options) # ret_args['mountConflictDelay'] = self._get_int_option( # options, 'mountConflictDelay', # volume.DEFAULT_MOUNT_CONFLICT_DELAY) # ret_args['cpg'] = self._get_str_option(options, 'cpg', None) # ret_args['snapcpg'] = self._get_str_option(options, 'snapcpg', None) # ret_args['replicationGroup'] = self._get_str_option( # options, 'replicationGroup', None) # # return ret_args # # def _validate_rcg_params(self, rcg_name, backend_name): # LOG.info("Validating RCG: %s, backend name: %s..." % (rcg_name, # backend_name)) # hpepluginconfig = self._backend_configs[backend_name] # replication_device = hpepluginconfig.replication_device # # LOG.info("Replication device: %s" % six.text_type( # replication_device)) # # if rcg_name and not replication_device: # msg = "Request to create replicated volume cannot be fulfilled"\ # "without defining 'replication_device' entry defined in"\ # "hpe.conf for the backend '%s'. Please add it and execute"\ # "the request again." % backend_name # raise exception.InvalidInput(reason=msg) # # if replication_device and not rcg_name: # backend_names = list(self._backend_configs.keys()) # backend_names.sort() # # msg = "'%s' is a replication enabled backend. " \ # "Request to create replicated volume cannot be fulfilled "\ # "without specifying 'replicationGroup' option in the "\ # "request. Please either specify 'replicationGroup' or use"\ # "a normal backend and execute the request again. List of"\ # "backends defined in hpe.conf: %s" % (backend_name, # backend_names) # raise exception.InvalidInput(reason=msg) # # if rcg_name and replication_device: # # def _check_valid_replication_mode(mode): # valid_modes = ['synchronous', 'asynchronous', 'streaming'] # if mode.lower() not in valid_modes: # msg = "Unknown replication mode '%s' specified. Valid "\ # "values are 'synchronous | asynchronous | " \ # "streaming'" % mode # raise exception.InvalidInput(reason=msg) # # rep_mode = replication_device['replication_mode'].lower() # _check_valid_replication_mode(rep_mode) # if replication_device.get('quorum_witness_ip'): # if rep_mode.lower() != 'synchronous': # msg = "For Peer Persistence, replication mode must be "\ # "synchronous" # raise exception.InvalidInput(reason=msg) # # sync_period = replication_device.get('sync_period') # if sync_period and rep_mode == 'synchronous': # msg = "'sync_period' can be defined only for 'asynchronous'"\ # " and 'streaming' replicate modes" # raise exception.InvalidInput(reason=msg) # # if (rep_mode == 'asynchronous' or rep_mode == 'streaming')\ # and sync_period: # try: # sync_period = int(sync_period) # except ValueError as ex: # msg = "Non-integer value '%s' not allowed for " \ # "'sync_period'. %s" % ( # replication_device.sync_period, ex) # raise exception.InvalidInput(reason=msg) # else: # SYNC_PERIOD_LOW = 300 # SYNC_PERIOD_HIGH = 31622400 # if sync_period < SYNC_PERIOD_LOW or \ # sync_period > SYNC_PERIOD_HIGH: # msg = "'sync_period' must be between 300 and " \ # "31622400 seconds." # raise exception.InvalidInput(reason=msg) # # @staticmethod # def _validate_name(vol_name): # is_valid_name = re.match("^[A-Za-z0-9]+[A-Za-z0-9_-]+$", vol_name) # if not is_valid_name: # msg = 'Invalid volume name: %s is passed.' % vol_name # raise exception.InvalidInput(reason=msg)

43.387727

79

0.567746

16,687

0.536404

0

6,461

0.207689

0

18,657

0.59973

0c51d4fd680a6be2f21491d3d55f99e1a13769ea

32,369

py

Python

scripts/train_image_.py

shafieelab/SPyDERMAN

1b3fe1d0fcb33dcaed85fb110c88575ffa6fb7b6

[ "MIT" ]

1

2021-01-26T18:07:56.000Z

scripts/train_image_.py

Deeksha-K/SPyDERMAN

8cb4a3efc2b8706133f81e7bf878439110402434

[ "MIT" ]

null

scripts/train_image_.py

Deeksha-K/SPyDERMAN

8cb4a3efc2b8706133f81e7bf878439110402434

[ "MIT" ]

3

2021-01-26T18:07:39.000Z

2021-04-07T22:07:01.000Z

import argparse import csv import os import os.path as osp import statistics import tqdm import time from datetime import datetime import copy import numpy as np import torch import torch.nn as nn import torch.optim as optim import helper_utils.network as network import helper_utils.loss as loss import helper_utils.pre_process as prep from sklearn.metrics import confusion_matrix from torch.utils.data import DataLoader import helper_utils.lr_schedule as lr_schedule from helper_utils.data_list_m import ImageList from helper_utils.logger import Logger from helper_utils.sampler import ImbalancedDatasetSampler class EarlyStopping: """Early stops the training if validation loss doesn't improve after a given patience.""" def __init__(self, patience=2000, verbose=False, model_path="../results/"): """ Args: patience (int): How long to wait after last time validation loss improved. Default: 7 verbose (bool): If True, prints a message for each validation loss improvement. Default: False """ self.patience = patience self.verbose = verbose self.counter = 0 self.best_score = None self.early_stop = False self.val_loss_min = np.Inf self.model_path = model_path def __call__(self, val_loss, model): score = -val_loss if self.best_score is None: self.best_score = score self.save_checkpoint(val_loss, model) elif score < self.best_score: self.counter += 1 # print(f'EarlyStopping counter: {self.counter} out of {self.patience}') if self.counter >= self.patience: self.early_stop = True else: self.best_score = score self.save_checkpoint(val_loss, model) self.counter = 0 def save_checkpoint(self, val_loss, model): '''Saves model when validation loss decrease.''' if self.verbose: if self.val_loss_min != val_loss: print('Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...') # torch.save(model, self.model_path) self.val_loss_min = val_loss def image_classification_test(loader, model, test_10crop=False, num_iterations=0, test='test', class_num=2): start_test = True with torch.no_grad(): if test_10crop: iter_test = [iter(loader[test][i]) for i in range(1)] for i in range(len(loader[test][0])): data = [iter_test[j].next() for j in range(1)] inputs = [data[j][0] for j in range(1)] labels = data[0][1] for j in range(1): inputs[j] = inputs[j].cuda() labels = labels outputs = [] for j in range(1): _, predict_out = model(inputs[j]) outputs.append(nn.Softmax(dim=1)(predict_out)) outputs = sum(outputs) if start_test: all_output = outputs.float().cpu() all_label = labels.float() start_test = False else: all_output = torch.cat((all_output, outputs.float().cpu()), 0) all_label = torch.cat((all_label, labels.float()), 0) else: iter_test = iter(loader[test]) for i in range(len(loader[test])): data = iter_test.next() inputs = data[0] labels = data[1] inputs = inputs.cuda() labels = labels.cuda() _, raw_outputs = model(inputs) outputs = nn.Softmax(dim=1)(raw_outputs) if start_test: all_output = outputs.float().cpu() all_label = labels.float() start_test = False else: all_output = torch.cat((all_output, outputs.float().cpu()), 0) all_label = torch.cat((all_label, labels.float()), 0) # print(len(all_output)) # print(len(all_label)) all_output_numpy = all_output.numpy() # all_label_numpy= all_label.cpu().numpy() _, predict = torch.max(all_output, 1) all_values_CSV = [] predict_numpy = predict.numpy() with open(config["logs_path"] + '/_' + str(num_iterations) + '_confidence_values_.csv', mode='w') as file: csv_writer = csv.writer(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['Image_Name', 'Prediction', 'class_0_conf', 'class_1_conf']) for value in range(len(all_output_numpy)): csv_writer.writerow( [all_label[value], predict_numpy[value], all_output_numpy[value][0], all_output_numpy[value][1]]) accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0]) cm = confusion_matrix(all_label, torch.squeeze(predict).float()) print(cm) print(accuracy) # with open(config["output_path"] + '/_' + str(num_iterations) + 'accuracy.csv', mode='w') as file: # csv_writer = csv.writer(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) # # # csv_writer.writerow(['Accuracy',str(num_iterations),accuracy]) # with open(config["output_path"] + '/_' + str(num_iterations) + '_CM.csv', 'a') as f: # writer = csv.writer(f) # writer.writerows(cm) # # f.close() # exit() return accuracy, cm # exit() # return no def validation_loss(loader, model, test_10crop=False, data_name='valid_source', num_iterations=0, class_num=2): start_test = True with torch.no_grad(): if test_10crop: iter_test = [iter(loader['test'][i]) for i in range(10)] for i in range(len(loader['test'][0])): data = [iter_test[j].next() for j in range(10)] inputs = [data[j][0] for j in range(10)] labels = data[0][1] for j in range(10): inputs[j] = inputs[j].cuda() labels = labels outputs = [] for j in range(10): _, predict_out = model(inputs[j]) outputs.append(nn.Softmax(dim=1)(predict_out)) outputs = sum(outputs) if start_test: all_output = outputs.float().cpu() all_label = labels.float() start_test = False else: all_output = torch.cat((all_output, outputs.float().cpu()), 0) all_label = torch.cat((all_label, labels.float()), 0) else: iter_test = iter(loader[data_name]) for i in range(len(loader[data_name])): data = iter_test.next() inputs = data[0] labels = data[1] inputs = inputs.cuda() # labels = labels.cuda() _, raw_outputs = model(inputs) outputs = nn.Softmax(dim=1)(raw_outputs) if start_test: all_output = outputs.cpu() all_label = labels start_test = False else: all_output = torch.cat((all_output, outputs.cpu()), 0) all_label = torch.cat((all_label, labels), 0) # _, predict = torch.max(all_output, 1) val_loss = nn.CrossEntropyLoss()(all_output, all_label) val_loss = val_loss.numpy().item() all_output = all_output.float() _, predict = torch.max(all_output, 1) all_label = all_label.float() val_accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0]) all_output_numpy = all_output.numpy() predict_numpy = predict.numpy() if class_num == 2: with open(config["logs_path"] + '/_' + str(num_iterations) + '_confidence_values_.csv', mode='w') as file: csv_writer = csv.writer(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['Image_Name', 'Prediction', 'class_0_conf', 'class_1_conf']) for value in range(len(all_output_numpy)): csv_writer.writerow( [all_label[value], predict_numpy[value], all_output_numpy[value][0], all_output_numpy[value][1]]) accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0]) cm = confusion_matrix(all_label, torch.squeeze(predict).float()) print(cm) print(accuracy) return val_accuracy, val_loss, accuracy, cm # return val_accuracy,val_loss def train(config): now = dt_string.replace(" ", "_").replace(":", "_").replace(".", "_") logger = Logger(config["logs_path"] + "tensorboard/" + now) model_path = osp.join(config["output_path"], "best_model.pth.tar") early_stopping = EarlyStopping(patience=2000, verbose=True, model_path=model_path) # temp_acc = 0.0 # temp_loss = 10000 ## set pre-process prep_dict = {} prep_config = config["prep"] prep_dict["source"] = prep.image_train(**config["prep"]['params']) prep_dict["target"] = prep.image_train(**config["prep"]['params']) if prep_config["test_10crop"]: prep_dict["test"] = prep.image_test_10crop(**config["prep"]['params']) else: prep_dict["test"] = prep.image_test(**config["prep"]['params']) prep_dict["valid_source"] = prep.image_test(**config["prep"]['params']) ## prepare data dsets = {} dset_loaders = {} data_config = config["data"] train_bs = data_config["source"]["batch_size"] test_bs = data_config["test"]["batch_size"] valid_bs = data_config["test"]["batch_size"] dsets["source"] = ImageList(open(data_config["source"]["list_path"]).readlines(), \ transform=prep_dict["source"], data="source") dset_loaders["source"] = DataLoader(dsets["source"], batch_size=train_bs, \ shuffle=False, num_workers=4, drop_last=True) #sampler=ImbalancedDatasetSampler(dsets["source"]) dsets["target"] = ImageList(open(data_config["target"]["list_path"]).readlines(), \ transform=prep_dict["target"], data="target") dset_loaders["target"] = DataLoader(dsets["target"], batch_size=train_bs, \ shuffle=True, num_workers=4, drop_last=True) dsets["valid_source"] = ImageList(open(data_config["valid_source"]["list_path"]).readlines(), \ transform=prep_dict["valid_source"], data="source") dset_loaders["valid_source"] = DataLoader(dsets["valid_source"], batch_size=test_bs, \ shuffle=False, num_workers=4) if prep_config["test_10crop"]: for i in range(10): dsets["test"] = [ImageList(open(data_config["test"]["list_path"]).readlines(), \ transform=prep_dict["test"][i]) for i in range(10)] dset_loaders["test"] = [DataLoader(dset, batch_size=test_bs, \ shuffle=False, num_workers=4) for dset in dsets['test']] else: dsets["test"] = ImageList(open(data_config["test"]["list_path"]).readlines(), \ transform=prep_dict["test"], data="target") dset_loaders["test"] = DataLoader(dsets["test"], batch_size=test_bs, \ shuffle=False, num_workers=4) class_num = config["network"]["params"]["class_num"] ## set base network net_config = config["network"] base_network = net_config["name"](**net_config["params"]) base_network = base_network.cuda() ## add additional network for some methods if config["loss"]["random"]: random_layer = network.RandomLayer([base_network.output_num(), class_num], config["loss"]["random_dim"]) ad_net = network.AdversarialNetwork(config["loss"]["random_dim"], 1024) else: random_layer = None ad_net = network.AdversarialNetwork(base_network.output_num() * class_num, 1024) if config["loss"]["random"]: random_layer.cuda() ad_net = ad_net.cuda() parameter_list = base_network.get_parameters() + ad_net.get_parameters() ## set optimizer optimizer_config = config["optimizer"] optimizer = optimizer_config["type"](parameter_list, \ **(optimizer_config["optim_params"])) param_lr = [] for param_group in optimizer.param_groups: param_lr.append(param_group["lr"]) schedule_param = optimizer_config["lr_param"] lr_scheduler = lr_schedule.schedule_dict[optimizer_config["lr_type"]] gpus = config['gpu'].split(',') if len(gpus) > 1: ad_net = nn.DataParallel(ad_net, device_ids=[int(i) for i in gpus]) base_network = nn.DataParallel(base_network, device_ids=[int(i) for i in gpus]) ## train len_train_source = len(dset_loaders["source"]) len_train_target = len(dset_loaders["target"]) len_train_valid_source = len(dset_loaders["valid_source"]) transfer_loss_value = classifier_loss_value = total_loss_value = 0.0 best_acc = 0.0 best_loss_valid = 10000 # total best_loss_transfer = 10000 # total best_total_loss_numpy_transfer = best_transfer_loss_numpy_transfer = best_classifier_loss_numpy_transfer = 100000 best_loss_total = 10000 # total best_total_loss_numpy_total = best_transfer_loss_numpy_total = best_classifier_loss_numpy_total = 100000 best_loss3 = 10000 # transfer best_total_loss_numpy_acc = best_transfer_loss_numpy_acc = best_classifier_loss_numpy_acc = 100000 val_accuracy = 0.0 val_loss = 10.0 for i in range(config["num_iterations"]): if i % config["test_interval"] == 0: itr_log = "num_iterations " + str(i) config["out_file"].write(itr_log + "\n") config["out_file"].flush() print(itr_log) train_time_end = time.time() test_time_start = time.time() base_network.train(False) # image_classification_test(dset_loaders, \ # base_network, # test_10crop=prep_config[ # "test_10crop"], # num_iterations=i) # val_accuracy, val_loss= validation_loss(dset_loaders, \ # base_network, # # ) val_accuracy, val_loss, best_acc_new, best_cm = validation_loss(dset_loaders, \ base_network, num_iterations=i, data_name='valid_source') # val_accuracy_target, val_loss_target, best_acc_new_target, best_cm_target = validation_loss(dset_loaders, \ # base_network, # # num_iterations=i, # data_name='test') temp_model = nn.Sequential(base_network) if val_loss < best_loss_valid: best_loss_valid = val_loss best_acc = val_accuracy best_model = copy.deepcopy(temp_model) torch.save(best_model, osp.join(config["model_path"], "model_" + str(i) + ".pth.tar")) best_itr = i # torch.save(temp_model, osp.join(config["model_path"], \ # "iter_{:05d}_model.pth.tar".format(i))) # if i % config["snapshot_interval"] == 0: # torch.save(nn.Sequential(base_network), osp.join(config["model_path"], \ # "iter_{:05d}_model.pth.tar".format(i))) loss_params = config["loss"] ## train one iter base_network.train(True) ad_net.train(True) optimizer = lr_scheduler(optimizer, i, **schedule_param) optimizer.zero_grad() if i % len_train_source == 0: iter_source = iter(dset_loaders["source"]) if i % len_train_target == 0: iter_target = iter(dset_loaders["target"]) inputs_source, labels_source = iter_source.next() inputs_target, labels_target = iter_target.next() inputs_source, inputs_target, labels_source = inputs_source.cuda(), inputs_target.cuda(), labels_source.cuda() features_source, outputs_source = base_network(inputs_source) features_target, outputs_target = base_network(inputs_target) features = torch.cat((features_source, features_target), dim=0) outputs = torch.cat((outputs_source, outputs_target), dim=0) softmax_out = nn.Softmax(dim=1)(outputs) if config['method'] == 'CDAN+E': entropy = loss.Entropy(softmax_out) transfer_loss = loss.CDAN([features, softmax_out], ad_net, entropy, network.calc_coeff(i), random_layer) elif config['method'] == 'CDAN': transfer_loss = loss.CDAN([features, softmax_out], ad_net, None, None, random_layer) elif config['method'] == 'DANN': transfer_loss = loss.DANN(features, ad_net) else: raise ValueError('Method cannot be recognized.') classifier_loss = nn.CrossEntropyLoss()(outputs_source, labels_source) total_loss = loss_params["trade_off"] * transfer_loss + classifier_loss total_loss.backward() optimizer.step() transfer_loss_numpy = transfer_loss.clone().cpu().detach().numpy() classifier_loss_numpy = classifier_loss.clone().cpu().detach().numpy() total_loss_numpy = total_loss.clone().cpu().detach().numpy() entropy_numpy = torch.sum(entropy).clone().cpu().detach().numpy() info = {'total_loss': total_loss_numpy.item(), 'classifier_loss': classifier_loss_numpy.item(), 'transfer_loss': transfer_loss_numpy.item(), 'entropy': entropy_numpy.item(), 'valid_source_loss': val_loss, 'valid_source_acc': val_accuracy, # 'target_valid_loss': val_loss_target, 'target_valid_acc': val_accuracy_target, } for tag, value in info.items(): logger.scalar_summary(tag, value, i) with open(config["logs_path"] + '/loss_values_.csv', mode='a') as file: csv_writer = csv.writer(file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow( [i, total_loss_numpy, transfer_loss_numpy, classifier_loss_numpy, entropy_numpy, val_loss, val_accuracy, ]) early_stopping(val_loss, nn.Sequential(base_network)) # print(i) if early_stopping.early_stop: print("Early stopping") break # temp_model_total = nn.Sequential(base_network) # temp_loss_total = transfer_loss_numpy # if temp_loss_total < best_loss_total: # best_loss_total = temp_loss_total # best_model_total = temp_model_total # best_itr_total = i # # best_classifier_loss_numpy_total = classifier_loss_numpy # best_total_loss_numpy_total = total_loss_numpy # best_transfer_loss_numpy_total = transfer_loss_numpy # # temp_model_transfer = nn.Sequential(base_network) # temp_loss_transfer = transfer_loss_numpy # if temp_loss_transfer < best_loss_transfer: # best_loss_transfer = temp_loss_transfer # best_model_transfer = temp_model_transfer # best_itr_transfer = i # # best_classifier_loss_numpy_transfer = classifier_loss_numpy # best_total_loss_numpy_transfer = total_loss_numpy # best_transfer_loss_numpy_transfer = transfer_loss_numpy # torch.save(best_model_transfer, osp.join(config["model_path"], "best_model_transfer.pth.tar")) # torch.save(best_model_total, osp.join(config["model_path"], "best_model_total.pth.tar")) # def post_training(model, best_itr, best_classifier_loss, best_total_loss, best_transfer_loss, metric_name): model.train(False) # if is_training: torch.save(best_model, osp.join(config["model_path"], "best_model_acc.pth.tar")) torch.save(best_model, osp.join(config["model_path"], "best_model" + str(best_itr) + ".pth.tar")) model.train(False) model = torch.load(osp.join(config["model_path"], "best_model" + str(best_itr) + ".pth.tar")) else: model = torch.load(model_path_for_testing) best_itr = config["best_itr"] source_val_accuracy, source_val_loss, best_acc_new, best_cm = validation_loss(dset_loaders, \ model, num_iterations=best_itr, data_name='valid_source') # val_accuracy_target, val_loss_target, best_acc_new_target, best_cm_target = validation_loss(dset_loaders, \ # model, # # num_iterations=best_itr, # data_name='test') #print(val_accuracy_target, val_loss_target, best_cm_target) config_array = ["trail-" + str(trial_number), metric_name, source_val_accuracy, source_val_loss, best_cm, #val_accuracy_target, val_loss_target, best_cm_target, best_classifier_loss, best_transfer_loss, best_total_loss , best_itr] + training_parameters config["trial_parameters_log"].writerow(config_array) if not is_training: # print("error") best_model = torch.load(model_path_for_testing) best_itr = config["best_itr"] post_training(best_model, best_itr, best_classifier_loss_numpy_acc, best_total_loss_numpy_acc, best_transfer_loss_numpy_acc, "Source Val_loss") # post_training(best_model_total,best_itr_total,best_classifier_loss_numpy_total,best_total_loss_numpy_total,best_transfer_loss_numpy_total,"Total") # post_training(best_model_transfer,best_itr_transfer,best_classifier_loss_numpy_transfer,best_total_loss_numpy_transfer,best_transfer_loss_numpy_transfer,"Transfer") return best_acc if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--gpu_id', type=str, nargs='?', default='0', help="device id to run") parser.add_argument('--dset', type=str, default='COVID19', help="The dataset or source dataset used") parser.add_argument('--trail', type=str, default='mb', help="The dataset or source dataset used") parser.add_argument('--lr', type=float, default=0.005) args = parser.parse_args() seed = 0 now = datetime.now() dt_string = now.strftime("%d/%m/%Y %H:%M:%S") dt_string = dt_string.replace("/", "_").replace(" ", "_").replace(":", "_").replace(".", "_") dataset = args.dset valid_or_test = "" # "valid or "test" or "" if whole dataset test_on_source_ed3 = False is_training = True if valid_or_test == "test": is_training = False testing = "testing" # testing = "train" if testing == "testing": is_training = False print(dataset) os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id log_output_dir_root = '../logs/' + dataset + '/' results_output_dir_root = '../experimental results/' + dataset + '/' models_output_dir_root = '../models/' + dataset + '/' trial_number = args.trail + "_" + dataset + "_" + testing + "_" + dt_string if dataset == 'HCV': source_path = "../Data/HCV/txt_80_20_tile/HCV_train_80.txt" valid_source_path = "../Data/HCV/txt_80_20_tile/HCV_val_20.txt" target_path = "../Data/HCV/HCV_target_tile.txt" no_of_classes = 2 elif dataset == 'HIV': source_path = "../Data/HIV/txt_80_20_tile/HIV_train_80.txt" valid_source_path = "../Data/HIV/txt_80_20_tile/HIV_val_20.txt" target_path = "../Data/HIV/HIV_target_tile.txt" no_of_classes = 2 elif dataset == 'ZIKA': source_path = "../Data/ZIKA/txt_90_10_tile/ZIKA_train_90.txt" valid_source_path = "../Data/ZIKA/txt_90_10_tile/ZIKA_val_10.txt" target_path = "../Data/ZIKA/ZIKA_target_tile.txt" no_of_classes = 2 elif dataset == 'HBV': source_path = "../Data/HBV/txt_80_20_tile/HBV_train_80.txt" valid_source_path = "../Data/HBV/txt_80_20_tile/HBV_val_20.txt" target_path = "../Data/HBV/HBV_target_tile.txt" no_of_classes = 2 elif dataset == 'COVID19': source_path = "../Data/COVID19/txt_80_20_tile/COVID19_train_80.txt" valid_source_path = "../Data/COVID19/txt_80_20_tile/COVID19_val_20.txt" target_path = "../Data/COVID19/COVID19_target_tile.txt" no_of_classes = 2 elif dataset == 'CAS12': source_path = "../Data/CAS12/txt_80_20_tile/CAS12_train_80.txt" valid_source_path = "../Data/CAS12/txt_80_20_tile/CAS12_val_20.txt" target_path = "../Data/CAS12/CAS12_target_tile.txt" no_of_classes = 2 else: no_of_classes = None net = 'Xception' # net = 'ResNet50' dset = dataset lr_ = args.lr gamma = 0.001 power = 0.75 # power = 0.9 momentum = 0.9 weight_decay = 0.0005 nesterov = True optimizer = optim.Adam config = {} config['method'] = 'CDAN+E' config["gpu"] = '0' config["num_iterations"] = 10000 config["test_interval"] = 50 config["snapshot_interval"] = 5000 batch_size = 8 batch_size_test = 128 use_bottleneck = False bottleneck_dim = 256 adv_lay_random = False random_dim = 1024 new_cls = True if not is_training: valid_source_path = "../Data/Test/mb1/mb_test.txt" target_path = "../Data/Test/mb1/mb_test.txt" model_path_for_testing = "../Final Models/CDAN + GAN/COVID19/model_1600.pth.tar" config["num_iterations"] = 0 best_itr = "testing" print("Testing:") config["best_itr"] = "testing" print("num_iterations", config["num_iterations"]) header_list = ["trail no ", 'metric name', 'source_val_accuracy', 'source_val_loss', 'best_cm', # 'val_accuracy_target', 'val_loss_target', 'best_cm_target', "best_classifier_loss", "best_transfer_loss", "best_total_loss" , "best_itr"] + \ ["lr", "gamma", "power", "momentum", "weight_decay", "nesterov", "optimizer", "batch_size", "batch_size_test", "use_bottleneck", "bottleneck_dim", "adv_lay_random", "random_dim", "no_of_classes", "new_cls", "dset", "net", "source_path", "target_path", "output_path", "model_path" , "logs_path", "gpu", "test_interval", "seed"] log_output_path = log_output_dir_root + net + '/' + 'trial-' + trial_number + '/' trial_results_path = net + '/trial-' + trial_number + '/' config["output_path"] = results_output_dir_root + trial_results_path config["model_path"] = models_output_dir_root + trial_results_path config["logs_path"] = log_output_path if not os.path.exists(config["logs_path"]): os.makedirs(config["logs_path"]) if is_training: if not os.path.exists(config["model_path"]): os.makedirs(config["model_path"]) # if not os.path.exists(config["output_path"]): # os.makedirs(config["output_path"]) if not os.path.isfile(osp.join(log_output_dir_root, "log.csv")): with open(osp.join(log_output_dir_root, "log.csv"), mode='w') as param_log_file: param_log_writer = csv.writer(param_log_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) param_log_writer.writerow(header_list) config["out_file"] = open(osp.join(config["logs_path"], "log.txt"), "w") config["trial_parameters_log"] = csv.writer(open(osp.join(log_output_dir_root, "log.csv"), "a")) config["prep"] = {"test_10crop": False, 'params': {"resize_size": 224, "crop_size": 224, 'alexnet': False}} config["loss"] = {"trade_off": 1.0} if "Xception" in net: config["network"] = \ {"name": network.XceptionFc, "params": {"use_bottleneck": use_bottleneck, "bottleneck_dim": bottleneck_dim, "new_cls": new_cls}} elif "ResNet50" in net: config["network"] = {"name": network.ResNetFc, "params": {"resnet_name": net, "use_bottleneck": use_bottleneck, "bottleneck_dim": bottleneck_dim, "new_cls": new_cls}} config["loss"]["random"] = adv_lay_random config["loss"]["random_dim"] = random_dim if optimizer == optim.SGD: config["optimizer"] = {"type": optim.SGD, "optim_params": {'lr': lr_, "momentum": momentum, "weight_decay": weight_decay, "nesterov": nesterov}, "lr_type": "inv", "lr_param": {"lr": lr_, "gamma": gamma, "power": power}} elif optimizer == optim.Adam: config["optimizer"] = {"type": optim.Adam, "optim_params": {'lr': lr_, "weight_decay": weight_decay}, "lr_type": "inv", "lr_param": {"lr": lr_, "gamma": gamma, "power": power}} config["dataset"] = dset config["data"] = {"source": {"list_path": source_path, "batch_size": batch_size}, "target": {"list_path": target_path, "batch_size": batch_size}, "test": {"list_path": target_path, "batch_size": batch_size_test}, "valid_source": {"list_path": valid_source_path, "batch_size": batch_size}} config["optimizer"]["lr_param"]["lr"] = lr_ config["network"]["params"]["class_num"] = no_of_classes config["out_file"].write(str(config)) config["out_file"].flush() training_parameters = [lr_, gamma, power, momentum, weight_decay, nesterov, optimizer, batch_size, batch_size_test, use_bottleneck, bottleneck_dim, adv_lay_random, random_dim, no_of_classes, new_cls, dset, net, source_path, target_path, config["output_path"], config["model_path"] , config["logs_path"], config["gpu"], config["test_interval"], str(seed)] print("source_path", source_path) print("target_path", target_path) print("lr_", lr_) print('GPU', os.environ["CUDA_VISIBLE_DEVICES"], config["gpu"]) train(config)

42.646904

170

0.575489

1,651

0.051006

0

9,515

0.293954

0c51eb0b9b67869087426ffee62488bbc0029d3f

1,230

py

Python

src/freshchat/client/configuration.py

twyla-ai/python-freshchat

5bb0ea730f82b63292688be61315b6b880896e1f

[ "MIT" ]

4

2019-10-15T11:03:28.000Z

2021-08-19T01:14:12.000Z

src/freshchat/client/configuration.py

twyla-ai/python-freshchat

5bb0ea730f82b63292688be61315b6b880896e1f

[ "MIT" ]

137

2019-10-18T04:36:21.000Z

2022-03-21T04:11:18.000Z

src/freshchat/client/configuration.py

twyla-ai/python-freshchat

5bb0ea730f82b63292688be61315b6b880896e1f

[ "MIT" ]

1

2021-08-19T01:14:14.000Z

import os from dataclasses import dataclass, field from typing import AnyStr, Dict, Optional from urllib.parse import urljoin @dataclass class FreshChatConfiguration: """ Class represents the base configuration for Freshchat """ app_id: str token: str = field(repr=False) default_channel_id: Optional[str] = field(default=None) default_initial_message: Optional[str] = field(default=None) url: Optional[str] = field( default_factory=lambda: os.environ.get( "FRESHCHAT_API_URL", "https://api.freshchat.com/v2/" ) ) @property def authorization_header(self) -> Dict[AnyStr, AnyStr]: """ Property which returns the proper format of the authorization header """ return { "Authorization": f"Bearer {self.token}" if "Bearer" not in self.token else self.token } def get_url(self, endpoint: str) -> str: """ Method responsible to build the url using the given endpoint :param endpoint: String with the endpoint which needs to attached to URL :return: a string which represents URL """ return urljoin(self.url, endpoint.lstrip("/"))

28.604651

80

0.64065

1,090

0.886179

0

1,101

0.895122

0

472

0.38374

0c521cb77fbca7152db05ece3eddd9a49ae59322

20,120

py

Python

get_headers.py

rupendrab/py_unstr_parse

3cece3fb7ca969734bf5e60fe5846a7148ce8be4

[ "MIT" ]

null

get_headers.py

rupendrab/py_unstr_parse

3cece3fb7ca969734bf5e60fe5846a7148ce8be4

[ "MIT" ]

null

get_headers.py

rupendrab/py_unstr_parse

3cece3fb7ca969734bf5e60fe5846a7148ce8be4

[ "MIT" ]

null

#!/usr/bin/env python3.5 import sys import re import os import csv import numpy as np from operator import itemgetter from time import time from multiprocessing import Pool from extract_toc import parseargs from predict_using_toc_mapper import Mapper, get_topic, read_model_file from find_topics import toc_entries, get_summary_map, read_topics import dateparser import check_309 import parse_name from get_ratings import Ratings, Ratings2 import delimiterwriter from predict_using_subtopic_mapper import SubtopicPredictor from analyze_topic import SubtopicReader from headerutil import * # NEWLINE_WITHIN_COLUMN = '\r\n' # NEWLINE_WITHIN_COLUMN = '\r\n' # CSV_LINE_TERMINATOR = '\r\n' # CSV_FIELD_DELIMITER = ',' # FD_REPLACED = None p_region = re.compile('(^|.*\s+)region(\s*[:\']\s*|\s+)(.*)?\s*$', re.IGNORECASE) p_region_with_other = re.compile('(.*)?\s{5,}(certificate\s+num[bh]e[ir]|certificate|charter\s+num[bh]er|charter|field\s+offic\s*e|url)\s*:?\s*(.*)?\s*$', re.IGNORECASE) p_blank = re.compile('^\s*$') p_from_first_uppercase_char = re.compile('^.*?([A-Z].*)$', re.MULTILINE) p_cert_direct = re.compile('(^|^.*\s+)(certificate\s+number)(\s*:\s*|\s+)(\w+).*$', re.IGNORECASE) p_region_direct = re.compile('(^|^.*\s+)(region)(\s*:\s*|\s+)(\w+).*$', re.IGNORECASE) p_patterns_str = { 'bank_name' : [ 'bank\s+name', 'institution\s+name', 'name' ], 'bank_location': [ 'location' ], 'examiner_in_charge': [ 'examiner[\s\-]*in[\s\-]*charge' ], 'exam_start_date': [ 'examination[\s\-]*start[\s\-]*date' ], 'exam_date': [ 'examination[\s\-]*date' ], 'exam_as_of_date': [ 'examination[\s\-]*as[\s\-]*of[\s\-]*date' ] } all_matched = {} for k,patterns in p_patterns_str.items(): all_matched[k] = [] p_patterns = {} for k,patterns in p_patterns_str.items(): p_patterns[k] = [re.compile('(^|.*\s+)' + p + '(\s*[:\'][\'\s\.]*|[\'\s\.]+)' + '(.*)?\s*$', re.IGNORECASE) for p in patterns] def get_pattern(line, pat): ret = [] for i, p in enumerate(p_patterns[pat]): quality = 0 m = p.match(line) if (m): st = m.group(1) sep = m.group(2) val = m.group(3) vals = re.split('\s{5,}', val) val = vals[0] if (not st.strip()): quality += 1 ## Higher quality for line starting pattern else: if (len(st) - len(st.rstrip()) < 2): ## Just one space # quality -= 1 quality -= 0 ## Ignore this one for now if (sep.strip() ==':'): quality += 1 ## Higher qiuality in presence of : if (len(vals) == 1): quality += 1 ret.append((p_patterns_str[pat][i], val, quality)) return ret def match_pattern(line, pat): global all_matched all_matched[pat] += get_pattern(line, pat) def match_all_patterns(line): for pat in p_patterns.keys(): match_pattern(line, pat) """ def best_match(pat): # print('In best match', pat) all_m = all_matched.get(pat) if (all_m): l = sorted(all_matched.get(pat), key=lambda x: (-1 * p_patterns_str[pat].index(x[0]), x[2]), reverse=True) # print('Best match sorted list', l) if (l): if (l[0][2] > 0): ## Quality more than zero return l[0] """ def best_match(pat, validationfn = None): # print('In best match', pat) all_m = all_matched.get(pat) if (all_m): l = sorted(all_matched.get(pat), key=lambda x: (-1 * p_patterns_str[pat].index(x[0]), x[2]), reverse=True) # print('Best match sorted list', l) if (l): if (validationfn): for item in l: if (item[2] >= 0 and validationfn(item[1])): ## Quality more than zero return item else: if (l[0][2] >= 0): ## Quality more than zero return l[0] """ def best_match_text(pat): bm_tuple = best_match(pat) if (bm_tuple and len(bm_tuple) == 3): return bm_tuple[1] else: return "" """ def best_match_text(pat, validationfn = None): bm_tuple = best_match(pat, validationfn) if (bm_tuple and len(bm_tuple) == 3): return bm_tuple[1] else: return "" def format_eic(eic_name): if (not eic_name): return eic_name words = re.split('\s+', eic_name) new_eic = [] for i, word in enumerate(words): if word.endswith(';'): new_eic += [word[:-1]] break if word[0].islower(): break new_eic += [word] return ' '.join(new_eic) def format_date(dt): if (not dt): return dt parts = re.split('\s{3,}', dt) if (len(parts) >= 1): return parts[0] else: return "" def get_cert_from_line(line): if not line: return ""; m = p_cert_direct.match(line) if m: return m.group(4) else: return "" def readfile(filename): for line in open(filename, 'r', encoding='latin1'): yield(line[:-1]) def remove_punct(str): return re.sub(r'[^\w\s]','',str).strip() def format_cert_number(cert): return remove_punct(cert) def format_region(region): if not region: return region split_by_extra_spaces = re.split('\s{5,}', remove_punct(region)) return split_by_extra_spaces[0] def format_bank_str(str): if not str: return str m = p_from_first_uppercase_char.match(str.strip()) if (m): return singlespace(m.group(1)) else: return singlespace(str) def singlespace(sent): return ' '.join(re.split('\s+', sent)) def separate_cert(str): cert = "" newstr = str m_str_with_other = p_region_with_other.match(str) if (m_str_with_other): newstr = m_str_with_other.group(1) if m_str_with_other.group(2).lower() == "charter" and m_str_with_other.group(3).lower() == "bank": return str, cert if (m_str_with_other.group(2).strip().lower().startswith('certificate')): cert = m_str_with_other.group(3) return newstr, cert def prev_nonblank_line(lines, lineno): # print('In prev_nonblank_line', lineno, len(lines)) while lineno > 0: lineno -= 1 line = lines[lineno] if (p_blank.match(line)): continue else: # print("Non Blank Line = ", line) return lineno, line return -1, "" def init_all_matched(): for k,patterns in p_patterns_str.items(): all_matched[k] = [] def get_header_for_file(filename): # global all_matched init_all_matched() region = "" cert = "" bank_name = "" bank_location = "" lines = [] lineno = 0 ff = chr(12) prev_region_match_quality = 0 for line in readfile(filename): # if line and ord(line[0]) == 12: ## Line starts with control-L # line = line[1:] if line: ## Delete all form feed characters line = line.replace(ff, "") # line = re.sub('\s+', ' ', line) ## Compress multile spaces to a single space character lines += [line] match_all_patterns(line) m_region = p_region.match(line) if (m_region): if (m_region.group(1).strip() == ""): if (m_region.group(2).strip() == ":"): region_match_quality = 3 else: region_match_quality = 2 else: region_match_quality = 1 if (region_match_quality >= prev_region_match_quality): prev_region_match_quality = region_match_quality region = m_region.group(3) # print("Region = ", region) region, certx = separate_cert(region) if (not cert): cert = certx # print("Evaluating previous lines:", line) if (m_region.group(1).strip() == ""): location_line, bank_location = prev_nonblank_line(lines, lineno) bank_location, cert2 = separate_cert(bank_location) if (not cert): cert = cert2 bank_line, bank_name = prev_nonblank_line(lines, location_line) # print("Bank Name = ", bank_name) bank_name, cert2 = separate_cert(bank_name) if (not cert): cert = cert2 # print("Bank Name = ", bank_name) if (not cert): cert = get_cert_from_line(line) lineno += 1 # print(all_matched) if (not bank_name): bank_name = best_match_text('bank_name') if (not bank_location): bank_location = best_match_text('bank_location') examiner_in_charge = format_eic(best_match_text('examiner_in_charge')) eic_first_name, eic_middle_name, eic_last_name, eic_suffix = parse_name.parse_name(examiner_in_charge) exam_start_date = format_date(best_match_text('exam_start_date', dateparser.get_date)) if (not exam_start_date): exam_start_date = format_date(best_match_text('exam_date', dateparser.get_date)) exam_start_year, exam_start_month, exam_start_day, exam_start_date_formatted = dateparser.get_year_month_day(exam_start_date) exam_as_of_date = format_date(best_match_text('exam_as_of_date', dateparser.get_date)) exam_as_of_year, exam_as_of_month, exam_as_of_day, exam_as_of_date_formatted = dateparser.get_year_month_day(exam_as_of_date) return (lines, format_region(region).title().replace(' ', '_'), format_cert_number(cert), format_bank_str(bank_name).replace(' ', '_'), format_bank_str(bank_location), eic_first_name, eic_middle_name, eic_last_name, exam_start_date_formatted, exam_start_year, exam_start_month, exam_as_of_date_formatted, exam_as_of_year, exam_as_of_month) def multiply_array(arr, factor): if (factor > 1): subscript = True else: subscript = False if subscript: return [newval + '_' + str(i+1) for subarr in [[val] * factor for val in arr] for i,newval in enumerate(subarr)] else: return [newval for subarr in [[val] * factor for val in arr] for i,newval in enumerate(subarr)] def format_headercol(header): header_new = re.sub('[\-\/]', ' ', header) header_new = re.sub('\'', '', header_new) header_new = re.sub(' +', '_', header_new) return header_new def replace_list(orig, start, end, new): orig[start:end] = new def find_in_list(lst, pattern): p = re.compile("^" + pattern + "_?[0-9]*$", re.IGNORECASE) inds = [i for i,val in enumerate(lst) if p.match(val)] if (inds and len(inds) > 0): return (inds[0], inds[-1] + 1) def process_single_file(inputTuple): (serialno, filename, topics, mapper, summary_map, nosplit, topic_split_times, ratings, smodelfile, stopic) = inputTuple file_time = time() # print("Processing file %s at %f" % (filename, (file_time - start_time))) filedata = get_header_for_file(filename) file_time = time() # print("Processing file %s at %f" % (filename, (file_time - start_time))) rowdata = [os.path.basename(filename)] lines = filedata[0] ## Only write to exception file if part 309 is not present # if not check_309.has_part_309_sents(lines): # exf.write(os.path.abspath(filename) + '\r\n') # continue # print(summary_map) no_topics = summary_map.get(os.path.basename(filename)) # print("No Topics", no_topics) guess_allowed = False if (not no_topics or no_topics == 0): guess_allowed = True topic_list = read_topics(filename, mapper, guess_allowed) # print(topic_list) if nosplit: topic_data = ["" for i in range(len(topics))] else: topic_data = ["" for i in range(len(topics) * topic_split_times)] # print("Topic Data length", len(topic_data)) no_topics_in_doc = len(topic_list) if smodelfile: stcol_topic_start, stcol_topic_end = find_in_list(topics, stopic) if (not nosplit): ## Readjust for split columns stcol_topic_start = stcol_topic_start * topic_split_times stcol_topic_end = stcol_topic_start + topic_split_times if smodelfile: subtopicReader = SubtopicReader(stopic, mapper, summary_map) subtopicPredictor = SubtopicPredictor(smodelfile) stopic_start_line, stopic_end_line = None,None for i, topic_line in enumerate(topic_list): start_line = topic_line[0] if (i < no_topics_in_doc -1): end_line = topic_list[i+1][0] if nosplit: topic_text = NEWLINE_WITHIN_COLUMN.join(lines[start_line:end_line]) else: topic_texts_split = break_into_pieces(lines[start_line:end_line], NEWLINE_WITHIN_COLUMN) else: end_line = None if nosplit: topic_text = NEWLINE_WITHIN_COLUMN.join(lines[start_line:]) else: topic_texts_split = break_into_pieces(lines[start_line:], NEWLINE_WITHIN_COLUMN) topic_name = topic_line[2] topic_index = topics.index(topic_name) if nosplit: topic_data[topic_index] = topic_text # topic_data[topic_index] = topic_text[:32000] else: if (len(topic_texts_split) > topic_split_times): print('Problem in file %s for topic %s, number of splits needed is %d' % (os.path.basename(filename), topic_name, len(topic_texts_split))) for topic_subindex in range(len(topic_texts_split)): # print('Setting:', topic_index, topic_index * topic_split_times + topic_subindex) topic_data[topic_index * topic_split_times + topic_subindex] = topic_texts_split[topic_subindex] ## Handle Subtopics if smodelfile and topic_name == stopic: stopic_start_line, stopic_end_line = start_line, end_line # topic_data[topic_index] = topic_text[:300] # if (len(topic_text) > 32000): # print(rowdata[0], topic_name, topic_index, len(topic_text)) # if (len(topic_text) > 32000): # print(topic_text) # print(topic_name, topic_index) # print('======================================================') # print(topic_text) if smodelfile: ## If Subtopic lines exit if stopic_start_line: subtopics_dict = subtopicReader.mapped_subtopics_from_lines(lines, stopic_start_line, stopic_end_line, subtopicPredictor) subtopics_arr = subtopicReader.subtopic_array(subtopics_dict) topic_data[stcol_topic_start:stcol_topic_end] = subtopics_arr else: topic_data[stcol_topic_start:stcol_topic_end] = subtopicReader.empty_subtopics(subtopicPredictor) rowdata.insert(0, serialno) rowdata += filedata[1:] ratings.process_file(filename) ratings.map_ratings() rowdata += ratings.get_column_data() rowdata += topic_data return rowdata def get_headers_for_files(files, topics, mapper, summary_map, outfile, exfile, nosplit, topic_split_times, ratings, smodels = None, stopics = None): if (not outfile): if (len(CSV_FIELD_DELIMITER) == 1): writer = csv.writer(sys.stdout, delimiter = CSV_FIELD_DELIMITER, lineterminator = CSV_LINE_TERMINATOR) else: writer = delimiterwriter.writer(sys.stdout, CSV_FIELD_DELIMITER, CSV_LINE_TERMINATOR, FD_REPLACED) else: outf = open(outfile, 'w') if (len(CSV_FIELD_DELIMITER) == 1): writer = csv.writer(outf, delimiter = CSV_FIELD_DELIMITER, lineterminator = CSV_LINE_TERMINATOR) else: writer = delimiterwriter.writer(outf, CSV_FIELD_DELIMITER, CSV_LINE_TERMINATOR, FD_REPLACED) exf = open(exfile, 'w') headerline = [ 'serial_no', 'file_name', 'region', 'certificate_number', 'bank_name', 'bank_location', 'examiner_in_charge_first_name', 'examiner_in_charge_middle_name', 'examiner_in_charge_last_name', 'exam_start_date', 'exam_start_year', 'exam_start_month', 'exam_as_of_date', 'exam_as_of_year', 'exam_as_of_month' ] headerline += ratings.get_column_headers() topic_start_index = len(headerline) # topic_split_times = 4 topics.append('Confidential') if (nosplit): headerline += topics else: headerline += multiply_array(topics, topic_split_times) # headerline_no_spaces = [headercol.replace(' ', '_') for headercol in headerline] smodelfile=None stopic=None if (smodels and len(smodels) > 0): smodelfile= smodels[0] if (stopics and len(stopics) > 0): stopic= stopics[0] # smodelfile = "model_sub_svc.pkl" # stopic = "IT Assessment" # print("smodelfile = %s, stopic = %s" % (smodelfile, stopic)) if smodelfile: subtopicReader = SubtopicReader(stopic, mapper, summary_map) subtopicPredictor = SubtopicPredictor(smodelfile) subtopic_columns = subtopicReader.get_column_names(subtopicPredictor) stcol_start, stcol_end = find_in_list(headerline, stopic) headerline[stcol_start:stcol_end] = subtopic_columns headerline_no_spaces = [format_headercol(headercol) for headercol in headerline] writer.writerow(headerline_no_spaces) serial=0 start_time = time() pool = Pool(processes=4) inputList = [] for filename in files: ### Placeholder for now serial += 1 # rowdata = process_single_file(serial, filename, topics, mapper, summary_map, nosplit, topic_split_times, ratings, smodelfile, stopic, subtopicReader, subtopicPredictor) inputList.append((serial, filename, topics, mapper, summary_map, nosplit, topic_split_times, ratings, smodelfile, stopic)) # print(inputList) for rowdata in pool.imap(process_single_file, inputList, chunksize=20): writer.writerow(rowdata) # for rowdata in pool.map(process_single_file, inputList): # writer.writerow(rowdata) if (outfile): outf.close() if (exf): exf.close() def break_into_pieces(lines, newlinechar, chunksize=32000): fields = [] field = "" fieldlen = 0 newlinelen = len(newlinechar) for i, line in enumerate(lines): if (fieldlen + len(line) + newlinelen) > chunksize: fields.append(field) field = "" fieldlen = 0 if (field): field += newlinechar field += line fieldlen = fieldlen + len(line) + newlinelen if field: fields.append(field) return fields def main(args): global NEWLINE_WITHIN_COLUMN argsmap = parseargs(args) files = argsmap.get('files') if (not files): sys.exit(0) summaryfile = argsmap.get("summary") if (not summaryfile or len(summaryfile) == 0): print('Summary file must be specified...') sys.exit(1) summary_map = get_summary_map(summaryfile[0]) # print(summary_map) modelfile = argsmap.get("model") if (not modelfile): print('Model must be specified...') sys.exit(1) modelfile = modelfile[0] (origmap, sorted_y, vectorizer, le, grid_search) = read_model_file(modelfile) topics = toc_entries(origmap) mapper = Mapper(origmap, sorted_y, vectorizer, le, grid_search) nosplit = argsmap.get('nosplit') if nosplit == []: nosplit = True else: nosplit = False if not nosplit: topic_split_times = argsmap.get('split') if (not topic_split_times): topic_split_times = 4 else: topic_split_times = int(topic_split_times[0]) else: topic_split_times = 0 NL = argsmap.get('NL') ## Set newline character for multiline columns if (NL): NL = NL[0] if (NL): NEWLINE_WITHIN_COLUMN = NL outfile = argsmap.get("out") if (outfile): outfile = outfile[0] exfile = argsmap.get("err") if exfile: exfile = exfile[0] if not exfile: print("Exception file name must be entered using the --err option...") sys.exit(1) ratings_mapper_file = argsmap.get("rmap") if ratings_mapper_file: ratings_mapper_file = ratings_mapper_file[0] if not ratings_mapper_file: print("Ratings Mapper File file name must be entered using the --rmap option...") sys.exit(1) ratings = Ratings(ratings_mapper_file) global CSV_FIELD_DELIMITER field_delim = argsmap.get('fd') if field_delim: field_delim = field_delim[0] if field_delim: CSV_FIELD_DELIMITER = field_delim global FD_REPLACED fd_replaced = argsmap.get('fdr') if fd_replaced: fd_replaced = fd_replaced[0] if fd_replaced: FD_REPLACED = fd_replaced smodels = argsmap.get("smodels") stopics = argsmap.get("stopics") get_headers_for_files(files, topics, mapper, summary_map, outfile, exfile, nosplit, topic_split_times, ratings, smodels, stopics) if __name__ == '__main__': args = sys.argv[1:] main(args)

31.885895

174

0.655964

0

98

0.004871

0

4,431

0.220229

0c52238d0be9f0af598966fd7664c6c79e85f8cb

6,214

py

Python

dalle_pytorch/dalle_pytorch.py

tensorfork/DALLE-pytorch

0e8f5d9a7fe054c587ed91d9c9616c7a883f393b

[ "MIT" ]

1

2021-06-22T08:26:20.000Z

dalle_pytorch/dalle_pytorch.py

tensorfork/DALLE-pytorch

0e8f5d9a7fe054c587ed91d9c9616c7a883f393b

[ "MIT" ]

null

dalle_pytorch/dalle_pytorch.py

tensorfork/DALLE-pytorch

0e8f5d9a7fe054c587ed91d9c9616c7a883f393b

[ "MIT" ]

null

from math import log2 import torch from torch import nn, einsum import torch.nn.functional as F from einops import rearrange from x_transformers import Encoder, Decoder # helpers def exists(val): return val is not None def masked_mean(t, mask, dim = 1): t = t.masked_fill(~mask[:, :, None], 0.) return t.sum(dim = 1) / mask.sum(dim = 1)[..., None] # classes class DiscreteVAE(nn.Module): def __init__( self, num_tokens, dim = 512, hidden_dim = 64 ): super().__init__() hdim = hidden_dim self.encoder = nn.Sequential( nn.Conv2d(3, hdim, 4, stride = 2, padding = 1), nn.ReLU(), nn.Conv2d(hdim, hdim, 4, stride = 2, padding = 1), nn.ReLU(), nn.Conv2d(hdim, hdim, 4, stride = 2, padding = 1), nn.ReLU(), nn.Conv2d(hdim, num_tokens, 1) ) self.decoder = nn.Sequential( nn.ConvTranspose2d(dim, hdim, 4, stride = 2, padding = 1), nn.ReLU(), nn.ConvTranspose2d(hdim, hdim, 4, stride = 2, padding = 1), nn.ReLU(), nn.ConvTranspose2d(hdim, hdim, 4, stride = 2, padding = 1), nn.ReLU(), nn.Conv2d(hdim, 3, 1) ) self.num_tokens = num_tokens self.codebook = nn.Embedding(num_tokens, dim) def forward( self, img, return_recon_loss = False, return_logits = False ): logits = self.encoder(img) if return_logits: return logits # return logits for getting hard image indices for DALL-E training soft_one_hot = F.gumbel_softmax(logits, tau = 1.) sampled = einsum('b n h w, n d -> b d h w', soft_one_hot, self.codebook.weight) out = self.decoder(sampled) if not return_recon_loss: return out loss = F.mse_loss(img, out) return loss # main classes class CLIP(nn.Module): def __init__( self, *, dim = 512, num_text_tokens = 10000, num_visual_tokens = 512, text_enc_depth = 6, visual_enc_depth = 6, text_seq_len = 256, visual_seq_len = 1024, text_heads = 8, visual_heads = 8 ): super().__init__() self.scale = dim ** -0.5 self.text_emb = nn.Embedding(num_text_tokens, dim) self.visual_emb = nn.Embedding(num_visual_tokens, dim) self.text_pos_emb = nn.Embedding(text_seq_len, dim) self.visual_pos_emb = nn.Embedding(visual_seq_len, dim) self.text_transformer = Encoder(dim = dim, depth = text_enc_depth, heads = text_heads) self.visual_transformer = Encoder(dim = dim, depth = visual_enc_depth, heads = visual_heads) def forward( self, text, image, text_mask = None, return_loss = False ): b, device = text.shape[0], text.device text_emb = self.text_emb(text) text_emb += self.text_pos_emb(torch.arange(text.shape[1], device = device)) image_emb = self.visual_emb(image) image_emb += self.visual_pos_emb(torch.arange(image.shape[1], device = device)) enc_text = self.text_transformer(text_emb, mask = text_mask) enc_image = self.visual_transformer(image_emb) if exists(text_mask): text_latents = masked_mean(enc_text, text_mask, dim = 1) else: text_latents = enc_text.mean(dim = 1) image_latents = enc_image.mean(dim = 1) sim = einsum('i d, j d -> i j', text_latents, image_latents) * self.scale if not return_loss: return sim labels = torch.arange(b, device = device) loss = F.cross_entropy(sim, labels) return loss class DALLE(nn.Module): def __init__( self, *, dim, num_text_tokens = 10000, num_image_tokens = 512, text_seq_len = 256, image_seq_len = 1024, depth = 6, # should be 64 heads = 8, vae = None ): super().__init__() self.text_emb = nn.Embedding(num_text_tokens, dim) self.image_emb = nn.Embedding(num_image_tokens, dim) self.text_pos_emb = nn.Embedding(text_seq_len, dim) self.image_pos_emb = nn.Embedding(image_seq_len, dim) self.num_text_tokens = num_text_tokens # for offsetting logits index and calculating cross entropy loss self.image_seq_len = image_seq_len self.total_tokens = num_text_tokens + num_image_tokens + 1 # extra for EOS self.vae = vae self.image_emb = vae.codebook self.transformer = Decoder(dim = dim, depth = depth, heads = heads) self.to_logits = nn.Sequential( nn.LayerNorm(dim), nn.Linear(dim, self.total_tokens), ) def forward( self, text, image, mask = None, return_loss = False ): device = text.device is_raw_image = len(image.shape) == 4 text_emb = self.text_emb(text) text_emb += self.text_pos_emb(torch.arange(text.shape[1], device = device)) if is_raw_image: assert exists(self.vae), 'VAE must be passed into constructor if you are to train directly on raw images' image_logits = self.vae(image, return_logits = True) codebook_indices = image_logits.argmax(dim = 1).flatten(1) image = codebook_indices image_emb = self.image_emb(image) image_emb += self.image_pos_emb(torch.arange(image.shape[1], device = device)) tokens = torch.cat((text_emb, image_emb), dim = 1) if exists(mask): mask = F.pad(mask, (0, self.image_seq_len), value = True) out = self.transformer(tokens, mask = mask) out = self.to_logits(out) if not return_loss: return out offsetted_image = image + self.num_text_tokens labels = torch.cat((text, offsetted_image), dim = 1) labels = F.pad(labels, (0, 1), value = (self.total_tokens - 1)) # last token predicts EOS loss = F.cross_entropy(out.transpose(1, 2), labels[:, 1:]) return loss

30.019324

117

0.587383

5,816

0.935951

0

338

0.054393

0c52795432861cbcf4e3ec45d893ec1acc331585

7,668

py

Python

aiida_phonopy/parsers/phonopy.py

giovannipizzi/aiida-phonopy

26e419c34415c68f815fa81ce2ac644aa387ae72

[ "MIT" ]

null

aiida_phonopy/parsers/phonopy.py

giovannipizzi/aiida-phonopy

26e419c34415c68f815fa81ce2ac644aa387ae72

[ "MIT" ]

null

aiida_phonopy/parsers/phonopy.py

giovannipizzi/aiida-phonopy

26e419c34415c68f815fa81ce2ac644aa387ae72

[ "MIT" ]

null

# -*- coding: utf-8 -*- from aiida.orm.data.folder import FolderData from aiida.parsers.parser import Parser from aiida.common.datastructures import calc_states from aiida.parsers.exceptions import OutputParsingError from aiida.common.exceptions import UniquenessError import numpy from aiida.orm.data.array import ArrayData from aiida.orm.data.array.bands import BandsData from aiida.orm.data.array.kpoints import KpointsData from aiida.orm.data.parameter import ParameterData from aiida.orm.data.structure import StructureData import json from aiida_phonopy.calculations.phonopy import PhonopyCalculation __copyright__ = u"Copyright (c), 2014-2015, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, Laboratory of Theory and Simulation of Materials (THEOS). All rights reserved." __license__ = "Non-Commercial, End-User Software License Agreement, see LICENSE.txt file" __version__ = "0.4.1" class PhonopyParser(Parser): """ This class is the implementation of the Parser class for a Phonopy calculator. """ _out_band_name = 'phonon_frequencies' _out_dos_name = 'phonon_dos' _out_thermal_name = 'thermal_properties' def __init__(self,calc): """ Initialize the instance of PhonopyParser """ # check for valid input if not isinstance(calc,PhonopyCalculation): raise OutputParsingError("Input calculation must be a PhonopyCalculation") self._calc = calc def parse_with_retrieved(self, retrieved): """ Parses the datafolder, stores results. This parser for this simple code does simply store in the DB a node representing the file of forces in real space """ from aiida.common.exceptions import InvalidOperation # suppose at the start that the job is successful successful = True # check that calculation is in the right state # state = self._calc.get_state() # if state != calc_states.PARSING: # raise InvalidOperation("Calculation not in {} state" # .format(calc_states.PARSING) ) # select the folder object # Check that the retrieved folder is there try: out_folder = retrieved[self._calc._get_linkname_retrieved()] except KeyError: self.logger.error("No retrieved folder found") return False, () # check what is inside the folder list_of_files = out_folder.get_folder_list() # at least the stdout should exist if not self._calc._OUTPUT_FILE_NAME in list_of_files or not self._calc._RESULT_FILE_NAME: successful = False self.logger.error("Output/results not found",extra=logger_extra) return successful,() # load the results dictionary json_outfile = out_folder.get_abs_path( self._calc._RESULT_FILE_NAME ) with open(json_outfile,'r') as f: json_params = json.load(f) # look at warnings warnings = [] with open(out_folder.get_abs_path( self._calc._SCHED_ERROR_FILE )) as f: errors = f.read() if errors: warnings = [errors] # I implicitly assume that all data inside the json are arrays # it should be very often the case for the phonon properties # ====================== prepare the output nodes ====================== # save the outputs new_nodes_list= [] # save dos try: frequencies_dos = json_params['frequencies_dos'] total_dos = json_params['total_dos'] array_dos = ArrayData() array_dos.set_array('frequency', frequencies_dos) array_dos.set_array('phonon_dos', total_dos) new_nodes_list.append( (self._out_dos_name, array_dos) ) except KeyError: # keys not found in json pass # save thermodynamic quantities try: temperature = json_params['temperature'] free_energy = json_params['free_energy'] entropy = json_params['entropy'] cv = json_params['cv'] array_thermal = ArrayData() array_thermal.set_array('temperature', temperature) array_thermal.set_array('free_energy', free_energy) array_thermal.set_array('entropy', entropy) array_thermal.set_array('specific_heat', cv) # TODO: in which units am I storing stuff??? new_nodes_list.append( (self._out_thermal_name, array_thermal) ) except KeyError: # keys not found in json pass # save frequencies array_freq = BandsData() try: structure = self._calc.inp.structure except AttributeError: structure = self._calc.inp.force_constants.structure inp_kpoints = self._calc.inp.qpoints try: inp_kpoints.get_kpoints() array_freq.set_kpointsdata(inp_kpoints) except AttributeError: # it had a mesh of kpoints in input try: cell = inp_kpoints.cell except AttributeError: cell = structure.cell try: pbc = inp_kpoints.pbc except AttributeError: pbc = structure.pbc try: the_kpoints = json_params['q_points'] except KeyError: the_kpoints = inp_kpoints.get_kpoints() try: the_weights = json_params['weights'] except KeyError: the_weights = None array_freq.cell = cell array_freq.pbc = pbc array_freq.set_kpoints(the_kpoints, weights=the_weights) array_freq.labels = inp_kpoints.labels try: frequencies = json_params['frequencies'] except KeyError: warnings.append('Unable to read phonon frequencies') new_nodes_list.append((self.get_linkname_outparams(), ParameterData(dict={'warnings': warnings}))) return False, new_nodes_list labels = 'frequencies' bands = frequencies try: group_velocities = json_params['group_velocities'] vx = [ _[0] for _ in group_velocities ] vy = [ _[1] for _ in group_velocities ] vz = [ _[2] for _ in group_velocities ] bands = [frequencies] labels = ['frequencies'] bands.append(vx) bands.append(vy) bands.append(vz) labels += ['vx','vy','vz'] except KeyError: pass array_freq.set_bands(frequencies, units='THz', occupations=None, labels='frequencies') #TODO: verify the units try: eigenvectors = json_params['eigenvectors'] array_freq.set_array('eigenvectors', eigenvectors) except KeyError: pass print 'here' new_nodes_list.append( (self._out_band_name, array_freq) ) #except KeyError as e: # keys not found in json # raise e # add the dictionary with warnings new_nodes_list.append( (self.get_linkname_outparams(), ParameterData(dict={'warnings': warnings}))) return successful, new_nodes_list

36.865385

185

0.594027

6,749

0.879807

0

2,151

0.280407

0c52883ec5869dd4ebaf9438c8845a04d78492ff

1,128

py

Python

bagua/bagua_define.py

jphgxq/bagua

3444f79b8fe9c9d2975a8994a1a613ebd14c3d33

[ "MIT" ]

1

2021-07-12T03:33:38.000Z

bagua/bagua_define.py

jphgxq/bagua

3444f79b8fe9c9d2975a8994a1a613ebd14c3d33

[ "MIT" ]

null

bagua/bagua_define.py

jphgxq/bagua

3444f79b8fe9c9d2975a8994a1a613ebd14c3d33

[ "MIT" ]

null

import enum from typing import List import sys if sys.version_info >= (3, 9): from typing import TypedDict # pytype: disable=not-supported-yet else: from typing_extensions import TypedDict # pytype: disable=not-supported-yet from pydantic import BaseModel class TensorDtype(str, enum.Enum): F32 = "f32" F16 = "f16" U8 = "u8" class TensorDeclaration(TypedDict): name: str num_elements: int dtype: TensorDtype def get_tensor_declaration_bytes(td: TensorDeclaration) -> int: dtype_unit_size = { TensorDtype.F32.value: 4, TensorDtype.F16.value: 2, TensorDtype.U8.value: 1, } return td["num_elements"] * dtype_unit_size[td["dtype"]] class BaguaHyperparameter(BaseModel): """ Structured all bagua hyperparameters """ buckets: List[List[TensorDeclaration]] = [] is_hierarchical_reduce: bool = False def update(self, param_dict: dict): tmp = self.dict() tmp.update(param_dict) for key, value in param_dict.items(): if key in tmp: self.__dict__[key] = value return self

22.56

80

0.656915

593

0.525709

0

157

0.139184

0c53db086eb0eb7f6a00e60d3b14eacbfe7ba92e

97

py

Python

instaphotos/apps.py

LekamCharity/insta-IG

0302440df3b2029297af54eb9c56090f82232973

[ "MIT" ]

null

instaphotos/apps.py

LekamCharity/insta-IG

0302440df3b2029297af54eb9c56090f82232973

[ "MIT" ]

null

instaphotos/apps.py

LekamCharity/insta-IG

0302440df3b2029297af54eb9c56090f82232973

[ "MIT" ]

null

from django.apps import AppConfig class InstaphotosConfig(AppConfig): name = 'instaphotos'

16.166667

35

0.773196

60

0.618557

0

13

0.134021

0c5539475c0da1f3dfc53cbf5dc335c43077d9cf

2,835

py

Python

services/backend/expiring_links/tests/test_expiring_link_generator_serializer.py

patpio/drf_images_api

ef689bac10ce8b9d2f03d6b647fa4bbd70b02f1c

[ "Beerware" ]

1

2022-02-27T16:34:46.000Z

services/backend/expiring_links/tests/test_expiring_link_generator_serializer.py

patpio/drf_images_api

ef689bac10ce8b9d2f03d6b647fa4bbd70b02f1c

[ "Beerware" ]

null

services/backend/expiring_links/tests/test_expiring_link_generator_serializer.py

patpio/drf_images_api

ef689bac10ce8b9d2f03d6b647fa4bbd70b02f1c

[ "Beerware" ]

null

import pytest from expiring_links.serializers import ExpiringLinkGeneratorSerializer @pytest.mark.serializers def test_fields(db, create_test_expiring_link_serializer_data): assert list(create_test_expiring_link_serializer_data.keys()) == ['image_id', 'expiration_time'] @pytest.mark.serializers def test_valid_serializer(db, create_test_expiring_link_serializer_data, create_test_image, create_test_user, remove_test_data): serializer = ExpiringLinkGeneratorSerializer(data=create_test_expiring_link_serializer_data, context={'user': create_test_user}) assert serializer.is_valid() @pytest.mark.serializers def test_user_without_permission(db, create_test_expiring_link_serializer_data, create_test_image, create_test_user, remove_test_data): create_test_user.tier.expired_link_flag = False serializer = ExpiringLinkGeneratorSerializer(data=create_test_expiring_link_serializer_data, context={'user': create_test_user}) assert not serializer.is_valid() assert set(serializer.errors) == {'non_field_errors'} @pytest.mark.serializers def test_wrong_image_id(db, create_test_expiring_link_serializer_data, create_test_image, create_test_user, remove_test_data): create_test_expiring_link_serializer_data['image_id'] = create_test_image.pk + 1 serializer = ExpiringLinkGeneratorSerializer(data=create_test_expiring_link_serializer_data, context={'user': create_test_user}) assert not serializer.is_valid() assert set(serializer.errors) == {'image_id'} @pytest.mark.serializers def test_too_short_expiration_time(db, create_test_expiring_link_serializer_data, create_test_image, create_test_user, remove_test_data): create_test_expiring_link_serializer_data['expiration_time'] = 200 serializer = ExpiringLinkGeneratorSerializer(data=create_test_expiring_link_serializer_data, context={'user': create_test_user}) assert not serializer.is_valid() assert set(serializer.errors) == {'expiration_time'} @pytest.mark.serializers def test_too_long_expiration_time(db, create_test_expiring_link_serializer_data, create_test_image, create_test_user, remove_test_data): create_test_expiring_link_serializer_data['expiration_time'] = 40000 serializer = ExpiringLinkGeneratorSerializer(data=create_test_expiring_link_serializer_data, context={'user': create_test_user}) assert not serializer.is_valid() assert set(serializer.errors) == {'expiration_time'}

42.954545

118

0.71358

0

2,731

0.963316

0

163

0.057496

0c553d8f4165e63fa177620f1fa3f79bb1b9cb45

91,609

py

Python

com/vmware/nsx/trust_management_client.py

adammillerio/vsphere-automation-sdk-python

c07e1be98615201139b26c28db3aa584c4254b66

[ "MIT" ]

null

com/vmware/nsx/trust_management_client.py

adammillerio/vsphere-automation-sdk-python

c07e1be98615201139b26c28db3aa584c4254b66

[ "MIT" ]

null

com/vmware/nsx/trust_management_client.py

adammillerio/vsphere-automation-sdk-python

c07e1be98615201139b26c28db3aa584c4254b66

[ "MIT" ]

null

# -*- coding: utf-8 -*- #--------------------------------------------------------------------------- # Copyright 2020 VMware, Inc. All rights reserved. # AUTO GENERATED FILE -- DO NOT MODIFY! # # vAPI stub file for package com.vmware.nsx.trust_management. #--------------------------------------------------------------------------- """ """ __author__ = 'VMware, Inc.' __docformat__ = 'restructuredtext en' import sys from vmware.vapi.bindings import type from vmware.vapi.bindings.converter import TypeConverter from vmware.vapi.bindings.enum import Enum from vmware.vapi.bindings.error import VapiError from vmware.vapi.bindings.struct import VapiStruct from vmware.vapi.bindings.stub import ( ApiInterfaceStub, StubFactoryBase, VapiInterface) from vmware.vapi.bindings.common import raise_core_exception from vmware.vapi.data.validator import (UnionValidator, HasFieldsOfValidator) from vmware.vapi.exception import CoreException from vmware.vapi.lib.constants import TaskType from vmware.vapi.lib.rest import OperationRestMetadata class Certificates(VapiInterface): """ """ LIST_TYPE_CERTIFICATE = "cluster_api_certificate" """ Possible value for ``type`` of method :func:`Certificates.list`. """ _VAPI_SERVICE_ID = 'com.vmware.nsx.trust_management.certificates' """ Identifier of the service in canonical form. """ def __init__(self, config): """ :type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for creating the stub. """ VapiInterface.__init__(self, config, _CertificatesStub) self._VAPI_OPERATION_IDS = {} def delete(self, cert_id, ): """ Removes the specified certificate. The private key associated with the certificate is also deleted. :type cert_id: :class:`str` :param cert_id: ID of certificate to delete (required) :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('delete', { 'cert_id': cert_id, }) def get(self, cert_id, details=None, ): """ Returns information for the specified certificate ID, including the certificate's UUID; resource_type (for example, certificate_self_signed, certificate_ca, or certificate_signed); pem_encoded data; and history of the certificate (who created or modified it and when). For additional information, include the ?details=true modifier at the end of the request URI. :type cert_id: :class:`str` :param cert_id: ID of certificate to read (required) :type details: :class:`bool` or ``None`` :param details: whether to expand the pem data and show all its details (optional, default to false) :rtype: :class:`com.vmware.nsx.model_client.Certificate` :return: com.vmware.nsx.model.Certificate :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('get', { 'cert_id': cert_id, 'details': details, }) def importcertificate(self, trust_object_data, ): """ Adds a new private-public certificate or a chain of certificates (CAs) and, optionally, a private key that can be applied to one of the user-facing components (appliance management or edge). The certificate and the key should be stored in PEM format. If no private key is provided, the certificate is used as a client certificate in the trust store. :type trust_object_data: :class:`com.vmware.nsx.model_client.TrustObjectData` :param trust_object_data: (required) :rtype: :class:`com.vmware.nsx.model_client.CertificateList` :return: com.vmware.nsx.model.CertificateList :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('importcertificate', { 'trust_object_data': trust_object_data, }) def list(self, cursor=None, details=None, included_fields=None, page_size=None, sort_ascending=None, sort_by=None, type=None, ): """ Returns all certificate information viewable by the user, including each certificate's UUID; resource_type (for example, certificate_self_signed, certificate_ca, or certificate_signed); pem_encoded data; and history of the certificate (who created or modified it and when). For additional information, include the ?details=true modifier at the end of the request URI. :type cursor: :class:`str` or ``None`` :param cursor: Opaque cursor to be used for getting next page of records (supplied by current result page) (optional) :type details: :class:`bool` or ``None`` :param details: whether to expand the pem data and show all its details (optional, default to false) :type included_fields: :class:`str` or ``None`` :param included_fields: Comma separated list of fields that should be included in query result (optional) :type page_size: :class:`long` or ``None`` :param page_size: Maximum number of results to return in this page (server may return fewer) (optional, default to 1000) :type sort_ascending: :class:`bool` or ``None`` :param sort_ascending: (optional) :type sort_by: :class:`str` or ``None`` :param sort_by: Field by which records are sorted (optional) :type type: :class:`str` or ``None`` :param type: Type of certificate to return (optional) :rtype: :class:`com.vmware.nsx.model_client.CertificateList` :return: com.vmware.nsx.model.CertificateList :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('list', { 'cursor': cursor, 'details': details, 'included_fields': included_fields, 'page_size': page_size, 'sort_ascending': sort_ascending, 'sort_by': sort_by, 'type': type, }) class CrlDistributionPoints(VapiInterface): """ """ _VAPI_SERVICE_ID = 'com.vmware.nsx.trust_management.crl_distribution_points' """ Identifier of the service in canonical form. """ def __init__(self, config): """ :type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for creating the stub. """ VapiInterface.__init__(self, config, _CrlDistributionPointsStub) self._VAPI_OPERATION_IDS = {} def create(self, crl_distribution_point, ): """ Create an entity that will represent a Crl Distribution Point :type crl_distribution_point: :class:`com.vmware.nsx.model_client.CrlDistributionPoint` :param crl_distribution_point: (required) :rtype: :class:`com.vmware.nsx.model_client.CrlDistributionPoint` :return: com.vmware.nsx.model.CrlDistributionPoint :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('create', { 'crl_distribution_point': crl_distribution_point, }) def delete(self, crl_distribution_point_id, ): """ Delete a CrlDistributionPoint. It does not delete the actual CRL. :type crl_distribution_point_id: :class:`str` :param crl_distribution_point_id: Unique id of the CrlDistributionPoint to delete (required) :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('delete', { 'crl_distribution_point_id': crl_distribution_point_id, }) def get(self, crl_distribution_point_id, ): """ :type crl_distribution_point_id: :class:`str` :param crl_distribution_point_id: (required) :rtype: :class:`com.vmware.nsx.model_client.CrlDistributionPoint` :return: com.vmware.nsx.model.CrlDistributionPoint :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('get', { 'crl_distribution_point_id': crl_distribution_point_id, }) def list(self, cursor=None, included_fields=None, page_size=None, sort_ascending=None, sort_by=None, ): """ Return the list of CrlDistributionPoints :type cursor: :class:`str` or ``None`` :param cursor: Opaque cursor to be used for getting next page of records (supplied by current result page) (optional) :type included_fields: :class:`str` or ``None`` :param included_fields: Comma separated list of fields that should be included in query result (optional) :type page_size: :class:`long` or ``None`` :param page_size: Maximum number of results to return in this page (server may return fewer) (optional, default to 1000) :type sort_ascending: :class:`bool` or ``None`` :param sort_ascending: (optional) :type sort_by: :class:`str` or ``None`` :param sort_by: Field by which records are sorted (optional) :rtype: :class:`com.vmware.nsx.model_client.CrlDistributionPointList` :return: com.vmware.nsx.model.CrlDistributionPointList :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('list', { 'cursor': cursor, 'included_fields': included_fields, 'page_size': page_size, 'sort_ascending': sort_ascending, 'sort_by': sort_by, }) def update(self, crl_distribution_point_id, crl_distribution_point, ): """ :type crl_distribution_point_id: :class:`str` :param crl_distribution_point_id: (required) :type crl_distribution_point: :class:`com.vmware.nsx.model_client.CrlDistributionPoint` :param crl_distribution_point: (required) :rtype: :class:`com.vmware.nsx.model_client.CrlDistributionPoint` :return: com.vmware.nsx.model.CrlDistributionPoint :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('update', { 'crl_distribution_point_id': crl_distribution_point_id, 'crl_distribution_point': crl_distribution_point, }) class Crls(VapiInterface): """ """ LIST_TYPE_CERTIFICATE = "cluster_api_certificate" """ Possible value for ``type`` of method :func:`Crls.list`. """ _VAPI_SERVICE_ID = 'com.vmware.nsx.trust_management.crls' """ Identifier of the service in canonical form. """ def __init__(self, config): """ :type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for creating the stub. """ VapiInterface.__init__(self, config, _CrlsStub) self._VAPI_OPERATION_IDS = {} def delete(self, crl_id, ): """ Deletes an existing CRL. :type crl_id: :class:`str` :param crl_id: ID of CRL to delete (required) :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('delete', { 'crl_id': crl_id, }) def get(self, crl_id, details=None, ): """ Returns information about the specified CRL. For additional information, include the ?details=true modifier at the end of the request URI. :type crl_id: :class:`str` :param crl_id: ID of CRL to read (required) :type details: :class:`bool` or ``None`` :param details: whether to expand the pem data and show all its details (optional, default to false) :rtype: :class:`com.vmware.nsx.model_client.Crl` :return: com.vmware.nsx.model.Crl :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('get', { 'crl_id': crl_id, 'details': details, }) def importcrl(self, crl_object_data, ): """ Adds a new certificate revocation list (CRL). The CRL is used to verify the client certificate status against the revocation lists published by the CA. For this reason, the administrator needs to add the CRL in certificate repository as well. :type crl_object_data: :class:`com.vmware.nsx.model_client.CrlObjectData` :param crl_object_data: (required) :rtype: :class:`com.vmware.nsx.model_client.CrlList` :return: com.vmware.nsx.model.CrlList :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('importcrl', { 'crl_object_data': crl_object_data, }) def list(self, cursor=None, details=None, included_fields=None, page_size=None, sort_ascending=None, sort_by=None, type=None, ): """ Returns information about all CRLs. For additional information, include the ?details=true modifier at the end of the request URI. :type cursor: :class:`str` or ``None`` :param cursor: Opaque cursor to be used for getting next page of records (supplied by current result page) (optional) :type details: :class:`bool` or ``None`` :param details: whether to expand the pem data and show all its details (optional, default to false) :type included_fields: :class:`str` or ``None`` :param included_fields: Comma separated list of fields that should be included in query result (optional) :type page_size: :class:`long` or ``None`` :param page_size: Maximum number of results to return in this page (server may return fewer) (optional, default to 1000) :type sort_ascending: :class:`bool` or ``None`` :param sort_ascending: (optional) :type sort_by: :class:`str` or ``None`` :param sort_by: Field by which records are sorted (optional) :type type: :class:`str` or ``None`` :param type: Type of certificate to return (optional) :rtype: :class:`com.vmware.nsx.model_client.CrlList` :return: com.vmware.nsx.model.CrlList :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('list', { 'cursor': cursor, 'details': details, 'included_fields': included_fields, 'page_size': page_size, 'sort_ascending': sort_ascending, 'sort_by': sort_by, 'type': type, }) def update(self, crl_id, crl, ): """ Updates an existing CRL. :type crl_id: :class:`str` :param crl_id: ID of CRL to update (required) :type crl: :class:`com.vmware.nsx.model_client.Crl` :param crl: (required) :rtype: :class:`com.vmware.nsx.model_client.Crl` :return: com.vmware.nsx.model.Crl :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('update', { 'crl_id': crl_id, 'crl': crl, }) class Csrs(VapiInterface): """ """ _VAPI_SERVICE_ID = 'com.vmware.nsx.trust_management.csrs' """ Identifier of the service in canonical form. """ def __init__(self, config): """ :type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for creating the stub. """ VapiInterface.__init__(self, config, _CsrsStub) self._VAPI_OPERATION_IDS = {} def create(self, csr, ): """ Creates a new certificate signing request (CSR). A CSR is encrypted text that contains information about your organization (organization name, country, and so on) and your Web server's public key, which is a public certificate the is generated on the server that can be used to forward this request to a certificate authority (CA). A private key is also usually created at the same time as the CSR. :type csr: :class:`com.vmware.nsx.model_client.Csr` :param csr: (required) :rtype: :class:`com.vmware.nsx.model_client.Csr` :return: com.vmware.nsx.model.Csr :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('create', { 'csr': csr, }) def delete(self, csr_id, ): """ Removes a specified CSR. If a CSR is not used for verification, you can delete it. Note that the CSR import and upload POST actions automatically delete the associated CSR. :type csr_id: :class:`str` :param csr_id: ID of CSR to delete (required) :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('delete', { 'csr_id': csr_id, }) def get(self, csr_id, ): """ Returns information about the specified CSR. :type csr_id: :class:`str` :param csr_id: ID of CSR to read (required) :rtype: :class:`com.vmware.nsx.model_client.Csr` :return: com.vmware.nsx.model.Csr :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('get', { 'csr_id': csr_id, }) def importcsr(self, csr_id, trust_object_data, ): """ Imports a certificate authority (CA)-signed certificate for a CSR. This action links the certificate to the private key created by the CSR. The pem_encoded string in the request body is the signed certificate provided by your CA in response to the CSR that you provide to them. The import POST action automatically deletes the associated CSR. :type csr_id: :class:`str` :param csr_id: CSR this certificate is associated with (required) :type trust_object_data: :class:`com.vmware.nsx.model_client.TrustObjectData` :param trust_object_data: (required) :rtype: :class:`com.vmware.nsx.model_client.CertificateList` :return: com.vmware.nsx.model.CertificateList :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('importcsr', { 'csr_id': csr_id, 'trust_object_data': trust_object_data, }) def list(self, cursor=None, included_fields=None, page_size=None, sort_ascending=None, sort_by=None, ): """ Returns information about all of the CSRs that have been created. :type cursor: :class:`str` or ``None`` :param cursor: Opaque cursor to be used for getting next page of records (supplied by current result page) (optional) :type included_fields: :class:`str` or ``None`` :param included_fields: Comma separated list of fields that should be included in query result (optional) :type page_size: :class:`long` or ``None`` :param page_size: Maximum number of results to return in this page (server may return fewer) (optional, default to 1000) :type sort_ascending: :class:`bool` or ``None`` :param sort_ascending: (optional) :type sort_by: :class:`str` or ``None`` :param sort_by: Field by which records are sorted (optional) :rtype: :class:`com.vmware.nsx.model_client.CsrList` :return: com.vmware.nsx.model.CsrList :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('list', { 'cursor': cursor, 'included_fields': included_fields, 'page_size': page_size, 'sort_ascending': sort_ascending, 'sort_by': sort_by, }) def selfsign(self, csr_id, days_valid, ): """ Self-signs the previously generated CSR. This action is similar to the import certificate action, but instead of using a public certificate signed by a CA, the self_sign POST action uses a certificate that is signed with NSX's own private key. :type csr_id: :class:`str` :param csr_id: CSR this certificate is associated with (required) :type days_valid: :class:`long` :param days_valid: Number of days the certificate will be valid, default 10 years (required) :rtype: :class:`com.vmware.nsx.model_client.Certificate` :return: com.vmware.nsx.model.Certificate :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('selfsign', { 'csr_id': csr_id, 'days_valid': days_valid, }) class PrincipalIdentities(VapiInterface): """ """ _VAPI_SERVICE_ID = 'com.vmware.nsx.trust_management.principal_identities' """ Identifier of the service in canonical form. """ def __init__(self, config): """ :type config: :class:`vmware.vapi.bindings.stub.StubConfiguration` :param config: Configuration to be used for creating the stub. """ VapiInterface.__init__(self, config, _PrincipalIdentitiesStub) self._VAPI_OPERATION_IDS = {} def create(self, principal_identity, ): """ Associates a principal's name with a certificate that is used to authenticate. Deprecated, use POST /trust-management/principal-identities/with-certificate instead. :type principal_identity: :class:`com.vmware.nsx.model_client.PrincipalIdentity` :param principal_identity: (required) :rtype: :class:`com.vmware.nsx.model_client.PrincipalIdentity` :return: com.vmware.nsx.model.PrincipalIdentity :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('create', { 'principal_identity': principal_identity, }) def delete(self, principal_identity_id, ): """ Delete a principal identity. It does not delete the certificate. :type principal_identity_id: :class:`str` :param principal_identity_id: Unique id of the principal identity to delete (required) :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('delete', { 'principal_identity_id': principal_identity_id, }) def get(self, principal_identity_id, ): """ Get a stored principal identity :type principal_identity_id: :class:`str` :param principal_identity_id: ID of Principal Identity to get (required) :rtype: :class:`com.vmware.nsx.model_client.PrincipalIdentity` :return: com.vmware.nsx.model.PrincipalIdentity :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('get', { 'principal_identity_id': principal_identity_id, }) def list(self): """ Returns the list of principals registered with a certificate. :rtype: :class:`com.vmware.nsx.model_client.PrincipalIdentityList` :return: com.vmware.nsx.model.PrincipalIdentityList :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('list', None) def updatecertificate(self, update_principal_identity_certificate_request, ): """ Update a principal identity's certificate :type update_principal_identity_certificate_request: :class:`com.vmware.nsx.model_client.UpdatePrincipalIdentityCertificateRequest` :param update_principal_identity_certificate_request: (required) :rtype: :class:`com.vmware.nsx.model_client.PrincipalIdentity` :return: com.vmware.nsx.model.PrincipalIdentity :raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable` Service Unavailable :raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest` Bad Request, Precondition Failed :raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError` Internal Server Error :raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized` Forbidden :raise: :class:`com.vmware.vapi.std.errors_client.NotFound` Not Found """ return self._invoke('updatecertificate', { 'update_principal_identity_certificate_request': update_principal_identity_certificate_request, }) class _CertificatesStub(ApiInterfaceStub): def __init__(self, config): # properties for delete operation delete_input_type = type.StructType('operation-input', { 'cert_id': type.StringType(), }) delete_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } delete_input_value_validator_list = [ ] delete_output_validator_list = [ ] delete_rest_metadata = OperationRestMetadata( http_method='DELETE', url_template='/api/v1/trust-management/certificates/{cert-id}', path_variables={ 'cert_id': 'cert-id', }, query_parameters={ }, content_type='application/json' ) # properties for get operation get_input_type = type.StructType('operation-input', { 'cert_id': type.StringType(), 'details': type.OptionalType(type.BooleanType()), }) get_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } get_input_value_validator_list = [ ] get_output_validator_list = [ ] get_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/certificates/{cert-id}', path_variables={ 'cert_id': 'cert-id', }, query_parameters={ 'details': 'details', }, content_type='application/json' ) # properties for importcertificate operation importcertificate_input_type = type.StructType('operation-input', { 'trust_object_data': type.ReferenceType('com.vmware.nsx.model_client', 'TrustObjectData'), }) importcertificate_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } importcertificate_input_value_validator_list = [ ] importcertificate_output_validator_list = [ ] importcertificate_rest_metadata = OperationRestMetadata( http_method='POST', url_template='/api/v1/trust-management/certificates?action=import', request_body_parameter='trust_object_data', path_variables={ }, query_parameters={ }, content_type='application/json' ) # properties for list operation list_input_type = type.StructType('operation-input', { 'cursor': type.OptionalType(type.StringType()), 'details': type.OptionalType(type.BooleanType()), 'included_fields': type.OptionalType(type.StringType()), 'page_size': type.OptionalType(type.IntegerType()), 'sort_ascending': type.OptionalType(type.BooleanType()), 'sort_by': type.OptionalType(type.StringType()), 'type': type.OptionalType(type.StringType()), }) list_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } list_input_value_validator_list = [ ] list_output_validator_list = [ ] list_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/certificates', path_variables={ }, query_parameters={ 'cursor': 'cursor', 'details': 'details', 'included_fields': 'included_fields', 'page_size': 'page_size', 'sort_ascending': 'sort_ascending', 'sort_by': 'sort_by', 'type': 'type', }, content_type='application/json' ) operations = { 'delete': { 'input_type': delete_input_type, 'output_type': type.VoidType(), 'errors': delete_error_dict, 'input_value_validator_list': delete_input_value_validator_list, 'output_validator_list': delete_output_validator_list, 'task_type': TaskType.NONE, }, 'get': { 'input_type': get_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'Certificate'), 'errors': get_error_dict, 'input_value_validator_list': get_input_value_validator_list, 'output_validator_list': get_output_validator_list, 'task_type': TaskType.NONE, }, 'importcertificate': { 'input_type': importcertificate_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CertificateList'), 'errors': importcertificate_error_dict, 'input_value_validator_list': importcertificate_input_value_validator_list, 'output_validator_list': importcertificate_output_validator_list, 'task_type': TaskType.NONE, }, 'list': { 'input_type': list_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CertificateList'), 'errors': list_error_dict, 'input_value_validator_list': list_input_value_validator_list, 'output_validator_list': list_output_validator_list, 'task_type': TaskType.NONE, }, } rest_metadata = { 'delete': delete_rest_metadata, 'get': get_rest_metadata, 'importcertificate': importcertificate_rest_metadata, 'list': list_rest_metadata, } ApiInterfaceStub.__init__( self, iface_name='com.vmware.nsx.trust_management.certificates', config=config, operations=operations, rest_metadata=rest_metadata, is_vapi_rest=False) class _CrlDistributionPointsStub(ApiInterfaceStub): def __init__(self, config): # properties for create operation create_input_type = type.StructType('operation-input', { 'crl_distribution_point': type.ReferenceType('com.vmware.nsx.model_client', 'CrlDistributionPoint'), }) create_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } create_input_value_validator_list = [ ] create_output_validator_list = [ ] create_rest_metadata = OperationRestMetadata( http_method='POST', url_template='/api/v1/trust-management/crl-distribution-points', request_body_parameter='crl_distribution_point', path_variables={ }, query_parameters={ }, content_type='application/json' ) # properties for delete operation delete_input_type = type.StructType('operation-input', { 'crl_distribution_point_id': type.StringType(), }) delete_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } delete_input_value_validator_list = [ ] delete_output_validator_list = [ ] delete_rest_metadata = OperationRestMetadata( http_method='DELETE', url_template='/api/v1/trust-management/crl-distribution-points/{crl-distribution-point-id}', path_variables={ 'crl_distribution_point_id': 'crl-distribution-point-id', }, query_parameters={ }, content_type='application/json' ) # properties for get operation get_input_type = type.StructType('operation-input', { 'crl_distribution_point_id': type.StringType(), }) get_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } get_input_value_validator_list = [ ] get_output_validator_list = [ ] get_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/crl-distribution-points/{crl-distribution-point-id}', path_variables={ 'crl_distribution_point_id': 'crl-distribution-point-id', }, query_parameters={ }, content_type='application/json' ) # properties for list operation list_input_type = type.StructType('operation-input', { 'cursor': type.OptionalType(type.StringType()), 'included_fields': type.OptionalType(type.StringType()), 'page_size': type.OptionalType(type.IntegerType()), 'sort_ascending': type.OptionalType(type.BooleanType()), 'sort_by': type.OptionalType(type.StringType()), }) list_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } list_input_value_validator_list = [ ] list_output_validator_list = [ ] list_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/crl-distribution-points', path_variables={ }, query_parameters={ 'cursor': 'cursor', 'included_fields': 'included_fields', 'page_size': 'page_size', 'sort_ascending': 'sort_ascending', 'sort_by': 'sort_by', }, content_type='application/json' ) # properties for update operation update_input_type = type.StructType('operation-input', { 'crl_distribution_point_id': type.StringType(), 'crl_distribution_point': type.ReferenceType('com.vmware.nsx.model_client', 'CrlDistributionPoint'), }) update_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } update_input_value_validator_list = [ ] update_output_validator_list = [ ] update_rest_metadata = OperationRestMetadata( http_method='PUT', url_template='/api/v1/trust-management/crl-distribution-points/{crl-distribution-point-id}', request_body_parameter='crl_distribution_point', path_variables={ 'crl_distribution_point_id': 'crl-distribution-point-id', }, query_parameters={ }, content_type='application/json' ) operations = { 'create': { 'input_type': create_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CrlDistributionPoint'), 'errors': create_error_dict, 'input_value_validator_list': create_input_value_validator_list, 'output_validator_list': create_output_validator_list, 'task_type': TaskType.NONE, }, 'delete': { 'input_type': delete_input_type, 'output_type': type.VoidType(), 'errors': delete_error_dict, 'input_value_validator_list': delete_input_value_validator_list, 'output_validator_list': delete_output_validator_list, 'task_type': TaskType.NONE, }, 'get': { 'input_type': get_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CrlDistributionPoint'), 'errors': get_error_dict, 'input_value_validator_list': get_input_value_validator_list, 'output_validator_list': get_output_validator_list, 'task_type': TaskType.NONE, }, 'list': { 'input_type': list_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CrlDistributionPointList'), 'errors': list_error_dict, 'input_value_validator_list': list_input_value_validator_list, 'output_validator_list': list_output_validator_list, 'task_type': TaskType.NONE, }, 'update': { 'input_type': update_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CrlDistributionPoint'), 'errors': update_error_dict, 'input_value_validator_list': update_input_value_validator_list, 'output_validator_list': update_output_validator_list, 'task_type': TaskType.NONE, }, } rest_metadata = { 'create': create_rest_metadata, 'delete': delete_rest_metadata, 'get': get_rest_metadata, 'list': list_rest_metadata, 'update': update_rest_metadata, } ApiInterfaceStub.__init__( self, iface_name='com.vmware.nsx.trust_management.crl_distribution_points', config=config, operations=operations, rest_metadata=rest_metadata, is_vapi_rest=False) class _CrlsStub(ApiInterfaceStub): def __init__(self, config): # properties for delete operation delete_input_type = type.StructType('operation-input', { 'crl_id': type.StringType(), }) delete_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } delete_input_value_validator_list = [ ] delete_output_validator_list = [ ] delete_rest_metadata = OperationRestMetadata( http_method='DELETE', url_template='/api/v1/trust-management/crls/{crl-id}', path_variables={ 'crl_id': 'crl-id', }, query_parameters={ }, content_type='application/json' ) # properties for get operation get_input_type = type.StructType('operation-input', { 'crl_id': type.StringType(), 'details': type.OptionalType(type.BooleanType()), }) get_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } get_input_value_validator_list = [ ] get_output_validator_list = [ ] get_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/crls/{crl-id}', path_variables={ 'crl_id': 'crl-id', }, query_parameters={ 'details': 'details', }, content_type='application/json' ) # properties for importcrl operation importcrl_input_type = type.StructType('operation-input', { 'crl_object_data': type.ReferenceType('com.vmware.nsx.model_client', 'CrlObjectData'), }) importcrl_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } importcrl_input_value_validator_list = [ ] importcrl_output_validator_list = [ ] importcrl_rest_metadata = OperationRestMetadata( http_method='POST', url_template='/api/v1/trust-management/crls?action=import', request_body_parameter='crl_object_data', path_variables={ }, query_parameters={ }, content_type='application/json' ) # properties for list operation list_input_type = type.StructType('operation-input', { 'cursor': type.OptionalType(type.StringType()), 'details': type.OptionalType(type.BooleanType()), 'included_fields': type.OptionalType(type.StringType()), 'page_size': type.OptionalType(type.IntegerType()), 'sort_ascending': type.OptionalType(type.BooleanType()), 'sort_by': type.OptionalType(type.StringType()), 'type': type.OptionalType(type.StringType()), }) list_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } list_input_value_validator_list = [ ] list_output_validator_list = [ ] list_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/crls', path_variables={ }, query_parameters={ 'cursor': 'cursor', 'details': 'details', 'included_fields': 'included_fields', 'page_size': 'page_size', 'sort_ascending': 'sort_ascending', 'sort_by': 'sort_by', 'type': 'type', }, content_type='application/json' ) # properties for update operation update_input_type = type.StructType('operation-input', { 'crl_id': type.StringType(), 'crl': type.ReferenceType('com.vmware.nsx.model_client', 'Crl'), }) update_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } update_input_value_validator_list = [ ] update_output_validator_list = [ ] update_rest_metadata = OperationRestMetadata( http_method='PUT', url_template='/api/v1/trust-management/crls/{crl-id}', request_body_parameter='crl', path_variables={ 'crl_id': 'crl-id', }, query_parameters={ }, content_type='application/json' ) operations = { 'delete': { 'input_type': delete_input_type, 'output_type': type.VoidType(), 'errors': delete_error_dict, 'input_value_validator_list': delete_input_value_validator_list, 'output_validator_list': delete_output_validator_list, 'task_type': TaskType.NONE, }, 'get': { 'input_type': get_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'Crl'), 'errors': get_error_dict, 'input_value_validator_list': get_input_value_validator_list, 'output_validator_list': get_output_validator_list, 'task_type': TaskType.NONE, }, 'importcrl': { 'input_type': importcrl_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CrlList'), 'errors': importcrl_error_dict, 'input_value_validator_list': importcrl_input_value_validator_list, 'output_validator_list': importcrl_output_validator_list, 'task_type': TaskType.NONE, }, 'list': { 'input_type': list_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CrlList'), 'errors': list_error_dict, 'input_value_validator_list': list_input_value_validator_list, 'output_validator_list': list_output_validator_list, 'task_type': TaskType.NONE, }, 'update': { 'input_type': update_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'Crl'), 'errors': update_error_dict, 'input_value_validator_list': update_input_value_validator_list, 'output_validator_list': update_output_validator_list, 'task_type': TaskType.NONE, }, } rest_metadata = { 'delete': delete_rest_metadata, 'get': get_rest_metadata, 'importcrl': importcrl_rest_metadata, 'list': list_rest_metadata, 'update': update_rest_metadata, } ApiInterfaceStub.__init__( self, iface_name='com.vmware.nsx.trust_management.crls', config=config, operations=operations, rest_metadata=rest_metadata, is_vapi_rest=False) class _CsrsStub(ApiInterfaceStub): def __init__(self, config): # properties for create operation create_input_type = type.StructType('operation-input', { 'csr': type.ReferenceType('com.vmware.nsx.model_client', 'Csr'), }) create_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } create_input_value_validator_list = [ ] create_output_validator_list = [ ] create_rest_metadata = OperationRestMetadata( http_method='POST', url_template='/api/v1/trust-management/csrs', request_body_parameter='csr', path_variables={ }, query_parameters={ }, content_type='application/json' ) # properties for delete operation delete_input_type = type.StructType('operation-input', { 'csr_id': type.StringType(), }) delete_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } delete_input_value_validator_list = [ ] delete_output_validator_list = [ ] delete_rest_metadata = OperationRestMetadata( http_method='DELETE', url_template='/api/v1/trust-management/csrs/{csr-id}', path_variables={ 'csr_id': 'csr-id', }, query_parameters={ }, content_type='application/json' ) # properties for get operation get_input_type = type.StructType('operation-input', { 'csr_id': type.StringType(), }) get_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } get_input_value_validator_list = [ ] get_output_validator_list = [ ] get_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/csrs/{csr-id}', path_variables={ 'csr_id': 'csr-id', }, query_parameters={ }, content_type='application/json' ) # properties for importcsr operation importcsr_input_type = type.StructType('operation-input', { 'csr_id': type.StringType(), 'trust_object_data': type.ReferenceType('com.vmware.nsx.model_client', 'TrustObjectData'), }) importcsr_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } importcsr_input_value_validator_list = [ ] importcsr_output_validator_list = [ ] importcsr_rest_metadata = OperationRestMetadata( http_method='POST', url_template='/api/v1/trust-management/csrs/{csr-id}?action=import', request_body_parameter='trust_object_data', path_variables={ 'csr_id': 'csr-id', }, query_parameters={ }, content_type='application/json' ) # properties for list operation list_input_type = type.StructType('operation-input', { 'cursor': type.OptionalType(type.StringType()), 'included_fields': type.OptionalType(type.StringType()), 'page_size': type.OptionalType(type.IntegerType()), 'sort_ascending': type.OptionalType(type.BooleanType()), 'sort_by': type.OptionalType(type.StringType()), }) list_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } list_input_value_validator_list = [ ] list_output_validator_list = [ ] list_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/csrs', path_variables={ }, query_parameters={ 'cursor': 'cursor', 'included_fields': 'included_fields', 'page_size': 'page_size', 'sort_ascending': 'sort_ascending', 'sort_by': 'sort_by', }, content_type='application/json' ) # properties for selfsign operation selfsign_input_type = type.StructType('operation-input', { 'csr_id': type.StringType(), 'days_valid': type.IntegerType(), }) selfsign_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } selfsign_input_value_validator_list = [ ] selfsign_output_validator_list = [ ] selfsign_rest_metadata = OperationRestMetadata( http_method='POST', url_template='/api/v1/trust-management/csrs/{csr-id}?action=self_sign', path_variables={ 'csr_id': 'csr-id', }, query_parameters={ 'days_valid': 'days_valid', }, content_type='application/json' ) operations = { 'create': { 'input_type': create_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'Csr'), 'errors': create_error_dict, 'input_value_validator_list': create_input_value_validator_list, 'output_validator_list': create_output_validator_list, 'task_type': TaskType.NONE, }, 'delete': { 'input_type': delete_input_type, 'output_type': type.VoidType(), 'errors': delete_error_dict, 'input_value_validator_list': delete_input_value_validator_list, 'output_validator_list': delete_output_validator_list, 'task_type': TaskType.NONE, }, 'get': { 'input_type': get_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'Csr'), 'errors': get_error_dict, 'input_value_validator_list': get_input_value_validator_list, 'output_validator_list': get_output_validator_list, 'task_type': TaskType.NONE, }, 'importcsr': { 'input_type': importcsr_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CertificateList'), 'errors': importcsr_error_dict, 'input_value_validator_list': importcsr_input_value_validator_list, 'output_validator_list': importcsr_output_validator_list, 'task_type': TaskType.NONE, }, 'list': { 'input_type': list_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'CsrList'), 'errors': list_error_dict, 'input_value_validator_list': list_input_value_validator_list, 'output_validator_list': list_output_validator_list, 'task_type': TaskType.NONE, }, 'selfsign': { 'input_type': selfsign_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'Certificate'), 'errors': selfsign_error_dict, 'input_value_validator_list': selfsign_input_value_validator_list, 'output_validator_list': selfsign_output_validator_list, 'task_type': TaskType.NONE, }, } rest_metadata = { 'create': create_rest_metadata, 'delete': delete_rest_metadata, 'get': get_rest_metadata, 'importcsr': importcsr_rest_metadata, 'list': list_rest_metadata, 'selfsign': selfsign_rest_metadata, } ApiInterfaceStub.__init__( self, iface_name='com.vmware.nsx.trust_management.csrs', config=config, operations=operations, rest_metadata=rest_metadata, is_vapi_rest=False) class _PrincipalIdentitiesStub(ApiInterfaceStub): def __init__(self, config): # properties for create operation create_input_type = type.StructType('operation-input', { 'principal_identity': type.ReferenceType('com.vmware.nsx.model_client', 'PrincipalIdentity'), }) create_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } create_input_value_validator_list = [ ] create_output_validator_list = [ ] create_rest_metadata = OperationRestMetadata( http_method='POST', url_template='/api/v1/trust-management/principal-identities', request_body_parameter='principal_identity', path_variables={ }, query_parameters={ }, content_type='application/json' ) # properties for delete operation delete_input_type = type.StructType('operation-input', { 'principal_identity_id': type.StringType(), }) delete_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } delete_input_value_validator_list = [ ] delete_output_validator_list = [ ] delete_rest_metadata = OperationRestMetadata( http_method='DELETE', url_template='/api/v1/trust-management/principal-identities/{principal-identity-id}', path_variables={ 'principal_identity_id': 'principal-identity-id', }, query_parameters={ }, content_type='application/json' ) # properties for get operation get_input_type = type.StructType('operation-input', { 'principal_identity_id': type.StringType(), }) get_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } get_input_value_validator_list = [ ] get_output_validator_list = [ ] get_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/principal-identities/{principal-identity-id}', path_variables={ 'principal_identity_id': 'principal-identity-id', }, query_parameters={ }, content_type='application/json' ) # properties for list operation list_input_type = type.StructType('operation-input', {}) list_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } list_input_value_validator_list = [ ] list_output_validator_list = [ ] list_rest_metadata = OperationRestMetadata( http_method='GET', url_template='/api/v1/trust-management/principal-identities', path_variables={ }, query_parameters={ }, content_type='application/json' ) # properties for updatecertificate operation updatecertificate_input_type = type.StructType('operation-input', { 'update_principal_identity_certificate_request': type.ReferenceType('com.vmware.nsx.model_client', 'UpdatePrincipalIdentityCertificateRequest'), }) updatecertificate_error_dict = { 'com.vmware.vapi.std.errors.service_unavailable': type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'), 'com.vmware.vapi.std.errors.invalid_request': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'), 'com.vmware.vapi.std.errors.internal_server_error': type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'), 'com.vmware.vapi.std.errors.unauthorized': type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'), 'com.vmware.vapi.std.errors.not_found': type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'), } updatecertificate_input_value_validator_list = [ ] updatecertificate_output_validator_list = [ ] updatecertificate_rest_metadata = OperationRestMetadata( http_method='POST', url_template='/api/v1/trust-management/principal-identities?action=update_certificate', request_body_parameter='update_principal_identity_certificate_request', path_variables={ }, query_parameters={ }, content_type='application/json' ) operations = { 'create': { 'input_type': create_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'PrincipalIdentity'), 'errors': create_error_dict, 'input_value_validator_list': create_input_value_validator_list, 'output_validator_list': create_output_validator_list, 'task_type': TaskType.NONE, }, 'delete': { 'input_type': delete_input_type, 'output_type': type.VoidType(), 'errors': delete_error_dict, 'input_value_validator_list': delete_input_value_validator_list, 'output_validator_list': delete_output_validator_list, 'task_type': TaskType.NONE, }, 'get': { 'input_type': get_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'PrincipalIdentity'), 'errors': get_error_dict, 'input_value_validator_list': get_input_value_validator_list, 'output_validator_list': get_output_validator_list, 'task_type': TaskType.NONE, }, 'list': { 'input_type': list_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'PrincipalIdentityList'), 'errors': list_error_dict, 'input_value_validator_list': list_input_value_validator_list, 'output_validator_list': list_output_validator_list, 'task_type': TaskType.NONE, }, 'updatecertificate': { 'input_type': updatecertificate_input_type, 'output_type': type.ReferenceType('com.vmware.nsx.model_client', 'PrincipalIdentity'), 'errors': updatecertificate_error_dict, 'input_value_validator_list': updatecertificate_input_value_validator_list, 'output_validator_list': updatecertificate_output_validator_list, 'task_type': TaskType.NONE, }, } rest_metadata = { 'create': create_rest_metadata, 'delete': delete_rest_metadata, 'get': get_rest_metadata, 'list': list_rest_metadata, 'updatecertificate': updatecertificate_rest_metadata, } ApiInterfaceStub.__init__( self, iface_name='com.vmware.nsx.trust_management.principal_identities', config=config, operations=operations, rest_metadata=rest_metadata, is_vapi_rest=False) class StubFactory(StubFactoryBase): _attrs = { 'Certificates': Certificates, 'CrlDistributionPoints': CrlDistributionPoints, 'Crls': Crls, 'Csrs': Csrs, 'PrincipalIdentities': PrincipalIdentities, 'crl_distribution_points': 'com.vmware.nsx.trust_management.crl_distribution_points_client.StubFactory', 'principal_identities': 'com.vmware.nsx.trust_management.principal_identities_client.StubFactory', }

44.40572

156

0.596492

90,542

0.988353

0

51,538

0.562587

0c5549700625606ae1bd959bf730c22c941eb303

4,255

py

Python

bottleneck/tests/list_input_test.py

stroxler/bottleneck

6e91bcb8a21170588ee9a3f2c425a4e307ae05de

[ "BSD-2-Clause" ]

2

2015-05-26T09:06:32.000Z

2015-05-26T09:06:46.000Z

bottleneck/tests/list_input_test.py

stroxler/bottleneck

6e91bcb8a21170588ee9a3f2c425a4e307ae05de

[ "BSD-2-Clause" ]

null

bottleneck/tests/list_input_test.py

stroxler/bottleneck

6e91bcb8a21170588ee9a3f2c425a4e307ae05de

[ "BSD-2-Clause" ]

null

"Test list input." # For support of python 2.5 from __future__ import with_statement import numpy as np from numpy.testing import assert_equal, assert_array_almost_equal import bottleneck as bn # --------------------------------------------------------------------------- # Check that functions can handle list input def lists(): "Iterator that yields lists to use for unit testing." ss = {} ss[1] = {'size': 4, 'shapes': [(4,)]} ss[2] = {'size': 6, 'shapes': [(1, 6), (2, 3)]} ss[3] = {'size': 6, 'shapes': [(1, 2, 3)]} ss[4] = {'size': 24, 'shapes': [(1, 2, 3, 4)]} # Unaccelerated for ndim in ss: size = ss[ndim]['size'] shapes = ss[ndim]['shapes'] a = np.arange(size) for shape in shapes: a = a.reshape(shape) yield a.tolist() def unit_maker(func, func0, args=tuple()): "Test that bn.xxx gives the same output as bn.slow.xxx for list input." msg = '\nfunc %s | input %s | shape %s\n' msg += '\nInput array:\n%s\n' for i, arr in enumerate(lists()): argsi = tuple([list(arr)] + list(args)) actual = func(*argsi) desired = func0(*argsi) tup = (func.__name__, 'a'+str(i), str(np.array(arr).shape), arr) err_msg = msg % tup assert_array_almost_equal(actual, desired, err_msg=err_msg) def test_nansum(): "Test nansum." yield unit_maker, bn.nansum, bn.slow.nansum def test_nanmax(): "Test nanmax." yield unit_maker, bn.nanmax, bn.slow.nanmax def test_nanargmin(): "Test nanargmin." yield unit_maker, bn.nanargmin, bn.slow.nanargmin def test_nanargmax(): "Test nanargmax." yield unit_maker, bn.nanargmax, bn.slow.nanargmax def test_nanmin(): "Test nanmin." yield unit_maker, bn.nanmin, bn.slow.nanmin def test_nanmean(): "Test nanmean." yield unit_maker, bn.nanmean, bn.slow.nanmean def test_nanstd(): "Test nanstd." yield unit_maker, bn.nanstd, bn.slow.nanstd def test_nanvar(): "Test nanvar." yield unit_maker, bn.nanvar, bn.slow.nanvar def test_median(): "Test median." yield unit_maker, bn.median, bn.slow.median def test_nanmedian(): "Test nanmedian." yield unit_maker, bn.nanmedian, bn.slow.nanmedian def test_rankdata(): "Test rankdata." yield unit_maker, bn.rankdata, bn.slow.rankdata def test_nanrankdata(): "Test nanrankdata." yield unit_maker, bn.nanrankdata, bn.slow.nanrankdata def test_partsort(): "Test partsort." yield unit_maker, bn.partsort, bn.slow.partsort, (2,) def test_argpartsort(): "Test argpartsort." yield unit_maker, bn.argpartsort, bn.slow.argpartsort, (2,) def test_ss(): "Test ss." yield unit_maker, bn.ss, bn.slow.ss def test_nn(): "Test nn." a = [[1, 2], [3, 4]] a0 = [1, 2] assert_equal(bn.nn(a, a0), bn.slow.nn(a, a0)) def test_anynan(): "Test anynan." yield unit_maker, bn.anynan, bn.slow.anynan def test_allnan(): "Test allnan." yield unit_maker, bn.allnan, bn.slow.allnan def test_move_sum(): "Test move_sum." yield unit_maker, bn.move_sum, bn.slow.move_sum, (2,) def test_move_nansum(): "Test move_nansum." yield unit_maker, bn.move_nansum, bn.slow.move_nansum, (2,) def test_move_mean(): "Test move_mean." yield unit_maker, bn.move_mean, bn.slow.move_mean, (2,) def test_move_median(): "Test move_median." yield unit_maker, bn.move_median, bn.slow.move_median, (2,) def test_move_nanmean(): "Test move_nanmean." yield unit_maker, bn.move_nanmean, bn.slow.move_nanmean, (2,) def test_move_std(): "Test move_std." yield unit_maker, bn.move_std, bn.slow.move_std, (2,) def test_move_nanstd(): "Test move_nanstd." yield unit_maker, bn.move_nanstd, bn.slow.move_nanstd, (2,) def test_move_min(): "Test move_min." yield unit_maker, bn.move_min, bn.slow.move_min, (2,) def test_move_max(): "Test move_max." yield unit_maker, bn.move_max, bn.slow.move_max, (2,) def test_move_nanmin(): "Test move_nanmin." yield unit_maker, bn.move_nanmin, bn.slow.move_nanmin, (2,) def test_move_nanmax(): "Test move_nanmax." yield unit_maker, bn.move_nanmax, bn.slow.move_nanmax, (2,)

22.632979

77

0.636193

0

3,207

0.753702

0

899

0.211281

0c587de94c3ee270415110f012b7d77cb256c5a4

1,475

py

Python

hanzo/warcindex.py

ukwa/warctools

f74061382d6bc37b6eec889a3aec26c5748d90d3

[ "MIT" ]

1

2020-09-03T00:51:50.000Z

hanzo/warcindex.py

martinsbalodis/warc-tools

d9d5e708e00bd0f6d9d0c2d95cbc9332f51b05e4

[ "MIT" ]

null

hanzo/warcindex.py

martinsbalodis/warc-tools

d9d5e708e00bd0f6d9d0c2d95cbc9332f51b05e4

[ "MIT" ]

1

2021-04-12T01:45:14.000Z

#!/usr/bin/env python """warcindex - dump warc index""" import os import sys import sys import os.path from optparse import OptionParser from .warctools import WarcRecord, expand_files parser = OptionParser(usage="%prog [options] warc warc warc") parser.add_option("-l", "--limit", dest="limit") parser.add_option("-O", "--output-format", dest="output_format", help="output format (ignored)") parser.add_option("-o", "--output", dest="output_format", help="output file (ignored)") parser.add_option("-L", "--log-level", dest="log_level") parser.set_defaults(output=None, limit=None, log_level="info") def main(argv): (options, input_files) = parser.parse_args(args=argv[1:]) out = sys.stdout if len(input_files) < 1: parser.error("no imput warc file(s)") print '#WARC filename offset warc-type warc-subject-uri warc-record-id content-type content-length' for name in expand_files(input_files): fh = WarcRecord.open_archive(name, gzip="auto") for (offset, record, errors) in fh.read_records(limit=None): if record: print name, offset, record.type, record.url, record.id, record.content_type, record.content_length elif errors: pass # ignore else: pass # no errors at tail fh.close() return 0 def run(): sys.exit(main(sys.argv)) if __name__ == '__main__': run()

23.412698

114

0.633898

0

412

0.279322

0c599f149ff2c8a006a46a9e33e3ef181a3cc037

1,469

py

Python

tsdata/migrations/0001_initial.py

OpenDataPolicingNC/Traffic-Stops

74e0d16ad2ac32addca6f04d34c2ddf36d023990

[ "MIT" ]

25

2015-09-12T23:10:52.000Z

2021-03-24T08:39:46.000Z

tsdata/migrations/0001_initial.py

OpenDataPolicingNC/Traffic-Stops

74e0d16ad2ac32addca6f04d34c2ddf36d023990

[ "MIT" ]

159

2015-07-01T03:57:23.000Z

2021-04-17T21:09:19.000Z

tsdata/migrations/0001_initial.py

copelco/NC-Traffic-Stops

74e0d16ad2ac32addca6f04d34c2ddf36d023990

[ "MIT" ]

8

2015-10-02T16:56:40.000Z

2020-10-18T01:16:29.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='Dataset', fields=[ ('id', models.AutoField(primary_key=True, auto_created=True, verbose_name='ID', serialize=False)), ('state', models.CharField(choices=[('nc', 'North Carolina'), ('md', 'Maryland')], max_length=2)), ('name', models.CharField(unique=True, max_length=255)), ('date_added', models.DateTimeField(auto_now_add=True)), ('date_received', models.DateField()), ('url', models.URLField(unique=True, verbose_name='URL')), ('destination', models.CharField(blank=True, max_length=1024, help_text='Absolute path to destination directory (helpful for testing)')), ], ), migrations.CreateModel( name='Import', fields=[ ('id', models.AutoField(primary_key=True, auto_created=True, verbose_name='ID', serialize=False)), ('date_started', models.DateTimeField(auto_now_add=True)), ('date_finished', models.DateTimeField(null=True)), ('successful', models.BooleanField(default=False)), ('dataset', models.ForeignKey(to='tsdata.Dataset')), ], ), ]

40.805556

153

0.582709

1,360

0.9258

0

281

0.191287

0c5b11a856de6baa5333d1f6f60e74187acb3fcd

1,836

py

Python

api/tests/opentrons/protocol_engine/execution/test_run_control_handler.py

mrod0101/opentrons

6450edb0421f1c2484c292f8583602d8f6fd13b8

[ "Apache-2.0" ]

235

2017-10-27T20:37:27.000Z

2022-03-30T14:09:49.000Z

api/tests/opentrons/protocol_engine/execution/test_run_control_handler.py

koji/opentrons

0f339f45de238183b2c433e67f839363d5177582

[ "Apache-2.0" ]

8,425

2017-10-26T15:25:43.000Z

2022-03-31T23:54:26.000Z

api/tests/opentrons/protocol_engine/execution/test_run_control_handler.py

mrod0101/opentrons

6450edb0421f1c2484c292f8583602d8f6fd13b8

[ "Apache-2.0" ]

130

2017-11-09T21:02:37.000Z

2022-03-15T18:01:24.000Z

"""Run control side-effect handler.""" import pytest from decoy import Decoy from opentrons.protocol_engine.state import StateStore from opentrons.protocol_engine.actions import ActionDispatcher, PauseAction from opentrons.protocol_engine.execution.run_control import RunControlHandler from opentrons.protocol_engine.state import EngineConfigs @pytest.fixture def state_store(decoy: Decoy) -> StateStore: """Get a mocked out StateStore.""" return decoy.mock(cls=StateStore) @pytest.fixture def action_dispatcher(decoy: Decoy) -> ActionDispatcher: """Get a mocked out ActionDispatcher.""" return decoy.mock(cls=ActionDispatcher) @pytest.fixture def subject( state_store: StateStore, action_dispatcher: ActionDispatcher, ) -> RunControlHandler: """Create a RunControlHandler with its dependencies mocked out.""" return RunControlHandler( state_store=state_store, action_dispatcher=action_dispatcher, ) async def test_pause( decoy: Decoy, state_store: StateStore, action_dispatcher: ActionDispatcher, subject: RunControlHandler, ) -> None: """It should be able to execute a pause.""" decoy.when(state_store.get_configs()).then_return(EngineConfigs(ignore_pause=False)) await subject.pause() decoy.verify( action_dispatcher.dispatch(PauseAction()), await state_store.wait_for(condition=state_store.commands.get_is_running), ) async def test_pause_analysis( decoy: Decoy, state_store: StateStore, action_dispatcher: ActionDispatcher, subject: RunControlHandler, ) -> None: """It should no op during a protocol analysis.""" decoy.when(state_store.get_configs()).then_return(EngineConfigs(ignore_pause=True)) await subject.pause() decoy.verify(action_dispatcher.dispatch(PauseAction()), times=0)

30.6

88

0.751634

0

605

0.329521

871

0.474401

270

0.147059

0c5b7ae73a2b618a79092df65cc9600f76dbf5e0

510

py

Python

Datasets/Generator/Healthcare/mergedrug.py

undraaa/m2bench

b661b61ca04470ed1c9c50531ce760a2cd5000d9

[ "RSA-MD" ]

null

Datasets/Generator/Healthcare/mergedrug.py

undraaa/m2bench

b661b61ca04470ed1c9c50531ce760a2cd5000d9

[ "RSA-MD" ]

null

Datasets/Generator/Healthcare/mergedrug.py

undraaa/m2bench

b661b61ca04470ed1c9c50531ce760a2cd5000d9

[ "RSA-MD" ]

1

2021-11-29T10:31:36.000Z

import json import glob def merge_drug(drug_dirpath): #start_time = time.time() print("\n----- MERGING json data into merged_drug.json -----") result = [] for f in glob.glob(drug_dirpath+'/*.json'): with open(f,"rb") as infile: result.append(json.load(infile)) with open("merged_drug.json","w") as outfile: json.dump(result,outfile) print("----- merged_drug.json DONE -----") #print("----- MERGE DRUG %s seconds -----" % (time.time() - start_time))

26.842105

76

0.592157

0

221

0.433333

0c5c225bea97b848df7068538bc1df5271634638

10,326

py

Python

tests/test_rundramatiq_command.py

BradleyKirton/django_dramatiq

93a4a9ae39aee643cc4a987b18030ad8d1fc8480

[ "Apache-2.0" ]

null

tests/test_rundramatiq_command.py

BradleyKirton/django_dramatiq

93a4a9ae39aee643cc4a987b18030ad8d1fc8480

[ "Apache-2.0" ]

null

tests/test_rundramatiq_command.py

BradleyKirton/django_dramatiq

93a4a9ae39aee643cc4a987b18030ad8d1fc8480

[ "Apache-2.0" ]

null

import os import sys from io import StringIO from unittest.mock import patch from django.core.management import call_command from django_dramatiq.management.commands import rundramatiq def test_rundramatiq_command_autodiscovers_modules(): assert rundramatiq.Command().discover_tasks_modules() == [ "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", ] @patch("os.execvp") def test_rundramatiq_can_run_dramatiq(execvp_mock): # Given an output buffer buff = StringIO() # When I call the rundramatiq command call_command("rundramatiq", stdout=buff) # Then stdout should contain a message about discovered task modules assert "Discovered tasks module: 'tests.testapp1.tasks'" in buff.getvalue() assert "Discovered tasks module: 'tests.testapp2.tasks'" in buff.getvalue() assert "Discovered tasks module: 'tests.testapp3.tasks.tasks'" in buff.getvalue() assert "Discovered tasks module: 'tests.testapp3.tasks.other_tasks'" in buff.getvalue() # And execvp should be called with the appropriate arguments cores = str(rundramatiq.CPU_COUNT) expected_exec_name = "dramatiq" expected_exec_path = os.path.join( os.path.dirname(sys.executable), expected_exec_name, ) execvp_mock.assert_called_once_with(expected_exec_path, [ expected_exec_name, "--path", ".", "--processes", cores, "--threads", cores, "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", ]) @patch("os.execvp") def test_rundramatiq_can_run_dramatiq_reload(execvp_mock): # Given an output buffer buff = StringIO() # When I call the rundramatiq command with --reload-use-polling call_command("rundramatiq", "--reload", stdout=buff) # Then execvp should be called with the appropriate arguments cores = str(rundramatiq.CPU_COUNT) expected_exec_name = "dramatiq" expected_exec_path = os.path.join( os.path.dirname(sys.executable), expected_exec_name, ) execvp_mock.assert_called_once_with(expected_exec_path, [ expected_exec_name, "--path", ".", "--processes", cores, "--threads", cores, "--watch", ".", "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", ]) @patch("os.execvp") def test_rundramatiq_can_run_dramatiq_with_polling(execvp_mock): # Given an output buffer buff = StringIO() # When I call the rundramatiq command with --reload-use-polling call_command("rundramatiq", "--reload", "--reload-use-polling", stdout=buff) # Then execvp should be called with the appropriate arguments cores = str(rundramatiq.CPU_COUNT) expected_exec_name = "dramatiq" expected_exec_path = os.path.join( os.path.dirname(sys.executable), expected_exec_name, ) execvp_mock.assert_called_once_with(expected_exec_path, [ expected_exec_name, "--path", ".", "--processes", cores, "--threads", cores, "--watch", ".", "--watch-use-polling", "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", ]) @patch("os.execvp") def test_rundramatiq_can_run_dramatiq_with_only_some_queues(execvp_mock): # Given an output buffer buff = StringIO() # When I call the rundramatiq command with --queues call_command("rundramatiq", "--queues", "A B C", stdout=buff) # Then execvp should be called with the appropriate arguments cores = str(rundramatiq.CPU_COUNT) expected_exec_name = "dramatiq" expected_exec_path = os.path.join( os.path.dirname(sys.executable), expected_exec_name, ) execvp_mock.assert_called_once_with(expected_exec_path, [ expected_exec_name, "--path", ".", "--processes", cores, "--threads", cores, "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", "--queues", "A B C" ]) @patch("os.execvp") def test_rundramatiq_can_run_dramatiq_with_specified_pid_file(execvp_mock): # Given an output buffer buff = StringIO() # When I call the rundramatiq command with --pid-file call_command("rundramatiq", "--pid-file", "drama.pid", stdout=buff) # Then execvp should be called with the appropriate arguments cores = str(rundramatiq.CPU_COUNT) expected_exec_name = "dramatiq" expected_exec_path = os.path.join( os.path.dirname(sys.executable), expected_exec_name, ) execvp_mock.assert_called_once_with(expected_exec_path, [ expected_exec_name, "--path", ".", "--processes", cores, "--threads", cores, "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", "--pid-file", "drama.pid" ]) @patch("os.execvp") def test_rundramatiq_can_run_dramatiq_with_specified_log_file(execvp_mock): # Given an output buffer buff = StringIO() # When I call the rundramatiq command with --log-file call_command("rundramatiq", "--log-file", "drama.log", stdout=buff) # Then execvp should be called with the appropriate arguments cores = str(rundramatiq.CPU_COUNT) expected_exec_name = "dramatiq" expected_exec_path = os.path.join( os.path.dirname(sys.executable), expected_exec_name, ) execvp_mock.assert_called_once_with(expected_exec_path, [ expected_exec_name, "--path", ".", "--processes", cores, "--threads", cores, "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", "--log-file", "drama.log" ]) @patch("os.execvp") def test_rundramatiq_can_ignore_modules(execvp_mock, settings): # Given an output buffer buff = StringIO() # And 'tests.testapp2.tasks' in DRAMATIQ_IGNORED_MODULES # And 'tests.testapp3.tasks.other_tasks' in DRAMATIQ_IGNORED_MODULES settings.DRAMATIQ_IGNORED_MODULES = ( "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.utils.*", ) # When I call the rundramatiq command call_command("rundramatiq", stdout=buff) # Then stdout should contain a message about ignored task modules assert "Discovered tasks module: 'tests.testapp1.tasks'" in buff.getvalue() assert "Discovered tasks module: 'tests.testapp3.tasks.tasks'" in buff.getvalue() assert "Discovered tasks module: 'tests.testapp3.tasks.utils'" in buff.getvalue() assert "Ignored tasks module: 'tests.testapp2.tasks'" in buff.getvalue() assert "Ignored tasks module: 'tests.testapp3.tasks.other_tasks'" in buff.getvalue() assert "Ignored tasks module: 'tests.testapp3.tasks.utils.not_a_task'" in buff.getvalue() # And execvp should be called with the appropriate arguments cores = str(rundramatiq.CPU_COUNT) expected_exec_name = "dramatiq" expected_exec_path = os.path.join( os.path.dirname(sys.executable), expected_exec_name, ) execvp_mock.assert_called_once_with(expected_exec_path, [ expected_exec_name, "--path", ".", "--processes", cores, "--threads", cores, "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", ]) @patch("os.execvp") def test_rundramatiq_can_fork(execvp_mock, settings): # Given an output buffer buff = StringIO() # When I call the rundramatiq command with --log-file call_command("rundramatiq", "--fork-function", "a", "--fork-function", "b", stdout=buff) # Then execvp should be called with the appropriate arguments cores = str(rundramatiq.CPU_COUNT) expected_exec_name = "dramatiq" expected_exec_path = os.path.join( os.path.dirname(sys.executable), expected_exec_name, ) execvp_mock.assert_called_once_with(expected_exec_path, [ expected_exec_name, "--path", ".", "--processes", cores, "--threads", cores, "--fork-function", "a", "--fork-function", "b", "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", ]) def test_rundramatiq_command_autodiscovers_additional_modules(settings): settings.DRAMATIQ_AUTODISCOVER_MODULES = ("services", ) assert rundramatiq.Command().discover_tasks_modules() == [ "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", "tests.testapp4.services", ]

35.122449

93

0.673833

0

9,155

0.886597

0

4,946

0.478985

0c5c924b0477b69417c6a0474627207f48573e2f

3,620

py

Python

WordRPG/data/states/new_game.py

ChristopherLBruce/WordRPG

e545cf313afc430e8191a7c813db9ee9759a6fd4

[ "Apache-2.0" ]

2

2018-12-15T15:06:35.000Z

2022-02-09T00:19:28.000Z

WordRPG/data/states/new_game.py

ChristopherLBruce/WordRPG

e545cf313afc430e8191a7c813db9ee9759a6fd4

[ "Apache-2.0" ]

null

WordRPG/data/states/new_game.py

ChristopherLBruce/WordRPG

e545cf313afc430e8191a7c813db9ee9759a6fd4

[ "Apache-2.0" ]

null

""" 'new_game' state. Includes character creation. """ from ...engine.gui.screen import const, Screen from ...engine.state_machine import State class New_Game(State): def __init__(self): """ Initiailize class and super class """ super(New_Game, self).__init__() self.screen = self._init_screen() def _init_screen(self): """ Create the main game screen """ screen = Screen() # creates standard double line frame around whole screen screen.add_frame(size=(80, 30), offset=(0, 0), fgcolor='BLUE', bgcolor='BLACK') # add menu menu = { 'text_format' : {'fgcolor':'CYAN','bgcolor':'BLACK','style':'NORMAL'}, 'hotkey_format' : {'fgcolor':'YELLOW','bgcolor':'BLACK','style':'BRIGHT'}, 'encap' : '()', 'sep' : ' - ', 'options' : [ ('BOB THE WARRIOR', '1'), ('TIM THE MAGE', '2'), # ('race', 'r'), ('SUE THE ARCHER', '3'), ('PEG THE ASSASSIN', '4'), ('start', 's'), ] } screen.add_menu(menu, offset=('center',12)) screen.add_footer() return screen def update_screen(self): """ Draws the screen """ self.screen.draw() def on_event(self, event, prev_state): """ Handles events that are delegated to this State. """ self.update_screen() while True: key = self.get_key_press() # if key == 'n': # return 'character_name' # if key == 'c': # return 'character_class' # if key == 'g': # return 'character_gender' if key == 's': return 'game' if key == 'esc': return 'main_menu' return self class Character_Name(State): def __init__(self): """ Initiailize class and super class """ super(Character_Name, self).__init__() def update_screen(self): """ Draws the screen """ gui.main.clear() print('PLACEHOLDER SCREEN FOR ENTERING CHARACTER NAME') print('PRESS ANY KEY TO RETURN TO NEW CHARACTER SCREEN.') def on_event(self, event, prev_state): """ Handles events that are delegated to this State. """ self.update_screen() self.wait_for_keypress() return 'new_game' class Character_Gender(State): def __init__(self): """ Initiailize class and super class """ super(Character_Gender, self).__init__() def update_screen(self): """ Draws the screen """ gui.main.clear() print('PLACEHOLDER SCREEN FOR SELECTING CHARACTER GENDER') print('PRESS ANY KEY TO RETURN TO NEW CHARACTER SCREEN.') def on_event(self, event, prev_state): """ Handles events that are delegated to this State. """ self.update_screen() self.wait_for_keypress() return 'new_game' class Character_Class(State): def __init__(self): """ Initiailize class and super class """ super(Character_Class, self).__init__() def update_screen(self): """ Draws the screen """ gui.main.clear() print('PLACEHOLDER SCREEN FOR SELECTING CHARACTER CLASS') print('PRESS ANY KEY TO RETURN TO NEW CHARACTER SCREEN.') def on_event(self, event, prev_state): """ Handles events that are delegated to this State. """ self.update_screen() self.wait_for_keypress() return 'new_game'

27.424242

86

0.551381

3,458

0.955249

0

1,408

0.38895

0c5da302d0cfb597c70f8c34fe51028d86ae2e18

2,106

py

Python

guestbook.py

Tycx2ry/FKRTimeline

11e784f4a3800336abf19c42c15a06c86af970bd

[ "Apache-2.0" ]

null

guestbook.py

Tycx2ry/FKRTimeline

11e784f4a3800336abf19c42c15a06c86af970bd

[ "Apache-2.0" ]

null

guestbook.py

Tycx2ry/FKRTimeline

11e784f4a3800336abf19c42c15a06c86af970bd

[ "Apache-2.0" ]

null

#!/usr /bin/env python # -*- coding: utf-8 -*- __author__ = 'jiangge' from flask_sqlalchemy import SQLAlchemy from datetime import datetime from flask import Flask, request, render_template, redirect application = Flask(__name__) application.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///guestbook.db' application.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False db = SQLAlchemy(application) class posts(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(50)) comment = db.Column(db.Text) url = db.Column(db.Text) create_at = db.Column(db.DateTime) def __init__(self, name, comment, url, create_at): self.name = name self.comment = comment self.url = url self.create_at = create_at def save_data(name, comment, url, create_at): """ save data from form submitted """ db.session.add(posts(name, comment, url, create_at)) db.session.commit() def load_data(page): """ load saved data """ record_list = posts.query.paginate(page, per_page=5, error_out=True) return record_list @application.route('/', methods=['GET', 'POST']) @application.route('/index/ ', methods=['GET', 'POST']) @application.route('/index/<int:page>', methods=['GET', 'POST']) def index(page = 1): """Top page Use template to show the page """ record_list = load_data(page) return render_template('index.html', record_list=record_list.items) @application.route('/post', methods=['POST']) def post(): """Comment's target url """ name = request.form.get('name') comment = request.form.get('comment') url = request.form.get('url') create_at = datetime.now() save_data(name, comment, url, create_at) return redirect('/') if __name__ == '__main__': if True: db.drop_all() db.create_all() db.session.add(posts(text=application.config["FIRST_MESSAGE"])) db.session.commit() application.run('0.0.0.0', port=80, debug=True)

27.350649

73

0.632479

393

0.18661

0

681

0.323362

0

451

0.21415

0c5db28673060acc0246927ee800263dd3a7f124

707

py

Python

dash_test_runner/testapp/migrations/0001_initial.py

Ilhasoft/dash

d9b900cc08d9238304a226d837a4c90dec6b46fc

[ "BSD-3-Clause" ]

null

dash_test_runner/testapp/migrations/0001_initial.py

Ilhasoft/dash

d9b900cc08d9238304a226d837a4c90dec6b46fc

[ "BSD-3-Clause" ]

null

dash_test_runner/testapp/migrations/0001_initial.py

Ilhasoft/dash

d9b900cc08d9238304a226d837a4c90dec6b46fc

[ "BSD-3-Clause" ]

1

2018-04-12T20:18:34.000Z

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('orgs', '0015_auto_20160209_0926'), ] operations = [ migrations.CreateModel( name='Contact', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('uuid', models.CharField(unique=True, max_length=36)), ('name', models.CharField(max_length=128, verbose_name='Name')), ('is_active', models.BooleanField(default=True)), ('org', models.ForeignKey(to='orgs.Org', on_delete=models.PROTECT)), ], ), ]

32.136364

114

0.575672

663

0.937765

0

92

0.130127

0c5e4893a61a507b2525a971a14202b85e75581a

6,596

py

Python

tests/test_integration.py

Radico/business-rules

7dd0551e8b33234fcea0abaf04f9982eb6f3426f

[ "MIT" ]

null

tests/test_integration.py

Radico/business-rules

7dd0551e8b33234fcea0abaf04f9982eb6f3426f

[ "MIT" ]

null

tests/test_integration.py

Radico/business-rules

7dd0551e8b33234fcea0abaf04f9982eb6f3426f

[ "MIT" ]

null

from __future__ import absolute_import from business_rules.actions import rule_action, BaseActions from business_rules.engine import check_condition, run_all from business_rules.fields import FIELD_TEXT, FIELD_NUMERIC, FIELD_SELECT from business_rules.variables import BaseVariables, string_rule_variable, numeric_rule_variable, boolean_rule_variable from . import TestCase class SomeVariables(BaseVariables): @string_rule_variable() def foo(self): return "foo" @numeric_rule_variable(label="Diez") def ten(self): return 10 @boolean_rule_variable() def true_bool(self): return True @numeric_rule_variable(params=[{'field_type': FIELD_NUMERIC, 'name': 'x', 'label': 'X'}]) def x_plus_one(self, x): return x + 1 @boolean_rule_variable() def rule_received(self, **kwargs): rule = kwargs.get('rule') assert rule is not None return rule is not None @string_rule_variable(label="StringLabel", options=['one', 'two', 'three']) def string_variable_with_options(self): return "foo" @string_rule_variable(public=False) def private_string_variable(self): return 'foo' class SomeActions(BaseActions): @rule_action(params={"foo": FIELD_NUMERIC}) def some_action(self, foo): pass @rule_action(label="woohoo", params={"bar": FIELD_TEXT}) def some_other_action(self, bar): pass @rule_action(params=[ { 'field_type': FIELD_SELECT, 'name': 'baz', 'label': 'Baz', 'options': [ {'label': 'Chose Me', 'name': 'chose_me'}, {'label': 'Or Me', 'name': 'or_me'} ] }]) def some_select_action(self, baz): pass @rule_action() def action_with_no_params(self): pass class IntegrationTests(TestCase): """ Integration test, using the library like a user would. """ def test_true_boolean_variable(self): condition = { 'name': 'true_bool', 'operator': 'is_true', 'value': '' } rule = { 'conditions': condition } condition_result = check_condition(condition, SomeVariables(), rule) self.assertTrue(condition_result.result) def test_false_boolean_variable(self): condition = { 'name': 'true_bool', 'operator': 'is_false', 'value': '' } rule = { 'conditions': condition } condition_result = check_condition(condition, SomeVariables(), rule) self.assertFalse(condition_result.result) def test_check_true_condition_happy_path(self): condition = {'name': 'foo', 'operator': 'contains', 'value': 'o'} rule = { 'conditions': condition } condition_result = check_condition(condition, SomeVariables(), rule) self.assertTrue(condition_result.result) def test_check_false_condition_happy_path(self): condition = {'name': 'foo', 'operator': 'contains', 'value': 'm'} rule = { 'conditions': condition } condition_result = check_condition(condition, SomeVariables(), rule) self.assertFalse(condition_result.result) def test_numeric_variable_with_params(self): condition = { 'name': 'x_plus_one', 'operator': 'equal_to', 'value': 10, 'params': {'x': 9} } rule = { 'conditions': condition } condition_result = check_condition(condition, SomeVariables(), rule) self.assertTrue(condition_result.result) def test_check_incorrect_method_name(self): condition = { 'name': 'food', 'operator': 'equal_to', 'value': 'm' } rule = { 'conditions': condition } err_string = 'Variable food is not defined in class SomeVariables' with self.assertRaisesRegexp(AssertionError, err_string): check_condition(condition, SomeVariables(), rule) def test_check_incorrect_operator_name(self): condition = { 'name': 'foo', 'operator': 'equal_tooooze', 'value': 'foo' } rule = { 'conditions': condition } with self.assertRaises(AssertionError): check_condition(condition, SomeVariables(), rule) def test_check_missing_params(self): condition = { 'name': 'x_plus_one', 'operator': 'equal_to', 'value': 10, 'params': {} } rule = { 'conditions': condition } err_string = 'Missing parameters x for variable x_plus_one' with self.assertRaisesRegexp(AssertionError, err_string): check_condition(condition, SomeVariables(), rule) def test_check_invalid_params(self): condition = { 'name': 'x_plus_one', 'operator': 'equal_to', 'value': 10, 'params': {'x': 9, 'y': 9} } rule = { 'conditions': condition } err_string = 'Invalid parameters y for variable x_plus_one' with self.assertRaisesRegexp(AssertionError, err_string): check_condition(condition, SomeVariables(), rule) def test_variable_received_rules(self): condition = { 'name': 'rule_received', 'operator': 'is_true', 'value': 'true', } rule = { 'conditions': condition } condition_result = check_condition(condition, SomeVariables(), rule) self.assertTrue(condition_result) def test_string_variable_with_options_with_wrong_value(self): condition = { 'name': 'string_variable_with_options', 'operator': 'equal_to', 'value': 'foo', } rule = { 'conditions': condition } condition_result = check_condition(condition, SomeVariables(), rule) self.assertTrue(condition_result) def test_run_with_no_conditions(self): actions = [ { 'name': 'action_with_no_params' } ] rule = { 'actions': actions } result = run_all(rule_list=[rule], defined_variables=SomeVariables(), defined_actions=SomeActions()) self.assertTrue(result)

27.256198

118

0.575045

6,213

0.941935

0

1,307

0.19815

0

1,143

0.173287

0c5f5d9ac8242efc8ccf5bafaa6e567b8ee2cc86

5,808

py

Python

cog/cli/user_argparser.py

Demonware/cog

b206066ebfd5faae000b1a1708988db8ca592b94

[ "BSD-3-Clause" ]

2

2016-06-02T02:15:56.000Z

2016-08-16T08:37:27.000Z

cog/cli/user_argparser.py

Demonware/cog

b206066ebfd5faae000b1a1708988db8ca592b94

[ "BSD-3-Clause" ]

null

cog/cli/user_argparser.py

Demonware/cog

b206066ebfd5faae000b1a1708988db8ca592b94

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import sys import argparse arg_no = len(sys.argv) tool_parser = argparse.ArgumentParser(add_help=False) tool_subparsers = tool_parser.add_subparsers(help='commands', dest='command') # The rename command. rename_parser = tool_subparsers.add_parser('rename', help='rename an existing user account.') rename_parser.add_argument( 'name', action='store', metavar='<name>', help='account name' ) rename_parser.add_argument( '--new-name', '-n', action='store', dest='newName', metavar='<new account name>' ) # The add command. add_parser = tool_subparsers.add_parser('add', help='add new user account to the directory.') add_parser.add_argument( '--type', '-t', action='store', default='generic', dest='account_type', metavar='<type of account>' ) add_parser.add_argument( 'name', action='store', help='account name', metavar='<name>' ) group1_parser = add_parser.add_argument_group('account specific') group1_parser.add_argument( '--password', '-P', action='store', dest='userPassword', metavar='<account\'s owner password>' ) group1_parser.add_argument( '--home', action='store', dest='homeDirectory', metavar='<path to the home directory>' ) group1_parser.add_argument( '--shell', action='store', dest='loginShell', metavar='<path to the shell interpreter>' ) group1_parser = add_parser.add_argument_group('personal information') group1_parser.add_argument( '--phone-no', action='append', dest='telephoneNumber', metavar='<phone number>' ) group1_parser.add_argument( '--last-name', action='store', dest='sn', metavar='<account owner\'s last name>' ) group1_parser.add_argument( '--first-name', action='store', dest='givenName', metavar='<account owner\'s first name>' ) group1_parser.add_argument( '--organization', '-o', action='store', dest='o', metavar='<organization>' ) group1_parser.add_argument( '--email', action='append', dest='mail', metavar='<email>' ) group1_parser.add_argument( '--full-name', action='store', dest='cn', metavar='<account owner\'s full name>' ) group1_parser = add_parser.add_argument_group('uid and group management') group1_parser.add_argument( '--uid', action='store', dest='uid', metavar='<user\'s uid>' ) group1_parser.add_argument( '--add-group', action='append', dest='group', metavar='<secondary group>' ) group1_parser.add_argument( '--uid-number', action='store', dest='uidNumber', metavar='<user id number>' ) group1_parser.add_argument( '--gid', action='store', dest='gidNumber', metavar='<primary group id>' ) # The show command. show_parser = tool_subparsers.add_parser('show', help='show account data') show_parser.add_argument( 'name', action='append', nargs='*', help='account name' ) show_parser.add_argument( '--verbose', '-v', action='store_true', dest='verbose', help='be verbose about it' ) # The edit command. edit_parser = tool_subparsers.add_parser('edit', help='edit existing user data in the directory') edit_parser.add_argument( '--type', '-t', action='store', dest='account_type', metavar='<change account type>' ) edit_parser.add_argument( 'name', action='store', help='account name' ) group1_parser = edit_parser.add_argument_group('account specific') group1_parser.add_argument( '--reset-password', '-r', dest='resetPassword', action='store_true', help='<reset user\'s password>' ) group1_parser.add_argument( '--home', action='store', dest='homeDirectory', metavar='<new home directory path>' ) group1_parser.add_argument( '--shell', action='store', dest='loginShell', metavar='<new shell interpreter path>' ) group1_parser = edit_parser.add_argument_group('personal information') group1_parser.add_argument( '--first-name', action='store', dest='givenName', metavar='<new first name>' ) group1_parser.add_argument( '--del-email', action='append', dest='delMail', metavar='<remove email address>' ) group1_parser.add_argument( '--last-name', action='store', dest='sn', metavar='<new last name>' ) group1_parser.add_argument( '--add-email', action='append', dest='addMail', metavar='<add new email address>' ) group1_parser.add_argument( '--del-phone-no', action='append', dest='delTelephoneNumber', metavar='<phone number to remove>' ) group1_parser.add_argument( '--organization', '-o', action='store', dest='o', metavar='<organization>' ) group1_parser.add_argument( '--add-phone-no', action='append', dest='addTelephoneNumber', metavar='<phone number to add>' ) group1_parser.add_argument( '--full-name', action='store', dest='cn', metavar='<new full name>' ) group1_parser = edit_parser.add_argument_group('uid and group management') group1_parser.add_argument( '--del-group', action='append', dest='delgroup', metavar='<remove user from the group>' ) group1_parser.add_argument( '--group-id', action='store', dest='gidNumber', metavar='<change primary group ID>' ) group1_parser.add_argument( '--add-group', action='append', dest='addgroup', metavar='<add user to the group>' ) group1_parser.add_argument( '--uid-number', action='store', dest='uidNumber', metavar='<change user ID number>' ) group1_parser.add_argument( '--uid', action='store', dest='uid', metavar='<user\'s uid>' ) # The retire command. retire_parser = tool_subparsers.add_parser('retire', help='retire an existing account and remove all its privileges.') retire_parser.add_argument( 'name', action='store', metavar='<name>', help='account name' ) # The type command. type_parser = tool_subparsers.add_parser('type', help='manage user types') type_parser.add_argument( '--list', '-l', action='store_true', dest='list_types', help='list user types' ) # The remove command. remove_parser = tool_subparsers.add_parser('remove', help='remove an existing account.') remove_parser.add_argument( 'name', action='store', metavar='<name>', help='account name' )

35.2

118

0.722968

0

2,622

0.451446

0c605a349671fad2588ca9a0e3c2afed9c2453f5

6,235

py

Python

custom_components/wisersmart/climate.py

tomtomfx/wiserSmartForHA

9878840b073250302e583bd2f6040a825de97803

[ "MIT" ]

1

2020-10-06T19:49:59.000Z

custom_components/wisersmart/climate.py

tomtomfx/wiserSmartForHA

9878840b073250302e583bd2f6040a825de97803

[ "MIT" ]

1

2020-10-06T20:18:32.000Z

2020-10-24T19:50:53.000Z

custom_components/wisersmart/climate.py

tomtomfx/wiserSmartForHA

9878840b073250302e583bd2f6040a825de97803

[ "MIT" ]

1

2021-04-12T16:37:40.000Z

""" Climate Platform Device for Wiser Smart https://github.com/tomtomfx/wiserSmartForHA thomas.fayoux@gmail.com """ import asyncio import logging import voluptuous as vol from functools import partial from ruamel.yaml import YAML as yaml from homeassistant.components.climate import ClimateEntity from homeassistant.core import callback from homeassistant.components.climate.const import ( SUPPORT_TARGET_TEMPERATURE, ATTR_CURRENT_TEMPERATURE, HVAC_MODE_HEAT, HVAC_MODE_OFF, ) from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, ) import homeassistant.helpers.config_validation as cv from homeassistant.helpers.dispatcher import async_dispatcher_connect from homeassistant.helpers.entity import Entity from .const import ( _LOGGER, DOMAIN, MANUFACTURER, ROOM, WISER_SMART_SERVICES, ) SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE async def async_setup_entry(hass, config_entry, async_add_entities): """Set up Wiser climate device""" data = hass.data[DOMAIN] wiser_rooms = [ WiserSmartRoom(hass, data, room) for room in data.wiserSmart.getWiserRoomsThermostat() ] async_add_entities(wiser_rooms, True) """ Definition of WiserSmartRoom """ class WiserSmartRoom(ClimateEntity): def __init__(self, hass, data, room_id): """Initialize the sensor.""" self.data = data self.hass = hass self.current_temp = None self.target_temp = None self.room_id = room_id self._force_update = False self._hvac_modes_list = [HVAC_MODE_HEAT, HVAC_MODE_OFF] _LOGGER.info( "WiserSmart Room: Initialisation for {}".format(self.room_id) ) async def async_update(self): _LOGGER.debug("WiserSmartRoom: Update requested for {}".format(self.name)) if self._force_update: await self.data.async_update(no_throttle=True) self._force_update = False room = self.data.wiserSmart.getWiserRoomInfo(self.room_id) self.current_temp = room.get("currentValue") self.target_temp = room.get("targetValue") if self.target_temp is None: self.target_temp = -1 @property def supported_features(self): """Return the list of supported features.""" return SUPPORT_FLAGS @property def should_poll(self): return False @property def state(self): room = self.data.wiserSmart.getWiserRoomInfo(self.room_id) self.current_temp = room.get("currentValue") self.target_temp = room.get("targetValue") if self.target_temp is None: self.target_temp = -1 if self.current_temp < self.target_temp: state = HVAC_MODE_HEAT else: state = HVAC_MODE_OFF return state @property def name(self): return "WiserSmart - Thermostat - " + self.room_id @property def temperature_unit(self): return TEMP_CELSIUS @property def min_temp(self): return self.data.minimum_temp @property def max_temp(self): return self.data.maximum_temp @property def current_temperature(self): temp = self.data.wiserSmart.getWiserRoomInfo(self.room_id).get("currentValue") return temp @property def icon(self): # Change icon to show if radiator is heating, not heating or set to off. room = self.data.wiserSmart.getWiserRoomInfo(self.room_id) self.current_temp = room.get("currentValue") self.target_temp = room.get("targetValue") if self.target_temp is None: self.target_temp = -1 if self.current_temp < self.target_temp: return "mdi:radiator" else: return "mdi:radiator-off" @property def unique_id(self): return "WiserSmartRoom - {}".format(self.room_id) @property def device_info(self): """Return device specific attributes.""" return { "name": self.name, "identifiers": {(DOMAIN, self.unique_id)}, "manufacturer": MANUFACTURER, "model": "Wiser Smart Room", } @property def hvac_mode(self): state = self.state() return state @property def hvac_modes(self): """Return the list of available operation modes.""" return self._hvac_modes_list @property def target_temperature(self): return self.data.wiserSmart.getWiserRoomInfo(self.room_id).get("targetValue") @property def state_attributes(self): """Return state attributes.""" # Generic attributes attrs = super().state_attributes # If VACT return valves infos i = 1 valves = self.data.wiserSmart.getWiserRoomInfo(self.room_id).get("valve") if (valves == None): return attrs for valve in valves: attrs["valvePosition_" + str(i)] = valve.get("valvePosition") attrs["calibrationStatus_" + str(i)] = valve.get("calibrationStatus") attrs["internalTemp_" + str(i)] = valve.get("internalTemp") i = i + 1 return attrs async def async_set_temperature(self, **kwargs): """Set new target temperatures.""" target_temperature = kwargs.get(ATTR_TEMPERATURE) if target_temperature is None: _LOGGER.debug( "No target temperature set for {}".format(self.name) ) return False _LOGGER.debug( "Setting temperature for {} to {}".format(self.name, target_temperature) ) await self.hass.async_add_executor_job( partial(self.data.wiserSmart.setWiserRoomTemp, self.room_id, target_temperature) ) self._force_update = True await self.async_update_ha_state(True) return True async def async_added_to_hass(self): """Subscribe for update from the Controller""" async def async_update_state(): """Update sensor state.""" await self.async_update_ha_state(True) async_dispatcher_connect(self.hass, "WiserSmartUpdateMessage", async_update_state)

28.865741

94

0.645549

5,001

0.802085

0

2,907

0.466239

1,800

0.288693

1,133

0.181716

0c60917a4d7a8f1d00442aa352ab85caf9e37f11

4,765

py

Python

src/dataset/utils/process_df.py

Fkaneko/kaggle_g2net_gravitational_wave_detection-

8bb32cc675e6b56171da8a3754fffeda41e934bb

[ "Apache-2.0" ]

null

src/dataset/utils/process_df.py

Fkaneko/kaggle_g2net_gravitational_wave_detection-

8bb32cc675e6b56171da8a3754fffeda41e934bb

[ "Apache-2.0" ]

null

src/dataset/utils/process_df.py

Fkaneko/kaggle_g2net_gravitational_wave_detection-

8bb32cc675e6b56171da8a3754fffeda41e934bb

[ "Apache-2.0" ]

null

import os from functools import partial from multiprocessing import Pool from typing import Any, Callable, Dict, List, Optional import numpy as np import pandas as pd from tqdm import tqdm from src.dataset.utils.waveform_preprocessings import preprocess_strain def id_2_path( image_id: str, is_train: bool = True, data_dir: str = "../input/g2net-gravitational-wave-detection", ) -> str: """ modify from https://www.kaggle.com/ihelon/g2net-eda-and-modeling """ folder = "train" if is_train else "test" return "{}/{}/{}/{}/{}/{}.npy".format( data_dir, folder, image_id[0], image_id[1], image_id[2], image_id ) def path_2_id(path: str) -> str: return os.path.basename(path).replace(".npy", "") def add_dir(df: pd.DataFrame) -> pd.DataFrame: df["top_dir"] = df["id"].apply(lambda x: x[0]) df["bottom_dir"] = df["id"].apply(lambda x: x[:3]) return df def add_data_path( df: pd.DataFrame, is_train: bool = False, data_dir: str = "../input/g2net-gravitational-wave-detection", ) -> pd.DataFrame: df = add_dir(df=df) df["path"] = df["id"].apply( lambda x: id_2_path(image_id=x, is_train=is_train, data_dir=data_dir) ) return df def get_agg_feats( path: str, interp_psd: Optional[Callable] = None, psds: Optional[np.ndarray] = None, window: str = "tukey", fs: int = 2048, fband: List[int] = [10, 912], psd_cache_path_suffix: Optional[str] = None, T: float = 2.0, ) -> Dict[str, Any]: sample_data = np.load(path) data_id = path_2_id(path) if interp_psd is None: for i, strain in enumerate(sample_data): _, strain_bp = preprocess_strain( strain=strain, interp_psd=interp_psd, psd=psds[i], window=window, fs=fs, fband=fband, ) sample_data[i] = strain_bp mean = sample_data.mean(axis=-1) std = sample_data.std(axis=-1) minim = sample_data.min(axis=-1) maxim = sample_data.max(axis=-1) ene = (sample_data ** 2).sum(axis=-1) agg_dict = { "id": data_id, "mean_site0": mean[0], "mean_site1": mean[1], "mean_site2": mean[2], "std_site0": std[0], "std_site1": std[1], "std_site2": std[2], "min_site0": minim[0], "min_site1": minim[1], "min_site2": minim[2], "max_site0": maxim[0], "max_site1": maxim[1], "max_site2": maxim[2], "ene_site0": ene[0], "ene_site1": ene[1], "ene_site2": ene[2], } if psd_cache_path_suffix is not None: cache_path = path.replace(".npy", psd_cache_path_suffix) if os.path.exists(cache_path): psd = np.load(cache_path) psd_ranges = [10, 35, 350, 500, 912] psd_hz_begin = 0 for psd_hz_end in psd_ranges: psd_mean = psd[:, int(psd_hz_begin * T) : int(psd_hz_end * T)].mean( axis=-1 ) for site_id, psd_mean_for_site in enumerate(psd_mean): agg_dict[ f"psd_{psd_hz_begin}-{psd_hz_end}hz_site{site_id}" ] = psd_mean_for_site psd_hz_begin = psd_hz_end for site_id, psd_mean_for_site in enumerate(psd.mean(axis=-1)): agg_dict[f"psd_all-hz_site{site_id}"] = psd_mean_for_site return agg_dict def get_site_metrics( df: pd.DataFrame, interp_psd: Optional[Callable] = None, psds: Optional[np.ndarray] = None, window: str = "tukey", fs: int = 2048, fband: List[int] = [10, 912], psd_cache_path_suffix: Optional[str] = None, num_workers: int = 8, ): """ Compute for each id the metrics for each site. df: the complete df modify from https://www.kaggle.com/andradaolteanu/g2net-searching-the-sky-pytorch-effnet-w-meta """ func_ = partial( get_agg_feats, interp_psd=interp_psd, psds=psds, window=window, fs=fs, fband=fband, psd_cache_path_suffix=psd_cache_path_suffix, ) if num_workers > 1: with Pool(processes=num_workers) as pool: agg_dicts = list( tqdm( pool.imap(func_, df["path"].tolist()), total=len(df), ) ) else: agg_dicts = [] for ID, path in tqdm(zip(df["id"].values, df["path"].values)): # First extract the cronological info agg_dict = func_(path=path) agg_dicts.append(agg_dict) agg_df = pd.DataFrame(agg_dicts) df = pd.merge(df, agg_df, on="id") return df

28.532934

87

0.570619

0

769

0.161385

0c60c978bb3233d48fef80aac1fbd85b7650f54f

637

py

Python

sa/migrations/0051_managedobject_set_profile.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

sa/migrations/0051_managedobject_set_profile.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

sa/migrations/0051_managedobject_set_profile.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# ---------------------------------------------------------------------- # managedobject set profile # ---------------------------------------------------------------------- # Copyright (C) 2007-2019 The NOC Project # See LICENSE for details # ---------------------------------------------------------------------- # NOC modules from noc.core.migration.base import BaseMigration class Migration(BaseMigration): def migrate(self): r = self.db.execute("SELECT id FROM sa_managedobjectprofile WHERE name='default'") p_id = r[0][0] self.db.execute("UPDATE sa_managedobject SET object_profile_id = %s", [p_id])

37.470588

90

0.470958

254

0.398744

0

435

0.682889

0c6180591c4611e118c4ac0d8c026f5d2d7c99fa

305

py

Python

oct2py/compat.py

sdvillal/oct2py

f7aa89b909cbb5959ddedf3ab3e743898eac3d45

[ "MIT" ]

8

2015-10-16T23:28:16.000Z

2020-06-19T18:49:18.000Z

oct2py/compat.py

sdvillal/oct2py

f7aa89b909cbb5959ddedf3ab3e743898eac3d45

[ "MIT" ]

8

2015-06-25T20:57:56.000Z

2020-04-03T22:33:16.000Z

oct2py/compat.py

sdvillal/oct2py

f7aa89b909cbb5959ddedf3ab3e743898eac3d45

[ "MIT" ]

6

2015-04-21T12:23:44.000Z

2021-10-01T00:08:47.000Z

# -*- coding: utf-8 -*- import sys PY2 = sys.version[0] == '2' PY3 = sys.version[0] == '3' if PY2: unicode = unicode long = long from StringIO import StringIO import Queue as queue else: # pragma : no cover unicode = str long = int from io import StringIO import queue

16.944444

33

0.606557

0

48

0.157377

0c61d0a37539223a22a77c96706aa91e5bab6637

1,563

py

Python

lambda_functions/compute/campaign/aws.py

pierrealixt/MapCampaigner

7845bda4b0f6ccb7d18905a8c77d91ba6a4f78ad

[ "BSD-3-Clause" ]

null

lambda_functions/compute/campaign/aws.py

pierrealixt/MapCampaigner

7845bda4b0f6ccb7d18905a8c77d91ba6a4f78ad

[ "BSD-3-Clause" ]

1

2018-07-24T13:57:03.000Z

lambda_functions/compute/campaign/aws.py

pierrealixt/MapCampaigner

7845bda4b0f6ccb7d18905a8c77d91ba6a4f78ad

[ "BSD-3-Clause" ]

null

import os import json import boto3 class S3Data(object): """ Class for AWS S3 """ def __init__(self): """ Initialize the s3 client. """ self.s3 = boto3.client('s3') self.bucket = os.environ['S3_BUCKET'] def list(self, prefix): """ List S3 objects in bucket starting with prefix. There aren't files or folders in a S3 bucket, only objects. A key is the name of an object. The key is used to retrieve an object. examples of keys: - campaign/ - campaign/uuid.json - campaign/uuid.geojson - surveys/ - surveys/buildings - kartoza.jpg :param prefix: keys has to start with prefix. :type prefix: string :returns: list of keys starting with prefix in the bucket. :rtype: list """ prefix = '{}/'.format(prefix) objects = [] try: for obj in self.s3.list_objects( Bucket=self.bucket, Prefix=prefix)['Contents']: if obj['Key'] != prefix: key = obj['Key'].replace(prefix, '') objects.append(key.split('/')[0]) return list(set(objects)) except KeyError: return [] def delete(self, key): """ Delete a key in the S3 bucket. :param key: pathname + filename :type key: string :returns: """ self.s3.delete_object( Bucket=self.bucket, Key=key)

24.809524

78

0.515035

1,526

0.976328

0

835

0.534229

0c6200ff0e4e0bec1acf3bffde906f26e624c332

5,980

py

Python

infra/utils/launch_ec2.py

philipmac/nephele2

50acba6b7bb00da6209c75e26c8c040ffacbaa1e

[ "CC0-1.0" ]

1

2021-02-26T23:00:10.000Z

infra/utils/launch_ec2.py

philipmac/nephele2

50acba6b7bb00da6209c75e26c8c040ffacbaa1e

[ "CC0-1.0" ]

1

2020-11-16T01:55:06.000Z

infra/utils/launch_ec2.py

philipmac/nephele2

50acba6b7bb00da6209c75e26c8c040ffacbaa1e

[ "CC0-1.0" ]

2

2021-08-12T13:59:49.000Z

2022-01-19T17:16:26.000Z

#!/usr/bin/env python3 import os import boto3 import botocore.exceptions import argparse import yaml from nephele2 import NepheleError mand_vars = ['AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY'] perm_error = """\n\nIt seems you have not set up your AWS correctly. Should you be running this with Awssume? Or have profile with appropriate role? Exiting now.\n""" def main(args): """Launch ec2 instance""" if args.profile is None: ec2_resource = boto3.Session(region_name='us-east-1').resource('ec2') else: ec2_resource = boto3.Session(region_name='us-east-1', profile_name=args.profile).resource('ec2') test_sanity(ec2_resource, args) envs = load_stack_vars(args.yaml_env.name) start_EC2(ec2_resource, args.ami_id, args.instance_type, args.key_path, args.label, envs, args.dry_run) def load_stack_vars(fname): try: with open(fname) as f: data_map = yaml.safe_load(f) return data_map except FileNotFoundError as fnf: print(fnf) print('Unable to find yaml file, exiting.') exit(1) except: raise def gen_mnt_str(efs_ip): mnt_opts = 'nfsvers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2,noresvport' # from AWS return 'mount -t nfs -o {opts} {trgt}:/ {mnt}/'.format(opts=mnt_opts, trgt=efs_ip, mnt='/mnt/EFS') def read_key(key_path): try: with open(key_path, 'r') as f: key = f.read() return key except: raise def test_sanity(ec2_resource, args): """Test if env vars are set, key exists, and can access ec2""" if args.profile is None: for var in mand_vars: if os.environ.get(var) is None: print(var + ' must be set as an evironment variable. \nExiting.') exit(1) if not os.path.exists(args.key_path): print('Unable to see your key: {}, exiting now :-('.format(args.key_path)) exit(1) try: ec2_resource.instances.all().__iter__().__next__() except botocore.exceptions.ClientError as expn: print(expn) print(perm_error) exit(1) def create_EC2(ec2_resource, ami_id, i_type, envs, u_data='', dry_run=True): """create ec2 instance. by default DryRun is T, and only checks perms.""" inst = ec2_resource.create_instances( DryRun=dry_run, SecurityGroupIds=[envs['INTERNAL_SECURITY_GROUP'], envs['ecs_cluster_security_group_id']], IamInstanceProfile={'Arn': envs['N2_WORKER_INSTANCE_PROFILE']}, InstanceType=i_type, ImageId=ami_id, MinCount=1, MaxCount=1, InstanceInitiatedShutdownBehavior='terminate', SubnetId=envs['VPC_SUBNET'], UserData=u_data ) return inst def start_EC2(ec2_resource, ami_id, i_type, key_path, label, envs, dry_run): """check if have perms to create instance. https://boto3.amazonaws.com/v1/documentation/api/latest/guide/ec2-example-managing-instances.html#start-and-stop-instances if so, the create instance and tag with label. """ try: create_EC2(ec2_resource, ami_id, i_type, envs) except botocore.exceptions.ClientError as e: if 'DryRunOperation' not in str(e): print(e.response['Error']['Message']) print(perm_error) exit(1) elif dry_run: print(e.response['Error']['Message']) exit(0) else: pass mnt_str = gen_mnt_str(envs['EFS_IP']) key_str = read_key(key_path) auth_key_str = 'printf "{}" >> /home/admin/.ssh/authorized_keys;'.format( key_str) u_data = '#!/bin/bash\n{mnt_str}\n{auth_key_str}\n'.format(mnt_str=mnt_str, auth_key_str=auth_key_str) print('Creating EC2...') try: instances = create_EC2(ec2_resource, ami_id, i_type, envs, u_data, False) except botocore.exceptions.ClientError as bce: print(bce) print('\nUnable to launch EC2. \nExiting.') exit(1) if len(instances) is not 1: msg = 'Instances launched: %s' % str(instances) raise NepheleError.UnableToStartEC2Exception(msg=msg) instance = instances[0] instance.wait_until_running() instance.create_tags(Tags=[{'Key': 'Name', 'Value': label}]) print(str(instance) + ' has been created.') print('To connect type:\nssh {ip_addr}'.format( ip_addr=instance.instance_id)) print('To terminate instance type:') print('awssume aws ec2 terminate-instances --instance-ids ' + instance.instance_id) if __name__ == "__main__": usage = 'Eg:\nsource ~/code/neph2-envs/dev/environment_vars\n'\ 'awssume launch_ec2.py -e ../../neph2-envs/dev/dev_outputs.yaml -a ami-0ae1b7201f4a236f9 -t m5.4xlarge -k ~/.ssh/id_rsa.pub --label instance_name_tag\n\n'\ 'Alternately, pass profile which has correct role/permissions:\n'\ 'launch_ec2.py -e dev_outputs.yaml -a ami-003eed27e5bf2ef91 -t t2.micro -k ~/.ssh/id_rsa.pub -l name_tag --profile aws_profile_name' parser = argparse.ArgumentParser( description='CLI Interface to N2.', usage=usage) req = parser.add_argument_group('required args') req.add_argument("-e", "--yaml_env", type=argparse.FileType('r'), required=True) req.add_argument("-t", "--instance_type", type=str, required=True) req.add_argument("-a", "--ami_id", type=str, required=True) req.add_argument("-k", "--key_path", type=str, required=True) req.add_argument("-l", "--label", type=str, required=True) parser.add_argument("-p", "--profile", type=str) parser.add_argument("-d", "--dry_run", action='store_true') args = parser.parse_args() main(args)

39.084967

167

0.623746

0

1,986

0.332107

0c6419af7c4ea362b8097a85b3a1cb0ca9746ce0

9,196

py

Python

tests/test_wvlns.py

seignovert/pyvims

a70b5b9b8bc5c37fa43b7db4d15407f312a31849

[ "BSD-3-Clause" ]

4

2019-09-16T15:50:22.000Z

2021-04-08T15:32:48.000Z

tests/test_wvlns.py

seignovert/pyvims

a70b5b9b8bc5c37fa43b7db4d15407f312a31849

[ "BSD-3-Clause" ]

3

2018-05-04T09:28:24.000Z

2018-12-03T09:00:31.000Z

tests/test_wvlns.py

seignovert/pyvims

a70b5b9b8bc5c37fa43b7db4d15407f312a31849

[ "BSD-3-Clause" ]

1

2020-10-12T15:14:17.000Z

"""Test VIMS wavelength module.""" from pathlib import Path import numpy as np from numpy.testing import assert_array_almost_equal as assert_array from pyvims import QUB from pyvims.vars import ROOT_DATA from pyvims.wvlns import (BAD_IR_PIXELS, CHANNELS, FWHM, SHIFT, VIMS_IR, VIMS_VIS, WLNS, YEARS, bad_ir_pixels, ir_multiplexer, ir_hot_pixels, is_hot_pixel, median_spectrum, moving_median, sample_line_axes) from pytest import approx, raises DATA = Path(__file__).parent / 'data' def test_vims_csv(): """Test CSV global variables.""" assert len(CHANNELS) == len(WLNS) == len(FWHM) == 352 assert CHANNELS[0] == 1 assert CHANNELS[-1] == 352 assert WLNS[0] == .350540 assert WLNS[-1] == 5.1225 assert FWHM[0] == .007368 assert FWHM[-1] == .016 assert len(YEARS) == len(SHIFT) == 58 assert YEARS[0] == 1999.6 assert YEARS[-1] == 2017.8 assert SHIFT[0] == -25.8 assert SHIFT[-1] == 9.8 def test_vims_ir(): """Test VIMS IR wavelengths.""" # Standard wavelengths wvlns = VIMS_IR() assert len(wvlns) == 256 assert wvlns[0] == .884210 assert wvlns[-1] == 5.122500 # Full-width at half maximum value fwhms = VIMS_IR(fwhm=True) assert len(fwhms) == 256 assert fwhms[0] == .012878 assert fwhms[-1] == .016 # Wavenumber (cm-1) wvnb = VIMS_IR(sigma=True) assert len(wvnb) == 256 assert wvnb[0] == approx(11309.53, abs=1e-2) assert wvnb[-1] == approx(1952.17, abs=1e-2) # Single band assert VIMS_IR(band=97) == .884210 assert VIMS_IR(band=97, fwhm=True) == .012878 assert VIMS_IR(band=97, sigma=True) == approx(11309.53, abs=1e-2) assert VIMS_IR(band=97, fwhm=True, sigma=True) == approx(164.72, abs=1e-2) # Selected bands array assert_array(VIMS_IR(band=[97, 352]), [.884210, 5.122500]) assert_array(VIMS_IR(band=[97, 352], fwhm=True), [.012878, .016]) # Time offset assert VIMS_IR(band=97, year=2002) == approx(.884210, abs=1e-6) assert VIMS_IR(band=97, year=2005) == approx(.884210, abs=1e-6) assert VIMS_IR(band=97, year=2001.5) == approx(.885410, abs=1e-6) # +.0012 assert VIMS_IR(band=97, year=2011) == approx(.890210, abs=1e-6) # +.006 # Time offset on all IR bands wvlns_2011 = VIMS_IR(year=2011) assert len(wvlns_2011) == 256 assert wvlns_2011[0] == approx(.890210, abs=1e-6) assert wvlns_2011[-1] == approx(5.128500, abs=1e-6) # No change in FWHM with time assert VIMS_IR(band=97, year=2001.5, fwhm=True) == .012878 # Outside IR band range assert np.isnan(VIMS_IR(band=0)) assert np.isnan(VIMS_IR(band=96, fwhm=True)) assert np.isnan(VIMS_IR(band=353, sigma=True)) def test_vims_vis(): """Test VIMS VIS wavelengths.""" # Standard wavelengths wvlns = VIMS_VIS() assert len(wvlns) == 96 assert wvlns[0] == .350540 assert wvlns[-1] == 1.045980 # Full-width at half maximum value fwhms = VIMS_VIS(fwhm=True) assert len(fwhms) == 96 assert fwhms[0] == .007368 assert fwhms[-1] == .012480 # Wavenumber (cm-1) wvnb = VIMS_VIS(sigma=True) assert len(wvnb) == 96 assert wvnb[0] == approx(28527.41, abs=1e-2) assert wvnb[-1] == approx(9560.41, abs=1e-2) # Single band assert VIMS_VIS(band=96) == 1.045980 assert VIMS_VIS(band=96, fwhm=True) == .012480 assert VIMS_VIS(band=96, sigma=True) == approx(9560.41, abs=1e-2) assert VIMS_VIS(band=96, fwhm=True, sigma=True) == approx(114.07, abs=1e-2) # Selected bands array assert_array(VIMS_VIS(band=[1, 96]), [.350540, 1.045980]) assert_array(VIMS_VIS(band=[1, 96], fwhm=True), [.007368, .012480]) # Time offset with raises(ValueError): _ = VIMS_VIS(band=97, year=2002) with raises(ValueError): _ = VIMS_VIS(year=2011) # Outside IR band range assert np.isnan(VIMS_VIS(band=0)) assert np.isnan(VIMS_VIS(band=97, fwhm=True)) assert np.isnan(VIMS_VIS(band=353, sigma=True)) def test_bad_ir_pixels(): """Test bad IR pixels list.""" csv = np.loadtxt(ROOT_DATA / 'wvlns_std.csv', delimiter=',', usecols=(0, 1, 2, 3), dtype=str, skiprows=98) # Extract bad pixels wvlns = np.transpose([ (int(channel), float(wvln) - .5 * float(fwhm), float(fwhm)) for channel, wvln, fwhm, comment in csv if comment ]) # Group bad pixels news = [True] + list((wvlns[0, 1:] - wvlns[0, :-1]) > 1.5) bads = [] for i, new in enumerate(news): if new: bads.append(list(wvlns[1:, i])) else: bads[-1][1] += wvlns[2, i] assert_array(BAD_IR_PIXELS, bads) coll = bad_ir_pixels() assert len(coll.get_paths()) == len(bads) def test_moving_median(): """Test moving median filter.""" a = [1, 2, 3, 4, 5] assert_array(moving_median(a, width=1), a) assert_array(moving_median(a, width=3), [1.5, 2, 3, 4, 4.5]) assert_array(moving_median(a, width=5), [2, 2.5, 3, 3.5, 4]) assert_array(moving_median(a, width=2), [1.5, 2.5, 3.5, 4.5, 5]) assert_array(moving_median(a, width=4), [2, 2.5, 3.5, 4, 4.5]) def test_is_hot_pixel(): """Test hot pixel detector.""" # Create random signal signal = np.random.default_rng().integers(20, size=100) # Add hot pixels signal[10::20] = 50 signal[10::30] = 150 hot_pix = is_hot_pixel(signal) assert len(hot_pix) == 100 assert 3 <= sum(hot_pix) < 6 assert all(hot_pix[10::30]) hot_pix = is_hot_pixel(signal, tol=1.5, frac=90) assert len(hot_pix) == 100 assert 6 <= sum(hot_pix) < 12 assert all(hot_pix[10::20]) def test_sample_line_axes(): """Test locatation sample and line axes.""" # 2D case assert sample_line_axes((64, 352)) == (0, ) assert sample_line_axes((256, 32)) == (1, ) # 3D case assert sample_line_axes((32, 64, 352)) == (0, 1) assert sample_line_axes((32, 352, 64)) == (0, 2) assert sample_line_axes((352, 32, 64)) == (1, 2) # 1D case with raises(TypeError): _ = sample_line_axes((352)) # No band axis with raises(ValueError): _ = sample_line_axes((64, 64)) def test_median_spectrum(): """Test the median spectrum extraction.""" # 2D cases spectra = [CHANNELS, CHANNELS] spectrum = median_spectrum(spectra) # (2, 352) assert spectrum.shape == (352,) assert spectrum[0] == 1 assert spectrum[-1] == 352 spectrum = median_spectrum(np.transpose(spectra)) # (352, 2) assert spectrum.shape == (352,) assert spectrum[0] == 1 assert spectrum[-1] == 352 # 3D cases spectra = [[CHANNELS, CHANNELS]] spectrum = median_spectrum(spectra) # (1, 2, 352) assert spectrum.shape == (352,) assert spectrum[0] == 1 assert spectrum[-1] == 352 spectrum = median_spectrum(np.moveaxis(spectra, 1, 2)) # (1, 352, 2) assert spectrum.shape == (352,) assert spectrum[0] == 1 assert spectrum[-1] == 352 spectrum = median_spectrum(np.moveaxis(spectra, 2, 0)) # (352, 1, 2) assert spectrum.shape == (352,) assert spectrum[0] == 1 assert spectrum[-1] == 352 def test_ir_multiplexer(): """Test spectrum split in each IR multiplexer.""" # Full spectrum spec_1, spec_2 = ir_multiplexer(CHANNELS) assert len(spec_1) == 128 assert len(spec_2) == 128 assert spec_1[0] == 97 assert spec_1[-1] == 351 assert spec_2[0] == 98 assert spec_2[-1] == 352 # IR spectrum only spec_1, spec_2 = ir_multiplexer(CHANNELS[96:]) assert len(spec_1) == 128 assert len(spec_2) == 128 assert spec_1[0] == 97 assert spec_1[-1] == 351 assert spec_2[0] == 98 assert spec_2[-1] == 352 # 2D spectra spectra = [CHANNELS, CHANNELS] spec_1, spec_2 = ir_multiplexer(spectra) assert len(spec_1) == 128 assert len(spec_2) == 128 assert spec_1[0] == 97 assert spec_1[-1] == 351 assert spec_2[0] == 98 assert spec_2[-1] == 352 # 3D spectra spectra = [[CHANNELS, CHANNELS]] spec_1, spec_2 = ir_multiplexer(spectra) assert len(spec_1) == 128 assert len(spec_2) == 128 assert spec_1[0] == 97 assert spec_1[-1] == 351 assert spec_2[0] == 98 assert spec_2[-1] == 352 # VIS spectrum only with raises(ValueError): _ = ir_multiplexer(CHANNELS[:96]) # Dimension too high with raises(ValueError): _ = ir_multiplexer([[[CHANNELS]]]) def test_ir_hot_pixels(): """Test IR hot pixel detector from spectra.""" qub = QUB('1787314297_1', root=DATA) # 1D spectrum hot_pixels = ir_hot_pixels(qub['BACKGROUND'][0]) assert len(hot_pixels) == 10 assert_array(hot_pixels, [105, 119, 124, 168, 239, 240, 275, 306, 317, 331]) # 2D spectra hot_pixels = ir_hot_pixels(qub['BACKGROUND']) assert len(hot_pixels) == 10 assert_array(hot_pixels, [105, 119, 124, 168, 239, 240, 275, 306, 317, 331])

27.450746

79

0.605154

0

1,145

0.124511

0c661084ef2dc9a119cb718b8362035d15b03067

909

py

Python

Outliers/loss/losses.py

MakotoTAKAMATSU013/Outliers

80043027d64b8f07355a05b281925f00bbf1a442

[ "MIT" ]

null

Outliers/loss/losses.py

MakotoTAKAMATSU013/Outliers

80043027d64b8f07355a05b281925f00bbf1a442

[ "MIT" ]

null

Outliers/loss/losses.py

MakotoTAKAMATSU013/Outliers

80043027d64b8f07355a05b281925f00bbf1a442

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F class LabelSmoothingCrossEntropy(nn.Module): def __init__(self, smoothing = 0.1): super(LabelSmoothingCrossEntropy, self).__init__() assert smoothing < 0.1 self.smoothing = smoothing self.confidence = 1. - smoothing def forward(self, x, target): logprobs = F.log_softmax(x, dim=-1) null_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) null_loss = null_loss.squeeze(1) smooth_loss = -logprobs.mean(dim=-1) loss = self.confidence * null_loss + self.smoothing * smooth_loss return loss.mean() class SoftTargetCrossEntropy(nn.module): def __init__(self): super(SoftTargetCrossEntropy, self).__init__() def forward(self, x, target): loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1) return loss.mean()

34.961538

73

0.660066

839

0.922992

0

0c663401e4bd928831a371cae4be0b6a743a91c8

5,783

py

Python

esiosdata/importdemdata.py

azogue/esiosdata

680c7918955bc6ceee5bded92b3a4485f5ea8151

[ "MIT" ]

20

2017-06-04T20:34:16.000Z

2021-10-31T22:55:22.000Z

esiosdata/importdemdata.py

azogue/esiosdata

680c7918955bc6ceee5bded92b3a4485f5ea8151

[ "MIT" ]

null

esiosdata/importdemdata.py

azogue/esiosdata

680c7918955bc6ceee5bded92b3a4485f5ea8151

[ "MIT" ]

4

2020-01-28T19:02:24.000Z

2022-03-08T15:59:11.000Z

# -*- coding: utf-8 -*- """ Created on Sat Feb 27 18:16:24 2015 @author: Eugenio Panadero A raíz del cambio previsto: DESCONEXIÓN DE LA WEB PÚBLICA CLÁSICA DE E·SIOS La Web pública clásica de e·sios (http://www.esios.ree.es) será desconectada el día 29 de marzo de 2016. Continuaremos ofreciendo servicio en la nueva Web del Operador del Sistema: https://www.esios.ree.es. Por favor, actualice sus favoritos apuntando a la nueva Web. IMPORTANTE!!! En la misma fecha (29/03/2016), también dejará de funcionar el servicio Solicitar y Descargar, utilizado para descargar información de la Web pública clásica de e·sios. Por favor, infórmese sobre descarga de información en https://www.esios.ree.es/es/pagina/api y actualice sus procesos de descarga. """ import json import pandas as pd import re from dataweb.requestweb import get_data_en_intervalo from esiosdata.esios_config import DATE_FMT, TZ, SERVER, HEADERS, D_TIPOS_REQ_DEM, KEYS_DATA_DEM from esiosdata.prettyprinting import print_redb, print_err __author__ = 'Eugenio Panadero' __copyright__ = "Copyright 2015, AzogueLabs" __credits__ = ["Eugenio Panadero"] __license__ = "GPL" __version__ = "1.0" __maintainer__ = "Eugenio Panadero" RG_FUNC_CONTENT = re.compile('(?P<func>.*)$(?P<json>.*)$;') def dem_url_dia(dt_day='2015-06-22'): """Obtiene las urls de descarga de los datos de demanda energética de un día concreto.""" def _url_tipo_dato(str_dia, k): url = SERVER + '/archives/{}/download_json?locale=es'.format(D_TIPOS_REQ_DEM[k]) if type(str_dia) is str: return url + '&date=' + str_dia else: return url + '&date=' + str_dia.date().isoformat() urls = [_url_tipo_dato(dt_day, k) for k in D_TIPOS_REQ_DEM.keys()] return urls def _extract_func_json_data(data_raw): try: busca = RG_FUNC_CONTENT.match(data_raw).groupdict() ind, data = busca['func'], None data = json.loads(busca['json']) if len(data.keys()) == 1: return ind, data[list(data.keys())[0]] else: return ind, data except AttributeError: # print('ERROR REG_EXP [{}] --> RAW: {}'.format(e, data_raw)) return None, None def _import_daily_max_min(data): # IND_MaxMinRenovEol / IND_MaxMin df = pd.DataFrame(data, index=[0]) cols_ts = df.columns.str.startswith('ts') is_max_min_renov = any(cols_ts) if is_max_min_renov: df.index = pd.DatetimeIndex([pd.Timestamp(df['tsMaxRenov'][0]).date()], freq='D') else: df = pd.DataFrame(df.set_index(pd.DatetimeIndex([pd.Timestamp(df['date'][0]).date()], freq='D') ).drop('date', axis=1)) cols_ts = df.columns.str.contains('timeStamp', regex=False) for c, is_ts in zip(df.columns, cols_ts): if is_ts: df[c] = df[c].apply(pd.Timestamp) else: df[c] = df[c].astype(float) return df def _import_json_ts_data(data): df = pd.DataFrame(data) try: return pd.DataFrame(df.set_index(pd.DatetimeIndex(df['ts'].apply(lambda x: pd.Timestamp(x, tz=TZ)), freq='10T', tz=TZ), verify_integrity=True ).drop('ts', axis=1)).sort_index().applymap(float) except ValueError: # ES DST df['ts'] = pd.DatetimeIndex(start=pd.Timestamp(df['ts'].iloc[0]), periods=len(df), freq='10T', tz=TZ) # , ambiguous="infer") return df.set_index('ts', verify_integrity=True).sort_index().applymap(float) def dem_procesa_datos_dia(key_day, response): """Procesa los datos descargados en JSON.""" dfs_import, df_import, dfs_maxmin, hay_errores = [], None, [], 0 for r in response: tipo_datos, data = _extract_func_json_data(r) if tipo_datos is not None: if ('IND_MaxMin' in tipo_datos) and data: df_import = _import_daily_max_min(data) dfs_maxmin.append(df_import) elif data: df_import = _import_json_ts_data(data) dfs_import.append(df_import) if tipo_datos is None or df_import is None: hay_errores += 1 if hay_errores == 4: # No hay nada, salida temprana sin retry: print_redb('** No hay datos para el día {}!'.format(key_day)) return None, -2 else: # if hay_errores < 3: # TODO formar datos incompletos!! (max-min con NaN's, etc.) data_import = {} if dfs_import: data_import[KEYS_DATA_DEM[0]] = dfs_import[0].join(dfs_import[1]) if len(dfs_maxmin) == 2: data_import[KEYS_DATA_DEM[1]] = dfs_maxmin[0].join(dfs_maxmin[1]) elif dfs_maxmin: data_import[KEYS_DATA_DEM[1]] = dfs_maxmin[0] if not data_import: print_err('DÍA: {} -> # ERRORES: {}'.format(key_day, hay_errores)) return None, -2 return data_import, 0 def dem_data_dia(str_dia='2015-10-10', str_dia_fin=None): """Obtiene datos de demanda energética en un día concreto o un intervalo, accediendo directamente a la web.""" params = {'date_fmt': DATE_FMT, 'usar_multithread': False, 'num_retries': 1, "timeout": 10, 'func_procesa_data_dia': dem_procesa_datos_dia, 'func_url_data_dia': dem_url_dia, 'data_extra_request': {'json_req': False, 'headers': HEADERS}} if str_dia_fin is not None: params['usar_multithread'] = True data, hay_errores, str_import = get_data_en_intervalo(str_dia, str_dia_fin, **params) else: data, hay_errores, str_import = get_data_en_intervalo(str_dia, str_dia, **params) if not hay_errores: return data else: print_err(str_import) return None

39.609589

114

0.639633

0

1,782

0.306871

a73d0e2b381469762428cb4845c16d12f86b59d9

4,744

py

Python

brainfrick.py

rium9/brainfrick

37f8e3417cde5828e3ed2c2099fc952259f12844

[ "MIT" ]

null

brainfrick.py

rium9/brainfrick

37f8e3417cde5828e3ed2c2099fc952259f12844

[ "MIT" ]

null

brainfrick.py

rium9/brainfrick

37f8e3417cde5828e3ed2c2099fc952259f12844

[ "MIT" ]

null

class BrainfuckException(Exception): pass class BLexer: """ Static class encapsulating functionality for lexing Brainfuck programs. """ symbols = [ '>', '<', '+', '-', '.', ',', '[', ']' ] @staticmethod def lex(code): """ Return a generator for tokens in some Brainfuck code. """ # The syntax of Brainfuck is so simple that nothing is really gained from converting # symbols to some sort of Token object. Just ignore everything that isn't in Brainfuck's # syntax. return (char for char in code if char in BLexer.symbols) class BrainfuckMachine: """ Class encapsulating the core operations of a brainfuck machine. Namely, - Move pointer left, - Move pointer right, - Increment cell under pointer, - Decrement cell under pointer, - Output value of cell under pointer, - Input value to cell under pointer. """ def __init__(self, cells=256, in_func=input, out_func=chr): self.cells = [0] * cells self.ptr = 0 self.in_func = in_func self.out_func = out_func self.looppos = [] self.out_buffer = [] def right(self): if self.ptr < len(self.cells)-1: self.ptr += 1 def left(self): if self.ptr > 0: self.ptr -= 1 def incr(self): self.cells[self.ptr] += 1 def decr(self): self.cells[self.ptr] -= 1 def value(self): """ Return the value of the cell under the pointer. """ return self.cells[self.ptr] def outf(self): return self.out_func(self.cells[self.ptr]) def inf(self): self.cells[self.ptr] = self.in_func() class BInterpreter: """ Class encapsulating interpretation functionality for brainfuck code. """ def __init__(self, machine=None): if machine: self.machine = machine else: self.machine = BrainfuckMachine() def interpret_code(self, code): """ Interpret each character in a list or string of brainfuck tokens. """ # Iterate through every character in the code. Use indexing so that we can # jump as necessary for square bracket loops. To identify matching brackets for forward # jumps, move forward a position at a time, keeping track of the nesting level (starts at 1). When a # open bracket ([) is encountered, increment the nesting level, and when a close bracket # (]) is found, decrement it. When nesting level reaches 0, the matching bracket has been found. # For finding the correct bracket for a backwards jump, do the same thing but with # backwards iteration and swap when you increment and decrement. pos = 0 while pos < len(code): if code[pos] == '[': if self.machine.value() == 0: nest = 1 while nest != 0: pos += 1 if code[pos] == '[': nest += 1 elif code[pos] == ']': nest -= 1 pos += 1 else: pos += 1 elif code[pos] == ']': if self.machine.value() != 0: nest = 1 while nest != 0: pos -= 1 if code[pos] == ']': nest += 1 elif code[pos] == '[': nest -= 1 pos += 1 else: pos += 1 else: self.interpret_one(code[pos]) pos += 1 def interpret_one(self, char): """ Perform the appropriate operation for a single brainfuck character. """ if char == '>': self.machine.right() elif char == '<': self.machine.left() elif char == '+': self.machine.incr() elif char == '-': self.machine.decr() elif char == '.': # TODO output checks print(self.machine.outf(), end='') elif char == ',': # TODO input checks self.machine.inf() if __name__ == '__main__': bfm = BrainfuckMachine(cells=8, out_func=chr) bi = BInterpreter(bfm) f = open('helloworld', 'r').read() code = list(BLexer.lex(f)) bi.interpret_code(code)

32.493151

110

0.490304

4,501

0.948777

0

381

0.080312

0

1,674

0.352867

a73f4577fe0a30a2fdd1d7b44615b63fb0d34f1e

3,476

bzl

Python

infra_macros/fbcode_macros/build_defs/build_info.bzl

martarozek/buckit

343cc5a5964c1d43902b6a77868652adaefa0caa

[ "BSD-3-Clause" ]

null

infra_macros/fbcode_macros/build_defs/build_info.bzl

martarozek/buckit

343cc5a5964c1d43902b6a77868652adaefa0caa

[ "BSD-3-Clause" ]

null

infra_macros/fbcode_macros/build_defs/build_info.bzl

martarozek/buckit

343cc5a5964c1d43902b6a77868652adaefa0caa

[ "BSD-3-Clause" ]

null

load("@fbcode_macros//build_defs:config.bzl", "config") load("@fbcode_macros//build_defs/config:read_configs.bzl", "read_int") load("@fbcode_macros//build_defs:core_tools.bzl", "core_tools") def _create_build_info( build_mode, buck_package, name, rule_type, platform, epochtime=0, host="", package_name="", package_version="", package_release="", path="", revision="", revision_epochtime=0, time="", time_iso8601="", upstream_revision="", upstream_revision_epochtime=0, user="", ): return struct( build_mode=build_mode, rule="fbcode:" + buck_package + ":" + name, platform=platform, rule_type=rule_type, epochtime=epochtime, host=host, package_name=package_name, package_version=package_version, package_release=package_release, path=path, revision=revision, revision_epochtime=revision_epochtime, time=time, time_iso8601=time_iso8601, upstream_revision=upstream_revision, upstream_revision_epochtime=upstream_revision_epochtime, user=user, ) def _get_build_info(package_name, name, rule_type, platform): """ Gets a build_info struct from various configurations (or default values) This struct has values passed in by the packaging system in order to stamp things like the build epoch, platform, etc into the final binary. This returns stable values by default so that non-release builds do not affect rulekeys. Args: package_name: The name of the package that contains the build rule that needs build info. No leading slashes name: The name of the rule that needs build info rule_type: The type of rule that is being built. This should be the macro name, not the underlying rule type. (e.g. cpp_binary, not cxx_binary) platform: The platform that is being built for """ build_mode = config.get_build_mode() if core_tools.is_core_tool(package_name,name): return _create_build_info( build_mode, package_name, name, rule_type, platform, ) else: return _create_build_info( build_mode, package_name, name, rule_type, platform, epochtime=read_int("build_info", "epochtime", 0), host=native.read_config("build_info", "host", ""), package_name=native.read_config("build_info", "package_name", ""), package_version=native.read_config("build_info", "package_version", ""), package_release=native.read_config("build_info", "package_release", ""), path=native.read_config("build_info", "path", ""), revision=native.read_config("build_info", "revision", ""), revision_epochtime=read_int("build_info", "revision_epochtime", 0), time=native.read_config("build_info", "time", ""), time_iso8601=native.read_config("build_info", "time_iso8601", ""), upstream_revision=native.read_config("build_info", "upstream_revision", ""), upstream_revision_epochtime=read_int("build_info", "upstream_revision_epochtime", 0), user=native.read_config("build_info", "user", ""), ) build_info = struct( get_build_info = _get_build_info, )

35.469388

97

0.635788

0

1,335

0.384062

a7401ff3c28629b2dc0848d7b3f999f8226d524f

1,885

py

Python

src/scan.py

Unitato/github-public-alert

29dbcf72dd8c18c45385c29f25174c28c3428560

[ "MIT" ]

null

src/scan.py

Unitato/github-public-alert

29dbcf72dd8c18c45385c29f25174c28c3428560

[ "MIT" ]

null

src/scan.py

Unitato/github-public-alert

29dbcf72dd8c18c45385c29f25174c28c3428560

[ "MIT" ]

null

#!#!/usr/bin/env python import os from github import Github from libraries.notify import Notify import json print("") print("Scanning Github repos") GITHUB_API_KEY = os.environ.get('GITHUB_API_KEY') WHITELIST = json.loads(os.environ.get('GITHUB_WHITELIST').lower()) GITHUB_SCAN = json.loads(os.environ.get('GITHUB_SCAN')) SENDGRID_API_KEY = os.environ.get('SENDGRID_API_KEY') SENDGRID_FROM = os.environ.get('SENDGRID_FROM') SENDGRID_SUBJECT = os.environ.get('SENDGRID_SUBJECT') SENDGRID_TEMPLATE = os.environ.get('SENDGRID_TEMPLATE') SENDGRID_NOTIFY = json.loads(os.environ.get('SENDGRID_NOTIFY')) results = [] print(" Target: {}".format(GITHUB_SCAN)) print(" Github:{}".format(len(GITHUB_API_KEY[:-4])*"#"+GITHUB_API_KEY[-4:])) print(" Whitelist: {}".format(WHITELIST)) print("") def load_template(_file): try: with open(_file) as f: # print(f.readlines()) return f.readlines() except IOError: print("Template file not accessible") # or using an access token g = Github(GITHUB_API_KEY) for ITEM in GITHUB_SCAN: print("Checking {}".format(ITEM)) for repo in g.get_user(ITEM).get_repos(): if repo.name.lower() in WHITELIST: print(" [-] {}".format(repo.name)) # commits = repo.get_commits() # for com in commits: # print(com) else: print(" [+] {}".format(repo.name)) results.append("{}/{}".format(ITEM,repo.name)) if results: print("FOUND NEW REPOs!!! SENDING EMAIL!!!") #exit() notify = Notify(SENDGRID_API_KEY) notify.add_from(SENDGRID_FROM) notify.add_mailto(SENDGRID_NOTIFY) notify.add_subject(SENDGRID_SUBJECT) notify.add_content_html(load_template(SENDGRID_TEMPLATE)) notify.update_content_html("", results) notify.send_mail() else: print("Nothing found, going to sleep")

30.901639

76

0.671088

0

501

0.265782

a7403e0780a57d1602d030f1189826ad5b0324b5

3,634

py

Python

models.py

YavorPaunov/await

0ea7ad1d0d48b66686e35702d39695268451b688

[ "MIT" ]

null

models.py

YavorPaunov/await

0ea7ad1d0d48b66686e35702d39695268451b688

[ "MIT" ]

null

models.py

YavorPaunov/await

0ea7ad1d0d48b66686e35702d39695268451b688

[ "MIT" ]

null

from flask.ext.sqlalchemy import SQLAlchemy from util import hex_to_rgb, rgb_to_hex from time2words import relative_time_to_text from datetime import datetime from dateutil.tz import tzutc import pytz db = SQLAlchemy() def created_on_default(): return datetime.utcnow() class Counter(db.Model): __tablename__ = 'counters' id = db.Column(db.Integer, primary_key=True) created_on = db.Column(db.DateTime, default=created_on_default) updated_on = db.Column( db.DateTime, default=created_on_default, onupdate=created_on_default) time = db.Column(db.DateTime) text_after = db.Column(db.String()) text_before = db.Column(db.String()) theme = db.Column( db.Enum('simple', 'trip', name='themes'), default='simple') url = db.Column(db.String, unique=True) secret = db.Column(db.String) # Foreign keys trip_theme = db.relationship( 'TripTheme', backref='counter', lazy='joined', uselist=False) def __repr__(self): return '<Counter (id: {0}, time:{1})>'.format(self.id, self.time) def time_left_in_text(self): time_in_seconds = int((self.time.replace(tzinfo=pytz.utc) - datetime.utcnow().replace(tzinfo=pytz.utc)).total_seconds()) return relative_time_to_text(seconds=abs(time_in_seconds)) def has_passed(self): return self.time.replace(tzinfo=pytz.utc) < datetime.utcnow().replace(tzinfo=pytz.utc) def full_text(self): full_text_list = [] if self.has_passed(): full_text_list = [self.time_left_in_text(), "ago"] else: if len(self.text_before) > 0: full_text_list.append(self.text_before) full_text_list.append(self.time_left_in_text()) if len(self.text_after) > 0: full_text_list.append(self.text_after) full_text = " ".join(full_text_list) full_text = full_text[0].upper() + full_text[1:] return full_text def to_dict(self, just_created=False): data = { 'id': self.id, 'created_on': self.created_on, 'updated_on': self.updated_on, 'time': self.time, 'text_after': self.text_after, 'text_before': self.text_before, 'full_text': self.full_text(), 'url': self.url, 'theme': self.theme } if self.theme == 'trip': data['city_origin'] = self.trip_theme.origin data['city_destination'] = self.trip_theme.destination if just_created: data['secret'] = self.secret return data def to_json(self): return jsonify(self.to_dict()) class TripTheme(db.Model): __tablename__ = 'trip_themes' id = db.Column(db.Integer, primary_key=True) created_on = db.Column(db.DateTime, default=created_on_default) updated_on = db.Column( db.DateTime, default=created_on_default, onupdate=created_on_default) origin = db.Column(db.String(255), nullable=False) destination = db.Column(db.String(255), nullable=False) # Relationships counter_id = db.Column( db.Integer, db.ForeignKey('counters.id'), nullable=False) def to_dict(self): data = { 'origin': self.origin, 'destination': self.destination } return data def get_or_create(session, model, **kwargs): instance = session.query(model).filter_by(**kwargs).first() if instance: return instance else: instance = model(**kwargs) session.add(instance) session.commit() return instance

30.79661

94

0.632911

3,077

0.846725

0

310

0.085305

a74179e3bf17c46fcdccafdc139bb260a2c60cb7

732

py

Python

setup.py

ManuelMeraz/ReinforcementLearning

5d42a88776428308d35c8031c01bf5afdf080079

[ "MIT" ]

1

2020-04-19T15:29:47.000Z

setup.py

ManuelMeraz/ReinforcementLearning

5d42a88776428308d35c8031c01bf5afdf080079

[ "MIT" ]

null

setup.py

ManuelMeraz/ReinforcementLearning

5d42a88776428308d35c8031c01bf5afdf080079

[ "MIT" ]

null

#!/usr/bin/env python3 import os import setuptools DIR = os.path.dirname(__file__) REQUIREMENTS = os.path.join(DIR, "requirements.txt") with open(REQUIREMENTS) as f: reqs = f.read().strip().split("\n") setuptools.setup( name="rl", version="0.0.1", description="Reinforcement Learning: An Introduction", url="github.com/manuelmeraz/ReinforcementLearning", author="Manuel Meraz-Rodriguez", license="MIT", packages=setuptools.find_packages(), install_requires=reqs, entry_points={ "console_scripts": [ "tictactoe = rl.book.chapter_1.tictactoe.main:main", "bandits = rl.book.chapter_2.main:main", "rlgrid = rl.rlgrid.main:main", ] }, )

25.241379

64

0.648907

0

308

0.420765

a743058f6e943a66d50447c9ef87971c35895cc0

169

py

Python

taxcalc/tbi/__init__.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

[ "CC0-1.0" ]

1

2021-02-23T21:03:43.000Z

taxcalc/tbi/__init__.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

[ "CC0-1.0" ]

null

taxcalc/tbi/__init__.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

[ "CC0-1.0" ]

null

from taxcalc.tbi.tbi import (run_nth_year_taxcalc_model, run_nth_year_gdp_elast_model, reform_warnings_errors)

42.25

58

0.585799

0

a7433e8c895ee751d0a668a187a9eb4c45927efe

6,223

py

Python

mooc_access_number.py

mengshouer/mooc_access_number

8de596ce34006f1f8c5d0404f5e40546fb438b2a

[ "MIT" ]

6

2020-05-12T14:36:17.000Z

2021-12-03T01:56:58.000Z

mooc_access_number.py

mengshouer/mooc_tools

8de596ce34006f1f8c5d0404f5e40546fb438b2a

[ "MIT" ]

2

2020-05-11T06:21:13.000Z

2020-05-23T12:34:18.000Z

mooc_access_number.py

mengshouer/mooc_tools

8de596ce34006f1f8c5d0404f5e40546fb438b2a

[ "MIT" ]

1

2020-05-11T04:19:15.000Z

import requests,time,json,re,base64 requests.packages.urllib3.disable_warnings() from io import BytesIO from PIL import Image,ImageDraw,ImageChops from lxml import etree from urllib.parse import urlparse, parse_qs username = "" #登录账号 password = "" #登录密码 s = requests.Session() s.headers.update({'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36'}) def login(): global uid,username,password if(username == "" or password == ""): username = input("登录账号：") password = input("登录密码：") #旧接口，已失效 #url="http://i.chaoxing.com/vlogin?passWord="+str(password)+"&userName="+str(username) url = f'https://passport2-api.chaoxing.com/v11/loginregister?uname='+str(username)+'&code='+str(password) res= s.get(url) if("验证通过" in str(res.text)): print('Login success!') for key, value in res.cookies.items(): if key=="_uid": uid=value return s else: print(username,password) print('账号密码有误，请重试。') username = "" password = "" login() ''' def captchalogin(username,password): if(username == "" or password == ""): username = input("登录账号：") password = input("登录密码：") #以下两个用于自动识别验证码，手动输入验证码可无视 #百度云内的人工智能文字识别orc创建应用获得，不保证识别成功率，可以试试 APIKey = "" SecretKey = "" #免费的每天有api限制 if(APIKey != "" or SecretKey != ""): getkeyurl = f'https://aip.baidubce.com/oauth/2.0/token' data = { "grant_type" : "client_credentials", "client_id" : APIKey, "client_secret" : SecretKey } getkey = requests.post(getkeyurl,data).text access_token = json.loads(getkey)["access_token"] numcode = "" while 1: t = int(round(time.time()*1000)) codeurl = f'http://passport2.chaoxing.com/num/code?'+ str(t) img_numcode = s.get(codeurl).content img = base64.b64encode(img_numcode) orcurl = f'https://aip.baidubce.com/rest/2.0/ocr/v1/accurate_basic?access_token='+access_token data = {"image":img} headers = {'content-type': 'application/x-www-form-urlencoded'} captcha = requests.post(orcurl,data=data,headers=headers).text numcodelen = json.loads(captcha)["words_result_num"] if numcodelen == 0: print("验证码识别错误，重新获取验证码识别") time.sleep(1) else: numcode = json.loads(captcha)["words_result"][0]["words"] numcode = re.sub("\D","",numcode) if len(numcode) < 4: print("验证码识别错误，重新获取验证码识别") time.sleep(1) else: print("识别成功") break else: t = int(round(time.time()*1000)) url = f'http://passport2.chaoxing.com/num/code?'+ str(t) web = s.get(url,verify=False) img = Image.open(BytesIO(web.content)) img.show() numcode = input('验证码：') url = 'http://passport2.chaoxing.com/login?refer=http://i.mooc.chaoxing.com' data = {'refer_0x001': 'http%3A%2F%2Fi.mooc.chaoxing.com', 'pid':'-1', 'pidName':'', 'fid':'1467', #院校id 1467:a系统 'fidName':'', 'allowJoin':'0', 'isCheckNumCode':'1', 'f':'0', 'productid':'', 'uname':username, 'password':password, 'numcode':numcode, 'verCode':'' } web = s.post(url,data=data,verify=False) time.sleep(2) if('账号管理' in str(web.text)): print('Login success!') return s else: print('账号密码或验证码有误，请重试。') username = "" password = "" captchalogin(username,password) ''' def getuserdata(): web = s.get('http://mooc1-1.chaoxing.com/visit/courses') h1 = etree.HTML(web.text) name = h1.xpath('//h3[@class = "clearfix"]/a/text()') print("-----------课程名称-----------") print(name) print("------------------------------") global count try: count except NameError: count_exist = False else: count_exist = True if(count_exist): pass else: if(len(name) == 1): count = 0 else: #count = 0 count = int(input("请用数字选择要访问的课程(从0开始)：")) geturl = h1.xpath('//div[@class = "Mcon1img httpsClass"]/a/@href') i = 0 courseurl = [] for temp in range(0,len(geturl)): if("course" in geturl[i]): courseurl.append(geturl[i]) i += 1 url = 'https://mooc1-1.chaoxing.com' + courseurl[count] url_query = urlparse(url).query userdata = dict([(k, v[0]) for k, v in parse_qs(url_query).items()]) global cpi, enc, courseId, classId, encode cpi = userdata["cpi"] #enc = userdata["enc"] courseId = userdata["courseid"] classId = userdata["clazzid"] web = s.get(url) h2 = etree.HTML(web.text) encodeurl = h2.xpath('//script[@type = "text/javascript"]/@src') i=0 for temp in range(0,len(encodeurl)): if("encode" in encodeurl[i]): break i += 1 url_query = urlparse(encodeurl[i]).query userdata = dict([(k, v[0]) for k, v in parse_qs(url_query).items()]) encode = userdata["encode"] def main(): getuserdata() url = 'https://fystat-ans.chaoxing.com/log/setlog?personid='+cpi+'&courseId='+courseId+'&classId='+classId+'&encode=' +encode i = 0 while 1: web = s.get(url,verify=False) time.sleep(5) i+=1 print(i) if(i == 500): break main() if __name__ == "__main__": print("登录成功后等待访问数慢慢增加，显示的数字并不代表访问数，只是用于计数") try: #captchalogin(username,password) login() main() except: print("登录信息尝试重新登录") #captchalogin(username,password) login() main()

32.752632

151

0.527398

0

4,263

0.635226

a743c86ba9ec1ed2c5e5910bec35a0fda5523988

11,174

py

Python

tests/test_json_api.py

Padraic-O-Mhuiris/fava

797ae1ee1f7378c8e7347d2970fc52c4be366b01

[ "MIT" ]

null

tests/test_json_api.py

Padraic-O-Mhuiris/fava

797ae1ee1f7378c8e7347d2970fc52c4be366b01

[ "MIT" ]

null

tests/test_json_api.py

Padraic-O-Mhuiris/fava

797ae1ee1f7378c8e7347d2970fc52c4be366b01

[ "MIT" ]

null

# pylint: disable=missing-docstring from __future__ import annotations import hashlib from io import BytesIO from pathlib import Path from typing import Any import pytest from beancount.core.compare import hash_entry from flask import url_for from flask.testing import FlaskClient from fava.context import g from fava.core import FavaLedger from fava.core.charts import PRETTY_ENCODER from fava.core.misc import align from fava.json_api import validate_func_arguments from fava.json_api import ValidationError dumps = PRETTY_ENCODER.encode def test_validate_get_args() -> None: def func(test: str): assert test and isinstance(test, str) validator = validate_func_arguments(func) with pytest.raises(ValidationError): validator({"notest": "value"}) assert validator({"test": "value"}) == ["value"] def assert_api_error(response, msg: str | None = None) -> None: """Asserts that the response errored and contains the message.""" assert response.status_code == 200 assert not response.json["success"], response.json if msg: assert msg == response.json["error"] def assert_api_success(response, data: Any | None = None) -> None: """Asserts that the request was successful and contains the data.""" assert response.status_code == 200 assert response.json["success"], response.json if data: assert data == response.json["data"] def test_api_changed(app, test_client: FlaskClient) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for("json_api.get_changed") response = test_client.get(url) assert_api_success(response, False) def test_api_add_document( app, test_client: FlaskClient, tmp_path, monkeypatch ) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() monkeypatch.setitem(g.ledger.options, "documents", [str(tmp_path)]) request_data = { "folder": str(tmp_path), "account": "Expenses:Food:Restaurant", "file": (BytesIO(b"asdfasdf"), "2015-12-12 test"), } url = url_for("json_api.put_add_document") response = test_client.put(url) assert_api_error(response, "No file uploaded.") filename = ( tmp_path / "Expenses" / "Food" / "Restaurant" / "2015-12-12 test" ) response = test_client.put(url, data=request_data) assert_api_success(response, f"Uploaded to {filename}") assert Path(filename).is_file() request_data["file"] = (BytesIO(b"asdfasdf"), "2015-12-12 test") response = test_client.put(url, data=request_data) assert_api_error(response, f"{filename} already exists.") def test_api_errors(app, test_client: FlaskClient) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for("json_api.get_errors") response = test_client.get(url) assert_api_success(response, 0) def test_api_context( app, test_client: FlaskClient, snapshot, example_ledger: FavaLedger ) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for("json_api.get_context") response = test_client.get(url) assert_api_error( response, "Invalid API request: Parameter `entry_hash` is missing." ) url = url_for( "json_api.get_context", entry_hash=hash_entry( next( entry for entry in example_ledger.all_entries if entry.meta["lineno"] == 3732 ) ), ) response = test_client.get(url) assert_api_success(response) snapshot(response.json) url = url_for( "json_api.get_context", entry_hash=hash_entry(example_ledger.entries[10]), ) response = test_client.get(url) assert_api_success(response) assert not response.json.get("balances_before") snapshot(response.json) def test_api_payee_accounts(app, test_client: FlaskClient) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for("json_api.get_payee_accounts", payee="test") response = test_client.get(url) assert_api_success(response, []) def test_api_move(app, test_client: FlaskClient) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for("json_api.get_move") response = test_client.get(url) assert_api_error( response, "Invalid API request: Parameter `account` is missing." ) def test_api_source_put(app, test_client: FlaskClient) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for("json_api.put_source") path = g.ledger.beancount_file_path # test bad request response = test_client.put(url) assert_api_error(response, "Invalid JSON request.") with open(path, encoding="utf-8") as file_handle: payload = file_handle.read() with open(path, mode="rb") as bfile_handle: sha256sum = hashlib.sha256(bfile_handle.read()).hexdigest() # change source response = test_client.put( url, data=dumps( { "source": "asdf" + payload, "sha256sum": sha256sum, "file_path": path, } ), content_type="application/json", ) with open(path, mode="rb") as bfile_handle: sha256sum = hashlib.sha256(bfile_handle.read()).hexdigest() assert_api_success(response, sha256sum) # check if the file has been written with open(path, encoding="utf-8") as file_handle: assert file_handle.read() == "asdf" + payload # write original source file result = test_client.put( url, data=dumps( {"source": payload, "sha256sum": sha256sum, "file_path": path} ), content_type="application/json", ) assert result.status_code == 200 with open(path, encoding="utf-8") as file_handle: assert file_handle.read() == payload def test_api_format_source(app, test_client: FlaskClient) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for("json_api.put_format_source") path = g.ledger.beancount_file_path with open(path, encoding="utf-8") as file_handle: payload = file_handle.read() response = test_client.put( url, data=dumps({"source": payload}), content_type="application/json", ) assert_api_success(response, align(payload, 61)) def test_api_format_source_options( app, test_client: FlaskClient, monkeypatch ) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() path = g.ledger.beancount_file_path with open(path, encoding="utf-8") as file_handle: payload = file_handle.read() url = url_for("json_api.put_format_source") monkeypatch.setattr(g.ledger.fava_options, "currency_column", 90) response = test_client.put( url, data=dumps({"source": payload}), content_type="application/json", ) assert_api_success(response, align(payload, 90)) def test_api_add_entries(app, test_client: FlaskClient, tmp_path, monkeypatch): with app.test_request_context("/long-example/"): app.preprocess_request() test_file = tmp_path / "test_file" test_file.open("a") monkeypatch.setattr(g.ledger, "beancount_file_path", str(test_file)) data = { "entries": [ { "type": "Transaction", "date": "2017-12-12", "flag": "*", "payee": "Test3", "narration": "", "meta": {}, "postings": [ { "account": "Assets:US:ETrade:Cash", "amount": "100 USD", }, {"account": "Assets:US:ETrade:GLD"}, ], }, { "type": "Transaction", "date": "2017-01-12", "flag": "*", "payee": "Test1", "narration": "", "meta": {}, "postings": [ { "account": "Assets:US:ETrade:Cash", "amount": "100 USD", }, {"account": "Assets:US:ETrade:GLD"}, ], }, { "type": "Transaction", "date": "2017-02-12", "flag": "*", "payee": "Test", "narration": "Test", "meta": {}, "postings": [ { "account": "Assets:US:ETrade:Cash", "amount": "100 USD", }, {"account": "Assets:US:ETrade:GLD"}, ], }, ] } url = url_for("json_api.put_add_entries") response = test_client.put( url, data=dumps(data), content_type="application/json" ) assert_api_success(response, "Stored 3 entries.") assert ( test_file.read_text("utf-8") == """ 2017-01-12 * "Test1" "" Assets:US:ETrade:Cash 100 USD Assets:US:ETrade:GLD 2017-02-12 * "Test" "Test" Assets:US:ETrade:Cash 100 USD Assets:US:ETrade:GLD 2017-12-12 * "Test3" "" Assets:US:ETrade:Cash 100 USD Assets:US:ETrade:GLD """ ) @pytest.mark.parametrize( "query_string,result_str", [ ("balances from year = 2014", "5086.65 USD"), ("nononono", "ERROR: Syntax error near"), ("select sum(day)", "43558"), ], ) def test_api_query_result( query_string, result_str, app, test_client: FlaskClient ) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for("json_api.get_query_result", query_string=query_string) response = test_client.get(url) assert response.status_code == 200 assert result_str in response.get_data(True) def test_api_query_result_filters(app, test_client: FlaskClient) -> None: with app.test_request_context("/long-example/"): app.preprocess_request() url = url_for( "json_api.get_query_result", query_string="select sum(day)", time="2021", ) response = test_client.get(url) assert response.status_code == 200 assert "6882" in response.get_data(True)

31.564972

79

0.578038

0

600

0.053696

0

2,586

0.23143

a747752e784483f13e0672fa7ef44261d743dd9f

403

py

Python

babybuddy/migrations/0017_promocode_max_usage_per_account.py

amcquistan/babyasst

310a7948f06b71ae0d62593a3b5932abfd4eb444

[ "BSD-2-Clause" ]

null

babybuddy/migrations/0017_promocode_max_usage_per_account.py

amcquistan/babyasst

310a7948f06b71ae0d62593a3b5932abfd4eb444

[ "BSD-2-Clause" ]

null

babybuddy/migrations/0017_promocode_max_usage_per_account.py

amcquistan/babyasst

310a7948f06b71ae0d62593a3b5932abfd4eb444

[ "BSD-2-Clause" ]

null

# Generated by Django 2.2.6 on 2019-11-27 20:28 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('babybuddy', '0016_auto_20191127_1424'), ] operations = [ migrations.AddField( model_name='promocode', name='max_usage_per_account', field=models.IntegerField(default=1), ), ]

21.210526

49

0.615385

310

0.769231

0

117

0.290323

a74a33620df33a15eb19e53e0f2d731815811072

6,218

py

Python

tests/test_upload.py

LuminosoInsight/luminoso-api-client-python

bae7db9b02123718ded5a8345a860bd12680b367

[ "MIT" ]

5

2016-09-14T02:02:30.000Z

2021-06-20T21:11:19.000Z

tests/test_upload.py

LuminosoInsight/luminoso-api-client-python

bae7db9b02123718ded5a8345a860bd12680b367

[ "MIT" ]

29

2015-01-13T15:07:38.000Z

2021-06-14T21:03:06.000Z

tests/test_upload.py

LuminosoInsight/luminoso-api-client-python

bae7db9b02123718ded5a8345a860bd12680b367

[ "MIT" ]

3

2016-03-07T13:04:34.000Z

2017-08-07T21:15:53.000Z

from luminoso_api.v5_client import LuminosoClient from luminoso_api.v5_upload import create_project_with_docs, BATCH_SIZE from unittest.mock import patch import pytest BASE_URL = 'http://mock-api.localhost/api/v5/' DOCS_TO_UPLOAD = [ {'title': 'Document 1', 'text': 'Bonjour', 'extra': 'field'}, {'title': 'Document 2', 'text': 'Au revoir'}, ] DOCS_UPLOADED = [ {'title': 'Document 1', 'text': 'Bonjour', 'metadata': []}, {'title': 'Document 2', 'text': 'Au revoir', 'metadata': []}, ] REPETITIVE_DOC = {'title': 'Yadda', 'text': 'yadda yadda', 'metadata': []} def _build_info_response(ndocs, language, done): """ Construct the expected response when we get the project's info after requesting a build. """ response = { 'json': { 'project_id': 'projid', 'document_count': ndocs, 'language': language, 'last_build_info': { 'number': 1, 'start_time': 0., 'stop_time': None, }, } } if done: response['json']['last_build_info']['success'] = True response['json']['last_build_info']['stop_time'] = 1. return response def test_project_creation(requests_mock): """ Test creating a project by mocking what happens when it is successful. """ # First, configure what the mock responses should be: # The initial response from creating the project requests_mock.post( BASE_URL + 'projects/', json={ 'project_id': 'projid', 'document_count': 0, 'language': 'fr', 'last_build_info': None, }, ) # Empty responses from further build steps requests_mock.post(BASE_URL + 'projects/projid/upload/', json={}) requests_mock.post(BASE_URL + 'projects/projid/build/', json={}) # Build status response, which isn't done yet the first time it's checked, # and is done the second time requests_mock.get( BASE_URL + 'projects/projid/', [ _build_info_response(2, 'fr', done=False), _build_info_response(2, 'fr', done=True), ], ) # Now run the main uploader function and get the result client = LuminosoClient.connect(BASE_URL, token='fake') with patch('time.sleep', return_value=None): response = create_project_with_docs( client, DOCS_TO_UPLOAD, language='fr', name='Projet test', progress=False, ) # Test that the right sequence of requests happened history = requests_mock.request_history assert history[0].method == 'POST' assert history[0].url == BASE_URL + 'projects/' params = history[0].json() assert params['name'] == 'Projet test' assert params['language'] == 'fr' assert history[1].method == 'POST' assert history[1].url == BASE_URL + 'projects/projid/upload/' params = history[1].json() assert params['docs'] == DOCS_UPLOADED assert history[2].method == 'POST' assert history[2].url == BASE_URL + 'projects/projid/build/' assert history[2].json() == {} assert history[3].method == 'GET' assert history[3].url == BASE_URL + 'projects/projid/' assert history[4].method == 'GET' assert history[4].url == BASE_URL + 'projects/projid/' assert len(history) == 5 assert response['last_build_info']['success'] def test_missing_text(requests_mock): """ Test a project that fails to be created, on the client side, because a bad document is supplied. """ # The initial response from creating the project requests_mock.post( BASE_URL + 'projects/', json={ 'project_id': 'projid', 'document_count': 0, 'language': 'en', 'last_build_info': None, }, ) with pytest.raises(ValueError): client = LuminosoClient.connect(BASE_URL, token='fake') create_project_with_docs( client, [{'bad': 'document'}], language='en', name='Bad project test', progress=False, ) def test_pagination(requests_mock): """ Test that we can create a project whose documents would be broken into multiple pages, and when we iterate over its documents, we correctly request all the pages. """ # The initial response from creating the project requests_mock.post( BASE_URL + 'projects/', json={ 'project_id': 'projid', 'document_count': 0, 'language': 'fr', 'last_build_info': None, }, ) # Empty responses from further build steps requests_mock.post(BASE_URL + 'projects/projid/upload/', json={}) requests_mock.post(BASE_URL + 'projects/projid/build/', json={}) ndocs = BATCH_SIZE + 2 # Build status response, which isn't done yet the first or second time # it's checked, and is done the third time requests_mock.get( BASE_URL + 'projects/projid/', [ _build_info_response(ndocs, 'fr', done=False), _build_info_response(ndocs, 'fr', done=False), _build_info_response(ndocs, 'fr', done=True), ], ) # Now run the main uploader function and get the result client = LuminosoClient.connect(BASE_URL, token='fake') with patch('time.sleep', return_value=None): create_project_with_docs( client, [REPETITIVE_DOC] * (BATCH_SIZE + 2), language='fr', name='Projet test', progress=False, ) # Test that the right sequence of requests happened, this time just as # a list of URLs history = requests_mock.request_history reqs = [(req.method, req.url) for req in history] assert reqs == [ ('POST', BASE_URL + 'projects/'), ('POST', BASE_URL + 'projects/projid/upload/'), ('POST', BASE_URL + 'projects/projid/upload/'), ('POST', BASE_URL + 'projects/projid/build/'), ('GET', BASE_URL + 'projects/projid/'), ('GET', BASE_URL + 'projects/projid/'), ('GET', BASE_URL + 'projects/projid/'), ]

31.72449

78

0.595529

0

2,525

0.406079

a74ad7dc8ca825fa0b64d0132540f37da6f4e67a

1,259

py

Python

src/oca_github_bot/webhooks/on_command.py

eLBati/oca-github-bot

4fa974f8ec123c9ccfd7bcad22e4baa939c985ac

[ "MIT" ]

null

src/oca_github_bot/webhooks/on_command.py

eLBati/oca-github-bot

4fa974f8ec123c9ccfd7bcad22e4baa939c985ac

[ "MIT" ]

null

src/oca_github_bot/webhooks/on_command.py

eLBati/oca-github-bot

4fa974f8ec123c9ccfd7bcad22e4baa939c985ac

[ "MIT" ]

null

# Copyright (c) initOS GmbH 2019 # Distributed under the MIT License (http://opensource.org/licenses/MIT). from ..commands import CommandError, parse_commands from ..config import OCABOT_EXTRA_DOCUMENTATION, OCABOT_USAGE from ..router import router from ..tasks.add_pr_comment import add_pr_comment @router.register("issue_comment", action="created") async def on_command(event, gh, *args, **kwargs): """On pull request review, tag if approved or ready to merge.""" if not event.data["issue"].get("pull_request"): # ignore issue comments return org, repo = event.data["repository"]["full_name"].split("/") pr = event.data["issue"]["number"] username = event.data["comment"]["user"]["login"] text = event.data["comment"]["body"] try: for command in parse_commands(text): command.delay(org, repo, pr, username) except CommandError as e: # Add a comment on the current PR, if # the command were misunderstood by the bot add_pr_comment.delay( org, repo, pr, f"Hi @{username}. Your command failed:\n\n" f"``{e}``.\n\n" f"{OCABOT_USAGE}\n\n" f"{OCABOT_EXTRA_DOCUMENTATION}", )

34.972222

73

0.629071

0

956

0.759333

904

0.71803

505

0.401112

a74cb2eb35421327d8faf002d2a0cd393a5579ab

1,151

py

Python

splitListToParts.py

pflun/learningAlgorithms

3101e989488dfc8a56f1bf256a1c03a837fe7d97

[ "MIT" ]

null

splitListToParts.py

pflun/learningAlgorithms

3101e989488dfc8a56f1bf256a1c03a837fe7d97

[ "MIT" ]

null

splitListToParts.py

pflun/learningAlgorithms

3101e989488dfc8a56f1bf256a1c03a837fe7d97

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Definition for singly-linked list. class ListNode(object): def __init__(self, x): self.val = x self.next = None class Solution(object): def splitListToParts(self, root, k): res = [] size = 0 traverse = root while traverse: size += 1 traverse = traverse.next # 制作一个queue，[4, 3, 3]，代表每一回合走几步 d, r = divmod(size, k) queue = [] for _ in range(d): queue.append(k) for i in range(r): queue[i] += 1 # 从queue里取该回合走的步数，一边走一边加入tmp，然后tmp加入res for q in queue: tmp = [] for _ in range(q): tmp.append(root) root = root.next res.append(tmp) return res head = ListNode(1) p1 = ListNode(2) p2 = ListNode(3) p3 = ListNode(4) p4 = ListNode(5) p5 = ListNode(6) p6 = ListNode(7) p7 = ListNode(8) p8 = ListNode(9) p9 = ListNode(10) head.next = p1 p1.next = p2 p2.next = p3 p3.next = p4 p4.next = p5 p5.next = p6 p6.next = p7 p7.next = p8 p8.next = p9 test = Solution() print test.splitListToParts(head, 3)

20.553571

47

0.536056

815

0.664222

0

205

0.167074

a74cdb915e99b5e47e7fb18dd30921d17381a256

7,744

py

Python

pmlearn/mixture/tests/test_dirichlet_process.py

john-veillette/pymc-learn

267b0084438616b869866194bc167c332c3e3547

[ "BSD-3-Clause" ]

187

2018-10-16T02:33:51.000Z

2022-03-27T14:06:36.000Z

pmlearn/mixture/tests/test_dirichlet_process.py

john-veillette/pymc-learn

267b0084438616b869866194bc167c332c3e3547

[ "BSD-3-Clause" ]

20

2018-10-31T15:13:29.000Z

2022-01-20T18:54:00.000Z

pmlearn/mixture/tests/test_dirichlet_process.py

john-veillette/pymc-learn

267b0084438616b869866194bc167c332c3e3547

[ "BSD-3-Clause" ]

20

2018-10-19T21:32:06.000Z

2022-02-07T06:04:55.000Z

import unittest import shutil import tempfile import numpy as np # import pandas as pd # import pymc3 as pm # from pymc3 import summary # from sklearn.mixture import BayesianGaussianMixture as skBayesianGaussianMixture from sklearn.model_selection import train_test_split from pmlearn.exceptions import NotFittedError from pmlearn.mixture import DirichletProcessMixture class DirichletProcessMixtureTestCase(unittest.TestCase): def setUp(self): self.num_truncate = 3 self.num_components = 3 self.num_pred = 1 self.num_training_samples = 100 self.pi = np.array([0.35, 0.4, 0.25]) self.means = np.array([0, 5, 10]) self.sigmas = np.array([0.5, 0.5, 1.0]) self.components = np.random.randint(0, self.num_components, self.num_training_samples) X = np.random.normal(loc=self.means[self.components], scale=self.sigmas[self.components]) X.shape = (self.num_training_samples, 1) self.X_train, self.X_test = train_test_split(X, test_size=0.3) self.test_DPMM = DirichletProcessMixture() self.test_nuts_DPMM = DirichletProcessMixture() self.test_dir = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.test_dir) # class DirichletProcessMixtureFitTestCase(DirichletProcessMixtureTestCase): # def test_advi_fit_returns_correct_model(self): # # This print statement ensures PyMC3 output won't overwrite the test name # print('') # self.test_DPMM.fit(self.X_train) # # self.assertEqual(self.num_pred, self.test_DPMM.num_pred) # self.assertEqual(self.num_components, self.test_DPMM.num_components) # self.assertEqual(self.num_truncate, self.test_DPMM.num_truncate) # # self.assertAlmostEqual(self.pi[0], # self.test_DPMM.summary['mean']['pi__0'], # 0) # self.assertAlmostEqual(self.pi[1], # self.test_DPMM.summary['mean']['pi__1'], # 0) # self.assertAlmostEqual(self.pi[2], # self.test_DPMM.summary['mean']['pi__2'], # 0) # # self.assertAlmostEqual( # self.means[0], # self.test_DPMM.summary['mean']['cluster_center_0__0'], # 0) # self.assertAlmostEqual( # self.means[1], # self.test_DPMM.summary['mean']['cluster_center_1__0'], # 0) # self.assertAlmostEqual( # self.means[2], # self.test_DPMM.summary['mean']['cluster_center_2__0'], # 0) # # self.assertAlmostEqual( # self.sigmas[0], # self.test_DPMM.summary['mean']['cluster_variance_0__0'], # 0) # self.assertAlmostEqual( # self.sigmas[1], # self.test_DPMM.summary['mean']['cluster_variance_1__0'], # 0) # self.assertAlmostEqual( # self.sigmas[2], # self.test_DPMM.summary['mean']['cluster_variance_2__0'], # 0) # # def test_nuts_fit_returns_correct_model(self): # # This print statement ensures PyMC3 output won't overwrite the test name # print('') # self.test_nuts_DPMM.fit(self.X_train, # inference_type='nuts', # inference_args={'draws': 1000, # 'chains': 2}) # # self.assertEqual(self.num_pred, self.test_nuts_DPMM.num_pred) # self.assertEqual(self.num_components, self.test_nuts_DPMM.num_components) # self.assertEqual(self.num_components, self.test_nuts_DPMM.num_truncate) # # self.assertAlmostEqual(self.pi[0], # self.test_nuts_DPMM.summary['mean']['pi__0'], # 0) # self.assertAlmostEqual(self.pi[1], # self.test_nuts_DPMM.summary['mean']['pi__1'], # 0) # self.assertAlmostEqual(self.pi[2], # self.test_nuts_DPMM.summary['mean']['pi__2'], # 0) # # self.assertAlmostEqual( # self.means[0], # self.test_nuts_DPMM.summary['mean']['cluster_center_0__0'], # 0) # self.assertAlmostEqual( # self.means[1], # self.test_nuts_DPMM.summary['mean']['cluster_center_1__0'], # 0) # self.assertAlmostEqual( # self.means[2], # self.test_nuts_DPMM.summary['mean']['cluster_center_2__0'], # 0) # # self.assertAlmostEqual( # self.sigmas[0], # self.test_nuts_DPMM.summary['mean']['cluster_variance_0__0'], # 0) # self.assertAlmostEqual( # self.sigmas[1], # self.test_nuts_DPMM.summary['mean']['cluster_variance_1__0'], # 0) # self.assertAlmostEqual( # self.sigmas[2], # self.test_nuts_DPMM.summary['mean']['cluster_variance_2__0'], # 0) # # class DirichletProcessMixturePredictTestCase(DirichletProcessMixtureTestCase): # def test_predict_returns_predictions(self): # print('') # self.test_DPMM.fit(self.X_train, self.y_train) # preds = self.test_DPMM.predict(self.X_test) # self.assertEqual(self.y_test.shape, preds.shape) # def test_predict_returns_mean_predictions_and_std(self): # print('') # self.test_DPMM.fit(self.X_train, self.y_train) # preds, stds = self.test_DPMM.predict(self.X_test, return_std=True) # self.assertEqual(self.y_test.shape, preds.shape) # self.assertEqual(self.y_test.shape, stds.shape) def test_predict_raises_error_if_not_fit(self): print('') with self.assertRaises(NotFittedError) as no_fit_error: test_DPMM = DirichletProcessMixture() test_DPMM.predict(self.X_train) expected = 'Run fit on the model before predict.' self.assertEqual(str(no_fit_error.exception), expected) # class DirichletProcessMixtureScoreTestCase(DirichletProcessMixtureTestCase): # def test_score_matches_sklearn_performance(self): # print('') # skDPMM = skBayesianGaussianMixture(n_components=3) # skDPMM.fit(self.X_train) # skDPMM_score = skDPMM.score(self.X_test) # # self.test_DPMM.fit(self.X_train) # test_DPMM_score = self.test_DPMM.score(self.X_test) # # self.assertAlmostEqual(skDPMM_score, test_DPMM_score, 0) # # # class DirichletProcessMixtureSaveAndLoadTestCase(DirichletProcessMixtureTestCase): # def test_save_and_load_work_correctly(self): # print('') # self.test_DPMM.fit(self.X_train) # score1 = self.test_DPMM.score(self.X_test) # self.test_DPMM.save(self.test_dir) # # DPMM2 = DirichletProcessMixture() # DPMM2.load(self.test_dir) # # self.assertEqual(self.test_DPMM.inference_type, DPMM2.inference_type) # self.assertEqual(self.test_DPMM.num_pred, DPMM2.num_pred) # self.assertEqual(self.test_DPMM.num_training_samples, # DPMM2.num_training_samples) # self.assertEqual(self.test_DPMM.num_truncate, DPMM2.num_truncate) # # pd.testing.assert_frame_equal(summary(self.test_DPMM.trace), # summary(DPMM2.trace)) # # score2 = DPMM2.score(self.X_test) # self.assertAlmostEqual(score1, score2, 0)

38.914573

84

0.594008

1,995

0.257619

0

5,949

0.768208

a74d82ac6813ed8153326a2d69c62b3256148e18

1,096

py

Python

algorithms/utils.py

billvb/oblio-game

c1c95b9d7bffe4e2841a978e4338cf72c38174ac

[ "MIT" ]

2

2016-03-20T03:03:18.000Z

2021-02-15T22:23:44.000Z

algorithms/utils.py

billvb/oblio-game

c1c95b9d7bffe4e2841a978e4338cf72c38174ac

[ "MIT" ]

null

algorithms/utils.py

billvb/oblio-game

c1c95b9d7bffe4e2841a978e4338cf72c38174ac

[ "MIT" ]

null

import random TUPLE_SIZE = 4 DIGIT_BASE = 10 MAX_GUESS = DIGIT_BASE ** TUPLE_SIZE def yield_all(): for i in xrange(DIGIT_BASE ** TUPLE_SIZE): tup = tuple([int(x) for x in '%04d' % i]) assert len(tup) == TUPLE_SIZE for l in tup: if tup.count(l) != 1: break else: yield OblioTuple(tup) def weighted_dice_roll(weight_map, exclusions): # Actually, this does an UNWEIGHTED dice roll. Never got around to doing weighted. # Don't think it would matter much anyway. new_map = {k: v for k, v in weight_map.iteritems() if k not in exclusions} return new_map.keys()[random.randint(0, len(new_map) - 1)] class OblioTuple(tuple): @staticmethod def get_random(): pile = range(0, DIGIT_BASE) secret = [] for i in xrange(0, TUPLE_SIZE): r = random.randint(0, len(pile) - 1) secret.append(pile[r]) del pile[r] # Assert that the tuple contains 4 distinct digits assert len(list(set(secret))) == 4 return OblioTuple(secret)

28.842105

86

0.603102

406

0.370438

277

0.252737

377

0.343978

0

180

0.164234

a74d8736deea9179712853219ede84e9608d42dd

1,276

py

Python

utils/utils.py

cheng052/H3DNet

872dabb37d8c2ca3581cf4e242014e6464debe57

[ "MIT" ]

212

2020-06-11T01:03:36.000Z

2022-03-17T17:29:21.000Z

utils/utils.py

cheng052/H3DNet

872dabb37d8c2ca3581cf4e242014e6464debe57

[ "MIT" ]

25

2020-06-15T13:35:13.000Z

2022-03-10T05:44:05.000Z

utils/utils.py

cheng052/H3DNet

872dabb37d8c2ca3581cf4e242014e6464debe57

[ "MIT" ]

24

2020-06-11T01:17:24.000Z

2022-03-30T13:34:45.000Z

import torch import torch.nn as nn import torch.nn.functional as F def conv3x3x3(in_planes, out_planes, stride): # 3x3x3 convolution with padding return nn.Conv3d( in_planes, out_planes, kernel_size=3, stride=stride, padding=1) def upconv3x3x3(in_planes, out_planes, stride): return nn.ConvTranspose3d( in_planes, out_planes, kernel_size=3, stride=1, padding=1, output_padding=1) def conv_block_3d(in_dim, out_dim, activation): return nn.Sequential( nn.Conv3d(in_dim, out_dim, kernel_size=3, stride=1, padding=1), nn.BatchNorm3d(out_dim), activation,) def conv_trans_block_3d(in_dim, out_dim, activation, stride=2): return nn.Sequential( nn.ConvTranspose3d(in_dim, out_dim, kernel_size=3, stride=stride, padding=1, output_padding=1), nn.BatchNorm3d(out_dim), activation,) def max_pooling_3d(): return nn.MaxPool3d(kernel_size=2, stride=2, padding=0) def conv_block_2_3d(in_dim, out_dim, activation, stride=1): return nn.Sequential( conv_block_3d(in_dim, out_dim, activation), nn.Conv3d(out_dim, out_dim, kernel_size=3, stride=stride, padding=1), nn.BatchNorm3d(out_dim),)

27.73913

103

0.670063

0

32

0.025078

a74f41b8c63e9716f46430fe18d6b543d0682cb3

8,258

py

Python

device/app.py

panjanek/IotCenter

e139617d14617c10a18c35515e2d3aaae797bcac

[ "MIT" ]

2

2016-12-12T15:16:16.000Z

2018-10-30T02:35:36.000Z

device/app.py

panjanek/IotCenter

e139617d14617c10a18c35515e2d3aaae797bcac

[ "MIT" ]

null

device/app.py

panjanek/IotCenter

e139617d14617c10a18c35515e2d3aaae797bcac

[ "MIT" ]

null

import logging import threading import json import base64 import os from subprocess import Popen import glob import time import urllib2 import re import string import datetime class DeviceHandler: logger = logging.getLogger() def __init__(self, config): self.service = None self.tunnel = None self.video = None self.config = config self.first = True self.counter = 1; self.uploadfile = '/tmp/upload.txt' def start(self): self.logger.info("starting device handler") def getMessagePayload(self): self.logger.debug("Preparing client->device message payload") gputemp = os.popen("vcgencmd measure_temp").readline().replace("temp=","").replace("'C","") cputemp = os.popen("cat /sys/class/thermal/thermal_zone0/temp").readline() payloadDict = {"values":{}} payloadDict["mid"] = self.counter self.counter += 1 payloadDict["values"]["status"] = 1 payloadDict["values"]["gpu_temp"] = float(gputemp) payloadDict["values"]["cpu_temp"] = float(cputemp) / 1000 log = self.getLogToUpload() if log is not None: payloadDict["log"] = log payload = json.dumps(payloadDict) return payload def getLogToUpload(self): log = None if self.first: self.first = False with open(self.uploadfile, "a") as upfile: upfile.write("First message, communucation started\n") uploadfiletmp = self.uploadfile + ".tmp" if os.path.exists(self.uploadfile) and os.path.getsize(self.uploadfile) > 0: with open(self.uploadfile, 'r+') as upfile: content = upfile.read() upfile.truncate(0) self.logger.info("found log data to upload: {0}, moving to {1}".format(content, uploadfiletmp)) with open(uploadfiletmp, "a") as tmpfile: tmpfile.write(content) if os.path.exists(uploadfiletmp) and os.path.getsize(uploadfiletmp) > 0: with open(uploadfiletmp, 'r') as tmpfile: toupload = tmpfile.read() log = toupload return log def handleServerCall(self, payload): self.logger.info("Handling server callback with payload {0}".format(payload)) payloadDict = json.loads(payload) if "ack" in payloadDict: mid = payloadDict["ack"] self.logger.info("received ack for mid {0}".format(mid)) uploadfiletmp = self.uploadfile + ".tmp" if mid == self.counter - 1 and os.path.exists(uploadfiletmp) and os.path.getsize(uploadfiletmp) > 0: self.logger.info("Removing file {0}".format(uploadfiletmp)) os.remove(uploadfiletmp) if "command" in payloadDict: command = payloadDict["command"] self.logger.info("Received command: {0}".format(command)) if command == "blink": self.logger.info("Blinking status LED") os.system("echo none | sudo tee /sys/class/leds/led0/trigger") os.system("echo 1 | sudo tee /sys/class/leds/led0/brightness") time.sleep(0.5) os.system("echo 0 | sudo tee /sys/class/leds/led0/brightness") time.sleep(0.5) os.system("echo 1 | sudo tee /sys/class/leds/led0/brightness") time.sleep(0.5) os.system("echo 0 | sudo tee /sys/class/leds/led0/brightness") time.sleep(0.5) os.system("echo 1 | sudo tee /sys/class/leds/led0/brightness") time.sleep(0.5) os.system("echo 0 | sudo tee /sys/class/leds/led0/brightness") time.sleep(0.5) os.system("echo 1 | sudo tee /sys/class/leds/led0/brightness") elif command == "reboot": self.logger.info("REBOOT!!!") os.system("sudo reboot") elif command == "photo": quality = payloadDict.get("quality", "sd") self.logger.info("Taking {0} photo".format(quality)) photoFile = "/tmp/snapshot_{0}.jpg".format(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')) if quality == "hd": os.system("raspistill -hf -t 1000 -o {0}".format(photoFile)) else: os.system("raspistill -hf -t 1000 -w 640 -h 480 -o {0}".format(photoFile)) with open(photoFile, mode='rb') as file: photoData = file.read() base64data = base64.b64encode(photoData) self.service.sendMessage(json.dumps({'image':base64data, 'type':'jpg'})) elif command == "relay": state = payloadDict.get("state", 1) self.logger.info("Changing relay state to: {0}".format(state)) os.system("curl {0}/?relay={1}".format(relay1_addr, state)) elif command == "light": state = payloadDict.get("state", 1) self.logger.info("Changing light state to: {0}".format(state)) if state == 0: led_rgb(0,0,0) else: led_rgb(1,1,0) elif command == "tunnel": if self.tunnel: self.logger.warning("Tunnel already active - ingoring command") else: remotePort = payloadDict.get("remotePort", 18888) localPort = payloadDict.get("localPort", 22) addr = payloadDict["addr"] self.startTunnel(remotePort, localPort, addr) elif command == "video": if self.tunnel: self.logger.warning("Tunnel already active - ingoring command") else: port = payloadDict.get("port", 8081) addr = payloadDict["addr"] self.startVideo(port, addr) elif command == "tunnel-close": if self.tunnel: self.logger.info("terminating tunnel process") self.tunnel.kill() self.tunnel = None else: self.logger.warning("no tunnel process active, ignoring command") if self.video: self.logger.info("terminating video process") self.video.kill() self.video = None else: self.logger.info("Command '{0}' unknown".format(command)) def startTunnel(self, remotePort, localPort, addr): sshPrivateKeyFile = self.config.get('client', 'sshPrivateKeyFile') self.logger.info("Opening SSH tunneling session for remotePort={0}, localPort={1}, addr={2} using privateKey={3}".format(remotePort, localPort, addr, sshPrivateKeyFile)) cmd = "/usr/bin/ssh -o BatchMode=yes -o StrictHostKeyChecking=no -i {0} -N -R {1}:localhost:{2} {3}".format(sshPrivateKeyFile, remotePort, localPort, addr) self.logger.info("Starting process: {0}".format(cmd)) self.tunnel = Popen(cmd.split()) self.logger.info("SSH tunneling process started") def startVideo(self, port, addr): sshPrivateKeyFile = self.config.get('client', 'sshPrivateKeyFile') self.logger.info("Starting video streaming session") self.logger.info("loading driver bcm2835-v4l2") os.system("sudo modprobe bcm2835-v4l2") time.sleep(0.5) cmdVideo = "sudo motion" self.logger.info("Starting processes: {0}".format(cmdVideo)) self.video = Popen(cmdVideo.split()) cmdTunnel = "sudo /usr/bin/ssh -o BatchMode=yes -o StrictHostKeyChecking=no -i {0} -N -R {1}:localhost:8081 {2}".format(sshPrivateKeyFile, port, addr) self.logger.info("Starting processes: {0}".format(cmdTunnel)) self.tunnel = Popen(cmdTunnel.split()) self.logger.info("SSH video tunneling session started")

47.45977

177

0.552434

8,073

0.977597

0

2,109

0.255389

a74fb2c9000b17ff11193cacddad30429c023b4c

7,882

py

Python

deepsource/utils.py

vafaei-ar/deepsource

cbb06f5a2105506b63539ae5bfe73a3e62d4055f

[ "BSD-3-Clause" ]

null

deepsource/utils.py

vafaei-ar/deepsource

cbb06f5a2105506b63539ae5bfe73a3e62d4055f

[ "BSD-3-Clause" ]

1

2020-12-15T10:03:50.000Z

2020-12-16T10:39:00.000Z

deepsource/utils.py

vafaei-ar/deepsource

cbb06f5a2105506b63539ae5bfe73a3e62d4055f

[ "BSD-3-Clause" ]

2

2019-09-02T10:24:22.000Z

2021-03-30T01:29:03.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import numpy as np from skimage import draw from skimage import measure from astropy.io import fits from astropy import units as u from astropy import wcs, coordinates from scipy.ndimage.filters import gaussian_filter def standard(X): """ standard : This function makes data ragbe between 0 and 1. Arguments: X (numoy array) : input data. -------- Returns: standard data. """ xmin = X.min() X = X-xmin xmax = X.max() X = X/xmax return X def fetch_data(image_file,model_file,do_standard=True,ignore_error=False): """ fetch_data : This function reads image and model. Arguments: image_file (string) : path to image file. model_file (string) : path to model file. do_standard (logical) (default=True) : if true, minimum/maximum value of image will be set to 0/1. -------- Returns: image, x coordinates, y coordinates """ with fits.open(image_file) as hdulist: data = hdulist[0].data header = hdulist[0].header lx = header['NAXIS1'] ly = header['NAXIS2'] coord_sys = wcs.WCS(header) model_file = model_file sources = np.loadtxt(model_file, dtype={'names': ('name', 'ra', 'dec', 'I'), 'formats': ('S10', 'f4', 'f4', 'f4')}) ra, dec = sources['ra'],sources['dec'] num_sources = len(ra) radec_coords = coordinates.SkyCoord(ra, dec, unit='deg', frame='fk5') coords_ar = np.vstack([radec_coords.ra*u.deg, radec_coords.dec*u.deg, np.zeros(num_sources), np.zeros(num_sources)]).T xy_coords = coord_sys.wcs_world2pix(coords_ar, 0) x_coords, y_coords = xy_coords[:,0], xy_coords[:,1] filt = (0<=x_coords) & (x_coords<lx) & (0<=y_coords) & (y_coords<ly) if ignore_error: x_coords, y_coords = x_coords[filt], y_coords[filt] else: assert np.sum(filt)==num_sources,'There are some sources out of images! The problem might be in coordinate conversion system or simulation!' if do_standard==True: data = standard(data) return np.moveaxis(data, 0, -1), x_coords, y_coords def fetch_data_3ch(image_file,model_file,do_standard=True): """ fetch_data_3ch : This function reads 3 images of 3 robust and model. Arguments: image_file (string) : path to robust 0 image file. model_file (string) : path to model file. do_standard (logical) (default=True) : if true, minimum/maximum value of image will be set to 0/1. -------- Returns: image, x coordinates, y coordinates """ data0, x_coords, y_coords = fetch_data(image_file,model_file,do_standard=do_standard) # lx,ly = data0[0,:,:,0].shape try: data1, x_coords, y_coords = fetch_data(image_file.replace('robust-0','robust-1'),model_file,do_standard=do_standard) except: assert 0,'Robust 1 does not exist.' try: data2, x_coords, y_coords = fetch_data(image_file.replace('robust-0','robust-2'),model_file,do_standard=do_standard) except: assert 0,'Robust 1 does not exist.' return np.concatenate((data0,data1,data2), axis=-1), x_coords, y_coords def cat2map(lx,ly,x_coords,y_coords): """ cat2map : This function converts a catalog to a 0/1 map which are representing background/point source. Arguments: lx (int): number of pixels of the image in first dimension. ly (int): number of pixels of the image in second dimension. x_coords (numpy array): list of the first dimension of point source positions. y_coords (numpy array): list of the second dimension of point source positions. -------- Returns: catalog image as nupmy array. """ cat = np.zeros((lx,ly)) for i,j in zip(x_coords.astype(int), y_coords.astype(int)): cat[j, i] = 1 return cat def magnifier(y,radius=15,value=1): """ magnifier (numpy array): This function magnifies any pixel with value one by a given value. Arguments: y : input 2D map. radius (int) (default=15) : radius of magnification. value (float) (default=True) : the value you want to use in magnified pixels. -------- Returns: image with magnified objects as numpy array. """ mag = np.zeros(y.shape) for i,j in np.argwhere(y==1): rr, cc = draw.circle(i, j, radius=radius, shape=mag.shape) mag[rr, cc] = value return mag def circle(y,radius=15): """ circle : This function add some circles around any pixel with value one. Arguments: y (numpy array): input 2D map. radius (int) (default=15): circle radius. -------- Returns: image with circles around objects. """ mag = np.zeros(y.shape) for i,j in np.argwhere(y==1): rr, cc = draw.circle_perimeter(i, j, radius=radius, shape=mag.shape) mag[rr, cc] = 1 return mag def horn_kernel(y,radius=10,step_height=1): """ horn_kernel : Horn shape kernel. Arguments: y (numpy array): input 2D map. radius (int) (default=15): effective radius of kernel. -------- Returns: kerneled image. """ mag = np.zeros(y.shape) for r in range(1,radius): for i,j in np.argwhere(y==1): rr, cc = draw.circle(i, j, radius=r, shape=mag.shape) mag[rr, cc] += 1.*step_height/radius return mag def gaussian_kernel(y,sigma=7): """ gaussian_kernel: Gaussian filter. Arguments: y (numpy array): input 2D map. sigma (float) (default=7): effective length of Gaussian smoothing. -------- Returns: kerneled image. """ return gaussian_filter(y, sigma) def ch_mkdir(directory): """ ch_mkdir : This function creates a directory if it does not exist. Arguments: directory (string): Path to the directory. -------- Returns: null. """ if not os.path.exists(directory): os.makedirs(directory) def the_print(text,style='bold',tc='gray',bgc='red'): """ prints table of formatted text format options """ colors = ['black','red','green','yellow','blue','purple','skyblue','gray'] if style == 'bold': style = 1 elif style == 'underlined': style = 4 else: style = 0 fg = 30+colors.index(tc) bg = 40+colors.index(bgc) form = ';'.join([str(style), str(fg), str(bg)]) print('\x1b[%sm %s \x1b[0m' % (form, text)) #def ps_extract(xp): # xp = xp-xp.min() # xp = xp/xp.max() # nb = [] # for trsh in np.linspace(0,0.2,200): # blobs = measure.label(xp>trsh) # nn = np.unique(blobs).shape[0] # nb.append(nn) # nb = np.array(nb) # nb = np.diff(nb) # trshs = np.linspace(0,0.2,200)[:-1] # thrsl = trshs[~((-5<nb) & (nb<5))] # if thrsl.shape[0]==0: # trsh = 0.1 # else: # trsh = thrsl[-1] #2: 15, 20 #3: 30,10 #4: 50, 10 # nnp = 0 # for tr in np.linspace(1,0,1000): # blobs = measure.label(xp>tr) # nn = np.unique(blobs).shape[0] # if nn-nnp>50: # break # nnp = nn # trsh = tr # blobs = measure.label(xp>trsh) # xl = [] # yl = [] # pl = [] # for v in np.unique(blobs)[1:]: # filt = blobs==v # pnt = np.round(np.mean(np.argwhere(filt),axis=0)).astype(int) # if filt.sum()>10: # xl.append(pnt[1]) # yl.append(pnt[0]) # pl.append(np.mean(xp[blobs==v])) # return np.array([xl,yl]).T,np.array(pl)

29.520599

148

0.586399

0

4,345

0.551256

a74fd79fe36c35a1329c69bf98a54c22cc8f9a55

12,349

py

Python

ftc/lib/net/network.py

efulet/ann_text_classification

fba05a1789a19aa6d607ee36069dda419bb98e28

[ "MIT" ]

null

ftc/lib/net/network.py

efulet/ann_text_classification

fba05a1789a19aa6d607ee36069dda419bb98e28

[ "MIT" ]

null

ftc/lib/net/network.py

efulet/ann_text_classification

fba05a1789a19aa6d607ee36069dda419bb98e28

[ "MIT" ]

null

""" @created_at 2015-01-18 @author Exequiel Fuentes Lettura <efulet@gmail.com> """ from pybrain.datasets import ClassificationDataSet from pybrain.tools.shortcuts import buildNetwork from pybrain.structure.modules import SoftmaxLayer from pybrain.supervised.trainers import BackpropTrainer from pybrain.utilities import percentError from pybrain.tools.validation import Validator # Only needed for data generation and graphical output import pylab as pl import numpy as np # Only needed for saving and loading trained network import pickle import os from lib.util import SystemUtils from network_exception import NetworkException class Network: """""" # Define the split proportion into 75% training and 25% test data sets SPLIT_PROPORTION = 0.25 # Define 5 hidden units HIDDEN_NEURONS = 5 # Define the momentum, which is the ratio by which the gradient of the last # timestep is used MOMENTUM = 0.1 # Weightdecay corresponds to the weightdecay rate, where 0 is no weight decay at all. WEIGHTDECAY = 0.01 # Define epochs EPOCHS = 100 def __init__(self, input, classes, options, logger=None): """ :param input: Dataset :param classes: Class values :param options: Optional vales :param logger: logger object [opcional] """ if input == None or len(input) == 0: raise NetworkException("Empty dataset") self._input = input if classes == None or len(classes) == 0: raise NetworkException("Empty class vector") self._classes = classes self._options = options if self._options.hidden_neurons: self._hidden_neurons = self._options.hidden_neurons else: self._hidden_neurons = self.HIDDEN_NEURONS if self._options.momentum: self._momentum = self._options.momentum else: self._momentum = self.MOMENTUM if self._options.weightdecay: self._weightdecay = self._options.weightdecay else: self._weightdecay = self.WEIGHTDECAY if self._options.epochs: self._epochs = self._options.epochs else: self._epochs = self.EPOCHS if self._options.verbose: self._verbose = True else: self._verbose = False self._logger = logger or SystemUtils().configure_log() self._dataset = None self._X_train = None self._X_test = None self._feed_forward_network = None self._X_train_results = [] self._X_test_results = [] def fit(self): """ Fit network using PyBrain library """ # Create the dataset # http://pybrain.org/docs/api/datasets/classificationdataset.html self._dataset = ClassificationDataSet(len(self._input[0][0]), 1, \ nb_classes=len(self._classes), \ class_labels=self._classes) # Add samples # http://pybrain.org/docs/tutorial/fnn.html for sample in self._input: self._dataset.addSample(sample[0], [sample[1]]) # Print statistics #print self._dataset.calculateStatistics() # Randomly split the dataset into 75% training and 25% test data sets. # Of course, we could also have created two different datasets to begin with. self._X_test, self._X_train = self._dataset.splitWithProportion(self.SPLIT_PROPORTION) # For neural network classification, it is highly advisable to encode # classes with one output neuron per class. Note that this operation # duplicates the original targets and stores them in an (integer) field # named 'class'. self._X_train._convertToOneOfMany() self._X_test._convertToOneOfMany() if self._verbose: # Test our dataset by printing a little information about it. self._logger.info("Number of training patterns: %4d" % len(self._X_train)) self._logger.info("Input dimensions: %4d" % self._X_train.indim) self._logger.info("Output dimensions: %4d" % self._X_train.outdim) #print "First sample (input, target, class):" #print self._X_train['input'][0], self._X_train['target'][0], self._X_train['class'][0] # Now build a feed-forward network with 5 hidden units. We use the shortcut # buildNetwork() for this. The input and output layer size must match # the dataset's input and target dimension. You could add additional # hidden layers by inserting more numbers giving the desired layer sizes. # The output layer uses a softmax function because we are doing classification. # There are more options to explore here, e.g. try changing the hidden # layer transfer function to linear instead of (the default) sigmoid. self._feed_forward_network = buildNetwork(self._X_train.indim, \ self._hidden_neurons, \ self._X_train.outdim, \ outclass=SoftmaxLayer) # Set up a trainer that basically takes the network and training dataset # as input. We are using a BackpropTrainer for this. trainer = BackpropTrainer(self._feed_forward_network, dataset=self._X_train, \ momentum=self._momentum, verbose=self._verbose, \ weightdecay=self._weightdecay) # Start the training iterations epoch_results = [] train_error_results = [] test_error_results = [] for i in xrange(self._epochs): # Train the network for some epochs. Usually you would set something # like 5 here, but for visualization purposes we do this one epoch # at a time. trainer.trainEpochs(1) # http://pybrain.org/docs/api/supervised/trainers.html X_train_result = percentError(trainer.testOnClassData(), self._X_train['class']) X_test_result = percentError(trainer.testOnClassData(dataset=self._X_test), self._X_test['class']) # Store the results epoch_results.append(trainer.totalepochs) train_error_results.append(X_train_result) test_error_results.append(X_test_result) if (trainer.totalepochs == 1 or trainer.totalepochs % 10 == 0 or \ trainer.totalepochs == self._epochs) and self._verbose: self._logger.info("Epoch: %4d" % trainer.totalepochs + " Train error: %5.2f%%" % X_train_result + " Test error: %5.2f%%" % X_test_result) # Now, plot the train and test data pl.figure(1) pl.ioff() # interactive graphics off pl.clf() # clear the plot pl.hold(True) # overplot on pl.plot(epoch_results, train_error_results, 'b', epoch_results, test_error_results, 'r') pl.xlabel('Epoch number') pl.ylabel('Error') pl.legend(['Training result', 'Test result']) pl.title('Training/Test results') pl.ion() # interactive graphics on pl.draw() # update the plot if self._verbose: # Print network coefs #self._logger.info(self._feed_forward_network['in'].outputbuffer[self._feed_forward_network['in'].offset]) #self._logger.info(self._feed_forward_network['hidden0'].outputbuffer[self._feed_forward_network['hidden0'].offset]) #self._logger.info(self._feed_forward_network['out'].outputbuffer[self._feed_forward_network['out'].offset]) # Finally, keep showing the plot. pl.ioff() # Store the results self._X_train_results = (epoch_results, train_error_results) self._X_test_results = (epoch_results, test_error_results) def predict(self, validation_dataset): """ Generate predictions :param validation_dataset: Validation dataset """ y_pred = [] for i in xrange(len(validation_dataset)): output = self._feed_forward_network.activate(validation_dataset[i][0]) class_index = max(xrange(len(output)), key=output.__getitem__) y_pred.append(class_index) return y_pred def classification_performance(self, output, target): """ Returns the hit rate of the outputs compared to the targets. http://pybrain.org/docs/api/tools.html#pybrain.tools.validation.Validator.classificationPerformance """ return Validator.classificationPerformance(np.array(output), np.array(target)) def explained_sum_squares(self, output, target): """ Returns the explained sum of squares (ESS). http://pybrain.org/docs/api/tools.html#pybrain.tools.validation.Validator.ESS """ return Validator.ESS(np.array(output), np.array(target)) def mean_squared_error(self, output, target): """ Returns the mean squared error. The multidimensional arrays will get flattened in order to compare them. http://pybrain.org/docs/api/tools.html#pybrain.tools.validation.Validator.MSE """ return Validator.MSE(np.array(output), np.array(target)) def show_plot(self): pl.show() def show_error(self): """ Show training and test process versus epochs """ pl.figure(1) pl.plot(self._X_train_results[0], self._X_train_results[1], 'b', self._X_test_results[0], self._X_test_results[1], 'r') pl.xlabel('Epoch number') pl.ylabel('Error') pl.legend(['Training result', 'Test result']) pl.title('Training/Test results') pl.draw() def show_layer(self): """ Show network layers in text format """ for mod in self._feed_forward_network.modules: print "Module:", mod.name if mod.paramdim > 0: print "--parameters:", mod.params for conn in self._feed_forward_network.connections[mod]: print "-connection to", conn.outmod.name if conn.paramdim > 0: print "- parameters", conn.params if hasattr(self._feed_forward_network, "recurrentConns"): print "Recurrent connections" for conn in self._feed_forward_network.recurrentConns: print "-", conn.inmod.name, " to", conn.outmod.name if conn.paramdim > 0: print "- parameters", conn.params def save(self, file_path): """ Save network """ try: file_net = None file_net = open(file_path, 'w') pickle.dump(self._feed_forward_network, file_net) except Exception, err: raise NetworkException(str(err)) finally: if file_net != None: file_net.close() def load(self, file_path): """ Load network from file """ try: file_net = None if os.path.isfile(file_path) == False: raise NetworkException("No such file: " + file_path) file_net = open(file_path,'r') self._feed_forward_network = pickle.load(file_net) except Exception, err: raise NetworkException(str(err)) finally: if file_net != None: file_net.close()

38.711599

128

0.581667

11,709

0.948174

0

4,274

0.346101

a75006d06757a5f27ac00ff68ada7211ab1bbdc4

342

py

Python

python2/probe_yd.py

Nzen/run_ydl

90d7075ba8ec5771b5edcbe2ad52211d95546f83

[ "WTFPL" ]

null

python2/probe_yd.py

Nzen/run_ydl

90d7075ba8ec5771b5edcbe2ad52211d95546f83

[ "WTFPL" ]

null

python2/probe_yd.py

Nzen/run_ydl

90d7075ba8ec5771b5edcbe2ad52211d95546f83

[ "WTFPL" ]

null

from sys import argv from subprocess import call try : link = argv[ 1 ] except IndexError: link = raw_input( " - which url interests you? " ) try: ydl_answ = call( "youtube-dl -F "+ link, shell = True ) if ydl_answ is not 0 : print "-- failed "+ link + " code "+ str(ydl_answ) except OSError as ose : print "Execution failed:", ose

21.375

56

0.663743

0

85

0.248538

a7538f1279770f7607c3e20bb1757708788234b0

9,689

py

Python

src/cogs/welcome.py

Cr4zi/SynatxBot

eeb59555c1cfa81e05c924b84c601c0b240e5ee3

[ "MIT" ]

4

2021-08-12T08:11:21.000Z

2021-08-12T08:15:22.000Z

src/cogs/welcome.py

Cr4zi/SynatxBot

eeb59555c1cfa81e05c924b84c601c0b240e5ee3

[ "MIT" ]

null

src/cogs/welcome.py

Cr4zi/SynatxBot

eeb59555c1cfa81e05c924b84c601c0b240e5ee3

[ "MIT" ]

null

import discord from discord.ext import commands from discord import Embed from discord.utils import get import datetime import psycopg2 from bot import DB_NAME, DB_PASS, DB_HOST, DB_USER, logger, private_message class Welcome(commands.Cog): def __init__(self, bot): self.bot = bot logger.info("Welcome Cog loaded") @commands.group(invoke_without_command=True, description="Welcome group command") @commands.check(private_message) async def welcome(self, ctx): await ctx.send("Welcome commands: set_channel, set_message, message_type and auto_role") @welcome.command(name='channel', aliases=['setChannel'], description="Setting the welcomew channel where all the messages will be sent.") @commands.has_permissions(manage_guild=True) @commands.check(private_message) async def channel(self, ctx): async def ask_channel(): def check(m): return m.author.id == ctx.author.id and m.channel.id == ctx.channel.id try: msg = await self.bot.wait_for("message", check=check) return await commands.TextChannelConverter().convert(ctx, msg.content) except commands.errors.ChannelNotFound as e: await ctx.send(f"Invalid channel `{e.argument}`. Please enter a channel name again.") return await ask_channel() await ctx.send("Please enter the channel name where all the welcome messages will be sent.") channel = await ask_channel() main = psycopg2.connect(dbname=DB_NAME, user=DB_USER, password=DB_PASS, host=DB_HOST) cur = main.cursor() cur.execute(f"SELECT channel_id FROM welcome WHERE guild_id = '{ctx.guild.id}'") result = cur.fetchone() if result is None: sql = ("INSERT INTO welcome(guild_id, channel_id) VALUES(%s,%s)") val = (str(ctx.guild.id), channel.id) else: sql = ("UPDATE welcome SET channel_id = %s WHERE guild_id = %s") val = (channel.id, str(ctx.guild.id)) cur.execute(sql, val) main.commit() cur.close() main.close() await ctx.send(f"Set welcome channel to: {channel.mention}") @welcome.command(name='set_message', aliases=['set_msg', 'setMessage', 'text', 'message'], description="Setting the welcome message.") @commands.has_permissions(manage_guild=True) @commands.check(private_message) async def set_message(self, ctx): async def ask_msg(): def check(m): return m.author.id == ctx.author.id and m.channel.id == ctx.channel.id message = await self.bot.wait_for("message", check=check) return message.content await ctx.send("Please enter the message that will show up for every welcome message.") message = await ask_msg() main = psycopg2.connect(dbname=DB_NAME, user=DB_USER, password=DB_PASS, host=DB_HOST) cur = main.cursor() cur.execute(f"SELECT msg FROM welcome WHERE guild_id = '{ctx.guild.id}'") result = cur.fetchone() if result is None: sql = ("INSERT INTO welcome(guild_id, msg) VALUES(%s,%s)") val = (str(ctx.guild.id), message) else: sql = ("UPDATE welcome SET msg = %s WHERE guild_id = %s") val = (message, str(ctx.guild.id)) cur.execute(sql, val) main.commit() cur.close() main.close() await ctx.send(f"Message has been set: `{message}`") @welcome.command(name='message_type', aliases=['msg_type', 'MessageType', 'type'], description="Setting welcome message type embed or text.") @commands.has_permissions(manage_guild=True) @commands.check(private_message) async def message_type(self, ctx): async def ask_type(): def check(m): return m.author.id == ctx.author.id and m.channel.id == ctx.channel.id msg_type = await self.bot.wait_for("message", check=check) if msg_type.content.lower() != "embed" and msg_type.content.lower() != "text": await ctx.send("Invalid type! You only have 2 types of messages: embed and text. Please a message type again.") return await ask_type() return msg_type.content.lower() await ctx.send("Please enter the message type that will show up for every welcome message. (text | embed)") msg_type = await ask_type() main = psycopg2.connect(dbname=DB_NAME, user=DB_USER, password=DB_PASS, host=DB_HOST) cur = main.cursor() cur.execute(f"SELECT msg_type FROM welcome WHERE guild_id = '{ctx.guild.id}'") result = cur.fetchone() if result is None: sql = ("INSERT INTO welcome(guild_id, msg_type) VALUES(%s,%s)") val = (str(ctx.guild.id), msg_type) else: sql = ("UPDATE welcome SET msg_type = %s WHERE guild_id = %s") val = (msg_type, str(ctx.guild.id)) cur.execute(sql, val) main.commit() cur.close() main.close() await ctx.send(f"Welcome message type has been set: `{msg_type}`") @welcome.command(name='auto_role', aliases=['auto_roles', 'autoRoles'], description="Setting auto role that will be given to a new users.") @commands.has_permissions(manage_guild=True) @commands.check(private_message) async def auto_role(self, ctx, *, roles_id=None): main = psycopg2.connect(dbname=DB_NAME, user=DB_USER, password=DB_PASS, host=DB_HOST) cur = main.cursor() cur.execute(f"SELECT roles_id FROM welcome WHERE guild_id = '{ctx.guild.id}'") result = cur.fetchone() if result is None: cur.execute(f"INSERT INTO welcome(roles_id, guild_id) VALUES(%s,%s)", (f"{roles_id}", f"{ctx.guild.id}")) else: cur.execute("UPDATE welcome SET roles_id = %s WHERE guild_id = %s", (f"{roles_id}", f"{ctx.guild.id}")) main.commit() cur.close() main.close() await ctx.send(f"Welcome roles id's set to: {roles_id}") @commands.Cog.listener() async def on_member_join(self, member): main = psycopg2.connect(dbname=DB_NAME, user=DB_USER, password=DB_PASS, host=DB_HOST) cur = main.cursor() cur.execute(f"SELECT channel_id FROM welcome WHERE guild_id = '{member.guild.id}'") result = cur.fetchone() if result is None: return else: cur.execute(f"SELECT roles_id FROM welcome WHERE guild_id = '{member.guild.id}'") result3 = cur.fetchone() try: for role_id in result3[0].split(" "): role = get(member.guild.roles, id=int(role_id)) await member.add_roles(role) except AttributeError as e: pass except ValueError: pass members_count = member.guild.member_count mention = member.mention user = member.name guild = member.guild.name cur.execute(f"SELECT msg_type FROM welcome WHERE guild_id = '{member.guild.id}'") result1 = cur.fetchone() cur.execute(f"SELECT msg FROM welcome WHERE guild_id = '{member.guild.id}'") result2 = cur.fetchone() if result1 is None: if result2 is None: embed = Embed( description=f"Welcome to my server! your are the {member.guild.member_count} member in the server") else: embed = Embed( description=f"{result2[0]}".format( members_count=members_count, mention=mention, user=user, guild=guild)) embed.set_thumbnail(url=f"{member.avatar_url}") embed.set_author(name=f"{member.name}", icon_url=f"{member.avatar_url}") embed.set_footer(text=f"{member.guild}", icon_url=f"{member.guild.icon_url}") embed.timestamp = datetime.datetime.utcnow() channel = self.bot.get_channel(id=int(result[0])) await channel.send(embed=embed) else: if result1[0].lower() == "embed": if result2 is None: embed = Embed( description=f"Welcome to my server! your are the {member.guild.member_count} member in the server") else: embed = Embed( description=f"{result2[0]}".format( members_count=members_count, mention=mention, user=user, guild=guild)) embed.set_thumbnail(url=f"{member.avatar_url}") embed.set_author(name=f"{member.name}", icon_url=f"{member.avatar_url}") embed.set_footer(text=f"{member.guild}", icon_url=f"{member.guild.icon_url}") embed.timestamp = datetime.datetime.utcnow() channel = self.bot.get_channel(id=int(result[0])) await channel.send(embed=embed) else: channel = self.bot.get_channel(id=int(result[0])) await channel.send(f"{result2[0]}".format(members_count=members_count, mention=mention, user=user, guild=guild)) cur.close() main.close() def setup(bot): bot.add_cog(Welcome(bot))

46.581731

146

0.58489

9,412

0.971411

0

9,239

0.953556

8,157

0.841883

2,520

0.260089

a7551310f1a028ec26dd2191bdc424bc482a29c5

468

py

Python

etcd_restore_rebuild_util/edit_yaml_for_rebuild.py

Cray-HPE/utils

dd6e13b46500e1c2f6ad887a8c1604044465d1d8

[ "MIT" ]

null

etcd_restore_rebuild_util/edit_yaml_for_rebuild.py

Cray-HPE/utils

dd6e13b46500e1c2f6ad887a8c1604044465d1d8

[ "MIT" ]

null

etcd_restore_rebuild_util/edit_yaml_for_rebuild.py

Cray-HPE/utils

dd6e13b46500e1c2f6ad887a8c1604044465d1d8

[ "MIT" ]

null

#!/usr/bin/env python3 import os import sys import yaml file_name=sys.argv[1] file_name = '/root/etcd/' + file_name + '.yaml' with open(file_name) as f: y=yaml.safe_load(f) del y['metadata']['creationTimestamp'] del y['metadata']['generation'] del y['metadata']['resourceVersion'] del y['metadata']['uid'] del y['status'] with open(file_name, 'w') as outputFile: yaml.dump(y,outputFile, default_flow_style=False, sort_keys=False)

22.285714

70

0.67094

0

146

0.311966

a75582560560cf86bc8bb8744feee3c442ea60e2

1,514

py

Python

src/Segmentation/segmentation.py

odigous-labs/video-summarization

c125bf9fa1016d76680d5e9389e4bdb0f83bc4fb

[ "MIT" ]

1

2019-03-05T06:00:38.000Z

src/Segmentation/segmentation.py

odigous-labs/video-summarization

c125bf9fa1016d76680d5e9389e4bdb0f83bc4fb

[ "MIT" ]

2

2019-03-02T05:12:59.000Z

2019-09-26T17:03:56.000Z

src/Segmentation/segmentation.py

odigous-labs/video-summarization

c125bf9fa1016d76680d5e9389e4bdb0f83bc4fb

[ "MIT" ]

null

import os import cv2 from Segmentation import CombinedHist, get_histograms, HistQueue import matplotlib.pyplot as plt import numpy as np listofFiles = os.listdir('generated_frames') # change the size of queue accordingly queue_of_hists = HistQueue.HistQueue(25) x = [] y_r = [] y_g = [] y_b = [] def compare(current_hist, frame_no): avg_histr = queue_of_hists.getAverageHist() red_result = cv2.compareHist(current_hist.getRedHistr(), avg_histr.getRedHistr(), 0) green_result = cv2.compareHist(current_hist.getGreenHistr(), avg_histr.getGreenHistr(), 0) blue_result = cv2.compareHist(current_hist.getBlueHistr(), avg_histr.getBlueHistr(), 0) x.append(i) y_r.append(red_result) y_g.append(green_result) y_b.append(blue_result) # print(red_result) for i in range(0, 4000): blue_histr, green_histr, red_histr = get_histograms.get_histograms('generated_frames/frame' + str(i) + ".jpg") hist_of_image = CombinedHist.CombinedHist(blue_histr, green_histr, red_histr) compare(hist_of_image, i) queue_of_hists.insert_histr(hist_of_image) print("frame" + str(i) + ".jpg") fig = plt.figure(figsize=(18, 5)) y = np.add(np.add(y_r, y_g), y_b) / 3 value = np.percentile(y, 5) median = np.median(y) minimum = np.amin(y) y_sorted = np.sort(y) getting_index = y_sorted[8] print("quartile" + str(value)) print("median" + str(median)) plt.plot(x, y, color='k') plt.axhline(y=value, color='r', linestyle='-') plt.xticks(np.arange(min(x), max(x) + 1, 100.0)) plt.show()

29.115385

114

0.718626

0

145

0.095773

a755b8f4c107bcf90ce08cbfeeeaa2d842ac3f66

12,369

py

Python

stickerbot.py

gumblex/stickerindexbot

8e8edaabac54d2747e4b620464670a60a65efcb5

[ "MIT" ]

1

2017-01-20T18:11:46.000Z

stickerbot.py

gumblex/stickerindexbot

8e8edaabac54d2747e4b620464670a60a65efcb5

[ "MIT" ]

null

stickerbot.py

gumblex/stickerindexbot

8e8edaabac54d2747e4b620464670a60a65efcb5

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- ''' Telegram Sticker Index Bot ''' import re import sys import time import json import queue import sqlite3 import logging import requests import functools import threading import collections import concurrent.futures import zhconv logging.basicConfig(stream=sys.stderr, format='%(asctime)s [%(name)s:%(levelname)s] %(message)s', level=logging.DEBUG if sys.argv[-1] == '-v' else logging.INFO) logger_botapi = logging.getLogger('botapi') executor = concurrent.futures.ThreadPoolExecutor(5) HSession = requests.Session() _re_one_emoji = ( '[🇦-🇿]|' '(?:(?:[🌀-🏺]|[🐀-🙏]|[🚀-\U0001f6ff]|[\U0001f900-\U0001f9ff])[🏻-🏿]?\u200d)*' '(?:[🌀-🏺]|[🐀-🙏]|[🚀-\U0001f6ff]|[\U0001f900-\U0001f9ff])[🏻-🏿]?' ) re_emoji = re.compile('(%s)' % _re_one_emoji) re_qtag = re.compile(r"#?\w+", re.UNICODE) re_tag = re.compile(r"#\w+", re.UNICODE) re_tags = re.compile(r"#\w+(?:\s+#\w+)*", re.UNICODE) class Sticker(collections.namedtuple('Sticker', 'file_id width height emoji file_size')): @classmethod def from_telegram(cls, sticker): return cls( sticker['file_id'], sticker['width'], sticker['height'], sticker.get('emoji'), sticker.get('file_size') ) class AttrDict(dict): def __init__(self, *args, **kwargs): super(AttrDict, self).__init__(*args, **kwargs) self.__dict__ = self class SQLiteStateStore(collections.UserDict): TABLE = 'bot_status' def __init__(self, connection): self.conn = connection cur = self.conn.cursor() data = {k: json.loads(v) for k,v in cur.execute( 'SELECT key, value FROM ' + self.TABLE)} super().__init__(data) def commit(self): cur = self.conn.cursor() for k, v in self.data.items(): cur.execute('REPLACE INTO %s (key, value) VALUES (?,?)' % self.TABLE, (k, json.dumps(v))) self.conn.commit() def close(self): self.commit() def nt_from_dict(nt, d, default=None): kwargs = dict.fromkeys(nt._fields, default) kwargs.update(d) return nt(**kwargs) # Bot API class BotAPIFailed(Exception): pass def async_func(func): @functools.wraps(func) def wrapped(*args, **kwargs): def func_noerr(*args, **kwargs): try: func(*args, **kwargs) except Exception: logger_botapi.exception('Async function failed.') executor.submit(func_noerr, *args, **kwargs) return wrapped def bot_api(method, **params): for att in range(3): try: req = HSession.post(('https://api.telegram.org/bot%s/' % CFG.apitoken) + method, data=params, timeout=45) retjson = req.content if not retjson: continue ret = json.loads(retjson.decode('utf-8')) break except Exception as ex: if att < 1: time.sleep((att + 1) * 2) else: raise ex if not ret['ok']: raise BotAPIFailed(repr(ret)) return ret['result'] @async_func def sendmsg(text, chat_id, reply_to_message_id=None, **kwargs): text = text.strip() if not text: logger_botapi.warning('Empty message ignored: %s, %s' % (chat_id, reply_to_message_id)) return logger_botapi.debug('sendMessage(%s): %s' % (len(text), text[:20])) if len(text) > 2000: text = text[:1999] + '…' reply_id = reply_to_message_id if reply_to_message_id and reply_to_message_id < 0: reply_id = None return bot_api('sendMessage', chat_id=chat_id, text=text, reply_to_message_id=reply_id, **kwargs) @async_func def answer(inline_query_id, results, **kwargs): return bot_api('answerInlineQuery', inline_query_id=inline_query_id, results=json.dumps(results), **kwargs) def getupdates(): global CFG, STATE while 1: try: updates = bot_api('getUpdates', offset=STATE.get('offset', 0), timeout=10) except Exception: logger_botapi.exception('Get updates failed.') continue if updates: STATE['offset'] = updates[-1]["update_id"] + 1 for upd in updates: MSG_Q.put(upd) time.sleep(.2) def parse_cmd(text: str): t = text.strip().replace('\xa0', ' ').split(' ', 1) if not t: return (None, None) cmd = t[0].rsplit('@', 1) if len(cmd[0]) < 2 or cmd[0][0] != "/": return (None, None) if len(cmd) > 1 and 'username' in CFG and cmd[-1] != CFG.username: return (None, None) expr = t[1] if len(t) > 1 else '' return (cmd[0][1:], expr.strip()) # DB stuff def init_db(filename): db = sqlite3.connect(filename) db.row_factory = sqlite3.Row cur = db.cursor() cur.execute('CREATE TABLE IF NOT EXISTS stickers (' 'file_id TEXT PRIMARY KEY,' 'width INTEGER,' 'height Integer,' 'emoji TEXT,' 'file_size INTEGER' ')') # should have some special tags cur.execute('CREATE TABLE IF NOT EXISTS tags (' 'sticker TEXT,' 'tag TEXT,' 'PRIMARY KEY (sticker, tag),' 'FOREIGN KEY (sticker) REFERENCES stickers(file_id)' ')') cur.execute('CREATE TABLE IF NOT EXISTS tag_index (' 'tag TEXT PRIMARY KEY,' 'indexed TEXT,' 'FOREIGN KEY (tag) REFERENCES tags(tag)' ')') cur.execute('CREATE TABLE IF NOT EXISTS bot_status (' 'key TEXT PRIMARY KEY,' 'value TEXT' ')') cur.execute('CREATE INDEX IF NOT EXISTS idx_emoji ON stickers (emoji)') cur.execute('CREATE INDEX IF NOT EXISTS idx_tags ON tags (sticker, tag)') cur.execute('CREATE INDEX IF NOT EXISTS idx_tag_index ON tag_index (indexed)') db.commit() state = SQLiteStateStore(db) return db, state @functools.lru_cache(maxsize=64) def normalize_tag(tag): ''' Normalize tag to be indexable `tag` must be \w+ ''' indexedtag = tag.lower().replace('_', '') return zhconv.convert(indexedtag, 'zh-hans') def add_sticker(sticker, tags=None): cur = DB.cursor() if isinstance(sticker, Sticker): cur.execute('INSERT OR IGNORE INTO stickers VALUES (?,?,?,?,?)', sticker) sticker_id = sticker.file_id emoji = sticker.emoji else: sticker_id = sticker emoji = '[?]' if tags: logging.info('Tags %r added for %s %s', tags, emoji, sticker_id) for tag in tags: indexed = normalize_tag(tag) cur.execute('INSERT OR IGNORE INTO tags VALUES (?,?)', (sticker_id, tag)) cur.execute('INSERT OR IGNORE INTO tag_index VALUES (?,?)', (tag, indexed)) else: logging.debug('Seen %s %s', emoji, sticker_id) DB.commit() def del_tag(sticker, tag): if isinstance(sticker, Sticker): sticker_id = sticker.file_id else: sticker_id = sticker cur = DB.cursor() cur.execute('DELETE FROM tags WHERE sticker=? AND tag=?', (sticker_id, tag)) DB.commit() def vacuum_db(): cur = DB.cursor() cur.execute('DELETE FROM tag_index WHERE tag NOT IN (SELECT DISTINCT tag from tags)') DB.commit() DB.vacuum() def get_sticker(text, num=50): text = text.strip() if not text: return [] emojis = [] tags = [] where, vals = '', [] for frag in re_emoji.split(text): if re_emoji.match(frag): emojis.append(frag) else: tags.extend(x.lstrip('#') for x in frag.strip().split() if re_qtag.match(x)) join = '' if emojis: where = ' OR '.join('emoji = ?' for x in emojis) vals = emojis.copy() if tags: if where: where = '(%s) AND ' % where where += ' AND '.join('t.indexed = ?' for x in tags) join = (' LEFT JOIN tags ON tags.sticker = stickers.file_id' ' LEFT JOIN tag_index t ON t.tag = tags.tag') vals.extend(normalize_tag(t) for t in tags) if not where: return [] sql = 'SELECT file_id, emoji FROM stickers%s WHERE %s LIMIT ?' % (join, where) vals.append(num) cur = DB.cursor() ret = [Sticker(file_id, None, None, emoji, None) for file_id, emoji in cur.execute(sql, vals)] if not ret and tags: where = ' OR '.join('t.indexed LIKE ?' for x in tags) vals = ['%' + normalize_tag(t) + '%' for t in tags] vals.append(num) sql = 'SELECT file_id, emoji FROM stickers%s WHERE %s LIMIT ?' % (join, where) ret = [Sticker(file_id, None, None, emoji, None) for file_id, emoji in cur.execute(sql, vals)] return ret # Query handling START = 'This is the Sticker Index Bot. Send /help, or directly use its inline mode.' HELP = ('You can search for stickers by tags or emoji in its inline mode.\n' 'This bot will collect tags for stickers in groups or private chat, ' 'after seeing stickers being replied to in the format "#tagone #tagtwo".' ) def handle_api_update(d: dict): logger_botapi.debug('Update: %r' % d) try: if 'inline_query' in d: query = d['inline_query'] text = query['query'].strip() if text: stickers = get_sticker(text) logging.info('Got %d stickers for %s', len(stickers), text) r = answer(query['id'], inline_result(stickers)) logger_botapi.debug(r) elif 'message' in d: msg = d['message'] text = msg.get('text', '') sticker = msg.get('sticker') ret = None if sticker: on_sticker(sticker, msg['chat'], msg) elif text: cmd, expr = parse_cmd(text) if not cmd: ret = on_text(text, msg['chat'], msg['message_id'], msg) elif msg['chat']['type'] == 'private': msgchatid = str(msg['chat']['id']) if msgchatid in STATE: STATE[msgchatid] = None if cmd == 'start': ret = START # elif cmd == 'help': else: ret = HELP if ret: sendmsg(ret, msg['chat']['id'], msg['message_id']) except Exception: logger_botapi.exception('Failed to process a message.') def inline_result(stickers): ret = [] for d in stickers: ret.append({ 'type': 'sticker', 'id': d.file_id, 'sticker_file_id': d.file_id }) return ret def on_text(text, chat, replyid, msg): if not re_tags.match(text): if chat['type'] == 'private': return 'Please send me a sticker and its tag(s).' if 'reply_to_message' in msg and 'sticker' in msg['reply_to_message']: tags = [] if chat['type'] != 'private': match = re_tags.match(text.strip()) if match: tags = [x.lstrip('#') for x in match.group(0).split()] else: tags = [x.lstrip('#') for x in text.strip().split() if re_qtag.match(x)] add_sticker(Sticker.from_telegram(msg['reply_to_message']['sticker']), tags) if chat['type'] == 'private': return 'Tags added.' elif chat['type'] == 'private': sticker = STATE.get(str(chat['id'])) if sticker: tags = [x.lstrip('#') for x in text.strip().split() if re_qtag.match(x)] add_sticker(sticker, tags) STATE[str(chat['id'])] = None return 'Tags added.' def on_sticker(sticker, chat, msg): sticker_obj = Sticker.from_telegram(sticker) add_sticker(sticker_obj) if chat['type'] == 'private': STATE[str(chat['id'])] = sticker_obj.file_id def load_config(): return AttrDict(json.load(open('config.json', encoding='utf-8'))) if __name__ == '__main__': CFG = load_config() MSG_Q = queue.Queue() DB, STATE = init_db(CFG.database) try: apithr = threading.Thread(target=getupdates) apithr.daemon = True apithr.start() logging.info('Satellite launched') while 1: handle_api_update(MSG_Q.get()) finally: STATE.close()

32.379581

160

0.570216

1,132

0.091151

0

1,552

0.12497

0

3,050

0.245591

a755c3e60d6f4943e03a99183eadd47ca1d97d29

4,571

py

Python

tests.py

AndreLouisCaron/requests-wsgi-adapter

5506c4785824673147449daabb5c4e06192e5078

[ "BSD-3-Clause" ]

null

tests.py

AndreLouisCaron/requests-wsgi-adapter

5506c4785824673147449daabb5c4e06192e5078

[ "BSD-3-Clause" ]

null

tests.py

AndreLouisCaron/requests-wsgi-adapter

5506c4785824673147449daabb5c4e06192e5078

[ "BSD-3-Clause" ]

null

import json import unittest import requests from urllib3._collections import HTTPHeaderDict from wsgiadapter import WSGIAdapter class WSGITestHandler(object): def __init__(self, extra_headers=None): self.extra_headers = extra_headers or tuple() def __call__(self, environ, start_response): headers = HTTPHeaderDict({'Content-Type': 'application/json'}) for key, value in self.extra_headers: headers.add(key, value) start_response('200 OK', headers, exc_info=None) return [bytes(json.dumps({ 'result': '__works__', 'body': environ['wsgi.input'].read().decode('utf-8'), 'content_type': environ['CONTENT_TYPE'], 'content_length': environ['CONTENT_LENGTH'], 'path_info': environ['PATH_INFO'], 'request_method': environ['REQUEST_METHOD'], 'server_name': environ['SERVER_NAME'], 'server_port': environ['SERVER_PORT'], }).encode('utf-8'))] class WSGIAdapterTest(unittest.TestCase): def setUp(self): self.session = requests.session() self.session.mount('http://localhost', WSGIAdapter(app=WSGITestHandler())) self.session.mount('https://localhost', WSGIAdapter(app=WSGITestHandler())) def test_basic_response(self): response = self.session.get('http://localhost/index', headers={'Content-Type': 'application/json'}) self.assertEqual(response.status_code, 200) self.assertEqual(response.headers['Content-Type'], 'application/json') self.assertEqual(response.json()['result'], '__works__') self.assertEqual(response.json()['content_type'], 'application/json') self.assertEqual(response.json()['path_info'], '/index') self.assertEqual(response.json()['request_method'], 'GET') self.assertEqual(response.json()['server_name'], 'localhost') def test_request_with_body(self): response = self.session.post('http://localhost/index', data='__test__') self.assertEqual(response.json()['body'], '__test__') self.assertEqual(response.json()['content_length'], len('__test__')) def test_request_with_https(self): response = self.session.get('https://localhost/index') self.assertEqual(response.json()['server_port'], '443') def test_request_with_json(self): response = self.session.post('http://localhost/index', json={}) self.assertEqual(response.json()['body'], '{}') self.assertEqual(response.json()['content_length'], len('{}')) class WSGIAdapterCookieTest(unittest.TestCase): def setUp(self): app = WSGITestHandler( extra_headers=[ ("Set-Cookie", "c1=v1; Path=/"), ("Set-Cookie", "c2=v2; Path=/")]) self.session = requests.session() self.session.mount('http://localhost', WSGIAdapter(app=app)) self.session.mount('https://localhost', WSGIAdapter(app=app)) def test_request_with_cookies(self): response = self.session.get("http://localhost/cookies") self.assertEqual(response.cookies['c1'], 'v1') self.assertEqual(self.session.cookies['c1'], 'v1') def test_multiple_cookies(): app = WSGITestHandler( extra_headers=[ ("Set-Cookie", "flimble=floop; Path=/"), ("Set-Cookie", "flamble=flaap; Path=/")]) session = requests.session() session.mount('http://localhost', WSGIAdapter(app=app)) session.get( "http://localhost/cookies/set?flimble=floop&flamble=flaap") assert session.cookies['flimble'] == "floop" assert session.cookies['flamble'] == "flaap" def test_delete_cookies(): session = requests.session() set_app = WSGITestHandler( extra_headers=[ ("Set-Cookie", "flimble=floop; Path=/"), ("Set-Cookie", "flamble=flaap; Path=/")]) delete_app = WSGITestHandler( extra_headers=[( "Set-Cookie", "flimble=; Expires=Thu, 01-Jan-1970 00:00:00 GMT; Max-Age=0; Path=/")]) session.mount( 'http://localhost/cookies/set', WSGIAdapter(app=set_app)) session.mount( 'http://localhost/cookies/delete', WSGIAdapter(app=delete_app)) session.get( "http://localhost/cookies/set?flimble=floop&flamble=flaap") assert session.cookies['flimble'] == "floop" assert session.cookies['flamble'] == "flaap" session.get( "http://localhost/cookies/delete?flimble") assert 'flimble' not in session.cookies assert session.cookies['flamble'] == "flaap"

39.068376

107

0.640123

3,051

0.667469

0

1,350

0.29534

a75611852715e07033587bffa7d94fdf7b98243d

548

py

Python

setup.py

ducandu/aiopening

214d8d6dfc928ab4f8db634018092dc43eaf0e3c

[ "MIT" ]

null

setup.py

ducandu/aiopening

214d8d6dfc928ab4f8db634018092dc43eaf0e3c

[ "MIT" ]

null

setup.py

ducandu/aiopening

214d8d6dfc928ab4f8db634018092dc43eaf0e3c

[ "MIT" ]

null

""" ------------------------------------------------------------------------- shine - setup !!TODO: add file description here!! created: 2017/06/04 in PyCharm (c) 2017 Sven - ducandu GmbH ------------------------------------------------------------------------- """ from setuptools import setup setup(name='aiopening', version='1.0', description='AI (but even opener)', url='http://github.com/sven1977/aiopening', author='Sven Mika', author_email='sven.mika@ducandu.com', license='MIT', packages=['aiopening'], zip_safe=False)

34.25

138

0.501825

0

405

0.739051

a756ca330f0702ca67f549b4365c53dd8dc05dbc

1,932

py

Python

podcast_dl/podcasts.py

RMPR/simple-podcast-dl

bb4419d3beb1a893bfac5aa6546ba25522531b00

[ "MIT" ]

null

podcast_dl/podcasts.py

RMPR/simple-podcast-dl

bb4419d3beb1a893bfac5aa6546ba25522531b00

[ "MIT" ]

null

podcast_dl/podcasts.py

RMPR/simple-podcast-dl

bb4419d3beb1a893bfac5aa6546ba25522531b00

[ "MIT" ]

null

""" List of podcasts and their filename parser types. """ from .rss_parsers import BaseItem, TalkPythonItem, ChangelogItem, IndieHackersItem import attr @attr.s(slots=True, frozen=True) class Podcast: name = attr.ib(type=str) title = attr.ib(type=str) url = attr.ib(type=str) rss = attr.ib(type=str) rss_parser = attr.ib(type=BaseItem) PODCASTS = [ Podcast( name="talkpython", title="Talk Python To Me", url="https://talkpython.fm", rss="https://talkpython.fm/episodes/rss", rss_parser=TalkPythonItem, ), Podcast( name="pythonbytes", title="Python Bytes", url="https://pythonbytes.fm/", rss="https://pythonbytes.fm/episodes/rss", rss_parser=TalkPythonItem, ), Podcast( name="changelog", title="The Changelog", url="https://changelog.com/podcast", rss="https://changelog.com/podcast/feed", rss_parser=ChangelogItem, ), Podcast( name="podcastinit", title="Podcast.__init__", url="https://www.podcastinit.com/", rss="https://www.podcastinit.com/feed/mp3/", rss_parser=BaseItem, ), Podcast( name="indiehackers", title="Indie Hackers", url="https://www.indiehackers.com/podcast", rss="http://feeds.backtracks.fm/feeds/indiehackers/indiehackers/feed.xml", rss_parser=IndieHackersItem, ), Podcast( name="realpython", title="Real Python", url="https://realpython.com/podcasts/rpp/", rss="https://realpython.com/podcasts/rpp/feed", rss_parser=BaseItem, ), Podcast( name="kubernetespodcast", title="Kubernetes Podcast", url="https://kubernetespodcast.com/", rss="https://kubernetespodcast.com/feeds/audio.xml", rss_parser=BaseItem, ), ] PODCAST_MAP = {p.name: p for p in PODCASTS}

27.6

82

0.608696

169

0.087474

0

202

0.104555

0

788

0.407867

a75700da032ade0f2e5909a09f4ffc60c4abd193

20,543

py

Python

07_spitzer_aor_extraction.py

rsiverd/ultracool

cbeb2e0e4aee0acc9f8ed2bde7ecdf8be5fa85a1

[ "BSD-2-Clause" ]

null

07_spitzer_aor_extraction.py

rsiverd/ultracool

cbeb2e0e4aee0acc9f8ed2bde7ecdf8be5fa85a1

[ "BSD-2-Clause" ]

null

07_spitzer_aor_extraction.py

rsiverd/ultracool

cbeb2e0e4aee0acc9f8ed2bde7ecdf8be5fa85a1

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python # vim: set fileencoding=utf-8 ts=4 sts=4 sw=4 et tw=80 : # # Extract and save extended object catalogs from the specified data and # uncertainty images. This version of the script jointly analyzes all # images from a specific AOR/channel to enable more sophisticated # analysis. # # Rob Siverd # Created: 2021-02-02 # Last modified: 2021-08-24 #-------------------------------------------------------------------------- #************************************************************************** #-------------------------------------------------------------------------- ## Logging setup: import logging #logging.basicConfig(level=logging.DEBUG) logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) #logger.setLevel(logging.DEBUG) logger.setLevel(logging.INFO) ## Current version: __version__ = "0.3.5" ## Python version-agnostic module reloading: try: reload # Python 2.7 except NameError: try: from importlib import reload # Python 3.4+ except ImportError: from imp import reload # Python 3.0 - 3.3 ## Modules: import argparse import shutil #import resource #import signal import glob #import gc import os import sys import time import numpy as np #from numpy.lib.recfunctions import append_fields #import datetime as dt #from dateutil import parser as dtp #from functools import partial #from collections import OrderedDict #from collections.abc import Iterable #import multiprocessing as mp #np.set_printoptions(suppress=True, linewidth=160) _have_np_vers = float('.'.join(np.__version__.split('.')[:2])) ##--------------------------------------------------------------------------## ## Disable buffering on stdout/stderr: class Unbuffered(object): def __init__(self, stream): self.stream = stream def write(self, data): self.stream.write(data) self.stream.flush() def __getattr__(self, attr): return getattr(self.stream, attr) sys.stdout = Unbuffered(sys.stdout) sys.stderr = Unbuffered(sys.stderr) ##--------------------------------------------------------------------------## ## Spitzer pipeline filesystem helpers: try: import spitz_fs_helpers reload(spitz_fs_helpers) except ImportError: logger.error("failed to import spitz_fs_helpers module!") sys.exit(1) sfh = spitz_fs_helpers ## Spitzer pipeline cross-correlation: try: import spitz_xcorr_stacking reload(spitz_xcorr_stacking) except ImportError: logger.error("failed to import spitz_xcor_stacking module!") sys.exit(1) sxc = spitz_xcorr_stacking.SpitzerXCorr() ## Catalog pruning helpers: try: import catalog_tools reload(catalog_tools) except ImportError: logger.error("failed to import catalog_tools module!") sys.exit(1) xcp = catalog_tools.XCorrPruner() ## Spitzer star detection routine: try: import spitz_extract reload(spitz_extract) spf = spitz_extract.SpitzFind() except ImportError: logger.error("spitz_extract module not found!") sys.exit(1) ## Hybrid stack+individual position calculator: try: import spitz_stack_astrom reload(spitz_stack_astrom) ha = spitz_stack_astrom.HybridAstrom() except ImportError: logger.error("failed to import spitz_stack_astrom module!") sys.exit(1) ## HORIZONS ephemeris tools: try: import jpl_eph_helpers reload(jpl_eph_helpers) except ImportError: logger.error("failed to import jpl_eph_helpers module!") sys.exit(1) eee = jpl_eph_helpers.EphTool() ##--------------------------------------------------------------------------## ## Fast FITS I/O: try: import fitsio except ImportError: logger.error("fitsio module not found! Install and retry.") sys.stderr.write("\nError: fitsio module not found!\n") sys.exit(1) ## Save FITS image with clobber (fitsio): def qsave(iname, idata, header=None, **kwargs): this_func = sys._getframe().f_code.co_name parent_func = sys._getframe(1).f_code.co_name sys.stderr.write("Writing to '%s' ... " % iname) fitsio.write(iname, idata, clobber=True, header=header, **kwargs) sys.stderr.write("done.\n") ##--------------------------------------------------------------------------## ##------------------ Parse Command Line ----------------## ##--------------------------------------------------------------------------## ## Dividers: halfdiv = '-' * 40 fulldiv = '-' * 80 ## Parse arguments and run script: class MyParser(argparse.ArgumentParser): def error(self, message): sys.stderr.write('error: %s\n' % message) self.print_help() sys.exit(2) ## Enable raw text AND display of defaults: class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter): pass ## Parse the command line: if __name__ == '__main__': # ------------------------------------------------------------------ prog_name = os.path.basename(__file__) descr_txt = """ Extract catalogs from the listed Spitzer data/uncertainty images. Version: %s """ % __version__ parser = MyParser(prog=prog_name, description=descr_txt) #formatter_class=argparse.RawTextHelpFormatter) # ------------------------------------------------------------------ parser.set_defaults(imtype=None) #'cbcd') #'clean') #parser.set_defaults(sigthresh=3.0) parser.set_defaults(sigthresh=2.0) parser.set_defaults(skip_existing=True) parser.set_defaults(save_registered=True) #parser.set_defaults(save_reg_subdir=None) # ------------------------------------------------------------------ #parser.add_argument('firstpos', help='first positional argument') #parser.add_argument('-w', '--whatever', required=False, default=5.0, # help='some option with default [def: %(default)s]', type=float) # ------------------------------------------------------------------ # ------------------------------------------------------------------ iogroup = parser.add_argument_group('File I/O') iogroup.add_argument('--overwrite', required=False, dest='skip_existing', action='store_false', help='overwrite existing catalogs') #iogroup.add_argument('-E', '--ephem_data', default=None, required=True, # help='CSV file with SST ephemeris data', type=str) iogroup.add_argument('-I', '--input_folder', default=None, required=True, help='where to find input images', type=str) iogroup.add_argument('-O', '--output_folder', default=None, required=False, help='where to save extended catalog outputs', type=str) iogroup.add_argument('-W', '--walk', default=False, action='store_true', help='recursively walk subfolders to find CBCD images') imtype = iogroup.add_mutually_exclusive_group() #imtype.add_argument('--cbcd', required=False, action='store_const', # dest='imtype', const='cbcd', help='use cbcd images') imtype.add_argument('--hcfix', required=False, action='store_const', dest='imtype', const='hcfix', help='use hcfix images') imtype.add_argument('--clean', required=False, action='store_const', dest='imtype', const='clean', help='use clean images') imtype.add_argument('--nudge', required=False, action='store_const', dest='imtype', const='nudge', help='use nudge images') #iogroup.add_argument('-R', '--ref_image', default=None, required=True, # help='KELT image with WCS') # ------------------------------------------------------------------ # ------------------------------------------------------------------ # Miscellany: miscgroup = parser.add_argument_group('Miscellany') miscgroup.add_argument('--debug', dest='debug', default=False, help='Enable extra debugging messages', action='store_true') miscgroup.add_argument('-q', '--quiet', action='count', default=0, help='less progress/status reporting') miscgroup.add_argument('-v', '--verbose', action='count', default=0, help='more progress/status reporting') # ------------------------------------------------------------------ context = parser.parse_args() context.vlevel = 99 if context.debug else (context.verbose-context.quiet) context.prog_name = prog_name # Unless otherwise specified, output goes into input folder: if not context.output_folder: context.output_folder = context.input_folder # Ensure an image type is selected: if not context.imtype: sys.stderr.write("\nNo image type selected!\n\n") sys.exit(1) ## Use imtype-specific folder for registered file output: #if not context.save_reg_subdir: # context.save_reg_subdir = 'aligned_%s' % context.imtype ##--------------------------------------------------------------------------## ##------------------ Make Input Image List ----------------## ##--------------------------------------------------------------------------## tstart = time.time() sys.stderr.write("Listing %s frames ... " % context.imtype) #im_wildpath = 'SPITZ*%s.fits' % context.imtype #im_wildcard = os.path.join(context.input_folder, 'SPIT*' #_img_types = ['cbcd', 'clean', 'cbunc'] #_type_suff = dict([(x, x+'.fits') for x in _im_types]) #img_list = {} #for imsuff in suffixes: # wpath = '%s/SPITZ*%s.fits' % (context.input_folder, imsuff) # img_list[imsuff] = sorted(glob.glob(os.path.join(context. #img_files = sorted(glob.glob(os.path.join(context.input_folder, im_wildpath))) if context.walk: img_files = sfh.get_files_walk(context.input_folder, flavor=context.imtype) else: img_files = sfh.get_files_single(context.input_folder, flavor=context.imtype) sys.stderr.write("done.\n") ## Abort in case of no input: if not img_files: sys.stderr.write("No input (%s) files found in folder:\n" % context.imtype) sys.stderr.write("--> %s\n\n" % context.input_folder) sys.exit(1) n_images = len(img_files) ## List of uncertainty frames (warn if any missing): #unc_files = [x.replace(context.imtype, 'cbunc') for x in img_files] #sys.stderr.write("Checking error-images ... ") #have_unc = [os.path.isfile(x) for x in unc_files] #if not all(have_unc): # sys.stderr.write("WARNING: some uncertainty frames missing!\n") #else: # sys.stderr.write("done.\n") ##--------------------------------------------------------------------------## ##------------------ Load SST Ephemeris Data ----------------## ##--------------------------------------------------------------------------## ### Ephemeris data file must exist: #if not context.ephem_data: # logger.error("context.ephem_data not set?!?!") # sys.exit(1) #if not os.path.isfile(context.ephem_data): # logger.error("Ephemeris file not found: %s" % context.ephem_data) # sys.exit(1) # ### Load ephemeris data: #eee.load(context.ephem_data) ##--------------------------------------------------------------------------## ##------------------ Unique AOR/Channel Combos ----------------## ##--------------------------------------------------------------------------## unique_tags = sorted(list(set([sfh.get_irac_aor_tag(x) for x in img_files]))) images_by_tag = {x:[] for x in unique_tags} for ii in img_files: images_by_tag[sfh.get_irac_aor_tag(ii)].append(ii) ##--------------------------------------------------------------------------## ##------------------ Diagnostic Region Files ----------------## ##--------------------------------------------------------------------------## def regify_excat_pix(data, rpath, win=False, rr=2.0): colnames = ('wx', 'wy') if win else ('x', 'y') xpix, ypix = [data[x] for x in colnames] with open(rpath, 'w') as rfile: for xx,yy in zip(xpix, ypix): rfile.write("image; circle(%8.3f, %8.3f, %8.3f)\n" % (xx, yy, rr)) return ##--------------------------------------------------------------------------## ##------------------ ExtendedCatalog Ephem Format ----------------## ##--------------------------------------------------------------------------## #def reformat_ephem(edata): ##--------------------------------------------------------------------------## ##------------------ Stack/Image Comparison ----------------## ##--------------------------------------------------------------------------## #def xcheck(idata, sdata): # nstack = len(sdata) # nimage = len(idata) # sys.stderr.write("nstack: %d\n" % nstack) # sys.stderr.write("nimage: %d\n" % nimage) # return ##--------------------------------------------------------------------------## ##------------------ Process All Images ----------------## ##--------------------------------------------------------------------------## ntodo = 0 nproc = 0 ntotal = len(img_files) min_sobj = 10 # bark if fewer than this many found in stack skip_stuff = False #context.save_registered = False #context.skip_existing = False ## Reduce bright pixel threshold: #sxc.set_bp_thresh(10.0) #sxc.set_bp_thresh(5.0) sxc.set_bp_thresh(10.0) #sxc.set_vlevel(10) sxc.set_roi_rfrac(0.90) sxc.set_roi_rfrac(2.00) #sys.exit(0) #for aor_tag,tag_files in images_by_tag.items(): for aor_tag in unique_tags: sys.stderr.write("\n\nProcessing images from %s ...\n" % aor_tag) tag_files = images_by_tag[aor_tag] n_tagged = len(tag_files) if n_tagged < 2: sys.stderr.write("WARNING: only %d images with tag %s\n" % (n_tagged, aor_tag)) sys.stderr.write("This case is not currently handled ...\n") sys.exit(1) # File/folder paths: aor_dir = os.path.dirname(tag_files[0]) stack_ibase = '%s_%s_stack.fits' % (aor_tag, context.imtype) stack_cbase = '%s_%s_stack.fcat' % (aor_tag, context.imtype) medze_ibase = '%s_%s_medze.fits' % (aor_tag, context.imtype) stack_ipath = os.path.join(aor_dir, stack_ibase) stack_cpath = os.path.join(aor_dir, stack_cbase) medze_ipath = os.path.join(aor_dir, medze_ibase) #sys.stderr.write("stack_ibase: %s\n" % stack_ibase) #sys.stderr.write("As of this point ...\n") #sys.stderr.write("sxc._roi_rfrac: %.5f\n" % sxc._roi_rfrac) sys.stderr.write("Cross-correlating and stacking ... ") result = sxc.shift_and_stack(tag_files) sys.stderr.write("done.\n") sxc.save_istack(stack_ipath) #sys.exit(0) #istack = sxc.get_stacked() #qsave(stack_ipath, istack) # Dump registered data to disk: if context.save_registered: save_reg_subdir = 'aligned_%s_%s' % (aor_tag, context.imtype) sys.stderr.write("Saving registered frames for inspection ...\n") #reg_dir = os.path.join(aor_dir, context.save_reg_subdir) reg_dir = os.path.join(aor_dir, save_reg_subdir) if os.path.isdir(reg_dir): shutil.rmtree(reg_dir) os.mkdir(reg_dir) sxc.dump_registered_images(reg_dir) sxc.dump_bright_pixel_masks(reg_dir) sys.stderr.write("\n") # Extract stars from stacked image: spf.use_images(ipath=stack_ipath) stack_cat = spf.find_stars(context.sigthresh) sdata = stack_cat.get_catalog() nsobj = len(sdata) sys.stderr.write(" \nFound %d sources in stacked image.\n\n" % nsobj) if (nsobj < min_sobj): sys.stderr.write("Fewer than %d objects found in stack ... \n" % min_sobj) sys.stderr.write("Found %d objects.\n\n" % nsobj) sys.stderr.write("--> %s\n\n" % stack_ipath) sys.exit(1) stack_cat.save_as_fits(stack_cpath, overwrite=True) # region file for diagnostics: stack_rfile = stack_ipath + '.reg' regify_excat_pix(sdata, stack_rfile, win=True) # Make/save 'medianize' stack for comparison: sxc.make_mstack() sxc.save_mstack(medze_ipath) # Set up pruning system: xshifts, yshifts = sxc.get_stackcat_offsets() xcp.set_master_catalog(sdata) xcp.set_image_offsets(xshifts, yshifts) # Set up hybrid astrometry system: ha.set_stack_excat(stack_cat) # catalog of detections ha.set_xcorr_metadata(sxc) # pixel offsets by image ## Stop here for now ... #if skip_stuff: # continue # process individual files with cross-correlation help: for ii,img_ipath in enumerate(tag_files, 1): sys.stderr.write("%s\n" % fulldiv) unc_ipath = img_ipath.replace(context.imtype, 'cbunc') if not os.path.isfile(unc_ipath): sys.stderr.write("WARNING: file not found:\n--> %s\n" % unc_ipath) continue img_ibase = os.path.basename(img_ipath) #cat_ibase = img_ibase.replace(context.imtype, 'fcat') cat_fbase = img_ibase + '.fcat' cat_pbase = img_ibase + '.pcat' cat_mbase = img_ibase + '.mcat' ### FIXME ### ### context.output_folder is not appropriate for walk mode ... save_dir = context.output_folder # NOT FOR WALK MODE save_dir = os.path.dirname(img_ipath) cat_fpath = os.path.join(save_dir, cat_fbase) cat_ppath = os.path.join(save_dir, cat_pbase) cat_mpath = os.path.join(save_dir, cat_mbase) ### FIXME ### sys.stderr.write("Catalog %s ... " % cat_fpath) if context.skip_existing: if os.path.isfile(cat_mpath): sys.stderr.write("exists! Skipping ... \n") continue nproc += 1 sys.stderr.write("not found ... creating ...\n") spf.use_images(ipath=img_ipath, upath=unc_ipath) result = spf.find_stars(context.sigthresh) ## FIXME: this just grabs the ephemeris from the header content ## of the first ExtendedCatalog produced. This should be obtained ## separately to make things easier to follow (and to eliminate ## the need to pre-modify the image headers ...) eph_data = eee.eph_from_header(result.get_header()) result.set_ephem(eph_data) result.save_as_fits(cat_fpath, overwrite=True) nfound = len(result.get_catalog()) frame_rfile = img_ipath + '.reg' regify_excat_pix(result.get_catalog(), frame_rfile, win=True) # prune sources not detected in stacked frame: pruned = xcp.prune_spurious(result.get_catalog(), img_ipath) npruned = len(pruned) sys.stderr.write("nfound: %d, npruned: %d\n" % (nfound, npruned)) if (len(pruned) < 5): sys.stderr.write("BARKBARKBARK\n") sys.exit(1) result.set_catalog(pruned) result.save_as_fits(cat_ppath, overwrite=True) # build and save hybrid catalog: mcat = ha.make_hybrid_excat(result) mcat.set_ephem(eph_data) mcat.save_as_fits(cat_mpath, overwrite=True) mxcat_rfile = img_ipath + '.mcat.reg' #regify_excat_pix(mcat.get_catalog(), mxcat_rfile, win=True) # stop early if requested: if (ntodo > 0) and (nproc >= ntodo): break #break #sys.exit(0) if (ntodo > 0) and (nproc >= ntodo): break tstop = time.time() ttook = tstop - tstart sys.stderr.write("Extraction completed in %.3f seconds.\n" % ttook) #import astropy.io.fits as pf # #imra = np.array([hh['CRVAL1'] for hh in sxc._im_hdrs]) #imde = np.array([hh['CRVAL2'] for hh in sxc._im_hdrs]) # ##sys.stderr.write("\n\n\n") ##sys.stderr.write("sxc.shift_and_stack(tag_files)\n") ##result = sxc.shift_and_stack(tag_files) #sys.exit(0) # #layers = sxc.pad_and_shift(sxc._im_data, sxc._x_shifts, sxc._y_shifts) #tstack = sxc.dumb_stack(layers) #pf.writeto('tstack.fits', tstack, overwrite=True) # #tdir = 'zzz' #if not os.path.isdir(tdir): # os.mkdir(tdir) # ##tag_bases = [os.path.basename(x) for x in tag_files] ##for ibase,idata in zip(tag_bases, layers): ## tsave = os.path.join(tdir, 'r' + ibase) ## sys.stderr.write("Saving %s ... \n" % tsave) ## pf.writeto(tsave, idata, overwrite=True) # #sys.stderr.write("\n\n\n") #sys.stderr.write("visual inspection with:\n") #sys.stderr.write("flztfs %s\n" % ' '.join(tag_files)) ##--------------------------------------------------------------------------## ###################################################################### # CHANGELOG (07_spitzer_aor_extraction.py): #--------------------------------------------------------------------- # # 2021-02-02: # -- Increased __version__ to 0.1.0. # -- First created 07_spitzer_aor_extraction.py. #

37.148282

82

0.577861

540

0.026286

0

11,171

0.543786

a7574a31d3793e68486c1afc1807fc0afcd14ce5

6,594

py

Python

project/apps/CI-producer/app/producers_test.py

Monxun/PortainerPractice

a3be077efe5c5eb2aa27b6a2fcf626989bdbbbe4

[ "MIT" ]

null

project/apps/CI-producer/app/producers_test.py

Monxun/PortainerPractice

a3be077efe5c5eb2aa27b6a2fcf626989bdbbbe4

[ "MIT" ]

1

2022-03-02T22:54:36.000Z

project/apps/CI-producer/app/producers_test.py

Monxun/PortainerPractice

a3be077efe5c5eb2aa27b6a2fcf626989bdbbbe4

[ "MIT" ]

null

from os import strerror import os import pytest import datetime import sqlalchemy from sqlalchemy import inspect from sqlalchemy import select from sqlalchemy.orm import session from sqlalchemy.sql.expression import func ################################################# # DATABASE CONNECTOR user = 'user' password = 'root' host = 'localhost' port = 3306 name = 'alinedb' engine = sqlalchemy.create_engine( f'mysql+pymysql://{user}:{password}@{host}:{port}/{name}', echo=True ) inspector = inspect(engine) for table_name in inspector.get_table_names(): print(table_name) Session = session.sessionmaker() Session.configure(bind=engine) my_session = Session() ################################################# # TEST ''' Module to test producers ''' from models import ( Applicant, Bank, Merchant, Application, Branch, Member, Account, User, OneTimePasscode, Transaction, UserRegistrationToken ) def test_create_applicants() -> None: test_object = my_session.query(Applicant).first() assert isinstance(test_object.id, int) assert isinstance(test_object.address, str) assert isinstance(test_object.city, str) assert isinstance(test_object.created_at, datetime.datetime) assert isinstance(test_object.date_of_birth, datetime.date) assert isinstance(test_object.drivers_license, str) assert isinstance(test_object.email, str) assert isinstance(test_object.first_name, str) assert isinstance(test_object.gender, str) assert isinstance(test_object.income, int) assert isinstance(test_object.last_modified_at_at, datetime.datetime) assert isinstance(test_object.last_name, str) assert isinstance(test_object.mailing_address, str) assert isinstance(test_object.mailing_city, str) assert isinstance(test_object.mailing_state, str) assert isinstance(test_object.mailing_zipcode, str) assert isinstance(test_object.middle_name, str) assert isinstance(test_object.phone, str) assert isinstance(test_object.social_security, str) assert isinstance(test_object.state, str) assert isinstance(test_object.zipcode, str) def test_create_banks() -> None: test_object = my_session.query(Bank).first() assert isinstance(test_object.id, int) assert isinstance(test_object.address, str) assert isinstance(test_object.city, str) assert isinstance(test_object.routing_number, str) assert isinstance(test_object.state, str) assert isinstance(test_object.zipcode, str) def test_create_merchants() -> None: test_object = my_session.query(Merchant).first() assert isinstance(test_object.code, str) assert isinstance(test_object.address, str) assert isinstance(test_object.city, str) assert isinstance(test_object.description, str) assert isinstance(test_object.name, str) assert isinstance(test_object.registered_at, datetime.datetime) assert isinstance(test_object.state, str) assert isinstance(test_object.zipcode, str) def test_create_applications() -> None: test_object = my_session.query(Application).first() assert isinstance(test_object.id, int) assert isinstance(test_object.application_status, str) assert isinstance(test_object.application_type, str) assert isinstance(test_object.primary_applicant_id, int) def test_create_branches() -> None: test_object = my_session.query(Branch).first() assert isinstance(test_object.id, int) assert isinstance(test_object.address, str) assert isinstance(test_object.city, str) assert isinstance(test_object.name, str) assert isinstance(test_object.phone, str) assert isinstance(test_object.state, str) assert isinstance(test_object.zipcode, str) assert isinstance(test_object.bank_id, int) def test_create_members() -> None: test_object = my_session.query(Member).first() assert isinstance(test_object.id, int) assert isinstance(test_object.membership_id, str) assert isinstance(test_object.applicant_id, int) assert isinstance(test_object.branch_id, int) def test_create_accounts() -> None: test_object = my_session.query(Account).first() assert isinstance(test_object.account_type, str) assert isinstance(test_object.id, int) assert isinstance(test_object.account_number, str) assert isinstance(test_object.balance, int) assert isinstance(test_object.status, str) assert isinstance(test_object.available_balance, int) assert isinstance(test_object.apy, float) assert isinstance(test_object.primary_account_holder_id, int) def test_create_users() -> None: test_object = my_session.query(User).first() assert isinstance(test_object.role, str) assert isinstance(test_object.id, int) assert isinstance(test_object.enabled, int) assert isinstance(test_object.password, str) assert isinstance(test_object.username, str) assert isinstance(test_object.email, str) assert isinstance(test_object.first_name, str) assert isinstance(test_object.last_name, str) assert isinstance(test_object.phone, str) assert isinstance(test_object.member_id, int) def test_create_one_time_passcodes() -> None: test_object = my_session.query(OneTimePasscode).first() assert isinstance(test_object.id, int) assert isinstance(test_object.checked, int) assert isinstance(test_object.otp, str) assert isinstance(test_object.user_id, int) def test_create_transactions() -> None: test_object = my_session.query(Transaction).first() assert isinstance(test_object.id, int) assert isinstance(test_object.amount, int) assert isinstance(test_object.date, datetime.datetime) assert isinstance(test_object.description, str) assert isinstance(test_object.initial_balance, int) assert isinstance(test_object.last_modified, datetime.date) assert isinstance(test_object.method, str) assert isinstance(test_object.posted_balance, int) assert isinstance(test_object.state, str) assert isinstance(test_object.status, str) assert isinstance(test_object.type, str) assert isinstance(test_object.account_id, int) assert isinstance(test_object.merchant_code, str) def test_create_user_registration_tokens() -> None: test_object = my_session.query(UserRegistrationToken).first() assert isinstance(test_object.token, str) assert isinstance(test_object.created, datetime.datetime) assert isinstance(test_object.expiration_delay, int) assert isinstance(test_object.user_id, int)

30.957746

74

0.746588

0

245

0.037155

a7574f04a38567a940cb678fc874747f83a2b6d9

223

py

Python

quran/domain/edition.py

octabytes/quran

974d351cf5e6a12a28a5ac9f29c8d2753ae6dd86

[ "Apache-2.0" ]

null

quran/domain/edition.py

octabytes/quran

974d351cf5e6a12a28a5ac9f29c8d2753ae6dd86

[ "Apache-2.0" ]

null

quran/domain/edition.py

octabytes/quran

974d351cf5e6a12a28a5ac9f29c8d2753ae6dd86

[ "Apache-2.0" ]

null

from dataclasses import dataclass from quran.domain.entity import Entity @dataclass class Edition(Entity): id: str language: str name: str translator: str type: str format: str direction: str

14.866667

38

0.690583

135

0.605381

0

146

0.654709

0

a75778c132db31042c63da3f963565d091dded6a

1,231

py

Python

dataflow/core/visualization.py

alphamatic/amp

5018137097159415c10eaa659a2e0de8c4e403d4

[ "BSD-3-Clause" ]

5

2021-08-10T23:16:44.000Z

2022-03-17T17:27:00.000Z

dataflow/core/visualization.py

alphamatic/amp

5018137097159415c10eaa659a2e0de8c4e403d4

[ "BSD-3-Clause" ]

330

2021-06-10T17:28:22.000Z

2022-03-31T00:55:48.000Z

dataflow/core/visualization.py

alphamatic/amp

5018137097159415c10eaa659a2e0de8c4e403d4

[ "BSD-3-Clause" ]

6

2021-06-10T17:20:32.000Z

2022-03-28T08:08:03.000Z

""" Helper functions to visualize a graph in a notebook or save the plot to file. Import as: import dataflow.core.visualization as dtfcorvisu """ import IPython import networkx as networ import pygraphviz import dataflow.core.dag as dtfcordag import helpers.hdbg as hdbg import helpers.hio as hio def draw(dag: dtfcordag.DAG) -> IPython.core.display.Image: """ Render DAG in a notebook. """ agraph = _extract_agraph_from_dag(dag) image = IPython.display.Image(agraph.draw(format="png", prog="dot")) return image def draw_to_file(dag: dtfcordag.DAG, file_name: str = "graph.png") -> str: """ Save DAG rendering to a file. """ agraph = _extract_agraph_from_dag(dag) # Save to file. hio.create_enclosing_dir(file_name) agraph.draw(file_name, prog="dot") return file_name def _extract_agraph_from_dag(dag: dtfcordag.DAG) -> pygraphviz.agraph.AGraph: """ Extract a pygraphviz `agraph` from a DAG. """ # Extract networkx DAG. hdbg.dassert_isinstance(dag, dtfcordag.DAG) graph = dag.dag hdbg.dassert_isinstance(graph, networ.Graph) # Convert the DAG into a pygraphviz graph. agraph = networ.nx_agraph.to_agraph(graph) return agraph

25.122449

77

0.707555

0

396

0.32169

a758f541fb2e3c2ec9bc820cd471a439cd2c4443

7,714

py

Python

scripts/pixel_error.py

ling-k/STOVE

fcf36139f41dee5ef892e90dedf1d2208da6fd3c

[ "MIT" ]

31

2019-10-14T01:48:44.000Z

2022-01-20T19:19:14.000Z

scripts/pixel_error.py

ling-k/STOVE

fcf36139f41dee5ef892e90dedf1d2208da6fd3c

[ "MIT" ]

3

2020-05-08T11:01:25.000Z

2021-05-24T07:50:10.000Z

scripts/pixel_error.py

ling-k/STOVE

fcf36139f41dee5ef892e90dedf1d2208da6fd3c

[ "MIT" ]

9

2020-01-13T11:25:16.000Z

2021-05-10T06:04:08.000Z

"""Calculate pixel errors for a single run or all runs in an experiment dir.""" import torch import itertools import numpy as np import imageio import argparse import os import glob from model.main import main as restore_model from model.utils.utils import bw_transform os.environ["CUDA_VISIBLE_DEVICES"] = '-1' def run_fmt(x, with_under=False): """Format array x of ints to become valid run folder names.""" return 'run{:03d}'.format(x) if not with_under else 'run_{:03d}'.format(x) def get_pixel_error(restore, linear=False, path='', real_mpe=False, checkpoint='checkpoint'): """Restore a model and calculate error from reconstructions.""" # do not write any new runs extras = { 'nolog': True, 'checkpoint_path': os.path.join(restore, checkpoint)} self = restore_model(restore=restore, extras=extras) # ignore supairvised runs for now if self.c.supairvised is True: return None # make sure all runs access the same data! print(self.c.testdata) step = self.c.frame_step visible = self.c.num_visible batch_size = self.c.batch_size skip = self.c.skip # make sure this is the same print(step, visible, batch_size, skip) long_rollout_length = self.c.num_frames // step - visible lrl = long_rollout_length total_images = self.test_dataset.total_img total_labels = self.test_dataset.total_data # apply step and batch size once total_images = total_images[:batch_size, ::step] total_labels = total_labels[:batch_size, ::step] # true data to compare against true_images = total_images[:, skip:(visible+long_rollout_length)] true_images = torch.tensor(true_images).to(self.c.device).type(self.c.dtype) # First obtain reconstruction of input. stove_input = total_images[:, :visible] stove_input = torch.tensor(stove_input).to(self.c.device).type(self.c.dtype) _, prop_dict2, _ = self.stove(stove_input, self.c.plot_every) z_recon = prop_dict2['z'] # Use last state to do rollout if not linear: z_pred, _ = self.stove.rollout(z_recon[:, -1], long_rollout_length) else: # propagate last speed v = z_recon[:, -1, :, 4:6].unsqueeze(1) v = v.repeat(1, long_rollout_length, 1, 1) t = torch.arange(1, long_rollout_length+1) t = t.repeat(v.shape[0], *v.shape[2:], 1).permute(0, 3, 1, 2).double() dx = v * t new_x = z_recon[:, -1, :, 2:4].unsqueeze(1) new_x = new_x.repeat(1, long_rollout_length, 1, 1) + dx z_pred = torch.cat( [z_recon[:, -1, :, :2].unsqueeze(1).repeat(1, lrl, 1, 1), new_x, v, z_recon[:, -1, :, 6:].unsqueeze(1).repeat(1, lrl, 1, 1)], -1 ) z_seq = torch.cat([z_recon, z_pred], 1) # sigmoid positions to make errors comparable if linear: print('clamp positions to 0.9') frame_lim = 0.8 if self.c.coord_lim == 10 else 0.9 z_seq = torch.cat([ z_seq[..., :2], torch.clamp(z_seq[..., 2:4], -frame_lim, frame_lim), z_seq[..., 6:]], -1) # Simple Reconstruction of Sequences # stove_input = total_images[:10] # stove_input = torch.tensor(stove_input).to(self.c.device).type(self.c.dtype) # elbo, prop_dict2, _ = self.stove(stove_input, self.c.plot_every) # z_recon = prop_dict2['z'] # if self.c.debug_bw: # img = stove_input.sum(2) # img = torch.clamp(img, 0, 1) # img = torch.unsqueeze(img, 2) # model_images = self.stove.reconstruct_from_z( # z_recon, img[:, skip:], max_activation=False, single_image=False) # use mpe to get reconstructed images if real_mpe: if self.c.debug_bw: img = stove_input[:, skip].sum(1) img = torch.clamp(img, 0, 1) img = torch.unsqueeze(img, 1) model_images = self.stove.reconstruct_from_z( z_seq, img, max_activation=False, single_image=True) else: model_images = self.stove.reconstruct_from_z(z_seq) if self.c.debug_bw: true_images = bw_transform(true_images) model_images = torch.clamp(model_images, 0, 1) mse = torch.mean(((true_images - model_images)**2), dim=(0, 2, 3, 4)) plot_sample = model_images[:10, :, 0].detach().cpu().numpy() plot_sample = (255 * plot_sample.reshape(-1, self.c.height, self.c.width)) plot_sample = plot_sample.astype(np.uint8) filename = 'linear_' if linear else '' filename += 'pixel_error_sample.gif' filepath = os.path.join(path, filename) print('Saving gif to ', filepath) imageio.mimsave( filepath, plot_sample, fps=24) # also log state differences # bug_potential... for some reason self.c.coord_lim is 30 but max # true_states is 10 for gravity true_states = total_labels[:, skip:(visible+long_rollout_length)] print(true_states.max(), ' is coord max.') true_states = torch.tensor(true_states).to(self.c.device).type(self.c.dtype) permutations = list(itertools.permutations(range(0, self.c.num_obj))) errors = [] for perm in permutations: error = ((true_states[:, :5, :, :2]-z_seq[:, :5, perm, 2:4])**2).sum(-1) error = torch.sqrt(error).mean((1, 2)) errors += [error] errors = torch.stack(errors, 1) _, idx = errors.min(1) selector = list(zip(range(idx.shape[0]), idx.cpu().tolist())) pos_matched = [z_seq[i, :, permutations[j]] for i, j in selector] pos_matched = torch.stack(pos_matched, 0) mse_states = torch.sqrt((( true_states[..., :2] - pos_matched[..., 2:4])**2).sum(-1)).mean((0, 2)) return mse, mse_states def main(script_args): """Parse arguments, find runs, execute pixel_error.""" parser = argparse.ArgumentParser() parser.add_argument( "-p", "--path", type=str, help="Set folder from which to create pixel errors for." + "Must contain runs of model.") parser.add_argument( '--linear', action='store_true', help='create linear errors') parser.add_argument( '--no-save', dest='no_save', action='store_true') parser.add_argument( '--real-mpe', dest='real_mpe', action='store_true') parser.add_argument( '--checkpoint', type=str, default='checkpoint') args = parser.parse_args(script_args) filename = 'pixel_errors.csv' if args.linear: filename = 'linear_' + filename if 'run' not in args.path[-10:]: restores = glob.glob(args.path+'run*') restores = sorted(restores) else: restores = [args.path] print(restores) if len(restores) == 0: raise ValueError('No runs found in path {}.'.format(args.path)) # debug # mse, mse_states = get_pixel_error( # restores[0], args.linear, args.path, args.real_mpe, args.checkpoint) # return 0 for restore in restores: try: mse, mse_states = get_pixel_error( restore, args.linear, args.path, args.real_mpe, args.checkpoint) except Exception as e: print(e) print('Not possible for run {}.'.format(restore)) continue mse = mse.cpu().detach().numpy() if args.no_save: continue save_dir = os.path.join(args.path, 'test') if not os.path.exists(save_dir): os.makedirs(save_dir) with open(os.path.join(save_dir, filename), 'a') as f: f.write(','.join(['{:.6f}'.format(i) for i in mse])+'\n') with open(os.path.join(save_dir, 'states_'+filename), 'a') as f: f.write(','.join(['{:.6f}'.format(i) for i in mse_states])+'\n')

34.4375

93

0.621208

0

1,907

0.247213

a75a94acdbd36e7b6da2d3d837a50b906558f9b8

770

py

Python

users/admin.py

JVacca12/FIRST

e3906209cae1198e1fbda4d00bc0a906e8294a69

[ "MIT" ]

null

users/admin.py

JVacca12/FIRST

e3906209cae1198e1fbda4d00bc0a906e8294a69

[ "MIT" ]

null

users/admin.py

JVacca12/FIRST

e3906209cae1198e1fbda4d00bc0a906e8294a69

[ "MIT" ]

null

from django.contrib import admin # Register your models here. """User admin classes.""" # Django from django.contrib import admin # Models from users.models import User @admin.register(User) class UserAdmin(admin.ModelAdmin): """User admin.""" list_display = ('pk', 'username', 'email','first_name','last_name') #Columnas de la tabla visibles, first and last name son campos intrínsecos de user list_display_links = ('pk', 'username', 'email','first_name','last_name') #Cliqueables search_fields = ( 'email', 'username', 'first_name', 'last_name', ) list_filter = ( 'is_active', 'is_staff', 'date_joined', 'modified', ) readonly_fields = ('date_joined', 'modified',)

23.333333

154

0.633766

576

0.747082

0

598

0.775616

0

376

0.487678

a75aa0bd60c43a11405b09d22589cf2d9c586cc5

3,469

py

Python

mud/migrations/0001_initial.py

lambda-mud-cs18/backend

060c5c1a317d8b6557e778cd539e75f24eff05dd

[ "MIT" ]

1

2022-01-12T17:44:26.000Z

mud/migrations/0001_initial.py

lambda-mud-cs18/backend

060c5c1a317d8b6557e778cd539e75f24eff05dd

[ "MIT" ]

8

2020-02-12T01:12:46.000Z

2022-02-10T10:17:28.000Z

mud/migrations/0001_initial.py

lambda-mud-cs18/backend

060c5c1a317d8b6557e778cd539e75f24eff05dd

[ "MIT" ]

2

2022-01-12T17:44:29.000Z

# Generated by Django 2.2.3 on 2019-07-31 17:10 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Item', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ('name', models.CharField(max_length=200)), ('description', models.CharField(max_length=200)), ('weight', models.IntegerField()), ('itemtype', models.CharField(max_length=200)), ('level', models.IntegerField()), ('exp', models.IntegerField()), ('attributes', models.CharField(max_length=200)), ], ), migrations.CreateModel( name='Map', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ], ), migrations.CreateModel( name='Player', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ('playername', models.CharField(max_length=200)), ('name', models.CharField(max_length=200)), ('password', models.CharField(max_length=200)), ('team_id', models.IntegerField()), ('current_room', models.IntegerField()), ('cooldown', models.FloatField()), ('encumbrance', models.IntegerField()), ('strength', models.IntegerField()), ('speed', models.IntegerField()), ('gold', models.IntegerField()), ('inventory', models.CharField(max_length=1000)), ('status', models.CharField(max_length=1000)), ('errors', models.CharField(max_length=1000)), ('messages', models.CharField(max_length=1000)), ('token', models.CharField(max_length=1000)), ], ), migrations.CreateModel( name='PlayerInventory', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ('player_id', models.IntegerField()), ('item_id', models.IntegerField()), ('quantity', models.IntegerField()), ], ), migrations.CreateModel( name='Room', fields=[ ('map_id', models.IntegerField()), ('room_id', models.IntegerField(primary_key=True, serialize=False)), ('title', models.CharField(max_length=200)), ('description', models.CharField(max_length=200)), ('coordinates', models.CharField(max_length=200)), ('elevation', models.IntegerField()), ('terrain', models.CharField(max_length=200)), ('north', models.CharField(max_length=200)), ('south', models.CharField(max_length=200)), ('east', models.CharField(max_length=200)), ('west', models.CharField(max_length=200)), ], ), migrations.CreateModel( name='Team', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ('name', models.CharField(max_length=200)), ('map_id', models.IntegerField()), ], ), ]

39.420455

84

0.51254

3,376

0.973191

0

457

0.131738

a75c1979034dbafe33e7945478e87745ce9ce8e5

918

py

Python

scripts/quest/q22504s.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

54

2019-04-16T23:24:48.000Z

2021-12-18T11:41:50.000Z

scripts/quest/q22504s.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

3

2019-05-19T15:19:41.000Z

2020-04-27T16:29:16.000Z

scripts/quest/q22504s.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

49

2020-11-25T23:29:16.000Z

2022-03-26T16:20:24.000Z

sm.setSpeakerID(1013000) sm.sendNext("Ugh. This isn't going to work. I need something else. No plants. No meat. What, you have no idea? But you're the master, and you're older than me, too. You must know what'd be good for me!") sm.setPlayerAsSpeaker() sm.sendSay("#bBut I don't. It's not like age has anything to do with this...") sm.setSpeakerID(1013000) if sm.sendAskAccept("Since you're older, you must be more experienced in the world, too. Makes sense that you'd know more than me. Oh, fine. I'll ask someone who's even older than you, master!"): if not sm.hasQuest(parentID): sm.startQuest(parentID) sm.setPlayerAsSpeaker() sm.sendSayOkay("#b#b(You already asked Dad once, but you don't have any better ideas. Time to ask him again!)") else: sm.sendNext("No use trying to find an answer to this on my own. I'd better look for #bsomeone older and wiser than master#k!") sm.dispose()

61.2

203

0.721133

0

637

0.6939

a75cf13072fe0194f0d08765f3c331975a5d8df7

424

py

Python

user/migrations/0002_user_photo.py

martinlehoux/erp-reloaded

db7dea603095dec558f4b0ad9a0d2dbd20f8703c

[ "MIT" ]

null

user/migrations/0002_user_photo.py

martinlehoux/erp-reloaded

db7dea603095dec558f4b0ad9a0d2dbd20f8703c

[ "MIT" ]

5

2021-04-08T18:54:04.000Z

2021-06-10T18:37:26.000Z

user/migrations/0002_user_photo.py

martinlehoux/erp-reloaded

db7dea603095dec558f4b0ad9a0d2dbd20f8703c

[ "MIT" ]

null

# Generated by Django 3.0.3 on 2020-03-01 00:58 from django.db import migrations, models import user.models class Migration(migrations.Migration): dependencies = [ ('user', '0001_initial'), ] operations = [ migrations.AddField( model_name='user', name='photo', field=models.ImageField(null=True, upload_to=user.models.UploadTo('photo')), ), ]

21.2

88

0.606132

312

0.735849

0

87

0.205189

a75f0071595f1cf5e30f78a377181f6b55570f76

61

py

Python

core/models/__init__.py

Bhaskers-Blu-Org1/bLVNet-TAM

feadcd3a1a25723dc28bed867580032181e824a3

[ "Apache-2.0" ]

62

2019-10-22T14:52:30.000Z

2021-07-27T12:07:38.000Z

core/models/__init__.py

Bhaskers-Blu-Org1/bLVNet-TAM

feadcd3a1a25723dc28bed867580032181e824a3

[ "Apache-2.0" ]

6

2019-12-16T06:03:42.000Z

2020-08-31T07:59:04.000Z

core/models/__init__.py

IBM/bLVNet-TAM

feadcd3a1a25723dc28bed867580032181e824a3

[ "Apache-2.0" ]

16

2019-11-02T06:49:19.000Z

2021-12-30T14:51:48.000Z

from .blvnet_tam import bLVNet_TAM __all__ = ['bLVNet_TAM']

15.25

34

0.770492

0

12

0.196721

a760fe286388453e9bf13c54cc23324198919723

438

py

Python

monodepth/geometry/utils.py

vguizilini/packnet-sfm

e462716837f24c11cb227ca99fe30bcf12b3cc56

[ "MIT" ]

1

2020-04-30T07:32:57.000Z

monodepth/geometry/utils.py

muzi2045/packnet-sfm

fec6d0b493b784cabe5e6bf9c65b996a83c63fe1

[ "MIT" ]

null

monodepth/geometry/utils.py

muzi2045/packnet-sfm

fec6d0b493b784cabe5e6bf9c65b996a83c63fe1

[ "MIT" ]

null

# Copyright 2020 Toyota Research Institute. All rights reserved. """ Geometry utilities """ import numpy as np def invert_pose_numpy(T): """ 'Invert' 4x4 extrinsic matrix Parameters ---------- T: 4x4 matrix (world to camera) Returns ------- 4x4 matrix (camera to world) """ Tc = np.copy(T) R, t = Tc[:3, :3], Tc[:3, 3] Tc[:3, :3], Tc[:3, 3] = R.T, - np.matmul(R.T, t) return Tc

16.222222

65

0.547945

0

261

0.59589

a7624496ee4975eb04a3c005275217a54323fb5d

27,209

py

Python

minesweeper.py

MrAttoAttoAtto/Cool-Programming-Project

68214d089b612fdcca7fe76dce3464edec35ce2b

[ "MIT" ]

null

minesweeper.py

MrAttoAttoAtto/Cool-Programming-Project

68214d089b612fdcca7fe76dce3464edec35ce2b

[ "MIT" ]

null

minesweeper.py

MrAttoAttoAtto/Cool-Programming-Project

68214d089b612fdcca7fe76dce3464edec35ce2b

[ "MIT" ]

null

#Minesweeper! from tkinter import * import random, time, math, threading, os.path, os #Tkinter Class class MinesweeperMain: #Initialising class def __init__(self, xLength, yLength, percentOfBombs, caller=None, winChoice=True): try: #kills the 'play again' host (if it exists) caller.root.destroy() except TclError: pass except AttributeError: pass self.gameStarted = False #makes the necessary variables self.gameOver = False self.vlc64bitInstalled = True self.squaresRevealed = 0 try: #checks if the user has vlc import vlc except OSError: self.vlc64bitInstalled = False self.xLength = xLength #sets these variables to the object self.yLength = yLength self.percentOfBombs = percentOfBombs #sets the variable self.numOfBombs = math.floor(self.percentOfBombs/100*self.xLength*self.yLength) #setting the number of bombs self.bombsLeftToReveal = self.numOfBombs #sets a variable that will allow for enough labels to be created if self.vlc64bitInstalled: self.explosionSound = vlc.MediaPlayer(os.path.join('sounds', 'explosion-sound.mp3')) #loads the sounds self.alertSound = vlc.MediaPlayer(os.path.join('sounds', 'alert-sound.mp3')) #alert sound self.winChoice = winChoice if self.winChoice: #chooses the sound to load self.winSound = vlc.MediaPlayer(os.path.join('sounds', 'win-sound.mp3')) else: self.winSound = vlc.MediaPlayer(os.path.join('sounds', 'win-sound.wav')) self.mapData = [] #creating the variable which holds the map data self.revealedSquareIds = [] #list so that, when the loss occurs and all tiles are revealed, already revealed squares are not affected self.bombLocationsReserved = [] #creates a list that will hold the locations where no more bombs can be placed self.root = Tk() self.root.title('Minesweeper') #sets up the tkinter window self.listOfNumberImages = [] #sets up this list for holding the images of the numbers for x in range(9): self.listOfNumberImages.append(PhotoImage(file='numbers'+os.sep+str(x)+'.PNG')) #fills said list self.transImage = PhotoImage(file=os.path.join('pictures', 'transparent.png')) self.flagImage = PhotoImage(file=os.path.join('pictures', 'flag.png')) self.bombImage = PhotoImage(file=os.path.join('pictures', 'mine2-11.png')) self.explosionImage = PhotoImage(file=os.path.join('pictures', 'explosion.png')) #sets up the rest of the images self.frame = Frame(self.root) #makes the frame widget self.frame.pack() self.bombLabelList = [] #list for storing the bomb pictures for i in range(self.numOfBombs): #adds all the necessary bomb picture labels self.bombLabelList.append(Label(self.frame, image=self.bombImage, width=62, height=51)) #adds the right amount of bomb pictures to the list if self.xLength % 2 == 0: timeXPos = int(self.xLength/2-1) #sets the positions so they are in the middle bombCountXPos = timeXPos + 1 else: timeXPos = int(self.xLength/2-1.5) bombCountXPos = timeXPos + 2 self.timeSecs = 0 #sets these time variables self.timeMins = 0 self.timeLabel = Label(self.frame, text='Time') #puts the time and bomb count onto the tkinter window self.timeLabel.grid(row=0, column=timeXPos) self.bombLabel = Label(self.frame, text='Bombs') self.bombLabel.grid(row=0, column=bombCountXPos) self.timeStrVar = StringVar() self.timeStrVar.set('00:00') self.timeClock = Label(self.frame, textvariable=self.timeStrVar) self.timeClock.grid(row=1, column=timeXPos) self.bombStrVar = StringVar() self.bombStrVar.set(str(self.numOfBombs)) self.bombsLeftLabel = Label(self.frame, textvariable=self.bombStrVar) self.bombsLeftLabel.grid(row=1, column=bombCountXPos) self.buttonList = [] #lists to hold data for buttons/labels self.buttonStringVarList = [] self.labelList = [] self.isFlaggedList = [] self.mapData = [] #creating the variable which holds the map data for l in range(self.yLength): #fills the lists with their required starting data self.buttonStringVarList.append([]) self.buttonList.append([]) self.labelList.append([]) self.isFlaggedList.append([]) self.mapData.append([]) for p in range(self.xLength): self.buttonStringVarList[l].append(StringVar()) self.buttonList[l].append('') self.labelList[l].append('') self.isFlaggedList[l].append(False) self.mapData[l].append('') xPos = 0 #sets the working positions of the button creation yPos = 0 for pos in range(0, self.xLength*self.yLength): #creates all of the buttons required if xPos == self.xLength: yPos += 1 xPos = 0 self.buttonList[yPos][xPos] = Button(self.frame, height=49, width=60, textvariable=self.buttonStringVarList[yPos][xPos], image=self.transImage) self.buttonList[yPos][xPos].grid(row=yPos+2, column=xPos) self.buttonList[yPos][xPos].bind('<Button-1>', lambda e, xPosLoc=xPos, yPosLoc=yPos: self.revealSquare(xPosLoc, yPosLoc)) #reveals the square if left-clicked self.buttonList[yPos][xPos].bind('<Button-3>', lambda e, xPosLoc=xPos, yPosLoc=yPos: self.markSquare(xPosLoc, yPosLoc)) #marks the square if right-clicked xPos += 1 self.timerThread = threading.Thread(target=self.timerCode, name="timer") #starts the timer self.timerThread.start() self.root.mainloop() #mainloop! def timerCode(self): while True: if self.gameOver: #if the game is over, exit this loop of the timer return self.timeSecs = int(self.timeSecs) #turns them back into ints (just in case they were converted into strings to add 0s to the front of them) self.timeMins = int(self.timeMins) start = time.time() self.timeSecs += 1 #increments the seconds if self.timeSecs == 60: #if it is a minute... self.timeSecs = 0 #change the seconds to 0 and add 1 to the mins self.timeMins += 1 if self.timeSecs < 10: #if either is lower than 10, make sure it has a 0 in front of the number self.timeSecs = '0'+str(self.timeSecs) if self.timeMins < 10: self.timeMins = '0'+str(self.timeMins) try: self.timeStrVar.set(str(self.timeMins)+':'+str(self.timeSecs)) #sets the visual time except RuntimeError: #if the window has been forcefully ended return while time.time() < start+1: #waits for a sec continue def generateBoard(self, xPos, yPos): #generating the board self.bombLocationsReserved.append(xPos+yPos*self.xLength) #reserving the 3x3 area around the button placed self.bombLocationsReserved.append(xPos+yPos*self.xLength-1) self.bombLocationsReserved.append(xPos+yPos*self.xLength+1) self.bombLocationsReserved.append(xPos+yPos*self.xLength-self.xLength) self.bombLocationsReserved.append(xPos+yPos*self.xLength+self.xLength) self.bombLocationsReserved.append(xPos+yPos*self.xLength-self.xLength-1) self.bombLocationsReserved.append(xPos+yPos*self.xLength-self.xLength+1) self.bombLocationsReserved.append(xPos+yPos*self.xLength+self.xLength-1) self.bombLocationsReserved.append(xPos+yPos*self.xLength+self.xLength+1) bombsLeftToPlace = self.numOfBombs #sets a helpful temporary variable while bombsLeftToPlace > 0: yPlace = 0 bombPlacement = random.randint(0, self.xLength*self.yLength-1) #random square id placementValue = bombPlacement #another helpful variable while bombPlacement >= self.xLength: #figures out the x and y from that bombPlacement = bombPlacement - self.xLength yPlace += 1 xPlace = bombPlacement if not placementValue in self.bombLocationsReserved: #checks the place isnt reserved self.mapData[yPlace][xPlace] = 'B' #updates the map bombsLeftToPlace = bombsLeftToPlace - 1 #self-explanatory self.bombLocationsReserved.append(placementValue) #reserves the place just taken for squareXPos in range(0, self.xLength): #for EVERY square... for squareYPos in range(0, self.yLength): bombsSurrounding = 0 #sets this to 0 if self.mapData[squareYPos][squareXPos] == 'B': #if a bomb... self.buttonStringVarList[squareYPos][squareXPos].set('B') #sets the strVar to B (debugging) continue #goes back to the loop if squareXPos > 0: #all of this next part finds how many bombs surround a square (and makes sure that it does not wrap around or throw an error) if squareYPos > 0: if self.mapData[squareYPos-1][squareXPos-1] == 'B': bombsSurrounding += 1 if self.mapData[squareYPos][squareXPos-1] == 'B': bombsSurrounding += 1 try: if self.mapData[squareYPos+1][squareXPos-1] == 'B': bombsSurrounding += 1 except IndexError: pass if squareYPos > 0: if self.mapData[squareYPos-1][squareXPos] == 'B': bombsSurrounding += 1 try: if self.mapData[squareYPos+1][squareXPos] == 'B': bombsSurrounding += 1 except IndexError: pass if squareYPos > 0: try: if self.mapData[squareYPos-1][squareXPos+1] == 'B': bombsSurrounding += 1 except IndexError: pass try: if self.mapData[squareYPos][squareXPos+1] == 'B': bombsSurrounding += 1 except IndexError: pass try: if self.mapData[squareYPos+1][squareXPos+1] == 'B': bombsSurrounding += 1 except IndexError: pass self.mapData[squareYPos][squareXPos] = bombsSurrounding #updates the mapData with the value of the square def revealSquare(self, xPos, yPos): #if a square is left-clicked... if not self.gameStarted: #is the game hasnt been generated yet... self.generateBoard(xPos, yPos) #generate it having been clicked at xPos, yPos self.gameStarted = True #the board has been generated if xPos+yPos*self.xLength in self.revealedSquareIds or (self.isFlaggedList[yPos][xPos] and not self.gameOver): #if the id has already been revealed or the square if flagged... return #exit the function self.squaresRevealed += 1 #increments the squares revealed self.revealedSquareIds.append(xPos+yPos*self.xLength) #append the id to the revealed ids self.buttonList[yPos][xPos].destroy() #destroy the button if self.mapData[yPos][xPos] != 'B': #if it is NOT a bomb... self.labelList[yPos][xPos] = Label(self.frame, width=62, height=51, image=self.listOfNumberImages[self.mapData[yPos][xPos]]) #create a label for it, self.labelList[yPos][xPos].grid(row=yPos+2, column=xPos) self.labelList[yPos][xPos].bind('<Button-2>', lambda e, xPos=xPos, yPos=yPos: self.chordSquare(xPos, yPos)) # and if middle-clicked, it will call chordSquare if not self.gameOver: #if the game hasn't been failed... self.root.update() #update the window (for nice looking 0 chain reactions) time.sleep(0.02) #sleep a bit if self.mapData[yPos][xPos] == 0 and not self.gameOver: #if it is a 0 and the game has not been lost... if xPos > 0: #reveal all round it (nice recursiveness) if yPos > 0: try: self.revealSquare(xPos-1, yPos-1) except Exception: pass try: self.revealSquare(xPos-1, yPos) except Exception: pass try: self.revealSquare(xPos-1, yPos+1) except Exception: pass if yPos > 0: try: self.revealSquare(xPos, yPos-1) except Exception: pass try: self.revealSquare(xPos, yPos+1) except Exception: pass if yPos > 0: try: self.revealSquare(xPos+1, yPos-1) except Exception: pass try: self.revealSquare(xPos+1, yPos) except Exception: pass try: self.revealSquare(xPos+1, yPos+1) except Exception: pass if self.mapData[yPos][xPos] == 'B': #if it's a bomb... self.bombLabelList[self.bombsLeftToReveal-1].grid(row=yPos+2, column=xPos) #put the pic in its place self.bombsLeftToReveal = self.bombsLeftToReveal-1 #self-explanatory if self.mapData[yPos][xPos] == 'B' and not self.gameOver: #if it is the bomb which made you lose... self.gameOver = True #you failed print('Working...') self.explosionLabel = Label(self.frame, width=62, height=51, image=self.explosionImage) #it becomes an explosion image self.explosionLabel.grid(row=yPos+2, column=xPos)# and is placed where it was if self.vlc64bitInstalled: #if vlc is installed... self.alertSound.play() #play alert time.sleep(0.3) self.explosionSound.play() #play the sound self.root.update() #update to show the explosion for xFail in range(self.xLength*self.yLength): #open all squares yFail = 0 while xFail >= self.xLength: xFail = xFail - self.xLength yFail += 1 self.revealSquare(xFail, yFail) self.root.update() #update after all this is done print('Done!') gameOver = GameOverBox(self, 'loss') #activate the game over dialog if self.squaresRevealed == self.xLength*self.yLength-self.numOfBombs and not self.gameOver: #if you have revealed all of the non-bomb squares and not failed... self.gameOver = True print('Working...') if self.vlc64bitInstalled: #if vlc is installed... self.winSound.play() #play the win sound bombLocIds = self.bombLocationsReserved[8:] #give the bomb ids for bombId in bombLocIds: #iterate through them yLocBomb = 0 while bombId >= self.xLength: #turn the ids into coordinates bombId = bombId - self.xLength yLocBomb += 1 xLocBomb = bombId self.revealSquare(xLocBomb, yLocBomb) #reveal those coords print('Done!') gameOver = GameOverBox(self, 'win') #open the win dialog box def markSquare(self, xPos, yPos): #flagging if not self.isFlaggedList[yPos][xPos]: #if the square is NOT flagged... self.buttonList[yPos][xPos].configure(image=self.flagImage, height=49, width=60) #flag it self.bombStrVar.set(int(self.bombStrVar.get())-1) #increment the bombs left self.isFlaggedList[yPos][xPos] = True else: self.buttonList[yPos][xPos].configure(image=self.transImage, height=49, width=60) #get rid of the flag self.bombStrVar.set(int(self.bombStrVar.get())+1) #increment the bombs left self.isFlaggedList[yPos][xPos] = False def chordSquare(self, xPos, yPos): #chording flagsSurrounding = 0 flagsNeeded = self.mapData[yPos][xPos] if xPos > 0: #all of this next part finds how many flags surround a square (and makes sure that it does not wrap around or throw an error) if yPos > 0: try: if self.isFlaggedList[yPos-1][xPos-1]: flagsSurrounding += 1 except Exception: pass try: if self.isFlaggedList[yPos][xPos-1]: flagsSurrounding += 1 except Exception: pass try: if self.isFlaggedList[yPos+1][xPos-1]: flagsSurrounding += 1 except IndexError: pass except Exception: pass if yPos > 0: try: if self.isFlaggedList[yPos-1][xPos]: flagsSurrounding += 1 except Exception: pass try: if self.isFlaggedList[yPos+1][xPos]: flagsSurrounding += 1 except IndexError: pass except Exception: pass if yPos > 0: try: if self.isFlaggedList[yPos-1][xPos+1]: flagsSurrounding += 1 except IndexError: pass except Exception: pass try: if self.isFlaggedList[yPos][xPos+1]: flagsSurrounding += 1 except IndexError: pass except Exception: pass try: if self.isFlaggedList[yPos+1][xPos+1]: flagsSurrounding += 1 except IndexError: pass except Exception: pass if flagsSurrounding == flagsNeeded: #if there are enough, but not too many flags... if xPos > 0: #reveal all around it if yPos > 0: try: self.revealSquare(xPos-1, yPos-1) except Exception: pass try: self.revealSquare(xPos-1, yPos) except Exception: pass try: self.revealSquare(xPos-1, yPos+1) except Exception: pass if yPos > 0: try: self.revealSquare(xPos, yPos-1) except Exception: pass try: self.revealSquare(xPos, yPos+1) except Exception: pass if yPos > 0: try: self.revealSquare(xPos+1, yPos-1) except Exception: pass try: self.revealSquare(xPos+1, yPos) except Exception: pass try: self.revealSquare(xPos+1, yPos+1) except Exception: pass class GameOverBox: #end of game dialog def __init__(self, master, state): if state == 'loss': #if you lost self.title = 'Game Over' #set these variables self.message = 'You Lost!' self.color = 'red' else: #if you won self.title = 'Congratulations' #set these variables self.message = 'You Won, Well Done! It took you '+master.timeStrVar.get()+'!' self.color = 'green' self.root = Tk() self.root.title(self.title) #create the window self.frame = Frame(self.root) #create the frame self.frame.pack() self.label = Label(self.frame, text=self.message, fg=self.color) #create the label self.label.grid(row=0, column=1) self.playAgainButton = Button(self.frame, text='Play Again', fg='green', command=lambda: self.restart(master)) #create the play again button self.playAgainButton.grid(row=0, column=0) self.exitButton = Button(self.frame, text='Exit and Close', fg='red', command=lambda: self.exit(master)) #create the exit button self.exitButton.grid(row=0, column=2) self.playOtherButton = Button(self.frame, text='Play another configuration', command=lambda: self.playOther(master)) #create the 'play another config' button self.playOtherButton.grid(row=1, column=1) self.root.mainloop() #Mainloop! def restart(self, master): #the restart function try: master.root.destroy() #kill the MinesweeperMain window except Exception: pass openMain(self, master=master) #re-call it def exit(self, master): #exit func try: master.root.destroy() #kill the MinesweepreMain window except Exception: pass self.root.destroy() #kill the end of game dialog def playOther(self, master): global start try: master.root.destroy() #kill the MinesweeperMain window except Exception: pass start = StartBox(self) #start the Start Box class StartBox: def __init__(self, caller=None): try: caller.root.destroy() #try killing the play again box (if it exists) except Exception: pass self.choice = True #choice defaults to true self.root = Tk() #creates the window self.root.title('Start Minesweeper') self.frame = Frame(self.root) #creates the frame self.frame.pack() self.xLabel = Label(self.frame, text='Enter the width of the minesweeper board') self.xLabel.grid(row=0, column=0) #creates the xLabel self.xLengthStrVar = StringVar() self.xInput = Entry(self.frame, width=5, textvariable=self.xLengthStrVar) self.xInput.grid(row=1, column=0) #creates the x entry box self.yLabel = Label(self.frame, text='Enter the height of the minesweeper board') self.yLabel.grid(row=3, column=0) #etc self.yLengthStrVar = StringVar() self.yInput = Entry(self.frame, width=5, textvariable=self.yLengthStrVar) self.yInput.grid(row=4, column=0) #etc self.bombPercentLabel = Label(self.frame, text='Enter the percentage of the squares you would like to be bombs') self.bombPercentLabel.grid(row=6, column=0) #etc self.bombPercentStrVar = StringVar() self.bombPercentInput = Entry(self.frame, width=5, textvariable=self.bombPercentStrVar) self.bombPercentInput.grid(row=7, column=0) #etc self.winChoiceLabel = Label(self.frame, text='Select either the orchestral or vocal win event') self.winChoiceLabel.grid(row=9, column=0) #creates the win choice label self.vocalWinButton = Button(self.frame, text='Change to vocal', command=lambda: self.setWin(True)) self.orchestralWinButton = Button(self.frame, text='Change to orchestral', command=lambda: self.setWin(False)) self.orchestralWinButton.grid(row=10, column=0) #creates both win choice buttons and activates the orchestral one self.winChoiceChoiceStrVar = StringVar() self.winChoiceChoiceStrVar.set('The vocal win event is selected') self.winChoiceChoiceLabel = Label(self.frame, textvariable=self.winChoiceChoiceStrVar) self.winChoiceChoiceLabel.grid(row=10, column=1) #creates the StringVar which will tell you which choice you have selected self.submitButton = Button(self.frame, text='Submit', fg='green', command=self.completeRequest) self.submitButton.grid(row=12, column=0) #submit button self.cancelButton = Button(self.frame, text='Cancel and Exit', fg='red', command=self.root.destroy) self.cancelButton.grid(row=12, column=1) #exit button self.root.mainloop() #Mainloop! def setWin(self, choice): self.choice = choice #sets the variable if self.choice: self.vocalWinButton.grid_forget() #updates which buttons you can press and the stringvar self.orchestralWinButton.grid(row=10, column=0) self.winChoiceChoiceStrVar.set('The vocal win event is selected') else: self.orchestralWinButton.grid_forget() #see above self.vocalWinButton.grid(row=10, column=0) self.winChoiceChoiceStrVar.set('The orchestral win event is selected') def completeRequest(self): #completes the request try: self.xLen = int(self.xLengthStrVar.get()) #tries to make them ints/floats self.yLen = int(self.yLengthStrVar.get()) self.bombPercent = float(self.bombPercentStrVar.get()) if not (self.xLen*self.yLen)-(self.xLen*self.yLen*self.bombPercent/100) >= 9: #if 9 squares cannot be reserved for the first click, dont allow them to play error = ErrorBox('The percentage of bombs is too high, the game will not generate') return openMain(self, self.xLen, self.yLen, self.bombPercent, self.choice) #opens the opener except ValueError: error = ErrorBox('One or more values you have entered is invalid (all have to be numbers but the percentage does not have to be an integer)') #these have to be numbers! pass class ErrorBox: def __init__(self, error): self.error = error #sets the error self.root = Tk() #creates the window self.root.title('Error') self.frame = Frame(self.root) #creates the frame self.frame.pack() self.label = Label(self.frame, text=error, fg='red') #shows the error self.label.grid(row=0, column=0) self.button = Button(self.frame, text='Ok', command=self.root.destroy) #button to kill the error box self.button.grid(row=1, column=0) self.root.mainloop() #Mainloop! def openMain(caller, xLength=None, yLength=None, percentOfBombs=None, winChoice=None, master=None): #restarts it outside of the class global minesweeper if master != None: #if it has been called from the play again box... minesweeper = MinesweeperMain(master.xLength, master.yLength, master.percentOfBombs, caller, master.winChoice) #use the old configs else: #else minesweeper = MinesweeperMain(xLength, yLength, percentOfBombs, caller, winChoice) #use the new configs if __name__ == '__main__': start = StartBox() minesweeper = None

40.429421

183

0.592745

26,522

0.974751

0

6,153

0.226138

a7625f42a7dd6cbf1419217f4da8ae9f6f00c5f6

5,431

py

Python

cannlytics/utils/scraper.py

mindthegrow/cannlytics

c266bc1169bef75214985901cd3165f415ad9ba7

[ "MIT" ]

7

2021-05-31T15:30:22.000Z

2022-02-05T14:12:31.000Z

cannlytics/utils/scraper.py

mindthegrow/cannlytics

c266bc1169bef75214985901cd3165f415ad9ba7

[ "MIT" ]

17

2021-06-09T01:04:27.000Z

2022-03-18T14:48:12.000Z

cannlytics/utils/scraper.py

mindthegrow/cannlytics

c266bc1169bef75214985901cd3165f415ad9ba7

[ "MIT" ]

5

2021-06-07T13:52:33.000Z

2021-08-04T00:09:39.000Z

# -*- coding: utf-8 -*- """ Scrape Website Data | Cannlytics Copyright © 2021 Cannlytics Author: Keegan Skeate <keegan@cannlytics.com> Created: 1/10/2021 License GPLv3+: GNU GPL version 3 or later <https://gnu.org/licenses/gpl.html> This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law. Resources: https://stackoverflow.com/questions/54416896/how-to-scrape-email-and-phone-numbers-from-a-list-of-websites https://hackersandslackers.com/scraping-urls-with-beautifulsoup/ TODO: Improve with requests-html - https://github.com/psf/requests-html - Get #about - Get absolute URLs - Search for text (prices/analyses) r.html.search('Python is a {} language')[0] """ import re import requests from bs4 import BeautifulSoup def get_page_metadata(url): """Scrape target URL for metadata.""" headers = { "Access-Control-Allow-Origin": "*", "Access-Control-Allow-Methods": "GET", "Access-Control-Allow-Headers": "Content-Type", "Access-Control-Max-Age": "3600", "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0", } # Handle URLs without http beginning if not url.startswith("http"): url = "http://" + url response = requests.get(url, headers=headers) html = BeautifulSoup(response.content, "html.parser") metadata = { "description": get_description(html), "image_url": get_image(html), # FIXME: Append URL if relative path. "favicon": get_favicon(html, url), "brand_color": get_theme_color(html), } return response, html, metadata def get_description(html): """Scrape page description.""" description = None if html.find("meta", property="description"): description = html.find("meta", property="description").get("content") elif html.find("meta", property="og:description"): description = html.find("meta", property="og:description").get("content") elif html.find("meta", property="twitter:description"): description = html.find("meta", property="twitter:description").get("content") elif html.find("p"): description = html.find("p").contents if isinstance(description, list): try: description = description[0] except IndexError: pass return description def get_image(html): """Scrape share image.""" image = None if html.find("meta", property="image"): image = html.find("meta", property="image").get("content") elif html.find("meta", property="og:image"): image = html.find("meta", property="og:image").get("content") elif html.find("meta", property="twitter:image"): image = html.find("meta", property="twitter:image").get("content") elif html.find("img", src=True): image = html.find_all("img")[0].get("src") return image def get_favicon(html, url): """Scrape favicon.""" if html.find("link", attrs={"rel": "icon"}): favicon = html.find("link", attrs={"rel": "icon"}).get("href") elif html.find("link", attrs={"rel": "shortcut icon"}): favicon = html.find("link", attrs={"rel": "shortcut icon"}).get("href") else: favicon = f'{url.rstrip("/")}/favicon.ico' return favicon def get_theme_color(html): """Scrape brand color.""" if html.find("meta", property="theme-color"): color = html.find("meta", property="theme-color").get("content") return color return None def get_phone(html, response): """Scrape phone number.""" try: phone = html.select("a[href*=callto]")[0].text return phone except: pass try: phone = re.findall( r"$?\b[2-9][0-9]{2}$?[-][2-9][0-9]{2}[-][0-9]{4}\b", response.text )[0] return phone except: pass try: phone = re.findall( r"$?\b[2-9][0-9]{2}$?[-. ]?[2-9][0-9]{2}[-. ]?[0-9]{4}\b", response.text )[-1] return phone except: print("Phone number not found") phone = "" return phone def get_email(html, response): """Get email.""" try: email = re.findall( r"([a-zA-Z0-9._-]+@[a-zA-Z0-9._-]+\.[a-zA-Z0-9_-]+)", response.text )[-1] return email except: pass try: email = html.select("a[href*=mailto]")[-1].text except: print("Email not found") email = "" return email def find_lab_address(): """ TODO: Tries to find a lab's address from their website, then Google Maps. """ street, city, state, zipcode = None, None, None, None return street, city, state, zipcode def find_lab_linkedin(): """ TODO: Tries to find a lab's LinkedIn URL. (Try to find LinkedIn on homepage?) """ return "" def find_lab_url(): """ TODO: Find a lab's website URL. (Google search for name?) """ return "" def clean_string_columns(df): """Clean string columns in a dataframe.""" for column in df.columns: try: df[column] = df[column].str.title() df[column] = df[column].str.replace("Llc", "LLC") df[column] = df[column].str.replace("L.L.C.", "LLC") df[column] = df[column].str.strip() except AttributeError: pass return df

30.511236

110

0.598048

0

2,377

0.437592

a762725a417c914c2de8c1cfaad398234b972ef4

22,326

py

Python

acsm/utils/bird_vis.py

eldar/acsm

04069e8bb4c12185473dc10c3355e5367fa98968

[ "Apache-2.0" ]

52

2020-04-02T12:35:55.000Z

2022-03-11T07:47:30.000Z

acsm/utils/bird_vis.py

eldar/acsm

04069e8bb4c12185473dc10c3355e5367fa98968

[ "Apache-2.0" ]

8

2020-06-04T07:34:34.000Z

2021-09-18T21:17:26.000Z

acsm/utils/bird_vis.py

eldar/acsm

04069e8bb4c12185473dc10c3355e5367fa98968

[ "Apache-2.0" ]

6

2020-07-12T02:12:18.000Z

2021-03-06T05:03:33.000Z

""" Code borrowed from https://github.com/akanazawa/cmr/blob/master/utils/bird_vis.py Visualization helpers specific to birds. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import torch from torch.autograd import Variable import numpy as np import os.path as osp import cv2 import pdb from . import cub_parse from ..nnutils.nmr import NeuralRenderer from ..utils import transformations from . import visutil import pdb class VisRenderer(object): """ Utility to render meshes using pytorch NMR faces are F x 3 or 1 x F x 3 numpy """ def __init__(self, img_size, faces, t_size=3): self.renderer = NeuralRenderer(img_size) self.faces = Variable( torch.IntTensor(faces).cuda(), requires_grad=False) if self.faces.dim() == 2: self.faces = torch.unsqueeze(self.faces, 0) default_tex = np.ones((1, self.faces.shape[1], t_size, t_size, t_size, 3)) blue = np.array([156, 199, 234.]) / 255. default_tex = default_tex * blue # Could make each triangle different color self.default_tex = Variable( torch.FloatTensor(default_tex).cuda(), requires_grad=False) # rot = transformations.quaternion_about_axis(np.pi/8, [1, 0, 0]) # This is median quaternion from sfm_pose # rot = np.array([ 0.66553962, 0.31033762, -0.02249813, 0.01267084]) # This is the side view: import cv2 R0 = cv2.Rodrigues(np.array([np.pi / 3, 0, 0]))[0] R1 = cv2.Rodrigues(np.array([0, np.pi / 2, 0]))[0] R = R1.dot(R0) R = np.vstack((np.hstack((R, np.zeros((3, 1)))), np.array([0, 0, 0, 1]))) rot = transformations.quaternion_from_matrix(R, isprecise=True) cam = np.hstack([0.75, 0, 0, rot]) self.default_cam = Variable( torch.FloatTensor(cam).cuda(), requires_grad=False) self.default_cam = torch.unsqueeze(self.default_cam, 0) def __call__(self, verts, cams=None, texture=None, rend_mask=False): """ verts is |V| x 3 cuda torch Variable cams is 7, cuda torch Variable Returns N x N x 3 numpy """ if texture is None: texture = self.default_tex elif texture.dim() == 5: # Here input it F x T x T x T x 3 (instead of F x T x T x 3) # So add batch dim. texture = torch.unsqueeze(texture, 0) if cams is None: cams = self.default_cam elif cams.dim() == 1: cams = torch.unsqueeze(cams, 0) if verts.dim() == 2: verts = torch.unsqueeze(verts, 0) verts = asVariable(verts) cams = asVariable(cams) texture = asVariable(texture) if rend_mask: rend = self.renderer.forward(verts, self.faces, cams) rend = rend.repeat(3, 1, 1) rend = rend.unsqueeze(0) else: rend = self.renderer.forward(verts, self.faces, cams, texture) rend = rend.data.cpu().numpy()[0].transpose((1, 2, 0)) rend = np.clip(rend, 0, 1) * 255.0 return rend.astype(np.uint8) def rotated(self, vert, deg, axis=[0, 1, 0], cam=None, texture=None): """ vert is N x 3, torch FloatTensor (or Variable) """ import cv2 new_rot = cv2.Rodrigues(np.deg2rad(deg) * np.array(axis))[0] new_rot = convert_as(torch.FloatTensor(new_rot), vert) center = vert.mean(0) new_vert = torch.t(torch.matmul(new_rot, torch.t(vert - center))) + center # new_vert = torch.matmul(vert - center, new_rot) + center return self.__call__(new_vert, cams=cam, texture=texture) def diff_vp(self, verts, cam=None, angle=90, axis=[1, 0, 0], texture=None, kp_verts=None, new_ext=None, extra_elev=False): if cam is None: cam = self.default_cam[0] if new_ext is None: new_ext = [0.6, 0, 0] # Cam is 7D: [s, tx, ty, rot] import cv2 cam = asVariable(cam) quat = cam[-4:].view(1, 1, -1) R = transformations.quaternion_matrix( quat.squeeze().data.cpu().numpy())[:3, :3] rad_angle = np.deg2rad(angle) rotate_by = cv2.Rodrigues(rad_angle * np.array(axis))[0] # new_R = R.dot(rotate_by) new_R = rotate_by.dot(R) if extra_elev: # Left multiply the camera by 30deg on X. R_elev = cv2.Rodrigues(np.array([np.pi / 9, 0, 0]))[0] new_R = R_elev.dot(new_R) # Make homogeneous new_R = np.vstack( [np.hstack((new_R, np.zeros((3, 1)))), np.array([0, 0, 0, 1])]) new_quat = transformations.quaternion_from_matrix( new_R, isprecise=True) new_quat = Variable(torch.Tensor(new_quat).cuda(), requires_grad=False) # new_cam = torch.cat([cam[:-4], new_quat], 0) new_ext = Variable(torch.Tensor(new_ext).cuda(), requires_grad=False) new_cam = torch.cat([new_ext, new_quat], 0) rend_img = self.__call__(verts, cams=new_cam, texture=texture) if kp_verts is None: return rend_img else: kps = self.renderer.project_points( kp_verts.unsqueeze(0), new_cam.unsqueeze(0)) kps = kps[0].data.cpu().numpy() return kp2im(kps, rend_img, radius=1) def set_bgcolor(self, color): self.renderer.set_bgcolor(color) def set_light_dir(self, direction, int_dir=0.8, int_amb=0.8): renderer = self.renderer.renderer.renderer renderer.light_direction = direction renderer.light_intensity_directional = int_dir renderer.light_intensity_ambient = int_amb def set_light_status(self, use_lights): renderer = self.renderer.renderer.renderer renderer.use_lights = use_lights return def contour_alphas(H, W, real_img=False): alphas = np.zeros((H,W)) n_lines_H = 20 n_lines_W = 10 if real_img: line_width_x = 10 line_width_y = 10 else: line_width_x = 10 line_width_y = 10 line_pos_x = (np.linspace(0, W-1, n_lines_W, endpoint=False) + 0.5*W/n_lines_W).astype(np.int) line_pos_y = (np.linspace(0, H-1, n_lines_H, endpoint=False) + 0.5*H/n_lines_H).astype(np.int) for x in line_pos_x: alphas[:, x-line_width_x: x+line_width_x+1] = 1 for y in line_pos_y: alphas[y-line_width_y: y+line_width_y+1, :] = 1 return torch.Tensor(alphas).unsqueeze(0) def sample_UV_contour(img, uv_map, uv_img, mask, real_img=False): img = img.unsqueeze(0) uv_map = uv_map.unsqueeze(0) uv_img = uv_img.unsqueeze(0) uv_sample = torch.nn.functional.grid_sample(uv_img, 2*uv_map-1).squeeze(0) uv_sample = uv_sample*mask + (1-mask) # alphas = contour_alphas(uv_img.shape[2], uv_img.shape[3], real_img).unsqueeze(0) alphas = contour_alphas(uv_img.shape[2], uv_img.shape[3], real_img).unsqueeze(0)* 0 + 1 # pdb.set_trace() alpha_sample = torch.nn.functional.grid_sample(alphas, 2*uv_map-1).squeeze(0) # alpha_sample = alpha_sample*0.95 + 0.05 alpha_sample = (alpha_sample>0.0).float()*0.7 # alpha_sample = (alpha_sample > 0.9).float() alpha_sample = alpha_sample*mask if real_img: # uv_rendering = (uv_sample*alpha_sample)*1.0 + img.squeeze(0)*(1-alpha_sample)*0.3 * (mask) + img.squeeze(0)*(1-alpha_sample)* (1-mask)*0.3 uv_rendering = (uv_sample*alpha_sample)*1.0 + img.squeeze(0)*(1-alpha_sample) uv_rendering = (uv_sample*alpha_sample)*1.0 + img.squeeze(0)*(1-alpha_sample)*0.4 * (mask) + img.squeeze(0)*(1-alpha_sample)* (1-mask) else: uv_rendering = (uv_sample*alpha_sample) + (img.squeeze(0)*(1-alpha_sample)) return uv_rendering def draw_points_on_image(img, points,): img = img.copy() for kpx, keypoint in enumerate(points): color = (0,0,0) img = cv2.circle(img,(keypoint[0],keypoint[1]), 5, (color[0]*255,color[1]*255,color[2]*255), -1) return img def draw_keypoint_on_image(img, keypoints, keypoints_vis, color_map=None): img = img.copy() for kpx, (keypoint, vis) in enumerate(zip(keypoints,keypoints_vis)): if vis > 0: color = (0,0,255) if color_map is not None: color = color_map[kpx] img = cv2.circle(img,(keypoint[0],keypoint[1]), 10, (color[0]*255,color[1]*255,color[2]*255), -1) return img def write_on_image(img, text, location): ## location is x,y img = img.copy() color = (0,0,255) img = cv2.putText(img,"{}".format(text), (location[0],location[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness=2) return img def draw_keypoint_and_text_on_image(img, keypoints, keypoints_vis, color_map=None, text=None, text_col=None): img = img.copy() for kpx, (keypoint, vis) in enumerate(zip(keypoints,keypoints_vis)): if vis > 0: color = (0,0,255) if color_map is not None: color = color_map[kpx] img = cv2.circle(img,(keypoint[0],keypoint[1]), 5, (color[0]*255,color[1]*255,color[2]*255), -1) color = (0,0,255) if text_col is not None: color = text_col[kpx] if text is not None: img = cv2.putText(img,"{}".format(text[kpx]), (keypoint[0],keypoint[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness=1) return img def save_obj_with_texture(name, results_path, texture_img, mean_shape): visutil.mkdir(results_path) verts = np.round(mean_shape['verts'],5) uv_verts = np.round(mean_shape['uv_verts'],5) faces = mean_shape['faces'] obj_file = osp.join(results_path, '{}.obj'.format(name)) with open(obj_file,'w') as f: f.write('mtllib {}.mtl\n'.format(name)) ## write vertices for v in verts: f.write("v {} {} {}\n".format(v[0], v[1], v[2])) ## write texture vertices for v in uv_verts: f.write("vt {} {}\n".format(v[0], 1 - v[1])) f.write('usemtl bird\n') f.write('s 1\n') ## write faces faces = faces + 1 for fv in faces: f.write('f {}/{}/ {}/{}/ {}/{}/ \n'.format(fv[0], fv[0], fv[1], fv[1], fv[2], fv[2])) # for fv in faces: # f.write('f {}// {}// {}// \n'.format(fv[0]+1, fv[1]+1, fv[2]+1)) ## mtl file mtl_file = osp.join(results_path, '{}.mtl'.format(name)) with open(mtl_file,'w') as f: f.write('# Material Count: 1\n') f.write('newmtl bird\n') f.write('Ns 96.078431\n') f.write('Ka 1.000000 1.000000 1.000000\n') f.write('Kd 0.640000 0.640000 0.640000\n') f.write('Ks 0.500000 0.500000 0.500000\n') f.write('Ke 0.000000 0.000000 0.000000\n') f.write('Ni 1.00000\n') f.write('d 1.000000\n') f.write('illum 2\n') f.write('map_Kd {}.png\n'.format(name,)) ## Dump the texture image # texture_img[:,:,0], texture_img[:,:,2] = texture_img[:,:,2], texture_img[:,:,0] # texture_img = texture_img[:,:,[2,1,0]] # pdb.set_trace() visutil.save_image(texture_img, osp.join(results_path,'{}.png'.format(name))) return def merge_textures(foreground, background,): ''' 3, H, W Assume foreground to have 1 in the A channel and 0 for the background. ''' texture = foreground * (foreground[3,None,...] > 0.5) + background * (foreground[3,None,...] <0.5) return texture def render_transfer_kps_imgs(keypoint_cmap, img1, img2, kps1, kps2, transfer_kps12, transfer_kps21, common_kps): visuals = {} common_vis = kps1[:,0]*0 common_vis[common_kps] = 1 img1_tfs = draw_keypoint_on_image(img1, kps1, common_vis, keypoint_cmap) img2_tfs = draw_keypoint_on_image(img2, kps2, common_vis, keypoint_cmap) img_tfs12 = draw_keypoint_on_image(img2, transfer_kps12, common_vis, keypoint_cmap) img_tfs21 = draw_keypoint_on_image(img1, transfer_kps21, common_vis, keypoint_cmap) visuals['tfs_a_img1'] = img1_tfs visuals['tfs_d_img2'] = img2_tfs visuals['tfs_b_1to2'] = img_tfs12 visuals['tfs_c_2to1'] = img_tfs21 return visuals def create_monocolor_texture(uvimg_H, uvimg_W, color=None): if color is None: color = [156, 199, 234., 0] default_tex = np.ones((uvimg_H, uvimg_W,4)) blue = np.array(color) / 255. blue[3] = color[3] default_tex = default_tex * blue.reshape(1,1,-1) return default_tex.transpose(2,0,1) def create_kp_heat_map_texture(uvimg_H, uvimg_W, u_cord=None, v_cord=None, color=None, transprancy=1): default_tex = create_monocolor_texture(uvimg_H, uvimg_W) if color is None: color = (1,0,0) box_size = 3 if v_cord is not None and u_cord is not None: default_tex = default_tex*0 default_tex[0, v_cord-box_size:v_cord+box_size, u_cord-box_size:u_cord+box_size] = color[0] default_tex[1, v_cord-box_size:v_cord+box_size, u_cord-box_size:u_cord+box_size] = color[1] default_tex[2, v_cord-box_size:v_cord+box_size, u_cord-box_size:u_cord+box_size] = color[2] default_tex[3, v_cord-box_size:v_cord+box_size, u_cord-box_size:u_cord+box_size] = transprancy return default_tex def upsample_img_mask_uv_map(img, mask, uv_map): uv_map = torch.nn.functional.upsample(uv_map.permute(2,0,1).unsqueeze(0), scale_factor=4, mode='bilinear') mask = torch.nn.functional.upsample(mask.unsqueeze(0), scale_factor=4, mode='nearest').squeeze(0) img = torch.nn.functional.upsample(img.unsqueeze(0), scale_factor=4, mode='nearest').squeeze(0) uv_map = uv_map.squeeze().permute(1,2,0) return img, mask, uv_map def create_texture_image_from_uv_map(uvimg_H, uvimg_W, uv_map, img, mask): default_tex = np.ones((uvimg_H, uvimg_W,3)) blue = np.array([156, 199, 234.]) / 255. default_tex = default_tex * blue.reshape(1,1,-1) count_tex = np.zeros((uvimg_H, uvimg_W,1)) uv_map_inds = uv_map.copy() uv_map_inds[:,:,0] = np.clip((uv_map[:,:,0] * uvimg_W).round(), 0, uvimg_W-1) uv_map_inds[:,:,1] = np.clip((uv_map[:,:,1] * uvimg_H).round(), 0, uvimg_H-1) uv_map_inds = uv_map_inds.astype(np.int32) non_zero_inds = np.where(mask.squeeze()) for rx,cx in zip(*non_zero_inds): u_ind = uv_map_inds[rx, cx, 0] v_ind = uv_map_inds[rx, cx, 1] if count_tex[v_ind, u_ind, 0] == 0: default_tex[v_ind, u_ind,:] = img[:, rx,cx] else: default_tex[v_ind, u_ind,:] += img[:, rx,cx] count_tex[v_ind, u_ind, 0] += 1 count_tex = count_tex + 1*(count_tex < 1) default_tex = default_tex / count_tex return default_tex.transpose(2,0,1) def wrap_texture_and_render(renderer, vert, camera, uv_sampler, texture_image, tex_size, other_vps, lights=True ): sampled_texture = torch.nn.functional.grid_sample(texture_image.unsqueeze(0), uv_sampler) sampled_texture = sampled_texture.squeeze().permute(1,2,0) sampled_texture = sampled_texture.view(sampled_texture.size(0), tex_size, tex_size, 3) sampled_texture = sampled_texture.unsqueeze(3).repeat(1, 1, 1, tex_size, 1) renderer.set_light_dir([0, 1, -1], 0.4) img_pred = renderer(vert, camera, texture=sampled_texture) if not lights: renderer.set_light_status(lights) if other_vps: new_camera = camera.clone() new_camera[3] = 1 new_camera[4:] *=0 vp1 = renderer.diff_vp( vert, new_camera, angle=90, axis=[0, 1, 0], texture=sampled_texture, extra_elev=True) vp2 = renderer.diff_vp( vert, new_camera, angle=180, axis=[0, 1, 0], texture=sampled_texture, extra_elev=True) vp3 = renderer.diff_vp( vert, new_camera, angle=180, axis=[1, 0, 0], texture=sampled_texture) return (img_pred, vp1, vp2, vp3), texture_image.cpu().numpy().transpose(1,2,0) else: return (img_pred), texture_image.cpu().numpy().transpose(1,2,0) def render_model_with_uv_map(renderer, vert, uvimg_H, uvimg_W, camera, uv_sampler, tex_size=6, other_vps=False): texture_image = cub_parse.get_sample_grid((uvimg_H,uvimg_W)) * 0.5 + 0.5 texture_image = torch.cat([texture_image[:,:,None, 0], texture_image[:,:,None, 0]*0, texture_image[:,:,None, 1]], dim=-1) texture_image = texture_image.permute(2,0,1).cuda() # pdb.set_trace() render_stuff, texture_image = wrap_texture_and_render(renderer, vert, camera, uv_sampler, texture_image, tex_size, other_vps) return render_stuff, texture_image def render_model_default(renderer, vert, uvimg_H, uvimg_W, camera, uv_sampler, tex_size=6, other_vps=False, color=None): # texture_image = cub_parse.get_sample_grid((uvimg_H,uvimg_W)) * 0.5 + 0.5 # texture_image = torch.cat([texture_image[:,:,None, 0], texture_image[:,:,None, 0]*0, texture_image[:,:,None, 1]], dim=-1) # texture_image = texture_image.permute(2,0,1).cuda() # pdb.set_trace() texture_image = torch.from_numpy(create_monocolor_texture(uvimg_H, uvimg_W, color=color)).float().cuda()[0:3] render_stuff, texture_image = wrap_texture_and_render(renderer, vert, camera, uv_sampler, texture_image, tex_size, other_vps) return render_stuff, texture_image def copy_texture_from_img(mask, img, xy_map): img = (visutil.undo_resnet_preprocess(img.unsqueeze(0))*mask).squeeze() img = img.permute(1,2,0) xy_map_inds = xy_map.clone() xy_map_inds[:,:,0] = (xy_map_inds[:,:,0] + 1)* (img.size(1)/2) xy_map_inds[:,:,1] = (xy_map_inds[:,:,1] + 1) * (img.size(0)/2) xy_map_inds = torch.clamp(xy_map_inds.long(), min=0, max=img.size(0) -1).long().view(-1,2) new_img = img[xy_map_inds[:,1], xy_map_inds[:,0],:].view(img.shape) # new_img = img new_img = new_img.permute(2,0,1) # new_img = new_img * mask new_img = (new_img*mask).unsqueeze(0) new_img = visutil.tensor2im(new_img) return new_img def render_model_with_texture(renderer, vert, uvimg_H, uvimg_W, uv_map, img, mask, camera, uv_sampler, tex_size=6, other_vps=False, undo_resnet=True): uv_map = uv_map.data.cpu().numpy() if undo_resnet: img = img.unsqueeze(0) img = visutil.undo_resnet_preprocess(img).squeeze() img = img.data.cpu().numpy() # camera = camera.data.cpu().numpy() mask = mask.data.cpu().numpy() texture_image = create_texture_image_from_uv_map(uvimg_H, uvimg_W, uv_map, img, mask) texture_image = torch.from_numpy(texture_image).float().cuda() render_stuff, texture_image = wrap_texture_and_render(renderer, vert, camera, uv_sampler, texture_image, tex_size, other_vps) return render_stuff, texture_image def asVariable(x): if type(x) is not torch.autograd.Variable: x = Variable(x, requires_grad=False) return x def convert_as(src, trg): src = src.type_as(trg) if src.is_cuda: src = src.cuda(device=trg.get_device()) if type(trg) is torch.autograd.Variable: src = Variable(src, requires_grad=False) return src def convert2np(x): # import ipdb; ipdb.set_trace() # if type(x) is torch.autograd.Variable: # x = x.data # Assumes x is gpu tensor.. if type(x) is not np.ndarray: return x.cpu().numpy() return x def tensor2mask(image_tensor, imtype=np.uint8): # Input is H x W image_numpy = image_tensor.cpu().float().numpy() image_numpy = np.expand_dims(image_numpy, 2) * 255.0 image_numpy = np.tile(image_numpy, (1, 1, 3)) return image_numpy.astype(imtype) def kp2im(kp, img, radius=None): """ Input is numpy array or torch.cuda.Tensor img can be H x W, H x W x C, or C x H x W kp is |KP| x 2 """ kp_norm = convert2np(kp) img = convert2np(img) if img.ndim == 2: img = np.dstack((img, ) * 3) # Make it H x W x C: elif img.shape[0] == 1 or img.shape[0] == 3: img = np.transpose(img, (1, 2, 0)) if img.shape[2] == 1: # Gray2RGB for H x W x 1 img = np.dstack((img, ) * 3) # kp_norm is still in [-1, 1], converts it to image coord. kp = (kp_norm[:, :2] + 1) * 0.5 * img.shape[0] if kp_norm.shape[1] == 3: vis = kp_norm[:, 2] > 0 kp[~vis] = 0 kp = np.hstack((kp, vis.reshape(-1, 1))) else: vis = np.ones((kp.shape[0], 1)) kp = np.hstack((kp, vis)) kp_img = draw_kp(kp, img, radius=radius) return kp_img def draw_kp(kp, img, radius=None): """ kp is 15 x 2 or 3 numpy. img can be either RGB or Gray Draws bird points. """ if radius is None: radius = max(4, (np.mean(img.shape[:2]) * 0.01).astype(int)) num_kp = kp.shape[0] # Generate colors import pylab cm = pylab.get_cmap('gist_rainbow') colors = 255 * np.array([cm(1. * i / num_kp)[:3] for i in range(num_kp)]) white = np.ones(3) * 255 image = img.copy() if isinstance(image.reshape(-1)[0], np.float32): # Convert to 255 and np.uint8 for cv2.. image = (image * 255).astype(np.uint8) kp = np.round(kp).astype(int) for kpi, color in zip(kp, colors): # This sometimes causes OverflowError,, if kpi[2] == 0: continue cv2.circle(image, (kpi[0], kpi[1]), radius + 1, white, -1) cv2.circle(image, (kpi[0], kpi[1]), radius, color, -1) # import matplotlib.pyplot as plt # plt.ion() # plt.clf() # plt.imshow(image) # import ipdb; ipdb.set_trace() return image def tensor2im(image_tensor, imtype=np.uint8, scale_to_range_1=False): image_numpy = image_tensor.cpu().float().numpy() image_numpy = np.transpose(image_numpy, (1, 2, 0)) if scale_to_range_1: image_numpy = image_numpy - np.min(image_numpy, axis=2, keepdims=True) image_numpy = image_numpy / np.max(image_numpy) else: # Clip to [0, 1] image_numpy = np.clip(image_numpy, 0, 1) return (image_numpy * 255).astype(imtype)

37.585859

150

0.61874

5,690

0.25486

0

3,243

0.145257

a7641eec8122f15991dc897dc20ebeb0e83b0d20

10,764

py

Python

gatenlp/corpora/files.py

joancf/python-gatenlp

21441d72ded19e9348052e99ac5bc1fc6af7ab6e

[ "Apache-2.0" ]

30

2020-04-18T12:28:15.000Z

2022-02-18T21:31:18.000Z

gatenlp/corpora/files.py

joancf/python-gatenlp

21441d72ded19e9348052e99ac5bc1fc6af7ab6e

[ "Apache-2.0" ]

133

2019-10-16T07:41:59.000Z

2022-03-31T07:27:07.000Z

gatenlp/corpora/files.py

joancf/python-gatenlp

21441d72ded19e9348052e99ac5bc1fc6af7ab6e

[ "Apache-2.0" ]

4

2021-01-20T08:12:19.000Z

2021-10-21T13:29:44.000Z

""" Module that defines Corpus and DocumentSource/DocumentDestination classes which access documents as lines or parts in a file. """ import json from gatenlp.urlfileutils import yield_lines_from from gatenlp.document import Document from gatenlp.corpora.base import DocumentSource, DocumentDestination from gatenlp.corpora.base import MultiProcessingAble class BdocjsLinesFileSource(DocumentSource, MultiProcessingAble): """ A document source which reads one bdoc json serialization of a document from each line of the given file. """ def __init__(self, file): """ Create a JsonLinesFileSource. Args: file: the file path (a string) or an open file handle. """ self.file = file def __iter__(self): with open(self.file, "rt", encoding="utf-8") as infp: for line in infp: yield Document.load_mem(line, fmt="json") class BdocjsLinesFileDestination(DocumentDestination): """ Writes one line of JSON per document to the a single output file. """ def __init__(self, file): """ Args: file: the file to write to. If it exists, it gets overwritten without warning. Expected to be a string or an open file handle. """ if isinstance(file, str): self.fh = open(file, "wt", encoding="utf-8") else: self.fh = file self.n = 0 def __enter__(self): return self def __exit__(self, extype, value, traceback): self.fh.close() def append(self, doc): """ Append a document to the destination. Args: doc: the document, if None, no action is performed. """ if doc is None: return assert isinstance(doc, Document) self.fh.write(doc.save_mem(fmt="json")) self.fh.write("\n") self.n += 1 def close(self): self.fh.close() class JsonLinesFileSource(DocumentSource, MultiProcessingAble): """ A document source which reads one json serialization per line, creates a document from one field in the json and optionally stores all or a selection of remaining fields as document feature "__data". """ def __init__(self, file, text_field="text", data_fields=None, data_feature="__data"): """ Create a JsonLinesFileSource. Args: file: the file path (a string) or an open file handle. text_field: the field name where to get the document text from. data_fields: if a list of names, store these fields in the "__data" feature. if True, store all fields. data_feature: the name of the data feature, default is "__data" """ # feature_fields: NOT YET IMPLEMENTED -- a mapping from original json fields to document features self.file = file self.text_field = text_field self.data_fields = data_fields self.data_feature = data_feature def __iter__(self): with open(self.file, "rt", encoding="utf-8") as infp: for line in infp: data = json.loads(line) # TODO: what if the field does not exist? should we use get(text_field, "") instead? text = data[self.text_field] doc = Document(text) if self.data_fields: if isinstance(self.data_fields, list): tmp = {} for fname in self.data_fields: # TODO: what if the field does not exist? tmp[fname] = data[fname] else: tmp = data doc.features[self.data_feature] = tmp yield doc class JsonLinesFileDestination(DocumentDestination): """ Writes one line of JSON per document to the a single output file. This will either write the document json as nested data or the document text to the field designated for the document and will write other json fields from the "__data" document feature. """ def __init__(self, file, document_field="text", document_bdocjs=False, data_fields=True, data_feature="__data"): """ Args: file: the file to write to. If it exists, it gets overwritten without warning. Expected to be a string or an open file handle. document_field: the name of the json field that will contain the document either just the text or the bdocjs representation if document_bdocjs is True. document_bdocjs: if True store the bdocjs serialization into the document_field instead of just the text data_fields: if a list, only store these fields in the json, if False, do not store any additional fields. Default is True: store all fields as is. data_feature: the name of the data feature, default is "__data" """ if isinstance(file, str): self.fh = open(file, "wt", encoding="utf-8") else: self.fh = file self.n = 0 self.document_field = document_field self.document_bdocjs = document_bdocjs self.data_fields = data_fields self.data_feature = data_feature def __enter__(self): return self def __exit__(self, _extype, _value, _traceback): self.fh.close() def append(self, doc): """ Append a document to the destination. Args: doc: the document, if None, no action is performed. """ if doc is None: return assert isinstance(doc, Document) data = {} if self.data_fields: if isinstance(self.data_fields, list): for fname in self.data_fields: data[fname] = doc.features[self.data_feature][fname] else: data.update(doc.features[self.data_feature]) # assign the document field last so it overwrites anything that comes from the data feature! if self.document_bdocjs: data[self.document_field] = doc.save_mem(fmt="json") else: data[self.document_field] = doc.text self.fh.write(json.dumps(data)) self.fh.write("\n") self.n += 1 def close(self): self.fh.close() class TsvFileSource(DocumentSource, MultiProcessingAble): """ A TsvFileSource is a DocumentSource which is a single TSV file with a fixed number of tab-separated values per row. Each document in sequence is created from the text in one of the columns and document features can be set from arbitrary columns as well. """ def __init__(self, source, hdr=True, text_col=None, feature_cols=None, data_cols=None, data_feature="__data"): """ Creates the TsvFileSource. Args: source: a file path or URL hdr: if True (default), expects a header line with the column names, if a list, should be the list of column names, if False/None, no header line is expected. text_col: the column which contains the text for creating the document. Either the column number, or the name of the column (only possible if there is a header line) or a function that should take the list of fields and arbitrary kwargs and return the text. Also passes "cols" and "n" as keyward arguments. feature_cols: if not None, must be either a dictionary mapping document feature names to the column numbers or column names of where to get the feature value from; or a function that should take the list of fields and arbitrary kwargs and return a dictionary with the features. Also passes "cols" (dict mapping column names to column indices, or None) and "n" (current line number) as keyword arguments. data_cols: if not None, either an iterable of the names of columns to store in the special document feature "__data" or if "True", stores all columns. At the moment this only works if the tsv file has a header line. The values are stored as a list in the order of the names given or the original order of the values in the TSV file. data_feature: the name of the document feature where to store the data, default is "__data" """ assert text_col is not None self.hdr = hdr self.text_col = text_col self.feature_cols = feature_cols self.data_cols = data_cols self.source = source self.n = 0 self.hdr2col = {} if data_cols and not hdr: raise Exception("Header must be present if data_cols should be used") self.data_feature = data_feature def __iter__(self): reader = yield_lines_from(self.source) if self.hdr and self.n == 0: self.n += 1 self.hdr = next(reader).rstrip("\n\r").split("\t") if self.hdr: self.hdr2col = {name: idx for idx, name in enumerate(self.hdr)} for line in reader: line = line.rstrip("\n\r") fields = line.split("\t") if isinstance(self.text_col, int): text = fields[self.text_col] elif callable(self.text_col): text = self.text_col(fields, cols=self.hdr2col, n=self.n) else: text = fields[self.hdr2col[self.text_col]] doc = Document(text) if self.feature_cols: if callable(self.feature_cols): doc.features.update( self.feature_cols(fields, cols=self.hdr2col, n=self.n) ) else: for fname, colid in self.feature_cols.items(): if isinstance(colid, int): value = fields[colid] else: value = fields[self.hdr2col[colid]] doc.features[fname] = value if self.data_cols: if isinstance(self.data_cols, list): data = {} for cname in self.data_cols: if isinstance(cname, str): data[cname] = fields[self.hdr2col[cname]] else: # assume it is the column index! data[cname] = fields[cname] else: data = fields doc.features[self.data_feature] = data self.n += 1 yield doc

40.164179

118

0.590673

10,392

0.96544

2,813

0.261334

0

5,001

0.464604

a7646b2e354d22868d6a6f4cc986b8c2069e186b

709

py

Python

src/ch5-viewmodels/web/services/AccountPageService.py

saryeHaddadi/Python.Course.WebAppFastAPI

ddc1f1850473c227e715c8ecd2afd741e53c4680

[ "MIT" ]

null

src/ch5-viewmodels/web/services/AccountPageService.py

saryeHaddadi/Python.Course.WebAppFastAPI

ddc1f1850473c227e715c8ecd2afd741e53c4680

[ "MIT" ]

null

src/ch5-viewmodels/web/services/AccountPageService.py

saryeHaddadi/Python.Course.WebAppFastAPI

ddc1f1850473c227e715c8ecd2afd741e53c4680

[ "MIT" ]

null

import fastapi from starlette.requests import Request from web.viewmodels.account.AccountViewModel import AccountViewModel from web.viewmodels.account.LoginViewModel import LoginViewModel from web.viewmodels.account.RegisterViewModel import RegisterViewModel router = fastapi.APIRouter() @router.get('/account') def index(request: Request): vm = AccountViewModel(request) return vm.to_dict() @router.get('/account/register') def register(request: Request): vm = RegisterViewModel(request) return vm.to_dict() @router.get('/account/login') def login(request: Request): vm = LoginViewModel(request) return vm.to_dict() @router.get('/account/logout') def logout(): return {}

22.870968

70

0.760226

0

408

0.575458

0

62

0.087447

a7659e9cd38acecd1d387852d0d503d7207e98a9

29,031

py

Python

src/opserver/uveserver.py

madkiss/contrail-controller

17f622dfe99f8ab4163436399e80f95dd564814c

[ "Apache-2.0" ]

null

src/opserver/uveserver.py

madkiss/contrail-controller

17f622dfe99f8ab4163436399e80f95dd564814c

[ "Apache-2.0" ]

null

src/opserver/uveserver.py

madkiss/contrail-controller

17f622dfe99f8ab4163436399e80f95dd564814c

[ "Apache-2.0" ]

null

# # Copyright (c) 2013 Juniper Networks, Inc. All rights reserved. # # # UVEServer # # Operational State Server for UVEs # import gevent import json import copy import xmltodict import redis import datetime import sys from opserver_util import OpServerUtils import re from gevent.coros import BoundedSemaphore from pysandesh.util import UTCTimestampUsec from pysandesh.connection_info import ConnectionState from sandesh.viz.constants import UVE_MAP class UVEServer(object): def __init__(self, redis_uve_server, logger, redis_password=None): self._local_redis_uve = redis_uve_server self._redis_uve_list = [] self._logger = logger self._sem = BoundedSemaphore(1) self._redis = None self._redis_password = redis_password if self._local_redis_uve: self._redis = redis.StrictRedis(self._local_redis_uve[0], self._local_redis_uve[1], password=self._redis_password, db=1) self._uve_reverse_map = {} for h,m in UVE_MAP.iteritems(): self._uve_reverse_map[m] = h #end __init__ def update_redis_uve_list(self, redis_uve_list): self._redis_uve_list = redis_uve_list # end update_redis_uve_list def fill_redis_uve_info(self, redis_uve_info): redis_uve_info.ip = self._local_redis_uve[0] redis_uve_info.port = self._local_redis_uve[1] try: self._redis.ping() except redis.exceptions.ConnectionError: redis_uve_info.status = 'DisConnected' else: redis_uve_info.status = 'Connected' #end fill_redis_uve_info @staticmethod def merge_previous(state, key, typ, attr, prevdict): print "%s New val is %s" % (attr, prevdict) nstate = copy.deepcopy(state) if UVEServer._is_agg_item(prevdict): count = int(state[key][typ][attr]['previous']['#text']) count += int(prevdict['#text']) nstate[key][typ][attr]['previous']['#text'] = str(count) if UVEServer._is_agg_list(prevdict): sname = ParallelAggregator.get_list_name( state[key][typ][attr]['previous']) count = len(prevdict['list'][sname]) + \ len(state[key][typ][attr]['previous']['list'][sname]) nstate[key][typ][attr]['previous']['list'][sname].extend( prevdict['list'][sname]) nstate[key][typ][attr]['previous']['list']['@size'] = \ str(count) tstate = {} tstate[typ] = {} tstate[typ][attr] = copy.deepcopy( nstate[key][typ][attr]['previous']) nstate[key][typ][attr]['previous'] =\ ParallelAggregator.consolidate_list(tstate, typ, attr) print "%s Merged val is %s"\ % (attr, nstate[key][typ][attr]['previous']) return nstate def run(self): lck = False while True: try: k, value = self._redis.brpop("DELETED") self._sem.acquire() lck = True self._logger.debug("%s del received for " % value) # value is of the format: # DEL:<key>:<src>:<node-type>:<module>:<instance-id>:<message-type>:<seqno> info = value.rsplit(":", 6) key = info[0].split(":", 1)[1] typ = info[5] existing = self._redis.hgetall("PREVIOUS:" + key + ":" + typ) tstate = {} tstate[key] = {} tstate[key][typ] = {} state = UVEServer.convert_previous(existing, tstate, key, typ) for attr, hval in self._redis.hgetall(value).iteritems(): snhdict = xmltodict.parse(hval) if UVEServer._is_agg_list(snhdict[attr]): if snhdict[attr]['list']['@size'] == "0": continue if snhdict[attr]['list']['@size'] == "1": sname = ParallelAggregator.get_list_name( snhdict[attr]) if not isinstance( snhdict[attr]['list'][sname], list): snhdict[attr]['list'][sname] = \ [snhdict[attr]['list'][sname]] if (attr not in state[key][typ]): # There is no existing entry for the UVE vstr = json.dumps(snhdict[attr]) else: # There is an existing entry # Merge the new entry with the existing one state = UVEServer.merge_previous( state, key, typ, attr, snhdict[attr]) vstr = json.dumps(state[key][typ][attr]['previous']) # Store the merged result back in the database self._redis.sadd("PUVES:" + typ, key) self._redis.sadd("PTYPES:" + key, typ) self._redis.hset("PREVIOUS:" + key + ":" + typ, attr, vstr) self._redis.delete(value) except redis.exceptions.ResponseError: #send redis connection down msg. Coule be bcos of authentication ConnectionState.update(conn_type = ConnectionType.REDIS, name = 'UVE', status = ConnectionStatus.DOWN, message = 'UVE result : Connection Error', server_addrs = ['%s:%d' % (self._local_redis_uve[0], self._local_redis_uve[1])]) sys.exit() except redis.exceptions.ConnectionError: if lck: self._sem.release() lck = False gevent.sleep(5) else: if lck: self._sem.release() lck = False self._logger.debug("Deleted %s" % value) self._logger.debug("UVE %s Type %s" % (key, typ)) @staticmethod def _is_agg_item(attr): if attr['@type'] in ['i8', 'i16', 'i32', 'i64', 'byte', 'u8', 'u16', 'u32', 'u64']: if '@aggtype' in attr: if attr['@aggtype'] == "counter": return True return False @staticmethod def _is_agg_list(attr): if attr['@type'] in ['list']: if '@aggtype' in attr: if attr['@aggtype'] == "append": return True return False @staticmethod def convert_previous(existing, state, key, typ, afilter=None): # Take the existing delete record, and load it into the state dict for attr, hval in existing.iteritems(): hdict = json.loads(hval) if afilter is not None and len(afilter): if attr not in afilter: continue # When recording deleted attributes, only record those # for which delete-time aggregation is needed if UVEServer._is_agg_item(hdict): if (typ not in state[key]): state[key][typ] = {} if (attr not in state[key][typ]): state[key][typ][attr] = {} state[key][typ][attr]["previous"] = hdict # For lists that require delete-time aggregation, we need # to normailize lists of size 1, and ignore those of size 0 if UVEServer._is_agg_list(hdict): if hdict['list']['@size'] != "0": if (typ not in state[key]): state[key][typ] = {} if (attr not in state[key][typ]): state[key][typ][attr] = {} state[key][typ][attr]["previous"] = hdict if hdict['list']['@size'] == "1": sname = ParallelAggregator.get_list_name(hdict) if not isinstance(hdict['list'][sname], list): hdict['list'][sname] = [hdict['list'][sname]] return state def get_part(self, part): uves = {} for redis_uve in self._redis_uve_list: gen_uves = {} redish = redis.StrictRedis(host=redis_uve[0], port=redis_uve[1], db=1) for elems in redish.smembers("PART2KEY:" + str(part)): info = elems.split(":", 5) gen = info[0] + ":" + info[1] + ":" + info[2] + ":" + info[3] key = info[5] if not gen_uves.has_key(gen): gen_uves[gen] = {} gen_uves[gen][key] = 0 uves[redis_uve[0] + ":" + str(redis_uve[1])] = gen_uves return uves def get_uve(self, key, flat, filters=None, multi=False, is_alarm=False, base_url=None): filters = filters or {} sfilter = filters.get('sfilt') mfilter = filters.get('mfilt') tfilter = filters.get('cfilt') ackfilter = filters.get('ackfilt') state = {} state[key] = {} statdict = {} for redis_uve in self._redis_uve_list: redish = redis.StrictRedis(host=redis_uve[0], port=redis_uve[1], password=self._redis_password, db=1) try: qmap = {} origins = redish.smembers("ALARM_ORIGINS:" + key) if not is_alarm: origins = origins.union(redish.smembers("ORIGINS:" + key)) for origs in origins: info = origs.rsplit(":", 1) sm = info[0].split(":", 1) source = sm[0] if sfilter is not None: if sfilter != source: continue mdule = sm[1] if mfilter is not None: if mfilter != mdule: continue dsource = source + ":" + mdule typ = info[1] if tfilter is not None: if typ not in tfilter: continue odict = redish.hgetall("VALUES:" + key + ":" + origs) afilter_list = set() if tfilter is not None: afilter_list = tfilter[typ] for attr, value in odict.iteritems(): if len(afilter_list): if attr not in afilter_list: continue if typ not in state[key]: state[key][typ] = {} if value[0] == '<': snhdict = xmltodict.parse(value) if snhdict[attr]['@type'] == 'list': sname = ParallelAggregator.get_list_name( snhdict[attr]) if snhdict[attr]['list']['@size'] == '0': continue elif snhdict[attr]['list']['@size'] == '1': if not isinstance( snhdict[attr]['list'][sname], list): snhdict[attr]['list'][sname] = [ snhdict[attr]['list'][sname]] if typ == 'UVEAlarms' and attr == 'alarms' and \ ackfilter is not None: alarms = [] for alarm in snhdict[attr]['list'][sname]: ack_attr = alarm.get('ack') if ack_attr: ack = ack_attr['#text'] else: ack = 'false' if ack == ackfilter: alarms.append(alarm) if not len(alarms): continue snhdict[attr]['list'][sname] = alarms snhdict[attr]['list']['@size'] = \ str(len(alarms)) else: continue # print "Attr %s Value %s" % (attr, snhdict) if attr not in state[key][typ]: state[key][typ][attr] = {} if dsource in state[key][typ][attr]: print "Found Dup %s:%s:%s:%s:%s = %s" % \ (key, typ, attr, source, mdule, state[ key][typ][attr][dsource]) state[key][typ][attr][dsource] = snhdict[attr] if sfilter is None and mfilter is None: for ptyp in redish.smembers("PTYPES:" + key): afilter = None if tfilter is not None: if ptyp not in tfilter: continue afilter = tfilter[ptyp] existing = redish.hgetall("PREVIOUS:" + key + ":" + ptyp) nstate = UVEServer.convert_previous( existing, state, key, ptyp, afilter) state = copy.deepcopy(nstate) pa = ParallelAggregator(state, self._uve_reverse_map) rsp = pa.aggregate(key, flat, base_url) except redis.exceptions.ConnectionError: self._logger.error("Failed to connect to redis-uve: %s:%d" \ % (redis_uve[0], redis_uve[1])) except Exception as e: self._logger.error("Exception: %s" % e) return {} else: self._logger.debug("Computed %s" % key) for k, v in statdict.iteritems(): if k in rsp: mp = dict(v.items() + rsp[k].items()) statdict[k] = mp return dict(rsp.items() + statdict.items()) # end get_uve def get_uve_regex(self, key): regex = '' if key[0] != '*': regex += '^' regex += key.replace('*', '.*?') if key[-1] != '*': regex += '$' return re.compile(regex) # end get_uve_regex def multi_uve_get(self, table, flat, filters=None, is_alarm=False, base_url=None): # get_uve_list cannot handle attribute names very efficiently, # so we don't pass them here uve_list = self.get_uve_list(table, filters, False, is_alarm) for uve_name in uve_list: uve_val = self.get_uve( table + ':' + uve_name, flat, filters, True, is_alarm, base_url) if uve_val == {}: continue else: uve = {'name': uve_name, 'value': uve_val} yield uve # end multi_uve_get def get_uve_list(self, table, filters=None, parse_afilter=False, is_alarm=False): filters = filters or {} uve_list = set() kfilter = filters.get('kfilt') if kfilter is not None: patterns = set() for filt in kfilter: patterns.add(self.get_uve_regex(filt)) for redis_uve in self._redis_uve_list: redish = redis.StrictRedis(host=redis_uve[0], port=redis_uve[1], password=self._redis_password, db=1) try: # For UVE queries, we wanna read both UVE and Alarm table entries = redish.smembers('ALARM_TABLE:' + table) if not is_alarm: entries = entries.union(redish.smembers('TABLE:' + table)) for entry in entries: info = (entry.split(':', 1)[1]).rsplit(':', 5) uve_key = info[0] if kfilter is not None: kfilter_match = False for pattern in patterns: if pattern.match(uve_key): kfilter_match = True break if not kfilter_match: continue src = info[1] sfilter = filters.get('sfilt') if sfilter is not None: if sfilter != src: continue module = info[2]+':'+info[3]+':'+info[4] mfilter = filters.get('mfilt') if mfilter is not None: if mfilter != module: continue typ = info[5] tfilter = filters.get('cfilt') if tfilter is not None: if typ not in tfilter: continue if parse_afilter: if tfilter is not None and len(tfilter[typ]): valkey = "VALUES:" + table + ":" + uve_key + \ ":" + src + ":" + module + ":" + typ for afilter in tfilter[typ]: attrval = redish.hget(valkey, afilter) if attrval is not None: break if attrval is None: continue uve_list.add(uve_key) except redis.exceptions.ConnectionError: self._logger.error('Failed to connect to redis-uve: %s:%d' \ % (redis_uve[0], redis_uve[1])) except Exception as e: self._logger.error('Exception: %s' % e) return set() return uve_list # end get_uve_list # end UVEServer class ParallelAggregator: def __init__(self, state, rev_map = {}): self._state = state self._rev_map = rev_map def _default_agg(self, oattr): itemset = set() result = [] for source in oattr.keys(): elem = oattr[source] hdelem = json.dumps(elem) if hdelem not in itemset: itemset.add(hdelem) result.append([elem, source]) else: for items in result: if elem in items: items.append(source) return result def _is_sum(self, oattr): akey = oattr.keys()[0] if '@aggtype' not in oattr[akey]: return False if oattr[akey]['@aggtype'] in ["sum"]: return True if oattr[akey]['@type'] in ['i8', 'i16', 'i32', 'i64', 'byte', 'u8', 'u16', 'u32', 'u64']: if oattr[akey]['@aggtype'] in ["counter"]: return True return False def _is_union(self, oattr): akey = oattr.keys()[0] if not oattr[akey]['@type'] in ["list"]: return False if '@aggtype' not in oattr[akey]: return False if oattr[akey]['@aggtype'] in ["union"]: return True else: return False def _is_append(self, oattr): akey = oattr.keys()[0] if not oattr[akey]['@type'] in ["list"]: return False if '@aggtype' not in oattr[akey]: return False if oattr[akey]['@aggtype'] in ["append"]: return True else: return False @staticmethod def get_list_name(attr): sname = "" for sattr in attr['list'].keys(): if sattr[0] not in ['@']: sname = sattr return sname @staticmethod def _get_list_key(elem): skey = "" for sattr in elem.keys(): if '@aggtype' in elem[sattr]: if elem[sattr]['@aggtype'] in ["listkey"]: skey = sattr return skey def _sum_agg(self, oattr): akey = oattr.keys()[0] result = copy.deepcopy(oattr[akey]) count = 0 for source in oattr.keys(): count += int(oattr[source]['#text']) result['#text'] = str(count) return result def _union_agg(self, oattr): akey = oattr.keys()[0] result = copy.deepcopy(oattr[akey]) itemset = set() sname = ParallelAggregator.get_list_name(oattr[akey]) result['list'][sname] = [] siz = 0 for source in oattr.keys(): if isinstance(oattr[source]['list'][sname], basestring): oattr[source]['list'][sname] = [oattr[source]['list'][sname]] for elem in oattr[source]['list'][sname]: hdelem = json.dumps(elem) if hdelem not in itemset: itemset.add(hdelem) result['list'][sname].append(elem) siz += 1 result['list']['@size'] = str(siz) return result def _append_agg(self, oattr): akey = oattr.keys()[0] result = copy.deepcopy(oattr[akey]) sname = ParallelAggregator.get_list_name(oattr[akey]) result['list'][sname] = [] siz = 0 for source in oattr.keys(): if not isinstance(oattr[source]['list'][sname], list): oattr[source]['list'][sname] = [oattr[source]['list'][sname]] for elem in oattr[source]['list'][sname]: result['list'][sname].append(elem) siz += 1 result['list']['@size'] = str(siz) return result @staticmethod def _list_agg_attrs(item): for ctrs in item.keys(): if '@aggtype'in item[ctrs]: if item[ctrs]['@aggtype'] in ["listkey"]: continue if item[ctrs]['@type'] in ['i8', 'i16', 'i32', 'i64', 'byte', 'u8', 'u16', 'u32', 'u64']: yield ctrs @staticmethod def consolidate_list(result, typ, objattr): applist = ParallelAggregator.get_list_name( result[typ][objattr]) appkey = ParallelAggregator._get_list_key( result[typ][objattr]['list'][applist][0]) # There is no listkey ; no consolidation is possible if len(appkey) == 0: return result # If the list's underlying struct has a listkey present, # we need to further aggregate entries that have the # same listkey mod_result = copy.deepcopy(result[typ][objattr]) mod_result['list'][applist] = [] res_size = 0 mod_result['list']['@size'] = int(res_size) # Add up stats for items in result[typ][objattr]['list'][applist]: matched = False for res_items in mod_result['list'][applist]: if items[appkey]['#text'] in [res_items[appkey]['#text']]: for ctrs in ParallelAggregator._list_agg_attrs(items): res_items[ctrs]['#text'] += int(items[ctrs]['#text']) matched = True if not matched: newitem = copy.deepcopy(items) for ctrs in ParallelAggregator._list_agg_attrs(items): newitem[ctrs]['#text'] = int(items[ctrs]['#text']) mod_result['list'][applist].append(newitem) res_size += 1 # Convert results back into strings for res_items in mod_result['list'][applist]: for ctrs in ParallelAggregator._list_agg_attrs(res_items): res_items[ctrs]['#text'] = str(res_items[ctrs]['#text']) mod_result['list']['@size'] = str(res_size) return mod_result def aggregate(self, key, flat, base_url = None): ''' This function does parallel aggregation of this UVE's state. It aggregates across all sources and return the global state of the UVE ''' result = {} try: for typ in self._state[key].keys(): result[typ] = {} for objattr in self._state[key][typ].keys(): if self._is_sum(self._state[key][typ][objattr]): sum_res = self._sum_agg(self._state[key][typ][objattr]) if flat: result[typ][objattr] = \ OpServerUtils.uve_attr_flatten(sum_res) else: result[typ][objattr] = sum_res elif self._is_union(self._state[key][typ][objattr]): union_res = self._union_agg( self._state[key][typ][objattr]) conv_res = None if union_res.has_key('@ulink') and base_url and \ union_res['list']['@type'] == 'string': uterms = union_res['@ulink'].split(":",1) # This is the linked UVE's table name m_table = uterms[0] if self._rev_map.has_key(m_table): h_table = self._rev_map[m_table] conv_res = [] sname = ParallelAggregator.get_list_name(union_res) for el in union_res['list'][sname]: lobj = {} lobj['name'] = el lobj['href'] = base_url + '/analytics/uves/' + \ h_table + '/' + el if len(uterms) == 2: lobj['href'] = lobj['href'] + '?cfilt=' + uterms[1] else: lobj['href'] = lobj['href'] + '?flat' conv_res.append(lobj) if flat: if not conv_res: result[typ][objattr] = \ OpServerUtils.uve_attr_flatten(union_res) else: result[typ][objattr] = conv_res else: result[typ][objattr] = union_res elif self._is_append(self._state[key][typ][objattr]): result[typ][objattr] = self._append_agg( self._state[key][typ][objattr]) append_res = ParallelAggregator.consolidate_list( result, typ, objattr) if flat: result[typ][objattr] =\ OpServerUtils.uve_attr_flatten(append_res) else: result[typ][objattr] = append_res else: default_res = self._default_agg( self._state[key][typ][objattr]) if flat: if (len(default_res) == 1): result[typ][objattr] =\ OpServerUtils.uve_attr_flatten( default_res[0][0]) else: nres = [] for idx in range(len(default_res)): nres.append(default_res[idx]) nres[idx][0] =\ OpServerUtils.uve_attr_flatten( default_res[idx][0]) result[typ][objattr] = nres else: result[typ][objattr] = default_res except KeyError: pass return result if __name__ == '__main__': uveserver = UVEServer(None, 0, None, None) gevent.spawn(uveserver.run()) uve_state = json.loads(uveserver.get_uve("abc-corp:vn02", False)) print json.dumps(uve_state, indent=4, sort_keys=True)

41.711207

91

0.450484

28,320

0.975509

923

0.031794

5,890

0.202887

0

3,300

0.113672

a765ce6d1c1eea007b73c094feaef3cfb92302b9

6,559

py

Python

tests/datasets/test_tonas.py

lucaspbastos/mirdata

e591c5411c41591e8606812df869dca1ad52ee0f

[ "BSD-3-Clause" ]

224

2019-05-08T14:46:05.000Z

2022-03-31T12:14:39.000Z

tests/datasets/test_tonas.py

oriolcolomefont/mirdata

e591c5411c41591e8606812df869dca1ad52ee0f

[ "BSD-3-Clause" ]

492

2019-04-08T16:59:33.000Z

2022-01-19T13:50:56.000Z

tests/datasets/test_tonas.py

oriolcolomefont/mirdata

e591c5411c41591e8606812df869dca1ad52ee0f

[ "BSD-3-Clause" ]

46

2019-04-11T15:12:18.000Z

2022-01-19T17:33:50.000Z

import numpy as np from tests.test_utils import run_track_tests from mirdata import annotations from mirdata.datasets import tonas TEST_DATA_HOME = "tests/resources/mir_datasets/tonas" def test_track(): default_trackid = "01-D_AMairena" dataset = tonas.Dataset(TEST_DATA_HOME) track = dataset.track(default_trackid) expected_attributes = { "singer": "En el barrio de Triana", "style": "Debla", "title": "Antonio Mairena", "tuning_frequency": 451.0654725341684, "f0_path": "tests/resources/mir_datasets/tonas/Deblas/01-D_AMairena.f0.Corrected", "notes_path": "tests/resources/mir_datasets/tonas/Deblas/01-D_AMairena.notes.Corrected", "audio_path": "tests/resources/mir_datasets/tonas/Deblas/01-D_AMairena.wav", "track_id": "01-D_AMairena", } expected_property_types = { "f0": annotations.F0Data, "f0_automatic": annotations.F0Data, "f0_corrected": annotations.F0Data, "notes": annotations.NoteData, "audio": tuple, "singer": str, "style": str, "title": str, "tuning_frequency": float, } run_track_tests(track, expected_attributes, expected_property_types) def test_to_jams(): default_trackid = "01-D_AMairena" dataset = tonas.Dataset(TEST_DATA_HOME) track = dataset.track(default_trackid) jam = track.to_jams() # Validate cante100 jam schema assert jam.validate() # Validate melody f0 = jam.search(namespace="pitch_contour")[0]["data"] assert [note.time for note in f0] == [0.197, 0.209, 0.221, 0.232] assert [note.duration for note in f0] == [0.0, 0.0, 0.0, 0.0] assert [note.value for note in f0] == [ {"index": 0, "frequency": 0.0, "voiced": False}, {"index": 0, "frequency": 379.299, "voiced": True}, {"index": 0, "frequency": 379.299, "voiced": True}, {"index": 0, "frequency": 379.299, "voiced": True}, ] print([note.confidence for note in f0]) assert [note.confidence for note in f0] == [3.09e-06, 2.86e-06, 7.15e-06, 1.545e-05] # Validate note transciption notes = jam.search(namespace="note_hz")[0]["data"] assert [note.time for note in notes] == [ 0.216667, 0.65, 2.183333, 2.566667, ] assert [note.duration for note in notes] == [ 0.433333, 1.016667, 0.3833329999999999, 0.3333330000000001, ] assert [note.value for note in notes] == [ 388.8382625732775, 411.9597888711769, 388.8382625732775, 411.9597888711769, ] assert [note.confidence for note in notes] == [None, None, None, None] def test_load_melody(): default_trackid = "01-D_AMairena" dataset = tonas.Dataset(TEST_DATA_HOME) track = dataset.track(default_trackid) f0_path = track.f0_path f0_data_corrected = tonas.load_f0(f0_path, True) f0_data_automatic = tonas.load_f0(f0_path, False) # check types assert type(f0_data_corrected) == annotations.F0Data assert type(f0_data_corrected.times) is np.ndarray assert type(f0_data_corrected.frequencies) is np.ndarray assert type(f0_data_corrected.voicing) is np.ndarray assert type(f0_data_corrected._confidence) is np.ndarray assert type(f0_data_automatic) == annotations.F0Data assert type(f0_data_automatic.times) is np.ndarray assert type(f0_data_automatic.frequencies) is np.ndarray assert type(f0_data_corrected.voicing) is np.ndarray assert type(f0_data_automatic._confidence) is np.ndarray # check values assert np.array_equal( f0_data_corrected.times, np.array([0.197, 0.209, 0.221, 0.232]), ) assert np.array_equal( f0_data_corrected.frequencies, np.array([0.000, 379.299, 379.299, 379.299]) ) assert np.array_equal( f0_data_corrected.voicing, np.array([0.0, 1.0, 1.0, 1.0]), ) assert np.array_equal( f0_data_corrected._confidence, np.array([3.090e-06, 0.00000286, 0.00000715, 0.00001545]), ) # check values assert np.array_equal( f0_data_automatic.times, np.array([0.197, 0.209, 0.221, 0.232]), ) assert np.array_equal( f0_data_automatic.frequencies, np.array( [ 0.000, 0.000, 143.918, 143.918, ] ), ) assert np.array_equal( f0_data_automatic.voicing, np.array([0.0, 0.0, 1.0, 1.0]), ) assert np.array_equal( f0_data_automatic._confidence, np.array([3.090e-06, 2.860e-06, 0.00000715, 0.00001545]), ) def test_load_notes(): default_trackid = "01-D_AMairena" dataset = tonas.Dataset(TEST_DATA_HOME) track = dataset.track(default_trackid) notes_path = track.notes_path notes_data = tonas.load_notes(notes_path) tuning_frequency = tonas._load_tuning_frequency(notes_path) # check types assert type(notes_data) == annotations.NoteData assert type(notes_data.intervals) is np.ndarray assert type(notes_data.pitches) is np.ndarray assert type(notes_data.confidence) is np.ndarray assert type(tuning_frequency) is float # check tuning frequency assert tuning_frequency == 451.0654725341684 # check values assert np.array_equal( notes_data.intervals[:, 0], np.array([0.216667, 0.65, 2.183333, 2.566667]) ) assert np.array_equal( notes_data.intervals[:, 1], np.array([0.65, 1.666667, 2.566666, 2.9]) ) assert np.array_equal( notes_data.pitches, np.array( [388.8382625732775, 411.9597888711769, 388.8382625732775, 411.9597888711769] ), ) assert np.array_equal( notes_data.confidence, np.array( [ 0.018007, 0.010794, 0.00698, 0.03265, ] ), ) def test_load_audio(): default_trackid = "01-D_AMairena" dataset = tonas.Dataset(TEST_DATA_HOME) track = dataset.track(default_trackid) audio_path = track.audio_path audio, sr = tonas.load_audio(audio_path) assert sr == 44100 assert type(audio) is np.ndarray def test_metadata(): default_trackid = "01-D_AMairena" dataset = tonas.Dataset(TEST_DATA_HOME) metadata = dataset._metadata assert metadata[default_trackid] == { "title": "En el barrio de Triana", "style": "Debla", "singer": "Antonio Mairena", }

30.649533

96

0.633938

0

939

0.143162

a765ee4d5ce159cb94158867be1e207d0bdc988c

1,064

py

Python

pycreds.py

Ennovar/aws-creds-test

fcc5c10c8cfb79bb0ea0fd52f2e2f137efd8a9ce

[ "Apache-2.0" ]

7

2017-06-13T15:55:23.000Z

2019-05-23T18:52:00.000Z

pycreds.py

Ennovar/aws-creds-test

fcc5c10c8cfb79bb0ea0fd52f2e2f137efd8a9ce

[ "Apache-2.0" ]

2

2019-02-16T12:56:33.000Z

2020-07-02T19:32:58.000Z

pycreds.py

Ennovar/aws-creds-test

fcc5c10c8cfb79bb0ea0fd52f2e2f137efd8a9ce

[ "Apache-2.0" ]

8

2017-05-17T22:46:07.000Z

2022-03-11T14:27:56.000Z

import os import hashlib import getpass import hmac import botocore.session import botocore.exceptions def _hash(value): return hmac.new(os.environ['TEST_KEY'], value, digestmod=hashlib.sha256).hexdigest() def main(): access_key = getpass.getpass("Access Key: ").strip() secret_access_key = getpass.getpass("Secret Access Key: ").strip() print("AKID hash: %s" % _hash(access_key)) print("AKID length: %s" % len(access_key)) print("\nSAK hash: %s" % _hash(secret_access_key)) print("SAK length: %s" % len(secret_access_key)) session = botocore.session.get_session() sts = session.create_client('sts', aws_access_key_id=access_key, aws_secret_access_key=secret_access_key) try: response = sts.get_caller_identity() print("Successfuly made an AWS request with the " "provided credentials.\n") except botocore.exceptions.ClientError as e: print("Error making AWS request: %s\n" % e) if __name__ == '__main__': main()

30.4

72

0.656015

0

230

0.216165