xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

dfbb0256287dd2cbd4ce421bc0c2333540e9d21b

245

py

Python

elf/types/section/header/sh_name.py

Valmarelox/elftoolsng

99c3f4913a7e477007b1d81df83274d7657bf693

[ "MIT" ]

null

elf/types/section/header/sh_name.py

Valmarelox/elftoolsng

99c3f4913a7e477007b1d81df83274d7657bf693

[ "MIT" ]

null

elf/types/section/header/sh_name.py

Valmarelox/elftoolsng

99c3f4913a7e477007b1d81df83274d7657bf693

[ "MIT" ]

null

from elf.types.base.elf_name_type import ElfNameType from elf.types.base import ElfOffset # TODO: Generify to ElfStringType class SHName(ElfNameType): @property def strtab_accessor(self): return int(self.elf.header.e_shstrndx)

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null

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1

0

6

dfef9bfa104382c3a5eda10715d69e669e03e902

218

py

Python

apps/life_sci/dgllife/utils/__init__.py

arangoml/dgl

d135058f9986fadcbdf6aa1011a00c3ad45a8ce3

[ "Apache-2.0" ]

3

2020-02-28T07:28:52.000Z

2020-06-03T08:41:55.000Z

apps/life_sci/python/dgllife/utils/__init__.py

sherry-1001/dgl

60d2e7d3c928d43bbb18e7ab17c066451c49f649

[ "Apache-2.0" ]

null

apps/life_sci/python/dgllife/utils/__init__.py

sherry-1001/dgl

60d2e7d3c928d43bbb18e7ab17c066451c49f649

[ "Apache-2.0" ]

2

2020-12-07T09:34:01.000Z

2020-12-13T06:18:58.000Z

"""Utils for data processing.""" from .complex_to_graph import * from .early_stop import * from .eval import * from .featurizers import * from .mol_to_graph import * from .rdkit_utils import * from .splitters import *

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1

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6

5f006ecb902171c6b66ca04d50152453bcda65fe

44

py

Python

janusbackup/core/__init__.py

NikitosnikN/janus-backup

413d365663b532a0611575be16ea0a4f0c7ffd20

[ "MIT" ]

null

janusbackup/core/__init__.py

NikitosnikN/janus-backup

413d365663b532a0611575be16ea0a4f0c7ffd20

[ "MIT" ]

null

janusbackup/core/__init__.py

NikitosnikN/janus-backup

413d365663b532a0611575be16ea0a4f0c7ffd20

[ "MIT" ]

null

from .backup_pipeline import BackupPipeline

22

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null

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1

0

6

a049f24b1bb3840c29bf99a96cacf661e921860e

20,810

py

Python

code/python/FactSetFunds/v1/fds/sdk/FactSetFunds/model/costs_fees.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

6

2022-02-07T16:34:18.000Z

2022-03-30T08:04:57.000Z

code/python/FactSetFunds/v1/fds/sdk/FactSetFunds/model/costs_fees.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

2

2022-02-07T05:25:57.000Z

2022-03-07T14:18:04.000Z

code/python/FactSetFunds/v1/fds/sdk/FactSetFunds/model/costs_fees.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

null

""" FactSet Funds API FactSet Mutual Funds data offers over 50 fund- and share class-specific data points for mutual funds listed in the United States. FactSet Mutual Funds Reference provides fund-specific reference information as well as FactSet's proprietary classification system. It includes but is not limited to the following coverage * Fund descriptions * A seven-tier classification system * Leverage information * Fees and expenses * Portfolio managers FactSet Mutual Funds Time Series provides quantitative data items on a historical basis. It includes but is not limited to the following coverage * Net asset value * Fund flows * Assets under management * Total return # noqa: E501 The version of the OpenAPI document: 1.0.0 Contact: api@factset.com Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from fds.sdk.FactSetFunds.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from fds.sdk.FactSetFunds.exceptions import ApiAttributeError class CostsFees(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { 'fsym_id': (str,), # noqa: E501 'management_expenses': (float,), # noqa: E501 'date': (str,), # noqa: E501 'currency': (str,), # noqa: E501 'entry_expense': (float,), # noqa: E501 'exit_expense': (float,), # noqa: E501 'front_expenses_max': (float,), # noqa: E501 'back_expenses_max': (float,), # noqa: E501 'expense_ratio': (float,), # noqa: E501 'expense_ratio_prospectus': (float,), # noqa: E501 'init_investment_min': (float,), # noqa: E501 'init_investment_ira': (float,), # noqa: E501 'swing_price': (float,), # noqa: E501 'swing_price_date': (str,), # noqa: E501 'sri_priips': (int,), # noqa: E501 'srri_ucits': (int,), # noqa: E501 'performance_fee': (float,), # noqa: E501 'trading_expense_ratio': (float,), # noqa: E501 'request_id': (str,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'fsym_id': 'fsymId', # noqa: E501 'management_expenses': 'managementExpenses', # noqa: E501 'date': 'date', # noqa: E501 'currency': 'currency', # noqa: E501 'entry_expense': 'entryExpense', # noqa: E501 'exit_expense': 'exitExpense', # noqa: E501 'front_expenses_max': 'frontExpensesMax', # noqa: E501 'back_expenses_max': 'backExpensesMax', # noqa: E501 'expense_ratio': 'expenseRatio', # noqa: E501 'expense_ratio_prospectus': 'expenseRatioProspectus', # noqa: E501 'init_investment_min': 'initInvestmentMin', # noqa: E501 'init_investment_ira': 'initInvestmentIra', # noqa: E501 'swing_price': 'swingPrice', # noqa: E501 'swing_price_date': 'swingPriceDate', # noqa: E501 'sri_priips': 'sriPriips', # noqa: E501 'srri_ucits': 'srriUcits', # noqa: E501 'performance_fee': 'performanceFee', # noqa: E501 'trading_expense_ratio': 'tradingExpenseRatio', # noqa: E501 'request_id': 'requestId', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """CostsFees - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) fsym_id (str): FactSet Security Identifier. Six alpha-numeric characters, excluding vowels, with a -S suffix (XXXXXX-S), resolved from the requestId of the Fund requested.. [optional] # noqa: E501 management_expenses (float): The management fee, or maintenance fee, is charged by the fund manager. This cost is usually between 0.5% and 2% of assets on average and is a periodic fee.. [optional] # noqa: E501 date (str): The Expense Date expressed in YYYY-MM-DD.. [optional] # noqa: E501 currency (str): ISO3 Currency. [optional] # noqa: E501 entry_expense (float): The transaction entry fee or purchase fee collected from investors when they join or leave a scheme. The fee is paid to the fund. [optional] # noqa: E501 exit_expense (float): The transaction exit fee is charged to investors when they redeem shares from a fund.. [optional] # noqa: E501 front_expenses_max (float): The Maximum sales load or initial Sales Fee is a reduction made from each investment in the fund, the maximum paid is dependent on the size of the purchase, it decreases as the investment increases. Often associated with class 'A' shares of a mutual fund it is also known as Sales Charge, this is a fee paid when shares are purchased. Also known as a \"front-end load\", this fee typically goes to the brokers that sell the fund's shares. (Under the Investment Company Act of 1940 is 9%. The maximum sales load under NASD Rules is 81⁄2%).\" . [optional] # noqa: E501 back_expenses_max (float): The Back Expense Maximum. [optional] # noqa: E501 expense_ratio (float): The Expense Ratio. [optional] # noqa: E501 expense_ratio_prospectus (float): The Expense Ratio Prospectus. [optional] # noqa: E501 init_investment_min (float): The Initial Investment Minimum. [optional] # noqa: E501 init_investment_ira (float): The Initial Investment Individual Retirement Accounts. [optional] # noqa: E501 swing_price (float): Swing Price. Swing pricing occurs when a fund provider adjusts the net asset value (NAV) of a fund in order to pass on trading costs to purchasing or redeeming shareholders. This anti-dilution technique is used to protect long-term shareholder’s interests.. [optional] # noqa: E501 swing_price_date (str): Swing Price Date. Swing pricing occurs when a fund provider adjusts the net asset value (NAV) of a fund in order to pass on trading costs to purchasing or redeeming shareholders. This anti-dilution technique is used to protect long-term shareholder’s interests.. [optional] # noqa: E501 sri_priips (int): The SRI (Summary Risk Indicator) illustrates PRIIPs’ risk and reward profile by measuring the market and credit risk level. Returns 1 for low risk up to 7 for higher risk.. [optional] # noqa: E501 srri_ucits (int): Synthetic Risk and Reward Indicator illustrates a UCITS or NURS (Non-UCITS Retail Scheme) fund’s risk and reward profile by measuring the market risk level. Returns 1 for low risk up to 7 for high risk.. [optional] # noqa: E501 performance_fee (float): Represents fees made to an investment manager as a percentage of investment profits for generating positive returns.. [optional] # noqa: E501 trading_expense_ratio (float): Represents the amount of trading commissions incurred to manage the portfolio as a percentage of the total assets of the fund.. [optional] # noqa: E501 request_id (str): The requested Id sent as input.. [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """CostsFees - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) fsym_id (str): FactSet Security Identifier. Six alpha-numeric characters, excluding vowels, with a -S suffix (XXXXXX-S), resolved from the requestId of the Fund requested.. [optional] # noqa: E501 management_expenses (float): The management fee, or maintenance fee, is charged by the fund manager. This cost is usually between 0.5% and 2% of assets on average and is a periodic fee.. [optional] # noqa: E501 date (str): The Expense Date expressed in YYYY-MM-DD.. [optional] # noqa: E501 currency (str): ISO3 Currency. [optional] # noqa: E501 entry_expense (float): The transaction entry fee or purchase fee collected from investors when they join or leave a scheme. The fee is paid to the fund. [optional] # noqa: E501 exit_expense (float): The transaction exit fee is charged to investors when they redeem shares from a fund.. [optional] # noqa: E501 front_expenses_max (float): The Maximum sales load or initial Sales Fee is a reduction made from each investment in the fund, the maximum paid is dependent on the size of the purchase, it decreases as the investment increases. Often associated with class 'A' shares of a mutual fund it is also known as Sales Charge, this is a fee paid when shares are purchased. Also known as a \"front-end load\", this fee typically goes to the brokers that sell the fund's shares. (Under the Investment Company Act of 1940 is 9%. The maximum sales load under NASD Rules is 81⁄2%).\" . [optional] # noqa: E501 back_expenses_max (float): The Back Expense Maximum. [optional] # noqa: E501 expense_ratio (float): The Expense Ratio. [optional] # noqa: E501 expense_ratio_prospectus (float): The Expense Ratio Prospectus. [optional] # noqa: E501 init_investment_min (float): The Initial Investment Minimum. [optional] # noqa: E501 init_investment_ira (float): The Initial Investment Individual Retirement Accounts. [optional] # noqa: E501 swing_price (float): Swing Price. Swing pricing occurs when a fund provider adjusts the net asset value (NAV) of a fund in order to pass on trading costs to purchasing or redeeming shareholders. This anti-dilution technique is used to protect long-term shareholder’s interests.. [optional] # noqa: E501 swing_price_date (str): Swing Price Date. Swing pricing occurs when a fund provider adjusts the net asset value (NAV) of a fund in order to pass on trading costs to purchasing or redeeming shareholders. This anti-dilution technique is used to protect long-term shareholder’s interests.. [optional] # noqa: E501 sri_priips (int): The SRI (Summary Risk Indicator) illustrates PRIIPs’ risk and reward profile by measuring the market and credit risk level. Returns 1 for low risk up to 7 for higher risk.. [optional] # noqa: E501 srri_ucits (int): Synthetic Risk and Reward Indicator illustrates a UCITS or NURS (Non-UCITS Retail Scheme) fund’s risk and reward profile by measuring the market risk level. Returns 1 for low risk up to 7 for high risk.. [optional] # noqa: E501 performance_fee (float): Represents fees made to an investment manager as a percentage of investment profits for generating positive returns.. [optional] # noqa: E501 trading_expense_ratio (float): Represents the amount of trading commissions incurred to manage the portfolio as a percentage of the total assets of the fund.. [optional] # noqa: E501 request_id (str): The requested Id sent as input.. [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

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0

1

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null

0

6

a06f446d3844839bd8a53b28fca1b6b8cee02277

8,105

py

Python

library/graph_processing.py

RomainClaret/CONVEX

b1477d3e332903846f21b5c1ac2d5cb8cfdac21e

[ "MIT" ]

29

2019-10-08T13:21:18.000Z

2021-12-30T03:32:23.000Z

library/graph_processing.py

RomainClaret/CONVEX

b1477d3e332903846f21b5c1ac2d5cb8cfdac21e

[ "MIT" ]

19

2019-10-31T13:43:33.000Z

2022-03-29T00:57:00.000Z

library/graph_processing.py

RomainClaret/CONVEX

b1477d3e332903846f21b5c1ac2d5cb8cfdac21e

[ "MIT" ]

11

2019-10-31T12:38:43.000Z

2022-03-23T11:02:34.000Z

import networkx as nx # used to distinguish between multiple predicate nodes with same label - next index for predicate predicate_nodes = {} qualifier_predicate_nodes = {} ##################################################### ### Graphs ##################################################### # one element of the answer_statements is a dictionary with 'entity', 'object', 'predicate' and 'qualifiers' attributes # see statement_structure.json for details def expand_context_with_statements(context, statements, turn = 1, qa=False): if not context: context = nx.Graph() # print statements for statement in statements: # add the entity and object node if not statement['entity']['id'] in context: context.add_node(statement['entity']['id'], type='entity', turn=turn, qa=qa) if not statement['object']['id'] in context: context.add_node(statement['object']['id'], type='entity', turn=turn, qa=qa) # get current index of predicate used if not predicate_nodes.get(statement['predicate']['id']): # the predicate did not occur yet => index 0 and new entry predicate_nodes[statement['predicate']['id']] = 1 predicate_index = 0 else: # the predicate already occured => fetch the next index available and increase the saved one predicate_index = predicate_nodes[statement['predicate']['id']] predicate_nodes[statement['predicate']['id']] += 1 # add the predicate node predicate_node_id = (statement['predicate']['id'] + "-" + str(predicate_index)) context.add_node(predicate_node_id, type='predicate', turn=turn) # add the two edges (entity->predicate->object) context.add_edge(statement['entity']['id'], predicate_node_id) context.add_edge(predicate_node_id, statement['object']['id']) # if there were qualifiers occuring in the statement if statement.get('qualifiers'): for qualifier_statement in statement['qualifiers']: # add the qualifier_statment object if not qualifier_statement['qualifier_object']['id'] in context: context.add_node(qualifier_statement['qualifier_object']['id'], type='entity', turn=turn, qa=qa) # get current index of qualifier_predicate used if not qualifier_predicate_nodes.get(qualifier_statement['qualifier_predicate']['id']): # the qualifier_predicate did not occur yet => index 0 and new entry qualifier_predicate_nodes[qualifier_statement['qualifier_predicate']['id']] = 1 predicate_index = 0 else: # the qualifier_predicate already occured => fetch the next index available and increase the saved one predicate_index = qualifier_predicate_nodes[qualifier_statement['qualifier_predicate']['id']] qualifier_predicate_nodes[qualifier_statement['qualifier_predicate']['id']] += 1 # add the qualifier_predicate qualifier_predicate_node_id = qualifier_statement['qualifier_predicate']['id'] + "-" + str(predicate_index) context.add_node(qualifier_predicate_node_id, type='qualifier_predicate', turn=turn) # add the two edges (qualifier_entity->qualifier_predicate->qualifier_object) context.add_edge(predicate_node_id, qualifier_predicate_node_id) context.add_edge(qualifier_predicate_node_id, qualifier_statement['qualifier_object']['id']) return context # one element of the answer_statements is a dictionary with 'entity', 'object', 'predicate' and 'qualifiers' attributes # see statement_structure.json for details def expand_context_with_frontier(context, frontier, frontier_statement, turn = 1): if not context: context = nx.Graph() # complete the statement with labels # statement = complete_statement(frontier_statement, True) statement = frontier_statement # add the entity and object node if not statement['entity']['id'] in context: context.add_node(statement['entity']['id'], type='entity', turn=turn, qa=False) if not statement['object']['id'] in context: context.add_node(statement['object']['id'], type='entity', turn=turn, qa=False) # get current index of predicate used if not predicate_nodes.get(statement['predicate']['id']): # the predicate did not occur yet => index 0 and new entry predicate_nodes[statement['predicate']['id']] = 1 predicate_index = 0 else: # the predicate already occured => fetch the next index available and increase the saved one predicate_index = predicate_nodes[statement['predicate']['id']] predicate_nodes[statement['predicate']['id']] += 1 # add the predicate node predicate_node_id = (statement['predicate']['id'] + "-" + str(predicate_index)) context.add_node(predicate_node_id, type='predicate', turn=turn) # if the frontier is the predicate node, set the label as frontier if frontier == statement['predicate']['id']: frontier = predicate_node_id # add the two edges (entity->predicate->object) context.add_edge(statement['entity']['id'], predicate_node_id) context.add_edge(predicate_node_id, statement['object']['id']) # if there were qualifiers occuring in the statement if statement.get('qualifiers'): for qualifier_statement in statement['qualifiers']: # add the qualfier_statment object if not qualifier_statement['qualifier_object']['id'] in context: context.add_node(qualifier_statement['qualifier_object']['id'], type='entity', turn=turn, qa=False) # get current index of qualifier_predicate used if not qualifier_predicate_nodes.get(qualifier_statement['qualifier_predicate']['id']): # the qualifier_predicate did not occur yet => index 0 and new entry qualifier_predicate_nodes[qualifier_statement['qualifier_predicate']['id']] = 1 predicate_index = 0 else: # the qualifier_predicate already occured => fetch the next index available and increase the saved one predicate_index = qualifier_predicate_nodes[qualifier_statement['qualifier_predicate']['id']] qualifier_predicate_nodes[qualifier_statement['qualifier_predicate']['id']] += 1 # add the qualifier_predicate qualifier_predicate_node_id = qualifier_statement['qualifier_predicate']['id'] + "-" + str(predicate_index) context.add_node(qualifier_predicate_node_id, type='qualifier_predicate', turn=turn) # if the frontier is the predicate node, set the label as frontier if frontier == qualifier_statement['qualifier_predicate']['id']: frontier = qualifier_predicate_node_id # add the two edges (qualifier_entity->qualifier_predicate->qualifier_object) context.add_edge(predicate_node_id, qualifier_predicate_node_id) context.add_edge(qualifier_predicate_node_id, qualifier_statement['qualifier_object']['id']) return context, frontier # expand the context by the top candidates def expand_context_with_candidates(graph, candidates, turn=1): statements = [] for candidate in candidates: statements.append(candidate['statement']) graph = expand_context_with_statements(graph, statements, turn) return graph # set all nodes as qa nodes in the given graph def set_all_nodes_as_qa_nodes(graph): for node in list(graph.nodes(data=True)): node[1]['qa'] = True # return a list of all entity nodes which where question words or answers of the graph def get_all_qa_nodes(graph): entity_nodes = [node for node in list(graph.nodes(data=True)) if node[1]['type'] == 'entity' and node[1]['qa']] return entity_nodes # return a list of all entity nodes which could be answers def get_all_answer_candidates(graph): entity_nodes = [node[0] for node in list(graph.nodes(data=True)) if node[1]['type'] == 'entity' and not node[1]['qa']] if entity_nodes: return entity_nodes else: return get_all_answer_candidates_with_qa(graph) # return a list of all entity nodes which could be answers def get_all_answer_candidates_with_qa(graph): entity_nodes = [node[0] for node in list(graph.nodes(data=True)) if node[1]['type'] == 'entity'] return entity_nodes def get_distance(graph, answer_candidate, entity_node): return float(nx.shortest_path_length(graph, source=answer_candidate, target=entity_node) + 1.0) # graph to file def write_graph(graph, file_path): nx.write_gpickle(graph, file_path) # load graph from file def load_graph(file_path): return nx.read_gpickle(file_path)

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a077406730a08b76ee288c4760edc7e8d5bfeb61

656

py

Python

src/pathway_forte/pipeline/__init__.py

pathwayforte/PathwayForte

07775f3e174bfc756f7cf8f03efe49ef95a1cfd9

[ "Apache-2.0" ]

10

2019-03-31T14:53:05.000Z

2021-01-16T07:33:41.000Z

src/pathway_forte/pipeline/__init__.py

pathwayforte/PathwayForte

07775f3e174bfc756f7cf8f03efe49ef95a1cfd9

[ "Apache-2.0" ]

16

2019-03-31T07:25:43.000Z

2019-08-21T09:47:26.000Z

src/pathway_forte/pipeline/__init__.py

pathwayforte/PathwayForte

07775f3e174bfc756f7cf8f03efe49ef95a1cfd9

[ "Apache-2.0" ]

3

2020-04-23T13:55:29.000Z

2020-08-28T16:10:27.000Z

# -*- coding: utf-8 -*- """Pipelines from Pathway Forte.""" from pathway_forte.pipeline.binary import do_binary_prediction from pathway_forte.pipeline.export import do_export from pathway_forte.pipeline.geometric import do_hypergeometric from pathway_forte.pipeline.gsea import do_gsea from pathway_forte.pipeline.gsea_msig import do_gsea_msig from pathway_forte.pipeline.import_gmt import gmt_parser from pathway_forte.pipeline.ssgsea import do_ssgsea from pathway_forte.pipeline.stability import do_stability_prediction from pathway_forte.pipeline.subtype import do_subtype_prediction from pathway_forte.pipeline.survival import do_survival_prediction

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a08049ff810ab9612712756497cbceb04ec51ad4

276

py

Python

Codewars/8kyu/grasshopper-combine-strings/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/8kyu/grasshopper-combine-strings/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/8kyu/grasshopper-combine-strings/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 2.7.6 Test.describe('combine names') Test.it('example tests') Test.assert_equals(combine_names('James', 'Stevens'), 'James Stevens') Test.assert_equals(combine_names('Davy', 'Back'), 'Davy Back') Test.assert_equals(combine_names('Arthur', 'Dent'), 'Arthur Dent')

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a0a32a6cfc506420c6aa632e0146a0b9266c6c28

80

py

Python

examples/testlib2/box/__init__.py

uibcdf/pyunitwizard

54cdce7369e1f2a3771a1f05a4a6ba1d7610a5e7

[ "MIT" ]

2

2021-07-01T14:33:58.000Z

2022-03-19T19:19:09.000Z

examples/testlib2/box/__init__.py

uibcdf/pyunitwizard

54cdce7369e1f2a3771a1f05a4a6ba1d7610a5e7

[ "MIT" ]

15

2021-02-11T18:54:16.000Z

2022-03-18T17:38:03.000Z

examples/testlib2/box/__init__.py

uibcdf/pyunitwizard

54cdce7369e1f2a3771a1f05a4a6ba1d7610a5e7

[ "MIT" ]

2

2021-06-17T18:56:02.000Z

2022-03-08T05:02:17.000Z

from .methods_a import get_default_form from .methods_b import set_default_form

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39ff9ac1ffd835711400efbeebe984e6ded6187f

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py

Python

FEMpy/tests/integration/test_poisson_1d.py

floydie7/FEMpy

50e11b88dc249ff7c599472b455b07b04df1afd7

[ "MIT" ]

null

FEMpy/tests/integration/test_poisson_1d.py

floydie7/FEMpy

50e11b88dc249ff7c599472b455b07b04df1afd7

[ "MIT" ]

null

FEMpy/tests/integration/test_poisson_1d.py

floydie7/FEMpy

50e11b88dc249ff7c599472b455b07b04df1afd7

[ "MIT" ]

1

2022-01-22T06:39:38.000Z

import numpy as np import FEMpy def coefficient_function(x): return np.exp(x) def source_function(x): return -np.exp(x) * (np.cos(x) - 2 * np.sin(x) - x * np.cos(x) - x * np.sin(x)) def dirichlet_function(x): if x == 0: return 0 def neumann_function(x): if x == 1: return np.cos(1) - np.sin(1) def analytical_sol(x): return x * np.cos(x) def dx_analytical_sol(x): return np.cos(x) - x * np.sin(x) class TestLinearElements(object): def setup(self): self.mesh = FEMpy.Interval1D(0, 1, 1/4, 'linear') self.basis = FEMpy.IntervalBasis1D('linear') self.bcs = FEMpy.BoundaryConditions(self.mesh, ['dirichlet', 'neumann'], dirichlet_fun=dirichlet_function, neumann_fun=neumann_function, coeff_fun=coefficient_function) self.poisson_eq = FEMpy.Poisson1D(self.mesh, self.basis, self.basis, self.bcs) def test_solution_vector(self): nodal_solution_vector = self.poisson_eq.solve(coeff_fun=coefficient_function, source_fun=source_function) assert np.allclose(nodal_solution_vector, np.array([2.9317e-16, 0.24174, 0.43690, 0.54469, 0.53351])) def test_l_infinity_norm_error(self): self.poisson_eq.solve(coeff_fun=coefficient_function, source_fun=source_function) l_infinity_norm_error = self.poisson_eq.l_inf_error(analytical_sol) assert np.abs(l_infinity_norm_error - 2.0464e-02) <= 1e-5 def test_l_2_norm_error(self): self.poisson_eq.solve(coeff_fun=coefficient_function, source_fun=source_function) l_2_norm_error = self.poisson_eq.l2_error(analytical_sol) assert np.abs(l_2_norm_error - 1.1205e-02) <= 1e-5 def test_h_1_seminorm_error(self): self.poisson_eq.solve(coeff_fun=coefficient_function, source_fun=source_function) h_1_seminorm_error = self.poisson_eq.h1_seminorm_error(dx_analytical_sol) assert np.abs(h_1_seminorm_error - 1.0542e-01) <= 1e-5 class TestQuadraticElements(object): def setup(self): self.mesh = FEMpy.Interval1D(0, 1, 1 / 4, 'quadratic') self.basis = FEMpy.IntervalBasis1D('quadratic') self.bcs = FEMpy.BoundaryConditions(self.mesh, ['dirichlet', 'neumann'], dirichlet_fun=dirichlet_function, neumann_fun=neumann_function, coeff_fun=coefficient_function) self.poisson_eq = FEMpy.Poisson1D(self.mesh, self.basis, self.basis, self.bcs) def test_solution_vector(self): nodal_solution_vector = self.poisson_eq.solve(coeff_fun=coefficient_function, source_fun=source_function) assert np.allclose(nodal_solution_vector, np.array([-1.3260e-15, 0.12407, 0.24223, 0.34899, 0.43880, 0.50689, 0.54878, 0.56090, 0.54031]), rtol=1e-4, atol=1e-7) def test_l_infinity_norm_error(self): self.poisson_eq.solve(coeff_fun=coefficient_function, source_fun=source_function) l_infinity_norm_error = self.poisson_eq.l_inf_error(analytical_sol) assert np.abs(l_infinity_norm_error - 3.3279e-04) <= 1e-5 def test_l_2_norm_error(self): self.poisson_eq.solve(coeff_fun=coefficient_function, source_fun=source_function) l_2_norm_error = self.poisson_eq.l2_error(analytical_sol) assert np.abs(l_2_norm_error - 2.1050e-04) <= 1e-5 def test_h_1_seminorm_error(self): self.poisson_eq.solve(coeff_fun=coefficient_function, source_fun=source_function) h_1_seminorm_error = self.poisson_eq.h1_seminorm_error(dx_analytical_sol) assert np.abs(h_1_seminorm_error - 5.4213e-03) <= 1e-5

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261753006962b6f5023a994d77457b91ece7c7c3

27

py

Python

__init__.py

rsgalloway/QRangeSlider

06cb61de8fa174e4eea556c1e6eda4b0d04e5b10

[ "BSD-3-Clause" ]

24

2015-03-20T08:02:46.000Z

2021-02-28T06:25:19.000Z

__init__.py

rsgalloway/QRangeSlider

06cb61de8fa174e4eea556c1e6eda4b0d04e5b10

[ "BSD-3-Clause" ]

3

2017-01-27T20:31:40.000Z

2021-01-19T20:05:19.000Z

__init__.py

rsgalloway/QRangeSlider

06cb61de8fa174e4eea556c1e6eda4b0d04e5b10

[ "BSD-3-Clause" ]

9

2016-08-24T16:14:25.000Z

2020-08-18T07:06:02.000Z

from qrangeslider import *

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26495e8d5e05ec83207013e49457b028a8e4266c

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py

Python

cubework/module/parallel_3d/module.py

kurisusnowdeng/Cubework

56c0d35f87765efc8f2b6d47a4ccea6f2ec626aa

[ "Apache-2.0" ]

null

cubework/module/parallel_3d/module.py

kurisusnowdeng/Cubework

56c0d35f87765efc8f2b6d47a4ccea6f2ec626aa

[ "Apache-2.0" ]

null

cubework/module/parallel_3d/module.py

kurisusnowdeng/Cubework

56c0d35f87765efc8f2b6d47a4ccea6f2ec626aa

[ "Apache-2.0" ]

null

import math from typing import Callable import torch import torch.nn as nn import torch.nn.functional as F from cubework.distributed import ParallelManager as pm from cubework.distributed import all_reduce, broadcast from cubework.global_vars import env from cubework.utils import get_current_device, seed from torch import Tensor from torch.nn import Parameter from .. import init from ..utils import set_tensor_parallel_attribute_by_partition, split_tensor, to_2tuple from ._operation import classifier_3d, layernorm_3d, linear_3d from ._utils import ( all_gather_weight_3d, broadcast_weight_3d_from_diagonal, get_depth_from_env, get_input_parallel_mode, get_output_parallel_mode, get_weight_parallel_mode, get_input_x_weight_parallel_mode, get_output_x_weight_parallel_mode, reduce_scatter_tensor_3d, split_batch_3d, swap_in_out_group, ) class LayerNorm3D(nn.Module): def __init__(self, normalized_shape: int, eps: float = 1e-12, dtype=None): super().__init__() self.input_parallel_mode = get_input_parallel_mode() self.weight_parallel_mode = get_weight_parallel_mode() self.output_parallel_mode = get_output_parallel_mode() self.input_x_weight_parallel_mode = get_input_x_weight_parallel_mode() self.output_x_weight_parallel_mode = get_output_x_weight_parallel_mode() self.depth = get_depth_from_env() self.normalized_shape = normalized_shape self.normalized_shape_per_partition = normalized_shape // self.depth self.weight = Parameter( torch.ones(self.normalized_shape_per_partition, device=get_current_device(), dtype=dtype) ) self.bias = Parameter( torch.zeros(self.normalized_shape_per_partition, device=get_current_device(), dtype=dtype) ) self.variance_epsilon = eps self._set_tensor_parallel_attributes() def _set_tensor_parallel_attributes(self) -> None: set_tensor_parallel_attribute_by_partition(self.weight, self.depth) set_tensor_parallel_attribute_by_partition(self.bias, self.depth) def reset_parameters(self) -> None: init.zeros_()(self.bias) init.ones_()(self.weight) def forward(self, input_: Tensor) -> Tensor: return layernorm_3d( input_, self.weight, self.bias, self.normalized_shape, self.variance_epsilon, self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode, self.input_x_weight_parallel_mode, ) class Linear3D(nn.Module): def __init__( self, in_features: int, out_features: int, bias: bool = True, dtype: torch.dtype = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), ): super().__init__() self.in_features = in_features self.out_features = out_features self.input_parallel_mode = get_input_parallel_mode() self.weight_parallel_mode = get_weight_parallel_mode() self.output_parallel_mode = get_output_parallel_mode() self.input_x_weight_parallel_mode = get_input_x_weight_parallel_mode() self.output_x_weight_parallel_mode = get_output_x_weight_parallel_mode() self.depth = get_depth_from_env() self.in_features_per_partition = in_features // self.depth self.out_features_per_partition = out_features // self.depth**2 self.bias_features_per_partition = out_features // self.depth self.weight = Parameter( torch.empty( self.in_features_per_partition, self.out_features_per_partition, device=get_current_device(), dtype=dtype, ) ) if bias: self.bias = Parameter( torch.zeros(self.bias_features_per_partition, device=get_current_device(), dtype=dtype) ) else: self.bias = None self.reset_parameters(weight_initializer, bias_initializer) self._set_tensor_parallel_attributes() swap_in_out_group() def _set_tensor_parallel_attributes(self) -> None: set_tensor_parallel_attribute_by_partition(self.weight, self.depth**3) if self.bias is not None: set_tensor_parallel_attribute_by_partition(self.bias, self.depth) def reset_parameters(self, weight_initializer, bias_initializer) -> None: with seed(pm.TENSOR): fan_in, fan_out = self.in_features, self.out_features weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) if self.bias is not None: bias_initializer(self.bias, fan_in=fan_in) # weight_src_rank = self.weight_parallel_mode.rank_by_idx(0) # output_src_rank = self.output_parallel_mode.rank_by_idx(0) # broadcast(self.bias, weight_src_rank, self.weight_parallel_mode) # broadcast(self.bias, output_src_rank, self.output_parallel_mode) broadcast( self.bias, self.output_x_weight_parallel_mode.rank_by_idx(0), self.output_x_weight_parallel_mode, ) def forward(self, input_: Tensor) -> Tensor: return linear_3d( input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode, ) class Classifier3D(nn.Module): def __init__( self, in_features: int, num_classes: int, weight: Parameter = None, bias: bool = True, dtype: torch.dtype = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), ): super().__init__() self.in_features = in_features self.num_classes = num_classes self.input_parallel_mode = get_input_parallel_mode() self.weight_parallel_mode = get_weight_parallel_mode() self.output_parallel_mode = get_output_parallel_mode() self.depth = get_depth_from_env() self.in_features_per_partition = in_features // self.depth if weight is not None: self.weight = weight self.has_weight = False else: self.weight = Parameter( torch.empty(self.num_classes, self.in_features_per_partition, device=get_current_device(), dtype=dtype) ) self.has_weight = True if bias: self.bias = Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype)) else: self.bias = None self.reset_parameters(weight_initializer, bias_initializer) self._set_tensor_parallel_attributes() def _set_tensor_parallel_attributes(self) -> None: if self.has_weight: set_tensor_parallel_attribute_by_partition(self.weight, self.depth) def reset_parameters(self, weight_initializer, bias_initializer) -> None: with seed(pm.TENSOR): fan_in, fan_out = self.in_features, self.num_classes weight_src_rank = self.weight_parallel_mode.rank_by_idx(0) output_src_rank = self.output_parallel_mode.rank_by_idx(0) input_src_rank = self.input_parallel_mode.rank_by_idx(0) if self.has_weight: weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) broadcast(self.weight, weight_src_rank, self.weight_parallel_mode) if self.bias is not None: bias_initializer(self.bias, fan_in=fan_in) broadcast(self.bias, weight_src_rank, self.weight_parallel_mode) broadcast(self.bias, output_src_rank, self.output_parallel_mode) broadcast(self.bias, input_src_rank, self.input_parallel_mode) def forward(self, input_: Tensor) -> Tensor: return classifier_3d( input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode, ) class VocabParallelClassifier3D(nn.Module): def __init__( self, in_features: int, num_classes: int, weight: Parameter = None, bias: bool = True, dtype: torch.dtype = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), ): super().__init__() self.in_features = in_features self.num_classes = num_classes self.input_parallel_mode = get_input_parallel_mode() self.weight_parallel_mode = get_weight_parallel_mode() self.output_parallel_mode = get_output_parallel_mode() self.input_x_weight_parallel_mode = get_input_x_weight_parallel_mode() self.output_x_weight_parallel_mode = get_output_x_weight_parallel_mode() self.depth = get_depth_from_env() self.in_features_per_partition = in_features // self.depth self.out_features_per_partition = num_classes // self.depth**2 self.bias_features_per_partition = num_classes // self.depth if weight is not None: self.weight = weight self.has_weight = False else: self.weight = Parameter( torch.empty( self.out_features_per_partition, self.in_features_per_partition, device=get_current_device(), dtype=dtype, ) ) self.has_weight = True if bias: self.bias = Parameter( torch.zeros(self.bias_features_per_partition, device=get_current_device(), dtype=dtype) ) else: self.bias = None self.reset_parameters(weight_initializer, bias_initializer) self._set_tensor_parallel_attributes() swap_in_out_group() env.vocab_parallel = True def _set_tensor_parallel_attributes(self) -> None: if self.has_weight: set_tensor_parallel_attribute_by_partition(self.weight, self.depth**3) if self.bias is not None: set_tensor_parallel_attribute_by_partition(self.bias, self.depth) def reset_parameters(self, weight_initializer, bias_initializer) -> None: with seed(pm.TENSOR): fan_in, fan_out = self.in_features, self.num_classes if self.has_weight: weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) if self.bias is not None: bias_initializer(self.bias, fan_in=fan_in) # weight_src_rank = self.weight_parallel_mode.rank_by_idx(0) # output_src_rank = self.output_parallel_mode.rank_by_idx(0) # broadcast(self.bias, weight_src_rank, self.weight_parallel_mode) # broadcast(self.bias, output_src_rank, self.output_parallel_mode) broadcast( self.bias, self.output_x_weight_parallel_mode.rank_by_idx(0), self.output_x_weight_parallel_mode, ) def forward(self, input_: Tensor) -> Tensor: return linear_3d( input_, self.weight.transpose(0, 1), self.bias, self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode, ) class PatchEmbedding3D(nn.Module): def __init__( self, img_size: int, patch_size: int, in_chans: int, embed_size: int, flatten: bool = True, dtype: torch.dtype = None, weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)), bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1), position_embed_initializer: Callable = init.zeros_(), ): super().__init__() self.depth = get_depth_from_env() self.input_parallel_mode = get_input_parallel_mode() self.weight_parallel_mode = get_weight_parallel_mode() self.output_parallel_mode = get_output_parallel_mode() self.patch_size = to_2tuple(patch_size) grid_size = to_2tuple(img_size // patch_size) num_patches = grid_size[0] * grid_size[1] self.embed_size = embed_size embed_size_per_partition = embed_size // self.depth self.flatten = flatten self.weight = nn.Parameter( torch.empty( (embed_size_per_partition, in_chans, *self.patch_size), device=get_current_device(), dtype=dtype ) ) self.bias = nn.Parameter(torch.empty(embed_size_per_partition, device=get_current_device(), dtype=dtype)) self.cls_token = nn.Parameter( torch.zeros((1, 1, embed_size_per_partition), device=get_current_device(), dtype=dtype) ) self.pos_embed = nn.Parameter( torch.zeros((1, num_patches + 1, embed_size_per_partition), device=get_current_device(), dtype=dtype) ) self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer) self._set_tensor_parallel_attributes() def _set_tensor_parallel_attributes(self) -> None: set_tensor_parallel_attribute_by_partition(self.weight, self.depth) set_tensor_parallel_attribute_by_partition(self.bias, self.depth) set_tensor_parallel_attribute_by_partition(self.cls_token, self.depth) set_tensor_parallel_attribute_by_partition(self.pos_embed, self.depth) def _sync_grad_hook(self, grad) -> Tensor: grad = all_reduce(grad.clone(), self.input_parallel_mode) grad = all_reduce(grad, self.weight_parallel_mode) return grad def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer) -> None: with seed(pm.TENSOR): fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight) fan_out = self.embed_size weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) bias_initializer(self.bias, fan_in=fan_in) position_embed_initializer(self.pos_embed) weight_src_rank = self.weight_parallel_mode.rank_by_idx(0) input_src_rank = self.input_parallel_mode.rank_by_idx(0) broadcast(self.weight, weight_src_rank, self.weight_parallel_mode) broadcast(self.bias, weight_src_rank, self.weight_parallel_mode) broadcast(self.pos_embed, weight_src_rank, self.weight_parallel_mode) broadcast(self.weight, input_src_rank, self.input_parallel_mode) broadcast(self.bias, input_src_rank, self.input_parallel_mode) broadcast(self.pos_embed, input_src_rank, self.input_parallel_mode) self.weight.register_hook(self._sync_grad_hook) self.bias.register_hook(self._sync_grad_hook) self.cls_token.register_hook(self._sync_grad_hook) self.pos_embed.register_hook(self._sync_grad_hook) def forward(self, input_: Tensor) -> Tensor: input_ = split_batch_3d( input_, input_parallel_mode=self.input_parallel_mode, weight_parallel_mode=self.weight_parallel_mode ) output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size) if self.flatten: output = output.flatten(2).transpose(1, 2) # BCHW -> BNC cls_token = self.cls_token.expand(output.shape[0], -1, -1) output = torch.cat((cls_token, output), dim=1) output = output + self.pos_embed return output class Embedding3D(nn.Module): def __init__( self, num_embeddings: int, embedding_dim: int, padding_idx: int = None, dtype: torch.dtype = None, weight_initializer: Callable = init.normal_(), *args, **kwargs ): super().__init__() self.depth = get_depth_from_env() self.input_parallel_mode = get_input_parallel_mode() self.weight_parallel_mode = get_weight_parallel_mode() self.output_parallel_mode = get_output_parallel_mode() self.input_x_weight_parallel_mode = get_input_x_weight_parallel_mode() self.output_x_weight_parallel_mode = get_output_x_weight_parallel_mode() self.num_embeddings = num_embeddings self.embed_dim = embedding_dim embed_dim_per_partition = embedding_dim // self.depth self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs self.weight = nn.Parameter( torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype) ) self.reset_parameters(weight_initializer) self._set_tensor_parallel_attributes() def _set_tensor_parallel_attributes(self) -> None: set_tensor_parallel_attribute_by_partition(self.weight, self.depth) def _sync_grad_hook(self, grad) -> Tensor: # grad = all_reduce(grad.clone(), self.input_parallel_mode) # grad = all_reduce(grad, self.weight_parallel_mode) grad = all_reduce(grad.clone(), self.input_x_weight_parallel_mode) return grad def reset_parameters(self, weight_initializer) -> None: with seed(pm.TENSOR): fan_in, fan_out = self.num_embeddings, self.embed_dim weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) self._fill_padding_idx_with_zero() # weight_src_rank = self.weight_parallel_mode.rank_by_idx(0) broadcast(self.weight, self.input_x_weight_parallel_mode.rank_by_idx(0), self.input_x_weight_parallel_mode) self.weight.register_hook(self._sync_grad_hook) def _fill_padding_idx_with_zero(self) -> None: if self.padding_idx is not None: with torch.no_grad(): self.weight[self.padding_idx].fill_(0) def forward(self, input_: Tensor) -> Tensor: input_ = split_batch_3d( input_, input_parallel_mode=self.input_parallel_mode, weight_parallel_mode=self.weight_parallel_mode ) # weight = broadcast_weight_3d_from_diagonal( # self.weight, self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode # ) output = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) return output class VocabParallelEmbedding3D(torch.nn.Module): def __init__( self, num_embeddings: int, embedding_dim: int, padding_idx: int = None, dtype: torch.dtype = None, weight_initializer: Callable = init.normal_(), *args, **kwargs ): super().__init__() self.num_embeddings = num_embeddings self.embed_dim = embedding_dim self.padding_idx = padding_idx self.embed_args = args self.embed_kwargs = kwargs self.depth = get_depth_from_env() self.input_parallel_mode = get_input_parallel_mode() self.weight_parallel_mode = get_weight_parallel_mode() self.output_parallel_mode = get_output_parallel_mode() self.num_embeddings_per_partition = self.num_embeddings // self.depth**2 self.embed_dim_per_partition = self.embed_dim // self.depth vocab_parallel_rank = self.input_parallel_mode.local_rank self.vocab_start_index = vocab_parallel_rank * self.num_embeddings_per_partition * self.depth self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition * self.depth self.weight = Parameter( torch.empty( (self.num_embeddings_per_partition, self.embed_dim_per_partition), device=get_current_device(), dtype=dtype, ) ) self.reset_parameters(weight_initializer) self._set_tensor_parallel_attributes() env.vocab_parallel = True def _set_tensor_parallel_attributes(self): set_tensor_parallel_attribute_by_partition(self.weight, self.depth**3) def reset_parameters(self, weight_initializer) -> None: with seed(pm.TENSOR): fan_in, fan_out = self.num_embeddings, self.embed_dim weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out) self._fill_padding_idx_with_zero() def _fill_padding_idx_with_zero(self) -> None: if ( self.padding_idx is not None and self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index ): with torch.no_grad(): self.weight[self.padding_idx - self.vocab_start_index].fill_(0) def forward(self, input_: Tensor) -> Tensor: input_ = split_tensor(input_, 0, self.weight_parallel_mode) input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index) masked_input = input_.clone() - self.vocab_start_index masked_input[input_mask] = 0 weight = all_gather_weight_3d(self.weight, 0, self.weight_parallel_mode) output_parallel = F.embedding(masked_input, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) output_parallel[input_mask, :] = 0.0 output = reduce_scatter_tensor_3d(output_parallel, 0, self.input_parallel_mode) return output

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0.797583

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0.250715

21,682

535

120

40.527103

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null

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null

0

6

cd1a7483160f9454132be4e3913db772b4a8e2ec

7,971

py

Python

model.py

gitlimlab/FeatureControlHRL-Tensorflow

7e611febd296bada68f44710992f9bcd284941d2

[ "MIT" ]

26

2017-12-11T21:13:15.000Z

2019-11-05T08:21:21.000Z

model.py

clvrai/FeatureControlHRL-Tensorflow

7e611febd296bada68f44710992f9bcd284941d2

[ "MIT" ]

null

model.py

clvrai/FeatureControlHRL-Tensorflow

7e611febd296bada68f44710992f9bcd284941d2

[ "MIT" ]

4

2018-02-05T08:23:09.000Z

2019-02-11T10:56:51.000Z

import numpy as np import tensorflow as tf import tensorflow.contrib.rnn as rnn from ops import flatten, conv2d, linear def normalized_columns_initializer(std=1.0): def _initializer(shape, dtype=None, partition_info=None): out = np.random.randn(*shape).astype(np.float32) out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True)) return tf.constant(out) return _initializer def categorical_sample(logits, d): value = tf.squeeze(tf.multinomial(logits - tf.reduce_max(logits, [1], keep_dims=True), 1), [1]) return tf.one_hot(value, d) class SubPolicy(object): def __init__(self, ob_space, ac_space, subgoal_space, intrinsic_type): self.x = x = tf.placeholder(tf.float32, [None] + list(ob_space), name='x') self.action_prev = action_prev = tf.placeholder(tf.float32, [None, ac_space], name='action_prev') self.reward_prev = reward_prev = tf.placeholder(tf.float32, [None, 1], name='reward_prev') self.subgoal = subgoal = tf.placeholder(tf.float32, [None, subgoal_space], name='subgoal') self.intrinsic_type = intrinsic_type with tf.variable_scope('encoder'): x = tf.image.resize_images(x, [84, 84]) x = x / 255.0 self.p = x x = tf.nn.relu(conv2d(x, 16, "l1", [8, 8], [4, 4])) x = tf.nn.relu(conv2d(x, 32, "l2", [4, 4], [2, 2])) self.f = tf.reduce_mean(x, axis=[1, 2]) x = flatten(x) with tf.variable_scope('sub_policy'): x = tf.nn.relu(linear(x, 256, "fc", normalized_columns_initializer(0.01))) x = tf.concat([x, action_prev], axis=1) x = tf.concat([x, reward_prev], axis=1) x = tf.concat([x, subgoal], axis=1) # introduce a "fake" batch dimension of 1 after flatten # so that we can do LSTM over time dim x = tf.expand_dims(x, [0]) size = 256 lstm = rnn.BasicLSTMCell(size, state_is_tuple=True) self.state_size = lstm.state_size step_size = tf.shape(self.x)[:1] c_init = np.zeros((1, lstm.state_size.c), np.float32) h_init = np.zeros((1, lstm.state_size.h), np.float32) self.state_init = [c_init, h_init] c_in = tf.placeholder(tf.float32, [1, lstm.state_size.c]) h_in = tf.placeholder(tf.float32, [1, lstm.state_size.h]) self.state_in = [c_in, h_in] state_in = rnn.LSTMStateTuple(c_in, h_in) lstm_outputs, lstm_state = tf.nn.dynamic_rnn( lstm, x, initial_state=state_in, sequence_length=step_size, time_major=False ) lstm_c, lstm_h = lstm_state lstm_outputs = tf.reshape(lstm_outputs, [-1, size]) self.logits = linear(lstm_outputs, ac_space, "action", normalized_columns_initializer(0.01)) self.vf = tf.reshape(linear(lstm_outputs, 1, "value", normalized_columns_initializer(1.0)), [-1]) self.state_out = [lstm_c[:1, :], lstm_h[:1, :]] self.sample = categorical_sample(self.logits, ac_space)[0, :] self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name) def get_initial_features(self): return self.state_init def act(self, ob, action_prev, reward_prev, subgoal, c, h): sess = tf.get_default_session() return sess.run([self.sample, self.vf] + self.state_out, {self.x: [ob], self.state_in[0]: c, self.state_in[1]: h, self.action_prev: [action_prev], self.reward_prev: [reward_prev], self.subgoal: [subgoal]}) def value(self, ob, action_prev, reward_prev, subgoal, c, h): sess = tf.get_default_session() return sess.run(self.vf, {self.x: [ob], self.state_in[0]: c, self.state_in[1]: h, self.action_prev: [action_prev], self.reward_prev: [reward_prev], self.subgoal: [subgoal]})[0] def feature(self, state): sess = tf.get_default_session() if self.intrinsic_type == 'feature': return sess.run(self.f, {self.x: [state]})[0, :] else: return sess.run(self.p, {self.x: [state]})[0, :] class MetaPolicy(object): def __init__(self, ob_space, subgoal_space, intrinsic_type): self.x = x = \ tf.placeholder(tf.float32, [None] + list(ob_space), name='x_meta') self.subgoal_prev = subgoal_prev = \ tf.placeholder(tf.float32, [None, subgoal_space], name='subgoal_prev') self.reward_prev = reward_prev = \ tf.placeholder(tf.float32, [None, 1], name='reward_prev_meta') self.intrinsic_type = intrinsic_type with tf.variable_scope('encoder', reuse=True): x = tf.image.resize_images(x, [84, 84]) x = x / 255.0 x = tf.nn.relu(conv2d(x, 16, "l1", [8, 8], [4, 4])) x = tf.nn.relu(conv2d(x, 32, "l2", [4, 4], [2, 2])) x = flatten(x) with tf.variable_scope('meta_policy'): x = tf.nn.relu(linear(x, 256, "fc", normalized_columns_initializer(0.01))) x = tf.concat([x, subgoal_prev], axis=1) x = tf.concat([x, reward_prev], axis=1) # introduce a "fake" batch dimension of 1 after flatten # so that we can do LSTM over time dim x = tf.expand_dims(x, [0]) size = 256 lstm = rnn.BasicLSTMCell(size, state_is_tuple=True) self.state_size = lstm.state_size step_size = tf.shape(self.x)[:1] c_init = np.zeros((1, lstm.state_size.c), np.float32) h_init = np.zeros((1, lstm.state_size.h), np.float32) self.state_init = [c_init, h_init] c_in = tf.placeholder(tf.float32, [1, lstm.state_size.c]) h_in = tf.placeholder(tf.float32, [1, lstm.state_size.h]) self.state_in = [c_in, h_in] state_in = rnn.LSTMStateTuple(c_in, h_in) lstm_outputs, lstm_state = tf.nn.dynamic_rnn( lstm, x, initial_state=state_in, sequence_length=step_size, time_major=False) lstm_c, lstm_h = lstm_state lstm_outputs = tf.reshape(lstm_outputs, [-1, size]) self.logits = linear(lstm_outputs, subgoal_space, "action", normalized_columns_initializer(0.01)) self.vf = tf.reshape(linear(lstm_outputs, 1, "value", normalized_columns_initializer(1.0)), [-1]) self.state_out = [lstm_c[:1, :], lstm_h[:1, :]] self.sample = categorical_sample(self.logits, subgoal_space)[0, :] self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name) def get_initial_features(self): return self.state_init def act(self, ob, subgoal_prev, reward_prev, c, h): sess = tf.get_default_session() return sess.run([self.sample, self.vf] + self.state_out, {self.x: [ob], self.state_in[0]: c, self.state_in[1]: h, self.subgoal_prev: [subgoal_prev], self.reward_prev: [reward_prev]}) def value(self, ob, subgoal_prev, reward_prev, c, h): sess = tf.get_default_session() return sess.run(self.vf, {self.x: [ob], self.state_in[0]: c, self.state_in[1]: h, self.subgoal_prev: [subgoal_prev], self.reward_prev: [reward_prev]})[0]

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26e98a4a18f62f4031d492d98a09e2069665f4da

81,255

py

Python

users/views.py

anshrathod/Lyrico

377bb884b95953d8853e939d920eadbc502cac66

[ "MIT" ]

null

users/views.py

anshrathod/Lyrico

377bb884b95953d8853e939d920eadbc502cac66

[ "MIT" ]

null

users/views.py

anshrathod/Lyrico

377bb884b95953d8853e939d920eadbc502cac66

[ "MIT" ]

1

2020-04-13T10:53:33.000Z

from django.contrib import messages from django.contrib.auth import (authenticate, login, logout, update_session_auth_hash) from django.contrib.auth.decorators import login_required from django.contrib.auth.forms import PasswordChangeForm from django.contrib.auth.models import User from django.shortcuts import redirect, render from django.urls import reverse from django.utils.datastructures import MultiValueDictKeyError from songs.models import Song from .forms import UserRegisterForm from .models import Profile def login_user(request): if request.method == "POST": username = request.POST['username'] password = request.POST['password'] user = authenticate(username=username, password=password) if user is not None: if user.is_active: login(request, user) messages.success(request, ('{},Just Logged In!'.format(username))) return redirect('songs-home') else: messages.warning(request,'Your account has been deactivated.') return render(request, 'users/login.html') else: messages.warning(request,'The account details entered were wrong.') return render(request, 'users/login.html') return render(request, 'users/login.html') def signup(request): if request.method=='POST': try: user = UserRegisterForm(request.POST) if user.is_valid(): password1 = user.cleaned_data.get('password1') password2 = user.cleaned_data.get('password2') username = user.cleaned_data.get('username') email=user.cleaned_data.get('email') fname =user.cleaned_data.get('fname') lname =user.cleaned_data.get('lname') context={ 'fname':fname, 'lname':lname, 'username':username, 'email':email, } if password1 == password2: passval = valid_pass(password1) if passval == 'True': if valid_email(email): user.save() useracc=User.objects.get(username=username) profile=Profile(user = useracc,age=0) profile.save() messages.success(request, ('Account has been created for {}! Please Fill the details to build up your Profile.'.format(username))) login(request,useracc) return redirect('songs-addprofile') else: messages.warning(request,'Your account couldn\'t be created...Enter Valid Email-id.') return render(request,'users/signup.html',context) else: messages.warning(request,passval) return render(request,'users/signup.html',context) else: messages.warning(request,'Both The Passwords Entered Didn\'t Match.') return render(request,'users/signup.html',context) except Exception as e: messages.warning(request,e) print(profile.age) return render(request,'users/signup.html',{}) else: user = UserRegisterForm() return render(request,'users/signup.html',{}) def valid_email(mail): domains = ['somaiya.edu', 'gmail.com' ,] user_domain = mail.split('@')[1] if user_domain in domains: return True return False def valid_pass(password): passlist=['aaa', 'abc', 'academia', 'academic', 'access', 'ada', 'admin', 'adrian', 'adrianna', 'aerobics', 'airplane', 'albany', 'albatross', 'albert', 'alex', 'alexander', 'alf', 'algebra', 'alias', 'aliases', 'alice', 'alicia', 'alisa', 'alison', 'allison', 'alpha', 'alphabet', 'ama', 'amadeus', 'amanda', 'amber', 'amorphous', 'amy', 'analog', 'anchor', 'andrea', 'andromache', 'andy', 'angela', 'angerine', 'angie', 'animals', 'anita', 'ann', 'anna', 'anne', 'annette', 'answer', 'anthropogenic', 'anvils', 'anything', 'april', 'aria', 'ariadne', 'arlene', 'arrow', 'arthur', 'asd', 'asm', 'asshole', 'athena', 'atmosphere', 'aztecs', 'azure', 'bacchus', 'badass', 'bailey', 'banana', 'bananas', 'bandit', 'banks', 'barbara', 'barber', 'baritone', 'bart', 'bartman', 'basic', 'bass', 'bassoon', 'batch', 'batman', 'beach', 'beater', 'beauty', 'beaver', 'becky', 'beethoven', 'beloved', 'benz', 'beowulf', 'berkeley', 'berlin', 'berliner', 'beryl', 'beta', 'beth', 'betsie', 'betty', 'beverly', 'bicameral', 'bishop', 'bitch', 'bob', 'bradley', 'brandi', 'brandy', 'brenda', 'brian', 'bridget', 'broadway', 'bsd', 'bumbling', 'burgess', 'cad', 'camille', 'campanile', 'candi', 'candy', 'cantor', 'cardinal', 'caren', 'carla', 'carmen', 'carol', 'carole', 'carolina', 'caroline', 'carrie', 'carson', 'cascades', 'castle', 'cat', 'catherine', 'cathy', 'cayuga', 'cecily', 'celtics', 'cerulean', 'change', 'charity', 'charles', 'charming', 'charon', 'chat', 'chem', 'chemistry', 'chess', 'chester', 'christina', 'christine', 'christy', 'cigar', 'cindy', 'class', 'classic', 'claudia', 'cluster', 'clusters', 'code', 'coffee', 'coke', 'collins', 'commrades', 'computer', 'comrade', 'comrades', 'condo', 'condom', 'connect', 'connie', 'console', 'cookie', 'cooper', 'cornelius', 'couscous', 'create', 'creation', 'creosote', 'cretin', 'criminal', 'cristina', 'crystal', 'cshrc', 'cynthia', 'daemon', 'daisy', 'dana', 'dancer', 'daniel', 'danielle', 'danny', 'dapper', 'data', 'dave', 'dawn', 'deb', 'debbie', 'deborah', 'december', 'default', 'defoe', 'deluge', 'denise', 'desiree', 'desperate', 'develop', 'device', 'dial', 'diana', 'diane', 'diet', 'dieter', 'digital', 'disc', 'discovery', 'disk', 'disney', 'dog', 'dos', 'drought', 'dulce', 'duncan', 'eager', 'earth', 'easier', 'easy', 'eatme', 'edges', 'edinburgh', 'edwin', 'edwina', 'egghead', 'eiderdown', 'eileen', 'einstein', 'elaine', 'elanor', 'elephant', 'elizabeth', 'ellen', 'email', 'emerald', 'emily', 'emmanuel', 'enemy', 'engine', 'engineer', 'enterprise', 'enzyme', 'erenity', 'erica', 'erika', 'erin', 'ersatz', 'establish', 'estate', 'eternity', 'euclid', 'evelyn', 'extension', 'fairway', 'felicia', 'fender', 'fermat', 'ferrari', 'fidelity', 'field', 'file', 'finite', 'fishers', 'flakes', 'float', 'flower', 'flowers', 'foolproof', 'football', 'foresight', 'format', 'forsythe', 'fourier', 'fred', 'friend', 'frighten', 'fun', 'function', 'fungible', 'gabriel', 'games', 'gardner', 'garfield', 'gatt', 'gauss', 'george', 'gertrude', 'gibson', 'gina', 'ginger', 'glacier', 'gnu', 'golf', 'golfer', 'gorgeous', 'gorges', 'gosling', 'gouge', 'graham', 'grahm', 'group', 'gryphon', 'gucci', 'guess', 'guest', 'guitar', 'gumption', 'guntis', 'hack', 'hacker', 'hal', 'hamlet', 'handily', 'happening', 'harmony', 'harold', 'harvey', 'hawaii', 'heather', 'hebrides', 'heidi', 'heinlein', 'hello', 'help', 'herbert', 'hiawatha', 'hibernia', 'hidden', 'holly', 'homework', 'honey', 'horse', 'horus', 'hutchins', 'hydrogen', 'ibm', 'imbroglio', 'imperial', 'include', 'ingres', 'ingress', 'ingrid', 'inna', 'innocuous', 'internet', 'irene', 'irishman', 'isis', 'jackie', 'jane', 'janet', 'janice', 'janie', 'japan', 'jasmin', 'jean', 'jeanne', 'jen', 'jenni', 'jennifer', 'jenny', 'jessica', 'jester', 'jill', 'jixian', 'joanne', 'jody', 'johnny', 'joseph', 'joshua', 'joy', 'joyce', 'judith', 'judy', 'juggle', 'julia', 'julie', 'june', 'jupiter', 'karen', 'karie', 'karina', 'kate', 'kathleen', 'kathrine', 'kathy', 'katina', 'katrina', 'kelly', 'keri', 'kermit', 'kernel', 'kerri', 'kerrie', 'kerry', 'key', 'kim', 'kimberly', 'kirkland', 'kitten', 'knight', 'krista', 'kristen', 'kristi', 'kristie', 'kristin', 'kristine', 'kristy', 'ladle', 'lambda', 'lamination', 'lana', 'lara', 'larkin', 'larry', 'laura', 'lazarus', 'leah', 'lebesgue', 'lee', 'leland', 'leroy', 'leslie', 'lewis', 'library', 'light', 'linda', 'lisa', 'lisp', 'liz', 'lock', 'lockout', 'lois', 'lori', 'lorin', 'lorraine', 'louis', 'love', 'lucy', 'lynn', 'lynne', 'macintosh', 'mack', 'maggot', 'magic', 'mail', 'maint', 'malcolm', 'malcom', 'manager', 'mara', 'marci', 'marcy', 'maria', 'marietta', 'mark', 'markus', 'marni', 'mars', 'marty', 'marvin', 'mary', 'master', 'math', 'maurice', 'meagan', 'megan', 'melissa', 'mellon', 'memory', 'mercury', 'merlin', 'mets', 'mgr', 'michael', 'michele', 'michelle', 'mickey', 'mike', 'minimum', 'minsky', 'mit', 'modem', 'mogul', 'moguls', 'monica', 'moose', 'morley', 'mouse', 'mozart', 'mutant', 'nagel', 'nancy', 'napoleon', 'nasa', 'nepenthe', 'neptune', 'ness', 'net', 'network', 'new', 'news', 'newton', 'next', 'nicole', 'nita', 'nobody', 'noreen', 'noxious', 'nuclear', 'nutrition', 'nyquist', 'oceanography', 'ocelot', 'office', 'olivetti', 'olivia', 'open', 'operator', 'oracle', 'orca', 'orwell', 'osiris', 'outlaw', 'oxford', 'pacific', 'pad', 'painless', 'pakistan', 'pam', 'pamela', 'paper', 'papers', 'pass', 'password', 'pat', 'patricia', 'patty', 'paula', 'pencil', 'penelope', 'penguin', 'penis', 'peoria', 'percolate', 'persimmon', 'persona', 'pete', 'peter', 'philip', 'phoenix', 'phone', 'pierre', 'pizza', 'plane', 'playboy', 'plover', 'pluto', 'plymouth', 'polly', 'polynomial', 'pondering', 'pork', 'porsche', 'poster', 'power', 'praise', 'precious', 'prelude', 'presto', 'prince', 'princeton', 'priv', 'private', 'privs', 'professor', 'profile', 'program', 'protect', 'protozoa', 'pub', 'public', 'pumpkin', 'puneet', 'puppet', 'qwerty', 'rabbit', 'rachel', 'rachelle', 'rachmaninoff', 'rainbow', 'raindrop', 'raleigh', 'random', 'rascal', 'reagan', 'really', 'rebecca', 'regional', 'remote', 'renee', 'rick', 'ripple', 'risc', 'rje', 'robin', 'robot', 'robotics', 'robyn', 'rochelle', 'rochester', 'rodent', 'rolex', 'romano', 'ronald', 'root', 'rose', 'rosebud', 'rosemary', 'roses', 'ruben', 'rules', 'ruth', 'sal', 'samantha', 'sandra', 'sandy', 'sara', 'sarah', 'saturn', 'saxon', 'scamper', 'scheme', 'school', 'scott', 'scotty', 'secret', 'security', 'sensor', 'serenity', 'service', 'sesame', 'sex', 'shannon', 'sharc', 'shark', 'sharks', 'sharon', 'sheffield', 'sheldon', 'shell', 'sherri', 'shirley', 'shit', 'shiva', 'shivers', 'shuttle', 'signature', 'simon', 'simple', 'simpsons', 'singer', 'single', 'smile', 'smiles', 'smooch', 'smother', 'snatch', 'snoopy', 'soap', 'socrates', 'somebody', 'sondra', 'sonia', 'sonya', 'sossina', 'sparrows', 'spit', 'spring', 'springer', 'squires', 'stacey', 'staci', 'stacie', 'stacy', 'steph', 'stephanie', 'strangle', 'stratford', 'student', 'stuttgart', 'subway', 'success', 'summer', 'sun', 'super', 'superstage', 'superuser', 'support', 'supported', 'surfer', 'susan', 'susanne', 'susie', 'suzanne', 'suzie', 'swearer', 'sybil', 'symmetry', 'sys', 'sysadmin', 'system', 'tamara', 'tami', 'tamie', 'tammy', 'tangerine', 'tape', 'tara', 'target', 'tarragon', 'taylor', 'tech', 'telephone', 'temptation', 'tennis', 'terminal', 'test', 'thailand', 'theresa', 'tiffany', 'tiger', 'tina', 'toggle', 'tomato', 'topography', 'tortoise', 'toxic', 'toyota', 'traci', 'tracie', 'tracy', 'trails', 'transfer', 'trisha', 'trivial', 'trombone', 'tty', 'tubas', 'tuttle', 'umesh', 'unhappy', 'unicorn', 'unix', 'unknown', 'uranus', 'urchin', 'ursula', 'util', 'utility', 'uucp', 'valerie', 'vasant', 'venus', 'veronica', 'vertigo', 'vicky', 'village', 'virgin', 'virginia', 'visitor', 'wargames', 'warren', 'water', 'weenie', 'wendi', 'wendy', 'whatever', 'whatnot', 'whiting', 'whitney', 'wholesale', 'will', 'william', 'williamsburg', 'willie', 'wilma', 'winston', 'wisconsin', 'wizard', 'wombat', 'woodwind', 'word', 'work', 'wormwood', 'wyoming', 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'0345555658', '503415985', '43453777', '13546073', '935165885', '621037979', '1005186348', '547416332', '2066897926', '63377305', '0182748056', '2900525818', '13077613', '3190016016', '07046498', '5058772088', '5955378122', '482965541', '423525197', '852886496', '7747587233', '884137307', '208876581', '58808123', '84883473', '7770938772', '7295501719', '76561929', '5967813835', '90336244', '179575899', '4752061914', '02585410', '117346749', '354984353', '9431376777', '404314761', '281346400', '2280922029', '05462701', '10925840', '20263454', '6521257963', '45277081', '7814010055', '4913772053', '581126665', '2734692205', '919427716', '24956709', '7019984427', '19928781', '9768078626', '5517560815', '097198081', '981729233', '892372599', '195920022', '9257811187', '80540216', '4436801371', '705063970', '10435230', '4074454383', '159356225', '8448344204', '717643121', '3985639040', '0738510708', '431769553', '5496625815'] if len(password)>7: a=0 if '@' in password: for i in password: if i in ['0','1','2','3','4','5','6','7','8','9']: a+=1 if a>0: if password in passlist: return 'Your Password is too Common...' return 'True' return 'Your Password Doesn\'t contain numbers...' return 'Your Password Doesn\'t contain @...' return 'Your Password must contain at least 8 characters.' def changepass(request): if request.method == 'POST': form = PasswordChangeForm(data=request.POST, user=request.user) if form.is_valid(): form.save() update_session_auth_hash(request, form.user) return redirect('songs-profile') else: return redirect(reverse('songs-password')) else: form = PasswordChangeForm(user=request.user) args = {'form': form} return render(request, 'users/change_password.html', args) @login_required def addsong(request): if request.method=='POST': title=request.POST['title'] lyrics=request.POST['lyrics'] composer=request.user.username featuring=request.POST['featuring'] album=request.POST['album'] img=request.FILES['img'] link=request.POST['link'] genre=request.POST['genre'] genre = genre.upper() audio=request.FILES['audio'] ytlink=link link='https://www.youtube.com/embed/'+ link[link.index('=')+1:]+'?rel=0' song = Song(title=title,lyrics=lyrics,composer=composer,featuring=featuring,album=album,img=img,link=link,ytlink=ytlink,audio=audio,genre=genre) song.save() return redirect('songs-profile') else: return render(request,'songs/addsong.html') def logout_user(request): logout(request) messages.warning(request,'You have successfully logged out.') return redirect('songs-home') @login_required def profile(request): mysongs = list() songs = Song.objects.all() username = request.user.username for song in songs: if song.composer == username or song.featuring == username: mysongs.append(song) size = len(mysongs) context = {'size': size,'songs':mysongs,'thisuser':username} return render(request,'users/display_profile.html',context) def addprofile(request): if request.user.profile.bio : messages.warning(request,'Your Profile can be added only Once.You can update your profile.To update your Profile Go to /profile/update') return redirect('songs-home') if request.method =="POST": print("heyy") username=request.POST.get('username') user = User.objects.filter(username=username).first() profile=Profile.objects.get(user=user) user.first_name=request.POST['fname'] user.last_name=request.POST['lname'] profile.gender=request.POST['gender'] profile.age=request.POST['age'] profile.bio=request.POST['bio'] if request.FILES['pic']: profile.image=request.FILES['pic'] user.save() profile.saave() else: return render(request,'users/addprofile.html') return render(request,'songs/base.html') def update(request): profile = request.user mysongs = list() a=0 userprofile = Profile.objects.get(user=profile) if request.method == 'POST': if request.POST['fname']!=profile.first_name: fname = request.POST['fname'] a+=1 else: fname=profile.first_name if request.POST['lname']!=profile.last_name: lname = request.POST['lname'] a+=1 else: lname=profile.last_name if request.POST['gender']!=userprofile.gender: gender = request.POST['gender'] a+=1 else: gender=userprofile.gender if request.POST['age']!=userprofile.age: age = request.POST['age'] a+=1 else: age=userprofile.age if request.POST['bio']!=userprofile.bio: bio = request.POST['bio'] a+=1 else: bio=userprofile.bio try: if request.FILES['pic']: pic = request.FILES['pic'] path = userprofile.image.path import os os.remove(path) a+=1 else: pic=userprofile.image except MultiValueDictKeyError : print("m") pic=userprofile.image profile.first_name = fname profile.last_name = lname userprofile.gender=gender userprofile.age=age userprofile.bio=bio userprofile.image=pic userprofile.saave() profile.save() if a>1: messages.success(request, 'Your Account has been updated!') return redirect('songs-profile') songs = Song.objects.all() username = profile.username for song in songs: if song.composer == username or song.featuring == username: mysongs.append(song) context={ 'songs':mysongs, 'profile':userprofile } return render(request,'users/profile.html',context)

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null

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6

f82f7aefc4dc6217cf7f3f7d7c2702a859202a36

30

py

Python

tests/test_inventory_service.py

accelero-cloud/tutorials

9a9580e60bc216bf45ec0011f6d9b6b14d5a8d03

[ "Apache-2.0" ]

2

2019-08-09T16:15:40.000Z

2020-01-12T09:46:28.000Z

tests/test_inventory_service.py

accelero-cloud/tutorials

9a9580e60bc216bf45ec0011f6d9b6b14d5a8d03

[ "Apache-2.0" ]

2

2021-03-31T18:48:41.000Z

2021-12-13T19:49:46.000Z

tests/test_inventory_service.py

accelero-cloud/tutorials

9a9580e60bc216bf45ec0011f6d9b6b14d5a8d03

[ "Apache-2.0" ]

null

def test_default(): pass

7.5

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6

f8892aaad6559f0cce09d9a2a58429a7d441eb47

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py

Python

Assignment.2/test.py

ash0x0/AUC-ProgrammingLanguagePython

3fc64c6acac981eef30d5b48278f06086ef4212b

[ "Apache-2.0" ]

null

Assignment.2/test.py

ash0x0/AUC-ProgrammingLanguagePython

3fc64c6acac981eef30d5b48278f06086ef4212b

[ "Apache-2.0" ]

null

Assignment.2/test.py

ash0x0/AUC-ProgrammingLanguagePython

3fc64c6acac981eef30d5b48278f06086ef4212b

[ "Apache-2.0" ]

null

import pickle file = open('file.txt.code' , 'rb') print(pickle.load(file)) print(pickle.load(file))

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44

py

Python

sdk/exception/service_exception.py

CLG0125/elemesdk

344466398bad7cf026e082e47c77d3ca98621ef3

[ "MIT" ]

1

2021-04-03T05:11:29.000Z

sdk/exception/service_exception.py

CLG0125/elemesdk

344466398bad7cf026e082e47c77d3ca98621ef3

[ "MIT" ]

null

sdk/exception/service_exception.py

CLG0125/elemesdk

344466398bad7cf026e082e47c77d3ca98621ef3

[ "MIT" ]

null

class ServiceException(Exception):pass

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3e05b8b904f5b29089ee4f538ee5a7e43627430c

7,243

py

Python

src/micropython/microbit/__model/constants.py

julianrendell/vscode-python-devicesimulator

8014a940c9a0551793bfb5694bff9a52df6c0155

[ "MIT" ]

151

2019-11-05T10:10:29.000Z

2022-02-18T11:46:27.000Z

src/micropython/microbit/__model/constants.py

julianrendell/vscode-python-devicesimulator

8014a940c9a0551793bfb5694bff9a52df6c0155

[ "MIT" ]

98

2019-11-09T17:41:30.000Z

2021-12-17T23:05:01.000Z

src/micropython/microbit/__model/constants.py

julianrendell/vscode-python-devicesimulator

8014a940c9a0551793bfb5694bff9a52df6c0155

[ "MIT" ]

42

2019-11-10T02:26:27.000Z

2022-03-22T01:43:01.000Z

MICROBIT = "micro:bit" # string arguments for constructor BLANK_5X5 = "00000:00000:00000:00000:00000:" # pre-defined image patterns IMAGE_PATTERNS = { "HEART": "09090:99999:99999:09990:00900:", "HEART_SMALL": "00000:09090:09990:00900:00000:", "HAPPY": "00000:09090:00000:90009:09990:", "SMILE": "00000:00000:00000:90009:09990:", "SAD": "00000:09090:00000:09990:90009:", "CONFUSED": "00000:09090:00000:09090:90909:", "ANGRY": "90009:09090:00000:99999:90909:", "ASLEEP": "00000:99099:00000:09990:00000:", "SURPRISED": "09090:00000:00900:09090:00900:", "SILLY": "90009:00000:99999:00909:00999:", "FABULOUS": "99999:99099:00000:09090:09990:", "MEH": "09090:00000:00090:00900:09000:", "YES": "00000:00009:00090:90900:09000:", "NO": "90009:09090:00900:09090:90009:", "CLOCK12": "00900:00900:00900:00000:00000:", "CLOCK11": "09000:09000:00900:00000:00000:", "CLOCK10": "00000:99000:00900:00000:00000:", "CLOCK9": "00000:00000:99900:00000:00000:", "CLOCK8": "00000:00000:00900:99000:00000:", "CLOCK7": "00000:00000:00900:09000:09000:", "CLOCK6": "00000:00000:00900:00900:00900:", "CLOCK5": "00000:00000:00900:00090:00090:", "CLOCK4": "00000:00000:00900:00099:00000:", "CLOCK3": "00000:00000:00999:00000:00000:", "CLOCK2": "00000:00099:00900:00000:00000:", "CLOCK1": "00090:00090:00900:00000:00000:", "ARROW_N": "00900:09990:90909:00900:00900:", "ARROW_NE": "00999:00099:00909:09000:90000:", "ARROW_E": "00900:00090:99999:00090:00900:", "ARROW_SE": "90000:09000:00909:00099:00999:", "ARROW_S": "00900:00900:90909:09990:00900:", "ARROW_SW": "00009:00090:90900:99000:99900:", "ARROW_W": "00900:09000:99999:09000:00900:", "ARROW_NW": "99900:99000:90900:00090:00009:", "TRIANGLE": "00000:00900:09090:99999:00000:", "TRIANGLE_LEFT": "90000:99000:90900:90090:99999:", "CHESSBOARD": "09090:90909:09090:90909:09090:", "DIAMOND": "00900:09090:90009:09090:00900:", "DIAMOND_SMALL": "00000:00900:09090:00900:00000:", "SQUARE": "99999:90009:90009:90009:99999:", "SQUARE_SMALL": "00000:09990:09090:09990:00000:", "RABBIT": "90900:90900:99990:99090:99990:", "COW": "90009:90009:99999:09990:00900:", "MUSIC_CROTCHET": "00900:00900:00900:99900:99900:", "MUSIC_QUAVER": "00900:00990:00909:99900:99900:", "MUSIC_QUAVERS": "09999:09009:09009:99099:99099:", "PITCHFORK": "90909:90909:99999:00900:00900:", "XMAS": "00900:09990:00900:09990:99999:", "PACMAN": "09999:99090:99900:99990:09999:", "TARGET": "00900:09990:99099:09990:00900:", "TSHIRT": "99099:99999:09990:09990:09990:", "ROLLERSKATE": "00099:00099:99999:99999:09090:", "DUCK": "09900:99900:09999:09990:00000:", "HOUSE": "00900:09990:99999:09990:09090:", "TORTOISE": "00000:09990:99999:09090:00000:", "BUTTERFLY": "99099:99999:00900:99999:99099:", "STICKFIGURE": "00900:99999:00900:09090:90009:", "GHOST": "99999:90909:99999:99999:90909:", "SWORD": "00900:00900:00900:09990:00900:", "GIRAFFE": "99000:09000:09000:09990:09090:", "SKULL": "09990:90909:99999:09990:09990:", "UMBRELLA": "09990:99999:00900:90900:09900:", "SNAKE": "99000:99099:09090:09990:00000:", } IMAGE_TUPLE_LOOKUP = { "ALL_CLOCKS": [ "CLOCK12", "CLOCK11", "CLOCK10", "CLOCK9", "CLOCK8", "CLOCK7", "CLOCK6", "CLOCK5", "CLOCK4", "CLOCK3", "CLOCK2", "CLOCK1", ], "ALL_ARROWS": [ "ARROW_N", "ARROW_NE", "ARROW_E", "ARROW_SE", "ARROW_S", "ARROW_SW", "ARROW_W", "ARROW_NW", ], } # 5x5 Alphabet # Taken from https://raw.githubusercontent.com/micropython/micropython/264d80c84e034541bd6e4b461bfece4443ffd0ac/ports/nrf/boards/microbit/modules/microbitfont.h ALPHABET = b"\x00\x00\x00\x00\x00\x08\x08\x08\x00\x08\x0a\x4a\x40\x00\x00\x0a\x5f\xea\x5f\xea\x0e\xd9\x2e\xd3\x6e\x19\x32\x44\x89\x33\x0c\x92\x4c\x92\x4d\x08\x08\x00\x00\x00\x04\x88\x08\x08\x04\x08\x04\x84\x84\x88\x00\x0a\x44\x8a\x40\x00\x04\x8e\xc4\x80\x00\x00\x00\x04\x88\x00\x00\x0e\xc0\x00\x00\x00\x00\x08\x00\x01\x22\x44\x88\x10\x0c\x92\x52\x52\x4c\x04\x8c\x84\x84\x8e\x1c\x82\x4c\x90\x1e\x1e\xc2\x44\x92\x4c\x06\xca\x52\x5f\xe2\x1f\xf0\x1e\xc1\x3e\x02\x44\x8e\xd1\x2e\x1f\xe2\x44\x88\x10\x0e\xd1\x2e\xd1\x2e\x0e\xd1\x2e\xc4\x88\x00\x08\x00\x08\x00\x00\x04\x80\x04\x88\x02\x44\x88\x04\x82\x00\x0e\xc0\x0e\xc0\x08\x04\x82\x44\x88\x0e\xd1\x26\xc0\x04\x0e\xd1\x35\xb3\x6c\x0c\x92\x5e\xd2\x52\x1c\x92\x5c\x92\x5c\x0e\xd0\x10\x10\x0e\x1c\x92\x52\x52\x5c\x1e\xd0\x1c\x90\x1e\x1e\xd0\x1c\x90\x10\x0e\xd0\x13\x71\x2e\x12\x52\x5e\xd2\x52\x1c\x88\x08\x08\x1c\x1f\xe2\x42\x52\x4c\x12\x54\x98\x14\x92\x10\x10\x10\x10\x1e\x11\x3b\x75\xb1\x31\x11\x39\x35\xb3\x71\x0c\x92\x52\x52\x4c\x1c\x92\x5c\x90\x10\x0c\x92\x52\x4c\x86\x1c\x92\x5c\x92\x51\x0e\xd0\x0c\x82\x5c\x1f\xe4\x84\x84\x84\x12\x52\x52\x52\x4c\x11\x31\x31\x2a\x44\x11\x31\x35\xbb\x71\x12\x52\x4c\x92\x52\x11\x2a\x44\x84\x84\x1e\xc4\x88\x10\x1e\x0e\xc8\x08\x08\x0e\x10\x08\x04\x82\x41\x0e\xc2\x42\x42\x4e\x04\x8a\x40\x00\x00\x00\x00\x00\x00\x1f\x08\x04\x80\x00\x00\x00\x0e\xd2\x52\x4f\x10\x10\x1c\x92\x5c\x00\x0e\xd0\x10\x0e\x02\x42\x4e\xd2\x4e\x0c\x92\x5c\x90\x0e\x06\xc8\x1c\x88\x08\x0e\xd2\x4e\xc2\x4c\x10\x10\x1c\x92\x52\x08\x00\x08\x08\x08\x02\x40\x02\x42\x4c\x10\x14\x98\x14\x92\x08\x08\x08\x08\x06\x00\x1b\x75\xb1\x31\x00\x1c\x92\x52\x52\x00\x0c\x92\x52\x4c\x00\x1c\x92\x5c\x90\x00\x0e\xd2\x4e\xc2\x00\x0e\xd0\x10\x10\x00\x06\xc8\x04\x98\x08\x08\x0e\xc8\x07\x00\x12\x52\x52\x4f\x00\x11\x31\x2a\x44\x00\x11\x31\x35\xbb\x00\x12\x4c\x8c\x92\x00\x11\x2a\x44\x98\x00\x1e\xc4\x88\x1e\x06\xc4\x8c\x84\x86\x08\x08\x08\x08\x08\x18\x08\x0c\x88\x18\x00\x00\x0c\x83\x60" # We support ASCII characters between these indexes on the microbit ASCII_START = 32 ASCII_END = 126 SPACE_BETWEEN_LETTERS_WIDTH = 1 WHITESPACE_WIDTH = 3 # numerical LED values LED_HEIGHT = 5 LED_WIDTH = 5 BRIGHTNESS_MIN = 0 BRIGHTNESS_MAX = 9 # sensor max/min values MAX_TEMPERATURE = 125 MIN_TEMPERATURE = -55 MAX_LIGHT_LEVEL = 255 MIN_LIGHT_LEVEL = 0 MAX_ACCELERATION = 1023 MIN_ACCELERATION = -1023 GESTURES = set( [ "up", "down", "left", "right", "face up", "face down", "freefall", "3g", "6g", "8g", "shake", ] ) # error messages BRIGHTNESS_ERR = "brightness out of bounds" COPY_ERR_MESSAGE = "please call copy function first" INCORR_IMAGE_SIZE = "image data is incorrect size" INDEX_ERR = "index out of bounds" NOT_IMPLEMENTED_ERROR = "This method is not implemented by the simulator" UNSUPPORTED_ADD_TYPE = "unsupported types for __add__:" SAME_SIZE_ERR = "images must be the same size" INVALID_GESTURE_ERR = "invalid gesture" INVALID_ACCEL_ERR = "invalid acceleration" INVALID_LIGHT_LEVEL_ERR = "invalid light level" INVALID_TEMPERATURE_ERR = "invalid temperature" TIME_DELAY = 0.03 EXPECTED_INPUT_BUTTONS = [ "button_a", "button_b", ] EXPECTED_INPUT_ACCEL = { "motion_x": "x", "motion_y": "y", "motion_z": "z", } EXPECTED_INPUT_LIGHT = "light" EXPECTED_INPUT_TEMP = "temperature" EXPECTED_INPUT_GESTURE = "gesture"

42.605882

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null

1

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6

3e4aa6aa7b822f47c6496278e880676fb0f3eb6f

64

py

Python

tests/01_import_test.py

andybrice/Pypework

d71c46cd0dfb41660f776ce8d435bb6893466c25

[ "MIT" ]

3

2019-07-25T09:31:14.000Z

2021-07-11T10:33:27.000Z

tests/01_import_test.py

andybrice/pypework

d71c46cd0dfb41660f776ce8d435bb6893466c25

[ "MIT" ]

null

tests/01_import_test.py

andybrice/pypework

d71c46cd0dfb41660f776ce8d435bb6893466c25

[ "MIT" ]

null

import pypework def test_module_imports(): assert pypework

12.8

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0

6

e43f8bf6a885551c25ef6c760f3ee1e16ea80cd9

35

py

Python

addons14/datamodel/tests/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

1

2021-06-10T14:59:13.000Z

addons14/datamodel/tests/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

null

addons14/datamodel/tests/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

1

2021-04-09T09:44:44.000Z

from . import test_build_datamodel

17.5

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6

e44c6a0598c6242a093a6f6d084b47ed8cf72c4a

176

py

Python

envs/water_tank/no_shield.py

safe-rl/safe-rl-shielding

287d540df6b26928eed512a57297d44d72f19832

[ "MIT" ]

26

2018-12-30T20:32:45.000Z

2022-03-15T06:11:40.000Z

envs/water_tank/no_shield.py

safe-rl/safe-rl-shielding

287d540df6b26928eed512a57297d44d72f19832

[ "MIT" ]

20

2018-08-29T10:34:48.000Z

2022-03-11T23:16:24.000Z

envs/water_tank/no_shield.py

safe-rl/safe-rl-shielding

287d540df6b26928eed512a57297d44d72f19832

[ "MIT" ]

13

2019-05-11T01:59:58.000Z

2022-03-15T14:12:40.000Z

class Shield: def __init__(self): self.water_level = 0 self.switch_state = 0 def tick(self, water_level, switch_state, action): return action

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null

0

1

0

1

0

6

e463045136a20cb2df10b62bfab5431c572d57b5

3,668

py

Python

tests/test_image_array.py

deepgreenAN/tracking_annotation

f13e2dbf0bd6400db07b26a13715b24b5b4fd7aa

[ "Apache-2.0" ]

null

tests/test_image_array.py

deepgreenAN/tracking_annotation

f13e2dbf0bd6400db07b26a13715b24b5b4fd7aa

[ "Apache-2.0" ]

1

2021-04-27T06:27:48.000Z

tests/test_image_array.py

deepgreenAN/tracking_annotation

f13e2dbf0bd6400db07b26a13715b24b5b4fd7aa

[ "Apache-2.0" ]

null

import unittest from pathlib import Path import time from image_array import MovieImageArray, MovieImageArrayFile, MovieImageArrayRaw class TestMovieImageArray(unittest.TestCase): def setUp(self): self.test_path = Path("tests") self.test_path_contensts = set(list(self.test_path.iterdir())) def tearDown(self): # ファイルの構造が変わってないか確認． end_test_path_contents = set(list(self.test_path.iterdir())) self.assertEqual(end_test_path_contents, self.test_path_contensts) def test_movie_image_array(self): image_array1 = MovieImageArray("tests/mini_movie.mp4", is_temp=False, temp_dir=Path("tests")) # 保存データは破棄されない image_array1.read_movie(is_update=True) #image_arrayから所得できるものか3階テンソルであり，最後の次数が3 for image in image_array1: self.assertEqual(len(image.shape), 3) self.assertEqual(image.shape[-1], 3) image_array1.close() image_array2 = MovieImageArray.from_file(image_array1.saved_path, is_temp=True) # 保存データが破棄される #image_arrayから所得できるものか3階テンソルであり，最後の次数が3 for image in image_array2: self.assertEqual(len(image.shape), 3) self.assertEqual(image.shape[-1], 3) image_array2.close() time.sleep(3) # ファイル削除のラグを考慮 def test_movie_image_array_temp(self): image_array1 = MovieImageArray("tests/mini_movie.mp4", is_temp=True, temp_dir=Path("tests")) # 保存データは破棄される image_array1.read_movie(is_update=True) #image_arrayから所得できるものか3階テンソルであり，最後の次数が3 for image in image_array1: self.assertEqual(len(image.shape), 3) self.assertEqual(image.shape[-1], 3) image_array1.close() time.sleep(3) # ファイル削除のラグを考慮 def test_movie_image_array_file(self): image_array1 = MovieImageArrayFile("tests/mini_movie.mp4", is_temp=False, temp_dir=Path("tests")) # 保存データは破棄されない image_array1.read_movie(is_update=True) #image_arrayから所得できるものか3階テンソルであり，最後の次数が3 for image in image_array1: self.assertEqual(len(image.shape), 3) self.assertEqual(image.shape[-1], 3) image_array1.close() image_array2 = MovieImageArrayFile.from_file(image_array1.saved_path, is_temp=True) # 保存データが破棄される #image_arrayから所得できるものか3階テンソルであり，最後の次数が3 for image in image_array2: self.assertEqual(len(image.shape), 3) self.assertEqual(image.shape[-1], 3) image_array2.close() time.sleep(3) # ファイル削除のラグを考慮 def test_movie_image_array_file_temp(self): image_array1 = MovieImageArrayFile("tests/mini_movie.mp4", is_temp=True, temp_dir=Path("tests")) # 保存データは破棄される image_array1.read_movie(is_update=True) #image_arrayから所得できるものか3階テンソルであり，最後の次数が3 for image in image_array1: self.assertEqual(len(image.shape), 3) self.assertEqual(image.shape[-1], 3) image_array1.close() time.sleep(3) # ファイル削除のラグを考慮 def test_movie_image_array_raw(self): image_array1 = MovieImageArrayRaw("tests/mini_movie.mp4", is_temp=True, temp_dir=Path("tests")) # 保存データは破棄される image_array1.read_movie() #image_arrayから所得できるものか3階テンソルであり，最後の次数が3 for image in image_array1: self.assertEqual(len(image.shape), 3) self.assertEqual(image.shape[-1], 3) image_array1.close() if __name__ == "__main__": unittest.main()

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0

null

0

1

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null

0

6

e472de48e2bb67ff515fab4fb5d997f1e5a72731

69

py

Python

datatypes/tuple_one.py

janbodnar/Python-Course

51705ab5a2adef52bcdb99a800e94c0d67144a38

[ "BSD-2-Clause" ]

13

2017-08-22T12:26:07.000Z

2021-07-29T16:13:50.000Z

datatypes/tuple_one.py

janbodnar/Python-Course

51705ab5a2adef52bcdb99a800e94c0d67144a38

[ "BSD-2-Clause" ]

1

2021-02-08T10:24:33.000Z

datatypes/tuple_one.py

janbodnar/Python-Course

51705ab5a2adef52bcdb99a800e94c0d67144a38

[ "BSD-2-Clause" ]

17

2018-08-13T11:10:33.000Z

2021-07-29T16:14:02.000Z

#!/usr/bin/python # tuple_one.py print ((3 + 7)) print ((3 + 7, ))

9.857143

17

0.536232

12

69

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0.333333

0.388889

0

0.072727

0.202899

69

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0.581818

0.42029

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0

1

0

null

0

1

0

1

0

6

e47f0d5e790cb4f203e804c47a4b4ce763015c33

85

py

Python

pydale/utils/__init__.py

sz144/TPy

689e38bdc2549015bc45cfacfe42e20a51c76e5a

[ "MIT" ]

4

2018-08-20T13:38:13.000Z

2020-08-31T08:57:12.000Z

pydale/utils/__init__.py

sz144/pydale

689e38bdc2549015bc45cfacfe42e20a51c76e5a

[ "MIT" ]

null

pydale/utils/__init__.py

sz144/pydale

689e38bdc2549015bc45cfacfe42e20a51c76e5a

[ "MIT" ]

2

2021-09-28T08:24:30.000Z

2022-01-29T08:29:46.000Z

from ._base import lap_norm from ._base import mmd_coef from ._base import base_init

21.25

28

0.823529

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85

4.266667

0.533333

0.375

0.65625

0

0.141176

85

3

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28.333333

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0

1

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true

0

1

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1

0

1

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null

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0

1

0

null

0

1

0

1

0

6

e485e0d5a58ff367650e9db686d5531ffd45d32a

28,833

py

Python

SchemaTerms/example-code/protobufs/schematerms_pb2.py

whiteslack/schemaorg

1e382bf7b40c2f85a865d2b30f911bcdac4e6da3

[ "Apache-2.0" ]

4,768

2015-01-08T04:45:33.000Z

2022-03-28T07:32:59.000Z

software/SchemaTerms/example-code/protobufs/schematerms_pb2.py

lioncorpo/schenmaorg

d863285fde9c50572b95ceca3f0391e46ea7ef88

[ "Apache-2.0" ]

2,599

2015-01-06T21:51:28.000Z

2022-03-30T12:40:09.000Z

software/SchemaTerms/example-code/protobufs/schematerms_pb2.py

lioncorpo/schenmaorg

d863285fde9c50572b95ceca3f0391e46ea7ef88

[ "Apache-2.0" ]

878

2015-01-10T00:03:30.000Z

2022-03-31T22:54:15.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: schematerms.proto from google.protobuf.internal import enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='schematerms.proto', package='SchemaTerms', syntax='proto2', serialized_options=None, create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x11schematerms.proto\x12\x0bSchemaTerms\"\x1e\n\tSuperPath\x12\x11\n\tsuperPath\x18\x01 \x03(\t\"\x97\x02\n\x07SDOTerm\x12\'\n\x08termType\x18\x01 \x02(\x0e\x32\x15.SchemaTerms.TermType\x12\x0b\n\x03uri\x18\x02 \x02(\t\x12\r\n\x05label\x18\x03 \x02(\t\x12\x0f\n\x07\x63omment\x18\x04 \x02(\t\x12\x0f\n\x07pending\x18\x05 \x02(\x08\x12\x0f\n\x07retired\x18\x06 \x02(\x08\x12*\n\nsuperPaths\x18\x07 \x03(\x0b\x32\x16.SchemaTerms.SuperPath\x12\x18\n\x10\x61\x63knowledgements\x18\x08 \x03(\t\x12\x13\n\x0b\x65quivalents\x18\t \x03(\t\x12\x14\n\x0csupersededBy\x18\n \x01(\t\x12\x12\n\nsupersedes\x18\x0b \x03(\t\x12\x0f\n\x07sources\x18\x0c \x03(\t\"\xd8\x01\n\x0bSDOBaseType\x12\n\n\x02id\x18\x01 \x02(\t\x12,\n\x0etermdescriptor\x18\x02 \x03(\x0b\x32\x14.SchemaTerms.SDOTerm\x12\x12\n\nproperties\x18\x03 \x03(\t\x12\x15\n\rallproperties\x18\x04 \x03(\t\x12\x17\n\x0f\x65xpectedTypeFor\x18\x05 \x03(\t\x12\x1a\n\x12\x65numerationMembers\x18\x06 \x03(\t\x12\x0c\n\x04subs\x18\x07 \x03(\t\x12\x0e\n\x06supers\x18\x08 \x03(\t\x12\x11\n\ttermStack\x18\t \x03(\t\"\xb8\x01\n\x0bSDOProperty\x12\n\n\x02id\x18\x01 \x02(\t\x12,\n\x0etermdescriptor\x18\x02 \x03(\x0b\x32\x14.SchemaTerms.SDOTerm\x12\x16\n\x0e\x64omainIncludes\x18\x03 \x03(\t\x12\x15\n\rrangeIncludes\x18\x04 \x03(\t\x12\x0c\n\x04subs\x18\x05 \x03(\t\x12\x0e\n\x06supers\x18\x06 \x03(\t\x12\x0f\n\x07inverse\x18\x07 \x01(\t\x12\x11\n\ttermStack\x18\x08 \x03(\t\"j\n\x13SDOEnumerationValue\x12\n\n\x02id\x18\x01 \x02(\t\x12,\n\x0etermdescriptor\x18\x02 \x03(\x0b\x32\x14.SchemaTerms.SDOTerm\x12\x19\n\x11\x65numerationParent\x18\x03 \x02(\t\"\'\n\x0cSDOReference\x12\n\n\x02id\x18\x01 \x02(\t\x12\x0b\n\x03uri\x18\x02 \x02(\t\"\xfd\x01\n\x13SDOBaseTypeExpanded\x12\n\n\x02id\x18\x01 \x02(\t\x12,\n\x0etermdescriptor\x18\x02 \x03(\x0b\x32\x14.SchemaTerms.SDOTerm\x12,\n\nproperties\x18\x03 \x03(\x0b\x32\x18.SchemaTerms.SDOProperty\x12\x31\n\x0f\x65xpectedTypeFor\x18\x04 \x03(\x0b\x32\x18.SchemaTerms.SDOProperty\x12\x1a\n\x12\x65numerationMembers\x18\x05 \x03(\t\x12\x0c\n\x04subs\x18\x06 \x03(\t\x12\x0e\n\x06supers\x18\x07 \x03(\t\x12\x11\n\ttermStack\x18\x08 \x03(\t*f\n\x08TermType\x12\x08\n\x04TYPE\x10\x00\x12\x0c\n\x08PROPERTY\x10\x01\x12\x0c\n\x08\x44\x41TATYPE\x10\x02\x12\x0f\n\x0b\x45NUMERATION\x10\x03\x12\x14\n\x10\x45NUMERATIONVALUE\x10\x04\x12\r\n\tREFERENCE\x10\x05' ) _TERMTYPE = _descriptor.EnumDescriptor( name='TermType', full_name='SchemaTerms.TermType', filename=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key, values=[ _descriptor.EnumValueDescriptor( name='TYPE', index=0, number=0, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='PROPERTY', index=1, number=1, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='DATATYPE', index=2, number=2, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ENUMERATION', index=3, number=3, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='ENUMERATIONVALUE', index=4, number=4, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), _descriptor.EnumValueDescriptor( name='REFERENCE', index=5, number=5, serialized_options=None, type=None, create_key=_descriptor._internal_create_key), ], containing_type=None, serialized_options=None, serialized_start=1159, serialized_end=1261, ) _sym_db.RegisterEnumDescriptor(_TERMTYPE) TermType = enum_type_wrapper.EnumTypeWrapper(_TERMTYPE) TYPE = 0 PROPERTY = 1 DATATYPE = 2 ENUMERATION = 3 ENUMERATIONVALUE = 4 REFERENCE = 5 _SUPERPATH = _descriptor.Descriptor( name='SuperPath', full_name='SchemaTerms.SuperPath', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='superPath', full_name='SchemaTerms.SuperPath.superPath', index=0, number=1, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=34, serialized_end=64, ) _SDOTERM = _descriptor.Descriptor( name='SDOTerm', full_name='SchemaTerms.SDOTerm', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='termType', full_name='SchemaTerms.SDOTerm.termType', index=0, number=1, type=14, cpp_type=8, label=2, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='uri', full_name='SchemaTerms.SDOTerm.uri', index=1, number=2, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='label', full_name='SchemaTerms.SDOTerm.label', index=2, number=3, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='comment', full_name='SchemaTerms.SDOTerm.comment', index=3, number=4, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='pending', full_name='SchemaTerms.SDOTerm.pending', index=4, number=5, type=8, cpp_type=7, label=2, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='retired', full_name='SchemaTerms.SDOTerm.retired', index=5, number=6, type=8, cpp_type=7, label=2, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='superPaths', full_name='SchemaTerms.SDOTerm.superPaths', index=6, number=7, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='acknowledgements', full_name='SchemaTerms.SDOTerm.acknowledgements', index=7, number=8, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='equivalents', full_name='SchemaTerms.SDOTerm.equivalents', index=8, number=9, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='supersededBy', full_name='SchemaTerms.SDOTerm.supersededBy', index=9, number=10, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='supersedes', full_name='SchemaTerms.SDOTerm.supersedes', index=10, number=11, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='sources', full_name='SchemaTerms.SDOTerm.sources', index=11, number=12, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=67, serialized_end=346, ) _SDOBASETYPE = _descriptor.Descriptor( name='SDOBaseType', full_name='SchemaTerms.SDOBaseType', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='id', full_name='SchemaTerms.SDOBaseType.id', index=0, number=1, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='termdescriptor', full_name='SchemaTerms.SDOBaseType.termdescriptor', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='properties', full_name='SchemaTerms.SDOBaseType.properties', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='allproperties', full_name='SchemaTerms.SDOBaseType.allproperties', index=3, number=4, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='expectedTypeFor', full_name='SchemaTerms.SDOBaseType.expectedTypeFor', index=4, number=5, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='enumerationMembers', full_name='SchemaTerms.SDOBaseType.enumerationMembers', index=5, number=6, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='subs', full_name='SchemaTerms.SDOBaseType.subs', index=6, number=7, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='supers', full_name='SchemaTerms.SDOBaseType.supers', index=7, number=8, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='termStack', full_name='SchemaTerms.SDOBaseType.termStack', index=8, number=9, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=349, serialized_end=565, ) _SDOPROPERTY = _descriptor.Descriptor( name='SDOProperty', full_name='SchemaTerms.SDOProperty', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='id', full_name='SchemaTerms.SDOProperty.id', index=0, number=1, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='termdescriptor', full_name='SchemaTerms.SDOProperty.termdescriptor', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domainIncludes', full_name='SchemaTerms.SDOProperty.domainIncludes', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='rangeIncludes', full_name='SchemaTerms.SDOProperty.rangeIncludes', index=3, number=4, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='subs', full_name='SchemaTerms.SDOProperty.subs', index=4, number=5, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='supers', full_name='SchemaTerms.SDOProperty.supers', index=5, number=6, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='inverse', full_name='SchemaTerms.SDOProperty.inverse', index=6, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='termStack', full_name='SchemaTerms.SDOProperty.termStack', index=7, number=8, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=568, serialized_end=752, ) _SDOENUMERATIONVALUE = _descriptor.Descriptor( name='SDOEnumerationValue', full_name='SchemaTerms.SDOEnumerationValue', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='id', full_name='SchemaTerms.SDOEnumerationValue.id', index=0, number=1, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='termdescriptor', full_name='SchemaTerms.SDOEnumerationValue.termdescriptor', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='enumerationParent', full_name='SchemaTerms.SDOEnumerationValue.enumerationParent', index=2, number=3, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=754, serialized_end=860, ) _SDOREFERENCE = _descriptor.Descriptor( name='SDOReference', full_name='SchemaTerms.SDOReference', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='id', full_name='SchemaTerms.SDOReference.id', index=0, number=1, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='uri', full_name='SchemaTerms.SDOReference.uri', index=1, number=2, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=862, serialized_end=901, ) _SDOBASETYPEEXPANDED = _descriptor.Descriptor( name='SDOBaseTypeExpanded', full_name='SchemaTerms.SDOBaseTypeExpanded', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='id', full_name='SchemaTerms.SDOBaseTypeExpanded.id', index=0, number=1, type=9, cpp_type=9, label=2, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='termdescriptor', full_name='SchemaTerms.SDOBaseTypeExpanded.termdescriptor', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='properties', full_name='SchemaTerms.SDOBaseTypeExpanded.properties', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='expectedTypeFor', full_name='SchemaTerms.SDOBaseTypeExpanded.expectedTypeFor', index=3, number=4, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='enumerationMembers', full_name='SchemaTerms.SDOBaseTypeExpanded.enumerationMembers', index=4, number=5, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='subs', full_name='SchemaTerms.SDOBaseTypeExpanded.subs', index=5, number=6, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='supers', full_name='SchemaTerms.SDOBaseTypeExpanded.supers', index=6, number=7, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='termStack', full_name='SchemaTerms.SDOBaseTypeExpanded.termStack', index=7, number=8, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=904, serialized_end=1157, ) _SDOTERM.fields_by_name['termType'].enum_type = _TERMTYPE _SDOTERM.fields_by_name['superPaths'].message_type = _SUPERPATH _SDOBASETYPE.fields_by_name['termdescriptor'].message_type = _SDOTERM _SDOPROPERTY.fields_by_name['termdescriptor'].message_type = _SDOTERM _SDOENUMERATIONVALUE.fields_by_name['termdescriptor'].message_type = _SDOTERM _SDOBASETYPEEXPANDED.fields_by_name['termdescriptor'].message_type = _SDOTERM _SDOBASETYPEEXPANDED.fields_by_name['properties'].message_type = _SDOPROPERTY _SDOBASETYPEEXPANDED.fields_by_name['expectedTypeFor'].message_type = _SDOPROPERTY DESCRIPTOR.message_types_by_name['SuperPath'] = _SUPERPATH DESCRIPTOR.message_types_by_name['SDOTerm'] = _SDOTERM DESCRIPTOR.message_types_by_name['SDOBaseType'] = _SDOBASETYPE DESCRIPTOR.message_types_by_name['SDOProperty'] = _SDOPROPERTY DESCRIPTOR.message_types_by_name['SDOEnumerationValue'] = _SDOENUMERATIONVALUE DESCRIPTOR.message_types_by_name['SDOReference'] = _SDOREFERENCE DESCRIPTOR.message_types_by_name['SDOBaseTypeExpanded'] = _SDOBASETYPEEXPANDED DESCRIPTOR.enum_types_by_name['TermType'] = _TERMTYPE _sym_db.RegisterFileDescriptor(DESCRIPTOR) SuperPath = _reflection.GeneratedProtocolMessageType('SuperPath', (_message.Message,), { 'DESCRIPTOR' : _SUPERPATH, '__module__' : 'schematerms_pb2' # @@protoc_insertion_point(class_scope:SchemaTerms.SuperPath) }) _sym_db.RegisterMessage(SuperPath) SDOTerm = _reflection.GeneratedProtocolMessageType('SDOTerm', (_message.Message,), { 'DESCRIPTOR' : _SDOTERM, '__module__' : 'schematerms_pb2' # @@protoc_insertion_point(class_scope:SchemaTerms.SDOTerm) }) _sym_db.RegisterMessage(SDOTerm) SDOBaseType = _reflection.GeneratedProtocolMessageType('SDOBaseType', (_message.Message,), { 'DESCRIPTOR' : _SDOBASETYPE, '__module__' : 'schematerms_pb2' # @@protoc_insertion_point(class_scope:SchemaTerms.SDOBaseType) }) _sym_db.RegisterMessage(SDOBaseType) SDOProperty = _reflection.GeneratedProtocolMessageType('SDOProperty', (_message.Message,), { 'DESCRIPTOR' : _SDOPROPERTY, '__module__' : 'schematerms_pb2' # @@protoc_insertion_point(class_scope:SchemaTerms.SDOProperty) }) _sym_db.RegisterMessage(SDOProperty) SDOEnumerationValue = _reflection.GeneratedProtocolMessageType('SDOEnumerationValue', (_message.Message,), { 'DESCRIPTOR' : _SDOENUMERATIONVALUE, '__module__' : 'schematerms_pb2' # @@protoc_insertion_point(class_scope:SchemaTerms.SDOEnumerationValue) }) _sym_db.RegisterMessage(SDOEnumerationValue) SDOReference = _reflection.GeneratedProtocolMessageType('SDOReference', (_message.Message,), { 'DESCRIPTOR' : _SDOREFERENCE, '__module__' : 'schematerms_pb2' # @@protoc_insertion_point(class_scope:SchemaTerms.SDOReference) }) _sym_db.RegisterMessage(SDOReference) SDOBaseTypeExpanded = _reflection.GeneratedProtocolMessageType('SDOBaseTypeExpanded', (_message.Message,), { 'DESCRIPTOR' : _SDOBASETYPEEXPANDED, '__module__' : 'schematerms_pb2' # @@protoc_insertion_point(class_scope:SchemaTerms.SDOBaseTypeExpanded) }) _sym_db.RegisterMessage(SDOBaseTypeExpanded) # @@protoc_insertion_point(module_scope)

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6

e499de222c437838f9677d111c351d2cc1d63933

80

py

Python

Intensivo-Python/Cap-8/printing_functions.py

RodrigoTAbreu/Python-3

9bf0578c1ed52283c8d8516a9052557bde038947

[ "MIT" ]

null

Intensivo-Python/Cap-8/printing_functions.py

RodrigoTAbreu/Python-3

9bf0578c1ed52283c8d8516a9052557bde038947

[ "MIT" ]

null

Intensivo-Python/Cap-8/printing_functions.py

RodrigoTAbreu/Python-3

9bf0578c1ed52283c8d8516a9052557bde038947

[ "MIT" ]

null

import print_models print_models.unprinted_models=['android','xiaomi','iphone']

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e4a7a235a5923962364ee54071e94b926eb89812

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py

Python

src/Python/Stage_1/pbmodel.py

ananthsridharan/vtol_sizing

3f754e1bd3cebdb5b5c68c8a2d84c47be1df2f02

[ "MIT" ]

10

2020-03-24T10:20:52.000Z

2021-11-22T18:49:25.000Z

src/Python/Stage_1/pbmodel.py

ananthsridharan/vtol_sizing

3f754e1bd3cebdb5b5c68c8a2d84c47be1df2f02

[ "MIT" ]

4

2020-12-08T10:26:41.000Z

2021-10-04T18:19:59.000Z

src/Python/Stage_1/pbmodel.py

ananthsridharan/vtol_sizing

3f754e1bd3cebdb5b5c68c8a2d84c47be1df2f02

[ "MIT" ]

5

2018-11-27T21:21:19.000Z

2021-04-20T15:44:18.000Z

#==================================================================== # python function to predict weight of rotor blades and hub # physics-based model for blades, parametric model for hubs #====================================================================

43.666667

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262

5.052632

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262

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6

e4de558f7701dcf21b7d815eebbda8a9ee4b1a1f

103

py

Python

pyqt_translucent_full_loading_screen_thread/__init__.py

yjg30737/pyqt-translucent-full-loading-screen-thread

8b9fe1422d672b1fa78a540f88a7cb4de15dc2c9

[ "MIT" ]

null

pyqt_translucent_full_loading_screen_thread/__init__.py

yjg30737/pyqt-translucent-full-loading-screen-thread

8b9fe1422d672b1fa78a540f88a7cb4de15dc2c9

[ "MIT" ]

null

pyqt_translucent_full_loading_screen_thread/__init__.py

yjg30737/pyqt-translucent-full-loading-screen-thread

8b9fe1422d672b1fa78a540f88a7cb4de15dc2c9

[ "MIT" ]

null

from .loadingThread import LoadingThread from .loadingTranslucentScreen import LoadingTranslucentScreen

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1

0

6

90208d48667d3a1e10f22f8d4a1e064f41fb3829

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py

Python

tests/transformer/test_transformer_layers.py

vikua/time-series-experiments

2f9d3fa842866c39c8c1a9906c8c5d4870a6f7da

[ "MIT" ]

null

tests/transformer/test_transformer_layers.py

vikua/time-series-experiments

2f9d3fa842866c39c8c1a9906c8c5d4870a6f7da

[ "MIT" ]

4

2020-10-11T15:14:48.000Z

2022-02-10T02:28:07.000Z

tests/transformer/test_transformer_layers.py

vikua/time-series-experiments

2f9d3fa842866c39c8c1a9906c8c5d4870a6f7da

[ "MIT" ]

null

import random import pytest import numpy as np import tensorflow as tf from tensorflow import keras from time_series_experiments.transformer.layers import ( MultiHeadAttention, PositionalEncoding, ) from time_series_experiments.utils import get_initializer from time_series_experiments.utils.metrics import rmse from ..conftest import simple_seq_data, RANDOM_SEED @pytest.fixture(scope="function", autouse=True) def clear_session(): tf.keras.backend.clear_session() tf.random.set_seed(RANDOM_SEED) np.random.seed(RANDOM_SEED) random.seed(RANDOM_SEED) def _positional_encoding_reference(seq_len, dims): def _angle_vec(pos): return [pos / np.power(10000.0, 2 * (u // 2) / dims) for u in range(dims)] table = np.array([_angle_vec(pos) for pos in range(seq_len)]) sines = np.sin(table[:, 0::2]) cosines = np.cos(table[:, 1::2]) # instead of concatentation: should be sin on even cos on odd positions # table[:, 0::2] = sines # table[:, 1::2] = cosines table = np.concatenate([sines, cosines], axis=-1) return table def test_scaled_dot_product_attention(): mha = MultiHeadAttention(32, 4) temp_q = tf.constant([[0, 10, 0]], dtype=tf.float32) temp_k = tf.constant( [[10, 0, 0], [0, 10, 0], [0, 0, 10], [0, 0, 10]], dtype=tf.float32 ) temp_v = tf.constant([[1, 0], [10, 0], [100, 5], [1000, 6]], dtype=tf.float32) outputs, weights = mha.scaled_dot_product_attention(temp_q, temp_k, temp_v, None) assert np.all(np.isclose(outputs, np.array([10.0, 0.0],), atol=1e-6)) assert np.all(np.isclose(weights, np.array([0.0, 1.0, 0.0, 0.0],), atol=1e-5)) temp_q = tf.constant([[0, 0, 10]], dtype=tf.float32) outputs, weights = mha.scaled_dot_product_attention(temp_q, temp_k, temp_v, None) assert np.all(np.isclose(outputs, np.array([550.0, 5.5],), atol=1e-6)) assert np.all(np.isclose(weights, np.array([0.0, 0.0, 0.5, 0.5],), atol=1e-5)) temp_q = tf.constant([[10, 10, 0]], dtype=tf.float32) outputs, weights = mha.scaled_dot_product_attention(temp_q, temp_k, temp_v, None) assert np.all(np.isclose(outputs, np.array([5.5, 0.0],), atol=1e-6)) assert np.all(np.isclose(weights, np.array([0.5, 0.5, 0.0, 0.0],), atol=1e-5)) temp_q = tf.constant([[0, 0, 10], [0, 10, 0], [10, 10, 0]], dtype=tf.float32) outputs, weights = mha.scaled_dot_product_attention(temp_q, temp_k, temp_v, None) assert np.all( np.isclose( outputs, np.array([[550.0, 5.5], [10.0, 0.0], [5.5, 0.0]]), atol=1e-6 ) ) assert np.all( np.isclose( weights, np.array( [[0.0, 0.0, 0.5, 0.5], [0.0, 1.0, 0.0, 0.0], [0.5, 0.5, 0.0, 0.0]] ), atol=1e-5, ) ) def test_multi_head_attention(): fdw = 28 fw = 7 attention_dim = 32 num_heads = 4 x_train, y_train, x_test, y_test = simple_seq_data( nrows=1000, freq="1H", fdw=fdw, fw=fw, test_size=0.2 ) inputs = keras.Input(shape=(fdw, 1)) outputs, attention_weights = MultiHeadAttention( attention_dim=attention_dim, num_heads=num_heads, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), )([inputs, inputs, inputs]) outputs = keras.layers.Reshape((fdw * attention_dim * num_heads,))(outputs) outputs = keras.layers.Dense( fw, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), activation="linear", )(outputs) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=keras.optimizers.Adam(0.01), loss=keras.losses.MeanSquaredError() ) model.fit(x_train, y_train, epochs=5, batch_size=32, shuffle=False) y_pred = model.predict(x_test) assert np.all(np.isfinite(y_pred)) error = rmse(y_test, y_pred) assert error < 0.5 def test_multi_head_attention_padding_mask(): fdw = 28 fw = 7 attention_dim = 32 num_heads = 4 x_train, y_train, x_test, y_test = simple_seq_data( nrows=1000, freq="1H", fdw=fdw, fw=fw, test_size=0.2 ) random_state = np.random.RandomState(RANDOM_SEED) mask = ( random_state.random((x_train.shape[0], 1, 1, x_train.shape[1])) > 0.3 ).astype(np.int) inputs = keras.Input(shape=(fdw, 1)) padding_mask = keras.Input(shape=(1, 1, fdw)) outputs, attention_weights = MultiHeadAttention( attention_dim=attention_dim, num_heads=num_heads, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), )([inputs, inputs, inputs], mask=padding_mask) outputs = keras.layers.Reshape((fdw * attention_dim * num_heads,))(outputs) outputs = keras.layers.Dense( fw, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), activation="linear", )(outputs) model = keras.Model(inputs=[inputs, padding_mask], outputs=outputs) model.compile( optimizer=keras.optimizers.Adam(0.01), loss=keras.losses.MeanSquaredError(), ) model.fit([x_train, mask], y_train, epochs=5, batch_size=32, shuffle=False) mask = (random_state.random((x_test.shape[0], 1, 1, x_test.shape[1])) > 0.3).astype( np.int ) y_pred = model.predict([x_test, mask]) assert np.all(np.isfinite(y_pred)) error = rmse(y_test, y_pred) assert error < 0.5 def test_multi_head_attention_lookahead_mask(): fdw = 28 fw = 7 attention_dim = 32 num_heads = 4 x_train, y_train, x_test, y_test = simple_seq_data( nrows=1000, freq="1H", fdw=fdw, fw=fw, test_size=0.2 ) triu = np.triu(np.ones((fdw, fdw))) mask = np.stack([triu for _ in range(x_train.shape[0])]) mask = np.expand_dims(mask, axis=1) inputs = keras.Input(shape=(fdw, 1)) lookahead_mask = keras.Input(shape=(1, fdw, fdw)) outputs, attention_weights = MultiHeadAttention( attention_dim=attention_dim, num_heads=num_heads, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), )([inputs, inputs, inputs], mask=lookahead_mask) outputs = keras.layers.Reshape((fdw * attention_dim * num_heads,))(outputs) outputs = keras.layers.Dense( fw, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), activation="linear", )(outputs) model = keras.Model(inputs=[inputs, lookahead_mask], outputs=outputs) model.compile( optimizer=keras.optimizers.Adam(0.01), loss=keras.losses.MeanSquaredError(), ) model.fit([x_train, mask], y_train, epochs=5, batch_size=32, shuffle=False) mask = np.stack([triu for _ in range(x_test.shape[0])]) mask = np.expand_dims(mask, axis=1) y_pred = model.predict([x_test, mask]) assert np.all(np.isfinite(y_pred)) error = rmse(y_test, y_pred) assert error < 0.5 def test_positional_encoding_table(): fdw = 28 fw = 7 x_train, _, _, _ = simple_seq_data( nrows=1000, freq="1H", fdw=fdw, fw=fw, test_size=0.2 ) x_train = tf.convert_to_tensor(x_train[0][np.newaxis, :, :]) pos = PositionalEncoding(128) pos_encoding = pos.call(x_train) pos_encoding = tf.squeeze(pos_encoding).numpy() reference_encoding = _positional_encoding_reference(fdw, 128) assert np.all(np.isclose(pos_encoding, reference_encoding, atol=1e-5)) def test_positional_encoding(): fdw = 28 fw = 7 x_train, y_train, x_test, y_test = simple_seq_data( nrows=1000, freq="1H", fdw=fdw, fw=fw, test_size=0.2 ) inputs = keras.Input(shape=(fdw, 1)) outputs = PositionalEncoding(8)(inputs) outputs = keras.layers.Concatenate()([inputs, outputs]) outputs = keras.layers.Flatten()(outputs) outputs = keras.layers.Dense( fw, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), activation="linear", )(outputs) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=keras.optimizers.Adam(0.01), loss=keras.losses.MeanSquaredError() ) model.fit(x_train, y_train, epochs=5, batch_size=32, shuffle=False) y_pred = model.predict(x_test) assert np.all(np.isfinite(y_pred)) error = rmse(y_test, y_pred) assert error < 0.5 def test_positional_encoding_and_attention(): fdw = 28 fw = 7 attention_dim = 32 num_heads = 4 x_train, y_train, x_test, y_test = simple_seq_data( nrows=1000, freq="1H", fdw=fdw, fw=fw, test_size=0.2 ) inputs = keras.Input(shape=(fdw, 1)) outputs = PositionalEncoding(8)(inputs) outputs = keras.layers.Concatenate()([inputs, outputs]) outputs, attention_weights = MultiHeadAttention( attention_dim=attention_dim, num_heads=num_heads, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), )([outputs, outputs, outputs]) outputs = keras.layers.Reshape((fdw * attention_dim * num_heads,))(outputs) outputs = keras.layers.Dense( fw, kernel_initializer=get_initializer("glorot_uniform", RANDOM_SEED), activation="linear", )(outputs) model = keras.Model(inputs=inputs, outputs=outputs) model.compile( optimizer=keras.optimizers.Adam(0.01), loss=keras.losses.MeanSquaredError() ) model.fit(x_train, y_train, epochs=5, batch_size=32, shuffle=False) y_pred = model.predict(x_test) assert np.all(np.isfinite(y_pred)) error = rmse(y_test, y_pred) assert error < 0.5

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9038251a0e5fb0f56a43fbbb1f3e39dc241f7a62

15,789

py

Python

2021/09.py

almazkun/advent

bcc18f83e7eaf6579a7b6b47fa9f0f6f0d2b31b4

[ "MIT" ]

null

2021/09.py

almazkun/advent

bcc18f83e7eaf6579a7b6b47fa9f0f6f0d2b31b4

[ "MIT" ]

null

2021/09.py

almazkun/advent

bcc18f83e7eaf6579a7b6b47fa9f0f6f0d2b31b4

[ "MIT" ]

null

from solution import Solution class Point: def __init__(self, loc: int, x: int, y: int): self.loc = int(loc) self.x = x self.y = y def __str__(self): return f"{self.loc}" def __repr__(self): return self.__str__() def lowest_x(self, field): return self.left(field) > self.loc < self.right(field) def lowest_y(self, field): return self.top(field) > self.loc < self.bottom(field) def left(self, field): if self.x == 0: return self.loc + 1 return field.field[self.y][self.x - 1].loc def right(self, field): try: return field.field[self.y][self.x + 1].loc except IndexError: return self.loc + 1 def top(self, field): if self.y == 0: return self.loc + 1 return field.field[self.y - 1][self.x].loc def bottom(self, field): try: return field.field[self.y + 1][self.x].loc except IndexError: return self.loc + 1 class Field: def __init__(self): self.field = [] def __str__(self): return "\n".join(["".join(map(str, row)) for row in self.field]) class Sol(Solution): """ --- Day 9: Smoke Basin --- These caves seem to be lava tubes. Parts are even still volcanically active; small hydrothermal vents release smoke into the caves that slowly settles like rain. If you can model how the smoke flows through the caves, you might be able to avoid it and be that much safer. The submarine generates a heightmap of the floor of the nearby caves for you (your puzzle input). Smoke flows to the lowest point of the area it's in. For example, consider the following heightmap: 2199943210 3987894921 9856789892 8767896789 9899965678 Each number corresponds to the height of a particular location, where 9 is the highest and 0 is the lowest a location can be. Your first goal is to find the low points - the locations that are lower than any of its adjacent locations. Most locations have four adjacent locations (up, down, left, and right); locations on the edge or corner of the map have three or two adjacent locations, respectively. (Diagonal locations do not count as adjacent.) In the above example, there are four low points, all highlighted: two are in the first row (a 1 and a 0), one is in the third row (a 5), and one is in the bottom row (also a 5). All other locations on the heightmap have some lower adjacent location, and so are not low points. The risk level of a low point is 1 plus its height. In the above example, the risk levels of the low points are 2, 1, 6, and 6. The sum of the risk levels of all low points in the heightmap is therefore 15. Find all of the low points on your heightmap. What is the sum of the risk levels of all low points on your heightmap? --- Part Two --- Next, you need to find the largest basins so you know what areas are most important to avoid. A basin is all locations that eventually flow downward to a single low point. Therefore, every low point has a basin, although some basins are very small. Locations of height 9 do not count as being in any basin, and all other locations will always be part of exactly one basin. The size of a basin is the number of locations within the basin, including the low point. The example above has four basins. The top-left basin, size 3: 2199943210 3987894921 9856789892 8767896789 9899965678 The top-right basin, size 9: 2199943210 3987894921 9856789892 8767896789 9899965678 The middle basin, size 14: 2199943210 3987894921 9856789892 8767896789 9899965678 The bottom-right basin, size 9: 2199943210 3987894921 9856789892 8767896789 9899965678 Find the three largest basins and multiply their sizes together. In the above example, this is 9 * 14 * 9 = 1134. What do you get if you multiply together the sizes of the three largest basins? """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @property def cleaned(self): return [x.strip() for x in self.input.split("\n") if x] def p1(self): f = Field() for i_line, line in enumerate(self.cleaned): f.field.append([]) for i_loc, loc in enumerate(line): p = Point(loc, i_loc, i_line) f.field[i_line].append(p) risk = 0 for row in f.field: for loc in row: if loc.lowest_x(f) and loc.lowest_y(f): risk += loc.loc + 1 return risk def p2(self): pass @property def solution(self): try: return f"p1: {self.p1()}\np2: {self.p2()}\n" except Exception as e: return f"Error: {e}" test_ = """ 2199943210 3987894921 9856789892 8767896789 9899965678 """ input_ = """ 6769876887698999876367898543212378997654321291098765432398767667989976543210123456987678999766598921 5456965476567999985457897654301456989743210989989898540987656545678987654323634679876569998757387892 4349864345476889876778999864323699879654329879878987659876543434569998789434545798765459879543296989 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6569899654989597989239876569985479876532123456899765656789979876543456943498964348798963458921234567 9699998799878456899123998698794321987843234569999876767898767987854567894579875499987654567894345789 8989219987656345678934599987653210198967347678989987898987654398765679965679876989999965878965756899 7478909876544234567895678998764321239878456789679998969898321239989989876989989878989876789879867998 6367899998432123456789899219965432346989767896568999656789432387895491987893498767678989894989878987 5256789987641015667899964329876563456799898965457898547896543456954320198912987654567899953492989876 4345896595432124588998765545987674567899999876345987658987659767896431239109876543456789432101299965 """ if __name__ == "__main__": try: Sol(test_).solve() except: pass Sol(input_).solve()

50.605769

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0.841219

875

15,789

15.118857

0.354286

0.004762

0.013606

0.018142

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0.032807

0.028271

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0.756176

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50.768489

0.210715

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false

0.011173

0.005587

0.03352

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0

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null

0

1

0

1

0

1

null

1

0

6

5f71be04bb4de360f940f744f1758c7d47fcb4fb

93

py

Python

lib/vpm/models/__init__.py

aligholami/kepler

a272ac427e09892cd44ade70e910272c4f69c638

[ "Unlicense" ]

null

lib/vpm/models/__init__.py

aligholami/kepler

a272ac427e09892cd44ade70e910272c4f69c638

[ "Unlicense" ]

null

lib/vpm/models/__init__.py

aligholami/kepler

a272ac427e09892cd44ade70e910272c4f69c638

[ "Unlicense" ]

null

from .naive import NaiveViewpointMatching from .quaternion import QuaternionViewpointMatching

46.5

51

0.903226

8

93

10.5

0.75

0

0.075269

93

2

51

46.5

0.976744

0

1

0

true

0

1

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1

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1

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1

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0

1

0

null

0

1

0

1

0

1

0

6

5f74f393f35e2c794a751b34f340e182dff67b73

128

py

Python

math_lib.py

cu-swe4s-fall-2019/version-control-tprossiter0

8957b8905e5b4302e802e68b4c4e2ac61eea054b

[ "MIT" ]

null

math_lib.py

cu-swe4s-fall-2019/version-control-tprossiter0

8957b8905e5b4302e802e68b4c4e2ac61eea054b

[ "MIT" ]

null

math_lib.py

cu-swe4s-fall-2019/version-control-tprossiter0

8957b8905e5b4302e802e68b4c4e2ac61eea054b

[ "MIT" ]

null

def div(a, b): if(b != 0): return a/b else: print("cannot divide by 0") return def add(a,b): return a+b

14.222222

32

0.515625

24

128

2.75

0.541667

0.121212

0.242424

0

0.023256

0.328125

128

9

33

14.222222

0.744186

0

0.139535

0

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0.25

false

0

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0.625

0.125

1

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null

0

1

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null

0

1

0

1

0

6

5fc764f6f305f1bef57aa8a36c50c0e2d3fb5f55

1,579

py

Python

admin/admin.py

mischievousdev/cogs-for-you

fd590a8ea9d6b99179b67c272f88a04e30392e11

[ "CC0-1.0" ]

1

2020-02-21T15:59:21.000Z

admin/admin.py

mischievousdev/cogs-for-you

fd590a8ea9d6b99179b67c272f88a04e30392e11

[ "CC0-1.0" ]

null

admin/admin.py

mischievousdev/cogs-for-you

fd590a8ea9d6b99179b67c272f88a04e30392e11

[ "CC0-1.0" ]

null

import discord from discord.ext import commands class Admin(commands.Cog): def __init__(self, bot): self.bot = bot @commands.command(hidden=True) @commands.is_owner() async def load(self, ctx, extension): self.bot.load_extension(f"cogs.{extension}") embed = discord.Embed(color=discord.Color.blurple()) embed.add_field(name=":inbox_tray: `Input`", value=f"`Requested for loading cogs.{extension}`", inline=True) embed.add_field(name=":outbox_tray: `Output`", value=f"`Successfully loaded cogs.{extension}`", inline=True) await ctx.send(embed=embed) @commands.command(hidden=True) @commands.is_owner() async def unload(self, ctx, extension): self.bot.unload_extension(f"cogs.{extension}") embed = discord.Embed(color=discord.Color.blurple()) embed.add_field(name=":inbox_tray: `Input`", value=f"`Requested for unloading cogs.{extension}`", inline=True) embed.add_field(name=":outbox_tray: `Output`", value=f"`Successfully unloaded cogs.{extension}`", inline=True) await ctx.send(embed=embed) @commands.command(hidden=True) @commands.is_owner() async def reload(self, ctx, extension): self.bot.unload_extension(f"cogs.{extension}") self.bot.load_extension(f"cogs.{extension}") embed = discord.Embed(color=discord.Color.blurple()) embed.add_field(name=":inbox_tray: `Input`", value=f"`Requested for re-loading cogs.{extension}`", inline=True) embed.add_field(name=":outbox_tray: `Output`", value=f"`Successfully re-loaded cogs.{extension}`", inline=True) await ctx.send(embed=embed) def setup(bot): bot.add_cog(Admin(bot))

41.552632

113

0.734642

224

1,579

5.071429

0.21875

0.114437

0.068662

0.089789

0.867077

0.860915

0.818662

0

0.098163

1,579

38

114

41.552632

0.797753

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false

0

0.0625

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0

null

0

1

0

null

0

6

395e61093a2eacc092d2a5b10d7c0cd0efce05e0

2,672

py

Python

alembic/versions/8c09a746d436_update_experiment_columns.py

jonathanzong/dmca

70157cff983310e5951024aa80e99e7a5404d758

[ "MIT" ]

2

2022-02-16T22:50:06.000Z

2022-02-21T19:38:02.000Z

alembic/versions/8c09a746d436_update_experiment_columns.py

jonathanzong/dmca

70157cff983310e5951024aa80e99e7a5404d758

[ "MIT" ]

2

2022-02-01T05:48:07.000Z

2022-02-01T05:49:29.000Z

alembic/versions/8c09a746d436_update_experiment_columns.py

jonathanzong/bartleby

70157cff983310e5951024aa80e99e7a5404d758

[ "MIT" ]

null

"""Update experiment columns Revision ID: 8c09a746d436 Revises: b6bb41e569e4 Create Date: 2017-12-19 14:23:51.293811 """ # revision identifiers, used by Alembic. revision = '8c09a746d436' down_revision = 'b6bb41e569e4' branch_labels = None depends_on = None from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import mysql def upgrade(engine_name): globals()["upgrade_%s" % engine_name]() def downgrade(engine_name): globals()["downgrade_%s" % engine_name]() def upgrade_development(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('experiments', sa.Column('controller', sa.String(length=64), nullable=True)) op.add_column('experiments', sa.Column('settings_json', sa.LargeBinary(), nullable=True)) op.drop_column('experiments', 'account_found') # ### end Alembic commands ### def downgrade_development(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('experiments', sa.Column('account_found', mysql.TINYINT(display_width=1), autoincrement=False, nullable=True)) op.drop_column('experiments', 'settings_json') op.drop_column('experiments', 'controller') # ### end Alembic commands ### def upgrade_test(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('experiments', sa.Column('controller', sa.String(length=64), nullable=True)) op.add_column('experiments', sa.Column('settings_json', sa.LargeBinary(), nullable=True)) op.drop_column('experiments', 'account_found') # ### end Alembic commands ### def downgrade_test(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('experiments', sa.Column('account_found', mysql.TINYINT(display_width=1), autoincrement=False, nullable=True)) op.drop_column('experiments', 'settings_json') op.drop_column('experiments', 'controller') # ### end Alembic commands ### def upgrade_production(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('experiments', sa.Column('controller', sa.String(length=64), nullable=True)) op.add_column('experiments', sa.Column('settings_json', sa.LargeBinary(), nullable=True)) op.drop_column('experiments', 'account_found') # ### end Alembic commands ### def downgrade_production(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('experiments', sa.Column('account_found', mysql.TINYINT(display_width=1), autoincrement=False, nullable=True)) op.drop_column('experiments', 'settings_json') op.drop_column('experiments', 'controller') # ### end Alembic commands ###

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128

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2,672

5.786834

0.225705

0.165764

0.053629

0.107259

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0

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0.140344

2,672

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129

35.157895

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false

0

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0

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0

null

0

1

0

null

0

1

0

6

39a6aa5ed87f23c48f7da5f9292acc9eecda8404

7,660

py

Python

src/conversation_analytics_toolkit/filtering.py

zzhang13/assistant-dialog-flow-analysis

d4e8d00ee0ff1aec33035ad654e7b5484d112040

[ "Apache-2.0" ]

19

2020-06-07T19:13:06.000Z

2022-01-22T02:34:11.000Z

src/conversation_analytics_toolkit/filtering.py

watson-developer-cloud/assistant-dialog-flow-analysis

0c7bcd9527636dce77c74b80f60dbe23e6682e13

[ "Apache-2.0" ]

32

2020-06-04T14:09:03.000Z

2021-02-11T15:05:07.000Z

src/conversation_analytics_toolkit/filtering.py

zzhang13/assistant-dialog-flow-analysis

d4e8d00ee0ff1aec33035ad654e7b5484d112040

[ "Apache-2.0" ]

10

2020-06-04T18:49:53.000Z

2021-11-26T12:42:08.000Z

# (C) Copyright IBM Corp. 2019, 2020. # # 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. import pandas as pd import numpy as np import datetime def by_included_node(node, df_logs_formated): """ return subset of conversations that contains specific node """ print("filtering.by_included_node() is DEPRECATED and will be removed in a future release. Use filtering2.by_node_id() instead ") # create an empty dataframe with the same column names and types filtered_df_logs = pd.DataFrame(data=None, columns=df_logs_formated.columns) for column in filtered_df_logs.columns: filtered_df_logs[column] = filtered_df_logs[column].astype(df_logs_formated[column].dtypes.name) # TODO: should we sort? df_by_conversation_id = df_logs_formated.sort_values(by=["response_timestamp"]).groupby(by="conversation_id") # TODO: potentially iterate over groupby ConvDict = {} for key, group in df_by_conversation_id: ConvDict[key] = group for i, (k, v) in enumerate(ConvDict.items()): # consider if we need to sort dfP1=pd.DataFrame(v, columns=v.columns).sort_values(by=['response_timestamp']) if any(dfP1['node_visited'] == node): filtered_df_logs = pd.concat([filtered_df_logs,dfP1]) if filtered_df_logs.empty: print('Filtering yielded an empty dataframe. Path flow analysis requires a non-empty dataframe.') print('Initial amount of records (before filtering):', len(df_logs_formated)) print('Amount of filtered records:', len(df_logs_formated)-len(filtered_df_logs)) print('Final amount of records (after filtering):', len(filtered_df_logs)) return filtered_df_logs def by_included_absolute_path(path, df_logs_formated): """ return subset of conversations that contains specific node @param path path: array """ print("filtering.by_included_absolute_path() is DEPRECATED and will be removed in a future release. ") # create an empty dataframe with the same column names and types filtered_df_logs = pd.DataFrame(data=None, columns=df_logs_formated.columns) for column in filtered_df_logs.columns: filtered_df_logs[column] = filtered_df_logs[column].astype(df_logs_formated[column].dtypes.name) # TODO: should we sort? df_by_conversation_id = df_logs_formated.sort_values(by=["response_timestamp"]).groupby(by="conversation_id") # TODO: potentially iterate over groupby ConvDict = {} for key, group in df_by_conversation_id: ConvDict[key] = group for i, (k, v) in enumerate(ConvDict.items()): # consider if we need to sort dfP1=pd.DataFrame(v, columns=v.columns).sort_values(by=['response_timestamp']) # length of conversation is at least length of path if len(dfP1) >= len(path): for i in range(len(path)): if path[i] != dfP1.iloc[i]['node_visited']: break # last element if i == len(path)-1: filtered_df_logs = pd.concat([filtered_df_logs,dfP1]) if filtered_df_logs.empty: print('Filtering yielded an empty dataframe. Path flow analysis requires a non-empty dataframe.') print('Initial amount of records (before filtering):', len(df_logs_formated)) print('Amount of filtered records:', len(df_logs_formated)-len(filtered_df_logs)) print('Final amount of records (after filtering):', len(filtered_df_logs)) return filtered_df_logs def by_initial_intent(intent, df_logs_formated): """ filter conversations starting with initial intent """ print("filtering.by_initial_intent() is DEPRECATED and will be removed in a future release. Use filtering2.by_node_id() instead.") # create an empty dataframe with the same column names and types filtered_df_logs = pd.DataFrame(data=None, columns=df_logs_formated.columns) for column in filtered_df_logs.columns: filtered_df_logs[column] = filtered_df_logs[column].astype(df_logs_formated[column].dtypes.name) df_by_conversation_id = df_logs_formated.sort_values(by=["response_timestamp"]).groupby(by="conversation_id") ConvDict = {} for key, group in df_by_conversation_id: ConvDict[key] = group for i, (k, v) in enumerate(ConvDict.items()): dfP1=pd.DataFrame(v, columns=v.columns).sort_values(by=['response_timestamp']) if (dfP1['intent'].iloc[0] == intent): filtered_df_logs = pd.concat([filtered_df_logs,dfP1]) if filtered_df_logs.empty: print('Filtering yielded an empty dataframe. Path flow analysis requires a non-empty dataframe.') print('Initial amount of records (before filtering):', len(df_logs_formated)) print('Amount of filtered records:', len(df_logs_formated)-len(filtered_df_logs)) print('Final amount of records (after filtering):', len(filtered_df_logs)) return filtered_df_logs def from_node_onwards(node, df_logs_formated): """ filtering by truncating conversations from selected node onwards """ print("filtering.from_node_onwards is DEPRECATED and will be removed in a future release. Use filtering2.trim_from_node_id instead.") # create an empty dataframe with the same column names and types filtered_df_logs = pd.DataFrame(data=None, columns=df_logs_formated.columns) for column in filtered_df_logs.columns: filtered_df_logs[column] = filtered_df_logs[column].astype(df_logs_formated[column].dtypes.name) ## TODO: should we move sorting into the loop? df_by_conversation_id = df_logs_formated.sort_values(by=["response_timestamp"]).groupby(by="conversation_id") for conversation_id, conversation_df in df_by_conversation_id: i=0 for index, row in conversation_df.iterrows(): i=i+1 node_visited = row["node_visited"] if node == node_visited: num_of_elements_to_copy = len(conversation_df)-i+1 filtered_df_logs = pd.concat([filtered_df_logs,conversation_df.tail(num_of_elements_to_copy)]) break if filtered_df_logs.empty: print('Filtering yielded an empty dataframe. Path flow analysis requires a non-empty dataframe.') print('Initial amount of records (before filtering):', len(df_logs_formated)) print('Amount of filtered records:', len(df_logs_formated)-len(filtered_df_logs)) print('Final amount of records (after filtering):', len(filtered_df_logs)) return filtered_df_logs def by_date_range(df, start_date, end_date): print("filtering.by_date_range is DEPRECATED and will be removed in a future release. Use filtering2.by_date_range instead.") mask = (df['response_timestamp'] >= start_date) & (df['response_timestamp'] <= end_date) df_1 = df.loc[mask].reset_index() if df_1.empty: print('Filtering yielded an empty dataframe. Path flow analysis requires a non-empty dataframe.') print('Initial amount of records (before filtering):', len(df)) print('Amount of filtered records:', len(df)-len(df_1)) print('Final amount of records (after filtering):', len(df_1)) return df_1

46.424242

137

0.715796

1,090

7,660

4.818349

0.166055

0.073115

0.106626

0.024372

0.740289

0.729246

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0.714966

0.70773

0

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0.188251

7,660

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46.424242

0.838855

0.176632

0

0.642105

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0

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1

0.052632

false

0

0.031579

0

0.136842

0.263158

0

null

0

1

0

null

0

1

0

6

f2dad5196463dee843f5096f0a717f3e7ebb43f5

178

py

Python

polls/admin.py

bomzheg/drf-api-test-task

00611073c198ab4fc3d601323587fce781123a3a

[ "MIT" ]

null

polls/admin.py

bomzheg/drf-api-test-task

00611073c198ab4fc3d601323587fce781123a3a

[ "MIT" ]

null

polls/admin.py

bomzheg/drf-api-test-task

00611073c198ab4fc3d601323587fce781123a3a

[ "MIT" ]

null

from django.contrib import admin from .models import Question, PossibleAnswer, Poll admin.site.register(Poll) admin.site.register(Question) admin.site.register(PossibleAnswer)

22.25

50

0.825843

23

178

6.391304

0.478261

0.183673

0.346939

0.285714

0

0.08427

178

7

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25.428571

0.90184

0

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true

0

0.4

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1

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0

null

0

1

0

1

0

6

f2f19d35215945981d5761608e6c3d44716308ce

137

py

Python

notebooks/lib/__init__.py

timtyree/crypto

cc4e5fff15d02402edc4d157c4a74fcb1e2ae834

[ "MIT" ]

null

notebooks/lib/__init__.py

timtyree/crypto

cc4e5fff15d02402edc4d157c4a74fcb1e2ae834

[ "MIT" ]

null

notebooks/lib/__init__.py

timtyree/crypto

cc4e5fff15d02402edc4d157c4a74fcb1e2ae834

[ "MIT" ]

null

from .utils import * from .viewer import * from .controller import * from .model import * from .measures import * from .routines import *

22.833333

25

0.744526

18

137

5.666667

0.444444

0.490196

0

0.167883

137

6

26

22.833333

0.894737

0

1

0

true

0

1

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

6

8408888717007f44a45ef85dbe032c0065a684fe

116

py

Python

dist/Basilisk/fswAlgorithms/magComm/__init__.py

ian-cooke/basilisk_mag

a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14

[ "0BSD" ]

null

dist/Basilisk/fswAlgorithms/magComm/__init__.py

ian-cooke/basilisk_mag

a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14

[ "0BSD" ]

1

2019-03-13T20:52:22.000Z

dist/Basilisk/fswAlgorithms/magComm/__init__.py

ian-cooke/basilisk_mag

a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14

[ "0BSD" ]

null

# This __init__.py file for the magComm package is automatically generated by the build system from magComm import *

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18

116

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0.888889

0

0.155172

116

2

95

58

0.928571

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0

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1

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true

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1

0

null

0

1

0

1

0

1

0

6

840ee113fb8f7ba082479d0471050df70f548c78

646

py

Python

autogen/openapi_server/models/__init__.py

jeanyjean/locatitude-api

b36759f372060a3726c63edb35516303d0e85d81

[ "MIT" ]

null

autogen/openapi_server/models/__init__.py

jeanyjean/locatitude-api

b36759f372060a3726c63edb35516303d0e85d81

[ "MIT" ]

null

autogen/openapi_server/models/__init__.py

jeanyjean/locatitude-api

b36759f372060a3726c63edb35516303d0e85d81

[ "MIT" ]

null

# coding: utf-8 # flake8: noqa from __future__ import absolute_import # import models into model package from openapi_server.models.all_details import AllDetails from openapi_server.models.covid19 import Covid19 from openapi_server.models.covid_trend import CovidTrend from openapi_server.models.lat_long import LatLong from openapi_server.models.new_covid_each_day import NewCovidEachDay from openapi_server.models.pm25 import PM25 from openapi_server.models.pm_trend import PmTrend from openapi_server.models.population import Population from openapi_server.models.province import Province from openapi_server.models.sum_covid import SumCovid

40.375

68

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93

646

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0.203704

0.314815

0.425926

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646

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null

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1

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1

0

1

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6

ffec802f4f43d7f3966e6705fa58059b934a3080

108

py

Python

ratbagd.py

staticssleever668/ratbag-python

285f73270cd3141a567f36536b96d24d747c6b27

[ "MIT" ]

null

ratbagd.py

staticssleever668/ratbag-python

285f73270cd3141a567f36536b96d24d747c6b27

[ "MIT" ]

null

ratbagd.py

staticssleever668/ratbag-python

285f73270cd3141a567f36536b96d24d747c6b27

[ "MIT" ]

null

#!/usr/bin/env python3 import ratbag.cli.ratbagd if __name__ == "__main__": ratbag.cli.ratbagd.main()

15.428571

29

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15

108

4.533333

0.733333

0.264706

0.470588

0

0.010753

0.138889

108

6

30

18

0.72043

0.194444

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0.093023

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true

0

0.333333

0

0.333333

0

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null

1

0

1

0

null

0

1

0

1

0

6

0832cf651bb687e158c907e0b630059b1f20f8ee

179

py

Python

demo_package/data/__init__.py

sdpython/demo_package

d057fe8e02a4c18b75696251b3e3bd4b3a9136a5

[ "MIT" ]

null

demo_package/data/__init__.py

sdpython/demo_package

d057fe8e02a4c18b75696251b3e3bd4b3a9136a5

[ "MIT" ]

2

2020-12-14T14:48:46.000Z

2020-12-14T15:00:18.000Z

demo_package/data/__init__.py

sdpython/demo_package

d057fe8e02a4c18b75696251b3e3bd4b3a9136a5

[ "MIT" ]

null

""" Shortcuts to *data*. """ from .data_insee import ( # noqa data_covid_france_departments_hospitals, data_covid_france_departments_tests, data_france_departments)

19.888889

44

0.759777

21

179

5.952381

0.571429

0.408

0.24

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0.156425

179

8

45

22.375

0.827815

0.145251

0

1

0

true

0

0.25

0

0.25

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1

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null

1

0

1

0

null

0

1

0

6

084f71e0cd63a232698b72a6a394d81d54b6d573

35

py

Python

jduargs/__init__.py

jeandemeusy/jdu_args

cbaf69d70c4cc25492989787ff97b4642b58078f

[ "MIT" ]

null

jduargs/__init__.py

jeandemeusy/jdu_args

cbaf69d70c4cc25492989787ff97b4642b58078f

[ "MIT" ]

null

jduargs/__init__.py

jeandemeusy/jdu_args

cbaf69d70c4cc25492989787ff97b4642b58078f

[ "MIT" ]

null

from .parser import ArgumentParser

17.5

34

0.857143

4

35

7.5

1

0

0.114286

35

1

35

0.967742

0

1

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true

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null

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null

0

1

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1

0

1

0

6

f25ba38ad9bc528a1cd74f62a8ce3c1bd7e1365a

67,406

py

Python

alerter/test/monitors/node/test_chainlink.py

SimplyVC/panic

2f5c327ea0d14b6a49dc8f4599a255048bc2ff6d

[ "Apache-2.0" ]

41

2019-08-23T12:40:42.000Z

2022-03-28T11:06:02.000Z

alerter/test/monitors/node/test_chainlink.py

SimplyVC/panic

2f5c327ea0d14b6a49dc8f4599a255048bc2ff6d

[ "Apache-2.0" ]

147

2019-08-30T22:09:48.000Z

2022-03-30T08:46:26.000Z

alerter/test/monitors/node/test_chainlink.py

SimplyVC/panic

2f5c327ea0d14b6a49dc8f4599a255048bc2ff6d

[ "Apache-2.0" ]

3

2019-09-03T21:12:28.000Z

2021-08-18T14:27:56.000Z

import copy import json import logging import unittest from collections import ChainMap from datetime import datetime from datetime import timedelta from http.client import IncompleteRead from typing import Dict from unittest import mock from unittest.mock import call import pika from freezegun import freeze_time from parameterized import parameterized from pika.exceptions import AMQPConnectionError, AMQPChannelError from requests.exceptions import (ConnectionError as ReqConnectionError, ReadTimeout, ChunkedEncodingError, MissingSchema, InvalidSchema, InvalidURL) from urllib3.exceptions import ProtocolError from src.configs.nodes.chainlink import ChainlinkNodeConfig from src.message_broker.rabbitmq import RabbitMQApi from src.monitors.node.chainlink import ChainlinkNodeMonitor from src.utils import env from src.utils.constants.rabbitmq import (HEALTH_CHECK_EXCHANGE, RAW_DATA_EXCHANGE, CHAINLINK_NODE_RAW_DATA_ROUTING_KEY, HEARTBEAT_OUTPUT_WORKER_ROUTING_KEY) from src.utils.exceptions import (PANICException, EnabledSourceIsEmptyException, MetricNotFoundException, NodeIsDownException, DataReadingException, InvalidUrlException, MessageWasNotDeliveredException) from test.utils.utils import (connect_to_rabbit, delete_queue_if_exists, delete_exchange_if_exists, disconnect_from_rabbit, assert_not_called_with) class TestChainlinkNodeMonitor(unittest.TestCase): def setUp(self) -> None: self.dummy_logger = logging.getLogger('Dummy') self.dummy_logger.disabled = True self.connection_check_time_interval = timedelta(seconds=0) self.rabbit_ip = env.RABBIT_IP self.rabbitmq = RabbitMQApi( self.dummy_logger, self.rabbit_ip, connection_check_time_interval=self.connection_check_time_interval) self.monitor_name = 'test_monitor' self.monitoring_period = 10 self.node_id = 'test_node_id' self.parent_id = 'test_parent_id' self.node_name = 'test_node' self.monitor_node = True self.monitor_prometheus = True self.node_prometheus_urls = ['https://test_ip_1:1000', 'https://test_ip_2:1000', 'https://test_ip_3:1000'] self.routing_key = 'test_routing_key' self.test_data_str = 'test data' self.test_data_dict = { 'test_key_1': 'test_val_1', 'test_key_2': 'test_val_2', } self.test_heartbeat = { 'component_name': 'Test Component', 'is_alive': True, 'timestamp': datetime(2012, 1, 1).timestamp(), } self.test_queue_name = 'Test Queue' self.prometheus_metrics = { 'head_tracker_current_head': 'strict', 'head_tracker_heads_received_total': 'strict', 'max_unconfirmed_blocks': 'strict', 'process_start_time_seconds': 'strict', 'tx_manager_num_gas_bumps_total': 'strict', 'tx_manager_gas_bump_exceeds_limit_total': 'strict', 'unconfirmed_transactions': 'strict', 'gas_updater_set_gas_price': 'optional', 'eth_balance': 'strict', 'run_status_update_total': 'optional', } self.retrieved_prometheus_data_example = { 'eth_balance': {'{"account": "eth_add_1"}': 26.043292035081947}, 'gas_updater_set_gas_price': { '{"percentile": "20%"}': 5000000000.0 }, 'head_tracker_current_head': 6924314.0, 'head_tracker_heads_received_total': 26392.0, 'max_unconfirmed_blocks': 0.0, 'process_start_time_seconds': 1619431240.24, 'run_status_update_total': { '{"from_status": "", "job_spec_id": ' '"03ba2f182d5e4245b8492e7f8672482e", ' '"status": "in_progress"}': 129.0, '{"from_status": "", "job_spec_id": ' '"0b7dd91f5e8a40d8b0493fc0799fe5d3", ' '"status": "in_progress"}': 189.0, '{"from_status": "in_progress", "job_spec_id": ' '"03ba2f182d5e4245b8492e7f8672482e", ' '"status": "completed"}': 389.0, '{"from_status": "in_progress", "job_spec_id": ' '"03ba2f182d5e4245b8492e7f8672482e", "status": ' '"pending_outgoing_confirmations"}': 1898.0, '{"from_status": "in_progress", "job_spec_id": ' '"0b7dd91f5e8a40d8b0493fc0799fe5d3", ' '"status": "completed"}': 569.0, '{"from_status": "in_progress", "job_spec_id": ' '"0b7dd91f5e8a40d8b0493fc0799fe5d3", ' '"status": "pending_outgoing_confirmations"}': 2780.0, '{"from_status": "in_progress", "job_spec_id": ' '"2aacf8ce6827410dae6ff2ce68938edb", "status": "errored"}': 1.0, '{"from_status": "in_progress", "job_spec_id": ' '"3cc0a79b77f8404fa193c1e56b3f29bf", ' '"status": "errored"}': 90.0, '{"from_status": "in_progress", ' '"job_spec_id": "4ae35b033a294c3db78a45db9ada9a57", ' '"status": "errored"}': 1.0, '{"from_status": "in_progress", "job_spec_id": ' '"7594586a567d4700b1a794f3363569e1", "status": "errored"}': 1.0, '{"from_status": "in_progress", "job_spec_id": ' '"834275814b3b46de83aa7770dbc90912", "status": "errored"}': 4.0, '{"from_status": "in_progress", "job_spec_id": ' '"8d2cde397b17415486bbd79de84c901e", ' '"status": "errored"}': 112.0, '{"from_status": "in_progress", "job_spec_id": ' '"d0dd062c26794ff1a9b9460cd5d529f6", "status": "errored"}': 2.0, '{"from_status": "in_progress", "job_spec_id": ' '"f2e35bcb37b04198a9241121cd936572", "status": "errored"}': 4.0, }, 'tx_manager_gas_bump_exceeds_limit_total': 0.0, 'tx_manager_num_gas_bumps_total': 2031.0, 'unconfirmed_transactions': 1.0 } self.retrieved_prometheus_data_example_optionals_none = copy.deepcopy( self.retrieved_prometheus_data_example) self.retrieved_prometheus_data_example_optionals_none[ 'gas_updater_set_gas_price'] = None self.retrieved_prometheus_data_example_optionals_none[ 'run_status_update_total'] = None self.processed_prometheus_data_example = { 'head_tracker_current_head': 6924314.0, 'head_tracker_heads_received_total': 26392.0, 'max_unconfirmed_blocks': 0.0, 'process_start_time_seconds': 1619431240.24, 'tx_manager_gas_bump_exceeds_limit_total': 0.0, 'tx_manager_num_gas_bumps_total': 2031.0, 'unconfirmed_transactions': 1.0, 'gas_updater_set_gas_price': { 'percentile': '20%', 'price': 5000000000.0 }, 'eth_balance': { 'address': 'eth_add_1', 'balance': 26.043292035081947 }, 'run_status_update_total_errors': 8 } self.processed_prometheus_data_example_optionals_none = copy.deepcopy( self.processed_prometheus_data_example) self.processed_prometheus_data_example_optionals_none[ 'gas_updater_set_gas_price'] = None self.processed_prometheus_data_example_optionals_none[ 'run_status_update_total_errors'] = 0 self.test_exception = PANICException('test_exception', 1) self.node_config = ChainlinkNodeConfig( self.node_id, self.parent_id, self.node_name, self.monitor_node, self.monitor_prometheus, self.node_prometheus_urls) self.test_monitor = ChainlinkNodeMonitor( self.monitor_name, self.node_config, self.dummy_logger, self.monitoring_period, self.rabbitmq ) # The dicts below will make more sense when more source types are added self.received_retrieval_info_all_source_types_enabled = { 'prometheus': { 'data': self.retrieved_prometheus_data_example, 'data_retrieval_failed': False, 'data_retrieval_exception': None, 'get_function': self.test_monitor._get_prometheus_data, 'processing_function': self.test_monitor._process_retrieved_prometheus_data, 'last_source_used_var': 'self._last_prometheus_source_used', 'monitoring_enabled_var': 'self.node_config._monitor_prometheus' } # When more sources are added this should contain source types with # successfully obtained data. } self.received_retrieval_info_prometheus_disabled = { 'prometheus': { 'data': {}, 'data_retrieval_failed': True, 'data_retrieval_exception': None, 'get_function': self.test_monitor._get_prometheus_data, 'processing_function': self.test_monitor._process_retrieved_prometheus_data, 'last_source_used_var': 'self._last_prometheus_source_used', 'monitoring_enabled_var': 'self.node_config._monitor_prometheus' } # When more sources are added this should contain source types with # successfully obtained data. } self.received_retrieval_info_all_source_types_enabled_err = { 'prometheus': { 'data': {}, 'data_retrieval_failed': True, 'data_retrieval_exception': None, 'get_function': self.test_monitor._get_prometheus_data, 'processing_function': self.test_monitor._process_retrieved_prometheus_data, 'last_source_used_var': 'self._last_prometheus_source_used', 'monitoring_enabled_var': 'self.node_config._monitor_prometheus' } # When more sources are added this should contain source types with # successfully obtained data. } # TODO: When more sources are added we can add # self.received_retrieval_info_prometheus_disabled_err def tearDown(self) -> None: # Delete any queues and exchanges which are common across many tests connect_to_rabbit(self.test_monitor.rabbitmq) delete_queue_if_exists(self.test_monitor.rabbitmq, self.test_queue_name) delete_exchange_if_exists(self.test_monitor.rabbitmq, HEALTH_CHECK_EXCHANGE) delete_exchange_if_exists(self.test_monitor.rabbitmq, RAW_DATA_EXCHANGE) disconnect_from_rabbit(self.test_monitor.rabbitmq) self.dummy_logger = None self.connection_check_time_interval = None self.rabbitmq = None self.test_exception = None self.node_config = None self.test_monitor = None def test_str_returns_monitor_name(self) -> None: self.assertEqual(self.monitor_name, str(self.test_monitor)) def test_get_monitor_period_returns_monitor_period(self) -> None: self.assertEqual(self.monitoring_period, self.test_monitor.monitor_period) def test_get_monitor_name_returns_monitor_name(self) -> None: self.assertEqual(self.monitor_name, self.test_monitor.monitor_name) def test_node_config_returns_node_config(self) -> None: self.assertEqual(self.node_config, self.test_monitor.node_config) def test_prometheus_metrics_returns_prometheus_metrics(self) -> None: self.assertEqual(self.prometheus_metrics, self.test_monitor.prometheus_metrics) def test_last_prometheus_source_used_returns_last_prometheus_source_used( self) -> None: # Check that on startup # last_prometheus_source_used = node_prometheus_urls[0] self.assertEqual(self.node_prometheus_urls[0], self.test_monitor.last_prometheus_source_used) # Check for any other value self.test_monitor._last_prometheus_source_used = \ self.node_prometheus_urls[1] self.assertEqual(self.node_prometheus_urls[1], self.test_monitor.last_prometheus_source_used) @parameterized.expand([ ([],) ]) def test_init_raises_EnabledSourceIsEmptyException_if_empty_enabled_source( self, node_prometheus_urls) -> None: """ This function should be parameterized further once we increase the number of data sources. """ node_config = ChainlinkNodeConfig( self.node_id, self.parent_id, self.node_name, self.monitor_node, self.monitor_prometheus, node_prometheus_urls) self.assertRaises( EnabledSourceIsEmptyException, ChainlinkNodeMonitor, self.monitor_name, node_config, self.dummy_logger, self.monitoring_period, self.rabbitmq) def test_initialise_rabbitmq_initialises_everything_as_expected( self) -> None: # To make sure that there is no connection/channel already # established self.assertIsNone(self.rabbitmq.connection) self.assertIsNone(self.rabbitmq.channel) # To make sure that the exchanges have not already been declared connect_to_rabbit(self.rabbitmq) self.rabbitmq.exchange_delete(RAW_DATA_EXCHANGE) self.rabbitmq.exchange_delete(HEALTH_CHECK_EXCHANGE) disconnect_from_rabbit(self.rabbitmq) self.test_monitor._initialise_rabbitmq() # Perform checks that the connection has been opened, marked as open # and that the delivery confirmation variable is set. self.assertTrue(self.test_monitor.rabbitmq.is_connected) self.assertTrue(self.test_monitor.rabbitmq.connection.is_open) self.assertTrue( self.test_monitor.rabbitmq.channel._delivery_confirmation) # Check whether the exchange has been creating by sending messages # to it. If this fails an exception is raised hence the test fails. self.test_monitor.rabbitmq.basic_publish_confirm( exchange=RAW_DATA_EXCHANGE, routing_key=self.routing_key, body=self.test_data_str, is_body_dict=False, properties=pika.BasicProperties(delivery_mode=2), mandatory=False) self.test_monitor.rabbitmq.basic_publish_confirm( exchange=HEALTH_CHECK_EXCHANGE, routing_key=self.routing_key, body=self.test_data_str, is_body_dict=False, properties=pika.BasicProperties(delivery_mode=2), mandatory=False) @mock.patch.object(ChainlinkNodeMonitor, "_process_retrieved_data") @mock.patch.object(ChainlinkNodeMonitor, "_process_error") def test_process_data_calls_process_error_on_retrieval_error( self, mock_process_error, mock_process_retrieved_data) -> None: # Do not test the processing of data for now mock_process_error.return_value = self.test_data_dict self.test_monitor._process_data(True, [self.test_exception], [self.test_data_dict]) # Test passes if _process_error is called once and # process_retrieved_data is not called self.assertEqual(1, mock_process_error.call_count) self.assertEqual(0, mock_process_retrieved_data.call_count) @mock.patch.object(ChainlinkNodeMonitor, "_process_retrieved_data") @mock.patch.object(ChainlinkNodeMonitor, "_process_error") def test_process_data_calls_process_retrieved_data_on_retrieval_success( self, mock_process_error, mock_process_retrieved_data) -> None: # Do not test the processing of data for now mock_process_retrieved_data.return_value = self.test_data_dict self.test_monitor._process_data(False, [self.test_exception], [self.test_data_dict]) # Test passes if _process_error is not called and process_retrieved_data # is called once self.assertEqual(0, mock_process_error.call_count) self.assertEqual(1, mock_process_retrieved_data.call_count) def test_send_heartbeat_sends_a_heartbeat_correctly(self) -> None: # This test creates a queue which receives messages with the same # routing key as the ones sent by send_heartbeat, and checks that the # heartbeat is received self.test_monitor._initialise_rabbitmq() # Delete the queue before to avoid messages in the queue on error. self.test_monitor.rabbitmq.queue_delete(self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=HEALTH_CHECK_EXCHANGE, routing_key=HEARTBEAT_OUTPUT_WORKER_ROUTING_KEY) self.test_monitor._send_heartbeat(self.test_heartbeat) # By re-declaring the queue again we can get the number of messages # in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) self.assertEqual(1, res.method.message_count) # Check that the message received is actually the HB _, _, body = self.test_monitor.rabbitmq.basic_get( self.test_queue_name) self.assertEqual(self.test_heartbeat, json.loads(body)) def test_display_data_returns_the_correct_string_if_all_metrics_present( self) -> None: # Test when optionals are not None expected_output = \ "head_tracker_current_head={}, " \ "head_tracker_heads_received_total={}, " \ "max_unconfirmed_blocks={}, process_start_time_seconds={}, " \ "tx_manager_num_gas_bumps_total={}, " \ "tx_manager_gas_bump_exceeds_limit_total={}, " \ "unconfirmed_transactions={}, gas_updater_set_gas_price={}, " \ "eth_balance={}, run_status_update_total_errors={}" \ "".format( self.processed_prometheus_data_example[ 'head_tracker_current_head'], self.processed_prometheus_data_example[ 'head_tracker_heads_received_total'], self.processed_prometheus_data_example[ 'max_unconfirmed_blocks'], self.processed_prometheus_data_example[ 'process_start_time_seconds'], self.processed_prometheus_data_example[ 'tx_manager_num_gas_bumps_total'], self.processed_prometheus_data_example[ 'tx_manager_gas_bump_exceeds_limit_total'], self.processed_prometheus_data_example[ 'unconfirmed_transactions'], self.processed_prometheus_data_example[ 'gas_updater_set_gas_price'], self.processed_prometheus_data_example['eth_balance'], self.processed_prometheus_data_example[ 'run_status_update_total_errors'] ) actual_output = self.test_monitor._display_data( self.processed_prometheus_data_example) self.assertEqual(expected_output, actual_output) # Test when optionals are None expected_output = \ "head_tracker_current_head={}, " \ "head_tracker_heads_received_total={}, " \ "max_unconfirmed_blocks={}, " \ "process_start_time_seconds={}, " \ "tx_manager_num_gas_bumps_total={}, " \ "tx_manager_gas_bump_exceeds_limit_total={}, " \ "unconfirmed_transactions={}, gas_updater_set_gas_price={}, " \ "eth_balance={}, run_status_update_total_errors={}" \ "".format( self.processed_prometheus_data_example_optionals_none[ 'head_tracker_current_head'], self.processed_prometheus_data_example_optionals_none[ 'head_tracker_heads_received_total'], self.processed_prometheus_data_example_optionals_none[ 'max_unconfirmed_blocks'], self.processed_prometheus_data_example_optionals_none[ 'process_start_time_seconds'], self.processed_prometheus_data_example_optionals_none[ 'tx_manager_num_gas_bumps_total'], self.processed_prometheus_data_example_optionals_none[ 'tx_manager_gas_bump_exceeds_limit_total'], self.processed_prometheus_data_example_optionals_none[ 'unconfirmed_transactions'], self.processed_prometheus_data_example_optionals_none[ 'gas_updater_set_gas_price'], self.processed_prometheus_data_example_optionals_none[ 'eth_balance'], self.processed_prometheus_data_example_optionals_none[ 'run_status_update_total_errors'] ) actual_output = self.test_monitor._display_data( self.processed_prometheus_data_example_optionals_none) self.assertEqual(expected_output, actual_output) def test_display_data_returns_the_correct_string_if_not_all_metrics_present( self) -> None: # Test when optionals are not None del self.processed_prometheus_data_example['head_tracker_current_head'] del self.processed_prometheus_data_example['eth_balance'] expected_output = \ "head_tracker_current_head={}, " \ "head_tracker_heads_received_total={}, " \ "max_unconfirmed_blocks={}, process_start_time_seconds={}, " \ "tx_manager_num_gas_bumps_total={}, " \ "tx_manager_gas_bump_exceeds_limit_total={}, " \ "unconfirmed_transactions={}, gas_updater_set_gas_price={}, " \ "eth_balance={}, run_status_update_total_errors={}" \ "".format( "Disabled", self.processed_prometheus_data_example[ 'head_tracker_heads_received_total'], self.processed_prometheus_data_example[ 'max_unconfirmed_blocks'], self.processed_prometheus_data_example[ 'process_start_time_seconds'], self.processed_prometheus_data_example[ 'tx_manager_num_gas_bumps_total'], self.processed_prometheus_data_example[ 'tx_manager_gas_bump_exceeds_limit_total'], self.processed_prometheus_data_example[ 'unconfirmed_transactions'], self.processed_prometheus_data_example[ 'gas_updater_set_gas_price'], "Disabled", self.processed_prometheus_data_example[ 'run_status_update_total_errors'] ) actual_output = self.test_monitor._display_data( self.processed_prometheus_data_example) self.assertEqual(expected_output, actual_output) # Test when optionals are None del self.processed_prometheus_data_example_optionals_none[ 'head_tracker_current_head'] del self.processed_prometheus_data_example_optionals_none[ 'eth_balance'] expected_output = \ "head_tracker_current_head={}, " \ "head_tracker_heads_received_total={}, " \ "max_unconfirmed_blocks={}, process_start_time_seconds={}, " \ "tx_manager_num_gas_bumps_total={}, " \ "tx_manager_gas_bump_exceeds_limit_total={}, " \ "unconfirmed_transactions={}, gas_updater_set_gas_price={}, " \ "eth_balance={}, run_status_update_total_errors={}" \ "".format( "Disabled", self.processed_prometheus_data_example_optionals_none[ 'head_tracker_heads_received_total'], self.processed_prometheus_data_example_optionals_none[ 'max_unconfirmed_blocks'], self.processed_prometheus_data_example_optionals_none[ 'process_start_time_seconds'], self.processed_prometheus_data_example_optionals_none[ 'tx_manager_num_gas_bumps_total'], self.processed_prometheus_data_example_optionals_none[ 'tx_manager_gas_bump_exceeds_limit_total'], self.processed_prometheus_data_example_optionals_none[ 'unconfirmed_transactions'], self.processed_prometheus_data_example_optionals_none[ 'gas_updater_set_gas_price'], "Disabled", self.processed_prometheus_data_example_optionals_none[ 'run_status_update_total_errors'] ) actual_output = self.test_monitor._display_data( self.processed_prometheus_data_example_optionals_none) self.assertEqual(expected_output, actual_output) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_first_attempts_retrieval_using_last_prom_source_used( self, mock_get_prometheus_metrics_data) -> None: mock_get_prometheus_metrics_data.return_value = \ self.processed_prometheus_data_example old_last_prometheus_source_used = \ self.test_monitor.last_prometheus_source_used actual_output = self.test_monitor._get_prometheus_data() mock_get_prometheus_metrics_data.assert_called_once_with( old_last_prometheus_source_used, self.prometheus_metrics, self.dummy_logger, verify=False) self.assertEqual(self.processed_prometheus_data_example, actual_output) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_does_not_change_last_prom_sourced_used_if_online( self, mock_get_prometheus_metrics_data) -> None: mock_get_prometheus_metrics_data.return_value = \ self.processed_prometheus_data_example old_last_prometheus_source_used = \ self.test_monitor.last_prometheus_source_used self.test_monitor._get_prometheus_data() self.assertEqual(old_last_prometheus_source_used, self.test_monitor.last_prometheus_source_used) @parameterized.expand([ (IncompleteRead, IncompleteRead('test'),), (ChunkedEncodingError, ChunkedEncodingError('test'),), (ProtocolError, ProtocolError('test'),), (InvalidURL, InvalidURL('test'),), (InvalidSchema, InvalidSchema('test'),), (MissingSchema, MissingSchema('test'),), (MetricNotFoundException, MetricNotFoundException('test', 'test'),), (Exception, Exception('test'),), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_raises_non_conn_err_if_last_source_used_on_and_errs( self, exception_class, exception_instance, mock_get_prometheus_metrics_data) -> None: mock_get_prometheus_metrics_data.side_effect = exception_instance old_last_prometheus_source_used = \ self.test_monitor.last_prometheus_source_used self.assertRaises(exception_class, self.test_monitor._get_prometheus_data) mock_get_prometheus_metrics_data.assert_called_once_with( old_last_prometheus_source_used, self.prometheus_metrics, self.dummy_logger, verify=False) @parameterized.expand([ (IncompleteRead, IncompleteRead('test'),), (ChunkedEncodingError, ChunkedEncodingError('test'),), (ProtocolError, ProtocolError('test'),), (InvalidURL, InvalidURL('test'),), (InvalidSchema, InvalidSchema('test'),), (MissingSchema, MissingSchema('test'),), (MetricNotFoundException, MetricNotFoundException('test', 'test'),), (Exception, Exception('test'),), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_no_change_last_source_used_if_online_and_it_errors( self, exception_class, exception_instance, mock_get_prometheus_metrics_data) -> None: # Here we are assuming that the error is not connection related mock_get_prometheus_metrics_data.side_effect = exception_instance old_last_prometheus_source_used = \ self.test_monitor.last_prometheus_source_used try: self.test_monitor._get_data() except exception_class: pass self.assertEqual(old_last_prometheus_source_used, self.test_monitor.last_prometheus_source_used) @parameterized.expand([ (ReadTimeout('test'),), (ReqConnectionError('test'),), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_gets_data_from_online_source_if_last_source_used_off( self, exception_instance, mock_get_prometheus_metrics_data) -> None: # In this case we are setting the final source to be online mock_get_prometheus_metrics_data.side_effect = [ exception_instance, exception_instance, exception_instance, self.processed_prometheus_data_example] old_last_prometheus_source_used = \ self.test_monitor.last_prometheus_source_used actual_output = self.test_monitor._get_prometheus_data() actual_calls = mock_get_prometheus_metrics_data.call_args_list self.assertEqual(4, len(actual_calls)) # In this case there are two calls to # self.test_monitor.node_config._node_prometheus_urls[0] because # initially this url was also the last prometheus source used. expected_calls = [call(old_last_prometheus_source_used, self.prometheus_metrics, self.dummy_logger, verify=False)] for i in range(0, len(self.node_prometheus_urls)): expected_calls.append(call( self.test_monitor.node_config.node_prometheus_urls[i], self.prometheus_metrics, self.dummy_logger, verify=False)) self.assertEqual(expected_calls, actual_calls) self.assertEqual(self.processed_prometheus_data_example, actual_output) @parameterized.expand([ (ReadTimeout('test'),), (ReqConnectionError('test'),), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_changes_last_source_if_last_source_off_other_node_on( self, exception_instance, mock_get_prometheus_metrics_data) -> None: # In this case we are setting the final source to be online mock_get_prometheus_metrics_data.side_effect = [ exception_instance, exception_instance, exception_instance, self.processed_prometheus_data_example] self.test_monitor._get_prometheus_data() self.assertEqual(self.test_monitor.node_config.node_prometheus_urls[-1], self.test_monitor.last_prometheus_source_used) @parameterized.expand([ (IncompleteRead, IncompleteRead('test'),), (ChunkedEncodingError, ChunkedEncodingError('test'),), (ProtocolError, ProtocolError('test'),), (InvalidURL, InvalidURL('test'),), (InvalidSchema, InvalidSchema('test'),), (MissingSchema, MissingSchema('test'),), (MetricNotFoundException, MetricNotFoundException('test', 'test'),), (Exception, Exception('test'),), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_raises_non_connection_err_if_online_source_errors( self, exception_class, exception_instance, mock_get_prometheus_metrics_data) -> None: # Here we will assume that the last prometheus source used was deemed as # offline as we have already tested the online case in a previous test. # We will also assume that the second source is online but it errors. mock_get_prometheus_metrics_data.side_effect = [ ReqConnectionError('test'), ReqConnectionError('test'), exception_instance] old_last_prometheus_source_used = \ self.test_monitor.last_prometheus_source_used self.assertRaises(exception_class, self.test_monitor._get_prometheus_data) actual_calls = mock_get_prometheus_metrics_data.call_args_list self.assertEqual(3, len(actual_calls)) self.assertEqual([ call(old_last_prometheus_source_used, self.prometheus_metrics, self.dummy_logger, verify=False), call(self.test_monitor.node_config._node_prometheus_urls[0], self.prometheus_metrics, self.dummy_logger, verify=False), call(self.test_monitor.node_config._node_prometheus_urls[1], self.prometheus_metrics, self.dummy_logger, verify=False)], actual_calls) @parameterized.expand([ (IncompleteRead, IncompleteRead('test'),), (ChunkedEncodingError, ChunkedEncodingError('test'),), (ProtocolError, ProtocolError('test'),), (InvalidURL, InvalidURL('test'),), (InvalidSchema, InvalidSchema('test'),), (MissingSchema, MissingSchema('test'),), (MetricNotFoundException, MetricNotFoundException('test', 'test'),), (Exception, Exception('test'),), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_changes_last_prom_source_used_if_online_source_errs( self, exception_class, exception_instance, mock_get_prometheus_metrics_data) -> None: # Here we will assume that the last prometheus source used was deemed as # offline as we have already tested when it is online in a previous # test. We will also assume that the second source is online but it # errors. mock_get_prometheus_metrics_data.side_effect = [ ReqConnectionError('test'), ReqConnectionError('test'), exception_instance] try: self.test_monitor._get_prometheus_data() except exception_class: pass self.assertEqual(self.test_monitor.node_config.node_prometheus_urls[1], self.test_monitor.last_prometheus_source_used) @parameterized.expand([ (ReadTimeout('test'),), (ReqConnectionError('test'),), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_raises_NodeIsDownException_if_all_prom_sources_down( self, exception_instance, mock_get_prometheus_metrics_data) -> None: mock_get_prometheus_metrics_data.side_effect = [ exception_instance, exception_instance, exception_instance, exception_instance] old_last_prometheus_source_used = \ self.test_monitor.last_prometheus_source_used self.assertRaises(NodeIsDownException, self.test_monitor._get_prometheus_data) actual_calls = mock_get_prometheus_metrics_data.call_args_list self.assertEqual(4, len(actual_calls)) # In this case there are two calls to # self.test_monitor.node_config._node_prometheus_urls[0] because # initially this url was also the last prometheus source used. expected_calls = [call(old_last_prometheus_source_used, self.prometheus_metrics, self.dummy_logger, verify=False)] for i in range(0, len(self.node_prometheus_urls)): expected_calls.append(call( self.test_monitor.node_config.node_prometheus_urls[i], self.prometheus_metrics, self.dummy_logger, verify=False)) self.assertEqual(expected_calls, actual_calls) @parameterized.expand([ (ReadTimeout('test'),), (ReqConnectionError('test'),), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_prom_data_does_not_change_last_prom_source_used_if_all_down( self, exception_instance, mock_get_prometheus_metrics_data) -> None: mock_get_prometheus_metrics_data.side_effect = [ exception_instance, exception_instance, exception_instance, exception_instance] old_last_prometheus_source_used = \ self.test_monitor.last_prometheus_source_used try: self.test_monitor._get_prometheus_data() except NodeIsDownException: pass self.assertEqual(old_last_prometheus_source_used, self.test_monitor.last_prometheus_source_used) @parameterized.expand([ ('self.received_retrieval_info_prometheus_disabled', [], False,), ('self.received_retrieval_info_all_source_types_enabled', ['self.retrieved_prometheus_data_example'], True,), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_data_return_if_no_errors_raised( self, expected_return, retrieved_prometheus_data, monitor_prometheus, mock_get_prometheus_metrics_data) -> None: get_prometheus_metrics_data_return = list(map( eval, retrieved_prometheus_data)) mock_get_prometheus_metrics_data.side_effect = \ get_prometheus_metrics_data_return self.test_monitor._node_config._monitor_prometheus = monitor_prometheus actual_ret = self.test_monitor._get_data() expected_ret = eval(expected_return) self.assertEqual(expected_ret, actual_ret) @parameterized.expand([ ("IncompleteRead('test')", "DataReadingException(self.test_monitor.monitor_name, " "self.test_monitor.last_prometheus_source_used)", 'self.received_retrieval_info_all_source_types_enabled_err', True, 'prometheus'), ("ChunkedEncodingError('test')", "DataReadingException(self.test_monitor.monitor_name, " "self.test_monitor.last_prometheus_source_used)", 'self.received_retrieval_info_all_source_types_enabled_err', True, 'prometheus'), ("ProtocolError('test')", "DataReadingException(self.test_monitor.monitor_name, " "self.test_monitor.last_prometheus_source_used)", 'self.received_retrieval_info_all_source_types_enabled_err', True, 'prometheus'), ("InvalidURL('test')", "InvalidUrlException(self.test_monitor.last_prometheus_source_used)", 'self.received_retrieval_info_all_source_types_enabled_err', True, 'prometheus'), ("InvalidSchema('test')", "InvalidUrlException(self.test_monitor.last_prometheus_source_used)", 'self.received_retrieval_info_all_source_types_enabled_err', True, 'prometheus'), ("MissingSchema('test')", "InvalidUrlException(self.test_monitor.last_prometheus_source_used)", 'self.received_retrieval_info_all_source_types_enabled_err', True, 'prometheus'), ("MetricNotFoundException('test_metric', 'test_endpoint')", "MetricNotFoundException('test_metric', 'test_endpoint')", 'self.received_retrieval_info_all_source_types_enabled_err', True, 'prometheus'), ('NodeIsDownException(self.node_name)', 'NodeIsDownException(self.node_name)', 'self.received_retrieval_info_all_source_types_enabled_err', True, 'prometheus'), ]) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_data_return_if_recognised_error_raised( self, raised_err, returned_err, expected_return, monitor_prometheus, errored_source_type, mock_get_prometheus_metrics_data) -> None: # This test will be expanded when adding more source types to cater for # when monitor_prometheus is False mock_get_prometheus_metrics_data.side_effect = \ eval(raised_err) if errored_source_type == "prometheus" else None self.test_monitor._node_config._monitor_prometheus = monitor_prometheus actual_ret = self.test_monitor._get_data() expected_ret = eval(expected_return) expected_ret[errored_source_type][ 'data_retrieval_exception'] = eval(returned_err) self.assertEqual(expected_ret, actual_ret) @mock.patch("src.monitors.node.chainlink.get_prometheus_metrics_data") def test_get_data_raises_unrecognised_error_if_raised( self, mock_get_prometheus_metrics_data) -> None: mock_get_prometheus_metrics_data.side_effect = self.test_exception self.assertRaises(PANICException, self.test_monitor._get_data) @parameterized.expand([ ("self.test_monitor.last_prometheus_source_used",), ]) @freeze_time("2012-01-01") def test_process_error_returns_expected_data(self, last_source_used) -> None: # We will add more parameters to this test as the source types increase expected_output = { 'error': { 'meta_data': { 'monitor_name': self.test_monitor.monitor_name, 'node_name': self.test_monitor.node_config.node_name, 'last_source_used': eval(last_source_used), 'node_id': self.test_monitor.node_config.node_id, 'node_parent_id': self.test_monitor.node_config.parent_id, 'time': datetime(2012, 1, 1).timestamp() }, 'message': self.test_exception.message, 'code': self.test_exception.code, } } actual_output = self.test_monitor._process_error(self.test_exception, eval(last_source_used)) self.assertEqual(actual_output, expected_output) @parameterized.expand([ ("self.processed_prometheus_data_example", "self.retrieved_prometheus_data_example"), ("self.processed_prometheus_data_example_optionals_none", "self.retrieved_prometheus_data_example_optionals_none"), ]) @freeze_time("2012-01-01") def test_process_retrieved_prometheus_data_returns_expected_data( self, expected_data_output, retrieved_data) -> None: expected_output = { 'result': { 'meta_data': { 'monitor_name': self.test_monitor.monitor_name, 'node_name': self.test_monitor.node_config.node_name, 'last_source_used': self.test_monitor.last_prometheus_source_used, 'node_id': self.test_monitor.node_config.node_id, 'node_parent_id': self.test_monitor.node_config.parent_id, 'time': datetime(2012, 1, 1).timestamp() }, 'data': eval(expected_data_output), } } actual_output = self.test_monitor._process_retrieved_prometheus_data( eval(retrieved_data)) self.assertEqual(expected_output, actual_output) def test_process_retrieved_data_returns_the_correct_dict(self) -> None: def test_fn(x: Dict): return x actual_ret = self.test_monitor._process_retrieved_data( test_fn, self.test_data_dict) expected_ret = test_fn(self.test_data_dict) self.assertEqual(expected_ret, actual_ret) def test_send_data_sends_data_correctly(self) -> None: # This test creates a queue which receives messages with the same # routing key as the ones sent by send_data, and checks that the # data is received self.test_monitor._initialise_rabbitmq() # Delete the queue before to avoid messages in the queue on error. self.test_monitor.rabbitmq.queue_delete(self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=RAW_DATA_EXCHANGE, routing_key=CHAINLINK_NODE_RAW_DATA_ROUTING_KEY) self.test_monitor._send_data(self.processed_prometheus_data_example) # By re-declaring the queue again we can get the number of messages # in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) self.assertEqual(1, res.method.message_count) # Check that the message received is actually the processed data _, _, body = self.test_monitor.rabbitmq.basic_get(self.test_queue_name) self.assertEqual(self.processed_prometheus_data_example, json.loads(body)) @freeze_time("2012-01-01") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_sends_data_and_hb_if_data_retrieve_and_processing_success( self, mock_get_data) -> None: # Here we are assuming that all sources are enabled. expected_output_data = { 'prometheus': { 'result': { 'meta_data': { 'monitor_name': self.test_monitor.monitor_name, 'node_name': self.test_monitor.node_config.node_name, 'last_source_used': self.test_monitor.last_prometheus_source_used, 'node_id': self.test_monitor.node_config.node_id, 'node_parent_id': self.test_monitor.node_config.parent_id, 'time': datetime(2012, 1, 1).timestamp() }, 'data': self.processed_prometheus_data_example, } } } expected_output_hb = { 'component_name': self.test_monitor.monitor_name, 'is_alive': True, 'timestamp': datetime(2012, 1, 1).timestamp() } mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled self.test_monitor._initialise_rabbitmq() # Delete the queue before to avoid messages in the queue on error. self.test_monitor.rabbitmq.queue_delete(self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=RAW_DATA_EXCHANGE, routing_key=CHAINLINK_NODE_RAW_DATA_ROUTING_KEY) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=HEALTH_CHECK_EXCHANGE, routing_key=HEARTBEAT_OUTPUT_WORKER_ROUTING_KEY) self.test_monitor._monitor() # By re-declaring the queue again we can get the number of messages # in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) # There must be 2 messages in the queue, the heartbeat and the # processed data self.assertEqual(2, res.method.message_count) # Check that the message received is actually the processed data _, _, body = self.test_monitor.rabbitmq.basic_get( self.test_queue_name) self.assertEqual(expected_output_data, json.loads(body)) # Check that the message received is actually the HB _, _, body = self.test_monitor.rabbitmq.basic_get( self.test_queue_name) self.assertEqual(expected_output_hb, json.loads(body)) @parameterized.expand([ (False, ['prometheus'],) ]) @freeze_time("2012-01-01") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_sends_empty_dict_for_disabled_source( self, monitor_prometheus, disabled_sources, mock_get_data) -> None: # Once more sources are added this test will make more sense. self.test_monitor.node_config._monitor_prometheus = monitor_prometheus expected_output_data = { 'prometheus': { 'result': { 'meta_data': { 'monitor_name': self.test_monitor.monitor_name, 'node_name': self.test_monitor.node_config.node_name, 'last_source_used': self.test_monitor.last_prometheus_source_used, 'node_id': self.test_monitor.node_config.node_id, 'node_parent_id': self.test_monitor.node_config.parent_id, 'time': datetime(2012, 1, 1).timestamp() }, 'data': self.processed_prometheus_data_example, } } } for disabled_source in disabled_sources: expected_output_data[disabled_source] = {} expected_output_hb = { 'component_name': self.test_monitor.monitor_name, 'is_alive': True, 'timestamp': datetime(2012, 1, 1).timestamp() } # We can get all data since that won't effect how _monitor() works mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled self.test_monitor._initialise_rabbitmq() # Delete the queue before to avoid messages in the queue on error. self.test_monitor.rabbitmq.queue_delete(self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=RAW_DATA_EXCHANGE, routing_key=CHAINLINK_NODE_RAW_DATA_ROUTING_KEY) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=HEALTH_CHECK_EXCHANGE, routing_key=HEARTBEAT_OUTPUT_WORKER_ROUTING_KEY) self.test_monitor._monitor() # By re-declaring the queue again we can get the number of messages # in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) # There must be 2 messages in the queue, the heartbeat and the # processed data self.assertEqual(2, res.method.message_count) # Check that the message received is actually the processed data _, _, body = self.test_monitor.rabbitmq.basic_get( self.test_queue_name) self.assertEqual(expected_output_data, json.loads(body)) # Check that the message received is actually the HB _, _, body = self.test_monitor.rabbitmq.basic_get( self.test_queue_name) self.assertEqual(expected_output_hb, json.loads(body)) @parameterized.expand([ (['self.test_exception'],) ]) @mock.patch.object(ChainlinkNodeMonitor, "_process_data") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_sends_no_data_and_hb_if_data_ret_success_and_proc_fails( self, process_data_side_effect, mock_get_data, mock_process_data) -> None: # This test will be expanded further once more sources are added. We # can eventually test for when example the first source is processed # correctly but the second fails. process_data_side_effect_eval = list(map( eval, process_data_side_effect)) mock_process_data.side_effect = process_data_side_effect_eval mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled self.test_monitor._initialise_rabbitmq() # Delete the queue before to avoid messages in the queue on error. self.test_monitor.rabbitmq.queue_delete(self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=RAW_DATA_EXCHANGE, routing_key=CHAINLINK_NODE_RAW_DATA_ROUTING_KEY) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=HEALTH_CHECK_EXCHANGE, routing_key=HEARTBEAT_OUTPUT_WORKER_ROUTING_KEY) self.test_monitor._monitor() # By re-declaring the queue again we can get the number of messages # in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) # There must be 0 messages in the queue. self.assertEqual(0, res.method.message_count) @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_sends_no_data_and_no_hb_on_get_data_unexpected_exception( self, mock_get_data) -> None: mock_get_data.side_effect = self.test_exception self.test_monitor._initialise_rabbitmq() # Delete the queue before to avoid messages in the queue on error. self.test_monitor.rabbitmq.queue_delete(self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=RAW_DATA_EXCHANGE, routing_key=CHAINLINK_NODE_RAW_DATA_ROUTING_KEY) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=HEALTH_CHECK_EXCHANGE, routing_key=HEARTBEAT_OUTPUT_WORKER_ROUTING_KEY) self.assertRaises(PANICException, self.test_monitor._monitor) # By re-declaring the queue again we can get the number of messages # in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) # There must be 0 messages in the queue. self.assertEqual(0, res.method.message_count) @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_raises_msg_not_delivered_exception_if_data_not_routed( self, mock_get_data) -> None: mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled self.test_monitor._initialise_rabbitmq() self.assertRaises(MessageWasNotDeliveredException, self.test_monitor._monitor) @parameterized.expand([ (AMQPConnectionError, AMQPConnectionError('test'),), (AMQPChannelError, AMQPChannelError('test'),), (InvalidUrlException, InvalidUrlException('test'),), (DataReadingException, DataReadingException('test', 'test'),), (Exception, Exception('test'),), ]) @mock.patch.object(ChainlinkNodeMonitor, "_send_data") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_raises_error_if_raised_by_send_data( self, exception_class, exception_instance, mock_get_data, mock_send_data) -> None: mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled mock_send_data.side_effect = exception_instance self.test_monitor._initialise_rabbitmq() self.assertRaises(exception_class, self.test_monitor._monitor) @parameterized.expand([ (AMQPConnectionError, AMQPConnectionError('test'),), (AMQPChannelError, AMQPChannelError('test'),), (MessageWasNotDeliveredException, MessageWasNotDeliveredException('test'),), (Exception, Exception('test'),), ]) @mock.patch.object(ChainlinkNodeMonitor, "_send_data") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_does_not_send_hb_and_data_if_send_data_fails( self, exception_class, exception_instance, mock_get_data, mock_send_data) -> None: mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled mock_send_data.side_effect = exception_instance self.test_monitor._initialise_rabbitmq() self.test_monitor.rabbitmq.queue_delete( self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=HEALTH_CHECK_EXCHANGE, routing_key=HEARTBEAT_OUTPUT_WORKER_ROUTING_KEY) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=RAW_DATA_EXCHANGE, routing_key=CHAINLINK_NODE_RAW_DATA_ROUTING_KEY) try: self.test_monitor._monitor() except exception_class: pass # By re-declaring the queue again we can get the number of # messages in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) # There must be no messages in the queue. self.assertEqual(0, res.method.message_count) @freeze_time("2012-01-01") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_raises_msg_not_del_except_if_hb_not_routed_and_sends_data( self, mock_get_data) -> None: mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled expected_output_data = { 'prometheus': { 'result': { 'meta_data': { 'monitor_name': self.test_monitor.monitor_name, 'node_name': self.test_monitor.node_config.node_name, 'last_source_used': self.test_monitor.last_prometheus_source_used, 'node_id': self.test_monitor.node_config.node_id, 'node_parent_id': self.test_monitor.node_config.parent_id, 'time': datetime(2012, 1, 1).timestamp() }, 'data': self.processed_prometheus_data_example, } } } self.test_monitor._initialise_rabbitmq() self.test_monitor.rabbitmq.queue_delete(self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=RAW_DATA_EXCHANGE, routing_key=CHAINLINK_NODE_RAW_DATA_ROUTING_KEY) self.assertRaises(MessageWasNotDeliveredException, self.test_monitor._monitor) # By re-declaring the queue again we can get the number of # messages in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) # There must be 1 message in the queue, the processed data self.assertEqual(1, res.method.message_count) # Check that the message received is actually the processed data _, _, body = self.test_monitor.rabbitmq.basic_get( self.test_queue_name) self.assertEqual(expected_output_data, json.loads(body)) @parameterized.expand([ (AMQPConnectionError, AMQPConnectionError('test'),), (AMQPChannelError, AMQPChannelError('test'),), (Exception, Exception('test'),), ]) @freeze_time("2012-01-01") @mock.patch.object(ChainlinkNodeMonitor, "_send_heartbeat") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_raises_error_if_raised_by_send_hb_and_sends_data( self, exception_class, exception_instance, mock_get_data, mock_send_hb) -> None: mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled expected_output_data = { 'prometheus': { 'result': { 'meta_data': { 'monitor_name': self.test_monitor.monitor_name, 'node_name': self.test_monitor.node_config.node_name, 'last_source_used': self.test_monitor.last_prometheus_source_used, 'node_id': self.test_monitor.node_config.node_id, 'node_parent_id': self.test_monitor.node_config.parent_id, 'time': datetime(2012, 1, 1).timestamp() }, 'data': self.processed_prometheus_data_example, } } } mock_send_hb.side_effect = exception_instance self.test_monitor._initialise_rabbitmq() self.test_monitor.rabbitmq.queue_delete(self.test_queue_name) res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=False ) self.assertEqual(0, res.method.message_count) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=HEALTH_CHECK_EXCHANGE, routing_key=HEARTBEAT_OUTPUT_WORKER_ROUTING_KEY) self.test_monitor.rabbitmq.queue_bind( queue=self.test_queue_name, exchange=RAW_DATA_EXCHANGE, routing_key=CHAINLINK_NODE_RAW_DATA_ROUTING_KEY) self.assertRaises(exception_class, self.test_monitor._monitor) # By re-declaring the queue again we can get the number of # messages in the queue. res = self.test_monitor.rabbitmq.queue_declare( queue=self.test_queue_name, durable=True, exclusive=False, auto_delete=False, passive=True ) # There must be 1 message in the queue, the processed data self.assertEqual(1, res.method.message_count) # Check that the message received is actually the processed data _, _, body = self.test_monitor.rabbitmq.basic_get( self.test_queue_name) self.assertEqual(expected_output_data, json.loads(body)) @mock.patch.object(logging.Logger, "info") @mock.patch.object(ChainlinkNodeMonitor, "_send_heartbeat") @mock.patch.object(ChainlinkNodeMonitor, "_send_data") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_logs_data_if_all_sources_enabled_and_no_retrieval_error( self, mock_get_data, mock_send_data, mock_send_hb, mock_log) -> None: mock_send_data.return_value = None mock_send_hb.return_value = None mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled self.test_monitor._monitor() mock_log.assert_called_with(self.test_monitor._display_data( self.processed_prometheus_data_example)) @mock.patch.object(logging.Logger, "info") @mock.patch.object(ChainlinkNodeMonitor, "_send_heartbeat") @mock.patch.object(ChainlinkNodeMonitor, "_send_data") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_does_not_log_if_no_retrieval_performed( self, mock_get_data, mock_send_data, mock_send_hb, mock_log) -> None: # This needs to be updated as we increase the number of sources mock_send_data.return_value = None mock_send_hb.return_value = None mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled self.test_monitor.node_config._monitor_prometheus = False processed_data = dict(ChainMap( *[self.processed_prometheus_data_example])) self.test_monitor._monitor() assert_not_called_with(mock_log, self.test_monitor._display_data(processed_data)) @mock.patch.object(logging.Logger, "info") @mock.patch.object(ChainlinkNodeMonitor, "_send_heartbeat") @mock.patch.object(ChainlinkNodeMonitor, "_send_data") @mock.patch.object(ChainlinkNodeMonitor, "_get_data") def test_monitor_does_not_log_if_retrieval_error( self, mock_get_data, mock_send_data, mock_send_hb, mock_log) -> None: # This needs to be updated as we increase the number of sources mock_send_data.return_value = None mock_send_hb.return_value = None mock_get_data.return_value = \ self.received_retrieval_info_all_source_types_enabled_err processed_data = {} self.test_monitor._monitor() assert_not_called_with(mock_log, self.test_monitor._display_data(processed_data)) # TODO: When more sources are added we need to test for when some sources # : are enabled and some disabled

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0

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null

0

6

f2989a63525352cb9639a86eb6d55e031e5791e9

2,935

py

Python

tests/test_projects.py

geraxe/dolib

2728db044a65b0bba15e7bfbc633d24a21b955d0

[ "MIT" ]

5

2020-05-30T05:20:06.000Z

2021-05-21T21:42:34.000Z

tests/test_projects.py

geraxe/dolib

2728db044a65b0bba15e7bfbc633d24a21b955d0

[ "MIT" ]

17

2020-05-30T08:17:10.000Z

2021-06-20T13:26:37.000Z

tests/test_projects.py

geraxe/dolib

2728db044a65b0bba15e7bfbc633d24a21b955d0

[ "MIT" ]

3

2020-05-30T05:28:08.000Z

2021-04-10T17:07:02.000Z

import pytest from dolib.client import AsyncClient, Client from dolib.models import Project @pytest.mark.vcr @pytest.mark.block_network() def test_crud_projects(client: Client) -> None: project = Project( name="dolib-test", description="Python library for digital ocean API", purpose="For test purposes", environment="Development", ) # create project created_project = client.projects.create(project) assert isinstance(created_project, Project) assert created_project.id is not None # list projects projects = client.projects.all() assert len(projects) > 0 # read project read_project = client.projects.get(str(projects[0].id)) assert read_project.id == projects[0].id assert isinstance(read_project, Project) # update project read_project.is_default = False read_project.name = "dolib-test-renamed" updated_project = client.projects.update(read_project) assert isinstance(updated_project, Project) assert read_project.name == updated_project.name # assign resource volume = client.volumes.all()[-1] client.projects.assign_resources( str(read_project.id), [Project.Resource(urn=f"do:volume:{volume.id}")], ) # list resources resources = client.projects.resources(str(read_project.id)) assert len(resources) > 0 # delete project client.projects.delete(project=read_project) @pytest.mark.vcr @pytest.mark.block_network() @pytest.mark.asyncio async def test_async_crud_projects(async_client: AsyncClient) -> None: project = Project( name="dolib-test", description="Python library for digital ocean API", purpose="For test purposes", environment="Development", ) # create project created_project = await async_client.projects.create(project) assert isinstance(created_project, Project) assert created_project.id is not None # list projects projects = await async_client.projects.all() assert len(projects) > 0 # read project read_project = await async_client.projects.get(str(projects[0].id)) assert read_project.id == projects[0].id assert isinstance(read_project, Project) # update project read_project.is_default = False read_project.name = "dolib-test-renamed" updated_project = await async_client.projects.update(read_project) assert isinstance(updated_project, Project) assert read_project.name == updated_project.name # assign resource volume = (await async_client.volumes.all())[-1] await async_client.projects.assign_resources( str(read_project.id), [Project.Resource(urn=f"do:volume:{volume.id}")], ) # list resources resources = await async_client.projects.resources(str(read_project.id)) assert len(resources) > 0 # delete project await async_client.projects.delete(project=read_project)

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null

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6

f2bf6f5f6f1b38f6e7f456740dadb32b5459f9e4

144

py

Python

ionyweb/plugin_app/plugin_text/admin.py

makinacorpus/ionyweb

2f18e3dc1fdc86c7e19bae3778e67e28a37567be

[ "BSD-3-Clause" ]

4

2015-09-28T10:07:39.000Z

2019-10-18T20:14:07.000Z

ionyweb/plugin_app/plugin_text/admin.py

makinacorpus/ionyweb

2f18e3dc1fdc86c7e19bae3778e67e28a37567be

[ "BSD-3-Clause" ]

1

2021-03-19T21:41:33.000Z

ionyweb/plugin_app/plugin_text/admin.py

makinacorpus/ionyweb

2f18e3dc1fdc86c7e19bae3778e67e28a37567be

[ "BSD-3-Clause" ]

1

2017-10-12T09:25:19.000Z

# -*- coding: utf-8 -*- from django.contrib import admin from ionyweb.plugin_app.plugin_text.models import * admin.site.register(Plugin_Text)

20.571429

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0

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1

0

1

0

6

4b4d6f551efd56925bdd292d5e3dd4563b2f53fb

246

py

Python

app/models/__init__.py

cmcunningham27/python-newsfeed

3eef42d693a41807d36946af47ff7e6442185c5c

[ "MIT" ]

null

app/models/__init__.py

cmcunningham27/python-newsfeed

3eef42d693a41807d36946af47ff7e6442185c5c

[ "MIT" ]

null

app/models/__init__.py

cmcunningham27/python-newsfeed

3eef42d693a41807d36946af47ff7e6442185c5c

[ "MIT" ]

null

# syncs User model with the database from .User import User # syncs Post model with the database from .Post import Post # sync Comment model with the database from .Comment import Comment # sync Vote model with the database from .Vote import Vote

30.75

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0.792683

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4.875

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0.246154

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0.492308

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246

8

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null

0

1

0

1

0

1

0

6

4b5a6b62ebb8551e0e1bd3c5bbdc61fe31bac4f5

57

py

Python

ahvl/options/set/__init__.py

gardar/ahvl

045b5882d94fc2d4ba7b194bf65ebfbf9d2e1d6d

[ "MIT" ]

4

2019-10-12T12:11:23.000Z

2021-12-20T13:53:28.000Z

ahvl/options/set/__init__.py

gardar/ahvl

045b5882d94fc2d4ba7b194bf65ebfbf9d2e1d6d

[ "MIT" ]

2

2021-02-05T12:52:55.000Z

2022-02-11T10:58:52.000Z

ahvl/options/set/__init__.py

gardar/ahvl

045b5882d94fc2d4ba7b194bf65ebfbf9d2e1d6d

[ "MIT" ]

1

2020-08-13T07:52:27.000Z

from ahvl.options.set.password import OptionsSetPassword

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7

57

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57

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57

0.943396

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0

1

0

null

0

1

0

1

0

6

4bc25cf81cee74db5d88de84d3613e690fbdf2a2

40

py

Python

rdm/wrappers/wordification/__init__.py

Alshak/rdm

0c969665a4a3c8e6258c3d603de8987bd9639fd1

[ "MIT" ]

null

rdm/wrappers/wordification/__init__.py

Alshak/rdm

0c969665a4a3c8e6258c3d603de8987bd9639fd1

[ "MIT" ]

null

rdm/wrappers/wordification/__init__.py

Alshak/rdm

0c969665a4a3c8e6258c3d603de8987bd9639fd1

[ "MIT" ]

1

2020-02-29T17:40:32.000Z

from wordification import Wordification

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6

4bc6239870be59dbed26ba81b2a0f52c2c74b8d6

4,824

py

Python

bot.py

AviationTools/Weather-Bot

44f946cef7d15428e2c4da8219ad609c56292727

[ "MIT" ]

null

bot.py

AviationTools/Weather-Bot

44f946cef7d15428e2c4da8219ad609c56292727

[ "MIT" ]

null

bot.py

AviationTools/Weather-Bot

44f946cef7d15428e2c4da8219ad609c56292727

[ "MIT" ]

null

import os import discord import requests import datetime from PIL import Image from dotenv import load_dotenv load_dotenv() TOKEN = os.getenv("DISCORD_TOKEN") client = discord.Client() @client.event async def on_message(message): if message.author == client.user: return if message.content == "!weather": print("handling !weather") utc = datetime.datetime.utcnow() plain_date = utc.strftime("%y%m%d") link_date = utc.strftime("%Y/%m/%d") times = ["0000", "0600", "1200", "1800"] last_url = None async with message.channel.typing(): for time in times: response = requests.get(f"https://www.zamg.ac.at/fix/wetter/bodenkarte/{link_date}/BK_BodAna_Sat_{plain_date}{time}.png") if response.status_code == 200: last_url = f"https://www.zamg.ac.at/fix/wetter/bodenkarte/{link_date}/BK_BodAna_Sat_{plain_date}{time}.png" elif response.status_code == 404: print("not found weather map for ", time) break if last_url: print("found weather map ", last_url) await message.channel.send(last_url) else: await message.channel.send("Could not fetch last weather map.") if message.content == "!satellite": images = [] utc = datetime.datetime.utcnow() plain_date = utc.strftime("%y%m%d") print(plain_date) times = ["0000", "0030", "0100", "0130", "0200", "0230", "0300", "0330", "0400", "0430", "0500", "0530", "0600", "0630", "0700", "0730", "0800", "0830", "0900", "0930", "1000", "1030", "1100", "1130", "1200", "1200", "1230", "1300", "1330", "1400", "1430", "1500", "1530", "1600", "1630", "01700", "1730", "1800", "1830", "1900", "1930", "2000", "2030", "2100", "2130", "2200", "2230", "2300", "2330", "2400"] async with message.channel.typing(): for time in times: response = requests.get(f"https://www.zamg.ac.at/dyn/pictures/Hsatimg/H{plain_date}{time}.gif", stream=True) if response.status_code == 200: img = Image.open(response.raw) images.append(img) elif response.status_code == 404: print("not found weather map for ", time) images[0].save('satellite.gif', save_all=True, append_images=images[1:], loop=0, duration=200) await message.channel.send(file=discord.File('satellite.gif')) os.remove("satellite.gif") if message.content == "!world": images = [] utc = datetime.datetime.utcnow() plain_date = utc.strftime("%Y%m%d") print(plain_date) times = ["0000", "0030", "0100", "0130", "0200", "0230", "0300", "0330", "0400", "0430", "0500", "0530", "0600", "0630", "0700", "0730", "0800", "0830", "0900", "0930", "1000", "1030", "1100", "1130", "1200", "1200", "1230", "1300", "1330", "1400", "1430", "1500", "1530", "1600", "1630", "01700", "1730", "1800", "1830", "1900", "1930", "2000", "2030", "2100", "2130", "2200", "2230", "2300", "2330", "2400"] async with message.channel.typing(): for time in times: response = requests.get(f"https://www.zamg.ac.at/zamgWeb/wetter/weltsatbilder/worldsatimg/WCM/SAT_WCM_{plain_date}{time}.gif", stream=True) if response.status_code == 200: img = Image.open(response.raw) images.append(img) elif response.status_code == 404: print("not found weather map for ", time) images[0].save('world.gif', save_all=True, append_images=images[1:], loop=0, duration=200) await message.channel.send(file=discord.File('world.gif')) os.remove("world.gif") if message.content == "!sigwx": print("handling !weather") utc = datetime.datetime.utcnow() plain_date = utc.strftime("%y%m%d") link_date = utc.strftime("%Y/%m/%d") times = ["0000", "0600", "1200", "1800"] last_url = None async with message.channel.typing(): for time in times: response = requests.get(f"http://brunnur.vedur.is/flugkort/PGDE14_EGRR_{time}.PNG") if response.status_code == 200: last_url = f"http://brunnur.vedur.is/flugkort/PGDE14_EGRR_{time}.PNG" elif response.status_code == 404: print("not found weather map for ", time) break if last_url: print("found weather map ", last_url) await message.channel.send(last_url) else: await message.channel.send("Could not fetch last weather map.") print("starting weather bot") client.run(TOKEN)

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null

0

1

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null

0

6

29d22f41a246483051d3caf02752368e644d0615

174

py

Python

hello.py

vanfalen/simple-pi-oled

b3534c795f7cda4fb67a6b844c311eedf3ba8525

[ "Apache-2.0" ]

null

hello.py

vanfalen/simple-pi-oled

b3534c795f7cda4fb67a6b844c311eedf3ba8525

[ "Apache-2.0" ]

null

hello.py

vanfalen/simple-pi-oled

b3534c795f7cda4fb67a6b844c311eedf3ba8525

[ "Apache-2.0" ]

null

from flask import Flask from flask import request app = Flask(__name__) @app.route("/terminal",methods=['POST']) def hello(): return "Hello, %s"%(request.form["user"])

19.333333

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0.258621

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0.132184

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null

0

1

0

6

4b066366a9e2c33bf2584337f289d21864090178

117

py

Python

rest_api/controller/__init__.py

ArzelaAscoIi/haystack

be8f50c9e3de4e264b3f345f5f4b9c9ec518ed08

[ "Apache-2.0" ]

4,544

2019-11-14T11:57:49.000Z

2022-03-31T17:41:18.000Z

rest_api/controller/__init__.py

ArzelaAscoIi/haystack

be8f50c9e3de4e264b3f345f5f4b9c9ec518ed08

[ "Apache-2.0" ]

1,679

2020-01-14T15:55:58.000Z

2022-03-31T20:55:25.000Z

rest_api/controller/__init__.py

ArzelaAscoIi/haystack

be8f50c9e3de4e264b3f345f5f4b9c9ec518ed08

[ "Apache-2.0" ]

820

2019-11-27T13:01:42.000Z

2022-03-31T12:54:34.000Z

from rest_api.pipeline import custom_component # this import is required for the Custom Components to be registered

58.5

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6

d9a86d35609ccc5e41e5471a6be676f5d69ff793

5,262

py

Python

Keras/1-Flowers/PhotoHandler.py

SSRMori/Tensorflow-Selflearning

931a015c81b955ab68a7042490a728db1cd29ff2

[ "MIT" ]

1

2019-07-06T02:11:00.000Z

Keras/1-Flowers/PhotoHandler.py

SSRMori/Tensorflow-Selflearning

931a015c81b955ab68a7042490a728db1cd29ff2

[ "MIT" ]

null

Keras/1-Flowers/PhotoHandler.py

SSRMori/Tensorflow-Selflearning

931a015c81b955ab68a7042490a728db1cd29ff2

[ "MIT" ]

null

from __future__ import print_function import tensorflow as tf import numpy as np import os import math from PIL import Image data_top_path = "./flowers/" flower_labels = os.listdir(data_top_path) label_size = len(flower_labels) jpg_width = 100 jpg_height = 100 def read_jpg_files(file_path): origin_jpg_file = Image.open(file_path) resized_jpg_file = origin_jpg_file.resize((jpg_width, jpg_height)) return resized_jpg_file def read_files_from_fold(fold_path): file_list = [] jpg_name_list = [] for file_name in os.listdir(fold_path): if file_name[-4:] != ".jpg": continue jpg_file_name = str(fold_path + "/" + file_name) jpg_name_list.append(jpg_file_name) file_list.append(read_jpg_files(jpg_file_name)) return (jpg_name_list, file_list) def get_feature_vector(image): ans_list = [] for i in range(0, jpg_width): for j in range(0, jpg_height): temp_rgba_info = image.getpixel((i, j)) for k in range(0, len(temp_rgba_info)): ans_list.append(temp_rgba_info[k]) return np.array(ans_list) def read_one_class(class_label_number): class_name = flower_labels[class_label_number] ans_matrix_list = [] image_file_name_list, image_file_list = read_files_from_fold(str(data_top_path + class_name)) for i in range(0, len(image_file_name_list)): ans_matrix_list.append(get_feature_vector(image_file_list[i])) print(str(flower_labels[class_label_number] + " loaded")) np_ans_matrix_list = np.array(ans_matrix_list) np_labels = np.ones((np_ans_matrix_list.shape[0], 1)) * class_label_number return (np_ans_matrix_list, np_labels) def load_data(): test_list = [] label_list = [] for i in range(0, len(flower_labels)): temp_test, temp_label = read_one_class(i) for j in range(0, len(temp_test)): test_list.append(temp_test[j]) label_list.append(temp_label[j]) return (np.array(test_list), np.array(label_list)) def shuffle(test_list, label_list): state = np.random.get_state() np.random.shuffle(test_list) np.random.set_state(state) np.random.shuffle(label_list) return (test_list, label_list) def get_training_set_and_test_set(): origin_data_set, origin_label_set = load_data() data_set, label_set = shuffle(origin_data_set, origin_label_set) divide_line = math.floor(4 * data_set.shape[0] / 5) x_train = data_set[:divide_line] y_train = label_set[:divide_line] x_test = data_set[divide_line:] y_test = label_set[divide_line:] return ((x_train, y_train), (x_test, y_test)) CNN_jpg_width = 100 CNN_jpg_height = 100 # def read_CNN_jpg_files(file_path): # origin_jpg_file = Image.open(file_path) # resized_jpg_file = origin_jpg_file.resize((CNN_jpg_width, CNN_jpg_height)) # return resized_jpg_file def read_CNN_jpg_files(file_path): origin_jpg_file = Image.open(file_path) # resized_jpg_file = origin_jpg_file.resize((CNN_jpg_width, CNN_jpg_height)) return origin_jpg_file# resized_jpg_file def read_CNN_files_from_fold(fold_path): file_list = [] jpg_name_list = [] for file_name in os.listdir(fold_path): if file_name[-4:] != ".jpg": continue jpg_file_name = str(fold_path + "/" + file_name) tempFile = read_CNN_jpg_files(jpg_file_name) if tempFile.shape[0] > 100 and tempFile.shape[1] > 100: jpg_name_list.append(jpg_file_name) file_list.append(tempFile) return (jpg_name_list, file_list) def get_CNN_feature_vector(image): np_ans = np.zeros((CNN_jpg_width, CNN_jpg_height, 3)) for i in range(0, CNN_jpg_width): for j in range(0, CNN_jpg_height): temp_rgba_info = image.getpixel((i, j)) for k in range(0, 3): np_ans[i][j][k] = temp_rgba_info[k] return np_ans def read_CNN_one_class(class_label_number): class_name = flower_labels[class_label_number] ans_matrix_list = [] image_file_name_list, image_file_list = read_CNN_files_from_fold(str(data_top_path + class_name)) for i in range(0, len(image_file_name_list)): ans_matrix_list.append(get_CNN_feature_vector(image_file_list[i])) print(str(flower_labels[class_label_number] + " loaded")) np_ans_matrix_list = np.array(ans_matrix_list) np_labels = np.ones((np_ans_matrix_list.shape[0], 1)) * class_label_number return (np_ans_matrix_list, np_labels) def CNN_load_data(): test_list = [] label_list = [] for i in range(0, len(flower_labels)): temp_test, temp_label = read_CNN_one_class(i) for j in range(0, len(temp_test)): test_list.append(temp_test[j]) label_list.append(temp_label[j]) return (np.array(test_list), np.array(label_list)) def get_CNN_training_set_and_test_set(): origin_data_set, origin_label_set = CNN_load_data() data_set, label_set = shuffle(origin_data_set, origin_label_set) divide_line = math.floor(4 * data_set.shape[0] / 5) x_train = data_set[:divide_line] y_train = label_set[:divide_line] x_test = data_set[divide_line:] y_test = label_set[divide_line:] return ((x_train, y_train), (x_test, y_test))

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null

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6

d9d5b91a01311b857fef40f3ab59bffc826a0995

17,948

py

Python

polaris/polaris/tests/sep6/test_withdraw.py

lijamie98/django-polaris

5cdda7434281988deb761b34f574dfcaf7ae9f5d

[ "Apache-2.0" ]

null

polaris/polaris/tests/sep6/test_withdraw.py

lijamie98/django-polaris

5cdda7434281988deb761b34f574dfcaf7ae9f5d

[ "Apache-2.0" ]

null

polaris/polaris/tests/sep6/test_withdraw.py

lijamie98/django-polaris

5cdda7434281988deb761b34f574dfcaf7ae9f5d

[ "Apache-2.0" ]

null

import pytest import json from typing import Dict from unittest.mock import patch, Mock from stellar_sdk.keypair import Keypair from rest_framework.request import Request from polaris.tests.conftest import USD_DISTRIBUTION_SEED from polaris.tests.helpers import ( mock_check_auth_success, mock_check_auth_success_client_domain, ) from polaris.integrations import WithdrawalIntegration from polaris.models import Transaction, Asset from polaris.sep10.token import SEP10Token WITHDRAW_PATH = "/sep6/withdraw" class GoodWithdrawalIntegration(WithdrawalIntegration): def process_sep6_request( self, token: SEP10Token, request: Request, params: Dict, transaction: Transaction, *args, **kwargs ) -> Dict: if params.get("type") == "bad type": raise ValueError() transaction.save() return {"extra_info": {"test": "test"}} @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi", GoodWithdrawalIntegration()) @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_good_withdrawal_integration(client, usd_asset_factory): asset = Asset.objects.create( code="USD", issuer=Keypair.random().public_key, sep6_enabled=True, withdrawal_enabled=True, withdrawal_min_amount=10, withdrawal_max_amount=1000, distribution_seed=Keypair.random().secret, ) response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "type": "bank_account", "dest": "test bank account number", }, ) content = response.json() assert response.status_code == 200 assert content.pop("memo") assert content.pop("memo_type") == Transaction.MEMO_TYPES.hash assert content == { "id": str(Transaction.objects.first().id), "account_id": asset.distribution_account, "min_amount": round(asset.withdrawal_min_amount, asset.significant_decimals), "max_amount": round(asset.withdrawal_max_amount, asset.significant_decimals), "fee_fixed": round(asset.withdrawal_fee_fixed, asset.significant_decimals), "fee_percent": asset.withdrawal_fee_percent, "extra_info": {"test": "test"}, } @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi.process_sep6_request") @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdrawal_success_no_min_max_amounts(mock_process_sep6_request, client): asset = Asset.objects.create( code="USD", issuer=Keypair.random().public_key, sep6_enabled=True, withdrawal_enabled=True, distribution_seed=Keypair.random().secret, ) mock_process_sep6_request.return_value = { "extra_info": {"test": "test"}, } response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "type": "bank_account", "dest": "test bank account number", }, ) mock_process_sep6_request.assert_called_once() assert Transaction.objects.count() == 1 assert response.status_code == 200 content = response.json() assert content.pop("memo") assert content.pop("memo_type") == Transaction.MEMO_TYPES.hash assert content == { "id": str(Transaction.objects.first().id), "account_id": asset.distribution_account, "extra_info": {"test": "test"}, "fee_fixed": round(asset.deposit_fee_fixed, asset.significant_decimals), "fee_percent": asset.deposit_fee_percent, } @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi.process_sep6_request") @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdrawal_success_custom_min_max_amounts(mock_process_sep6_request, client): asset = Asset.objects.create( code="USD", issuer=Keypair.random().public_key, sep6_enabled=True, withdrawal_enabled=True, withdrawal_min_amount=10, withdrawal_max_amount=1000, distribution_seed=Keypair.random().secret, ) mock_process_sep6_request.return_value = { "extra_info": {"test": "test"}, "min_amount": 1000, "max_amount": 10000, } response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "type": "bank_account", "dest": "test bank account number", }, ) mock_process_sep6_request.assert_called_once() assert Transaction.objects.count() == 1 content = response.json() assert response.status_code == 200, content assert content.pop("memo") assert content.pop("memo_type") == Transaction.MEMO_TYPES.hash assert content == { "id": str(Transaction.objects.first().id), "account_id": asset.distribution_account, "min_amount": 1000, "max_amount": 10000, "extra_info": {"test": "test"}, "fee_fixed": round(asset.deposit_fee_fixed, asset.significant_decimals), "fee_percent": asset.deposit_fee_percent, } @pytest.mark.django_db @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_bad_memo_type(client, acc1_usd_withdrawal_transaction_factory): withdraw = acc1_usd_withdrawal_transaction_factory( protocol=Transaction.PROTOCOL.sep6 ) asset = withdraw.asset response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "type": "good type", "dest": "test", "memo_type": "none", }, ) content = json.loads(response.content) assert response.status_code == 400 assert content == {"error": "invalid 'memo_type'"} @pytest.mark.django_db @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_bad_memo(client, acc1_usd_withdrawal_transaction_factory): withdraw = acc1_usd_withdrawal_transaction_factory( protocol=Transaction.PROTOCOL.sep6 ) asset = withdraw.asset response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "type": "good type", "dest": "test", "memo_type": "id", "memo": "not an id", }, ) content = json.loads(response.content) assert response.status_code == 400 assert content == {"error": "invalid 'memo' for 'memo_type'"} @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi", GoodWithdrawalIntegration()) @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_bad_type(client, acc1_usd_withdrawal_transaction_factory): withdraw = acc1_usd_withdrawal_transaction_factory( protocol=Transaction.PROTOCOL.sep6 ) response = client.get( WITHDRAW_PATH, { "asset_code": withdraw.asset.code, "type": "bad type", "dest": "test bank account number", }, ) content = json.loads(response.content) assert response.status_code == 400 assert "error" in content class MissingHowDepositIntegration(WithdrawalIntegration): def process_sep6_request( self, token: SEP10Token, request: Request, params: Dict, transaction: Transaction, *args, **kwargs ) -> Dict: return {} @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi", MissingHowDepositIntegration()) @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_empty_integration_response(client, usd_asset_factory): asset = usd_asset_factory(protocols=[Transaction.PROTOCOL.sep6]) response = client.get( WITHDRAW_PATH, {"asset_code": asset.code, "type": "good type", "dest": "test"}, ) content = json.loads(response.content) assert response.status_code == 200 assert content.pop("memo") assert content.pop("memo_type") == Transaction.MEMO_TYPES.hash assert content == { "id": str(Transaction.objects.first().id), "account_id": Keypair.from_secret(USD_DISTRIBUTION_SEED).public_key, "min_amount": round(asset.withdrawal_min_amount, asset.significant_decimals), "max_amount": round(asset.withdrawal_max_amount, asset.significant_decimals), "fee_fixed": round(asset.withdrawal_fee_fixed, asset.significant_decimals), "fee_percent": asset.withdrawal_fee_percent, } class BadExtraInfoWithdrawalIntegration(WithdrawalIntegration): def process_sep6_request( self, token: SEP10Token, request: Request, params: Dict, transaction: Transaction, *args, **kwargs ) -> Dict: return {"extra_info": "not a dict"} @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi", BadExtraInfoWithdrawalIntegration()) @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_bad_extra_info_integration( client, acc1_usd_withdrawal_transaction_factory ): withdraw = acc1_usd_withdrawal_transaction_factory( protocol=Transaction.PROTOCOL.sep6 ) response = client.get( WITHDRAW_PATH, {"asset_code": withdraw.asset.code, "type": "good type", "dest": "test"}, ) content = json.loads(response.content) assert response.status_code == 500 assert content == {"error": "unable to process the request"} @pytest.mark.django_db @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_missing_asset(client, acc1_usd_withdrawal_transaction_factory): acc1_usd_withdrawal_transaction_factory(protocol=Transaction.PROTOCOL.sep6) response = client.get(WITHDRAW_PATH, {"type": "good type", "dest": "test"}) content = json.loads(response.content) assert response.status_code == 400 assert content == {"error": "invalid 'asset_code'"} @pytest.mark.django_db @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_invalid_asset(client): response = client.get( WITHDRAW_PATH, {"asset_code": "USD", "type": "good type", "dest": "test"} ) content = json.loads(response.content) assert response.status_code == 400 assert content == {"error": "invalid 'asset_code'"} @pytest.mark.django_db @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_missing_type(client, acc1_usd_withdrawal_transaction_factory): withdraw = acc1_usd_withdrawal_transaction_factory( protocol=Transaction.PROTOCOL.sep6 ) response = client.get( WITHDRAW_PATH, {"asset_code": withdraw.asset.code, "dest": "test"} ) content = json.loads(response.content) assert response.status_code == 400 assert content == {"error": "'type' is required"} @pytest.mark.django_db @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_missing_dest(client, acc1_usd_withdrawal_transaction_factory): withdraw = acc1_usd_withdrawal_transaction_factory( protocol=Transaction.PROTOCOL.sep6 ) response = client.get( WITHDRAW_PATH, {"asset_code": withdraw.asset.code, "type": "good type"} ) content = json.loads(response.content) assert response.status_code == 400 assert content == {"error": "'dest' is required"} @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi", GoodWithdrawalIntegration()) @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdrawal_transaction_created( client, acc1_usd_withdrawal_transaction_factory ): withdraw = acc1_usd_withdrawal_transaction_factory( protocol=Transaction.PROTOCOL.sep6 ) distribution_address = Keypair.from_secret(USD_DISTRIBUTION_SEED).public_key response = client.get( WITHDRAW_PATH, { "asset_code": withdraw.asset.code, "type": "good type", "dest": "test", "amount": "100", }, ) assert response.status_code == 200 t = ( Transaction.objects.filter(kind=Transaction.KIND.withdrawal) .order_by("-started_at") .first() ) assert t assert t.memo_type == Transaction.MEMO_TYPES.hash assert t.receiving_anchor_account == distribution_address assert t.stellar_account == "test source address" assert t.amount_in == 100 assert t.amount_expected == 100 assert t.asset == withdraw.asset assert t.kind == Transaction.KIND.withdrawal assert t.status == Transaction.STATUS.pending_user_transfer_start assert t.protocol == Transaction.PROTOCOL.sep6 class GoodInfoNeededWithdrawalIntegration(WithdrawalIntegration): def process_sep6_request( self, token: SEP10Token, request: Request, params: Dict, transaction: Transaction, *args, **kwargs ) -> Dict: return { "type": "non_interactive_customer_info_needed", "fields": ["first_name", "last_name"], } @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi", GoodInfoNeededWithdrawalIntegration()) @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_withdraw_non_interactive_customer_info_needed( client, acc1_usd_withdrawal_transaction_factory ): withdraw = acc1_usd_withdrawal_transaction_factory( protocol=Transaction.PROTOCOL.sep6 ) response = client.get( WITHDRAW_PATH, {"asset_code": withdraw.asset.code, "type": "good type", "dest": "test"}, ) content = json.loads(response.content) assert response.status_code == 403 assert content == { "type": "non_interactive_customer_info_needed", "fields": ["first_name", "last_name"], } @pytest.mark.django_db def test_deposit_bad_auth(client): response = client.get(WITHDRAW_PATH, {}) content = json.loads(response.content) assert response.status_code == 403 assert content == {"type": "authentication_required"} class BadSaveWithdrawalIntegration(WithdrawalIntegration): def process_sep6_request( self, token: SEP10Token, request: Request, params: Dict, transaction: Transaction, *args, **kwargs ) -> Dict: transaction.save() return { "type": "non_interactive_customer_info_needed", "fields": ["first_name", "last_name"], } @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi", BadSaveWithdrawalIntegration()) @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_saved_transaction_on_failure_response(client, usd_asset_factory): asset = usd_asset_factory(protocols=[Transaction.PROTOCOL.sep6]) response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "type": "bank_account", "dest": "test bank account number", }, ) assert response.status_code == 500 @pytest.mark.django_db @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_bad_amount(client, usd_asset_factory): asset = usd_asset_factory(protocols=[Transaction.PROTOCOL.sep6]) response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "account": Keypair.random().public_key, "type": "good type", "amount": "not an amount", "dest": "test bank account number", }, ) assert response.status_code == 400 assert "amount" in json.loads(response.content)["error"] @pytest.mark.django_db @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_amount_too_large(client, usd_asset_factory): asset = usd_asset_factory(protocols=[Transaction.PROTOCOL.sep6]) response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "account": Keypair.random().public_key, "type": "good type", "dest": "test bank account number", "amount": asset.deposit_max_amount + 1, }, ) assert response.status_code == 400 assert "amount" in json.loads(response.content)["error"] @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi") @patch("polaris.sep10.utils.check_auth", mock_check_auth_success) def test_good_amount(mock_deposit, client, usd_asset_factory): asset = usd_asset_factory(protocols=[Transaction.PROTOCOL.sep6]) mock_deposit.process_sep6_request = Mock(return_value={"how": "test"}) response = client.get( WITHDRAW_PATH, { "asset_code": asset.code, "account": Keypair.random().public_key, "type": "good type", "dest": "test bank account number", "amount": asset.deposit_max_amount - 1, }, ) assert response.status_code == 200 kwargs = mock_deposit.process_sep6_request.call_args_list[0][1] assert kwargs.get("params", {}).get("amount") == asset.deposit_max_amount - 1 @pytest.mark.django_db @patch("polaris.sep6.withdraw.rwi") @patch("polaris.sep10.utils.check_auth", mock_check_auth_success_client_domain) def test_withdraw_client_domain_saved(mock_withdraw, client): kp = Keypair.random() usd = Asset.objects.create( code="USD", issuer=Keypair.random().public_key, sep6_enabled=True, withdrawal_enabled=True, distribution_seed=Keypair.random().secret, ) mock_withdraw.process_sep6_request = Mock(return_value={"how": "test"}) response = client.get( WITHDRAW_PATH, { "asset_code": usd.code, "account": kp.public_key, "type": "good type", "dest": "test bank account number", }, ) content = response.json() assert response.status_code == 200, json.dumps(content, indent=2) assert Transaction.objects.count() == 1 transaction = Transaction.objects.first() assert transaction.client_domain == "test.com"

33.800377

87

0.673836

2,023

17,948

5.708354

0.088482

0.031174

0.02364

0.03637

0.85045

0.836941

0.815899

0.810097

0.791739

0.779702

0

0.015865

0.209828

17,948

530

88

33.864151

0.798406

0

0.655462

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0.148875

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1

0.052521

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0.023109

0.006303

0.096639

0

null

0

1

0

null

0

6

d9e41d15c261b7b3a2fc68883869e9ef0cae31dc

26

py

Python

network_scanner_module/dns_scan.py

ikoyfman/Network---Tooling

5654b818feabc82c9a58bd2aecbec7436a5fc764

[ "MIT" ]

null

network_scanner_module/dns_scan.py

ikoyfman/Network---Tooling

5654b818feabc82c9a58bd2aecbec7436a5fc764

[ "MIT" ]

null

network_scanner_module/dns_scan.py

ikoyfman/Network---Tooling

5654b818feabc82c9a58bd2aecbec7436a5fc764

[ "MIT" ]

null

#FUTURE PORTION import dns

13

15

0.846154

4

26

5.5

1

0

0.115385

26

2

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13

0.956522

0.538462

0

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null

0

1

0

1

0

1

0

6

d9ec9c40e632c92d6734c77b36854b807c433979

12,832

py

Python

dataio.py

sudo-michael/deepreach

a8affc4cc53b7671fda54dc159129315ec6b7ca8

[ "MIT" ]

null

dataio.py

sudo-michael/deepreach

a8affc4cc53b7671fda54dc159129315ec6b7ca8

[ "MIT" ]

null

dataio.py

sudo-michael/deepreach

a8affc4cc53b7671fda54dc159129315ec6b7ca8

[ "MIT" ]

null

import csv import glob import math import os from unicodedata import normalize import matplotlib.colors as colors import numpy as np import scipy.io as spio import torch from torch.utils.data import Dataset from torchvision.transforms import Resize, Compose, ToTensor, Normalize import utils import pickle def get_mgrid(sidelen, dim=2): """Generates a flattened grid of (x,y,...) coordinates in a range of -1 to 1.""" if isinstance(sidelen, int): sidelen = dim * (sidelen,) # (sidelen, sidelen, ...) if dim == 2: pixel_coords = np.stack(np.mgrid[: sidelen[0], : sidelen[1]], axis=-1)[ None, ... ].astype(np.float32) pixel_coords[0, :, :, 0] = pixel_coords[0, :, :, 0] / (sidelen[0] - 1) pixel_coords[0, :, :, 1] = pixel_coords[0, :, :, 1] / (sidelen[1] - 1) elif dim == 3: pixel_coords = np.stack( np.mgrid[: sidelen[0], : sidelen[1], : sidelen[2]], axis=-1 )[None, ...].astype(np.float32) pixel_coords[..., 0] = pixel_coords[..., 0] / max(sidelen[0] - 1, 1) pixel_coords[..., 1] = pixel_coords[..., 1] / (sidelen[1] - 1) pixel_coords[..., 2] = pixel_coords[..., 2] / (sidelen[2] - 1) else: raise NotImplementedError("Not implemented for dim=%d" % dim) pixel_coords -= 0.5 pixel_coords *= 2.0 pixel_coords = torch.Tensor(pixel_coords).view(-1, dim) return pixel_coords def to_uint8(x): return (255.0 * x).astype(np.uint8) def to_numpy(x): return x.detach().cpu().numpy() def gaussian(x, mu=[0, 0], sigma=1e-4, d=2): x = x.numpy() if isinstance(mu, torch.Tensor): mu = mu.numpy() q = -0.5 * ((x - mu) ** 2).sum(1) return torch.from_numpy( 1 / np.sqrt(sigma ** d * (2 * np.pi) ** d) * np.exp(q / sigma) ).float() class ReachabilityMultiVehicleCollisionSourceNE(Dataset): def __init__( self, numpoints, collisionR=0.25, velocity=0.6, omega_max=1.1, pretrain=False, tMin=0.0, tMax=0.5, counter_start=0, counter_end=100e3, numEvaders=1, pretrain_iters=2000, angle_alpha=1.0, time_alpha=1.0, num_src_samples=1000, ): super().__init__() torch.manual_seed(0) self.pretrain = pretrain self.numpoints = numpoints self.velocity = velocity self.omega_max = omega_max self.collisionR = collisionR self.alpha_angle = angle_alpha * math.pi self.alpha_time = time_alpha self.numEvaders = numEvaders self.num_states_per_vehicle = 3 self.num_states = self.num_states_per_vehicle * (numEvaders + 1) self.num_pos_states = 2 * (numEvaders + 1) # The state sequence will be as follows # [x-y position of vehicle 1, x-y position of vehicle 2, ...., x-y position of vehicle N, heading of vehicle 1, heading of vehicle 2, ...., heading of vehicle N] self.tMin = tMin self.tMax = tMax self.N_src_samples = num_src_samples self.pretrain_counter = 0 self.counter = counter_start self.pretrain_iters = pretrain_iters self.full_count = counter_end def __len__(self): return 1 def __getitem__(self, idx): start_time = 0.0 # time to apply initial conditions # uniformly sample domain and include coordinates where source is non-zero coords = torch.zeros(self.numpoints, self.num_states).uniform_(-1, 1) if self.pretrain: # only sample in time around the initial condition # time = torch.zeros(self.numpoints, 1).uniform_(start_time - 0.001, start_time + 0.001) time = torch.ones(self.numpoints, 1) * start_time coords = torch.cat((time, coords), dim=1) else: # slowly grow time values from start time # this currently assumes start_time = tMin and max time value is tMax time = self.tMin + torch.zeros(self.numpoints, 1).uniform_( 0, (self.tMax - self.tMin) * (self.counter / self.full_count) ) coords = torch.cat((time, coords), dim=1) # make sure we always have training samples at the initial time coords[-self.N_src_samples :, 0] = start_time # set up the initial value function # Collision cost between the pursuer and the evaders boundary_values = ( torch.norm(coords[:, 1:3] - coords[:, 3:5], dim=1, keepdim=True) - self.collisionR ) for i in range(1, self.numEvaders): boundary_values_current = ( torch.norm( coords[:, 1:3] - coords[:, 2 * (i + 1) + 1 : 2 * (i + 1) + 3], dim=1, keepdim=True, ) - self.collisionR ) boundary_values = torch.min(boundary_values, boundary_values_current) # Collision cost between the evaders themselves for i in range(self.numEvaders): for j in range(i + 1, self.numEvaders): evader1_coords_index = 1 + (i + 1) * 2 evader2_coords_index = 1 + (j + 1) * 2 boundary_values_current = ( torch.norm( coords[:, evader1_coords_index : evader1_coords_index + 2] - coords[:, evader2_coords_index : evader2_coords_index + 2], dim=1, keepdim=True, ) - self.collisionR ) boundary_values = torch.min(boundary_values, boundary_values_current) # normalize the value function norm_to = 0.02 mean = 0.25 var = 0.5 boundary_values = (boundary_values - mean) * norm_to / var if self.pretrain: dirichlet_mask = torch.ones(coords.shape[0], 1) > 0 else: # only enforce initial conditions around start_time dirichlet_mask = coords[:, 0, None] == start_time if self.pretrain: self.pretrain_counter += 1 elif self.counter < self.full_count: self.counter += 1 if self.pretrain and self.pretrain_counter == self.pretrain_iters: self.pretrain = False return ( {"coords": coords}, { "source_boundary_values": boundary_values, "dirichlet_mask": dirichlet_mask, }, ) class ReachabilityAir3DSource(Dataset): def __init__( self, numpoints, collisionR=0.25, velocity=0.6, omega_max=1.1, pretrain=False, tMin=0.0, tMax=0.5, counter_start=0, counter_end=100e3, pretrain_iters=2000, angle_alpha=1.0, num_src_samples=1000, seed=0, ): super().__init__() torch.manual_seed(0) self.pretrain = pretrain self.numpoints = numpoints self.velocity = velocity self.omega_max = omega_max self.collisionR = collisionR self.alpha_angle = angle_alpha * math.pi self.num_states = 3 self.tMax = tMax self.tMin = tMin self.N_src_samples = num_src_samples self.pretrain_counter = 0 self.counter = counter_start self.pretrain_iters = pretrain_iters self.full_count = counter_end # Set the seed torch.manual_seed(seed) def __len__(self): return 1 def __getitem__(self, idx): start_time = 0.0 # time to apply initial conditions # uniformly sample domain and include coordinates where source is non-zero coords = torch.zeros(self.numpoints, self.num_states).uniform_(-1, 1) if self.pretrain: # only sample in time around the initial condition time = torch.ones(self.numpoints, 1) * start_time coords = torch.cat((time, coords), dim=1) else: # slowly grow time values from start time # this currently assumes start_time = 0 and max time value is tMax time = self.tMin + torch.zeros(self.numpoints, 1).uniform_( 0, (self.tMax - self.tMin) * (self.counter / self.full_count) ) coords = torch.cat((time, coords), dim=1) # make sure we always have training samples at the initial time coords[-self.N_src_samples :, 0] = start_time # set up the initial value function boundary_values = ( torch.norm(coords[:, 1:3], dim=1, keepdim=True) - self.collisionR ) # normalize the value function norm_to = 0.02 mean = 0.25 var = 0.5 boundary_values = (boundary_values - mean) * norm_to / var if self.pretrain: dirichlet_mask = torch.ones(coords.shape[0], 1) > 0 else: # only enforce initial conditions around start_time dirichlet_mask = coords[:, 0, None] == start_time if self.pretrain: self.pretrain_counter += 1 elif self.counter < self.full_count: self.counter += 1 if self.pretrain and self.pretrain_counter == self.pretrain_iters: self.pretrain = False return ( {"coords": coords}, { "source_boundary_values": boundary_values, "dirichlet_mask": dirichlet_mask, }, ) class ReachabilityAir3DSource(Dataset): def __init__( self, numpoints, collisionR=0.25, velocity=0.6, omega_max=1.1, pretrain=False, tMin=0.0, tMax=0.5, counter_start=0, counter_end=100e3, pretrain_iters=2000, angle_alpha=1.0, num_src_samples=1000, seed=0, ): super().__init__() torch.manual_seed(0) self.pretrain = pretrain self.numpoints = numpoints self.velocity = velocity self.omega_max = omega_max self.collisionR = collisionR self.alpha_angle = angle_alpha * math.pi self.num_states = 3 self.tMax = tMax self.tMin = tMin self.N_src_samples = num_src_samples self.pretrain_counter = 0 self.counter = counter_start self.pretrain_iters = pretrain_iters self.full_count = counter_end # Set the seed torch.manual_seed(seed) def __len__(self): return 1 def __getitem__(self, idx): start_time = 0.0 # time to apply initial conditions # uniformly sample domain and include coordinates where source is non-zero coords = torch.zeros(self.numpoints, self.num_states).uniform_(-1, 1) if self.pretrain: # only sample in time around the initial condition time = torch.ones(self.numpoints, 1) * start_time # cat t with coords # numpoints x ([t x y relative_heading]) coords = torch.cat((time, coords), dim=1) else: # create samples of t from 0 to (t_max - t_min) / count # this currently assumes start_time = 0 and max time value is tMax time = self.tMin + torch.zeros(self.numpoints, 1).uniform_( 0, (self.tMax - self.tMin) * (self.counter / self.full_count) ) coords = torch.cat((time, coords), dim=1) # ensure training samples at the initial time coords[-self.N_src_samples :, 0] = start_time # set up the initial value function # sqrt(x^2 + y^2) - R boundary_values = ( # coords[:, 0] is t # coords[:, 1:3] is x and y torch.norm(coords[:, 1:3], dim=1, keepdim=True) - self.collisionR ) # normalize the value function norm_to = 0.02 mean = 0.25 var = 0.5 boundary_values = (boundary_values - mean) * norm_to / var if self.pretrain: # all true dirichlet_mask = torch.ones(coords.shape[0], 1) > 0 else: # only enforce initial conditions around start_time dirichlet_mask = coords[:, 0, None] == start_time if self.pretrain: self.pretrain_counter += 1 elif self.counter < self.full_count: self.counter += 1 if self.pretrain and self.pretrain_counter == self.pretrain_iters: self.pretrain = False return ( {"coords": coords}, { "source_boundary_values": boundary_values, "dirichlet_mask": dirichlet_mask, }, )

31.221411

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0.56515

1,559

12,832

4.481719

0.130212

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0.03206

0.772435

0.757693

0.733505

0.719479

0.714613

0.704308

0

0.034341

0.3351

12,832

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0.784576

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0

0.713311

0

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1

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false

0

0.044369

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0

null

0

1

0

1

0

null

0

6

8a258f9c0b65813f0d1f31d7bcfe33d60776054a

121

py

Python

django_uicomponents/settings.py

koenwoortman/django-uicomponents

833dd219ebbbdaa7dc2b41730d5f21afa55641f1

[ "MIT" ]

3

2021-05-22T10:45:51.000Z

2021-08-12T14:40:45.000Z

django_uicomponents/settings.py

koenwoortman/django-uicomponents

833dd219ebbbdaa7dc2b41730d5f21afa55641f1

[ "MIT" ]

null

django_uicomponents/settings.py

koenwoortman/django-uicomponents

833dd219ebbbdaa7dc2b41730d5f21afa55641f1

[ "MIT" ]

null

from django.conf import settings if not hasattr(settings, 'COMPONENTS_DIR'): settings.COMPONENTS_DIR = 'components'

24.2

43

0.77686

15

121

6.133333

0.666667

0.391304

0.456522

0

0.132231

121

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0

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0

1

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true

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0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

8a3d4ba2ae42ed05c83016768ef91833512499da

21,899

py

Python

app/models.py

JeremyKimotho/blogs

f0fc056032534dc93f1ac725b60c5ae46d9407ea

[ "MIT" ]

null

app/models.py

JeremyKimotho/blogs

f0fc056032534dc93f1ac725b60c5ae46d9407ea

[ "MIT" ]

null

app/models.py

JeremyKimotho/blogs

f0fc056032534dc93f1ac725b60c5ae46d9407ea

[ "MIT" ]

null

from . import db from werkzeug.security import generate_password_hash, check_password_hash from flask_login import UserMixin from datetime import datetime from . import login_manager ACCESS = { 'user': 0, 'admin': 1 } @login_manager.user_loader def load_user(user_id): return User.query.get(int(user_id)) class User(UserMixin, db.Model): __tablename__='users' id = db.Column(db.Integer, primary_key = True) username = db.Column(db.String(255)) email = db.Column(db.String(255)) joined=db.Column(db.DateTime,default=datetime.now) first_name = db.Column(db.String(255)) surname = db.Column(db.String(255)) pass_secure = db.Column(db.String(255)) access=db.Column(db.String(255), default=ACCESS['user']) comments = db.relationship('Comments', backref='user', lazy='dynamic') @property def password(self): raise AttributeError('You do not have the permissions to access this') @password.setter def password(self, password): self.pass_secure = generate_password_hash(password) def verify_password(self, password): return check_password_hash(self.pass_secure, password) def save_user(self): db.session.add(self) db.session.commit() def find_by_username(username): user = User.query.filter_by(username=username).first() return user def is_admin(self): return self.access == ACCESS['admin'] def allowed(self, access_level): return self.access >= access_level def init_db(): if User.query.count() == 0: master = User(username='master', password='master', first_name='Jeremy', surname='Kimotho', email='projectsjeremy1000@gmail.com', access=ACCESS['admin']) db.session.add(master) db.session.commit() def __repr__(self): return f'User {self.username}' class Comments(db.Model): __tablename__='comments' id = db.Column(db.Integer, primary_key = True) comment = db.Column(db.String) user_id = db.Column(db.Integer, db.ForeignKey('users.id')) posted = db.Column(db.DateTime,default=datetime.now) post = db.Column(db.Integer, db.ForeignKey('posts.id')) def save_comment(self): db.session.add(self) db.session.commit() def delete_comments(self): db.session.delete(self) db.session.commit() @classmethod def get_comments(cls, id): comments = Comments.query.filter_by(posted=id).all() return comments class Post(db.Model): __tablename__='posts' id = db.Column(db.Integer, primary_key = True) title = db.Column(db.String(255)) body = db.Column(db.String) posted = db.Column(db.DateTime,default=datetime.utcnow) comments = db.relationship('Comments', backref='post_comments', lazy='dynamic') def save_post(self): db.session.add(self) db.session.commit() def delete_post(self): db.session.delete(self) db.session.commit() def get_specific_post(id): post = Post.query.filter_by(id=id).first() return post @classmethod def get_posts(cls): posts = Post.query.all() return posts def get_comments(self): post = Post.query.filter_by(id = self.id).first() comments = Comments.query.filter_by(post=post.id) return comments def default_posts(): if Post.query.count() == 0: post1=Post(title='Let\'s talk Behavioral Psychology?', body="Let's define behavioral psychology. Behavioral psychology is the study of the connection between our minds and our behavior. Sometimes you will hear behavioral psychology referred to as behaviorism. The researchers and scientists who study behavioral psychology are trying to understand why we behave the way we do and they are concerned with discovering patterns in our actions and behaviors. The hope is that if we can use behavioral psychology to help us predict how humans will behave, we can build better habits as individuals, create better products as companies, and develop better living spaces as communities.") post2=Post(title='Motivation?', body="So what is motivation, exactly? The author Steven Pressfield has a great line in his book, The War of Art, which I think gets at the core of motivation. To paraphrase Pressfield, 'At some point, the pain of not doing it becomes greater than the pain of doing it.' In other words, at some point, it is easier to change than to stay the same. It is easier to take action and feel insecure at the gym than to sit still and experience self-loathing on the couch. It is easier to feel awkward while making the sales call than to feel disappointed about your dwindling bank account. This, I think, is the essence of motivation. Every choice has a price, but when we are motivated, it is easier to bear the inconvenience of action than the pain of remaining the same. Somehow we cross a mental threshold—usually after weeks of procrastination and in the face of an impending deadline—and it becomes more painful to not do the work than to actually do it.") post3=Post(title='What is Procrastination?', body="Human beings have been procrastinating for centuries. The problem is so timeless, in fact, that ancient Greek philosophers like Socrates and Aristotle developed a word to describe this type of behavior: Akrasia. Akrasia is the state of acting against your better judgment. It is when you do one thing even though you know you should do something else. Loosely translated, you could say that akrasia is procrastination or a lack of self-control.' Ok, definitions are great and all, but why do we procrastinate? What is going on in the brain that causes us to avoid the things we know we should be doing? This is a good time to bring some science into our discussion. Behavioral psychology research has revealed a phenomenon called “time inconsistency,” which helps explain why procrastination seems to pull us in despite our good intentions. Time inconsistency refers to the tendency of the human brain to value immediate rewards more highly than future rewards. The best way to understand this is by imagining that you have two selves: your Present Self and your Future Self. When you set goals for yourself — like losing weight or writing a book or learning a language — you are actually making plans for your Future Self. You are envisioning what you want your life to be like in the future. Researchers have found that when you think about your Future Self, it is quite easy for your brain to see the value in taking actions with long-term benefits. The Future Self values long-term rewards. However, while the Future Self can set goals, only the Present Self can take action. When the time comes to make a decision, you are no longer making a choice for your Future Self. Now you are in the present moment, and your brain is thinking about the Present Self. Researchers have discovered that the Present Self really likes instant gratification, not long-term payoff. So, the Present Self and the Future Self are often at odds with one another. The Future Self wants to be trim and fit, but the Present Self wants a donut. Sure, everyone knows you should eat healthy today to avoid being overweight in 10 years. But consequences like an increased risk for diabetes or heart failure are years away. Similarly, many young people know that saving for retirement in their 20s and 30s is crucial, but the benefit of doing so is decades off. It is far easier for the Present Self to see the value in buying a new pair of shoes than in socking away $100 for 70-year-old you. (If you're curious, there are some very good evolutionary reasons for why our brain values immediate rewards more highly than long-term rewards.) This is one reason why you might go to bed feeling motivated to make a change in your life, but when you wake up you find yourself falling back into old patterns. Your brain values long-term benefits when they are in the future (tomorrow), but it values immediate gratification when it comes to the present moment (today).") post4=Post(title='Creativity', body="The creative process is the act of making new connections between old ideas or recognizing relationships between concepts. Creative thinking is not about generating something new from a blank slate, but rather about taking what is already present and combining those bits and pieces in a way that has not been done previously. While being creative isn't easy, nearly all great ideas follow a similar creative process. In 1940, an advertising executive named James Webb Young published a short guide titled, A Technique for Producing Ideas. Young believed the process of creative connection always occurred in five steps. The Creative Process. Step 1: Gather new material. At first, you learn. During this stage you focus on 1) learning specific material directly related to your task and 2) learning general material by becoming fascinated with a wide range of concepts. Step 2: Thoroughly work over the materials in your mind. During this stage, you examine what you have learned by looking at the facts from different angles and experimenting with fitting various ideas together. Step 3: Step away from the problem. Next, you put the problem completely out of your mind and go do something else that excites you and energizes you. Step 4: Let your idea return to you. At some point, but only after you have stopped thinking about it, your idea will come back to you with a flash of insight and renewed energy. Step 5: Shape and develop your idea based on feedback. For any idea to succeed, you must release it out into the world, submit it to criticism, and adapt it as needed. While we often think of creativity as an event or as a natural skill that some people have and some don't, research actually suggests that both creativity and non-creativity are learned. According to psychology professor Barbara Kerr, “approximately 22 percent of the variance [in creativity] is due to the influence of genes.” This discovery was made by studying the differences in creative thinking between sets of twins. All of this to say, claiming that “I'm just not the creative type” is a pretty weak excuse for avoiding creative thinking. Certainly, some people are primed to be more creative than others. However, nearly every person is born with some level of creative skill and the majority of our creative thinking abilities are trainable.") post5=Post(title='How to Make Smart Decisions and Avoid Bad Ones', body="Decision making is just what it sounds like: the action or process of making decisions. Sometimes we make logical decisions, but there are many times when we make emotional, irrational, and confusing choices. This page covers why we make poor decisions and discusses useful frameworks to expand your decision-making toolbox. I like to think of myself as a rational person, but I’m not one. The good news is it’s not just me — or you. We are all irrational. For a long time, researchers and economists believed that humans made logical, well-considered decisions. In recent decades, however, researchers have uncovered a wide range of mental errors that derail our thinking. 5 Common Mental Errors That Sway You From Making Good Decisions: Let's talk about the mental errors that show up most frequently in our lives and break them down in easy-to-understand language. This blog outlines how survivorship bias, loss aversion, the availability heuristic, anchoring, and confirmation bias sway you from making good decisions. How to Spot a Common Mental Error That Leads to Misguided Thinking: Hundreds of psychology studies have proven that we tend to overestimate the importance of events we can easily recall and underestimate the importance of events we have trouble recalling. Psychologists refer to this little brain mistake as an “illusory correlation.” In this article, we talk about a simple strategy you can use to spot your hidden assumptions and prevent yourself from making an illusory correlation. Two Harvard Professors Reveal One Reason Our Brains Love to Procrastinate: We have a tendency to care too much about our present selves and not enough about our future selves. If you want to beat procrastination and make better long-term choices, then you have to find a way to make your present self act in the best interest of your future self. This article breaks down three simple ways to do just that. How to Use Mental Models for Smart Decision Making The smartest way to improve your decision making skills is to learn mental models. A mental model is a framework or theory that helps to explain why the world works the way it does. Each mental model is a concept that helps us make sense of the world and offers a way of looking at the problems of life.") post6=Post(title='Be more productive everyday', body="Let's define productivity. Productivity is a measure of efficiency of a person completing a task. We often assume that productivity means getting more things done each day. Wrong. Productivity is getting important things done consistently. And no matter what you are working on, there are only a few things that are truly important. Being productive is about maintaining a steady, average speed on a few things, not maximum speed on everything. Before we talk about how to get started, I wanted to let you know I researched and compiled science-backed ways to stick to good habits and stop procrastinating. My Top Productivity Strategies One: Eliminate Time Wasting Activities by Using the Eisenhower Box: This simple decision matrix will help you take action, organize tasks, and get more done. The great thing about this matrix is that it can be used for broad productivity plans (“How should I spend my time each week?”) and for smaller, daily plans (“What should I do today?”). Two: Warren Buffett’s “2 List” Strategy: How to Maximize Your Focus and Master Your Priorities: This method comes from the famous investor Warren Buffett and uses a simple 3-step productivity strategy to help you determine your priorities and actions. You may find this method useful for making decisions and getting yourself to commit to doing one thing right away. Three: The Ivy Lee Method: The Daily Routine Experts Recommend for Peak Productivity: This productivity strategy is straightforward: Do the most important thing first each day. The Ivy Lee Method is a dead simple way to implement this strategy. Four: The 15-Minute Routine Anthony Trollope Used to Write 40+ Books: There is one common problem with the approach of ranking your priorities and doing the most important thing first, though. After ranking your priorities for the day, if the number one task is a really big project then it can leave you feeling frustrated because it takes a long time to finish. Writer Anthony Trollope, however, developed a solution to this common problem. Most productivity strategies focus on short-term efficiency: how to manage your to-do list effectively, how to get more done each morning, how to shorten your weekly meetings, and so on. These are all reasonable ideas. We often fail to realize, however, that there are certain strategic choices we need to make if we want to maximize our productivity for the long-term. In these articles below, I break down some ideas about long-term productivity. Here Are More Simple Ways to Be More Productive Every Day: Step 1: Manage your energy, not your time. If you take a moment to think about it, you’ll probably realize that you are better at doing certain tasks at certain times. What type of energy do you have in the morning? Afternoon? Evening? Determine what tasks each energy level and time of day are best suited for. Step 2: Prepare the night before. If you only do one thing each day then spend a few minutes each night organizing your to–do list for tomorrow. When I do it right, I’ll outline the article I’m going to write the next day and develop a short list of the most important items for me to accomplish. It takes 10 minutes that night and saves 3 hours the next day. Step 3: Don’t open email until noon. Sounds simple. Nobody does it. It took me awhile to get over the urge to open my inbox, but eventually I realized that everything can wait a few hours. Nobody is going to email you about a true emergency (a death in the family, etc.), so leave your email alone for the first few hours of each day. Use the morning to do what’s important rather than responding to what is “urgent.” Step 4: Turn your phone off and leave it in another room. Or on your colleague's desk. Or at the very least, put it somewhere that is out of sight. This eliminates the urge to check text messages, Facebook, Twitter, and so on. This simple strategy eliminates the likelihood of slipping into half–work where you waste time dividing your attention among meaningless tasks. Step 5: Work in a cool place. Have you ever noticed how you feel groggy and sluggish in a hot room? Turning the temperature down or moving to a cooler place is an easy way to focus your mind and body. (Hat tip to Michael Hyatt for this one.) Step 6: Sit up or stand up. When you sit hunched over, your chest is in a collapsed position and your diaphragm is pressing against the bottom of your lungs, which hinders your ability to breathe easily and deeply. Sit up straight or stand up and you’ll find that you can breathe easier and more fully. As a result, your brain will get more oxygen and you’ll be able to concentrate better. Step 7: Develop a “pre–game routine” to start your day. My morning routine starts by pouring a cold glass of water. Some people kick off their day with ten minutes of meditation. Similarly, you should have a sequence that starts your morning ritual. This tiny routine signals to your brain that it’s time to get into work mode or exercise mode or whatever mode you need to be in to accomplish your task. Additionally, a pre–game routine helps you overcome a lack of motivation and get things done even when you don’t feel like it.") post7=Post(title='Continous Improvement', body="Let's define continuous improvement. Continuous improvement is a dedication to making small changes and improvements every day, with the expectation that those small improvements will add up to something significant. The typical approach to self-improvement is to set a large goal, then try to take big leaps in order to accomplish the goal in as little time as possible. While this may sound good in theory, it often ends in burnout, frustration, and failure. Instead, we should focus on continuous improvement by slowly and slightly adjusting our normal everyday habits and behaviors. It is so easy to dismiss the value of making slightly better decisions on a daily basis. Sticking with the fundamentals is not impressive. Falling in love with boredom is not sexy. Getting one percent better isn't going to make headlines. There is one thing about it though: it works. How Does Continuous Improvement Work? So often we convince ourselves that change is only meaningful if there is some large, visible outcome associated with it. Whether it is losing weight, building a business, traveling the world or any other goal, we often put pressure on ourselves to make some earth-shattering improvement that everyone will talk about. Meanwhile, improving by just 1 percent isn't notable (and sometimes it isn't even noticeable). But it can be just as meaningful, especially in the long run. In the beginning, there is basically no difference between making a choice that is 1 percent better or 1 percent worse. (In other words, it won't impact you very much today.) But as time goes on, these small improvements or declines compound and you suddenly find a very big gap between people who make slightly better decisions on a daily basis and those who don't. Here's the punchline: If you get one percent better each day for one year, you'll end up thirty-seven times better by the time you’re done. This is why small choices don't make much of a difference at the time, but add up over the long-term.") post8=Post(title='We\'re talking about practise', body="Deliberate practice refers to a special type of practice that is purposeful and systematic. While regular practice might include mindless repetitions, deliberate practice requires focused attention and is conducted with the specific goal of improving performance.Can You Achieve Anything With Enough Practice?Deliberate practice does not mean that you can fashion yourself into anything with enough work and effort, though. While human beings do possess a remarkable ability to develop their skills, there are limits to how far any individual can go. Your genes set a boundary around what is possible. However, while genetics influence performance, they do not determine performance. Do not confuse destiny with opportunity. Genes provide opportunity. They do not determine our destiny. It’s similar to a game of cards. You have a better opportunity if you are dealt a better hand, but you also need to play the hand well to win. Regardless of where we choose to apply ourselves, deliberate practice can help us maximize our potential—no matter what cards we were dealt. It turns potential into reality. Read The Myth and Magic of Deliberate Practice for more on genetics, practice, and how to maximize your genetic potential in life. Examples of Deliberate Practice: Joe DiMaggio was one of the greatest hitters in baseball history. I recently heard a little-known story about how DiMaggio developed his exceptional ability. In some circles, golfer Ben Hogan is credited with “inventing practice.” Hogan methodically broke the game of golf down into chunks and figured out how he could master each section. Today, experts have a new term for his rigorous style of improvement.") db.session.add(post1) db.session.add(post2) db.session.add(post3) db.session.add(post4) db.session.add(post5) db.session.add(post6) db.session.add(post7) db.session.add(post8) db.session.commit()

157.546763

5,231

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3,673

21,899

4.661312

0.280969

0.011039

0.009929

0.008411

0.066819

0.038199

0.02862

0.017581

0.007359

0.00514

0

0.004891

0.16914

21,899

139

5,232

157.546763

0.935532

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1

0.074074

0.808539

0.001279

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1

0.175926

false

0.083333

0.064815

0.046296

0.564815

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1

0

null

0

1

0

1

0

6

8a50c79907852fccf1b106fa066ff0c1f6430154

9,187

py

Python

UnityEngine/Collider/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

UnityEngine/Collider/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

UnityEngine/Collider/__init__.py

Grim-es/udon-pie-auto-completion

c2cd86554ed615cdbbb01e19fa40665eafdfaedc

[ "MIT" ]

null

from typing import overload from UdonPie import System from UdonPie import UnityEngine from UdonPie.Undefined import * class Collider: def __new__(cls, arg1=None): ''' :returns: Collider :rtype: UnityEngine.Collider ''' pass @staticmethod def op_Implicit(arg1): ''' :param arg1: Object :type arg1: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def op_Equality(arg1, arg2): ''' :param arg1: Object :type arg1: UnityEngine.Object :param arg2: Object :type arg2: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def op_Inequality(arg1, arg2): ''' :param arg1: Object :type arg1: UnityEngine.Object :param arg2: Object :type arg2: UnityEngine.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def get_enabled(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def set_enabled(arg1): ''' :param arg1: Boolean :type arg1: System.Boolean or bool ''' pass @staticmethod def get_attachedRigidbody(): ''' :returns: Rigidbody :rtype: UnityEngine.Rigidbody ''' pass @staticmethod def get_isTrigger(): ''' :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def set_isTrigger(arg1): ''' :param arg1: Boolean :type arg1: System.Boolean or bool ''' pass @staticmethod def get_contactOffset(): ''' :returns: Single :rtype: System.Single ''' pass @staticmethod def set_contactOffset(arg1): ''' :param arg1: Single :type arg1: System.Single or float ''' pass @staticmethod def ClosestPoint(arg1): ''' :param arg1: Vector3 :type arg1: UnityEngine.Vector3 :returns: Vector3 :rtype: UnityEngine.Vector3 ''' pass @staticmethod def get_bounds(): ''' :returns: Bounds :rtype: UnityEngine.Bounds ''' pass @staticmethod def get_sharedMaterial(): ''' :returns: PhysicMaterial :rtype: UnityEngine.PhysicMaterial ''' pass @staticmethod def set_sharedMaterial(arg1): ''' :param arg1: PhysicMaterial :type arg1: UnityEngine.PhysicMaterial ''' pass @staticmethod def get_material(): ''' :returns: PhysicMaterial :rtype: UnityEngine.PhysicMaterial ''' pass @staticmethod def set_material(arg1): ''' :param arg1: PhysicMaterial :type arg1: UnityEngine.PhysicMaterial ''' pass @staticmethod def Raycast(arg1, arg2, arg3): ''' :param arg1: Ray :type arg1: UnityEngine.Ray :param arg2: Undefined variable :type arg2: RaycastHitRef.RaycastHitRef :param arg3: Single :type arg3: System.Single or float :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def ClosestPointOnBounds(arg1): ''' :param arg1: Vector3 :type arg1: UnityEngine.Vector3 :returns: Vector3 :rtype: UnityEngine.Vector3 ''' pass @staticmethod def get_transform(): ''' :returns: Transform :rtype: UnityEngine.Transform ''' pass @staticmethod def get_gameObject(): ''' :returns: GameObject :rtype: UnityEngine.GameObject ''' pass @staticmethod @overload def GetComponent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod @overload def GetComponent(arg1): ''' :param arg1: String :type arg1: System.String or str :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponent(arg1=None): pass @staticmethod @overload def GetComponentInChildren(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod @overload def GetComponentInChildren(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponentInChildren(arg1=None, arg2=None): pass @staticmethod @overload def GetComponentsInChildren(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInChildren(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInChildren(arg1, arg2): ''' :param arg1: Boolean :type arg1: System.Boolean or bool :param arg2: Undefined variable :type arg2: ListT.ListT ''' pass @staticmethod @overload def GetComponentsInChildren(arg1): ''' :param arg1: Undefined variable :type arg1: ListT.ListT ''' pass @staticmethod def GetComponentsInChildren(arg1=None, arg2=None): pass @staticmethod @overload def GetComponentInParent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: Component :rtype: UnityEngine.Component ''' pass @staticmethod def GetComponentInParent(arg1=None): pass @staticmethod @overload def GetComponentsInParent(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Boolean :type arg2: System.Boolean or bool :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInParent(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponentsInParent(arg1, arg2): ''' :param arg1: Boolean :type arg1: System.Boolean or bool :param arg2: Undefined variable :type arg2: ListT.ListT ''' pass @staticmethod def GetComponentsInParent(arg1=None, arg2=None): pass @staticmethod @overload def GetComponents(arg1): ''' :param arg1: Type :type arg1: System.Type :returns: ComponentArray :rtype: UnityEngine.ComponentArray ''' pass @staticmethod @overload def GetComponents(arg1, arg2): ''' :param arg1: Type :type arg1: System.Type :param arg2: Undefined variable :type arg2: SystemCollectionsGenericList.SystemCollectionsGenericList ''' pass @staticmethod @overload def GetComponents(arg1): ''' :param arg1: Undefined variable :type arg1: ListT.ListT ''' pass @staticmethod def GetComponents(arg1=None, arg2=None): pass @staticmethod def GetInstanceID(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def GetHashCode(): ''' :returns: Int32 :rtype: System.Int32 ''' pass @staticmethod def Equals(arg1): ''' :param arg1: Object :type arg1: System.Object :returns: Boolean :rtype: System.Boolean ''' pass @staticmethod def get_name(): ''' :returns: String :rtype: System.String ''' pass @staticmethod def set_name(arg1): ''' :param arg1: String :type arg1: System.String or str ''' pass @staticmethod def ToString(): ''' :returns: String :rtype: System.String ''' pass @staticmethod def GetType(): ''' :returns: Type :rtype: System.Type ''' pass

20.691441

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762

9,187

6.437008

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0.127829

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0.602039

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0

0.024416

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9,187

443

78

20.738149

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1

0

null

0

1

0

1

0

6

8a5aa53a45f3630def4820dcdef3fb71478123ba

28

py

Python

test.py

ror-bot/ror-records

3ed9b35afe6b49e7415d5609b7bf7106370808c8

[ "MIT" ]

null

test.py

ror-bot/ror-records

3ed9b35afe6b49e7415d5609b7bf7106370808c8

[ "MIT" ]

null

test.py

ror-bot/ror-records

3ed9b35afe6b49e7415d5609b7bf7106370808c8

[ "MIT" ]

null

print("hi from the script")

14

27

0.714286

5

28

4

1

0

0.142857

28

1

28

0.833333

0

0.642857

0

1

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true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

8ac814b7464d4c9e36a7f4f27861aefded46b663

7,889

py

Python

test/test_processor_qa.py

skiran252/FARM

8460d78910a20d19a5da12de6e9bff11f68332a7

[ "Apache-2.0" ]

1,551

2019-07-17T18:21:08.000Z

2022-03-24T18:09:07.000Z

test/test_processor_qa.py

skiran252/FARM

8460d78910a20d19a5da12de6e9bff11f68332a7

[ "Apache-2.0" ]

555

2019-07-23T09:00:54.000Z

2022-03-31T15:31:06.000Z

test/test_processor_qa.py

skiran252/FARM

8460d78910a20d19a5da12de6e9bff11f68332a7

[ "Apache-2.0" ]

259

2019-07-22T08:12:01.000Z

2022-03-26T09:41:00.000Z

import logging import json from farm.data_handler.processor import SquadProcessor from farm.modeling.tokenization import Tokenizer # during inference (parameter return_baskets = False) we do not convert labels def test_dataset_from_dicts_qa_inference(caplog=None): if caplog: caplog.set_level(logging.CRITICAL) models = [ "deepset/roberta-base-squad2", "deepset/bert-base-cased-squad2", "deepset/xlm-roberta-large-squad2", "deepset/minilm-uncased-squad2", "deepset/electra-base-squad2", ] sample_types = ["answer-wrong", "answer-offset-wrong", "noanswer", "vanilla"] for model in models: tokenizer = Tokenizer.load(pretrained_model_name_or_path=model, use_fast=True) processor = SquadProcessor(tokenizer, max_seq_len=256, data_dir=None) for sample_type in sample_types: dicts = processor.file_to_dicts(f"samples/qa/{sample_type}.json") dataset, tensor_names, problematic_sample_ids, baskets = processor.dataset_from_dicts(dicts, indices=[1], return_baskets=True) assert tensor_names == ['input_ids', 'padding_mask', 'segment_ids', 'passage_start_t', 'start_of_word', 'labels', 'id', 'seq_2_start_t', 'span_mask'], f"Processing for {model} has changed." assert len(problematic_sample_ids) == 0, f"Processing for {model} has changed." assert baskets[0].id_external == '5ad3d560604f3c001a3ff2c8', f"Processing for {model} has changed." assert baskets[0].id_internal == '1-0', f"Processing for {model} has changed." # roberta if model == "deepset/roberta-base-squad2": assert len(baskets[0].samples[0].tokenized["passage_tokens"]) == 6, f"Processing for {model} has changed." assert len(baskets[0].samples[0].tokenized["question_tokens"]) == 7, f"Processing for {model} has changed." if sample_type == "noanswer": assert baskets[0].samples[0].features[0]["input_ids"][:13] == \ [0, 6179, 171, 82, 697, 11, 2201, 116, 2, 2, 26795, 2614, 34], \ f"Processing for {model} and {sample_type}-testsample has changed." else: assert baskets[0].samples[0].features[0]["input_ids"][:13] == \ [0, 6179, 171, 82, 697, 11, 5459, 116, 2, 2, 26795, 2614, 34], \ f"Processing for {model} and {sample_type}-testsample has changed." # bert if model == "deepset/bert-base-cased-squad2": assert len(baskets[0].samples[0].tokenized["passage_tokens"]) == 5, f"Processing for {model} has changed." assert len(baskets[0].samples[0].tokenized["question_tokens"]) == 7, f"Processing for {model} has changed." if sample_type == "noanswer": assert baskets[0].samples[0].features[0]["input_ids"][:10] == \ [101, 1731, 1242, 1234, 1686, 1107, 2123, 136, 102, 3206], \ f"Processing for {model} and {sample_type}-testsample has changed." else: assert baskets[0].samples[0].features[0]["input_ids"][:10] == \ [101, 1731, 1242, 1234, 1686, 1107, 3206, 136, 102, 3206], \ f"Processing for {model} and {sample_type}-testsample has changed." # xlm-roberta if model == "deepset/xlm-roberta-large-squad2": assert len(baskets[0].samples[0].tokenized["passage_tokens"]) == 7, f"Processing for {model} has changed." assert len(baskets[0].samples[0].tokenized["question_tokens"]) == 7, f"Processing for {model} has changed." if sample_type == "noanswer": assert baskets[0].samples[0].features[0]["input_ids"][:12] == \ [0, 11249, 5941, 3395, 6867, 23, 7270, 32, 2, 2, 10271, 1556], \ f"Processing for {model} and {sample_type}-testsample has changed." else: assert baskets[0].samples[0].features[0]["input_ids"][:12] == \ [0, 11249, 5941, 3395, 6867, 23, 10271, 32, 2, 2, 10271, 1556], \ f"Processing for {model} and {sample_type}-testsample has changed." # minilm and electra have same vocab + tokenizer if model == "deepset/minilm-uncased-squad2" or model == "deepset/electra-base-squad2": assert len(baskets[0].samples[0].tokenized["passage_tokens"]) == 5, f"Processing for {model} has changed." assert len(baskets[0].samples[0].tokenized["question_tokens"]) == 7, f"Processing for {model} has changed." if sample_type == "noanswer": assert baskets[0].samples[0].features[0]["input_ids"][:10] == \ [101, 2129, 2116, 2111, 2444, 1999, 3000, 1029, 102, 4068], \ f"Processing for {model} and {sample_type}-testsample has changed." else: assert baskets[0].samples[0].features[0]["input_ids"][:10] == \ [101, 2129, 2116, 2111, 2444, 1999, 4068, 1029, 102, 4068], \ f"Processing for {model} and {sample_type}-testsample has changed." def test_dataset_from_dicts_qa_labelconversion(caplog=None): if caplog: caplog.set_level(logging.CRITICAL) models = [ "deepset/roberta-base-squad2", "deepset/bert-base-cased-squad2", "deepset/xlm-roberta-large-squad2", "deepset/minilm-uncased-squad2", "deepset/electra-base-squad2", ] sample_types = ["answer-wrong", "answer-offset-wrong", "noanswer", "vanilla"] for model in models: tokenizer = Tokenizer.load(pretrained_model_name_or_path=model, use_fast=True) processor = SquadProcessor(tokenizer, max_seq_len=256, data_dir=None) for sample_type in sample_types: dicts = processor.file_to_dicts(f"samples/qa/{sample_type}.json") dataset, tensor_names, problematic_sample_ids = processor.dataset_from_dicts(dicts, indices=[1], return_baskets=False) if sample_type == "answer-wrong" or sample_type == "answer-offset-wrong": assert len(problematic_sample_ids) == 1, f"Processing labels for {model} has changed." if sample_type == "noanswer": assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0, 0, :]) == [0, 0], f"Processing labels for {model} has changed." assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0, 1, :]) == [-1, -1], f"Processing labels for {model} has changed." if sample_type == "vanilla": # roberta if model == "deepset/roberta-base-squad2": assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0,0,:]) == [13, 13], f"Processing labels for {model} has changed." assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0,1,:]) == [13, 14], f"Processing labels for {model} has changed." # bert, minilm, electra if model == "deepset/bert-base-cased-squad2" or model == "deepset/minilm-uncased-squad2" or model == "deepset/electra-base-squad2": assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0,0,:]) == [11, 11], f"Processing labels for {model} has changed." # xlm-roberta if model == "deepset/xlm-roberta-large-squad2": assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0,0,:]) == [12, 12], f"Processing labels for {model} has changed." if(__name__=="__main__"): test_dataset_from_dicts_qa_labelconversion()

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Python

django/contrib/messages/__init__.py

egenerat/gae-django

f12379483cf3917ed3cb46ca5ff0b94daf89fc50

[ "MIT" ]

3

2016-07-08T23:49:32.000Z

2018-04-15T22:55:01.000Z

django/contrib/messages/__init__.py

egenerat/gae-django

f12379483cf3917ed3cb46ca5ff0b94daf89fc50

[ "MIT" ]

27

2017-02-05T15:57:04.000Z

2018-04-15T22:57:26.000Z

django/contrib/messages/__init__.py

egenerat/gae-django

f12379483cf3917ed3cb46ca5ff0b94daf89fc50

[ "MIT" ]

null

from api import * from constants import *

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Python

istio/datadog_checks/istio/metrics.py

grosser/integrations-core

4afe8e448fec0e152e0e2a8deb70b1efff7b2128

[ "BSD-3-Clause" ]

null

istio/datadog_checks/istio/metrics.py

grosser/integrations-core

4afe8e448fec0e152e0e2a8deb70b1efff7b2128

[ "BSD-3-Clause" ]

null

istio/datadog_checks/istio/metrics.py

grosser/integrations-core

4afe8e448fec0e152e0e2a8deb70b1efff7b2128

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2020 - Present # All rights reserved # Licensed under Simplified BSD License (see LICENSE) GENERIC_METRICS = { 'go_gc_duration_seconds': 'go.gc_duration_seconds', 'go_goroutines': 'go.goroutines', 'go_info': 'go.info', 'go_memstats_alloc_bytes': 'go.memstats.alloc_bytes', 'go_memstats_alloc_bytes_total': 'go.memstats.alloc_bytes_total', 'go_memstats_buck_hash_sys_bytes': 'go.memstats.buck_hash_sys_bytes', 'go_memstats_frees_total': 'go.memstats.frees_total', 'go_memstats_gc_cpu_fraction': 'go.memstats.gc_cpu_fraction', 'go_memstats_gc_sys_bytes': 'go.memstats.gc_sys_bytes', 'go_memstats_heap_alloc_bytes': 'go.memstats.heap_alloc_bytes', 'go_memstats_heap_idle_bytes': 'go.memstats.heap_idle_bytes', 'go_memstats_heap_inuse_bytes': 'go.memstats.heap_inuse_bytes', 'go_memstats_heap_objects': 'go.memstats.heap_objects', 'go_memstats_heap_released_bytes': 'go.memstats.heap_released_bytes', 'go_memstats_heap_sys_bytes': 'go.memstats.heap_sys_bytes', 'go_memstats_last_gc_time_seconds': 'go.memstats.last_gc_time_seconds', 'go_memstats_lookups_total': 'go.memstats.lookups_total', 'go_memstats_mallocs_total': 'go.memstats.mallocs_total', 'go_memstats_mcache_inuse_bytes': 'go.memstats.mcache_inuse_bytes', 'go_memstats_mcache_sys_bytes': 'go.memstats.mcache_sys_bytes', 'go_memstats_mspan_inuse_bytes': 'go.memstats.mspan_inuse_bytes', 'go_memstats_mspan_sys_bytes': 'go.memstats.mspan_sys_bytes', 'go_memstats_next_gc_bytes': 'go.memstats.next_gc_bytes', 'go_memstats_other_sys_bytes': 'go.memstats.other_sys_bytes', 'go_memstats_stack_inuse_bytes': 'go.memstats.stack_inuse_bytes', 'go_memstats_stack_sys_bytes': 'go.memstats.stack_sys_bytes', 'go_memstats_sys_bytes': 'go.memstats.sys_bytes', 'go_threads': 'go.threads', 'process_cpu_seconds_total': 'process.cpu_seconds_total', 'process_max_fds': 'process.max_fds', 'process_open_fds': 'process.open_fds', 'process_resident_memory_bytes': 'process.resident_memory_bytes', 'process_start_time_seconds': 'process.start_time_seconds', 'process_virtual_memory_bytes': 'process.virtual_memory_bytes', } CITADEL_METRICS = { 'citadel_secret_controller_csr_err_count': 'secret_controller.csr_err_count', 'citadel_secret_controller_secret_deleted_cert_count': ('secret_controller.secret_deleted_cert_count'), 'citadel_secret_controller_svc_acc_created_cert_count': ('secret_controller.svc_acc_created_cert_count'), 'citadel_secret_controller_svc_acc_deleted_cert_count': ('secret_controller.svc_acc_deleted_cert_count'), 'citadel_server_authentication_failure_count': 'server.authentication_failure_count', 'citadel_server_citadel_root_cert_expiry_timestamp': ('server.citadel_root_cert_expiry_timestamp'), 'citadel_server_csr_count': 'server.csr_count', 'citadel_server_csr_parsing_err_count': 'server.csr_parsing_err_count', 'citadel_server_id_extraction_err_count': 'server.id_extraction_err_count', 'citadel_server_success_cert_issuance_count': 'server.success_cert_issuance_count', 'citadel_server_root_cert_expiry_timestamp': 'server.root_cert_expiry_timestamp', } GALLEY_METRICS = { 'endpoint_no_pod': 'endpoint_no_pod', 'galley_mcp_source_clients_total': 'mcp_source.clients_total', 'galley_runtime_processor_event_span_duration_milliseconds': ('runtime_processor.event_span_duration_milliseconds'), 'galley_runtime_processor_events_processed_total': 'runtime_processor.events_processed_total', 'galley_runtime_processor_snapshot_events_total': 'runtime_processor.snapshot_events_total', 'galley_runtime_processor_snapshot_lifetime_duration_milliseconds': ( 'runtime_processor.snapshot_lifetime_duration_milliseconds' ), 'galley_runtime_processor_snapshots_published_total': ('runtime_processor.snapshots_published_total'), 'galley_runtime_state_type_instances_total': 'runtime_state_type_instances_total', 'galley_runtime_strategy_on_change_total': 'runtime_strategy.on_change_total', 'galley_runtime_strategy_timer_max_time_reached_total': ('runtime_strategy.timer_max_time_reached_total'), 'galley_runtime_strategy_timer_quiesce_reached_total': 'runtime_strategy.quiesce_reached_total', 'galley_runtime_strategy_timer_resets_total': 'runtime_strategy.timer_resets_total', 'galley_source_kube_dynamic_converter_success_total': ('source_kube.dynamic_converter_success_total'), 'galley_source_kube_event_success_total': 'source_kube.event_success_total', 'galley_validation_cert_key_updates': 'validation.cert_key_updates', 'galley_validation_config_load': 'validation.config_load', 'galley_validation_config_updates': 'validation.config_update', 'galley_validation_passed': 'validation.passed', # These metrics supported Istio 1.5 'galley_validation_config_update_error': 'validation.config_update_error', } MESH_METRICS = { # These metrics support Istio 1.5 'istio_request_duration_milliseconds': 'request.duration.milliseconds', # These metrics support Istio 1.0 'istio_requests_total': 'request.count', 'istio_request_duration_seconds': 'request.duration', 'istio_request_bytes': 'request.size', 'istio_response_bytes': 'response.size', # These metrics support Istio 0.8 'istio_request_count': 'request.count', 'istio_request_duration': 'request.duration', 'istio_request_size': 'request.size', 'istio_response_size': 'response.size', # TCP metrics 'istio_tcp_connections_closed_total': 'tcp.connections_closed.total', 'istio_tcp_connections_opened_total': 'tcp.connections_opened.total', 'istio_tcp_received_bytes_total': 'tcp.received_bytes.total', 'istio_tcp_sent_bytes_total': 'tcp.send_bytes.total', } MIXER_METRICS = { # Pre 1.1 metrics 'grpc_server_handled_total': 'grpc.server.handled_total', 'grpc_server_handling_seconds': 'grpc.server.handling_seconds', 'grpc_server_msg_received_total': 'grpc.server.msg_received_total', 'grpc_server_msg_sent_total': 'grpc.server.msg_sent_total', 'grpc_server_started_total': 'grpc.server.started_total', 'mixer_adapter_dispatch_count': 'adapter.dispatch_count', 'mixer_adapter_dispatch_duration': 'adapter.dispatch_duration', 'mixer_adapter_old_dispatch_count': 'adapter.old_dispatch_count', 'mixer_adapter_old_dispatch_duration': 'adapter.old_dispatch_duration', 'mixer_config_resolve_actions': 'config.resolve_actions', 'mixer_config_resolve_count': 'config.resolve_count', 'mixer_config_resolve_duration': 'config.resolve_duration', 'mixer_config_resolve_rules': 'config.resolve_rules', # 1.1 metrics 'grpc_io_server_completed_rpcs': 'grpc_io_server.completed_rpcs', 'grpc_io_server_received_bytes_per_rpc': 'grpc_io_server.received_bytes_per_rpc', 'grpc_io_server_sent_bytes_per_rpc': 'grpc_io_server.sent_bytes_per_rpc', 'grpc_io_server_server_latency': 'grpc_io_server.server_latency', 'mixer_config_attributes_total': 'config.attributes_total', 'mixer_config_handler_configs_total': 'config.handler_configs_total', 'mixer_config_instance_configs_total': 'config.instance_configs_total', 'mixer_config_rule_configs_total': 'config.rule_configs_total', 'mixer_dispatcher_destinations_per_request': 'dispatcher.destinations_per_request', 'mixer_dispatcher_instances_per_request': 'dispatcher.instances_per_request', 'mixer_handler_daemons_total': 'handler.daemons_total', 'mixer_handler_new_handlers_total': 'handler.new_handlers_total', 'mixer_mcp_sink_reconnections': 'mcp_sink.reconnections', 'mixer_mcp_sink_request_acks_total': 'mcp_sink.request_acks_total', 'mixer_runtime_dispatches_total': 'runtime.dispatches_total', 'mixer_runtime_dispatch_duration_seconds': 'runtime.dispatch_duration_seconds', } PILOT_METRICS = { 'pilot_conflict_inbound_listener': 'conflict.inbound_listener', 'pilot_conflict_outbound_listener_http_over_current_tcp': ('conflict.outbound_listener.http_over_current_tcp'), 'pilot_conflict_outbound_listener_tcp_over_current_http': ('conflict.outbound_listener.tcp_over_current_http'), 'pilot_conflict_outbound_listener_tcp_over_current_tcp': ('conflict.outbound_listener.tcp_over_current_tcp'), 'pilot_destrule_subsets': 'destrule_subsets', 'pilot_duplicate_envoy_clusters': 'duplicate_envoy_clusters', 'pilot_eds_no_instances': 'eds_no_instances', 'pilot_endpoint_not_ready': 'endpoint_not_ready', 'pilot_invalid_out_listeners': 'invalid_out_listeners', 'pilot_mcp_sink_reconnections': 'mcp_sink.reconnections', 'pilot_mcp_sink_recv_failures_total': 'mcp_sink.recv_failures_total', 'pilot_mcp_sink_request_acks_total': 'mcp_sink.request_acks_total', 'pilot_no_ip': 'no_ip', 'pilot_proxy_convergence_time': 'proxy_convergence_time', 'pilot_rds_expired_nonce': 'rds_expired_nonce', 'pilot_services': 'services', 'pilot_total_xds_internal_errors': 'total_xds_internal_errors', 'pilot_total_xds_rejects': 'total_xds_rejects', 'pilot_virt_services': 'virt_services', 'pilot_vservice_dup_domain': 'vservice_dup_domain', 'pilot_xds': 'xds', 'pilot_xds_eds_instances': 'xds.eds_instances', 'pilot_xds_push_context_errors': 'xds.push.context_errors', 'pilot_xds_push_timeout': 'xds.push.timeout', 'pilot_xds_push_timeout_failures': 'xds.push.timeout_failures', 'pilot_xds_pushes': 'xds.pushes', 'pilot_xds_write_timeout': 'xds.write_timeout', 'pilot_xds_rds_reject': 'pilot.xds.rds_reject', 'pilot_xds_eds_reject': 'pilot.xds.eds_reject', 'pilot_xds_cds_reject': 'pilot.xds.cds_reject', 'pilot_xds_lds_reject': 'pilot.xds.lds_reject', } ISTIOD_METRICS = { # Maintain namespace compatibility from legacy components # Generic metrics 'go_gc_duration_seconds': 'go.gc_duration_seconds', 'go_goroutines': 'go.goroutines', 'go_info': 'go.info', 'go_memstats_alloc_bytes': 'go.memstats.alloc_bytes', 'go_memstats_alloc_bytes_total': 'go.memstats.alloc_bytes_total', 'go_memstats_buck_hash_sys_bytes': 'go.memstats.buck_hash_sys_bytes', 'go_memstats_frees_total': 'go.memstats.frees_total', 'go_memstats_gc_cpu_fraction': 'go.memstats.gc_cpu_fraction', 'go_memstats_gc_sys_bytes': 'go.memstats.gc_sys_bytes', 'go_memstats_heap_alloc_bytes': 'go.memstats.heap_alloc_bytes', 'go_memstats_heap_idle_bytes': 'go.memstats.heap_idle_bytes', 'go_memstats_heap_inuse_bytes': 'go.memstats.heap_inuse_bytes', 'go_memstats_heap_objects': 'go.memstats.heap_objects', 'go_memstats_heap_released_bytes': 'go.memstats.heap_released_bytes', 'go_memstats_heap_sys_bytes': 'go.memstats.heap_sys_bytes', 'go_memstats_last_gc_time_seconds': 'go.memstats.last_gc_time_seconds', 'go_memstats_lookups_total': 'go.memstats.lookups_total', 'go_memstats_mallocs_total': 'go.memstats.mallocs_total', 'go_memstats_mcache_inuse_bytes': 'go.memstats.mcache_inuse_bytes', 'go_memstats_mcache_sys_bytes': 'go.memstats.mcache_sys_bytes', 'go_memstats_mspan_inuse_bytes': 'go.memstats.mspan_inuse_bytes', 'go_memstats_mspan_sys_bytes': 'go.memstats.mspan_sys_bytes', 'go_memstats_next_gc_bytes': 'go.memstats.next_gc_bytes', 'go_memstats_other_sys_bytes': 'go.memstats.other_sys_bytes', 'go_memstats_stack_inuse_bytes': 'go.memstats.stack_inuse_bytes', 'go_memstats_stack_sys_bytes': 'go.memstats.stack_sys_bytes', 'go_memstats_sys_bytes': 'go.memstats.sys_bytes', 'go_threads': 'go.threads', 'process_cpu_seconds_total': 'process.cpu_seconds_total', 'process_max_fds': 'process.max_fds', 'process_open_fds': 'process.open_fds', 'process_resident_memory_bytes': 'process.resident_memory_bytes', 'process_start_time_seconds': 'process.start_time_seconds', 'process_virtual_memory_bytes': 'process.virtual_memory_bytes', 'pilot_conflict_inbound_listener': 'pilot.conflict.inbound_listener', 'pilot_conflict_outbound_listener_http_over_current_tcp': ( 'pilot.conflict.outbound_listener.http_over_current_tcp' ), 'pilot_conflict_outbound_listener_tcp_over_current_http': ( 'pilot.conflict.outbound_listener.tcp_over_current_http' ), 'pilot_conflict_outbound_listener_tcp_over_current_tcp': ('pilot.conflict.outbound_listener.tcp_over_current_tcp'), 'pilot_destrule_subsets': 'pilot.destrule_subsets', 'pilot_duplicate_envoy_clusters': 'pilot.duplicate_envoy_clusters', 'pilot_eds_no_instances': 'pilot.eds_no_instances', 'pilot_endpoint_not_ready': 'pilot.endpoint_not_ready', 'pilot_invalid_out_listeners': 'pilot.invalid_out_listeners', 'pilot_mcp_sink_reconnections': 'pilot.mcp_sink.reconnections', 'pilot_mcp_sink_recv_failures_total': 'pilot.mcp_sink.recv_failures_total', 'pilot_mcp_sink_request_acks_total': 'pilot.mcp_sink.request_acks_total', 'pilot_no_ip': 'pilot.no_ip', 'pilot_proxy_convergence_time': 'pilot.proxy_convergence_time', 'pilot_rds_expired_nonce': 'pilot.rds_expired_nonce', 'pilot_services': 'pilot.services', 'pilot_total_xds_internal_errors': 'pilot.total_xds_internal_errors', 'pilot_total_xds_rejects': 'pilot.total_xds_rejects', 'pilot_virt_services': 'pilot.virt_services', 'pilot_vservice_dup_domain': 'pilot.vservice_dup_domain', 'pilot_xds': 'pilot.xds', 'pilot_xds_eds_instances': 'pilot.xds.eds_instances', 'pilot_xds_push_context_errors': 'pilot.xds.push.context_errors', 'pilot_xds_push_timeout': 'pilot.xds.push.timeout', 'pilot_xds_push_timeout_failures': 'pilot.xds.push.timeout_failures', 'pilot_xds_pushes': 'pilot.xds.pushes', 'pilot_xds_write_timeout': 'pilot.xds.write_timeout', 'pilot_xds_rds_reject': 'pilot.xds.rds_reject', 'pilot_xds_eds_reject': 'pilot.xds.eds_reject', 'pilot_xds_cds_reject': 'pilot.xds.cds_reject', 'pilot_xds_lds_reject': 'pilot.xds.lds_reject', 'grpc_server_handled_total': 'grpc.server.handled_total', 'grpc_server_handling_seconds': 'grpc.server.handling_seconds', 'grpc_server_msg_received_total': 'grpc.server.msg_received_total', 'grpc_server_msg_sent_total': 'grpc.server.msg_sent_total', 'grpc_server_started_total': 'grpc.server.started_total', 'grpc_io_server_completed_rpcs': 'mixer.grpc_io_server.completed_rpcs', 'grpc_io_server_received_bytes_per_rpc': 'mixer.grpc_io_server.received_bytes_per_rpc', 'grpc_io_server_sent_bytes_per_rpc': 'mixer.grpc_io_server.sent_bytes_per_rpc', 'grpc_io_server_server_latency': 'mixer.grpc_io_server.server_latency', 'mixer_config_attributes_total': 'mixer.config.attributes_total', 'mixer_config_handler_configs_total': 'mixer.config.handler_configs_total', 'mixer_config_instance_configs_total': 'mixer.config.instance_configs_total', 'mixer_config_rule_configs_total': 'mixer.config.rule_configs_total', 'mixer_dispatcher_destinations_per_request': 'mixer.dispatcher.destinations_per_request', 'mixer_dispatcher_instances_per_request': 'mixer.dispatcher.instances_per_request', 'mixer_handler_daemons_total': 'mixer.handler.daemons_total', 'mixer_handler_new_handlers_total': 'mixer.handler.new_handlers_total', 'mixer_mcp_sink_reconnections': 'mixer.mcp_sink.reconnections', 'mixer_mcp_sink_request_acks_total': 'mixer.mcp_sink.request_acks_total', 'mixer_runtime_dispatches_total': 'mixer.runtime.dispatches_total', 'mixer_runtime_dispatch_duration_seconds': 'mixer.runtime.dispatch_duration_seconds', 'endpoint_no_pod': 'galley.endpoint_no_pod', 'galley_mcp_source_clients_total': 'galley.mcp_source.clients_total', 'galley_runtime_processor_event_span_duration_milliseconds': ( 'galley.runtime_processor.event_span_duration_milliseconds' ), 'galley_runtime_processor_events_processed_total': 'galley.runtime_processor.events_processed_total', 'galley_runtime_processor_snapshot_events_total': 'galley.runtime_processor.snapshot_events_total', 'galley_runtime_processor_snapshot_lifetime_duration_milliseconds': ( 'galley.runtime_processor.snapshot_lifetime_duration_milliseconds' ), 'galley_runtime_processor_snapshots_published_total': ('galley.runtime_processor.snapshots_published_total'), 'galley_runtime_state_type_instances_total': 'galley.runtime_state_type_instances_total', 'galley_runtime_strategy_on_change_total': 'galley.runtime_strategy.on_change_total', 'galley_runtime_strategy_timer_max_time_reached_total': ('galley.runtime_strategy.timer_max_time_reached_total'), 'galley_runtime_strategy_timer_quiesce_reached_total': 'galley.runtime_strategy.quiesce_reached_total', 'galley_runtime_strategy_timer_resets_total': 'galley.runtime_strategy.timer_resets_total', 'galley_source_kube_dynamic_converter_success_total': ('galley.source_kube.dynamic_converter_success_total'), 'galley_source_kube_event_success_total': 'galley.source_kube.event_success_total', 'galley_validation_config_load': 'galley.validation.config_load', 'galley_validation_config_updates': 'galley.validation.config_update', 'citadel_secret_controller_csr_err_count': 'citadel.secret_controller.csr_err_count', 'citadel_secret_controller_secret_deleted_cert_count': ('citadel.secret_controller.secret_deleted_cert_count'), 'citadel_secret_controller_svc_acc_created_cert_count': ('citadel.secret_controller.svc_acc_created_cert_count'), 'citadel_secret_controller_svc_acc_deleted_cert_count': ('citadel.secret_controller.svc_acc_deleted_cert_count'), 'citadel_server_authentication_failure_count': 'citadel.server.authentication_failure_count', 'citadel_server_citadel_root_cert_expiry_timestamp': ('citadel.server.citadel_root_cert_expiry_timestamp'), 'citadel_server_csr_count': 'citadel.server.csr_count', 'citadel_server_csr_parsing_err_count': 'citadel.server.csr_parsing_err_count', 'citadel_server_id_extraction_err_count': 'citadel.server.id_extraction_err_count', 'citadel_server_success_cert_issuance_count': 'citadel.server.success_cert_issuance_count', # These metrics supported Istio 1.5 'galley_validation_config_update_error': 'galley.validation.config_update_error', 'citadel_server_root_cert_expiry_timestamp': 'citadel.server.root_cert_expiry_timestamp', 'galley_validation_passed': 'galley.validation.passed', 'galley_validation_failed': 'galley.validation.failed', 'pilot_conflict_outbound_listener_http_over_https': 'pilot.conflict.outbound_listener.http_over_https', 'pilot_inbound_updates': 'pilot.inbound_updates', 'pilot_k8s_cfg_events': 'pilot.k8s.cfg_events', 'pilot_k8s_reg_events': 'pilot.k8s.reg_events', 'pilot_proxy_queue_time': 'pilot.proxy_queue_time', 'pilot_push_triggers': 'pilot.push.triggers', 'pilot_xds_eds_all_locality_endpoints': 'pilot.xds.eds_all_locality_endpoints', 'pilot_xds_push_time': 'pilot.xds.push.time', 'process_virtual_memory_max_bytes': 'process.virtual_memory_max_bytes', 'sidecar_injection_requests_total': 'sidecar_injection.requests_total', 'sidecar_injection_success_total': 'sidecar_injection.success_total', 'sidecar_injection_failure_total': 'sidecar_injection.failure_total', 'sidecar_injection_skip_total': 'sidecar_injection.skip_total', }

61.233438

120

0.798774

2,511

19,411

5.57268

0.092393

0.068606

0.07075

0.038591

0.893375

0.853212

0.798542

0.733938

0.646609

0.57343

0

0.00125

0.093555

19,411

316

121

61.427215

0.794032

0.019577

0

0.32069

0

0.81485

0.737025

0

1

0

false

0.006897

0

null

0

1

0

1

null

0

6

0a0e09eca6f88004a43e7b6443d11f7c60dd37b9

159

py

Python

src/bindings/python/__init__.py

Georepublic/valhalla

079c11978093608e730b22a52c2363d39eefdc15

[ "MIT" ]

1

2022-02-19T05:31:55.000Z

src/bindings/python/__init__.py

Georepublic/valhalla

079c11978093608e730b22a52c2363d39eefdc15

[ "MIT" ]

null

src/bindings/python/__init__.py

Georepublic/valhalla

079c11978093608e730b22a52c2363d39eefdc15

[ "MIT" ]

null

try: from .python_valhalla import * except ModuleNotFoundError: from python_valhalla import * from .actor import Actor from .config import get_config

19.875

34

0.779874

20

159

6.05

0.5

0.165289

0.297521

0.396694

0

0.176101

159

7

35

22.714286

0.923664

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

0a29088fbf5324b45efa5699af22995f9ebcd2e8

45

py

Python

sharpy/__init__.py

MadManSC2/sharpy-sc2

13950357df2db58033daab24f076e3ae83f0b2a8

[ "MIT" ]

1

2020-03-05T19:21:56.000Z

sharpy/__init__.py

MadManSC2/sharpy-sc2

13950357df2db58033daab24f076e3ae83f0b2a8

[ "MIT" ]

null

sharpy/__init__.py

MadManSC2/sharpy-sc2

13950357df2db58033daab24f076e3ae83f0b2a8

[ "MIT" ]

null

# re-export from .constants import Constants

22.5

32

0.8

6

45

6

0.833333

0

0.133333

45

2

32

22.5

0.923077

0.2

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0a3d0752a0ca7e9afbc6e991f12468ea88b23894

216

py

Python

Server/test/apply/extension/test_extension_map_get.py

miraedbswo/DMS-Backend-V3-Student

37269d1d24b2c57b5edb96b69c74eecd3995fd2f

[ "MIT" ]

10

2019-08-19T09:46:09.000Z

2021-04-29T10:47:54.000Z

Server/test/apply/extension/test_extension_map_get.py

DSM-DMS/DMS-Backend-V3-Student

3eedd071fab46ae29e9cc3d7b05ef9e5cfd446b6

[ "MIT" ]

6

2018-10-10T23:37:20.000Z

2018-12-27T04:57:32.000Z

Server/test/apply/extension/test_extension_map_get.py

DSM-DMS/DMS-Backend-V3-Student

3eedd071fab46ae29e9cc3d7b05ef9e5cfd446b6

[ "MIT" ]

3

2021-02-27T05:41:11.000Z

2021-06-28T03:10:31.000Z

from flask import Response from app.model.apply import ExtensionApplyModel from test import TCBase, check_status_code from test.request import ApplyRequest class TestGetExtensionMap(TCBase, ApplyRequest): pass

24

48

0.837963

27

216

6.62963

0.666667

0.089385

0

0.125

216

9

49

24

0.94709

0

1

0

true

0.166667

0.666667

0

0.833333

0

1

0

null

0

1

0

null

0

1

0

1

0

6

6a7e8c4cb7dc46112f2a9c9d7d76bc7361af2318

3,137

py

Python

tests/test_engine/test_update/test_update_currentDate.py

bobuk/montydb

9ee299e7f1d3a7236abb683e0dfe4f7817859b2c

[ "BSD-3-Clause" ]

478

2019-07-31T00:48:11.000Z

2022-03-18T09:12:29.000Z

tests/test_engine/test_update/test_update_currentDate.py

bobuk/montydb

9ee299e7f1d3a7236abb683e0dfe4f7817859b2c

[ "BSD-3-Clause" ]

47

2019-07-28T10:12:22.000Z

2022-01-04T16:25:12.000Z

tests/test_engine/test_update/test_update_currentDate.py

bobuk/montydb

9ee299e7f1d3a7236abb683e0dfe4f7817859b2c

[ "BSD-3-Clause" ]

26

2019-08-09T14:28:29.000Z

2022-02-22T02:49:51.000Z

import pytest from pymongo.errors import WriteError as mongo_write_err from montydb.errors import WriteError as monty_write_err from ...conftest import skip_if_no_bson def test_update_currentDate_1(monty_update, mongo_update): docs = [ {"a": None} ] spec = {"$currentDate": {"a": True}} monty_c = monty_update(docs, spec) mongo_c = mongo_update(docs, spec) mg_date = next(mongo_c)["a"] mt_date = next(monty_c)["a"] assert mg_date.date() == mt_date.date() assert mg_date.hour == mt_date.hour assert mg_date.minute == mt_date.minute def test_update_currentDate_2(monty_update, mongo_update): docs = [ {"a": None} ] spec = {"$currentDate": {"a": False}} # still set date monty_c = monty_update(docs, spec) mongo_c = mongo_update(docs, spec) mg_date = next(mongo_c)["a"] mt_date = next(monty_c)["a"] assert mg_date.date() == mt_date.date() assert mg_date.hour == mt_date.hour assert mg_date.minute == mt_date.minute def test_update_currentDate_3(monty_update, mongo_update): docs = [ {"a": None} ] spec = {"$currentDate": {"a": {"$type": "date"}}} monty_c = monty_update(docs, spec) mongo_c = mongo_update(docs, spec) mg_date = next(mongo_c)["a"] mt_date = next(monty_c)["a"] assert mg_date.date() == mt_date.date() assert mg_date.hour == mt_date.hour assert mg_date.minute == mt_date.minute @skip_if_no_bson def test_update_currentDate_4(monty_update, mongo_update): docs = [ {"a": None} ] spec = {"$currentDate": {"a": {"$type": "timestamp"}}} monty_c = monty_update(docs, spec) mongo_c = mongo_update(docs, spec) mg_tstamp = next(mongo_c)["a"] mt_tstamp = next(monty_c)["a"] assert mg_tstamp.time - mt_tstamp.time < 10 assert mg_tstamp.inc == mt_tstamp.inc def test_update_currentDate_5(monty_update, mongo_update): docs = [ {"a": None} ] spec = {"$currentDate": {"a": 1}} with pytest.raises(mongo_write_err) as mongo_err: mongo_update(docs, spec) with pytest.raises(monty_write_err) as monty_err: monty_update(docs, spec) # ignore comparing error code # assert mongo_err.value.code == monty_err.value.code def test_update_currentDate_6(monty_update, mongo_update): docs = [ {"a": None} ] spec = {"$currentDate": {"a": {"not_op": True}}} with pytest.raises(mongo_write_err) as mongo_err: mongo_update(docs, spec) with pytest.raises(monty_write_err) as monty_err: monty_update(docs, spec) # ignore comparing error code # assert mongo_err.value.code == monty_err.value.code def test_update_currentDate_7(monty_update, mongo_update): docs = [ {"a": None} ] spec = {"$currentDate": {"a": {"$type": "not date nor timestamp"}}} with pytest.raises(mongo_write_err) as mongo_err: mongo_update(docs, spec) with pytest.raises(monty_write_err) as monty_err: monty_update(docs, spec) # ignore comparing error code # assert mongo_err.value.code == monty_err.value.code

25.504065

71

0.651897

445

3,137

4.303371

0.134831

0.109661

0.087728

0.855875

0.849086

0.839164

0.801567

0

0.004067

0.21613

3,137

122

72

25.713115

0.774705

0.080969

0

0.620253

0

0.056367

0

0.139241

1

0.088608

false

0

0.050633

0

0.139241

0

null

0

1

0

null

0

6

6aa6908338b46c742f8e827b7137e230aeec8285

14,116

py

Python

dandi/tests/test_delete.py

TheChymera/dandi-cli

9a42b1fa2f9af3be01254f7457f5a21d834c1864

[ "Apache-2.0" ]

null

dandi/tests/test_delete.py

TheChymera/dandi-cli

9a42b1fa2f9af3be01254f7457f5a21d834c1864

[ "Apache-2.0" ]

null

dandi/tests/test_delete.py

TheChymera/dandi-cli

9a42b1fa2f9af3be01254f7457f5a21d834c1864

[ "Apache-2.0" ]

null

from pathlib import Path from typing import List import pytest from pytest_mock import MockerFixture from .fixtures import DandiAPI, SampleDandiset from ..consts import DRAFT, dandiset_metadata_file from ..dandiapi import RESTFullAPIClient from ..delete import delete from ..download import download from ..exceptions import NotFoundError from ..utils import list_paths @pytest.mark.parametrize( "paths,remainder", [ ( ["subdir2/coconut.txt"], [ Path("file.txt"), Path("subdir1", "apple.txt"), Path("subdir2", "banana.txt"), ], ), (["subdir2"], [Path("file.txt"), Path("subdir1", "apple.txt")]), ( ["subdir2", "subdir2/coconut.txt"], [Path("file.txt"), Path("subdir1", "apple.txt")], ), ( ["dandi://{instance}/{dandiset_id}/subdir2/coconut.txt"], [ Path("file.txt"), Path("subdir1", "apple.txt"), Path("subdir2", "banana.txt"), ], ), ( ["dandi://{instance}/{dandiset_id}/subdir2/"], [Path("file.txt"), Path("subdir1", "apple.txt")], ), ( [ "dandi://{instance}/{dandiset_id}/subdir2/", "dandi://{instance}/{dandiset_id}/subdir2/coconut.txt", ], [Path("file.txt"), Path("subdir1", "apple.txt")], ), ( [ "subdir1", "dandi://{instance}/{dandiset_id}/subdir2/coconut.txt", ], [Path("file.txt"), Path("subdir2", "banana.txt")], ), ], ) def test_delete_paths( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, tmp_path: Path, paths: List[str], remainder: List[Path], ) -> None: monkeypatch.chdir(text_dandiset.dspath) monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") delete( [p.format(instance=instance, dandiset_id=dandiset_id) for p in paths], dandi_instance=instance, devel_debug=True, force=True, ) delete_spy.assert_called() download(text_dandiset.dandiset.version_api_url, tmp_path) assert list_paths(tmp_path) == [ tmp_path / dandiset_id / f for f in [Path("dandiset.yaml")] + remainder ] @pytest.mark.parametrize("confirm", [True, False]) def test_delete_path_confirm( confirm: bool, mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, ) -> None: monkeypatch.chdir(text_dandiset.dspath) monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") confirm_mock = mocker.patch("click.confirm", return_value=confirm) delete(["subdir2/coconut.txt"], dandi_instance=instance, devel_debug=True) confirm_mock.assert_called_with( f"Delete 1 assets on server from Dandiset {dandiset_id}?" ) if confirm: delete_spy.assert_called() else: delete_spy.assert_not_called() def test_delete_path_pyout( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, ) -> None: monkeypatch.chdir(text_dandiset.dspath) monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") delete(["subdir2/coconut.txt"], dandi_instance=instance, force=True) delete_spy.assert_called() @pytest.mark.parametrize( "paths", [ ["dandi://{instance}/{dandiset_id}"], ["dandi://{instance}/{dandiset_id}", "file.txt"], ["file.txt", "dandi://{instance}/{dandiset_id}"], [ "dandi://{instance}/{dandiset_id}", "dandi://{instance}/{dandiset_id}/subdir2/coconut.txt", ], [ "dandi://{instance}/{dandiset_id}/subdir2/coconut.txt", "dandi://{instance}/{dandiset_id}", ], ], ) def test_delete_dandiset( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, paths: List[str], ) -> None: monkeypatch.chdir(text_dandiset.dspath) monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") delete( [p.format(instance=instance, dandiset_id=dandiset_id) for p in paths], dandi_instance=instance, devel_debug=True, force=True, ) delete_spy.assert_called() with pytest.raises(NotFoundError): text_dandiset.client.get_dandiset(dandiset_id, DRAFT, lazy=False) @pytest.mark.parametrize("confirm", [True, False]) def test_delete_dandiset_confirm( confirm: bool, mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, ) -> None: monkeypatch.chdir(text_dandiset.dspath) monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") confirm_mock = mocker.patch("click.confirm", return_value=confirm) delete( [f"dandi://{instance}/{dandiset_id}"], dandi_instance=instance, devel_debug=True ) confirm_mock.assert_called_with(f"Delete Dandiset {dandiset_id}?") if confirm: delete_spy.assert_called() else: delete_spy.assert_not_called() def test_delete_dandiset_mismatch( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, ) -> None: monkeypatch.chdir(text_dandiset.dspath) monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id not_dandiset = str(int(dandiset_id) - 1).zfill(6) delete_spy = mocker.spy(RESTFullAPIClient, "delete") for paths in [ [ "subdir1/apple.txt", f"dandi://{instance}/{not_dandiset}/subdir2/coconut.txt", ], [ f"dandi://{instance}/{dandiset_id}/subdir1/apple.txt", f"dandi://{instance}/{not_dandiset}/subdir2/coconut.txt", ], ]: with pytest.raises(ValueError) as excinfo: delete(paths, dandi_instance=instance, devel_debug=True, force=True) assert ( str(excinfo.value) == "Cannot delete assets from multiple Dandisets at once" ) delete_spy.assert_not_called() def test_delete_instance_mismatch( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, ) -> None: monkeypatch.chdir(text_dandiset.dspath) monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") for paths in [ [ "subdir1/apple.txt", f"dandi://dandi/{dandiset_id}/subdir2/coconut.txt", ], [ f"dandi://{instance}/{dandiset_id}/subdir2/coconut.txt", f"dandi://dandi/{dandiset_id}/subdir1/apple.txt", ], ]: with pytest.raises(ValueError) as excinfo: delete(paths, dandi_instance=instance, devel_debug=True, force=True) assert ( str(excinfo.value) == "Cannot delete assets from multiple API instances at once" ) delete_spy.assert_not_called() def test_delete_nonexistent_dandiset( local_dandi_api: DandiAPI, mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch ) -> None: monkeypatch.setenv("DANDI_API_KEY", local_dandi_api.api_key) instance = local_dandi_api.instance_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") with pytest.raises(NotFoundError) as excinfo: delete( [f"dandi://{instance}/999999/subdir1/apple.txt"], dandi_instance=instance, devel_debug=True, force=True, ) assert str(excinfo.value) == "No such Dandiset: '999999'" delete_spy.assert_not_called() def test_delete_nonexistent_dandiset_skip_missing( local_dandi_api: DandiAPI, mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch ) -> None: monkeypatch.setenv("DANDI_API_KEY", local_dandi_api.api_key) instance = local_dandi_api.instance_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") delete( [f"dandi://{instance}/999999/subdir1/apple.txt"], dandi_instance=instance, devel_debug=True, force=True, skip_missing=True, ) delete_spy.assert_not_called() def test_delete_nonexistent_asset( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, ) -> None: monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") with pytest.raises(NotFoundError) as excinfo: delete( [ f"dandi://{instance}/{dandiset_id}/file.txt", f"dandi://{instance}/{dandiset_id}/subdir3/mango.txt", ], dandi_instance=instance, devel_debug=True, force=True, ) assert ( str(excinfo.value) == f"No assets found for dandi://{instance}/{dandiset_id}/subdir3/mango.txt" ) delete_spy.assert_not_called() def test_delete_nonexistent_asset_skip_missing( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, tmp_path: Path, ) -> None: monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") delete( [ f"dandi://{instance}/{dandiset_id}/file.txt", f"dandi://{instance}/{dandiset_id}/subdir3/mango.txt", ], dandi_instance=instance, devel_debug=True, force=True, skip_missing=True, ) delete_spy.assert_called() download(text_dandiset.dandiset.version_api_url, tmp_path) assert list_paths(tmp_path) == [ tmp_path / dandiset_id / "dandiset.yaml", tmp_path / dandiset_id / "subdir1" / "apple.txt", tmp_path / dandiset_id / "subdir2" / "banana.txt", tmp_path / dandiset_id / "subdir2" / "coconut.txt", ] def test_delete_nonexistent_asset_folder( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, ) -> None: monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") with pytest.raises(NotFoundError) as excinfo: delete( [ f"dandi://{instance}/{dandiset_id}/subdir1/", f"dandi://{instance}/{dandiset_id}/subdir3/", ], dandi_instance=instance, devel_debug=True, force=True, ) assert ( str(excinfo.value) == f"No assets found for dandi://{instance}/{dandiset_id}/subdir3/" ) delete_spy.assert_not_called() def test_delete_nonexistent_asset_folder_skip_missing( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, text_dandiset: SampleDandiset, tmp_path: Path, ) -> None: monkeypatch.setenv("DANDI_API_KEY", text_dandiset.api.api_key) instance = text_dandiset.api.instance_id dandiset_id = text_dandiset.dandiset_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") delete( [ f"dandi://{instance}/{dandiset_id}/subdir1/", f"dandi://{instance}/{dandiset_id}/subdir3/", ], dandi_instance=instance, devel_debug=True, force=True, skip_missing=True, ) delete_spy.assert_called() download(text_dandiset.dandiset.version_api_url, tmp_path) assert list_paths(tmp_path) == [ tmp_path / dandiset_id / "dandiset.yaml", tmp_path / dandiset_id / "file.txt", tmp_path / dandiset_id / "subdir2" / "banana.txt", tmp_path / dandiset_id / "subdir2" / "coconut.txt", ] def test_delete_version( local_dandi_api: DandiAPI, mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch ) -> None: monkeypatch.setenv("DANDI_API_KEY", local_dandi_api.api_key) instance = local_dandi_api.instance_id delete_spy = mocker.spy(RESTFullAPIClient, "delete") with pytest.raises(NotImplementedError) as excinfo: delete( [f"dandi://{instance}/999999@draft"], dandi_instance=instance, devel_debug=True, force=True, ) assert str(excinfo.value) == ( "Dandi API server does not support deletion of individual versions of a" " dandiset" ) delete_spy.assert_not_called() def test_delete_no_dandiset( mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, tmp_path: Path ) -> None: monkeypatch.chdir(tmp_path) delete_spy = mocker.spy(RESTFullAPIClient, "delete") with pytest.raises(RuntimeError) as excinfo: delete( ["dir/file.txt"], dandi_instance="dandi", devel_debug=True, force=True, ) assert str(excinfo.value) == ( f"Found no {dandiset_metadata_file} anywhere. " "Use 'dandi download' or 'organize' first" ) delete_spy.assert_not_called()

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58,222

py

Python

ironic/tests/unit/drivers/modules/test_pxe.py

isabella232/ironic

9a0bd8a774143e6f767aaa3031be6b70554bc332

[ "Apache-2.0" ]

2

2019-06-17T21:37:53.000Z

2020-07-11T03:58:39.000Z

ironic/tests/unit/drivers/modules/test_pxe.py

openshift/ironic

9a0bd8a774143e6f767aaa3031be6b70554bc332

[ "Apache-2.0" ]

1

2019-06-16T22:53:49.000Z

2019-09-16T09:37:35.000Z

ironic/tests/unit/drivers/modules/test_pxe.py

isabella232/ironic

9a0bd8a774143e6f767aaa3031be6b70554bc332

[ "Apache-2.0" ]

6

2019-06-13T12:49:33.000Z

2021-04-17T16:33:19.000Z

# Copyright 2013 Hewlett-Packard Development Company, L.P. # 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. """Test class for PXE driver.""" import os import tempfile import mock from oslo_config import cfg from oslo_serialization import jsonutils as json from oslo_utils import uuidutils from ironic.common import boot_devices from ironic.common import boot_modes from ironic.common import dhcp_factory from ironic.common import exception from ironic.common.glance_service import base_image_service from ironic.common import pxe_utils from ironic.common import states from ironic.common import utils as common_utils from ironic.conductor import task_manager from ironic.conductor import utils as manager_utils from ironic.drivers import base as drivers_base from ironic.drivers.modules import agent_base_vendor from ironic.drivers.modules import deploy_utils from ironic.drivers.modules import fake from ironic.drivers.modules import ipxe from ironic.drivers.modules import pxe from ironic.drivers.modules.storage import noop as noop_storage from ironic.tests.unit.db import base as db_base from ironic.tests.unit.db import utils as db_utils from ironic.tests.unit.objects import utils as obj_utils CONF = cfg.CONF INST_INFO_DICT = db_utils.get_test_pxe_instance_info() DRV_INFO_DICT = db_utils.get_test_pxe_driver_info() DRV_INTERNAL_INFO_DICT = db_utils.get_test_pxe_driver_internal_info() # NOTE(TheJulia): Mark pxe interface loading as None in order # to prent false counts for individual method tests. @mock.patch.object(ipxe.iPXEBoot, '__init__', lambda self: None) @mock.patch.object(pxe.PXEBoot, '__init__', lambda self: None) class PXEBootTestCase(db_base.DbTestCase): driver = 'fake-hardware' boot_interface = 'pxe' driver_info = DRV_INFO_DICT driver_internal_info = DRV_INTERNAL_INFO_DICT def setUp(self): super(PXEBootTestCase, self).setUp() self.context.auth_token = 'fake' self.config_temp_dir('tftp_root', group='pxe') self.config_temp_dir('images_path', group='pxe') self.config_temp_dir('http_root', group='deploy') instance_info = INST_INFO_DICT instance_info['deploy_key'] = 'fake-56789' self.config(enabled_boot_interfaces=[self.boot_interface, 'ipxe', 'fake']) self.node = obj_utils.create_test_node( self.context, driver=self.driver, boot_interface=self.boot_interface, # Avoid fake properties in get_properties() output vendor_interface='no-vendor', instance_info=instance_info, driver_info=self.driver_info, driver_internal_info=self.driver_internal_info) self.port = obj_utils.create_test_port(self.context, node_id=self.node.id) self.config(group='conductor', api_url='http://127.0.0.1:1234/') def test_get_properties(self): expected = pxe.COMMON_PROPERTIES expected.update(agent_base_vendor.VENDOR_PROPERTIES) with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: self.assertEqual(expected, task.driver.get_properties()) @mock.patch.object(base_image_service.BaseImageService, '_show', autospec=True) def test_validate_good(self, mock_glance): mock_glance.return_value = {'properties': {'kernel_id': 'fake-kernel', 'ramdisk_id': 'fake-initr'}} with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: task.driver.boot.validate(task) @mock.patch.object(base_image_service.BaseImageService, '_show', autospec=True) def test_validate_good_whole_disk_image(self, mock_glance): with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: task.node.driver_internal_info['is_whole_disk_image'] = True task.driver.boot.validate(task) @mock.patch.object(base_image_service.BaseImageService, '_show', autospec=True) @mock.patch.object(noop_storage.NoopStorage, 'should_write_image', autospec=True) def test_validate_skip_check_write_image_false(self, mock_write, mock_glance): mock_write.return_value = False with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: task.driver.boot.validate(task) self.assertFalse(mock_glance.called) def test_validate_fail_missing_deploy_kernel(self): with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: del task.node.driver_info['deploy_kernel'] self.assertRaises(exception.MissingParameterValue, task.driver.boot.validate, task) def test_validate_fail_missing_deploy_ramdisk(self): with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: del task.node.driver_info['deploy_ramdisk'] self.assertRaises(exception.MissingParameterValue, task.driver.boot.validate, task) def test_validate_fail_missing_image_source(self): info = dict(INST_INFO_DICT) del info['image_source'] self.node.instance_info = json.dumps(info) with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: task.node['instance_info'] = json.dumps(info) self.assertRaises(exception.MissingParameterValue, task.driver.boot.validate, task) def test_validate_fail_no_port(self): new_node = obj_utils.create_test_node( self.context, uuid='aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee', driver=self.driver, boot_interface=self.boot_interface, instance_info=INST_INFO_DICT, driver_info=DRV_INFO_DICT) with task_manager.acquire(self.context, new_node.uuid, shared=True) as task: self.assertRaises(exception.MissingParameterValue, task.driver.boot.validate, task) def test_validate_fail_trusted_boot_with_secure_boot(self): instance_info = {"boot_option": "netboot", "secure_boot": "true", "trusted_boot": "true"} properties = {'capabilities': 'trusted_boot:true'} with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: task.node.instance_info['capabilities'] = instance_info task.node.properties = properties task.node.driver_internal_info['is_whole_disk_image'] = False self.assertRaises(exception.InvalidParameterValue, task.driver.boot.validate, task) def test_validate_fail_invalid_trusted_boot_value(self): properties = {'capabilities': 'trusted_boot:value'} instance_info = {"trusted_boot": "value"} with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: task.node.properties = properties task.node.instance_info['capabilities'] = instance_info self.assertRaises(exception.InvalidParameterValue, task.driver.boot.validate, task) @mock.patch.object(base_image_service.BaseImageService, '_show', autospec=True) def test_validate_fail_no_image_kernel_ramdisk_props(self, mock_glance): mock_glance.return_value = {'properties': {}} with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: self.assertRaises(exception.MissingParameterValue, task.driver.boot.validate, task) @mock.patch.object(base_image_service.BaseImageService, '_show', autospec=True) def test_validate_fail_glance_image_doesnt_exists(self, mock_glance): mock_glance.side_effect = exception.ImageNotFound('not found') with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: self.assertRaises(exception.InvalidParameterValue, task.driver.boot.validate, task) @mock.patch.object(base_image_service.BaseImageService, '_show', autospec=True) def test_validate_fail_glance_conn_problem(self, mock_glance): exceptions = (exception.GlanceConnectionFailed('connection fail'), exception.ImageNotAuthorized('not authorized'), exception.Invalid('invalid')) mock_glance.side_effect = exceptions for exc in exceptions: with task_manager.acquire(self.context, self.node.uuid, shared=True) as task: self.assertRaises(exception.InvalidParameterValue, task.driver.boot.validate, task) @mock.patch.object(manager_utils, 'node_get_boot_mode', autospec=True) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory') @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) @mock.patch.object(pxe_utils, 'get_image_info', autospec=True) @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'build_pxe_config_options', autospec=True) @mock.patch.object(pxe_utils, 'create_pxe_config', autospec=True) def _test_prepare_ramdisk(self, mock_pxe_config, mock_build_pxe, mock_cache_r_k, mock_deploy_img_info, mock_instance_img_info, dhcp_factory_mock, set_boot_device_mock, get_boot_mode_mock, uefi=False, cleaning=False, ipxe_use_swift=False, whole_disk_image=False, mode='deploy', node_boot_mode=None, persistent=False): mock_build_pxe.return_value = {} kernel_label = '%s_kernel' % mode ramdisk_label = '%s_ramdisk' % mode mock_deploy_img_info.return_value = {kernel_label: 'a', ramdisk_label: 'r'} if whole_disk_image: mock_instance_img_info.return_value = {} else: mock_instance_img_info.return_value = {'kernel': 'b'} mock_pxe_config.return_value = None mock_cache_r_k.return_value = None provider_mock = mock.MagicMock() dhcp_factory_mock.return_value = provider_mock get_boot_mode_mock.return_value = node_boot_mode driver_internal_info = self.node.driver_internal_info driver_internal_info['is_whole_disk_image'] = whole_disk_image self.node.driver_internal_info = driver_internal_info if mode == 'rescue': mock_deploy_img_info.return_value = { 'rescue_kernel': 'a', 'rescue_ramdisk': 'r'} self.node.save() with task_manager.acquire(self.context, self.node.uuid) as task: dhcp_opts = pxe_utils.dhcp_options_for_instance( task, ipxe_enabled=CONF.pxe.ipxe_enabled) task.driver.boot.prepare_ramdisk(task, {'foo': 'bar'}) mock_deploy_img_info.assert_called_once_with(task.node, mode=mode) provider_mock.update_dhcp.assert_called_once_with(task, dhcp_opts) if self.node.provision_state == states.DEPLOYING: get_boot_mode_mock.assert_called_once_with(task) set_boot_device_mock.assert_called_once_with(task, boot_devices.PXE, persistent=persistent) if ipxe_use_swift: if whole_disk_image: self.assertFalse(mock_cache_r_k.called) else: mock_cache_r_k.assert_called_once_with( task, {'kernel': 'b'}, ipxe_enabled=CONF.pxe.ipxe_enabled) mock_instance_img_info.assert_called_once_with(task) elif not cleaning and mode == 'deploy': mock_cache_r_k.assert_called_once_with( task, {'deploy_kernel': 'a', 'deploy_ramdisk': 'r', 'kernel': 'b'}, ipxe_enabled=CONF.pxe.ipxe_enabled) mock_instance_img_info.assert_called_once_with(task) elif mode == 'deploy': mock_cache_r_k.assert_called_once_with( task, {'deploy_kernel': 'a', 'deploy_ramdisk': 'r'}, ipxe_enabled=CONF.pxe.ipxe_enabled) elif mode == 'rescue': mock_cache_r_k.assert_called_once_with( task, {'rescue_kernel': 'a', 'rescue_ramdisk': 'r'}, ipxe_enabled=CONF.pxe.ipxe_enabled) if uefi: mock_pxe_config.assert_called_once_with( task, {'foo': 'bar'}, CONF.pxe.uefi_pxe_config_template, ipxe_enabled=CONF.pxe.ipxe_enabled) else: mock_pxe_config.assert_called_once_with( task, {'foo': 'bar'}, CONF.pxe.pxe_config_template, ipxe_enabled=CONF.pxe.ipxe_enabled) def test_prepare_ramdisk(self): self.node.provision_state = states.DEPLOYING self.node.save() self._test_prepare_ramdisk() def test_prepare_ramdisk_force_persistent_boot_device_true(self): self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = 'True' self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk(persistent=True) def test_prepare_ramdisk_force_persistent_boot_device_bool_true(self): self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = True self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk(persistent=True) def test_prepare_ramdisk_force_persistent_boot_device_sloppy_true(self): for value in ['true', 't', '1', 'on', 'y', 'YES']: self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = value self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk(persistent=True) def test_prepare_ramdisk_force_persistent_boot_device_false(self): self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = 'False' self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk() def test_prepare_ramdisk_force_persistent_boot_device_bool_false(self): self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = False self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk(persistent=False) def test_prepare_ramdisk_force_persistent_boot_device_sloppy_false(self): for value in ['false', 'f', '0', 'off', 'n', 'NO', 'yxz']: self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = value self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk() def test_prepare_ramdisk_force_persistent_boot_device_default(self): self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = 'Default' self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk(persistent=False) def test_prepare_ramdisk_force_persistent_boot_device_always(self): self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = 'Always' self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk(persistent=True) def test_prepare_ramdisk_force_persistent_boot_device_never(self): self.node.provision_state = states.DEPLOYING driver_info = self.node.driver_info driver_info['force_persistent_boot_device'] = 'Never' self.node.driver_info = driver_info self.node.save() self._test_prepare_ramdisk(persistent=False) def test_prepare_ramdisk_rescue(self): self.node.provision_state = states.RESCUING self.node.save() self._test_prepare_ramdisk(mode='rescue') def test_prepare_ramdisk_uefi(self): self.node.provision_state = states.DEPLOYING self.node.save() properties = self.node.properties properties['capabilities'] = 'boot_mode:uefi' self.node.properties = properties self.node.save() self._test_prepare_ramdisk(uefi=True) @mock.patch.object(os.path, 'isfile', lambda path: True) @mock.patch.object(common_utils, 'file_has_content', lambda *args: False) @mock.patch('ironic.common.utils.write_to_file', autospec=True) @mock.patch('ironic.common.utils.render_template', autospec=True) def test_prepare_ramdisk_ipxe_with_copy_file_different( self, render_mock, write_mock): self.node.provision_state = states.DEPLOYING self.node.save() self.config(group='pxe', ipxe_enabled=True) self.config(group='deploy', http_url='http://myserver') render_mock.return_value = 'foo' self._test_prepare_ramdisk() write_mock.assert_called_once_with( os.path.join( CONF.deploy.http_root, os.path.basename(CONF.pxe.ipxe_boot_script)), 'foo') render_mock.assert_called_once_with( CONF.pxe.ipxe_boot_script, {'ipxe_for_mac_uri': 'pxelinux.cfg/'}) @mock.patch.object(os.path, 'isfile', lambda path: False) @mock.patch('ironic.common.utils.file_has_content', autospec=True) @mock.patch('ironic.common.utils.write_to_file', autospec=True) @mock.patch('ironic.common.utils.render_template', autospec=True) def test_prepare_ramdisk_ipxe_with_copy_no_file( self, render_mock, write_mock, file_has_content_mock): self.node.provision_state = states.DEPLOYING self.node.save() self.config(group='pxe', ipxe_enabled=True) self.config(group='deploy', http_url='http://myserver') render_mock.return_value = 'foo' self._test_prepare_ramdisk() self.assertFalse(file_has_content_mock.called) write_mock.assert_called_once_with( os.path.join( CONF.deploy.http_root, os.path.basename(CONF.pxe.ipxe_boot_script)), 'foo') render_mock.assert_called_once_with( CONF.pxe.ipxe_boot_script, {'ipxe_for_mac_uri': 'pxelinux.cfg/'}) @mock.patch.object(os.path, 'isfile', lambda path: True) @mock.patch.object(common_utils, 'file_has_content', lambda *args: True) @mock.patch('ironic.common.utils.write_to_file', autospec=True) @mock.patch('ironic.common.utils.render_template', autospec=True) def test_prepare_ramdisk_ipxe_without_copy( self, render_mock, write_mock): self.node.provision_state = states.DEPLOYING self.node.save() self.config(group='pxe', ipxe_enabled=True) self.config(group='deploy', http_url='http://myserver') self._test_prepare_ramdisk() self.assertFalse(write_mock.called) @mock.patch.object(common_utils, 'render_template', lambda *args: 'foo') @mock.patch('ironic.common.utils.write_to_file', autospec=True) def test_prepare_ramdisk_ipxe_swift(self, write_mock): self.node.provision_state = states.DEPLOYING self.node.save() self.config(group='pxe', ipxe_enabled=True) self.config(group='pxe', ipxe_use_swift=True) self.config(group='deploy', http_url='http://myserver') self._test_prepare_ramdisk(ipxe_use_swift=True) write_mock.assert_called_once_with( os.path.join( CONF.deploy.http_root, os.path.basename(CONF.pxe.ipxe_boot_script)), 'foo') @mock.patch.object(common_utils, 'render_template', lambda *args: 'foo') @mock.patch('ironic.common.utils.write_to_file', autospec=True) def test_prepare_ramdisk_ipxe_swift_whole_disk_image( self, write_mock): self.node.provision_state = states.DEPLOYING self.node.save() self.config(group='pxe', ipxe_enabled=True) self.config(group='pxe', ipxe_use_swift=True) self.config(group='deploy', http_url='http://myserver') self._test_prepare_ramdisk(ipxe_use_swift=True, whole_disk_image=True) write_mock.assert_called_once_with( os.path.join( CONF.deploy.http_root, os.path.basename(CONF.pxe.ipxe_boot_script)), 'foo') def test_prepare_ramdisk_cleaning(self): self.node.provision_state = states.CLEANING self.node.save() self._test_prepare_ramdisk(cleaning=True) @mock.patch.object(manager_utils, 'node_set_boot_mode', autospec=True) def test_prepare_ramdisk_set_boot_mode_on_bm( self, set_boot_mode_mock): self.node.provision_state = states.DEPLOYING properties = self.node.properties properties['capabilities'] = 'boot_mode:uefi' self.node.properties = properties self.node.save() self._test_prepare_ramdisk(uefi=True) set_boot_mode_mock.assert_called_once_with(mock.ANY, boot_modes.UEFI) @mock.patch.object(manager_utils, 'node_set_boot_mode', autospec=True) def test_prepare_ramdisk_set_boot_mode_on_ironic( self, set_boot_mode_mock): self.node.provision_state = states.DEPLOYING self.node.save() self._test_prepare_ramdisk(node_boot_mode=boot_modes.LEGACY_BIOS) with task_manager.acquire(self.context, self.node.uuid) as task: driver_internal_info = task.node.driver_internal_info self.assertIn('deploy_boot_mode', driver_internal_info) self.assertEqual(boot_modes.LEGACY_BIOS, driver_internal_info['deploy_boot_mode']) self.assertEqual(set_boot_mode_mock.call_count, 0) @mock.patch.object(manager_utils, 'node_set_boot_mode', autospec=True) def test_prepare_ramdisk_set_default_boot_mode_on_ironic_bios( self, set_boot_mode_mock): self.node.provision_state = states.DEPLOYING self.node.save() self.config(default_boot_mode=boot_modes.LEGACY_BIOS, group='deploy') self._test_prepare_ramdisk() with task_manager.acquire(self.context, self.node.uuid) as task: driver_internal_info = task.node.driver_internal_info self.assertIn('deploy_boot_mode', driver_internal_info) self.assertEqual(boot_modes.LEGACY_BIOS, driver_internal_info['deploy_boot_mode']) self.assertEqual(set_boot_mode_mock.call_count, 1) @mock.patch.object(manager_utils, 'node_set_boot_mode', autospec=True) def test_prepare_ramdisk_set_default_boot_mode_on_ironic_uefi( self, set_boot_mode_mock): self.node.provision_state = states.DEPLOYING self.node.save() self.config(default_boot_mode=boot_modes.UEFI, group='deploy') self._test_prepare_ramdisk(uefi=True) with task_manager.acquire(self.context, self.node.uuid) as task: driver_internal_info = task.node.driver_internal_info self.assertIn('deploy_boot_mode', driver_internal_info) self.assertEqual(boot_modes.UEFI, driver_internal_info['deploy_boot_mode']) self.assertEqual(set_boot_mode_mock.call_count, 1) @mock.patch.object(manager_utils, 'node_set_boot_mode', autospec=True) def test_prepare_ramdisk_conflicting_boot_modes( self, set_boot_mode_mock): self.node.provision_state = states.DEPLOYING properties = self.node.properties properties['capabilities'] = 'boot_mode:uefi' self.node.properties = properties self.node.save() self._test_prepare_ramdisk(uefi=True, node_boot_mode=boot_modes.LEGACY_BIOS) set_boot_mode_mock.assert_called_once_with(mock.ANY, boot_modes.UEFI) @mock.patch.object(manager_utils, 'node_set_boot_mode', autospec=True) def test_prepare_ramdisk_conflicting_boot_modes_set_unsupported( self, set_boot_mode_mock): self.node.provision_state = states.DEPLOYING properties = self.node.properties properties['capabilities'] = 'boot_mode:uefi' self.node.properties = properties self.node.save() set_boot_mode_mock.side_effect = exception.UnsupportedDriverExtension( extension='management', driver='test-driver' ) self.assertRaises(exception.UnsupportedDriverExtension, self._test_prepare_ramdisk, uefi=True, node_boot_mode=boot_modes.LEGACY_BIOS) @mock.patch.object(manager_utils, 'node_set_boot_mode', autospec=True) def test_prepare_ramdisk_set_boot_mode_not_called( self, set_boot_mode_mock): self.node.provision_state = states.DEPLOYING self.node.save() properties = self.node.properties properties['capabilities'] = 'boot_mode:uefi' self.node.properties = properties self.node.save() self._test_prepare_ramdisk(uefi=True, node_boot_mode=boot_modes.UEFI) self.assertEqual(set_boot_mode_mock.call_count, 0) @mock.patch.object(pxe_utils, 'clean_up_pxe_env', autospec=True) @mock.patch.object(pxe_utils, 'get_image_info', autospec=True) def _test_clean_up_ramdisk(self, get_image_info_mock, clean_up_pxe_env_mock, mode='deploy'): with task_manager.acquire(self.context, self.node.uuid) as task: kernel_label = '%s_kernel' % mode ramdisk_label = '%s_ramdisk' % mode image_info = {kernel_label: ['', '/path/to/' + kernel_label], ramdisk_label: ['', '/path/to/' + ramdisk_label]} get_image_info_mock.return_value = image_info task.driver.boot.clean_up_ramdisk(task) clean_up_pxe_env_mock.assert_called_once_with(task, image_info) get_image_info_mock.assert_called_once_with(task.node, mode=mode) def test_clean_up_ramdisk(self): self.node.provision_state = states.DEPLOYING self.node.save() self._test_clean_up_ramdisk() def test_clean_up_ramdisk_rescue(self): self.node.provision_state = states.RESCUING self.node.save() self._test_clean_up_ramdisk(mode='rescue') @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(deploy_utils, 'switch_pxe_config', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory', autospec=True) @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_prepare_instance_netboot( self, get_image_info_mock, cache_mock, dhcp_factory_mock, switch_pxe_config_mock, set_boot_device_mock): provider_mock = mock.MagicMock() dhcp_factory_mock.return_value = provider_mock image_info = {'kernel': ('', '/path/to/kernel'), 'ramdisk': ('', '/path/to/ramdisk')} get_image_info_mock.return_value = image_info with task_manager.acquire(self.context, self.node.uuid) as task: dhcp_opts = pxe_utils.dhcp_options_for_instance( task, ipxe_enabled=CONF.pxe.ipxe_enabled) pxe_config_path = pxe_utils.get_pxe_config_file_path( task.node.uuid) task.node.properties['capabilities'] = 'boot_mode:bios' task.node.driver_internal_info['root_uuid_or_disk_id'] = ( "30212642-09d3-467f-8e09-21685826ab50") task.node.driver_internal_info['is_whole_disk_image'] = False task.driver.boot.prepare_instance(task) get_image_info_mock.assert_called_once_with( task) cache_mock.assert_called_once_with( task, image_info, ipxe_enabled=CONF.pxe.ipxe_enabled) provider_mock.update_dhcp.assert_called_once_with(task, dhcp_opts) switch_pxe_config_mock.assert_called_once_with( pxe_config_path, "30212642-09d3-467f-8e09-21685826ab50", 'bios', False, False, False, False, ipxe_enabled=False) set_boot_device_mock.assert_called_once_with(task, boot_devices.PXE, persistent=True) @mock.patch('os.path.isfile', return_value=False) @mock.patch.object(pxe_utils, 'create_pxe_config', autospec=True) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(deploy_utils, 'switch_pxe_config', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory', autospec=True) @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_prepare_instance_netboot_active( self, get_image_info_mock, cache_mock, dhcp_factory_mock, switch_pxe_config_mock, set_boot_device_mock, create_pxe_config_mock, isfile_mock): provider_mock = mock.MagicMock() dhcp_factory_mock.return_value = provider_mock image_info = {'kernel': ('', '/path/to/kernel'), 'ramdisk': ('', '/path/to/ramdisk')} get_image_info_mock.return_value = image_info self.node.provision_state = states.ACTIVE self.node.save() with task_manager.acquire(self.context, self.node.uuid) as task: dhcp_opts = pxe_utils.dhcp_options_for_instance( task, ipxe_enabled=CONF.pxe.ipxe_enabled) pxe_config_path = pxe_utils.get_pxe_config_file_path( task.node.uuid) task.node.properties['capabilities'] = 'boot_mode:bios' task.node.driver_internal_info['root_uuid_or_disk_id'] = ( "30212642-09d3-467f-8e09-21685826ab50") task.node.driver_internal_info['is_whole_disk_image'] = False task.driver.boot.prepare_instance(task) get_image_info_mock.assert_called_once_with( task) cache_mock.assert_called_once_with( task, image_info, ipxe_enabled=CONF.pxe.ipxe_enabled) provider_mock.update_dhcp.assert_called_once_with(task, dhcp_opts) create_pxe_config_mock.assert_called_once_with( task, mock.ANY, CONF.pxe.pxe_config_template, ipxe_enabled=False) switch_pxe_config_mock.assert_called_once_with( pxe_config_path, "30212642-09d3-467f-8e09-21685826ab50", 'bios', False, False, False, False, ipxe_enabled=False) self.assertFalse(set_boot_device_mock.called) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(deploy_utils, 'switch_pxe_config', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory') @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_prepare_instance_netboot_missing_root_uuid( self, get_image_info_mock, cache_mock, dhcp_factory_mock, switch_pxe_config_mock, set_boot_device_mock): provider_mock = mock.MagicMock() dhcp_factory_mock.return_value = provider_mock image_info = {'kernel': ('', '/path/to/kernel'), 'ramdisk': ('', '/path/to/ramdisk')} get_image_info_mock.return_value = image_info with task_manager.acquire(self.context, self.node.uuid) as task: dhcp_opts = pxe_utils.dhcp_options_for_instance( task, ipxe_enabled=CONF.pxe.ipxe_enabled) task.node.properties['capabilities'] = 'boot_mode:bios' task.node.driver_internal_info['is_whole_disk_image'] = False task.driver.boot.prepare_instance(task) get_image_info_mock.assert_called_once_with(task) cache_mock.assert_called_once_with( task, image_info, ipxe_enabled=CONF.pxe.ipxe_enabled) provider_mock.update_dhcp.assert_called_once_with(task, dhcp_opts) self.assertFalse(switch_pxe_config_mock.called) self.assertFalse(set_boot_device_mock.called) @mock.patch.object(pxe.LOG, 'warning', autospec=True) @mock.patch.object(pxe_utils, 'clean_up_pxe_config', autospec=True) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory') @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_prepare_instance_whole_disk_image_missing_root_uuid( self, get_image_info_mock, cache_mock, dhcp_factory_mock, set_boot_device_mock, clean_up_pxe_mock, log_mock): provider_mock = mock.MagicMock() dhcp_factory_mock.return_value = provider_mock get_image_info_mock.return_value = {} with task_manager.acquire(self.context, self.node.uuid) as task: dhcp_opts = pxe_utils.dhcp_options_for_instance( task, CONF.pxe.ipxe_enabled) task.node.properties['capabilities'] = 'boot_mode:bios' task.node.driver_internal_info['is_whole_disk_image'] = True task.driver.boot.prepare_instance(task) get_image_info_mock.assert_called_once_with(task) cache_mock.assert_called_once_with( task, {}, ipxe_enabled=CONF.pxe.ipxe_enabled) provider_mock.update_dhcp.assert_called_once_with(task, dhcp_opts) self.assertTrue(log_mock.called) clean_up_pxe_mock.assert_called_once_with( task, ipxe_enabled=CONF.pxe.ipxe_enabled) set_boot_device_mock.assert_called_once_with( task, boot_devices.DISK, persistent=True) @mock.patch('os.path.isfile', lambda filename: False) @mock.patch.object(pxe_utils, 'create_pxe_config', autospec=True) @mock.patch.object(deploy_utils, 'is_iscsi_boot', lambda task: True) @mock.patch.object(noop_storage.NoopStorage, 'should_write_image', lambda task: False) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(deploy_utils, 'switch_pxe_config', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory', autospec=True) @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_prepare_instance_netboot_iscsi( self, get_image_info_mock, cache_mock, dhcp_factory_mock, switch_pxe_config_mock, set_boot_device_mock, create_pxe_config_mock): http_url = 'http://192.1.2.3:1234' self.config(ipxe_enabled=True, group='pxe') self.config(http_url=http_url, group='deploy') provider_mock = mock.MagicMock() dhcp_factory_mock.return_value = provider_mock vol_id = uuidutils.generate_uuid() obj_utils.create_test_volume_target( self.context, node_id=self.node.id, volume_type='iscsi', boot_index=0, volume_id='1234', uuid=vol_id, properties={'target_lun': 0, 'target_portal': 'fake_host:3260', 'target_iqn': 'fake_iqn', 'auth_username': 'fake_username', 'auth_password': 'fake_password'}) with task_manager.acquire(self.context, self.node.uuid) as task: task.node.driver_internal_info = { 'boot_from_volume': vol_id} dhcp_opts = pxe_utils.dhcp_options_for_instance(task, ipxe_enabled=True) pxe_config_path = pxe_utils.get_pxe_config_file_path( task.node.uuid) task.node.properties['capabilities'] = 'boot_mode:bios' task.driver.boot.prepare_instance(task) self.assertFalse(get_image_info_mock.called) self.assertFalse(cache_mock.called) provider_mock.update_dhcp.assert_called_once_with(task, dhcp_opts) create_pxe_config_mock.assert_called_once_with( task, mock.ANY, CONF.pxe.pxe_config_template, ipxe_enabled=True) switch_pxe_config_mock.assert_called_once_with( pxe_config_path, None, boot_modes.LEGACY_BIOS, False, ipxe_enabled=True, ramdisk_boot=False, iscsi_boot=True) set_boot_device_mock.assert_called_once_with(task, boot_devices.PXE, persistent=True) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(pxe_utils, 'clean_up_pxe_config', autospec=True) def test_prepare_instance_localboot(self, clean_up_pxe_config_mock, set_boot_device_mock): with task_manager.acquire(self.context, self.node.uuid) as task: instance_info = task.node.instance_info instance_info['capabilities'] = {'boot_option': 'local'} task.node.instance_info = instance_info task.node.save() task.driver.boot.prepare_instance(task) clean_up_pxe_config_mock.assert_called_once_with( task, ipxe_enabled=CONF.pxe.ipxe_enabled) set_boot_device_mock.assert_called_once_with(task, boot_devices.DISK, persistent=True) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(pxe_utils, 'clean_up_pxe_config', autospec=True) def test_prepare_instance_localboot_active(self, clean_up_pxe_config_mock, set_boot_device_mock): self.node.provision_state = states.ACTIVE self.node.save() with task_manager.acquire(self.context, self.node.uuid) as task: instance_info = task.node.instance_info instance_info['capabilities'] = {'boot_option': 'local'} task.node.instance_info = instance_info task.node.save() task.driver.boot.prepare_instance(task) clean_up_pxe_config_mock.assert_called_once_with( task, ipxe_enabled=CONF.pxe.ipxe_enabled) self.assertFalse(set_boot_device_mock.called) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(deploy_utils, 'switch_pxe_config', autospec=True) @mock.patch.object(pxe_utils, 'create_pxe_config', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory', autospec=True) @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def _test_prepare_instance_ramdisk( self, get_image_info_mock, cache_mock, dhcp_factory_mock, create_pxe_config_mock, switch_pxe_config_mock, set_boot_device_mock, config_file_exits=False): image_info = {'kernel': ['', '/path/to/kernel'], 'ramdisk': ['', '/path/to/ramdisk']} get_image_info_mock.return_value = image_info provider_mock = mock.MagicMock() dhcp_factory_mock.return_value = provider_mock self.node.provision_state = states.DEPLOYING get_image_info_mock.return_value = image_info with task_manager.acquire(self.context, self.node.uuid) as task: instance_info = task.node.instance_info instance_info['capabilities'] = {'boot_option': 'ramdisk'} task.node.instance_info = instance_info task.node.save() dhcp_opts = pxe_utils.dhcp_options_for_instance( task, ipxe_enabled=CONF.pxe.ipxe_enabled) pxe_config_path = pxe_utils.get_pxe_config_file_path( task.node.uuid) task.driver.boot.prepare_instance(task) get_image_info_mock.assert_called_once_with(task) cache_mock.assert_called_once_with( task, image_info, CONF.pxe.ipxe_enabled) provider_mock.update_dhcp.assert_called_once_with(task, dhcp_opts) if config_file_exits: self.assertFalse(create_pxe_config_mock.called) else: create_pxe_config_mock.assert_called_once_with( task, mock.ANY, CONF.pxe.pxe_config_template, ipxe_enabled=False) switch_pxe_config_mock.assert_called_once_with( pxe_config_path, None, 'bios', False, ipxe_enabled=False, iscsi_boot=False, ramdisk_boot=True) set_boot_device_mock.assert_called_once_with(task, boot_devices.PXE, persistent=True) @mock.patch.object(os.path, 'isfile', lambda path: True) def test_prepare_instance_ramdisk_pxe_conf_missing(self): self._test_prepare_instance_ramdisk(config_file_exits=True) @mock.patch.object(os.path, 'isfile', lambda path: False) def test_prepare_instance_ramdisk_pxe_conf_exists(self): self._test_prepare_instance_ramdisk(config_file_exits=False) @mock.patch.object(pxe_utils, 'clean_up_pxe_env', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_clean_up_instance(self, get_image_info_mock, clean_up_pxe_env_mock): with task_manager.acquire(self.context, self.node.uuid) as task: image_info = {'kernel': ['', '/path/to/kernel'], 'ramdisk': ['', '/path/to/ramdisk']} get_image_info_mock.return_value = image_info task.driver.boot.clean_up_instance(task) clean_up_pxe_env_mock.assert_called_once_with(task, image_info) get_image_info_mock.assert_called_once_with(task) class PXERamdiskDeployTestCase(db_base.DbTestCase): def setUp(self): super(PXERamdiskDeployTestCase, self).setUp() self.temp_dir = tempfile.mkdtemp() self.config(tftp_root=self.temp_dir, group='pxe') self.temp_dir = tempfile.mkdtemp() self.config(images_path=self.temp_dir, group='pxe') self.config(enabled_deploy_interfaces=['ramdisk']) self.config(enabled_boot_interfaces=['pxe']) for iface in drivers_base.ALL_INTERFACES: impl = 'fake' if iface == 'network': impl = 'noop' if iface == 'deploy': impl = 'ramdisk' if iface == 'boot': impl = 'pxe' config_kwarg = {'enabled_%s_interfaces' % iface: [impl], 'default_%s_interface' % iface: impl} self.config(**config_kwarg) self.config(enabled_hardware_types=['fake-hardware']) instance_info = INST_INFO_DICT self.node = obj_utils.create_test_node( self.context, driver='fake-hardware', instance_info=instance_info, driver_info=DRV_INFO_DICT, driver_internal_info=DRV_INTERNAL_INFO_DICT) self.port = obj_utils.create_test_port(self.context, node_id=self.node.id) @mock.patch.object(manager_utils, 'node_set_boot_device', autospec=True) @mock.patch.object(deploy_utils, 'switch_pxe_config', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory', autospec=True) @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_prepare_instance_ramdisk( self, get_image_info_mock, cache_mock, dhcp_factory_mock, switch_pxe_config_mock, set_boot_device_mock): provider_mock = mock.MagicMock() dhcp_factory_mock.return_value = provider_mock self.node.provision_state = states.DEPLOYING image_info = {'kernel': ('', '/path/to/kernel'), 'ramdisk': ('', '/path/to/ramdisk')} get_image_info_mock.return_value = image_info with task_manager.acquire(self.context, self.node.uuid) as task: dhcp_opts = pxe_utils.dhcp_options_for_instance( task, ipxe_enabled=CONF.pxe.ipxe_enabled) pxe_config_path = pxe_utils.get_pxe_config_file_path( task.node.uuid) task.node.properties['capabilities'] = 'boot_option:netboot' task.node.driver_internal_info['is_whole_disk_image'] = False task.driver.deploy.prepare(task) task.driver.deploy.deploy(task) get_image_info_mock.assert_called_once_with(task) cache_mock.assert_called_once_with( task, image_info, ipxe_enabled=CONF.pxe.ipxe_enabled) provider_mock.update_dhcp.assert_called_once_with(task, dhcp_opts) switch_pxe_config_mock.assert_called_once_with( pxe_config_path, None, 'bios', False, ipxe_enabled=False, iscsi_boot=False, ramdisk_boot=True) set_boot_device_mock.assert_called_once_with(task, boot_devices.PXE, persistent=True) @mock.patch.object(pxe.LOG, 'warning', autospec=True) @mock.patch.object(deploy_utils, 'switch_pxe_config', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory', autospec=True) @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_deploy(self, mock_image_info, mock_cache, mock_dhcp_factory, mock_switch_config, mock_warning): image_info = {'kernel': ('', '/path/to/kernel'), 'ramdisk': ('', '/path/to/ramdisk')} mock_image_info.return_value = image_info i_info = self.node.instance_info i_info.update({'capabilities': {'boot_option': 'ramdisk'}}) self.node.instance_info = i_info self.node.save() with task_manager.acquire(self.context, self.node.uuid) as task: self.assertIsNone(task.driver.deploy.deploy(task)) mock_image_info.assert_called_once_with(task) mock_cache.assert_called_once_with( task, image_info, ipxe_enabled=CONF.pxe.ipxe_enabled) self.assertFalse(mock_warning.called) i_info['configdrive'] = 'meow' self.node.instance_info = i_info self.node.save() mock_warning.reset_mock() with task_manager.acquire(self.context, self.node.uuid) as task: self.assertIsNone(task.driver.deploy.deploy(task)) self.assertTrue(mock_warning.called) @mock.patch.object(pxe.PXEBoot, 'prepare_instance', autospec=True) def test_prepare(self, mock_prepare_instance): node = self.node node.provision_state = states.DEPLOYING node.instance_info = {} node.save() with task_manager.acquire(self.context, node.uuid) as task: task.driver.deploy.prepare(task) self.assertFalse(mock_prepare_instance.called) self.assertEqual({'boot_option': 'ramdisk'}, task.node.instance_info['capabilities']) @mock.patch.object(pxe.PXEBoot, 'prepare_instance', autospec=True) def test_prepare_active(self, mock_prepare_instance): node = self.node node.provision_state = states.ACTIVE node.save() with task_manager.acquire(self.context, node.uuid) as task: task.driver.deploy.prepare(task) mock_prepare_instance.assert_called_once_with(mock.ANY, task) @mock.patch.object(pxe.PXEBoot, 'prepare_instance', autospec=True) def test_prepare_unrescuing(self, mock_prepare_instance): node = self.node node.provision_state = states.UNRESCUING node.save() with task_manager.acquire(self.context, node.uuid) as task: task.driver.deploy.prepare(task) mock_prepare_instance.assert_called_once_with(mock.ANY, task) @mock.patch.object(pxe.LOG, 'warning', autospec=True) @mock.patch.object(pxe.PXEBoot, 'prepare_instance', autospec=True) def test_prepare_fixes_and_logs_boot_option_warning( self, mock_prepare_instance, mock_warning): node = self.node node.properties['capabilities'] = 'boot_option:ramdisk' node.provision_state = states.DEPLOYING node.instance_info = {} node.save() with task_manager.acquire(self.context, node.uuid) as task: task.driver.deploy.prepare(task) self.assertFalse(mock_prepare_instance.called) self.assertEqual({'boot_option': 'ramdisk'}, task.node.instance_info['capabilities']) self.assertTrue(mock_warning.called) @mock.patch.object(deploy_utils, 'validate_image_properties', autospec=True) def test_validate(self, mock_validate_img): node = self.node node.properties['capabilities'] = 'boot_option:netboot' node.save() with task_manager.acquire(self.context, node.uuid) as task: task.driver.deploy.validate(task) self.assertTrue(mock_validate_img.called) @mock.patch.object(fake.FakeBoot, 'validate', autospec=True) @mock.patch.object(deploy_utils, 'validate_image_properties', autospec=True) def test_validate_interface_mismatch(self, mock_validate_image, mock_boot_validate): node = self.node node.boot_interface = 'fake' node.save() self.config(enabled_boot_interfaces=['fake'], default_boot_interface='fake') with task_manager.acquire(self.context, node.uuid) as task: error = self.assertRaises(exception.InvalidParameterValue, task.driver.deploy.validate, task) error_message = ('Invalid configuration: The boot interface must ' 'have the `ramdisk_boot` capability. You are ' 'using an incompatible boot interface.') self.assertEqual(error_message, str(error)) self.assertFalse(mock_boot_validate.called) self.assertFalse(mock_validate_image.called) @mock.patch.object(pxe.PXEBoot, 'validate', autospec=True) def test_validate_calls_boot_validate(self, mock_validate): with task_manager.acquire(self.context, self.node.uuid) as task: task.driver.deploy.validate(task) mock_validate.assert_called_once_with(mock.ANY, task) @mock.patch.object(manager_utils, 'restore_power_state_if_needed', autospec=True) @mock.patch.object(manager_utils, 'power_on_node_if_needed', autospec=True) @mock.patch.object(pxe.LOG, 'warning', autospec=True) @mock.patch.object(deploy_utils, 'switch_pxe_config', autospec=True) @mock.patch.object(dhcp_factory, 'DHCPFactory', autospec=True) @mock.patch.object(pxe_utils, 'cache_ramdisk_kernel', autospec=True) @mock.patch.object(pxe_utils, 'get_instance_image_info', autospec=True) def test_deploy_with_smartnic_port( self, mock_image_info, mock_cache, mock_dhcp_factory, mock_switch_config, mock_warning, power_on_node_if_needed_mock, restore_power_state_mock): image_info = {'kernel': ('', '/path/to/kernel'), 'ramdisk': ('', '/path/to/ramdisk')} mock_image_info.return_value = image_info i_info = self.node.instance_info i_info.update({'capabilities': {'boot_option': 'ramdisk'}}) self.node.instance_info = i_info self.node.save() with task_manager.acquire(self.context, self.node.uuid) as task: power_on_node_if_needed_mock.return_value = states.POWER_OFF self.assertIsNone(task.driver.deploy.deploy(task)) mock_image_info.assert_called_once_with(task) mock_cache.assert_called_once_with( task, image_info, ipxe_enabled=CONF.pxe.ipxe_enabled) self.assertFalse(mock_warning.called) power_on_node_if_needed_mock.assert_called_once_with(task) restore_power_state_mock.assert_called_once_with( task, states.POWER_OFF) i_info['configdrive'] = 'meow' self.node.instance_info = i_info self.node.save() mock_warning.reset_mock() with task_manager.acquire(self.context, self.node.uuid) as task: self.assertIsNone(task.driver.deploy.deploy(task)) self.assertTrue(mock_warning.called) class PXEValidateRescueTestCase(db_base.DbTestCase): def setUp(self): super(PXEValidateRescueTestCase, self).setUp() for iface in drivers_base.ALL_INTERFACES: impl = 'fake' if iface == 'network': impl = 'flat' if iface == 'rescue': impl = 'agent' if iface == 'boot': impl = 'pxe' config_kwarg = {'enabled_%s_interfaces' % iface: [impl], 'default_%s_interface' % iface: impl} self.config(**config_kwarg) self.config(enabled_hardware_types=['fake-hardware']) driver_info = DRV_INFO_DICT driver_info.update({'rescue_ramdisk': 'my_ramdisk', 'rescue_kernel': 'my_kernel'}) instance_info = INST_INFO_DICT instance_info.update({'rescue_password': 'password'}) n = { 'driver': 'fake-hardware', 'instance_info': instance_info, 'driver_info': driver_info, 'driver_internal_info': DRV_INTERNAL_INFO_DICT, } self.node = obj_utils.create_test_node(self.context, **n) def test_validate_rescue(self): with task_manager.acquire(self.context, self.node.uuid) as task: task.driver.boot.validate_rescue(task) def test_validate_rescue_no_rescue_ramdisk(self): driver_info = self.node.driver_info del driver_info['rescue_ramdisk'] self.node.driver_info = driver_info self.node.save() with task_manager.acquire(self.context, self.node.uuid) as task: self.assertRaisesRegex(exception.MissingParameterValue, 'Missing.*rescue_ramdisk', task.driver.boot.validate_rescue, task) def test_validate_rescue_fails_no_rescue_kernel(self): driver_info = self.node.driver_info del driver_info['rescue_kernel'] self.node.driver_info = driver_info self.node.save() with task_manager.acquire(self.context, self.node.uuid) as task: self.assertRaisesRegex(exception.MissingParameterValue, 'Missing.*rescue_kernel', task.driver.boot.validate_rescue, task)

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0.844767

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0.791759

0.769514

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0.257841

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6aea03aee927de9288877466e8b780cf42cff828

22,578

py

Python

yardstick/tests/unit/benchmark/scenarios/networking/test_pktgen.py

mythwm/yardstick

ea13581f450c9c44f6f73d383e6a192697a95cc1

[ "Apache-2.0" ]

null

yardstick/tests/unit/benchmark/scenarios/networking/test_pktgen.py

mythwm/yardstick

ea13581f450c9c44f6f73d383e6a192697a95cc1

[ "Apache-2.0" ]

null

yardstick/tests/unit/benchmark/scenarios/networking/test_pktgen.py

mythwm/yardstick

ea13581f450c9c44f6f73d383e6a192697a95cc1

[ "Apache-2.0" ]

null

############################################################################## # Copyright (c) 2015 Ericsson AB and others. # # All rights reserved. This program and the accompanying materials # are made available under the terms of the Apache License, Version 2.0 # which accompanies this distribution, and is available at # http://www.apache.org/licenses/LICENSE-2.0 ############################################################################## import mock import unittest from oslo_serialization import jsonutils from yardstick.benchmark.scenarios.networking import pktgen @mock.patch('yardstick.benchmark.scenarios.networking.pktgen.ssh') class PktgenTestCase(unittest.TestCase): def setUp(self): self.ctx = { 'host': { 'ip': '172.16.0.137', 'user': 'root', 'key_filename': 'mykey.key' }, 'target': { 'ip': '172.16.0.138', 'user': 'root', 'key_filename': 'mykey.key', 'ipaddr': '172.16.0.138' } } def test_pktgen_successful_setup(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.setup() mock_ssh.SSH.from_node().execute.return_value = (0, '', '') self.assertIsNotNone(p.server) self.assertIsNotNone(p.client) self.assertTrue(p.setup_done) def test_pktgen_successful_iptables_setup(self, mock_ssh): args = { 'options': {'packetsize': 60, 'number_of_ports': 10}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.number_of_ports = args['options']['number_of_ports'] mock_ssh.SSH.from_node().execute.return_value = (0, '', '') p._iptables_setup() mock_ssh.SSH.from_node().execute.assert_called_with( "sudo iptables -F; " "sudo iptables -A INPUT -p udp --dport 1000:%s -j DROP" % 1010, timeout=60) def test_pktgen_unsuccessful_iptables_setup(self, mock_ssh): args = { 'options': {'packetsize': 60, 'number_of_ports': 10}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.number_of_ports = args['options']['number_of_ports'] mock_ssh.SSH.from_node().execute.return_value = (1, '', 'FOOBAR') self.assertRaises(RuntimeError, p._iptables_setup) def test_pktgen_successful_iptables_get_result(self, mock_ssh): args = { 'options': {'packetsize': 60, 'number_of_ports': 10}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.number_of_ports = args['options']['number_of_ports'] mock_ssh.SSH.from_node().execute.return_value = (0, '150000', '') p._iptables_get_result() mock_ssh.SSH.from_node().execute.assert_called_with( "sudo iptables -L INPUT -vnx |" "awk '/dpts:1000:%s/ {{printf \"%%s\", $1}}'" % 1010) def test_pktgen_unsuccessful_iptables_get_result(self, mock_ssh): args = { 'options': {'packetsize': 60, 'number_of_ports': 10}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.number_of_ports = args['options']['number_of_ports'] mock_ssh.SSH.from_node().execute.return_value = (1, '', 'FOOBAR') self.assertRaises(RuntimeError, p._iptables_get_result) def test_pktgen_successful_no_sla(self, mock_ssh): args = { 'options': {'packetsize': 60, 'number_of_ports': 10}, } result = {} p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() p._iptables_get_result = mock.Mock(return_value=149300) sample_output = '{"packets_per_second": 9753, "errors": 0, \ "packets_sent": 149776, "packetsize": 60, "flows": 110, "ppm": 3179}' mock_ssh.SSH.from_node().execute.return_value = (0, sample_output, '') p.run(result) expected_result = jsonutils.loads(sample_output) expected_result["packets_received"] = 149300 expected_result["packetsize"] = 60 self.assertEqual(result, expected_result) def test_pktgen_successful_sla(self, mock_ssh): args = { 'options': {'packetsize': 60, 'number_of_ports': 10}, 'sla': {'max_ppm': 10000} } result = {} p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() p._iptables_get_result = mock.Mock(return_value=149300) sample_output = '{"packets_per_second": 9753, "errors": 0, \ "packets_sent": 149776, "packetsize": 60, "flows": 110, "ppm": 3179}' mock_ssh.SSH.from_node().execute.return_value = (0, sample_output, '') p.run(result) expected_result = jsonutils.loads(sample_output) expected_result["packets_received"] = 149300 expected_result["packetsize"] = 60 self.assertEqual(result, expected_result) def test_pktgen_unsuccessful_sla(self, mock_ssh): args = { 'options': {'packetsize': 60, 'number_of_ports': 10}, 'sla': {'max_ppm': 1000} } result = {} p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() p._iptables_get_result = mock.Mock(return_value=149300) sample_output = '{"packets_per_second": 9753, "errors": 0, \ "packets_sent": 149776, "packetsize": 60, "flows": 110}' mock_ssh.SSH.from_node().execute.return_value = (0, sample_output, '') self.assertRaises(AssertionError, p.run, result) def test_pktgen_unsuccessful_script_error(self, mock_ssh): args = { 'options': {'packetsize': 60, 'number_of_ports': 10}, 'sla': {'max_ppm': 1000} } result = {} p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', 'FOOBAR') self.assertRaises(RuntimeError, p.run, result) def test_pktgen_get_vnic_driver_name(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, 'ixgbevf', '') vnic_driver_name = p._get_vnic_driver_name() self.assertEqual(vnic_driver_name, 'ixgbevf') def test_pktgen_unsuccessful_get_vnic_driver_name(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._get_vnic_driver_name) def test_pktgen_get_sriov_queue_number(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '2', '') p.queue_number = p._get_sriov_queue_number() self.assertEqual(p.queue_number, 2) def test_pktgen_unsuccessful_get_sriov_queue_number(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._get_sriov_queue_number) def test_pktgen_get_available_queue_number(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '4', '') p._get_available_queue_number() mock_ssh.SSH.from_node().execute.assert_called_with( "sudo ethtool -l eth0 | grep Combined | head -1 |" "awk '{printf $2}'") def test_pktgen_unsuccessful_get_available_queue_number(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._get_available_queue_number) def test_pktgen_get_usable_queue_number(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '1', '') p._get_usable_queue_number() mock_ssh.SSH.from_node().execute.assert_called_with( "sudo ethtool -l eth0 | grep Combined | tail -1 |" "awk '{printf $2}'") def test_pktgen_unsuccessful_get_usable_queue_number(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._get_usable_queue_number) def test_pktgen_enable_ovs_multiqueue(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '4', '') p._get_usable_queue_number = mock.Mock(return_value=1) p._get_available_queue_number = mock.Mock(return_value=4) p.queue_number = p._enable_ovs_multiqueue() self.assertEqual(p.queue_number, 4) def test_pktgen_enable_ovs_multiqueue_1q(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '1', '') p._get_usable_queue_number = mock.Mock(return_value=1) p._get_available_queue_number = mock.Mock(return_value=1) p.queue_number = p._enable_ovs_multiqueue() self.assertEqual(p.queue_number, 1) def test_pktgen_unsuccessful_enable_ovs_multiqueue(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') p._get_usable_queue_number = mock.Mock(return_value=1) p._get_available_queue_number = mock.Mock(return_value=4) self.assertRaises(RuntimeError, p._enable_ovs_multiqueue) def test_pktgen_setup_irqmapping_ovs(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '10', '') p._setup_irqmapping_ovs(4) mock_ssh.SSH.from_node().execute.assert_called_with( "echo 8 | sudo tee /proc/irq/10/smp_affinity") def test_pktgen_setup_irqmapping_ovs_1q(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '10', '') p._setup_irqmapping_ovs(1) mock_ssh.SSH.from_node().execute.assert_called_with( "echo 1 | sudo tee /proc/irq/10/smp_affinity") def test_pktgen_unsuccessful_setup_irqmapping_ovs(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._setup_irqmapping_ovs, 4) def test_pktgen_unsuccessful_setup_irqmapping_ovs_1q(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._setup_irqmapping_ovs, 1) def test_pktgen_setup_irqmapping_sriov(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '10', '') p._setup_irqmapping_sriov(2) mock_ssh.SSH.from_node().execute.assert_called_with( "echo 2 | sudo tee /proc/irq/10/smp_affinity") def test_pktgen_setup_irqmapping_sriov_1q(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '10', '') p._setup_irqmapping_sriov(1) mock_ssh.SSH.from_node().execute.assert_called_with( "echo 1 | sudo tee /proc/irq/10/smp_affinity") def test_pktgen_unsuccessful_setup_irqmapping_sriov(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._setup_irqmapping_sriov, 2) def test_pktgen_unsuccessful_setup_irqmapping_sriov_1q(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._setup_irqmapping_sriov, 1) def test_pktgen_is_irqbalance_disabled(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '', '') p._is_irqbalance_disabled() mock_ssh.SSH.from_node().execute.assert_called_with( "grep ENABLED /etc/default/irqbalance") def test_pktgen_unsuccessful_is_irqbalance_disabled(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._is_irqbalance_disabled) def test_pktgen_disable_irqbalance(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '', '') p._disable_irqbalance() mock_ssh.SSH.from_node().execute.assert_called_with( "sudo service irqbalance disable") def test_pktgen_unsuccessful_disable_irqbalance(self, mock_ssh): args = { 'options': {'packetsize': 60}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (1, '', '') self.assertRaises(RuntimeError, p._disable_irqbalance) def test_pktgen_multiqueue_setup_ovs(self, mock_ssh): args = { 'options': {'packetsize': 60, 'multiqueue': True}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '4', '') p._is_irqbalance_disabled = mock.Mock(return_value=False) p._get_vnic_driver_name = mock.Mock(return_value="virtio_net") p._get_usable_queue_number = mock.Mock(return_value=1) p._get_available_queue_number = mock.Mock(return_value=4) p.multiqueue_setup() self.assertEqual(p.queue_number, 4) def test_pktgen_multiqueue_setup_ovs_1q(self, mock_ssh): args = { 'options': {'packetsize': 60, 'multiqueue': True}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '1', '') p._is_irqbalance_disabled = mock.Mock(return_value=False) p._get_vnic_driver_name = mock.Mock(return_value="virtio_net") p._get_usable_queue_number = mock.Mock(return_value=1) p._get_available_queue_number = mock.Mock(return_value=1) p.multiqueue_setup() self.assertEqual(p.queue_number, 1) def test_pktgen_multiqueue_setup_sriov(self, mock_ssh): args = { 'options': {'packetsize': 60, 'multiqueue': True}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '2', '') p._is_irqbalance_disabled = mock.Mock(return_value=False) p._get_vnic_driver_name = mock.Mock(return_value="ixgbevf") p.multiqueue_setup() self.assertEqual(p.queue_number, 2) def test_pktgen_multiqueue_setup_sriov_1q(self, mock_ssh): args = { 'options': {'packetsize': 60, 'multiqueue': True}, } p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() mock_ssh.SSH.from_node().execute.return_value = (0, '1', '') p._is_irqbalance_disabled = mock.Mock(return_value=False) p._get_vnic_driver_name = mock.Mock(return_value="ixgbevf") p.multiqueue_setup() self.assertEqual(p.queue_number, 1) def test_pktgen_run_with_setup_done(self, mock_ssh): args = { 'options': { 'packetsize': 60, 'number_of_ports': 10, 'duration': 20, 'multiqueue': True}, 'sla': { 'max_ppm': 1}} result = {} p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() p.setup_done = True p.multiqueue_setup_done = True mock_iptables_result = mock.Mock() mock_iptables_result.return_value = 149300 p._iptables_get_result = mock_iptables_result sample_output = '{"packets_per_second": 9753, "errors": 0, \ "packets_sent": 149300, "flows": 110, "ppm": 0}' mock_ssh.SSH.from_node().execute.return_value = (0, sample_output, '') p.run(result) expected_result = jsonutils.loads(sample_output) expected_result["packets_received"] = 149300 expected_result["packetsize"] = 60 self.assertEqual(result, expected_result) def test_pktgen_run_with_ovs_multiqueque(self, mock_ssh): args = { 'options': { 'packetsize': 60, 'number_of_ports': 10, 'duration': 20, 'multiqueue': True}, 'sla': { 'max_ppm': 1}} result = {} p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() p._get_vnic_driver_name = mock.Mock(return_value="virtio_net") p._get_usable_queue_number = mock.Mock(return_value=1) p._get_available_queue_number = mock.Mock(return_value=4) p._enable_ovs_multiqueue = mock.Mock(return_value=4) p._setup_irqmapping_ovs = mock.Mock() p._iptables_get_result = mock.Mock(return_value=149300) sample_output = '{"packets_per_second": 9753, "errors": 0, \ "packets_sent": 149300, "flows": 110, "ppm": 0}' mock_ssh.SSH.from_node().execute.return_value = (0, sample_output, '') p.run(result) expected_result = jsonutils.loads(sample_output) expected_result["packets_received"] = 149300 expected_result["packetsize"] = 60 self.assertEqual(result, expected_result) def test_pktgen_run_with_sriov_multiqueque(self, mock_ssh): args = { 'options': { 'packetsize': 60, 'number_of_ports': 10, 'duration': 20, 'multiqueue': True}, 'sla': { 'max_ppm': 1}} result = {} p = pktgen.Pktgen(args, self.ctx) p.server = mock_ssh.SSH.from_node() p.client = mock_ssh.SSH.from_node() p._get_vnic_driver_name = mock.Mock(return_value="ixgbevf") p._get_sriov_queue_number = mock.Mock(return_value=2) p._setup_irqmapping_sriov = mock.Mock() p._iptables_get_result = mock.Mock(return_value=149300) sample_output = '{"packets_per_second": 9753, "errors": 0, \ "packets_sent": 149300, "flows": 110, "ppm": 0}' mock_ssh.SSH.from_node().execute.return_value = (0, sample_output, '') p.run(result) expected_result = jsonutils.loads(sample_output) expected_result["packets_received"] = 149300 expected_result["packetsize"] = 60 self.assertEqual(result, expected_result)

33.799401

81

0.601825

2,796

22,578

4.54721

0.065451

0.082586

0.087305

0.122227

0.900346

0.87266

0.845131

0.840412

0.83821

0.81603

0

0.028077

0.260165

22,578

667

82

33.850075

0.733058

0.012269

0

0.693252

0

0.102448

0.00786

0

0.083845

1

0.0818

false

0

0.00818

0

0.092025

0.006135

0

null

0

1

0

null

0

6

0a7afd02bf2f95c23824af798fe6e8b3168e798a

47

py

Python

sr/utils/__init__.py

marcocamma/sr

221026b6e5bcaf1aab5e418260adf3724e517287

[ "MIT" ]

null

sr/utils/__init__.py

marcocamma/sr

221026b6e5bcaf1aab5e418260adf3724e517287

[ "MIT" ]

null

sr/utils/__init__.py

marcocamma/sr

221026b6e5bcaf1aab5e418260adf3724e517287

[ "MIT" ]

null

from . import unicode from . import conversion

15.666667

24

0.787234

6

47

6.166667

0.666667

0.540541

0

0.170213

47

2

25

23.5

0.948718

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

0a8197d1b7bc435e7e509cf003502a33ebc433f7

7,948

py

Python

xlsxwriter/test/styles/test_write_font.py

haiyangd/XlsxWriter

81f8c9435b3e03a1458bf9ba314b5d3f7508290f

[ "BSD-2-Clause-FreeBSD" ]

3

2018-02-26T12:31:41.000Z

2020-10-10T14:14:11.000Z

xlsxwriter/test/styles/test_write_font.py

haiyangd/XlsxWriter

81f8c9435b3e03a1458bf9ba314b5d3f7508290f

[ "BSD-2-Clause-FreeBSD" ]

null

xlsxwriter/test/styles/test_write_font.py

haiyangd/XlsxWriter

81f8c9435b3e03a1458bf9ba314b5d3f7508290f

[ "BSD-2-Clause-FreeBSD" ]

null

############################################################################### # # Tests for XlsxWriter. # # Copyright (c), 2013-2017, John McNamara, jmcnamara@cpan.org # import unittest from ...compatibility import StringIO from ...styles import Styles from ...format import Format class TestWriteFont(unittest.TestCase): """ Test the Styles _write_font() method. """ def setUp(self): self.fh = StringIO() self.styles = Styles() self.styles._set_filehandle(self.fh) def test_write_font_1(self): """Test the _write_font() method. Default properties.""" properties = {} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_2(self): """Test the _write_font() method. Bold.""" properties = {'bold': 1} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_3(self): """Test the _write_font() method. Italic.""" properties = {'italic': 1} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_4(self): """Test the _write_font() method. Underline.""" properties = {'underline': 1} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_5(self): """Test the _write_font() method. Strikeout.""" properties = {'font_strikeout': 1} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<strike/><sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_6(self): """Test the _write_font() method. Superscript.""" properties = {'font_script': 1} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<vertAlign val="superscript"/><sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_7(self): """Test the _write_font() method. Subscript.""" properties = {'font_script': 2} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<vertAlign val="subscript"/><sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_8(self): """Test the _write_font() method. Font name.""" properties = {'font_name': 'Arial'} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="1"/><name val="Arial"/><family val="2"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_9(self): """Test the _write_font() method. Font size.""" properties = {'size': 12} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="12"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_10(self): """Test the _write_font() method. Outline.""" properties = {'font_outline': 1} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<outline/><sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_11(self): """Test the _write_font() method. Shadow.""" properties = {'font_shadow': 1} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<shadow/><sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_12(self): """Test the _write_font() method. Colour = red.""" properties = {'color': '#FF0000'} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color rgb="FFFF0000"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_13(self): """Test the _write_font() method. All font attributes to check order.""" properties = { 'bold': 1, 'color': '#FF0000', 'font_outline': 1, 'font_script': 1, 'font_shadow': 1, 'font_strikeout': 1, 'italic': 1, 'size': 12, 'underline': 1, } xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<strike/><outline/><shadow/><vertAlign val="superscript"/><sz val="12"/><color rgb="FFFF0000"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_14(self): """Test the _write_font() method. Double underline.""" properties = {'underline': 2} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_15(self): """Test the _write_font() method. Double underline.""" properties = {'underline': 33} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_16(self): """Test the _write_font() method. Double underline.""" properties = {'underline': 34} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="1"/><name val="Calibri"/><family val="2"/><scheme val="minor"/>""" got = self.fh.getvalue() self.assertEqual(got, exp) def test_write_font_17(self): """Test the _write_font() method. Hyperlink.""" properties = {'hyperlink': 1} xf_format = Format(properties) self.styles._write_font(xf_format) exp = """<sz val="11"/><color theme="10"/><name val="Calibri"/><family val="2"/>""" got = self.fh.getvalue() self.assertEqual(got, exp)

31.168627

199

0.576623

979

7,948

4.510725

0.100102

0.105978

0.061141

0.061594

0.800045

0.791214

0.717391

0.703804

0

0.02302

0.229366

7,948

254

200

31.291339

0.697959

0.110846

0

0.492754

0

0.123188

0.320401

0.079628

0

0.123188

1

0.130435

false

0

0.028986

0

0.166667

0

null

0

1

0

null

0

6

0abe383ca309301febf25e471bffd58ee3e40cd1

17,936

py

Python

system/t09_repo/include.py

Yelp/aptly

59a0c0140ba0f0f12554d57d99110511eb3e6229

[ "MIT" ]

null

system/t09_repo/include.py

Yelp/aptly

59a0c0140ba0f0f12554d57d99110511eb3e6229

[ "MIT" ]

null

system/t09_repo/include.py

Yelp/aptly

59a0c0140ba0f0f12554d57d99110511eb3e6229

[ "MIT" ]

1

2022-03-18T11:33:21.000Z

import tempfile import shutil import os import inspect import re from lib import BaseTest def gpgRemove(_, s): return re.sub(r'Signature made .* using|gpgv: keyblock resource .*$|gpgv: Can\'t check signature: .*$', '', s, flags=re.MULTILINE) def changesRemove(_, s): return s.replace(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), "") def tempDirRemove(self, s): return s.replace(self.tempSrcDir, "") class IncludeRepo1Test(BaseTest): """ include packages to local repo: .changes file from directory """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -no-remove-files -keyring=${files}/aptly.pub ${changes}" outputMatchPrepare = gpgRemove def check(self): self.check_output() self.check_cmd_output("aptly repo show -with-packages unstable", "repo_show") # check pool self.check_exists('pool/66/83/99580590bf1ffcd9eb161b6e5747_hardlink_0.2.1_amd64.deb') self.check_exists('pool/c0/d7/458aa2ca3886cd6885f395a289ef_hardlink_0.2.1.dsc') self.check_exists('pool/4d/f0/adce005526a1f0e1b38171ddb1f0_hardlink_0.2.1.tar.gz') class IncludeRepo2Test(BaseTest): """ include packages to local repo: .changes file from file + custom repo """ fixtureCmds = [ "aptly repo create my-unstable", "aptly repo add my-unstable ${files}", ] runCmd = "aptly repo include -no-remove-files -keyring=${files}/aptly.pub -repo=my-{{.Distribution}} ${changes}/hardlink_0.2.1_amd64.changes" outputMatchPrepare = gpgRemove def check(self): self.check_output() self.check_cmd_output("aptly repo show -with-packages my-unstable", "repo_show") # check pool self.check_exists('pool/66/83/99580590bf1ffcd9eb161b6e5747_hardlink_0.2.1_amd64.deb') self.check_exists('pool/c0/d7/458aa2ca3886cd6885f395a289ef_hardlink_0.2.1.dsc') self.check_exists('pool/4d/f0/adce005526a1f0e1b38171ddb1f0_hardlink_0.2.1.tar.gz') class IncludeRepo3Test(BaseTest): """ include packages to local repo: broken repo flag """ fixtureCmds = [ ] runCmd = "aptly repo include -no-remove-files -keyring=${files}/aptly.pub -repo=my-{{.Distribution} ${changes}" expectedCode = 1 def outputMatchPrepare(_, s): return s.replace('; missing space?', '') class IncludeRepo4Test(BaseTest): """ include packages to local repo: missing repo """ fixtureCmds = [ ] runCmd = "aptly repo include -no-remove-files -ignore-signatures -keyring=${files}/aptly.pub ${changes}" outputMatchPrepare = changesRemove expectedCode = 1 class IncludeRepo5Test(BaseTest): """ include packages to local repo: remove files being added """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -keyring=${files}/aptly.pub " outputMatchPrepare = gpgRemove def prepare(self): super(IncludeRepo5Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() shutil.copytree(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), os.path.join(self.tempSrcDir, "01")) shutil.copy(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "files", "pyspi_0.6.1-1.3.diff.gz"), os.path.join(self.tempSrcDir, "01", "pyspi_0.6.1-1.3.diff.gz")) self.runCmd += self.tempSrcDir def check(self): try: self.check_output() self.check_cmd_output("aptly repo show -with-packages unstable", "repo_show") # check pool self.check_exists('pool/66/83/99580590bf1ffcd9eb161b6e5747_hardlink_0.2.1_amd64.deb') self.check_exists('pool/c0/d7/458aa2ca3886cd6885f395a289ef_hardlink_0.2.1.dsc') self.check_exists('pool/4d/f0/adce005526a1f0e1b38171ddb1f0_hardlink_0.2.1.tar.gz') for path in ["hardlink_0.2.1.dsc", "hardlink_0.2.1.tar.gz", "hardlink_0.2.1_amd64.changes", "hardlink_0.2.1_amd64.deb"]: path = os.path.join(self.tempSrcDir, "01", path) if os.path.exists(path): raise Exception("path %s shouldn't exist" % (path, )) path = os.path.join(self.tempSrcDir, "01", "pyspi_0.6.1-1.3.diff.gz") if not os.path.exists(path): raise Exception("path %s doesn't exist" % (path, )) finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo6Test(BaseTest): """ include packages to local repo: missing files """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -keyring=${files}/aptly.pub " expectedCode = 1 def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo6Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() os.makedirs(os.path.join(self.tempSrcDir, "01"), 0o755) for path in ["hardlink_0.2.1.dsc", "hardlink_0.2.1_amd64.changes", "hardlink_0.2.1_amd64.deb"]: shutil.copy(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes", path), os.path.join(self.tempSrcDir, "01", path)) self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo6Test, self).check() for path in ["hardlink_0.2.1.dsc", "hardlink_0.2.1_amd64.changes", "hardlink_0.2.1_amd64.deb"]: path = os.path.join(self.tempSrcDir, "01", path) if not os.path.exists(path): raise Exception("path %s doesn't exist" % (path, )) finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo7Test(BaseTest): """ include packages to local repo: wrong checksum """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -keyring=${files}/aptly.pub " expectedCode = 1 def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo7Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() shutil.copytree(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), os.path.join(self.tempSrcDir, "01")) with open(os.path.join(self.tempSrcDir, "01", "hardlink_0.2.1.dsc"), "w") as f: f.write("A" * 949) # file size self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo7Test, self).check() finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo8Test(BaseTest): """ include packages to local repo: wrong signature """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -keyring=${files}/aptly.pub " expectedCode = 1 def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo8Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() shutil.copytree(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), os.path.join(self.tempSrcDir, "01")) with open(os.path.join(self.tempSrcDir, "01", "hardlink_0.2.1_amd64.changes"), "r+") as f: contents = f.read() f.seek(0, 0) f.write(contents.replace('Julian', 'Andrey')) f.truncate() self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo8Test, self).check() finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo9Test(BaseTest): """ include packages to local repo: unsigned """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -keyring=${files}/aptly.pub " expectedCode = 1 def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo9Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() shutil.copytree(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), os.path.join(self.tempSrcDir, "01")) with open(os.path.join(self.tempSrcDir, "01", "hardlink_0.2.1_amd64.changes"), "r+") as f: contents = f.readlines() contents = contents[3:31] f.seek(0, 0) f.write("".join(contents)) f.truncate() self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo9Test, self).check() finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo10Test(BaseTest): """ include packages to local repo: wrong signature + -ignore-signatures """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -ignore-signatures " def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo10Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() shutil.copytree(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), os.path.join(self.tempSrcDir, "01")) with open(os.path.join(self.tempSrcDir, "01", "hardlink_0.2.1_amd64.changes"), "r+") as f: contents = f.read() f.seek(0, 0) f.write(contents.replace('Julian', 'Andrey')) f.truncate() self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo10Test, self).check() finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo11Test(BaseTest): """ include packages to local repo: unsigned + -accept-unsigned """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -accept-unsigned -keyring=${files}/aptly.pub " def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo11Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() shutil.copytree(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), os.path.join(self.tempSrcDir, "01")) with open(os.path.join(self.tempSrcDir, "01", "hardlink_0.2.1_amd64.changes"), "r+") as f: contents = f.readlines() contents = contents[3:31] f.seek(0, 0) f.write("".join(contents)) f.truncate() self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo11Test, self).check() finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo12Test(BaseTest): """ include packages to local repo: unsigned + -accept-unsigned + restriction breakage """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -accept-unsigned -keyring=${files}/aptly.pub " expectedCode = 1 def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo12Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() shutil.copytree(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), os.path.join(self.tempSrcDir, "01")) with open(os.path.join(self.tempSrcDir, "01", "hardlink_0.2.1_amd64.changes"), "r+") as f: contents = f.readlines() contents = contents[3:31] contents[3] = "Binary: hardlink-dbg\n" f.seek(0, 0) f.write("".join(contents)) f.truncate() self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo12Test, self).check() finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo13Test(BaseTest): """ include packages to local repo: with denying uploaders.json """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -uploaders-file=${changes}/uploaders1.json -no-remove-files -keyring=${files}/aptly.pub ${changes}" expectedCode = 1 def outputMatchPrepare(_, s): return changesRemove(_, gpgRemove(_, s)) class IncludeRepo14Test(BaseTest): """ include packages to local repo: allow with uploaders.json """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -uploaders-file=${changes}/uploaders2.json -no-remove-files -keyring=${files}/aptly.pub ${changes}" def outputMatchPrepare(_, s): return changesRemove(_, gpgRemove(_, s)) class IncludeRepo15Test(BaseTest): """ include packages to local repo: no uploaders.json """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -uploaders-file=${changes}/uploaders-404.json -no-remove-files -keyring=${files}/aptly.pub ${changes}" expectedCode = 1 def outputMatchPrepare(_, s): return changesRemove(_, gpgRemove(_, s)) class IncludeRepo16Test(BaseTest): """ include packages to local repo: malformed JSON """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -uploaders-file=${changes}/uploaders3.json -no-remove-files -keyring=${files}/aptly.pub ${changes}" expectedCode = 1 def outputMatchPrepare(_, s): return changesRemove(_, gpgRemove(_, s)) class IncludeRepo17Test(BaseTest): """ include packages to local repo: malformed rule """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -uploaders-file=${changes}/uploaders4.json -no-remove-files -keyring=${files}/aptly.pub ${changes}" expectedCode = 1 def outputMatchPrepare(_, s): return changesRemove(_, gpgRemove(_, s)) class IncludeRepo18Test(BaseTest): """ include packages to local repo: repo uploaders.json + global uploaders.json """ fixtureCmds = [ "aptly repo create -uploaders-file=${changes}/uploaders2.json unstable", ] runCmd = "aptly repo include -uploaders-file=${changes}/uploaders1.json -no-remove-files -keyring=${files}/aptly.pub ${changes}" def outputMatchPrepare(_, s): return changesRemove(_, gpgRemove(_, s)) class IncludeRepo19Test(BaseTest): """ include packages to local repo: per-repo uploaders.json """ fixtureCmds = [ "aptly repo create -uploaders-file=${changes}/uploaders1.json unstable", ] runCmd = "aptly repo include -no-remove-files -keyring=${files}/aptly.pub ${changes}" expectedCode = 1 def outputMatchPrepare(_, s): return changesRemove(_, gpgRemove(_, s)) class IncludeRepo20Test(BaseTest): """ include packages to local repo: .changes file from directory (internal PGP implementation) """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -no-remove-files -keyring=${files}/aptly.pub ${changes}" outputMatchPrepare = gpgRemove configOverride = {"gpgProvider": "internal"} class IncludeRepo21Test(BaseTest): """ include packages to local repo: wrong signature (internal PGP implementation) """ fixtureCmds = [ "aptly repo create unstable", ] runCmd = "aptly repo include -keyring=${files}/aptly.pub " expectedCode = 1 configOverride = {"gpgProvider": "internal"} def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo21Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() shutil.copytree(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes"), os.path.join(self.tempSrcDir, "01")) with open(os.path.join(self.tempSrcDir, "01", "hardlink_0.2.1_amd64.changes"), "r+") as f: contents = f.read() f.seek(0, 0) f.write(contents.replace('Julian', 'Andrey')) f.truncate() self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo21Test, self).check() finally: shutil.rmtree(self.tempSrcDir) class IncludeRepo22Test(BaseTest): """ include packages to local repo: missing files, but files aready in the pool """ fixtureCmds = [ "aptly repo create stable", "aptly repo create unstable", "aptly repo add stable ${changes}" ] runCmd = "aptly repo include -ignore-signatures -keyring=${files}/aptly.pub " def outputMatchPrepare(self, s): return gpgRemove(self, tempDirRemove(self, s)) def prepare(self): super(IncludeRepo22Test, self).prepare() self.tempSrcDir = tempfile.mkdtemp() os.makedirs(os.path.join(self.tempSrcDir, "01"), 0o755) for path in ["hardlink_0.2.1.dsc", "hardlink_0.2.1_amd64.deb"]: shutil.copy(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes", path), os.path.join(self.tempSrcDir, "01", path)) path = "hardlink_0.2.1_amd64.changes" with open(os.path.join(os.path.dirname(inspect.getsourcefile(BaseTest)), "changes", path), "r") as source: with open(os.path.join(self.tempSrcDir, "01", path), "w") as dest: content = source.readlines() # remove reference to .tar.gz file content = [line for line in content if "hardlink_0.2.1.tar.gz" not in line] dest.write("".join(content)) self.runCmd += self.tempSrcDir def check(self): try: super(IncludeRepo22Test, self).check() finally: shutil.rmtree(self.tempSrcDir)

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0

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0.435028

0

null

0

1

0

null

0

6

0ac5b265377df4976a6598a69e82f04731e923b6

290

py

Python

app/exception.py

zhangmingkai4315/ContactCli

5a7030bed2374179b65b70d53f01eb7400ef865f

[ "MIT" ]

null

app/exception.py

zhangmingkai4315/ContactCli

5a7030bed2374179b65b70d53f01eb7400ef865f

[ "MIT" ]

null

app/exception.py

zhangmingkai4315/ContactCli

5a7030bed2374179b65b70d53f01eb7400ef865f

[ "MIT" ]

null

class UserNotValidException(Exception): pass class PhoneNotValidException(Exception): pass class ConfigFileParseException(Exception): pass class DuplicateUserException(Exception): pass class IndexOutofRangeException(Exception): pass class IndexNotGivenException(Exception): pass

24.166667

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true

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null

0

1

0

6

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29,451

py

Python

module_statistics.py

ambra-dipiano/thesis

c24bb1a19c2fad652202527145851f1b22980bfe

[ "BSD-3-Clause" ]

null

module_statistics.py

ambra-dipiano/thesis

c24bb1a19c2fad652202527145851f1b22980bfe

[ "BSD-3-Clause" ]

null

module_statistics.py

ambra-dipiano/thesis

c24bb1a19c2fad652202527145851f1b22980bfe

[ "BSD-3-Clause" ]

1

2020-10-01T12:37:35.000Z

# MIT License # ----------------------------- # # Copyright 2020 Ambra Di Piano # # ----------------------------- # -------------------------------------------------- # # Redistribution and use in source and binary forms, with or without modification, # # are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # # this list of conditions and the following disclaimer in the documentation and/or # # other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # # may be used to endorse or promote products derived from this software without # # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED # # OF THE POSSIBILITY OF SUCH DAMAGE. # # ---------------------------------------------------------------------------------- # # ============================================ # !!! MODULE FOR STATISTICS AND HISTOGRAMS !!! # ============================================ import matplotlib.pyplot as plt import seaborn as sns import numpy as np from matplotlib.patches import Rectangle from scipy import stats from scipy.stats import rayleigh, norm, chi2 import pandas as pd from matplotlib.colors import LogNorm from matplotlib.lines import Line2D from matplotlib.patches import Ellipse, Circle from scipy.ndimage.filters import gaussian_filter from matplotlib.ticker import FormatStrFormatter import matplotlib as mpl import scipy.ndimage as sp from matplotlib.image import NonUniformImage from scipy.ndimage.filters import gaussian_filter extra = Rectangle((0, 0), 1, 1, fc="w", fill=False, edgecolor='none', linewidth=0) extra2 = Line2D([0], [0], ls='-.', color='k', lw='1') def hist1d(x, mean, nbin=20, hist=True, fontsize=20, color='b', xscale='linear', figsize=(15,12), rotation=0, alpha=0.5, title='gaussian fit', ax_thresh=None, xlabel='x', ylabel='y', leglabel='data', filename='hist1d_gauss.png', show=True): fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111, xscale=xscale) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) # plt.plot([],[], color='none', label='wbin=%.2fdeg' %width) for index, el in enumerate(x): if el[0] is list(): el=el[0] sns.distplot(el, bins=nbin, kde=False, hist=hist, fit=norm, norm_hist=True, fit_kws={"color": color[index]}, color=color[index], hist_kws={'alpha':alpha}, label=leglabel[index]) plt.axvline(mean[index], c=color[index], ls='--', lw=2, label='mean $\\approx$ %.1E' %mean[index]) if mean != None else None plt.title(title, fontsize=fontsize) plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.legend(fontsize=fontsize) plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # HIST 1D GAUSSIAN DISTRIBUTION ---! def hist1d_gauss(x, mean, loc=0, threshold=1, nbin=20, width=None, hist=True, fontsize=20, figsize=(15,12), color='b', alpha=0.5, title='gaussian fit', ax_thresh=0.2, xlabel='x', ylabel='y', leglabel='data', rotation=0, filename='hist1d_gauss.png', show=True): if nbin == None: if width == None: print('Error: set either nbin or width') nbin = int(threshold/width) fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) # plt.plot([],[], color='none', label='wbin=%.2fdeg' %width) for index, el in enumerate(x): if el[0] is list(): el=el[0] sns.distplot(el, bins=nbin, kde=False, hist=hist, fit=norm, norm_hist=True, fit_kws={"color": color[index]}, color=color[index], hist_kws={'alpha':alpha, 'range':[loc-threshold, loc+threshold]}, label=leglabel[index]) plt.axvline(mean[index], c=color[index], ls='--', lw=2, label='mean $\\approx$ %.3fdeg' %mean[index]) if mean != None else None plt.axvline(loc, c='k', ls='-', lw=2, label='true $\\approx$ %.3fdeg' %loc) if loc != None else None plt.title(title, fontsize=fontsize) plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.legend(fontsize=fontsize) plt.xlim([loc-ax_thresh, loc+ax_thresh]) plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # HIST 1D RAYLEIGH DISTRIBUTION ---! def hist1d_rayleigh(x, mean, rayleigh_prms={'loc':0, 'scale':[1]}, threshold=1, nbin=None, width=None, hist=True, fontsize=20, figsize=(15,12), rotation=0, color='b', alpha=0.5, title='rayleigh fit', ax_thresh=0.2, xlabel='x', ylabel='y', leglabel='data', filename='hist1d_rayleigh.png', show=True): if width == None: width = threshold/nbin if nbin == None: nbin = int(threshold/width) if nbin == None and width == None: print('Error: set either nbin or width') fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) # plt.plot([],[], color='none', label='wbin=%.2fdeg' %width) for index, el in enumerate(x): if el[0] is list(): el=el[0] sns.distplot(el, bins=nbin, kde=False, hist=hist, fit=rayleigh, norm_hist=True, fit_kws={"color": color[index]}, color=color[index], hist_kws={'alpha':alpha, 'range':[0.0, threshold]}, label=leglabel[index]) plt.axvline(mean[index], c=color[index], ls='--', lw=2, label='mean $\\approx$ %.3fdeg' %mean[index]) if mean != None else None if rayleigh_prms['scale'] != None: plt.axvline(rayleigh_prms['scale'][index], c=color[index], ls='-', lw=2, label='mode $\\approx$ %.3fdeg' %rayleigh_prms['scale'][index]) plt.title(title, fontsize=fontsize) plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.legend(fontsize=fontsize) plt.xlim([rayleigh_prms['loc'], rayleigh_prms['loc']+ax_thresh]) if rayleigh_prms['loc'] != None else None plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # RAYLEIGH CDF WITH CONFIDENCE INTERVAL ---! def rayleigh_cdf(x, loc=0, scale=1, if_CI=True, probs=(0.6827, 0.9545, 0.9973, 0.99994), xlabel='x', title='x$\\sim$ RA($\\gamma$) CDF', colors=('k', 'r', 'orange', 'm'), fontsize=20, figsize=(15,12), rotation=0, filename='theo_rayleigh_cdf.png', show=False): fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) ax.plot(np.sort(x), stats.rayleigh.cdf(np.sort(x), loc=loc, scale=scale), ls='-', label='cdf') ax.axvline(scale, c='maroon', label='$\gamma$') ax.axvline(np.std(x), c='maroon', ls=':', label='1 std =%.2f' %(np.std(x))) if if_CI is True: x_critical = [] for i in range(len(probs)): x_critical.append(stats.rayleigh.ppf(q=probs[i], loc=loc, scale=scale)) ax.axvline(x_critical[i], c=colors[i], ls='-.', label='x=%.2f, %.2f' %(x_critical[i],probs[i]*100)+'%') plt.ylabel('1-$\\alpha$', rotation=90, fontsize=fontsize) plt.xlabel(xlabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) ax.set_xlim(left=0) ax.set_ylim(bottom=0) plt.legend(loc=0) plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # RAYLEIGH PDF WITH CONFIDENCE INTERVAL ---! def rayleigh_pdf(x, loc=0, scale=1, if_CI=True, probs=(0.6827, 0.9545, 0.9973, 0.99994), xlabel='x', title='x$\\sim$ RA($\\gamma$) CDF', colors=('k', 'r', 'orange', 'm'), fontsize=20, figsize=(15,12), rotation=0, filename='theo_rayleigh_cdf.png', show=False): fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) ax.plot(np.sort(x), stats.rayleigh.pdf(np.sort(x), loc=loc, scale=scale), ls='-', label='cdf') ax.axvline(scale, c='maroon', label='$\gamma$') ax.axvline(np.std(x), c='maroon', ls=':', label='1 std =%.2f' %(np.std(x))) if if_CI is True: x_critical = [] for i in range(len(probs)): x_critical.append(stats.rayleigh.ppf(q=probs[i], loc=loc, scale=scale)) ax.axvline(x_critical[i], c=colors[i], ls='-.', label='x=%.2f, %.2f' %(x_critical[i],probs[i]*100)+'%') plt.ylabel('counts density', rotation=90, fontsize=fontsize) plt.xlabel(xlabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) ax.set_xlim(left=0) ax.set_ylim(bottom=0) plt.legend(loc=0) plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # 2D HISTOGRAM WITH RAYLEIGH CONFIDENCE INTERVAL ---! def hist2d_rayleigh_CI(x, y, nbin=None, width=None, rayleigh_prms={'loc':0, 'scale':1}, xcentre=0, ycentre=0, interp=None, threshold=1, probs=(0.6827, 0.9545, 0.9973, 0.99994), colors=('k', 'r', 'orange', 'm'), lw=2, ms=2e2, ax_thresh=0.2, xlabel='x', ylabel='y', title='confidence intervals from theoretical distribution', fontsize=20 , figsize=(10,8), rotation=0, filename='hist2d_CIrayleigh.png', show=False): xmean = np.mean(x) ymean = np.mean(y) if width is None: width = threshold/nbin if nbin is None: nbin = int(threshold/width) if nbin is None and width is None: print('Error: set either nbin or width') fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) if interp == None: h = plt.hist2d(x, y, bins=nbin, cmap='jet', range=[[xcentre - threshold, xcentre + threshold], [ycentre - threshold, ycentre + threshold]]) else: h, xedges, yedges = np.histogram2d(x, y, bins=nbin, range=[[xcentre - threshold, xcentre + threshold], [ycentre - threshold, ycentre + threshold]]) h = h.T plt.imshow(h, interpolation=interp, cmap='gist_heat', extent=[[xcentre - threshold, xcentre + threshold], [ycentre - threshold, ycentre + threshold]]) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) plt.scatter(xcentre, ycentre, c='w', marker='*', s=ms) plt.plot([], [], c='none', label='Reyleigh') for i in range(len(probs)): plt.plot([], [], c=colors[i], label='%.2f' % (probs[i] * 100) + '\%') r = stats.rayleigh.ppf(q=probs[i], loc=rayleigh_prms['loc'], scale=rayleigh_prms['scale']) # q = rayleigh['scale'] * np.sqrt(-2 * np.log(probs[i])) # r = stats.rayleigh.ppf(q=q, loc=rayleigh['loc'], scale=rayleigh['scale']) cir = Circle(xy=(float(xmean), float(ymean)), radius=r, color=colors[i], lw=lw) cir.set_facecolor('none') ax.add_artist(cir) if interp == None: cbar = plt.colorbar(h[3], ax=ax).set_label('counts', fontsize=fontsize) plt.axis([xcentre - ax_thresh, xcentre + ax_thresh, ycentre - ax_thresh, ycentre + ax_thresh], 'equal') if ax_thresh != None else None plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) plt.legend(ncol=3, fontsize=fontsize) plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # COVARIANCE EIGENVALUES ---! def eigsorted(cov): vals, vecs = np.linalg.eigh(cov) order = vals.argsort()[::-1] return vals[order], vecs[:, order] # 2D HISTOGRAM WITH GAUSSIAN COVARIANCE CONFIDENCE INTERVAL ---! def hist2d_gauss_CI(x, y, nbin=None, width=None, xcentre=0, ycentre=0, threshold=1, nstd=(1, 2, 3, 5), lw=2, colors=('k', 'r', 'orange', 'm'), ax_thresh=0.2, xlabel='x', ylabel='y', interp=None, ms=2e2, title='confidence intervals from theoretical distribution', fontsize=20, figsize=(10,8), rotation=0, filename='hist2d_CIgauss.png', show=False): xmean = np.mean(x) ymean = np.mean(y) if width is None: width = threshold/nbin if nbin is None: nbin = int(threshold/width) if nbin is None and width is None: print('Error: set either nbin or width') fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) h = plt.hist2d(x, y, bins=nbin, cmap='jet', range=[[xcentre - threshold, xcentre + threshold], [ycentre - threshold, ycentre + threshold]]) if interp != None: plt.cla() plt.imshow(h[0], origin='lower', interpolation=interp, cmap='gist_heat') plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) plt.scatter(xcentre, ycentre, c='w', marker='*', s=ms) plt.plot([], [], c='none', label='gauss') for i in range(len(nstd)): plt.plot([], [], c=colors[i], label='%d $\sigma$' % (nstd[i])) cov = np.cov(x, y) vals, vecs = eigsorted(cov) theta = np.degrees(np.arctan2(*vecs[:, 0][::-1])) w, v = 2 * nstd[i] * np.sqrt(vals) ell = Ellipse(xy=(float(xmean), float(ymean)), width=w, height=v, angle=float(theta), color=colors[i], lw=lw) ell.set_facecolor('none') ax.add_artist(ell) if interp == None: cbar = plt.colorbar(h[3], ax=ax).set_label('counts', fontsize=fontsize) plt.axis([xcentre - ax_thresh, xcentre + ax_thresh, ycentre - ax_thresh, ycentre + ax_thresh], 'equal') if ax_thresh != None else None plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) plt.legend(ncol=3, fontsize=fontsize) plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # 2D HISTOGRAM WITH GAUSSIAN COVARIANCE CONFIDENCE INTERVAL ---! def contour_gauss_CI(x, y, nbin=None, width=None, xcentre=0, ycentre=0, threshold=1, nstd=(1, 2, 3, 5), colors=('k', 'r', 'orange', 'm'), ax_thresh=0.2, xlabel='x', ylabel='y', interp=None, title='confidence intervals from theoretical distribution', fontsize=20, figsize=(10,8), rotation=0, filename='hist2d_CIgauss.png', show=False): xmean = np.mean(x) ymean = np.mean(y) if width is None: width = threshold/nbin if nbin is None: nbin = int(threshold/width) if nbin is None and width is None: print('Error: set either nbin or width') fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) h = plt.hist2d(x, y, bins=nbin, cmap='jet', range=[[xcentre - threshold, xcentre + threshold], [ycentre - threshold, ycentre + threshold]]) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) plt.scatter(xcentre, ycentre, c='w', marker='*', s=1e2) plt.plot([], [], c='none', label='gauss') for i in range(len(nstd)): plt.plot([], [], c=colors[i], label='%d $\sigma$' % (nstd[i])) cov = np.cov(x, y) vals, vecs = eigsorted(cov) theta = np.degrees(np.arctan2(*vecs[:, 0][::-1])) w, v = 2 * nstd[i] * np.sqrt(vals) ell = Ellipse(xy=(xmean, ymean), width=w, height=v, angle=theta, color=colors[i], lw=2) ell.set_facecolor('none') ax.add_artist(ell) cbar = plt.colorbar(h[3], ax=ax).set_label('counts', fontsize=fontsize) plt.axis([xcentre - ax_thresh, xcentre + ax_thresh, ycentre - ax_thresh, ycentre + ax_thresh], 'equal') if ax_thresh != None else None plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) plt.legend(ncol=3, fontsize=fontsize) plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # 2D HISTOGRAM MAP ---! def hist2d_map(x, y, trials, nbin=None, width=None, xcentre=0, ycentre=0, threshold=1, ax_thresh=0.2, xlabel='x', ylabel='y', title='probability map', fontsize=20, figsize=(10,8), rotation=0, filename='hist2d_map.png', if_CI=None, rayleigh={'loc':0, 'scale':1}, nstd=(1, 2, 3, 5), colors=('k', 'r', 'orange', 'm'), probs=(0.6827, 0.9545, 0.9973, 0.99994), smooth=True, show=False): if width is None: width = threshold/nbin if nbin is None: nbin = int(threshold/width) if nbin is None and width is None: print('Error: set either nbin or width') fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) h = ax.hist2d(x, y, bins=nbin, cmap='jet', vmin=0.0, vmax=trials, range=[[xcentre - threshold, xcentre + threshold], [ycentre - threshold, ycentre + threshold]]) if smooth: plt.clf() # hist2d with numpy (invert axis since imshow stumbles them) ---! # sigma=2 # X = gaussian_filter(x, sigma) # Y = gaussian_filter(y, sigma) heatmap, xedges, yedges = np.histogram2d(x, y, bins=nbin, range=[[xcentre - threshold, xcentre + threshold], [ycentre - threshold, ycentre + threshold]]) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] ax.imshow(heatmap, extent=extent, cmap='jet', interpolation='gaussian', filterrad=1, filternorm=True, resample=False, origin='lower') plt.scatter(xcentre, ycentre, c='w', marker='*', s=1e2) if if_CI is None: pass elif if_CI.lower() is 'rayleigh': xmean = np.mean(x) ymean = np.mean(y) plt.plot([], [], c='none', label='Reyleigh') for i in range(len(probs)): plt.plot([], [], c=colors[i], label='%.2f' % (probs[i] * 100) + '%') r = stats.rayleigh.ppf(q=probs[i], loc=rayleigh['loc'], scale=rayleigh['scale']) cir = Circle(xy=(xmean, ymean), radius=r, color=colors[i], lw=2) cir.set_facecolor('none') ax.add_artist(cir) elif if_CI.lower() is 'gauss' or if_CI.lower is 'covariance' or if_CI.lower is 'cov': xmean = np.mean(x) ymean = np.mean(y) plt.plot([], [], c='none', label='gauss') for i in range(len(nstd)): plt.plot([], [], c=colors[i], label='%.2f' % (nstd[i] * 100) + '%') cov = np.cov(x, y) vals, vecs = eigsorted(cov) theta = np.degrees(np.arctan2(*vecs[:, 0][::-1])) w, v = 2 * nstd[i] * np.sqrt(vals) ell = Ellipse(xy=(xmean, ymean), width=w, height=v, angle=theta, color=colors[i], lw=2) ell.set_facecolor('none') ax.add_artist(ell) else: print('Error: if_CI parameter value not understood') m = plt.cm.ScalarMappable(cmap='jet') m.set_clim(0., trials/100) cbar = plt.colorbar(m, boundaries=np.linspace(0, 100, 11)).set_label('cts \%', fontsize=fontsize) plt.axis([xcentre - ax_thresh, xcentre + ax_thresh, ycentre - ax_thresh, ycentre + ax_thresh], 'equal') plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) if title!=None else None plt.axis('equal') plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # WILKS THEOREM DIST FOR EMPTY FIELDS ---! def ts_wilks(x, trials, df=1, nbin=None, width=None, ylim=None, xlim=None, show=False, fontsize=20, figsize=(15,12), rotation=0, xlabel='TS', ylabel='normalised counts', title='TS distribution (empty fields)', filename='wilks_preTrials.png'): if width is None: width = (max(x)-min(x))/nbin if nbin is None: nbin = int((max(x)-min(x))/width) if nbin is None and width is None: print('Error: set either nbin or width') fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111, yscale='log') plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) h, edges = np.histogram(x, bins=int(nbin), density=False, range=(min(x), max(x))) yerr = np.sqrt(h)/trials h = h/trials cbin = (edges[1:] + edges[:-1]) / 2 xerr = (edges[:-1] - edges[1:]) / 2 x2 = np.arange(0, 30, 1) plt.errorbar(cbin, h, fmt='k+', yerr=yerr, xerr=xerr, markersize=5, label='ts') plt.plot(x2, stats.chi2.pdf(x2, df=df), c='orange', lw=1, ls='--', label='$\\chi^2$(dof=%d)' %df) plt.plot(x2, stats.chi2.pdf(x2, df=df)/2, c='b', lw=1, ls='--', label='$\\chi^2$/2(dof=%d)' %df) plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) plt.legend(loc=0, fontsize=fontsize) plt.xlim(xlim) if xlim is not None else None plt.ylim(ylim) if ylim is not None else None plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # WILKS THEOREM P-VALUES FOR EMPTY FIELDS ---! def p_values(x, trials, df=1, nbin=None, width=None, ylim=None, xlim=None, show=False, fontsize=20, figsize=(15,12), rotation=0, xlabel='h', ylabel='p-values', title='p-value (empty fields)', filename='pvalue_preTrials.png'): if width is None: width = (max(x)-min(x))/nbin if nbin is None: nbin = int((max(x)-min(x))/width) if nbin is None and width is None: print('Error: set either nbin or width') fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111, yscale='log') plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) h = np.empty(len(np.arange(int(max(x))))) p = np.empty(len(np.arange(int(max(x))))) cbin, xerr = [], [] for i in range(int(max(x))): cbin.append(i+1) xerr.append(0.5) for idx, val in enumerate(x): if val >= i+1: h[i] += 1 p = h/trials yerr = np.sqrt(h)/trials x2 = np.arange(min(x), max(x)+5, 1) plt.errorbar(cbin[0], p[0], yerr=yerr[0], xerr=xerr[0], fmt='k+', markersize=5) plt.errorbar(cbin[1:], p[1:], yerr=yerr[1:], xerr=xerr[1:], fmt='k+', markersize=5, label='ts') plt.plot(x2, (1 - stats.chi2.cdf(x2, df=df)), lw=1, ls='-.', c='green', label='$\\chi^2$(dof=%d)' %df) plt.plot(x2, (1 - stats.chi2.cdf(x2, df=df))/2, lw=1, ls='-.', c='maroon', label='$\\chi^2$/2(dof=%d)' %df) # plt.legend(('$\\chi^2$/2(dof=%d)', '$\\chi^2$(dof=%d)', 'ts'), loc=0, fontsize=fontsize) plt.axhline(3e-7, c='gray', ls=':', alpha=1, lw=2) plt.text(23, 2e-7, '5$\sigma$', fontsize=fontsize, alpha=1) plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) plt.legend(loc=0, fontsize=fontsize) plt.xlim(xlim) if xlim is not None else None plt.ylim(ylim) if ylim is not None else None plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax # WILKS THEOREM P-VALUES FOR EMPTY FIELDS ---! def ts_wilks_cumulative(x, trials, df=1, nbin=None, width=None, ylim=None, xlim=None, show=False, fontsize=20, figsize=(15,12), rotation=0, xlabel='h', ylabel='cumulative probability', title='p-value (empty fields)', filename='cumulative_preTrials.png'): if width is None: width = (max(x)-min(x))/nbin if nbin is None: nbin = int((max(x)-min(x))/width) if nbin is None and width is None: print('Error: set either nbin or width') fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) sns.set() ax = plt.subplot(111) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) h = np.empty(len(np.arange(int(max(x))))) p = np.empty(len(np.arange(int(max(x))))) cbin, xerr = [], [] for i in range(int(max(x))): cbin.append(i+1) xerr.append(0.5) for idx, val in enumerate(x): if val >= i+1: h[i] += 1 p = 1 - h/trials yerr = np.sqrt(h)/trials x2 = np.arange(min(x), max(x)+5, 1) plt.errorbar(cbin[0], p[0], yerr=yerr[0], xerr=xerr[0], fmt='k+', markersize=5) plt.errorbar(cbin[1:], p[1:], yerr=yerr[1:], xerr=xerr[1:], fmt='k+', markersize=5, label='ts') plt.plot(x2, stats.chi2.cdf(x2, df=df), lw=1, ls='-.', c='maroon', label='$P$(dof=%d)' %df) # plt.legend(('$\\chi^2$/2(dof=%d)', '$\\chi^2$(dof=%d)', 'ts'), loc=0, fontsize=fontsize) plt.axhline(1-3e-7, c='gray', ls=':', lw=2, alpha=1) plt.text(1, 0.95, '5$\sigma$', fontsize=fontsize, alpha=1) plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) plt.legend(loc=0, fontsize=fontsize) plt.xlim(xlim) if xlim is not None else None plt.ylim(ylim) if ylim is not None else None plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show == True else None plt.close() return fig, ax def chi2_reduced(x, trials, df=1, nbin=None, width=None, var=True): np.seterr(divide='ignore', invalid='ignore') if width is None: width = (max(x)-min(x))/nbin if nbin is None: nbin = int((max(x)-min(x))/width) if nbin is None and width is None: print('Error: set either nbin or width') h, edges = np.histogram(x, bins=int(nbin), density=False, range=(0., max(x))) yerr = np.sqrt(h)/trials h = h/trials cbin = (edges[1:] + edges[:-1])/2 p = (1 - stats.chi2.pdf(cbin, df=df))/2 #p = stats.chi2.pdf(cbin, df=df)/2 #err = yerr/h #print('values', h, '\nerrors', yerr, '\nerror perc', err) with np.errstate(invalid='raise'): if var: chi2 = 2*np.sum((h[1:] - p[1:])**2/h[1:]) #chi2 = np.sum((h[1:] - p[1:])**2/err[1:]) else: chi2 = 2*np.sum((h[1:] - p[1:])**2/h[1:]) #chi2 = np.sum((h[1:] - p[1:])**2/err[1:]) h[1:] = np.array(h[1:]) N = np.count_nonzero(h[1:]) chi2r = chi2 / (N - 1) return chi2, chi2r # MANUAL NORMALISED HISTOGRAM ---! def normedHist(x, trials=None, step=None, nbin=None, ylim=None, xlim=None, show=False, normed=True, xscale='linear', yscale='log', fontsize=20, figsize=(15,12), rotation=0, xlabel='x', ylabel='normalised counts', leglabel='legend', title='normed histogram', usetex=True, usesns=False, filename='normed_histogram.png'): x = np.sort(x) if step is None: step = (max(x)-min(x))/nbin if nbin is None: nbin = int((max(x)-min(x))/step) if nbin is None and step is None: print('Error: set either nbin or step') if trials is None: trials = len(x) fig = plt.figure(figsize=figsize) plt.rc('text', usetex=True) if usetex else None if usesns: sns.set() else: plt.grid() ax = plt.subplot(111, yscale=yscale, xscale=xscale) plt.xticks(fontsize=fontsize, rotation=rotation) plt.yticks(fontsize=fontsize, rotation=rotation) h = np.empty(len(np.arange(nbin))) cbin, xerr = [], [] for i in range(nbin): cbin.append(step*i + step/2) xerr.append(step/2) for idx, val in enumerate(x): if val <= cbin[i]: h[i] += 1 x=x[(x>=cbin[i])] h_norm = h/trials if normed: yerr = np.sqrt(h) / trials plt.errorbar(cbin, h_norm, yerr=yerr, xerr=xerr, fmt='k+', markersize=5, label=leglabel) else: yerr = h / trials plt.errorbar(cbin, h, yerr=yerr, xerr=xerr, fmt='k+', markersize=5, label=leglabel) plt.xlabel(xlabel, fontsize=fontsize) plt.ylabel(ylabel, fontsize=fontsize) plt.title(title, fontsize=fontsize) plt.legend(loc=0, fontsize=fontsize) plt.xlim(xlim) if xlim is not None else None plt.ylim(ylim) if ylim is not None else None plt.tight_layout() fig.savefig(filename) # show fig ---! plt.show() if show else None plt.close() return fig, ax

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0ad0134e50901402cc76e39899d74112cd4600f2

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Python

econml/ortho_iv.py

jaronowitz/EconML

3df959d120d429537a62ebfb22a84b9b28530457

[ "MIT" ]

null

econml/ortho_iv.py

jaronowitz/EconML

3df959d120d429537a62ebfb22a84b9b28530457

[ "MIT" ]

null

econml/ortho_iv.py

jaronowitz/EconML

3df959d120d429537a62ebfb22a84b9b28530457

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. """Orthogonal IV for Heterogeneous Treatment Effects. A Double/Orthogonal machine learning approach to estimation of heterogeneous treatment effect with an endogenous treatment and an instrument. It implements the DMLIV and related algorithms from the paper: Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis https://arxiv.org/abs/1905.10176 """ import numpy as np from sklearn.base import clone from sklearn.linear_model import LinearRegression from sklearn.pipeline import Pipeline from sklearn.preprocessing import FunctionTransformer from ._ortho_learner import _OrthoLearner from .cate_estimator import StatsModelsCateEstimatorMixin from .dml import _FinalWrapper from .inference import StatsModelsInference from .sklearn_extensions.linear_model import StatsModelsLinearRegression from .utilities import (_deprecate_positional, add_intercept, fit_with_groups, filter_none_kwargs, hstack, inverse_onehot) # A cut-down version of the DML first stage wrapper, since we don't need to support linear first stages class _FirstStageWrapper: def __init__(self, model, discrete_target): self._model = clone(model, safe=False) self._discrete_target = discrete_target def _combine(self, X, W, Z, n_samples, fitting=True): # output is # * a column of ones if X, W, and Z are all None # * just X or W or Z if both of the others are None # * hstack([arrs]) for whatever subset are not None otherwise # ensure Z is 2D if Z is not None: Z = Z.reshape(n_samples, -1) if X is None and W is None and Z is None: return np.ones((n_samples, 1)) arrs = [arr for arr in [X, W, Z] if arr is not None] if len(arrs) == 1: return arrs[0] else: return hstack(arrs) def fit(self, *, X, W, Target, Z=None, sample_weight=None, groups=None): if self._discrete_target: # In this case, the Target is the one-hot-encoding of the treatment variable # We need to go back to the label representation of the one-hot so as to call # the classifier. if np.any(np.all(Target == 0, axis=0)) or (not np.any(np.all(Target == 0, axis=1))): raise AttributeError("Provided crossfit folds contain training splits that " + "don't contain all treatments") Target = inverse_onehot(Target) if sample_weight is not None: fit_with_groups(self._model, self._combine(X, W, Z, Target.shape[0]), Target, groups=groups, sample_weight=sample_weight) else: fit_with_groups(self._model, self._combine(X, W, Z, Target.shape[0]), Target, groups=groups) def score(self, *, X, W, Target, Z=None, sample_weight=None): if hasattr(self._model, 'score'): if self._discrete_target: # In this case, the Target is the one-hot-encoding of the treatment variable # We need to go back to the label representation of the one-hot so as to call # the classifier. if np.any(np.all(Target == 0, axis=0)) or (not np.any(np.all(Target == 0, axis=1))): raise AttributeError("Provided crossfit folds contain training splits that " + "don't contain all treatments") Target = inverse_onehot(Target) if sample_weight is not None: return self._model.score(self._combine(X, W, Z, Target.shape[0]), Target, sample_weight=sample_weight) else: return self._model.score(self._combine(X, W, Z, Target.shape[0]), Target) else: return None def predict(self, X, W, Z=None): arrs = [arr for arr in [X, W, Z] if arr is not None] n_samples = arrs[0].shape[0] if arrs else 1 if self._discrete_target: return self._model.predict_proba(self._combine(X, W, Z, n_samples, fitting=False))[:, 1:] else: return self._model.predict(self._combine(X, W, Z, n_samples, fitting=False)) class _BaseDMLATEIVModelFinal: def __init__(self): self._first_stage = LinearRegression(fit_intercept=False) self._model_final = _FinalWrapper(LinearRegression(fit_intercept=False), fit_cate_intercept=True, featurizer=None, use_weight_trick=False) def fit(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_weight=None, sample_var=None): Y_res, T_res, Z_res = nuisances if Z_res.ndim == 1: Z_res = Z_res.reshape(-1, 1) # DMLATEIV is just like 2SLS; first regress T_res on Z_res, then regress Y_res on predicted T_res T_res_pred = self._first_stage.fit(Z_res, T_res, sample_weight=sample_weight).predict(Z_res) # TODO: allow the final model to actually use X? Then we'd need to rename the class # since we would actually be calculating a CATE rather than ATE. self._model_final.fit(X=None, T_res=T_res_pred, Y_res=Y_res, sample_weight=sample_weight) return self def predict(self, X=None): # TODO: allow the final model to actually use X? return self._model_final.predict(X=None) def score(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_weight=None, sample_var=None): Y_res, T_res, Z_res = nuisances if Y_res.ndim == 1: Y_res = Y_res.reshape((-1, 1)) if T_res.ndim == 1: T_res = T_res.reshape((-1, 1)) # TODO: allow the final model to actually use X? effects = self._model_final.predict(X=None).reshape((-1, Y_res.shape[1], T_res.shape[1])) Y_res_pred = np.einsum('ijk,ik->ij', effects, T_res).reshape(Y_res.shape) if sample_weight is not None: return np.mean(np.average((Y_res - Y_res_pred)**2, weights=sample_weight, axis=0)) else: return np.mean((Y_res - Y_res_pred) ** 2) class _BaseDMLATEIV(_OrthoLearner): def __init__(self, model_nuisance, discrete_instrument=False, discrete_treatment=False, categories='auto', n_splits=2, random_state=None): super().__init__(model_nuisance, _BaseDMLATEIVModelFinal(), discrete_treatment=discrete_treatment, discrete_instrument=discrete_instrument, categories=categories, n_splits=n_splits, random_state=random_state) @_deprecate_positional("W and Z should be passed by keyword only. In a future release " "we will disallow passing W and Z by position.", ['W', 'Z']) def fit(self, Y, T, Z, W=None, *, sample_weight=None, sample_var=None, groups=None, inference=None): """ Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`. Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: (n, d_z) matrix Instruments for each sample X: optional(n, d_x) matrix or None (Default=None) Features for each sample sample_weight: optional(n,) vector or None (Default=None) Weights for each samples sample_var: optional(n,) vector or None (Default=None) Sample variance for each sample groups: (n,) vector, optional All rows corresponding to the same group will be kept together during splitting. If groups is not None, the n_splits argument passed to this class's initializer must support a 'groups' argument to its split method. inference: string,:class:`.Inference` instance, or None Method for performing inference. This estimator supports 'bootstrap' (or an instance of:class:`.BootstrapInference`). Returns ------- self: _BaseDMLATEIV instance """ # Replacing fit from _OrthoLearner, to enforce W=None and improve the docstring return super().fit(Y, T, W=W, Z=Z, sample_weight=sample_weight, sample_var=sample_var, groups=groups, inference=inference) def score(self, Y, T, Z, W=None): """ Score the fitted CATE model on a new data set. Generates nuisance parameters for the new data set based on the fitted residual nuisance models created at fit time. It uses the mean prediction of the models fitted by the different crossfit folds. Then calculates the MSE of the final residual Y on residual T regression. If model_final does not have a score method, then it raises an :exc:`.AttributeError` Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: optional(n, d_z) matrix Instruments for each sample X: optional(n, d_x) matrix or None (Default=None) Features for each sample Returns ------- score: float The MSE of the final CATE model on the new data. """ # Replacing score from _OrthoLearner, to enforce X=None and improve the docstring return super().score(Y, T, W=W, Z=Z) class _DMLATEIVModelNuisance: def __init__(self, model_Y_W, model_T_W, model_Z_W): self._model_Y_W = clone(model_Y_W, safe=False) self._model_T_W = clone(model_T_W, safe=False) self._model_Z_W = clone(model_Z_W, safe=False) def fit(self, Y, T, X=None, W=None, Z=None, sample_weight=None, groups=None): assert X is None, "DML ATE IV does not accept features" self._model_Y_W.fit(X=X, W=W, Target=Y, sample_weight=sample_weight, groups=groups) self._model_T_W.fit(X=X, W=W, Target=T, sample_weight=sample_weight, groups=groups) self._model_Z_W.fit(X=X, W=W, Target=Z, sample_weight=sample_weight, groups=groups) return self def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None): assert X is None, "DML ATE IV does not accept features" if hasattr(self._model_Y_W, 'score'): Y_X_score = self._model_Y_W.score(X=X, W=W, Target=Y, sample_weight=sample_weight) else: Y_X_score = None if hasattr(self._model_T_W, 'score'): T_X_score = self._model_T_W.score(X=X, W=W, Target=T, sample_weight=sample_weight) else: T_X_score = None if hasattr(self._model_Z_W, 'score'): Z_X_score = self._model_Z_W.score(X=X, W=W, Target=Z, sample_weight=sample_weight) else: Z_X_score = None return Y_X_score, T_X_score, Z_X_score def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None): assert X is None, "DML ATE IV does not accept features" Y_pred = self._model_Y_W.predict(X=X, W=W) T_pred = self._model_T_W.predict(X=X, W=W) Z_pred = self._model_Z_W.predict(X=X, W=W) if W is None: # In this case predict above returns a single row Y_pred = np.tile(Y_pred.reshape(1, -1), (Y.shape[0], 1)) T_pred = np.tile(T_pred.reshape(1, -1), (T.shape[0], 1)) Z_pred = np.tile(Z_pred.reshape(1, -1), (Z.shape[0], 1)) Y_res = Y - Y_pred.reshape(Y.shape) T_res = T - T_pred.reshape(T.shape) Z_res = Z - Z_pred.reshape(Z.shape) return Y_res, T_res, Z_res class DMLATEIV(_BaseDMLATEIV): """ Implementation of the orthogonal/double ml method for ATE estimation with IV as described in Double/Debiased Machine Learning for Treatment and Causal Parameters Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins https://arxiv.org/abs/1608.00060 Requires that either co-variance of T, Z is independent of X or that effect is not heterogeneous in X for correct recovery. Otherwise it estimates a biased ATE. """ def __init__(self, model_Y_W, model_T_W, model_Z_W, discrete_treatment=False, discrete_instrument=False, categories='auto', n_splits=2, random_state=None): super().__init__(_DMLATEIVModelNuisance(model_Y_W=_FirstStageWrapper(model_Y_W, discrete_target=False), model_T_W=_FirstStageWrapper( model_T_W, discrete_target=discrete_treatment), model_Z_W=_FirstStageWrapper( model_Z_W, discrete_target=discrete_instrument)), discrete_instrument=discrete_instrument, discrete_treatment=discrete_treatment, categories=categories, n_splits=n_splits, random_state=random_state) class _ProjectedDMLATEIVModelNuisance: def __init__(self, model_Y_W, model_T_W, model_T_WZ): self._model_Y_W = clone(model_Y_W, safe=False) self._model_T_W = clone(model_T_W, safe=False) self._model_T_WZ = clone(model_T_WZ, safe=False) def fit(self, Y, T, X=None, W=None, Z=None, sample_weight=None, groups=None): assert X is None, "DML ATE IV does not accept features" self._model_Y_W.fit(X=X, W=W, Target=Y, sample_weight=sample_weight, groups=groups) self._model_T_W.fit(X=X, W=W, Target=T, sample_weight=sample_weight, groups=groups) self._model_T_WZ.fit(X=X, W=W, Z=Z, Target=T, sample_weight=sample_weight, groups=groups) return self def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None): assert X is None, "DML ATE IV does not accept features" if hasattr(self._model_Y_W, 'score'): Y_X_score = self._model_Y_W.score(X=X, W=W, Target=Y, sample_weight=sample_weight) else: Y_X_score = None if hasattr(self._model_T_W, 'score'): T_X_score = self._model_T_W.score(X=X, W=W, Target=T, sample_weight=sample_weight) else: T_X_score = None if hasattr(self._model_T_WZ, 'score'): T_XZ_score = self._model_T_WZ.score(X=X, W=W, Z=Z, Target=T, sample_weight=sample_weight) else: T_XZ_score = None return Y_X_score, T_X_score, T_XZ_score def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None): assert X is None, "DML ATE IV does not accept features" Y_pred = self._model_Y_W.predict(X, W) TX_pred = self._model_T_W.predict(X, W) TXZ_pred = self._model_T_WZ.predict(X, W, Z) if W is None: # In this case predict above returns a single row Y_pred = np.tile(Y_pred.reshape(1, -1), (Y.shape[0], 1)) TX_pred = np.tile(TX_pred.reshape(1, -1), (T.shape[0], 1)) Y_res = Y - Y_pred.reshape(Y.shape) T_res = T - TX_pred.reshape(T.shape) Z_res = TXZ_pred.reshape(T.shape) - TX_pred.reshape(T.shape) return Y_res, T_res, Z_res class ProjectedDMLATEIV(_BaseDMLATEIV): def __init__(self, model_Y_W, model_T_W, model_T_WZ, discrete_treatment=False, discrete_instrument=False, categories='auto', n_splits=2, random_state=None): super().__init__(_ProjectedDMLATEIVModelNuisance( model_Y_W=_FirstStageWrapper( model_Y_W, discrete_target=False), model_T_W=_FirstStageWrapper( model_T_W, discrete_target=discrete_treatment), model_T_WZ=_FirstStageWrapper( model_T_WZ, discrete_target=discrete_treatment)), discrete_treatment=discrete_treatment, discrete_instrument=discrete_instrument, categories=categories, n_splits=n_splits, random_state=random_state) class _BaseDMLIVModelNuisance: """ Nuisance model fits the three models at fit time and at predict time returns :math:`Y-\\E[Y|X]` and :math:`\\E[T|X,Z]-\\E[T|X]` as residuals. """ def __init__(self, model_Y_X, model_T_X, model_T_XZ): self._model_Y_X = clone(model_Y_X, safe=False) self._model_T_X = clone(model_T_X, safe=False) self._model_T_XZ = clone(model_T_XZ, safe=False) def fit(self, Y, T, X=None, W=None, Z=None, sample_weight=None, groups=None): # TODO: would it be useful to extend to handle controls ala vanilla DML? assert W is None, "DML IV does not accept controls" self._model_Y_X.fit(X=X, W=None, Target=Y, sample_weight=sample_weight, groups=groups) self._model_T_X.fit(X=X, W=None, Target=T, sample_weight=sample_weight, groups=groups) self._model_T_XZ.fit(X=X, W=None, Z=Z, Target=T, sample_weight=sample_weight, groups=groups) return self def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None): assert W is None, "DML IV does not accept controls" if hasattr(self._model_Y_X, 'score'): Y_X_score = self._model_Y_X.score(X=X, W=W, Target=Y, sample_weight=sample_weight) else: Y_X_score = None if hasattr(self._model_T_X, 'score'): T_X_score = self._model_T_X.score(X=X, W=W, Target=T, sample_weight=sample_weight) else: T_X_score = None if hasattr(self._model_T_XZ, 'score'): T_XZ_score = self._model_T_XZ.score(X=X, W=W, Z=Z, Target=T, sample_weight=sample_weight) else: T_XZ_score = None return Y_X_score, T_X_score, T_XZ_score def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None): assert W is None, "DML IV does not accept controls" Y_pred = self._model_Y_X.predict(X, W) TXZ_pred = self._model_T_XZ.predict(X, W, Z) TX_pred = self._model_T_X.predict(X, W) if X is None: # In this case predict above returns a single row Y_pred = np.tile(Y_pred.reshape(1, -1), (Y.shape[0], 1)) TX_pred = np.tile(TX_pred.reshape(1, -1), (T.shape[0], 1)) Y_res = Y - Y_pred.reshape(Y.shape) T_res = TXZ_pred.reshape(T.shape) - TX_pred.reshape(T.shape) return Y_res, T_res class _BaseDMLIVModelFinal: """ Final model at fit time, fits a residual on residual regression with a heterogeneous coefficient that depends on X, i.e. .. math :: Y - \\E[Y | X] = \\theta(X) \\cdot (\\E[T | X, Z] - \\E[T | X]) + \\epsilon and at predict time returns :math:`\\theta(X)`. The score method returns the MSE of this final residual on residual regression. """ def __init__(self, model_final): self._model_final = clone(model_final, safe=False) def fit(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_weight=None, sample_var=None): Y_res, T_res = nuisances self._model_final.fit(X, T_res, Y_res, sample_weight=sample_weight, sample_var=sample_var) return self def predict(self, X=None): return self._model_final.predict(X) def score(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_weight=None, sample_var=None): Y_res, T_res = nuisances if Y_res.ndim == 1: Y_res = Y_res.reshape((-1, 1)) if T_res.ndim == 1: T_res = T_res.reshape((-1, 1)) effects = self._model_final.predict(X).reshape((-1, Y_res.shape[1], T_res.shape[1])) Y_res_pred = np.einsum('ijk,ik->ij', effects, T_res).reshape(Y_res.shape) if sample_weight is not None: return np.mean(np.average((Y_res - Y_res_pred)**2, weights=sample_weight, axis=0)) else: return np.mean((Y_res - Y_res_pred)**2) class _BaseDMLIV(_OrthoLearner): """ The class _BaseDMLIV implements the base class of the DMLIV algorithm for estimating a CATE. It accepts three generic machine learning models: 1) model_Y_X that estimates :math:`\\E[Y | X]` 2) model_T_X that estimates :math:`\\E[T | X]` 3) model_T_XZ that estimates :math:`\\E[T | X, Z]` These are estimated in a cross-fitting manner for each sample in the training set. Then it minimizes the square loss: .. math:: \\sum_i (Y_i - \\E[Y|X_i] - \theta(X) * (\\E[T|X_i, Z_i] - \\E[T|X_i]))^2 This loss is minimized by the model_final class, which is passed as an input. In the two children classes {DMLIV, GenericDMLIV}, we implement different strategies of how to invoke machine learning algorithms to minimize this final square loss. Parameters ---------- model_Y_X : estimator model to estimate :math:`\\E[Y | X]`. Must support `fit` and `predict` methods. model_T_X : estimator model to estimate :math:`\\E[T | X]`. Must support `fit` and `predict` methods model_T_XZ : estimator model to estimate :math:`\\E[T | X, Z]`. Must support `fit(X, Z, T, *, sample_weights)` and `predict(X, Z)` methods. model_final : estimator final model that at fit time takes as input :math:`(Y-\\E[Y|X])`, :math:`(\\E[T|X,Z]-\\E[T|X])` and X and supports method predict(X) that produces the CATE at X discrete_instrument: bool, optional, default False Whether the instrument values should be treated as categorical, rather than continuous, quantities discrete_treatment: bool, optional, default False Whether the treatment values should be treated as categorical, rather than continuous, quantities categories: 'auto' or list, default 'auto' The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values). The first category will be treated as the control treatment. n_splits: int, cross-validation generator or an iterable, optional, default 2 Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - :term:`cv splitter` - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the treatment is discrete :class:`~sklearn.model_selection.StratifiedKFold` is used, else, :class:`~sklearn.model_selection.KFold` is used (with a random shuffle in either case). Unless an iterable is used, we call `split(concat[W, X], T)` to generate the splits. If all W, X are None, then we call `split(ones((T.shape[0], 1)), T)`. random_state: int, :class:`~numpy.random.mtrand.RandomState` instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If :class:`~numpy.random.mtrand.RandomState` instance, random_state is the random number generator; If None, the random number generator is the :class:`~numpy.random.mtrand.RandomState` instance used by :mod:`np.random<numpy.random>`. """ def __init__(self, model_Y_X, model_T_X, model_T_XZ, model_final, discrete_instrument=False, discrete_treatment=False, categories='auto', n_splits=2, random_state=None): super().__init__(_BaseDMLIVModelNuisance(model_Y_X, model_T_X, model_T_XZ), _BaseDMLIVModelFinal(model_final), discrete_treatment=discrete_treatment, discrete_instrument=discrete_instrument, categories=categories, n_splits=n_splits, random_state=random_state) @_deprecate_positional("Z and X should be passed by keyword only. In a future release " "we will disallow passing Z and X by position.", ['X', 'Z']) def fit(self, Y, T, Z, X=None, *, sample_weight=None, sample_var=None, groups=None, inference=None): """ Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`. Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: (n, d_z) matrix Instruments for each sample X: optional(n, d_x) matrix or None (Default=None) Features for each sample sample_weight: optional(n,) vector or None (Default=None) Weights for each samples sample_var: optional(n,) vector or None (Default=None) Sample variance for each sample groups: (n,) vector, optional All rows corresponding to the same group will be kept together during splitting. If groups is not None, the n_splits argument passed to this class's initializer must support a 'groups' argument to its split method. inference: string,:class:`.Inference` instance, or None Method for performing inference. This estimator supports 'bootstrap' (or an instance of:class:`.BootstrapInference`). Returns ------- self: _BaseDMLIV """ # Replacing fit from _OrthoLearner, to enforce W=None and improve the docstring return super().fit(Y, T, X=X, Z=Z, sample_weight=sample_weight, sample_var=sample_var, groups=groups, inference=inference) def score(self, Y, T, Z, X=None): """ Score the fitted CATE model on a new data set. Generates nuisance parameters for the new data set based on the fitted residual nuisance models created at fit time. It uses the mean prediction of the models fitted by the different crossfit folds. Then calculates the MSE of the final residual Y on residual T regression. If model_final does not have a score method, then it raises an :exc:`.AttributeError` Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: optional(n, d_z) matrix Instruments for each sample X: optional(n, d_x) matrix or None (Default=None) Features for each sample Returns ------- score: float The MSE of the final CATE model on the new data. """ # Replacing score from _OrthoLearner, to enforce W=None and improve the docstring return super().score(Y, T, X=X, Z=Z) @property def original_featurizer(self): return super().model_final._model_final._original_featurizer @property def featurizer(self): # NOTE This is used by the inference methods and has to be the overall featurizer. intended # for internal use by the library return super().model_final._model_final._featurizer @property def model_final(self): # NOTE This is used by the inference methods and is more for internal use to the library return super().model_final._model_final._model @property def model_cate(self): """ Get the fitted final CATE model. Returns ------- model_cate: object of type(model_final) An instance of the model_final object that was fitted after calling fit which corresponds to the constant marginal CATE model. """ return super().model_final._model_final._model @property def models_Y_X(self): """ Get the fitted models for :math:`\\E[Y | X]`. Returns ------- models_Y_X: list of objects of type(`model_Y_X`) A list of instances of the `model_Y_X` object. Each element corresponds to a crossfitting fold and is the model instance that was fitted for that training fold. """ return [mdl._model for mdl in super().models_Y_X] @property def models_T_X(self): """ Get the fitted models for :math:`\\E[T | X]`. Returns ------- models_T_X: list of objects of type(`model_T_X`) A list of instances of the `model_T_X` object. Each element corresponds to a crossfitting fold and is the model instance that was fitted for that training fold. """ return [mdl._model for mdl in super().models_T_X] @property def models_T_XZ(self): """ Get the fitted models for :math:`\\E[T | X, Z]`. Returns ------- models_T_XZ: list of objects of type(`model_T_XZ`) A list of instances of the `model_T_XZ` object. Each element corresponds to a crossfitting fold and is the model instance that was fitted for that training fold. """ return [mdl._model for mdl in super().models_T_XZ] @property def nuisance_scores_Y_X(self): """ Get the scores for Y_X model on the out-of-sample training data """ return self.nuisance_scores_[0] @property def nuisance_scores_T_X(self): """ Get the scores for T_X model on the out-of-sample training data """ return self.nuisance_scores_[1] @property def nuisance_scores_T_XZ(self): """ Get the scores for T_XZ model on the out-of-sample training data """ return self.nuisance_scores_[2] def cate_feature_names(self, feature_names=None): """ Get the output feature names. Parameters ---------- feature_names: list of strings of length X.shape[1] or None The names of the input features. If None and X is a dataframe, it defaults to the column names from the dataframe. Returns ------- out_feature_names: list of strings or None The names of the output features :math:`\\phi(X)`, i.e. the features with respect to which the final constant marginal CATE model is linear. It is the names of the features that are associated with each entry of the :meth:`coef_` parameter. Not available when the featurizer is not None and does not have a method: `get_feature_names(feature_names)`. Otherwise None is returned. """ if feature_names is None: feature_names = self._input_names["feature_names"] if self.original_featurizer is None: return feature_names elif hasattr(self.original_featurizer, 'get_feature_names'): return self.original_featurizer.get_feature_names(feature_names) else: raise AttributeError("Featurizer does not have a method: get_feature_names!") class DMLIV(_BaseDMLIV): """ A child of the _BaseDMLIV class that specifies a particular effect model where the treatment effect is linear in some featurization of the variable X The features are created by a provided featurizer that supports fit_transform. Then an arbitrary model fits on the composite set of features. Concretely, it assumes that :math:`\\theta(X)=<\\theta, \\phi(X)>` for some features :math:`\\phi(X)` and runs a linear model regression of :math:`Y-\\E[Y|X]` on :math:`phi(X)*(\\E[T|X,Z]-\\E[T|X])`. The features are created by the featurizer provided by the user. The particular linear model regression is also specified by the user (e.g. Lasso, ElasticNet) Parameters ---------- model_Y_X : estimator model to estimate :math:`\\E[Y | X]`. Must support `fit` and `predict` methods. model_T_X : estimator model to estimate :math:`\\E[T | X]`. Must support `fit` and either `predict` or `predict_proba` methods, depending on whether the treatment is discrete. model_T_XZ : estimator model to estimate :math:`\\E[T | X, Z]`. Must support `fit` and either `predict` or `predict_proba` methods, depending on whether the treatment is discrete. model_final : estimator final linear model for predicting :math:`(Y-\\E[Y|X])` from :math:`\\phi(X) \\cdot (\\E[T|X,Z]-\\E[T|X])` Method is incorrect if this model is not linear (e.g. Lasso, ElasticNet, LinearRegression). featurizer: :term:`transformer`, optional, default None Must support fit_transform and transform. Used to create composite features in the final CATE regression. It is ignored if X is None. The final CATE will be trained on the outcome of featurizer.fit_transform(X). If featurizer=None, then CATE is trained on X. fit_cate_intercept : bool, optional, default True Whether the linear CATE model should have a constant term. n_splits: int, cross-validation generator or an iterable, optional, default 2 Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - :term:`cv splitter` - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the treatment is discrete :class:`~sklearn.model_selection.StratifiedKFold` is used, else, :class:`~sklearn.model_selection.KFold` is used (with a random shuffle in either case). Unless an iterable is used, we call `split(concat[W, X], T)` to generate the splits. If all W, X are None, then we call `split(ones((T.shape[0], 1)), T)`. discrete_instrument: bool, optional, default False Whether the instrument values should be treated as categorical, rather than continuous, quantities discrete_treatment: bool, optional, default False Whether the treatment values should be treated as categorical, rather than continuous, quantities categories: 'auto' or list, default 'auto' The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values). The first category will be treated as the control treatment. random_state: int, :class:`~numpy.random.mtrand.RandomState` instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If :class:`~numpy.random.mtrand.RandomState` instance, random_state is the random number generator; If None, the random number generator is the :class:`~numpy.random.mtrand.RandomState` instance used by :mod:`np.random<numpy.random>`. """ def __init__(self, model_Y_X, model_T_X, model_T_XZ, model_final, featurizer=None, fit_cate_intercept=True, n_splits=2, discrete_instrument=False, discrete_treatment=False, categories='auto', random_state=None): self.bias_part_of_coef = fit_cate_intercept self.fit_cate_intercept = fit_cate_intercept super().__init__(_FirstStageWrapper(model_Y_X, False), _FirstStageWrapper(model_T_X, discrete_treatment), _FirstStageWrapper(model_T_XZ, discrete_treatment), _FinalWrapper(model_final, fit_cate_intercept=fit_cate_intercept, featurizer=featurizer, use_weight_trick=False), n_splits=n_splits, discrete_instrument=discrete_instrument, discrete_treatment=discrete_treatment, categories=categories, random_state=random_state) class NonParamDMLIV(_BaseDMLIV): """ A child of the _BaseDMLIV class that allows for an arbitrary square loss based ML method in the final stage of the DMLIV algorithm. The method has to support sample weights and the fit method has to take as input sample_weights (e.g. random forests), i.e. fit(X, y, sample_weight=None) It achieves this by re-writing the final stage square loss of the DMLIV algorithm as: .. math :: \\sum_i (\\E[T|X_i, Z_i] - \\E[T|X_i])^2 * ((Y_i - \\E[Y|X_i])/(\\E[T|X_i, Z_i] - \\E[T|X_i]) - \\theta(X))^2 Then this can be viewed as a weighted square loss regression, where the target label is .. math :: \\tilde{Y}_i = (Y_i - \\E[Y|X_i])/(\\E[T|X_i, Z_i] - \\E[T|X_i]) and each sample has a weight of .. math :: V(X_i) = (\\E[T|X_i, Z_i] - \\E[T|X_i])^2 Thus we can call any regression model with inputs: fit(X, :math:`\\tilde{Y}_i`, sample_weight= :math:`V(X_i)`) Parameters ---------- model_Y_X : estimator model to estimate :math:`\\E[Y | X]`. Must support `fit` and `predict` methods. model_T_X : estimator model to estimate :math:`\\E[T | X]`. Must support `fit` and either `predict` or `predict_proba` methods, depending on whether the treatment is discrete. model_T_XZ : estimator model to estimate :math:`\\E[T | X, Z]`. Must support `fit` and either `predict` or `predict_proba` methods, depending on whether the treatment is discrete. model_final : estimator final model for predicting :math:`\\tilde{Y}` from X with sample weights V(X) n_splits: int, cross-validation generator or an iterable, optional, default 2 Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - :term:`cv splitter` - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the treatment is discrete :class:`~sklearn.model_selection.StratifiedKFold` is used, else, :class:`~sklearn.model_selection.KFold` is used (with a random shuffle in either case). Unless an iterable is used, we call `split(concat[W, X], T)` to generate the splits. If all W, X are None, then we call `split(ones((T.shape[0], 1)), T)`. discrete_instrument: bool, optional, default False Whether the instrument values should be treated as categorical, rather than continuous, quantities discrete_treatment: bool, optional, default False Whether the treatment values should be treated as categorical, rather than continuous, quantities categories: 'auto' or list, default 'auto' The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values). The first category will be treated as the control treatment. random_state: int, :class:`~numpy.random.mtrand.RandomState` instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If :class:`~numpy.random.mtrand.RandomState` instance, random_state is the random number generator; If None, the random number generator is the :class:`~numpy.random.mtrand.RandomState` instance used by :mod:`np.random<numpy.random>`. """ def __init__(self, model_Y_X, model_T_X, model_T_XZ, model_final, featurizer=None, fit_cate_intercept=True, n_splits=2, discrete_instrument=False, discrete_treatment=False, categories='auto', random_state=None): super().__init__(_FirstStageWrapper(model_Y_X, False), _FirstStageWrapper(model_T_X, discrete_treatment), _FirstStageWrapper(model_T_XZ, discrete_treatment), _FinalWrapper(model_final, fit_cate_intercept=fit_cate_intercept, featurizer=featurizer, use_weight_trick=True), n_splits=n_splits, discrete_instrument=discrete_instrument, discrete_treatment=discrete_treatment, categories=categories, random_state=random_state) class _BaseDRIVModelFinal: """ Final model at fit time, fits a residual on residual regression with a heterogeneous coefficient that depends on X, i.e. .. math :: Y - \\E[Y | X] = \\theta(X) \\cdot (\\E[T | X, Z] - \\E[T | X]) + \\epsilon and at predict time returns :math:`\\theta(X)`. The score method returns the MSE of this final residual on residual regression. """ def __init__(self, model_final, featurizer, discrete_treatment, discrete_instrument, fit_cate_intercept, cov_clip, opt_reweighted): self._model_final = clone(model_final, safe=False) self._fit_cate_intercept = fit_cate_intercept self._original_featurizer = clone(featurizer, safe=False) self._discrete_treatment = discrete_treatment self._discrete_instrument = discrete_instrument if self._fit_cate_intercept: add_intercept_trans = FunctionTransformer(add_intercept, validate=True) if featurizer: self._featurizer = Pipeline([('featurize', self._original_featurizer), ('add_intercept', add_intercept_trans)]) else: self._featurizer = add_intercept_trans else: self._featurizer = self._original_featurizer self._cov_clip = cov_clip self._opt_reweighted = opt_reweighted def _effect_estimate(self, nuisances): prel_theta, res_t, res_y, res_z, cov = [nuisance.reshape(nuisances[0].shape) for nuisance in nuisances] # Estimate final model of theta(X) by minimizing the square loss: # (prel_theta(X) + (Y_res - prel_theta(X) * T_res) * Z_res / cov[T,Z | X] - theta(X))^2 # We clip the covariance so that it is bounded away from zero, so as to reduce variance # at the expense of some small bias. For points with very small covariance we revert # to the model-based preliminary estimate and do not add the correction term. cov_sign = np.sign(cov) cov_sign[cov_sign == 0] = 1 clipped_cov = cov_sign * np.clip(np.abs(cov), self._cov_clip, np.inf) return prel_theta + (res_y - prel_theta * res_t) * res_z / clipped_cov, clipped_cov def fit(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_weight=None, sample_var=None): self.d_y = Y.shape[1:] self.d_t = nuisances[1].shape[1:] self.d_z = nuisances[3].shape[1:] # TODO: if opt_reweighted is False, we could change the logic to support multidimensional treatments, # instruments, and outcomes if self.d_y and self.d_y[0] > 2: raise AttributeError("DRIV only supports a single outcome") if self.d_t and self.d_t[0] > 1: if self._discrete_treatment: raise AttributeError("DRIV only supports binary treatments") else: raise AttributeError("DRIV only supports single-dimensional continuous treatments") if self.d_z and self.d_z[0] > 1: if self._discrete_instrument: raise AttributeError("DRIV only supports binary instruments") else: raise AttributeError("DRIV only supports single-dimensional continuous instruments") theta_dr, clipped_cov = self._effect_estimate(nuisances) if (X is not None) and (self._featurizer is not None): X = self._featurizer.fit_transform(X) if self._opt_reweighted and (sample_weight is not None): sample_weight = sample_weight * clipped_cov.ravel()**2 elif self._opt_reweighted: sample_weight = clipped_cov.ravel()**2 self._model_final.fit(X, theta_dr, **filter_none_kwargs(sample_weight=sample_weight, sample_var=sample_var)) return self def predict(self, X=None): if (X is not None) and (self._featurizer is not None): X = self._featurizer.transform(X) return self._model_final.predict(X).reshape((-1,) + self.d_y + self.d_t) def score(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_weight=None, sample_var=None): theta_dr, clipped_cov = self._effect_estimate(nuisances) if (X is not None) and (self._featurizer is not None): X = self._featurizer.transform(X) if self._opt_reweighted and (sample_weight is not None): sample_weight = sample_weight * clipped_cov.ravel()**2 elif self._opt_reweighted: sample_weight = clipped_cov.ravel()**2 return np.average((theta_dr.ravel() - self._model_final.predict(X).ravel())**2, weights=sample_weight, axis=0) class _BaseDRIV(_OrthoLearner): """ The _BaseDRIV algorithm for estimating CATE with IVs. It is the parent of the two public classes {DRIV, ProjectedDRIV} Parameters ---------- nuisance_models : dictionary of nuisance models, with {'name_of_model' : EstimatorObject, ...} model_final : estimator model compatible with the sklearn regression API, used to fit the effect on X featurizer : :term:`transformer`, optional, default None Must support fit_transform and transform. Used to create composite features in the final CATE regression. It is ignored if X is None. The final CATE will be trained on the outcome of featurizer.fit_transform(X). If featurizer=None, then CATE is trained on X. fit_cate_intercept : bool, optional, default True Whether the linear CATE model should have a constant term. cov_clip : float, optional, default 0.1 clipping of the covariate for regions with low "overlap", to reduce variance opt_reweighted : bool, optional, default False Whether to reweight the samples to minimize variance. If True then model_final.fit must accept sample_weight as a kw argument. If True then assumes the model_final is flexible enough to fit the true CATE model. Otherwise, it method will return a biased projection to the model_final space, biased to give more weight on parts of the feature space where the instrument is strong. discrete_instrument: bool, optional, default False Whether the instrument values should be treated as categorical, rather than continuous, quantities discrete_treatment: bool, optional, default False Whether the treatment values should be treated as categorical, rather than continuous, quantities categories: 'auto' or list, default 'auto' The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values). The first category will be treated as the control treatment. n_splits: int, cross-validation generator or an iterable, optional, default 2 Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - :term:`cv splitter` - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the treatment is discrete :class:`~sklearn.model_selection.StratifiedKFold` is used, else, :class:`~sklearn.model_selection.KFold` is used (with a random shuffle in either case). Unless an iterable is used, we call `split(concat[W, X], T)` to generate the splits. If all W, X are None, then we call `split(ones((T.shape[0], 1)), T)`. random_state: int, :class:`~numpy.random.mtrand.RandomState` instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If :class:`~numpy.random.mtrand.RandomState` instance, random_state is the random number generator; If None, the random number generator is the :class:`~numpy.random.mtrand.RandomState` instance used by :mod:`np.random<numpy.random>`. """ def __init__(self, nuisance_models, model_final, featurizer=None, fit_cate_intercept=True, cov_clip=0.1, opt_reweighted=False, discrete_instrument=False, discrete_treatment=False, categories='auto', n_splits=2, random_state=None): self.fit_cate_intercept = fit_cate_intercept self.bias_part_of_coef = fit_cate_intercept self.cov_clip = cov_clip self.opt_reweighted = opt_reweighted super().__init__(nuisance_models, _BaseDRIVModelFinal(model_final, featurizer, discrete_treatment, discrete_instrument, fit_cate_intercept, cov_clip, opt_reweighted), discrete_instrument=discrete_instrument, discrete_treatment=discrete_treatment, categories=categories, n_splits=n_splits, random_state=random_state) @_deprecate_positional("X, W, and Z should be passed by keyword only. In a future release " "we will disallow passing X, W, and Z by position.", ['X', 'W', 'Z']) def fit(self, Y, T, Z, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference=None): """ Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`. Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: (n, d_z) matrix Instruments for each sample X: optional(n, d_x) matrix or None (Default=None) Features for each sample W: optional(n, d_w) matrix or None (Default=None) Controls for each sample sample_weight: optional(n,) vector or None (Default=None) Weights for each samples sample_var: optional(n,) vector or None (Default=None) Sample variance for each sample groups: (n,) vector, optional All rows corresponding to the same group will be kept together during splitting. If groups is not None, the n_splits argument passed to this class's initializer must support a 'groups' argument to its split method. inference: string,:class:`.Inference` instance, or None Method for performing inference. This estimator supports 'bootstrap' (or an instance of:class:`.BootstrapInference`). Returns ------- self: _BaseDRIV instance """ # Replacing fit from _OrthoLearner, to reorder arguments and improve the docstring return super().fit(Y, T, X=X, W=W, Z=Z, sample_weight=sample_weight, sample_var=sample_var, groups=groups, inference=inference) def score(self, Y, T, Z, X=None, W=None, sample_weight=None): """ Score the fitted CATE model on a new data set. Generates nuisance parameters for the new data set based on the fitted nuisance models created at fit time. It uses the mean prediction of the models fitted by the different crossfit folds. Then calls the score function of the model_final and returns the calculated score. The model_final model must have a score method. If model_final does not have a score method, then it raises an :exc:`.AttributeError` Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: (n, d_z) matrix or None (Default=None) Instruments for each sample X: optional (n, d_x) matrix or None (Default=None) Features for each sample W: optional(n, d_w) matrix or None (Default=None) Controls for each sample sample_weight: optional(n,) vector or None (Default=None) Weights for each samples Returns ------- score : float or (array of float) The score of the final CATE model on the new data. Same type as the return type of the model_final.score method. """ # Replacing score from _OrthoLearner, to reorder arguments and improve the docstring return super().score(Y, T, X=X, W=W, Z=Z, sample_weight=sample_weight) @property def original_featurizer(self): return super().model_final._original_featurizer @property def featurizer(self): # NOTE This is used by the inference methods and has to be the overall featurizer. intended # for internal use by the library return super().model_final._featurizer @property def model_final(self): # NOTE This is used by the inference methods and is more for internal use to the library return super().model_final._model_final def cate_feature_names(self, feature_names=None): """ Get the output feature names. Parameters ---------- feature_names: list of strings of length X.shape[1] or None The names of the input features. If None and X is a dataframe, it defaults to the column names from the dataframe. Returns ------- out_feature_names: list of strings or None The names of the output features :math:`\\phi(X)`, i.e. the features with respect to which the final constant marginal CATE model is linear. It is the names of the features that are associated with each entry of the :meth:`coef_` parameter. Not available when the featurizer is not None and does not have a method: `get_feature_names(feature_names)`. Otherwise None is returned. """ if feature_names is None: feature_names = self._input_names["feature_names"] if self.original_featurizer is None: return feature_names elif hasattr(self.original_featurizer, 'get_feature_names'): return self.original_featurizer.get_feature_names(feature_names) else: raise AttributeError("Featurizer does not have a method: get_feature_names!") class _IntentToTreatDRIVModelNuisance: """ Nuisance model fits the three models at fit time and at predict time returns :math:`Y-\\E[Y|X]` and :math:`\\E[T|X,Z]-\\E[T|X]` as residuals. """ def __init__(self, model_Y_X, model_T_XZ, prel_model_effect): self._model_Y_X = clone(model_Y_X, safe=False) self._model_T_XZ = clone(model_T_XZ, safe=False) self._prel_model_effect = clone(prel_model_effect, safe=False) def fit(self, Y, T, X=None, W=None, Z=None, sample_weight=None, groups=None): self._model_Y_X.fit(X=X, W=W, Target=Y, sample_weight=sample_weight, groups=groups) self._model_T_XZ.fit(X=X, W=W, Z=Z, Target=T, sample_weight=sample_weight, groups=groups) # we need to undo the one-hot encoding for calling effect, # since it expects raw values self._prel_model_effect.fit(Y, inverse_onehot(T), Z=inverse_onehot(Z), X=X, W=W, sample_weight=sample_weight, groups=groups) return self def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None): if hasattr(self._model_Y_X, 'score'): Y_X_score = self._model_Y_X.score(X=X, W=W, Target=Y, sample_weight=sample_weight) else: Y_X_score = None if hasattr(self._model_T_XZ, 'score'): T_XZ_score = self._model_T_XZ.score(X=X, W=W, Z=Z, Target=T, sample_weight=sample_weight) else: T_XZ_score = None if hasattr(self._prel_model_effect, 'score'): # we need to undo the one-hot encoding for calling effect, # since it expects raw values effect_score = self._prel_model_effect.score(Y, inverse_onehot(T), Z=inverse_onehot(Z), X=X, W=W, sample_weight=sample_weight) else: effect_score = None return Y_X_score, T_XZ_score, effect_score def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None): Y_pred = self._model_Y_X.predict(X, W) T_pred_zero = self._model_T_XZ.predict(X, W, np.zeros(Z.shape)) T_pred_one = self._model_T_XZ.predict(X, W, np.ones(Z.shape)) delta = (T_pred_one - T_pred_zero) / 2 T_pred_mean = (T_pred_one + T_pred_zero) / 2 prel_theta = self._prel_model_effect.effect(X) if X is None: # In this case predict above returns a single row Y_pred = np.tile(Y_pred.reshape(1, -1), (Y.shape[0], 1)) prel_theta = np.tile(prel_theta.reshape(1, -1), (T.shape[0], 1)) Y_res = Y - Y_pred.reshape(Y.shape) T_res = T - T_pred_mean.reshape(T.shape) return prel_theta, T_res, Y_res, 2 * Z - 1, delta class _IntentToTreatDRIV(_BaseDRIV): """ Helper class for the DRIV algorithm for the intent-to-treat A/B test setting """ def __init__(self, model_Y_X, model_T_XZ, prel_model_effect, model_effect, featurizer=None, fit_cate_intercept=True, cov_clip=.1, n_splits=3, opt_reweighted=False, categories='auto', random_state=None): """ """ # TODO: check that Y, T, Z do not have multiple columns super().__init__(_IntentToTreatDRIVModelNuisance(model_Y_X, model_T_XZ, prel_model_effect), model_effect, featurizer=featurizer, fit_cate_intercept=fit_cate_intercept, cov_clip=cov_clip, n_splits=n_splits, discrete_instrument=True, discrete_treatment=True, categories=categories, opt_reweighted=opt_reweighted, random_state=random_state) class _DummyCATE: """ A dummy cate effect model that always returns zero effect """ def __init__(self): return def fit(self, y, T, *, Z, X, W=None, sample_weight=None, groups=None): return self def effect(self, X): if X is None: return np.zeros(1) return np.zeros(X.shape[0]) class IntentToTreatDRIV(_IntentToTreatDRIV): """ Implements the DRIV algorithm for the intent-to-treat A/B test setting Parameters ---------- model_Y_X : estimator model to estimate :math:`\\E[Y | X]`. Must support `fit` and `predict` methods. model_T_XZ : estimator model to estimate :math:`\\E[T | X, Z]`. Must support `fit` and `predict_proba` methods. flexible_model_effect : estimator a flexible model for a preliminary version of the CATE, must accept sample_weight at fit time. final_model_effect : estimator, optional a final model for the CATE and projections. If None, then flexible_model_effect is also used as a final model featurizer : :term:`transformer`, optional, default None Must support fit_transform and transform. Used to create composite features in the final CATE regression. It is ignored if X is None. The final CATE will be trained on the outcome of featurizer.fit_transform(X). If featurizer=None, then CATE is trained on X. fit_cate_intercept : bool, optional, default True Whether the linear CATE model should have a constant term. cov_clip : float, optional, default 0.1 clipping of the covariate for regions with low "overlap", to reduce variance n_splits: int, cross-validation generator or an iterable, optional, default 3 Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - :term:`cv splitter` - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the treatment is discrete :class:`~sklearn.model_selection.StratifiedKFold` is used, else, :class:`~sklearn.model_selection.KFold` is used (with a random shuffle in either case). Unless an iterable is used, we call `split(concat[W, X], T)` to generate the splits. If all W, X are None, then we call `split(ones((T.shape[0], 1)), T)`. opt_reweighted : bool, optional, default False Whether to reweight the samples to minimize variance. If True then final_model_effect.fit must accept sample_weight as a kw argument (WeightWrapper from utilities can be used for any linear model to enable sample_weights). If True then assumes the final_model_effect is flexible enough to fit the true CATE model. Otherwise, it method will return a biased projection to the model_effect space, biased to give more weight on parts of the feature space where the instrument is strong. categories: 'auto' or list, default 'auto' The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values). The first category will be treated as the control treatment. random_state: int, :class:`~numpy.random.mtrand.RandomState` instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If :class:`~numpy.random.mtrand.RandomState` instance, random_state is the random number generator; If None, the random number generator is the :class:`~numpy.random.mtrand.RandomState` instance used by :mod:`np.random<numpy.random>`. """ def __init__(self, model_Y_X, model_T_XZ, flexible_model_effect, final_model_effect=None, featurizer=None, fit_cate_intercept=True, cov_clip=.1, n_splits=3, opt_reweighted=False, categories='auto', random_state=None): model_Y_X = _FirstStageWrapper(model_Y_X, discrete_target=False) model_T_XZ = _FirstStageWrapper(model_T_XZ, discrete_target=True) prel_model_effect = _IntentToTreatDRIV(model_Y_X, model_T_XZ, _DummyCATE(), flexible_model_effect, cov_clip=1e-7, n_splits=1, opt_reweighted=True, random_state=random_state) if final_model_effect is None: final_model_effect = flexible_model_effect super().__init__(model_Y_X, model_T_XZ, prel_model_effect, final_model_effect, featurizer=featurizer, fit_cate_intercept=fit_cate_intercept, cov_clip=cov_clip, n_splits=n_splits, opt_reweighted=opt_reweighted, categories=categories, random_state=random_state) @property def models_Y_X(self): return [mdl._model_Y_X._model for mdl in super().models_nuisance] @property def models_T_XZ(self): return [mdl._model_T_XZ._model for mdl in super().models_nuisance] @property def nuisance_scores_Y_X(self): return self.nuisance_scores_[0] @property def nuisance_scores_T_XZ(self): return self.nuisance_scores_[1] @property def nuisance_scores_effect(self): return self.nuisance_scores_[2] class LinearIntentToTreatDRIV(StatsModelsCateEstimatorMixin, IntentToTreatDRIV): """ Implements the DRIV algorithm for the intent-to-treat A/B test setting Parameters ---------- model_Y_X : estimator model to estimate :math:`\\E[Y | X]`. Must support `fit` and `predict` methods. model_T_XZ : estimator model to estimate :math:`\\E[T | X, Z]`. Must support `fit` and `predict_proba` methods. flexible_model_effect : estimator a flexible model for a preliminary version of the CATE, must accept sample_weight at fit time. featurizer : :term:`transformer`, optional, default None Must support fit_transform and transform. Used to create composite features in the final CATE regression. It is ignored if X is None. The final CATE will be trained on the outcome of featurizer.fit_transform(X). If featurizer=None, then CATE is trained on X. fit_cate_intercept : bool, optional, default True Whether the linear CATE model should have a constant term. cov_clip : float, optional, default 0.1 clipping of the covariate for regions with low "overlap", to reduce variance n_splits: int, cross-validation generator or an iterable, optional, default 3 Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - :term:`cv splitter` - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the treatment is discrete :class:`~sklearn.model_selection.StratifiedKFold` is used, else, :class:`~sklearn.model_selection.KFold` is used (with a random shuffle in either case). Unless an iterable is used, we call `split(concat[W, X], T)` to generate the splits. If all W, X are None, then we call `split(ones((T.shape[0], 1)), T)`. categories: 'auto' or list, default 'auto' The categories to use when encoding discrete treatments (or 'auto' to use the unique sorted values). The first category will be treated as the control treatment. random_state: int, :class:`~numpy.random.mtrand.RandomState` instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If :class:`~numpy.random.mtrand.RandomState` instance, random_state is the random number generator; If None, the random number generator is the :class:`~numpy.random.mtrand.RandomState` instance used by :mod:`np.random<numpy.random>`. """ def __init__(self, model_Y_X, model_T_XZ, flexible_model_effect, featurizer=None, fit_cate_intercept=True, cov_clip=.1, n_splits=3, categories='auto', random_state=None): super().__init__(model_Y_X, model_T_XZ, flexible_model_effect=flexible_model_effect, featurizer=featurizer, fit_cate_intercept=fit_cate_intercept, final_model_effect=StatsModelsLinearRegression(fit_intercept=False), cov_clip=cov_clip, n_splits=n_splits, opt_reweighted=False, categories=categories, random_state=random_state) # override only so that we can update the docstring to indicate support for `StatsModelsInference` @_deprecate_positional("X, W, and Z should be passed by keyword only. In a future release " "we will disallow passing X, W, and Z by position.", ['X', 'W', 'Z']) def fit(self, Y, T, Z, X=None, W=None, *, sample_weight=None, sample_var=None, groups=None, inference='auto'): """ Estimate the counterfactual model from data, i.e. estimates function :math:`\\theta(\\cdot)`. Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: (n, d_z) matrix or vector of length n Instruments for each sample X: optional(n, d_x) matrix or None (Default=None) Features for each sample W: optional(n, d_w) matrix or None (Default=None) Controls for each sample sample_weight: optional(n,) vector or None (Default=None) Weights for each samples sample_var: optional(n,) vector or None (Default=None) Sample variance for each sample groups: (n,) vector, optional All rows corresponding to the same group will be kept together during splitting. If groups is not None, the n_splits argument passed to this class's initializer must support a 'groups' argument to its split method. inference: string,:class:`.Inference` instance, or None Method for performing inference. This estimator supports 'bootstrap' (or an instance of:class:`.BootstrapInference`) and 'statsmodels' (or an instance of :class:`.StatsModelsInference`). Returns ------- self : instance """ return super().fit(Y, T, Z=Z, X=X, W=W, sample_weight=sample_weight, sample_var=sample_var, groups=groups, inference=inference)

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Python

tests/test_stream_xep_0060.py

imo/SleekXMPP

8175ed572888551314fe43304ab8acd2278c809b

[ "BSD-3-Clause" ]

3

2019-02-01T06:50:08.000Z

2020-03-24T00:45:31.000Z

tests/test_stream_xep_0060.py

imo/SleekXMPP

8175ed572888551314fe43304ab8acd2278c809b

[ "BSD-3-Clause" ]

null

tests/test_stream_xep_0060.py

imo/SleekXMPP

8175ed572888551314fe43304ab8acd2278c809b

[ "BSD-3-Clause" ]

null

import sys import time import threading from sleekxmpp.test import * from sleekxmpp.stanza.atom import AtomEntry from sleekxmpp.xmlstream import register_stanza_plugin class TestStreamPubsub(SleekTest): """ Test using the XEP-0030 plugin. """ def setUp(self): self.stream_start() def tearDown(self): self.stream_close() def testCreateInstantNode(self): """Test creating an instant node""" t = threading.Thread(name='create_node', target=self.xmpp['xep_0060'].create_node, args=('pubsub.example.com', None)) t.start() self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <create /> </pubsub> </iq> """) self.recv(""" <iq type="result" id="1" to="tester@localhost" from="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <create node="25e3d37dabbab9541f7523321421edc5bfeb2dae" /> </pubsub> </iq> """) t.join() def testCreateNodeNoConfig(self): """Test creating a node without a config""" self.xmpp['xep_0060'].create_node( 'pubsub.example.com', 'princely_musings', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <create node="princely_musings" /> </pubsub> </iq> """) def testCreateNodeConfig(self): """Test creating a node with a config""" form = self.xmpp['xep_0004'].stanza.Form() form['type'] = 'submit' form.add_field(var='pubsub#access_model', value='whitelist') self.xmpp['xep_0060'].create_node( 'pubsub.example.com', 'princely_musings', config=form, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <create node="princely_musings" /> <configure> <x xmlns="jabber:x:data" type="submit"> <field var="pubsub#access_model"> <value>whitelist</value> </field> <field var="FORM_TYPE"> <value>http://jabber.org/protocol/pubsub#node_config</value> </field> </x> </configure> </pubsub> </iq> """) def testDeleteNode(self): """Test deleting a node""" self.xmpp['xep_0060'].delete_node( 'pubsub.example.com', 'some_node', block=False) self.send(""" <iq type="set" to="pubsub.example.com" id="1"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <delete node="some_node" /> </pubsub> </iq> """) def testSubscribeCase1(self): """ Test subscribing to a node: Case 1: No subscribee, default 'from' JID, bare JID """ self.xmpp['xep_0060'].subscribe( 'pubsub.example.com', 'somenode', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <subscribe node="somenode" jid="tester@localhost" /> </pubsub> </iq> """) def testSubscribeCase2(self): """ Test subscribing to a node: Case 2: No subscribee, given 'from' JID, bare JID """ self.xmpp['xep_0060'].subscribe( 'pubsub.example.com', 'somenode', ifrom='foo@comp.example.com/bar', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com" from="foo@comp.example.com/bar"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <subscribe node="somenode" jid="foo@comp.example.com" /> </pubsub> </iq> """) def testSubscribeCase3(self): """ Test subscribing to a node: Case 3: No subscribee, given 'from' JID, full JID """ self.xmpp['xep_0060'].subscribe( 'pubsub.example.com', 'somenode', ifrom='foo@comp.example.com/bar', bare=False, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com" from="foo@comp.example.com/bar"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <subscribe node="somenode" jid="foo@comp.example.com/bar" /> </pubsub> </iq> """) def testSubscribeCase4(self): """ Test subscribing to a node: Case 4: No subscribee, no 'from' JID, full JID """ self.stream_close() self.stream_start(jid='tester@localhost/full') self.xmpp['xep_0060'].subscribe( 'pubsub.example.com', 'somenode', bare=False, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <subscribe node="somenode" jid="tester@localhost/full" /> </pubsub> </iq> """) def testSubscribeCase5(self): """ Test subscribing to a node: Case 5: Subscribee given """ self.xmpp['xep_0060'].subscribe( 'pubsub.example.com', 'somenode', subscribee='user@example.com/foo', ifrom='foo@comp.example.com/bar', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com" from="foo@comp.example.com/bar"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <subscribe node="somenode" jid="user@example.com/foo" /> </pubsub> </iq> """) def testSubscribeWithOptions(self): """Test subscribing to a node, with options.""" opts = self.xmpp['xep_0004'].make_form() opts.add_field( var='FORM_TYPE', value='http://jabber.org/protocol/pubsub#subscribe_options', ftype='hidden') opts.add_field( var='pubsub#digest', value=False, ftype='boolean') opts['type'] = 'submit' self.xmpp['xep_0060'].subscribe( 'pubsub.example.com', 'somenode', options=opts, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <subscribe node="somenode" jid="tester@localhost" /> <options> <x xmlns="jabber:x:data" type="submit"> <field var="FORM_TYPE"> <value>http://jabber.org/protocol/pubsub#subscribe_options</value> </field> <field var="pubsub#digest"> <value>0</value> </field> </x> </options> </pubsub> </iq> """) def testUnsubscribeCase1(self): """ Test unsubscribing from a node: Case 1: No subscribee, default 'from' JID, bare JID """ self.xmpp['xep_0060'].unsubscribe( 'pubsub.example.com', 'somenode', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <unsubscribe node="somenode" jid="tester@localhost" /> </pubsub> </iq> """) def testUnsubscribeCase2(self): """ Test unsubscribing from a node: Case 2: No subscribee, given 'from' JID, bare JID """ self.xmpp['xep_0060'].unsubscribe( 'pubsub.example.com', 'somenode', ifrom='foo@comp.example.com/bar', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com" from="foo@comp.example.com/bar"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <unsubscribe node="somenode" jid="foo@comp.example.com" /> </pubsub> </iq> """) def testUnsubscribeCase3(self): """ Test unsubscribing from a node: Case 3: No subscribee, given 'from' JID, full JID """ self.xmpp['xep_0060'].unsubscribe( 'pubsub.example.com', 'somenode', ifrom='foo@comp.example.com/bar', bare=False, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com" from="foo@comp.example.com/bar"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <unsubscribe node="somenode" jid="foo@comp.example.com/bar" /> </pubsub> </iq> """) def testUnsubscribeCase4(self): """ Test unsubscribing from a node: Case 4: No subscribee, no 'from' JID, full JID """ self.stream_close() self.stream_start(jid='tester@localhost/full') self.xmpp['xep_0060'].unsubscribe( 'pubsub.example.com', 'somenode', bare=False, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <unsubscribe node="somenode" jid="tester@localhost/full" /> </pubsub> </iq> """) def testUnsubscribeCase5(self): """ Test unsubscribing from a node: Case 5: Subscribee given """ self.xmpp['xep_0060'].unsubscribe( 'pubsub.example.com', 'somenode', subscribee='user@example.com/foo', ifrom='foo@comp.example.com/bar', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com" from="foo@comp.example.com/bar"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <unsubscribe node="somenode" jid="user@example.com/foo" /> </pubsub> </iq> """) def testGetDefaultNodeConfig(self): """Test retrieving the default node config for a pubsub service.""" self.xmpp['xep_0060'].get_node_config( 'pubsub.example.com', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <default /> </pubsub> </iq> """, use_values=False) def testGetNodeConfig(self): """Test getting the config for a given node.""" self.xmpp['xep_0060'].get_node_config( 'pubsub.example.com', 'somenode', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <configure node="somenode" /> </pubsub> </iq> """, use_values=False) def testSetNodeConfig(self): """Test setting the configuration for a node.""" form = self.xmpp['xep_0004'].make_form() form.add_field(var='FORM_TYPE', ftype='hidden', value='http://jabber.org/protocol/pubsub#node_config') form.add_field(var='pubsub#title', ftype='text-single', value='This is awesome!') form['type'] = 'submit' self.xmpp['xep_0060'].set_node_config( 'pubsub.example.com', 'somenode', form, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <configure node="somenode"> <x xmlns="jabber:x:data" type="submit"> <field var="FORM_TYPE"> <value>http://jabber.org/protocol/pubsub#node_config</value> </field> <field var="pubsub#title"> <value>This is awesome!</value> </field> </x> </configure> </pubsub> </iq> """) def testPublishNoItems(self): """Test publishing no items (in order to generate events)""" self.xmpp['xep_0060'].publish( 'pubsub.example.com', 'somenode', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <publish node="somenode" /> </pubsub> </iq> """) def testPublishSingle(self): """Test publishing a single item.""" payload = AtomEntry() payload['title'] = 'Test' register_stanza_plugin(self.xmpp['xep_0060'].stanza.Item, AtomEntry) self.xmpp['xep_0060'].publish( 'pubsub.example.com', 'somenode', id='id42', payload=payload, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <publish node="somenode"> <item id="id42"> <entry xmlns="http://www.w3.org/2005/Atom"> <title>Test</title> </entry> </item> </publish> </pubsub> </iq> """, use_values=False) def testPublishSingleOptions(self): """Test publishing a single item, with options.""" payload = AtomEntry() payload['title'] = 'Test' register_stanza_plugin(self.xmpp['xep_0060'].stanza.Item, AtomEntry) options = self.xmpp['xep_0004'].make_form() options.add_field(var='FORM_TYPE', ftype='hidden', value='http://jabber.org/protocol/pubsub#publish-options') options.add_field(var='pubsub#access_model', ftype='text-single', value='presence') options['type'] = 'submit' self.xmpp['xep_0060'].publish( 'pubsub.example.com', 'somenode', id='ID42', payload=payload, options=options, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <publish node="somenode"> <item id="ID42"> <entry xmlns="http://www.w3.org/2005/Atom"> <title>Test</title> </entry> </item> </publish> <publish-options> <x xmlns="jabber:x:data" type="submit"> <field var="FORM_TYPE"> <value>http://jabber.org/protocol/pubsub#publish-options</value> </field> <field var="pubsub#access_model"> <value>presence</value> </field> </x> </publish-options> </pubsub> </iq> """, use_values=False) def testRetract(self): """Test deleting an item.""" self.xmpp['xep_0060'].retract( 'pubsub.example.com', 'somenode', 'ID1', notify=True, block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <retract node="somenode" notify="true"> <item id="ID1" /> </retract> </pubsub> </iq> """) def testRetract(self): """Test deleting an item.""" self.xmpp['xep_0060'].retract( 'pubsub.example.com', 'somenode', 'ID1', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <retract node="somenode"> <item id="ID1" /> </retract> </pubsub> </iq> """) def testPurge(self): """Test removing all items from a node.""" self.xmpp['xep_0060'].purge( 'pubsub.example.com', 'somenode', block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <purge node="somenode" /> </pubsub> </iq> """) def testGetItem(self): """Test retrieving a single item.""" self.xmpp['xep_0060'].get_item( 'pubsub.example.com', 'somenode', 'id42', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <items node="somenode"> <item id="id42" /> </items> </pubsub> </iq> """) def testGetLatestItems(self): """Test retrieving the most recent N items.""" self.xmpp['xep_0060'].get_items( 'pubsub.example.com', 'somenode', max_items=3, block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <items node="somenode" max_items="3" /> </pubsub> </iq> """) def testGetAllItems(self): """Test retrieving all items.""" self.xmpp['xep_0060'].get_items( 'pubsub.example.com', 'somenode', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <items node="somenode" /> </pubsub> </iq> """) def testGetSpecificItems(self): """Test retrieving a specific set of items.""" self.xmpp['xep_0060'].get_items( 'pubsub.example.com', 'somenode', item_ids=['A', 'B', 'C'], block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <items node="somenode"> <item id="A" /> <item id="B" /> <item id="C" /> </items> </pubsub> </iq> """) def testGetSubscriptionGlobalDefaultOptions(self): """Test getting the subscription options for a node/JID.""" self.xmpp['xep_0060'].get_subscription_options( 'pubsub.example.com', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <default /> </pubsub> </iq> """, use_values=False) def testGetSubscriptionNodeDefaultOptions(self): """Test getting the subscription options for a node/JID.""" self.xmpp['xep_0060'].get_subscription_options( 'pubsub.example.com', node='somenode', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <default node="somenode" /> </pubsub> </iq> """, use_values=False) def testGetSubscriptionOptions(self): """Test getting the subscription options for a node/JID.""" self.xmpp['xep_0060'].get_subscription_options( 'pubsub.example.com', 'somenode', 'tester@localhost', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <options node="somenode" jid="tester@localhost" /> </pubsub> </iq> """, use_values=False) def testSetSubscriptionOptions(self): """Test setting the subscription options for a node/JID.""" opts = self.xmpp['xep_0004'].make_form() opts.add_field( var='FORM_TYPE', value='http://jabber.org/protocol/pubsub#subscribe_options', ftype='hidden') opts.add_field( var='pubsub#digest', value=False, ftype='boolean') opts['type'] = 'submit' self.xmpp['xep_0060'].set_subscription_options( 'pubsub.example.com', 'somenode', 'tester@localhost', opts, block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <options node="somenode" jid="tester@localhost"> <x xmlns="jabber:x:data" type="submit"> <field var="FORM_TYPE"> <value>http://jabber.org/protocol/pubsub#subscribe_options</value> </field> <field var="pubsub#digest"> <value>0</value> </field> </x> </options> </pubsub> </iq> """) def testGetNodeSubscriptions(self): """Test retrieving all subscriptions for a node.""" self.xmpp['xep_0060'].get_node_subscriptions( 'pubsub.example.com', 'somenode', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <subscriptions node="somenode" /> </pubsub> </iq> """) def testGetSubscriptions(self): """Test retrieving a users's subscriptions.""" self.xmpp['xep_0060'].get_subscriptions( 'pubsub.example.com', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <subscriptions /> </pubsub> </iq> """) def testGetSubscriptionsForNode(self): """Test retrieving a users's subscriptions for a given node.""" self.xmpp['xep_0060'].get_subscriptions( 'pubsub.example.com', node='somenode', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <subscriptions node="somenode" /> </pubsub> </iq> """) def testGetAffiliations(self): """Test retrieving a users's affiliations.""" self.xmpp['xep_0060'].get_affiliations( 'pubsub.example.com', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <affiliations /> </pubsub> </iq> """) def testGetAffiliatinssForNode(self): """Test retrieving a users's affiliations for a given node.""" self.xmpp['xep_0060'].get_affiliations( 'pubsub.example.com', node='somenode', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub"> <affiliations node="somenode" /> </pubsub> </iq> """) def testGetNodeAffiliations(self): """Test getting the affiliations for a node.""" self.xmpp['xep_0060'].get_node_affiliations( 'pubsub.example.com', 'somenode', block=False) self.send(""" <iq type="get" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <affiliations node="somenode" /> </pubsub> </iq> """) def testModifySubscriptions(self): """Test owner modifying node subscriptions.""" self.xmpp['xep_0060'].modify_subscriptions( 'pubsub.example.com', 'somenode', subscriptions=[('user@example.com', 'subscribed'), ('foo@example.net', 'none')], block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <subscriptions node="somenode"> <subscription jid="user@example.com" subscription="subscribed" /> <subscription jid="foo@example.net" subscription="none" /> </subscriptions> </pubsub> </iq> """) def testModifyAffiliations(self): """Test owner modifying node affiliations.""" self.xmpp['xep_0060'].modify_affiliations( 'pubsub.example.com', 'somenode', affiliations=[('user@example.com', 'publisher'), ('foo@example.net', 'none')], block=False) self.send(""" <iq type="set" id="1" to="pubsub.example.com"> <pubsub xmlns="http://jabber.org/protocol/pubsub#owner"> <affiliations node="somenode"> <affiliation jid="user@example.com" affiliation="publisher" /> <affiliation jid="foo@example.net" affiliation="none" /> </affiliations> </pubsub> </iq> """) suite = unittest.TestLoader().loadTestsFromTestCase(TestStreamPubsub)

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py

Python

tests/test_prior.py

jmeyers314/DP_SNe

d5a91e96425fe99c8f4450a9f11256f3abbd43eb

[ "BSD-2-Clause" ]

20

2017-11-20T09:44:14.000Z

2022-02-11T17:38:24.000Z

tests/test_prior.py

jmeyers314/DP_SNe

d5a91e96425fe99c8f4450a9f11256f3abbd43eb

[ "BSD-2-Clause" ]

1

2016-07-08T01:12:51.000Z

tests/test_prior.py

jmeyers314/DP_SNe

d5a91e96425fe99c8f4450a9f11256f3abbd43eb

[ "BSD-2-Clause" ]

9

2017-06-30T20:46:57.000Z

2021-08-17T06:47:58.000Z

import warnings import numpy as np from scipy.integrate import quad, dblquad, tplquad import dpmm from test_utils import timer @timer def test_GaussianMeanKnownVariance(): mu_0 = 0.15 sigsqr_0 = 1.2 sigsqr = 0.15 model = dpmm.GaussianMeanKnownVariance(mu_0, sigsqr_0, sigsqr) D = np.r_[1.0, 2.2, 1.1, -1.13] mus = np.r_[1.1, 2.0, 0.1] # Check prior density r = quad(model, -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "GaussianMeanKnownVariance prior density does not integrate to 1.0") # Check prior predictive density r = quad(model.pred, -np.inf, np.inf) np.testing.assert_almost_equal( r[0], 1.0, 10, "GaussianMeanKnownVariance prior predictive density does not integrate to 1.0") # Check posterior density r = quad(model.post(D), -np.inf, np.inf) np.testing.assert_almost_equal( r[0], 1.0, 10, "GaussianMeanKnownVariance posterior density does not integrate to 1.0") # Check posterior predictive density r = quad(model.post(D).pred, -np.inf, np.inf) np.testing.assert_almost_equal( r[0], 1.0, 10, "GaussianMeanKnownVariance posterior predictive density does not integrate to 1.0") # Check that the likelihood integrates to 1. r = quad(lambda x: model.like1(x, mu=1.1), -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "GaussianMeanKnownVariance likelihood does not integrate to 1.0") # # Check that evidence (of single data point) integrates to 1. # r = quad(lambda x: model.evidence(x), -np.inf, np.inf) # np.testing.assert_almost_equal(r[0], 1.0, 10, # "GaussianMeanKnownVariance evidence does not integrate to 1.0") # # Check evidence for two data points. # r = dblquad(lambda x, y: model.evidence([x, y]), # -np.inf, np.inf, # lambda x: -np.inf, lambda x: np.inf) # np.testing.assert_almost_equal(r[0], 1.0, 5, # "GaussianMeanKnownVariance evidence does not integrate to 1.0") # # Check that posterior = prior * likelihood / evidence # post = model.post(D) # post1 = [model(mu)*model.likelihood(mu, D=D) / model.evidence(D) for mu in mus] # post2 = [post(mu) for mu in mus] # np.testing.assert_array_almost_equal( # post1, post2, 10, # "GaussianMeanKnownVariance posterior != prior * likelihood / evidence") # Check that posterior is proportional to prior * likelihood # Add some more data points posts = [model.post(D)(mu) for mu in mus] posts2 = [model(mu)*model.likelihood(D, mu) for mu in mus] np.testing.assert_array_almost_equal( posts/posts[0], posts2/posts2[0], 5, "GaussianMeanKnownVariance posterior not proportional to prior * likelihood.") # Check that integrating out theta yields the prior predictive. xs = [0.1, 0.2, 0.3, 0.4] preds1 = np.array([quad(lambda theta: model(theta) * model.like1(x, theta), -np.inf, np.inf)[0] for x in xs]) preds2 = np.array([model.pred(x) for x in xs]) np.testing.assert_array_almost_equal( preds1/preds1[0], preds2/preds2[0], 5, "Prior predictive not proportional to integral of likelihood * prior") @timer def test_InvGamma(): alpha = 1.1 beta = 1.2 mu = 0.1 ig = dpmm.InvGamma(alpha, beta, mu) ig.sample() # Check prior density r = quad(ig, 0.0, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 5, "InvGamma prior density does not integrate to 1.0") # Check prior predictive density r = quad(ig.pred, -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "InvGamma prior predictive density does not integrate to 1.0") # Check posterior density D = [1.0, 2.0, 3.0] r = quad(ig.post(D), 0.0, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 7, "InvGamma posterior density does not integrate to 1.0") # Check posterior predictive density r = quad(ig.post(D).pred, -np.inf, np.inf) np.testing.assert_almost_equal( r[0], 1.0, 10, "InvGamma posterior predictive density does not integrate to 1.0") # Check that the likelihood integrates to 1. r = quad(lambda x: ig.like1(x, var=2.1), -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "InvGamma likelihood does not integrate to 1.0") # Check that posterior is proportional to prior * likelihood # Add some more data points D = np.array([1.0, 2.0, 3.0, 2.2, 2.3, 1.2]) vars_ = [0.7, 1.1, 1.2, 1.5] posts = [ig.post(D)(var) for var in vars_] posts2 = [ig(var)*ig.likelihood(D, var) for var in vars_] np.testing.assert_array_almost_equal( posts/posts[0], posts2/posts2[0], 5, "InvGamma posterior not proportional to prior * likelihood.") # Check mean and variance mean = 1./beta/(alpha-1.0) np.testing.assert_almost_equal(quad(lambda x: ig(x)*x, 0.0, np.inf)[0], mean, 10, "InvGamma has wrong mean.") var = beta**(-2)/(alpha-1)**2/(alpha-2) with warnings.catch_warnings(): warnings.simplefilter('ignore') np.testing.assert_almost_equal(quad(lambda x: ig(x)*(x-mean)**2, 0.0, np.inf)[0], var, 5, "InvGamma has wrong variance.") # Check that integrating out theta yields the prior predictive. xs = [0.1, 0.2, 0.3, 0.4] preds1 = np.array([quad(lambda theta: ig(theta) * ig.like1(x, theta), 0, np.inf)[0] for x in xs]) preds2 = np.array([ig.pred(x) for x in xs]) np.testing.assert_array_almost_equal( preds1/preds1[0], preds2/preds2[0], 5, "Prior predictive not proportional to integral of likelihood * prior") @timer def test_InvGamma2D(full=False): alpha = 1.1 beta = 1.2 mu = np.r_[0.1, 0.2] ig2d = dpmm.InvGamma2D(alpha, beta, mu) ig2d.sample() # Check prior density r = quad(ig2d, 0.0, np.inf) np.testing.assert_almost_equal( r[0], 1.0, 5, "InvGamma2D prior density does not integrate to 1.0") if __name__ == '__main__' and full: # Check prior predictive density r = dblquad(lambda x, y: ig2d.pred(np.r_[x, y]), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal( r[0], 1.0, 5, "InvGamma2D prior predictive density does not integrate to 1.0") # Check posterior density D = np.array([[0.1, 0.2], [0.2, 0.3]]) r = quad(ig2d.post(D), 0.0, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 7, "InvGamma2D posterior density does not integrate to 1.0") # Check posterior predictive density r = dblquad(lambda x, y: ig2d.post(D).pred(np.r_[x, y]), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal( r[0], 1.0, 5, "InvGamma2D posterior predictive density does not integrate to 1.0") # Check that the likelihood integrates to 1. r = dblquad(lambda x, y: ig2d.like1(np.r_[x, y], var=2.1), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "InvGamma2D likelihood does not integrate to 1.0") # Check that posterior is proportional to prior * likelihood vars_ = [0.7, 1.1, 1.2, 1.5] posts = np.array([ig2d.post(D)(var) for var in vars_]) posts2 = np.array([ig2d(var)*ig2d.likelihood(D, var) for var in vars_]) np.testing.assert_array_almost_equal( posts/posts[0], posts2/posts2[0], 5, "InvGamma2D posterior not proportional to prior * likelihood.") # Check mean and variance mean = 1./beta/(alpha-1.0) np.testing.assert_almost_equal(quad(lambda x: ig2d(x)*x, 0.0, np.inf)[0], mean, 10, "InvGamma2D has wrong mean.") var = beta**(-2)/(alpha-1)**2/(alpha-2) with warnings.catch_warnings(): warnings.simplefilter('ignore') np.testing.assert_almost_equal(quad(lambda x: ig2d(x)*(x-mean)**2, 0.0, np.inf)[0], var, 5, "InvGamma2D has wrong variance.") # Check that integrating out theta yields the prior predictive. xs = [np.r_[0.1, 0.2], np.r_[0.2, 0.3], np.r_[0.1, 0.3]] preds1 = np.array([quad(lambda theta: ig2d(theta) * ig2d.like1(x, theta), 0, np.inf)[0] for x in xs]) preds2 = np.array([ig2d.pred(x) for x in xs]) np.testing.assert_array_almost_equal( preds1/preds1[0], preds2/preds2[0], 5, "Prior predictive not proportional to integral of likelihood * prior") @timer def test_NormInvChi2(): mu_0 = -0.1 sigsqr_0 = 1.1 kappa_0 = 2 nu_0 = 3 nix = dpmm.NormInvChi2(mu_0, kappa_0, sigsqr_0, nu_0) D = np.r_[1.0, 2.0, 3.0] mus = np.r_[1.1, 1.2, 1.3] vars_ = np.r_[1.2, 3.2, 2.3] # Check prior density with warnings.catch_warnings(): warnings.simplefilter('ignore') r = dblquad(nix, 0.0, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 5, "NormInvChi2 prior density does not integrate to 1.0") # Check prior predictive density r = quad(nix.pred, -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "NormInvChi2 prior predictive density does not integrate to 1.0") # Check posterior density r = dblquad(nix.post(D), 0.0, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 7, "NormInvChi2 posterior density does not integrate to 1.0") # Check posterior predictive density r = quad(nix.post(D).pred, -np.inf, np.inf) np.testing.assert_almost_equal( r[0], 1.0, 10, "NormInvChi2 posterior predictive density does not integrate to 1.0") # Check that the likelihood integrates to 1. r = quad(lambda x: nix.like1(x, 1.1, 2.1), -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "NormInvChi2 likelihood does not integrate to 1.0") # Check that evidence (of single data point) integrates to 1. r = quad(lambda x: nix.evidence(x), -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "NormInvChi2 evidence does not integrate to 1.0") # Check evidence for two data points. r = dblquad(lambda x, y: nix.evidence([x, y]), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 5, "NormInvChi2 evidence does not integrate to 1.0") # Check that posterior = prior * likelihood / evidence post = nix.post(D) post1 = [nix(mu, var)*nix.likelihood(D, mu, var) / nix.evidence(D) for mu, var in zip(mus, vars_)] post2 = [post(mu, var) for mu, var in zip(mus, vars_)] np.testing.assert_array_almost_equal(post1, post2, 10, "NormInvChi2 posterior != prior * likelihood / evidence") # Test that marginal variance probability method matches integrated result. Pr_var1 = [nix.marginal_var(var) for var in vars_] Pr_var2 = [quad(lambda mu: nix(mu, var), -np.inf, np.inf)[0] for var in vars_] np.testing.assert_array_almost_equal( Pr_var1, Pr_var2, 10, "Pr(var) method calculation does not match integrated result.") # Test that marginal mean probability method matches integrated result. Pr_mu1 = [nix.marginal_mu(mu) for mu in mus] Pr_mu2 = [quad(lambda var: nix(mu, var), 0.0, np.inf)[0] for mu in mus] np.testing.assert_array_almost_equal( Pr_mu1, Pr_mu2, 10, "Pr(mu) method calculation does not match integrated result.") # Check that integrating out theta yields the prior predictive. xs = [0.1, 0.2, 0.3, 0.4] preds1 = np.array([dblquad(lambda mu, var: nix(mu, var) * nix.like1(x, mu, var), 0, np.inf, lambda var: -np.inf, lambda var: np.inf)[0] for x in xs]) preds2 = np.array([nix.pred(x) for x in xs]) np.testing.assert_array_almost_equal( preds1/preds1[0], preds2/preds2[0], 5, "Prior predictive not proportional to integral of likelihood * prior") @timer def test_NormInvGamma(): m_0 = -0.1 V_0 = 1.1 a_0 = 2.0 b_0 = 3.0 nig = dpmm.NormInvGamma(m_0, V_0, a_0, b_0) D = np.r_[1.0, 2.0, 3.0] mus = np.r_[1.1, 1.2, 1.3] vars_ = np.r_[1.2, 3.2, 2.3] # Check prior density with warnings.catch_warnings(): warnings.simplefilter('ignore') r = dblquad(nig, 0.0, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 5, "NormInvGamma prior density does not integrate to 1.0") # Check prior predictive density r = quad(nig.pred, -np.inf, np.inf) np.testing.assert_almost_equal( r[0], 1.0, 10, "NormInvGamma prior predictive density does not integrate to 1.0") # Check posterior density r = dblquad(nig.post(D), 0.0, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 7, "NormInvGamma posterior density does not integrate to 1.0") # Check posterior predictive density r = quad(nig.post(D).pred, -np.inf, np.inf) np.testing.assert_almost_equal( r[0], 1.0, 10, "NormInvGamma posterior predictive density does not integrate to 1.0") # Check that the likelihood integrates to 1. r = quad(lambda x: nig.like1(x, 1.1, 2.1), -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "NormInvGamma likelihood does not integrate to 1.0") # Check that evidence (of single data point) integrates to 1. r = quad(lambda x: nig.evidence(x), -np.inf, np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "NormInvGamma evidence does not integrate to 1.0") # Check evidence for two data points. r = dblquad(lambda x, y: nig.evidence([x, y]), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 5, "NormInvGamma evidence does not integrate to 1.0") # Check that posterior = prior * likelihood / evidence post = nig.post(D) post1 = [nig(mu, var)*nig.likelihood(D, mu, var) / nig.evidence(D) for mu, var in zip(mus, vars_)] post2 = [post(mu, var) for mu, var in zip(mus, vars_)] np.testing.assert_array_almost_equal(post1, post2, 10, "NormInvGamma posterior != prior * likelihood / evidence") # Test that marginal variance probability method matches integrated result. Pr_var1 = [nig.marginal_var(var) for var in vars_] Pr_var2 = [quad(lambda mu: nig(mu, var), -np.inf, np.inf)[0] for var in vars_] np.testing.assert_array_almost_equal( Pr_var1, Pr_var2, 10, "Pr(var) method calculation does not match integrated result.") # Test that marginal mean probability method matches integrated result. Pr_mu1 = [nig.marginal_mu(mu) for mu in mus] Pr_mu2 = [quad(lambda var: nig(mu, var), 0.0, np.inf)[0] for mu in mus] np.testing.assert_array_almost_equal( Pr_mu1, Pr_mu2, 10, "Pr(mu) method calculation does not match integrated result.") # Check that integrating out theta yields the prior predictive. xs = [0.1, 0.2, 0.3, 0.4] preds1 = np.array([dblquad(lambda mu, var: nig(mu, var) * nig.like1(x, mu, var), 0, np.inf, lambda var: -np.inf, lambda var: np.inf)[0] for x in xs]) preds2 = np.array([nig.pred(x) for x in xs]) np.testing.assert_array_almost_equal( preds1/preds1[0], preds2/preds2[0], 5, "Prior predictive not proportional to integral of likelihood * prior") @timer def test_NormInvChi2_eq_NormInvGamma(): mu_0 = 0.1 sigsqr_0 = 1.1 kappa_0 = 2 nu_0 = 3 m_0 = mu_0 V_0 = 1./kappa_0 a_0 = nu_0/2.0 b_0 = nu_0*sigsqr_0/2.0 model1 = dpmm.NormInvChi2(mu_0, kappa_0, sigsqr_0, nu_0) model2 = dpmm.NormInvGamma(m_0, V_0, a_0, b_0) mus = np.linspace(-2.2, 2.2, 5) vars_ = np.linspace(1.0, 4.0, 5) xs = np.arange(-1.1, 1.1, 5) for x in xs: np.testing.assert_equal( model1.pred(x), model2.pred(x), "NormInvChi2 and NormInvGamma prior predictive densities don't agree at x = ".format(x)) np.testing.assert_equal( model1.post(x).pred(x), model2.post(x).pred(x), "NormInvChi2 and NormInvGamma posterior " + "predictive densities don't agree at x = {}".format(x)) for mu, var in zip(mus, vars_): np.testing.assert_almost_equal( model1(mu, var), model2(mu, var), 10, "NormInvChi2 and NormInvGamma prior densities " + "don't agree at mu, var = {}, {}".format(mu, var)) post1 = model1.post(xs) post2 = model2.post(xs) for mu, var in zip(mus, vars_): np.testing.assert_almost_equal( post1(mu, var), post2(mu, var), 10, "NormInvChi2 and NormInvGamma posterior densities " + "don't agree at mu, var = {}, {}".format(mu, var)) for mu, var, x in zip(mus, vars_, xs): np.testing.assert_almost_equal( model1.like1(x, mu, var), model2.like1(x, mu, var), 10, "NormInvChi2 and NormInvGamma likelihoods don't " + "agree at mu, var, x = {}, {}, {}".format(mu, var, x)) np.testing.assert_almost_equal( model1.evidence(xs), model2.evidence(xs), 10, "NormInvChi2 and NormInvGamma evidences don't agree") @timer def test_NormInvWish(full=False): mu_0 = np.r_[0.2, 0.1] kappa_0 = 2.0 Lam_0 = np.eye(2)+0.1 nu_0 = 3 # Create a Normal-Inverse-Wishart prior. niw = dpmm.NormInvWish(mu_0, kappa_0, Lam_0, nu_0) # Check that we can draw samples from NormInvWish. niw.sample() niw.sample(size=10) # Check that we can evaluate a likelihood given data. theta = np.zeros(1, dtype=niw.model_dtype) theta['mu'] = np.r_[1.0, 1.0] theta['Sig'] = np.eye(2)+0.12 D = np.array([[0.1, 0.2], [0.2, 0.3], [0.1, 0.2], [0.4, 0.3]]) niw.likelihood(D, theta) # Evaluate prior niw(theta) if __name__ == "__main__" and full: # Check prior predictive density with warnings.catch_warnings(): warnings.simplefilter('ignore') r = dblquad(lambda x, y: niw.pred(np.r_[x, y]), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 5, "NormInvWish prior predictive density does not integrate to 1.0") # Check posterior predictive density r = dblquad(lambda x, y: niw.post(D).pred(np.r_[x, y]), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal( r[0], 1.0, 5, "NormInvWish posterior predictive density does not integrate to 1.0") # Check that the likelihood of a single point in 2 dimensions integrates to 1. r = dblquad(lambda x, y: niw.like1(np.r_[x, y], np.r_[1.2, 1.1], np.eye(2)+0.12), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 10, "NormInvWish likelihood does not integrate to 1.0") if __name__ == "__main__" and full: # Check that likelihood of a single point in 3 dimensions integrates to 1. niw3 = dpmm.NormInvWish(np.r_[1, 1, 1], 2.0, np.eye(3), 3) r = tplquad(lambda x, y, z: niw3.like1(np.r_[x, y, z], np.r_[0.1, 0.2, 0.3], np.eye(3)+0.1), -np.inf, np.inf, lambda x: -np.inf, lambda x: np.inf, lambda x, y: -np.inf, lambda x, y: np.inf) np.testing.assert_almost_equal(r[0], 1.0, 8, "NormInvWish likelihood does not integrate to 1.0") # Check that posterior is proportional to prior * likelihood D = np.array([[0.1, 0.2], [0.2, 0.3], [0.1, 0.2], [0.4, 0.3]]) mus = [np.r_[2.1, 1.1], np.r_[0.9, 1.2], np.r_[0.9, 1.1]] Sigs = [np.eye(2)*1.5, np.eye(2)*0.7, np.array([[1.1, -0.1], [-0.1, 1.2]])] posts = [niw.post(D)(mu, Sig) for mu, Sig in zip(mus, Sigs)] posts2 = [niw(mu, Sig)*niw.likelihood(D, mu, Sig) for mu, Sig, in zip(mus, Sigs)] np.testing.assert_array_almost_equal( posts/posts[0], posts2/posts2[0], 5, "NormInvWish posterior not proportional to prior * likelihood.") # Check that posterior = prior * likelihood / evidence mus = [np.r_[1.1, 1.1], np.r_[1.1, 1.2], np.r_[0.7, 1.3]] Sigs = [np.eye(2)*0.2, np.eye(2)*0.1, np.array([[2.1, -0.1], [-0.1, 2.2]])] post = niw.post(D) post1 = [niw(mu, Sig) * niw.likelihood(D, mu, Sig) / niw.evidence(D) for mu, Sig in zip(mus, Sigs)] post2 = [post(mu, Sig) for mu, Sig in zip(mus, Sigs)] np.testing.assert_array_almost_equal(post1, post2, 10, "NormInvWish posterior != prior * likelihood / evidence") # Would like to check that pred(x) == int prior(theta) * like1(x, theta) d(theta), but I don't # know how to integrate over all covariance matrices. Plus, integrating over a 2D covariance # matrix plus a 2D mean is a 5 dimensional integral, which sounds nasty to do. if __name__ == "__main__": from argparse import ArgumentParser parser = ArgumentParser() parser.add_argument('--full', action='store_true', help="Run full test suite (slow).") args = parser.parse_args() test_GaussianMeanKnownVariance() test_InvGamma() test_InvGamma2D() test_NormInvChi2() test_NormInvGamma() test_NormInvChi2_eq_NormInvGamma() test_NormInvWish(args.full)

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7c21be921705f6079de57f15c3dd0aff925ce379

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py

Python

tests/test_equilibrium.py

MartinKliemank/lettuce

ee1b4dbfdbcf1bd87ac6b867b091a923d033403e

[ "MIT" ]

53

2021-04-01T06:17:52.000Z

2022-03-21T18:27:13.000Z

tests/test_equilibrium.py

MartinKliemank/lettuce

ee1b4dbfdbcf1bd87ac6b867b091a923d033403e

[ "MIT" ]

48

2019-09-17T14:07:57.000Z

2020-11-18T19:53:24.000Z

tests/test_equilibrium.py

MartinKliemank/lettuce

ee1b4dbfdbcf1bd87ac6b867b091a923d033403e

[ "MIT" ]

9

2021-04-16T12:57:19.000Z

2022-03-08T11:40:50.000Z

""" Tests for equilibria """ import pytest from lettuce.equilibrium import * @pytest.mark.parametrize("Equilibrium", [QuadraticEquilibrium]) def test_equilibrium_conserves_mass(f_all_lattices, Equilibrium): f, lattice = f_all_lattices equilibrium = Equilibrium(lattice) feq = equilibrium(rho=lattice.rho(f), u=lattice.u(f)) assert lattice.rho(feq).cpu().numpy() == pytest.approx(lattice.rho(f).cpu().numpy()) @pytest.mark.parametrize("Equilibrium", [QuadraticEquilibrium]) def test_equilibrium_conserves_momentum(f_all_lattices, Equilibrium): f, lattice = f_all_lattices equilibrium = Equilibrium(lattice) feq = equilibrium(rho=lattice.rho(f), u=lattice.u(f)) assert lattice.j(feq).cpu().numpy() == pytest.approx(lattice.j(f).cpu().numpy(), abs=1e-6)

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py

Python

02_ldprofile_pybind11/src/wrap/__init__.py

martinschwinzerl/pyhep2020-cxx-bindings

27562b6500210dbb9066e86bba5dd3abe4a47328

[ "MIT" ]

1

2020-07-17T14:00:52.000Z

02_ldprofile_pybind11/src/wrap/__init__.py

martinschwinzerl/pyhep2020-cxx-bindings

27562b6500210dbb9066e86bba5dd3abe4a47328

[ "MIT" ]

null

02_ldprofile_pybind11/src/wrap/__init__.py

martinschwinzerl/pyhep2020-cxx-bindings

27562b6500210dbb9066e86bba5dd3abe4a47328

[ "MIT" ]

null

from .ldprofile_pybind11 import CoastingLDProfile, QGaussianLDProfile, LinInterpolLDProfile

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py

Python

pjtk2/tests/api/test_project_points_polygons_api.py

AdamCottrill/Project-Tracker-2

ba37441e1936b7825b2cfc1507858d207ff874f8

[ "MIT" ]

null

pjtk2/tests/api/test_project_points_polygons_api.py

AdamCottrill/Project-Tracker-2

ba37441e1936b7825b2cfc1507858d207ff874f8

[ "MIT" ]

null

pjtk2/tests/api/test_project_points_polygons_api.py

AdamCottrill/Project-Tracker-2

ba37441e1936b7825b2cfc1507858d207ff874f8

[ "MIT" ]

null

from django.contrib.gis.geos import GEOSGeometry from django.test import TestCase, Client, RequestFactory from django.urls import reverse from rest_framework import status from pjtk2.models import SamplePoint, ProjectPolygon from pjtk2.api.serializers import ProjectPolygonSerializer, ProjectPointSerializer from pjtk2.tests.factories import ProjectFactory, SamplePointFactory from rest_framework.test import APITestCase class ProjectAPITest(APITestCase): def setUp(self): self.factory = RequestFactory() # we need to create some models with different years - starting # with the current year. prj_cd = "LHA_IA16_INN" self.project1 = ProjectFactory.create(prj_cd=prj_cd, prj_nm="All In Roi") # these are four randomly selected points that all fall within the roi pts = [ "POINT(-82.081126628131 44.000970817096)", "POINT(-82.0456637754061 44.0649121962459)", "POINT(-82.024922507764 44.0171801372301)", "POINT(-82.0017671634393 44.0513359855003)", ] for i, pt in enumerate(pts): SamplePointFactory.create( project=self.project1, label="In-{}".format(i), geom=GEOSGeometry(pt) ) self.project1.update_convex_hull() prj_cd = "LHA_IA16_000" self.project4 = ProjectFactory.create(prj_cd=prj_cd, prj_nm="No Points") # =================== # PROJECT POINTS def test_project_points_api_get_good_project_code(self): """The project points api should return a series of geojson points corresponding to sampling locations associated with a project. """ slug = self.project1.slug url = reverse("api:project_points", kwargs={"slug": slug}) request = self.factory.get(url) response = self.client.get(url) # get data from db points = SamplePoint.objects.filter(project__slug=slug) serializer = ProjectPointSerializer( points, many=True, context={"request": request} ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data, serializer.data) def test_project_points_api_get_bad_project_code(self): """If we try to access the project points api with a malformed project code it will return an error. """ slug = "LHA_XX15_X01" url = reverse("api:project_points", kwargs={"slug": slug}) response = self.client.get(url.replace("X", "")) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_project_points_api_get_project_code_doesnot_exist(self): """If we try to access the project points api for a project that does not exists, code it will return a 404 error. """ slug = "LHA_IA15_ABC" url = reverse("api:project_points", kwargs={"slug": slug}) request = self.factory.get(url) response = self.client.get(url) points = SamplePoint.objects.filter(project__slug=slug) serializer = ProjectPointSerializer( points, many=True, context={"request": request} ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data, serializer.data) self.assertEqual(len(response.data), 0) self.assertEqual(len(serializer.data), 0) def test_project_points_api_get_project_code_without_points(self): """If we try to access the project points api for a project without sample points, it will handle it gracefully. """ slug = self.project4.slug url = reverse("api:project_points", kwargs={"slug": slug}) request = self.factory.get(url) response = self.client.get(url) points = SamplePoint.objects.filter(project__slug=slug) serializer = ProjectPointSerializer( points, many=True, context={"request": request} ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data, serializer.data) self.assertEqual(len(response.data), 0) self.assertEqual(len(serializer.data), 0) def test_project_points_api_post_put_delete(self): """the project points api is currently readonly - any other request type should throw an error. """ slug = self.project4.slug url = reverse("api:project_points", kwargs={"slug": slug}) data = {"label": "Test-1"} response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED) # ================================= # PROJECT POLYGON def test_project_polygon_api_get_good_project_code(self): """The project polygon api should return a series of geojson polygon corresponding to sampling locations associated with a project. """ slug = self.project1.slug url = reverse("api:project_polygon", kwargs={"slug": slug}) request = self.factory.get(url) response = self.client.get(url) # get data from db polygon = ProjectPolygon.objects.filter(project__slug=slug) serializer = ProjectPolygonSerializer( polygon, many=True, context={"request": request} ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data, serializer.data) def test_project_polygon_api_get_bad_project_code(self): """If we try to access the project polygon api with a malformed project code it will return an error. """ slug = "LHA_XX15_X01" url = reverse("api:project_polygon", kwargs={"slug": slug}) response = self.client.get(url.replace("X", "")) self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND) def test_project_polygon_api_get_project_code_doesnot_exist(self): """If we try to access the project polygon api for a project that does not exists, code it will return an empty array. """ slug = "LHA_IA15_ABC" url = reverse("api:project_polygon", kwargs={"slug": slug}) request = self.factory.get(url) response = self.client.get(url) # get data from db polygon = ProjectPolygon.objects.filter(project__slug=slug) serializer = ProjectPolygonSerializer( polygon, many=True, context={"request": request} ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data, serializer.data) self.assertEqual(len(response.data), 0) self.assertEqual(len(serializer.data), 0) def test_project_polygon_api_get_project_code_without_polygon(self): """If we try to access the project polygon api for a project without sample polygon, it will handle it gracefully. """ slug = self.project4.slug url = reverse("api:project_polygon", kwargs={"slug": slug}) request = self.factory.get(url) response = self.client.get(url) # get data from db polygon = ProjectPolygon.objects.filter(project__slug=slug) serializer = ProjectPolygonSerializer( polygon, many=True, context={"request": request} ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data, serializer.data) self.assertEqual(len(response.data), 0) self.assertEqual(len(serializer.data), 0) def test_project_polygon_api_post_put_delete(self): """the project polygon api is currently readonly - any other request type should throw an error. """ slug = self.project4.slug url = reverse("api:project_polygon", kwargs={"slug": slug}) data = {"label": "Test-1"} response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)

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0.658199

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8,031

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0.173597

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0.071707

0.038971

0.809821

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null

0

1

0

null

0

6

7c884e67d811cd4cebb9e894c032818c505e522d

27

py

Python

src/detector/__init__.py

ymlsam/deepgaau-detector

3f6d8195b4b1857bc1317035d999e9bca226ce7c

[ "MIT" ]

null

src/detector/__init__.py

ymlsam/deepgaau-detector

3f6d8195b4b1857bc1317035d999e9bca226ce7c

[ "MIT" ]

null

src/detector/__init__.py

ymlsam/deepgaau-detector

3f6d8195b4b1857bc1317035d999e9bca226ce7c

[ "MIT" ]

null

from .detector import main

13.5

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null

0

1

0

1

0

1

0

6

7cda91da3939985ac1873e9204a896cc248eac5e

142

py

Python

app/schema/mutations/todo/__init__.py

rjNemo/graphql_python_template

14bc5fd657f6bdba8d7293f21cfcec821fa6374f

[ "MIT" ]

1

2021-05-02T01:47:57.000Z

app/schema/mutations/todo/__init__.py

rjNemo/graphql_python_template

14bc5fd657f6bdba8d7293f21cfcec821fa6374f

[ "MIT" ]

null

app/schema/mutations/todo/__init__.py

rjNemo/graphql_python_template

14bc5fd657f6bdba8d7293f21cfcec821fa6374f

[ "MIT" ]

null

from .close_todo import CloseTodo from .create_todo import CreateTodo from .delete_todo import DeleteTodo from .update_todo import UpdateTodo

28.4

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35.5

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true

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null

0

1

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6

7cef326530a204235c99ed313d3a473debe866ed

47

py

Python

taotao-cloud-python/taotao-cloud-oldboy/day42-python-js/test.py

shuigedeng/taotao-cloud-paren

3d281b919490f7cbee4520211e2eee5da7387564

[ "Apache-2.0" ]

47

2021-04-13T10:32:13.000Z

2022-03-31T10:30:30.000Z

taotao-cloud-python/taotao-cloud-oldboy/day42-python-js/test.py

shuigedeng/taotao-cloud-paren

3d281b919490f7cbee4520211e2eee5da7387564

[ "Apache-2.0" ]

1

2021-11-01T07:41:04.000Z

2021-11-01T07:41:10.000Z

taotao-cloud-python/taotao-cloud-oldboy/day42-python-js/test.py

shuigedeng/taotao-cloud-paren

3d281b919490f7cbee4520211e2eee5da7387564

[ "Apache-2.0" ]

21

2021-04-13T10:32:17.000Z

2022-03-26T07:43:22.000Z

# def f(): # x=10 if 1: x=10 print(x)

6.714286

10

0.404255

10

47

1.9

0.7

0.315789

0

0.172414

0.382979

47

7

11

6.714286

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0.361702

0

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null

1

0

1

0

1

0

null

0

6

6b1856d785c18d72b922e94ed4d2792383baa9d9

107

py

Python

generator_app/__init__.py

badf00d21/JSD2021

0af83e671bdc2570b617ed29b395db4193dd7daf

[ "MIT" ]

null

generator_app/__init__.py

badf00d21/JSD2021

0af83e671bdc2570b617ed29b395db4193dd7daf

[ "MIT" ]

null

generator_app/__init__.py

badf00d21/JSD2021

0af83e671bdc2570b617ed29b395db4193dd7daf

[ "MIT" ]

null

import generator_app.generator_app as app def call_generate(): print('call_generate') app.main()

15.285714

41

0.728972

15

107

4.933333

0.6

0.324324

0

0.168224

107

6

42

17.833333

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0

1

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0.121495

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1

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true

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0.25

0

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0.25

1

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null

1

0

1

0

1

0

null

0

1

0

6

6b219f509501b5473aa18d8201d2069a3706934f

1,320

py

Python

dusk/script/math.py

mroethlin/dusk

7c494d5270b2a9ecd40ce90bd122d36d27cdc5d9

[ "MIT" ]

null

dusk/script/math.py

mroethlin/dusk

7c494d5270b2a9ecd40ce90bd122d36d27cdc5d9

[ "MIT" ]

1

2020-09-29T12:14:44.000Z

2020-10-13T07:15:12.000Z

dusk/script/math.py

mroethlin/dusk

7c494d5270b2a9ecd40ce90bd122d36d27cdc5d9

[ "MIT" ]

null

__all__ = [ "max", "min", "pow", "sqrt", "exp", "log", "sin", "cos", "tan", "arcsin", "arccos", "arctan", "fabs", "floor", "ceil", "isinf", "isnan", ] def max(a: float, b: float) -> float: raise NotImplementedError def min(a: float, b: float) -> float: raise NotImplementedError def pow(base: float, exp: float) -> float: raise NotImplementedError def sqrt(arg: float) -> float: raise NotImplementedError def exp(exp: float) -> float: raise NotImplementedError def log(arg: float) -> float: raise NotImplementedError def sin(arg: float) -> float: raise NotImplementedError def cos(arg: float) -> float: raise NotImplementedError def tan(arg: float) -> float: raise NotImplementedError def arcsin(arg: float) -> float: raise NotImplementedError def arccos(arg: float) -> float: raise NotImplementedError def arctan(arg: float) -> float: raise NotImplementedError def fabs(arg: float) -> float: raise NotImplementedError def floor(arg: float) -> float: raise NotImplementedError def ceil(arg: float) -> float: raise NotImplementedError def isinf(arg: float) -> float: raise NotImplementedError def isnan(arg: float) -> float: raise NotImplementedError

15

42

0.637879

143

1,320

5.86014

0.181818

0.202864

0.304296

0.689737

0.817422

0.77327

0.105012

0

0.239394

1,320

87

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15.172414

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0

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0

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false

0

0.320755

0

null

1

0

null

0

1

0

6

862e2d8c378243ec96afd025d93354f3ade461cc

187

py

Python

include/const.py

Ayvytr/PythonBox

ab15c4fad79be827e7e04cdce30335d6a979655a

[ "Apache-2.0" ]

1

2020-01-03T00:18:36.000Z

include/const.py

Ayvytr/PyBox

ab15c4fad79be827e7e04cdce30335d6a979655a

[ "Apache-2.0" ]

null

include/const.py

Ayvytr/PyBox

ab15c4fad79be827e7e04cdce30335d6a979655a

[ "Apache-2.0" ]

null

class Const: GITHUB = "https://github.com/ayvytr/PythonBox" ISSUE = "https://github.com/Ayvytr/PythonBox/issues" MAIL = "mailto:ayvytr@163.com?subject=Bug-Report&body={}"

37.4

62

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24

187

5.291667

0.666667

0.173228

0.220472

0.314961

0.456693

0

0.01875

0.144385

187

4

63

46.75

0.775

0

0.68306

0.262295

0

1

0

false

0

1

0

1

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null

0

1

0

1

0

1

0

null

0

1

0

6

8649d1c5e53f71e0eb876bb6163f00cb8e794a8f

4,092

py

Python

src/transformers/utils/dummy_flax_objects.py

theainerd/transformers

f7328de46dbeda4992a093a0501932bf0fc7b76f

[ "Apache-2.0" ]

34

2021-07-05T02:44:31.000Z

2022-03-28T14:39:57.000Z

src/transformers/utils/dummy_flax_objects.py

theainerd/transformers

f7328de46dbeda4992a093a0501932bf0fc7b76f

[ "Apache-2.0" ]

3

2021-07-22T15:49:44.000Z

2022-03-19T08:46:27.000Z

src/transformers/utils/dummy_flax_objects.py

theainerd/transformers

f7328de46dbeda4992a093a0501932bf0fc7b76f

[ "Apache-2.0" ]

6

2021-07-05T02:44:32.000Z

2022-02-14T10:10:13.000Z

# This file is autogenerated by the command `make fix-copies`, do not edit. from ..file_utils import requires_flax class FlaxPreTrainedModel: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) FLAX_MODEL_FOR_MASKED_LM_MAPPING = None FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None FLAX_MODEL_FOR_PRETRAINING_MAPPING = None FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = None FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None FLAX_MODEL_MAPPING = None class FlaxAutoModel: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxAutoModelForMaskedLM: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxAutoModelForMultipleChoice: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxAutoModelForNextSentencePrediction: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxAutoModelForPreTraining: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxAutoModelForQuestionAnswering: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxAutoModelForSequenceClassification: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxAutoModelForTokenClassification: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxBertForMaskedLM: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxBertForMultipleChoice: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxBertForNextSentencePrediction: def __init__(self, *args, **kwargs): requires_flax(self) class FlaxBertForPreTraining: def __init__(self, *args, **kwargs): requires_flax(self) class FlaxBertForQuestionAnswering: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxBertForSequenceClassification: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxBertForTokenClassification: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxBertModel: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self) class FlaxRobertaModel: def __init__(self, *args, **kwargs): requires_flax(self) @classmethod def from_pretrained(self, *args, **kwargs): requires_flax(self)

21.536842

75

0.692326

432

4,092

6.168981

0.143519

0.166604

0.189118

0.297186

0.73546

0.665666

0.634146

0

0.206989

4,092

189

76

21.650794

0.821263

0.01784

0

0.760684

1

0

1

0.307692

false

0

0.008547

0

0.478632

0

null

0

1

0

1

0

1

0

null

0

1

0

6

8696259ca5e3968fea17fb25752d8c6fd5fe54f1

103

py

Python

personalcapital/exceptions.py

aagnone3/personalcapital

55471602885c6c495a1dff9229c84213f2220ab2

[ "MIT" ]

9

2019-04-01T01:16:38.000Z

2021-12-26T20:38:32.000Z

personalcapital/exceptions.py

Kpasha/personal-capital-plus

55471602885c6c495a1dff9229c84213f2220ab2

[ "MIT" ]

null

personalcapital/exceptions.py

Kpasha/personal-capital-plus

55471602885c6c495a1dff9229c84213f2220ab2

[ "MIT" ]

3

2020-06-23T02:58:53.000Z

2021-04-03T05:31:32.000Z

class RequireTwoFactorException(Exception): pass class LoginFailedException(Exception): pass

14.714286

43

0.786408

8

103

10.125

0.625

0.320988

0

0.15534

103

6

44

17.166667

0.931034

0

0.5

0

1

0

true

0.5

0

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0

1

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1

null

1

0

1

0

null

0

1

0

6

86d17965ffceecd8ef98d35a760dfa3656b18a60

76

py

Python

tinycat/__init__.py

kbobrowski/tinycat

4f1e177530ac891123e1df6beeee6d7833936ddf

[ "MIT" ]

null

tinycat/__init__.py

kbobrowski/tinycat

4f1e177530ac891123e1df6beeee6d7833936ddf

[ "MIT" ]

null

tinycat/__init__.py

kbobrowski/tinycat

4f1e177530ac891123e1df6beeee6d7833936ddf

[ "MIT" ]

null

from .translate import generate_task, paragraph_parser, process_translation

38

75

0.881579

9

76

7.111111

1

0

0.078947

76

1

76

0.914286

0

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

811003e6d6c4c5ee2768dc99ff4b20f807b78bec

19

py

Python

pygridtools/viz/__init__.py

phobson/gridtools

53c5792ed6826bb9487d3e6b8e943760ab421a75

[ "BSD-3-Clause" ]

26

2016-01-20T15:40:21.000Z

2021-10-08T00:35:15.000Z

pygridtools/viz/__init__.py

phobson/gridtools

53c5792ed6826bb9487d3e6b8e943760ab421a75

[ "BSD-3-Clause" ]

48

2015-10-01T02:51:52.000Z

2021-05-05T15:31:11.000Z

pygridtools/viz/__init__.py

phobson/gridtools

53c5792ed6826bb9487d3e6b8e943760ab421a75

[ "BSD-3-Clause" ]

8

2015-09-30T19:53:03.000Z

2022-02-23T03:29:24.000Z

from .viz import *

9.5

18

0.684211

3

19

4.333333

1

0

0.210526

19

1

19

0.866667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d4f5a1d4f3f9712da91744b84ef61eda79f0d2e4

95

py

Python

r2base/processors/readers/reader_cli.py

ariafyy/R2Base

19c65a7907d2b40fcad15f31574f6d4e4901776c

[ "Apache-2.0" ]

null

r2base/processors/readers/reader_cli.py

ariafyy/R2Base

19c65a7907d2b40fcad15f31574f6d4e4901776c

[ "Apache-2.0" ]

null

r2base/processors/readers/reader_cli.py

ariafyy/R2Base

19c65a7907d2b40fcad15f31574f6d4e4901776c

[ "Apache-2.0" ]

1

2021-08-02T05:07:44.000Z

from r2base.processors.bases import ProcessorBase class ReaderClient(ProcessorBase): pass

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077310e94bb5a1b0bbcd1ef9b405981f93aeaf7e

43

py

Python

rofm/__init__.py

PurelyApplied/roll_one_for_me

1130d6bb29db4795f3ef84ea0540e94290b6e58d

[ "Apache-2.0" ]

13

2016-05-10T22:11:46.000Z

2019-02-15T03:44:01.000Z

rofm/__init__.py

PurelyApplied/roll_one_for_me

1130d6bb29db4795f3ef84ea0540e94290b6e58d

[ "Apache-2.0" ]

6

2017-07-06T22:13:18.000Z

2017-07-07T18:18:12.000Z

rofm/__init__.py

PurelyApplied/roll_one_for_me

1130d6bb29db4795f3ef84ea0540e94290b6e58d

[ "Apache-2.0" ]

10

2016-02-10T20:23:51.000Z

2022-03-25T14:06:05.000Z

from . import classes from . import legacy

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6

079cd6e8ac3bf0849751bf8fc8c9e7d66a644886

118

py

Python

oommfc/tests/test_init.py

fgr1986/oommfc

8c57683fd480b910c0eca0fbca57d8d0b009ed7a

[ "BSD-3-Clause" ]

23

2019-09-18T10:58:00.000Z

2022-02-07T07:05:49.000Z

oommfc/tests/test_init.py

fgr1986/oommfc

8c57683fd480b910c0eca0fbca57d8d0b009ed7a

[ "BSD-3-Clause" ]

43

2019-08-22T04:31:36.000Z

2022-03-28T09:09:15.000Z

oommfc/tests/test_init.py

fgr1986/oommfc

8c57683fd480b910c0eca0fbca57d8d0b009ed7a

[ "BSD-3-Clause" ]

7

2020-04-25T13:25:25.000Z

2021-12-06T15:06:28.000Z

import oommfc as oc def test_version(): assert isinstance(oc.__version__, str) assert '.' in oc.__version__

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null

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6

079cdd52f25ed79982d9e2d2b0264a235121c7ed

164

py

Python

core/admin.py

johncmacy/django-react-graphql

723ea2fb7d482d3d955e336dbd099b24cf0c6d3c

[ "MIT" ]

null

core/admin.py

johncmacy/django-react-graphql

723ea2fb7d482d3d955e336dbd099b24cf0c6d3c

[ "MIT" ]

null

core/admin.py

johncmacy/django-react-graphql

723ea2fb7d482d3d955e336dbd099b24cf0c6d3c

[ "MIT" ]

null

from django.contrib import admin from .models import * admin.site.register(Shape) admin.site.register(Color) admin.site.register(Thing) admin.site.register(Widget)

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6

07aab67200eaadf6a6aa65bb96d6f76c7a9f8e97

32

py

Python

graff/query/__init__.py

apontzen/graff

edbc7dcb292bba5a723d5acd9478af75a601038c

[ "BSD-3-Clause" ]

3

2020-08-28T18:52:16.000Z

2020-09-05T01:51:40.000Z

graff/query/__init__.py

apontzen/graff

edbc7dcb292bba5a723d5acd9478af75a601038c

[ "BSD-3-Clause" ]

null

graff/query/__init__.py

apontzen/graff

edbc7dcb292bba5a723d5acd9478af75a601038c

[ "BSD-3-Clause" ]

null

from . import base, node, edge

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6

07c118b365beb31deda561082ceb91d2f2e90d8a

23,204

py

Python

semi_supervised_learning_pca.py

hkaneko1985/semi_supervised_learning

b2d0cd2d6e734eccd591a4e5ad984c002bdc9476

[ "MIT" ]

4

2019-11-08T08:58:14.000Z

2021-01-23T08:50:27.000Z

semi_supervised_learning_pca.py

1309822673/semi_supervised_learning

b2d0cd2d6e734eccd591a4e5ad984c002bdc9476

[ "MIT" ]

null

semi_supervised_learning_pca.py

1309822673/semi_supervised_learning

b2d0cd2d6e734eccd591a4e5ad984c002bdc9476

[ "MIT" ]

1

2020-11-20T10:48:22.000Z

# -*- coding: utf-8 -*- """ @author: Hiromasa Kaneko """ import math import warnings import matplotlib.figure as figure import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn import model_selection, svm, tree from sklearn.cross_decomposition import PLSRegression from sklearn.decomposition import PCA from sklearn.ensemble import RandomForestRegressor from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import Matern, DotProduct, WhiteKernel, RBF, ConstantKernel from sklearn.linear_model import Ridge, Lasso, ElasticNet, ElasticNetCV from sklearn.model_selection import GridSearchCV warnings.filterwarnings('ignore') regression_method = 'pls' # 'pls' or 'rr' or 'lasso' or 'en' or 'lsvr' or 'nsvr' or 'dt' or 'rf' or 'gp' max_pca_component_number = 150 threshold_of_rate_of_same_value = 1 fold_number = 2 max_pls_component_number = 30 ridge_lambdas = 2 ** np.arange(-5, 10, dtype=float) # L2 weight in ridge regression lasso_lambdas = np.arange(0.01, 0.71, 0.01, dtype=float) # L1 weight in LASSO elastic_net_lambdas = np.arange(0.01, 0.71, 0.01, dtype=float) # Lambda in elastic net elastic_net_alphas = np.arange(0.01, 1.00, 0.01, dtype=float) # Alpha in elastic net linear_svr_cs = 2 ** np.arange(-5, 5, dtype=float) # C for linear svr linear_svr_epsilons = 2 ** np.arange(-10, 0, dtype=float) # Epsilon for linear svr nonlinear_svr_cs = 2 ** np.arange(-5, 10, dtype=float) # C for nonlinear svr nonlinear_svr_epsilons = 2 ** np.arange(-10, 0, dtype=float) # Epsilon for nonlinear svr nonlinear_svr_gammas = 2 ** np.arange(-20, 10, dtype=float) # Gamma for nonlinear svr dt_max_max_depth = 30 # 木の深さの最大値、の最大値 dt_min_samples_leaf = 3 # 葉ごとのサンプル数の最小値 random_forest_number_of_trees = 300 # Number of decision trees for random forest random_forest_x_variables_rates = np.arange(1, 10, dtype=float) / 10 # Ratio of the number of X-variables for random forest # load data set supervised_dataset = pd.read_csv('descriptors_with_logS.csv', encoding='SHIFT-JIS', index_col=0) unsupervised_dataset = pd.read_csv('descriptors_for_prediction.csv', encoding='SHIFT-JIS', index_col=0) number_of_supervised_samples = supervised_dataset.shape[0] x_all_dataset = pd.concat([supervised_dataset.iloc[:, 1:], unsupervised_dataset], axis=0) x_all_dataset = x_all_dataset.loc[:, x_all_dataset.mean().index] # 平均を計算できる変数だけ選択 x_all_dataset = x_all_dataset.replace(np.inf, np.nan).fillna(np.nan) # infをnanに置き換えておく x_all_dataset = x_all_dataset.dropna(axis=1) # nanのある変数を削除 y_train = supervised_dataset.iloc[:, 0] rate_of_same_value = list() num = 0 for X_variable_name in x_all_dataset.columns: num += 1 # print('{0} / {1}'.format(num, x_all_dataset.shape[1])) same_value_number = x_all_dataset[X_variable_name].value_counts() rate_of_same_value.append(float(same_value_number[same_value_number.index[0]] / x_all_dataset.shape[0])) deleting_variable_numbers = np.where(np.array(rate_of_same_value) >= threshold_of_rate_of_same_value) """ # delete descriptors with zero variance deleting_variable_numbers = np.where( raw_Xtrain.var() == 0 ) """ if len(deleting_variable_numbers[0]) == 0: x_all = x_all_dataset.copy() else: x_all = x_all_dataset.drop(x_all_dataset.columns[deleting_variable_numbers], axis=1) print('Variable numbers zero variance: {0}'.format(deleting_variable_numbers[0] + 1)) print('# of X-variables: {0}'.format(x_all.shape[1])) # autoscaling autoscaled_x_all = (x_all - x_all.mean(axis=0)) / x_all.std(axis=0, ddof=1) autoscaled_y_train = (y_train - y_train.mean(axis=0)) / y_train.std(axis=0, ddof=1) # PCA pca = PCA() # PCA を行ったり PCA の結果を格納したりするための変数を、pca として宣言 pca.fit(autoscaled_x_all) # PCA を実行 # score score_all = pd.DataFrame(pca.transform(autoscaled_x_all), index=x_all.index) # 主成分スコアの計算した後、pandas の DataFrame 型に変換 score_train = score_all.iloc[:number_of_supervised_samples, :] score_test = score_all.iloc[number_of_supervised_samples:, :] # scaling autoscaled_score_train = score_train / score_train.std(axis=0, ddof=1) autoscaled_score_test = score_test / score_train.std(axis=0, ddof=1) # optimization of number of PCs set_max_pca_component_number = min(np.linalg.matrix_rank(autoscaled_score_train), max_pca_component_number) r2cvs = [] for number_of_pcs in range(set_max_pca_component_number): print('PC:', number_of_pcs + 1, '/', set_max_pca_component_number) autoscaled_x_train = autoscaled_score_train.iloc[:, :number_of_pcs + 1] if regression_method == 'pls': # Partial Least Squares pls_components = np.arange(1, min(np.linalg.matrix_rank(autoscaled_x_train) + 1, max_pls_component_number + 1), 1) r2cvall = [] for pls_component in pls_components: pls_model_in_cv = PLSRegression(n_components=pls_component) estimated_y_in_cv = np.ndarray.flatten( model_selection.cross_val_predict(pls_model_in_cv, autoscaled_x_train, autoscaled_y_train, cv=fold_number)) estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() r2cvall.append(float(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2))) optimal_pls_component_number = np.where(r2cvall == np.max(r2cvall))[0][0] + 1 regression_model = PLSRegression(n_components=optimal_pls_component_number) elif regression_method == 'rr': # ridge regression r2cvall = list() for ridge_lambda in ridge_lambdas: rr_model_in_cv = Ridge(alpha=ridge_lambda) estimated_y_in_cv = model_selection.cross_val_predict(rr_model_in_cv, autoscaled_x_train, autoscaled_y_train, cv=fold_number) estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() r2cvall.append(float(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2))) optimal_ridge_lambda = ridge_lambdas[np.where(r2cvall == np.max(r2cvall))[0][0]] regression_model = Ridge(alpha=optimal_ridge_lambda) elif regression_method == 'lasso': # LASSO r2cvall = list() for lasso_lambda in lasso_lambdas: lasso_model_in_cv = Lasso(alpha=lasso_lambda) estimated_y_in_cv = model_selection.cross_val_predict(lasso_model_in_cv, autoscaled_x_train, autoscaled_y_train, cv=fold_number) estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() r2cvall.append(float(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2))) optimal_lasso_lambda = lasso_lambdas[np.where(r2cvall == np.max(r2cvall))[0][0]] regression_model = Lasso(alpha=optimal_lasso_lambda) elif regression_method == 'en': # Elastic net elastic_net_in_cv = ElasticNetCV(cv=fold_number, l1_ratio=elastic_net_lambdas, alphas=elastic_net_alphas) elastic_net_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_elastic_net_alpha = elastic_net_in_cv.alpha_ optimal_elastic_net_lambda = elastic_net_in_cv.l1_ratio_ regression_model = ElasticNet(l1_ratio=optimal_elastic_net_lambda, alpha=optimal_elastic_net_alpha) elif regression_method == 'lsvr': # Linear SVR linear_svr_in_cv = GridSearchCV(svm.SVR(kernel='linear'), {'C': linear_svr_cs, 'epsilon': linear_svr_epsilons}, cv=fold_number) linear_svr_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_linear_svr_c = linear_svr_in_cv.best_params_['C'] optimal_linear_svr_epsilon = linear_svr_in_cv.best_params_['epsilon'] regression_model = svm.SVR(kernel='linear', C=optimal_linear_svr_c, epsilon=optimal_linear_svr_epsilon) elif regression_method == 'nsvr': # Nonlinear SVR variance_of_gram_matrix = list() numpy_autoscaled_Xtrain = np.array(autoscaled_x_train) for nonlinear_svr_gamma in nonlinear_svr_gammas: gram_matrix = np.exp( -nonlinear_svr_gamma * ((numpy_autoscaled_Xtrain[:, np.newaxis] - numpy_autoscaled_Xtrain) ** 2).sum( axis=2)) variance_of_gram_matrix.append(gram_matrix.var(ddof=1)) optimal_nonlinear_gamma = nonlinear_svr_gammas[ np.where(variance_of_gram_matrix == np.max(variance_of_gram_matrix))[0][0]] # CV による ε の最適化 model_in_cv = GridSearchCV(svm.SVR(kernel='rbf', C=3, gamma=optimal_nonlinear_gamma), {'epsilon': nonlinear_svr_epsilons}, cv=fold_number, iid=False, verbose=0) model_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_nonlinear_epsilon = model_in_cv.best_params_['epsilon'] # CV による C の最適化 model_in_cv = GridSearchCV( svm.SVR(kernel='rbf', epsilon=optimal_nonlinear_epsilon, gamma=optimal_nonlinear_gamma), {'C': nonlinear_svr_cs}, cv=fold_number, iid=False, verbose=0) model_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_nonlinear_c = model_in_cv.best_params_['C'] # CV による γ の最適化 model_in_cv = GridSearchCV(svm.SVR(kernel='rbf', epsilon=optimal_nonlinear_epsilon, C=optimal_nonlinear_c), {'gamma': nonlinear_svr_gammas}, cv=fold_number, iid=False, verbose=0) model_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_nonlinear_gamma = model_in_cv.best_params_['gamma'] regression_model = svm.SVR(kernel='rbf', C=optimal_nonlinear_c, epsilon=optimal_nonlinear_epsilon, gamma=optimal_nonlinear_gamma) elif regression_method == 'dt': # Decision tree # クロスバリデーションによる木の深さの最適化 r2cv_all = [] for max_depth in range(2, dt_max_max_depth): model_in_cv = tree.DecisionTreeRegressor(max_depth=max_depth, min_samples_leaf=dt_min_samples_leaf) estimated_y_in_cv = model_selection.cross_val_predict(model_in_cv, autoscaled_x_train, autoscaled_y_train, cv=fold_number) * y_train.std(ddof=1) + y_train.mean() r2cv_all.append(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2)) optimal_max_depth = np.where(r2cv_all == np.max(r2cv_all))[0][0] + 2 # r2cvが最も大きい木の深さ regression_model = tree.DecisionTreeRegressor(max_depth=optimal_max_depth, min_samples_leaf=dt_min_samples_leaf) # DTモデルの宣言 elif regression_method == 'rf': # Random forest rmse_oob_all = list() for random_forest_x_variables_rate in random_forest_x_variables_rates: RandomForestResult = RandomForestRegressor(n_estimators=random_forest_number_of_trees, max_features=int( max(math.ceil(autoscaled_x_train.shape[1] * random_forest_x_variables_rate), 1)), oob_score=True) RandomForestResult.fit(autoscaled_x_train, autoscaled_y_train) estimated_y_in_cv = RandomForestResult.oob_prediction_ estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() rmse_oob_all.append((sum((y_train - estimated_y_in_cv) ** 2) / len(y_train)) ** 0.5) optimal_random_forest_x_variables_rate = random_forest_x_variables_rates[ np.where(rmse_oob_all == np.min(rmse_oob_all))[0][0]] regression_model = RandomForestRegressor(n_estimators=random_forest_number_of_trees, max_features=int( max(math.ceil(autoscaled_x_train.shape[1] * optimal_random_forest_x_variables_rate), 1)), oob_score=True) elif regression_method == 'gp': # Gaussian process regression_model = GaussianProcessRegressor(ConstantKernel() * RBF() + WhiteKernel()) estimated_y_in_cv = np.ndarray.flatten( model_selection.cross_val_predict(regression_model, autoscaled_x_train, autoscaled_y_train, cv=fold_number)) estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() r2cvs.append(float(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2))) plt.rcParams['font.size'] = 18 # 横軸や縦軸の名前の文字などのフォントのサイズ plt.plot(np.arange(set_max_pca_component_number) + 1, r2cvs, 'b.-') plt.ylim(0, 1) plt.xlabel('Number of PCA components') plt.ylabel('r2cv') plt.show() optimal_pca_component_number = np.where(r2cvs == np.max(r2cvs))[0][0] + 1 print('Optimal PCA component number : {0}'.format(optimal_pca_component_number)) autoscaled_x_train = autoscaled_score_train.iloc[:, :optimal_pca_component_number] autoscaled_x_test = autoscaled_score_test.iloc[:, :optimal_pca_component_number] if regression_method == 'pls': # Partial Least Squares pls_components = np.arange(1, min(np.linalg.matrix_rank(autoscaled_x_train) + 1, max_pls_component_number + 1), 1) r2cvall = [] for pls_component in pls_components: pls_model_in_cv = PLSRegression(n_components=pls_component) estimated_y_in_cv = np.ndarray.flatten( model_selection.cross_val_predict(pls_model_in_cv, autoscaled_x_train, autoscaled_y_train, cv=fold_number)) estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() r2cvall.append(float(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2))) optimal_pls_component_number = np.where(r2cvall == np.max(r2cvall))[0][0] + 1 regression_model = PLSRegression(n_components=optimal_pls_component_number) elif regression_method == 'rr': # ridge regression r2cvall = list() for ridge_lambda in ridge_lambdas: rr_model_in_cv = Ridge(alpha=ridge_lambda) estimated_y_in_cv = model_selection.cross_val_predict(rr_model_in_cv, autoscaled_x_train, autoscaled_y_train, cv=fold_number) estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() r2cvall.append(float(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2))) optimal_ridge_lambda = ridge_lambdas[np.where(r2cvall == np.max(r2cvall))[0][0]] regression_model = Ridge(alpha=optimal_ridge_lambda) elif regression_method == 'lasso': # LASSO r2cvall = list() for lasso_lambda in lasso_lambdas: lasso_model_in_cv = Lasso(alpha=lasso_lambda) estimated_y_in_cv = model_selection.cross_val_predict(lasso_model_in_cv, autoscaled_x_train, autoscaled_y_train, cv=fold_number) estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() r2cvall.append(float(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2))) optimal_lasso_lambda = lasso_lambdas[np.where(r2cvall == np.max(r2cvall))[0][0]] regression_model = Lasso(alpha=optimal_lasso_lambda) elif regression_method == 'en': # Elastic net elastic_net_in_cv = ElasticNetCV(cv=fold_number, l1_ratio=elastic_net_lambdas, alphas=elastic_net_alphas) elastic_net_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_elastic_net_alpha = elastic_net_in_cv.alpha_ optimal_elastic_net_lambda = elastic_net_in_cv.l1_ratio_ regression_model = ElasticNet(l1_ratio=optimal_elastic_net_lambda, alpha=optimal_elastic_net_alpha) elif regression_method == 'lsvr': # Linear SVR linear_svr_in_cv = GridSearchCV(svm.SVR(kernel='linear'), {'C': linear_svr_cs, 'epsilon': linear_svr_epsilons}, cv=fold_number) linear_svr_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_linear_svr_c = linear_svr_in_cv.best_params_['C'] optimal_linear_svr_epsilon = linear_svr_in_cv.best_params_['epsilon'] regression_model = svm.SVR(kernel='linear', C=optimal_linear_svr_c, epsilon=optimal_linear_svr_epsilon) elif regression_method == 'nsvr': # Nonlinear SVR variance_of_gram_matrix = list() numpy_autoscaled_Xtrain = np.array(autoscaled_x_train) for nonlinear_svr_gamma in nonlinear_svr_gammas: gram_matrix = np.exp( -nonlinear_svr_gamma * ((numpy_autoscaled_Xtrain[:, np.newaxis] - numpy_autoscaled_Xtrain) ** 2).sum( axis=2)) variance_of_gram_matrix.append(gram_matrix.var(ddof=1)) optimal_nonlinear_gamma = nonlinear_svr_gammas[ np.where(variance_of_gram_matrix == np.max(variance_of_gram_matrix))[0][0]] # CV による ε の最適化 model_in_cv = GridSearchCV(svm.SVR(kernel='rbf', C=3, gamma=optimal_nonlinear_gamma), {'epsilon': nonlinear_svr_epsilons}, cv=fold_number, iid=False, verbose=0) model_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_nonlinear_epsilon = model_in_cv.best_params_['epsilon'] # CV による C の最適化 model_in_cv = GridSearchCV(svm.SVR(kernel='rbf', epsilon=optimal_nonlinear_epsilon, gamma=optimal_nonlinear_gamma), {'C': nonlinear_svr_cs}, cv=fold_number, iid=False, verbose=0) model_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_nonlinear_c = model_in_cv.best_params_['C'] # CV による γ の最適化 model_in_cv = GridSearchCV(svm.SVR(kernel='rbf', epsilon=optimal_nonlinear_epsilon, C=optimal_nonlinear_c), {'gamma': nonlinear_svr_gammas}, cv=fold_number, iid=False, verbose=0) model_in_cv.fit(autoscaled_x_train, autoscaled_y_train) optimal_nonlinear_gamma = model_in_cv.best_params_['gamma'] regression_model = svm.SVR(kernel='rbf', C=optimal_nonlinear_c, epsilon=optimal_nonlinear_epsilon, gamma=optimal_nonlinear_gamma) elif regression_method == 'dt': # Decision tree # クロスバリデーションによる木の深さの最適化 r2cv_all = [] for max_depth in range(2, dt_max_max_depth): model_in_cv = tree.DecisionTreeRegressor(max_depth=max_depth, min_samples_leaf=dt_min_samples_leaf) estimated_y_in_cv = model_selection.cross_val_predict(model_in_cv, autoscaled_x_train, autoscaled_y_train, cv=fold_number) * y_train.std(ddof=1) + y_train.mean() r2cv_all.append(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2)) optimal_max_depth = np.where(r2cv_all == np.max(r2cv_all))[0][0] + 2 # r2cvが最も大きい木の深さ regression_model = tree.DecisionTreeRegressor(max_depth=optimal_max_depth, min_samples_leaf=dt_min_samples_leaf) # DTモデルの宣言 elif regression_method == 'rf': # Random forest rmse_oob_all = list() for random_forest_x_variables_rate in random_forest_x_variables_rates: RandomForestResult = RandomForestRegressor(n_estimators=random_forest_number_of_trees, max_features=int( max(math.ceil(autoscaled_x_train.shape[1] * random_forest_x_variables_rate), 1)), oob_score=True) RandomForestResult.fit(autoscaled_x_train, autoscaled_y_train) estimated_y_in_cv = RandomForestResult.oob_prediction_ estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() rmse_oob_all.append((sum((y_train - estimated_y_in_cv) ** 2) / len(y_train)) ** 0.5) optimal_random_forest_x_variables_rate = random_forest_x_variables_rates[ np.where(rmse_oob_all == np.min(rmse_oob_all))[0][0]] regression_model = RandomForestRegressor(n_estimators=random_forest_number_of_trees, max_features=int( max(math.ceil(autoscaled_x_train.shape[1] * optimal_random_forest_x_variables_rate), 1)), oob_score=True) elif regression_method == 'gp': # Gaussian process regression_model = GaussianProcessRegressor(ConstantKernel() * RBF() + WhiteKernel()) regression_model.fit(autoscaled_x_train, autoscaled_y_train) # calculate y for training data calculated_ytrain = np.ndarray.flatten(regression_model.predict(autoscaled_x_train)) calculated_ytrain = calculated_ytrain * y_train.std(ddof=1) + y_train.mean() # yy-plot plt.figure(figsize=figure.figaspect(1)) plt.scatter(y_train, calculated_ytrain) y_max = np.max(np.array([np.array(y_train), calculated_ytrain])) y_min = np.min(np.array([np.array(y_train), calculated_ytrain])) plt.plot([y_min - 0.05 * (y_max - y_min), y_max + 0.05 * (y_max - y_min)], [y_min - 0.05 * (y_max - y_min), y_max + 0.05 * (y_max - y_min)], 'k-') plt.ylim(y_min - 0.05 * (y_max - y_min), y_max + 0.05 * (y_max - y_min)) plt.xlim(y_min - 0.05 * (y_max - y_min), y_max + 0.05 * (y_max - y_min)) plt.xlabel('Actual Y') plt.ylabel('Calculated Y') plt.show() # r2, RMSE, MAE print('r2: {0}'.format(float(1 - sum((y_train - calculated_ytrain) ** 2) / sum((y_train - y_train.mean()) ** 2)))) print('RMSE: {0}'.format(float((sum((y_train - calculated_ytrain) ** 2) / len(y_train)) ** 0.5))) print('MAE: {0}'.format(float(sum(abs(y_train - calculated_ytrain)) / len(y_train)))) # estimated_y in cross-validation estimated_y_in_cv = np.ndarray.flatten( model_selection.cross_val_predict(regression_model, autoscaled_x_train, autoscaled_y_train, cv=fold_number)) estimated_y_in_cv = estimated_y_in_cv * y_train.std(ddof=1) + y_train.mean() # yy-plot plt.figure(figsize=figure.figaspect(1)) plt.scatter(y_train, estimated_y_in_cv) y_max = np.max(np.array([np.array(y_train), estimated_y_in_cv])) y_min = np.min(np.array([np.array(y_train), estimated_y_in_cv])) plt.plot([y_min - 0.05 * (y_max - y_min), y_max + 0.05 * (y_max - y_min)], [y_min - 0.05 * (y_max - y_min), y_max + 0.05 * (y_max - y_min)], 'k-') plt.ylim(y_min - 0.05 * (y_max - y_min), y_max + 0.05 * (y_max - y_min)) plt.xlim(y_min - 0.05 * (y_max - y_min), y_max + 0.05 * (y_max - y_min)) plt.xlabel('Actual Y') plt.ylabel('Estimated Y in CV') plt.show() # r2cv, RMSEcv, MAEcv print('r2cv: {0}'.format(float(1 - sum((y_train - estimated_y_in_cv) ** 2) / sum((y_train - y_train.mean()) ** 2)))) print('RMSEcv: {0}'.format(float((sum((y_train - estimated_y_in_cv) ** 2) / len(y_train)) ** 0.5))) print('MAEcv: {0}'.format(float(sum(abs(y_train - estimated_y_in_cv)) / len(y_train)))) # estimate y for test data autoscaled_x_test = np.ndarray.flatten(regression_model.predict(autoscaled_x_test)) autoscaled_x_test = autoscaled_x_test * y_train.std(ddof=1) + y_train.mean() autoscaled_x_test = pd.DataFrame(autoscaled_x_test, index=unsupervised_dataset.index, columns=['estimated y']) autoscaled_x_test.to_csv('estimated_y.csv')

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07c8bd084ca8f2e3113088632c40e18e88256820

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py

Python

mobile/migrations/0001_initial.py

hyperoslo/django-mobile

7ce65c51b45c167d096021852ce8f367732eb754

[ "MIT" ]

3

2016-10-04T08:03:26.000Z

2017-06-30T10:25:35.000Z

mobile/migrations/0001_initial.py

hyperoslo/django-mobile

7ce65c51b45c167d096021852ce8f367732eb754

[ "MIT" ]

1

2021-04-30T09:51:29.000Z

mobile/migrations/0001_initial.py

hyperoslo/django-mobile

7ce65c51b45c167d096021852ce8f367732eb754

[ "MIT" ]

2

2016-10-04T08:03:28.000Z

2017-09-21T02:12:56.000Z

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'OutgoingSMS' db.create_table('mobile_outgoingsms', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('recipient', self.gf('django.db.models.fields.CharField')(max_length=255)), ('sender', self.gf('django.db.models.fields.CharField')(default=2210, max_length=255)), ('message', self.gf('django.db.models.fields.TextField')()), ('sent', self.gf('django.db.models.fields.BooleanField')(default=False)), ('price', self.gf('django.db.models.fields.IntegerField')()), ('country', self.gf('django.db.models.fields.CharField')(default='NO', max_length=255)), ('delivery_status', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('sent_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), )) db.send_create_signal('mobile', ['OutgoingSMS']) # Adding model 'IncomingSMS' db.create_table('mobile_incomingsms', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('recipient', self.gf('django.db.models.fields.CharField')(max_length=255)), ('sender', self.gf('django.db.models.fields.CharField')(max_length=255)), ('message', self.gf('django.db.models.fields.TextField')()), ('country', self.gf('django.db.models.fields.CharField')(default='NO', max_length=255)), ('keyword', self.gf('django.db.models.fields.CharField')(max_length=255, blank=True)), ('parameter', self.gf('django.db.models.fields.CharField')(max_length=255, blank=True)), ('received_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), ('source', self.gf('django.db.models.fields.TextField')()), )) db.send_create_signal('mobile', ['IncomingSMS']) # Adding model 'IncomingMMS' db.create_table('mobile_incomingmms', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('recipient', self.gf('django.db.models.fields.CharField')(max_length=255)), ('country', self.gf('django.db.models.fields.CharField')(default='NO', max_length=255)), ('sender', self.gf('django.db.models.fields.CharField')(max_length=255)), ('subject', self.gf('django.db.models.fields.CharField')(max_length=255)), ('received_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), ('source', self.gf('django.db.models.fields.TextField')()), )) db.send_create_signal('mobile', ['IncomingMMS']) # Adding model 'MMSFile' db.create_table('mobile_mmsfile', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('file', self.gf('django.db.models.fields.files.FileField')(max_length=100)), ('mms', self.gf('django.db.models.fields.related.ForeignKey')(related_name='files', to=orm['mobile.IncomingMMS'])), ('content_type', self.gf('django.db.models.fields.CharField')(max_length=255)), )) db.send_create_signal('mobile', ['MMSFile']) def backwards(self, orm): # Deleting model 'OutgoingSMS' db.delete_table('mobile_outgoingsms') # Deleting model 'IncomingSMS' db.delete_table('mobile_incomingsms') # Deleting model 'IncomingMMS' db.delete_table('mobile_incomingmms') # Deleting model 'MMSFile' db.delete_table('mobile_mmsfile') models = { 'mobile.incomingmms': { 'Meta': {'object_name': 'IncomingMMS'}, 'country': ('django.db.models.fields.CharField', [], {'default': "'NO'", 'max_length': '255'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'received_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'recipient': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'sender': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'source': ('django.db.models.fields.TextField', [], {}), 'subject': ('django.db.models.fields.CharField', [], {'max_length': '255'}) }, 'mobile.incomingsms': { 'Meta': {'object_name': 'IncomingSMS'}, 'country': ('django.db.models.fields.CharField', [], {'default': "'NO'", 'max_length': '255'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'keyword': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'message': ('django.db.models.fields.TextField', [], {}), 'parameter': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'received_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'recipient': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'sender': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'source': ('django.db.models.fields.TextField', [], {}) }, 'mobile.mmsfile': { 'Meta': {'object_name': 'MMSFile'}, 'content_type': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'file': ('django.db.models.fields.files.FileField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'mms': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'files'", 'to': "orm['mobile.IncomingMMS']"}) }, 'mobile.outgoingsms': { 'Meta': {'object_name': 'OutgoingSMS'}, 'country': ('django.db.models.fields.CharField', [], {'default': "'NO'", 'max_length': '255'}), 'delivery_status': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'message': ('django.db.models.fields.TextField', [], {}), 'price': ('django.db.models.fields.IntegerField', [], {}), 'recipient': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'sender': ('django.db.models.fields.CharField', [], {'default': '2210', 'max_length': '255'}), 'sent': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'sent_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}) } } complete_apps = ['mobile']

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58028b45994c9e5bc8269a5c988cb586f930ea3e

3,249

py

Python

tests/async/test_dealer_dealer.py

calcite/zmq_tubes

ab501639b310f818a85b4fa190f3a70ae28390ff

[ "MIT" ]

1

2021-10-07T11:29:02.000Z

tests/async/test_dealer_dealer.py

calcite/tubes

ab501639b310f818a85b4fa190f3a70ae28390ff

[ "MIT" ]

null

tests/async/test_dealer_dealer.py

calcite/tubes

ab501639b310f818a85b4fa190f3a70ae28390ff

[ "MIT" ]

null

import asyncio import zmq from ..helpers import run_test_tasks from zmq_tubes import Tube, TubeNode, TubeMessage ADDR = 'ipc:///tmp/dealer_dealer.pipe' TOPIC = 'req' def test_dealer_dealer(): data = ['request-DEALER1_REQ-0', 'request-DEALER1_REQ-1', 'request-DEALER2_REQ-0', 'request-DEALER2_REQ-1'] async def request_task(node, topic, name): asyncio.current_task().set_name(name) for it in range(0, 2): node.send(topic, f"request-{name}-{it}") await asyncio.sleep(3) async def response_dealer_task(node, topic, name): async def __process(response: TubeMessage): assert response.payload in data data.remove(response.payload) asyncio.current_task().set_name(name) node.register_handler(topic, __process) await node.start() tube_dealer1 = Tube( name='DEALER1', addr=ADDR, server=True, tube_type=zmq.DEALER ) tube_dealer2 = Tube( name='DEALER2', addr=ADDR, tube_type=zmq.DEALER ) node_dealer1 = TubeNode() node_dealer1.register_tube(tube_dealer1, f"{TOPIC}/#") node_dealer1.connect() node_dealer2 = TubeNode() node_dealer2.register_tube(tube_dealer2, f"{TOPIC}/#") node_dealer2.connect() asyncio.run( run_test_tasks( [request_task(node_dealer1, TOPIC, 'DEALER1_REQ'), request_task(node_dealer2, TOPIC, 'DEALER2_REQ')], [response_dealer_task(node_dealer1, f'{TOPIC}/#', 'DEALER1_RESP'), response_dealer_task(node_dealer2, f'{TOPIC}/#', 'DEALER2_RESP')] ) ) assert len(data) == 0 def test_dealer_dealer_on_same_node(): data = ['request-DEALER1_REQ-0', 'request-DEALER1_REQ-1', 'request-DEALER2_REQ-0', 'request-DEALER2_REQ-1'] async def request_task(node, topic, name): asyncio.current_task().set_name(name) for it in range(0, 2): node.send(topic, f"request-{name}-{it}") await asyncio.sleep(3) async def response_dealer_task(node, topic, name, tube): async def __process(response: TubeMessage): assert response.payload in data data.remove(response.payload) asyncio.current_task().set_name(name) node.register_handler(topic, __process, tube) await node.start() tube_dealer1 = Tube( name='DEALER1', addr=ADDR, server=True, tube_type=zmq.DEALER ) tube_dealer2 = Tube( name='DEALER2', addr=ADDR, tube_type=zmq.DEALER ) node_dealer1 = TubeNode() node_dealer1.register_tube(tube_dealer1, f"{TOPIC}/#") node_dealer1.register_tube(tube_dealer2, f"{TOPIC}/#") node_dealer1.connect() asyncio.run( run_test_tasks( [request_task(node_dealer1, TOPIC, 'DEALER1_REQ'), request_task(node_dealer1, TOPIC, 'DEALER2_REQ')], [response_dealer_task(node_dealer1, f'{TOPIC}/#', 'DEALER1_RESP', tube_dealer1), response_dealer_task(node_dealer1, f'{TOPIC}/#', 'DEALER2_RESP', tube_dealer2)] ) ) assert len(data) == 0

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6

ed09228aea18bcccf2db2b9eebc43a355daf1da7

130

py

Python

__init__.py

smwa/round_robin_tournament

8a2959e21d77f9fac722e787de57293062aa89dc

[ "MIT" ]

1

2022-01-12T20:49:31.000Z

__init__.py

smwa/round_robin_tournament

8a2959e21d77f9fac722e787de57293062aa89dc

[ "MIT" ]

null

__init__.py

smwa/round_robin_tournament

8a2959e21d77f9fac722e787de57293062aa89dc

[ "MIT" ]

null

""" Import the classes Tournament, Match, and Participant. """ from .round_robin_tournament import Tournament, Match, Participant

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ed17458a8c8eeb8fcee3108042c2b829a5cde2c1

1,279

py

Python

dbReports/iondb/bin/fix_experimentChipTypeWithExtraQuotes.py

sequencer2014/TS

465804570349d46b47c1bdf131bdafea5c582dee

[ "Apache-2.0" ]

null

dbReports/iondb/bin/fix_experimentChipTypeWithExtraQuotes.py

sequencer2014/TS

465804570349d46b47c1bdf131bdafea5c582dee

[ "Apache-2.0" ]

null

dbReports/iondb/bin/fix_experimentChipTypeWithExtraQuotes.py

sequencer2014/TS

465804570349d46b47c1bdf131bdafea5c582dee

[ "Apache-2.0" ]

null

#!/usr/bin/env python # Copyright (C) 2013 Ion Torrent Systems, Inc. All Rights Reserved from djangoinit import * import sys import os from iondb.rundb import models ''' remove extra quotes or backslash in Experiment.chipType ''' chipTypes = models.Experiment.objects.all().values_list('chipType', flat=True).distinct('chipType') for chipType in filter(lambda x: '\\' in x, chipTypes): badChars = '\\' clean_chipType = chipType.replace(badChars, '') exps = models.Experiment.objects.all().filter(chipType=chipType) print("FIX-1: Going to fix %d experiments by replacing chipType %s with %s" % (exps.count(), badChars, clean_chipType)) models.Experiment.objects.filter(chipType=chipType).update(chipType=clean_chipType) chipTypes = models.Experiment.objects.all().values_list('chipType', flat=True).distinct('chipType') for chipType in filter(lambda x: '"' in x, chipTypes): badChars = '"' clean_chipType = chipType.replace(badChars, '') exps = models.Experiment.objects.all().filter(chipType=chipType) print("FIX-2: Going to fix %d experiments by replacing chipType %s with %s" % (exps.count(), badChars, clean_chipType)) models.Experiment.objects.filter(chipType=chipType).update(chipType=clean_chipType)

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Python

application/migrations/0035_auto_20200831_1609.py

City-of-Helsinki/events-helsinki-cms

64e4c1ce6cc058fb3783e417560dc244bd753d05

[ "MIT" ]

2

2020-04-20T05:37:28.000Z

2021-02-19T10:33:45.000Z

application/migrations/0035_auto_20200831_1609.py

City-of-Helsinki/events-helsinki-cms

64e4c1ce6cc058fb3783e417560dc244bd753d05

[ "MIT" ]

6

2020-02-12T12:55:37.000Z

2021-03-30T12:56:28.000Z

application/migrations/0035_auto_20200831_1609.py

City-of-Helsinki/events-helsinki-cms

64e4c1ce6cc058fb3783e417560dc244bd753d05

[ "MIT" ]

1

2021-02-18T12:11:18.000Z

# Generated by Django 2.2.9 on 2020-08-31 16:09 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('application', '0034_auto_20200806_1101'), ] operations = [ migrations.AlterField( model_name='landingpages', name='hero_background_image_color_en', field=models.CharField(blank=True, choices=[('FOG', 'Sumu'), ('ENGEL', 'Engel'), ('COPPER', 'Kupari'), ('SUOMENLINNA', 'Suomenlinna')], max_length=255, null=True), ), migrations.AlterField( model_name='landingpages', name='hero_background_image_color_fi', field=models.CharField(blank=True, choices=[('FOG', 'Sumu'), ('ENGEL', 'Engel'), ('COPPER', 'Kupari'), ('SUOMENLINNA', 'Suomenlinna')], max_length=255, null=True), ), migrations.AlterField( model_name='landingpages', name='hero_background_image_color_sv', field=models.CharField(blank=True, choices=[('FOG', 'Sumu'), ('ENGEL', 'Engel'), ('COPPER', 'Kupari'), ('SUOMENLINNA', 'Suomenlinna')], max_length=255, null=True), ), ]

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