xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

9122373b59bc6d414c05d8fc25de317a2c149243

86

py

Python

move_randomly/move_randomly.py

VeryHardBit/turtlebot2-slam-nav-bash

1f8e24f886182eca6cbfad2a380501a53158f1a9

[ "Apache-2.0" ]

null

move_randomly/move_randomly.py

VeryHardBit/turtlebot2-slam-nav-bash

1f8e24f886182eca6cbfad2a380501a53158f1a9

[ "Apache-2.0" ]

null

move_randomly/move_randomly.py

VeryHardBit/turtlebot2-slam-nav-bash

1f8e24f886182eca6cbfad2a380501a53158f1a9

[ "Apache-2.0" ]

null

import rospy from sensor_msgs import LaserScan rospy.init_node("druken_turtlebot")

12.285714

35

0.825581

12

86

5.666667

0.833333

0

0.116279

86

6

36

14.333333

0.894737

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0.188235

0

1

0

true

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0.666667

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0.666667

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null

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1

0

null

0

1

0

1

0

1

0

5

91412141c5573fcede9af51a40cf2cd18453d71a

41

py

Python

backend/tornado_api/app/__init__.py

andredias/spa-study-case

f1cf7f011f0be761c4d3ed2df61c1ef139cf6168

[ "MIT" ]

1

2021-05-26T13:44:20.000Z

backend/tornado_api/app/__init__.py

andredias/spa-study-case

f1cf7f011f0be761c4d3ed2df61c1ef139cf6168

[ "MIT" ]

2

2020-07-29T23:08:19.000Z

2020-08-12T01:58:20.000Z

backend/tornado_api/app/__init__.py

andredias/spa-study-case

f1cf7f011f0be761c4d3ed2df61c1ef139cf6168

[ "MIT" ]

null

from .handlers import login # noqa:F401

20.5

40

0.756098

6

41

5.166667

1

0

0.088235

0.170732

41

1

41

0.823529

0.219512

0

1

0

true

0

1

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1

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null

0

1

0

null

0

1

0

1

0

5

e66c5e4cdf37f92cef88b60a1d1c626fa866f770

153

py

Python

feedbackform/errors.py

aminbeigi/Feedback-Form

2d3b9a96feba35d9e94b6b8cd8f5f287377cdce5

[ "MIT" ]

null

feedbackform/errors.py

aminbeigi/Feedback-Form

2d3b9a96feba35d9e94b6b8cd8f5f287377cdce5

[ "MIT" ]

null

feedbackform/errors.py

aminbeigi/Feedback-Form

2d3b9a96feba35d9e94b6b8cd8f5f287377cdce5

[ "MIT" ]

null

from flask import render_template from feedbackform import app @app.errorhandler(404) def page_not_found(e): return render_template('404.html'), 404

25.5

43

0.797386

23

153

5.130435

0.695652

0.237288

0

0.066667

0.117647

153

6

43

25.5

0.807407

0

0.051948

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0.2

false

0

0.4

0.2

0.8

0

1

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null

1

0

1

0

null

0

1

0

5

e6773d5c5a531ca67aeafc572b8ef7a9345594aa

75

py

Python

Art/myname.py

chanchon11/artchanchon

9642dc530539c9ccbf4066c362a42d93bf1bcc53

[ "MIT" ]

null

Art/myname.py

chanchon11/artchanchon

9642dc530539c9ccbf4066c362a42d93bf1bcc53

[ "MIT" ]

null

Art/myname.py

chanchon11/artchanchon

9642dc530539c9ccbf4066c362a42d93bf1bcc53

[ "MIT" ]

null

#myname.py def fullname(): print('My name is Chanchon') print('Helllo')

12.5

29

0.68

11

75

4.636364

0.909091

0

0.146667

75

5

30

15

0.796875

0.12

0

0.390625

0

1

0.333333

true

0

0.333333

0.666667

1

0

null

0

1

0

null

0

1

0

1

0

5

e686d336550b6cd1055234d06136442a5f632cbf

62

py

Python

l3ns/ldc/__init__.py

OlegJakushkin/l3ns

320184cb03837b9d6d13cb6ff006263ad1a99544

[ "MIT" ]

3

2021-04-02T11:05:54.000Z

2021-12-17T17:46:02.000Z

l3ns/ldc/__init__.py

OlegJakushkin/l3ns

320184cb03837b9d6d13cb6ff006263ad1a99544

[ "MIT" ]

1

2020-10-31T08:36:11.000Z

l3ns/ldc/__init__.py

OlegJakushkin/l3ns

320184cb03837b9d6d13cb6ff006263ad1a99544

[ "MIT" ]

1

2020-06-08T03:48:58.000Z

from .node import DockerNode from .subnet import DockerSubnet

20.666667

32

0.83871

8

62

6.5

0.75

0

0.129032

62

2

33

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e6bc215ea269bd43323b8976cd9b417cb4b7728e

174

py

Python

src/drstorage/models/__init__.py

chelling87/drstorage

5d69cdd01306c8d890ace1b4277b64f50efa5114

[ "BSD-3-Clause" ]

null

src/drstorage/models/__init__.py

chelling87/drstorage

5d69cdd01306c8d890ace1b4277b64f50efa5114

[ "BSD-3-Clause" ]

null

src/drstorage/models/__init__.py

chelling87/drstorage

5d69cdd01306c8d890ace1b4277b64f50efa5114

[ "BSD-3-Clause" ]

null

from .base import generic from .f1 import F1_600, F1_1200 from .x2m import X2M_157 from .x2b import X2B_400 __all__ = ["generic", "F1_600", "F1_1200", "X2M_157", "X2B_400"]

24.857143

64

0.729885

31

174

3.709677

0.387097

0.086957

0.121739

0.191304

0

0.248322

0.143678

174

6

65

29

0.52349

0

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0

1

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false

0

0.8

0

0.8

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

5

e6c43b41b14e7eba597babe81215f2c90026e207

310

py

Python

09/00/0.py

pylangstudy/201711

be6222dde61373f67d25a2c926868b602463c5cc

[ "CC0-1.0" ]

null

09/00/0.py

pylangstudy/201711

be6222dde61373f67d25a2c926868b602463c5cc

[ "CC0-1.0" ]

2

2017-10-31T23:37:36.000Z

2017-11-02T23:31:07.000Z

09/00/0.py

pylangstudy/201711

be6222dde61373f67d25a2c926868b602463c5cc

[ "CC0-1.0" ]

null

import secrets print(secrets.choice([100,200,300]))#100,200,300 print(secrets.randbelow(10))#0〜10 print(secrets.randbits(8))#0〜255(2**8()) print(secrets.token_bytes(8)) print(secrets.token_hex(8)) print(secrets.token_urlsafe(8)) print(secrets.compare_digest('a','a')) print(secrets.compare_digest('a','b'))

23.846154

48

0.741935

54

310

4.203704

0.425926

0.422907

0.229075

0.237885

0.229075

0

0.111111

0.041935

310

12

49

25.833333

0.646465

0.090323

0

0.014337

0

1

0

true

0

0.111111

0

0.111111

0.888889

0

null

1

0

null

0

1

0

1

0

5

e6e7f6ff02dad13afb720fc3e025d5dc6b36dc2b

26,736

py

Python

kwhelp/rules/__init__.py

Amourspirit/python-kwargshelper

4851ad69cf26f0656bc4264c70f956226bf5017e

[ "MIT" ]

null

kwhelp/rules/__init__.py

Amourspirit/python-kwargshelper

4851ad69cf26f0656bc4264c70f956226bf5017e

[ "MIT" ]

4

2021-10-16T20:11:42.000Z

2021-12-11T09:54:06.000Z

kwhelp/rules/__init__.py

Amourspirit/python-kwargshelper

4851ad69cf26f0656bc4264c70f956226bf5017e

[ "MIT" ]

null

# coding: utf-8 from abc import ABC, abstractmethod import numbers import os from pathlib import Path from typing import Optional from ..helper import is_iterable # region Interface class IRule(ABC): """ Abstract Interface Class for rules See Also: :doc:`/source/general/rules` """ def __init__(self, key: str, name: str, value: object, raise_errors: bool, originator: object): """ Constructor Args: key (str): the key that rule is to apply to. name (str): the name of the field that value was assigned value (object): the value that is assigned to ``field_name`` raise_errors (bool): determinins if rule could raise an error when validation fails originator (object): the object that attributes validated for Raises: TypeError: If any arg is not of the correct type """ if not isinstance(name, str): msg = self._get_type_error_msg(name, 'name', 'str') raise TypeError(msg) self._name: str = name if not isinstance(key, str): msg = self._get_type_error_msg(key, 'key', 'str') raise TypeError(msg) self._key: str = key if not isinstance(raise_errors, bool): msg = self._get_type_error_msg( raise_errors, 'raise_errors', 'bool') raise TypeError(msg) self._raise_errors = raise_errors self._value: object = value self._originator: object = originator # region Abstract Methods @abstractmethod def validate(self) -> bool: '''Gets attrib field and value are valid''' # endregion Abstract Methods def _get_type_error_msg(self, arg: Optional[object] = None, arg_name: Optional[str] = None, expected_type: Optional[str] = None) -> str: _arg = self.field_value if arg is None else arg _arg_name = self.key if arg_name is None else arg_name if expected_type: msg = f"Argument Error: '{_arg_name}' is expecting type of '{expected_type}'. Got type of '{type(_arg).__name__}'" else: msg = f"Argument Error: '{_arg_name}' is not expecting '{type(_arg).__name__}'" return msg def _get_not_type_error_msg(self, arg: Optional[object] = None, arg_name: Optional[str] = None, not_type: Optional[str] = None) -> str: _arg = self.field_value if arg is None else arg _arg_name = self.key if arg_name is None else arg_name if not_type: msg = f"Argument Error: '{_arg_name}' is expecting non '{not_type}'. Got type of '{type(_arg).__name__}'" else: msg = f"Argument Error: '{_arg_name}' is expecting non '{type(_arg).__name__}'." return msg # region Properties @property def field_name(self) -> str: ''' Name of the field assigned. :getter: Gets the name of the field assigned :setter: Sets the name of the field assigned ''' return self._name @field_name.setter def field_name(self, value: str): if not isinstance(value, str): msg = self._get_type_error_msg(value, 'field_name', 'str') raise TypeError(msg) self._name = value @property def field_value(self) -> object: """ The value assigned to ``field_name`` :getter: Gets value assigned to ``field_name`` :setter: Sets value assigned to ``field_name`` """ return self._value @field_value.setter def field_value(self, value: object): self._value = value @property def key(self) -> str: '''Gets the key currently being read''' return self._key @key.setter def key(self, value: str): if not isinstance(value, str): msg = self._get_type_error_msg(value, 'key', 'str') raise TypeError(msg) self._key = value @property def raise_errors(self) -> bool: """ Determines if a rule can raise an error when validation fails. :getter: Gets if a rule could raise an error when validation fails :setter: Sets if a rule could raise an error when validation fails """ return self._raise_errors @raise_errors.setter def raise_errors(self, value: bool): if not isinstance(value, bool): msg = self._get_type_error_msg(value, 'raise_errors', 'bool') raise TypeError(msg) self._raise_errors = value @property def originator(self) -> object: '''Gets object that attributes validated for''' return self._originator # endregion Properties # endregion Interface # region Attrib rules class RuleAttrNotExist(IRule): ''' Rule to ensure an attribute does not exist before it is added to class. ''' def validate(self) -> bool: """ Validates that ``field_name`` is not an existing attribute of ``originator`` instance. Raises: AttributeError: If ``raise_errors`` is ``True`` and ``field_name`` is already an attribue of ``originator`` instance. Returns: bool: ``True`` if ``field_name`` is not an existing attribue of ``originator`` instance; Otherwise, ``False``. """ result = not hasattr(self.originator, self.field_name) if result == False and self.raise_errors == True: raise AttributeError( f"'{self.field_name}' attribute already exist in current instance of '{type(self.originator).__name__}'") return result class RuleAttrExist(IRule): ''' Rule to ensure an attribute does exist before its value is set. ''' def validate(self) -> bool: """ Validates that ``field_name`` is an existing attribute of ``originator`` instance. Raises: AttributeError: If ``raise_errors`` is ``True`` and ``field_name`` is not an attribue of ``originator`` instance. Returns: bool: ``True`` if ``field_name`` is an existing attribue of ``originator`` instance; Otherwise, ``False``. """ result = hasattr(self.originator, self.field_name) if result == False and self.raise_errors == True: raise AttributeError( f"'{self.field_name}' attribute does not exist in current instance of '{type(self.originator).__name__}'") return result # endregion Attrib rules # region None class RuleNone(IRule): ''' Rule that matched only if value is ``None``. ''' def validate(self) -> bool: """ Validates that value to assign to attribute is ``None``. Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not ``None``. Returns: bool: ``True`` if ``field_value`` is ``None``; Otherwise, ``False``. """ if self.field_value is not None: if self.raise_errors: raise ValueError( f"Arg error: {self.key} must be assigned a value") return False return True class RuleNotNone(IRule): ''' Rule that matched only if value is not ``None``. ''' def validate(self) -> bool: """ Validates that value to assign to attribute is not ``None``. Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is ``None``. Returns: bool: ``True`` if ``field_value`` is not ``None``; Otherwise, ``False``. """ if self.field_value is None: if self.raise_errors: raise ValueError( f"Arg error: {self.key} must be assigned a value") return False return True # endregion None # region Number class RuleNumber(IRule): ''' Rule that matched only if value is a valid number. Note: If value is a of type ``bool`` then validation will fail for this rule. ''' def validate(self) -> bool: """ Validates that value to assign is a number Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not a number. Returns: bool: ``True`` if ``field_value`` is a number; Otherwise, ``False``. """ # isinstance(False, int) is True # print(int(True)) 1 # print(int(False)) 0 if not isinstance(self.field_value, numbers.Number) or isinstance(self.field_value, bool): if self.raise_errors: raise TypeError(self._get_type_error_msg( self.field_value, self.key, 'Number')) return False return True # region Integer class RuleInt(IRule): ''' Rule that matched only if value is instance of ``int``. Note: If value is a of type ``bool`` then validation will fail for this rule. ''' def validate(self) -> bool: """ Validates that value to assign is an int Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not an int. Returns: bool: ``True`` if ``field_value`` is an ``int``; Otherwise, ``False``. """ # isinstance(False, int) is True # print(int(True)) 1 # print(int(False)) 0 if not isinstance(self.field_value, int) or isinstance(self.field_value, bool): if self.raise_errors: raise TypeError(self._get_type_error_msg(expected_type='int')) return False return True class RuleIntZero(RuleInt): ''' Rule that matched only if value is equal to ``0``. ''' def validate(self) -> bool: """ Validates that value to assign is equal to ``0`` int. Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not equal to ``0`` int. Returns: bool: ``True`` if ``field_value`` equals ``0`` int; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value != 0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be equal to 0 int value") return False return True class RuleIntPositive(RuleInt): ''' Rule that matched only if value is equal or greater than ``0``. ''' def validate(self) -> bool: """ Validates that value to assign is a posivite int Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not a positive int. Returns: bool: ``True`` if ``field_value`` is a positive int; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value < 0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be a positive int value") return False return True class RuleIntNegative(RuleInt): ''' Rule that matched only if value is less than ``0``. ''' def validate(self) -> bool: """ Validates that value to assign is a negative int Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not a negative int. Returns: bool: ``True`` if ``field_value`` is a negative int; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value >= 0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be a negative int value") return False return True class RuleIntNegativeOrZero(RuleInt): ''' Rule that matched only if value is equal or less than ``0``. ''' def validate(self) -> bool: """ Validates that value to assign is equal to zero or a negative int Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not a negative int. Returns: bool: ``True`` if ``field_value`` is equal to zero or a negative int; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value > 0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be equal to zero or a negative int value") return False return True class RuleByteUnsigned(RuleInt): ''' Unsigned Byte rule, range from ``0`` to ``255``. ''' def validate(self) -> bool: """ Valids Raises: ValueError: If ``raise_errors`` is ``False`` and value is less then ``0`` or greater than ``255``. Returns: bool: ``True`` if Validation passes; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value < 0 or self.field_value > 255: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be a num from 0 to 255") return False return True class RuleByteSigned(RuleInt): ''' Signed Byte rule, range from ``-128`` to ``127``. ''' def validate(self) -> bool: """ Valids Raises: ValueError: If ``raise_errors`` is ``False`` and value is less then ``-128`` or greater than ``128``. Returns: bool: ``True`` if Validation passes; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value < -128 or self.field_value > 127: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be a num from -128 to 127") return False return True # endregion Integer # region Float Rules class RuleFloat(IRule): ''' Rule that matched only if value is to type ``float``. ''' def validate(self) -> bool: """ Validates that value to assign is a float Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not a float. Returns: bool: ``True`` if ``field_value`` is a positive float; Otherwise, ``False``. """ if not isinstance(self.field_value, float): if self.raise_errors: raise TypeError(self._get_type_error_msg( self.field_value, self.key, 'float')) return False return True class RuleFloatZero(RuleFloat): ''' Rule that matched only if value is equal to ``0.0``. ''' def validate(self) -> bool: """ Validates that value to assign equals ``0.0`` float Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not equal to ``0.0`` float. Returns: bool: ``True`` if ``field_value`` equals ``0.0`` float; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value != 0.0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be equal to 0.0 float value") return False return True class RuleFloatPositive(RuleFloat): ''' Rule that matched only if value is equal or greater than ``0.0``. ''' def validate(self) -> bool: """ Validates that value to assign is a positive float Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not a positive float. Returns: bool: ``True`` if ``field_value`` is a positive float; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value < 0.0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be a positive float value") return False return True class RuleFloatNegative(RuleFloat): ''' Rule that matched only if value is less than ``0.0``. ''' def validate(self) -> bool: """ Validates that value to assign is a negative float Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not a negative float. Returns: bool: ``True`` if ``field_value`` is a negative float; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value >= 0.0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be a negative float value") return False return True class RuleFloatNegativeOrZero(RuleFloat): ''' Rule that matched only if value is equal or less than ``0.0``. ''' def validate(self) -> bool: """ Validates that value to assign is equal to ``0.0`` or a negative float Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not a negative float. Returns: bool: ``True`` if ``field_value`` is equal to ``0.0`` or a negative float; Otherwise, ``False``. """ if not super().validate(): return False if self.field_value > 0.0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must be equal to 0.0 or a negative float value") return False return True # endregion Float Rules # endregion Number # region String class RuleStr(IRule): ''' Rule that matched only if value is of type ``str``. ''' def validate(self) -> bool: """ Validates that value to assign is a string Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not instance of string. Returns: bool: ``True`` if ``field_value`` is a string; Otherwise, ``False``. """ if not isinstance(self.field_value, str): if self.raise_errors: raise TypeError(self._get_type_error_msg( self.field_value, self.key, 'str')) return False return True class RuleStrEmpty(RuleStr): ''' Rule that matched only if value is equal to empty string. ''' def validate(self) -> bool: """ Validates that value to assign is a string and is an empty string. Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not an empty string. Returns: bool: ``True`` if value is an empty string; Otherwise; ``False``. """ if not super().validate(): return False value = self.field_value if len(value) != 0: if self.raise_errors: raise ValueError( f"Arg error: {self.key} must be empty str") return False return True class RuleStrNotNullOrEmpty(RuleStr): ''' Rule that matched only if value is not ``None`` or empty string. ''' def validate(self) -> bool: """ Validates that value to assign is a string and is not a empty string. Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not instance of string or is empty string Returns: bool: ``True`` if value is valid; Otherwise; ``False``. """ if not super().validate(): return False value = self.field_value if len(value) == 0: if self.raise_errors: raise ValueError( f"Arg error: {self.key} must not be empty str") return False return True class RuleStrNotNullEmptyWs(RuleStrNotNullOrEmpty): ''' Rule that matched only if value is not ``None``, empty or whitespace. ''' def validate(self) -> bool: """ Validates that value to assign is a string and is not a empty or whitespace string. Raises: ValueError: If ``raise_errors`` is ``True`` and ``field_value`` is not instance of string or is empty or whitespace string Returns: bool: ``True`` if value is valid; Otherwise; ``False``. """ if not super().validate(): return False value = self.field_value.strip() if len(value) == 0: if self.raise_errors: raise ValueError( f"Arg error: '{self.key}' must not be empty or whitespace str") return False return True # endregion String # region boolean class RuleBool(IRule): """ Rule that matched only if value is instance of bool. """ def validate(self) -> bool: """ Validates that value to assign is a bool Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not instance of bool. Returns: bool: ``True`` if ``field_value`` is a bool; Otherwise, ``False``. """ if not isinstance(self.field_value, bool): if self.raise_errors: raise TypeError(self._get_type_error_msg(expected_type='bool')) return False return True # endregion boolean # region Iterable class RuleIterable(IRule): """ Rule that matched only if value is iterable such as list, tuple, set. """ def validate(self) -> bool: """ Validates that value to assign is iterable Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not iterable. Returns: bool: ``True`` if ``field_value`` is a iterable; Otherwise, ``False``. """ if not is_iterable(self.field_value): if self.raise_errors: raise TypeError(self._get_type_error_msg(expected_type="iterable")) return False return True class RuleNotIterable(IRule): """ Rule that matched only if value is not iterable. """ def validate(self) -> bool: """ Validates that value to assign is not iterable Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is iterable. Returns: bool: ``True`` if ``field_value`` is a not iterable; Otherwise, ``False``. """ if is_iterable(self.field_value): if self.raise_errors: raise TypeError(self._get_not_type_error_msg(not_type="iterable")) return False return True # endregion Iterable # region Path class RulePath(IRule): """ Rule that matched only if value is instance of ``Path``. """ def validate(self) -> bool: """ Validates that value to assign is a Path Raises: TypeError: If ``raise_errors`` is ``True`` and ``field_value`` is not instance of Path. Returns: bool: ``True`` if ``field_value`` is a bool; Otherwise, ``False``. """ if not isinstance(self.field_value, Path): if self.raise_errors: raise TypeError(self._get_type_error_msg(expected_type='Path')) return False return True class RulePathExist(RulePath): """ Rule that matched only if value is instance of ``Path`` and path exist. """ def validate(self) -> bool: """ Validates that value to assign is a path that exist Raises: FileNotFoundError: If ``raise_errors`` is ``True`` and ``field_value`` is path does not exist. Returns: bool: ``True`` if ``field_value`` is an existing path; Otherwise, ``False``. """ if not super().validate(): return False if not os.path.exists(self.field_value): if self.raise_errors: raise FileNotFoundError( f"Unable to find path: '{self.field_value}'") return False return True class RulePathNotExist(RulePath): """ Rule that matched only if value is instance of ``Path`` and path does not exist. """ def validate(self) -> bool: """ Validates that value to assign is a Path that does not exist Raises: FileExistsError: If ``raise_errors`` is ``True`` and ``field_value`` is path that is existing. Returns: bool: ``True`` if ``field_value`` is a p ath that does not exist; Otherwise, ``False``. """ if not super().validate(): return False if os.path.exists(self.field_value): if self.raise_errors: raise FileExistsError( f"File already exist: '{self.field_value}'") return False return True class RuleStrPathExist(RuleStr): """ Rule that matched only if value is instance of str and path exist. """ def validate(self) -> bool: """ Validates that value to assign is a str path that exist Raises: FileNotFoundError: If ``raise_errors`` is ``True`` and ``field_value`` is path does not exist. Returns: bool: ``True`` if ``field_value`` is a path that does exist; Otherwise, ``False``. """ if not super().validate(): return False if not os.path.exists(self.field_value): if self.raise_errors: raise FileNotFoundError( f"Unable to find path: '{self.field_value}'") return False return True class RuleStrPathNotExist(RuleStr): """ Rule that matched only if value is instance of str and path is not existing. """ def validate(self) -> bool: """ Validates that value to assign is a path not existing Raises: FileExistsError: If ``raise_errors`` is ``True`` and ``field_value`` is a path that is not existing. Returns: bool: ``True`` if ``field_value`` is a path that does not exist; Otherwise, ``False``. """ if not super().validate(): return False if os.path.exists(self.field_value): if self.raise_errors: raise FileExistsError( f"File already exist: '{self.field_value}'") return False return True # end Region Path

30.278596

140

0.565305

3,224

26,736

4.587159

0.064826

0.060856

0.038136

0.038542

0.818987

0.78457

0.77037

0.738454

0.684901

0.627899

0

0.005012

0.328396

26,736

882

141

30.312925

0.818612

0.401855

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0.011799

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0

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0

null

0

1

0

1

0

null

0

5

fc17317887686c685f1e523699fe5f4bb72bdb2e

100

py

Python

homework/numtowords/greetings.py

maksym-bielyshev/dp_189_taqc

67e0ecb68bef7dd710ef1a8248816efd7d834e59

[ "MIT" ]

null

homework/numtowords/greetings.py

maksym-bielyshev/dp_189_taqc

67e0ecb68bef7dd710ef1a8248816efd7d834e59

[ "MIT" ]

null

homework/numtowords/greetings.py

maksym-bielyshev/dp_189_taqc

67e0ecb68bef7dd710ef1a8248816efd7d834e59

[ "MIT" ]

null

"""Function with a greeting.""" def hello(): """Printing the greeting.""" print("Hello!")

14.285714

32

0.58

11

100

5.272727

0.818182

0

0.2

100

6

33

16.666667

0.725

0.48

0

0.146341

0

1

0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

fc1dab790df79850b337cf2249f753dc77d6a885

7,280

py

Python

tests/tests_task_manager.py

Bizarious/mra-discord

9d98d7dd15be00ffa9f24958bbaea2980272e713

[ "MIT" ]

null

tests/tests_task_manager.py

Bizarious/mra-discord

9d98d7dd15be00ffa9f24958bbaea2980272e713

[ "MIT" ]

6

2021-12-09T16:41:15.000Z

2022-01-25T23:44:14.000Z

tests/tests_task_manager.py

Bizarious/mra-discord

9d98d7dd15be00ffa9f24958bbaea2980272e713

[ "MIT" ]

null

from unittest import TestCase from datetime import datetime as dt, timedelta as td from core.task.task_control import TaskManager from core.system import IPC from core.database import Data from core.containers import TransferPackage class TaskManagerTests(TestCase): tm: TaskManager ipc: IPC data: Data t: TransferPackage def setUp(self) -> None: self.ipc = IPC() self.ipc.create_queues("bot", "task") self.data = Data() self.tm = TaskManager(data=self.data, ipc=self.ipc) self.tm.paths = {"../src/tasks": "tasks"} self.tm.register_all_tasks() self.t = TransferPackage() self.t.pack(author_id=0, channel_id=0, message="test", message_args="", date_string="1h", label="test", number=0 ) self.t.label(dst="task", cmd="task", task="Reminder", author_id=0, channel_id=0 ) self.tm.add_task(self.t) def tearDown(self) -> None: self.data.set_json(file="tasks", data=[]) def test_add_task_right_next_date(self): self.tm.add_task(self.t) right_next_time = dt.now().replace(microsecond=0) + td(hours=1) self.assertEqual(self.tm.next_date, right_next_time, msg="Wrong next date") def test_add_second_later_task_right_date(self): t2 = TransferPackage() t2.pack(author_id=0, channel_id=0, message="test", message_args="", date_string="2h", label="test", number=0 ) t2.label(dst="task", cmd="task", task="Reminder", author_id=0, channel_id=0 ) self.tm.add_task(t2) right_next_time = dt.now().replace(microsecond=0) + td(hours=1) self.assertEqual(self.tm.next_date, right_next_time, msg="Wrong next date") def test_add_second_earlier_task_right_date(self): t2 = TransferPackage() t2.pack(author_id=0, channel_id=0, message="test", message_args="", date_string="30m", label="test", number=0 ) t2.label(dst="task", cmd="task", task="Reminder", author_id=0, channel_id=0 ) self.tm.add_task(t2) right_next_time = dt.now().replace(microsecond=0) + td(minutes=30) self.assertEqual(self.tm.next_date, right_next_time, msg="Wrong next date") def test_remove_only_task_right_date(self): task = self.tm.get_task(0, 0) self.tm.delete_task(task) self.assertEqual(None, self.tm.next_date) def test_remove_first_task_right_date(self): t2 = TransferPackage() t2.pack(author_id=0, channel_id=0, message="test", message_args="", date_string="2h", label="test", number=0 ) t2.label(dst="task", cmd="task", task="Reminder", author_id=0, channel_id=0 ) self.tm.add_task(t2) task = self.tm.get_task(0, 0) self.tm.delete_task(task) right_next_time = dt.now().replace(microsecond=0) + td(hours=2) self.assertEqual(self.tm.next_date, right_next_time, msg="Wrong next date") def test_remove_second_task_right_date(self): t2 = TransferPackage() t2.pack(author_id=0, channel_id=0, message="test", message_args="", date_string="2h", label="test", number=0 ) t2.label(dst="task", cmd="task", task="Reminder", author_id=0, channel_id=0 ) self.tm.add_task(t2) task = self.tm.get_task(1, 0) self.tm.delete_task(task) right_next_time = dt.now().replace(microsecond=0) + td(hours=1) self.assertEqual(self.tm.next_date, right_next_time, msg="Wrong next date") def test_remove_all_tasks_right_date(self): t2 = TransferPackage() t2.pack(author_id=0, channel_id=0, message="test", message_args="", date_string="2h", label="test", number=0 ) t2.label(dst="task", cmd="task", task="Reminder", author_id=0, channel_id=0 ) self.tm.add_task(t2) self.tm.delete_all_tasks(0) self.assertEqual(self.tm.next_date, None, msg="Wrong next date") def test_add_second_task_different_user_right_date(self): t2 = TransferPackage() t2.pack(author_id=1, channel_id=0, message="test", message_args="", date_string="30m", label="test", number=0 ) t2.label(dst="task", cmd="task", task="Reminder", author_id=1, channel_id=0 ) self.tm.add_task(t2) right_next_time = dt.now().replace(microsecond=0) + td(minutes=30) self.assertEqual(self.tm.next_date, right_next_time, msg="Wrong next date") def test_load_tasks_right_date(self): t2 = TransferPackage() t2.pack(author_id=1, channel_id=0, message="test", message_args="", date_string="30m", label="test", number=0 ) t2.label(dst="task", cmd="task", task="Reminder", author_id=1, channel_id=0 ) self.tm.add_task(t2) right_next_time = dt.now().replace(microsecond=0) + td(minutes=30) self.tm.next_date = None while not self.tm.task_queue.empty(): self.tm.task_queue.get() self.tm.tasks = {} self.tm.import_tasks(self.data.get_json(file="tasks")) self.assertEqual(right_next_time, self.tm.next_date) def test_delete_all_tasks_multiple_users(self): t2 = TransferPackage() t2.pack(author_id=1, channel_id=0, message="test", message_args="", date_string="30m", label="test", number=0 ) t2.label(dst="task", cmd="task", task="Reminder", author_id=1, channel_id=0 ) self.tm.add_task(t2) self.tm.delete_all_tasks(0) self.tm.delete_all_tasks(1) self.assertEqual(None, self.tm.next_date)

29.236948

83

0.493544

819

7,280

4.17094

0.10989

0.064988

0.052693

0.056206

0.775176

0.763466

0.741218

0.721897

0.712529

0

0.026394

0.396291

7,280

248

84

29.354839

0.750853

0

0.699029

0

0.051786

0

0.048544

1

0.058252

false

0

0.033981

0

0.116505

0

null

0

1

0

null

0

5

fc2495005e183e82eac1729a0ed330d070b59e8c

1,047

py

Python

setup.py

doorknob6/WCLApi

f9edcb11b74dbbd7664c308f286a9c23dbd5d88b

[ "MIT" ]

null

setup.py

doorknob6/WCLApi

f9edcb11b74dbbd7664c308f286a9c23dbd5d88b

[ "MIT" ]

null

setup.py

doorknob6/WCLApi

f9edcb11b74dbbd7664c308f286a9c23dbd5d88b

[ "MIT" ]

null

from distutils.core import setup setup( name="WCLApi", packages=["WCLApi"], version="0.4.0", license="MIT", description="Python tools to communicate with the Wacraftlogs website API.", author="doorknob6", author_email="joopkjongste@gmail.com", url="https://github.com/doorknob6/WCLApi", download_url="https://github.com/doorknob6/WCLApi/archive/master.tar.gz", keywords=["Nexushub", "API"], install_requires=["requests", "requests-toolbelt"], classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Topic :: Software Development :: Build Tools", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", ], )

37.392857

80

0.617956

110

1,047

5.854545

0.572727

0.206522

0.271739

0.282609

0.099379

0

0.024631

0.224451

1,047

28

81

37.392857

0.768473

0

0.611641

0.020992

0

1

0

true

0

0.037037

0

0.037037

0

null

1

0

1

0

null

0

1

0

5

fc73e9640c75aeb1925c131b52e05412b7b89f08

1,577

py

Python

flockos/apis/channels.py

bilmyers/pyflock

b440ffbcd6a18c0d81b81dcdcbae7ae16c025d39

[ "Apache-2.0" ]

14

2017-02-14T07:02:59.000Z

2022-03-30T13:59:59.000Z

flockos/apis/channels.py

bilmyers/pyflock

b440ffbcd6a18c0d81b81dcdcbae7ae16c025d39

[ "Apache-2.0" ]

10

2016-10-22T20:52:00.000Z

2021-05-10T10:40:30.000Z

flockos/apis/channels.py

bilmyers/pyflock

b440ffbcd6a18c0d81b81dcdcbae7ae16c025d39

[ "Apache-2.0" ]

8

2017-03-03T13:16:34.000Z

2020-07-23T17:59:54.000Z

# coding: utf-8 # python 2 and python 3 compatibility library from six import iteritems from ..api_client import call_api def get_info(token, channel_id, **kwargs): """ This method makes a synchronous HTTP request. :param str token: (required) :param str channel_id: (required) :return: response dict """ params = locals() for key, val in iteritems(params['kwargs']): params[key] = val del params['kwargs'] resource_path = '/channels.getInfo'.replace('{format}', 'json') response = call_api(resource_path, params=params) return response def get_members(token, channel_id, show_public_profile, **kwargs): """ This method makes a synchronous HTTP request. :param str token: (required) :param str channel_id: (required) :param bool show_public_profile: (required) :return: response dict """ params = locals() for key, val in iteritems(params['kwargs']): params[key] = val del params['kwargs'] resource_path = '/channels.getMembers'.replace('{format}', 'json') response = call_api(resource_path, params=params) return response def list(token, **kwargs): """ This method makes a synchronous HTTP request. :param str token: (required) :return: response dict """ params = locals() for key, val in iteritems(params['kwargs']): params[key] = val del params['kwargs'] resource_path = '/channels.list'.replace('{format}', 'json') response = call_api(resource_path, params=params) return response

24.261538

70

0.655041

192

1,577

5.270833

0.296875

0.083004

0.047431

0.062253

0.786561

0

0.002449

0.223209

1,577

64

71

24.640625

0.823673

0.298034

0

0.692308

0

0.120825

0

1

0.115385

false

0

0.076923

0

0.307692

0

null

0

1

0

null

0

5

fc9e3e24c05b5ba684fdcc29a564d41d693692a1

89

py

Python

django-celery/home/admin.py

mrseyfi/django-celery

2da50b020d903eabb877aed6ffba00c118c62eaf

[ "MIT" ]

null

django-celery/home/admin.py

mrseyfi/django-celery

2da50b020d903eabb877aed6ffba00c118c62eaf

[ "MIT" ]

null

django-celery/home/admin.py

mrseyfi/django-celery

2da50b020d903eabb877aed6ffba00c118c62eaf

[ "MIT" ]

null

from django.contrib import admin from .models import Number admin.site.register(Number)

17.8

32

0.820225

13

89

5.615385

0.692308

0

0.11236

89

5

33

17.8

0.924051

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5da38113f75e1dd83be5794caed72f37218b969a

134

py

Python

test/__init__.py

lionel-/BeetsPluginStructuredComments

cdd0ae28d0b5ff3db6ac227a65b522be673cadcf

[ "MIT" ]

3

2020-12-23T10:16:51.000Z

2021-12-23T23:44:00.000Z

test/__init__.py

lionel-/BeetsPluginStructuredComments

cdd0ae28d0b5ff3db6ac227a65b522be673cadcf

[ "MIT" ]

null

test/__init__.py

lionel-/BeetsPluginStructuredComments

cdd0ae28d0b5ff3db6ac227a65b522be673cadcf

[ "MIT" ]

1

2020-12-30T14:07:18.000Z

33.5

55

0.731343

18

134

5.444444

0.666667

0.265306

0

0.036036

0.171642

134

3

56

44.666667

0.846847

0.940299

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

1

0

null

0

1

0

5

5dcdf39051ce8b4b546a96f2dc55aa51ab5d327d

114

py

Python

Hello World.py

Futurist-Forever/playground-for-python

a6f569c5f689dc83ec087b6e00a582123af9f732

[ "MIT" ]

null

Hello World.py

Futurist-Forever/playground-for-python

a6f569c5f689dc83ec087b6e00a582123af9f732

[ "MIT" ]

null

Hello World.py

Futurist-Forever/playground-for-python

a6f569c5f689dc83ec087b6e00a582123af9f732

[ "MIT" ]

null

# playground-for-python # Program 1: Hello World print("Hello World") # It prints "Hello World" as Output

22.8

60

0.684211

16

114

4.875

0.75

0.384615

0

0.011111

0.210526

114

4

61

28.5

0.855556

0.684211

0

0.392857

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

5ddd0825c6ad1a50b91af7658478c063e2051e6e

205

py

Python

manager/models/__init__.py

Exanis/dataset-manager

af2f2d4242417eb14240129ac6312a0ebdfd24ee

[ "MIT" ]

null

manager/models/__init__.py

Exanis/dataset-manager

af2f2d4242417eb14240129ac6312a0ebdfd24ee

[ "MIT" ]

5

2018-11-22T13:32:17.000Z

2018-11-22T13:34:39.000Z

manager/models/__init__.py

Exanis/dataset-manager

af2f2d4242417eb14240129ac6312a0ebdfd24ee

[ "MIT" ]

null

from .datatype import DataType, DataTypeElement, DataTypeOption from .collection import Collection, CollectionElement, CollectionElementValue from .export import Export, ExportParam from .task import Task

41

77

0.853659

21

205

8.333333

0.52381

0

0.102439

205

4

78

51.25

0.951087

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5dee49d22e2c8b88db1bbb18ac402f1635d8390e

320

py

Python

tests/test_parse_params_helpers.py

Dakhnovskiy/pomogator_bot

9a8b9d5f79b800020d99ffd6034df054d405e434

[ "Apache-2.0" ]

null

tests/test_parse_params_helpers.py

Dakhnovskiy/pomogator_bot

9a8b9d5f79b800020d99ffd6034df054d405e434

[ "Apache-2.0" ]

null

tests/test_parse_params_helpers.py

Dakhnovskiy/pomogator_bot

9a8b9d5f79b800020d99ffd6034df054d405e434

[ "Apache-2.0" ]

null

from fixtures import params_weather_forecast from bot.handlers.parse_params_helpers import parse_params_weather_forecast def test_parse_params_weather_forecast(params_weather_forecast): result = parse_params_weather_forecast(params_weather_forecast['param']) assert result == params_weather_forecast['result']

40

76

0.859375

41

320

6.219512

0.365854

0.356863

0.576471

0.305882

0.368627

0

0.084375

320

7

77

45.714286

0.870307

0

0.034375

0

0.2

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0.2

false

0

0.4

0

0.6

0

null

1

0

1

0

null

0

1

0

5

f8d65b960c578870b80d01a9a5d044c84b174c02

81

py

Python

Programmers/Lv.1/lack_money.py

kangjunseo/C-

eafdf57a22b3a794d09cab045d6d60c2842ba347

[ "MIT" ]

2

2021-08-30T12:37:57.000Z

2021-11-29T05:42:05.000Z

Programmers/Lv.1/lack_money.py

kangjunseo/C-

eafdf57a22b3a794d09cab045d6d60c2842ba347

[ "MIT" ]

null

Programmers/Lv.1/lack_money.py

kangjunseo/C-

eafdf57a22b3a794d09cab045d6d60c2842ba347

[ "MIT" ]

null

def solution(price, money, count): return max(0,price*count*(count+1)/2 - money)

40.5

80

0.716049

14

81

4.142857

0.714286

0

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f8f6ae6d2b78ee832987b97f16bfc3b842c327cc

6,597

py

Python

tests/networking/arista/test_save_config.py

QualiSystems/cloudshell-networking-arista-

011ff605244a98bb488fec985bd0e053af9855d0

[ "Apache-2.0" ]

null

tests/networking/arista/test_save_config.py

QualiSystems/cloudshell-networking-arista-

011ff605244a98bb488fec985bd0e053af9855d0

[ "Apache-2.0" ]

9

2018-04-03T12:02:29.000Z

2021-07-08T09:07:29.000Z

tests/networking/arista/test_save_config.py

QualiSystems/cloudshell-networking-arista-

011ff605244a98bb488fec985bd0e053af9855d0

[ "Apache-2.0" ]

2

2017-02-08T23:52:21.000Z

2018-07-04T15:33:36.000Z

from mock import MagicMock, patch from cloudshell.networking.arista.runners.arista_configuration_runner import ( AristaConfigurationRunner, ) from tests.networking.arista.base_test import ( ENABLE_PROMPT, VRF_PROMPT, BaseAristaTestCase, CliEmulator, Command, ) @patch("cloudshell.cli.session.ssh_session.paramiko", MagicMock()) @patch( "cloudshell.cli.session.ssh_session.SSHSession._clear_buffer", MagicMock(return_value=""), ) class TestSaveConfig(BaseAristaTestCase): def _setUp(self, attrs=None): super(TestSaveConfig, self)._setUp(attrs) self.runner = AristaConfigurationRunner( self.logger, self.resource_config, self.api, self.cli_handler ) @patch("cloudshell.cli.session.ssh_session.SSHSession._receive_all") @patch("cloudshell.cli.session.ssh_session.SSHSession.send_line") def test_save_anonymous(self, send_mock, recv_mock): self._setUp() host = "192.168.122.10" ftp_path = "ftp://{}".format(host) configuration_type = "running" emu = CliEmulator( [ Command( r"^copy {0} {1}/Arista-{0}-\d+-\d+$".format( configuration_type, ftp_path ), "Copy complete\n" "{}".format(ENABLE_PROMPT), regexp=True, ), ] ) send_mock.side_effect = emu.send_line recv_mock.side_effect = emu.receive_all self.runner.save(ftp_path, configuration_type) emu.check_calls() @patch("cloudshell.cli.session.ssh_session.SSHSession._receive_all") @patch("cloudshell.cli.session.ssh_session.SSHSession.send_line") def test_save_ftp(self, send_mock, recv_mock): self._setUp() user = "user" password = "password" host = "192.168.122.10" ftp_path = "ftp://{}:{}@{}".format(user, password, host) configuration_type = "running" emu = CliEmulator( [ Command( r"^copy {0} {1}/Arista-{0}-\d+-\d+$".format( configuration_type, ftp_path ), "Copy complete\n" "{}".format(ENABLE_PROMPT), regexp=True, ) ] ) send_mock.side_effect = emu.send_line recv_mock.side_effect = emu.receive_all self.runner.save(ftp_path, configuration_type) emu.check_calls() @patch("cloudshell.cli.session.ssh_session.SSHSession._receive_all") @patch("cloudshell.cli.session.ssh_session.SSHSession.send_line") def test_save_with_vrf(self, send_mock, recv_mock): vrf_name = "vrf_name" self._setUp({"VRF Management Name": vrf_name}) user = "user" password = "password" host = "192.168.122.10" ftp_path = "ftp://{}:{}@{}".format(user, password, host) configuration_type = "running" emu = CliEmulator( [ Command( "routing-context vrf {}".format(vrf_name), VRF_PROMPT.format(vrf_name=vrf_name), ), Command( r"^copy {0} {1}/Arista-{0}-\d+-\d+$".format( configuration_type, ftp_path ), "Copy complete\n" "{}".format(VRF_PROMPT.format(vrf_name=vrf_name)), regexp=True, ), Command("routing-context vrf default", ENABLE_PROMPT), ] ) send_mock.side_effect = emu.send_line recv_mock.side_effect = emu.receive_all self.runner.save(ftp_path, configuration_type) emu.check_calls() @patch("cloudshell.cli.session.ssh_session.SSHSession._receive_all") @patch("cloudshell.cli.session.ssh_session.SSHSession.send_line") def test_save_startup(self, send_mock, recv_mock): self._setUp() user = "user" password = "password" host = "192.168.122.10" ftp_path = "ftp://{}:{}@{}".format(user, password, host) configuration_type = "startup" emu = CliEmulator( [ Command( r"^copy {0} {1}/Arista-{0}-\d+-\d+$".format( configuration_type, ftp_path ), "Copy complete\n" "{}".format(ENABLE_PROMPT), regexp=True, ) ] ) send_mock.side_effect = emu.send_line recv_mock.side_effect = emu.receive_all self.runner.save(ftp_path, configuration_type) emu.check_calls() @patch("cloudshell.cli.session.ssh_session.SSHSession._receive_all") @patch("cloudshell.cli.session.ssh_session.SSHSession.send_line") def test_fail_to_save(self, send_mock, recv_mock): self._setUp() host = "192.168.122.10" ftp_path = "ftp://{}".format(host) configuration_type = "running" emu = CliEmulator( [ Command( r"^copy {0} {1}/Arista-{0}-\d+-\d+$".format( configuration_type, ftp_path ), "Error\n" "{}".format(ENABLE_PROMPT), regexp=True, ) ] ) send_mock.side_effect = emu.send_line recv_mock.side_effect = emu.receive_all self.assertRaisesRegexp( Exception, "Copy Command failed", self.runner.save, ftp_path, configuration_type, ) emu.check_calls() @patch("cloudshell.cli.session.ssh_session.SSHSession._receive_all") @patch("cloudshell.cli.session.ssh_session.SSHSession.send_line") def test_save_to_device(self, send_mock, recv_mock): self._setUp( { "Backup Location": "", "Backup Type": AristaConfigurationRunner.DEFAULT_FILE_SYSTEM, } ) path = "" configuration_type = "running" emu = CliEmulator( [ Command( r"copy {0} flash:/Arista-{0}-\d+-\d+".format(configuration_type), "Copy complete\n" "{}".format(ENABLE_PROMPT), regexp=True, ) ] ) send_mock.side_effect = emu.send_line recv_mock.side_effect = emu.receive_all self.runner.save(path, configuration_type) emu.check_calls()

32.497537

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6,597

5.145833

0.141369

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0.072874

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0

null

0

1

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null

0

5

5d3122194d423b3cf500448fe1ac778162db1ff1

266

py

Python

pynocle/depgraph/__init__.py

Totti20/pynocle

05f781a932bfb4f78c02f3a8f3c5cf6cf6186356

[ "MIT" ]

null

pynocle/depgraph/__init__.py

Totti20/pynocle

05f781a932bfb4f78c02f3a8f3c5cf6cf6186356

[ "MIT" ]

null

pynocle/depgraph/__init__.py

Totti20/pynocle

05f781a932bfb4f78c02f3a8f3c5cf6cf6186356

[ "MIT" ]

null

#!/usr/bin/env python from _doc import about_coupling, about_rank from depbuilder import DepBuilder, DependencyGroup from formatting import RankGoogleChartFormatter, CouplingGoogleChartFormatter from rendering import IRenderer, DefaultRenderer, DefaultStyler

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null

0

1

0

1

0

1

0

5

53cfec3d88179e004a687c0a56aec1569941e444

6,862

py

Python

test/swig/Ceil.py

AyishaR/deepC

1dc9707ef5ca9000fc13c3da7f1129685a83b494

[ "Apache-2.0" ]

null

test/swig/Ceil.py

AyishaR/deepC

1dc9707ef5ca9000fc13c3da7f1129685a83b494

[ "Apache-2.0" ]

null

test/swig/Ceil.py

AyishaR/deepC

1dc9707ef5ca9000fc13c3da7f1129685a83b494

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License") you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=invalid-name, unused-argument # # This file is part of DNN compiler maintained at # https://github.com/ai-techsystems/dnnCompiler import common import deepC.dnnc as dc import numpy as np import unittest class CeilTest(unittest.TestCase): def setUp(self): self.len = 48 self.np_float_a = np.random.randn(self.len).astype(np.float32) self.dc_float_a = dc.array(list(self.np_float_a)) self.np_double_a = np.random.randn(self.len).astype(np.float64) self.dc_double_a = dc.array(list(self.np_double_a)) def test_Ceil1D_float (self): npr = np.ceil(self.np_float_a) dcr = dc.ceil(self.dc_float_a) np.testing.assert_allclose(npr, np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3) def test_Ceil1D_double (self): npr = np.ceil(self.np_double_a) dcr = dc.ceil(self.dc_double_a) np.testing.assert_allclose(npr, np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3) def test_Ceil2D_float_1 (self): np_float_a = np.reshape(self.np_float_a, (3,16)) dc_float_a = dc.reshape(self.dc_float_a, (3,16)) npr = np.ceil(np_float_a) dcr = dc.ceil(dc_float_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3) def test_Ceil2D_float_2 (self): np_float_a = np.reshape(self.np_float_a, (6,8)) dc_float_a = dc.reshape(self.dc_float_a, (6,8)) npr = np.ceil(np_float_a) dcr = dc.ceil(dc_float_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3) def test_Ceil2D_float_3 (self): np_float_a = np.reshape(self.np_float_a, (12,4)) dc_float_a = dc.reshape(self.dc_float_a, (12,4)) npr = np.ceil(np_float_a) dcr = dc.ceil(dc_float_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3) def test_Ceil2D_double_1 (self): np_double_a = np.reshape(self.np_double_a, (3,16)) dc_double_a = dc.reshape(self.dc_double_a, (3,16)) npr = np.ceil(np_double_a) dcr = dc.ceil(dc_double_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3) def test_Ceil2D_double_2 (self): np_double_a = np.reshape(self.np_double_a, (6,8)) dc_double_a = dc.reshape(self.dc_double_a, (6,8)) npr = np.ceil(np_double_a) dcr = dc.ceil(dc_double_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3) def test_Ceil2D_double_3 (self): np_double_a = np.reshape(self.np_double_a, (12,4)) dc_double_a = dc.reshape(self.dc_double_a, (12,4)) npr = np.ceil(np_double_a) dcr = dc.ceil(dc_double_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3) def test_Ceil3D_float_1 (self): np_float_a = np.reshape(self.np_float_a, (4,4,3)) dc_float_a = dc.reshape(self.dc_float_a, (4,4,3)) npr = np.ceil(np_float_a) dcr = dc.ceil(dc_float_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3) def test_Ceil3D_float_2 (self): np_float_a = np.reshape(self.np_float_a, (8,2,3)) dc_float_a = dc.reshape(self.dc_float_a, (8,2,3)) npr = np.ceil(np_float_a) dcr = dc.ceil(dc_float_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3) def test_Ceil3D_float_3 (self): np_float_a = np.reshape(self.np_float_a, (2,4,6)) dc_float_a = dc.reshape(self.dc_float_a, (2,4,6)) npr = np.ceil(np_float_a) dcr = dc.ceil(dc_float_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3) def test_Ceil3D_double_1 (self): np_double_a = np.reshape(self.np_double_a, (4,4,3)) dc_double_a = dc.reshape(self.dc_double_a, (4,4,3)) npr = np.ceil(np_double_a) dcr = dc.ceil(dc_double_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3) def test_Ceil3D_double_2 (self): np_double_a = np.reshape(self.np_double_a, (8,2,3)) dc_double_a = dc.reshape(self.dc_double_a, (8,2,3)) npr = np.ceil(np_double_a) dcr = dc.ceil(dc_double_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3) def test_Ceil3D_double_3 (self): np_double_a = np.reshape(self.np_double_a, (2,4,6)) dc_double_a = dc.reshape(self.dc_double_a, (2,4,6)) npr = np.ceil(np_double_a) dcr = dc.ceil(dc_double_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3) def test_Ceil4D_float (self): np_float_a = np.reshape(self.np_float_a, (4,2,2,3)) dc_float_a = dc.reshape(self.dc_float_a, (4,2,2,3)) npr = np.ceil(np_float_a) dcr = dc.ceil(dc_float_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float32), rtol=1e-3, atol=1e-3) def test_Ceil4D_double (self): np_double_a = np.reshape(self.np_double_a, (4,2,2,3)) dc_double_a = dc.reshape(self.dc_double_a, (4,2,2,3)) npr = np.ceil(np_double_a) dcr = dc.ceil(dc_double_a) np.testing.assert_allclose(npr.flatten(), np.array(dcr.data()).astype(np.float64), rtol=1e-3, atol=1e-3) def tearDown(self): return "test finished" if __name__ == '__main__': unittest.main()

40.845238

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0.049419

0.759932

0.751938

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0.720203

0.700097

0.638566

0

0.04087

0.222676

6,862

167

91

41.08982

0.733033

0.129846

0

0.471545

0

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0

0.130081

1

0.146341

false

0

0.03252

0.00813

0.195122

0

null

0

1

0

null

0

5

53eec8cc11efb5d6965b6da361d30f6823aadc64

143

py

Python

exercicios/exercicio113.py

Helton-Rubens/Python-3

eb6d5ee71bcb2a2a80de4eaea942bd0c41d846b7

[ "MIT" ]

null

exercicios/exercicio113.py

Helton-Rubens/Python-3

eb6d5ee71bcb2a2a80de4eaea942bd0c41d846b7

[ "MIT" ]

null

exercicios/exercicio113.py

Helton-Rubens/Python-3

eb6d5ee71bcb2a2a80de4eaea942bd0c41d846b7

[ "MIT" ]

null

from ex112.UtilidadesdeDev import moeda from ex112.UtilidadesdeDev import dado p = dado.leiaValor('Digite um preço: R$') moeda.resumo(p, 5, 2)

28.6

41

0.776224

22

143

5.045455

0.681818

0.162162

0.432432

0.540541

0

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0.118881

143

4

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null

0

1

0

1

0

null

0

1

0

5

54e7294726ab7cfe2d663d202a30caec78f84f36

49,294

py

Python

packages/python/plotly/plotly/graph_objs/_image.py

adehad/plotly.py

bca292530c400c61e8b7f8a6571262a9dde43ee3

[ "MIT" ]

null

packages/python/plotly/plotly/graph_objs/_image.py

adehad/plotly.py

bca292530c400c61e8b7f8a6571262a9dde43ee3

[ "MIT" ]

null

packages/python/plotly/plotly/graph_objs/_image.py

adehad/plotly.py

bca292530c400c61e8b7f8a6571262a9dde43ee3

[ "MIT" ]

null

from plotly.basedatatypes import BaseTraceType as _BaseTraceType import copy as _copy class Image(_BaseTraceType): # class properties # -------------------- _parent_path_str = "" _path_str = "image" _valid_props = { "colormodel", "customdata", "customdatasrc", "dx", "dy", "hoverinfo", "hoverinfosrc", "hoverlabel", "hovertemplate", "hovertemplatesrc", "hovertext", "hovertextsrc", "ids", "idssrc", "meta", "metasrc", "name", "opacity", "source", "stream", "text", "textsrc", "type", "uid", "uirevision", "visible", "x0", "xaxis", "y0", "yaxis", "z", "zmax", "zmin", "zsmooth", "zsrc", } # colormodel # ---------- @property def colormodel(self): """ Color model used to map the numerical color components described in `z` into colors. If `source` is specified, this attribute will be set to `rgba256` otherwise it defaults to `rgb`. The 'colormodel' property is an enumeration that may be specified as: - One of the following enumeration values: ['rgb', 'rgba', 'rgba256', 'hsl', 'hsla'] Returns ------- Any """ return self["colormodel"] @colormodel.setter def colormodel(self, val): self["colormodel"] = val # customdata # ---------- @property def customdata(self): """ Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements The 'customdata' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["customdata"] @customdata.setter def customdata(self, val): self["customdata"] = val # customdatasrc # ------------- @property def customdatasrc(self): """ Sets the source reference on Chart Studio Cloud for customdata . The 'customdatasrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["customdatasrc"] @customdatasrc.setter def customdatasrc(self, val): self["customdatasrc"] = val # dx # -- @property def dx(self): """ Set the pixel's horizontal size. The 'dx' property is a number and may be specified as: - An int or float Returns ------- int|float """ return self["dx"] @dx.setter def dx(self, val): self["dx"] = val # dy # -- @property def dy(self): """ Set the pixel's vertical size The 'dy' property is a number and may be specified as: - An int or float Returns ------- int|float """ return self["dy"] @dy.setter def dy(self, val): self["dy"] = val # hoverinfo # --------- @property def hoverinfo(self): """ Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. The 'hoverinfo' property is a flaglist and may be specified as a string containing: - Any combination of ['x', 'y', 'z', 'color', 'name', 'text'] joined with '+' characters (e.g. 'x+y') OR exactly one of ['all', 'none', 'skip'] (e.g. 'skip') - A list or array of the above Returns ------- Any|numpy.ndarray """ return self["hoverinfo"] @hoverinfo.setter def hoverinfo(self, val): self["hoverinfo"] = val # hoverinfosrc # ------------ @property def hoverinfosrc(self): """ Sets the source reference on Chart Studio Cloud for hoverinfo . The 'hoverinfosrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["hoverinfosrc"] @hoverinfosrc.setter def hoverinfosrc(self, val): self["hoverinfosrc"] = val # hoverlabel # ---------- @property def hoverlabel(self): """ The 'hoverlabel' property is an instance of Hoverlabel that may be specified as: - An instance of :class:`plotly.graph_objs.image.Hoverlabel` - A dict of string/value properties that will be passed to the Hoverlabel constructor Supported dict properties: align Sets the horizontal alignment of the text content within hover label box. Has an effect only if the hover label text spans more two or more lines alignsrc Sets the source reference on Chart Studio Cloud for align . bgcolor Sets the background color of the hover labels for this trace bgcolorsrc Sets the source reference on Chart Studio Cloud for bgcolor . bordercolor Sets the border color of the hover labels for this trace. bordercolorsrc Sets the source reference on Chart Studio Cloud for bordercolor . font Sets the font used in hover labels. namelength Sets the default length (in number of characters) of the trace name in the hover labels for all traces. -1 shows the whole name regardless of length. 0-3 shows the first 0-3 characters, and an integer >3 will show the whole name if it is less than that many characters, but if it is longer, will truncate to `namelength - 3` characters and add an ellipsis. namelengthsrc Sets the source reference on Chart Studio Cloud for namelength . Returns ------- plotly.graph_objs.image.Hoverlabel """ return self["hoverlabel"] @hoverlabel.setter def hoverlabel(self, val): self["hoverlabel"] = val # hovertemplate # ------------- @property def hovertemplate(self): """ Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}" as well as %{xother}, {%_xother}, {%_xother_}, {%xother_}. When showing info for several points, "xother" will be added to those with different x positions from the first point. An underscore before or after "(x|y)other" will add a space on that side, only when this field is shown. Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format for details on the formatting syntax. Dates are formatted using d3-time- format's syntax %{variable|d3-time-format}, for example "Day: %{2019-01-01|%A}". https://github.com/d3/d3-time- format#locale_format for details on the date formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plotly.com/javascript/plotlyjs-events/#event-data. Additionally, every attributes that can be specified per-point (the ones that are `arrayOk: true`) are available. variables `z`, `color` and `colormodel`. Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". To hide the secondary box completely, use an empty tag `<extra></extra>`. The 'hovertemplate' property is a string and must be specified as: - A string - A number that will be converted to a string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self["hovertemplate"] @hovertemplate.setter def hovertemplate(self, val): self["hovertemplate"] = val # hovertemplatesrc # ---------------- @property def hovertemplatesrc(self): """ Sets the source reference on Chart Studio Cloud for hovertemplate . The 'hovertemplatesrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["hovertemplatesrc"] @hovertemplatesrc.setter def hovertemplatesrc(self, val): self["hovertemplatesrc"] = val # hovertext # --------- @property def hovertext(self): """ Same as `text`. The 'hovertext' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["hovertext"] @hovertext.setter def hovertext(self, val): self["hovertext"] = val # hovertextsrc # ------------ @property def hovertextsrc(self): """ Sets the source reference on Chart Studio Cloud for hovertext . The 'hovertextsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["hovertextsrc"] @hovertextsrc.setter def hovertextsrc(self, val): self["hovertextsrc"] = val # ids # --- @property def ids(self): """ Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. The 'ids' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["ids"] @ids.setter def ids(self, val): self["ids"] = val # idssrc # ------ @property def idssrc(self): """ Sets the source reference on Chart Studio Cloud for ids . The 'idssrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["idssrc"] @idssrc.setter def idssrc(self, val): self["idssrc"] = val # meta # ---- @property def meta(self): """ Assigns extra meta information associated with this trace that can be used in various text attributes. Attributes such as trace `name`, graph, axis and colorbar `title.text`, annotation `text` `rangeselector`, `updatemenues` and `sliders` `label` text all support `meta`. To access the trace `meta` values in an attribute in the same trace, simply use `%{meta[i]}` where `i` is the index or key of the `meta` item in question. To access trace `meta` in layout attributes, use `%{data[n[.meta[i]}` where `i` is the index or key of the `meta` and `n` is the trace index. The 'meta' property accepts values of any type Returns ------- Any|numpy.ndarray """ return self["meta"] @meta.setter def meta(self, val): self["meta"] = val # metasrc # ------- @property def metasrc(self): """ Sets the source reference on Chart Studio Cloud for meta . The 'metasrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["metasrc"] @metasrc.setter def metasrc(self, val): self["metasrc"] = val # name # ---- @property def name(self): """ Sets the trace name. The trace name appear as the legend item and on hover. The 'name' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["name"] @name.setter def name(self, val): self["name"] = val # opacity # ------- @property def opacity(self): """ Sets the opacity of the trace. The 'opacity' property is a number and may be specified as: - An int or float in the interval [0, 1] Returns ------- int|float """ return self["opacity"] @opacity.setter def opacity(self, val): self["opacity"] = val # source # ------ @property def source(self): """ Specifies the data URI of the image to be visualized. The URI consists of "data:image/[<media subtype>][;base64],<data>" The 'source' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["source"] @source.setter def source(self, val): self["source"] = val # stream # ------ @property def stream(self): """ The 'stream' property is an instance of Stream that may be specified as: - An instance of :class:`plotly.graph_objs.image.Stream` - A dict of string/value properties that will be passed to the Stream constructor Supported dict properties: maxpoints Sets the maximum number of points to keep on the plots from an incoming stream. If `maxpoints` is set to 50, only the newest 50 points will be displayed on the plot. token The stream id number links a data trace on a plot with a stream. See https://chart- studio.plotly.com/settings for more details. Returns ------- plotly.graph_objs.image.Stream """ return self["stream"] @stream.setter def stream(self, val): self["stream"] = val # text # ---- @property def text(self): """ Sets the text elements associated with each z value. The 'text' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["text"] @text.setter def text(self, val): self["text"] = val # textsrc # ------- @property def textsrc(self): """ Sets the source reference on Chart Studio Cloud for text . The 'textsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["textsrc"] @textsrc.setter def textsrc(self, val): self["textsrc"] = val # uid # --- @property def uid(self): """ Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. The 'uid' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["uid"] @uid.setter def uid(self, val): self["uid"] = val # uirevision # ---------- @property def uirevision(self): """ Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. The 'uirevision' property accepts values of any type Returns ------- Any """ return self["uirevision"] @uirevision.setter def uirevision(self, val): self["uirevision"] = val # visible # ------- @property def visible(self): """ Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). The 'visible' property is an enumeration that may be specified as: - One of the following enumeration values: [True, False, 'legendonly'] Returns ------- Any """ return self["visible"] @visible.setter def visible(self, val): self["visible"] = val # x0 # -- @property def x0(self): """ Set the image's x position. The 'x0' property accepts values of any type Returns ------- Any """ return self["x0"] @x0.setter def x0(self, val): self["x0"] = val # xaxis # ----- @property def xaxis(self): """ Sets a reference between this trace's x coordinates and a 2D cartesian x axis. If "x" (the default value), the x coordinates refer to `layout.xaxis`. If "x2", the x coordinates refer to `layout.xaxis2`, and so on. The 'xaxis' property is an identifier of a particular subplot, of type 'x', that may be specified as the string 'x' optionally followed by an integer >= 1 (e.g. 'x', 'x1', 'x2', 'x3', etc.) Returns ------- str """ return self["xaxis"] @xaxis.setter def xaxis(self, val): self["xaxis"] = val # y0 # -- @property def y0(self): """ Set the image's y position. The 'y0' property accepts values of any type Returns ------- Any """ return self["y0"] @y0.setter def y0(self, val): self["y0"] = val # yaxis # ----- @property def yaxis(self): """ Sets a reference between this trace's y coordinates and a 2D cartesian y axis. If "y" (the default value), the y coordinates refer to `layout.yaxis`. If "y2", the y coordinates refer to `layout.yaxis2`, and so on. The 'yaxis' property is an identifier of a particular subplot, of type 'y', that may be specified as the string 'y' optionally followed by an integer >= 1 (e.g. 'y', 'y1', 'y2', 'y3', etc.) Returns ------- str """ return self["yaxis"] @yaxis.setter def yaxis(self, val): self["yaxis"] = val # z # - @property def z(self): """ A 2-dimensional array in which each element is an array of 3 or 4 numbers representing a color. The 'z' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["z"] @z.setter def z(self, val): self["z"] = val # zmax # ---- @property def zmax(self): """ Array defining the higher bound for each color component. Note that the default value will depend on the colormodel. For the `rgb` colormodel, it is [255, 255, 255]. For the `rgba` colormodel, it is [255, 255, 255, 1]. For the `rgba256` colormodel, it is [255, 255, 255, 255]. For the `hsl` colormodel, it is [360, 100, 100]. For the `hsla` colormodel, it is [360, 100, 100, 1]. The 'zmax' property is an info array that may be specified as: * a list or tuple of 4 elements where: (0) The 'zmax[0]' property is a number and may be specified as: - An int or float (1) The 'zmax[1]' property is a number and may be specified as: - An int or float (2) The 'zmax[2]' property is a number and may be specified as: - An int or float (3) The 'zmax[3]' property is a number and may be specified as: - An int or float Returns ------- list """ return self["zmax"] @zmax.setter def zmax(self, val): self["zmax"] = val # zmin # ---- @property def zmin(self): """ Array defining the lower bound for each color component. Note that the default value will depend on the colormodel. For the `rgb` colormodel, it is [0, 0, 0]. For the `rgba` colormodel, it is [0, 0, 0, 0]. For the `rgba256` colormodel, it is [0, 0, 0, 0]. For the `hsl` colormodel, it is [0, 0, 0]. For the `hsla` colormodel, it is [0, 0, 0, 0]. The 'zmin' property is an info array that may be specified as: * a list or tuple of 4 elements where: (0) The 'zmin[0]' property is a number and may be specified as: - An int or float (1) The 'zmin[1]' property is a number and may be specified as: - An int or float (2) The 'zmin[2]' property is a number and may be specified as: - An int or float (3) The 'zmin[3]' property is a number and may be specified as: - An int or float Returns ------- list """ return self["zmin"] @zmin.setter def zmin(self, val): self["zmin"] = val # zsmooth # ------- @property def zsmooth(self): """ Picks a smoothing algorithm used to smooth `z` data. This only applies for image traces that use the `source` attribute. The 'zsmooth' property is an enumeration that may be specified as: - One of the following enumeration values: ['fast', False] Returns ------- Any """ return self["zsmooth"] @zsmooth.setter def zsmooth(self, val): self["zsmooth"] = val # zsrc # ---- @property def zsrc(self): """ Sets the source reference on Chart Studio Cloud for z . The 'zsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["zsrc"] @zsrc.setter def zsrc(self, val): self["zsrc"] = val # type # ---- @property def type(self): return self._props["type"] # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ colormodel Color model used to map the numerical color components described in `z` into colors. If `source` is specified, this attribute will be set to `rgba256` otherwise it defaults to `rgb`. customdata Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements customdatasrc Sets the source reference on Chart Studio Cloud for customdata . dx Set the pixel's horizontal size. dy Set the pixel's vertical size hoverinfo Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. hoverinfosrc Sets the source reference on Chart Studio Cloud for hoverinfo . hoverlabel :class:`plotly.graph_objects.image.Hoverlabel` instance or dict with compatible properties hovertemplate Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}" as well as %{xother}, {%_xother}, {%_xother_}, {%xother_}. When showing info for several points, "xother" will be added to those with different x positions from the first point. An underscore before or after "(x|y)other" will add a space on that side, only when this field is shown. Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format for details on the formatting syntax. Dates are formatted using d3-time-format's syntax %{variable|d3-time- format}, for example "Day: %{2019-01-01|%A}". https://github.com/d3/d3-time-format#locale_format for details on the date formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plotly.com/javascript/plotlyjs-events/#event- data. Additionally, every attributes that can be specified per-point (the ones that are `arrayOk: true`) are available. variables `z`, `color` and `colormodel`. Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". To hide the secondary box completely, use an empty tag `<extra></extra>`. hovertemplatesrc Sets the source reference on Chart Studio Cloud for hovertemplate . hovertext Same as `text`. hovertextsrc Sets the source reference on Chart Studio Cloud for hovertext . ids Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. idssrc Sets the source reference on Chart Studio Cloud for ids . meta Assigns extra meta information associated with this trace that can be used in various text attributes. Attributes such as trace `name`, graph, axis and colorbar `title.text`, annotation `text` `rangeselector`, `updatemenues` and `sliders` `label` text all support `meta`. To access the trace `meta` values in an attribute in the same trace, simply use `%{meta[i]}` where `i` is the index or key of the `meta` item in question. To access trace `meta` in layout attributes, use `%{data[n[.meta[i]}` where `i` is the index or key of the `meta` and `n` is the trace index. metasrc Sets the source reference on Chart Studio Cloud for meta . name Sets the trace name. The trace name appear as the legend item and on hover. opacity Sets the opacity of the trace. source Specifies the data URI of the image to be visualized. The URI consists of "data:image/[<media subtype>][;base64],<data>" stream :class:`plotly.graph_objects.image.Stream` instance or dict with compatible properties text Sets the text elements associated with each z value. textsrc Sets the source reference on Chart Studio Cloud for text . uid Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. uirevision Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. visible Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). x0 Set the image's x position. xaxis Sets a reference between this trace's x coordinates and a 2D cartesian x axis. If "x" (the default value), the x coordinates refer to `layout.xaxis`. If "x2", the x coordinates refer to `layout.xaxis2`, and so on. y0 Set the image's y position. yaxis Sets a reference between this trace's y coordinates and a 2D cartesian y axis. If "y" (the default value), the y coordinates refer to `layout.yaxis`. If "y2", the y coordinates refer to `layout.yaxis2`, and so on. z A 2-dimensional array in which each element is an array of 3 or 4 numbers representing a color. zmax Array defining the higher bound for each color component. Note that the default value will depend on the colormodel. For the `rgb` colormodel, it is [255, 255, 255]. For the `rgba` colormodel, it is [255, 255, 255, 1]. For the `rgba256` colormodel, it is [255, 255, 255, 255]. For the `hsl` colormodel, it is [360, 100, 100]. For the `hsla` colormodel, it is [360, 100, 100, 1]. zmin Array defining the lower bound for each color component. Note that the default value will depend on the colormodel. For the `rgb` colormodel, it is [0, 0, 0]. For the `rgba` colormodel, it is [0, 0, 0, 0]. For the `rgba256` colormodel, it is [0, 0, 0, 0]. For the `hsl` colormodel, it is [0, 0, 0]. For the `hsla` colormodel, it is [0, 0, 0, 0]. zsmooth Picks a smoothing algorithm used to smooth `z` data. This only applies for image traces that use the `source` attribute. zsrc Sets the source reference on Chart Studio Cloud for z . """ def __init__( self, arg=None, colormodel=None, customdata=None, customdatasrc=None, dx=None, dy=None, hoverinfo=None, hoverinfosrc=None, hoverlabel=None, hovertemplate=None, hovertemplatesrc=None, hovertext=None, hovertextsrc=None, ids=None, idssrc=None, meta=None, metasrc=None, name=None, opacity=None, source=None, stream=None, text=None, textsrc=None, uid=None, uirevision=None, visible=None, x0=None, xaxis=None, y0=None, yaxis=None, z=None, zmax=None, zmin=None, zsmooth=None, zsrc=None, **kwargs ): """ Construct a new Image object Display an image, i.e. data on a 2D regular raster. By default, when an image is displayed in a subplot, its y axis will be reversed (ie. `autorange: 'reversed'`), constrained to the domain (ie. `constrain: 'domain'`) and it will have the same scale as its x axis (ie. `scaleanchor: 'x,`) in order for pixels to be rendered as squares. Parameters ---------- arg dict of properties compatible with this constructor or an instance of :class:`plotly.graph_objs.Image` colormodel Color model used to map the numerical color components described in `z` into colors. If `source` is specified, this attribute will be set to `rgba256` otherwise it defaults to `rgb`. customdata Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements customdatasrc Sets the source reference on Chart Studio Cloud for customdata . dx Set the pixel's horizontal size. dy Set the pixel's vertical size hoverinfo Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. hoverinfosrc Sets the source reference on Chart Studio Cloud for hoverinfo . hoverlabel :class:`plotly.graph_objects.image.Hoverlabel` instance or dict with compatible properties hovertemplate Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}" as well as %{xother}, {%_xother}, {%_xother_}, {%xother_}. When showing info for several points, "xother" will be added to those with different x positions from the first point. An underscore before or after "(x|y)other" will add a space on that side, only when this field is shown. Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format for details on the formatting syntax. Dates are formatted using d3-time-format's syntax %{variable|d3-time- format}, for example "Day: %{2019-01-01|%A}". https://github.com/d3/d3-time-format#locale_format for details on the date formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plotly.com/javascript/plotlyjs-events/#event- data. Additionally, every attributes that can be specified per-point (the ones that are `arrayOk: true`) are available. variables `z`, `color` and `colormodel`. Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". To hide the secondary box completely, use an empty tag `<extra></extra>`. hovertemplatesrc Sets the source reference on Chart Studio Cloud for hovertemplate . hovertext Same as `text`. hovertextsrc Sets the source reference on Chart Studio Cloud for hovertext . ids Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. idssrc Sets the source reference on Chart Studio Cloud for ids . meta Assigns extra meta information associated with this trace that can be used in various text attributes. Attributes such as trace `name`, graph, axis and colorbar `title.text`, annotation `text` `rangeselector`, `updatemenues` and `sliders` `label` text all support `meta`. To access the trace `meta` values in an attribute in the same trace, simply use `%{meta[i]}` where `i` is the index or key of the `meta` item in question. To access trace `meta` in layout attributes, use `%{data[n[.meta[i]}` where `i` is the index or key of the `meta` and `n` is the trace index. metasrc Sets the source reference on Chart Studio Cloud for meta . name Sets the trace name. The trace name appear as the legend item and on hover. opacity Sets the opacity of the trace. source Specifies the data URI of the image to be visualized. The URI consists of "data:image/[<media subtype>][;base64],<data>" stream :class:`plotly.graph_objects.image.Stream` instance or dict with compatible properties text Sets the text elements associated with each z value. textsrc Sets the source reference on Chart Studio Cloud for text . uid Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. uirevision Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. visible Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). x0 Set the image's x position. xaxis Sets a reference between this trace's x coordinates and a 2D cartesian x axis. If "x" (the default value), the x coordinates refer to `layout.xaxis`. If "x2", the x coordinates refer to `layout.xaxis2`, and so on. y0 Set the image's y position. yaxis Sets a reference between this trace's y coordinates and a 2D cartesian y axis. If "y" (the default value), the y coordinates refer to `layout.yaxis`. If "y2", the y coordinates refer to `layout.yaxis2`, and so on. z A 2-dimensional array in which each element is an array of 3 or 4 numbers representing a color. zmax Array defining the higher bound for each color component. Note that the default value will depend on the colormodel. For the `rgb` colormodel, it is [255, 255, 255]. For the `rgba` colormodel, it is [255, 255, 255, 1]. For the `rgba256` colormodel, it is [255, 255, 255, 255]. For the `hsl` colormodel, it is [360, 100, 100]. For the `hsla` colormodel, it is [360, 100, 100, 1]. zmin Array defining the lower bound for each color component. Note that the default value will depend on the colormodel. For the `rgb` colormodel, it is [0, 0, 0]. For the `rgba` colormodel, it is [0, 0, 0, 0]. For the `rgba256` colormodel, it is [0, 0, 0, 0]. For the `hsl` colormodel, it is [0, 0, 0]. For the `hsla` colormodel, it is [0, 0, 0, 0]. zsmooth Picks a smoothing algorithm used to smooth `z` data. This only applies for image traces that use the `source` attribute. zsrc Sets the source reference on Chart Studio Cloud for z . Returns ------- Image """ super(Image, self).__init__("image") if "_parent" in kwargs: self._parent = kwargs["_parent"] return # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.Image constructor must be a dict or an instance of :class:`plotly.graph_objs.Image`""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) self._validate = kwargs.pop("_validate", True) # Populate data dict with properties # ---------------------------------- _v = arg.pop("colormodel", None) _v = colormodel if colormodel is not None else _v if _v is not None: self["colormodel"] = _v _v = arg.pop("customdata", None) _v = customdata if customdata is not None else _v if _v is not None: self["customdata"] = _v _v = arg.pop("customdatasrc", None) _v = customdatasrc if customdatasrc is not None else _v if _v is not None: self["customdatasrc"] = _v _v = arg.pop("dx", None) _v = dx if dx is not None else _v if _v is not None: self["dx"] = _v _v = arg.pop("dy", None) _v = dy if dy is not None else _v if _v is not None: self["dy"] = _v _v = arg.pop("hoverinfo", None) _v = hoverinfo if hoverinfo is not None else _v if _v is not None: self["hoverinfo"] = _v _v = arg.pop("hoverinfosrc", None) _v = hoverinfosrc if hoverinfosrc is not None else _v if _v is not None: self["hoverinfosrc"] = _v _v = arg.pop("hoverlabel", None) _v = hoverlabel if hoverlabel is not None else _v if _v is not None: self["hoverlabel"] = _v _v = arg.pop("hovertemplate", None) _v = hovertemplate if hovertemplate is not None else _v if _v is not None: self["hovertemplate"] = _v _v = arg.pop("hovertemplatesrc", None) _v = hovertemplatesrc if hovertemplatesrc is not None else _v if _v is not None: self["hovertemplatesrc"] = _v _v = arg.pop("hovertext", None) _v = hovertext if hovertext is not None else _v if _v is not None: self["hovertext"] = _v _v = arg.pop("hovertextsrc", None) _v = hovertextsrc if hovertextsrc is not None else _v if _v is not None: self["hovertextsrc"] = _v _v = arg.pop("ids", None) _v = ids if ids is not None else _v if _v is not None: self["ids"] = _v _v = arg.pop("idssrc", None) _v = idssrc if idssrc is not None else _v if _v is not None: self["idssrc"] = _v _v = arg.pop("meta", None) _v = meta if meta is not None else _v if _v is not None: self["meta"] = _v _v = arg.pop("metasrc", None) _v = metasrc if metasrc is not None else _v if _v is not None: self["metasrc"] = _v _v = arg.pop("name", None) _v = name if name is not None else _v if _v is not None: self["name"] = _v _v = arg.pop("opacity", None) _v = opacity if opacity is not None else _v if _v is not None: self["opacity"] = _v _v = arg.pop("source", None) _v = source if source is not None else _v if _v is not None: self["source"] = _v _v = arg.pop("stream", None) _v = stream if stream is not None else _v if _v is not None: self["stream"] = _v _v = arg.pop("text", None) _v = text if text is not None else _v if _v is not None: self["text"] = _v _v = arg.pop("textsrc", None) _v = textsrc if textsrc is not None else _v if _v is not None: self["textsrc"] = _v _v = arg.pop("uid", None) _v = uid if uid is not None else _v if _v is not None: self["uid"] = _v _v = arg.pop("uirevision", None) _v = uirevision if uirevision is not None else _v if _v is not None: self["uirevision"] = _v _v = arg.pop("visible", None) _v = visible if visible is not None else _v if _v is not None: self["visible"] = _v _v = arg.pop("x0", None) _v = x0 if x0 is not None else _v if _v is not None: self["x0"] = _v _v = arg.pop("xaxis", None) _v = xaxis if xaxis is not None else _v if _v is not None: self["xaxis"] = _v _v = arg.pop("y0", None) _v = y0 if y0 is not None else _v if _v is not None: self["y0"] = _v _v = arg.pop("yaxis", None) _v = yaxis if yaxis is not None else _v if _v is not None: self["yaxis"] = _v _v = arg.pop("z", None) _v = z if z is not None else _v if _v is not None: self["z"] = _v _v = arg.pop("zmax", None) _v = zmax if zmax is not None else _v if _v is not None: self["zmax"] = _v _v = arg.pop("zmin", None) _v = zmin if zmin is not None else _v if _v is not None: self["zmin"] = _v _v = arg.pop("zsmooth", None) _v = zsmooth if zsmooth is not None else _v if _v is not None: self["zsmooth"] = _v _v = arg.pop("zsrc", None) _v = zsrc if zsrc is not None else _v if _v is not None: self["zsrc"] = _v # Read-only literals # ------------------ self._props["type"] = "image" arg.pop("type", None) # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False

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0

null

0

1

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null

0

5

54ef94ee07bd7ed468f1f20cd0a8784070138fe9

35

py

Python

main.py

istommao/toolbox-api

1d7186d9668bcd396346d4154b7ff6ae00b0f59b

[ "MIT" ]

null

main.py

istommao/toolbox-api

1d7186d9668bcd396346d4154b7ff6ae00b0f59b

[ "MIT" ]

null

main.py

istommao/toolbox-api

1d7186d9668bcd396346d4154b7ff6ae00b0f59b

[ "MIT" ]

null

from src.app import APP app = APP

8.75

23

0.714286

7

35

3.571429

0.571429

0.48

0

0.228571

35

3

24

11.666667

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0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

54f6853a0e415fbedcf4255398607e5a767d1482

244

py

Python

vendor-local/lib/python/celery/loaders/app.py

Mozilla-GitHub-Standards/6f0d85288b5b0ef8beecb60345173dc14c98e40f48e1307a444ab1e08231e695

bf6a382913901ad193d907f022086931df0de8c4

[ "BSD-3-Clause" ]

1

2015-07-13T03:29:04.000Z

vendor-local/lib/python/celery/loaders/app.py

Mozilla-GitHub-Standards/6f0d85288b5b0ef8beecb60345173dc14c98e40f48e1307a444ab1e08231e695

bf6a382913901ad193d907f022086931df0de8c4

[ "BSD-3-Clause" ]

2

2015-03-03T23:02:19.000Z

2019-03-30T04:45:51.000Z

vendor-local/lib/python/celery/loaders/app.py

Mozilla-GitHub-Standards/6f0d85288b5b0ef8beecb60345173dc14c98e40f48e1307a444ab1e08231e695

bf6a382913901ad193d907f022086931df0de8c4

[ "BSD-3-Clause" ]

2

2016-04-15T11:43:05.000Z

2016-04-15T11:43:15.000Z

# -*- coding: utf-8 -*- """ celery.loaders.app ~~~~~~~~~~~~~~~~~~ The default loader used with custom app instances. """ from __future__ import absolute_import from .base import BaseLoader class AppLoader(BaseLoader): pass

15.25

54

0.635246

27

244

5.555556

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0

0.005128

0.20082

244

15

55

16.266667

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null

0

1

0

1

0

5

54f6abc955f7e6fdf7284f3837cf0284e2f71d17

20

py

Python

test/tokenize/t23.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

2,671

2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

test/tokenize/t23.py

csev/skulpt

9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

test/tokenize/t23.py

csev/skulpt

9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

x = u'abc' + U'ABC'

10

19

0.45

5

20

1.8

0.6

0.888889

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0.25

20

1

20

0.6

0

0.3

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false

0

1

null

1

0

1

0

null

0

5

0712314f22dc1dedf01c65d39dbc722b19510a17

190

py

Python

pywiktionary/parsers/__init__.py

alessandrome/pywiktionary

b9378ca1e2dfe704eaa8a044bd82519b12f81226

[ "MIT" ]

4

2019-08-08T21:15:01.000Z

2021-01-14T01:32:18.000Z

pywiktionary/parsers/__init__.py

alessandrome/pywiktionary

b9378ca1e2dfe704eaa8a044bd82519b12f81226

[ "MIT" ]

1

2021-09-02T17:24:12.000Z

pywiktionary/parsers/__init__.py

alessandrome/pywiktionary

b9378ca1e2dfe704eaa8a044bd82519b12f81226

[ "MIT" ]

1

2020-03-19T12:57:45.000Z

from .basic_parser import BasicParser from .english_parser import EnglishParser, SECTION_ID as ENGLISH_SECTION_ID from .italian_parser import ItalianParser, SECTION_ID as ITALIAN_SECTION_ID

47.5

75

0.878947

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190

5.851852

0.444444

0.227848

0.139241

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190

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63.333333

0.918605

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true

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null

0

1

0

1

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5

4acc308f7ced794ea469367cbc389cd63c66dbe5

192

py

Python

backend/api/__init__.py

XoriensLair/XoriensLair.github.io

61675ba296ee747a2a0bd729ec50becb6c903a18

[ "MIT" ]

null

backend/api/__init__.py

XoriensLair/XoriensLair.github.io

61675ba296ee747a2a0bd729ec50becb6c903a18

[ "MIT" ]

null

backend/api/__init__.py

XoriensLair/XoriensLair.github.io

61675ba296ee747a2a0bd729ec50becb6c903a18

[ "MIT" ]

null

from api.accessapi import get from api.dndutil import * from api.pycritter import APIError, api_get_bestiary, api_get_creature from api.pylink import PyLink from api.character import Character

38.4

70

0.848958

30

192

5.3

0.4

0.220126

0

0.109375

192

5

71

38.4

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0

1

0

true

0

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null

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1

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null

0

1

0

1

0

1

0

5

4ad6da871c1344379b1a9116ad308b49431dbbe0

84

py

Python

aiproteomics/frag/models/__init__.py

ai-proteomics/aiproteomics

125aed4b3528bfd40349ef932034d9532ab969c3

[ "Apache-2.0" ]

null

aiproteomics/frag/models/__init__.py

ai-proteomics/aiproteomics

125aed4b3528bfd40349ef932034d9532ab969c3

[ "Apache-2.0" ]

14

2022-03-30T19:49:30.000Z

2022-03-31T11:39:27.000Z

aiproteomics/frag/models/__init__.py

ai-proteomics/aiproteomics

125aed4b3528bfd40349ef932034d9532ab969c3

[ "Apache-2.0" ]

null

from .transformer_frag import * from . import prosit1 from .prosit1_model import *

16.8

31

0.785714

11

84

5.818182

0.545455

0

0.028169

0.154762

84

4

32

21

0.873239

0

1

0

true

0

1

0

1

0

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0

null

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1

0

null

0

1

0

1

0

1

0

5

4ae4eb58cea9a7732bfd549ff5497c462452cf33

87

py

Python

quotetools/__init__.py

entchen66/sinbad3.1

3353118b8693c84d5572ab2a7a2278a32be2a76c

[ "MIT" ]

null

quotetools/__init__.py

entchen66/sinbad3.1

3353118b8693c84d5572ab2a7a2278a32be2a76c

[ "MIT" ]

null

quotetools/__init__.py

entchen66/sinbad3.1

3353118b8693c84d5572ab2a7a2278a32be2a76c

[ "MIT" ]

1

2020-02-29T10:57:21.000Z

from . import quotetools def setup(bot): bot.add_cog(quotetools.QuoteTools(bot))

14.5

43

0.735632

12

87

5.25

0.666667

0

0.149425

87

5

44

17.4

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0

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false

0

0.333333

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0.666667

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null

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null

0

1

0

1

0

1

0

5

4af4d357f0c14ab60bb4e2f65dd8f512e91de2ac

1,677

py

Python

Decoder.py

eym55/mango-client-python

2cb1ce77d785343c24ecba913eaa9693c3db1181

[ "MIT" ]

null

Decoder.py

eym55/mango-client-python

2cb1ce77d785343c24ecba913eaa9693c3db1181

[ "MIT" ]

null

Decoder.py

eym55/mango-client-python

2cb1ce77d785343c24ecba913eaa9693c3db1181

[ "MIT" ]

null

import base64 import base58 import logging import typing from solana.publickey import PublicKey def decode_binary(encoded: typing.List) -> bytes: if isinstance(encoded, str): return base58.b58decode(encoded) elif encoded[1] == "base64": return base64.b64decode(encoded[0]) else: return base58.b58decode(encoded[0]) def encode_binary(decoded: bytes) -> typing.List: return [base64.b64encode(decoded), "base64"] def encode_key(key: PublicKey) -> str: return str(key) if __name__ == "__main__": logging.getLogger().setLevel(logging.INFO) data = decode_binary(['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', 'base64']) print(f"Data length (should be 744): {len(data)}")

62.111111

1,032

0.860465

108

1,677

13.25

0.592593

0.016771

0.02935

0.039133

0

0.108752

0.073345

1,677

27

1,033

62.111111

0.812098

0

0.05

0.630513

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0

1

0

1

0.15

false

0

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1

null

0

1

0

1

null

1

0

5

ab1a13db2a9437b692b17ebed60791632b4f6f96

18,784

py

Python

local_epifx/tests/test_seir_forecast.py

ruarai/epifx.covid

be7aecbf9e86c3402f6851ea65f6705cdb59f3cf

[ "BSD-3-Clause" ]

null

local_epifx/tests/test_seir_forecast.py

ruarai/epifx.covid

be7aecbf9e86c3402f6851ea65f6705cdb59f3cf

[ "BSD-3-Clause" ]

null

local_epifx/tests/test_seir_forecast.py

ruarai/epifx.covid

be7aecbf9e86c3402f6851ea65f6705cdb59f3cf

[ "BSD-3-Clause" ]

null

"""Test cases for the SEIR forecasting example.""" import datetime import epifx.cmd.decl_fs as fs import logging import numpy as np import os import pkgutil import pypfilt.config import pypfilt.sweep def two_forecast_dates(all_obs, fs_from): """Select only two forecasting dates, to reduce computation time.""" first_obs = min(obs['date'] for obs in all_obs if obs['date'] >= fs_from) twelve_weeks_later = first_obs + datetime.timedelta(days=12 * 7) return [first_obs, twelve_weeks_later] def two_forecast_times(all_obs, fs_from): """Select only two forecasting times, to reduce computation time.""" first_obs = min(obs['date'] for obs in all_obs if obs['date'] >= fs_from) twelve_weeks_later = first_obs + 12 * 7 return [first_obs, twelve_weeks_later] def simulate_seir_observations(): """Generate synthetic observations from a known model.""" toml_file = 'seir.toml' pr_file = 'pr-obs.ssv' obs_file_dt = 'simulated-weekly-cases-datetime.ssv' obs_file_sc = 'simulated-weekly-cases-scalar.ssv' toml_data = pkgutil.get_data('epifx.example.seir', toml_file).decode() config = pypfilt.config.from_string(toml_data) pr_data = pkgutil.get_data('epifx.example.seir', pr_file).decode() with open(pr_file, mode='w') as f: f.write(pr_data) forecasts = list(pypfilt.sweep.forecasts(config, load_obs=False)) assert len(forecasts) == 1 forecast = forecasts[0] # NOTE: define the fixed ground truth for the model simulation. params = forecast.params params['model']['prior'] = { 'R0': lambda r, size=None: r.uniform(low=1.45, high=1.45, size=size), 'sigma': lambda r, size=None: r.uniform(low=0.25, high=0.25, size=size), 'gamma': lambda r, size=None: r.uniform(low=0.25, high=0.25, size=size), 'eta': lambda r, size=None: r.uniform(low=1.0, high=1.0, size=size), 'alpha': lambda r, size=None: r.uniform(low=0.0, high=0.0, size=size), 't0': lambda r, size=None: r.uniform(low=14.0, high=14.0, size=size), } obs_table = pypfilt.simulate_from_model(params, px_count=1) # Extract weekly observations from the simulated data. def to_date(bs): return datetime.datetime.strptime(bs.decode(), '%Y-%m-%d %H:%M:%S').date() obs_list = [(to_date(row['date']), row['value'].astype(int)) for row in obs_table if to_date(row['date']).isoweekday() == 7] # Save date-indexed observations to disk. dt_obs = [(row[0].strftime('%Y-%m-%d'), row[1]) for row in obs_list] dt_obs = np.array(dt_obs, dtype=[('date', 'O'), ('value', np.int_)]) np.savetxt(obs_file_dt, dt_obs, fmt='%s %d', header='date cases', comments='') # Save day-indexed observations to disk. sc_obs = [(int(row[0].strftime('%-j')), row[1]) for row in obs_list] sc_obs = np.array(sc_obs, dtype=[('day', np.int_), ('value', np.int_)]) np.savetxt(obs_file_sc, sc_obs, fmt='%d %d', header='day cases', comments='') return (obs_list, obs_file_dt, obs_file_sc) def test_simulate(): """ Generate synthetic observations from a known model, and check that the serialised results are consistent. """ (obs_list, obs_file_dt, obs_file_sc) = simulate_seir_observations() peak_size = max(o[1] for o in obs_list) peak_time = [o[0] for o in obs_list if o[1] == peak_size][0] assert peak_size == 2678 assert peak_time == datetime.date(2014, 9, 14) peak_day = int(peak_time.strftime('%-j')) # Check that the date-indexed peak is consistent with the above results. dt_cols = [pypfilt.io.date_column('date'), ('cases', int)] dt_obs = pypfilt.io.read_table(obs_file_dt, dt_cols) dt_mask = dt_obs['cases'] == peak_size assert np.sum(dt_mask) == 1 assert dt_obs['date'][dt_mask].item().date() == peak_time # Check that the day-indexed peak is consistent with the above results. sc_cols = [('day', int), ('cases', int)] sc_obs = pypfilt.io.read_table(obs_file_sc, sc_cols) sc_mask = sc_obs['cases'] == peak_size assert np.sum(sc_mask) == 1 assert sc_obs['day'][sc_mask] == peak_day # Check that the observations are the same. assert np.array_equal(sc_obs['cases'], dt_obs['cases']) # Clean up: remove created files. os.remove(obs_file_dt) os.remove(obs_file_sc) def test_seeiir_forecast(): """ Use the SEEIIR forecasting example to compare peak size and time predictions at two forecasting dates. Note that the observation probability is set to 0.5 (much too high) and so we should only obtain sensible forecasts if the observation model is able to use the lookup table and obtain observation probabilities from the ``pr-obs.ssv`` data file. """ logging.basicConfig(level=logging.INFO) toml_file = 'seeiir.toml' obs_file = 'weekly-cases.ssv' pr_file = 'pr-obs.ssv' toml_data = pkgutil.get_data('epifx.example.seir', toml_file).decode() config = pypfilt.config.from_string(toml_data) obs_data = pkgutil.get_data('epifx.example.seir', obs_file).decode() with open(obs_file, mode='w') as f: f.write(obs_data) pr_data = pkgutil.get_data('epifx.example.seir', pr_file).decode() with open(pr_file, mode='w') as f: f.write(pr_data) forecast_from = datetime.datetime(2014, 4, 1) # Check that there is only one set of forecasts (i.e., only one location # and only one set of observation model parameters). forecasts = pypfilt.sweep.forecasts(config) forecasts = list(forecasts) assert len(forecasts) == 1 # Check that forecasts were run for two forecasting dates. forecast = forecasts[0] forecast_dates = two_forecast_dates(forecast.all_observations, forecast_from) state = fs.run(forecast, forecast_dates) fs_dates = list(state.keys()) assert len(fs_dates) == 2 fs_date_n1 = fs_dates[0] fs_date_n2 = fs_dates[1] # Retrieve the list of observations obs = state[fs_date_n1]['obs'] peak_size = max(o['value'] for o in obs) peak_date = [o['date'] for o in obs if o['value'] == peak_size][0] # Check that the peak size and date is as expected. assert peak_size == 2678 assert peak_date == datetime.datetime(2014, 9, 14) # Compare the forecast predictions to the observed peak size and date. forecast_n1 = state[fs_date_n1][fs_date_n1]['summary'] forecast_n2 = state[fs_date_n2][fs_date_n2]['summary'] dt_format = '%Y-%m-%d %H:%M:%S' # Ensure that all of the expected tables have been created, and that no # other tables have been created. expected_tables = { 'model_cints', 'param_covar', 'pr_epi', 'forecasts', 'obs_llhd', 'peak_size_acc', 'peak_time_acc', 'peak_cints', 'peak_ensemble', 'obs/cases', 'exceed_500', 'exceed_1000', 'expected_obs'} tables_n1 = set(forecast_n1.keys()) tables_n2 = set(forecast_n2.keys()) assert tables_n1 == expected_tables assert tables_n2 == expected_tables # Ensure that no tables are empty. for name in expected_tables: shape_n1 = forecast_n1[name].shape shape_n2 = forecast_n1[name].shape assert len(shape_n1) == 1 assert len(shape_n2) == 1 assert shape_n1[0] > 0 assert shape_n2[0] > 0 # Ensure that the exceed_500 and exceed_1000 tables differ. pr_exc_low_n1 = forecast_n1['exceed_500'][()]['prob'] pr_exc_high_n1 = forecast_n1['exceed_1000'][()]['prob'] assert pr_exc_low_n1.shape == pr_exc_high_n1.shape assert not np.allclose(pr_exc_low_n1, pr_exc_high_n1) # Ensure that the cumulative probability of exceeding 500 cases is greater # than that of exceeding 1000 cases, until they both equal 1.0. cum_pr_low_n1 = np.cumsum(pr_exc_low_n1) cum_pr_high_n1 = np.cumsum(pr_exc_high_n1) mask_lt_1 = np.logical_and(cum_pr_low_n1 < 1.0, cum_pr_high_n1 < 1.0) mask_gt_0 = np.logical_or(cum_pr_low_n1 > 0.0, cum_pr_high_n1 > 0.0) mask = np.logical_and(mask_lt_1, mask_gt_0) assert np.all(cum_pr_low_n1[mask] > cum_pr_high_n1[mask]) # The earlier forecast should include the peak size and time in its 95% # credible intervals. cints_n1 = forecast_n1['peak_cints'] ci_n1 = cints_n1[cints_n1['prob'] == 95] ci_n1_size_lower = ci_n1['sizemin'].item() ci_n1_size_upper = ci_n1['sizemax'].item() ci_n1_date_lower = datetime.datetime.strptime( ci_n1['timemin'].item().decode(), dt_format) ci_n1_date_upper = datetime.datetime.strptime( ci_n1['timemax'].item().decode(), dt_format) assert ci_n1_size_lower <= peak_size <= ci_n1_size_upper assert ci_n1_date_lower <= peak_date <= ci_n1_date_upper # The later forecast will have narrowed, and it should still include the # peak size and time in its 95% credible intervals. cints_n2 = forecast_n2['peak_cints'] ci_n2 = cints_n2[cints_n2['prob'] == 95] ci_n2_size_lower = ci_n2['sizemin'].item() ci_n2_size_upper = ci_n2['sizemax'].item() ci_n2_date_lower = datetime.datetime.strptime( ci_n2['timemin'].item().decode(), dt_format) ci_n2_date_upper = datetime.datetime.strptime( ci_n2['timemax'].item().decode(), dt_format) assert ci_n2_size_lower <= peak_size <= ci_n2_size_upper assert ci_n2_date_lower <= peak_date <= ci_n2_date_upper # The later forecast should have more accurate predictions of peak size. size_acc_n1 = forecast_n1['peak_size_acc']['acc'] size_acc_n2 = forecast_n2['peak_size_acc']['acc'] assert all(size_acc_n1 > 0.3) assert any(size_acc_n1 < 0.7) assert all(size_acc_n2 > 0.7) # The later forecast should have more accurate predictions of peak time. time_acc_n1 = forecast_n1['peak_time_acc']['acc'] time_acc_n2 = forecast_n2['peak_time_acc']['acc'] assert all(time_acc_n1 > 0.1) assert any(time_acc_n1 < 0.3) assert all(time_acc_n2 > 0.7) # Clean up: remove created files. os.remove(obs_file) os.remove(pr_file) os.remove(state[fs_date_n1]['forecast_file']) os.remove(state[fs_date_n2]['forecast_file']) def test_seir_forecast(): """ Use the SEIR forecasting example to compare peak size and time predictions at two forecasting dates. Note that the observation probability is set to 0.5 (much too high) and so we should only obtain sensible forecasts if the observation model is able to use the lookup table and obtain observation probabilities from the ``pr-obs.ssv`` data file. """ logging.basicConfig(level=logging.INFO) toml_file = 'seir.toml' obs_file = 'weekly-cases.ssv' pr_file = 'pr-obs.ssv' toml_data = pkgutil.get_data('epifx.example.seir', toml_file).decode() config = pypfilt.config.from_string(toml_data) obs_data = pkgutil.get_data('epifx.example.seir', obs_file).decode() with open(obs_file, mode='w') as f: f.write(obs_data) pr_data = pkgutil.get_data('epifx.example.seir', pr_file).decode() with open(pr_file, mode='w') as f: f.write(pr_data) forecast_from = datetime.datetime(2014, 4, 1) # Check that there is only one set of forecasts (i.e., only one location # and only one set of observation model parameters). forecasts = pypfilt.sweep.forecasts(config) forecasts = list(forecasts) assert len(forecasts) == 1 # Check that forecasts were run for two forecasting dates. forecast = forecasts[0] forecast_dates = two_forecast_dates(forecast.all_observations, forecast_from) state = fs.run(forecast, forecast_dates) fs_dates = list(state.keys()) assert len(fs_dates) == 2 fs_date_n1 = fs_dates[0] fs_date_n2 = fs_dates[1] # Retrieve the list of observations obs = state[fs_date_n1]['obs'] peak_size = max(o['value'] for o in obs) peak_date = [o['date'] for o in obs if o['value'] == peak_size][0] # Check that the peak size and date is as expected. assert peak_size == 2678 assert peak_date == datetime.datetime(2014, 9, 14) # Compare the forecast predictions to the observed peak size and date. forecast_n1 = state[fs_date_n1][fs_date_n1]['summary'] forecast_n2 = state[fs_date_n2][fs_date_n2]['summary'] dt_format = '%Y-%m-%d %H:%M:%S' # The earlier forecast should include the peak size and time in its 95% # credible intervals. cints_n1 = forecast_n1['peak_cints'] ci_n1 = cints_n1[cints_n1['prob'] == 95] ci_n1_size_lower = ci_n1['sizemin'].item() ci_n1_size_upper = ci_n1['sizemax'].item() ci_n1_date_lower = datetime.datetime.strptime( ci_n1['timemin'].item().decode(), dt_format) ci_n1_date_upper = datetime.datetime.strptime( ci_n1['timemax'].item().decode(), dt_format) assert ci_n1_size_lower <= peak_size <= ci_n1_size_upper assert ci_n1_date_lower <= peak_date <= ci_n1_date_upper # The later forecast will have narrowed, and it should still include the # peak size and time in its 95% credible intervals. cints_n2 = forecast_n2['peak_cints'] ci_n2 = cints_n2[cints_n2['prob'] == 95] ci_n2_size_lower = ci_n2['sizemin'].item() ci_n2_size_upper = ci_n2['sizemax'].item() ci_n2_date_lower = datetime.datetime.strptime( ci_n2['timemin'].item().decode(), dt_format) ci_n2_date_upper = datetime.datetime.strptime( ci_n2['timemax'].item().decode(), dt_format) assert ci_n2_size_lower <= peak_size <= ci_n2_size_upper assert ci_n2_date_lower <= peak_date <= ci_n2_date_upper # The later forecast should have more accurate predictions of peak size. size_acc_n1 = forecast_n1['peak_size_acc']['acc'] size_acc_n2 = forecast_n2['peak_size_acc']['acc'] assert all(size_acc_n1 > 0.3) assert any(size_acc_n1 < 0.7) assert all(size_acc_n2 > 0.7) # The later forecast should have more accurate predictions of peak time. time_acc_n1 = forecast_n1['peak_time_acc']['acc'] time_acc_n2 = forecast_n2['peak_time_acc']['acc'] assert all(time_acc_n1 > 0.1) assert any(time_acc_n1 < 0.3) assert all(time_acc_n2 > 0.7) # Clean up: remove created files. os.remove(obs_file) os.remove(pr_file) os.remove(state[fs_date_n1]['forecast_file']) os.remove(state[fs_date_n2]['forecast_file']) def test_seeiir_scalar_forecast(): """ Use the SEEIIR forecasting example to compare peak size and time predictions at two forecasting dates. Note that the observation probability is set to 0.5 (much too high) and so we should only obtain sensible forecasts if the observation model is able to use the lookup table and obtain observation probabilities from the ``pr-obs.ssv`` data file. """ logging.basicConfig(level=logging.INFO) toml_file = 'seeiir_scalar.toml' obs_file = 'weekly-cases-scalar.ssv' pr_file = 'pr-obs-scalar.ssv' toml_data = pkgutil.get_data('epifx.example.seir', toml_file).decode() config = pypfilt.config.from_string(toml_data) obs_data = pkgutil.get_data('epifx.example.seir', obs_file).decode() with open(obs_file, mode='w') as f: f.write(obs_data) pr_data = pkgutil.get_data('epifx.example.seir', pr_file).decode() with open(pr_file, mode='w') as f: f.write(pr_data) forecast_from = 91 # Check that there is only one set of forecasts (i.e., only one location # and only one set of observation model parameters). forecasts = pypfilt.sweep.forecasts(config) forecasts = list(forecasts) assert len(forecasts) == 1 # Check that forecasts were run for two forecasting dates. forecast = forecasts[0] forecast_dates = two_forecast_times(forecast.all_observations, forecast_from) state = fs.run(forecast, forecast_dates) fs_dates = list(state.keys()) assert len(fs_dates) == 2 fs_date_n1 = fs_dates[0] fs_date_n2 = fs_dates[1] # Retrieve the list of observations obs = state[fs_date_n1]['obs'] peak_size = max(o['value'] for o in obs) peak_date = [o['date'] for o in obs if o['value'] == peak_size][0] # Check that the peak size and date is as expected. assert peak_size == 2678 assert peak_date == 257 # Compare the forecast predictions to the observed peak size and date. forecast_n1 = state[fs_date_n1][fs_date_n1]['summary'] forecast_n2 = state[fs_date_n2][fs_date_n2]['summary'] # The earlier forecast should include the peak size and time in its 95% # credible intervals. cints_n1 = forecast_n1['peak_cints'] ci_n1 = cints_n1[cints_n1['prob'] == 95] ci_n1_size_lower = ci_n1['sizemin'].item() ci_n1_size_upper = ci_n1['sizemax'].item() ci_n1_date_lower = ci_n1['timemin'].item() ci_n1_date_upper = ci_n1['timemax'].item() assert ci_n1_size_lower <= peak_size <= ci_n1_size_upper assert ci_n1_date_lower <= peak_date <= ci_n1_date_upper # The later forecast will have narrowed, and it should still include the # peak size and time in its 95% credible intervals. cints_n2 = forecast_n2['peak_cints'] ci_n2 = cints_n2[cints_n2['prob'] == 95] ci_n2_size_lower = ci_n2['sizemin'].item() ci_n2_size_upper = ci_n2['sizemax'].item() ci_n2_date_lower = ci_n2['timemin'].item() ci_n2_date_upper = ci_n2['timemax'].item() assert ci_n2_size_lower <= peak_size <= ci_n2_size_upper assert ci_n2_date_lower <= peak_date <= ci_n2_date_upper # The later forecast should have more accurate predictions of peak size. size_acc_n1 = forecast_n1['peak_size_acc']['acc'] size_acc_n2 = forecast_n2['peak_size_acc']['acc'] assert all(size_acc_n1 > 0.3) assert any(size_acc_n1 < 0.7) assert all(size_acc_n2 > 0.7) # The later forecast should have more accurate predictions of peak time. time_acc_n1 = forecast_n1['peak_time_acc']['acc'] time_acc_n2 = forecast_n2['peak_time_acc']['acc'] assert all(time_acc_n1 > 0.1) assert any(time_acc_n1 < 0.3) assert all(time_acc_n2 > 0.7) # Clean up: remove created files. os.remove(obs_file) os.remove(pr_file) os.remove(state[fs_date_n1]['forecast_file']) os.remove(state[fs_date_n2]['forecast_file'])

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ab615923f1a3ef1a3d29045702c634df1497f66d

34,607

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Python

oscar_support/migrations/0002_auto__chg_field_message_type.py

snowball-one/django-oscar-support

57d82200f0905e17df683652327e9102b7b34129

[ "BSD-3-Clause" ]

14

2015-01-10T05:06:33.000Z

2021-02-08T03:37:32.000Z

oscar_support/migrations/0002_auto__chg_field_message_type.py

snowball-one/django-oscar-support

57d82200f0905e17df683652327e9102b7b34129

[ "BSD-3-Clause" ]

2

2017-08-25T20:14:41.000Z

2019-02-25T22:08:09.000Z

oscar_support/migrations/0002_auto__chg_field_message_type.py

snowball-one/django-oscar-support

57d82200f0905e17df683652327e9102b7b34129

[ "BSD-3-Clause" ]

8

2015-07-29T21:39:06.000Z

2018-12-06T04:14:56.000Z

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models from oscar.core.compat import AUTH_USER_MODEL, AUTH_USER_MODEL_NAME class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'Message.type' db.alter_column(u'oscar_support_message', 'type', self.gf('django.db.models.fields.CharField')(max_length=30)) def backwards(self, orm): # Changing field 'Message.type' db.alter_column(u'oscar_support_message', 'type', self.gf('django.db.models.fields.CharField')(max_length=3)) models = { u'address.country': { 'Meta': {'ordering': "('-display_order', 'name')", 'object_name': 'Country'}, 'display_order': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0', 'db_index': 'True'}), 'is_shipping_country': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True'}), 'iso_3166_1_a2': ('django.db.models.fields.CharField', [], {'max_length': '2', 'primary_key': 'True'}), 'iso_3166_1_a3': ('django.db.models.fields.CharField', [], {'max_length': '3', 'null': 'True', 'db_index': 'True'}), 'iso_3166_1_numeric': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'db_index': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'printable_name': ('django.db.models.fields.CharField', [], {'max_length': '128'}) }, u'auth.group': { 'Meta': {'object_name': 'Group'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, u'auth.permission': { 'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, AUTH_USER_MODEL: { 'Meta': {'object_name': AUTH_USER_MODEL_NAME}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, u'catalogue.attributeentity': { 'Meta': {'object_name': 'AttributeEntity'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '255', 'blank': 'True'}), 'type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'entities'", 'to': u"orm['catalogue.AttributeEntityType']"}) }, u'catalogue.attributeentitytype': { 'Meta': {'object_name': 'AttributeEntityType'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '255', 'blank': 'True'}) }, u'catalogue.attributeoption': { 'Meta': {'object_name': 'AttributeOption'}, 'group': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'options'", 'to': u"orm['catalogue.AttributeOptionGroup']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'option': ('django.db.models.fields.CharField', [], {'max_length': '255'}) }, u'catalogue.attributeoptiongroup': { 'Meta': {'object_name': 'AttributeOptionGroup'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}) }, u'catalogue.category': { 'Meta': {'ordering': "['full_name']", 'object_name': 'Category'}, 'depth': ('django.db.models.fields.PositiveIntegerField', [], {}), 'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'full_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'numchild': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'path': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '255'}) }, u'catalogue.option': { 'Meta': {'object_name': 'Option'}, 'code': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '128'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'type': ('django.db.models.fields.CharField', [], {'default': "'Required'", 'max_length': '128'}) }, u'catalogue.product': { 'Meta': {'ordering': "['-date_created']", 'object_name': 'Product'}, 'attributes': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['catalogue.ProductAttribute']", 'through': u"orm['catalogue.ProductAttributeValue']", 'symmetrical': 'False'}), 'categories': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['catalogue.Category']", 'through': u"orm['catalogue.ProductCategory']", 'symmetrical': 'False'}), 'date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'date_updated': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'db_index': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_discountable': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'variants'", 'null': 'True', 'to': u"orm['catalogue.Product']"}), 'product_class': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'products'", 'null': 'True', 'to': u"orm['catalogue.ProductClass']"}), 'product_options': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['catalogue.Option']", 'symmetrical': 'False', 'blank': 'True'}), 'rating': ('django.db.models.fields.FloatField', [], {'null': 'True'}), 'recommended_products': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['catalogue.Product']", 'symmetrical': 'False', 'through': u"orm['catalogue.ProductRecommendation']", 'blank': 'True'}), 'related_products': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'relations'", 'blank': 'True', 'to': u"orm['catalogue.Product']"}), 'score': ('django.db.models.fields.FloatField', [], {'default': '0.0', 'db_index': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '255'}), 'status': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '128', 'null': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'upc': ('django.db.models.fields.CharField', [], {'max_length': '64', 'unique': 'True', 'null': 'True', 'blank': 'True'}) }, u'catalogue.productattribute': { 'Meta': {'ordering': "['code']", 'object_name': 'ProductAttribute'}, 'code': ('django.db.models.fields.SlugField', [], {'max_length': '128'}), 'entity_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.AttributeEntityType']", 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'option_group': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.AttributeOptionGroup']", 'null': 'True', 'blank': 'True'}), 'product_class': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'attributes'", 'null': 'True', 'to': u"orm['catalogue.ProductClass']"}), 'required': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'type': ('django.db.models.fields.CharField', [], {'default': "'text'", 'max_length': '20'}) }, u'catalogue.productattributevalue': { 'Meta': {'object_name': 'ProductAttributeValue'}, 'attribute': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.ProductAttribute']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'attribute_values'", 'to': u"orm['catalogue.Product']"}), 'value_boolean': ('django.db.models.fields.NullBooleanField', [], {'null': 'True', 'blank': 'True'}), 'value_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), 'value_entity': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.AttributeEntity']", 'null': 'True', 'blank': 'True'}), 'value_file': ('django.db.models.fields.files.FileField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'value_float': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}), 'value_image': ('django.db.models.fields.files.ImageField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'value_integer': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'value_option': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.AttributeOption']", 'null': 'True', 'blank': 'True'}), 'value_richtext': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'value_text': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}) }, u'catalogue.productcategory': { 'Meta': {'ordering': "['-is_canonical']", 'object_name': 'ProductCategory'}, 'category': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.Category']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_canonical': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.Product']"}) }, u'catalogue.productclass': { 'Meta': {'ordering': "['name']", 'object_name': 'ProductClass'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'options': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['catalogue.Option']", 'symmetrical': 'False', 'blank': 'True'}), 'requires_shipping': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '128'}), 'track_stock': ('django.db.models.fields.BooleanField', [], {'default': 'True'}) }, u'catalogue.productrecommendation': { 'Meta': {'object_name': 'ProductRecommendation'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'primary': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'primary_recommendations'", 'to': u"orm['catalogue.Product']"}), 'ranking': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}), 'recommendation': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.Product']"}) }, u'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, u'order.billingaddress': { 'Meta': {'object_name': 'BillingAddress'}, 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['address.Country']"}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'line1': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'line2': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'line3': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'line4': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'postcode': ('oscar.models.fields.UppercaseCharField', [], {'max_length': '64', 'null': 'True', 'blank': 'True'}), 'search_text': ('django.db.models.fields.CharField', [], {'max_length': '1000'}), 'state': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '64', 'null': 'True', 'blank': 'True'}) }, u'order.line': { 'Meta': {'object_name': 'Line'}, 'est_dispatch_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'line_price_before_discounts_excl_tax': ('django.db.models.fields.DecimalField', [], {'max_digits': '12', 'decimal_places': '2'}), 'line_price_before_discounts_incl_tax': ('django.db.models.fields.DecimalField', [], {'max_digits': '12', 'decimal_places': '2'}), 'line_price_excl_tax': ('django.db.models.fields.DecimalField', [], {'max_digits': '12', 'decimal_places': '2'}), 'line_price_incl_tax': ('django.db.models.fields.DecimalField', [], {'max_digits': '12', 'decimal_places': '2'}), 'order': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'lines'", 'to': u"orm['order.Order']"}), 'partner': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'order_lines'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['partner.Partner']"}), 'partner_line_notes': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'partner_line_reference': ('django.db.models.fields.CharField', [], {'max_length': '128', 'null': 'True', 'blank': 'True'}), 'partner_name': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'partner_sku': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['catalogue.Product']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}), 'quantity': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}), 'status': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'stockrecord': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['partner.StockRecord']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'unit_cost_price': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '12', 'decimal_places': '2', 'blank': 'True'}), 'unit_price_excl_tax': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '12', 'decimal_places': '2', 'blank': 'True'}), 'unit_price_incl_tax': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '12', 'decimal_places': '2', 'blank': 'True'}), 'unit_retail_price': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '12', 'decimal_places': '2', 'blank': 'True'}), 'upc': ('django.db.models.fields.CharField', [], {'max_length': '128', 'null': 'True', 'blank': 'True'}) }, u'order.order': { 'Meta': {'ordering': "['-date_placed']", 'object_name': 'Order'}, 'basket_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'billing_address': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['order.BillingAddress']", 'null': 'True', 'blank': 'True'}), 'currency': ('django.db.models.fields.CharField', [], {'default': "'GBP'", 'max_length': '12'}), 'date_placed': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'db_index': 'True', 'blank': 'True'}), 'guest_email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'number': ('django.db.models.fields.CharField', [], {'max_length': '128', 'db_index': 'True'}), 'shipping_address': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['order.ShippingAddress']", 'null': 'True', 'blank': 'True'}), 'shipping_code': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '128', 'blank': 'True'}), 'shipping_excl_tax': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '12', 'decimal_places': '2'}), 'shipping_incl_tax': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '12', 'decimal_places': '2'}), 'shipping_method': ('django.db.models.fields.CharField', [], {'max_length': '128', 'null': 'True', 'blank': 'True'}), 'site': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['sites.Site']"}), 'status': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'total_excl_tax': ('django.db.models.fields.DecimalField', [], {'max_digits': '12', 'decimal_places': '2'}), 'total_incl_tax': ('django.db.models.fields.DecimalField', [], {'max_digits': '12', 'decimal_places': '2'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'orders'", 'null': 'True', 'to': u"orm['{}']".format(AUTH_USER_MODEL)}) }, u'order.shippingaddress': { 'Meta': {'object_name': 'ShippingAddress'}, 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['address.Country']"}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'line1': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'line2': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'line3': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'line4': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'notes': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'phone_number': ('oscar.models.fields.PhoneNumberField', [], {'max_length': '128', 'blank': 'True'}), 'postcode': ('oscar.models.fields.UppercaseCharField', [], {'max_length': '64', 'null': 'True', 'blank': 'True'}), 'search_text': ('django.db.models.fields.CharField', [], {'max_length': '1000'}), 'state': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '64', 'null': 'True', 'blank': 'True'}) }, u'oscar_support.attachment': { 'Meta': {'object_name': 'Attachment'}, 'date_created': ('django.db.models.fields.DateTimeField', [], {}), 'date_updated': ('django.db.models.fields.DateTimeField', [], {}), 'file': ('django.db.models.fields.files.FileField', [], {'max_length': '100'}), 'ticket': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'attachments'", 'to': u"orm['oscar_support.Ticket']"}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'attachments'", 'to': u"orm['{}']".format(AUTH_USER_MODEL)}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'oscar_support.message': { 'Meta': {'ordering': "['-date_created']", 'object_name': 'Message'}, 'date_created': ('django.db.models.fields.DateTimeField', [], {}), 'date_updated': ('django.db.models.fields.DateTimeField', [], {}), 'text': ('django.db.models.fields.TextField', [], {}), 'ticket': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'messages'", 'to': u"orm['oscar_support.Ticket']"}), 'type': ('django.db.models.fields.CharField', [], {'default': "u'public'", 'max_length': '30'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'messages'", 'to': u"orm['{}']".format(AUTH_USER_MODEL)}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'oscar_support.priority': { 'Meta': {'object_name': 'Priority'}, 'comment': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'slug': ('django_extensions.db.fields.AutoSlugField', [], {'allow_duplicates': 'False', 'max_length': '50', 'separator': "u'-'", 'blank': 'True', 'populate_from': "'name'", 'overwrite': 'False'}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'oscar_support.relatedorder': { 'Meta': {'object_name': 'RelatedOrder'}, 'date_created': ('django.db.models.fields.DateTimeField', [], {}), 'date_updated': ('django.db.models.fields.DateTimeField', [], {}), 'order': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'ticket_related_orders'", 'to': u"orm['order.Order']"}), 'ticket': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'relatedorders'", 'to': u"orm['oscar_support.Ticket']"}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'relatedorders'", 'to': u"orm['{}']".format(AUTH_USER_MODEL)}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'oscar_support.relatedorderline': { 'Meta': {'object_name': 'RelatedOrderLine'}, 'date_created': ('django.db.models.fields.DateTimeField', [], {}), 'date_updated': ('django.db.models.fields.DateTimeField', [], {}), 'line': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'ticket_related_order_lines'", 'to': u"orm['order.Line']"}), 'ticket': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'relatedorderlines'", 'to': u"orm['oscar_support.Ticket']"}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'relatedorderlines'", 'to': u"orm['{}']".format(AUTH_USER_MODEL)}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'oscar_support.relatedproduct': { 'Meta': {'object_name': 'RelatedProduct'}, 'date_created': ('django.db.models.fields.DateTimeField', [], {}), 'date_updated': ('django.db.models.fields.DateTimeField', [], {}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'ticket_related_products'", 'to': u"orm['catalogue.Product']"}), 'ticket': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'relatedproducts'", 'to': u"orm['oscar_support.Ticket']"}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'relatedproducts'", 'to': u"orm['{}']".format(AUTH_USER_MODEL)}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'oscar_support.ticket': { 'Meta': {'ordering': "['-date_updated']", 'unique_together': "(('number', 'subticket_id'),)", 'object_name': 'Ticket'}, 'assigned_group': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'tickets'", 'null': 'True', 'to': u"orm['auth.Group']"}), 'assignee': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'assigned_tickets'", 'null': 'True', 'to': u"orm['{}']".format(AUTH_USER_MODEL)}), 'body': ('django.db.models.fields.TextField', [], {}), 'date_created': ('django.db.models.fields.DateTimeField', [], {}), 'date_updated': ('django.db.models.fields.DateTimeField', [], {}), 'is_internal': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'number': ('django.db.models.fields.CharField', [], {'max_length': '64', 'db_index': 'True'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'subtickets'", 'null': 'True', 'to': u"orm['oscar_support.Ticket']"}), 'priority': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'tickets'", 'null': 'True', 'to': u"orm['oscar_support.Priority']"}), 'related_lines': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'tickets'", 'blank': 'True', 'through': u"orm['oscar_support.RelatedOrderLine']", 'to': u"orm['order.Line']"}), 'related_orders': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'tickets'", 'blank': 'True', 'through': u"orm['oscar_support.RelatedOrder']", 'to': u"orm['order.Order']"}), 'related_products': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'tickets'", 'blank': 'True', 'through': u"orm['oscar_support.RelatedProduct']", 'to': u"orm['catalogue.Product']"}), 'requester': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'submitted_tickets'", 'to': u"orm['{}']".format(AUTH_USER_MODEL)}), 'status': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'tickets'", 'to': u"orm['oscar_support.TicketStatus']"}), 'subject': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'subticket_id': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'tickets'", 'to': u"orm['oscar_support.TicketType']"}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'oscar_support.ticketstatus': { 'Meta': {'object_name': 'TicketStatus'}, 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '64'}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'oscar_support.tickettype': { 'Meta': {'object_name': 'TicketType'}, 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '64'}), 'uuid': ('shortuuidfield.fields.ShortUUIDField', [], {'max_length': '22', 'primary_key': 'True'}) }, u'partner.partner': { 'Meta': {'object_name': 'Partner'}, 'code': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '128'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128', 'null': 'True', 'blank': 'True'}), 'users': ('django.db.models.fields.related.ManyToManyField', [], {'blank': 'True', 'related_name': "'partners'", 'null': 'True', 'symmetrical': 'False', 'to': u"orm['{}']".format(AUTH_USER_MODEL)}) }, u'partner.stockrecord': { 'Meta': {'unique_together': "(('partner', 'partner_sku'),)", 'object_name': 'StockRecord'}, 'cost_price': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '12', 'decimal_places': '2', 'blank': 'True'}), 'date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'date_updated': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'db_index': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'low_stock_threshold': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'num_allocated': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'num_in_stock': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'partner': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'stockrecords'", 'to': u"orm['partner.Partner']"}), 'partner_sku': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'price_currency': ('django.db.models.fields.CharField', [], {'default': "'GBP'", 'max_length': '12'}), 'price_excl_tax': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '12', 'decimal_places': '2', 'blank': 'True'}), 'price_retail': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '12', 'decimal_places': '2', 'blank': 'True'}), 'product': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'stockrecords'", 'to': u"orm['catalogue.Product']"}) }, u'sites.site': { 'Meta': {'ordering': "('domain',)", 'object_name': 'Site', 'db_table': "'django_site'"}, 'domain': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) } } complete_apps = ['oscar_support']

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1

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null

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db4a31c7635bd900d42bfe2e3d644c35a7789fb4

156

py

Python

application.py

r2mars/r2mars.github.io

de718e682806ffbea9762b2ae4511ee4555687b2

[ "MIT" ]

null

application.py

r2mars/r2mars.github.io

de718e682806ffbea9762b2ae4511ee4555687b2

[ "MIT" ]

null

application.py

r2mars/r2mars.github.io

de718e682806ffbea9762b2ae4511ee4555687b2

[ "MIT" ]

null

from flask import Flask, session, render_template app = Flask(__name__) # Main page @app.route("/") def index(): return render_template('index.html')

17.333333

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156

5.047619

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1

0

null

1

0

1

0

null

0

1

0

5

db4ce52165b02e24ed724663528a74597e979535

162

py

Python

lambdatav3peggyk1/__init__.py/classnumcell.py

PeggyK1/lambdatav3

8b016bd4c278a5a9d5dbaaa25733bde92bae3b7d

[ "MIT" ]

null

lambdatav3peggyk1/__init__.py/classnumcell.py

PeggyK1/lambdatav3

8b016bd4c278a5a9d5dbaaa25733bde92bae3b7d

[ "MIT" ]

null

lambdatav3peggyk1/__init__.py/classnumcell.py

PeggyK1/lambdatav3

8b016bd4c278a5a9d5dbaaa25733bde92bae3b7d

[ "MIT" ]

null

import pandas as pd class MyDataFrame(pd.MyDataFrame): """ Reports number of cells """ def num_cells(self): return self.shape[0] * self.shape[1]

23.142857

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0.660494

23

162

4.608696

0.73913

0.169811

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0.015748

0.216049

162

7

44

23.142857

0.818898

0.141975

0

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0

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1

0

1

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null

0

1

0

null

0

1

0

1

0

5

db53d2461a718bba302e4782e560c97ec3251b6a

93

py

Python

foods/admin.py

Glucemy/Glucemy-back

c9fcf7996b3f13c67697aadd449e3e32afb1fa1b

[ "MIT" ]

null

foods/admin.py

Glucemy/Glucemy-back

c9fcf7996b3f13c67697aadd449e3e32afb1fa1b

[ "MIT" ]

null

foods/admin.py

Glucemy/Glucemy-back

c9fcf7996b3f13c67697aadd449e3e32afb1fa1b

[ "MIT" ]

null

from django.contrib import admin from foods.models import Foods admin.site.register(Foods)

15.5

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93

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0.642857

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0.11828

93

5

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null

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1

0

1

0

5

db6c4da596bb1523cafcef59e8de2ce2d65da99b

240

py

Python

vendor/paypal/standard/pdt/forms.py

starsep/NewsBlur

6c59416ca82377ca1bbc7d044890bdead3eba904

[ "MIT" ]

24

2016-08-06T18:10:54.000Z

2022-03-04T11:47:39.000Z

vendor/paypal/standard/pdt/forms.py

starsep/NewsBlur

6c59416ca82377ca1bbc7d044890bdead3eba904

[ "MIT" ]

21

2020-03-24T18:18:22.000Z

2021-03-31T20:18:53.000Z

vendor/paypal/standard/pdt/forms.py

starsep/NewsBlur

6c59416ca82377ca1bbc7d044890bdead3eba904

[ "MIT" ]

13

2017-03-28T02:35:32.000Z

2022-02-21T23:36:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from paypal.standard.forms import PayPalStandardBaseForm from paypal.standard.pdt.models import PayPalPDT class PayPalPDTForm(PayPalStandardBaseForm): class Meta: model = PayPalPDT

26.666667

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27

240

6.703704

0.740741

0.110497

0.198895

0

0.004878

0.145833

240

9

57

26.666667

0.878049

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0

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false

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null

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1

0

1

0

null

0

1

0

1

0

5

9155a971b5da10cf5833703f3cb25d69e8548f7a

46

py

Python

ss/settings.py

jeffchen81/stock-starer

baf2128acdaa3e32aff3b2fc1f79816b0b4d1df6

[ "MIT" ]

4

2018-11-19T09:51:28.000Z

2020-12-19T13:07:53.000Z

ss/settings.py

jeffchen81/stock-starer

baf2128acdaa3e32aff3b2fc1f79816b0b4d1df6

[ "MIT" ]

1

2021-06-01T22:57:02.000Z

ss/settings.py

jeffchen81/stock-starer

baf2128acdaa3e32aff3b2fc1f79816b0b4d1df6

[ "MIT" ]

4

2018-11-19T09:50:54.000Z

2020-12-19T13:07:54.000Z

# -*- coding: utf-8 -*- # another: Jeff.Chen

11.5

23

0.543478

6

46

4.166667

1

0

0.027027

0.195652

46

3

24

15.333333

0.648649

0.869565

0

null

0

null

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null

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null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

915e58a613d9b7af8efa29c31e2abfca8e40eb8b

137

py

Python

brane-ide/kernels/bscript/bscript_kernel/__main__.py

romnn/brane

03752edd85a09a5ffb817b9f6a0fa03c8e9b277a

[ "Apache-2.0" ]

null

brane-ide/kernels/bscript/bscript_kernel/__main__.py

romnn/brane

03752edd85a09a5ffb817b9f6a0fa03c8e9b277a

[ "Apache-2.0" ]

null

brane-ide/kernels/bscript/bscript_kernel/__main__.py

romnn/brane

03752edd85a09a5ffb817b9f6a0fa03c8e9b277a

[ "Apache-2.0" ]

null

from ipykernel.kernelapp import IPKernelApp from . import BraneScriptKernel IPKernelApp.launch_instance(kernel_class=BraneScriptKernel)

27.4

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137

8.5

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137

4

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1

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true

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0.666667

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0.666667

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null

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1

0

null

0

1

0

1

0

1

0

5

916aa484933ac5770f79eae97f6679a3ee08cbbb

21,247

py

Python

etl/parsers/etw/Microsoft_Windows_Hyper_V_VID.py

IMULMUL/etl-parser

76b7c046866ce0469cd129ee3f7bb3799b34e271

[ "Apache-2.0" ]

104

2020-03-04T14:31:31.000Z

2022-03-28T02:59:36.000Z

etl/parsers/etw/Microsoft_Windows_Hyper_V_VID.py

IMULMUL/etl-parser

76b7c046866ce0469cd129ee3f7bb3799b34e271

[ "Apache-2.0" ]

7

2020-04-20T09:18:39.000Z

2022-03-19T17:06:19.000Z

etl/parsers/etw/Microsoft_Windows_Hyper_V_VID.py

IMULMUL/etl-parser

76b7c046866ce0469cd129ee3f7bb3799b34e271

[ "Apache-2.0" ]

16

2020-03-05T18:55:59.000Z

2022-03-01T10:19:28.000Z

# -*- coding: utf-8 -*- """ Microsoft-Windows-Hyper-V-VID GUID : 5931d877-4860-4ee7-a95c-610a5f0d1407 """ from construct import Int8sl, Int8ul, Int16ul, Int16sl, Int32sl, Int32ul, Int64sl, Int64ul, Bytes, Double, Float32l, Struct from etl.utils import WString, CString, SystemTime, Guid from etl.dtyp import Sid from etl.parsers.etw.core import Etw, declare, guid @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1101, version=0) class Microsoft_Windows_Hyper_V_VID_1101_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1102, version=0) class Microsoft_Windows_Hyper_V_VID_1102_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1103, version=0) class Microsoft_Windows_Hyper_V_VID_1103_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1104, version=0) class Microsoft_Windows_Hyper_V_VID_1104_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1105, version=0) class Microsoft_Windows_Hyper_V_VID_1105_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1106, version=0) class Microsoft_Windows_Hyper_V_VID_1106_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1107, version=0) class Microsoft_Windows_Hyper_V_VID_1107_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1108, version=0) class Microsoft_Windows_Hyper_V_VID_1108_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1109, version=0) class Microsoft_Windows_Hyper_V_VID_1109_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1110, version=0) class Microsoft_Windows_Hyper_V_VID_1110_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=1111, version=0) class Microsoft_Windows_Hyper_V_VID_1111_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=3000, version=0) class Microsoft_Windows_Hyper_V_VID_3000_0(Etw): pattern = Struct( "PartitionId" / WString, "Parameter0" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5001, version=0) class Microsoft_Windows_Hyper_V_VID_5001_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5002, version=0) class Microsoft_Windows_Hyper_V_VID_5002_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl, "Parameter3" / Int64sl, "Parameter4" / Int64sl, "Parameter5" / Int64sl, "Parameter6" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5003, version=0) class Microsoft_Windows_Hyper_V_VID_5003_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5004, version=0) class Microsoft_Windows_Hyper_V_VID_5004_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5005, version=0) class Microsoft_Windows_Hyper_V_VID_5005_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5006, version=0) class Microsoft_Windows_Hyper_V_VID_5006_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5007, version=0) class Microsoft_Windows_Hyper_V_VID_5007_0(Etw): pattern = Struct( "Parameter0" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5008, version=0) class Microsoft_Windows_Hyper_V_VID_5008_0(Etw): pattern = Struct( "Parameter0" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5009, version=0) class Microsoft_Windows_Hyper_V_VID_5009_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5010, version=0) class Microsoft_Windows_Hyper_V_VID_5010_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5011, version=0) class Microsoft_Windows_Hyper_V_VID_5011_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5012, version=0) class Microsoft_Windows_Hyper_V_VID_5012_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5013, version=0) class Microsoft_Windows_Hyper_V_VID_5013_0(Etw): pattern = Struct( "Parameter0" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5014, version=0) class Microsoft_Windows_Hyper_V_VID_5014_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5017, version=0) class Microsoft_Windows_Hyper_V_VID_5017_0(Etw): pattern = Struct( "Parameter0" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5018, version=0) class Microsoft_Windows_Hyper_V_VID_5018_0(Etw): pattern = Struct( "Parameter0" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5019, version=0) class Microsoft_Windows_Hyper_V_VID_5019_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5020, version=0) class Microsoft_Windows_Hyper_V_VID_5020_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5021, version=0) class Microsoft_Windows_Hyper_V_VID_5021_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5022, version=0) class Microsoft_Windows_Hyper_V_VID_5022_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5023, version=0) class Microsoft_Windows_Hyper_V_VID_5023_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5024, version=0) class Microsoft_Windows_Hyper_V_VID_5024_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5025, version=0) class Microsoft_Windows_Hyper_V_VID_5025_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5026, version=0) class Microsoft_Windows_Hyper_V_VID_5026_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5029, version=0) class Microsoft_Windows_Hyper_V_VID_5029_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5030, version=0) class Microsoft_Windows_Hyper_V_VID_5030_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5031, version=0) class Microsoft_Windows_Hyper_V_VID_5031_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5032, version=0) class Microsoft_Windows_Hyper_V_VID_5032_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5033, version=0) class Microsoft_Windows_Hyper_V_VID_5033_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5034, version=0) class Microsoft_Windows_Hyper_V_VID_5034_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5035, version=0) class Microsoft_Windows_Hyper_V_VID_5035_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5036, version=0) class Microsoft_Windows_Hyper_V_VID_5036_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl, "Parameter3" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5037, version=0) class Microsoft_Windows_Hyper_V_VID_5037_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5038, version=0) class Microsoft_Windows_Hyper_V_VID_5038_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul, "PartitionFriendlyName" / WString, "PartitionName" / WString, "Consumed" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5039, version=0) class Microsoft_Windows_Hyper_V_VID_5039_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul, "PartitionFriendlyName" / WString, "PartitionName" / WString, "Consumed" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5040, version=0) class Microsoft_Windows_Hyper_V_VID_5040_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5041, version=0) class Microsoft_Windows_Hyper_V_VID_5041_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5042, version=0) class Microsoft_Windows_Hyper_V_VID_5042_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul, "PartitionFriendlyName" / WString, "PartitionName" / WString, "Consumed" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5043, version=0) class Microsoft_Windows_Hyper_V_VID_5043_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul, "PartitionFriendlyName" / WString, "PartitionName" / WString, "Consumed" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5044, version=0) class Microsoft_Windows_Hyper_V_VID_5044_0(Etw): pattern = Struct( "PartitionId" / WString, "Parameter0" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5045, version=0) class Microsoft_Windows_Hyper_V_VID_5045_0(Etw): pattern = Struct( "PartitionId" / WString, "Parameter0" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5046, version=0) class Microsoft_Windows_Hyper_V_VID_5046_0(Etw): pattern = Struct( "LowAddress" / Int64ul, "HighAddress" / Int64ul, "SkipBytes" / Int64ul, "TotalBytes" / Int64ul, "CacheType" / Int32ul, "NodeIndex" / Int8ul, "Flags" / Int32ul, "MemoryPartition" / Int64ul, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5047, version=0) class Microsoft_Windows_Hyper_V_VID_5047_0(Etw): pattern = Struct( "Mdl" / Int64ul, "TotalBytes" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5048, version=0) class Microsoft_Windows_Hyper_V_VID_5048_0(Etw): pattern = Struct( "MbpArraySize" / Int64ul, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5049, version=0) class Microsoft_Windows_Hyper_V_VID_5049_0(Etw): pattern = Struct( "Status" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5050, version=0) class Microsoft_Windows_Hyper_V_VID_5050_0(Etw): pattern = Struct( "PageCountToBack" / Int64ul, "KsrBlockId" / Int64ul, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5051, version=0) class Microsoft_Windows_Hyper_V_VID_5051_0(Etw): pattern = Struct( "MbpArraySize" / Int64ul, "KsrRunCount" / Int32ul, "Status" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5052, version=0) class Microsoft_Windows_Hyper_V_VID_5052_0(Etw): pattern = Struct( "KsrMemoryRunCount" / Int32ul, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5053, version=0) class Microsoft_Windows_Hyper_V_VID_5053_0(Etw): pattern = Struct( "Status" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5054, version=0) class Microsoft_Windows_Hyper_V_VID_5054_0(Etw): pattern = Struct( "MbpArraySize" / Int64ul, "KsrRunCount" / Int32ul, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5056, version=0) class Microsoft_Windows_Hyper_V_VID_5056_0(Etw): pattern = Struct( "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5057, version=0) class Microsoft_Windows_Hyper_V_VID_5057_0(Etw): pattern = Struct( "Status" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5058, version=0) class Microsoft_Windows_Hyper_V_VID_5058_0(Etw): pattern = Struct( "KsrPersisted" / Int8sl, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5059, version=0) class Microsoft_Windows_Hyper_V_VID_5059_0(Etw): pattern = Struct( "Status" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5060, version=0) class Microsoft_Windows_Hyper_V_VID_5060_0(Etw): pattern = Struct( "KsrPersisted" / Int8sl, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5061, version=0) class Microsoft_Windows_Hyper_V_VID_5061_0(Etw): pattern = Struct( "Status" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5062, version=0) class Microsoft_Windows_Hyper_V_VID_5062_0(Etw): pattern = Struct( "StartGpaPage" / Int64ul, "StartMbp" / Int64ul, "MbpCount" / Int64ul, "InterceptOverrideFlags" / Int32ul, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5063, version=0) class Microsoft_Windows_Hyper_V_VID_5063_0(Etw): pattern = Struct( "Status" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5064, version=0) class Microsoft_Windows_Hyper_V_VID_5064_0(Etw): pattern = Struct( "FirstPage" / Int64ul, "LastPage" / Int64ul, "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5065, version=0) class Microsoft_Windows_Hyper_V_VID_5065_0(Etw): pattern = Struct( "Status" / Int64ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5066, version=0) class Microsoft_Windows_Hyper_V_VID_5066_0(Etw): pattern = Struct( "PartitionGuid" / Guid ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5068, version=0) class Microsoft_Windows_Hyper_V_VID_5068_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5069, version=0) class Microsoft_Windows_Hyper_V_VID_5069_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5070, version=0) class Microsoft_Windows_Hyper_V_VID_5070_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5071, version=0) class Microsoft_Windows_Hyper_V_VID_5071_0(Etw): pattern = Struct( "Parameter0" / Int64sl, "Parameter1" / Int64sl, "Parameter2" / Int64sl ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5072, version=0) class Microsoft_Windows_Hyper_V_VID_5072_0(Etw): pattern = Struct( "TotalPages" / Int64ul, "PlatformDirected" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5073, version=0) class Microsoft_Windows_Hyper_V_VID_5073_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul, "PartitionFriendlyName" / WString, "PartitionName" / WString, "Consumed" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5074, version=0) class Microsoft_Windows_Hyper_V_VID_5074_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul, "PartitionFriendlyName" / WString, "PartitionName" / WString, "Consumed" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5075, version=0) class Microsoft_Windows_Hyper_V_VID_5075_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul, "PartitionFriendlyName" / WString, "PartitionName" / WString, "Consumed" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5076, version=0) class Microsoft_Windows_Hyper_V_VID_5076_0(Etw): pattern = Struct( "PhysicalAddress" / Int64ul, "PlatformDirected" / Int8ul ) @declare(guid=guid("5931d877-4860-4ee7-a95c-610a5f0d1407"), event_id=5077, version=0) class Microsoft_Windows_Hyper_V_VID_5077_0(Etw): pattern = Struct( "TotalPages" / Int64ul, "PlatformDirected" / Int8ul, "PartitionFriendlyName" / WString, "PartitionName" / WString, "Consumed" / Int8ul )

29.105479

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0.683532

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5.610463

0.066801

0.096399

0.126524

0.132549

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1

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null

0

5

91e01cf677b2ce229a1bea73d8fb8c795ccce440

248

py

Python

spider1/admin.py

EricMbuthia/SeleniumDjangoWebscraping

27954bcf02b895b3c1001f5924433d6aaf3f195e

[ "MIT" ]

null

spider1/admin.py

EricMbuthia/SeleniumDjangoWebscraping

27954bcf02b895b3c1001f5924433d6aaf3f195e

[ "MIT" ]

null

spider1/admin.py

EricMbuthia/SeleniumDjangoWebscraping

27954bcf02b895b3c1001f5924433d6aaf3f195e

[ "MIT" ]

null

from django.contrib import admin from .models import ScrapeRecordsInventory,ScrapeRecords,UndoneNotices # Register your models here. admin.site.register(ScrapeRecordsInventory) admin.site.register(ScrapeRecords) admin.site.register(UndoneNotices)

31

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27

248

7.888889

0.481481

0.126761

0.239437

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0.068548

248

8

71

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0.922078

0.104839

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true

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0.4

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null

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1

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null

0

1

0

1

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5

91ea2aedc62c92db0056df0c19a6e38619de6104

148

py

Python

src-python/main.py

DevParapalli/ca-adhyaaya-svelte

9a987b04c9c4dbcd9f30f92fa136eaed426fe356

[ "MIT" ]

null

src-python/main.py

DevParapalli/ca-adhyaaya-svelte

9a987b04c9c4dbcd9f30f92fa136eaed426fe356

[ "MIT" ]

1

2022-02-27T17:34:16.000Z

2022-02-27T19:00:33.000Z

src-python/main.py

DevParapalli/ca-adhyaaya-svelte

9a987b04c9c4dbcd9f30f92fa136eaed426fe356

[ "MIT" ]

1

2022-02-27T15:12:09.000Z

import clone_collection import create_CA_code_mapping import sync_refferal_codes import create_mailing_csv_from_registration import update_rankings

24.666667

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0.932432

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148

6.047619

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148

5

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true

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null

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1

0

1

0

1

0

5

91ec70bde5c7ecb4998d63ba2e3ad759a9cac2d6

4,913

py

Python

javifi.py

LEGEND-LX/PYTHONBOT.py.pkg

897b05528990acf76fbb2a05538429cd5d178733

[ "CC0-1.0" ]

2

2021-09-09T06:50:21.000Z

2021-10-01T16:59:30.000Z

javifi.py

LEGEND-LX/PYTHONBOT.py.pkg

897b05528990acf76fbb2a05538429cd5d178733

[ "CC0-1.0" ]

null

javifi.py

LEGEND-LX/PYTHONBOT.py.pkg

897b05528990acf76fbb2a05538429cd5d178733

[ "CC0-1.0" ]

null

import datetime import asyncio from telethon import events from telethon.errors.rpcerrorlist import YouBlockedUserError, UserAlreadyParticipantError from telethon.tl.functions.account import UpdateNotifySettingsRequest from telethon.tl.functions.messages import ImportChatInviteRequest from LEGENDBOT.utils import admin_cmd, edit_or_reply, sudo_cmd import time from userbot import ALIVE_NAME naam = str(ALIVE_NAME) bot = "@ceowhitehatcracks" bluebot = "@ceowhitehatcracks" freebot = "@ceowhitehatcracks" @bot.on(admin_cmd("jav ?(.*)")) async def _(event): if event.fwd_from: return sysarg = event.pattern_match.group(1) if sysarg == "h": async with bot.conversation(bot) as conv: try: await conv.send_message("/start") response = await conv.get_response() await conv.send_message("/hello") audio = await conv.get_response() await bot.send_file(event.chat_id, audio, caption="➡️**TO BOSS : **" + naam +"\n`Check This Bot out` [Sensible Userbot](ttps://github.com/spandey112/SensibleUserbot)") await event.delete() except YouBlockedUserError: await event.edit("**Error:** `unblock` @Ceowhitehatcracks `and retry!") elif sysarg == "ss": async with bot.conversation(bot) as conv: try: await conv.send_message("/start") response = await conv.get_response() await conv.send_message("/ss") audio = await conv.get_response() await bot.send_file(event.chat_id, audio, caption="**CREDITS : Dr.jr Genesis**\n`Check out` [Sensible Userbot Support](t.me/sensible_userbot)") await event.delete() except YouBlockedUserError: await event.edit("**Error:** `unblock` @ceowhitehatcracks `and retry!`") elif sysarg == "--h": async with bot.conversation(bot) as conv: try: await conv.send_message("/start") response = await conv.get_response() await conv.send_message("/help") audio = await conv.get_response() await bot.send_file(event.chat_id, audio, caption="**Dr.Bot Is Here To Help**\n`Check out` [Sensible Userbot Support](t.me/sensible_userbot)") await event.delete() except YouBlockedUserError: await event.edit("**Error:** `unblock` @ceowhitehatcracks `and retry!`") elif sysarg == "npic": async with bot.conversation(bot) as conv: try: await conv.send_message("/start") response = await conv.get_response() await conv.send_message("/nudepic") audio = await conv.get_response() await bot.send_file(event.chat_id, audio, caption="**For" + naam +" **\n`Check out` [Sensible Userbot Support](t.me/sensible_userbot)") await event.delete() except YouBlockedUserError: await event.edit("**Error:** `unblock` @sensible_userbot `and retry!`") elif sysarg == "rs": async with bot.conversation(bot) as conv: try: await conv.send_message("/start") response = await conv.get_response() await conv.send_message("/rs") audio = await conv.get_response() await bot.send_file(event.chat_id, audio, caption="**CREDITS : @CEOWHITEHATCRACKS**\n`Check out` [Sensible Userbot Support](t.me/sensible_userbot)") await event.delete() except YouBlockedUserError: await event.edit("**Error:** `unblock` @ceowhitehatcracks `and retry!`") elif sysarg == "ib": async with bot.conversation(bot) as conv: try: await conv.send_message("/start") response = await conv.get_response() await conv.send_message("/ib") audio = await conv.get_response() await bot.send_file(event.chat_id, audio, caption="**CREDITS : Ceowhitehatcracks**\n`Check out` [Sensible Userbot](ttps://github.com/spandey112/SensibleUserbot)") await event.delete() except YouBlockedUserError: await event.edit("**Error:** `unblock` @Ceowhitehatcracks `and retry!`") elif sysarg == "acc": async with bot.conversation(bot) as conv: try: await conv.send_message("/start") response = await conv.get_response() await conv.send_message("/acc") audio = await conv.get_response() await bot.send_file(event.chat_id, audio) await event.delete() except YouBlockedUserError: await event.edit("**Error:** `unblock` @ceowhitehatcracks `and retry!`") else: await brog.send_message(event.chat_id, "**INVALID** -- FOR HELP COMMAND IS **.jav --h**") await event.delete()

46.349057

181

0.615103

547

4,913

5.420475

0.193784

0.084992

0.061383

0.094435

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0

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4,913

105

182

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0

null

0

1

0

null

0

5

37e7b27a67560fa5bdb43b33348639adcb3b687b

2,457

py

Python

tests/_apis/league_of_legends/test_ChampionMasteryApiV4.py

TheBoringBakery/Riot-Watcher

6e05fffe127530a75fd63e67da37ba81489fd4fe

[ "MIT" ]

489

2015-01-04T22:49:51.000Z

2022-03-28T03:15:54.000Z

tests/_apis/league_of_legends/test_ChampionMasteryApiV4.py

TheBoringBakery/Riot-Watcher

6e05fffe127530a75fd63e67da37ba81489fd4fe

[ "MIT" ]

162

2015-02-09T22:10:40.000Z

2022-02-22T13:48:50.000Z

tests/_apis/league_of_legends/test_ChampionMasteryApiV4.py

TheBoringBakery/Riot-Watcher

6e05fffe127530a75fd63e67da37ba81489fd4fe

[ "MIT" ]

221

2015-01-07T18:01:57.000Z

2022-03-26T21:18:48.000Z

from unittest.mock import MagicMock import pytest from riotwatcher._apis.league_of_legends import ChampionMasteryApiV4 @pytest.mark.lol @pytest.mark.unit class TestChampionMasteryApiV4: def test_by_summoner(self): mock_base_api = MagicMock() expected_return = object() mock_base_api.raw_request.return_value = expected_return mastery = ChampionMasteryApiV4(mock_base_api) region = "afas" encrypted_summoner_id = "15462" ret = mastery.by_summoner(region, encrypted_summoner_id) mock_base_api.raw_request.assert_called_once_with( ChampionMasteryApiV4.__name__, mastery.by_summoner.__name__, region, f"https://{region}.api.riotgames.com/lol/champion-mastery/v4/champion-masteries/by-summoner/{encrypted_summoner_id}", {}, ) assert ret is expected_return def test_summoner_by_champion(self): mock_base_api = MagicMock() expected_return = object() mock_base_api.raw_request.return_value = expected_return mastery = ChampionMasteryApiV4(mock_base_api) region = "fsgs" encrypted_summoner_id = "53526" champion_id = 7 ret = mastery.by_summoner_by_champion( region, encrypted_summoner_id, champion_id ) mock_base_api.raw_request.assert_called_once_with( ChampionMasteryApiV4.__name__, mastery.by_summoner_by_champion.__name__, region, f"https://{region}.api.riotgames.com/lol/champion-mastery/v4/champion-masteries/by-summoner/{encrypted_summoner_id}/by-champion/{champion_id}", {}, ) assert ret is expected_return def test_scored_by_summoner(self): mock_base_api = MagicMock() expected_return = object() mock_base_api.raw_request.return_value = expected_return mastery = ChampionMasteryApiV4(mock_base_api) region = "fsgs" encrypted_summoner_id = "6243" ret = mastery.scores_by_summoner(region, encrypted_summoner_id) mock_base_api.raw_request.assert_called_once_with( ChampionMasteryApiV4.__name__, mastery.scores_by_summoner.__name__, region, f"https://{region}.api.riotgames.com/lol/champion-mastery/v4/scores/by-summoner/{encrypted_summoner_id}", {}, ) assert ret is expected_return

32.328947

155

0.673586

273

2,457

5.626374

0.205128

0.0625

0.085938

0.054688

0.785156

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0.052632

0

0.122807

0

null

0

1

0

null

0

5

530e0e5beb9d0ac8500102c7178a5aaa8ee33885

245

py

Python

src/flask_wtf/__init__.py

Bonifacio2/flask-wtf

7fc4a618ecc7880bcb7b03f69fe58d340be986c7

[ "BSD-3-Clause" ]

null

src/flask_wtf/__init__.py

Bonifacio2/flask-wtf

7fc4a618ecc7880bcb7b03f69fe58d340be986c7

[ "BSD-3-Clause" ]

null

src/flask_wtf/__init__.py

Bonifacio2/flask-wtf

7fc4a618ecc7880bcb7b03f69fe58d340be986c7

[ "BSD-3-Clause" ]

null

from .csrf import CSRFProtect from .csrf import CsrfProtect from .form import FlaskForm from .form import Form from .recaptcha import Recaptcha from .recaptcha import RecaptchaField from .recaptcha import RecaptchaWidget __version__ = "0.15.1"

24.5

38

0.820408

32

245

6.15625

0.40625

0.19797

0.28934

0.253807

0.274112

0

0.018779

0.130612

245

9

39

27.222222

0.906103

0

0.02449

0

1

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false

0

0.875

0

0.875

0

null

0

1

0

1

0

null

0

1

0

1

0

5

531940a33e3eb67f74d98bbb8c5bb50230bc5a41

38

py

Python

pandapower/converter/powerfactory/__init__.py

lschmelting/pandapower

1f24eb4946366bb761c26f529149e941da2d6fb0

[ "BSD-3-Clause" ]

2

2019-11-01T11:01:41.000Z

2022-02-07T12:55:55.000Z

pandapower/converter/powerfactory/__init__.py

lschmelting/pandapower

1f24eb4946366bb761c26f529149e941da2d6fb0

[ "BSD-3-Clause" ]

null

pandapower/converter/powerfactory/__init__.py

lschmelting/pandapower

1f24eb4946366bb761c26f529149e941da2d6fb0

[ "BSD-3-Clause" ]

null

from .export_pfd_to_pp import from_pfd

38

0.894737

8

38

3.75

0.75

0

0.078947

38

1

38

0.857143

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

531981d7099cf967674c939fc95b4caed3a6ee90

2,579

py

Python

tests/test_configure_logging.py

pedroburon/python-envtools

c1f00c2f2df3cb01e55a31c3616ebd8f6bd8dda9

[ "MIT" ]

3

2017-11-28T14:53:54.000Z

2019-11-05T17:50:21.000Z

tests/test_configure_logging.py

pedroburon/python-envtools

c1f00c2f2df3cb01e55a31c3616ebd8f6bd8dda9

[ "MIT" ]

null

tests/test_configure_logging.py

pedroburon/python-envtools

c1f00c2f2df3cb01e55a31c3616ebd8f6bd8dda9

[ "MIT" ]

null

import unittest from envtools import override_environment from envtools.logging_config import configure_logging class TestConfigureLoggingLevel(unittest.TestCase): @override_environment(LOGGING_LEVEL_module='DEBUG') def test_change_level(self): result = configure_logging({ 'loggers': { 'module': { 'handlers': ['console'], 'level': 'INFO', }, }, }) self.assertEqual( { 'loggers': { 'module': { 'handlers': ['console'], 'level': 'DEBUG', }, }, }, result ) @override_environment(LOGGING_LEVEL_module='INFO') def test_maintain_level(self): result = configure_logging({ 'loggers': { 'module': { 'handlers': ['console'], 'level': 'INFO', }, }, }) self.assertEqual( { 'loggers': { 'module': { 'handlers': ['console'], 'level': 'INFO', }, }, }, result ) @override_environment(LOGGING_LEVEL_module_submodule_subsub='DEBUG') def test_dotpath_level(self): result = configure_logging({ 'loggers': { 'module.submodule.subsub': { 'handlers': ['console'], 'level': 'INFO', }, }, }) self.assertEqual( { 'loggers': { 'module.submodule.subsub': { 'handlers': ['console'], 'level': 'DEBUG', }, }, }, result ) @override_environment(LOGGING_LEVEL_module_submodule_subsub='DEBUG') def test_non_existent(self): result = configure_logging({ 'loggers': { 'module': { 'handlers': ['console'], 'level': 'INFO', }, }, }) self.assertEqual( { 'loggers': { 'module': { 'handlers': ['console'], 'level': 'INFO', }, }, }, result )

25.79

72

0.373401

142

2,579

6.56338

0.21831

0.111588

0.171674

0.180258

0.786481

0.746781

0.667382

0.611588

0

0.511439

2,579

99

73

26.050505

0.739683

0

0.568182

0

0.136152

0.017843

0

0.045455

1

0.045455

false

0

0.034091

0

0.090909

0

null

0

1

0

1

0

1

0

1

0

null

0

5

533a79bf8ba1df68d29b739965c4e3117edbb65c

117

py

Python

ThisIsTheProject/app1/main.py

AlexandreSiedschlag/ProMaxima

d74865030214ec708af8d268965d709e73de4199

[ "Unlicense" ]

null

ThisIsTheProject/app1/main.py

AlexandreSiedschlag/ProMaxima

d74865030214ec708af8d268965d709e73de4199

[ "Unlicense" ]

null

ThisIsTheProject/app1/main.py

AlexandreSiedschlag/ProMaxima

d74865030214ec708af8d268965d709e73de4199

[ "Unlicense" ]

null

from functions import do_something do_something() #Login: admin #Password: 1234 #email = alexandresieds@gmail.com

13

34

0.786325

15

117

6

0.866667

0.244444

0

0.039216

0.128205

117

8

35

14.625

0.843137

0.495727

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

534bd579a21dfdeaa9b1ff2eb464aad6f9ec2d0e

354

py

Python

ddi_search_engine/Bio/expressions/__init__.py

dbmi-pitt/DIKB-Evidence-analytics

9ffd629db30c41ced224ff2afdf132ce9276ae3f

[ "MIT" ]

3

2015-06-08T17:58:54.000Z

2022-03-10T18:49:44.000Z

ddi_search_engine/Bio/expressions/__init__.py

dbmi-pitt/DIKB-Evidence-analytics

9ffd629db30c41ced224ff2afdf132ce9276ae3f

[ "MIT" ]

null

ddi_search_engine/Bio/expressions/__init__.py

dbmi-pitt/DIKB-Evidence-analytics

9ffd629db30c41ced224ff2afdf132ce9276ae3f

[ "MIT" ]

null

# This is a Python module. import warnings warnings.warn("Bio.expressions was deprecated, as it does not work with recent versions of mxTextTools. If you want to continue to use this module, please get in contact with the Biopython developers at biopython-dev@biopython.org to avoid permanent removal of this module from Biopython", DeprecationWarning)

70.8

309

0.813559

55

354

5.236364

0.781818

0.069444

0

0.144068

354

4

310

88.5

0.950495

0.067797

0

0.5

0.829268

0.082317

0

1

0

true

0

0.5

0

0.5

0

null

0

1

0

1

0

null

0

1

0

1

0

5

536daa8cef5714ea9320732f7aa943945ccd3067

271

py

Python

visualpriors/__init__.py

memmelma/visual-prior

6b9c65f291c587fcbb3fcc3f61f76cdd1c3eb175

[ "MIT" ]

1

2022-01-13T17:08:51.000Z

visualpriors/__init__.py

memmelma/visual-prior

6b9c65f291c587fcbb3fcc3f61f76cdd1c3eb175

[ "MIT" ]

null

visualpriors/__init__.py

memmelma/visual-prior

6b9c65f291c587fcbb3fcc3f61f76cdd1c3eb175

[ "MIT" ]

null

from .transforms import representation_transform, multi_representation_transform, max_coverage_featureset_transform from .transforms import feature_readout, multi_feature_readout from .transforms import get_networks, get_viable_feature_tasks, get_max_coverate_featuresets

90.333333

115

0.911439

33

271

7

0.515152

0.181818

0.25974

0

0.059041

271

3

116

90.333333

0.905882

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

536e744953d2f364f8c3c9126e16e25dd49ebd6f

14,901

py

Python

target_describe/target_describe.py

DanielR59/target_description

51e0f7a7ece1b45a55f1eed17abdb31532c5b2c2

[ "MIT" ]

null

target_describe/target_describe.py

DanielR59/target_description

51e0f7a7ece1b45a55f1eed17abdb31532c5b2c2

[ "MIT" ]

null

target_describe/target_describe.py

DanielR59/target_description

51e0f7a7ece1b45a55f1eed17abdb31532c5b2c2

[ "MIT" ]

null

from typing import List, Optional, Union from typing_extensions import Literal import pandas as pd from .utils import ( calculate_bins, get_variable_and_target, plot_numerical_variable, sample_and_get_distribution, select_categorical_text, select_numeric, plot_variable, calculate_distribution, ) class targetDescribe: def __init__( self, data: pd.DataFrame, target: Union[pd.Series, str], problem: Literal["binary_classification", "regression"], max_categories: int = 30, target_described: Optional[str] = None, nbins: int = 15, ) -> None: __target_in_df = False if isinstance(target, str): try: self._target_name = target self.target = data[target].copy() __target_in_df = True except KeyError: raise KeyError(f"{target} not in DataFrame") else: self._target_name = target.name self.target = target.copy() if target_described: self.target_value_described = target_described else: self.target_value_described = str(self.target.unique()[-1]) self.nbins = nbins self.max_categories = max_categories self.data = data.copy() self.problem = problem self.split_variables() self.numeric_variables = self._append_target( variables=self.numeric_variables, target_in_df=__target_in_df ) self.categorical_variables = self._append_target( variables=self.categorical_variables, target_in_df=__target_in_df ) def split_variables(self) -> None: self.numeric_variables = select_numeric(self.data) self.categorical_variables = select_categorical_text(self.data) def _append_target( self, variables: pd.DataFrame, target_in_df: bool ) -> pd.DataFrame: if target_in_df: if self.target.name in list(variables.columns): return variables else: return pd.concat([variables, self.target], axis=1) else: return pd.concat([variables, self.target], axis=1) def all_associations( self, target_value_described: Optional[str] = None, export: bool = False, max_categories: Optional[int] = None, nbins: Optional[int] = None, random_state: Optional[int] = None, nbins_round_2: Optional[dict] = None, sort_by: Literal["rows", "variable", "target_asc", "target_desc"] = "rows" ): if nbins: self.nbins = nbins if max_categories: self.max_categories = max_categories if target_value_described: self.target_value_described = target_value_described if sort_by not in ["rows", "variable", "target_asc", "target_desc"]: print("Incorrect sort_by option using default") sort_by = "rows" if self.problem == "binary_classification": if self.target.nunique() != 2: raise ("Not binary target") numeric_columns = [ name for name in list(self.numeric_variables.columns) if name not in [self._target_name] ] categorical_columns = [ name for name in list(self.categorical_variables.columns) if name not in [self._target_name] ] for nombre in numeric_columns: if self.numeric_variables[nombre].dtype in ["int64", "int32", "int16"]: num_categorias = self.numeric_variables[nombre].nunique() if num_categorias <= self.max_categories: proporcion = calculate_distribution( df=self.numeric_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, sort_by=sort_by ) plot_variable( df=proporcion, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, export=export, ) else: proporcion = sample_and_get_distribution( df=self.numeric_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, sample_size=self.max_categories, random_state=random_state, ) plot_variable( df=proporcion, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, export=export, ) elif self.numeric_variables[nombre].dtype in ["float64", "float32"]: proporcion, counts, bins = calculate_bins( df=self.numeric_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, nbins=self.nbins, ) plot_numerical_variable( get_variable_and_target( self.numeric_variables, nombre, self._target_name ), proporcion, counts, bins, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, nbins=self.nbins, nbins_round_2=nbins_round_2, export=export, ) for nombre in categorical_columns: if self.categorical_variables[nombre].dtype == "object": num_categorias = self.categorical_variables[nombre].nunique( ) if num_categorias <= self.max_categories: proporcion = calculate_distribution( df=self.categorical_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, sort_by=sort_by ) plot_variable( df=proporcion, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, export=export, ) else: proporcion = sample_and_get_distribution( df=self.categorical_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, sample_size=self.max_categories, random_state=random_state, ) plot_variable( df=proporcion, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, export=export, ) def describe_some(self, columns: List[str], target_value_described: Optional[str] = None, export: bool = False, max_categories: Optional[int] = None, nbins: Optional[int] = None, random_state: Optional[int] = None, nbins_round_2: Optional[dict] = None, sort_by: Literal["rows", "variable", "target_asc", "target_desc"] = "rows"): if nbins: self.nbins = nbins if max_categories: self.max_categories = max_categories if target_value_described: self.target_value_described = target_value_described if sort_by not in ["rows", "variable", "target_asc", "target_desc"]: print("Incorrect sort_by option using default") sort_by = "rows" if self.problem == "binary_classification": if self.target.nunique() != 2: raise ("Not binary target") for nombre in columns: if nombre in self.numeric_variables.columns: if self.numeric_variables[nombre].dtype in ["int64", "int32", "int16"]: num_categorias = self.numeric_variables[nombre].nunique( ) if num_categorias <= self.max_categories: proporcion = calculate_distribution( df=self.numeric_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, sort_by=sort_by ) plot_variable( df=proporcion, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, export=export, ) else: proporcion = sample_and_get_distribution( df=self.numeric_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, sample_size=self.max_categories, random_state=random_state, ) plot_variable( df=proporcion, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, export=export, ) elif self.numeric_variables[nombre].dtype in ["float64", "float32"]: proporcion, counts, bins = calculate_bins( df=self.numeric_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, nbins=self.nbins, ) plot_numerical_variable( get_variable_and_target( self.numeric_variables, nombre, self._target_name ), proporcion, counts, bins, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, nbins=self.nbins, nbins_round_2=nbins_round_2, export=export, ) elif nombre in self.categorical_variables.columns: if self.categorical_variables[nombre].dtype == "object": num_categorias = self.categorical_variables[nombre].nunique( ) if num_categorias <= self.max_categories: proporcion = calculate_distribution( df=self.categorical_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, sort_by=sort_by ) plot_variable( df=proporcion, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, export=export, ) else: proporcion = sample_and_get_distribution( df=self.categorical_variables, variable=nombre, target_name=self._target_name, target_value_described=self.target_value_described, sample_size=self.max_categories, random_state=random_state, ) plot_variable( df=proporcion, variable_name=nombre, target_name=self._target_name, target_value_described=self.target_value_described, export=export, ) else: print(f"{nombre} no esta en el dataframe cainal") if __name__ == "__main__": df = pd.read_csv( "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv" ) hola = df["Survived"].astype(str) df.drop(axis=1, labels="Survived", inplace=True) # print(df.columns) # a = targetDescribe(data=df, target="Survived", problem="binary_classification") b = targetDescribe(data=df, target=hola, problem="binary_classification") b.all_associations( max_categories=10, export=True, ) # b.all_associations(export=True, target_value_described="0")

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725e9cc850d6955931097cf0184c06b23e2acb03

269

py

Python

notes/design/low-level/case-studies/auction-system/auction/commands/AbstractCommand.py

Anmol-Singh-Jaggi/interview-notes

65af75e2b5725894fa5e13bb5cd9ecf152a0d652

[ "MIT" ]

6

2020-07-05T05:15:19.000Z

2021-01-24T20:17:14.000Z

notes/design/low-level/case-studies/auction-system/auction/commands/AbstractCommand.py

Anmol-Singh-Jaggi/interview-notes

65af75e2b5725894fa5e13bb5cd9ecf152a0d652

[ "MIT" ]

null

notes/design/low-level/case-studies/auction-system/auction/commands/AbstractCommand.py

Anmol-Singh-Jaggi/interview-notes

65af75e2b5725894fa5e13bb5cd9ecf152a0d652

[ "MIT" ]

2

2020-09-14T06:46:37.000Z

2021-06-15T09:17:21.000Z

from abc import ABC, abstractmethod from model.AuctionSystem import AuctionSystem class AbstractCommand(ABC): def __init__(self, auction_system: AuctionSystem): self.auction_system = auction_system @abstractmethod def execute(self): pass

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72b15f81c8b9afe16649daf9402a10d6c03eae76

2,214

py

Python

tests/test_dynamic_rerun_disabled_option.py

gnikonorov/pytest-dynamicrerun

200f45830be5de3d088d092aaac2b11626c04668

[ "MIT" ]

null

tests/test_dynamic_rerun_disabled_option.py

gnikonorov/pytest-dynamicrerun

200f45830be5de3d088d092aaac2b11626c04668

[ "MIT" ]

1

2020-08-10T00:58:07.000Z

2020-08-10T03:47:55.000Z

tests/test_dynamic_rerun_disabled_option.py

gnikonorov/pytest-dynamicrerun

200f45830be5de3d088d092aaac2b11626c04668

[ "MIT" ]

null

# This file contains tests specific to the dynamic_rerun_disabled option import pytest from helpers import _assert_result_outcomes def test_dynamic_rerun_disabled_false_by_default(testdir): dynamic_rerun_attempts = 3 testdir.makeini( """ [pytest] dynamic_rerun_attempts = {} dynamic_rerun_schedule = * * * * * * """.format( dynamic_rerun_attempts ) ) testdir.makepyfile("def test_always_false(): assert False") result = testdir.runpytest("-v") assert result.ret == pytest.ExitCode.TESTS_FAILED _assert_result_outcomes(result, dynamic_rerun=dynamic_rerun_attempts, failed=1) @pytest.mark.parametrize( "dynamic_rerun_disabled", ["TRUE", "True", "TrUe", "true", "y", "yes", "t", "true", "on", "1"], ) def test_dynamic_rerun_disabled_works_for_true_values(testdir, dynamic_rerun_disabled): testdir.makeini( """ [pytest] dynamic_rerun_attempts = 3 dynamic_rerun_disabled = {} dynamic_rerun_schedule = * * * * * * """.format( dynamic_rerun_disabled ) ) testdir.makepyfile("def test_always_false(): assert False") result = testdir.runpytest("-v") assert result.ret == pytest.ExitCode.TESTS_FAILED _assert_result_outcomes(result, dynamic_rerun=0, failed=1) @pytest.mark.parametrize( "dynamic_rerun_disabled", [ "doit", "ok", "fine", "n", "NO", "FaLsE", "OFF", "noway", "", "stopit", "123", "0", ], ) def test_dynamic_rerun_disabled_works_for_false_values(testdir, dynamic_rerun_disabled): dynamic_rerun_attempts = 3 testdir.makeini( """ [pytest] dynamic_rerun_attempts = {} dynamic_rerun_disabled = {} dynamic_rerun_schedule = * * * * * * """.format( dynamic_rerun_attempts, dynamic_rerun_disabled ) ) testdir.makepyfile("def test_always_false(): assert False") result = testdir.runpytest("-v") assert result.ret == pytest.ExitCode.TESTS_FAILED _assert_result_outcomes(result, dynamic_rerun=dynamic_rerun_attempts, failed=1)

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72cafeef823f6f538d3416ee440ce47f37267f54

191

py

Python

backend/restapi/models/__init__.py

IlluminateMedia/prompts-ai

04723f21f165811ab784f8eebab10b7df2d02075

[ "MIT" ]

null

backend/restapi/models/__init__.py

IlluminateMedia/prompts-ai

04723f21f165811ab784f8eebab10b7df2d02075

[ "MIT" ]

null

backend/restapi/models/__init__.py

IlluminateMedia/prompts-ai

04723f21f165811ab784f8eebab10b7df2d02075

[ "MIT" ]

null

from .custom_model import CustomModel from .shared_prompt import SharedPrompt from .workspace import Workspace from .airtable import Airtable from .airtable_workspace import AirtableWorkspace

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1

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72e07b62b5e8e09cb90d3bbe3b808367cc8a6dd2

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py

Python

project/editorial/migrations/0091_auto_20190512_1710.py

ProjectFacet/facet

dc6bc79d450f7e2bdf59cfbcd306d05a736e4db9

[ "MIT" ]

25

2015-07-13T22:16:36.000Z

2021-11-11T02:45:32.000Z

project/editorial/migrations/0091_auto_20190512_1710.py

ProjectFacet/facet

dc6bc79d450f7e2bdf59cfbcd306d05a736e4db9

[ "MIT" ]

74

2015-12-01T18:57:47.000Z

2022-03-11T23:25:47.000Z

project/editorial/migrations/0091_auto_20190512_1710.py

ProjectFacet/facet

dc6bc79d450f7e2bdf59cfbcd306d05a736e4db9

[ "MIT" ]

6

2016-01-08T21:12:43.000Z

2019-05-20T16:07:56.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.9 on 2019-05-13 00:10 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('editorial', '0090_auto_20190512_1558'), ] operations = [ migrations.RemoveField( model_name='organizationdiscoveryprofile', name='platforms', ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_cable_tv', field=models.BooleanField(default=False, help_text=b'Organization airs on cable television.'), ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_network_tv', field=models.BooleanField(default=False, help_text=b'Organization airs on network television.'), ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_newsletter', field=models.BooleanField(default=False, help_text=b'Organization publishes newsletters.'), ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_online', field=models.BooleanField(default=False, help_text=b'Organization publishes online.'), ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_podcast', field=models.BooleanField(default=False, help_text=b'Organization produces podcasts.'), ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_print', field=models.BooleanField(default=False, help_text=b'Organization publishes in print.'), ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_radio', field=models.BooleanField(default=False, help_text=b'Organization airs on radio.'), ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_social', field=models.BooleanField(default=False, help_text=b'Organization publishes content on social platforms.'), ), migrations.AddField( model_name='organizationdiscoveryprofile', name='platform_streaming_video', field=models.BooleanField(default=False, help_text=b'Organization content airs on streaming video.'), ), ]

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1

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null

0

5

72ecfa820dcbb2cb3632b9f36b274f5326a60330

30,736

py

Python

seaice/data/test/test_gridset_filters.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

2

2020-08-27T08:40:22.000Z

2021-04-14T15:42:09.000Z

seaice/data/test/test_gridset_filters.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

null

seaice/data/test/test_gridset_filters.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

null

import datetime as dt import unittest from unittest.mock import patch import numpy as np import numpy.testing as npt import pandas as pd import pandas.util.testing as pdt import seaice.data.gridset_filters as gf from seaice.data.gridset_filters import apply_largest_pole_hole from seaice.data.gridset_filters import concentration_cutoff from seaice.data.gridset_filters import drop_invalid_ice from seaice.data.gridset_filters import drop_bad_dates from seaice.data.gridset_filters import drop_land from seaice.data.gridset_filters import prevent_empty from seaice.data.gridset_filters import ensure_full_nrt_month import seaice.data.errors as e import seaice.nasateam as nt LAND = nt.FLAGS['land'] COAST = nt.FLAGS['coast'] class Test_apply_largest_pole_hole(unittest.TestCase): def test_no_pole_hole(self): gridset = {'data': np.array([[1, 1], [1, 1]]), 'metadata': {'flags': {'pole': 251}, 'missing_value': 255}} actual = apply_largest_pole_hole(gridset) npt.assert_array_equal(gridset['data'], actual['data']) def test_one_pole_hole(self): layer1 = np.array([[251, 1], [1, 1]]) layer2 = np.array([[2, 2], [2, 2]]) gridset = {'data': np.dstack([layer1, layer2]), 'metadata': {'flags': {'pole': 251}, 'missing_value': 255}} actual = apply_largest_pole_hole(gridset) expected_layer2 = np.array([[251, 2], [2, 2]]) expected = np.dstack([layer1, expected_layer2]) npt.assert_array_equal(expected, actual['data']) def test_different_pole_holes(self): layer1 = np.array([[251, 1], [1, 1]]) layer2 = np.array([[251, 251], [2, 2]]) gridset = {'data': np.dstack([layer1, layer2]), 'metadata': {'flags': {'pole': 251}, 'missing_value': 255}} actual = apply_largest_pole_hole(gridset) expected_layer1 = np.array([[251, 251], [1, 1]]) expected_layer2 = np.array([[251, 251], [2, 2]]) expected = np.dstack([expected_layer1, expected_layer2]) npt.assert_array_equal(expected, actual['data']) def test_with_layer_of_all_missing(self): layer1 = np.array([[251, 1], [1, 1]]) layer2 = np.array([[251, 251], [2, 2]]) layer3 = np.array([[255, 255], [255, 255]]) gridset = {'data': np.dstack([layer1, layer2, layer3]), 'metadata': {'flags': {'pole': 251}, 'missing_value': 255}} actual = apply_largest_pole_hole(gridset) expected_layer1 = np.array([[251, 251], [1, 1]]) expected_layer2 = np.array([[251, 251], [2, 2]]) expected_layer3 = np.array([[255, 255], [255, 255]]) expected = np.dstack([expected_layer1, expected_layer2, expected_layer3]) npt.assert_array_equal(expected, actual['data']) class Test_concentration_cutoff(unittest.TestCase): @patch('seaice.data.grid_filters.concentration_cutoff') def test_calls_grid_filters_concentration_cutoff(self, mock_concentration_cutoff): gridset = { 'data': np.array([[20, 10], [5, 50]]) } mock_concentration_cutoff.return_value = np.array([[20, 0], [0, 50]]) expected = { 'data': np.array([[20, 0], [0, 50]]) } actual = concentration_cutoff(15, gridset) npt.assert_array_equal(expected['data'], actual['data']) self.assertEqual(15, mock_concentration_cutoff.call_args[0][0]) npt.assert_array_equal(np.array([[20, 10], [5, 50]]), mock_concentration_cutoff.call_args[0][1]) class Test_drop_bad_dates(unittest.TestCase): @patch('seaice.datastore.get_bad_days_for_hemisphere') def test_no_bad_data(self, mock_get_bad_days_for_hemisphere): gridset = { 'data': np.full((5, 5, 3), 10, dtype=np.int), 'metadata': { 'hemi': 'N', 'temporality': 'D', 'files': ['1.bin', '2.bin', '3.bin'], 'period_index': pd.period_range('2016-01-01', '2016-01-03', freq='D') } } bad_dates_index = pd.PeriodIndex([], freq='D') mock_get_bad_days_for_hemisphere.return_value = bad_dates_index actual = drop_bad_dates(gridset) self.assertEqual(actual['metadata']['files'], ['1.bin', '2.bin', '3.bin']) pdt.assert_index_equal(actual['metadata']['period_index'], pd.period_range('2016-01-01', '2016-01-03', freq='D')) npt.assert_array_equal(actual['data'], np.full((5, 5, 3), 10, dtype=np.int)) @patch('seaice.datastore.get_bad_days_for_hemisphere') def test_all_bad_data(self, mock_get_bad_days_for_hemisphere): the_period_index = pd.period_range('2016-01-01', '2016-01-03', freq='D') gridset = { 'data': np.full((5, 5, 3), 10, dtype=np.int), 'metadata': { 'hemi': 'N', 'temporality': 'D', 'files': ['1.bin', '2.bin', '3.bin'], 'period_index': the_period_index, 'period': pd.Period(dt.date(2016, 2, 2), freq='D'), 'missing_value': 255 } } bad_dates_index = the_period_index.copy() mock_get_bad_days_for_hemisphere.return_value = bad_dates_index actual = drop_bad_dates(gridset) self.assertEqual(actual['metadata']['files'], []) pdt.assert_index_equal(actual['metadata']['period_index'], pd.PeriodIndex([], dtype='period[D]')) npt.assert_array_equal(actual['data'], np.full((5, 5), 255, dtype=np.int)) @patch('seaice.datastore.get_bad_days_for_hemisphere') def test_middle_day_bad(self, mock_get_bad_days_for_hemisphere): zeroth_grid = np.full((5, 5), 0, dtype=np.int) first_grid = np.full((5, 5), 1, dtype=np.int) second_grid = np.full((5, 5), 2, dtype=np.int) gridset = { 'data': np.dstack([zeroth_grid, first_grid, second_grid]), 'metadata': { 'hemi': 'N', 'temporality': 'D', 'files': ['1.bin', '2.bin', '3.bin'], 'period_index': pd.period_range('2016-01-01', '2016-01-03', freq='D') } } bad_dates_index = pd.PeriodIndex(['2016-01-02'], freq='D') mock_get_bad_days_for_hemisphere.return_value = bad_dates_index actual = drop_bad_dates(gridset) self.assertEqual(actual['metadata']['files'], ['1.bin', '3.bin']) pdt.assert_index_equal(actual['metadata']['period_index'], pd.PeriodIndex(['2016-01-01', '2016-01-03'], freq='D')) npt.assert_array_equal(actual['data'], np.dstack([zeroth_grid, second_grid])) @patch('seaice.datastore.get_bad_days_for_hemisphere') def test_two_days_bad(self, mock_get_bad_days_for_hemisphere): zeroth_grid = np.full((5, 5), 0, dtype=np.int) first_grid = np.full((5, 5), 1, dtype=np.int) second_grid = np.full((5, 5), 2, dtype=np.int) gridset = { 'data': np.dstack([zeroth_grid, first_grid, second_grid]), 'metadata': { 'hemi': 'N', 'temporality': 'D', 'files': ['1.bin', '2.bin', '3.bin'], 'period_index': pd.period_range('2016-01-01', '2016-01-03', freq='D') } } bad_dates_index = pd.PeriodIndex(['2016-01-02', '2016-01-03'], freq='D') mock_get_bad_days_for_hemisphere.return_value = bad_dates_index actual = drop_bad_dates(gridset) self.assertEqual(actual['metadata']['files'], ['1.bin']) pdt.assert_index_equal(actual['metadata']['period_index'], pd.PeriodIndex(['2016-01-01'], freq='D')) npt.assert_array_equal(actual['data'], zeroth_grid) @patch('seaice.datastore.get_bad_days_for_hemisphere') def test_no_bad_data_double_weighted_dates(self, mock_get_bad_days_for_hemisphere): gridset = { 'data': np.full((5, 5, 3), 10, dtype=np.int), 'metadata': { 'hemi': 'N', 'temporality': 'D', 'files': ['1.bin', '1.bin', '2.bin'], 'period_index': pd.PeriodIndex(['2016-01-01', '2016-01-01', '2016-01-02'], freq='D') } } bad_dates_index = pd.PeriodIndex([], freq='D') mock_get_bad_days_for_hemisphere.return_value = bad_dates_index actual = drop_bad_dates(gridset) self.assertEqual(actual['metadata']['files'], ['1.bin', '1.bin', '2.bin']) pdt.assert_index_equal(actual['metadata']['period_index'], pd.PeriodIndex(['2016-01-01', '2016-01-01', '2016-01-02'], freq='D')) npt.assert_array_equal(actual['data'], np.full((5, 5, 3), 10, dtype=np.int)) @patch('seaice.datastore.get_bad_days_for_hemisphere') def test_all_bad_data_double_weighted_dates(self, mock_get_bad_days_for_hemisphere): the_period_index = pd.PeriodIndex(['2016-01-01', '2016-01-01', '2016-01-02'], freq='D') gridset = { 'data': np.full((5, 5, 3), 10, dtype=np.int), 'metadata': { 'hemi': 'N', 'temporality': 'D', 'files': ['1.bin', '1.bin', '2.bin'], 'period_index': the_period_index, 'period': pd.Period(dt.date(2016, 2, 2), freq='D'), 'missing_value': 255 } } bad_dates_index = the_period_index.copy() mock_get_bad_days_for_hemisphere.return_value = bad_dates_index actual = drop_bad_dates(gridset) self.assertEqual(actual['metadata']['files'], []) pdt.assert_index_equal(actual['metadata']['period_index'], pd.PeriodIndex([], dtype='period[D]')) npt.assert_array_equal(actual['data'], np.full((5, 5), 255, dtype=np.int)) @patch('seaice.datastore.get_bad_days_for_hemisphere') def test_day_bad_double_weighted_dates(self, mock_get_bad_days_for_hemisphere): zeroth_grid = np.full((5, 5), 0, dtype=np.int) first_grid = np.full((5, 5), 1, dtype=np.int) second_grid = np.full((5, 5), 2, dtype=np.int) gridset = { 'data': np.dstack([zeroth_grid, zeroth_grid, first_grid, second_grid]), 'metadata': { 'hemi': 'N', 'temporality': 'D', 'files': ['1.bin', '1.bin', '2.bin', '3.bin'], 'period_index': pd.PeriodIndex(['2016-01-01', '2016-01-01', '2016-01-02', '2016-01-03'], freq='D') } } bad_dates_index = pd.PeriodIndex(['2016-01-02'], freq='D') mock_get_bad_days_for_hemisphere.return_value = bad_dates_index actual = drop_bad_dates(gridset) self.assertEqual(actual['metadata']['files'], ['1.bin', '1.bin', '3.bin']) pdt.assert_index_equal(actual['metadata']['period_index'], pd.PeriodIndex(['2016-01-01', '2016-01-01', '2016-01-03'], freq='D')) npt.assert_array_equal(actual['data'], np.dstack([zeroth_grid, zeroth_grid, second_grid])) @patch('seaice.datastore.get_bad_days_for_hemisphere') def test_double_weighted_day_bad_double_weighted_dates(self, mock_get_bad_days_for_hemisphere): zeroth_grid = np.full((5, 5), 0, dtype=np.int) first_grid = np.full((5, 5), 1, dtype=np.int) second_grid = np.full((5, 5), 2, dtype=np.int) gridset = { 'data': np.dstack([zeroth_grid, zeroth_grid, first_grid, second_grid]), 'metadata': { 'hemi': 'N', 'temporality': 'D', 'files': ['1.bin', '1.bin', '2.bin', '3.bin'], 'period_index': pd.PeriodIndex(['2016-01-01', '2016-01-01', '2016-01-02', '2016-01-03'], freq='D') } } bad_dates_index = pd.PeriodIndex(['2016-01-01', '2016-01-02'], freq='D') mock_get_bad_days_for_hemisphere.return_value = bad_dates_index actual = drop_bad_dates(gridset) self.assertEqual(actual['metadata']['files'], ['3.bin']) pdt.assert_index_equal(actual['metadata']['period_index'], pd.PeriodIndex(['2016-01-03'], freq='D')) npt.assert_array_equal(actual['data'], second_grid) class Test_drop_invalid_ice(unittest.TestCase): def test_preserves_flag_and_removes_invalid_ice(self): invalid_ice_mask = np.array([[False, False], [False, True]]) gridset = { 'data': np.array([[100, 251], [100, 100]]), 'metadata': {'valid_data_range': (0, 100), 'missing_value': 255} } actual = drop_invalid_ice(invalid_ice_mask, gridset) expected_data = np.array([[100, 251], [100, 0]]) self.assertTrue(actual['metadata']['drop_invalid_ice']) npt.assert_array_equal(actual['data'], expected_data) def test_preserves_flag_where_invalid_mask_is_present(self): invalid_ice_mask = np.array([[False, True], [False, True]]) gridset = { 'data': np.array([[100, 251], [100, 100]]), 'metadata': {'valid_data_range': (0, 100), 'missing_value': 255} } actual = drop_invalid_ice(invalid_ice_mask, gridset) expected_data = np.array([[100, 251], [100, 0]]) self.assertTrue(actual['metadata']['drop_invalid_ice']) npt.assert_array_equal(actual['data'], expected_data) def test_does_nothing_when_no_invalid_ice(self): invalid_ice_mask = np.array([[False, False], [False, False]]) gridset = { 'data': np.array([[100, 251], [100, 100]]), 'metadata': {'valid_data_range': (0, 100), 'missing_value': 255} } actual = drop_invalid_ice(invalid_ice_mask, gridset) expected_data = np.array([[100, 251], [100, 100]]) self.assertTrue(actual['metadata']['drop_invalid_ice']) npt.assert_array_equal(actual['data'], expected_data) def test_removes_missing_in_invalid_regions_and_leaves_it_in_valid_ones(self): invalid_ice_mask = np.array([[True, False], [True, False], [True, False]]) gridset = { 'data': np.array([[100, 251], [100, 100], [255, 255]]), 'metadata': {'valid_data_range': (0, 100), 'missing_value': 255} } actual = drop_invalid_ice(invalid_ice_mask, gridset) expected_data = np.array([[0, 251], [0, 100], [0, 255]]) self.assertTrue(actual['metadata']['drop_invalid_ice']) npt.assert_array_equal(expected_data, actual['data']) def test_leaves_all_missing_alone(self): invalid_ice_mask = np.array([[True, False], [True, False], [True, False]]) gridset = { 'data': np.array([[255, 255], [255, 255], [255, 255]]), 'metadata': {'valid_data_range': (0, 100), 'missing_value': 255} } actual = drop_invalid_ice(invalid_ice_mask, gridset) expected_data = np.array([[255, 255], [255, 255], [255, 255]]) self.assertNotIn('drop_invalid_ice', actual['metadata']) npt.assert_array_equal(expected_data, actual['data']) class Test_drop_land(unittest.TestCase): def test_drops_land_values(self): gridset = { 'data': np.array([[100, LAND], [100, 100]]), 'metadata': {} } actual = drop_land(LAND, COAST, gridset) expected_data = np.array([[100, 0], [100, 100]]) self.assertTrue(actual['metadata']['drop_land']) npt.assert_array_equal(actual['data'], expected_data) def test_drops_coast_values(self): gridset = { 'data': np.array([[100, 100], [100, COAST]]), 'metadata': {} } actual = drop_land(LAND, COAST, gridset) expected_data = np.array([[100, 100], [100, 0]]) self.assertTrue(actual['metadata']['drop_land']) npt.assert_array_equal(actual['data'], expected_data) def test_drops_land_and_coast_values(self): gridset = { 'data': np.array([[100, LAND], [100, COAST]]), 'metadata': {} } actual = drop_land(LAND, COAST, gridset) expected_data = np.array([[100, 0], [100, 0]]) self.assertTrue(actual['metadata']['drop_land']) npt.assert_array_equal(actual['data'], expected_data) def test_does_nothing_when_no_land(self): gridset = { 'data': np.array([[100, 76], [100, 100]]), 'metadata': {} } actual = drop_land(LAND, COAST, gridset) expected_data = np.array([[100, 76], [100, 100]]) self.assertTrue(actual['metadata']['drop_land']) npt.assert_array_equal(actual['data'], expected_data) class Test_ensure_full_nrt_monthly(unittest.TestCase): def setUp(self): self.gridset = { 'data': 'NA for this test', 'metadata': { 'files': [None] * 31, 'period_index': pd.period_range('1/1/2001', '1/31/2001', freq='D'), 'period': pd.Period('2001-01', freq='M'), 'temporality': 'M' } } def test_returns_exception_when_period_index_is_daily_and_nrt_filelist_is_incomplete(self): self.gridset['metadata']['files'] = [None] * 20 with self.assertRaises(e.IncompleteNRTGridsetError): ensure_full_nrt_month(self.gridset) def test_returns_gridset_when_period_index_is_daily_and_nrt_filelist_is_complete(self): actual = ensure_full_nrt_month(self.gridset) expected = self.gridset self.assertEqual(actual, expected) def test_returns_gridset_when_temporality_is_incorrect(self): self.gridset['metadata']['temporality'] = 'D' expected = self.gridset actual = ensure_full_nrt_month(self.gridset) self.assertEqual(actual, expected) def test_returns_gridset_when_period_is_monthly(self): self.gridset['metadata']['files'] = [None] self.gridset['metadata']['period_index'] = pd.period_range('1/1/2001', '1/1/2001', freq='M') self.gridset['metadata']['period'] = pd.Period('2001-01', freq='M') expected = self.gridset actual = ensure_full_nrt_month(self.gridset) self.assertEqual(actual, expected) class Test_prevent_empty(unittest.TestCase): def test_returns_same_nonempty_gridset(self): gridset = { 'data': np.array([[100, 76], [255, 100]]), 'metadata': {'missing_value': 255} } actual = prevent_empty(gridset) expected_data = np.array([[100, 76], [255, 100]]) self.assertEqual(actual['metadata'], {'missing_value': 255}) npt.assert_array_equal(actual['data'], expected_data) def test_raises_error_with_all_missing_gridset(self): gridset = { 'data': np.array([[255, 255], [255, 255]]), 'metadata': {'missing_value': 255} } with self.assertRaises(e.SeaIceDataNoData): prevent_empty(gridset) class Test__interpolate_missing(unittest.TestCase): def test_when_no_missing_data(self): data_grid = np.ma.array([[50., 50.], [100., 100.]]) zeros = np.zeros_like(data_grid) interpolation_grids = np.expand_dims(zeros, axis=2) # expected is just the data grid when there's no missing data expected = data_grid actual = gf._interpolate_missing(data_grid, interpolation_grids) npt.assert_array_equal(expected.data, actual.data) def test_when_data_is_masked(self): missing = 255 data_grid = np.ma.array([[50., missing], [100., 100.]]) zeros = np.zeros_like(data_grid) interpolation_grids = np.expand_dims(zeros, axis=2) data_grid = np.ma.masked_equal(data_grid, missing) expected = np.ma.array([[50., 0], [100., 100.]]) actual = gf._interpolate_missing(data_grid, interpolation_grids, missing_value=missing) npt.assert_array_equal(expected, actual) npt.assert_array_equal(expected.data, actual.data) def test_when_target_grid_is_all_missing(self): data_grid = np.ma.array([[50., 50.], [100., 100.]]) zeros = np.zeros_like(data_grid) interpolation_grids = np.dstack([data_grid, zeros]) target_grid = np.full(data_grid.shape, 255, dtype=np.int) expected = np.ma.array([[25., 25.], [50., 50.]]) actual = gf._interpolate_missing(target_grid, interpolation_grids) npt.assert_array_equal(expected.data, actual.data) def test_masked_flagged_values_unchanged_and_masked_no_missing(self): data_grid = np.array([[50., 251.], [100., 253.]]) interpolation_grids = np.expand_dims(data_grid.copy(), axis=2) expected = np.array([[50., 251.], [100., 253.]]) actual = gf._interpolate_missing(data_grid, interpolation_grids) npt.assert_array_equal(expected, actual) npt.assert_array_equal(expected.data, actual.data) def test_flagged_values_unchanged_with_missing(self): data_grid = np.ma.array([[50., 251.], [255., 253.]]) data_grid2 = np.ma.array([[50., 251.], [75., 253.]]) interpolation_grids = np.expand_dims(data_grid2, axis=2) expected = np.ma.array([[50., 251.], [75., 253.]]) actual = gf._interpolate_missing(data_grid, interpolation_grids) npt.assert_array_equal(expected, actual) def test_flagged_values_are_minimum_anded_with_missing(self): """Test pole hole is replaced by data if it shrinks or grows""" data_grid = np.ma.array([[50., 251.], [255., 253.]]) data_grid2 = np.ma.array([[251., 251.], # extra pole in this data [75., 253.]]) interpolation_grids = np.dstack([data_grid, data_grid2]) target_grid = np.full(data_grid.shape, 255, dtype=np.int) expected = np.ma.array([[50., 251.], # extra pole is replaced [75., 253.]]) # with data from data_grid actual = gf._interpolate_missing(target_grid, interpolation_grids) npt.assert_array_equal(expected, actual) def test_flagged_values_and_missing_mixed_together_return_missing(self): data_grid = np.ma.array([[255., 251.], [255., 253.]]) data_grid2 = np.ma.array([[251., 251.], # extra pole in this data [75., 253.]]) target_grid = np.full(data_grid.shape, 255, dtype=np.int) interpolation_grids = np.dstack([data_grid, data_grid2]) expected = np.ma.array([[255., 251.], [75., 253.]]) actual = gf._interpolate_missing(target_grid, interpolation_grids) npt.assert_array_equal(expected, actual) npt.assert_array_equal(expected.data, actual.data) def test_flagged_values_and_missing_and_data_mixed_together_return_data(self): data_grid = np.ma.array([[255., 251.], [255., 253.]]) data_grid2 = np.ma.array([[251., 251.], # extra pole in this data [75., 253.]]) data_grid3 = np.ma.array([[50., 251.], [255., 253.]]) interpolation_grids = np.dstack([data_grid, data_grid3]) target_grid = data_grid2 expected = np.ma.array([[50., 251.], [75., 253.]]) actual = gf._interpolate_missing(target_grid, interpolation_grids) npt.assert_array_equal(expected, actual) def test_with_missing_values(self): data_grid = np.array([[55., 0.], [55., 53.]]) data_grid2 = np.array([[60., 255.], [75., 23.]]) data_grid3 = np.array([[50., 100.], [55., 53.]]) interpolation_grids = np.dstack([data_grid, data_grid3]) target_grid = data_grid2 actual = gf._interpolate_missing(target_grid, interpolation_grids) expected_data = np.array([[60., 50.], [75., 23.]]) npt.assert_array_equal(expected_data, actual) class Test__index_by_date(unittest.TestCase): def test__index_by_date(self): filelist = ['nt_20120918_f17_v1.1_s.bin', 'nt_20120919_f13_v1.1_s.bin', 'nt_20120920_f17_v1.1_s.bin'] date_ = dt.date(2012, 9, 19) expected = 1 actual = gf._index_by_date(filelist, date_) self.assertEquals(expected, actual) def test__index_by_date_with_no_matches(self): filelist = ['nt_20120918_f17_v1.1_s.bin', 'nt_20120919_f13_v1.1_s.bin', 'nt_20120920_f17_v1.1_s.bin'] date = dt.date(2014, 9, 19) with self.assertRaises(e.IndexNotFoundError): gf._index_by_date(filelist, date) class Test__extent_grid_from_conc_grid(unittest.TestCase): def test_base_case(self): conc = np.array([[100, 100], [100, 100]]) actual = gf._extent_grid_from_conc_grid(conc) expected = np.array([[1, 1], [1, 1]]) npt.assert_array_equal(actual, expected) def test_default_valid_range(self): conc = np.array([[15, 100], [14, 100]]) actual = gf._extent_grid_from_conc_grid(conc) expected = np.array([[1, 1], [0, 1]]) npt.assert_array_equal(actual, expected) def test_only_counts_conc_within_range(self): conc = np.array([[50, 51], [27, 26]]) actual = gf._extent_grid_from_conc_grid(conc, valid_extent_range=(27, 50)) expected = np.array([[1, 0], [1, 0]]) npt.assert_array_equal(actual, expected) def test_preserves_flag_values(self): conc = np.array([[100, 1977], [100, 100]]) flags = { 'starwars': 1977 } actual = gf._extent_grid_from_conc_grid(conc, flags=flags) expected = np.array([[1, 1977], [1, 1]]) npt.assert_array_equal(actual, expected) def test_counts_pole_flag_as_extent(self): conc = np.array([[100, 1977], [100, 100]]) flags = { 'pole': 1977 } actual = gf._extent_grid_from_conc_grid(conc, flags=flags) expected = np.array([[1, 1], [1, 1]]) npt.assert_array_equal(actual, expected) def test_all_the_options(self): conc = np.array([[13, 75, 100], [12, 1977, 100], [2389, 50, 100]]) flags = { 'pole': 1977, 'special': 2389 } actual = gf._extent_grid_from_conc_grid(conc, valid_extent_range=(13, 99), flags=flags) expected = np.array([[1, 1, 0], [0, 1, 0], [2389, 1, 0]]) npt.assert_array_equal(actual, expected) def test_empty_gridset(self): conc = np.array([[255, 255], [255, 255]]) flags = { 'missing': 255 } actual = gf._extent_grid_from_conc_grid(conc, flags=flags) expected = np.array([[255, 255], [255, 255]]) npt.assert_array_equal(actual, expected)

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5

f400e606600c55f35fa1e8fcce3a914f8a7314bd

3,958

py

Python

backend/api/migrations/0001_initial.py

projectpai/paipass

8b8e70b6808bf026cf957e240c7eed7bfcf4c55d

[ "MIT" ]

3

2021-04-17T10:20:26.000Z

2022-03-08T07:36:13.000Z

backend/api/migrations/0001_initial.py

projectpai/paipass

8b8e70b6808bf026cf957e240c7eed7bfcf4c55d

[ "MIT" ]

null

backend/api/migrations/0001_initial.py

projectpai/paipass

8b8e70b6808bf026cf957e240c7eed7bfcf4c55d

[ "MIT" ]

null

# Generated by Django 3.0.1 on 2020-05-21 10:15 import api.models from django.db import migrations, models import uuid class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='EmailVerificationSession', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('email', models.EmailField(max_length=254, unique=True, verbose_name='email address')), ('status', models.CharField(choices=[('Accepted', 'ACCEPTED'), ('Pending', 'PENDING'), ('Cancelled', 'CANCELLED')], default='Cancelled', max_length=32, verbose_name='status')), ('ip_address', models.CharField(max_length=64, verbose_name='ip address')), ('created_on', models.DateTimeField(auto_now_add=True)), ('verified_on', models.DateTimeField(default=api.models.long_time_from_now)), ('verification_code', models.CharField(max_length=128, verbose_name='Verification Code')), ], ), migrations.CreateModel( name='IdentityVerificationSession', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), migrations.CreateModel( name='PhoneVerificationSession', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('phone_number', models.CharField(max_length=31, verbose_name='phone number')), ('status', models.CharField(choices=[('Accepted', 'ACCEPTED'), ('Pending', 'PENDING'), ('Cancelled', 'CANCELLED')], default='Pending', max_length=32, verbose_name='status')), ('ip_address', models.CharField(max_length=64, verbose_name='ip address')), ('created_on', models.DateTimeField(auto_now_add=True)), ('verified_on', models.DateTimeField(default=api.models.long_time_from_now)), ('verification_code', models.CharField(max_length=128, verbose_name='Verification Code')), ], ), migrations.CreateModel( name='ResetPasswordSession', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('status', models.CharField(choices=[('ACCEPTED', 'Accepted'), ('PENDING', 'Pending'), ('CANCELLED', 'Cancelled')], default='PENDING', max_length=32, verbose_name='status')), ('ip_address', models.CharField(max_length=64, verbose_name='ip address')), ('created_on', models.DateTimeField(auto_now_add=True)), ('verification_code', models.CharField(max_length=128, verbose_name='Verification Code')), ], ), migrations.CreateModel( name='SecondFactorAuthSession', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('encoded_code', models.CharField(max_length=128, verbose_name='encoded code')), ('created_on', models.DateTimeField(auto_now_add=True)), ('verified_on', models.DateTimeField()), ('exchanged_on', models.DateTimeField()), ], ), migrations.CreateModel( name='UnsubscribedEmail', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_on', models.DateTimeField(auto_now_add=True)), ('nonce', models.CharField(max_length=256, verbose_name='nonce')), ('hash', models.CharField(max_length=128, verbose_name='hash')), ], ), ]

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1

0

null

0

5

f45e0c8b107ef72334958d4edb367f310df3f904

38

py

Python

main/__init__.py

CrewSY/KovalAgent

74a5848e5ef5baf52924a739cd33d1196a958205

[ "MIT" ]

null

main/__init__.py

CrewSY/KovalAgent

74a5848e5ef5baf52924a739cd33d1196a958205

[ "MIT" ]

13

2018-03-03T17:23:13.000Z

2018-03-05T16:20:40.000Z

main/__init__.py

CrewSY/KovalAgent

74a5848e5ef5baf52924a739cd33d1196a958205

[ "MIT" ]

null

"""Main app of KovalAgent project."""

19

37

0.684211

5

38

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1

38

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null

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null

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null

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null

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null

0

1

0

1

0

null

0

1

0

5

f4834442cb847dad5458c418789ee7af84fe15b6

12,180

py

Python

tests/test_cond.py

kadeng/tensorflow-onnx

db91f5b25cc2a053f46af3b2c04b65a679cff03b

[ "MIT" ]

null

tests/test_cond.py

kadeng/tensorflow-onnx

db91f5b25cc2a053f46af3b2c04b65a679cff03b

[ "MIT" ]

null

tests/test_cond.py

kadeng/tensorflow-onnx

db91f5b25cc2a053f46af3b2c04b65a679cff03b

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT license. """Unit Tests for tf.cond and tf.case.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import unittest import numpy as np import tensorflow as tf from backend_test_base import Tf2OnnxBackendTestBase # pylint: disable=missing-docstring,invalid-name,unused-argument,using-constant-test # pylint: disable=abstract-method,arguments-differ class CondTests(Tf2OnnxBackendTestBase): def test_simple_cond(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 res = tf.cond(x[0] < y[0], lambda: x+y, lambda: x-y, name="test_cond") _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) @unittest.skip("known issue about onnxruntime that initilizer is subgraph input") def test_cond_with_const_branch(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") true_const = tf.constant(True, name="true_const", dtype=tf.bool) def cond_graph(): with tf.name_scope("cond_graph", "cond_graph", [x, y]): b = tf.constant(np.array([2, 1, 3], dtype=np.float32), name="b", dtype=tf.float32) return b res = tf.cond(true_const, lambda: x+y, cond_graph, name="test_cond") _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) @unittest.skip("a very special case that true and false branch of tf.cond only \ contain a const node, which depends on Switch per control inputs") def test_cond_with_only_const(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") def cond_graph(): with tf.name_scope("cond_graph", "cond_graph", [x, y]): b = tf.constant(10, name="b", dtype=tf.float32) return b res = tf.cond(x[0] < y[0], cond_graph, cond_graph, name="test_cond") _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) def test_cond_with_multi_merge(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 res = tf.cond(x[0] < y[0], lambda: [x, x+y], lambda: [x, x-y], name="test") _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) def test_cond_with_replicate_output(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 res = tf.cond(x[0] < y[0], lambda: [x, x], lambda: [y, y], name="test_cond") _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) def test_nest_cond(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 def cond_graph(): def cond_graph1(): def cond_graph2(): return tf.cond(x[0] < y[0], lambda: x + y, lambda: tf.square(y)) return tf.cond(tf.reduce_any(x < y), cond_graph2, cond_graph2) return tf.cond(x[0] > y[0], cond_graph1, cond_graph1) res = tf.cond(x[0] < y[0], cond_graph, cond_graph, name="test_cond") _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) @unittest.skip("not support for now") def test_cond_with_while_loop(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") def cond_graph(): b = tf.constant(np.array([0], dtype=np.int32), dtype=tf.int32) z = tf.gather_nd(x, b) # while_loop c = lambda y: tf.reduce_any(tf.less(y, 10)) b = lambda i: tf.add(y, 1) r = tf.while_loop(c, b, [y]) return tf.cond(x[0] > y[0], lambda: z, lambda: r) res = x[2] * tf.cond(x[0] < y[0], lambda: x, cond_graph, name="test_cond") _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) @unittest.skip("not support for now") def test_cond_in_while_loop(self): i = tf.placeholder(tf.int32, (), name="input_1") inputs = tf.placeholder(tf.float32, (10,), name="input_2") inputs_2 = tf.identity(inputs) input_ta = tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True).unstack(inputs_2) output_ta = tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True) c = lambda i, *_: tf.logical_and(tf.less(i, 10), i >= 0) def b(i, out_ta): new_i = tf.add(i, 1) x = input_ta.read(i) x = tf.cond(x >= 0, lambda: x - 1, lambda: x + 3) out_ta_new = out_ta.write(i, x) return new_i, out_ta_new i_final, out_final = tf.while_loop(c, b, [i, output_ta]) _ = tf.identity(i_final, name="i") _ = tf.identity(out_final.stack(), name="output_ta") input_names_with_port = ["input_1:0", "input_2:0"] feed_dict = {"input_1:0": np.array(0, dtype=np.int32), "input_2:0": np.array([2.0, 16.0, 5.0, 1.6, 5.0, 6.0, 7.0, 8.0, 9.0, 10.], dtype=np.float32)} output_names_with_port = ["i:0", "output_ta:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port, rtol=1e-06) def test_simple_case(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 res = tf.case([(tf.reduce_all(x < 1), lambda: x+y), (tf.reduce_all(y > 0), lambda: tf.square(y))], default=lambda: x, name="test_case") _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) def test_case_with_exclusive(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 res = tf.case([(tf.reduce_all(x < 1), lambda: x+y), (tf.reduce_all(y > 0), lambda: tf.square(y))], default=lambda: x, name="test_case", exclusive=True) _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) def test_case_without_default_branch(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 res = tf.case([(tf.reduce_all(x < 1), lambda: x+y), (tf.reduce_all(y > 0), lambda: tf.square(y))]) _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) def test_case_with_multi_merge(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 res = tf.case( [(tf.reduce_all(x < 1), lambda: [x+y, x-y]), (tf.reduce_all(y > 0), lambda: [tf.abs(x), tf.square(y)])], default=lambda: [x, y], name="test_case" ) _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) def test_nest_case(self): x_val = np.array([1, 2, 3], dtype=np.float32) y_val = np.array([4, 5, 6], dtype=np.float32) x = tf.placeholder(tf.float32, x_val.shape, name="input_1") y = tf.placeholder(tf.float32, y_val.shape, name="input_2") x = x + 1 y = y + 1 def case_graph(): return tf.case( [(tf.reduce_all(x < 1), lambda: x+y), (tf.reduce_all(y > 0), lambda: tf.square(y))], default=lambda: x - y, name="test_case" ) res = tf.case([(x[0] > 0, case_graph), (x[0] < 0, case_graph)], default=lambda: x - y) _ = tf.identity(res, name="output") feed_dict = {"input_1:0": x_val, "input_2:0": y_val} input_names_with_port = ["input_1:0", "input_2:0"] output_names_with_port = ["output:0"] self.run_test_case(feed_dict, input_names_with_port, output_names_with_port) if __name__ == '__main__': Tf2OnnxBackendTestBase.trigger(CondTests)

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1,960

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0.004566

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null

0

1

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null

0

5

f48576ca06150b59dd8047a7b71b99ca57812f4e

172

py

Python

data/strategies/publishers/sage.py

jamesrharwood/journal-guidelines

fe6c0a6d3c0443df6fc816b9503fad24459ddb4a

[ "MIT" ]

null

data/strategies/publishers/sage.py

jamesrharwood/journal-guidelines

fe6c0a6d3c0443df6fc816b9503fad24459ddb4a

[ "MIT" ]

null

data/strategies/publishers/sage.py

jamesrharwood/journal-guidelines

fe6c0a6d3c0443df6fc816b9503fad24459ddb4a

[ "MIT" ]

null

url = "journals.sagepub.com/home/{ID}" extractor_args = dict(restrict_text=[r"submission\s*guidelines"]) template = "https://journals.sagepub.com/author-instructions/{ID}"

43

66

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23

172

5.608696

0.826087

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0.27907

0

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172

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0.616279

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1

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1

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1

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null

0

5

be44d8527e6b58c546459f50a38c8c28c7b75321

7,487

py

Python

test/common/test_inventory_record.py

polarG/splunk-connect-for-snmp

d1e85675edd5caa5bad9114d1611411e15cec063

[ "Apache-2.0" ]

null

test/common/test_inventory_record.py

polarG/splunk-connect-for-snmp

d1e85675edd5caa5bad9114d1611411e15cec063

[ "Apache-2.0" ]

null

test/common/test_inventory_record.py

polarG/splunk-connect-for-snmp

d1e85675edd5caa5bad9114d1611411e15cec063

[ "Apache-2.0" ]

null

from unittest import TestCase from splunk_connect_for_snmp.common.inventory_record import InventoryRecord class TestInventoryRecord(TestCase): def test_address_not_none(self): ir_dict = {"address": None} with self.assertRaises(ValueError) as e: InventoryRecord(**ir_dict) self.assertEqual( "field address cannot be null", e.exception.args[0][0].exc.args[0] ) def test_address_not_commented(self): ir_dict = {"address": "#asd"} with self.assertRaises(ValueError) as e: InventoryRecord(**ir_dict) self.assertEqual( "field address cannot be commented", e.exception.args[0][0].exc.args[0] ) def test_address_not_resolvable(self): ir_dict = {"address": "12313sdfsf"} with self.assertRaises(ValueError) as e: InventoryRecord(**ir_dict) self.assertEqual( "field address must be an IP or a resolvable hostname 12313sdfsf", e.exception.args[0][0].exc.args[0], ) def test_port_too_high(self): ir_dict = { "address": "192.168.0.1", "port": 65537, "version": "2c", "walk_interval": 1850, "SmartProfiles": True, "delete": "", } with self.assertRaises(ValueError) as e: InventoryRecord(**ir_dict) self.assertEqual("Port out of range 65537", e.exception.args[0][0].exc.args[0]) def test_version_none(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": None, "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 1850, "profiles": "", "SmartProfiles": True, "delete": False, } ir = InventoryRecord(**ir_dict) self.assertEqual("2c", ir.version) def test_version_out_of_range(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "5a", "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 1850, "profiles": "", "SmartProfiles": True, "delete": False, } with self.assertRaises(ValueError) as e: InventoryRecord(**ir_dict) self.assertEqual( "version out of range 5a accepted is 1 or 2c or 3", e.exception.args[0][0].exc.args[0], ) def test_empty_community(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "3", "community": "", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 1850, "profiles": "", "SmartProfiles": True, "delete": False, } ir = InventoryRecord(**ir_dict) self.assertIsNone(ir.community) def test_empty_walk_interval(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "3", "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": None, "profiles": "", "SmartProfiles": True, "delete": False, } ir = InventoryRecord(**ir_dict) self.assertEqual(42000, ir.walk_interval) def test_too_low_walk_interval(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "3", "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 20, "profiles": "", "SmartProfiles": True, "delete": False, } ir = InventoryRecord(**ir_dict) self.assertEqual(1800, ir.walk_interval) def test_too_high_walk_interval(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "3", "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 50000, "profiles": "", "SmartProfiles": True, "delete": False, } ir = InventoryRecord(**ir_dict) self.assertEqual(42000, ir.walk_interval) def test_profiles_not_string(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "3", "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 1850, "profiles": [], "SmartProfiles": True, "delete": False, } ir = InventoryRecord(**ir_dict) self.assertEqual([], ir.profiles) def test_smart_profiles_empty(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "3", "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 1850, "profiles": "", "SmartProfiles": True, "delete": False, } ir = InventoryRecord(**ir_dict) self.assertTrue(ir.SmartProfiles) def test_delete_empty(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "3", "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 1850, "profiles": "", "SmartProfiles": True, "delete": "", } ir = InventoryRecord(**ir_dict) self.assertFalse(ir.delete) def test_from_json(self): ir = InventoryRecord.from_json( '{"address": "192.168.0.1", "port": "34", "version": "3", "community": ' '"public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": ' '1850, "profiles": "", "SmartProfiles": true, "delete": ""}' ) self.assertEqual(ir.address, "192.168.0.1") self.assertEqual(ir.port, 34) self.assertEqual(ir.version, "3") self.assertEqual(ir.community, "public") self.assertEqual(ir.secret, "secret") self.assertEqual(ir.securityEngine, "ENGINE") self.assertEqual(ir.walk_interval, 1850) self.assertEqual(ir.profiles, []) self.assertEqual(ir.SmartProfiles, True) self.assertEqual(ir.delete, False) def test_to_json(self): ir_dict = { "address": "192.168.0.1", "port": "34", "version": "3", "community": "public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": 1850, "profiles": "", "SmartProfiles": True, "delete": "", } ir = InventoryRecord(**ir_dict) self.assertEqual( '{"address": "192.168.0.1", "port": 34, "version": "3", "community": ' '"public", "secret": "secret", "securityEngine": "ENGINE", "walk_interval": ' '1850, "profiles": [], "SmartProfiles": true, "delete": false}', ir.to_json(), )

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0

0.052784

0.352211

7,487

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90

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0

0.084906

0

null

0

1

0

null

0

5

be76cdc119d092e0171003568528e58a8611795b

288

py

Python

vmlib/custom_exceptions.py

valentinmetraux/vmlib

0a04aeb486b1b6ed4973041807e49ca5a59600e9

[ "MIT" ]

null

vmlib/custom_exceptions.py

valentinmetraux/vmlib

0a04aeb486b1b6ed4973041807e49ca5a59600e9

[ "MIT" ]

null

vmlib/custom_exceptions.py

valentinmetraux/vmlib

0a04aeb486b1b6ed4973041807e49ca5a59600e9

[ "MIT" ]

null

# -*- coding: utf-8 -*- class SimpleExitException(Exception): """ Custom exception showing a simple exit program message """ def __init__(self): pass def __str__(self): return 'Script terminated'.upper() def __repr__(self): return ''

16

58

0.59375

29

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0.793103

0.125786

0

0.004878

0.288194

288

17

59

16.941176

0.770732

0.267361

0

0.087179

0

1

0.428571

false

0.142857

0

0.285714

0.857143

0

1

0

null

0

null

0

1

0

1

0

1

0

5

be8b1eb0fcc45323576643008912f03114d49df4

87

py

Python

pub_data_visualization/production/load/eco2mix/__init__.py

cre-os/pub-data-visualization

e5ec45e6397258646290836fc1a3b39ad69bf266

[ "MIT" ]

10

2020-10-08T11:35:49.000Z

2021-01-22T16:47:59.000Z

pub_data_visualization/production/load/eco2mix/__init__.py

l-leo/pub-data-visualization

68eea00491424581b057495a7f0f69cf74e16e7d

[ "MIT" ]

3

2021-03-15T14:26:43.000Z

2021-12-02T15:27:49.000Z

pub_data_visualization/production/load/eco2mix/__init__.py

cre-dev/pub-data-visualization

229bb7a543684be2cb06935299345ce3263da946

[ "MIT" ]

1

2021-01-22T16:47:10.000Z

""" Module to load production data provided by eCO2mix. """ from .load import *

10.875

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0.241379

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null

0

1

0

1

0

1

0

5

be8cf68de1d5d3990d599543d2348b7009a5e004

8,516

py

Python

tests/unit/transports/socket_transport_test.py

simomo/haigha

59f59e58fd6edb6a4dc2a9e89e7aeff8bfaceaf0

[ "BSD-3-Clause" ]

null

tests/unit/transports/socket_transport_test.py

simomo/haigha

59f59e58fd6edb6a4dc2a9e89e7aeff8bfaceaf0

[ "BSD-3-Clause" ]

null

tests/unit/transports/socket_transport_test.py

simomo/haigha

59f59e58fd6edb6a4dc2a9e89e7aeff8bfaceaf0

[ "BSD-3-Clause" ]

null

''' Copyright (c) 2011-2014, Agora Games, LLC All rights reserved. https://github.com/agoragames/haigha/blob/master/LICENSE.txt ''' from chai import Chai import errno import socket from haigha.transports import socket_transport from haigha.transports.socket_transport import * class SocketTransportTest(Chai): def setUp(self): super(SocketTransportTest, self).setUp() self.connection = mock() self.transport = SocketTransport(self.connection) self.transport._host = 'server:1234' def test_init(self): assert_equals(bytearray(), self.transport._buffer) assert_true(self.transport._synchronous) def test_connect_with_no_klass_arg(self): klass = mock() sock = mock() orig_defaults = self.transport.connect.im_func.func_defaults self.transport.connect.im_func.func_defaults = (klass,) expect(klass).returns(sock) self.connection._connect_timeout = 4.12 self.connection._sock_opts = { ('family', 'tcp'): 34, ('range', 'ipv6'): 'hex' } expect(sock.setblocking).args(True) expect(sock.settimeout).args(4.12) expect(sock.setsockopt).any_order().args( 'family', 'tcp', 34).any_order() expect(sock.setsockopt).any_order().args( 'range', 'ipv6', 'hex').any_order() expect(sock.connect).args(('host', 5309)) expect(sock.settimeout).args(None) self.transport.connect(('host', 5309)) self.transport.connect.im_func.func_defaults = orig_defaults def test_connect_with_klass_arg(self): klass = mock() sock = mock() expect(klass).returns(sock) self.connection._connect_timeout = 4.12 self.connection._sock_opts = { ('family', 'tcp'): 34, ('range', 'ipv6'): 'hex' } expect(sock.setblocking).args(True) expect(sock.settimeout).args(4.12) expect(sock.setsockopt).any_order().args( 'family', 'tcp', 34).any_order() expect(sock.setsockopt).any_order().args( 'range', 'ipv6', 'hex').any_order() expect(sock.connect).args(('host', 5309)) expect(sock.settimeout).args(None) self.transport.connect(('host', 5309), klass=klass) def test_read(self): self.transport._sock = mock() self.transport.connection.debug = False expect(self.transport._sock.settimeout).args(None) expect(self.transport._sock.getsockopt).args( socket.SOL_SOCKET, socket.SO_RCVBUF).returns(4095) expect(self.transport._sock.recv).args(4095).returns('buffereddata') assert_equals('buffereddata', self.transport.read()) def test_read_when_data_buffered(self): self.transport._sock = mock() self.transport.connection.debug = False self.transport._buffer = bytearray('buffered') expect(self.transport._sock.settimeout).args(3) expect(self.transport._sock.getsockopt).any_args().returns(4095) expect(self.transport._sock.recv).args(4095).returns('data') assert_equals('buffereddata', self.transport.read(3)) assert_equals(bytearray(), self.transport._buffer) def test_read_when_debugging(self): self.transport._sock = mock() self.transport.connection.debug = 2 expect(self.transport._sock.settimeout).args(None) expect(self.transport._sock.getsockopt).any_args().returns(4095) expect(self.transport._sock.recv).args(4095).returns('buffereddata') expect(self.transport.connection.logger.debug).args( 'read 12 bytes from server:1234') assert_equals('buffereddata', self.transport.read(0)) def test_read_when_socket_closes(self): self.transport._sock = mock() self.transport.connection.debug = 2 expect(self.transport._sock.settimeout).args(None) expect(self.transport._sock.getsockopt).any_args().returns(4095) expect(self.transport._sock.recv).args(4095).returns('') expect(self.transport.connection.transport_closed).args( msg='error reading from server:1234') self.transport.read() def test_read_when_socket_timeout(self): self.transport._sock = mock() self.transport.connection.debug = 2 expect(self.transport._sock.settimeout).args(42) expect(self.transport._sock.getsockopt).any_args().returns(4095) expect(self.transport._sock.recv).args(4095).raises( socket.timeout('not now')) assert_equals(None, self.transport.read(42)) def test_read_when_raises_eagain(self): self.transport._sock = mock() self.transport.connection.debug = 2 expect(self.transport._sock.settimeout).args(42) expect(self.transport._sock.getsockopt).any_args().returns(4095) expect(self.transport._sock.recv).args(4095).raises( EnvironmentError(errno.EAGAIN, 'tryagainlater')) assert_equals(None, self.transport.read(42)) def test_read_when_raises_socket_timeout(self): self.transport._sock = mock() self.transport.connection.debug = 2 expect(self.transport._sock.settimeout).args(42) expect(self.transport._sock.getsockopt).any_args().returns(4095) expect(self.transport._sock.recv).args(4095).raises( socket.timeout()) assert_equals(None, self.transport.read(42)) def test_read_when_raises_other_errno(self): self.transport._sock = mock() self.transport.connection.debug = 2 expect(self.transport._sock.settimeout).args(42) expect(self.transport._sock.getsockopt).any_args().returns(4095) expect(self.transport._sock.recv).args(4095).raises( EnvironmentError(errno.EBADF, 'baddog')) expect(self.transport.connection.logger.exception).args( 'error reading from server:1234') expect(self.transport.connection.transport_closed).args( msg='error reading from server:1234') with assert_raises(EnvironmentError): self.transport.read(42) def test_read_when_no_sock(self): self.transport.read() def test_buffer(self): self.transport._sock = mock() self.transport.buffer(bytearray('somedata')) assert_equals(bytearray('somedata'), self.transport._buffer) def test_buffer_when_already_buffered(self): self.transport._sock = mock() self.transport._buffer = bytearray('some') self.transport.buffer(bytearray('data')) assert_equals(bytearray('somedata'), self.transport._buffer) def test_buffer_when_no_sock(self): self.transport.buffer('somedata') def test_write(self): self.transport._sock = mock() self.transport.connection.debug = False expect(self.transport._sock.sendall).args('somedata') self.transport.write('somedata') def test_write_when_sendall_fails(self): self.transport._sock = mock() self.transport.connection.debug = False expect(self.transport._sock.sendall).args( 'somedata').raises(Exception('fail')) assert_raises(Exception, self.transport.write, 'somedata') def test_write_when_sendall_raises_environmenterror(self): self.transport._sock = mock() self.transport.connection.debug = False expect(self.transport._sock.sendall).args('somedata').raises( EnvironmentError(errno.EAGAIN, 'tryagainlater')) expect(self.transport.connection.logger.exception).args( 'error writing to server:1234') expect(self.transport.connection.transport_closed).args( msg='error writing to server:1234') self.transport.write('somedata') def test_write_when_debugging(self): self.transport._sock = mock() self.transport.connection.debug = 2 expect(self.transport._sock.sendall).args('somedata') expect(self.transport.connection.logger.debug).args( 'sent 8 bytes to server:1234') self.transport.write('somedata') def test_write_when_no_sock(self): self.transport.write('somedata') def test_disconnect(self): self.transport._sock = mock() expect(self.transport._sock.close) self.transport.disconnect() assert_equals(None, self.transport._sock) def test_disconnect_when_no_sock(self): self.transport.disconnect()

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be9d09fbbc96ef769180ca6f4343d3c8bae7bfdd

137

py

Python

django_pwned_passwords/apps.py

jamiecounsell/django-pwned-passwords

b18c4a73b497555c2d6672455eb1fd9d69201e70

[ "MIT" ]

24

2017-08-05T22:48:21.000Z

2022-01-31T08:10:28.000Z

django_pwned_passwords/apps.py

jamiecounsell/django-pwned-passwords

b18c4a73b497555c2d6672455eb1fd9d69201e70

[ "MIT" ]

9

2018-03-06T14:49:24.000Z

2020-10-22T18:05:23.000Z

django_pwned_passwords/apps.py

jamiecounsell/django-pwned-passwords

b18c4a73b497555c2d6672455eb1fd9d69201e70

[ "MIT" ]

7

2018-02-28T22:00:39.000Z

2022-01-08T18:59:34.000Z

# -*- coding: utf-8 from django.apps import AppConfig class DjangoPwnedPasswordsConfig(AppConfig): name = 'django_pwned_passwords'

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bebffe549a5895c2a2397685dfbac64d5feef6ae

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py

Python

transforms/spectrogram.py

koukyo1994/kaggle-rfcx

c3573d014d99312b58882e7b939de6c1055129b1

[ "MIT" ]

6

2021-02-18T05:18:17.000Z

2022-02-19T02:49:32.000Z

transforms/spectrogram.py

koukyo1994/kaggle-rfcx

c3573d014d99312b58882e7b939de6c1055129b1

[ "MIT" ]

null

transforms/spectrogram.py

koukyo1994/kaggle-rfcx

c3573d014d99312b58882e7b939de6c1055129b1

[ "MIT" ]

2

2021-02-18T11:31:50.000Z

2022-02-19T02:49:07.000Z

def get_spectrogram_transforms(config: dict, phase: str): return None

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null

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bec1fe4224049fad313dcde78e37ad76a44f0053

319

py

Python

oslo/torch/nn/parallel/data_parallel/__init__.py

lipovsek/oslo

c2cde6229068808bf691e200f8af8c97c1631eb4

[ "Apache-2.0" ]

249

2021-12-21T05:25:53.000Z

2022-03-21T21:03:58.000Z

oslo/torch/nn/parallel/data_parallel/__init__.py

lipovsek/oslo

c2cde6229068808bf691e200f8af8c97c1631eb4

[ "Apache-2.0" ]

21

2021-12-22T13:22:18.000Z

2022-03-31T17:38:53.000Z

oslo/torch/nn/parallel/data_parallel/__init__.py

lipovsek/oslo

c2cde6229068808bf691e200f8af8c97c1631eb4

[ "Apache-2.0" ]

14

2021-12-21T10:28:36.000Z

2022-03-29T12:35:44.000Z

from oslo.torch.nn.parallel.data_parallel.distributed_data_parallel import ( DistributedDataParallel, ) from oslo.torch.nn.parallel.data_parallel.fully_sharded_data_parallel import ( FullyShardedDataParallel, ) from oslo.torch.nn.parallel.data_parallel.sharded_data_parallel import ( ShardedDataParallel, )

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bec3ed24d5ca0dc2379aae7dc04764241cfe56e7

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py

Python

src/devapps/version.py

axiros/DevApps

8a246ab08f2ed6b4dcbf5efb50326f9add66df49

[ "Apache-2.0", "MIT" ]

null

src/devapps/version.py

axiros/DevApps

8a246ab08f2ed6b4dcbf5efb50326f9add66df49

[ "Apache-2.0", "MIT" ]

null

src/devapps/version.py

axiros/DevApps

8a246ab08f2ed6b4dcbf5efb50326f9add66df49

[ "Apache-2.0", "MIT" ]

null

__version__ = 20181022

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null

0

5

fe3fd2d6885fb16a7822305b2cc927d43e677a62

44

py

Python

python/testData/codeInsight/smartEnter/forFirst_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/codeInsight/smartEnter/forFirst_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/codeInsight/smartEnter/forFirst_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def foo(): for <caret> in : pass

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fe4da2163dd6138e07e65ec7f4fb6c921c3a439f

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py

Python

tests/query/v2/test_optimizer.py

critical27/nebula-graph

04d00e779e860ed3ddb226c416c335a22acc1147

[ "Apache-2.0" ]

null

tests/query/v2/test_optimizer.py

critical27/nebula-graph

04d00e779e860ed3ddb226c416c335a22acc1147

[ "Apache-2.0" ]

null

tests/query/v2/test_optimizer.py

critical27/nebula-graph

04d00e779e860ed3ddb226c416c335a22acc1147

[ "Apache-2.0" ]

null

# --coding:utf-8-- # # Copyright (c) 2020 vesoft inc. All rights reserved. # # This source code is licensed under Apache 2.0 License, # attached with Common Clause Condition 1.0, found in the LICENSES directory. import pytest from tests.common.nebula_test_suite import NebulaTestSuite class TestOptimizer(NebulaTestSuite): @classmethod def prepare(cls): cls.use_nba() def test_PushFilterDownGetNbrsRule(self): resp = self.execute_query(''' GO 1 STEPS FROM "Boris Diaw" OVER serve WHERE $^.player.age > 18 YIELD serve.start_year as start_year ''') expected_plan = [ ["Project", [1]], ["GetNeighbors", [2], ['($^.player.age>18)']], ["Start", []] ] expected_data = [[2003], [2005], [2008], [2012], [2016]] self.check_exec_plan(resp, expected_plan) self.check_out_of_order_result(resp, expected_data) resp = self.execute_query(''' GO 1 STEPS FROM "James Harden" OVER like REVERSELY WHERE $^.player.age > 18 YIELD like.likeness as likeness ''') expected_plan = [ ["Project", [1]], ["GetNeighbors", [2], ['($^.player.age>18)']], ["Start", []] ] expected_data = [[90], [80], [99]] self.check_exec_plan(resp, expected_plan) self.check_out_of_order_result(resp, expected_data) resp = self.execute_query(''' GO 1 STEPS FROM "Boris Diaw" OVER serve WHERE serve.start_year > 2005 YIELD serve.start_year as start_year ''') expected_plan = [ ["Project", [1]], ["GetNeighbors", [2], ['(serve.start_year>2005)']], ["Start", []] ] expected_data = [[2008], [2012], [2016]] self.check_exec_plan(resp, expected_plan) self.check_out_of_order_result(resp, expected_data) resp = self.execute_query(''' GO 1 STEPS FROM "Lakers" OVER serve REVERSELY WHERE serve.start_year < 2017 YIELD serve.start_year as start_year ''') expected_plan = [ ["Project", [1]], ["GetNeighbors", [2], ['(serve.start_year<2017)']], ["Start", []] ] expected_data = [[2012], [1996], [2008], [1996], [2012]] self.check_exec_plan(resp, expected_plan) self.check_out_of_order_result(resp, expected_data) @pytest.mark.skip(reason="Depends on other opt rules to eliminate duplicate project nodes") def test_PushFilterDownGetNbrsRule_Failed(self): resp = self.execute_query(''' GO 1 STEPS FROM "Boris Diaw" OVER serve WHERE $^.player.age > 18 AND $$.team.name == "Lakers" YIELD $^.player.name AS name ''') expected_plan = [ ["Project", [1]], ["Filter", [2], ['($$.team.name=="Lakers")']], ["GetNeighbors", [3], ['($^.player.age>18)']], ["Start", []] ] expected_data = [['Boris Diaw']] self.check_exec_plan(resp, expected_plan) self.check_out_of_order_result(resp, expected_data) resp = self.execute_query(''' GO 1 STEPS FROM "Boris Diaw" OVER serve WHERE $^.player.age > 18 OR $$.team.name == "Lakers" YIELD $^.player.name AS name ''') expected_plan = [ ["Project", [1]], ["Filter", [2], ['($^.player.age>18) OR ($$.team.name=="Lakers")']] ["GetNeighbors", [3]], ["Start", []] ] expected_data = [['Boris Diaw']] self.check_exec_plan(resp, expected_plan) self.check_out_of_order_result(resp, expected_data) # fail to optimize cases resp = self.execute_query(''' GO 1 STEPS FROM "Boris Diaw" OVER serve \ WHERE $$.team.name == "Lakers" YIELD $^.player.name AS name ''') expected_plan = [ ["Project", [1]], ["Filter", [2]], ["GetNeighbors", [3]], ["Start", []] ] expected_data = [['Boris Diaw']] self.check_exec_plan(resp, expected_plan) self.check_out_of_order_result(resp, expected_data) def test_TopNRule(self): resp = self.execute_query(''' GO 1 STEPS FROM "Marco Belinelli" OVER like YIELD like.likeness AS likeness | ORDER BY likeness | LIMIT 2 ''') expected_plan = [ ["DataCollect", [1]], ["TopN", [2]], ["Project", [3]], ["GetNeighbors", [4]], ["Start", []] ] expected_data = [[50], [55]] self.check_exec_plan(resp, expected_plan) self.check_result(resp, expected_data) resp = self.execute_query(''' GO 1 STEPS FROM "Marco Belinelli" OVER like REVERSELY YIELD like.likeness AS likeness | ORDER BY likeness | LIMIT 1 ''') expected_plan = [ ["DataCollect", [1]], ["TopN", [2]], ["Project", [3]], ["GetNeighbors", [4]], ["Start", []] ] expected_data = [[83]] self.check_exec_plan(resp, expected_plan) self.check_result(resp, expected_data) def test_TopNRule_Failed(self): resp = self.execute_query(''' GO 1 STEPS FROM "Marco Belinelli" OVER like YIELD like.likeness as likeness | ORDER BY likeness | LIMIT 2, 3 ''') expected_plan = [ ["DataCollect", [1]], ["Limit", [2]], ["Sort", [3]], ["Project", [4]], ["GetNeighbors", [5]], ["Start", []] ] expected_data = [[60]] self.check_exec_plan(resp, expected_plan) self.check_result(resp, expected_data) resp = self.execute_query(''' GO 1 STEPS FROM "Marco Belinelli" OVER like YIELD like.likeness AS likeness | ORDER BY likeness ''') expected_plan = [ ["DataCollect", [1]], ["Sort", [2]], ["Project", [3]], ["GetNeighbors", [4]], ["Start", []] ] expected_data = [[50], [55], [60]] self.check_exec_plan(resp, expected_plan) self.check_result(resp, expected_data) def test_LimitPushDownRule(self): resp = self.execute_query(''' GO 1 STEPS FROM "James Harden" OVER like REVERSELY | Limit 2 ''') expected_plan = [ ["DataCollect", [1]], ["Limit", [2]], ["Project", [3]], ["GetNeighbors", [4], ['2']], ["Start", []] ] # expected_data = [[90], [80], [99]] self.check_exec_plan(resp, expected_plan) if resp.data is None: assert False, 'resp.data is None' assert len(resp.data.rows) == 2 resp = self.execute_query(''' GO 1 STEPS FROM "Vince Carter" OVER serve YIELD serve.start_year as start_year | Limit 3, 4 ''') expected_plan = [ ["DataCollect", [1]], ["Limit", [2]], ["Project", [3]], ["GetNeighbors", [4], ['7']], ["Start", []] ] # expected_data = [[1998], [2004], [2009], [2010], [2011], [2014], [2017], [2018]] self.check_exec_plan(resp, expected_plan) assert resp.data is not None, 'resp.data is None' assert len(resp.data.rows) == 4

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null

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null

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5

fe5aca361e7879ff77b515afd0fbb1ef94da56df

1,658

py

Python

zhmccli/__init__.py

zhmcclient/zhmccli

946e104ee37606afed9376c7a5ee935d5fcfcda2

[ "Apache-2.0" ]

7

2019-05-14T10:03:39.000Z

2022-02-22T08:57:29.000Z

zhmccli/__init__.py

zhmcclient/zhmccli

946e104ee37606afed9376c7a5ee935d5fcfcda2

[ "Apache-2.0" ]

257

2017-09-21T09:11:46.000Z

2022-03-31T13:59:01.000Z

zhmccli/__init__.py

zhmcclient/zhmccli

946e104ee37606afed9376c7a5ee935d5fcfcda2

[ "Apache-2.0" ]

4

2018-11-27T14:49:49.000Z

2021-02-20T04:59:40.000Z

# Copyright 2016-2019 IBM Corp. 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. """ zhmccli - A CLI for the IBM Z HMC, written in pure Python. """ from __future__ import absolute_import from ._version import * # noqa: F401 from ._cmd_info import * # noqa: F401 from ._cmd_session import * # noqa: F401 from ._cmd_cpc import * # noqa: F401 from ._cmd_lpar import * # noqa: F401 from ._cmd_partition import * # noqa: F401 from ._cmd_adapter import * # noqa: F401 from ._cmd_port import * # noqa: F401 from ._cmd_hba import * # noqa: F401 from ._cmd_nic import * # noqa: F401 from ._cmd_vfunction import * # noqa: F401 from ._cmd_vswitch import * # noqa: F401 from ._cmd_metrics import * # noqa: F401 from ._cmd_storagegroup import * # noqa: F401 from ._cmd_storagevolume import * # noqa: F401 from ._cmd_vstorageresource import * # noqa: F401 from ._cmd_capacitygroup import * # noqa: F401 from ._cmd_user import * # noqa: F401 from ._cmd_user_role import * # noqa: F401 from ._cmd_password_rule import * # noqa: F401 from ._cmd_character_rule import * # noqa: F401

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py

Python

pecos/qeccs/surface_4444/instructions.py

quantum-pecos/PECOS

44bc614a9152f3b316bacef6ca034f6a8a611293

[ "Apache-2.0" ]

15

2019-04-11T16:02:38.000Z

2022-03-15T16:56:36.000Z

pecos/qeccs/surface_4444/instructions.py

quantum-pecos/PECOS

44bc614a9152f3b316bacef6ca034f6a8a611293

[ "Apache-2.0" ]

4

2018-10-04T19:30:09.000Z

2019-03-12T19:00:34.000Z

pecos/qeccs/surface_4444/instructions.py

quantum-pecos/PECOS

44bc614a9152f3b316bacef6ca034f6a8a611293

[ "Apache-2.0" ]

3

2020-10-07T16:47:16.000Z

2022-02-01T05:34:54.000Z

from ..instruction_parent_class import LogicalInstruction from ...circuits.quantum_circuit import QuantumCircuit from ..helper_functions import pos2qudit class InstrSynExtraction(LogicalInstruction): """ Instruction for a round of syndrome extraction. Parent class sets self.qecc. """ def __init__(self, qecc, symbol, **gate_params): super().__init__(qecc, symbol, **gate_params) self.symbol = 'instr_syn_extract' self.init_ticks = gate_params.get('init_ticks', 0) self.meas_ticks = gate_params.get('meas_ticks', 7) self.data_ticks = gate_params.get('data_ticks', [2, 4, 3, 5]) self.abstract_circuit = QuantumCircuit(track_qudits=False, **gate_params) self.data_qudit_set = self.qecc.data_qudit_set self.ancilla_qudit_set = self.qecc.ancilla_qudit_set self.ancilla_x_check = set([]) self.ancilla_z_check = set([]) # Go through the ancillas and grab the data qubits that are on either side of it. layout = qecc.layout # qudit_id => (x, y) self.pos2qudit = pos2qudit(layout) for q, (x, y) in layout.items(): if x % 2 == 1 and y % 2 == 0: # X ancilla self._create_x_check(q, x, y) elif x % 2 == 0 and y % 2 == 1: # Z ancilla self._create_z_check(q, x, y) # Determine the logical operations # -------------------------------- z_qudits = set(qecc.sides['top']) x_qudits = set(qecc.sides['left']) logical_ops = [ # Each element in the list corresponds to a logical qubit # The keys label the type of logical operator {'X': QuantumCircuit([{'X': x_qudits}]), 'Z': QuantumCircuit([{'Z': z_qudits}])}, ] self.initial_logical_ops = logical_ops logical_ops = [ # Each element in the list corresponds to a logical qubit # The keys label the type of logical operator {'X': QuantumCircuit([{'X': x_qudits}]), 'Z': QuantumCircuit([{'Z': z_qudits}])}, ] self.final_logical_ops = logical_ops self.logical_signs = None self.logical_stabilizers = None # Must be called at the end of initiation. self._compile_circuit(self.abstract_circuit) def _create_x_check(self, ancilla, x, y): """ Creates X-checks for circuit_extended. """ # register the x syndrome ancillas self.ancilla_x_check.add(ancilla) # get where the position of where the data qubits should be relative to the ancilla data_pos = self._data_pos_x_check(x, y) # Get the actual, available data-qubits and their ticks that correspond to the possible data qubit positions datas, my_data_ticks = self._find_data(position_to_qudit=self.pos2qudit, positions=data_pos, # ticks=self.x_ticks) ticks=self.data_ticks) # Now add the check to the extended circuit locations = set(datas) locations.add(ancilla) self.abstract_circuit.append('X check', locations=locations, datas=datas, ancillas=ancilla, ancilla_ticks=self.init_ticks, data_ticks=my_data_ticks, meas_ticks=self.meas_ticks) def _create_z_check(self, ancilla, x, y): """ Creates Z-checks for circuit_extended. """ # register the z syndrome ancillas self.ancilla_z_check.add(ancilla) # get where the position of where the data qubits should be relative to the ancilla data_pos = self._data_pos_z_check(x, y) # Get the actual, available data-qubits and their ticks that correspond to the possible data qubit positions datas, my_data_ticks = self._find_data(position_to_qudit=self.pos2qudit, positions=data_pos, # ticks=self.z_ticks) ticks=self.data_ticks) # Now add the check to the extended circuit locations = set(datas) locations.add(ancilla) self.abstract_circuit.append('Z check', locations=locations, datas=datas, ancillas=ancilla, ancilla_ticks=self.init_ticks, data_ticks=my_data_ticks, meas_ticks=self.meas_ticks) @staticmethod def _find_data(position_to_qudit, positions, ticks): """ From the positions given for possible data qudits, add the qudits and their corresponding ticks for each qudit that does exist. """ data_list = [] tick_list = [] for i, p in enumerate(positions): data = position_to_qudit.get(p, None) if data is not None: data_list.append(data) tick_list.append(ticks[i]) return data_list, tick_list @staticmethod def _data_pos_z_check(x, y): """ Determines the position of data qudits in a Z check in order of ticks. Check direction: 1 | 2 | ---+--- | 3 | 4 """ data_pos = [ (x - 1, y), (x, y + 1), (x, y - 1), (x + 1, y) ] return data_pos @staticmethod def _data_pos_x_check(x, y): """ Determines the position of data qudits in a Z check in order of ticks. Check direction: 1 | 3 | ---+--- | 2 | 4 """ data_pos = [ (x - 1, y), (x, y - 1), (x, y + 1), (x + 1, y) ] return data_pos class InstrInitZero(LogicalInstruction): """ Instruction for initializing a logical zero. It is just like syndrome extraction except the data qubits are initialized in the zero state at tick = 0. `ideal_meas` == True will cause the measurements to be replace with ideal measurements. Parent class sets self.qecc. """ def __init__(self, qecc, symbol, **gate_params): super().__init__(qecc, symbol, **gate_params) self.symbol = 'instr_init_zero' self.data_qudit_set = self.qecc.data_qudit_set self.ancilla_qudit_set = self.qecc.ancilla_qudit_set # This is basically syndrome extraction round where all the data qubits are initialized to zero. syn_ext = qecc.instruction('instr_syn_extract', **gate_params) # Make a shallow copy of the abstract circuits. self.abstract_circuit = syn_ext.abstract_circuit.copy() self.abstract_circuit.params.update(gate_params) self.ancilla_x_check = syn_ext.ancilla_x_check self.ancilla_z_check = syn_ext.ancilla_z_check data_qudits = syn_ext.data_qudit_set self.abstract_circuit.append('init |0>', locations=data_qudits, tick=0) self.initial_logical_ops = [ # Each element in the list corresponds to a logical qubit # The keys label the type of logical operator {'X': None, 'Z': None}, # None => can be anything ] # Special for state initialization: # --------------------------------- # list of tuples of logical check and delogical stabilizer for each logical qudit. self.final_logical_ops = [ {'Z': QuantumCircuit([{'Z': set(qecc.sides['top'])}]), 'X': QuantumCircuit([{'X': set(qecc.sides['left'])}])} ] # List of corresponding logical sign. (The logical sign if the instruction is preformed ideally.) self.logical_signs = [0] self.logical_stabilizers = ['Z'] # --------------------------------- # Must be called at the end of initiation. self._compile_circuit(self.abstract_circuit) class InstrInitPlus(LogicalInstruction): """ Instruction for initializing a logical plus. It is just like syndrome extraction except the data qubits are initialized in the plus state at tick = 0. `ideal_meas` == True will cause the measurements to be replace with ideal measurements. Parent class sets self.qecc. """ def __init__(self, qecc, symbol, **gate_params): super().__init__(qecc, symbol, **gate_params) self.symbol = 'instr_init_plus' self.data_qudit_set = self.qecc.data_qudit_set self.ancilla_qudit_set = self.qecc.ancilla_qudit_set # This is basically syndrome extraction round where all the data qubits are initialized to zero. syn_ext = qecc.instruction('instr_syn_extract', **gate_params) # Make a shallow copy of the abstract circuits. self.abstract_circuit = syn_ext.abstract_circuit.copy() self.abstract_circuit.params.update(gate_params) self.ancilla_x_check = syn_ext.ancilla_x_check self.ancilla_z_check = syn_ext.ancilla_z_check data_qudits = syn_ext.data_qudit_set self.abstract_circuit.append('init |0>', locations=data_qudits, tick=0) self.abstract_circuit.append('H', locations=data_qudits, tick=1) self.initial_logical_ops = [ # Each element in the list corresponds to a logical qubit # The keys label the type of logical operator {'X': None, 'Z': None}, # None => can be anything ] # Special for state initialization: # --------------------------------- # list of tuples of logical check and delogical stabilizer for each logical qudit. self.final_logical_ops = [ {'X': QuantumCircuit([{'X': set(qecc.sides['left'])}]), 'Z': QuantumCircuit([{'Z': set(qecc.sides['top'])}])} ] # List of corresponding logical sign. (The logical sign if the instruction is preformed ideally.) self.logical_signs = [0] self.logical_stabilizers = ['X'] # --------------------------------- # Must be called at the end of initiation. self._compile_circuit(self.abstract_circuit)

36.257951

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1,253

10,261

4.634477

0.141261

0.038746

0.042535

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0.727226

0.705528

0

0.006178

0.305916

10,261

282

122

36.386525

0.809183

0.330572

0

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0

0.028253

0

1

0.0625

false

0

0.023438

0

0.132813

0

null

0

1

0

null

0

5

fe736262cc3f69d303fe69eb226e61901d59dbe1

752

py

Python

project/__main__.py

jaroslaw-wieczorek/TIIK

74d72831735834d43e5965778851a2d2951346ec

[ "MIT" ]

null

project/__main__.py

jaroslaw-wieczorek/TIIK

74d72831735834d43e5965778851a2d2951346ec

[ "MIT" ]

null

project/__main__.py

jaroslaw-wieczorek/TIIK

74d72831735834d43e5965778851a2d2951346ec

[ "MIT" ]

null

from PyQt5.QtWidgets import QApplication from PyQt5.QtWidgets import QMainWindow from PyQt5.QtWidgets import QWidget from PyQt5.QtWidgets import QFileDialog from PyQt5.QtWidgets import QAction from PyQt5.QtWidgets import QPushButton from PyQt5.QtWidgets import QLabel from PyQt5.QtWidgets import QLineEdit from PyQt5.QtWidgets import QTextEdit from PyQt5.QtWidgets import QPlainTextEdit from PyQt5.QtWidgets import QLayout from PyQt5.QtWidgets import QHBoxLayout from PyQt5.QtWidgets import QVBoxLayout from PyQt5.QtGui import QIcon from pathlib import Path import os import sys from project.src.gui.mainwindow import MainWindow if __name__ == '__main__': app = QApplication(sys.argv) ex = MainWindow() sys.exit(app.exec_())

22.117647

49

0.8125

98

752

6.142857

0.357143

0.209302

0.388704

0.518272

0

0.021672

0.140957

752

33

50

22.787879

0.910217

0

0.010638

0

1

0

false

0

0.818182

0

0.818182

0

null

1

0

null

0

1

0

1

0

5

feab1d1cebedbf8a85b5199b0acb490847b20798

57

py

Python

day_ok/schedule/bl/__init__.py

bostud/day_ok

2bcee68252b698f5818808d1766fb3ec3f07fce8

[ "MIT" ]

null

day_ok/schedule/bl/__init__.py

bostud/day_ok

2bcee68252b698f5818808d1766fb3ec3f07fce8

[ "MIT" ]

16

2021-02-27T08:36:19.000Z

2021-04-07T11:43:31.000Z

day_ok/schedule/bl/__init__.py

bostud/day_ok

2bcee68252b698f5818808d1766fb3ec3f07fce8

[ "MIT" ]

null

from .lessons import get_weekly_classroom_lessons_by_day

28.5

56

0.912281

9

57

5.222222

0.888889

0

0.070175

57

1

57

0.886792

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

22829b7017334a3083c6f8ff5d0f6c6998e13398

5,261

py

Python

tests/test_weasyprint.py

danihodovic/wagtail-resume

a4283bd37f2ea8137f4d9ab84f066397112287de

[ "MIT" ]

null

tests/test_weasyprint.py

danihodovic/wagtail-resume

a4283bd37f2ea8137f4d9ab84f066397112287de

[ "MIT" ]

1

2019-12-10T09:30:26.000Z

tests/test_weasyprint.py

danihodovic/wagtail-resume

a4283bd37f2ea8137f4d9ab84f066397112287de

[ "MIT" ]

null

import logging import pytest from django.urls import reverse from wagtail.core.models import Site from wagtail_resume.views import resume_pdf from .models import CustomResumePage pytestmark = pytest.mark.django_db def test_weasyprint(client, mocker): mocker.patch("wagtail_resume.views.HTML") site = Site.objects.first() resume = CustomResumePage( title="Resume", full_name="Adin Hodovic", role="Software engineer", pdf_generation_visibility="always", ) site.root_page.add_child(instance=resume) # Test random page pdf generation url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}" res = client.get(url) assert "adin-hodovic" in res["content-disposition"] assert res.status_code == 200 assert res["content-type"] == "application/pdf" def test_weasyprint_with_font(client, mocker): mocker.patch("wagtail_resume.views.HTML") site = Site.objects.first() resume = CustomResumePage( title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato", pdf_generation_visibility="always", ) site.root_page.add_child(instance=resume) # Test random page pdf generation url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}" res = client.get(url) assert "adin-hodovic" in res["content-disposition"] assert res.status_code == 200 assert res["content-type"] == "application/pdf" def test_weasyprint_unauthenticated(client, mocker): mocker.patch("wagtail_resume.views.HTML") site = Site.objects.first() resume = CustomResumePage( title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato", pdf_generation_visibility="authenticated", ) site.root_page.add_child(instance=resume) # Test random page pdf generation url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}" res = client.get(url) assert b"You need to be authenticated to generate a resume PDF file." in res.content assert res.status_code == 403 def test_weasyprint_authenticated(rf, django_user_model, mocker): mocker.patch("wagtail_resume.views.HTML") site = Site.objects.first() resume = CustomResumePage( title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato", pdf_generation_visibility="authenticated", ) site.root_page.add_child(instance=resume) # Test random page pdf generation url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}" request = rf.get(url) username = "user1" password = "bar" user = django_user_model.objects.create_user(username=username, password=password) request.user = user res = resume_pdf(request) print(res) assert "adin-hodovic" in res["content-disposition"] assert res.status_code == 200 assert res["content-type"] == "application/pdf" def test_weasyprint_disabled(client, mocker): mocker.patch("wagtail_resume.views.HTML") site = Site.objects.first() resume = CustomResumePage( title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato", pdf_generation_visibility="never", ) site.root_page.add_child(instance=resume) # Test random page pdf generation url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}" res = client.get(url) assert b"<h1>PDF generation is disabled for this resume.</h1>" in res.content assert res.status_code == 400 def test_weasyprint_with_no_page_id(client, mocker): mocker.patch("wagtail_resume.views.HTML") site = Site.objects.first() resume = CustomResumePage( title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato", ) site.root_page.add_child(instance=resume) # Test random page pdf generation url = f"{reverse('generate_resume_pdf')}" res = client.get(url) assert b"Missing page id for resume generation" in res.content assert res.status_code == 400 def test_weasyprint_with_no_number(client, mocker): mocker.patch("wagtail_resume.views.HTML") site = Site.objects.first() resume = CustomResumePage( title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato", ) site.root_page.add_child(instance=resume) # Test random page pdf generation url = f"{reverse('generate_resume_pdf')}?page_id={resume.id}'" res = client.get(url) assert b"Page id is not a number" in res.content assert res.status_code == 400 def test_weasyprint_no_resume(client, mocker): mocker.patch("wagtail_resume.views.HTML") site = Site.objects.first() resume = CustomResumePage( title="Resume", full_name="Adin Hodovic", role="Software engineer", font="lato", ) site.root_page.add_child(instance=resume) # Test non existent resume url = f"{reverse('generate_resume_pdf')}?page_id=9999" res = client.get(url) assert res.status_code == 404 def test_weasyprint_logger_warnings_disabled(): logger = logging.getLogger("weasyprint") assert logger.level == 40

31.315476

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661

5,261

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0.154312

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0.046699

0.055347

0.793312

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0.780917

0.77198

0.762179

0

0.007863

0.202243

5,261

167

89

31.502994

0.81868

0.047139

0

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0

0.260592

0.117906

0

0.141791

1

0.067164

false

0.014925

0.044776

0

0.11194

0.08209

0

null

0

1

0

null

0

5

22a4680db02b6c7e142f036f3039c7f703c6cad0

6,987

py

Python

lab3/lab3d_tests.py

tyler274/CS1

155fad58f1d714ebd71fa178194711d1ee5dfe20

[ "MIT" ]

null

lab3/lab3d_tests.py

tyler274/CS1

155fad58f1d714ebd71fa178194711d1ee5dfe20

[ "MIT" ]

null

lab3/lab3d_tests.py

tyler274/CS1

155fad58f1d714ebd71fa178194711d1ee5dfe20

[ "MIT" ]

null

# lab3d_tests.py import nose from lab3d import * SMALL = 1.0e-4 # ---------------------------------------------------------------------- # Data we need for testing. # ---------------------------------------------------------------------- # # L-system strings. # # Koch snowflake. k1 = 'F-F++F-F++F-F++F-F++F-F++F-F' k2 = 'F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F' k3 = 'F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F' # # Lists of drawing commands. # # Koch snowflake. kc1 = ['F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1'] kc2 = ['F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'L 60', 'F 1', 'R 60', 'R 60', 'F 1', 'L 60', 'F 1'] # # Bounds. # # Koch snowflake. kb1 = (-6.661338147750939e-16, 3.0, -2.598076211353315, 0.8660254037844386) kb2 = (-2.220446049250313e-16, 9.0, -7.794228634059945, 2.598076211353316) # # Lists of locations. # # Koch snowflake. klocs1 = [(0.0, 0.0, 0.0), (1.0, 0.0, 0.0), (1.0, 0.0, 60.0), (1.5, 0.8660254037844386, 60.0), (1.5, 0.8660254037844386, 0.0), (1.5, 0.8660254037844386, 300.0), (2.0, 0.0, 300.0), (2.0, 0.0, 0.0), (3.0, 0.0, 0.0), (3.0, 0.0, 300.0), (3.0, 0.0, 240.0), (2.4999999999999996, -0.8660254037844384, 240.0), (2.4999999999999996, -0.8660254037844384, 300.0), (2.9999999999999996, -1.732050807568877, 300.0), (2.9999999999999996, -1.732050807568877, 240.0), (2.9999999999999996, -1.732050807568877, 180.0), (1.9999999999999996, -1.7320508075688767, 180.0), (1.9999999999999996, -1.7320508075688767, 240.0), (1.4999999999999991, -2.598076211353315, 240.0), (1.4999999999999991, -2.598076211353315, 180.0), (1.4999999999999991, -2.598076211353315, 120.0), (0.9999999999999993, -1.7320508075688763, 120.0), (0.9999999999999993, -1.7320508075688763, 180.0), (-6.661338147750939e-16, -1.732050807568876, 180.0), (-6.661338147750939e-16, -1.732050807568876, 120.0), (-6.661338147750939e-16, -1.732050807568876, 60.0), (0.49999999999999944, -0.8660254037844375, 60.0), (0.49999999999999944, -0.8660254037844375, 120.0), (-3.3306690738754696e-16, 1.3322676295501878e-15, 120.0)] # ---------------------------------------------------------------------- # Helper functions. # ---------------------------------------------------------------------- def floatEquals(f1, f2): ''' Compare two floats for equality. ''' return (abs(f1 - f2) < SMALL) def compareDrawingCommandLists(l1, l2): ''' Return True if the lists 'l1' and 'l2' contain the same drawing commands. ''' if len(l1) != len(l2): return False for i, _ in enumerate(l1): line1 = l1[i].split() line2 = l2[i].split() if line1[0] != line2[0]: return False for (s1, s2) in zip(line1[1:], line2[1:]): v1 = float(s1) v2 = float(s2) if abs(v1 - v2) >= SMALL: return False return True def compareTuples(t1, t2, l): ''' Return True if the tuples 't1' and 't2' contain the same values (within a certain tolerance). 'l' is the expected tuple length. ''' if len(t1) != len(t2): return False if len(t1) != l: return False for (x, y) in zip(t1, t2): if abs(x - y) >= SMALL: return False return True def compareBounds(b1, b2): ''' Return True if the bounds 'b1' and 'b2' contain the same values (within a certain tolerance). ''' return compareTuples(b1, b2, 4) def compareLocations(l1, l2): ''' Return True, if locations 'l1' and 'l2' contain the same values (within a certain tolerance). ''' return compareTuples(l1, l2, 3) def allLocations(cmds): ''' Return a list of all the locations/angles encountered while executing a list of commands. ''' loc = (0.0, 0.0, 0.0) locs = [loc] for cmd in cmds: next_loc = nextLocation(loc[0], loc[1], loc[2], cmd) locs.append(next_loc) loc = next_loc return locs def compareListOfLocations(cmds1, cmds2): if len(cmds1) != len(cmds2): return False for l1, l2 in zip(cmds1, cmds2): if not compareLocations(l1, l2): return False return True # ---------------------------------------------------------------------- # The tests themselves. # ---------------------------------------------------------------------- def test_update(): assert update(koch, koch['start']) == k1 assert update(koch, k1) == k2 assert update(koch, k2) == k3 def test_iterate(): assert iterate(koch, 1) == k1 assert iterate(koch, 2) == k2 assert iterate(koch, 3) == k3 def test_lsystemToDrawingCommands(): assert compareDrawingCommandLists( lsystemToDrawingCommands(koch_draw, iterate(koch, 1)), kc1) assert compareDrawingCommandLists( lsystemToDrawingCommands(koch_draw, iterate(koch, 2)), kc2) def test_bounds(): assert compareBounds(bounds(kc1), kb1) assert compareBounds(bounds(kc2), kb2) def test_nextLocation(): assert compareLocations(nextLocation(0.0, 0.0, 0.0, 'F 1'), (1.0, 0.0, 0.0)) assert compareLocations(nextLocation(0.0, 0.0, 0.0, 'L 10'), (0.0, 0.0, 10.0)) assert compareLocations(nextLocation(0.0, 0.0, 0.0, 'R 10'), (0.0, 0.0, 350.0)) assert compareListOfLocations(klocs1, allLocations(kc1)) def test_saveDrawing(): saveDrawing('koch_2', kb2, kc2) f = open('koch_2', 'r') lines = f.readlines() f.close() b2 = tuple(map(float, lines[0].split())) assert compareBounds(b2, kb2) assert compareDrawingCommandLists(lines[1:], kc2) if __name__ == '__main__': nose.runmodule()

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1

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1

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null

0

5

22bfde7b71ad6ac11153c93ce5bd1f4ff7c47859

127

py

Python

python/missingNumber.py

caleberi/LeetCode

fa170244648f73e76d316a6d7fc0e813adccaa82

[ "MIT" ]

1

2021-08-10T20:00:24.000Z

python/missingNumber.py

caleberi/LeetCode

fa170244648f73e76d316a6d7fc0e813adccaa82

[ "MIT" ]

null

python/missingNumber.py

caleberi/LeetCode

fa170244648f73e76d316a6d7fc0e813adccaa82

[ "MIT" ]

3

2021-06-11T11:56:39.000Z

2021-08-10T08:50:49.000Z

class Solution: def missingNumber(self, nums: List[int]) -> int: return int((len(nums)*(len(nums)+1)/2)-sum(nums))

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22f30c346fb55d22d67de624ddf1a819e2a5b82b

165

py

Python

http1/__init__.py

c4s4/http1

ab2610823f060632227f9ca60e98320800b5c5be

[ "Apache-2.0" ]

1

2019-11-30T14:24:25.000Z

http1/__init__.py

c4s4/http1

ab2610823f060632227f9ca60e98320800b5c5be

[ "Apache-2.0" ]

2

2015-04-25T08:14:49.000Z

2015-04-26T09:08:08.000Z

http1/__init__.py

c4s4/http1

ab2610823f060632227f9ca60e98320800b5c5be

[ "Apache-2.0" ]

1

2015-04-25T09:12:59.000Z

#!/usr/bin/env python # encoding: UTF-8 from http1.http1 import request, Response, TooManyRedirectsException, get, head, post, put, delete, connect, options, trace

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fe0a3bbb48b2015af1f91e79d56560e1dc9830e7

188

py

Python

app/helper.py

yangshun/cs4243-project

b41af28ab27fc9ec0993a98d91e8f05616949500

[ "MIT" ]

3

2021-03-08T17:32:08.000Z

2021-06-15T13:05:45.000Z

app/helper.py

yangshun/cs4243-project

b41af28ab27fc9ec0993a98d91e8f05616949500

[ "MIT" ]

null

app/helper.py

yangshun/cs4243-project

b41af28ab27fc9ec0993a98d91e8f05616949500

[ "MIT" ]

1

2020-03-14T22:50:44.000Z

MAX_FLOAT32_COORD = 1e11 def box_coord(a): if a > MAX_FLOAT32_COORD: return MAX_FLOAT32_COORD elif a < -MAX_FLOAT32_COORD: return -MAX_FLOAT32_COORD return a

18.8

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1

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null

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1

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5

fe0d270f02b564bdc909388e35eec51b1c1dead2

704

py

Python

clearml/automation/__init__.py

allegroai/clearml

0bc43a31f49702077f91ffca2a36d0cb00d7e1a5

[ "Apache-2.0" ]

1,118

2020-12-23T09:28:43.000Z

2022-03-31T14:22:31.000Z

clearml/automation/__init__.py

allegroai/clearml

0bc43a31f49702077f91ffca2a36d0cb00d7e1a5

[ "Apache-2.0" ]

347

2020-12-23T22:38:48.000Z

2022-03-31T20:01:06.000Z

clearml/automation/__init__.py

allegroai/clearml

0bc43a31f49702077f91ffca2a36d0cb00d7e1a5

[ "Apache-2.0" ]

228

2020-12-23T14:44:51.000Z

2022-03-27T08:56:48.000Z

from .parameters import (UniformParameterRange, DiscreteParameterRange, UniformIntegerParameterRange, ParameterSet, LogUniformParameterRange) from .optimization import GridSearch, RandomSearch, HyperParameterOptimizer, Objective from .job import ClearmlJob from .controller import PipelineController from .scheduler import TaskScheduler from .trigger import TriggerScheduler __all__ = ["UniformParameterRange", "DiscreteParameterRange", "UniformIntegerParameterRange", "ParameterSet", "LogUniformParameterRange", "GridSearch", "RandomSearch", "HyperParameterOptimizer", "Objective", "ClearmlJob", "PipelineController", "TaskScheduler", "TriggerScheduler"]

58.666667

115

0.786932

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704

12.222222

0.488889

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0.258182

0.301818

0.389091

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0.133523

704

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116

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1

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null

0

1

0

1

0

5

a3d84d6384d4bca7b25d9cf304485dc9a745acfd

35

py

Python

8393/8393.py3.py

isac322/BOJ

35959dd1a63d75ebca9ed606051f7a649d5c0c7b

[ "MIT" ]

14

2017-05-02T02:00:42.000Z

2021-11-16T07:25:29.000Z

8393/8393.py3.py

isac322/BOJ

35959dd1a63d75ebca9ed606051f7a649d5c0c7b

[ "MIT" ]

1

2017-12-25T14:18:14.000Z

2018-02-07T06:49:44.000Z

8393/8393.py3.py

isac322/BOJ

35959dd1a63d75ebca9ed606051f7a649d5c0c7b

[ "MIT" ]

9

2016-03-03T22:06:52.000Z

2020-04-30T22:06:24.000Z

print(sum(range(int(input()) + 1)))

35

0.628571

6

35

3.666667

1

0

0.030303

0.057143

35

1

35

0.636364

0

1

0

true

0

1

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null

0

1

0

null

0

1

0

1

0

5

a3e59ad072513f39bb11e1aa214c1e8d520e0025

674

py

Python

actions/__init__.py

fallcat/synst

0fa4adffa825af4a62b6e739b59c4125a7b6698e

[ "BSD-3-Clause" ]

1

2019-09-08T13:55:21.000Z

actions/__init__.py

fallcat/synst

0fa4adffa825af4a62b6e739b59c4125a7b6698e

[ "BSD-3-Clause" ]

2

2019-10-02T15:23:55.000Z

2019-10-16T02:38:25.000Z

actions/__init__.py

fallcat/synst

0fa4adffa825af4a62b6e739b59c4125a7b6698e

[ "BSD-3-Clause" ]

null

''' Initialize the actions module ''' from actions.train import Trainer from actions.evaluate import Evaluator from actions.translate import Translator from actions.probe import Prober from actions.probe_train import ProbeTrainer from actions.probe_evaluate import ProbeEvaluator from actions.probe_new_translate import ProbeNewTranslator from actions.probe_attn_stats import ProbeStatsGetter from actions.probe_off_diagonal import ProbeOffDiagonal class Pass(object): ''' Action that does nothing... ''' def __init__(self, *args, **kwargs): ''' Do nothing ''' pass def __call__(self, *args, **kwargs): ''' Do nothing ''' pass

28.083333

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0.747774

80

674

6.1

0.475

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0.196721

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0.110656

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0.170623

674

23

59

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null

1

0

null

0

1

0

1

0

5

432b51eddb77eca1d54b0538a17e5450c9bda9eb

235

py

Python

python/testData/inspections/PyTypeCheckerInspection/CallOperator.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/inspections/PyTypeCheckerInspection/CallOperator.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/inspections/PyTypeCheckerInspection/CallOperator.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class Foo: def __call__(self, arg: int): return arg bar = Foo() bar.__call__(<warning descr="Expected type 'int', got 'str' instead">"s"</warning>) bar(<warning descr="Expected type 'int', got 'str' instead">"s"</warning>)

33.571429

83

0.655319

34

235

4.294118

0.5

0.164384

0.273973

0.328767

0.657534

0

0.157447

235

7

84

33.571429

0.737374

0

0.330508

0

null

0

null

0

null

0

1

0

1

0

1

0

null

0

1

0

5

4a31b62c5d1800a565b4caefc2706dfedca80a34

53

py

Python

softlearning/environments/gym/wrappers/__init__.py

pedrodbs/mbpo

42c3be5eca050116c0cbada91588184e97af7c12

[ "MIT" ]

362

2019-04-16T22:45:21.000Z

2022-03-30T06:13:22.000Z

softlearning/environments/gym/wrappers/__init__.py

pedrodbs/mbpo

42c3be5eca050116c0cbada91588184e97af7c12

[ "MIT" ]

39

2019-05-03T04:21:14.000Z

2022-03-11T23:45:03.000Z

softlearning/environments/gym/wrappers/__init__.py

pedrodbs/mbpo

42c3be5eca050116c0cbada91588184e97af7c12

[ "MIT" ]

67

2019-04-17T03:35:29.000Z

2021-12-26T05:39:37.000Z

from .normalize_action import NormalizeActionWrapper

26.5

52

0.90566

5

53

9.4

1

0

0.075472

53

1

53

0.959184

0

1

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true

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0

null

0

1

0

1

0

1

0

5

4a37f192cd1f291208d745d53286211df639ed45

129

py

Python

mysite/plans/admin.py

tenderghost/FlyPersonalAssistant

f9b379a42c32ff1ea73803d25cce7be04f8ec497

[ "MIT" ]

1

2018-01-07T16:45:31.000Z

mysite/plans/admin.py

tenderghost/FlyPersonalAssistant

f9b379a42c32ff1ea73803d25cce7be04f8ec497

[ "MIT" ]

null

mysite/plans/admin.py

tenderghost/FlyPersonalAssistant

f9b379a42c32ff1ea73803d25cce7be04f8ec497

[ "MIT" ]

null

from django.contrib import admin from .models import Plan, PlanChange admin.site.register(Plan) admin.site.register(PlanChange)

21.5

36

0.821705

18

129

5.888889

0.555556

0.169811

0.320755

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0.093023

129

6

37

21.5

0.905983

0

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true

0

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null

0

1

0

1

0

5

4a3bcc70aa3d8d9869d2f4b2e945e0ba0dba767f

463

py

Python

CURSO PYTHON/pythonsexercicios/ex006.py

Sabrinaparussoli/PYTHON

77436608ffd799e9e2bbe4fa5084443fb7382793

[ "MIT" ]

null

CURSO PYTHON/pythonsexercicios/ex006.py

Sabrinaparussoli/PYTHON

77436608ffd799e9e2bbe4fa5084443fb7382793

[ "MIT" ]

null

CURSO PYTHON/pythonsexercicios/ex006.py

Sabrinaparussoli/PYTHON

77436608ffd799e9e2bbe4fa5084443fb7382793

[ "MIT" ]

null

n = int(input('Digite um numero: ')) d = n * 2 t = n * 3 r = n**(1/2) print('O dobro do valor de {} é: {}'.format(n, d)) print('O triplo do valor de {} é: {}'.format(n, t)) print('A raiz do valor de {} é: {:.2f}'.format(n, r)) # outra possibilidade n = int(input('Digite um numero: ')) print('O dobro do valor de {} é: {}'.format(n, (n*2))) print('O triplo do valor de {} é: {}'.format(n, (n*3))) print('A raiz do valor de {} é: {:.2f}'.format(n, pow(n,(1/2))))

33.071429

64

0.559395

91

463

2.846154

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0.162162

0.208494

0.23166

0.849421

0.671815

0.664093

0.223938

0

0.026316

0.179266

463

14

64

33.071429

0.655263

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0

0.181818

0

0.478555

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0

false

0

0.545455

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null

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null

0

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4a3ef977fd68e95cd023de83f2029d043f28d410

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py

Python

gwaripper_webGUI/__main__.py

nilfoer/gwaripper

28492b9894973633612471094d24907b2bc47728

[ "MIT" ]

6

2021-03-12T08:57:18.000Z

2022-03-27T00:28:17.000Z

gwaripper_webGUI/__main__.py

nilfoer/gwaripper

28492b9894973633612471094d24907b2bc47728

[ "MIT" ]

1

2020-10-05T04:25:53.000Z

2020-10-05T14:20:07.000Z

gwaripper_webGUI/__main__.py

nilfoer/gwaripper

28492b9894973633612471094d24907b2bc47728

[ "MIT" ]

2

2021-03-12T11:05:46.000Z

2021-09-12T22:53:58.000Z

from .start_webgui import main main()

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py

Python

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/digitalocean/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

1

2016-12-22T18:40:40.000Z

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/digitalocean/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

6

2020-06-05T18:44:19.000Z

2022-01-13T00:48:56.000Z

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/digitalocean/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

1

2022-02-01T17:19:28.000Z

from allauth.socialaccount.providers.oauth2.urls import default_urlpatterns from .provider import DigitalOceanProvider urlpatterns = default_urlpatterns(DigitalOceanProvider)

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5

4a98404bb1c711b53f7122eefc153fe6a05754ba

131

py

Python

tests/test_mkpy.py

wenshuin/mkpy

52d22b9bac50eede794bacd756869b1600b71ec0

[ "BSD-3-Clause" ]

null

tests/test_mkpy.py

wenshuin/mkpy

52d22b9bac50eede794bacd756869b1600b71ec0

[ "BSD-3-Clause" ]

25

2019-09-29T22:35:34.000Z

2020-12-18T01:05:20.000Z

tests/test_mkpy.py

wenshuin/mkpy

52d22b9bac50eede794bacd756869b1600b71ec0

[ "BSD-3-Clause" ]

1

2020-09-28T23:32:31.000Z

from pathlib import Path import mkpy from mkpy import get_ver def test_get_ver(): # screen version for updates get_ver()

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py

Python

python/oneroll/who_owe.py

exposit/katamoiran

f5ad354c102e3e24a666777a1a934fe79b73aea8

[ "BSD-Source-Code" ]

6

2017-10-09T05:07:50.000Z

2019-07-11T05:10:15.000Z

python/oneroll/who_owe.py

exposit/katamoiran

f5ad354c102e3e24a666777a1a934fe79b73aea8

[ "BSD-Source-Code" ]

1

2018-09-18T13:40:55.000Z

2018-11-22T16:41:04.000Z

python/oneroll/who_owe.py

exposit/katamoiran

f5ad354c102e3e24a666777a1a934fe79b73aea8

[ "BSD-Source-Code" ]

2

2017-11-05T19:50:28.000Z

2019-03-10T08:35:50.000Z

import random # not fancy; just goes down the list and prints each choice, handling the exceptions in a minimal way roll = [] print("\nYou owe the money to...") roll.append(random.randint(0,11)) print(["1. a sibling or close relative", "2. a noble", "3. a government body", "4. a church", "5. a gangster", "6. an outcast or monster", "7. a warlord or barbarian", "8. a wizard", "9. a thief", "10. a merchant", "11. a spymaster", "12. a courtesan"][roll[-1]]) print("\nYou owe the money because...") roll.append(random.randint(0,7)) print(["1. of gambling", "2. of an expensive vice", "3. of someone else", "4. of a joke or prank gone wrong", "5. they trusted you and you messed up", "6. you trusted a pretty face", "7. you stole it and they know", "8. it wasn't your fault but they hold you accountable anyway"][roll[-1]]) print("\nIf you don't pay up they'll...") roll.append(random.randint(0,5)) print(["1. take it out of your hide (possibly not fatal but you like both of your arms)", "2. make an example out of you (fatal and messy)", "3. have a legal claim to something you value", "4. take everything, including your person", "5. send a really skilled assassin out looking for you", "6. hurt people you care about"][roll[-1]]) print("\nYou have...") roll.append(random.randint(0,3)) print(["1. until they notice you're not dead yet", "2. until you want to go back to your home town", "3. as long as you can keep dealing with the hired thugs they keep sending", "4. a bargain buying you time but you'd better deliver"][roll[-1]]) print("\nYou know that one path to freedom is to...") roll.append(random.randint(0,9)) print(["1. retrieve something the one you owe values higher than the debt", "2. confront the one you owe directly", "3. pay it back in full", "4. convince someone to sacrifice for you", "5. flee far enough that they can't reach you", "6. find a powerful patron", "7. find a powerful item of protection", "8. turn the tables on them, but you're going to need allies", "9. lay low, attracting no attention, for a long, long time", "10. become someone else"][roll[-1]]) print("\nFinally, there's a wrinkle.") chart = ["1. Everybody you used to know knows you're toxic now.", "2. There's a price out on your head and even if you satisfy the debt it'll take time for word to get out.", "3. The law is looking for you but it's a total frame job.", "4. The law is looking for you, and you can totally explain...", "5. You did something terrible that you never thought you were capable of to escape.", "6. Someone you cared about betrayed you to them but you escaped.", "7. They actually killed you, or near enough, and think you're (still) dead.", "8. One of your former lovers or allies is working for them and they know you *very* well.", "9. They have a lackey who isn't directly dangerous, but follows you from place to place making sure everyone knows you owe.", "10. An ancestral spirit only you can see is attached to you. It's disappointed with you and finds everything about you depressing and repugnant and tells you what a failure you are whenever it thinks of it, which is all the time.", "11. You're cursed and slowly wasting away. Only collecting towards repayment of your debt eases the symptoms.", "12. You're cursed; at night your Shadow possesses your body and makes it do things you'd never do normally.", "13. The debt has a component you can't repay with money, only with blood -- or perhaps not at all.", "14. The person you owe money to will forgive the debt if you do something for them that is a. horrifying, b. extremely dangerous, c. illegal. Choose two.", "15. An innocent paid a terrible price when you incurred the debt and their family will do something about it. Roll again on the d6 chart and on the d4 chart to see what.", "16. Someone who didn't deserve it was ruined or suffered terribly because of your actions or lack of action.", "17. You're wracked with terrible nightmares about the money and you don't know why.", "18. You've been framed for the most heinous crime you can think of -- not just larceny or petty theft. The person you owe is just that vindictive.", "19. Roll until you have two results and combine them.", "20. You actually owe money to 1+1d4 different groups. Roll a d4, then roll up that many more groups."] roll.append(random.randint(0,19)) wrinkle = chart[roll[-1]] if "19" in wrinkle: chart = chart[:-2] wrinkle = " AND ".join(random.sample(chart,2)) print(wrinkle) print("\nThe amount you owe is %s" % ((max(roll) + 1) * 1000)) print("\nYou owe something more difficult to repay than mere money. You owe...") s = min(roll) roll.remove(s) ss = min(item for item in roll if item >= s) total = s + ss print(["2. A favor.", "3. A life. Yours, perhaps, or maybe a sacrificial victim's. Or a fellow debtor you could turn in...", "4. A rare spell component that's going to be hell to replace.", "5. A rare antique in a specific style.", "6. A rare animal. Don't even ask.", "7. A magic effect or power that you usurped from the expected owner. You can do something extraordinary now but have no control over it. And they're mad they lost out.", "8. An inconvenient magical animal that has imprinted on you instead of the intended owner (or died in your care if you don't want a pet).", "9. Your skin. Part of a powerful ritual is inked indelibly on your skin. You're the last copy.", "10. A powerful magical item, now bonded to you. Not necessarily a useful item, just a powerful one."][total])

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4aa128ee254d15024fc9297beafbaa9cc6e9ae4c

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py

Python

kd_common/kd_common/google/__init__.py

konovalovdmitry/catsnap

d5f1d7c37dcee1ad3fee2cdc12a3b44b56f4c63f

[ "MIT" ]

null

kd_common/kd_common/google/__init__.py

konovalovdmitry/catsnap

d5f1d7c37dcee1ad3fee2cdc12a3b44b56f4c63f

[ "MIT" ]

null

kd_common/kd_common/google/__init__.py

konovalovdmitry/catsnap

d5f1d7c37dcee1ad3fee2cdc12a3b44b56f4c63f

[ "MIT" ]

1

2021-09-30T08:06:20.000Z

from kd_common.google import sheet

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5

43508d97ec7d8908f8fa6d7f29824c472e27b8c9

163

py

Python

rlp/sedes/__init__.py

vaporyco/pyrlp

bdef65842a310d610e277096b403d284999ecbaa

[ "MIT" ]

null

rlp/sedes/__init__.py

vaporyco/pyrlp

bdef65842a310d610e277096b403d284999ecbaa

[ "MIT" ]

null

rlp/sedes/__init__.py

vaporyco/pyrlp

bdef65842a310d610e277096b403d284999ecbaa

[ "MIT" ]

null

from . import raw from .binary import Binary, binary from .big_endian_int import BigEndianInt, big_endian_int from .lists import CountableList, List, Serializable

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null

0

1

0

1

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5

43969d63b05383ba59d781e69732a657e9c1ae73

132

py

Python

qcfractal/storage_sockets/__init__.py

ChayaSt/QCFractal

2d3c737b0e755d6e5bac743a0beb0714b5a92d0b

[ "BSD-3-Clause" ]

null

qcfractal/storage_sockets/__init__.py

ChayaSt/QCFractal

2d3c737b0e755d6e5bac743a0beb0714b5a92d0b

[ "BSD-3-Clause" ]

null

qcfractal/storage_sockets/__init__.py

ChayaSt/QCFractal

2d3c737b0e755d6e5bac743a0beb0714b5a92d0b

[ "BSD-3-Clause" ]

null

""" Importer for the DB socket class. """ __all__ = ["storage_socket_factory"] from .storage_socket import storage_socket_factory

16.5

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0.772727

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0.430108

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18.857143

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0

1

0

null

0

1

0

5

4398f8355c06457343cda94fd4d31dfc0bebe099

462

py

Python

boards/tasks.py

oscarsiles/jotlet

361f7ad0d32ea96d012020a67493931482207036

[ "BSD-3-Clause" ]

null

boards/tasks.py

oscarsiles/jotlet

361f7ad0d32ea96d012020a67493931482207036

[ "BSD-3-Clause" ]

2

2022-03-21T22:22:33.000Z

2022-03-28T22:18:33.000Z

boards/tasks.py

oscarsiles/jotlet

361f7ad0d32ea96d012020a67493931482207036

[ "BSD-3-Clause" ]

null

from django.core import management def create_thumbnails(img): img.get_webp img.get_thumbnail img.get_thumbnail_webp def thumbnail_cleanup_command(): return management.call_command("thumbnail", "cleanup") def history_clean_duplicates_past_hour_command(): return management.call_command("clean_duplicate_history", "-m", "60", "--auto") def history_clean_old_command(): return management.call_command("clean_old_history", "--auto")

23.1

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null

0

1

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1

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5

78e0d8373f70c637a9b34a5bfcc6c1993f18efd2

65

py

Python

godaddypy/exceptions/__init__.py

avitko001c/godaddypy

c5bd91e414cb4831e57fa3bf310d639df29ed4e7

[ "BSD-3-Clause" ]

null

godaddypy/exceptions/__init__.py

avitko001c/godaddypy

c5bd91e414cb4831e57fa3bf310d639df29ed4e7

[ "BSD-3-Clause" ]

null

godaddypy/exceptions/__init__.py

avitko001c/godaddypy

c5bd91e414cb4831e57fa3bf310d639df29ed4e7

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import from . import exceptions

16.25

38

0.846154

8

65

6.25

0.625

0

0.138462

65

3

39

21.666667

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0

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true

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1

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1

0

null

0

1

0

1

0

1

0

5

604b0ef043ea017e5c80f2df3a559ba9f126fc7f

236

py

Python

shops/models.py

Jackintoshh/Webmappingtest

71a1aca53ed048dde75b4b673f707680c7f9d551

[ "MIT" ]

null

shops/models.py

Jackintoshh/Webmappingtest

71a1aca53ed048dde75b4b673f707680c7f9d551

[ "MIT" ]

7

2020-02-12T03:09:10.000Z

2022-02-10T11:15:31.000Z

shops/models.py

Jackintoshh/Webmappingtest

71a1aca53ed048dde75b4b673f707680c7f9d551

[ "MIT" ]

null

from django.contrib.gis.db import models class Shop(models.Model): name = models.CharField(max_length=100) location = models.PointField() address = models.CharField(max_length=100) city = models.CharField(max_length=50)

33.714286

46

0.745763

32

236

5.40625

0.625

0.260116

0.312139

0.416185

0.312139

0

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236

7

47

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1

0

1

0

null

0

1

0

5

605e68508b2617bdf5352354dd5821d35ced1f48

210

py

Python

avaland/sources/__init__.py

PSD79/avaland

142547e48b1728db6efe8a6b9f02af18a1b42bc5

[ "MIT" ]

27

2020-05-12T22:02:57.000Z

2021-07-27T10:53:24.000Z

avaland/sources/__init__.py

PSD79/avaland

142547e48b1728db6efe8a6b9f02af18a1b42bc5

[ "MIT" ]

null

avaland/sources/__init__.py

PSD79/avaland

142547e48b1728db6efe8a6b9f02af18a1b42bc5

[ "MIT" ]

2

2020-05-13T18:40:03.000Z

2020-05-14T15:01:07.000Z

from .bia2 import Bia2 from .navahang import Navahang from .nex1music import Nex1 from .radiojavan import RadioJavan from .rapfarsi import RapFarsi from .wikiseda import WikiSeda from .mrtehran import MrTehran

26.25

34

0.833333

28

210

6.25

0.357143

0

0.021978

0.133333

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7

35

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null

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1

0

1

0

1

0

5

6064f778529b5f669dec3ada49cfea9c818c27a2

139

py

Python

tests/gnn/test_combine.py

BatsResearch/zsl-kg

9bc4d4537a0f90ee3bbcefdf90ceae6dbcf48572

[ "Apache-2.0" ]

83

2021-08-30T02:50:37.000Z

2022-02-22T09:37:36.000Z

tests/gnn/test_combine.py

BatsResearch/zsl-kg

9bc4d4537a0f90ee3bbcefdf90ceae6dbcf48572

[ "Apache-2.0" ]

2

2021-09-10T08:44:13.000Z

2022-01-23T17:33:35.000Z

tests/gnn/test_combine.py

BatsResearch/zsl-kg

9bc4d4537a0f90ee3bbcefdf90ceae6dbcf48572

[ "Apache-2.0" ]

6

2021-09-10T07:09:41.000Z

2021-11-07T14:31:33.000Z

""" Files contains tests for the combine classes """ import unittest from zsl_kg.gnn.combine import Combine, RCGNCombine, AttentionCombine

23.166667

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0.805755

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139

6.166667

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0

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139

5

70

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0

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true

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1

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60a82b6266af795737cd9b74fdeff78ac13477ca

97

py

Python

training_codes/biophys2lifmodel_lr/run_lr2_g8_8_test500ms_inh_lif_syn_z104.py

zqwei/LIF_Vis_model

16f651ac827ba5f0feb40a0e619e600f1251d009

[ "MIT" ]

null

training_codes/biophys2lifmodel_lr/run_lr2_g8_8_test500ms_inh_lif_syn_z104.py

zqwei/LIF_Vis_model

16f651ac827ba5f0feb40a0e619e600f1251d009

[ "MIT" ]

null

training_codes/biophys2lifmodel_lr/run_lr2_g8_8_test500ms_inh_lif_syn_z104.py

zqwei/LIF_Vis_model

16f651ac827ba5f0feb40a0e619e600f1251d009

[ "MIT" ]

null

import start0 as start start.run_simulation('config_lr2_g8_8_test500ms_inh_lif_syn_z104.json')

19.4

71

0.865979

17

97

4.411765

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0

0.111111

0.072165

97

4

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true

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60cb70fa676e537ad2b8dc7e1d2d9c3ab2e3e9cb

1,280

py

Python

polog/tests/handlers/file/rotation/rules/rules/tokenization/tokens/test_size_token.py

pomponchik/polog

104c5068a65b0eaeab59327aac1a583e2606e77e

[ "MIT" ]

30

2020-07-16T16:52:46.000Z

2022-03-24T16:56:29.000Z

polog/tests/handlers/file/rotation/rules/rules/tokenization/tokens/test_size_token.py

pomponchik/polog

104c5068a65b0eaeab59327aac1a583e2606e77e

[ "MIT" ]

6

2021-02-07T22:08:01.000Z

2021-12-07T21:56:46.000Z

polog/tests/handlers/file/rotation/rules/rules/tokenization/tokens/test_size_token.py

pomponchik/polog

104c5068a65b0eaeab59327aac1a583e2606e77e

[ "MIT" ]

4

2020-12-22T07:05:34.000Z

2022-03-24T16:56:50.000Z

import pytest from polog.handlers.file.rotation.rules.rules.tokenization.tokens.size_token import SizeToken def test_content_extraction_for_size_token(): """ Проверяем, что значение из строки извлекается корректно. """ assert SizeToken('b').content == 1 assert SizeToken('kb').content == 1024 assert SizeToken('mb').content == 1024 * 1024 assert SizeToken('gb').content == 1024 * 1024 * 1024 assert SizeToken('tb').content == 1024 * 1024 * 1024 * 1024 assert SizeToken('pb').content == 1024 * 1024 * 1024 * 1024 * 1024 assert SizeToken('byte').content == 1 assert SizeToken('kilobyte').content == 1024 assert SizeToken('megabyte').content == 1024 * 1024 assert SizeToken('gigabyte').content == 1024 * 1024 * 1024 assert SizeToken('terabyte').content == 1024 * 1024 * 1024 * 1024 assert SizeToken('petabyte').content == 1024 * 1024 * 1024 * 1024 * 1024 assert SizeToken('bytes').content == 1 assert SizeToken('kilobytes').content == 1024 assert SizeToken('megabytes').content == 1024 * 1024 assert SizeToken('gigabytes').content == 1024 * 1024 * 1024 assert SizeToken('terabytes').content == 1024 * 1024 * 1024 * 1024 assert SizeToken('petabytes').content == 1024 * 1024 * 1024 * 1024 * 1024

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